WO2024098148A1 - Targeting irf2 for cancer therapy - Google Patents
Targeting irf2 for cancer therapy Download PDFInfo
- Publication number
- WO2024098148A1 WO2024098148A1 PCT/CA2023/051492 CA2023051492W WO2024098148A1 WO 2024098148 A1 WO2024098148 A1 WO 2024098148A1 CA 2023051492 W CA2023051492 W CA 2023051492W WO 2024098148 A1 WO2024098148 A1 WO 2024098148A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- cell
- irf2
- cells
- modified
- tumor
- Prior art date
Links
- 238000011275 oncology therapy Methods 0.000 title abstract description 4
- 230000008685 targeting Effects 0.000 title description 17
- 210000001744 T-lymphocyte Anatomy 0.000 claims abstract description 365
- 102100029838 Interferon regulatory factor 2 Human genes 0.000 claims abstract description 296
- 230000014509 gene expression Effects 0.000 claims abstract description 154
- 230000003247 decreasing effect Effects 0.000 claims abstract description 13
- 101001011393 Homo sapiens Interferon regulatory factor 2 Proteins 0.000 claims abstract 3
- 206010028980 Neoplasm Diseases 0.000 claims description 203
- 108091033409 CRISPR Proteins 0.000 claims description 53
- 238000011282 treatment Methods 0.000 claims description 37
- 201000011510 cancer Diseases 0.000 claims description 26
- 230000001404 mediated effect Effects 0.000 claims description 25
- 238000000034 method Methods 0.000 claims description 23
- 238000010354 CRISPR gene editing Methods 0.000 claims description 17
- 238000001727 in vivo Methods 0.000 claims description 8
- 239000003112 inhibitor Substances 0.000 claims description 7
- 210000003289 regulatory T cell Anatomy 0.000 claims description 7
- 238000011467 adoptive cell therapy Methods 0.000 claims description 6
- 230000000259 anti-tumor effect Effects 0.000 claims description 5
- 230000001976 improved effect Effects 0.000 claims description 5
- 101150054876 IRF2 gene Proteins 0.000 claims description 4
- 238000002360 preparation method Methods 0.000 claims description 4
- 238000002560 therapeutic procedure Methods 0.000 claims description 4
- 229940076838 Immune checkpoint inhibitor Drugs 0.000 claims description 3
- 108091034117 Oligonucleotide Proteins 0.000 claims description 3
- 241000700605 Viruses Species 0.000 claims description 3
- 239000003814 drug Substances 0.000 claims description 3
- 239000012274 immune-checkpoint protein inhibitor Substances 0.000 claims description 3
- 108091027967 Small hairpin RNA Proteins 0.000 claims description 2
- 108020004459 Small interfering RNA Proteins 0.000 claims description 2
- 239000000074 antisense oligonucleotide Substances 0.000 claims description 2
- 238000012230 antisense oligonucleotides Methods 0.000 claims description 2
- 210000003162 effector t lymphocyte Anatomy 0.000 claims description 2
- 108091070501 miRNA Proteins 0.000 claims description 2
- 239000002679 microRNA Substances 0.000 claims description 2
- 239000002924 silencing RNA Substances 0.000 claims description 2
- 239000004055 small Interfering RNA Substances 0.000 claims description 2
- 150000003384 small molecules Chemical group 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 abstract description 3
- 108090000908 Interferon regulatory factor 2 Proteins 0.000 description 293
- 102100034922 T-cell surface glycoprotein CD8 alpha chain Human genes 0.000 description 272
- 210000004027 cell Anatomy 0.000 description 151
- 241000699670 Mus sp. Species 0.000 description 137
- 108090000623 proteins and genes Proteins 0.000 description 98
- 210000003171 tumor-infiltrating lymphocyte Anatomy 0.000 description 87
- 102100040019 Interferon alpha-1/13 Human genes 0.000 description 45
- 102000004389 Ribonucleoproteins Human genes 0.000 description 43
- 108010081734 Ribonucleoproteins Proteins 0.000 description 43
- 230000001965 increasing effect Effects 0.000 description 39
- 230000011664 signaling Effects 0.000 description 39
- 238000012217 deletion Methods 0.000 description 34
- 238000000684 flow cytometry Methods 0.000 description 34
- 241000699666 Mus <mouse, genus> Species 0.000 description 31
- 102000004169 proteins and genes Human genes 0.000 description 31
- 230000037430 deletion Effects 0.000 description 30
- 230000006870 function Effects 0.000 description 30
- 108020005004 Guide RNA Proteins 0.000 description 28
- 238000004458 analytical method Methods 0.000 description 28
- 238000002474 experimental method Methods 0.000 description 25
- 230000004614 tumor growth Effects 0.000 description 25
- 101100046669 Mus musculus Tox gene Proteins 0.000 description 24
- 102100024216 Programmed cell death 1 ligand 1 Human genes 0.000 description 22
- 239000002609 medium Substances 0.000 description 22
- 238000001890 transfection Methods 0.000 description 22
- 201000001441 melanoma Diseases 0.000 description 21
- 239000012636 effector Substances 0.000 description 19
- 102000014150 Interferons Human genes 0.000 description 18
- 108010050904 Interferons Proteins 0.000 description 18
- 210000004881 tumor cell Anatomy 0.000 description 18
- 101000611936 Homo sapiens Programmed cell death protein 1 Proteins 0.000 description 17
- 102100040678 Programmed cell death protein 1 Human genes 0.000 description 17
- 230000037361 pathway Effects 0.000 description 17
- 238000010186 staining Methods 0.000 description 17
- 102100022970 Basic leucine zipper transcriptional factor ATF-like Human genes 0.000 description 16
- 101000834898 Homo sapiens Alpha-synuclein Proteins 0.000 description 16
- 101000903742 Homo sapiens Basic leucine zipper transcriptional factor ATF-like Proteins 0.000 description 16
- 101000652359 Homo sapiens Spermatogenesis-associated protein 2 Proteins 0.000 description 16
- 230000004913 activation Effects 0.000 description 16
- 238000004520 electroporation Methods 0.000 description 16
- 230000027455 binding Effects 0.000 description 15
- 230000000694 effects Effects 0.000 description 15
- 230000004044 response Effects 0.000 description 15
- 101001117317 Homo sapiens Programmed cell death 1 ligand 1 Proteins 0.000 description 14
- 108091023040 Transcription factor Proteins 0.000 description 14
- 102000040945 Transcription factor Human genes 0.000 description 14
- 239000003623 enhancer Substances 0.000 description 14
- 210000002865 immune cell Anatomy 0.000 description 14
- 229940047124 interferons Drugs 0.000 description 14
- 230000001105 regulatory effect Effects 0.000 description 14
- UCSJYZPVAKXKNQ-HZYVHMACSA-N streptomycin Chemical compound CN[C@H]1[C@H](O)[C@@H](O)[C@H](CO)O[C@H]1O[C@@H]1[C@](C=O)(O)[C@H](C)O[C@H]1O[C@@H]1[C@@H](NC(N)=N)[C@H](O)[C@@H](NC(N)=N)[C@H](O)[C@H]1O UCSJYZPVAKXKNQ-HZYVHMACSA-N 0.000 description 14
- 230000002950 deficient Effects 0.000 description 13
- 230000000977 initiatory effect Effects 0.000 description 13
- 210000000952 spleen Anatomy 0.000 description 13
- 230000002103 transcriptional effect Effects 0.000 description 13
- 108091003079 Bovine Serum Albumin Proteins 0.000 description 12
- 238000010356 CRISPR-Cas9 genome editing Methods 0.000 description 12
- 239000012091 fetal bovine serum Substances 0.000 description 12
- 238000010199 gene set enrichment analysis Methods 0.000 description 12
- 238000002513 implantation Methods 0.000 description 12
- 108091008042 inhibitory receptors Proteins 0.000 description 12
- 239000000523 sample Substances 0.000 description 12
- 230000000638 stimulation Effects 0.000 description 12
- 230000002459 sustained effect Effects 0.000 description 12
- 108091032973 (ribonucleotides)n+m Proteins 0.000 description 11
- 102100022057 Hepatocyte nuclear factor 1-alpha Human genes 0.000 description 11
- 101001045751 Homo sapiens Hepatocyte nuclear factor 1-alpha Proteins 0.000 description 11
- 241001529936 Murinae Species 0.000 description 11
- 230000007774 longterm Effects 0.000 description 11
- 238000012546 transfer Methods 0.000 description 11
- 102100029722 Ectonucleoside triphosphate diphosphohydrolase 1 Human genes 0.000 description 10
- 101001012447 Homo sapiens Ectonucleoside triphosphate diphosphohydrolase 1 Proteins 0.000 description 10
- 239000006285 cell suspension Substances 0.000 description 10
- 230000004083 survival effect Effects 0.000 description 10
- 108020004414 DNA Proteins 0.000 description 9
- 101001011441 Homo sapiens Interferon regulatory factor 4 Proteins 0.000 description 9
- 101001043807 Homo sapiens Interleukin-7 Proteins 0.000 description 9
- 102100036981 Interferon regulatory factor 1 Human genes 0.000 description 9
- 102100030126 Interferon regulatory factor 4 Human genes 0.000 description 9
- -1 antibodies Chemical class 0.000 description 9
- 230000000903 blocking effect Effects 0.000 description 9
- 239000000872 buffer Substances 0.000 description 9
- 102000052622 human IL7 Human genes 0.000 description 9
- 210000003819 peripheral blood mononuclear cell Anatomy 0.000 description 9
- 238000012163 sequencing technique Methods 0.000 description 9
- 108010074708 B7-H1 Antigen Proteins 0.000 description 8
- 108010077544 Chromatin Proteins 0.000 description 8
- 101000598002 Homo sapiens Interferon regulatory factor 1 Proteins 0.000 description 8
- 101000738771 Homo sapiens Receptor-type tyrosine-protein phosphatase C Proteins 0.000 description 8
- 101150030213 Lag3 gene Proteins 0.000 description 8
- 102100037422 Receptor-type tyrosine-protein phosphatase C Human genes 0.000 description 8
- 230000008859 change Effects 0.000 description 8
- 210000003483 chromatin Anatomy 0.000 description 8
- 239000012634 fragment Substances 0.000 description 8
- 230000000770 proinflammatory effect Effects 0.000 description 8
- 238000011144 upstream manufacturing Methods 0.000 description 8
- 208000026310 Breast neoplasm Diseases 0.000 description 7
- 101000914514 Homo sapiens T-cell-specific surface glycoprotein CD28 Proteins 0.000 description 7
- ZDXPYRJPNDTMRX-VKHMYHEASA-N L-glutamine Chemical compound OC(=O)[C@@H](N)CCC(N)=O ZDXPYRJPNDTMRX-VKHMYHEASA-N 0.000 description 7
- 229930182555 Penicillin Natural products 0.000 description 7
- JGSARLDLIJGVTE-MBNYWOFBSA-N Penicillin G Chemical compound N([C@H]1[C@H]2SC([C@@H](N2C1=O)C(O)=O)(C)C)C(=O)CC1=CC=CC=C1 JGSARLDLIJGVTE-MBNYWOFBSA-N 0.000 description 7
- 102100027213 T-cell-specific surface glycoprotein CD28 Human genes 0.000 description 7
- 238000003556 assay Methods 0.000 description 7
- 238000009826 distribution Methods 0.000 description 7
- 230000005934 immune activation Effects 0.000 description 7
- 238000009169 immunotherapy Methods 0.000 description 7
- 210000001165 lymph node Anatomy 0.000 description 7
- 229940049954 penicillin Drugs 0.000 description 7
- 108090000765 processed proteins & peptides Proteins 0.000 description 7
- 229960005322 streptomycin Drugs 0.000 description 7
- 238000011740 C57BL/6 mouse Methods 0.000 description 6
- 108091079001 CRISPR RNA Proteins 0.000 description 6
- 102000004127 Cytokines Human genes 0.000 description 6
- 108090000695 Cytokines Proteins 0.000 description 6
- 102000001398 Granzyme Human genes 0.000 description 6
- 108060005986 Granzyme Proteins 0.000 description 6
- 102000010029 Homer Scaffolding Proteins Human genes 0.000 description 6
- 108010077223 Homer Scaffolding Proteins Proteins 0.000 description 6
- 101001057508 Homo sapiens Ubiquitin-like protein ISG15 Proteins 0.000 description 6
- 229930182816 L-glutamine Natural products 0.000 description 6
- 102100027266 Ubiquitin-like protein ISG15 Human genes 0.000 description 6
- 230000003915 cell function Effects 0.000 description 6
- 230000007812 deficiency Effects 0.000 description 6
- 238000005457 optimization Methods 0.000 description 6
- RXWNCPJZOCPEPQ-NVWDDTSBSA-N puromycin Chemical compound C1=CC(OC)=CC=C1C[C@H](N)C(=O)N[C@H]1[C@@H](O)[C@H](N2C3=NC=NC(=C3N=C2)N(C)C)O[C@@H]1CO RXWNCPJZOCPEPQ-NVWDDTSBSA-N 0.000 description 6
- 238000012360 testing method Methods 0.000 description 6
- 230000014616 translation Effects 0.000 description 6
- 230000035899 viability Effects 0.000 description 6
- 206010006187 Breast cancer Diseases 0.000 description 5
- 101100005713 Homo sapiens CD4 gene Proteins 0.000 description 5
- 101000852870 Homo sapiens Interferon alpha/beta receptor 1 Proteins 0.000 description 5
- 102100036714 Interferon alpha/beta receptor 1 Human genes 0.000 description 5
- 101710089751 Interferon-induced, double-stranded RNA-activated protein kinase Proteins 0.000 description 5
- 102100034170 Interferon-induced, double-stranded RNA-activated protein kinase Human genes 0.000 description 5
- 102000000588 Interleukin-2 Human genes 0.000 description 5
- 108010002350 Interleukin-2 Proteins 0.000 description 5
- 108700012920 TNF Proteins 0.000 description 5
- 230000000692 anti-sense effect Effects 0.000 description 5
- 230000001684 chronic effect Effects 0.000 description 5
- 238000002955 isolation Methods 0.000 description 5
- 239000000203 mixture Substances 0.000 description 5
- 210000004940 nucleus Anatomy 0.000 description 5
- 238000001543 one-way ANOVA Methods 0.000 description 5
- 239000002953 phosphate buffered saline Substances 0.000 description 5
- 230000000717 retained effect Effects 0.000 description 5
- 238000012174 single-cell RNA sequencing Methods 0.000 description 5
- 230000001225 therapeutic effect Effects 0.000 description 5
- 101150063370 Gzmb gene Proteins 0.000 description 4
- 108091054729 IRF family Proteins 0.000 description 4
- 102000016854 Interferon Regulatory Factors Human genes 0.000 description 4
- 241000712899 Lymphocytic choriomeningitis mammarenavirus Species 0.000 description 4
- 238000003559 RNA-seq method Methods 0.000 description 4
- 108010044012 STAT1 Transcription Factor Proteins 0.000 description 4
- 102100029904 Signal transducer and activator of transcription 1-alpha/beta Human genes 0.000 description 4
- 238000000692 Student's t-test Methods 0.000 description 4
- 108091027544 Subgenomic mRNA Proteins 0.000 description 4
- 230000005867 T cell response Effects 0.000 description 4
- 208000009956 adenocarcinoma Diseases 0.000 description 4
- 230000000875 corresponding effect Effects 0.000 description 4
- 231100000433 cytotoxic Toxicity 0.000 description 4
- 230000001472 cytotoxic effect Effects 0.000 description 4
- 230000003292 diminished effect Effects 0.000 description 4
- 230000001973 epigenetic effect Effects 0.000 description 4
- 238000002347 injection Methods 0.000 description 4
- 239000007924 injection Substances 0.000 description 4
- 229940079322 interferon Drugs 0.000 description 4
- 238000012083 mass cytometry Methods 0.000 description 4
- 239000013642 negative control Substances 0.000 description 4
- 239000002831 pharmacologic agent Substances 0.000 description 4
- 230000002035 prolonged effect Effects 0.000 description 4
- 230000003827 upregulation Effects 0.000 description 4
- 230000009385 viral infection Effects 0.000 description 4
- 101150080672 Bst2 gene Proteins 0.000 description 3
- 102100032912 CD44 antigen Human genes 0.000 description 3
- 210000001266 CD8-positive T-lymphocyte Anatomy 0.000 description 3
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 3
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 3
- 101000868273 Homo sapiens CD44 antigen Proteins 0.000 description 3
- 101001032342 Homo sapiens Interferon regulatory factor 7 Proteins 0.000 description 3
- 101000599037 Homo sapiens Zinc finger protein Helios Proteins 0.000 description 3
- 101150106931 IFNG gene Proteins 0.000 description 3
- 102100038070 Interferon regulatory factor 7 Human genes 0.000 description 3
- 108010014426 Lymphocytic choriomeningitis virus glycoprotein peptide 33-41 Proteins 0.000 description 3
- 238000000585 Mann–Whitney U test Methods 0.000 description 3
- 101100260032 Mus musculus Tbx21 gene Proteins 0.000 description 3
- 239000006146 Roswell Park Memorial Institute medium Substances 0.000 description 3
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 description 3
- 230000006044 T cell activation Effects 0.000 description 3
- 108091028113 Trans-activating crRNA Proteins 0.000 description 3
- 208000036142 Viral infection Diseases 0.000 description 3
- 102100037796 Zinc finger protein Helios Human genes 0.000 description 3
- 239000000427 antigen Substances 0.000 description 3
- 108091007433 antigens Proteins 0.000 description 3
- 102000036639 antigens Human genes 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 230000031018 biological processes and functions Effects 0.000 description 3
- 230000033228 biological regulation Effects 0.000 description 3
- 230000020411 cell activation Effects 0.000 description 3
- 238000004113 cell culture Methods 0.000 description 3
- 230000001413 cellular effect Effects 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 230000004186 co-expression Effects 0.000 description 3
- 230000001351 cycling effect Effects 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 3
- 239000003937 drug carrier Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000002708 enhancing effect Effects 0.000 description 3
- 238000001914 filtration Methods 0.000 description 3
- 230000004547 gene signature Effects 0.000 description 3
- 230000012010 growth Effects 0.000 description 3
- 230000005746 immune checkpoint blockade Effects 0.000 description 3
- 230000002163 immunogen Effects 0.000 description 3
- 238000000338 in vitro Methods 0.000 description 3
- 230000001939 inductive effect Effects 0.000 description 3
- 230000004968 inflammatory condition Effects 0.000 description 3
- 230000002401 inhibitory effect Effects 0.000 description 3
- 230000005764 inhibitory process Effects 0.000 description 3
- 230000003834 intracellular effect Effects 0.000 description 3
- 229910052751 metal Inorganic materials 0.000 description 3
- 239000002184 metal Substances 0.000 description 3
- 230000002438 mitochondrial effect Effects 0.000 description 3
- 101150107865 prf1 gene Proteins 0.000 description 3
- 229950010131 puromycin Drugs 0.000 description 3
- 238000011002 quantification Methods 0.000 description 3
- 230000002829 reductive effect Effects 0.000 description 3
- 239000007787 solid Substances 0.000 description 3
- 210000000130 stem cell Anatomy 0.000 description 3
- 230000004936 stimulating effect Effects 0.000 description 3
- 210000001519 tissue Anatomy 0.000 description 3
- 238000013518 transcription Methods 0.000 description 3
- 230000035897 transcription Effects 0.000 description 3
- CJDRUOGAGYHKKD-XMTJACRCSA-N (+)-Ajmaline Natural products O[C@H]1[C@@H](CC)[C@@H]2[C@@H]3[C@H](O)[C@@]45[C@@H](N(C)c6c4cccc6)[C@@H](N1[C@H]3C5)C2 CJDRUOGAGYHKKD-XMTJACRCSA-N 0.000 description 2
- 102000040650 (ribonucleotides)n+m Human genes 0.000 description 2
- JKMHFZQWWAIEOD-UHFFFAOYSA-N 2-[4-(2-hydroxyethyl)piperazin-1-yl]ethanesulfonic acid Chemical compound OCC[NH+]1CCN(CCS([O-])(=O)=O)CC1 JKMHFZQWWAIEOD-UHFFFAOYSA-N 0.000 description 2
- 102100030379 Acyl-coenzyme A synthetase ACSM2A, mitochondrial Human genes 0.000 description 2
- 238000011357 CAR T-cell therapy Methods 0.000 description 2
- 102100033471 Cbp/p300-interacting transactivator 2 Human genes 0.000 description 2
- 102000053602 DNA Human genes 0.000 description 2
- 108010014303 DNA-directed DNA polymerase Proteins 0.000 description 2
- 102000016928 DNA-directed DNA polymerase Human genes 0.000 description 2
- 108700039887 Essential Genes Proteins 0.000 description 2
- 239000007995 HEPES buffer Substances 0.000 description 2
- 101100054737 Homo sapiens ACSM2A gene Proteins 0.000 description 2
- 101000944098 Homo sapiens Cbp/p300-interacting transactivator 2 Proteins 0.000 description 2
- 101001032341 Homo sapiens Interferon regulatory factor 9 Proteins 0.000 description 2
- 101001057504 Homo sapiens Interferon-stimulated gene 20 kDa protein Proteins 0.000 description 2
- 101001055144 Homo sapiens Interleukin-2 receptor subunit alpha Proteins 0.000 description 2
- 101001018097 Homo sapiens L-selectin Proteins 0.000 description 2
- 101000804764 Homo sapiens Lymphotactin Proteins 0.000 description 2
- 101000831496 Homo sapiens Toll-like receptor 3 Proteins 0.000 description 2
- 102000037982 Immune checkpoint proteins Human genes 0.000 description 2
- 108091008036 Immune checkpoint proteins Proteins 0.000 description 2
- 206010061218 Inflammation Diseases 0.000 description 2
- 108010014726 Interferon Type I Proteins 0.000 description 2
- 102000002227 Interferon Type I Human genes 0.000 description 2
- 102100038251 Interferon regulatory factor 9 Human genes 0.000 description 2
- 102100027268 Interferon-stimulated gene 20 kDa protein Human genes 0.000 description 2
- 102000000704 Interleukin-7 Human genes 0.000 description 2
- 108010002586 Interleukin-7 Proteins 0.000 description 2
- 102100033467 L-selectin Human genes 0.000 description 2
- 206010058467 Lung neoplasm malignant Diseases 0.000 description 2
- 102100035304 Lymphotactin Human genes 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 2
- 241000699660 Mus musculus Species 0.000 description 2
- 101001043808 Mus musculus Interleukin-7 Proteins 0.000 description 2
- 101100396694 Mus musculus Nfkbid gene Proteins 0.000 description 2
- 108010057466 NF-kappa B Proteins 0.000 description 2
- 102000003945 NF-kappa B Human genes 0.000 description 2
- 229930193140 Neomycin Natural products 0.000 description 2
- 239000012979 RPMI medium Substances 0.000 description 2
- 108010074687 Signaling Lymphocytic Activation Molecule Family Member 1 Proteins 0.000 description 2
- 102100029215 Signaling lymphocytic activation molecule Human genes 0.000 description 2
- 102100024324 Toll-like receptor 3 Human genes 0.000 description 2
- 230000003321 amplification Effects 0.000 description 2
- 230000003042 antagnostic effect Effects 0.000 description 2
- 230000005975 antitumor immune response Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- 239000011324 bead Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000012240 conditional targeting Methods 0.000 description 2
- 230000016396 cytokine production Effects 0.000 description 2
- 230000001461 cytolytic effect Effects 0.000 description 2
- 210000001151 cytotoxic T lymphocyte Anatomy 0.000 description 2
- 210000004443 dendritic cell Anatomy 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 230000000779 depleting effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000004069 differentiation Effects 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 238000010353 genetic engineering Methods 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 230000006801 homologous recombination Effects 0.000 description 2
- 238000002744 homologous recombination Methods 0.000 description 2
- 230000008629 immune suppression Effects 0.000 description 2
- 208000026278 immune system disease Diseases 0.000 description 2
- 230000036039 immunity Effects 0.000 description 2
- 238000011534 incubation Methods 0.000 description 2
- 230000002757 inflammatory effect Effects 0.000 description 2
- 230000004054 inflammatory process Effects 0.000 description 2
- 238000011081 inoculation Methods 0.000 description 2
- 239000007951 isotonicity adjuster Substances 0.000 description 2
- 210000003141 lower extremity Anatomy 0.000 description 2
- 201000005202 lung cancer Diseases 0.000 description 2
- 208000020816 lung neoplasm Diseases 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 229960004927 neomycin Drugs 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000003199 nucleic acid amplification method Methods 0.000 description 2
- 210000005105 peripheral blood lymphocyte Anatomy 0.000 description 2
- 230000008823 permeabilization Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000001737 promoting effect Effects 0.000 description 2
- 238000003908 quality control method Methods 0.000 description 2
- 102000005962 receptors Human genes 0.000 description 2
- 108020003175 receptors Proteins 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000004043 responsiveness Effects 0.000 description 2
- 239000011780 sodium chloride Substances 0.000 description 2
- DAEPDZWVDSPTHF-UHFFFAOYSA-M sodium pyruvate Chemical compound [Na+].CC(=O)C([O-])=O DAEPDZWVDSPTHF-UHFFFAOYSA-M 0.000 description 2
- 230000003393 splenic effect Effects 0.000 description 2
- 230000009261 transgenic effect Effects 0.000 description 2
- 238000011830 transgenic mouse model Methods 0.000 description 2
- DGVVWUTYPXICAM-UHFFFAOYSA-N β‐Mercaptoethanol Chemical compound OCCS DGVVWUTYPXICAM-UHFFFAOYSA-N 0.000 description 2
- 102100022464 5'-nucleotidase Human genes 0.000 description 1
- 108700028369 Alleles Proteins 0.000 description 1
- 108020000948 Antisense Oligonucleotides Proteins 0.000 description 1
- 102100021631 B-cell lymphoma 6 protein Human genes 0.000 description 1
- 101150115284 BIRC5 gene Proteins 0.000 description 1
- 101150013999 CRBN gene Proteins 0.000 description 1
- 238000010453 CRISPR/Cas method Methods 0.000 description 1
- 101100454807 Caenorhabditis elegans lgg-1 gene Proteins 0.000 description 1
- 102100037403 Carbohydrate-responsive element-binding protein Human genes 0.000 description 1
- 208000005623 Carcinogenesis Diseases 0.000 description 1
- 208000017667 Chronic Disease Diseases 0.000 description 1
- 206010052360 Colorectal adenocarcinoma Diseases 0.000 description 1
- 102100039498 Cytotoxic T-lymphocyte protein 4 Human genes 0.000 description 1
- FBPFZTCFMRRESA-FSIIMWSLSA-N D-Glucitol Natural products OC[C@H](O)[C@H](O)[C@@H](O)[C@H](O)CO FBPFZTCFMRRESA-FSIIMWSLSA-N 0.000 description 1
- FBPFZTCFMRRESA-KVTDHHQDSA-N D-Mannitol Chemical compound OC[C@@H](O)[C@@H](O)[C@H](O)[C@H](O)CO FBPFZTCFMRRESA-KVTDHHQDSA-N 0.000 description 1
- FBPFZTCFMRRESA-JGWLITMVSA-N D-glucitol Chemical compound OC[C@H](O)[C@@H](O)[C@H](O)[C@H](O)CO FBPFZTCFMRRESA-JGWLITMVSA-N 0.000 description 1
- 230000004568 DNA-binding Effects 0.000 description 1
- 108010008532 Deoxyribonuclease I Proteins 0.000 description 1
- 102000007260 Deoxyribonuclease I Human genes 0.000 description 1
- 102000016607 Diphtheria Toxin Human genes 0.000 description 1
- 108010053187 Diphtheria Toxin Proteins 0.000 description 1
- 239000006144 Dulbecco’s modified Eagle's medium Substances 0.000 description 1
- 101150107922 EEF1A1 gene Proteins 0.000 description 1
- 101150033008 EIF2AK4 gene Proteins 0.000 description 1
- 101150039757 EIF3E gene Proteins 0.000 description 1
- 102100025137 Early activation antigen CD69 Human genes 0.000 description 1
- 101150028132 Eif3h gene Proteins 0.000 description 1
- 208000000461 Esophageal Neoplasms Diseases 0.000 description 1
- 101150043363 GZMK gene Proteins 0.000 description 1
- 108700039691 Genetic Promoter Regions Proteins 0.000 description 1
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 description 1
- 108090000288 Glycoproteins Proteins 0.000 description 1
- 102000003886 Glycoproteins Human genes 0.000 description 1
- 102100036242 HLA class II histocompatibility antigen, DQ alpha 2 chain Human genes 0.000 description 1
- 239000012981 Hank's balanced salt solution Substances 0.000 description 1
- 101150046249 Havcr2 gene Proteins 0.000 description 1
- 102100034458 Hepatitis A virus cellular receptor 2 Human genes 0.000 description 1
- 101000678236 Homo sapiens 5'-nucleotidase Proteins 0.000 description 1
- 101000971234 Homo sapiens B-cell lymphoma 6 protein Proteins 0.000 description 1
- 101000952179 Homo sapiens Carbohydrate-responsive element-binding protein Proteins 0.000 description 1
- 101000889276 Homo sapiens Cytotoxic T-lymphocyte protein 4 Proteins 0.000 description 1
- 101000934374 Homo sapiens Early activation antigen CD69 Proteins 0.000 description 1
- 101000930801 Homo sapiens HLA class II histocompatibility antigen, DQ alpha 2 chain Proteins 0.000 description 1
- 101100341519 Homo sapiens ITGAX gene Proteins 0.000 description 1
- 101001043764 Homo sapiens Inhibitor of nuclear factor kappa-B kinase subunit alpha Proteins 0.000 description 1
- 101000852865 Homo sapiens Interferon alpha/beta receptor 2 Proteins 0.000 description 1
- 101001011382 Homo sapiens Interferon regulatory factor 3 Proteins 0.000 description 1
- 101001032345 Homo sapiens Interferon regulatory factor 8 Proteins 0.000 description 1
- 101001083151 Homo sapiens Interleukin-10 receptor subunit alpha Proteins 0.000 description 1
- 101001138022 Homo sapiens La-related protein 1 Proteins 0.000 description 1
- 101001030211 Homo sapiens Myc proto-oncogene protein Proteins 0.000 description 1
- 101000961071 Homo sapiens NF-kappa-B inhibitor alpha Proteins 0.000 description 1
- 101000941994 Homo sapiens Protein cereblon Proteins 0.000 description 1
- 101000893493 Homo sapiens Protein flightless-1 homolog Proteins 0.000 description 1
- 101100087590 Homo sapiens RICTOR gene Proteins 0.000 description 1
- 101000914484 Homo sapiens T-lymphocyte activation antigen CD80 Proteins 0.000 description 1
- 101000669447 Homo sapiens Toll-like receptor 4 Proteins 0.000 description 1
- 101000669402 Homo sapiens Toll-like receptor 7 Proteins 0.000 description 1
- 102000001284 I-kappa-B kinase Human genes 0.000 description 1
- 108060006678 I-kappa-B kinase Proteins 0.000 description 1
- 101150055020 Il2rb gene Proteins 0.000 description 1
- 102100021892 Inhibitor of nuclear factor kappa-B kinase subunit alpha Human genes 0.000 description 1
- 238000012351 Integrated analysis Methods 0.000 description 1
- 102100022297 Integrin alpha-X Human genes 0.000 description 1
- 102100036718 Interferon alpha/beta receptor 2 Human genes 0.000 description 1
- 108090000890 Interferon regulatory factor 1 Proteins 0.000 description 1
- 102100029843 Interferon regulatory factor 3 Human genes 0.000 description 1
- 102100038069 Interferon regulatory factor 8 Human genes 0.000 description 1
- 108010047761 Interferon-alpha Proteins 0.000 description 1
- 102000006992 Interferon-alpha Human genes 0.000 description 1
- 102000003996 Interferon-beta Human genes 0.000 description 1
- 108090000467 Interferon-beta Proteins 0.000 description 1
- 108010074328 Interferon-gamma Proteins 0.000 description 1
- 102000008070 Interferon-gamma Human genes 0.000 description 1
- 102100030236 Interleukin-10 receptor subunit alpha Human genes 0.000 description 1
- 102000013462 Interleukin-12 Human genes 0.000 description 1
- 108010065805 Interleukin-12 Proteins 0.000 description 1
- 108010032774 Interleukin-2 Receptor alpha Subunit Proteins 0.000 description 1
- 102000007351 Interleukin-2 Receptor alpha Subunit Human genes 0.000 description 1
- 102100030704 Interleukin-21 Human genes 0.000 description 1
- 101150009057 JAK2 gene Proteins 0.000 description 1
- 101150032862 LEF-1 gene Proteins 0.000 description 1
- 102100020859 La-related protein 1 Human genes 0.000 description 1
- 108700012912 MYCN Proteins 0.000 description 1
- 101150022024 MYCN gene Proteins 0.000 description 1
- 229930195725 Mannitol Natural products 0.000 description 1
- 102100025169 Max-binding protein MNT Human genes 0.000 description 1
- 101710202709 Middle T antigen Proteins 0.000 description 1
- 101100500670 Mus musculus Eef1b gene Proteins 0.000 description 1
- 101100232352 Mus musculus Il12rb1 gene Proteins 0.000 description 1
- 101001011398 Mus musculus Interferon regulatory factor 2 Proteins 0.000 description 1
- 101100072782 Mus musculus Irf2 gene Proteins 0.000 description 1
- 101100396696 Mus musculus Nfkbie gene Proteins 0.000 description 1
- 102100038895 Myc proto-oncogene protein Human genes 0.000 description 1
- 102000010168 Myeloid Differentiation Factor 88 Human genes 0.000 description 1
- 108010077432 Myeloid Differentiation Factor 88 Proteins 0.000 description 1
- 108700026495 N-Myc Proto-Oncogene Proteins 0.000 description 1
- 102100030124 N-myc proto-oncogene protein Human genes 0.000 description 1
- 102100039337 NF-kappa-B inhibitor alpha Human genes 0.000 description 1
- 101150026563 NR4A2 gene Proteins 0.000 description 1
- 101150091206 Nfkbia gene Proteins 0.000 description 1
- 101150075616 Nr4a3 gene Proteins 0.000 description 1
- 101150101087 Nt5e gene Proteins 0.000 description 1
- 108091028043 Nucleic acid sequence Proteins 0.000 description 1
- 108010047956 Nucleosomes Proteins 0.000 description 1
- 238000010222 PCR analysis Methods 0.000 description 1
- 102100024894 PR domain zinc finger protein 1 Human genes 0.000 description 1
- 229930040373 Paraformaldehyde Natural products 0.000 description 1
- 101150050396 Pclaf gene Proteins 0.000 description 1
- 108091000080 Phosphotransferase Proteins 0.000 description 1
- 108010009975 Positive Regulatory Domain I-Binding Factor 1 Proteins 0.000 description 1
- 101710089372 Programmed cell death protein 1 Proteins 0.000 description 1
- 102100020847 Protein FosB Human genes 0.000 description 1
- 102100032783 Protein cereblon Human genes 0.000 description 1
- 102100040923 Protein flightless-1 homolog Human genes 0.000 description 1
- 102000046941 Rapamycin-Insensitive Companion of mTOR Human genes 0.000 description 1
- 108700019586 Rapamycin-Insensitive Companion of mTOR Proteins 0.000 description 1
- 208000035999 Recurrence Diseases 0.000 description 1
- 101150029963 Rrm2 gene Proteins 0.000 description 1
- 102000004265 STAT2 Transcription Factor Human genes 0.000 description 1
- 108010081691 STAT2 Transcription Factor Proteins 0.000 description 1
- 101150099493 STAT3 gene Proteins 0.000 description 1
- 101150058731 STAT5A gene Proteins 0.000 description 1
- 101150045565 Socs1 gene Proteins 0.000 description 1
- 238000012352 Spearman correlation analysis Methods 0.000 description 1
- 241000193996 Streptococcus pyogenes Species 0.000 description 1
- 108700027336 Suppressor of Cytokine Signaling 1 Proteins 0.000 description 1
- 102100024779 Suppressor of cytokine signaling 1 Human genes 0.000 description 1
- 230000037453 T cell priming Effects 0.000 description 1
- 102100027222 T-lymphocyte activation antigen CD80 Human genes 0.000 description 1
- 210000000662 T-lymphocyte subset Anatomy 0.000 description 1
- 101150056647 TNFRSF4 gene Proteins 0.000 description 1
- 102000013530 TOR Serine-Threonine Kinases Human genes 0.000 description 1
- 108010065917 TOR Serine-Threonine Kinases Proteins 0.000 description 1
- 101150052454 TTC39C gene Proteins 0.000 description 1
- 108010006785 Taq Polymerase Proteins 0.000 description 1
- 108010060818 Toll-Like Receptor 9 Proteins 0.000 description 1
- 102000002689 Toll-like receptor Human genes 0.000 description 1
- 108020000411 Toll-like receptor Proteins 0.000 description 1
- 102100039360 Toll-like receptor 4 Human genes 0.000 description 1
- 102100039390 Toll-like receptor 7 Human genes 0.000 description 1
- 102100033117 Toll-like receptor 9 Human genes 0.000 description 1
- 101150017997 Tox gene Proteins 0.000 description 1
- 108010018242 Transcription Factor AP-1 Proteins 0.000 description 1
- 108700009124 Transcription Initiation Site Proteins 0.000 description 1
- 102000004060 Transforming Growth Factor-beta Type II Receptor Human genes 0.000 description 1
- 108010082684 Transforming Growth Factor-beta Type II Receptor Proteins 0.000 description 1
- 102100033178 Vascular endothelial growth factor receptor 1 Human genes 0.000 description 1
- JLCPHMBAVCMARE-UHFFFAOYSA-N [3-[[3-[[3-[[3-[[3-[[3-[[3-[[3-[[3-[[3-[[3-[[5-(2-amino-6-oxo-1H-purin-9-yl)-3-[[3-[[3-[[3-[[3-[[3-[[5-(2-amino-6-oxo-1H-purin-9-yl)-3-[[5-(2-amino-6-oxo-1H-purin-9-yl)-3-hydroxyoxolan-2-yl]methoxy-hydroxyphosphoryl]oxyoxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(5-methyl-2,4-dioxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxyoxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(5-methyl-2,4-dioxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(4-amino-2-oxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(5-methyl-2,4-dioxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(5-methyl-2,4-dioxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(4-amino-2-oxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(4-amino-2-oxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(4-amino-2-oxopyrimidin-1-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(6-aminopurin-9-yl)oxolan-2-yl]methoxy-hydroxyphosphoryl]oxy-5-(4-amino-2-oxopyrimidin-1-yl)oxolan-2-yl]methyl [5-(6-aminopurin-9-yl)-2-(hydroxymethyl)oxolan-3-yl] hydrogen phosphate Polymers Cc1cn(C2CC(OP(O)(=O)OCC3OC(CC3OP(O)(=O)OCC3OC(CC3O)n3cnc4c3nc(N)[nH]c4=O)n3cnc4c3nc(N)[nH]c4=O)C(COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3COP(O)(=O)OC3CC(OC3CO)n3cnc4c(N)ncnc34)n3ccc(N)nc3=O)n3cnc4c(N)ncnc34)n3ccc(N)nc3=O)n3ccc(N)nc3=O)n3ccc(N)nc3=O)n3cnc4c(N)ncnc34)n3cnc4c(N)ncnc34)n3cc(C)c(=O)[nH]c3=O)n3cc(C)c(=O)[nH]c3=O)n3ccc(N)nc3=O)n3cc(C)c(=O)[nH]c3=O)n3cnc4c3nc(N)[nH]c4=O)n3cnc4c(N)ncnc34)n3cnc4c(N)ncnc34)n3cnc4c(N)ncnc34)n3cnc4c(N)ncnc34)O2)c(=O)[nH]c1=O JLCPHMBAVCMARE-UHFFFAOYSA-N 0.000 description 1
- 239000003070 absorption delaying agent Substances 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 210000000577 adipose tissue Anatomy 0.000 description 1
- 150000001413 amino acids Chemical class 0.000 description 1
- 238000012197 amplification kit Methods 0.000 description 1
- 239000003242 anti bacterial agent Substances 0.000 description 1
- 230000000844 anti-bacterial effect Effects 0.000 description 1
- 230000001093 anti-cancer Effects 0.000 description 1
- 229940124650 anti-cancer therapies Drugs 0.000 description 1
- 230000003466 anti-cipated effect Effects 0.000 description 1
- 230000003110 anti-inflammatory effect Effects 0.000 description 1
- 230000003443 anti-oncogenic effect Effects 0.000 description 1
- 238000011319 anticancer therapy Methods 0.000 description 1
- 229940121375 antifungal agent Drugs 0.000 description 1
- 239000003429 antifungal agent Substances 0.000 description 1
- 230000030741 antigen processing and presentation Effects 0.000 description 1
- 230000006907 apoptotic process Effects 0.000 description 1
- 230000005784 autoimmunity Effects 0.000 description 1
- 210000003719 b-lymphocyte Anatomy 0.000 description 1
- 101150092503 batf gene Proteins 0.000 description 1
- WQZGKKKJIJFFOK-VFUOTHLCSA-N beta-D-glucose Chemical compound OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-VFUOTHLCSA-N 0.000 description 1
- 230000002715 bioenergetic effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000001574 biopsy Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000005422 blasting Methods 0.000 description 1
- 210000002459 blastocyst Anatomy 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 238000009395 breeding Methods 0.000 description 1
- 230000001488 breeding effect Effects 0.000 description 1
- KQNZDYYTLMIZCT-KQPMLPITSA-N brefeldin A Chemical compound O[C@@H]1\C=C\C(=O)O[C@@H](C)CCC\C=C\[C@@H]2C[C@H](O)C[C@H]21 KQNZDYYTLMIZCT-KQPMLPITSA-N 0.000 description 1
- JUMGSHROWPPKFX-UHFFFAOYSA-N brefeldin-A Natural products CC1CCCC=CC2(C)CC(O)CC2(C)C(O)C=CC(=O)O1 JUMGSHROWPPKFX-UHFFFAOYSA-N 0.000 description 1
- 230000036952 cancer formation Effects 0.000 description 1
- 230000005907 cancer growth Effects 0.000 description 1
- 231100000504 carcinogenesis Toxicity 0.000 description 1
- 108700021031 cdc Genes Proteins 0.000 description 1
- 230000022131 cell cycle Effects 0.000 description 1
- 230000030833 cell death Effects 0.000 description 1
- 230000011712 cell development Effects 0.000 description 1
- 230000024245 cell differentiation Effects 0.000 description 1
- 230000006037 cell lysis Effects 0.000 description 1
- 230000004663 cell proliferation Effects 0.000 description 1
- 238000002659 cell therapy Methods 0.000 description 1
- 230000019522 cellular metabolic process Effects 0.000 description 1
- 230000036755 cellular response Effects 0.000 description 1
- YRQNKMKHABXEJZ-UVQQGXFZSA-N chembl176323 Chemical compound C1C[C@]2(C)[C@@]3(C)CC(N=C4C[C@]5(C)CCC6[C@]7(C)CC[C@@H]([C@]7(CC[C@]6(C)[C@@]5(C)CC4=N4)C)CCCCCCCC)=C4C[C@]3(C)CCC2[C@]2(C)CC[C@H](CCCCCCCC)[C@]21C YRQNKMKHABXEJZ-UVQQGXFZSA-N 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- DQLATGHUWYMOKM-UHFFFAOYSA-L cisplatin Chemical compound N[Pt](N)(Cl)Cl DQLATGHUWYMOKM-UHFFFAOYSA-L 0.000 description 1
- 229960004316 cisplatin Drugs 0.000 description 1
- 238000003776 cleavage reaction Methods 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 201000010897 colon adenocarcinoma Diseases 0.000 description 1
- 208000029742 colonic neoplasm Diseases 0.000 description 1
- 239000002299 complementary DNA Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 208000030381 cutaneous melanoma Diseases 0.000 description 1
- 231100000135 cytotoxicity Toxicity 0.000 description 1
- 230000003013 cytotoxicity Effects 0.000 description 1
- 230000005860 defense response to virus Effects 0.000 description 1
- 238000004925 denaturation Methods 0.000 description 1
- 230000036425 denaturation Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000001627 detrimental effect Effects 0.000 description 1
- 239000008121 dextrose Substances 0.000 description 1
- 239000005546 dideoxynucleotide Substances 0.000 description 1
- 230000009274 differential gene expression Effects 0.000 description 1
- LOKCTEFSRHRXRJ-UHFFFAOYSA-I dipotassium trisodium dihydrogen phosphate hydrogen phosphate dichloride Chemical compound P(=O)(O)(O)[O-].[K+].P(=O)(O)([O-])[O-].[Na+].[Na+].[Cl-].[K+].[Cl-].[Na+] LOKCTEFSRHRXRJ-UHFFFAOYSA-I 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 239000002612 dispersion medium Substances 0.000 description 1
- 238000002224 dissection Methods 0.000 description 1
- 230000003828 downregulation Effects 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 239000003995 emulsifying agent Substances 0.000 description 1
- 210000003743 erythrocyte Anatomy 0.000 description 1
- 239000003797 essential amino acid Substances 0.000 description 1
- 235000020776 essential amino acid Nutrition 0.000 description 1
- 230000017188 evasion or tolerance of host immune response Effects 0.000 description 1
- 230000001747 exhibiting effect Effects 0.000 description 1
- 230000008713 feedback mechanism Effects 0.000 description 1
- 238000001943 fluorescence-activated cell sorting Methods 0.000 description 1
- 238000012239 gene modification Methods 0.000 description 1
- 230000005017 genetic modification Effects 0.000 description 1
- 235000013617 genetically modified food Nutrition 0.000 description 1
- 238000003205 genotyping method Methods 0.000 description 1
- 238000009650 gentamicin protection assay Methods 0.000 description 1
- 210000004602 germ cell Anatomy 0.000 description 1
- 239000001963 growth medium Substances 0.000 description 1
- 102000048362 human PDCD1 Human genes 0.000 description 1
- 210000005260 human cell Anatomy 0.000 description 1
- 230000001900 immune effect Effects 0.000 description 1
- 230000028993 immune response Effects 0.000 description 1
- 230000006028 immune-suppresssive effect Effects 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 230000008595 infiltration Effects 0.000 description 1
- 238000001764 infiltration Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000010468 interferon response Effects 0.000 description 1
- 108010074108 interleukin-21 Proteins 0.000 description 1
- 238000010212 intracellular staining Methods 0.000 description 1
- 230000006623 intrinsic pathway Effects 0.000 description 1
- 229910052741 iridium Inorganic materials 0.000 description 1
- GKOZUEZYRPOHIO-UHFFFAOYSA-N iridium atom Chemical compound [Ir] GKOZUEZYRPOHIO-UHFFFAOYSA-N 0.000 description 1
- 230000002147 killing effect Effects 0.000 description 1
- 238000011813 knockout mouse model Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000001325 log-rank test Methods 0.000 description 1
- 210000002540 macrophage Anatomy 0.000 description 1
- 210000005075 mammary gland Anatomy 0.000 description 1
- 239000000594 mannitol Substances 0.000 description 1
- 235000010355 mannitol Nutrition 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 102000027540 membrane-bound PRRs Human genes 0.000 description 1
- 108091008872 membrane-bound PRRs Proteins 0.000 description 1
- 230000002503 metabolic effect Effects 0.000 description 1
- 239000011325 microbead Substances 0.000 description 1
- 238000000520 microinjection Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 108700039855 mouse a Proteins 0.000 description 1
- 230000009125 negative feedback regulation Effects 0.000 description 1
- 108091027963 non-coding RNA Proteins 0.000 description 1
- 102000042567 non-coding RNA Human genes 0.000 description 1
- 230000005937 nuclear translocation Effects 0.000 description 1
- 108020004707 nucleic acids Proteins 0.000 description 1
- 102000039446 nucleic acids Human genes 0.000 description 1
- 150000007523 nucleic acids Chemical class 0.000 description 1
- 210000001623 nucleosome Anatomy 0.000 description 1
- 239000002773 nucleotide Substances 0.000 description 1
- 125000003729 nucleotide group Chemical group 0.000 description 1
- 101150016677 ohgt gene Proteins 0.000 description 1
- 231100000590 oncogenic Toxicity 0.000 description 1
- 230000002246 oncogenic effect Effects 0.000 description 1
- 229920002866 paraformaldehyde Polymers 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- XEBWQGVWTUSTLN-UHFFFAOYSA-M phenylmercury acetate Chemical compound CC(=O)O[Hg]C1=CC=CC=C1 XEBWQGVWTUSTLN-UHFFFAOYSA-M 0.000 description 1
- 230000026731 phosphorylation Effects 0.000 description 1
- 238000006366 phosphorylation reaction Methods 0.000 description 1
- 102000020233 phosphotransferase Human genes 0.000 description 1
- 239000003755 preservative agent Substances 0.000 description 1
- 230000037452 priming Effects 0.000 description 1
- 238000004393 prognosis Methods 0.000 description 1
- 230000008741 proinflammatory signaling process Effects 0.000 description 1
- 230000002062 proliferating effect Effects 0.000 description 1
- 230000035755 proliferation Effects 0.000 description 1
- 238000013138 pruning Methods 0.000 description 1
- 238000010791 quenching Methods 0.000 description 1
- 230000000171 quenching effect Effects 0.000 description 1
- 239000001397 quillaja saponaria molina bark Substances 0.000 description 1
- 230000006798 recombination Effects 0.000 description 1
- 238000005215 recombination Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
- 230000000754 repressing effect Effects 0.000 description 1
- 230000001718 repressive effect Effects 0.000 description 1
- 230000008672 reprogramming Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 108091008146 restriction endonucleases Proteins 0.000 description 1
- 230000001177 retroviral effect Effects 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 210000003705 ribosome Anatomy 0.000 description 1
- 229930182490 saponin Natural products 0.000 description 1
- 150000007949 saponins Chemical class 0.000 description 1
- 230000007017 scission Effects 0.000 description 1
- 238000013515 script Methods 0.000 description 1
- 230000001235 sensitizing effect Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000019491 signal transduction Effects 0.000 description 1
- 208000017520 skin disease Diseases 0.000 description 1
- 201000003708 skin melanoma Diseases 0.000 description 1
- 229940054269 sodium pyruvate Drugs 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 239000000600 sorbitol Substances 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 210000004988 splenocyte Anatomy 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 235000000346 sugar Nutrition 0.000 description 1
- 150000005846 sugar alcohols Polymers 0.000 description 1
- 150000008163 sugars Chemical class 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
- 238000004448 titration Methods 0.000 description 1
- 231100000331 toxic Toxicity 0.000 description 1
- 230000002588 toxic effect Effects 0.000 description 1
- 238000012085 transcriptional profiling Methods 0.000 description 1
- 108091008023 transcriptional regulators Proteins 0.000 description 1
- 108091006107 transcriptional repressors Proteins 0.000 description 1
- 238000011222 transcriptome analysis Methods 0.000 description 1
- 238000010361 transduction Methods 0.000 description 1
- 230000026683 transduction Effects 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- 230000032258 transport Effects 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 230000003612 virological effect Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- 238000009736 wetting Methods 0.000 description 1
- 239000000080 wetting agent Substances 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
- A61P35/00—Antineoplastic agents
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
- A01K67/00—Rearing or breeding animals, not otherwise provided for; New or modified breeds of animals
- A01K67/027—New or modified breeds of vertebrates
- A01K67/0275—Genetically modified vertebrates, e.g. transgenic
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
- A61K35/00—Medicinal preparations containing materials or reaction products thereof with undetermined constitution
- A61K35/12—Materials from mammals; Compositions comprising non-specified tissues or cells; Compositions comprising non-embryonic stem cells; Genetically modified cells
- A61K35/14—Blood; Artificial blood
- A61K35/17—Lymphocytes; B-cells; T-cells; Natural killer cells; Interferon-activated or cytokine-activated lymphocytes
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
- A61K39/00—Medicinal preparations containing antigens or antibodies
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
- A61K39/00—Medicinal preparations containing antigens or antibodies
- A61K39/395—Antibodies; Immunoglobulins; Immune serum, e.g. antilymphocytic serum
- A61K39/39533—Antibodies; Immunoglobulins; Immune serum, e.g. antilymphocytic serum against materials from animals
- A61K39/39541—Antibodies; Immunoglobulins; Immune serum, e.g. antilymphocytic serum against materials from animals against normal tissues, cells
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
- A61P37/00—Drugs for immunological or allergic disorders
- A61P37/02—Immunomodulators
- A61P37/04—Immunostimulants
-
- C—CHEMISTRY; METALLURGY
- C07—ORGANIC CHEMISTRY
- C07K—PEPTIDES
- C07K14/00—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
- C07K14/435—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans
-
- C—CHEMISTRY; METALLURGY
- C07—ORGANIC CHEMISTRY
- C07K—PEPTIDES
- C07K16/00—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies
- C07K16/18—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans
- C07K16/28—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
- C07K16/2803—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants against the immunoglobulin superfamily
- C07K16/2818—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants against the immunoglobulin superfamily against CD28 or CD152
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
- A01K2207/00—Modified animals
- A01K2207/12—Animals modified by administration of exogenous cells
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
- A01K2207/00—Modified animals
- A01K2207/15—Humanized animals
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
- A01K2217/00—Genetically modified animals
- A01K2217/07—Animals genetically altered by homologous recombination
- A01K2217/075—Animals genetically altered by homologous recombination inducing loss of function, i.e. knock out
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
- A01K2217/00—Genetically modified animals
- A01K2217/20—Animal model comprising regulated expression system
- A01K2217/206—Animal model comprising tissue-specific expression system, e.g. tissue specific expression of transgene, of Cre recombinase
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
- A61K39/00—Medicinal preparations containing antigens or antibodies
- A61K2039/505—Medicinal preparations containing antigens or antibodies comprising antibodies
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
- A61K39/00—Medicinal preparations containing antigens or antibodies
- A61K2039/505—Medicinal preparations containing antigens or antibodies comprising antibodies
- A61K2039/507—Comprising a combination of two or more separate antibodies
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
- A61K39/00—Medicinal preparations containing antigens or antibodies
- A61K39/46—Cellular immunotherapy
- A61K39/461—Cellular immunotherapy characterised by the cell type used
- A61K39/4611—T-cells, e.g. tumor infiltrating lymphocytes [TIL], lymphokine-activated killer cells [LAK] or regulatory T cells [Treg]
-
- C—CHEMISTRY; METALLURGY
- C07—ORGANIC CHEMISTRY
- C07K—PEPTIDES
- C07K16/00—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies
- C07K16/18—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans
- C07K16/24—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans against cytokines, lymphokines or interferons
- C07K16/249—Interferons
-
- C—CHEMISTRY; METALLURGY
- C07—ORGANIC CHEMISTRY
- C07K—PEPTIDES
- C07K16/00—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies
- C07K16/18—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans
- C07K16/28—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
- C07K16/2866—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants against receptors for cytokines, lymphokines, interferons
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
- C12N2510/00—Genetically modified cells
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
- C12N5/00—Undifferentiated human, animal or plant cells, e.g. cell lines; Tissues; Cultivation or maintenance thereof; Culture media therefor
- C12N5/06—Animal cells or tissues; Human cells or tissues
- C12N5/0602—Vertebrate cells
- C12N5/0634—Cells from the blood or the immune system
- C12N5/0636—T lymphocytes
Definitions
- the invention relates to compositions for treating cancer and particularly to targeting IRF2 for cancer therapy, including T cell therapy.
- Type I interferons IFN-I; IFNa/p
- type II interferon IFNy
- IFN-ls and IFN-II are also emerging as central regulators of both the chronic immune activation and the suppression that drive cancer progression (Boukhaled et al., 2021 ; Snell et al., 2017).
- the efficacy of many types of anti-cancer therapies, including checkpoint blockades, are associated with increased IFN signaling (Boukhaled et al., 2021 ; Snell et al., 2017).
- IFN-II increases PDL1 expression on the surface of tumor cells. PDL1 binds to PD1 on activated CD8 + T cells in the tumor microenvironment (TME), driving their apoptotic cell death (Dong et al., 2002).
- IFN-ls signal through a dimeric IFNAR1/IFNAR2 receptor that activates the kinases Jak1 and Tyk2 to initiate STAT1 and STAT2 phosphorylation (among other pathways) to induce expression of hundreds of IFN-I stimulated genes (ISGs), including interferon regulatory factors (IRFs) (Lukhele et al., 2019).
- IFN-ls signal through a dimeric IFNAR1/IFNAR2 receptor that activates the kinases Jak1 and Tyk2 to initiate STAT1 and STAT2 phosphorylation (among other pathways) to induce expression of hundreds of IFN-I stimulated genes (ISGs), including interferon regulatory factors (IRFs) (Lukhele et al., 2019).
- IRFs interferon regulatory factors
- IRF1 Central to these outcomes is the interplay between IRF1 and IRF2, known for their positive and negative regulation, respectively, of IFN-I and IFN-II signaling.
- IRF1 is activated by IFN-II [as well as IFN-I and nuclear factor-kappa p (NF- K p)] to induce the pro-inflammatory and immune stimulatory functions critical to prevent tumor growth (Drew et al., 1995a; Harada et al., 1993).
- IRF2 is constitutively expressed in many immune cells and is upregulated in response to either IFN-I or IFN- II (Harada et al., 1989; Taniguchi and Takaoka, 2001).
- IRF2 antagonizes IRF1 by competing for binding to the same promoter elements of IFN-I and IFN-I l-inducible genes (Harada et al., 1989), and by inhibiting nuclear translocation of IRF1 (Wang et al., 2007).
- IRF2 also interacts with NF-KP (Chae et al., 2008; Drew et al., 1995b), STAT1 (Rouyez et al., 2005), IRF8 (Bovolenta et al., 1994; Sharf et al., 1995), and IRF9 (Hida et al., 2000; Tanaka et al., 1993), factors that influence the ability of immune cells to control tumors.
- NF-KP Choe et al., 2008; Drew et al., 1995b
- STAT1 Rayez et al., 2005
- IRF8 Bovolenta et al., 1994; Sharf et al., 1995
- IRF9 Hida et al., 2000; Tanaka et al., 1993
- IRF2 activates gene transcription in certain contexts (Vaughan et al., 1995; Vaughan et al., 1998; Yamamoto et al., 1994), for example, IRF2 cooperates with IRF1 to induce TLR3 in HeLa cells (Ren et al., 2015). Thus, IRF2 balances IFN stimulation by differentially inducing and antagonizing key transcriptional regulators.
- IRF2 expression by tumor cells themselves generally correlates with development and progression of many human cancers, potentially through repressing cancer cell intrinsic IFN signaling (Chen et al., 2021a; Mei et al., 2017; Sakai et al., 2014; Wang et al., 2007; Yi et al., 2013).
- IRF2 promotes tumor survival by inhibiting transcription of the IFNyR, thereby enhancing tumor-intrinsic resistance to IFN-II (Wang et al., 2008).
- some tumor types downregulate IRF2 to evade immune targeting.
- IRF2 directly represses PDL1 expression and activates components of the MHC-I pathway, both of which increase susceptibility to T cell mediated killing (Kriegsman et al., 2019; Yan et al., 2020).
- IFNs IFN-I and II
- IRF2 as a central regulator of CD8 + T cell exhaustion in cancer.
- adoptive transfer of IRF2- deficient CD8 + T cells provided superior ability to control established tumors.
- the CD8 + T cell exhaustion signature normally observed within the tumor was instead replaced with a program of functional cytotoxic T cells.
- IRF2 is a CD8 + T cell-intrinsic nexus that translates signals from the inflammatory TME to adjust gene expression, attenuate cell activation and transcriptionally program T cell exhaustion to prevent tumor control.
- a modified T cell engineered to have decreased IRF2 expression is provided.
- the modified T cell described herein for use in the treatment of cancer.
- the modified T cell described herein for use in adoptive cell therapy.
- modified T cell described herein in the preparation of a medicament for the treatment of cancer.
- a method of treating a subject with cancer comprising administering to the subject a therapeutically effective amount of the modified T cell described herein.
- a method treating a subject with cancer comprising engineering the modified T cell described herein, in vivo in the subject.
- a method treating a subject with cancer comprising administering to the subject a therapeutically effective amount of an inhibitor of IRF2 function or expression.
- B Graph showing IRF2 expression (gMFI) in the spleens of naive (N) mice or from mice with MC38 tumors (T), as well as from tumor-infiltrating immune cells. Numbers next to the cell type indicate the fold change between IRF2 expression in the tumor compared to the spleens from those same mice. * p ⁇ 0.01 .
- Data are representative of at least two independent experiments containing 5 or more mice per group in each experiment. A total of 5 human melanoma tumors were assessed for IRF2 expression., *p ⁇ 0.01 , ** p ⁇ 0.001 , *** p ⁇ 0.0001.
- Tumor growth kinetics in WT or Irf - 1 - mice that received isotype control or anti-CD8 depleting antibody either (A) one day before (early CD8 + T cell depletion) or (B) 21 days after (late CD8 + T cell depletion) MC38 initiation.
- Irf2-'- mice were used since WT mice had already reached endpoint by day 21. Shaded region indicates duration of antibody treatment.
- C MC38 tumor growth in WT control (i.e., Irf2 +I+ , lightest), CD8-IRF2cWT (i.e., Irf 1 * CD8Cre + mice, black), CD8-IRF2cKO (IRF2-deficient only in CD8 + T cells; medium dark), or Irf2-'- (light) mice.
- WT control i.e., Irf2 +I+ , lightest
- CD8-IRF2cWT i.e., Irf 1 * CD8Cre + mice, black
- CD8-IRF2cKO IRF2-deficient only in CD8 + T cells; medium dark
- FIG. 1 IRF2-deficient CD8 + T cells resist exhaustion and maintain functionality in the TME.
- FIG. 1 UMAP plots of CyTOF data showing PhenoGraph-defined clusters of WT and Irf2 ⁇ /_ CD8 + TILs on day 12 after MC38 initiation. The bar graph depicts the proportion of each cluster in WT and Irf2-'- mice.
- the heatmap represents relative expression (normalized z-scores of the arcsinh transformed mean signal intensity; MSI) of the indicated protein in each cluster from panel A compared to the other clusters combined using Wilcoxon rank-sum test.
- Data are representative of at least three independent experiments. In each experiment, tumors from 4-7 mice were pooled from WT or Irf2-'- mice to obtain sufficient numbers of CD8 + TILs for analysis. * p ⁇ 0.05, ** p ⁇ 0.01 , *** p ⁇ 0.001 , **** P ⁇ 0.0001.
- IRF2 is highly expressed in activated and ISG-producing mouse and human CD8 + TILs .
- CD8 + TILs were divided into IRF2 high (upper 30%) and low (lower 30%) levels of IRF2 expression.
- CD8 + TILs were divided into IRF2 high and low fractions and then clustered as in panel A. Shown is one representative tumor. Bar graph depicts the proportion of each cluster in their respective groups.
- mice MC38 data are representative of 3 independent experiments, each with at least 4 mice. * p ⁇ 0.05, ** p ⁇ 0.01 , *** p ⁇ 0.0001. Unpaired, two-tailed Student’s t-test used to analyze significance of cluster proportions between IRF2 high and IRF2 low groups.
- FIG. 5 Transcriptional, epigenetic, and gene-binding profiling.
- A WT and /rf2“ / “CD8 + TILs derived from scRNA-seq data and clustered using Seurat.
- Bar graph depicts the proportion of each cluster within their respective group.
- FIG. 6 IRF2 re-routes transcriptional networks and programming.
- A Bar graph depicts z-scores of IPA-predicted upstream regulator molecules from the DEG dataset comparing c3. Irf2 ' to cO.WT CD8 + TILs. Upstream regulators predicted to be most enriched (Activated) in c3.lrf2-''- CD8 + T cells are shown at the top and those most activated in cO.WT at the bottom.
- C shows IRF2 expression (gMFI) in dLNs of MC38 tumor-bearing WT mice following treatment beginning at day 9 with either isotype control, anti-IFNAR blocking, anti-IFNy blocking, or dual (anti-IFNy and IFNAR) blocking antibodies.
- Data are representative of two independent experiments, each with at least 5 mice per treatment condition. *** p ⁇ 0.0001 , unpaired, two-tailed Student’s t-test.
- Bar graph depicts the number of MC38-infiltrating CD8 + T cells in each cluster in their respective WT and Irf - 1 - groups. A total of 591 cells were analyzed in each group.
- H Histogram showing BATF expression tumor-specific (P14) WT (black) and Irf - 1 - (light) CD8 + TILs.
- the numbers in the histograms show the gMFI of BATF expression.
- the graph summarizes gMFI of BATF expression of tumor-specific CD8 + TILs from five mice.
- Data are representative of at least three independent experiments.
- tumors from 4 - 7 mice were pooled from each group (WT or Irf2-'-) in order to obtain sufficient numbers of CD8 + TILs for analysis.
- Bar graph depicts the number of MC38-infiltrating CD8 + T cells in each cluster in their respective IRF2 high and IRF2 low groups. A total of 3300 cells were analyzed in each group.
- (D) Bar graph depicts the number of human melanoma-infiltrating CD8 + T cells in each cluster in their respective IRF2 high and IRF2 low groups. A total of 3000 cells were analyzed in each group.
- E CyTOF plot showing gating of IRF2 high and IRF2 low fractions in human melanoma CD8 + TILs .
- the flow plots show one representative tumor from the five melanoma samples.
- F Bar plots show Spearman correlation (r) of IRF2 with the indicated protein in human melanoma CD8 + TILs.
- Bar graph depicts the number of MC38-infiltrating CD8 + T cells in each cluster in their respective WT and Irf2 ' groups. A total of 679 cells were used in the scRNA-seq data analysis. Data representation is downsampled to 110 cells for each group.
- Target/Background is the enrichment of motifs in the target peaks divided by the enrichment of those motifs in a random Homer simulated k-mer background; i.e., the number of target-peaks that have the indicated motif divided by the number of background-peaks that have the same motif.
- CD8 T cells counts in naive and anti-CD3/CD28 stimulated conditions Absolute number of total T cells and CD8 T cells after 3 days incubation with anti- CD3/CD28 antibodies (i.e., activated cells) or medium alone (i.e., naive cells).
- Human T cells were isolated and transfected with either nontargeting control ribonucleoprotein (RNP) gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. The absolute number of CD8 T cells was calculated based on the total T cell manual counting followed by the percentage of CD8 T cells detected by flow cytometry.
- RNP nontargeting control ribonucleoprotein
- FIG. 13 CRISPR-mediated IRF2 deletion in primary human CD8 T cells -prior to culture.
- Total T cells including CD4 T cells, CD4 Treg cells and CD8 T cells
- T cells were isolated from primary human PBMC. Following isolation, the T cells were electroporated with IRF2 targeting (sglRF2) and control non-targeting (sgControl) sgRNAs and then cultured for 4 days.
- the plots show IRF2 expression in the control and IRF2 targeted CD8 T cells 4 days after electroporation. Numbers in each plot show the percentage of IRF2 experessing CD8 T cells.
- To achieve CRISPR deletion of IRF2 Primary human T cells were isolated using EasySep Human T cell isolation kit (STEMCELL, cat. no.
- a crRNA targeting a non-essential gene was used for negative control (Alt-R® CRISPR-Cas9 Negative Control crRNA #1 ; IDT, cat. no. 1072544).
- the RNP complex was mixed with the cell suspension and transferred into a 16-well nucleocuvette strip (Lonza, cat. no. V4XP-3032). Cells were transfected using program EH113 and buffer P3 on the 4D-Nucleofector system (4D- Nucleofector X unit, Lonza, cat. no. AAF-1003X).
- FIG. 14 Activation of control and IRF2 deleted primary human CD8 T cells.
- Total T cells including CD4 T cells, CD4 Treg cells and CD8 T cells
- T cells were isolated from primary human PBMC. Following isolation, the T cells were electroporated with IRF2 targeting (sglRF2) and control non-targeting (sgControl) sgRNAs and then cultured for 4 days. IRF2 deletion was confirmed by flow cytometry.
- the control and IRF2-deleted T cells were activated with anti-CD3 (10ug/ml), anti-CD28 (1 ug/ml) and IL-2 (20ng/ml) in T cell complete media. Cells that are kept naive have their media changed in place of activation.
- Activated and naive cells are cultured for 3 days and are then assessed via flow cytometry.
- the plots show Forward Scatter (FSC) and Side Scatter (SSC) of CD8 T cell in the indicated condition. This data show that without activation, the IRF2 deleted T cells remain in an unactivated/naive state similar to the control cells and that the IRF2-deleted T cells can be activated and maintained similar to control treated cells.
- FSC Forward Scatter
- SSC Side Scatter
- FIG. 15 CRISPR-mediated IRF2 deletion in primary human CD4 T cells prior to culture. Same as Figure 14, except the plots now show CD4 T cells.
- FIG. 16 Assessment of IRF2 expression in CD8 T cells - Day 2 post-transfection. Optimization of Cas9/gRNA ribonucleoprotein (RNP) transfection for CRISPR/Cas9- Mediated IRF2 knockout (KO) in human T cells at different pulse codes (EH113: and CM137:). Half offset (left) or overlaid (right) histograms show IRF2 expression on human CD8 T cells transfected with nontargeting (nt) control RNP gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. Flow cytometric analysis was performed on day 2 post-transfection.
- RNP Cas9/gRNA ribonucleoprotein
- FIG. 17 Assessment of IRF2 expression in CD4 T cells - Day 2 post-transfection. Optimization of Cas9/gRNA ribonucleoprotein (RNP) transfection for CRISPR/Cas9- Mediated IRF2 knockout (KO) in human T cells at different pulse codes (EH113: and CM137:). Half offset (left) or overlaid (right) histograms show IRF2 expression on human CD4 T cells transfected with nontargeting (nt) control RNP gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. Flow cytometric analysis was performed at day 2 post-transfection.
- RNP Cas9/gRNA ribonucleoprotein
- FIG. 18 Assessment of IRF2 expression in CD8 T cells - Day 4 post-transfection. Optimization of Cas9/gRNA ribonucleoprotein (RNP) transfection for CRISPR/Cas9- Mediated IRF2 knockout (KO) in human T cells at different pulse codes (EH113: and CM137:). Half offset (left) or overlaid (right) histograms show IRF2 expression on human CD8hi T cells transfected with nontargeting (nt) control RNP gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. Flow cytometric analysis was performed on day 4 post-transfection. Black histogram depicts no antibody staining control on the same flow cytometry channel as IRF2 (this is a way to measure inherent autofluorescence in the stain).
- RNP Cas9/gRNA ribonucleoprotein
- FIG. 19 Assessment of IRF2 expression in CD4 T cells - Day 4 post-transfection. Optimization of Cas9/gRNA ribonucleoprotein (RNP) transfection for CRISPR/Cas9- Mediated IRF2 knockout (KO) in human T cells at different pulse codes (EH113: and CM137:). Half offset (left) or overlaid (right) histograms show IRF2 expression on human CD4 T cells transfected with nontargeting (nt) control RNP gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. Flow cytometric analysis was performed on day 4 post-transfection. Black histogram depicts no antibody staining control on the same flow cytometry channel as IRF2 (this is a way to measure inherent autofluorescence in the stain).
- RNP Cas9/gRNA ribonucleoprotein
- Figure 20 Assessment of IRF2 expression in CD8 T cells - Day 7 post-transfection. Optimization of Cas9/gRNA ribonucleoprotein (RNP) transfection for CRISPR/Cas9- Mediated IRF2 knockout (KO) in human T cells at different pulse codes (EH113: and CM137:). Half offset (left) or overlaid (right) histograms show IRF2 expression on human CD8 T cells transfected with nontargeting (nt) control RNP gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. Flow cytometric analysis was performed on day 7 post-transfection.
- RNP Cas9/gRNA ribonucleoprotein
- Black histogram depicts no antibody staining control on the same flow cytometry channel as IRF2 (this is a way to measure inherent autofluorescence in the stain). Overall, this figure shows that the IRF2 deleted CD8 T cells can be maintained in culture for at least 7 days.
- Figure 21 Assessment of IRF2 expression in CD4 T cells - Day 7 post-transfection. Optimization of Cas9/gRNA ribonucleoprotein (RNP) transfection for CRISPR/Cas9- Mediated IRF2 knockout (KO) in human T cells at different pulse codes (EH113: and CM137:). Half offset (left) or overlaid (right) histograms show IRF2 expression on human CD4 T cells transfected with nontargeting (nt) control RNP gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. Flow cytometric analysis was performed on day 7 post-transfection.
- RNP Cas9/gRNA ribonucleoprotein
- Black histogram depicts no antibody staining control on the same flow cytometry channel as IRF2 (this is a way to measure inherent autofluorescence in the stain). Overall, this figure shows that the IRF2 deleted CD4 T cells can be maintained in culture for at least 7 days.
- IRF2 deletion increases ISG upregulation in response to IFN
- T cells were electroporated with IRF2 deleting sgRNAs or control sgRNAs and cultured for 4 days (time for IRF2 protein to be downregulated). The T cells were then stimulated with media (unstim), IFN
- Expression of ISG 15 measured by flow cytometry comparing IFNp-stimulated, IFNv-stimulated and unstimulated T cells within Irf2-deleted and non-deleted CD4 (left) and CD8 (right) T cells. Numbers indicate geometric mean fluorescence intensity for each histogram.
- FIG. 23 Total T cells were negatively selected from viably frozen human PBMC. Different volumes of control (WT) or IRF2-targeted (KO) Cas9 RNP mix were then tested for IRF2 deletion in CD8 and CD4 T cells. IRF2 expression was analyzed by flow cytometry two and four days after CRISPR-Cas9 RNP treatment. Electroporation for IRF2 RNP versus control RNP by electroporation was performed in the presence of an Enhancer. Following treatment, the cells were cultured in complete medium + human IL-7. The number in each histogram indicates the percentage of IRF2- expressing cells.
- the data show that IRF2 can be deleted in 91% of CD8 T cells and 84% of CD4 T cells by four days after CRISPR-Cas9 deletion.
- the 2.5ul Cas9-RNP concentration is used in subsequent experiments, although both concentrations tested effectively delete IRF2 from human, primary CD8 and CD4 T cells.
- FIG. 24 Total T cells were negatively selected from viably frozen human PBMC.
- a carrier DNA (enhancer) was added to the control (WT) or IRF2-targeted (KO) CRISPR- Cas9 reaction, and the IRF2 deletion efficiency (%) in human CD8 and CD4 T cell was analyzed by flow cytometry 4 days later. Following treatment, the cells were cultured in complete medium + human IL-7. The number in each histogram indicates the percentage of IRF2-expressing cells. The data show that the reaction works quite effectively without the enhancer, but that the deletion efficiency is increased using the enhancer.
- FIG 25 Total T cells were negatively selected from viably frozen human PBMC.
- the T cells were then electroporated with control (WT) or IRF2-targeted (KO) Cas9 RNP mix either a single time (as in figures 1 and 2) or two times. After 2 days, half of the cells were electroporated again for the control or the IRF2-targeted RNP. After 4 days (6 days in total), all cells were assessed for IRF2 expression by flow cytometry. Following treatment, the cells were cultured in complete medium + human IL-7. The number in each histogram indicates the percentage of IRF2-expressing cells. No enhancer was used in these experiments. The 2-times treatment worked somewhat better, but also substantially decreased viability of the cells. Therefore, the single electroporation approach is used in subsequent protocols.
- Jurkat T cells are an immortal human T lymphocyte line.
- Jurkat T cells were thawed, expanded and electroporated with the control (WT) or IRF2-targeted (KO) Cas9-RNP in the presence of an Enhancer. Cells were seeded in complete medium + human IL-7. After 4 days, single cells were FACSorted and subsequently expanded. One of these control (top) and 5 of these IRF2-targeted clones were again assessed for IRF2 expression by flow cytometry. The second IRF2-deleted clone will be used for further experiments. Use of these Jurkat T cell clones provides controls for IRF2 expression. These will be used to as controls to confirm IRF2 expression in the human T cells.
- FIG. 27 Total T cells were isolated from viably frozen PBMCs by negative selection. Electroporation with control (WT) or IRF2-targeted Cas9-RNP versus control RNP was performed in the presence of an Enhancer. Cells were seeded in complete medium + human IL-7 and after 4 days, IRF2 expression was measured by flow cytometry. In this experiment, the IRF2-targeted Cas9-RNP deleted IRF2 expression in 90% of CD8 T cells and 83% of CD4 T cells. On day 7 after the initial CRISPR-Cas9 treatment, the cells were split and treated with media alone or stimulated with 1000 Units IFN[3.
- ISG15 and PD-L1 two IFN-stimulated proteins
- flow cytometry The data show that IRF2-deletion led to increased expression of ISG15 and to a lesser extent PD-L1 following IFN[3 treatment.
- the increased PDL1 expression is consistent with the data showing that mice with IRF2 deleted CD8 T cells exhibited enhanced responses interferons.
- FIG. 28 Total T cells were isolated from viably frozen PBMCs by negative selection. Electroporation with control (WT) or IRF2-targeted Cas9-RNP versus control RNP was performed in the presence of an Enhancer. Cells were seeded in complete medium + human IL-7 and after 4 days, IRF2 expression was measured by flow cytometry. In this experiment, the IRF2-targeted Cas9-RNP deleted IRF2 expression in 90% of CD8 T cells and 83% of CD4 T cells. On day 7 after the initial CRISPR-Cas9 treatment, the cells were split and treated with media alone or stimulated with 1000ng IFNy.
- ISG15 and PD-L1 two IFN-stimulated proteins
- flow cytometry The data show that IRF2-deletion led to slightly increased expression of ISG15 and PD-L1 following IFNy treatment.
- the increased PDL1 expression is consistent with the data showing that mice with IRF2 deleted CD8 T cells exhibited enhanced responses to interferons.
- FIG. 29 Total T cells were isolated from viably frozen PBMCs by negative selection. Electroporation with control (WT) or IRF2-targeted Cas9-RNP versus control RNP was performed in the presence of an Enhancer. Cells were seeded in complete medium + human IL-7 and after 4 days, IRF2 expression was measured by flow cytometry. In this experiment, the IRF2-targeted Cas9-RNP deleted IRF2 expression in 90% of CD8 T cells and 83% of CD4 T cells. On day 7 after the initial CRISPR-Cas9 treatment, the cells were split and treated with media alone or stimulated with 1000 Units IFN[3 + 1000ng IFNy.
- ISG15 and PD-L1 two IFN- stimulated proteins
- flow cytometry The data show that IRF2- deletion led to increased expression of ISG15 and to a lesser extent PD-L1 following IFN[3 treatment.
- the increased PDL1 expression is consistent with the data showing that mice with IRF2 deleted CD8 T cells exhibited enhanced responses to interferons.
- FIG. 30 Total T cells were isolated from viably frozen PBMCs by negative selection. Electroporation with control (WT) or IRF2-targeted Cas9-RNP versus control RNP was performed in the presence of an Enhancer. Cells were seeded in complete medium + human IL-7. After 4 days, CRISPR-mediated IRF2 deletion was confirmed by flow cytometry (CD8 T cells: 90% deleted for IRF2). Half of the cells were kept unstimulated, while the other half was activated for 3 days with anti-CD3 (2ug/mL), anti-CD28 (1 ug/mL) and IL-2 (20ng/mL). (A) Numbers in each plot indicate the percent of live CD8 T cells. (B) Histograms show the FSC of unstimulated and anti-CD3 + CD28 stimulated CD8 T cells. Increase in FSC indicates CD8 T cell activation and blasting.
- FIG. 31 CRISPR-mediated IRF2 deletion in mouse tumor-specific (P14) CD8 T cells.
- P14 T cells are murine T cells that transgenically express rearranged TCR genes recognizing the LCMV-GP33-41 peptide presented in the context of murine H2Db.
- P14 T cells were isolated by negative selection from the spleen of naive P14 mice. The P14 T cells were then electroporated with control (WT) or IRF-2-targeted (KO) Cas9- RNP and cultured in complete medium + mouse IL-7. After 4 days, all cells were assessed for IRF2 expression by flow cytometry. The number in each histogram indicates the percent of P14 T cells that express IRF2.
- mice receiving control or IRF2-targeted P14 T cells were stimulated with anti-CD3 + anti-CD28 antibodies for 24 hours. Then 350,000 of the control or IRF2-targeted P14 cells were adoptively transferred into mice with established MC38-GP tumors (day 8 after tumor inoculation).
- MC38 is an adenocarcinoma tumor cell line that we engineered to express the LCMV-GP1-100 peptide sequence.
- the tumor size in mice receiving control or IRF2-targeted P14 T cells was measured on day 8 (just prior to adoptive P14 T cell transfer) and on day 23 after tumor implantation. Error bars represent mean ⁇ SEM. One-way ANOVA for multiple comparisons.
- IFNs Interferon Regulatory Factor 2
- IRF2 Interferon Regulatory Factor 2
- CD8 + T cell-specific deletion of IRF2 prevented acquisition of the T cell exhaustion program within the tumor, instead enabling sustained effector functions that promoted long-term tumor control, and increased responsiveness of immune-checkpoint and adoptive cell therapies.
- the long-term tumor control by IRF2-deficient CD8 + T cells required continuous integration of both IFN-I and IFN-II signals.
- IRF2 is a foundational feedback molecule that redirects IFN signals to suppress T cell responses and represents a new target to enhance cancer control.
- a modified T cell engineered to have decreased IRF2 expression In an aspect therefore, there is provided a modified T cell engineered to have decreased IRF2 expression.
- T cells may be modified using methods of genetic engineering (also referred to as genetic modification or genetic manipulation) that modify and manipulate a cell’s genes using techniques known in the art.
- genetic engineering also referred to as genetic modification or genetic manipulation
- the modified T cell has been engineered using virus-mediated knockdown. In some embodiments, the modified T cell has been engineered using CRISPR/Cas9.
- the T-cell is a regulatory T cell.
- the T-cell is an effector T cell.
- the T-cell is CD4+.
- the T-cell is CD8+.
- the T-cell is CD4- and/or CD8-.
- the T-cell is a CAR T cell.
- the IRF2 gene has been knocked-down in the modified T cell.
- the IRF2 gene, or portions thereof, have been deleted in the modified T cell.
- the T cell is a human T cell.
- the modified T cell has improved anti-tumor activity compared to the corresponding wild-type T-cell.
- the modified T cell has improved resistance to T cell exhaustion compared to the corresponding wild-type T-cell.
- the modified T cell described herein for use in the treatment of cancer.
- the modified T cell described herein for use in adoptive cell therapy.
- modified T cell described herein in the preparation of a medicament for the treatment of cancer.
- a method of treating a subject with cancer comprising administering to the subject a therapeutically effective amount of the modified T cell described herein.
- a method treating a subject with cancer comprising engineering the modified T cell described herein, in vivo in the subject.
- the method further comprises treating the subject with a checkpoint inhibitor.
- the method further comprises treating the subject with anti-PDL1 and/or anti-PD1 blockade therapy.
- a method treating a subject with cancer comprising administering to the subject a therapeutically effective amount of an inhibitor of IRF2 function or expression.
- inhibitor refers to molecules having the ability to specifically inhibit, wholly or partially, the expression or function of IRF2.
- inhibitors can include, without limitation, small molecules, antibodies, and nucleic acids (e.g. antisense oligonucleotides, siRNA, shRNA, miRNA...etc.).
- therapeutically effective amount refers to an amount effective, at dosages and for a particular period of time necessary, to achieve the desired therapeutic result.
- a therapeutically effective amount of the pharmacological agent may vary according to factors such as the disease state, age, sex, and weight of the individual, and the ability of the pharmacological agent to elicit a desired response in the individual.
- a therapeutically effective amount is also one in which any toxic or detrimental effects of the pharmacological agent are outweighed by the therapeutically beneficial effects.
- pharmaceutically acceptable carrier means any and all solvents, dispersion media, coatings, antibacterial and antifungal agents, isotonic and absorption delaying agents, and the like that are physiologically compatible.
- pharmaceutically acceptable carriers include one or more of water, saline, phosphate buffered saline, dextrose, glycerol, ethanol and the like, as well as combinations thereof.
- isotonic agents for example, sugars, polyalcohols such as mannitol, sorbitol, or sodium chloride in the composition.
- Pharmaceutically acceptable carriers may further comprise minor amounts of auxiliary substances such as wetting or emulsifying agents, preservatives or buffers, which enhance the shelf life or effectiveness of the pharmacological agent.
- auxiliary substances such as wetting or emulsifying agents, preservatives or buffers, which enhance the shelf life or effectiveness of the pharmacological agent.
- RNA-seq Single-cell RNA-seq, ATAC-Seq and CUT&Tag data have been deposited at GEO. Accession numbers are listed Table 1 : Key Resource Table.
- mice C57BL/6 mice were purchased from The Jackson Laboratory or the breeding colony at the Princess Margaret Cancer Center, University Health Network (PMCC, UHN).
- Irf ⁇ - mice (Matsuyama et al., 1993) were kindly provided by Dr. Tak Mak at the PMCC (UHN). Briefly, the Irf?-'- were generated by replacing exon 3 of the Irf2 gene (which encodes amino acids 30 - 63 and is part of the DNA-binding domain of the protein) with a neomycin resistance cassette.
- mice The Irf2 fll/fl mice (described below) were crossed with CD8a-Cre (E8iii-Cre) transgenic mice (C57BL/6-Tg(CD8a-cre)1 ltan/J, Stock No. 008766, The Jackson Laboratory) to delete IRF2 from peripheral CD8a-expressing T cells.
- LCMV-GP33-specific CD8 + TCR transgenic (P14) mice have been described previously (Brooks et al., 2006). Mice were housed under specific pathogen-free conditions at the PMCC (UHN). Mouse handling conformed to the experimental protocols approved by the OCI Animal Care Committee at the PMCC (UHN). Experiments were performed using sex and age matched male and/or female mice. Mice used were between 7 and 10 weeks old at the initiation of each experiment.
- a targeting construct was designed to conditionally delete the 3 rd exon of the mouse interferon regulatory factor 2 gene.
- primers based on mouse mlrf2 genomic sequence (GenBank Accession No. NC_000074) to use in PCR from mouse C57BL/6 genomic DNA (Jackson Laboratory, Bar Harbor, Maine).
- PCR primers 5'- GCA CTT AGC GAT CGC AGC TGC TCC TTG GAC CAA TGA CCT T -3'(Jrf2 AsiSI sense) and 5'- AAG TTA AAT CGA TAG AAG ACT CCT GGC GCA TGC TCA GTC -3' (Jrf2 Clal antisense) were used to amplify a 4497 bp 5’ homology-arm fragment (corresponding to mlrf2 intron 2 sequence) from 200 ng of C57BI/6J genomic DNA using the PfuUltra II fusion HS DNA polymerase (Agilent Technologies, Santa Clara CA).
- a 4555 bp 3’ homology-arm fragment (encompassing exon 4, exon 5, and part of intron 5 of mlrf2) was amplified from C57BI/6J Genomic DNA using the PCR primers 5'- TGG ACC AGT TTA AAC ATA TTG GAA GCT CGT CTC TGC -3' (Jrf2 Pmel sense) and 5'- ATT TAT GCG GCC GCT CAC TTC CTG GAT GAA CAT GGC -3' (Jrf2 Notl antisense).
- PfuUltra II fusion HS DNA polymerase was also used to amplify a 1146 bp fragment from C57BI/6J genomic DNA spanning the targeted mlrf2 exon 3 using the primers 5' TTC TGG TCT TAA TTA ACT TTA GCA GGA CTA GGA TTA CAG 3' (mlrf2 Ex3 Pad sense) and 5' AAT ATG ATT AAT TAA AAG GTC CAC ATC TAA AGA TAT CTC C 3' (mlrf2 Ex3 Pad antisense).
- the resulting PCR products were gel-purified using the Nucleospin® Gel and PCR Clean-up system (Machery-Nagel Gmbh & Co., Germany), and TA-overhangs were added via a 20-minute incubation with Taq DNA polymerase (New England BioLabs, Ipswich, MA) at 68°C in the presence of 1 mM dNTPs and 1X Buffer.
- Nucleospin® Gel and PCR Clean-up system Machery-Nagel Gmbh & Co., Germany
- TA-overhangs were added via a 20-minute incubation with Taq DNA polymerase (New England BioLabs, Ipswich, MA) at 68°C in the presence of 1 mM dNTPs and 1X Buffer.
- the fragments were then TA-cloned into pCR2.1-TOPO (Invitrogen, San Diego, CA) and subcloned into a modified pBluescript II KS (Stratagene, La Jolla, CA) vector containing a PGK- neomycin cassette flanked by both LOXP and FRT sequences.
- pCR2.1-TOPO Invitrogen, San Diego, CA
- KS Stratagene, La Jolla, CA
- a diphtheria toxin (OTA) gene was inserted 3’ of the long arm to negatively select against non- homologous targeting. Insert sequence was validated using fluorescent dideoxynucleotide sequencing and automated detection (ABI/Perkin Elmer, Forest City, CA).
- Targeted disruption of the murine Irf2 gene in ES cells The mlrf2 conditional targeting vector (25 pg) was linearized with Notl restriction endonuclease at the 3’ end of the 3’homology-arm and electroporated into C57BI6/N ES cells (NIH Knockout Mouse Project (KOMP) repository; University of California Davis) using a Bio-Rad Gene Pulser, 0.34 kV, and 0.25 mF. ES cell culture was carried out as previously described (Hakem et al., 1996).
- homologous recombinants were identified by 5’ and 3’ flanking PCR and confirmed by sequencing following published protocols (Hakem et al., 1996). Homologous recombination at the 5’ homology-arm was confirmed by TerraTM PCR Direct (Takara Bio, Mountain View, CA, USA) amplification of a 4657 bp fragment using the primers I rf2 5PCR Sense: 5’ GCC AGG CCA TTT GTT TAG GAA TGC AGG AG -3’, in the flanking sequence of mlrf2 intron 2, and the vector-specific primer PCRA antisense: 5'- CGA CGG TCA ACG AGC AGT CCA GCG TAT CC -3'.
- mice were produced by microinjection of independent mlrf2 + /- ES cell clones into E3.5 C57BL/6J blastocysts and transferred to pseudopregnant foster mothers. Chimeric males were mated with C57BI/6J females (Jackson Laboratory). Germ line transmission of the mutant allele was confirmed by PCR analysis of tail DNA from mice with an agouti coat color.
- the PGK-Neo cassette was removed from mouse Irf2 +t mice by crossing with Flp-deleter mice (Jackson Laboratory stock #009086) (Farley et al., 2000) and PCR genotyping and sequence validation of recombination at the FRT sites.
- MC38 tumor cells derived from C57BL/6 murine colon adenocarcinoma were a generous gift from Dr. Daniel de Carvalho at the PMCC (UHN) and were cultured in McCoy 5A medium supplemented with 10% heat-inactivated fetal bovine serum (FBS), 1% penicillin, 1% streptomycin and 1% L-glutamine.
- FBS heat-inactivated fetal bovine serum
- penicillin 1%
- streptomycin 1% L-glutamine
- 1x10 5 B16-F10 tumor cells were injected s.c. into one hind leg of each mouse.
- the MMTV-PyMT cell line was a generous gift from Dr. Christopher Page at the PMCC (UHN) and was cultured in McCoy 5A medium supplemented with 10% heat-inactivated fetal bovine serum (FBS), 1% penicillin, 1% streptomycin and 1% L- glutamine.
- FBS heat-inactivated fetal bovine serum
- penicillin 1%
- streptomycin 1% L- glutamine
- LCMV-GP33-41 -specific CD8 + P14 T cells were isolated from the spleens and lymph nodes of C57BL/6 and Irf2 ' transgenic mice by negative selection (Stem Cell Technologies). For pre-tumor transfer, 2x10 5 naive P14 T cells were transferred i.v. in the retro-orbital sinus. For therapeutic transfer experiments, P14 T cells were activated for 24 hours using plate-bound anti-CD3 (clone 2C11 , 10pg/mL) and soluble anti- CD28 (1 g/mL) antibodies. Cells were then counted and 2x10 5 P14 cells were transferred i.v. in the retro-orbital sinus nine days following tumor implantation.
- tumors were cut into small fragments and digested in RPMI media containing collagenase I (100U/mL, ThermoFisher), DNasel (10ug/mL, Sigma) and 2% FBS in a gentleMACS dissociator (Cat#130-093-235 Miltenyi Biotec) using the gentleMACS program 37c_m_TDK_1.
- Cells were filtered through a 70pm pre-separation filters (Cat#130-095-823 Miltenyi Biotec) and subjected to red blood cell lysis.
- CD45 + tumor infiltrating lymphocytes were isolated from the digested single-cell suspensions using Mouse CD45 (TIL) Microbeads (Cat#110-021- 618 Miltenyi Biotec), autoMACS columns (Cat#130-021 -101 Miltenyi Biotec) and an autoMACS Pro Separator (Miltenyi Biotec) following the manufacturer’s instructions. Purified CD45 + single-cell suspensions from tumors from at least 5 mice per condition were pooled before staining.
- Antibodies directly conjugated to metal tags were purchased from Fluidigm. Purified unconjugated antibodies were labeled with metal tags at the SickKids-UHN Flow and Mass Cytometry Facility using the MaxPar Antibody Labeling Kit from Fluidigm. CyTOF staining was performed as previously described(Snell et al., 2021). Briefly, mouse single cell suspensions (up to 5x10 6 cells) were pulsed with 12.5 mM Cisplatin (BioVision) in PBS for 1 min at room temperature (RT) prior to quenching with CyTOF staining media (Mg + /Ca + HBSS containing 2% FBS (Multicell), 10mM HEPES (Corning), and FBS underlay.
- CyTOF staining media Mg + /Ca + HBSS containing 2% FBS (Multicell), 10mM HEPES (Corning), and FBS underlay.
- Heatmaps were generated from arcsinh-transformed median of spectral indices (MSI) values, plotted in R using the viridis color package and the ggplots package.
- the CyTOF data was clustered and analyzed using the Phenograph(Levine et al., 2015) algorithm together with the R implementation of UMAP. An equal number of cells was used for clustering, determined based on the lowest common denominator between WT and Irf - 1 - tumor infiltrating CD8 + T cells.
- Flow cytometry and intracellular cytokine stimulation were generated from arcsinh-transformed median of spectral indices (MSI) values, plotted in R using the viridis color package and the ggplots package.
- the CyTOF data was clustered and analyzed using the Phenograph(Levine et al., 2015) algorithm together with the R implementation of UMAP. An equal number of cells was used for clustering, determined based on the lowest common denomin
- Single-cell suspensions of cells from the indicated tissue or tumor were stained with a zombie aqua viability stain (Biolegend) at 4°C and then surface stained using antibodies against targeted molecules CD45 (30-F11), CD45.2 (104), CD45.1 (A20), CD8a (53-6.7), CD4 (GK1.5), Tcr(3 (H57-597), PD1 (29F.1A12), Lag3 (C9B7W), CD39 (24DMS1), PDL1 (10F.9G2), B220 (RA3-6B2), CD62L (MEL-14), CD25 (PC61), CD86 (GL-1), CD11C (3.9), CD11 b (M1/70), CD44 (IM7), NK1.1 (PK136), Ly6C (HK1.4), Ly6G (1A8).
- CD45 (30-F11), CD45.2 (104), CD45.1 (A20), CD8a (53-6.7), CD4 (GK1.5), Tcr(3 (H57-597), PD1 (29F.1
- Single-cell suspensions of tumor cells were counted and restimulated with 2 mg/ml of MHC class l-restricted LCMV peptide GP33-40 in standard complete T cell medium (RPMI supplemented with 10% FBS, 1% penicillin and streptomycin, HEPES, 1% Sodium pyruvate, 1% non-essential amino acids, 1% L-glutamine and 2- mercaptoethanol) containing 50 U/mL recombinant murine IL-2 and 1 mg/mL brefeldin A (Sigma), at 37°C.
- standard complete T cell medium RPMI supplemented with 10% FBS, 1% penicillin and streptomycin, HEPES, 1% Sodium pyruvate, 1% non-essential amino acids, 1% L-glutamine and 2- mercaptoethanol
- IFN[3 stimulation of mouse cells Single-cell suspensions of splenocytes from naive mice were counted and incubated with 100U/ml_ IFN[3 for 8 hours in T cell RPMI medium supplemented with 10% fetal bovine serum, 1% penicillin and streptomycin and 1% L-glutamine. Cells were then surface stained for CD8 (53-6.7), CD4 (GK1.5), B220 (RA3-6B2) and then intracellularly stained for FoxP3 (MF-14) and IRF2 [EPR4644(2), Abeam],
- CD8 + and CD4 + T cells were depleted by administering 250pg of anti-CD8a (2.43) antibody or anti-CD4(GK1 .5) one day prior to tumor injection.
- 250pg of anti-CD8a (2.43) antibody or isotype control (LTF-2) was injected from 3 weeks after MC38 tumor injection, for a total of three anti-CD8a treatments (every 2 days).
- 500pg of anti-PDL1 (10F.9G2) or isotype control (LTF-2) antibody treatment was initiated i.p.
- mice were treated every 2 days with 500pg of anti-IFNAR (MAR1-5A3) of isotype control (mouse lgG1) for the first 2 treatments and 250pg for subsequent treatments.
- 500pg of anti-IFNy (XMG1.2) was administered every 2 days. Isotype control antibodies were injected in a similar fashion.
- Single-cell suspensions of tumor cells were counted and stained with a zombie aqua viability dye (Biolegend) and then surface stained with anti-CD45.2 (A20) antibody. Stained cells were washed and resuspended in RPMI medium supplemented with 10% FBS, 1% penicillin and streptomycin and 1% L-glutamine. At least 8 mice were pooled per group and CD45 + tumor-infiltrating cellsimmune cells were FACSorted on a Moflo Astrios (Beckman Coulter). Sorted cells were labelled with sample multi-plexing antibodies and AbSeq Ab-Oligos (antibody-oligonucleotides) using the BD Mouse Immune Single-Cell Multiplexing Kit (Cat.
- Seurat analyses Data was further analysed using the R Seurat (v 4.0.4) package (Hao et al., 2021). To filter out low quality cells and poorly expressed genes, we applied the following quality control steps: First, cells with ⁇ 500 read counts, with >25% mitochondrial reads, and expressing ⁇ 700 and >7000 genes in Tumor WT were removed. Second, cells with ⁇ 500 read counts, with >20% mitochondrial reads, and expressing ⁇ 700 and >6000 genes in Tumor IRF2 Z - were excluded. Third, genes expressed in ⁇ 10 cells in each condition were also excluded. Finally, doublets were removed using the Chord R package (v1.0.0).
- RNA assay Gene expression (RNA assay) data was then normalized using the logNormalize method and the antibody-derived tags (ADTs) assay data using the CLR method. RNA assay was scaled and adjusted by regressing out cell cycle genes and mitochondrial proportion. PCA was performed on most variable genes.
- WNN weighted nearest neighbor
- PCs principal components
- CD8 + T cells from both WT and Irf2 / were identified using both RNA and ADT and reclustered (0.4 resolution). Markers of each cluster were identified for RNA and ADT assays using the FindAIIMarkers functions with default parameters.
- RNA gene expression was also analyzed using SeqGeq software (v.1.6 or 1.7, BD Biosciences).
- GSEA Gene set enrichment analysis
- Irf2 ' vs cO.WT were identified by the FindMarkers function, as mentioned previously, without filtering any genes, and were ranked by sign(log2FC) * -Iog10(pval), where log2FC is the Iog2 fold change of average of gene expression in IRF2 /_ and average in WT and pval represents the P-value calculated using Wilcoxon rank-sum test.
- Enrichment Map To identify biological processes enriched in cO.WT and c3.lrf2 ' cells, we performed a GSEA analysis for RNA assay data and Gene Ontology Biological Processes gene sets using the GSEA application with default parameters. An Enrichment Map of the GSEA results was generated using the Enrichment Map Visualization tool from the GSEA application. Gene sets included in the Enrichment Map have a P-value ⁇ 0.005 and an FDR ⁇ 0.05 and a cutoff of 0.5 for the overlap coefficient used as a similarity metric between the different gene sets. Enrichment Map was illustrated in Cytoscape (Shannon et al., 2003).
- Upstream regulator analysis Predicted upstream regulator analysis of differentially expressed genes of c3.lrf2-''- vs cO.WT was performed using the Ingenuity Pathway Analysis software (Qiagen) according to the developer’s instructions.
- SCENIC analyses' We assessed regulon activity in cO.WT and c3.1 rf 2 ⁇ - CD8 + T cells by performing an analysis of the gene regulatory network using the pyscenic implementation of SCENIC (Aibar et al., 2017) consisting of three steps: 1) identifying regulons based of TF-targets co-expression using GRN, 2) pruning regulons to keep direct targets of TFs using RcisTarget, and finally 3) assessing regulon activity score (RAS) for each regulon on cellular level using AUCell. Due to the stochastic nature of GRN, SCENIC might generate slightly different results on each run.
- regulons having an FDR ⁇ 0.05, an absolute value of Iog2 fold change (c3./rf2- / - 1 cO.WT) > 0.25 and regulons active in at least 10% of the cells of either of the two compared clusters.
- Single-cell suspensions of WT, Irf ⁇ -, CD8-IRF2cWT and CD8-IRF2cKO MC38- infiltrating CD8 + T cells were isolated using autoMACS (Miltenyi Biotec), washed and treated with DNase I, and nuclei isolated according to the 10x Genomics nuclei isolation for single cell ATAC sequencing protocol.
- AT AC libraries were prepared and sequenced (to a target depth of 25,000 reads per cell) on the Novaseq6000 at the Princess Margaret Genomics Center, following the 10x Genomics sequencing workflow and protocol.
- Reads alignment and quantification base calls were generated using Illumina RTA v3.4.4 as bcl files which were then converted into fastq files using bcl2fastq v2.20 with default parameters. Reads in fastq files were aligned using cellranger-atac count (v 2.0.0) to the reference mm10 genome (i.e., refdata-cellranger-arc-mm10-2020-A- 2.0.0). The chemistry parameter of cellranger-atac count function was set to ARC-v1 , since only scATAC-Seq data was analysed from the multiome data.
- Peaks annotation and inference of IRF2 motifs The TFBStools (v 1 .32.0) R package in combination with position weight matrices (PWM) taken from the JASPAR2020 (v 0.99.10) database were used to infer occurrence of IRF2 motifs within each peak. Promoters were predicted using the GenomicFeatures (v 1.46.1) R package as 3000 bases upstream and 3000 bases downstream the transcription start site. Peaks that occur at least once in at least 10 cells were considered accessible.
- Peaks were called using MACS2 v2.2.7.1 (Zhang et al., 2008) with the following parameters: ‘-q 0.01 -nomodel --shift -100 -extsize 200'.
- Genome-wide IRF2 transcription binding site accessibility was predicted at a p-cutoff of 0.0005 using the TFBStools v1.28.0 R package (Tan and Lenhard, 2016) in coordination with position frequency matrices obtained via the JASPAR2020 v0.99.10 database (Fornes et al., 2020). Bedgraph files from each sample and a bed file of all predicted IRF2 binding sites were visualized using IGV v2.10.3. Additionally, pre-processed normalized counts for the ATAC data were obtained from GSE89308.
- Peak annotations were cross validated using the ChlPSeeker v1.26.2 R package (Yu et al., 2015). Differential accessibility was inferred using DESeq2 v1.30.1 (Love et al., 2014) using the apeglm v1.12.0 R package (Zhu et al., 2019) for the effect size shrinkage estimations. Finally, all gene-set enrichment analysis and over-representation analyses were done using the clusterprofiler v3.18.1 R package (Yu et al., 2012).
- Naive CD8 + T cells were isolated from the spleens and lymph nodes of C57BL/6 by negative selection (Stem Cell Technologies). Isolated CD8 + T cells were activated for 3 days using 5pg/mL plate-bound anti-CD3 (16-0031-86 Invitrogen) and 2pg/mL soluble anti-CD28 (16-0281-86 Invitrogen) antibodies in 10% RPMI complete media containing 100U/ml rhlL2 (200-02 PeproTech). Nuclei were isolated and CUT&Tag performed using the protocol from Epicypher (EpiCypher® CUTANATM Direct-to-PCR CUT&Tag Protocol v1.6 Revised: 11.04.2021).
- Samples were aligned to the mm 10 genome using bowtie v2.4.1 with the following parameters: ‘--local -very-sensitive -no-mixed -no-discordant -phred33 -I 10 -X 700’ and preprocessed using samtools v1.10.
- a bedgraph file was generated by scaling the reads by a scaling factor defined as 1 ,000,000/number_of_aligned_reads. Regions overlapping the ENCODE mm10 blacklist regions, as defined by Amemiya et al. (Amemiya et al., 2019) were then removed.
- Peaks were called using SEACR v1.3 (Meers et al., 2019) with a signal_threshold of 1 and run in 'stringent' and 'norm' mode. IRF2 motifs were then mapped to peaks using the catalogue of predicted IRF2 motifs in the mm10 genome described above. By calculating the proportion of peaks containing an IRF2 motif in the IRF2 sample, we established a minimum quantile-based cut-off of 0.81 for the totalsignal which corresponded to the largest increase in IRF2-motif containing peaks.
- the DNA sequences corresponding to the IRF2 peaks were derived using the bedtools v2.27.1 getfasta tool (Quinlan and Hall, 2010), which was then used as an input for the HOMER v4.8 findMotifsGenome.pl (Duttke et al., 2019) tool with a size parameter of 200. Analysis of transcription factor motifs enriched in IRF2 target peaks was performed using HOMER and ranked based on their Target/Background scores.
- CRISPR RNAs Pre-designed CRISPR RNAs (crRNAs) targeting IRF2 were annealed to transactivating crRNAs (tracrRNAs) to form a guide RNA (gRNA).
- the gRNAs were further complexed with Streptococcus pyogenes Cas9 protein (InvitrogenTM TrueCutTM Cas9 Protein v2. Cat.no. A36499) to form the CRISPR-Cas9-gRNA-ribonucleoprotein (RNP) complex at a molar ratio of 1 :2.5 i.e., 30 pmol Cas9 protein to 75 pmol of gRNA).
- RNP CRISPR-Cas9-gRNA-ribonucleoprotein
- a combination of different crRNAs targeting IRF2 were used (see table below).
- a crRNA targeting a non- essential gene (IDT, Ref. 442435748) was annealed with Cas9 at the same molar ratio.
- 10 6 to 10 7 freshly isolated (primary human or mouse) T cells were resuspended in 20 pl buffer P3 (P3 Primary Cell 4D-Nucleofector X Kit S, Lonza, cat. no. V4XP-3032).
- the cell suspension, RNP complex mix and where indicated 4uM of an electroporation enhancer non-homologous ssDNA oligonucleotide, IDT, cat. 1075916) were combined, transferred into a 16-well nucleocuvette strip (Lonza, cat. no.
- transfection control or IRF2-deleted T cells were activated for 3 days with anti-CD3 (2ug/mL), anti-CD28 (1 ug/mL) and IL-2 (20ng/mL).
- transfection control or IRF2-deleted T cells were treated with media alone or stimulated with 1000 Units IFN(3 and/or 1000ng IFNy.
- P14 T cells are murine T cells that transgenically express rearranged TCR genes recognizing the LCMV-GP33-41 peptide presented in the context of murine H2D b .
- P14 T cells were isolated by negative selection from the spleen of naive P14 mice. The P14 T cells were then electroporated with control (WT) or IRF-2-targeted (KO) Cas9-RNP and cultured in complete medium + mouse IL-7. After 4 days, all cells were assessed for IRF2 expression by flow cytometry to determine percent IRF2 deletion.
- the control or IRF2-targeted P14 T cells were stimulated with anti-CD3 + anti-CD28 antibodies for 24 hours. Then 200,000 to 350,000 of the control or IRF2-targeted P14 cells were adoptively transferred into mice with established MC38-GP tumors (day 8 after tumor inoculation).
- MC38 is an adenocarcinoma tumor cell line that we engineered to express the LCMV-GP-MOQ peptide sequence.
- IRF2 is expressed across immune subsets and its deficiency enables tumor control
- IRF2 immune cell expression within the TME
- WT IRF2-sufficient wild-type mice
- MC38 colorectal adenocarcinoma cells and isolated total tumor-infiltrating cells 14 days after tumor initiation.
- Cells were then analyzed by mass cytometry (CyTOF) with a panel identifying all major and most minor immune cell populations (data not shown).
- IRF2 was widely expressed across immune cells from mouse MC38 tumors (Fig 1A).
- TILs T tumor-infiltrating lymphocytes
- Fig 1 B T tumor-infiltrating lymphocytes
- human melanoma tumor-infiltrating immune cells possessed a broad IRF2 expression pattern (Fig 1C), indicating conserved immune-wide IRF2 expression within the mouse and human TME.
- Irf2 ⁇ l - mice had prolonged survival and enhanced control of minimally immunogenic B16-F10 melanoma and an orthotopic polyoma middle T antigen (PyMT) breast tumor (Fig 1 E), indicating that the absence of IRF2 enables control of diverse tumor types.
- CD4 + T cell subsets in Irf - 1 - mice based on their increased expression of IRF2 within the tumor (Fig 1 B).
- CD8 + T cell depletion prior to MC38 initiation abolished the tumor control observed in isotype antibody treated Irf - 1 - mice (Fig 2A), underscoring a key role of CD8 + T cells in enabling tumor control in the lrf2- , ⁇ mice.
- CD8 + T cell depletion in Irf - 1 - mice 3 weeks after MC38 tumor injection rapidly led to rebound of tumor growth (Fig 2B), indicating that CD8 + T cells actively and continually maintained the long-term tumor control in Irf2 ' mice.
- CD8 + TILs The number of CD8 + TILs was similar between WT and 1 ⁇ 2-'- CD8 + T cells (Fig 8C), indicating comparable expansion/maintenance of the CD8 + TILs.
- Irf2 floxed mice we created Irf2 floxed mice and crossed them with CD8-E8iii-Cre mice, to generate mice that only lack IRF2 expression in the CD8 + T cells (termed CD8-IRF2cKO mice; Fig 8D).
- the lack of IRF2 expression in CD8-IRF2cKO mice did not affect the expansion or survival of CD8 + TILs as their numbers were similar to WT (Fig 8E).
- the CD8- IRF2cKO mice efficiently controlled MC38 tumors in a manner like 1 ⁇ 2-'- mice (Fig 2C), indicating that /rf2-deletion in CD8 + T cells specifically enabled the long-term tumor control.
- MC38-GP tumor cells To further test the direct role of IRF2 in CD8 + T cells toward tumor control, we engineered MC38 cells to stably express the MHC-I Derestricted lymphocytic choriomeningitis virus (LCMV) glycoprotein (GP) 33 -4i epitope (referred to as MC38-GP tumor cells).
- LCMV lymphocytic choriomeningitis virus
- GP tumor-GP tumor cells MHC-I Derestricted lymphocytic choriomeningitis virus
- GP tumor-GP tumor cells MHC-I Derestricted lymphocytic choriomeningitis virus (LCMV) glycoprotein (GP) 33 -4i epitope.
- LCMV lymphocytic choriomeningitis virus
- GP tumor-GP tumor cells we adoptively transferred naive WT or Irf2 ' LCM -GP 3 3-4i-specific (i.e., tumor
- mice that received the Irf ⁇ '-PM T cells exhibited enhanced tumor control (Fig 2D). Both the WT and Irf ⁇ -P T cells maintained similar tumor infiltration prior to divergence in tumor sizes (Fig 8F). Further, whereas all mice receiving WT P14 T cells reached endpoint by day 21 , the mice that received Irf2-'- P14 T cells were all alive at day 21 , and 60% were still alive at 25 days (Fig 8G), indicating that a small fraction of Irf2 ' tumor-specific CD8 + T cells can effectively inhibit tumor growth.
- mice with established PyMT breast tumors with anti-PDL1 blocking antibody were treated with established PyMT breast tumors with anti-PDL1 blocking antibody. Although PyMT breast tumors are controlled better in Irf2 ' compared to WT mice, the tumors still progress, allowing the opportunity for therapeutic intervention to control established tumors.
- Anti-PDL1 treatment was initiated at day 15, at a time after T cell priming and when tumors in WT and Irf2 ' mice had reached ⁇ 50mm 3 but were still comparable in size.
- PDL1 blockade in WT mice induced a 2-fold reduction in PyMT tumor growth, whereas a 10-fold reduction was observed in mice (Fig 2E, 8H).
- the enhanced efficacy of PDL1 blockade was also observed in the CD8-IRF2cKO mice (Fig 8I), indicating that IRF2-deficiency within CD8 + T cells enhances the efficacy of anti-PDL1 immunotherapy.
- IRF2-deficient CD8 + T cells resist exhaustion and maintain functionality in the TME
- the Irf?-'- CD8 + T cells exhibited increased expression of CD80, SLAMF1 , Blimpl , Ki67 and CD25 [a protein that associates with an enhanced effector phenotype (Kalia et al., 2010)] compared to their WT counterparts (Fig 3B, 9B).
- TCF1 + PD1 + CD8 + T cells that have been shown to be capable of self-renewing as well as generating terminally differentiated cytotoxic T cells (Im et al., 2016; Miller et al., 2019; Siddiqui et al., 2019; Utzschneider et al., 2016; Wu et al., 2016) were also comparable between the WT and Irf - 1 - CD8 + T cells (Fig 9D), indicating that IRF2 deficiency does not deplete the TCF1 + stem-like population (Miller et al., 2019; Siddiqui et al., 2019).
- IR-int population co-expressed Ki67 with BATF and Blimpl (Fig 3H, Fig 9G, 9H), proteins that are associated with sustained effector function (Chen et al., 2021 b; Shin et al., 2009; Xin et al., 2015).
- the BATF interacting partner, IRF4 that cooperatively limits T cell exhaustion to favor robust effector functions in the tumor (Seo et al., 2021) and in chronic viral infections (Grusdat et al., 2014; Xin et al., 2015), was highly expressed in the Irf2-'- IR-int population (Fig 9I).
- the Irf2-’- CD8 + T cells expressed increased levels of multiple activation proteins (Fig 3G), indicating that even within the phenotypically similar populations, the IRF2-deficient CD8 + T cells exhibited increased activation and proliferation, and decreased exhaustion profiles.
- CD8 + T cells expressed the cytolytic protein granzyme B (GzmB) specifically within the IR-int population and did so at higher single cell expression levels (Fig 3J).
- GzmB cytolytic protein granzyme B
- WT and Irf - 1 - TILs were stimulated ex vivo with tumor-specific GP33-41 peptide on day 12 after MC38-GP tumor initiation (when tumor sizes were similar).
- a larger proportion of Irf - 1 - CD8 + TILs produced IFNy and TNFa, with the increase specifically within the IR-int cells (Fig 3K).
- Irf2-'- CD8 + T cells coexpressed TNFa and IFNy, unlike their WT CD8 + T cell counterparts (Fig 9J).
- Irf2-'- CD8 + T cells exhibit lower expression levels of IRs, increased expression of cytotoxic molecules, and elevated polyfunctional cytokine production within the TME.
- IRF2 is preferentially expressed in activated and ISG-producing CD8 + T cells from mouse and human tumors
- IRF2 expression was analyzed in CD8 + TILs.
- CD8 + T cells were clustered based on the upper and lower third of IRF2 expression, distinct enrichment patterns emerged (Fig 4A, 10A, 10B).
- the IRF2-low cells were enriched in TCF1 + clusters (c5, c7, c8) that also expressed low amounts of most activation-induced proteins (Fig 4A-C, 10B), consistent with a less activated state.
- c4, c6 and almost exclusively c1 were enriched in the IRF2-high fraction and expressed the highest levels of CD44, CD39, CD69, PD1 , Ki67, BATF, GzmB, Helios, and Tbet (Fig 4A-C, 10B).
- the IRF2-high subsets also expressed increased levels of the ISG Protein Kinase R (PKR), suggesting that IRF2 expression is linked to the strength of IFN-I signaling (Fig 4C).
- PSR Protein Kinase R
- the cluster with the highest amount of IRF2 (c4) is also the most activated/terminally differentiated, and is almost entirely absent from the IRF2-low fraction (Fig 4D, 4E, 10E). Indeed, in total melanoma-infiltrating CD8 + T cells, expression of these activation proteins positively correlated with IRF2 expression (Fig 10F). Overall, in both mouse and human CD8 + TILs, IRF2-high expressing cells were enriched for co-expression of all activation-induced proteins measured (Fig 4G + , with IRF2 highest expressed in the most activated subsets.
- Cluster 0 (accounting for half of the WT cells) was largely absent in the Irf2 ' cells, while c3 accounted for half of the Irf2 ' CD8 + TILs and was almost absent in WT CD8 + TILs (Fig 5A, 11 A).
- the abundance of c1 , c2, and c4 were largely comparable between WT and Irf - 1 - cells (Fig 5A).
- the TCF1 + c2 was largely comprised of naive CD8 + T cells [Tcf7 + , SelT, Lef1 + , Cx3crT, PdcdT, EntpdT, Havcr , cd44'°, itgaf (encoding Cd11a), and Icos] and potentially some Tcf7 + , Satb1 + regenerative, stem-like cells, and was equally present in both the WT and /rf? 7- CD8 + TILs (Fig 5A, 5B, and data not shown), suggesting that Irf2 ⁇ does not skew the differentiation of the TCF1 + populations.
- Cluster 4 are y ⁇ 5 T cells that were present at the same frequencies in WT and /rf2- / - CD8 + TILs (Fig 5A, 5B).
- cO cO.WT
- Tox a genes associated with immune dysfunction. These genes included Tox, Nr4a2 [which functions with Tox to drive exhaustion (Chen et al., 2019; Seo et al., 2019)], Irf8 (Mognol et al., 2017), Nt5e [encoding CD73 (Briceno et al., 2021)], Klrel [which negatively regulates cytotoxicity (Westgaard et al., 2003)], Crbn [which is associated with decreased CD8 + T cell activation and effector function (Hesterberg et al., 2020)], and Il2rb [which has been shown to drive terminal exhaustion in chronic viral infections (Beltra et al., 2016)] (Fig 5B, 5C, and data not shown).
- the predominating ZrfZ 7- CD8 + TIL c3 (c3.lrf2 ⁇ 1 -) exhibited increased expression of cytotoxic genes (Gzma, Gzmb, Gzmk, Stx11, Srgri), inflammatory cytokines and receptors [Cc/3, H2ra, Il12rb2, Tnfrsf4 (encoding 0X40)], NFkb-signaling factors (Trafl, Nfkbid, Nfkbiz, Nfkbia), factors that sustain effector functionality [Batf (Chen et al., 2021b; Grusdat et al., 2014; Xin et al., 2015)], Ttc39c [(encoding Bach2) (Yao et al., 2021)] and numerous ISGs (e.g., Bst2, Ifitl, lfitm1/2, lsg15, Slfnl, lrf7, CD274, G
- the WT CD8 + TILs also expressed some of the cytotoxic and immune-stimulatory genes, such as Gzmb, Prf1 and Ifng, however they did so at reduced levels compared to the /rf2- z - cells (both in proportions and at a single-cell expression) and with a large fraction of the WT cells co-expressing Tox (Fig 5E). Since the RNA expression is from the cells directly ex vivo (i.e., no in vitro stimulation), the increased levels of Ifng, Prf1 and Gzmb RNAs represent increased production of these anti-tumor factors by the Irf2 CD8 + TILs.
- the RNA expression is from the cells directly ex vivo (i.e., no in vitro stimulation)
- the increased levels of Ifng, Prf1 and Gzmb RNAs represent increased production of these anti-tumor factors by the Irf2 CD8 + TILs.
- CD8 + TILs were enriched in the signature of genes upregulated in Tox-deficient CD8 + T cells (Khan et al., 2019) (Fig 5F + ).
- IRF2 epigenetically regulates CD8 + TILs we performed ATAC-seq on CD8 + TILs from WT vs /rfZ 7- mice or from CD8- IRF2cWT and CD8-IRF2cKO mice.
- CUT&Tag Cleavage Under Targets & Tagmentation
- IRF2 interacted with many genes including those associated with immune dysfunction (Tox, Nr4a3, Lag3, Ctla4), immune-stimulatory and pro- inflammatory functions (Statl, Traf2, Nfkbid, Nfkbie, Ifnab, Tbx21, Prdml, Cd3e, Il12rb1), protein translation (Eif2ak4, Eifla, Eif3e, Eif3h, Eif4e3), as well as numerous ISGs (Isg15, lsg20, Ifitl, Bst2, Usp18, CD274, Gbps2-10) (Fig 5J, and data not shown).
- IRF2 binding genes were also enriched in motifs for other transcription factors (TFs).
- HOMER motif analysis indicated that the IRF2 target genes were enriched for TF motifs involved in immune activation (IRF1 , ISRE, BATF, AP-1 , PRDM1 , IRF4) as well as repression of effector responses including Fli1 (Chen et al., 2021c) (Fig 11 C, and data not shown).
- IRF2 independently and/or through transcription factor complexes
- IRF2 interacts with genes enriched in a variety of pathways including IFN signaling, TNFO/NF-K
- Regulators for other pro-inflammatory and immune-activating pathways were also predicted to be activated in the 1 ⁇ - cells, including CD3 (TCR), cytokines involved in sustained T cell function (IL2, IL21 , IL12), NF-K
- upstream regulators for networks controlling cellular proliferation and metabolism were inhibited in the Irf2-'- cells (Fig 6A, and data not shown).
- IFN signaling was increased in mouse CD8 + T cells above its constitutive expression levels (Fig 7A), and many (but not all) IFN-I and IFN-II signaling-associated molecules were increased at the protein level in /rf2- z - CD8 + T cells (Fig 7B), demonstrating that in vivo in the TME, IRF2 restricts the level of IFN-I and IFN-II signaling by CD8 + T cells.
- IFNs To next determine whether IFNs continue to increase IRF2 expression in the context of cancer, we used anti-IFNAR and anti-IFNy antibodies to respectively block IFN-I and IFN-II signaling in WT mice with established MC38-tumors. Blocking either IFN-I or IFN-II signaling alone, and particularly in combination, substantially reduced IRF2 levels in CD8 + T cells (Fig 7C). Thus, IFNs induce and actively sustain the heightened IRF2 expression by CD8 + T cells, which subsequently feeds-back to limit IFN signaling.
- IRF2 is a CD8 + T cell-intrinsic feedback inhibitor that translates IFN signals from the TME to transcriptionally program T cell exhaustion and, consequently, prevents long-term tumor control.
- IRF2 IRF2-deleted CD8 T cells can be efficiently activated (Figs 12-15) which is necessary for transduction of the CAR T constructs. Additionally, referring to Figs 16-21 , we show knockdown on IRF2 expression in human CD4 and CD8 T cells (at Days 2, 4, and 7 post-transfection).
- FIG 22 shows expression of ISG 15 measured by flow cytometry comparing IFN
- FIG. 25 shows that the double treatment worked somewhat better, but also substantially decreased viability of the cells. Therefore, the single transfection approach is used in subsequent protocols.
- Figure 26 demonstrates our development of a CRISPR-Cas9 mediated IRF2-deleted human Jurkat T cell line that (A) can be used as a flow cytometry staining control for IRF2 deletion in human T cells; and, (B) can be used for experimental purposes to probe the function of IRF2 in T cells.
- FIGS 27-29 demonstrate that IRF2-deletion lead to increased expression of the interferon-stimulated proteins ISG 15 and to a lesser extent PD-L1 following IFN
- the increased PDL1 expression is consistent with the data showing that murine IRF2-deleted CD8 T cells exhibited enhanced responses to interferons.
- the data in Figure 30 demonstrates that primary human T cells deleted of IRF2 can be activated, induced to blast, and have viability similar to their electroporation control CRISRP-Cas9 T cell counterparts following anti-CD3 + anti- CD28 stimulation (mimicking TCR and CD28 mediated stimulation).
- Figure 31 demonstrates that compared to control tumor-specific CD8 T cells, adoptive transfer of CRISPR-Cas9 IRF2-deleted murine tumor-specific CD8 T cells delay growth of established MC38 adenocarcinoma tumors and prolong survival of tumor-bearing mice.
- IFNs molecularly switch from pro-inflammatory to suppressive outcomes has long been a subject of interest.
- IRF2 as a keystone factor that translated IFN signals within the TME to temper inflammation. This feedback inhibition then suppressed the CD8 + T cell response and allowed tumor escape from immune control. In essence, IRF2 re-routed IFN induced transcriptional programming from pro- to antiinflammatory signaling. In the absence of IRF2, these exhaustion promoting functions of IFNs were bypassed and CD8 + T cells instead retained high levels of anti-tumor activity and were able to effectively control tumor growth within the otherwise suppressive TME.
- IFN-I signaling would continue to promote robust CD8 + T cell effector function is counter-intuitive based on the established role of prolonged IFN signaling to instead switch to drive immune suppression and CD8 + T cell exhaustion in multiple chronic disease contexts.
- the fact that this entire shift in the IFN programming focuses down to a single molecule was also unexpected, given the diversity of the ISGs driven by IFN signaling.
- the similarity between IRF2 expression and distribution in mouse and human CD8 + TILs implies a conserved role of IRF2.
- IRF2 is a foundational feedback mechanism that quells IFN-induced inflammatory reactions, and as a result, is a central regulator of T cell exhaustion in cancer.
- IRF2 insulin receptor 2
- Blimpl a transcription factor that directly mediate and counter T cell exhaustion
- BATF a transcription factor that directly mediate and counter T cell exhaustion
- IRF4a NFAT
- Nr4a NFAT
- Tox a transcription factor that directly mediate and counter T cell exhaustion
- cytotoxic genes include Blimpl , BATF, IRF4, NFAT, Nr4a and Tox.
- proinflammatory cytokines as well as factors that sustain effector CD8 + T cells functions were enhanced following deletion of IRF2.
- the CD8 + T cells lose the Tox gene expression signature and resist exhaustion but resist the overstimulation-induced cell death in the TME.
- IRF2 regulates a multi-directional response that instills exhaustion, while also suppressing the expression of multiple transcriptional nodes that sustain effector function in times of chronic antigen stimulation.
- Irf2-'- CD8 + T cells strong candidates for adoptive CAR T cell therapies at cell numbers well below those required for WT CD8 + T cells.
- modulating IRF2 represents a new target for multiple therapeutic modalities aimed to enhance tumor-specific T cell functions and control tumor growth.
- IRF2 is a key factor that negatively regulates IFN signaling, immune activation, and CD8 + T cell function, ultimately impeding the ability to control multiple mouse tumors.
- IRF2 expression is similar in mouse and human melanoma TILs.
- Irf - 1 - CD8 + T cells for adoptive CAR T cell therapies are anticipated to be effective against human cancers.
- IRF2 may synergize with immune- checkpoint therapies to fight less immunogenic tumors.
- Cancer control is a complex process, involving a variety of IRF2-expressing cell types that are also capable of IFN signaling. It is likely that IRF2 in these other cells also contribute to the anti-tumor immune response and cancer control.
- IRF-2 regulates NF-kappaB activity by modulating the subcellular localization of NF-kappaB. Biochemical and biophysical research communications 370, 519-524.
- NR4A transcription factors limit CAR T cell function in solid tumours. Nature 567, 530-534.
- BATF regulates progenitor to cytolytic effector CD8(+) T cell transition during chronic viral infection. Nat Immunol 22, 996-1007.
- Tumor-associated B7- H1 promotes T-cell apoptosis: a potential mechanism of immune evasion. Nat Med 8, 793-800.
- NF kappa B and interferon regulatory factor 1 physically interact and synergistically induce major histocompatibility class I gene expression. J Interferon Cytokine Res 15, 1037-1045.
- Interferon regulatory factor-2 physically interacts with NF- kappa B in vitro and inhibits NF-kappa B induction of major histocompatibility class I and beta 2-microglobulin gene expression in transfected human neuroblastoma cells. Journal of neuroimmunology 63, 157-162.
- JASPAR 2020 update of the open-access database of transcription factor binding profiles. Nucleic Acids Res 48, D87-D92.
- IRF2 interferon regulatory factor 2
- IRF4 and BATF are critical for CD8(+) T-cell function following infection with LCMV.
- Cell Death Differ 21 1050-1060.
- the tumor suppressor gene Brcal is required for embryonic cellular proliferation in the mouse. Cell 85, 1009-1023.
- the NF-kappaB regulator Bcl-3 restricts terminal differentiation and promotes memory cell formation of CD8+ T cells during viral infection.
- MSigDB The Molecular Signatures Database
- MSigDB Molecular signatures database 3.0. Bioinformatics 27, 1739-1740. Lou, X., Sun, S., Chen, W., Zhou, Y., Huang, Y., Liu, X., Shan, Y., and Wang, C. (2011). Negative feedback regulation of NF-kappaB action by CITED2 in the nucleus. J Immunol 186, 539-548.
- IFN regulatory factor-2 cooperates with STAT1 to regulate transporter associated with antigen processing- 1 promoter activity. Journal of immunology 174, 3948-3958.
- TOX is a critical regulator of tumour-specific T cell differentiation. Nature 571 , 270-274. 60.
- IFN regulatory factor (IRF)-1 and IRF-2 involvement of IFN regulatory factor (IRF)-1 and IRF-2 in the formation and progression of human esophageal cancers. Cancer research 67, 2535-2543.
- clusterprofiler 4.0 A universal enrichment tool for interpreting omics data. Innovation (Camb) 2, 100141.
- IRF-2 The oncogenic transcription factor IRF-2 possesses a transcriptional repression and a latent activation domain. Oncogene 9, 1423-1428. 85. Yan, Y., Zheng, L, Du, Q., Yan, B., and Geller, D.A. (2020). Interferon regulatory factor 1 (IRF-1) and IRF-2 regulate PD-L1 expression in hepatocellular carcinoma (HOC) cells. Cancer Immunol Immunother 69, 1891- 1903.
- Interferon regulatory factor (I RF)-1 and IRF- 2 are associated with prognosis and tumor invasion in HCC. Annals of surgical oncology 20, 267-276.
- clusterProfiler an R package for comparing biological themes among gene clusters. OMICS 16, 284-287.
- ChlPseeker an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31 , 2382-2383.
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Immunology (AREA)
- General Health & Medical Sciences (AREA)
- Medicinal Chemistry (AREA)
- Veterinary Medicine (AREA)
- Animal Behavior & Ethology (AREA)
- Organic Chemistry (AREA)
- Public Health (AREA)
- Engineering & Computer Science (AREA)
- Pharmacology & Pharmacy (AREA)
- Zoology (AREA)
- Epidemiology (AREA)
- Mycology (AREA)
- Molecular Biology (AREA)
- General Chemical & Material Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Environmental Sciences (AREA)
- Cell Biology (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Biochemistry (AREA)
- Biomedical Technology (AREA)
- Genetics & Genomics (AREA)
- Biophysics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Microbiology (AREA)
- Biotechnology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Animal Husbandry (AREA)
- Gastroenterology & Hepatology (AREA)
- Toxicology (AREA)
- Virology (AREA)
- Developmental Biology & Embryology (AREA)
- Hematology (AREA)
- Biodiversity & Conservation Biology (AREA)
- Medicines That Contain Protein Lipid Enzymes And Other Medicines (AREA)
Abstract
There is described herein modified T cells engineered to have decreased IRF2 expression and method of making and using the same for cancer therapy.
Description
TARGETING IRF2 FOR CANCER THERAPY
RELATED APPLICATIONS
This application claims priority to U.S. Provisional Application No. 63/423839 filed on November 9, 2022, which is incorporated herein by reference in its entirety.
FIELD OF THE INVENTION
The invention relates to compositions for treating cancer and particularly to targeting IRF2 for cancer therapy, including T cell therapy.
BACKGROUND OF THE INVENTION
Type I interferons (IFN-I; IFNa/p) and type II interferon (IFN-II, IFNy) have long been recognized for their immune stimulatory activities (Alspach et al., 2019; Lukhele et al., 2019). Yet, IFN-ls and IFN-II are also emerging as central regulators of both the chronic immune activation and the suppression that drive cancer progression (Boukhaled et al., 2021 ; Snell et al., 2017). The efficacy of many types of anti-cancer therapies, including checkpoint blockades, are associated with increased IFN signaling (Boukhaled et al., 2021 ; Snell et al., 2017). However, elevated and sustained IFN-I signaling also drives T cell exhaustion (Boukhaled et al., 2021 ; Budhwani et al., 2018; Minn, 2015; Snell et al., 2017), a process culminating in the attenuation of CD8+ T cell function through distinct transcriptional, epigenetic and metabolic reprogramming (McLane et al., 2019; Wherry, 2011). In addition, IFN-II increases PDL1 expression on the surface of tumor cells. PDL1 binds to PD1 on activated CD8+ T cells in the tumor microenvironment (TME), driving their apoptotic cell death (Dong et al., 2002). Thus, a paradox emerges wherein IFN signaling is critical for the induction and maintenance of CD8+ T cell activity, but simultaneously abolishes that same activity. Yet, how IFNs exert these distinct functions and the CD8+ T cell intrinsic pathways they induce to reroute proinflammatory to suppressive signals remain ill-defined.
IFN-ls signal through a dimeric IFNAR1/IFNAR2 receptor that activates the kinases Jak1 and Tyk2 to initiate STAT1 and STAT2 phosphorylation (among other pathways) to induce expression of hundreds of IFN-I stimulated genes (ISGs), including interferon regulatory factors (IRFs) (Lukhele et al., 2019). On the other hand, IFN-II signals through its IFNy receptor (composed of IFNyRI and IFNyR2 chains) to activate Jak1 and Jak2 that, in turn, phosphorylate STAT1 (Alspach et al., 2019). STAT1 then homodimerizes to form gamma-activated factors that translocate into the nucleus to induce ISG expression including the IRFs. By differentially inducing and antagonizing IRFs, IFN-I and IFN-II signaling triggers a broad range of immunologic programs (Antonczyk et al., 2019). Central to these outcomes is the interplay between IRF1 and IRF2, known for their positive and negative regulation, respectively, of IFN-I and IFN-II signaling. IRF1 is activated by IFN-II [as well as IFN-I and nuclear factor-kappa p (NF- Kp)] to induce the pro-inflammatory and immune stimulatory functions critical to prevent tumor growth (Drew et al., 1995a; Harada et al., 1993). IRF2 is constitutively expressed in many immune cells and is upregulated in response to either IFN-I or IFN- II (Harada et al., 1989; Taniguchi and Takaoka, 2001). Although the exact functions of IRF2 are mechanistically unclear, one important role is limiting immune activation and resultant autoimmunity (Hida et al., 2000). IRF2 antagonizes IRF1 by competing for binding to the same promoter elements of IFN-I and IFN-I l-inducible genes (Harada et al., 1989), and by inhibiting nuclear translocation of IRF1 (Wang et al., 2007). IRF2 also interacts with NF-KP (Chae et al., 2008; Drew et al., 1995b), STAT1 (Rouyez et al., 2005), IRF8 (Bovolenta et al., 1994; Sharf et al., 1995), and IRF9 (Hida et al., 2000; Tanaka et al., 1993), factors that influence the ability of immune cells to control tumors. While the transcriptional role of IRF2 is largely repressive, IRF2 activates gene transcription in certain contexts (Vaughan et al., 1995; Vaughan et al., 1998; Yamamoto et al., 1994), for example, IRF2 cooperates with IRF1 to induce TLR3 in HeLa cells (Ren et al., 2015). Thus, IRF2 balances IFN stimulation by differentially inducing and antagonizing key transcriptional regulators.
Increased IRF2 expression by tumor cells themselves generally correlates with development and progression of many human cancers, potentially through repressing cancer cell intrinsic IFN signaling (Chen et al., 2021a; Mei et al., 2017; Sakai et al., 2014; Wang et al., 2007; Yi et al., 2013). In high IRF2 expressing tumors such as esophageal cancers, IRF2 promotes tumor survival by inhibiting transcription of the IFNyR, thereby enhancing tumor-intrinsic resistance to IFN-II (Wang et al., 2008).
Conversely, some tumor types downregulate IRF2 to evade immune targeting. For example, IRF2 directly represses PDL1 expression and activates components of the MHC-I pathway, both of which increase susceptibility to T cell mediated killing (Kriegsman et al., 2019; Yan et al., 2020). Overall, these studies underscore an important role of IRF2 expression by tumor cells themselves, however, how IRF2 expression in the immune cell compartment influences their responses to IFN-I and II (collectively referred to as IFNs) and ability to control tumors is unknown.
SUMMARY OF THE INVENTION
Herein, we identified IRF2 as a central regulator of CD8+ T cell exhaustion in cancer. Deletion of IRF2 intrinsically programmed CD8+ T cells to resist exhaustion and maintain anti-tumor functions in the otherwise suppressive TME, thereby enabling long-term tumor control and increased responsiveness to immune checkpoint blockade. Compared to IRF2-sufficient CD8+ T cells, adoptive transfer of IRF2- deficient CD8+ T cells provided superior ability to control established tumors. In the absence of IRF2, the CD8+ T cell exhaustion signature normally observed within the tumor was instead replaced with a program of functional cytotoxic T cells. Similarly, the suppressive effects of IFN-I and IFN-II signaling were prevented in IRF2-deficient CD8+ T cells, and instead IFNs enhanced and sustained CD8+ T cell function for longterm tumor control. Thus, IRF2 is a CD8+ T cell-intrinsic nexus that translates signals from the inflammatory TME to adjust gene expression, attenuate cell activation and transcriptionally program T cell exhaustion to prevent tumor control.
Therefore, in an aspect, there is provided a modified T cell engineered to have decreased IRF2 expression.
In an aspect, there is provided the modified T cell described herein, for use in the treatment of cancer.
In an aspect, there is provided the modified T cell described herein, for use in adoptive cell therapy.
In an aspect, there is provided a use of modified T cell described herein, in the preparation of a medicament for the treatment of cancer.
In an aspect, there is provided a method of treating a subject with cancer, comprising administering to the subject a therapeutically effective amount of the modified T cell described herein.
In an aspect, there is provided a method treating a subject with cancer, comprising engineering the modified T cell described herein, in vivo in the subject.
In an aspect, there is provided a method treating a subject with cancer, comprising administering to the subject a therapeutically effective amount of an inhibitor of IRF2 function or expression.
BRIEF DESCRIPTION OF FIGURES
These and other features of the preferred embodiments of the invention will become more apparent in the following detailed description in which reference is made to the appended drawings wherein:
Figure 1. IRF2 expression across immune subsets and IRF2 deficiency enables tumor control.
(A) UMAP plots of CyTOF data showing IRF2 expression in PhenoGraph-defined CD45+ tumor-infiltrating immune cell clusters from mouse MC38 adenocarcinoma tumors.
(B) Graph showing IRF2 expression (gMFI) in the spleens of naive (N) mice or from mice with MC38 tumors (T), as well as from tumor-infiltrating immune cells. Numbers next to the cell type indicate the fold change between IRF2 expression in the tumor compared to the spleens from those same mice. * p<0.01 .
(C) UMAP plots of CyTOF data showing IRF2 expression and distribution in PhenoGraph-defined CD45+ tumor-infiltrating immune cell clusters from human melanoma tumors.
(D) Tumor growth kinetics of wildtype (WT, black) and Irf -1- (light) mice following implantation with MC38 tumor cells. Longitudinal line graphs show the average tumor volumes +/- standard error from the mean (SEM; left) and the tumor volumes of the individual mice (right).
(E) Average tumor volumes ± SEM of wildtype (WT, black) and Irf?-1- (light) mice following implantation with B16-F10 cells or PyMT cells.
Data are representative of at least two independent experiments containing 5 or more mice per group in each experiment. A total of 5 human melanoma tumors were assessed for IRF2 expression., *p<0.01 , ** p<0.001 , *** p< 0.0001. One-way ANOVA for multiple comparisons used for tumor growth kinetics.
Figure 2. Tumor control required CD8+ T cell-intrinsic IRF2 expression.
Tumor growth kinetics in WT or Irf -1- mice that received isotype control or anti-CD8 depleting antibody either (A) one day before (early CD8+ T cell depletion) or (B) 21 days after (late CD8+ T cell depletion) MC38 initiation. For late depletion, only Irf2-'- mice were used since WT mice had already reached endpoint by day 21. Shaded region indicates duration of antibody treatment.
(C) MC38 tumor growth in WT control (i.e., Irf2+I+, lightest), CD8-IRF2cWT (i.e., Irf 1* CD8Cre+ mice, black), CD8-IRF2cKO (IRF2-deficient only in CD8+ T cells; medium dark), or Irf2-'- (light) mice.
(D) Tumor size after WT mice received 2x105 naive WT (black) or lrf2-!~ (red) P14 T cells one day prior to receiving MC38-GP tumor.
(E) Tumor growth in WT or Irf2-'- mice with orthotopic PyMT breast tumor cells that were treated with isotype or anti-PDL1 blocking antibody beginning on day 15 after tumor implantation. Number in graph indicates the fold change in tumor size between the isotype vs. anti-PDL1 treatment for WT or Irf2-'- mice. Shaded region indicates duration of antibody treatment.
(F) MC38-GP tumor size after WT mice received 2x105 pre-activated WT (black) or Irf2-'- (light) P14 T cells (i.v.) on day 9 after tumor initiation.
Data are representative of at least two independent experiments. Error bars represent mean ± SEM. ** p<0.001 , *** p< 0.0001. One-way ANOVA for multiple comparisons used for tumor growth kinetics.
Figure 3. IRF2-deficient CD8+ T cells resist exhaustion and maintain functionality in the TME.
(A) UMAP plots of CyTOF data showing PhenoGraph-defined clusters of WT and Irf2~ /_CD8+ TILs on day 12 after MC38 initiation. The bar graph depicts the proportion of each cluster in WT and Irf2-'- mice.
(B) UMAP plots show the single-cell expression of the indicated protein in CD8+ TILs from panel A.
(C) The heatmap represents relative expression (normalized z-scores of the arcsinh transformed mean signal intensity; MSI) of the indicated protein in each cluster from panel A compared to the other clusters combined using Wilcoxon rank-sum test.
(D) Expression of the inhibitory receptors (IR) CD39, PD1 and Lag3 in WT and Irf2 ' CD8+ TILs. Numbers in the plots show the percent of cells in each gate. The graph compares proportions of CD8+ T cells expressing low, intermediate, or high levels of IRs combined from 4 independent experiments. Error bars represent SEM.
(E) Expression of CD39, PD1 and Lag3 in WT and IRF2 /_ tumor-specific CD8+ P14 T cells from mice implanted with MC38-GP tumors. Numbers show percent of cells in each gate.
(F) Ki67 expression in WT and Irf2-,~CD8+ TILs. Numbers indicate the percent of cells in each quadrant.
(G) Heatmap depicting expression (z-score of median) of the indicated protein in IR- low, IR-int and IR-hi WT (W) or lrf2 '- (K) CD8+ TILs.
(H) BATF, Blimpl and Ki67 expression by IR-int WT and /rf2-/-CD8+ TILs. Numbers indicate the percent of cells in each quadrant.
(I and J) PD1 , Tox and GzmB expression in WT and //'f2_/“CD8+ TILs. Graphs indicate the proportions of cells expressing and the per-cell expression levels (gMFI) of the indicated protein.
(K) Flow plots show IFNy and TNFa production in ex vivo GP33-41 peptide stimulated CD8+ TILs on day 12 after MC38-GP initiation. Graphs indicate the proportions of cells expressing IFNy and TNFa.
Data are representative of at least three independent experiments. In each experiment, tumors from 4-7 mice were pooled from WT or Irf2-'- mice to obtain
sufficient numbers of CD8+ TILs for analysis. * p<0.05, ** p<0.01 , *** p<0.001 , **** P< 0.0001.
Figure 4. IRF2 is highly expressed in activated and ISG-producing mouse and human CD8+ TILs .
CD8+ TILs were divided into IRF2 high (upper 30%) and low (lower 30%) levels of IRF2 expression.
(A) PhenoGraph-defined clusters divided into IRF2 high and IRF2 low CD8+ T cells from mouse MC38 tumors (day 14). Bar graph depicts the proportion of each cluster in their respective groups.
(B) Heatmap represents expression of the indicated protein in each cluster.
(C) Expression and distribution of the indicated protein in the IRF2 high and low clusters of MC38-infiltrating CD8+ T cells.
(D-F) IRF2 distribution in human melanoma tissue biopsies.
(D) CD8+ TILs were divided into IRF2 high and low fractions and then clustered as in panel A. Shown is one representative tumor. Bar graph depicts the proportion of each cluster in their respective groups.
(E) Heatmap represents expression of the indicated protein in each cluster.
(F) Expression and distribution of the indicated protein in the IRF2 high and low clusters of CD8+ TILs.
(G) Heatmaps compare arcsinh transformed z-score of the MSI of the indicated protein in the IRF2 high and low CD8+ TILs in mouse MC38 tumors (top) and human melanoma (bottom). Each row represents CD8+ T cells from a different tumor.
The mouse MC38 data are representative of 3 independent experiments, each with at least 4 mice. * p<0.05, ** p<0.01 , *** p< 0.0001. Unpaired, two-tailed Student’s t-test used to analyze significance of cluster proportions between IRF2 high and IRF2 low groups.
Figure 5: Transcriptional, epigenetic, and gene-binding profiling.
(A) WT and /rf2“/“CD8+ TILs derived from scRNA-seq data and clustered using Seurat.
Bar graph depicts the proportion of each cluster within their respective group.
(B) Heatmap of top 20 up-regulated genes defining the clusters.
(C) Heatmap of differentially expressed genes between WT cluster 0 (cO.WT) and Irf2~ cluster 3 (cS./r^- .
(D) GSEA plot showing enrichment of cO.WT and cZ.Irt -1- CD8+ T cells in gene signatures of effector vs exhausted CD8+ T cell pathway (from ImmuneSigDB).
(E) 2D plots showing Gzmb, Ifng, Prf1 and Tox RNA expression by WT and /rf2“/“CD8+ TILs.
(F) GSEA plot showing enrichment of c3. Irf -'- CD8+ T cells in the gene signature of Tox-deficient CD8+ T cells, from (Khan et al., 2019).
(G) scATAC-seq analysis indicating the number of accessible peaks in each region of WT and Irf2-I~CD8+ TILs.
(H) Open chromatin state at IRF2-binding sites in the Tox promoter of WT (dark) and Irf2-'- (light) CD8+ TILs. Solid vertical line represents predicted IRF2 motif with a p<0.00005.
(I) ATAC-seq plot indicating open chromatin state at IRF2-binding sites in the Tox promoter of CD8+ T cells isolated from peripheral blood lymphocytes obtained from (light peaks) three healthy donors; and (dark peaks) PD1 hi CD8+ TILs from 2 melanoma patients (tumor 1 and 2) and 1 lung cancer patient (tumor 3). Solid vertical lines represent predicted IRF2 motifs with a p<0.00005.
(J) Representative alignments of CUT&Tag peaks depicting IRF2 and IgG control antibody binding to the indicated loci of in vitro activated CD8+ T cells from the spleen and lymph nodes of WT mice.
(K) Selective list of pathways (and their respective adjusted P value) based on the genes that interact with IRF2. The size of each dot indicates the number of genes in that pathway.
Figure 6. IRF2 re-routes transcriptional networks and programming.
(A) Bar graph depicts z-scores of IPA-predicted upstream regulator molecules from the DEG dataset comparing c3. Irf2 ' to cO.WT CD8+ TILs. Upstream regulators predicted to be most enriched (Activated) in c3.lrf2-''- CD8+ T cells are shown at the top and those most activated in cO.WT at the bottom.
(B) SCENIC-based fold changes in average regulon activity indicating whether a regulatory network is more active or inhibited in c3.lrf2-''- vs cO.WT tumor infiltrating CD8+ T cells.
(C) Enrichment map showing biological processes enriched in up-regulated genes in cO.WT and up-regulated genes in c3. lrf2-' CD8+ TILs.
Figure 7. IFN-I and IFN-II are required for long-term tumor control in Irf2-'- mice.
(A) IRF2 expression (gMFI) following media control and IFN|3 stimulation of naive WT mouse CD8+ T cells. *** p< 0.0001 , paired Student’s t-test.
(B) Expression of IFN-I and IFN-II signaling-associated molecules in WT and /rf2-/_ CD8+ T cells from the spleen and tumor of mice on day 12 after MC38 initiation. Data are representative of two independent experiments, each with at least 5 mice per group pooled.
(C) Graph shows IRF2 expression (gMFI) in dLNs of MC38 tumor-bearing WT mice following treatment beginning at day 9 with either isotype control, anti-IFNAR blocking, anti-IFNy blocking, or dual (anti-IFNy and IFNAR) blocking antibodies. Data are representative of two independent experiments, each with at least 5 mice per treatment condition. *** p< 0.0001 , unpaired, two-tailed Student’s t-test.
(D) Tumor growth kinetics of (left) MC38 tumor-bearing Irf -1- mice treated with either isotype (light line), or a combination of anti-IFNy and anti-IFNAR (medium dark line) antibodies. Antibody treatments were initiated at 22 days post tumor implantation, after WT mice had reached endpoint. (Right) Tumor growth kinetics of MC38 tumor-bearing Irf2^~ mice treated with either isotype (light), anti-IFNy (medium dark) or anti-IFNAR (black) blocking antibodies. Antibody treatments were initiated at 23 days and after WT mice (lightest) had reached endpoint. Shaded area indicates duration of antibody treatment. Data are representative of two independent experiments, each with at least 5 mice per treatment condition.
(E) Tumor growth kinetics of MC38 tumor-bearing CD8-IRF2cKO mice treated with either isotype (light line) or a combination of anti-IFNy and anti-IFNAR (medium line) antibodies, beginning at 19 days post tumor implantation, after CD8-IRF2cWT mice (black) had reached endpoint. Shaded area indicates duration of antibody treatment. Data are representative of three independent experiments.
* p<0.05, ** p<0.01 , *** p< 0.0001 . One-way ANOVA for multiple comparisons used for tumor growth kinetics..
Figure 8. Tumor growth, IRF2 expression and immunotherapy in WT and /rf2'/_ mice, Related to Figures 1 and 2.
(A) Graph showing survival kinetics of WT (black) and Irf2-'- (light) mice implanted with MC38 tumors.
(B) Tumor growth kinetics in WT or Irf2 ' mice that received isotype control or anti- CD4 depleting antibody one day before MC38 tumor implantation.
(C) Graph shows the number of WT and lrf2~,~CD8+ TILs from mice implanted with MC38 tumors.
(D) Flow plots show level of IRF2 expression in various immune cells from spleens of WT control (lightest), CD8-IRF2cWT (IRF2 sufficient mice, black), CD8-IRF2cKO (IRF2-deficient only in CD8+ T cells, medium dark) and Irf2-'- (light) mice upon completion of a tumor kinetic study.
(E) Graph showing the number of CD8-IRF2cWT (WT) and CD8-IRF2cKO (cKO) CD8+ TILs from mice implanted with MC38 tumors.
(F) Graph depicting the number of tumor-specific (P14s) WT and lrf2~,~CD8+ TILs from mice implanted with MC38 tumors.
(G) Graph showing survival kinetics of WT and Irf -1- mice implanted with MC38 tumors.
(H) Individual mouse tumor growth kinetics in WT or Irf -1- mice with orthotopic PyMT breast tumor cells that were treated with isotype or anti-PDL1 blocking antibody beginning on day 15 after tumor implantation.
(I) Tumor growth kinetics in CD8-IRF2cKO mice with orthotopic PyMT breast tumor cells that were treated with isotype or anti-PDL1 blocking antibody beginning on day 19 after implantation.
Figure 9. Immune inhibitory and stimulatory protein expression in WT and Irf2'' CD8+ TILs, Related to Figure 3.
(A) Bar graph depicts the number of MC38-infiltrating CD8+ T cells in each cluster in their respective WT and Irf -1- groups. A total of 591 cells were analyzed in each group.
(B) Heatmap depicting log2FoldChange Irf2 / vs WT) of differentially expressed proteins between the WT and Irf -1- CD8+ TILs within the same cluster. Light indicates increased in Irf2-'- and medium dark indicates increased in WT.
(C) The graphs indicated the number of naive CD8+ T cells from the spleens and lymph nodes (LN) of WT and lrf2 ‘ mice, as well as the proportions of dLN CD8+ T cells expressing the indicated protein markers of immune activation.
(D) Flow plots indicating PD1 and TCF1 expression in WT and /rf2“/“CD8+ TILs.
(E) Flow plots indicating Ki67 and Lag3 expression in tumor-specific (P14) WT and /rf2-/-CD8+ TILs.
(F) Violin plot showing total Ki67 expression in WT and /rf2“/“CD8+ TILs. Each dot represents a single cell.
(G) CyTOF plot showing BATF and Blimpl expression by IR-int WT and Irf -1- CD8+ T cells. The numbers in the plots indicate the percent of cells in each quadrant.
(H) Histogram showing BATF expression tumor-specific (P14) WT (black) and Irf -1- (light) CD8+ TILs. The numbers in the histograms show the gMFI of BATF expression.
The graph summarizes gMFI of BATF expression of tumor-specific CD8+ TILs from five mice.
(I) Histogram showing IRF4 expression by IR-int WT (black) and Irf2-'- (light) CD8+ TILs. The numbers in the histograms show the gMFI of IRF4 expression.
(J) Flow plots indicating IFNy and TNFa co-expression in GP33-41 peptide stimulated WT and /rf2“/“CD8+ TILs. The numbers in the plots show the percent of cells in each quadrant.
Data are representative of at least three independent experiments. In each experiment, tumors from 4 - 7 mice were pooled from each group (WT or Irf2-'-) in order to obtain sufficient numbers of CD8+ TILs for analysis. * p<0.05, ** p<0.01 , *** p<0.001 , **** p< 0.0001.
Figure 10. IRF2 relationship to activation and IFN stimulated genes, Related to Figure 4.
(A) Bar graph depicts the number of MC38-infiltrating CD8+ T cells in each cluster in their respective IRF2 high and IRF2 low groups. A total of 3300 cells were analyzed in each group.
(B) CyTOF plot showing gating of IRF2 high and IRF2 low fractions in MC38 CD8+ TILs .
(C) Bar plots show Spearman correlation (r) of IRF2 with the indicated protein in MC38-infiltrating CD8+ T cells
(D) Bar graph depicts the number of human melanoma-infiltrating CD8+ T cells in each cluster in their respective IRF2 high and IRF2 low groups. A total of 3000 cells were analyzed in each group.
(E) CyTOF plot showing gating of IRF2 high and IRF2 low fractions in human melanoma CD8+ TILs . The flow plots show one representative tumor from the five melanoma samples.
(F) Bar plots show Spearman correlation (r) of IRF2 with the indicated protein in human melanoma CD8+ TILs.
Figure 11. Transcriptional and epigenetic analysis of CD8+ TILs, Related to Figure 5.
(A) Bar graph depicts the number of MC38-infiltrating CD8+ T cells in each cluster in their respective WT and Irf2 ' groups. A total of 679 cells were used in the scRNA-seq data analysis. Data representation is downsampled to 110 cells for each group.
(B) Bar graph depicting the number of accessible peaks in each indicated region of CD8-IRF2cWT (WT) and CD8-IRF2cKO (cKO) CD8+ TILs.
(C) Top transcription factor motifs enriched in IRF2 target peaks as determined by HOMER motif analysis. Target/Background is the enrichment of motifs in the target peaks divided by the enrichment of those motifs in a random Homer simulated k-mer background; i.e., the number of target-peaks that have the indicated motif divided by the number of background-peaks that have the same motif.
Figure 12. CD8 T cells counts in naive and anti-CD3/CD28 stimulated conditions. Absolute number of total T cells and CD8 T cells after 3 days incubation with anti- CD3/CD28 antibodies (i.e., activated cells) or medium alone (i.e., naive cells). Human T cells were isolated and transfected with either nontargeting control ribonucleoprotein (RNP) gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. The absolute number of CD8 T cells was calculated based on the total T cell manual counting followed by the percentage of CD8 T cells detected by flow cytometry.
Figure 13. CRISPR-mediated IRF2 deletion in primary human CD8 T cells -prior to culture. Total T cells (including CD4 T cells, CD4 Treg cells and CD8 T cells) were isolated from primary human PBMC. Following isolation, the T cells were electroporated with IRF2 targeting (sglRF2) and control non-targeting (sgControl) sgRNAs and then cultured for 4 days. The plots show IRF2 expression in the control and IRF2 targeted CD8 T cells 4 days after electroporation. Numbers in each plot show the percentage of IRF2 experessing CD8 T cells.
To achieve CRISPR deletion of IRF2: Primary human T cells were isolated using EasySep Human T cell isolation kit (STEMCELL, cat. no. 17951) according to the manufacturer’s protocol using the easySep magnets. Freshly isolated T cells (between 105 to 107 cells per nucleofection) were washed and resuspended in 20 pl buffer P3 (P3 Primary Cell 4D-Nucleofector X Kit S, Lonza, cat. no. V4XP-3032). In parallel, synthetic crRNAs (IDT) were complexed with tracrRNA (IDT, cat. no. 1072534) at 1 :1 ratio to form a gRNA. The annealed gRNA was mixed with recombinant Cas9 (Thermo Fisher Scientific, cat. no. A36499) for 15 min at room temperature, at a ratio of 1 :2.5 (i.e., 40 pmol Cas9 protein per 100 pmol gRNAs) to form the CRISPR-Cas9-gRNA- ribonucleoprotein (RNP) complex. To increase the KO efficiency, a combination of three different pre-validated crRNAs against human IFR2 gene was used (#1 Hs.Cas9.IRF2.1.AA: 5’-TAAACTCCAACACGATCCCG-3’; #2 Hs.Cas9.IRF2.1 .AB: 5’- GGATGCATGCGGCTAGACAT-3’ and #3 Hs.Cas9.IRF2.1 .AC: 5’-
CAGCATTCGGTAGACCCTGA-3’), whereas a crRNA targeting a non-essential gene was used for negative control (Alt-R® CRISPR-Cas9 Negative Control crRNA #1 ; IDT, cat. no. 1072544). The RNP complex was mixed with the cell suspension and transferred into a 16-well nucleocuvette strip (Lonza, cat. no. V4XP-3032). Cells were transfected using program EH113 and buffer P3 on the 4D-Nucleofector system (4D- Nucleofector X unit, Lonza, cat. no. AAF-1003X). Then, 150 pl of pre-warmed complete culture medium supplemented with human IL-7 (10ng/ml) was added to each well and cells were transferred to a 96-well plate for recovery at 37°C. After electroporation, cells are kept in T-cell complete media with IL-7 (10ng/mL) for 4 days with a media change on day two. IRF2 expression was then measured by flow cytometry.
Figure 14. Activation of control and IRF2 deleted primary human CD8 T cells. Total T cells (including CD4 T cells, CD4 Treg cells and CD8 T cells) were isolated from primary human PBMC. Following isolation, the T cells were electroporated with IRF2 targeting (sglRF2) and control non-targeting (sgControl) sgRNAs and then cultured for 4 days. IRF2 deletion was confirmed by flow cytometry. On day 4 after electroporation, the control and IRF2-deleted T cells were activated with anti-CD3 (10ug/ml), anti-CD28 (1 ug/ml) and IL-2 (20ng/ml) in T cell complete media. Cells that are kept naive have their media changed in place of activation. Activated and naive cells are cultured for 3 days and are then assessed via flow cytometry. The plots show Forward Scatter (FSC) and Side Scatter (SSC) of CD8 T cell in the indicated condition. This data show that
without activation, the IRF2 deleted T cells remain in an unactivated/naive state similar to the control cells and that the IRF2-deleted T cells can be activated and maintained similar to control treated cells.
Figure 15. CRISPR-mediated IRF2 deletion in primary human CD4 T cells prior to culture. Same as Figure 14, except the plots now show CD4 T cells.
Figure 16. Assessment of IRF2 expression in CD8 T cells - Day 2 post-transfection. Optimization of Cas9/gRNA ribonucleoprotein (RNP) transfection for CRISPR/Cas9- Mediated IRF2 knockout (KO) in human T cells at different pulse codes (EH113: and CM137:). Half offset (left) or overlaid (right) histograms show IRF2 expression on human CD8 T cells transfected with nontargeting (nt) control RNP gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. Flow cytometric analysis was performed on day 2 post-transfection.
Figure 17. Assessment of IRF2 expression in CD4 T cells - Day 2 post-transfection. Optimization of Cas9/gRNA ribonucleoprotein (RNP) transfection for CRISPR/Cas9- Mediated IRF2 knockout (KO) in human T cells at different pulse codes (EH113: and CM137:). Half offset (left) or overlaid (right) histograms show IRF2 expression on human CD4 T cells transfected with nontargeting (nt) control RNP gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. Flow cytometric analysis was performed at day 2 post-transfection.
Figure 18. Assessment of IRF2 expression in CD8 T cells - Day 4 post-transfection. Optimization of Cas9/gRNA ribonucleoprotein (RNP) transfection for CRISPR/Cas9- Mediated IRF2 knockout (KO) in human T cells at different pulse codes (EH113: and CM137:). Half offset (left) or overlaid (right) histograms show IRF2 expression on human CD8hi T cells transfected with nontargeting (nt) control RNP gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. Flow cytometric analysis was performed on day 4 post-transfection. Black histogram depicts no antibody staining control on the same flow cytometry channel as IRF2 (this is a way to measure inherent autofluorescence in the stain).
Figure 19. Assessment of IRF2 expression in CD4 T cells - Day 4 post-transfection. Optimization of Cas9/gRNA ribonucleoprotein (RNP) transfection for CRISPR/Cas9- Mediated IRF2 knockout (KO) in human T cells at different pulse codes (EH113: and
CM137:). Half offset (left) or overlaid (right) histograms show IRF2 expression on human CD4 T cells transfected with nontargeting (nt) control RNP gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. Flow cytometric analysis was performed on day 4 post-transfection. Black histogram depicts no antibody staining control on the same flow cytometry channel as IRF2 (this is a way to measure inherent autofluorescence in the stain).
Figure 20. Assessment of IRF2 expression in CD8 T cells - Day 7 post-transfection. Optimization of Cas9/gRNA ribonucleoprotein (RNP) transfection for CRISPR/Cas9- Mediated IRF2 knockout (KO) in human T cells at different pulse codes (EH113: and CM137:). Half offset (left) or overlaid (right) histograms show IRF2 expression on human CD8 T cells transfected with nontargeting (nt) control RNP gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. Flow cytometric analysis was performed on day 7 post-transfection. Black histogram depicts no antibody staining control on the same flow cytometry channel as IRF2 (this is a way to measure inherent autofluorescence in the stain). Overall, this figure shows that the IRF2 deleted CD8 T cells can be maintained in culture for at least 7 days.
Figure 21. Assessment of IRF2 expression in CD4 T cells - Day 7 post-transfection. Optimization of Cas9/gRNA ribonucleoprotein (RNP) transfection for CRISPR/Cas9- Mediated IRF2 knockout (KO) in human T cells at different pulse codes (EH113: and CM137:). Half offset (left) or overlaid (right) histograms show IRF2 expression on human CD4 T cells transfected with nontargeting (nt) control RNP gRNA/Cas9 complex or IRF2 RNP gRNA/Cas9 complex. Flow cytometric analysis was performed on day 7 post-transfection. Black histogram depicts no antibody staining control on the same flow cytometry channel as IRF2 (this is a way to measure inherent autofluorescence in the stain). Overall, this figure shows that the IRF2 deleted CD4 T cells can be maintained in culture for at least 7 days.
Figure 22. IRF2 deletion increases ISG upregulation in response to IFN|3 and IFNy. T cells were electroporated with IRF2 deleting sgRNAs or control sgRNAs and cultured for 4 days (time for IRF2 protein to be downregulated). The T cells were then stimulated with media (unstim), IFN|3 or IFNy for 16 hours. Expression of ISG 15 measured by flow cytometry comparing IFNp-stimulated, IFNv-stimulated and
unstimulated T cells within Irf2-deleted and non-deleted CD4 (left) and CD8 (right) T cells. Numbers indicate geometric mean fluorescence intensity for each histogram.
Figure 23. Total T cells were negatively selected from viably frozen human PBMC. Different volumes of control (WT) or IRF2-targeted (KO) Cas9 RNP mix were then tested for IRF2 deletion in CD8 and CD4 T cells. IRF2 expression was analyzed by flow cytometry two and four days after CRISPR-Cas9 RNP treatment. Electroporation for IRF2 RNP versus control RNP by electroporation was performed in the presence of an Enhancer. Following treatment, the cells were cultured in complete medium + human IL-7. The number in each histogram indicates the percentage of IRF2- expressing cells. The data show that IRF2 can be deleted in 91% of CD8 T cells and 84% of CD4 T cells by four days after CRISPR-Cas9 deletion. The 2.5ul Cas9-RNP concentration is used in subsequent experiments, although both concentrations tested effectively delete IRF2 from human, primary CD8 and CD4 T cells.
Figure 24. Total T cells were negatively selected from viably frozen human PBMC. A carrier DNA (enhancer) was added to the control (WT) or IRF2-targeted (KO) CRISPR- Cas9 reaction, and the IRF2 deletion efficiency (%) in human CD8 and CD4 T cell was analyzed by flow cytometry 4 days later. Following treatment, the cells were cultured in complete medium + human IL-7. The number in each histogram indicates the percentage of IRF2-expressing cells. The data show that the reaction works quite effectively without the enhancer, but that the deletion efficiency is increased using the enhancer.
Figure 25. Total T cells were negatively selected from viably frozen human PBMC. The T cells were then electroporated with control (WT) or IRF2-targeted (KO) Cas9 RNP mix either a single time (as in figures 1 and 2) or two times. After 2 days, half of the cells were electroporated again for the control or the IRF2-targeted RNP. After 4 days (6 days in total), all cells were assessed for IRF2 expression by flow cytometry. Following treatment, the cells were cultured in complete medium + human IL-7. The number in each histogram indicates the percentage of IRF2-expressing cells. No enhancer was used in these experiments. The 2-times treatment worked somewhat better, but also substantially decreased viability of the cells. Therefore, the single electroporation approach is used in subsequent protocols.
Figure 26. Jurkat T cells are an immortal human T lymphocyte line. Jurkat T cells were thawed, expanded and electroporated with the control (WT) or IRF2-targeted (KO)
Cas9-RNP in the presence of an Enhancer. Cells were seeded in complete medium + human IL-7. After 4 days, single cells were FACSorted and subsequently expanded. One of these control (top) and 5 of these IRF2-targeted clones were again assessed for IRF2 expression by flow cytometry. The second IRF2-deleted clone will be used for further experiments. Use of these Jurkat T cell clones provides controls for IRF2 expression. These will be used to as controls to confirm IRF2 expression in the human T cells.
Figure 27. Total T cells were isolated from viably frozen PBMCs by negative selection. Electroporation with control (WT) or IRF2-targeted Cas9-RNP versus control RNP was performed in the presence of an Enhancer. Cells were seeded in complete medium + human IL-7 and after 4 days, IRF2 expression was measured by flow cytometry. In this experiment, the IRF2-targeted Cas9-RNP deleted IRF2 expression in 90% of CD8 T cells and 83% of CD4 T cells. On day 7 after the initial CRISPR-Cas9 treatment, the cells were split and treated with media alone or stimulated with 1000 Units IFN[3. After 64h, the expression of ISG15 and PD-L1 (two IFN-stimulated proteins) was assessed by flow cytometry. The data show that IRF2-deletion led to increased expression of ISG15 and to a lesser extent PD-L1 following IFN[3 treatment. The increased PDL1 expression is consistent with the data showing that mice with IRF2 deleted CD8 T cells exhibited enhanced responses interferons.
Figure 28. Total T cells were isolated from viably frozen PBMCs by negative selection. Electroporation with control (WT) or IRF2-targeted Cas9-RNP versus control RNP was performed in the presence of an Enhancer. Cells were seeded in complete medium + human IL-7 and after 4 days, IRF2 expression was measured by flow cytometry. In this experiment, the IRF2-targeted Cas9-RNP deleted IRF2 expression in 90% of CD8 T cells and 83% of CD4 T cells. On day 7 after the initial CRISPR-Cas9 treatment, the cells were split and treated with media alone or stimulated with 1000ng IFNy. After 64h, the expression of ISG15 and PD-L1 (two IFN-stimulated proteins) was assessed by flow cytometry. The data show that IRF2-deletion led to slightly increased expression of ISG15 and PD-L1 following IFNy treatment. The increased PDL1 expression is consistent with the data showing that mice with IRF2 deleted CD8 T cells exhibited enhanced responses to interferons.
Figure 29. Total T cells were isolated from viably frozen PBMCs by negative selection. Electroporation with control (WT) or IRF2-targeted Cas9-RNP versus control
RNP was performed in the presence of an Enhancer. Cells were seeded in complete medium + human IL-7 and after 4 days, IRF2 expression was measured by flow cytometry. In this experiment, the IRF2-targeted Cas9-RNP deleted IRF2 expression in 90% of CD8 T cells and 83% of CD4 T cells. On day 7 after the initial CRISPR-Cas9 treatment, the cells were split and treated with media alone or stimulated with 1000 Units IFN[3 + 1000ng IFNy. After 64h, the expression of ISG15 and PD-L1 (two IFN- stimulated proteins) was assessed by flow cytometry. The data show that IRF2- deletion led to increased expression of ISG15 and to a lesser extent PD-L1 following IFN[3 treatment. The increased PDL1 expression is consistent with the data showing that mice with IRF2 deleted CD8 T cells exhibited enhanced responses to interferons.
Figure 30. Total T cells were isolated from viably frozen PBMCs by negative selection. Electroporation with control (WT) or IRF2-targeted Cas9-RNP versus control RNP was performed in the presence of an Enhancer. Cells were seeded in complete medium + human IL-7. After 4 days, CRISPR-mediated IRF2 deletion was confirmed by flow cytometry (CD8 T cells: 90% deleted for IRF2). Half of the cells were kept unstimulated, while the other half was activated for 3 days with anti-CD3 (2ug/mL), anti-CD28 (1 ug/mL) and IL-2 (20ng/mL). (A) Numbers in each plot indicate the percent of live CD8 T cells. (B) Histograms show the FSC of unstimulated and anti-CD3 + CD28 stimulated CD8 T cells. Increase in FSC indicates CD8 T cell activation and blasting.
Figure 31. (A) CRISPR-mediated IRF2 deletion in mouse tumor-specific (P14) CD8 T cells. P14 T cells are murine T cells that transgenically express rearranged TCR genes recognizing the LCMV-GP33-41 peptide presented in the context of murine H2Db. P14 T cells were isolated by negative selection from the spleen of naive P14 mice. The P14 T cells were then electroporated with control (WT) or IRF-2-targeted (KO) Cas9- RNP and cultured in complete medium + mouse IL-7. After 4 days, all cells were assessed for IRF2 expression by flow cytometry. The number in each histogram indicates the percent of P14 T cells that express IRF2. (B) The control or IRF2- targeted P14 T cells from panel A were stimulated with anti-CD3 + anti-CD28 antibodies for 24 hours. Then 350,000 of the control or IRF2-targeted P14 cells were adoptively transferred into mice with established MC38-GP tumors (day 8 after tumor inoculation). MC38 is an adenocarcinoma tumor cell line that we engineered to express the LCMV-GP1-100 peptide sequence. The tumor size in mice receiving control or IRF2-targeted P14 T cells was measured on day 8 (just prior to adoptive P14
T cell transfer) and on day 23 after tumor implantation. Error bars represent mean ± SEM. One-way ANOVA for multiple comparisons. C) Percent survival of the MC38-GP tumor bearing mice following control (solid) or IRF2-targeted P14 T cell adoptive transfer. Kaplan-Meier curve, log-rank test. All the data in this figure is from a single experiment, and the experiment has been repeated 3 times.
DETAILED DESCRIPTION
In the following description, numerous specific details are set forth to provide a thorough understanding of the invention. However, it is understood that the invention may be practiced without these specific details.
Type I and II interferons (IFNs) stimulate pro-inflammatory programs critical for immune activation, but also induce immune-suppressive feedback circuits that impede control of cancer growth. Here, we sought to determine how these opposing programs are differentially induced. We demonstrated that the transcription factor Interferon Regulatory Factor 2 (IRF2) was expressed by many immune cells in the tumor in response to sustained IFN signaling. CD8+ T cell-specific deletion of IRF2 prevented acquisition of the T cell exhaustion program within the tumor, instead enabling sustained effector functions that promoted long-term tumor control, and increased responsiveness of immune-checkpoint and adoptive cell therapies. The long-term tumor control by IRF2-deficient CD8+ T cells required continuous integration of both IFN-I and IFN-II signals. Thus, IRF2 is a foundational feedback molecule that redirects IFN signals to suppress T cell responses and represents a new target to enhance cancer control.
In an aspect therefore, there is provided a modified T cell engineered to have decreased IRF2 expression.
T cells may be modified using methods of genetic engineering (also referred to as genetic modification or genetic manipulation) that modify and manipulate a cell’s genes using techniques known in the art.
In some embodiments, the modified T cell has been engineered using virus-mediated knockdown.
In some embodiments, the modified T cell has been engineered using CRISPR/Cas9.
In some embodiments, the T-cell is a regulatory T cell.
In some embodiments, the T-cell is an effector T cell.
In some embodiments, the T-cell is CD4+.
In some embodiments, the T-cell is CD8+.
In some embodiments, the T-cell is CD4- and/or CD8-.
In some embodiments, the T-cell is a CAR T cell.
In some embodiments, the IRF2 gene has been knocked-down in the modified T cell.
In some embodiments, the IRF2 gene, or portions thereof, have been deleted in the modified T cell.
In some embodiments, the T cell is a human T cell.
In some embodiments, the modified T cell has improved anti-tumor activity compared to the corresponding wild-type T-cell.
In some embodiments, the modified T cell has improved resistance to T cell exhaustion compared to the corresponding wild-type T-cell.
In an aspect, there is provided the modified T cell described herein, for use in the treatment of cancer.
In an aspect, there is provided the modified T cell described herein, for use in adoptive cell therapy.
In an aspect, there is provided a use of modified T cell described herein, in the preparation of a medicament for the treatment of cancer.
In an aspect, there is provided a method of treating a subject with cancer, comprising administering to the subject a therapeutically effective amount of the modified T cell described herein.
In an aspect, there is provided a method treating a subject with cancer, comprising engineering the modified T cell described herein, in vivo in the subject.
In some embodiments, the method further comprises treating the subject with a checkpoint inhibitor.
Preferably, the method further comprises treating the subject with anti-PDL1 and/or anti-PD1 blockade therapy.
In an aspect, there is provided a method treating a subject with cancer, comprising administering to the subject a therapeutically effective amount of an inhibitor of IRF2 function or expression.
The term inhibitor as used herein with respect to IRF2 refers to molecules having the ability to specifically inhibit, wholly or partially, the expression or function of IRF2. Such inhibitors can include, without limitation, small molecules, antibodies, and nucleic acids (e.g. antisense oligonucleotides, siRNA, shRNA, miRNA...etc.).
As used herein, “therapeutically effective amount refers to an amount effective, at dosages and for a particular period of time necessary, to achieve the desired therapeutic result. A therapeutically effective amount of the pharmacological agent may vary according to factors such as the disease state, age, sex, and weight of the individual, and the ability of the pharmacological agent to elicit a desired response in the individual. A therapeutically effective amount is also one in which any toxic or detrimental effects of the pharmacological agent are outweighed by the therapeutically beneficial effects.
As used herein, “pharmaceutically acceptable carrier" means any and all solvents, dispersion media, coatings, antibacterial and antifungal agents, isotonic and absorption delaying agents, and the like that are physiologically compatible. Examples of pharmaceutically acceptable carriers include one or more of water, saline, phosphate buffered saline, dextrose, glycerol, ethanol and the like, as well as combinations thereof. In many cases, it will be preferable to include isotonic agents, for example, sugars, polyalcohols such as mannitol, sorbitol, or sodium chloride in the composition. Pharmaceutically acceptable carriers may further comprise minor amounts of auxiliary substances such as wetting or emulsifying agents, preservatives or buffers, which enhance the shelf life or effectiveness of the pharmacological agent.
The advantages of the present invention are further illustrated by the following examples. The examples and their particular details set forth herein are presented for illustration only and should not be construed as a limitation on the claims of the present invention.
EXAMPLES
Methods and Materials
Data and code availability
Single-cell RNA-seq, ATAC-Seq and CUT&Tag data have been deposited at GEO. Accession numbers are listed Table 1 : Key Resource Table.
All original codes (sc-RNA-Seq, scATAC-Seq and CUT&Tag analyses codes) have been deposited at the BrooksLab repository in github (https://github.com/diala- ar/BrooksLab). DOIs are listed in the key resources table
Mice
C57BL/6 mice were purchased from The Jackson Laboratory or the breeding colony at the Princess Margaret Cancer Center, University Health Network (PMCC, UHN). Irf^- mice (Matsuyama et al., 1993) were kindly provided by Dr. Tak Mak at the PMCC (UHN). Briefly, the Irf?-'- were generated by replacing exon 3 of the Irf2 gene (which encodes amino acids 30 - 63 and is part of the DNA-binding domain of the protein) with a neomycin resistance cassette. The Irf2fll/fl mice (described below) were crossed with CD8a-Cre (E8iii-Cre) transgenic mice (C57BL/6-Tg(CD8a-cre)1 ltan/J, Stock No. 008766, The Jackson Laboratory) to delete IRF2 from peripheral CD8a-expressing T cells. LCMV-GP33-specific CD8+ TCR transgenic (P14) mice have been described previously (Brooks et al., 2006). Mice were housed under specific pathogen-free conditions at the PMCC (UHN). Mouse handling conformed to the experimental protocols approved by the OCI Animal Care Committee at the PMCC (UHN). Experiments were performed using sex and age matched male and/or female mice. Mice used were between 7 and 10 weeks old at the initiation of each experiment.
Generation of IRF2 floxed mice
Construction of mlrf 2 conditional targeting vector: A targeting construct was designed to conditionally delete the 3rd exon of the mouse interferon regulatory factor 2 gene. To generate the genomic fragments for the 5’ and 3’ homology arms, we designed primers based on mouse mlrf2 genomic sequence (GenBank Accession No. NC_000074) to use in PCR from mouse C57BL/6 genomic DNA (Jackson Laboratory, Bar Harbor, Maine). In brief, PCR primers 5'- GCA CTT AGC GAT CGC AGC TGC TCC TTG GAC CAA TGA CCT T -3'(Jrf2 AsiSI sense) and 5'- AAG TTA AAT CGA TAG AAG ACT CCT GGC GCA TGC TCA GTC -3' (Jrf2 Clal antisense) were used to amplify a 4497 bp 5’ homology-arm fragment (corresponding to mlrf2 intron 2 sequence) from 200 ng of C57BI/6J genomic DNA using the PfuUltra II fusion HS DNA polymerase (Agilent Technologies, Santa Clara CA). Using the same PCR conditions, a 4555 bp 3’ homology-arm fragment (encompassing exon 4, exon 5, and part of intron 5 of mlrf2) was amplified from C57BI/6J Genomic DNA using the PCR primers 5'- TGG ACC AGT TTA AAC ATA TTG GAA GCT CGT CTC TGC -3' (Jrf2 Pmel sense) and 5'- ATT TAT GCG GCC GCT CAC TTC CTG GAT GAA CAT GGC -3' (Jrf2 Notl antisense). PfuUltra II fusion HS DNA polymerase was also used to amplify a 1146 bp fragment from C57BI/6J genomic DNA spanning the targeted mlrf2 exon 3 using the primers 5' TTC TGG TCT TAA TTA ACT TTA GCA GGA CTA GGA TTA CAG 3' (mlrf2 Ex3 Pad sense) and 5' AAT ATG ATT AAT TAA AAG GTC CAC ATC TAA AGA TAT CTC C 3' (mlrf2 Ex3 Pad antisense).
The resulting PCR products were gel-purified using the Nucleospin® Gel and PCR Clean-up system (Machery-Nagel Gmbh & Co., Germany), and TA-overhangs were added via a 20-minute incubation with Taq DNA polymerase (New England BioLabs, Ipswich, MA) at 68°C in the presence of 1 mM dNTPs and 1X Buffer. The fragments were then TA-cloned into pCR2.1-TOPO (Invitrogen, San Diego, CA) and subcloned into a modified pBluescript II KS (Stratagene, La Jolla, CA) vector containing a PGK- neomycin cassette flanked by both LOXP and FRT sequences. A diphtheria toxin (OTA) gene was inserted 3’ of the long arm to negatively select against non- homologous targeting. Insert sequence was validated using fluorescent dideoxynucleotide sequencing and automated detection (ABI/Perkin Elmer, Forest City, CA).
Targeted disruption of the murine Irf2 gene in ES cells: The mlrf2 conditional targeting vector (25 pg) was linearized with Notl restriction endonuclease at the 3’ end of the 3’homology-arm and electroporated into C57BI6/N ES cells (NIH Knockout Mouse Project (KOMP) repository; University of California Davis) using a Bio-Rad Gene
Pulser, 0.34 kV, and 0.25 mF. ES cell culture was carried out as previously described (Hakem et al., 1996). After G418 selection (250 pg/ml), homologous recombinants were identified by 5’ and 3’ flanking PCR and confirmed by sequencing following published protocols (Hakem et al., 1996). Homologous recombination at the 5’ homology-arm was confirmed by Terra™ PCR Direct (Takara Bio, Mountain View, CA, USA) amplification of a 4657 bp fragment using the primers I rf2 5PCR Sense: 5’ GCC AGG CCA TTT GTT TAG GAA TGC AGG AG -3’, in the flanking sequence of mlrf2 intron 2, and the vector-specific primer PCRA antisense: 5'- CGA CGG TCA ACG AGC AGT CCA GCG TAT CC -3'. Homologous recombination at the 3’ homology-arm was confirmed by Terra™ PCR Direct amplification of a 4858 bp fragment using the vector-specific primer PCRB sense: 5'- GCT TGA CTC GCT ACG TGG ATC GTC GAA C -3' and the mlrf2 intron 5’ flanking primer: Irf2 3PCR antisense 5'- AAA CCC ACC GGC CTG ATA CAC GTT CTA C -3'.
Generation of Irf2 flox mice: Chimeric mice were produced by microinjection of independent mlrf2 +/- ES cell clones into E3.5 C57BL/6J blastocysts and transferred to pseudopregnant foster mothers. Chimeric males were mated with C57BI/6J females (Jackson Laboratory). Germ line transmission of the mutant allele was confirmed by PCR analysis of tail DNA from mice with an agouti coat color. The PGK-Neo cassette was removed from mouse Irf2+t mice by crossing with Flp-deleter mice (Jackson Laboratory stock #009086) (Farley et al., 2000) and PCR genotyping and sequence validation of recombination at the FRT sites.
Human melanoma samples
All human tissue specimens were obtained through protocols approved by the institutional review board (University Health Network Research Ethics Board). Surgical specimens were obtained from the Princess Margaret Cancer Biobank. Written informed consent was obtained from all donors.
Tumor models, cell culture and tumor injections
MC38 tumor cells derived from C57BL/6 murine colon adenocarcinoma were a generous gift from Dr. Daniel de Carvalho at the PMCC (UHN) and were cultured in McCoy 5A medium supplemented with 10% heat-inactivated fetal bovine serum (FBS), 1% penicillin, 1% streptomycin and 1% L-glutamine. To generate the MC38-GP cells, MC38 cells were stably transduced with pMIG retroviral vector expressing the LCMV
GP-i-100 sequence and a Puromycin resistance gene. Puromycin was used to select for successfully transduced cells. To select single cell MC38-GP clones, cells were sorted and grown from a single cell per well in McCoy 5A medium supplemented with 10% FBS, 1% penicillin and streptomycin and 1% L-glutamine and puromycin. 2x105 MC38 or MC38-GP tumor cells were injected subcutaneously (s.c.) into one hind leg of each mouse. B16-F10 tumor cells derived from C57BL/6 murine B16-F10 skin melanoma were generously provided by Dr. Tracy McGaha at the PMCC (UHN) and cultured in DMEM medium (Gibco) supplemented with 10% FBS, 1% penicillin, 1% streptomycin and 1% L-glutamine. 1x105 B16-F10 tumor cells were injected s.c. into one hind leg of each mouse. The MMTV-PyMT cell line was a generous gift from Dr. Christopher Page at the PMCC (UHN) and was cultured in McCoy 5A medium supplemented with 10% heat-inactivated fetal bovine serum (FBS), 1% penicillin, 1% streptomycin and 1% L- glutamine. 1x106 PyMT cells were injected into a single mammary gland fat pad of each mouse.
P14 T cell adoptive transfer
LCMV-GP33-41 -specific CD8+ P14 T cells were isolated from the spleens and lymph nodes of C57BL/6 and Irf2 ' transgenic mice by negative selection (Stem Cell Technologies). For pre-tumor transfer, 2x105 naive P14 T cells were transferred i.v. in the retro-orbital sinus. For therapeutic transfer experiments, P14 T cells were activated for 24 hours using plate-bound anti-CD3 (clone 2C11 , 10pg/mL) and soluble anti- CD28 (1 g/mL) antibodies. Cells were then counted and 2x105 P14 cells were transferred i.v. in the retro-orbital sinus nine days following tumor implantation.
Isolation of tumor-infiltrating cells
To isolate immune cells from the tumor, tumors were cut into small fragments and digested in RPMI media containing collagenase I (100U/mL, ThermoFisher), DNasel (10ug/mL, Sigma) and 2% FBS in a gentleMACS dissociator (Cat#130-093-235 Miltenyi Biotec) using the gentleMACS program 37c_m_TDK_1. Cells were filtered through a 70pm pre-separation filters (Cat#130-095-823 Miltenyi Biotec) and subjected to red blood cell lysis. CD45+ tumor infiltrating lymphocytes were isolated from the digested single-cell suspensions using Mouse CD45 (TIL) Microbeads (Cat#110-021- 618 Miltenyi Biotec), autoMACS columns (Cat#130-021 -101 Miltenyi Biotec) and an autoMACS Pro Separator (Miltenyi Biotec) following the manufacturer’s instructions.
Purified CD45+ single-cell suspensions from tumors from at least 5 mice per condition were pooled before staining.
Time-of-Flight mass cytometry (CyTOF)
Antibodies directly conjugated to metal tags were purchased from Fluidigm. Purified unconjugated antibodies were labeled with metal tags at the SickKids-UHN Flow and Mass Cytometry Facility using the MaxPar Antibody Labeling Kit from Fluidigm. CyTOF staining was performed as previously described(Snell et al., 2021). Briefly, mouse single cell suspensions (up to 5x106 cells) were pulsed with 12.5 mM Cisplatin (BioVision) in PBS for 1 min at room temperature (RT) prior to quenching with CyTOF staining media (Mg+/Ca+ HBSS containing 2% FBS (Multicell), 10mM HEPES (Corning), and FBS underlay. Cells were then resuspended in CyTOF staining media containing Fc block (prepared in house) for 15 mins at 4°C and a 2x CyTOF staining media containing metal-tagged surface antibodies (data not shown) was added for an additional 30 min at 4°C. Cells were then fixed and permeabilized using the eBioscience Foxp3 I Transcription Factor Staining set (ThermoFisher) according to manufacturer’s instructions. Cells were then stained with metal tagged intracellular antibodies (data not shown) prepared in the permeabilization buffer from the same kit. All antibody concentrations were used at concentrations previously determined by titration. Cells were then incubated overnight in PBS (Multicell) containing 0.3% (w/v) saponin, 1.6% (v/v) paraformaldehyde (Polysciences Inc) and 50 nM Iridium (Fluidigm). Human melanoma samples were stained in a similar manner except the Fc block (ThermoFisher, catalog #16-9161-73) was for 10 min at RT. Cells were analyzed on a Fluidigm Helios or CyTOF-2 mass cytometer instruments at the SickKids-UHN Flow and Mass cytometry Facility and at the PMCC Tumor Immunotherapy Profiling Facility. FCS data files were manually de-barcoded and analyzed using Cytobank 6.2 (Cytobank, Inc), FlowJo software (v.9 or v.10, Treestar) and the GraphPad Prism v8 or v9.
Heatmaps were generated from arcsinh-transformed median of spectral indices (MSI) values, plotted in R using the viridis color package and the ggplots package. The CyTOF data was clustered and analyzed using the Phenograph(Levine et al., 2015) algorithm together with the R implementation of UMAP. An equal number of cells was used for clustering, determined based on the lowest common denominator between WT and Irf -1- tumor infiltrating CD8+ T cells.
Flow cytometry and intracellular cytokine stimulation
Single-cell suspensions of cells from the indicated tissue or tumor were stained with a zombie aqua viability stain (Biolegend) at 4°C and then surface stained using antibodies against targeted molecules CD45 (30-F11), CD45.2 (104), CD45.1 (A20), CD8a (53-6.7), CD4 (GK1.5), Tcr(3 (H57-597), PD1 (29F.1A12), Lag3 (C9B7W), CD39 (24DMS1), PDL1 (10F.9G2), B220 (RA3-6B2), CD62L (MEL-14), CD25 (PC61), CD86 (GL-1), CD11C (3.9), CD11 b (M1/70), CD44 (IM7), NK1.1 (PK136), Ly6C (HK1.4), Ly6G (1A8). Cells were then washed in PBS and staining buffer, then fixed and permeabilized using the eBioscience Foxp3 I Transcription Factor Staining Kit (ThermoFisher) according to manufacturer’s instructions. Cells were then stained with intracellular antibodies [Granzyme B (GB11), Ki67 (35/Ki-67RUO), Tbet (4B10), TCF1 (S33-966), Tox (txrxlO), BATF (D7C5), IRF4 (IRF4.3E4), IRF1 (D5E4), Stat (1/stat1), PKR (EPR19374, IRF7 (MNGPKL), Blimpl (5E7), FoxP3 (MF-14, FJK-16S), IRF2 (EPR4644(2))] prepared in the permeabilization buffer from the same kit, for 30 min at 37°C. Samples were then washed and, where necessary, the intracellular staining was repeated using secondary fluor-tagged antibodies.
Single-cell suspensions of tumor cells were counted and restimulated with 2 mg/ml of MHC class l-restricted LCMV peptide GP33-40 in standard complete T cell medium (RPMI supplemented with 10% FBS, 1% penicillin and streptomycin, HEPES, 1% Sodium pyruvate, 1% non-essential amino acids, 1% L-glutamine and 2- mercaptoethanol) containing 50 U/mL recombinant murine IL-2 and 1 mg/mL brefeldin A (Sigma), at 37°C. After 5 hours, cells were stained with a zombie aqua viability stain (Biolegend) and then stained for surface molecules using antibodies against CD45.2 (104), CD8a (53-6.7), CD4(GK1.5), Tcr(3 (H57-597) antibodies. Cells were then fixed and permeabilized (Biolegend cytokine staining kit) and then stained with anti-IFNy (XMG1.2) and anti-TNFa (MP6-XT22), both from BioLegend. Cells cultured similarly but in the absence of the GP33-40 peptide served as negative controls when quantifying cytokine production. Samples were then washed and resuspended in FACS staining buffer and acquired using a FACSVerse or FACSLyric analyzer (BD Biosciences). Data was analyzed using FlowJo software (v.9 or v.10, Treestar) and the GraphPad Prism v8 or v9.
IFN[3 stimulation of mouse cells
Single-cell suspensions of splenocytes from naive mice were counted and incubated with 100U/ml_ IFN[3 for 8 hours in T cell RPMI medium supplemented with 10% fetal bovine serum, 1% penicillin and streptomycin and 1% L-glutamine. Cells were then surface stained for CD8 (53-6.7), CD4 (GK1.5), B220 (RA3-6B2) and then intracellularly stained for FoxP3 (MF-14) and IRF2 [EPR4644(2), Abeam],
In vivo antibody treatments
All in vivo antibodies were purchased from BioXcell, diluted in PBS and injected intraperitoneally (i.p.). CD8+ and CD4+ T cells were depleted by administering 250pg of anti-CD8a (2.43) antibody or anti-CD4(GK1 .5) one day prior to tumor injection. For late CD8+ T cell depletion, 250pg of anti-CD8a (2.43) antibody or isotype control (LTF-2) was injected from 3 weeks after MC38 tumor injection, for a total of three anti-CD8a treatments (every 2 days). To block PDL1 in vivo, 500pg of anti-PDL1 (10F.9G2) or isotype control (LTF-2) antibody treatment was initiated i.p. at 15 days after PyMT tumor cell initiation (when tumors in WT and Irf2 ' mice had reached ~50mm3 but were still comparable in size) and then every 3 days for a total of 5 treatments. To block IFN-I signaling in established MC38-tumors, mice were treated every 2 days with 500pg of anti-IFNAR (MAR1-5A3) of isotype control (mouse lgG1) for the first 2 treatments and 250pg for subsequent treatments. To block IFNy signaling, 500pg of anti-IFNy (XMG1.2) was administered every 2 days. Isotype control antibodies were injected in a similar fashion.
Cell Sorting and single-cell RNA Sequencing
Single-cell suspensions of tumor cells were counted and stained with a zombie aqua viability dye (Biolegend) and then surface stained with anti-CD45.2 (A20) antibody. Stained cells were washed and resuspended in RPMI medium supplemented with 10% FBS, 1% penicillin and streptomycin and 1% L-glutamine. At least 8 mice were pooled per group and CD45+ tumor-infiltrating cellsimmune cells were FACSorted on a Moflo Astrios (Beckman Coulter). Sorted cells were labelled with sample multi-plexing antibodies and AbSeq Ab-Oligos (antibody-oligonucleotides) using the BD Mouse Immune Single-Cell Multiplexing Kit (Cat. No. 633793). Whole transcriptome Analysis (WTA), AbSeq and Sample Tag libraries from approximately 32,000 cells were constructed using the BD Rhapsody™ WTA Amplification Kit (Cat. No. 633801 , Becton, Dickinson and Company, Franklin Lakes, New Jersey) from cDNA encoded on beads after cell capture on the BD Rhapsody™ Single-Cell Analysis System, using the
manufacturer's recommended protocol. Briefly, the WTA library was amplified via random priming while in parallel, AbSeq and Sample Tag libraries were amplified by PCR after denaturation from cell capture beads. All libraries underwent index PCR to add full-length Illumina sequencing adapters and indices. Libraries were evaluated for quantity and quality on an Agilent Bioanalyzer, pooled and sequenced on an Illumina NovaSeq6000 instrument targeting approximately 2 billion reads on an S2 flowcell for paired-end 2x100 bp sequencing and 20% PhiX spike-in. Library preparation and sequencing were performed by The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada.
Seven Bridges pipeline: Sequence reads of tumor (WT and Irf2 / ') cells were aligned to the GRCm38 mouse genome and demultiplexed using the standard BD Rhapsody™ WTA Analysis pipeline on the Seven Bridges server (https://www.sevenbridges.com). Cells annotated as multiplets or undetermined by the pipeline were excluded. All bioinformatics analyses were performed using customized scripts in R (v4.1.1) unless mentioned otherwise.
Seurat analyses: Data was further analysed using the R Seurat (v 4.0.4) package (Hao et al., 2021). To filter out low quality cells and poorly expressed genes, we applied the following quality control steps: First, cells with <500 read counts, with >25% mitochondrial reads, and expressing <700 and >7000 genes in Tumor WT were removed. Second, cells with <500 read counts, with >20% mitochondrial reads, and expressing <700 and >6000 genes in Tumor IRF2 Z- were excluded. Third, genes expressed in <10 cells in each condition were also excluded. Finally, doublets were removed using the Chord R package (v1.0.0). Gene expression (RNA assay) data was then normalized using the logNormalize method and the antibody-derived tags (ADTs) assay data using the CLR method. RNA assay was scaled and adjusted by regressing out cell cycle genes and mitochondrial proportion. PCA was performed on most variable genes. To cluster cells, we constructed a weighted nearest neighbor (WNN) graph using the FindMultiModalNeighbors function with the first 20 principal components (PCs) for the RNA assay and 12 PCs for the ADT assay. CD8+ T cells from both WT and Irf2 / were identified using both RNA and ADT and reclustered (0.4 resolution). Markers of each cluster were identified for RNA and ADT assays using the FindAIIMarkers functions with default parameters. Differential gene expression between clusters and conditions (WT and Irf2 ' ) were performed using the FindMarkers function without filtering any gene with the following parameters:
logfc.threshold=0, min.pct=0, min.diff.pct=0. The gene list was used to rank the genes for the GSEA analyses and to identify differentially expressed genes (DEGs). DEGs were identified by filtering out genes with an FDR> 0.05, with an absolute value of Iog2 fold change <0.25 and expressed in less than 1% in either of the two compared subpopulations. RNA gene expression was also analyzed using SeqGeq software (v.1.6 or 1.7, BD Biosciences).
Gene set enrichment analysis (GSEA): We performed a GSEA analysis to test whether EFFECTOR_VS_EXHAUSTED_CD8_TCELL (UP and DN) from C7 of MSigDB (Liberzon et al., 2011 ; Subramanian et al., 2005) and DEGs in TOX/_ CD8+ T cells were enriched for up- or down-regulated genes in c3.lrf2-''- vs cO.WT. The up- and down-regulated genes in TOXZ- gene sets were taken from (Khan et al., 2019) and filtered to keep genes with adjusted P-value < 105. DEGs in c3. Irf2 ' vs cO.WT were identified by the FindMarkers function, as mentioned previously, without filtering any genes, and were ranked by sign(log2FC) * -Iog10(pval), where log2FC is the Iog2 fold change of average of gene expression in IRF2/_ and average in WT and pval represents the P-value calculated using Wilcoxon rank-sum test. Enriched pathways were identified using the fgsea R package (v1.18.0) with 1 ,000 permutations, the following parameters: minSize=5, maxSize=500, nPermSimple=100000, eps=0, nproc=1 and the Hallmark gene sets taken from
Enrichment Map: To identify biological processes enriched in cO.WT and c3.lrf2 ' cells, we performed a GSEA analysis for RNA assay data and Gene Ontology Biological Processes gene sets using the GSEA application with default parameters. An Enrichment Map of the GSEA results was generated using the Enrichment Map Visualization tool from the GSEA application. Gene sets included in the Enrichment Map have a P-value <0.005 and an FDR <0.05 and a cutoff of 0.5 for the overlap coefficient used as a similarity metric between the different gene sets. Enrichment Map was illustrated in Cytoscape (Shannon et al., 2003).
Upstream regulator analysis’. Predicted upstream regulator analysis of differentially expressed genes of c3.lrf2-''- vs cO.WT was performed using the Ingenuity Pathway Analysis software (Qiagen) according to the developer’s instructions.
SCENIC analyses'. We assessed regulon activity in cO.WT and c3.1 rf 2^- CD8+ T cells by performing an analysis of the gene regulatory network using the pyscenic implementation of SCENIC (Aibar et al., 2017) consisting of three steps: 1) identifying
regulons based of TF-targets co-expression using GRN, 2) pruning regulons to keep direct targets of TFs using RcisTarget, and finally 3) assessing regulon activity score (RAS) for each regulon on cellular level using AUCell. Due to the stochastic nature of GRN, SCENIC might generate slightly different results on each run. To define regulons in a robust manner, we ran the first two steps of SCENIC using arboreto_with_multiprocessing and ctx functions from pyscenic library 100 times with default parameters. Results of the 100 runs were aggregated as follows: TFs and TF- target pairs occurring 70 out of 100 times were retained. Regulons were redefined using the retained TFs and TF-target pairs. Then RAS scores were generated and binarized using aucell and binarize functions from the pyscenic library. RAS scores of the two clusters were compared using the Wilcoxon rank-sum test in R and p-values were adjusted using the FDR method. We retained regulons having an FDR < 0.05, an absolute value of Iog2 fold change (c3./rf2-/- 1 cO.WT) > 0.25 and regulons active in at least 10% of the cells of either of the two compared clusters.
ATAC-seq
Single-cell suspensions of WT, Irf^-, CD8-IRF2cWT and CD8-IRF2cKO MC38- infiltrating CD8+ T cells were isolated using autoMACS (Miltenyi Biotec), washed and treated with DNase I, and nuclei isolated according to the 10x Genomics nuclei isolation for single cell ATAC sequencing protocol. AT AC libraries were prepared and sequenced (to a target depth of 25,000 reads per cell) on the Novaseq6000 at the Princess Margaret Genomics Center, following the 10x Genomics sequencing workflow and protocol.
Reads alignment and quantification: base calls were generated using Illumina RTA v3.4.4 as bcl files which were then converted into fastq files using bcl2fastq v2.20 with default parameters. Reads in fastq files were aligned using cellranger-atac count (v 2.0.0) to the reference mm10 genome (i.e., refdata-cellranger-arc-mm10-2020-A- 2.0.0). The chemistry parameter of cellranger-atac count function was set to ARC-v1 , since only scATAC-Seq data was analysed from the multiome data.
Peaks calling, Quality control, and normalization: R (v 4.1.3) and the Signac (v 1 .6.0) R package were used for scATAC-Seq data analyses. Reads occurring in less than 10 cells and less than 200 genes were excluded from further analyses. Peaks were called using MACS2 V2.2.7.1 (Zhang et al., 2008) with the following parameters: format- BEDPE', effective. genome. size='mm', cleanup=F. Reads occurring in genomic
blacklist regions as defined in (Amemiya et al., 2019) were identified. The following criteria were used to exclude outlier cells from subsequent analyses: 1) reads within peak < 2000 in CD8-IRF2cWT and CD8-IRF2cKO,_and 800 in lrf2^~ and WT; 2) reads within peaks > the 99th percentile of reads within peaks in each sample; 3) percentage of unique reads occurring within peaks < 35% in CD8-IRF2cKO, 37% in CD8- IRF2cWT and 30% in Irf^- and WT, 4) ratio of reads in genomic blacklist region > 0.1 in CD8-IRF2cWT and CD8-IRF2cKO and 0.12 in Irf2 ' and WT, 5) nucleosome signal > 4, and 6) TSS enrichment score < 2. Term frequency-inverse document frequency (TF-IDF) was used for normalization using the Signac R package.
Peaks annotation and inference of IRF2 motifs: The TFBStools (v 1 .32.0) R package in combination with position weight matrices (PWM) taken from the JASPAR2020 (v 0.99.10) database were used to infer occurrence of IRF2 motifs within each peak. Promoters were predicted using the GenomicFeatures (v 1.46.1) R package as 3000 bases upstream and 3000 bases downstream the transcription start site. Peaks that occur at least once in at least 10 cells were considered accessible. The following parameters were used to annotate peaks using the annotatePeak function in the ChlPseeker (v 1.30.0) R package: tssRegion=c(-3000, 3000), level- gene', genomicAnnotationPriority=c("Promoter", "5UTR", "3UTR", "Exon", "Intron",
"Downstream", "Intergenic"). Finally, peaks were visualized using the CoveragePlot function from the R Signac package.
Human melanoma samples from published ATAC-seq data: Triplicates of ATAC-seq data from PD1 hi CD8+ T cells of a melanoma patient and from CD8+/CD45RA+ peripheral blood lymphocytes of healthy donors were obtained from published ATAC- seq data (Philip et al., 2017). Samples were aligned to the mm10 genome using bowtie v2.4.1 , and the resulting bam files were further preprocessed using samtools v1.10 and picard v2.22.1. Peaks were called using MACS2 v2.2.7.1 (Zhang et al., 2008) with the following parameters: ‘-q 0.01 -nomodel --shift -100 -extsize 200'. Genome-wide IRF2 transcription binding site accessibility was predicted at a p-cutoff of 0.0005 using the TFBStools v1.28.0 R package (Tan and Lenhard, 2016) in coordination with position frequency matrices obtained via the JASPAR2020 v0.99.10 database (Fornes et al., 2020). Bedgraph files from each sample and a bed file of all predicted IRF2 binding sites were visualized using IGV v2.10.3. Additionally, pre-processed normalized counts for the ATAC data were obtained from GSE89308. Peak annotations were cross validated using the ChlPSeeker v1.26.2 R package (Yu et al.,
2015). Differential accessibility was inferred using DESeq2 v1.30.1 (Love et al., 2014) using the apeglm v1.12.0 R package (Zhu et al., 2019) for the effect size shrinkage estimations. Finally, all gene-set enrichment analysis and over-representation analyses were done using the clusterprofiler v3.18.1 R package (Yu et al., 2012). Two type of gene sets were created for GSEA, one was comprised of transcription factors and all genes with their predicted binding site within their promoter region (TSS +/- 1 kb), and the other comprised the standard mSigDB gene sets from the msigdb v7.4.1 R package (Liberzon et al., 2015).
CUT&Tag
Naive CD8+ T cells were isolated from the spleens and lymph nodes of C57BL/6 by negative selection (Stem Cell Technologies). Isolated CD8+ T cells were activated for 3 days using 5pg/mL plate-bound anti-CD3 (16-0031-86 Invitrogen) and 2pg/mL soluble anti-CD28 (16-0281-86 Invitrogen) antibodies in 10% RPMI complete media containing 100U/ml rhlL2 (200-02 PeproTech). Nuclei were isolated and CUT&Tag performed using the protocol from Epicypher (EpiCypher® CUTANA™ Direct-to-PCR CUT&Tag Protocol v1.6 Revised: 11.04.2021). Prior to sequencing, samples were normalized based on their DNA concentrations and pooled. A high-output 75-cycle Nextseq kit [NextSeq 500/550 High Output Kit v2.5 (75 Cycles), 20024906 Illumina] was used for paired-end sequencing, giving each sample 10-12 million reads.
Data analysis’. Samples were aligned to the mm 10 genome using bowtie v2.4.1 with the following parameters: ‘--local -very-sensitive -no-mixed -no-discordant -phred33 -I 10 -X 700’ and preprocessed using samtools v1.10. A bedgraph file was generated by scaling the reads by a scaling factor defined as 1 ,000,000/number_of_aligned_reads. Regions overlapping the ENCODE mm10 blacklist regions, as defined by Amemiya et al. (Amemiya et al., 2019) were then removed. Peaks were called using SEACR v1.3 (Meers et al., 2019) with a signal_threshold of 1 and run in 'stringent' and 'norm' mode. IRF2 motifs were then mapped to peaks using the catalogue of predicted IRF2 motifs in the mm10 genome described above. By calculating the proportion of peaks containing an IRF2 motif in the IRF2 sample, we established a minimum quantile-based cut-off of 0.81 for the totalsignal which corresponded to the largest increase in IRF2-motif containing peaks. We did a similar analysis for peaks that were found to overlap between IRF2 and the IgG control; we calculated a maximum quantile-based cut-off of 0.82 for the IgG sample
which corresponded to the largest drop off of IRF2-containing motifs in overlapping peaks. Using this refined IRF2 catalogue of peaks, we then performed an overrepresentation analysis of the HALLMARK and C7:IMMUNESIGDB categories of msigdbr v7.4.1 (Liberzon et al., 2015) using clusterProfiler v4.2.2 (Wu et al., 2021)]. Significant HALLMARK and IMMUNESIGDB “exhausted” related pathways were extracted at a BH-corrected p-value of 0.05. The DNA sequences corresponding to the IRF2 peaks were derived using the bedtools v2.27.1 getfasta tool (Quinlan and Hall, 2010), which was then used as an input for the HOMER v4.8 findMotifsGenome.pl (Duttke et al., 2019) tool with a size parameter of 200. Analysis of transcription factor motifs enriched in IRF2 target peaks was performed using HOMER and ranked based on their Target/Background scores. The Target/Background ratio reflects the enrichment of motifs in the target peaks divided by the enrichment of those motifs in a random HOMER simulated k-mer (k=3 nucleotides) background, i.e., the number of target-peaks that have the indicated motif divided by the number of background-peaks that have the same motif.
CRISPR-mediated IRF2 knockout (KO) in T cells
Pre-designed CRISPR RNAs (crRNAs) targeting IRF2 were annealed to transactivating crRNAs (tracrRNAs) to form a guide RNA (gRNA). The gRNAs were further complexed with Streptococcus pyogenes Cas9 protein (Invitrogen™ TrueCut™ Cas9 Protein v2. Cat.no. A36499) to form the CRISPR-Cas9-gRNA-ribonucleoprotein (RNP) complex at a molar ratio of 1 :2.5 i.e., 30 pmol Cas9 protein to 75 pmol of gRNA). To increase the KO efficiency, a combination of different crRNAs targeting IRF2 were used (see table below). For negative control, a crRNA targeting a non- essential gene (IDT, Ref. 442435748) was annealed with Cas9 at the same molar ratio. In parallel, 106 to 107 freshly isolated (primary human or mouse) T cells were resuspended in 20 pl buffer P3 (P3 Primary Cell 4D-Nucleofector X Kit S, Lonza, cat. no. V4XP-3032). The cell suspension, RNP complex mix and where indicated 4uM of an electroporation enhancer (non-homologous ssDNA oligonucleotide, IDT, cat. 1075916) were combined, transferred into a 16-well nucleocuvette strip (Lonza, cat. no. V4XP-3032) and electroporated using program DS137 for mouse cells or program EH113 for human cells on the 4D-Nucleofector system (4D-Nucleofector X unit, Lonza, cat. no. AAF-1003X).
Table 1. Pre-designed crRNAs targeting IRF2 from IDT used in the present study.
T cell culture and stimulation
After electroporation, cells were incubated in standard media supplemented with IL-7 (10ng/ml) at 1x106 cells/ml at 37C for 3-4 days. The KO efficiency based on the frequency (%) of IRF2 expression was assessed by flow cytometry. Where indicated, transfection control or IRF2-deleted T cells were activated for 3 days with anti-CD3 (2ug/mL), anti-CD28 (1 ug/mL) and IL-2 (20ng/mL). Where indicated, transfection control or IRF2-deleted T cells were treated with media alone or stimulated with 1000 Units IFN(3 and/or 1000ng IFNy. Adoptive cell transfer of murine tumor-specific T cells
CRISPR-mediated IRF2 deletion in mouse tumor-specific (P14) CD8 T cells. P14 T cells are murine T cells that transgenically express rearranged TCR genes recognizing the LCMV-GP33-41 peptide presented in the context of murine H2Db. P14 T cells were isolated by negative selection from the spleen of naive P14 mice. The P14 T cells were then electroporated with control (WT) or IRF-2-targeted (KO) Cas9-RNP and cultured in complete medium + mouse IL-7. After 4 days, all cells were assessed for
IRF2 expression by flow cytometry to determine percent IRF2 deletion. The control or IRF2-targeted P14 T cells were stimulated with anti-CD3 + anti-CD28 antibodies for 24 hours. Then 200,000 to 350,000 of the control or IRF2-targeted P14 cells were adoptively transferred into mice with established MC38-GP tumors (day 8 after tumor inoculation). MC38 is an adenocarcinoma tumor cell line that we engineered to express the LCMV-GP-MOQ peptide sequence.
Quantification and Statistical Analysis
All statistical parameters are described in the figure legends. In all figures, error bars indicate the standard error of the mean (SEM). Student’s t-tests (two-tailed, unpaired) for comparing two conditions and One-way ANOVA (two-tailed, unpaired) for multiple comparisons were performed using GraphPad Prism v8 or v9. For survival assays, log-rank analysis (Mantel-Cox test) was performed using GraphPad Prism software v8 or v9.
RESULTS AND DISCUSSION
IRF2 is expressed across immune subsets and its deficiency enables tumor control
To determine the immune cell expression of IRF2 within the TME, we subcutaneously (s.c.) inoculated IRF2-sufficient wild-type (WT) mice with MC38 colorectal adenocarcinoma cells and isolated total tumor-infiltrating cells 14 days after tumor initiation. Cells were then analyzed by mass cytometry (CyTOF) with a panel identifying all major and most minor immune cell populations (data not shown). IRF2 was widely expressed across immune cells from mouse MC38 tumors (Fig 1A). Levels of IRF2 were minimally changed in tumor-infiltrating B cells, macrophages, and dendritic cells (DCs) compared to their splenic counterparts, with the spleen being largely immunologically unaffected by the MC38 tumor growth. In contrast, CD4+ and CD8+ T tumor-infiltrating lymphocytes (TILs) showed significant IRF2 upregulation compared to their splenic counterparts (Fig 1 B), suggestive that IRF2 levels in T cells are increased within the TME. Similarly, human melanoma tumor-infiltrating immune cells possessed a broad IRF2 expression pattern (Fig 1C), indicating conserved immune-wide IRF2 expression within the mouse and human TME.
To investigate the immune-mediated role of IRF2 in tumor control, WT mice or IRF2- deficient lrf2-,~) mice (Matsuyama et al., 1993) were given MC38 tumor cells. The
tumors grew similarly through 11 days in both the WT and Irf2~l- mice (Fig 1 D), indicating normal tumor initiation in the absence of IRF2. However, while the MC38 tumors progressed in WT mice, and all reached endpoint by 25 days, the tumors were controlled in /rf2-/_ mice, with some mice exhibiting no detectable tumors and all mice surviving the duration of the 50-day experimental period (Fig 1 D, 8A). Like MC38 tumors, Irf2~l- mice had prolonged survival and enhanced control of minimally immunogenic B16-F10 melanoma and an orthotopic polyoma middle T antigen (PyMT) breast tumor (Fig 1 E), indicating that the absence of IRF2 enables control of diverse tumor types.
Tumor control required CD8+ T cell intrinsic IRF2 expression
To identify the specific cell type(s) that mediated tumor control in the absence of IRF2, we depleted CD4+ or CD8+ T cell subsets in Irf -1- mice, based on their increased expression of IRF2 within the tumor (Fig 1 B). CD4+ T cell depletion in lrf2-,~ mice prior to MC38 implantation to some extent abrogated tumor control, although tumor control was also enhanced in CD4-depleted WT mice (Fig 8B), likely reflecting the beneficial effects of Treg cell depletion. In contrast, CD8+ T cell depletion prior to MC38 initiation abolished the tumor control observed in isotype antibody treated Irf -1- mice (Fig 2A), underscoring a key role of CD8+ T cells in enabling tumor control in the lrf2-,~ mice. Further, CD8+ T cell depletion in Irf -1- mice 3 weeks after MC38 tumor injection (at a time after tumors in WT mice had reached endpoint) rapidly led to rebound of tumor growth (Fig 2B), indicating that CD8+ T cells actively and continually maintained the long-term tumor control in Irf2 ' mice. The number of CD8+ TILs was similar between WT and 1^2-'- CD8+ T cells (Fig 8C), indicating comparable expansion/maintenance of the CD8+ TILs. To directly test the role of IRF2 deficiency within the CD8+ T cells, we created Irf2 floxed mice and crossed them with CD8-E8iii-Cre mice, to generate mice that only lack IRF2 expression in the CD8+ T cells (termed CD8-IRF2cKO mice; Fig 8D). The lack of IRF2 expression in CD8-IRF2cKO mice did not affect the expansion or survival of CD8+ TILs as their numbers were similar to WT (Fig 8E). The CD8- IRF2cKO mice efficiently controlled MC38 tumors in a manner like 1^2-'- mice (Fig 2C), indicating that /rf2-deletion in CD8+ T cells specifically enabled the long-term tumor control.
To further test the direct role of IRF2 in CD8+ T cells toward tumor control, we engineered MC38 cells to stably express the MHC-I Derestricted lymphocytic
choriomeningitis virus (LCMV) glycoprotein (GP)33-4i epitope (referred to as MC38-GP tumor cells). We adoptively transferred naive WT or Irf2 ' LCM -GP33-4i-specific (i.e., tumor-specific) CD8+ P14 transgenic T cells into WT mice prior to MC38-GP initiation. While the tumors grew initially alike between WT and Irf2 ' P14 CD8+ T cell recipients (like WT and Irf2-'- mice), mice that received the Irf^'-PM T cells exhibited enhanced tumor control (Fig 2D). Both the WT and Irf^-P T cells maintained similar tumor infiltration prior to divergence in tumor sizes (Fig 8F). Further, whereas all mice receiving WT P14 T cells reached endpoint by day 21 , the mice that received Irf2-'- P14 T cells were all alive at day 21 , and 60% were still alive at 25 days (Fig 8G), indicating that a small fraction of Irf2 ' tumor-specific CD8+ T cells can effectively inhibit tumor growth.
Enhanced immune checkpoint blockade and adoptive cell therapy by IRF2 CD8 T cells
The improved CD8+ T cell mediated tumor control in Irf -1- mice suggested that these cells may be more amenable to CD8+ T cell enhancing immune therapy. To test this, we treated mice with established PyMT breast tumors with anti-PDL1 blocking antibody. Although PyMT breast tumors are controlled better in Irf2 ' compared to WT mice, the tumors still progress, allowing the opportunity for therapeutic intervention to control established tumors. Anti-PDL1 treatment was initiated at day 15, at a time after T cell priming and when tumors in WT and Irf2 ' mice had reached ~50mm3 but were still comparable in size. PDL1 blockade in WT mice induced a 2-fold reduction in PyMT tumor growth, whereas a 10-fold reduction was observed in
mice (Fig 2E, 8H). The enhanced efficacy of PDL1 blockade was also observed in the CD8-IRF2cKO mice (Fig 8I), indicating that IRF2-deficiency within CD8+ T cells enhances the efficacy of anti-PDL1 immunotherapy.
We next tested the therapeutic benefit of adoptively transferring WT or lrf2-,~ tumorspecific CD8+ P14 T cells to control established tumors. Naive WT or Irf^1- P14 T cells were stimulated ex vivo for 24 hours with anti-CD3/CD28 antibodies and 2x105 cells were transferred into WT mice harboring established MC38-GP tumors. Transfer of this very low amount of WT P14 T cells had no effect on tumor growth (Fig 2F). On the other hand, transfer of Irf2-,~CD8+ P14 T cells into mice with established tumors effectively impeded tumor growth (Fig 2F), demonstrating the efficacy of adoptively
transferred Irf?-1- CD8+ T cells to control growth of established tumors at much lower numbers than required for WT CD8+ T cells.
IRF2-deficient CD8+ T cells resist exhaustion and maintain functionality in the TME
To understand how IRF2-deficiency within the CD8+ T cells facilitated long-term tumor control, we analyzed MC38-infiltrating CD8+ T cells from WT and Irf^- mice by CyTOF (data not shown). Analyses were performed at day 12 after MC38 implantation, when tumor sizes were similar between WT and Irf2 ' mice. PhenoGraph-based clustering of WT and Irf2 / CD8+ T cells yielded 5 clusters (Fig 3A). In WT CD8+ T cells, cluster (c)3 comprised most cells (64% WT vs.
whereas c4 (5% WT vs 14% Irf2^! ) and c5 (6% WT vs 39% Irf -'-) made up the majority in the Irf?-'- CD8+ T cells (Fig 3A, 9A). To define these clusters, we compared the protein expression in each cluster relative to its expression in the other clusters (Fig 3B, 3C). The proportionally dominant WT CD8+ T cell cluster (c3) expressed high levels of Helios, Lag3, Tim3, CD39 and PD1 (Fig 3B, 3C), indicating a terminally differentiated/exhausted subset predominating within the tumors of WT mice. Conversely, the proportionally dominant clusters (c4, c5) in Irf?-'- CD8+ T cells, expressed lower levels of these inhibitory receptors, in combination with increased expression of the activation-induced proteins SLAMF1 (CD150) and in c5, Ki67 (a protein expressed in cycling cells) (Fig 3B, 3C), suggesting that c4 and c5 are activated but have not differentiated into an exhausted state. Consistent with a generally enhanced activation profile, even within the same clusters, the Irf?-'- CD8+ T cells exhibited increased expression of CD80, SLAMF1 , Blimpl , Ki67 and CD25 [a protein that associates with an enhanced effector phenotype (Kalia et al., 2010)] compared to their WT counterparts (Fig 3B, 9B). A small, but significant, increase in cellular activation was also observed in Irf?-'- CD8+ T cells within the spleens and lymph nodes of naive mice (Fig 9C), indicating a role of IRF2 in suppressing immune activation, likely to self or commensal antigens (Gao et al., 2012; Hida et al., 2000). The proportions of non-activated, naTve/central memory TCF1+ cells (c2, TCF1+PD1“CD39“) were comparable between the WT and Irf2 ' CD8+ TILs (Fig 3A, 9D). Similarly, the proportions of TCF1+ PD1+ CD8+ T cells that have been shown to be capable of self-renewing as well as generating terminally differentiated cytotoxic T cells (Im et al., 2016; Miller et al., 2019; Siddiqui et al., 2019; Utzschneider et al., 2016; Wu et al., 2016) were also comparable between the WT and Irf -1- CD8+ T cells (Fig 9D), indicating that IRF2 deficiency does not deplete the TCF1 + stem-like population (Miller et al., 2019; Siddiqui et al., 2019).
Dividing the CD8+ TILs based on their expression of the inhibitory receptors (IRs) PD1 , Lag3 and CD39 revealed a dominant PD1hi, CD39hi, Lag3hi exhausted-phenotype population in WT mice (Fig 3D). Conversely, this IR-high population was diminished in the Irf -1- CD8+ T cells, and instead an IR-intermediate (IR-int) population Expressing lower levels of PD1 , Lag3 and CD39 dominated (Fig 3D). A similar phenotype was observed following adoptive transfer of WT and Irf2-'- tumor-specific P14 T cells into otherwise WT mice (Fig 3E), indicating the cell intrinsic role of IRF2 in CD8+ TILs. The IR-int population of CD8+ T cells in Irf2-'- mice and in adoptively transferred Irf2-'- tumor-specific P14 T cells also expressed the highest levels of Ki67, consistent with this population consisting of an activated, but not exhausted fraction (Fig 3F, 3G, 9E). Overall, Ki67 expression was increased in CD8+ TILs from Irf2-'- mice and Irf2-'- tumor-specific P14 T cells (Fig 3F, 9E, 9F). In particular, a larger proportion of the Irf2~
IR-int population co-expressed Ki67 with BATF and Blimpl (Fig 3H, Fig 9G, 9H), proteins that are associated with sustained effector function (Chen et al., 2021 b; Shin et al., 2009; Xin et al., 2015). Similarly, the BATF interacting partner, IRF4, that cooperatively limits T cell exhaustion to favor robust effector functions in the tumor (Seo et al., 2021) and in chronic viral infections (Grusdat et al., 2014; Xin et al., 2015), was highly expressed in the Irf2-'- IR-int population (Fig 9I). Throughout the different IR-expressing populations, the Irf2-’- CD8+ T cells expressed increased levels of multiple activation proteins (Fig 3G), indicating that even within the phenotypically similar populations, the IRF2-deficient CD8+ T cells exhibited increased activation and proliferation, and decreased exhaustion profiles.
The expression of the exhaustion “master-regulator” Tox was almost completely absent in the Irf2-'- CD8+ T cells (Fig 31). In WT CD8+ T cells, Tox expression was largest in the PD-1hi population, consistent with these being the most exhausted (Khan et al., 2019) (Fig 3I). However, even within the PD-1hi subset of Irf2-'- CD8+ T cells, Tox expression was minimal (Fig 3I), indicating an overall abrogation of the exhaustion master-regulator Tox in the global absence of IRF2. The intermediate IR expression and diminished Tox suggested that deletion of IRF2 may permit CD8+ T cells to retain functionality within the TME. Consistent with this hypothesis, a large proportion of Irf2~
CD8+ T cells expressed the cytolytic protein granzyme B (GzmB) specifically within the IR-int population and did so at higher single cell expression levels (Fig 3J). To further probe sustained functionality, WT and Irf -1- TILs were stimulated ex vivo with tumor-specific GP33-41 peptide on day 12 after MC38-GP tumor initiation (when tumor
sizes were similar). Compared to WT mice, a larger proportion of Irf -1- CD8+ TILs produced IFNy and TNFa, with the increase specifically within the IR-int cells (Fig 3K). Consistent with the retained functionality, the tumor-specific Irf2-'- CD8+ T cells coexpressed TNFa and IFNy, unlike their WT CD8+ T cell counterparts (Fig 9J). Thus, Irf2-'- CD8+ T cells exhibit lower expression levels of IRs, increased expression of cytotoxic molecules, and elevated polyfunctional cytokine production within the TME.
IRF2 is preferentially expressed in activated and ISG-producing CD8+ T cells from mouse and human tumors
To further understand the relationship between IRF2 expression and cellular activation, we analyzed IRF2 distribution in CD8+ TILs. When CD8+ T cells were clustered based on the upper and lower third of IRF2 expression, distinct enrichment patterns emerged (Fig 4A, 10A, 10B). The IRF2-low cells were enriched in TCF1 + clusters (c5, c7, c8) that also expressed low amounts of most activation-induced proteins (Fig 4A-C, 10B), consistent with a less activated state. In contrast, c4, c6 and almost exclusively c1 , were enriched in the IRF2-high fraction and expressed the highest levels of CD44, CD39, CD69, PD1 , Ki67, BATF, GzmB, Helios, and Tbet (Fig 4A-C, 10B). The IRF2-high subsets also expressed increased levels of the ISG Protein Kinase R (PKR), suggesting that IRF2 expression is linked to the strength of IFN-I signaling (Fig 4C). Spearman correlation analysis in all CD8+ TILs (not divided into IRF2 high or low subsets) further revealed positive correlations of IRF2 with CD44, PD1 , CD39, PKR, Tbet, BATF and Ki67, and a negative association with TCF1 and CD62L, markers of naive CD8+ T cells (Fig 10C). Thus, increasing IRF2 expression correlates with heightened expression of activation, cycling and ISG proteins in the TME.
Human melanoma-infiltrating CD8+ T cells also exhibited differential distribution of IRF2 among clusters (Fig 4D). When CD8+ TILs were divided into the upper and lower third of IRF2 expression (Fig 4D, 10D), the TCF1+ cells (the naive c6, and potentially the progenitor-like c7) and another less activated c8 were enriched with the IRF2-low subset (Fig 4D, 4E, 10E). On the other hand, increased levels of activation (CD39, PD1 , ICOS, BATF), cycling (Ki67) and ISG (PKR, Bst2) proteins were consistently observed in the I RF2-high subset of human melanomas (Fig 4E, 4F). The cluster with the highest amount of IRF2 (c4), is also the most activated/terminally differentiated, and is almost entirely absent from the IRF2-low fraction (Fig 4D, 4E, 10E). Indeed, in
total melanoma-infiltrating CD8+ T cells, expression of these activation proteins positively correlated with IRF2 expression (Fig 10F). Overall, in both mouse and human CD8+ TILs, IRF2-high expressing cells were enriched for co-expression of all activation-induced proteins measured (Fig 4G+, with IRF2 highest expressed in the most activated subsets.
Transcriptional profiling revealed sustained effector programming and resistance to exhaustion
To decipher how IRF2 transcriptionally programs CD8+ TILs, we performed combined single-cell (sc)RNA-seq plus antibody (Ab) staining on CD45-enriched tumor-infiltrating cells from WT and 1^2-'- mice. The Ab staining enabled identification of CD8+ T cells by protein expression, as RNA often under-represents the CD8+ T cell population (Mair et al., 2020), and the ability to further exclude cell doublets. Seurat-based clustering of the WT and Irf?-'- CD8+ TILs resolved 5 clusters (Fig 5A, 11 A, and data not shown). Cluster 0 (accounting for half of the WT cells) was largely absent in the Irf2 ' cells, while c3 accounted for half of the Irf2 ' CD8+ TILs and was almost absent in WT CD8+ TILs (Fig 5A, 11 A). The abundance of c1 , c2, and c4 were largely comparable between WT and Irf -1- cells (Fig 5A). Cluster 1 expressed cell cycle and survival associated genes Mki67, Birc5, Ube2c, Pclaf, Stmnl and Rrm2 (Fig 5B). Similarly, the TCF1+ c2 was largely comprised of naive CD8+ T cells [Tcf7+, SelT, Lef1+, Cx3crT, PdcdT, EntpdT, Havcr , cd44'°, itgaf (encoding Cd11a), and Icos] and potentially some Tcf7+, Satb1+ regenerative, stem-like cells, and was equally present in both the WT and /rf?7- CD8+ TILs (Fig 5A, 5B, and data not shown), suggesting that Irf2^ does not skew the differentiation of the TCF1+ populations. Cluster 4 are y<5 T cells that were present at the same frequencies in WT and /rf2-/- CD8+ TILs (Fig 5A, 5B).
Almost exclusively within the WT CD8+ TIL population, cO (cO.WT) was defined by genes associated with immune dysfunction. These genes included Tox, Nr4a2 [which functions with Tox to drive exhaustion (Chen et al., 2019; Seo et al., 2019)], Irf8 (Mognol et al., 2017), Nt5e [encoding CD73 (Briceno et al., 2021)], Klrel [which negatively regulates cytotoxicity (Westgaard et al., 2003)], Crbn [which is associated with decreased CD8+ T cell activation and effector function (Hesterberg et al., 2020)], and Il2rb [which has been shown to drive terminal exhaustion in chronic viral infections (Beltra et al., 2016)] (Fig 5B, 5C, and data not shown). In contrast, the predominating ZrfZ7- CD8+ TIL c3 (c3.lrf2~1-) exhibited increased expression of cytotoxic genes
(Gzma, Gzmb, Gzmk, Stx11, Srgri), inflammatory cytokines and receptors [Cc/3, H2ra, Il12rb2, Tnfrsf4 (encoding 0X40)], NFkb-signaling factors (Trafl, Nfkbid, Nfkbiz, Nfkbia), factors that sustain effector functionality [Batf (Chen et al., 2021b; Grusdat et al., 2014; Xin et al., 2015)], Ttc39c [(encoding Bach2) (Yao et al., 2021)] and numerous ISGs (e.g., Bst2, Ifitl, lfitm1/2, lsg15, Slfnl, lrf7, CD274, Gbps2-7) (Fig 5B, 5C, and data not shown). Genes associated with protein translation (Eif4b, Eef1a1, Eef1b2) and ribosomal assembly were decreased in the /rf?7- CD8+ TILs (Fig 5C, and data not shown), suggesting the heightened effector cell response phase associated with suppressed protein translation (Araki et al., 2017; Wherry et al., 2007). Further, the c3. Irf2 ' CD8+ TILs had an overall gene signature enriched in T cell effector function, compared to the exhaustion programming observed in cO.WT CD8+ TILs (Fig 5D). The WT CD8+ TILs also expressed some of the cytotoxic and immune-stimulatory genes, such as Gzmb, Prf1 and Ifng, however they did so at reduced levels compared to the /rf2-z- cells (both in proportions and at a single-cell expression) and with a large fraction of the WT cells co-expressing Tox (Fig 5E). Since the RNA expression is from the cells directly ex vivo (i.e., no in vitro stimulation), the increased levels of Ifng, Prf1 and Gzmb RNAs represent increased production of these anti-tumor factors by the Irf2 CD8+ TILs.
Consistent with the lack of Tox protein and gene expression, c3.//'f2_/“ CD8+ TILs were enriched in the signature of genes upregulated in Tox-deficient CD8+ T cells (Khan et al., 2019) (Fig 5F+). To further understand how IRF2 epigenetically regulates CD8+ TILs, we performed ATAC-seq on CD8+ TILs from WT vs /rfZ7- mice or from CD8- IRF2cWT and CD8-IRF2cKO mice. Overall, the number of accessible peaks was largely comparable between CD8+ TILs from WT and 1^2-'- mice, as well as from CD8- IRF2cWT and CD8-IRF2cKO mice (Fig 5G, Fig 11 B, and data not shown), indicating that IRF2 does not generally affect chromatin accessibility in this context. Further, several of the DEGs identified by scRNA-seq, including Tox, exhibited similar chromatin accessibility between Irf2*l+ and irf2'- CD8+ TILs (Fig 5H). Despite the similar chromatin accessibility, many of the DEGs contain IRF2 binding sites within open chromatin regions (Fig 5H, and data not shown), suggesting that IRF2 regulates expression of these genes rather than acting as an epigenetic regulator. A similar open chromatin state at IRF2-binding sites in the Tox promoter was observed in ATAC-seq data (Philip et al., 2017) from human PD1+ CD8+ TILs from melanoma and lung cancer patients (Fig 5I). In human naive CD8+ T cells, the chromatin at the IRF2 binding sites
in the Tox promoters were also open (Fig 5I), suggesting potential IRF2 binding to the Tox promoter in the naive state as well. To directly probe for gene targets of IRF2, we performed Cleavage Under Targets & Tagmentation (CUT&Tag) (Kaya-Okur et al., 2019) on in vitro activated CD8+ T cells from the spleen and lymph nodes of WT (Jrf2*,+) mice. Consistent with many DEGs containing IRF2 binding sites (Fig 5H, and data not shown), IRF2 interacted with many genes including those associated with immune dysfunction (Tox, Nr4a3, Lag3, Ctla4), immune-stimulatory and pro- inflammatory functions (Statl, Traf2, Nfkbid, Nfkbie, Ifnab, Tbx21, Prdml, Cd3e, Il12rb1), protein translation (Eif2ak4, Eifla, Eif3e, Eif3h, Eif4e3), as well as numerous ISGs (Isg15, lsg20, Ifitl, Bst2, Usp18, CD274, Gbps2-10) (Fig 5J, and data not shown). Given the diverse nature or functions of the IRF2 target genes, we determined whether IRF2 binding genes were also enriched in motifs for other transcription factors (TFs). HOMER motif analysis indicated that the IRF2 target genes were enriched for TF motifs involved in immune activation (IRF1 , ISRE, BATF, AP-1 , PRDM1 , IRF4) as well as repression of effector responses including Fli1 (Chen et al., 2021c) (Fig 11 C, and data not shown). Thus, instead of epigenetically regulating genes, IRF2 (independently and/or through transcription factor complexes) interacts with genes enriched in a variety of pathways including IFN signaling, TNFO/NF-K|3 signaling, immune exhaustion, and protein translation (Fig 5K), ultimately resulting in inhibition of CD8+ T cell responses.
Network and pathway profiling identifies transcriptional modifications mediated by IRF2
To understand the changes in transcriptional networks, we performed the upstream regulator analysis using IPA on WT and Irf2-,~CD8+ TILs. Comparing the dominant clusters in WT (cO) and 1^2-'- (c3) CD8+ TILs, the upstream regulator analysis predicted IFN-related pathways (IFNy, IFNa, IFN|3, IRF1 , IRF3, IRF7, Statl , Stat4) to be most enriched in 1^2-'- CD8+ T cells were (Fig 6A, and data not shown), indicating enhanced IFN-I and IFN-II. This was further complemented by decreased regulation by the SOCS1 pathway (a negative regulator of IFN-II signaling; Fig 6A). Regulators for other pro-inflammatory and immune-activating pathways were also predicted to be activated in the 1^- cells, including CD3 (TCR), cytokines involved in sustained T cell function (IL2, IL21 , IL12), NF-K|3 (CHUK, NFKBIA, NFKB complex; IKBKB), mTOR (LARP1 , RICTOR, MTORC1) and Toll-like receptors (TLR3, TLR4, TLR7, TLR9, MyD88) (Fig 6A, and data not shown). In contrast, negative regulators of proinflammatory signaling pathways were predicted to be decreased in 1^2-'- CD8+
TILs, including networks regulated by inhibitory receptors and suppressive cytokines (CTLA4, IL10RA, TGFBR2), by CITED2 [which inhibits NF-K|3 activation, IFN responses and pro-inflammatory activity (Lou et al., 2011 ; Pong Ng et al., 2020)]; by BCL6 [which can antagonize GzmB expression to limit CD8+ T cell cytolytic activity (Yoshida et al., 2006)]; as well as Tox stimulated pathways. In line with the reduced expression of translation-related molecules (Fig 5C), upstream regulators for networks controlling cellular proliferation and metabolism (MLXIPL,MYCN, MYC, Irgml) were inhibited in the Irf2-'- cells (Fig 6A, and data not shown).
To further probe changes in TF activity networks in WT and Irf -1- CD8+ T cells, we used SCENIC (single-cell regulatory network interference and clustering) (Aibar et al., 2017), an algorithm that pairs each TF to its putative direct binding target(s) [regulon(s)] within the same cell, indicating its activity level and whether the transcription factor and its targets are expressed in the same direction. Quantification of the fold change in average regulon activity indicated that the increased gene expression by CyTOF and scRNA-seq of multiple transcription factors in Irf -1- CD8+ TILs corresponded to a similar increase in their target RNAs, including Nfkbl , Rel, BATF, IRF4, Stat3, Stat5a, and pathways involved in ISG expression (IRF7, IRF9) (Fig 6B). The target regulons of the Nfkbl regulator Bcl3 also exhibited increased activity, potentially promoting survival, and enhancing effector responses (Jaiswal et al., 2021 ; Mitchell et al., 2001 ; Valenzuela et al., 2005). The increased activity of both BATF and IRF4 regulons in c3.lrf2-''- cells provided further downstream evidence for their role in countering exhaustion in the absence of IRF2, and their increased activity is consistent with the decreased levels of Tox observed in IRF2-deficient CD8+ T cells (Chen et al., 2021 b; Grusdat et al., 2014; Seo et al., 2021 ; Xin et al., 2015). Thus, the individual gene expression changes in the Irf -1- CD8+ TILs translate to transcriptional network programs that sustain effector function.
While the pathways enriched in the elevated genes in cO.WT CD8+ TILs involved protein translation and trafficking, many of the transcriptional pathways defining the c3.lrf2-''- CD8+ TILs were related to antiviral responses, T cell activation and differentiation, and responses to IFN-I and IFN-II signaling (Fig 6C, and data not shown). The increase in the IFN-I, IFN-II and viral-related response signaling pathways in the Irf2-'- CD8+ TILs (Fig 6C, and data not shown) was consistent with the role of IRF2 as a transcriptional repressor of IFN signaling (Harada et al., 1989; Lukhele et al., 2019; Matsuyama et al., 1993), but was unexpected to also be associated with
sustained CD8+ T cell function since increased/ongoing IFN signaling drives immune suppression (Snell et al., 2017).
We next sought to determine the role of IFN signaling in modulating tumor control in IRF2 deficiency. Treatment with IFN[3 increased IRF2 expression in mouse CD8+ T cells above its constitutive expression levels (Fig 7A), and many (but not all) IFN-I and IFN-II signaling-associated molecules were increased at the protein level in /rf2-z- CD8+ T cells (Fig 7B), demonstrating that in vivo in the TME, IRF2 restricts the level of IFN-I and IFN-II signaling by CD8+ T cells. To next determine whether IFNs continue to increase IRF2 expression in the context of cancer, we used anti-IFNAR and anti-IFNy antibodies to respectively block IFN-I and IFN-II signaling in WT mice with established MC38-tumors. Blocking either IFN-I or IFN-II signaling alone, and particularly in combination, substantially reduced IRF2 levels in CD8+ T cells (Fig 7C). Thus, IFNs induce and actively sustain the heightened IRF2 expression by CD8+ T cells, which subsequently feeds-back to limit IFN signaling.
We next determined whether the long-term tumor control in Irf -1- mice required continual interferon stimulation. We blocked IFN-I and IFN-II signaling in /rf2-z- mice with established MC38-tumors (at a time after the tumors in WT mice had reached endpoint). Blockade of IFNAR alone, IFNy alone, or IFNAR and IFNy together three weeks after MC38-tumor initiation in 1^2-'- mice led to rapid rebound of tumor growth and diminished survival in all treated animals, while isotype antibody recipients continued to suppress tumor growth (Fig 7D). To directly test if this dependence on continual interferon stimulation required IRF2 deficiency specifically within the CD8+ T cells, we blocked IFN-I and IFN-II signaling in CD8-IRF2cKO mice with established MC38-tumors. IFNAR and IFNy blockade also induced rapid tumor recrudescence in CD8-IRF2cKO mice (Fig 7E). Thus, IRF2 is a CD8+ T cell-intrinsic feedback inhibitor that translates IFN signals from the TME to transcriptionally program T cell exhaustion and, consequently, prevents long-term tumor control.
CRISPR/Cas 9 deletion of IRF2 in human T cells
We next tested the ability to delete IRF2 from naive human CD8 T cells. To do this we utilized the CRISPR/Cas9 method. Isolated total T cells (including CD4 T cells, CD4
Treg cells, and CD8 T cells) were electroporated with control or IRF2 targeting guide RNAs. Within 4 days after electroporation, IRF2 protein was deleted from >80% of all CD8 T cells (Figure 13) in the absence of cell activation (Figure 14). The same IRF2 deletion efficiency without cell activation was observed for CD4 T cells and CD4 Treg cells. Importantly, we show that the IRF2-deleted CD8 T cells can be efficiently activated (Figs 12-15) which is necessary for transduction of the CAR T constructs. Additionally, referring to Figs 16-21 , we show knockdown on IRF2 expression in human CD4 and CD8 T cells (at Days 2, 4, and 7 post-transfection).
We also show that IRF2 deletion increases ISG upregulation in response to IFN|3 and IFNy. Fig 22 shows expression of ISG 15 measured by flow cytometry comparing IFN|3- stimulated, IFNy-stimulated and unstimulated T cells within Irf2-deleted and nondeleted CD4 (left) and CD8 (right) T cells. Numbers indicate geometric mean fluorescence intensity for each histogram.
CRISPR-Mediated IRF2 knockout in T cells
We first determined the optimal CRISPR-Cas9 concentration for deletion of IRF2 in human CD8 T cells. The data in Figure 23 show that IRF2 can be deleted in 91% of CD8 T cells and 84% of CD4 T cells by four days after CRISPR-Cas9 deletion. Although both concentrations tested effectively delete IRF2 from human, primary CD8 and CD4 T cells, based on these results, the 2.5ul Cas9-RNP concentration was used in subsequent experiments. We next tested whether adding an electroporation enhancer (non-homologous ssDNA oligonucleotide) would increase CRISPR-Cas9 mediated IRF2 deletion. Figure 24 shows the deletion reaction works quite effectively without the enhancer, but that the deletion efficiency is increased using the enhancer. We subsequently tested whether one or two CRISPR-Cas9 electroporations would increase IRF2 deletion efficiency. Figure 25 shows that the double treatment worked somewhat better, but also substantially decreased viability of the cells. Therefore, the single transfection approach is used in subsequent protocols. Figure 26 demonstrates our development of a CRISPR-Cas9 mediated IRF2-deleted human Jurkat T cell line that (A) can be used as a flow cytometry staining control for IRF2 deletion in human T cells; and, (B) can be used for experimental purposes to probe the function of IRF2 in T cells. Figures 27-29 demonstrate that IRF2-deletion lead to increased expression of the interferon-stimulated proteins ISG 15 and to a lesser extent PD-L1 following IFN|3 and/or IFNy treatment. The increased PDL1 expression is consistent with the data
showing that murine IRF2-deleted CD8 T cells exhibited enhanced responses to interferons. The data in Figure 30 demonstrates that primary human T cells deleted of IRF2 can be activated, induced to blast, and have viability similar to their electroporation control CRISRP-Cas9 T cell counterparts following anti-CD3 + anti- CD28 stimulation (mimicking TCR and CD28 mediated stimulation). Figure 31 demonstrates that compared to control tumor-specific CD8 T cells, adoptive transfer of CRISPR-Cas9 IRF2-deleted murine tumor-specific CD8 T cells delay growth of established MC38 adenocarcinoma tumors and prolong survival of tumor-bearing mice.
Discussion
How IFNs molecularly switch from pro-inflammatory to suppressive outcomes has long been a subject of interest. Here, we identified IRF2 as a keystone factor that translated IFN signals within the TME to temper inflammation. This feedback inhibition then suppressed the CD8+ T cell response and allowed tumor escape from immune control. In essence, IRF2 re-routed IFN induced transcriptional programming from pro- to antiinflammatory signaling. In the absence of IRF2, these exhaustion promoting functions of IFNs were bypassed and CD8+ T cells instead retained high levels of anti-tumor activity and were able to effectively control tumor growth within the otherwise suppressive TME. That the sustained IFN-I signaling would continue to promote robust CD8+ T cell effector function is counter-intuitive based on the established role of prolonged IFN signaling to instead switch to drive immune suppression and CD8+ T cell exhaustion in multiple chronic disease contexts. The fact that this entire shift in the IFN programming focuses down to a single molecule was also unexpected, given the diversity of the ISGs driven by IFN signaling. The similarity between IRF2 expression and distribution in mouse and human CD8+ TILs implies a conserved role of IRF2. Thus, IRF2 is a foundational feedback mechanism that quells IFN-induced inflammatory reactions, and as a result, is a central regulator of T cell exhaustion in cancer.
The highly inflammatory conditions and prolonged IFN signaling within the TME necessitate counter measures to prevent excessive immunopathology, and tumors can co-opt these mechanisms to silence T cells for their own growth. Exposed to these inflammatory conditions, a wide variety of immune cells in the tumor exhibit IRF2
expression. Our study establishes that the failure of the immune response to control a variety of tumor types was highly dependent on IRF2 expression within the CD8+ TILs. Deletion of IRF2 only in CD8+ T cells was sufficient to induce long-term tumor control, and to enhance multiple immunotherapy-based strategies to control established tumors. While our data certainly does not exclude or negate potential IRF2-mediated contributions from other cell types within the tumor, it indicates that the attenuation of IFN signals by IRF2 is a CD8+ T cell intrinsic inflection point skewing from effector to exhausted functional states. These observations consolidate the complex roles of IFN- Is in tumorigenesis (Boukhaled et al., 2021 ; Lukhele et al., 2019; Snell et al., 2017) wherein IFNs stimulate antitumor immune responses while secondarily amplifying IRF2 expression to suppress and deactivate the functional CD8+ T cells to drive cancer progression. In essence, deleting IRF2 uncouples the sustained benefit of IFN signaling from the negative feedback inhibition IFN signaling also generates, placing IRF2 as the key lynchpin in the opposing regulation of IFN signaling.
Our study identifies IRF2 as a key factor that regulates the specific transcriptional programs that drive exhaustion. To this end, several transcription factors that directly mediate and counter T cell exhaustion are differentially regulated in the absence of IRF2 expression, including Blimpl , BATF, IRF4, NFAT, Nr4a and Tox. In conjunction, the expression of cytotoxic genes, proinflammatory cytokines as well as factors that sustain effector CD8+ T cells functions were enhanced following deletion of IRF2. It is interesting that in the absence of IRF2, the CD8+ T cells lose the Tox gene expression signature and resist exhaustion but resist the overstimulation-induced cell death in the TME. Their sustained presence is likely due at least in part to the fact that they still express intermediary levels of inhibitory receptors (Odorizzi et al., 2015; Scott et al., 2019), as well as increased expression of factors that sustain antigen stimulated (Grusdat et al., 2014; Xin et al., 2015) and tumor infiltrating T cells, such as BATF and IRF4 (Seo et al., 2021). Thus, IRF2 regulates a multi-directional response that instills exhaustion, while also suppressing the expression of multiple transcriptional nodes that sustain effector function in times of chronic antigen stimulation.
Resistance to checkpoint inhibitors and adoptive cell therapies remain a significant challenge. Based on its negative regulatory role in CD8+ T cells, deletion of IRF2 synergized with anti-PDL1 blockade to durably control a relatively non-immunogenic PyMT model of breast cancer. The efficacy of PDL1 blockade heavily relies on the ability to amplify CD8+ T cell responses, and although there are likely several
mechanisms sensitizing tumors to immunotherapy, the enhanced functional state of IRF2-deficient CD8+ T cells and their diminished expression of inhibitory receptors situates them ready-to-respond to immunotherapy. In addition, the ability to avert immune exhaustion and retain their superior effector functions under inflammatory conditions makes Irf2-'- CD8+ T cells strong candidates for adoptive CAR T cell therapies at cell numbers well below those required for WT CD8+ T cells. Thus, modulating IRF2 represents a new target for multiple therapeutic modalities aimed to enhance tumor-specific T cell functions and control tumor growth.
Our study identifies IRF2 as a key factor that negatively regulates IFN signaling, immune activation, and CD8+ T cell function, ultimately impeding the ability to control multiple mouse tumors. IRF2 expression is similar in mouse and human melanoma TILs. Irf -1- CD8+ T cells for adoptive CAR T cell therapies are anticipated to be effective against human cancers. In addition, IRF2 may synergize with immune- checkpoint therapies to fight less immunogenic tumors. Cancer control is a complex process, involving a variety of IRF2-expressing cell types that are also capable of IFN signaling. It is likely that IRF2 in these other cells also contribute to the anti-tumor immune response and cancer control.
Although preferred embodiments of the invention have been described herein, it will be understood by those skilled in the art that variations may be made thereto without departing from the spirit of the invention or the scope of the appended claims. All documents disclosed herein, including those in the following reference list, are incorporated by reference.
Reference List
1. Aibar, S., Gonzalez-Blas, C.B., Moerman, T., Huynh-Thu, V.A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.C., Geurts, P., Aerts, J., et al. (2017). SCENIC: single-cell regulatory network inference and clustering. Nat Methods 14, 1083-1086.
2. Alspach, E., Lussier, D.M., and Schreiber, R.D. (2019). Interferon gamma and Its Important Roles in Promoting and Inhibiting Spontaneous and Therapeutic Cancer Immunity. Cold Spring Harb Perspect Biol 11 .
3. Amemiya, H.M., Kundaje, A., and Boyle, A.P. (2019). The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Sci Rep 9, 9354.
4. Antonczyk, A., Krist, B., Sajek, M., Michalska, A., Piaszyk-Borychowska, A., Plens-Galaska, M., Wesoly, J., and Bluyssen, H.A.R. (2019). Direct Inhibition of IRF-Dependent Transcriptional Regulatory Mechanisms Associated With Disease. Front Immunol 10, 1176.
5. Araki, K., Morita, M., Bederman, A.G., Konieczny, B.T., Kissick, H.T., Sonenberg, N., and Ahmed, R. (2017). Translation is actively regulated during the differentiation of CD8(+) effector T cells. Nat Immunol 18, 1046-1057.
6. Beltra, J.C., Bourbonnais, S., Bedard, N., Charpentier, T., Boulange, M., Michaud, E., Boufaied, I., Bruneau, J., Shoukry, N.H., Lamarre, A., and Decaluwe, H. (2016). IL2Rbeta-dependent signals drive terminal exhaustion and suppress memory development during chronic viral infection. Proc Natl Acad Sci U S A 113, E5444-5453.
7. Boukhaled, G.M., Harding, S., and Brooks, D.G. (2021). Opposing Roles of Type I Interferons in Cancer Immunity. Annual Review Pathology 16, 167-198.
8. Bovolenta, C., Driggers, P.H., Marks, M.S., Medin, J.A., Politis, A.D., Vogel, S.N., Levy, D.E., Sakaguchi, K., Appella, E., Coligan, J.E., and et al. (1994). Molecular interactions between interferon consensus sequence binding protein and members of the interferon regulatory factor family. Proc Natl Acad Sci U S A 91 , 5046-5050.
9. Briceno, P., Rivas-Yanez, E., Rosemblatt, M.V., Parra-Tello, B., Farias, P., Vargas, L, Simon, V., Cardenas, C., Lladser, A., Salazar-Onfray, F., et al. (2021). CD73 Ectonucleotidase Restrains CD8+ T Cell Metabolic Fitness and Anti-tumoral Activity. Front Cell Dev Biol 9, 638037.
10. Brooks, D.G., McGavern, D.B., and Oldstone, M.B. (2006). Reprogramming of antiviral T cells prevents inactivation and restores T cell activity during persistent viral infection. J Clin Invest 116, 1675-1685.
11. Budhwani, M., Mazzieri, R., and Dolcetti, R. (2018). Plasticity of Type I Interferon-Mediated Responses in Cancer Therapy: From Anti-tumor Immunity to Resistance. Front Oncol 8, 322.
12. Chae, M., Kim, K., Park, S.M., Jang, I.S., Seo, T., Kim, D.M., Kim, I.C., Lee, J.H., and Park, J. (2008). IRF-2 regulates NF-kappaB activity by modulating the subcellular localization of NF-kappaB. Biochemical and biophysical research communications 370, 519-524.
13. Chen, J., Lopez-Moyado, I.F., Seo, H., Lio, C.J., Hempieman, L.J., Sekiya, T., Yoshimura, A., Scott-Browne, J.P., and Rao, A. (2019). NR4A transcription factors limit CAR T cell function in solid tumours. Nature 567, 530-534.
14. Chen, S.Y., Chen, S., Feng, W., Li, Z., Luo, Y., and Zhu, X. (2021a). A STING- related prognostic score predicts high-risk patients of colorectal cancer and provides insights into immunotherapy. Ann Transl Med 9, 14.
15. Chen, Y., Zander, R.A., Wu, X., Schauder, D.M., Kasmani, M.Y., Shen, J., Zheng, S., Burns, R., Taparowsky, E.J., and Cui, W. (2021 b). BATF regulates progenitor to cytolytic effector CD8(+) T cell transition during chronic viral infection. Nat Immunol 22, 996-1007.
16. Chen, Z., Arai, E., Khan, O., Zhang, Z., Ngiow, S.F., He, Y., Huang, H., Manne, S., Cao, Z., Baxter, A.E., et al. (2021c). In vivo CD8(+) T cell CRISPR screening reveals control by Fli1 in infection and cancer. Cell 184, 1262-1280 e1222.
17. Dong, H., Strome, S.E., Salomao, D.R., Tamura, H., Hirano, F., Flies, D.B., Roche, P.C., Lu, J., Zhu, G., Tamada, K., et al. (2002). Tumor-associated B7-
H1 promotes T-cell apoptosis: a potential mechanism of immune evasion. Nat Med 8, 793-800.
18. Drew, P.D., Franzoso, G., Becker, K.G., Bours, V., Carlson, L.M., Siebenlist, U., and Ozato, K. (1995a). NF kappa B and interferon regulatory factor 1 physically interact and synergistically induce major histocompatibility class I gene expression. J Interferon Cytokine Res 15, 1037-1045.
19. Drew, P.D., Franzoso, G., Carlson, L.M., Biddison, W.E., Siebenlist, U., and Ozato, K. (1995b). Interferon regulatory factor-2 physically interacts with NF- kappa B in vitro and inhibits NF-kappa B induction of major histocompatibility class I and beta 2-microglobulin gene expression in transfected human neuroblastoma cells. Journal of neuroimmunology 63, 157-162.
20. Duttke, S.H., Chang, M.W., Heinz, S., and Benner, C. (2019). Identification and dynamic quantification of regulatory elements using total RNA. Genome Res 29, 1836-1846.
21. Farley, F.W., Soriano, P., Steffen, L.S., and Dymecki, S.M. (2000). Widespread recombinase expression using FLPeR (flipper) mice. Genesis 28, 106-110.
22. Fornes, O., Castro-Mondragon, J.A., Khan, A., van der Lee, R., Zhang, X., Richmond, P.A., Modi, B.P., Correard, S., Gheorghe, M., Baranasic, D., et al. (2020). JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res 48, D87-D92.
23. Gao, P.S., Leung, D.Y., Rafaels, N.M., Boguniewicz, M., Hand, T., Gao, L., Hata, T.R., Schneider, L.C., Hanifin, J.M., Beaty, T.H., et al. (2012). Genetic variants in interferon regulatory factor 2 (IRF2) are associated with atopic dermatitis and eczema herpeticum. J Invest Dermatol 132, 650-657.
24. Grusdat, M., McIlwain, D.R., Xu, H.C., Pozdeev, V.I., Knievel, J., Crome, S.Q., Robert-Tissot, C., Dress, R.J., Pandyra, A.A., Speiser, D.E., et al. (2014). IRF4 and BATF are critical for CD8(+) T-cell function following infection with LCMV. Cell Death Differ 21 , 1050-1060.
25. Hakem, R., de la Pompa, J.L., Sirard, C., Mo, R., Woo, M., Hakem, A., Wakeham, A., Potter, J., Reitmair, A., Billia, F., et al. (1996). The tumor
suppressor gene Brcal is required for embryonic cellular proliferation in the mouse. Cell 85, 1009-1023. Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W.M., 3rd, Zheng, S., Butler, A., Lee, M.J., Wilk, A.J., Darby, C., Zager, M., et al. (2021). Integrated analysis of multimodal single-cell data. Cell 184, 3573-3587 e3529. Harada, H., Fujita, T., Miyamoto, M., Kimura, Y., Maruyama, M., Furia, A., Miyata, T., and Taniguchi, T. (1989). Structurally similar but functionally distinct factors, IRF-1 and IRF-2, bind to the same regulatory elements of IFN and IFN- inducible genes. Cell 58, 729-739. Harada, H., Kitagawa, M., Tanaka, N., Yamamoto, H., Harada, K., Ishihara, M., and Taniguchi, T. (1993). Anti-oncogenic and oncogenic potentials of interferon regulatory factors-1 and -2. Science 259, 971-974. Hesterberg, R.S., Beatty, M.S., Han, Y., Fernandez, M.R., Akuffo, A.A., Goodheart, W.E., Yang, C., Chang, S., Colin, C.M., Alontaga, A.Y., et al. (2020). Cereblon harnesses Myc-dependent bioenergetics and activity of CD8+ T lymphocytes. Blood 136, 857-870. Hida, S., Ogasawara, K., Sato, K., Abe, M., Takayanagi, H., Yokochi, T., Sato, T., Hirose, S., Shirai, T., Taki, S., and Taniguchi, T. (2000). CD8(+) T cell- mediated skin disease in mice lacking IRF-2, the transcriptional attenuator of interferon-alpha/beta signaling. Immunity 13, 643-655. Im, S.J., Hashimoto, M., Gerner, M.Y., Lee, J., Kissick, H.T., Burger, M.C., Shan, Q., Hale, J.S., Lee, J., Nasti, T.H., et al. (2016). Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417-421 . Jaiswal, H., Ciucci, T., Wang, H., Tang, W., Claudio, E., Murphy, P.M., Bosselut, R., and Siebenlist, U. (2021). The NF-kappaB regulator Bcl-3 restricts terminal differentiation and promotes memory cell formation of CD8+ T cells during viral infection. PLoS Pathog 17, e1009249. Kalia, V., Sarkar, S., Subramaniam, S., Haining, W.N., Smith, K.A., and Ahmed, R. (2010). Prolonged interleukin-2Ralpha expression on virus-specific
CD8+ T cells favors terminal-effector differentiation in vivo. Immunity 32, 91- 103. Kaya-Okur, H.S., Wu, S.J., Codomo, C.A., Pledger, E.S., Bryson, T.D., Henikoff, J.G., Ahmad, K., and Henikoff, S. (2019). CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat Commun 10, 1930. Khan, O., Giles, J.R., McDonald, S., Manne, S., Ngiow, S.F., Patel, K.P., Werner, M.T., Huang, A.C., Alexander, K.A., Wu, J.E., et al. (2019). TOX transcriptionally and epigenetically programs CD8(+) T cell exhaustion. Nature 571 , 211-218. Kriegsman, B.A., Vangala, P., Chen, B.J., Meraner, P., Brass, A.L., Garber, M., and Rock, K.L. (2019). Frequent Loss of IRF2 in Cancers Leads to Immune Evasion through Decreased MHC Class I Antigen Presentation and Increased PD-L1 Expression. J Immunol 203, 1999-2010. Levine, J.H., Simonds, E.F., Bendall, S.C., Davis, K.L., Amir el, A.D., Tadmor, M.D., Litvin, O., Fienberg, H.G., Jager, A., Zunder, E.R., et al. (2015). Data- Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell 162, 184-197. Liberzon, A., Birger, C., Thorvaldsdottir, H., Ghandi, M., Mesirov, J.P., and Tamayo, P. (2015). The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 1 , 417-425. Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdottir, H., Tamayo, P., and Mesirov, J.P. (2011). Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739-1740. Lou, X., Sun, S., Chen, W., Zhou, Y., Huang, Y., Liu, X., Shan, Y., and Wang, C. (2011). Negative feedback regulation of NF-kappaB action by CITED2 in the nucleus. J Immunol 186, 539-548. Love, M.I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550.
42. Lukhele, S., Boukhaled, G.M., and Brooks, D.G. (2019). Type I interferon signaling, regulation and gene stimulation in chronic virus infection. Semin Immunol 43, 101277.
43. Mair, F., Erickson, J.R., Voillet, V., Simoni, Y., Bi, T., Tyznik, A.J., Martin, J., Gottardo, R., Newell, E.W., and Prlic, M. (2020). A Targeted Multi-omic Analysis Approach Measures Protein Expression and Low-Abundance Transcripts on the Single-Cell Level. Cell Rep 31 , 107499.
44. Matsuyama, T., Kimura, T., Kitagawa, M., Pfeffer, K., Kawakami, T., Watanabe, N., Kundig, T.M., Amakawa, R., Kishihara, K., Wakeham, A., and et al. (1993). Targeted disruption of IRF-1 or IRF-2 results in abnormal type I IFN gene induction and aberrant lymphocyte development. Cell 75, 83-97.
45. McLane, L.M., Abdel-Hakeem, M.S., and Wherry, E.J. (2019). CD8 T Cell Exhaustion During Chronic Viral Infection and Cancer. Annu Rev Immunol 37, 457-495.
46. Meers, M.P., Tenenbaum, D., and Henikoff, S. (2019). Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling. Epigenetics Chromatin 12, 42.
47. Mei, Z., Wang, G., Liang, Z., Cui, A., Xu, A., Liu, Y., Liu, C., Yang, Y., and Cui, L. (2017). Prognostic value of IRF-2 expression in colorectal cancer. Oncotarget 8, 38969-38977.
48. Miller, B.C., Sen, D.R., Al Abosy, R., Bi, K., Virkud, Y.V., LaFleur, M.W., Yates, K.B., Lako, A., Felt, K., Naik, G.S., et al. (2019). Subsets of exhausted CD8(+) T cells differentially mediate tumor control and respond to checkpoint blockade. Nat Immunol 20, 326-336.
49. Minn, A. J. (2015). Interferons and the Immunogenic Effects of Cancer Therapy. Trends Immunol 36, 725-737.
50. Mitchell, T.C., Hildeman, D., Kedl, R.M., Teague, T.K., Schaefer, B.C., White, J., Zhu, Y., Kappler, J., and Marrack, P. (2001). Immunological adjuvants promote activated T cell survival via induction of Bcl-3. Nat Immunol 2, 397- 402.
51. Mognol, G.P., Spreafico, R., Wong, V., Scott-Browne, J.P., Togher, S., Hoffmann, A., Hogan, P.G., Rao, A., and Trifari, S. (2017). Exhaustion- associated regulatory regions in CD8(+) tumor-infiltrating T cells. Proc Natl Acad Sci U S A 114, E2776-E2785.
52. Odorizzi, P.M., Pauken, K.E., Paley, M.A., Sharpe, A., and Wherry, E.J. (2015). Genetic absence of PD-1 promotes accumulation of terminally differentiated exhausted CD8+ T cells. J Exp Med 212, 1125-1137.
53. Philip, M., Fairchild, L, Sun, L, Horste, E.L., Camara, S., Shakiba, M., Scott, A.C., Viale, A., Lauer, P., Merghoub, T., et al. (2017). Chromatin states define tumour-specific T cell dysfunction and reprogramming. Nature 545, 452-456.
54. Pong Ng, H., Kim, G.D., Ricky Chan, E., Dunwoodie, S.L., and Mahabeleshwar, G.H. (2020). CITED2 limits pathogenic inflammatory gene programs in myeloid cells. FASEB J 34, 12100-12113.
55. Quinlan, A.R., and Hall, I.M. (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841-842.
56. Ren, G., Cui, K., Zhang, Z., and Zhao, K. (2015). Division of labor between IRF1 and IRF2 in regulating different stages of transcriptional activation in cellular antiviral activities. Cell Biosci 5, 17.
57. Rouyez, M.C., Lestingi, M., Charon, M., Fichelson, S., Buzyn, A., and Dusanter-Fourt, I. (2005). IFN regulatory factor-2 cooperates with STAT1 to regulate transporter associated with antigen processing- 1 promoter activity. Journal of immunology 174, 3948-3958.
58. Sakai, T., Mashima, H., Yamada, Y., Goto, T., Sato, W., Dohmen, T., Kamada, K., Yoshioka, M., Uchinami, H., Yamamoto, Y., and Ohnishi, H. (2014). The roles of interferon regulatory factors 1 and 2 in the progression of human pancreatic cancer. Pancreas 43, 909-916.
59. Scott, A.C., Dundar, F., Zumbo, P., Chandran, S.S., Klebanoff, C.A., Shakiba, M., Trivedi, P., Menocal, L., Appleby, H., Camara, S., et al. (2019). TOX is a critical regulator of tumour-specific T cell differentiation. Nature 571 , 270-274.
60. Seo, H., Chen, J., Gonzalez-Avalos, E., Samaniego-Castruita, D., Das, A., Wang, Y.H., Lopez-Moyado, I.F., Georges, R.O., Zhang, W., Onodera, A., et al. (2019). TOX and TOX2 transcription factors cooperate with NR4A transcription factors to impose CD8(+) T cell exhaustion. Proc Natl Acad Sci U S A 116, 12410-12415.
61. Seo, H., Gonzalez-Avalos, E., Zhang, W., Ramchandani, P., Yang, C., Lio, C.J., Rao, A., and Hogan, P.G. (2021). BATF and IRF4 cooperate to counter exhaustion in tumor-infiltrating CAR T cells. Nat Immunol 22, 983-995.
62. Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., and Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13, 2498-2504.
63. Sharf, R., Azriel, A., Lejbkowicz, F., Winograd, S.S., Ehrlich, R., and Levi, B.Z. (1995). Functional domain analysis of interferon consensus sequence binding protein (ICSBP) and its association with interferon regulatory factors. The Journal of biological chemistry 270, 13063-13069.
64. Shin, H., Blackburn, S.D., Intlekofer, A.M., Kao, C., Angelosanto, J.M., Reiner, S.L., and Wherry, E.J. (2009). A role for the transcriptional repressor Blimp-1 in CD8(+) T cell exhaustion during chronic viral infection. Immunity 31 , 309-320.
65. Siddiqui, I., Schaeuble, K., Chennupati, V., Fuertes Marraco, S.A., Calderon- Copete, S., Pais Ferreira, D., Carmona, S.J., Scarpellino, L., Gfeller, D., Pradervand, S., et al. (2019). Intratumoral Tcf1 (+)PD-1 (+)CD8(+) T Cells with Stem-like Properties Promote Tumor Control in Response to Vaccination and Checkpoint Blockade Immunotherapy. Immunity 50, 195-211 e110.
66. Snell, L.M., McGaha, T.L., and Brooks, D.G. (2017). Type I Interferon in Chronic Virus Infection and Cancer. Trends Immunol 38, 542-557.
67. Snell, L.M., Xu, W., Abd-Rabbo, D., Boukhaled, G., Guo, M., Macleod, B.L., Elsaesser, H.J., Hezaveh, K., Alsahafi, N., Lukhele, S., et al. (2021). Dynamic CD4(+) T cell heterogeneity defines subset-specific suppression and PD-L1- blockade-driven functional restoration in chronic infection. Nat Immunol 22, 1524-1537.
68. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., and Mesirov, J.P. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545-15550.
69. Tan, G., and Lenhard, B. (2016). TFBSTools: an R/bioconductor package for transcription factor binding site analysis. Bioinformatics 32, 1555-1556.
70. Tanaka, N., Kawakami, T., and Taniguchi, T. (1993). Recognition DNA sequences of interferon regulatory factor 1 (IRF-1) and IRF-2, regulators of cell growth and the interferon system. Mol Cell Biol 13, 4531-4538.
71. Taniguchi, T., and Takaoka, A. (2001). A weak signal for strong responses: interferon-alpha/beta revisited. Nature reviews. Molecular cell biology 2, 378- 386.
72. Utzschneider, D.T., Charmoy, M., Chennupati, V., Pousse, L., Ferreira, D.P., Calderon-Copete, S., Danilo, M., Alfei, F., Hofmann, M., Wieland, D., et al. (2016). T Cell Factor 1-Expressing Memory-like CD8(+) T Cells Sustain the Immune Response to Chronic Viral Infections. Immunity 45, 415-427.
73. Valenzuela, J.O., Hammerbeck, C.D., and Mescher, M.F. (2005). Cutting edge: Bcl-3 up-regulation by signal 3 cytokine (IL-12) prolongs survival of antigen- activated CD8 T cells. J Immunol 174, 600-604.
74. Vaughan, P.S., Aziz, F., van Wijnen, A.J., Wu, S., Harada, H., Taniguchi, T., Soprano, K.J., Stein, J.L., and Stein, G.S. (1995). Activation of a cell-cycle- regulated histone gene by the oncogenic transcription factor IRF-2. Nature 377, 362-365.
75. Vaughan, P.S., van der Meijden, C.M., Aziz, F., Harada, H., Taniguchi, T., van Wijnen, A. J., Stein, J.L., and Stein, G.S. (1998). Cell cycle regulation of histone H4 gene transcription requires the oncogenic factor IRF-2. The Journal of biological chemistry 273, 194-199.
76. Wang, Y„ Liu, D„ Chen, P„ Koeffler, H.P., Tong, X., and Xie, D. (2008). Negative feedback regulation of IFN-gamma pathway by IFN regulatory factor 2 in esophageal cancers. Cancer research 68, 1136-1143.
77. Wang, Y., Liu, D.P., Chen, P.P., Koeffler, H.P., Tong, X.J., and Xie, D. (2007). Involvement of IFN regulatory factor (IRF)-1 and IRF-2 in the formation and progression of human esophageal cancers. Cancer research 67, 2535-2543.
78. Westgaard, I.H., Dissen, E., Torgersen, K.M., Lazetic, S., Lanier, L.L., Phillips, J.H., and Fossum, S. (2003). The lectin-like receptor KLRE1 inhibits natural killer cell cytotoxicity. J Exp Med 197, 1551-1561.
79. Wherry, E.J. (2011). T cell exhaustion. Nat Immunol 12, 492-499.
80. Wherry, E.J., Ha, S.J., Kaech, S.M., Haining, W.N., Sarkar, S., Kalia, V., Subramaniam, S., Blattman, J.N., Barber, D.L., and Ahmed, R. (2007). Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity 27, 670-684.
81. Wu, T., Hu, E., Xu, S., Chen, M., Guo, P., Dai, Z., Feng, T., Zhou, L., Tang, W., Zhan, L., et al. (2021). clusterprofiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb) 2, 100141.
82. Wu, T., Ji, Y., Moseman, E.A., Xu, H.C., Manglani, M., Kirby, M., Anderson, S.M., Handon, R., Kenyon, E., Elkahloun, A., et al. (2016). The TCF1-Bcl6 axis counteracts type I interferon to repress exhaustion and maintain T cell sternness. Sci Immunol 1.
83. Xin, G., Schauder, D.M., Lainez, B., Weinstein, J.S., Dai, Z., Chen, Y., Esplugues, E., Wen, R., Wang, D., Parish, I.A., et al. (2015). A Critical Role of IL-21-lnduced BATF in Sustaining CD8-T-Cell-Mediated Chronic Viral Control. Cell Rep 13, 1118-1124.
84. Yamamoto, H., Lamphier, M.S., Fujita, T., Taniguchi, T., and Harada, H. (1994). The oncogenic transcription factor IRF-2 possesses a transcriptional repression and a latent activation domain. Oncogene 9, 1423-1428.
85. Yan, Y., Zheng, L, Du, Q., Yan, B., and Geller, D.A. (2020). Interferon regulatory factor 1 (IRF-1) and IRF-2 regulate PD-L1 expression in hepatocellular carcinoma (HOC) cells. Cancer Immunol Immunother 69, 1891- 1903.
86. Yao, C., Lou, G., Sun, H.W., Zhu, Z., Sun, Y., Chen, Z., Chauss, D., Moseman, E.A., Cheng, J., D'Antonio, M.A., et al. (2021). BACH2 enforces the transcriptional and epigenetic programs of stem-like CD8(+) T cells. Nat Immunol 22, 370-380.
87. Yi, Y., Wu, H., Gao, Q., He, H.W., Li, Y.W., Cai, X.Y., Wang, J.X., Zhou, J., Cheng, Y.F., Jin, J. J., et al. (2013). Interferon regulatory factor (I RF)-1 and IRF- 2 are associated with prognosis and tumor invasion in HCC. Annals of surgical oncology 20, 267-276.
88. Yoshida, K., Sakamoto, A., Yamashita, K., Arguni, E., Horigome, S., Arima, M., Hatano, M., Seki, N., Ichikawa, T., and Tokuhisa, T. (2006). Bcl6 controls granzyme B expression in effector CD8+ T cells. Eur J Immunol 36, 3146- 3156.
89. Yu, G., Wang, L.G., Han, Y., and He, Q.Y. (2012). clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284-287.
90. Yu, G., Wang, L.G., and He, Q.Y. (2015). ChlPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31 , 2382-2383.
91. Zhang, Y., Liu, T., Meyer, C.A., Eeckhoute, J., Johnson, D.S., Bernstein, B.E., Nusbaum, C., Myers, R.M., Brown, M., Li, W., and Liu, X.S. (2008). Modelbased analysis of ChlP-Seq (MACS). Genome Biol 9, R137.
92. Zhu, A., Ibrahim, J.G., and Love, M.l. (2019). Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 35, 2084-2092.
Claims
1 . A modified T cell engineered to have decreased IRF2 expression.
2. The modified T cell of claim 1 , wherein the T-cell is a regulatory T cell.
3. The modified T cell of claim 1 , wherein the T-cell is an effector T cell.
4. The modified T cell of claim 1 , wherein the T-cell is CD4+.
5. The modified T cell of claim 1 , wherein the T-cell is CD8+.
6. The modified T cell of claim 1 , wherein the T-cell is CD4- and/or CD8-.
7. The modified T cell of any one of claims 1-6, wherein the T-cell is a CAR T cell.
8. The modified T cell of any one of claims 1-7, wherein the IRF2 gene has been knocked-down in the modified T cell.
9. The modified T cell of any one of claims 1-8, wherein the IRF2 gene, or portions thereof, have been deleted in the modified T cell.
10. The modified T cell of any one of claims 1-9, wherein the T cell is a human T cell.
11. The modified T cell of any one of claims 1-10, wherein the modified T cell has been engineered using virus-mediated knockdown.
12. The modified T cell of any one of claims 1-10, wherein the modified T cell has been engineered using CRISPR/Cas9.
13. The modified T cell of any one of claims 1-12, wherein the modified T cell has improved anti-tumor activity compared to the corresponding wild-type T-cell.
14. The modified T cell of any one of claims 1-12, wherein the modified T cell has improved resistance to T cell exhaustion compared to the corresponding wildtype T-cell.
15. The modified T cell of any one of claims 1-14, for use in the treatment of cancer.
The modified T cell of any one of claims 1-15, for use in adoptive cell therapy. Use of modified T cell of any one of claims 1-14, in the preparation of a medicament for the treatment of cancer. A method of treating a subject with cancer, comprising administering to the subject a therapeutically effective amount of the modified T cell of any one of claims 1-13. A method treating a subject with cancer, comprising engineering the modified T cell of any one of claims 1-13 in vivo in the subject. The method of claim 17 or 19, further comprising treating the subject with a checkpoint inhibitor. The method of claim 20, comprising treating the subject with anti-PDL1 and/or anti-PD1 blockade therapy. A method of treating a subject with cancer, comprising administering to the subject a therapeutically effective amount of an inhibitor of IRF2 function or expression. The method of claim 22, wherein the inhibitor is a small molecule, antibody, antisense oligonucleotide, siRNA, shRNA, or miRNA.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US202263423839P | 2022-11-09 | 2022-11-09 | |
US63/423,839 | 2022-11-09 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2024098148A1 true WO2024098148A1 (en) | 2024-05-16 |
Family
ID=91031577
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CA2023/051492 WO2024098148A1 (en) | 2022-11-09 | 2023-11-08 | Targeting irf2 for cancer therapy |
Country Status (1)
Country | Link |
---|---|
WO (1) | WO2024098148A1 (en) |
-
2023
- 2023-11-08 WO PCT/CA2023/051492 patent/WO2024098148A1/en unknown
Non-Patent Citations (6)
Title |
---|
HIDA SHIGEAKI, OGASAWARA KOUETSU; SATO KOJIRO; ABE MASAAKI; TAKAYANAGI HIROSHI; YOKOCHI TAEKO; SATO TAKEO; HIROSE SACHIKO; SHIRAI : "CD8+ T Cell-Mediated Skin Disease in Mice Lacking IRF-2, the Transcriptional Attenuator of Interferon-a/B Signaling", IMMUNITY, vol. 13, 1 November 2000 (2000-11-01), pages 643 - 655, XP093172457 * |
KRIEGSMAN BARRY A, VANGALA PRANITHA; CHEN BENJAMIN J.; MERANER PAUL; BRASS ABRAHAM L.; GARBER MANUEL; ROCK KENNETH L.: "Frequent Loss of IRF2 in Cancers Leads to Immune Evasion through Decreased MHC Class I Antigen Presentation and Increased PD-L1 Expression", THE JOURNAL OF IMMUNOLOGY, WILLIAMS & WILKINS CO., US, vol. 203, no. 7, 1 October 2019 (2019-10-01), US , pages 1999 - 2010, XP093172462, ISSN: 0022-1767, DOI: 10.4049/jimmunol.1900475 * |
LUKHELE SABELO; RABBO DIALA ABD; GUO MENGDI; SHEN JIAN; ELSAESSER HEIDI J.; QUEVEDO RENE; CAREW MADELEINE; GADALLA RAMY; SNELL LAU: "The transcription factor IRF2 drives interferon-mediated CD8+ T cell exhaustion to restrict anti-tumor immunity", IMMUNITY, CELL PRESS, AMSTERDAM, NL, vol. 55, no. 12, 11 November 2022 (2022-11-11), AMSTERDAM, NL , pages 2369, XP087227095, ISSN: 1074-7613, DOI: 10.1016/j.immuni.2022.10.020 * |
MEI ZUBING, GUANGHUI WANG, ZHONGLIN LIANG, ANG CUI, ANDONG XU, YUN LIU, CHENYING LIU, YILI YANG, LONG CUI: "Prognostic value of IRF-2 expression in colorectal cancer", ONCOTARGET, vol. 8, no. 24, 1 January 2017 (2017-01-01), pages 38969 - 38977, XP093172461 * |
SAKAI TOSHITAKA, MASHIMA HIROSATO, YAMADA YUMI, GOTO TAKASHI, SATO WATARU, DOHMEN TAKAHIRO, KAMADA KENTARO, YOSHIOKA MASATO, UCHIN: "The Roles of interferon regulatory factors 1 and 2 in the progression of human pancreatic cancer", PANCREAS, RAVEN PRESS, NEW YORK, NY., US, vol. 43, no. 6, 1 August 2014 (2014-08-01), US , pages 909 - 916, XP009554754, ISSN: 0885-3177, DOI: 10.1097/MPA.0000000000000116 * |
SUN SHANWEN, ZHI ZHENGKE; SU YANG; SUN JINGXIAN; LI QIANJUN: "A CD8+ T cell-associated immune gene panel for prediction of the prognosis and immunotherapeutic effect of melanoma", FRONTIERS IN IMMUNOLOGY, FRONTIERS MEDIA, LAUSANNE, CH, vol. 13, 20 October 2022 (2022-10-20), Lausanne, CH , XP093172466, ISSN: 1664-3224, DOI: 10.3389/fimmu.2022.1039565 * |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
LaFleur et al. | PTPN2 regulates the generation of exhausted CD8+ T cell subpopulations and restrains tumor immunity | |
JP6949728B2 (en) | Compositions and Methods for Modulating Inhibitory Interactions in Genetically Engineered Cells | |
Lukhele et al. | The transcription factor IRF2 drives interferon-mediated CD8+ T cell exhaustion to restrict anti-tumor immunity | |
WO2020057666A1 (en) | T-cell expressing chimeric receptor | |
US20200347386A1 (en) | Combination gene targets for improved immunotherapy | |
EP3452499A2 (en) | Genetically engineered cells and methods of making the same | |
US11786550B2 (en) | gRNA targeting HPK1 and a method for editing HPK1 gene | |
WO2018183485A1 (en) | Methods of isolating neoantigen-specific t cell receptor sequences | |
JP2021522188A (en) | Compositions and Methods for Multiple Tumor Vaccination with Endogenous Gene Activation | |
JP2020517247A (en) | Platform for manipulation of T lymphocyte genome and its in vivo high throughput screening method | |
CN118389602A (en) | Human-manipulated immune cells | |
WO2020160489A1 (en) | Gene-regulating compositions and methods for improved immunotherapy | |
EP3090751A1 (en) | Molecular profiling of cd8 t-cells in autochthonous melanoma identifies maf as driver of exhaustion | |
US20220017715A1 (en) | Compositions and Methods for Efficacy Enhancement of T-Cell Based Immunotherapy | |
US20220202900A1 (en) | Compositions and methods for crispr/cas9 knock-out of cd33 in human hematopoietic stem / progenitor cells for allogenic transplantation in patients with relapsed - refractory acute myeloid leukemia | |
WO2024098148A1 (en) | Targeting irf2 for cancer therapy | |
Espinosa-Carrasco et al. | Intratumoral immune triads are required for adoptive T cell therapy-mediated elimination of solid tumors | |
JP2024512261A (en) | In vivo CRISPR screening system to discover therapeutic targets in CD8 T cells | |
WO2024059821A2 (en) | Car t cell compositions for treatment of cancer | |
Althoff | Improvement of adoptive T cell therapy by T cell specific targeting of T cell inhibitory receptors instead of systemic antibody treatment | |
Vesely | Tumor antigens revealed by exome sequencing drive editing of tumor immunogenicity | |
Leonard | Ex vivo manipulation of CD8 T cells to improve adoptive cell therapy against cancer | |
JP2024512368A (en) | Compositions and methods for assessing and treating T cell dysfunction | |
Chen | Functional analysis of candidate regulatory genes in regulatory T cells |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 23887239 Country of ref document: EP Kind code of ref document: A1 |