CN112029858B - Predictive index for tumor immunotherapy - Google Patents
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Abstract
The invention discloses a method and a system for predicting the curative effect of PD-1/PD-L1 inhibitor immunotherapy. The method comprises the following steps: obtaining the type and abundance of T cell receptors of tumor-infiltrating lymphocytes of a subject and CD8 of baseline peripheral blood + PD1 + The class of T cell receptors for T cells; statistics of tumor infiltrating lymphocytes and CD8 of subjects + PD1 + Calculating the average proportion of tumor-infiltrating lymphocytes with the T cell receptor in the tumor-infiltrating lymphocytes as an IR index; judging the therapeutic effect of the PD-1/PD-L1 inhibitor immunotherapy through the IR index. The method can provide the predictive indicators of the curative effect and prognosis of tumor immunotherapy, and can effectively screen the patient group responding to the immunotherapy.
Description
Technical Field
The invention belongs to the technical field of biology, and particularly relates to a prediction index for tumor immunotherapy.
Background
PD-1/PD-L1 inhibitors have met with great success in the treatment of non-small cell lung cancer (NSCLC), but there are also cases of inconsistent patient response rates. However, for patients not selected for biomarkers, the effective rate of PD-1/PD-L1 immune checkpoint inhibitor is only about 20% [1] . Therefore, the curative effect prediction index of the inhibitor becomes a research hot spot.
The existing reported predictive markers of the curative effect of the immunotherapy comprise PD-L1 expression level, tumor mutation load, heterogeneity of tumor neoantigens, tumor infiltrating lymphocytes and the like, but the markers have some limitations.
In view of the fact that PD-1/PD-L1 inhibitors have a certain response rate in patients positive and negative for PD-L1 expression. Therefore, whether PD-L1 expression positive can be used as a therapeutic effect predictor is further determined. Furthermore, due to the different sensitivity of the PD-L1 antibodies used in the different studies; different studies determine that the low accuracy of the PD-L1 immunohistochemical results caused by different threshold settings of PD-L1 positivity, tumor heterogeneity and the like is also one of the reasons limiting the use of PD-L1 as a therapeutic effect prediction marker [2].
The limitation of tumor mutation burden is that the existing research results are all based on the whole exon sequencing results of tumor tissues before treatment. Whereas the tumor burden at a single time point lacks a universally applicable threshold for grouping patients. Also, mutation load may be outliers in both groups of good and bad efficacy, i.e., there may be extremely high mutation load in non-responsive patients, and lower mutation load in responsive patients. In addition, patients with different efficacy groupings have a degree of overlap in their mutational burden levels, which also places limitations on their clinical use [3,4] 。
There is a need in the art for a new evaluation index for immunotherapy for predicting the efficacy and prognosis of immune checkpoint inhibitors.
Disclosure of Invention
The invention aims to provide a therapeutic effect prediction analysis marker and a therapeutic effect prediction analysis method capable of predicting the therapeutic effect and prognosis of an immune checkpoint inhibitor. The inventors have found that subjects with high IR indices can benefit from PD1/PD-L1 inhibitor immunotherapy and have a better prognosis.
Accordingly, in one aspect, the present invention provides a method of predicting the efficacy of an immunotherapy with a PD-1/PD-L1 inhibitor, the method comprising:
obtaining the type and abundance of T cell receptors of tumor-infiltrating lymphocytes of a subject and CD8 of baseline peripheral blood + PD1 + The class of T cell receptors for T cells;
statistics of tumor infiltrating lymphocytes and CD8 of subjects + PD1 + T cells share a class of T cell receptors,
calculating the average proportion of tumor-infiltrating lymphocytes with a shared T cell receptor in the tumor-infiltrating lymphocytes as an IR index;
judging the therapeutic effect of the PD-1/PD-L1 inhibitor immunotherapy through the IR index.
In one embodiment, a greater IR index indicates a better therapeutic effect of the PD-1/PD-L1 inhibitor immunotherapy.
In one embodiment, the T cell receptor is characterized by the CDR3 region (CDR 3 beta sequence) of the T cell receptor beta chain.
In one embodiment, the method comprises:
tumor infiltrating lymphocytes and CD8 for a subject + PD1 + Sequencing the CDR3 beta sequence of the T cells to obtain a sequencing sequence;
obtaining tumor infiltrating lymphocytes and CD8 from the sequencing sequence + PD1 + A CDR3 β sequence class common to T cells;
the average proportion of the consensus CDR3 β sequence to all CDR3 β sequences in the tumor infiltrating lymphocytes was counted from the sequencing sequence as an IR index.
In one embodiment, the method comprises:
tumor infiltrating lymphocytes and CD8 for a subject + PD1 + Sequencing a CDR3 region (CDR 3 beta sequence) of a T cell receptor beta chain of the T cell to obtain a sequencing read length;
splicing the selected reading lengths to form a plurality of splicing sequences;
performing CDR3 beta sequence characteristic recognition on the spliced sequences to obtain the tumor infiltrating lymphocytes and CD8 + PD1 + A T cell consensus CDR3 β sequence species;
the average proportion of the consensus CDR3 β sequence species in tumor infiltrating lymphocytes was calculated as the IR index:
t is the CDR3 beta sequence class of tumor infiltrating lymphocytes;
p is CD8 in peripheral blood + PD1 + CDR3 beta sequence species of T cell samples;
n is the number of common CDR3 β sequence species both contained;
Tfreq i is the ratio of the ith shared CDR3 beta species to the total CDR3 beta in tumor infiltrating lymphocytes.
In one embodiment, the tissue sample comprises fresh pathological tissue or formaldehyde-fixed paraffin-embedded case tissue and paraffin sections.
In one embodiment, the subject is a non-small cell lung cancer patient.
In one embodiment, the blood sample comprises blood cells or sorted cells.
In one embodiment, the threshold value of the IR index is 1.295%.
In a second aspect, the present invention provides a system for predicting the efficacy of an immunotherapy with a PD-1/PD-L1 inhibitor, the system comprising:
a sequencing information acquisition unit forObtaining the type and abundance of T cell receptors of tumor-infiltrating lymphocytes of a subject and CD8 of baseline peripheral blood + PD1 + The class of T cell receptors for T cells;
a statistics unit for counting tumor-infiltrating lymphocytes and CD8 of the subject + PD1 + T cells share a class of T cell receptors,
a calculation unit for calculating an average proportion of tumor-infiltrating lymphocytes having a common T cell receptor among the tumor-infiltrating lymphocytes as an IR index;
and the judging unit is used for judging the curative effect of the PD-1/PD-L1 inhibitor immunotherapy through the IR index.
In one embodiment, a greater IR index indicates a better therapeutic effect of the PD-1/PD-L1 inhibitor immunotherapy.
In one embodiment, the T cell receptor is characterized by the CDR3 region (CDR 3 beta sequence) of the T cell receptor beta chain.
In one embodiment of the present invention, in one embodiment,
the sequencing information acquisition unit is used for acquiring tumor-infiltrating lymphocytes and CD8 of a subject + PD1 + Sequencing of CDR3 beta sequences of T cells;
the statistical unit is used for counting tumor infiltrating lymphocytes and CD8 from the sequencing sequence + PD1 + A CDR3 β sequence class common to T cells;
the calculation unit is used for calculating the average proportion of the shared CDR3 beta sequences to all CDR3 beta sequences in the tumor-infiltrating lymphocytes from the sequencing sequences as an IR index.
In one embodiment of the present invention, in one embodiment,
the sequencing information acquisition unit is used for acquiring tumor-infiltrating lymphocytes and CD8 of baseline peripheral blood of a subject + PD1 + Sequencing data of CDR3 beta sequences of T cells;
the statistical unit is used for carrying out CDR3 beta sequence characteristic recognition on the sequencing data of the CDR3 beta sequence to obtain the CDR3 beta sequence type, and counting tumor infiltrating lymphocytes of the patient and the CD8 + PD1 + A T cell sample comprising a consensus CDR3 β sequence species;
the calculation unit is used for calculating the average proportion of the common CDR3 beta type in tumor infiltrating lymphocytes, namely IR index:
t is the CDR3 beta sequence class of tumor infiltrating lymphocytes;
p is CD8 in peripheral blood + PD1 + CDR3 beta sequence species of T cell samples;
n is the number of common CDR3 β sequence species both contained;
Tfreq i is the average proportion of the common CDR3 beta species in tumor infiltrating lymphocytes.
In one embodiment, the sequencing data of the CDR3 β sequence acquired by the sequencing information acquisition unit is a sequencing read length sequence, the system further comprising: and the sequence splicing unit is used for selecting the sequencing read length containing the primer sequence and splicing the selected read length to form a plurality of spliced sequences.
The method can provide the predictive indicators of the curative effect and prognosis of tumor immunotherapy, and can effectively screen the patient group responding to the immunotherapy.
Drawings
The invention is illustrated by the following figures.
Figure 1 shows the IR index-treatment response.
Fig. 2 shows ROC curve-IR index.
FIG. 3 shows the PFS-IR index.
Detailed Description
In the present invention, baseline peripheral blood refers to peripheral blood collected from a patient prior to receiving immunotherapy.
CD8 isolated from tumor infiltrating lymphocytes and baseline peripheral blood + PD1 + T cells were subjected to high throughput sequencing of T cell receptors, respectively, and first T cell receptors (tumor infiltrating lymphocytes) and T cell receptors (CD 8 in peripheral blood) were calculated + PD1 + T cells) in the tumor tissue, the number of the consensus clones, (the consensus clones being present in both tumor tissue and peripheral blood CD 8) + PD1 + T cell receptor clones of (a); the average frequency of the consensus clones in the tumor tissue is then calculated, i.e. the sum of the frequencies of the consensus clones in the tumor tissue divided by the number of consensus clones.
IR index refers to tumor infiltrating lymphocytes and baseline peripheral CD8 + PD1 + The average frequency of T cell receptor-sharing clones of T cells in tumor infiltrating lymphocytes can be calculated by the following formula:
n is tumor infiltrating lymphocytes and baseline peripheral CD8 + PD1 + Number of T cell receptor clones shared by T cells;
i is tumor infiltrating lymphocytes and baseline peripheral CD8 + PD1 + Numbering of T cell-shared T cell receptor clones;
Tfreq i is tumor infiltrating lymphocytes and baseline peripheral CD8 + PD1 + Frequency of T cell-shared T cell receptor clone i.
In one embodiment, the method comprises:
isolating CD8 from baseline peripheral blood of a subject + PD1 + T cells;
tumor infiltrating lymphocytes and said CD8 for said subject + PD1 + Extracting DNA from T cells respectively;
respectively amplifying CDR3 regions of T cell receptor beta chains of the DNA to obtain amplified products;
sequencing the amplification product;
for said tumor infiltrating lymphocytes and said CD8 + PD1 + Sequencing data of T cells, following 1) -3), respectively:
1) Comparing the sequencing read length with the PCR primer sequence, and selecting a sequencing read length containing the PCR primer sequence;
2) Splicing the selected reading lengths to form a plurality of splicing sequences;
3) Performing CDR3 beta sequence feature recognition on the spliced sequences to obtain the types of the CDR3 beta sequences and the number of the occurrence of each type of CDR3 beta, namely the types and the number of T Cell Receptor (TCR) sequences;
tumor infiltrating lymphocytes for the patient with the CD8 + PD1 + T cell samples, which were taken to include the consensus CDR3 beta sequence species, and the average proportion of the consensus CDR3 beta species in tumor infiltrating lymphocytes, i.e., IR index, was calculated:
t is the CDR3 beta sequence class of tumor infiltrating lymphocytes;
p is CD8 in peripheral blood + PD1 + CDR3 beta sequence species of T cell samples;
n is the number of common CDR3 β sequence species both contained;
Tfreq i is the average proportion of the common CDR3 beta species in tumor infiltrating lymphocytes.
The invention is further illustrated below in conjunction with specific examples. It should be understood that these examples are only for the purpose of the present invention and are not intended to limit the scope of the present invention. Unless defined or otherwise indicated, the scientific and technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.
Example 1 an IR index is described for 30 NSCLC patients receiving PD-1/PD L1 inhibitor treatment to predict the efficacy and prognosis of immunotherapy in lung cancer patients.
Collecting 30 cases of tumor tissues and baseline peripheral blood of lung cancer patients treated with PD-1/PD L1 inhibitor, and separating CD8 of baseline peripheral blood by flow cytometry + PD1 + The sorted cells of the T cells and PD1-T cells were subjected to subsequent experimental procedures.
DNA extraction
The sample application range includes fresh pathological tissue, blood cells and sorted cells which are excised by surgery, namely three samples can be obtained for each patient: fresh pathological tissue, CD8 + PD1 + T cells and PD1 - T cells. The gDNA was extracted from the samples such as the sorted cells and tissues according to the instructions for QIAamp DNA Mini Kit extraction reagent. Then adopting the Qubit to quantify, and requiring the gDNA of the sorted cells to be more than 200ng; tissue DNA was greater than 200ng.
2. Library construction
1) Multiplex PCR amplification: t cell receptor specific primers were designed based on the structure of the CDR3 region V (D) J gene of the T cell receptor beta chain, and the CDR3 region of the T cell receptor beta chain was amplified and manipulated using a QIAGEN Multiplex PCR Kit (Qiagen) kit. Taking a qualified quality control sample, and preparing a multiple PCR reaction system:
| component (A) | Volume (quality) |
| 2×QIAGEN Multiplex PCR Master Mix | 25μL |
| 5 XQ solution | 5μL |
| Specific primer sets (for primer information see Table 1) | 2μL |
| Sample DNA | 1ug |
| NF-H 2 O | Supplement to50μL |
| Total volume of | 50μL |
PCR reaction conditions:
(3) 1.0 times XP magnetic bead purification:
the PCR reaction mixture was transferred to a 1.5mL centrifuge tube, and amplified samples were purified using the AMPure XP DNA purification kit (SPRI beads), and the experimental procedure was followed according to the instructions for the reagents used.
(4) PCR2 amplifying target products, and introducing sequencing primers: the amplification of the last step product by using Illumina common primers and Index primers was performed using KAPA HiFi PCR Kit (kapa biosystems) kit. A PCR reaction system was prepared in a 200. Mu.L PCR tube according to the following system:
| component (A) | Volume (mu L) |
| Purification of DNA | 23 |
| Primer1 public (10 uM) | 1 |
| Primer Index_X(10uM) | 1 |
| 2×KAPA hifi hot start Master Mix | 25 |
| Total volume of | 50 |
PCR reaction conditions
(5) 1.0 times XP magnetic bead purification:
the PCR reaction mixture was transferred to 1.5mL centrifuge tubes and amplified samples were purified using the AMPure XP DNA purification kit (SPRI beads).
(6) 2% agarose gel recovery: the T cell receptor is excised and the target fragment with the length of 250bp-350bp is recovered. 30ul volume NF-H 2 O is dissolved and preserved, and library construction is completed.
3. Library quality control
The library was then quality controlled for DNA fragments and concentration using a Bioanalyzer 2100 (Agilent).
4. Sequencing on machine
On-machine sequencing was performed using NextSeq 500 (Illumina), sequencing experiments performed on-machine sequencing operations according to the manufacturer's instructions.
5. Information analysis
After qualification of the double-ended sequencing data generated by sequencing, the analysis was performed according to the software MIXCR (https:// MIXCR. Readthes. Org/en/latest/index. Html).
Sequencing data and baseline peripheral blood isolated CD8 for tumor-infiltrating lymphocyte samples for each patient + PD1 + Sequencing data of T cell samples, performed as follows 1) -3), respectively:
1) Comparing the read length with the PCR primer sequence, and selecting a sequencing read length containing a specific primer sequence;
2) Splicing the selected reading lengths to form a plurality of splicing sequences;
3) Performing CDR3 beta sequence feature recognition on the spliced sequences to obtain the types of the CDR3 beta sequences and the number of the occurrences of each type of CDR3 beta, namely the types and the numbers of TCR sequences;
read Length 4) tumor infiltrating lymphocyte samples (T) for each patient with CD8 in peripheral blood + PD1 + T cell sample (P), selecting the common CDR3 beta sequence types (the number is n) contained in the T cell sample and calculating the average proportion (Tfieq) of the common CDR3 beta types in the tumor-infiltrating lymphocyte sample i ) I.e. IR index:
t is the CDR3 beta sequence type of the tumor infiltrating lymphocyte sample;
p is CD8 in peripheral blood + PD1 + CDR3 beta sequence species of T cell samples;
n is the number of common CDR3 β sequence species both contained;
Tfreq i is the average proportion of the shared CDR3 β species in the tumor-infiltrating lymphocyte sample.
6. Results statistics
Note that: response: treatment response status (1-response, 0-non-response);
disease Control: disease control (1-benefit, 0-not benefit);
progress (1-progress, 0-no progress, empty-deletion);
PFS (progress-free maintenance): progression free survival (month).
5.1 Predictive value of IR index for anti-PD 1 treatment efficacy (ORR) of NSCLC
As can be seen in fig. 1, the IR indices of the PR patient group and the pd+sd patient group have significant differences, P <0.01. Patients with high IR indices are more prone to benefit from anti-PD 1 treatment. The IR index can be used as a predictor of the efficacy (ORR) of NSCLC anti-PD 1 treatment. PD: disease progression; SD: the disease is stable; PR: the disease is partially alleviated.
5.2 Predictive value of IR index for prognosis of NSCLC anti-PD 1 treatment
FIG. 2ROC curve gives AUC 0.879, critical (cut-off) value 1.295, sensitivity 85.7%, specificity 78.3%. The critical value of the IR index was determined to be 1.295%.
As can be seen in fig. 3, the patient group with high IR index had longer PFS than the patient group with low IR index, which was a significant difference, and the IR index could be used as a predictor of NSCLC anti-PD 1 treatment prognosis.
The invention has the beneficial effects that the IR index can effectively distinguish between a responding patient and a non-responding patient before treatment and effectively predict the prognosis of the patient for immunotherapy. Improving the effective rate of immunotherapy, assisting the treatment decision of the clinical PD-L1 inhibitor, and having important scientific significance and good clinical application prospect.
Table 1: primer sequences (5 'to 3')
B-VB-TRB2F(SEQ ID NO.1)
GAACGACATGGCTACGATCCGACTTAGGGATCAAATTTCACTCTGAAGATCC
B-TRBV3-1-F4(SEQ ID NO.2)
GAACGACATGGCTACGATCCGACTTAGAAACAGTTCCAAATCGMTTCTCAC
B-TRBV4-1/2/3-F4(SEQ ID NO.3)
GAACGACATGGCTACGATCCGACTTAGCAAGTCGCTTCTCACCTGAATG
B-TRBV5-1-F4(SEQ ID NO.4)
GAACGACATGGCTACGATCCGACTTAGGCCAGTTCTCTAACTCTCGCTCT
B-TRBV5-4/5/6/8-F4(SEQ ID NO.5)
GAACGACATGGCTACGATCCGACTTAGCTCAGGTCTCCAGTTCCCTAATTA
B-TRBV6-4.1-F(SEQ ID NO.6)
GAACGACATGGCTACGATCCGACTTAGCCCTCACGTTGGCGTCTGCTGTA
B-TRBV6-8/5/1.2-F(SEQ ID NO.7)
GAACGACATGGCTACGATCCGACTTAGCAGGCTGGTGTCGGCTGCTCCCT
B-TRBV6-9/7/1.1/6-F(SEQ ID NO.8)
GAACGACATGGCTACGATCCGACTTAGCCGCTCAGGCTGGAGTCAGCTGC
B-TRBV6-4.2-F(SEQ ID NO.9)
GAACGACATGGCTACGATCCGACTTAGAGTCGCTTGCTGTACCCTCTCAG
B-TRRBV6-2/3-F(SEQ ID NO.10)
GAACGACATGGCTACGATCCGACTTAGGGGGTTGGAGTCGGCTGCTCCCT
B-TRBV7-2/4/6/7/8-F4(SEQ ID NO.11)
GAACGACATGGCTACGATCCGACTTAGGGGATCCGTCTCCACTCTGAMGAT
B-TRBV7-3-F4(SEQ ID NO.12)
GAACGACATGGCTACGATCCGACTTAGGGGATCCGTCTCTACTCTGAAGAT
B-TRBV7-9-F4(SEQ ID NO.13)
GAACGACATGGCTACGATCCGACTTAGTGAGGGATCCGTCTCCACTCTGAA
B-TRBV9F(SEQ ID NO.14)
GAACGACATGGCTACGATCCGACTTAGCCCTGACTTGCACTCTGAACTAAACC
B-TRBV10-1-F4(SEQ ID NO.15)
GAACGACATGGCTACGATCCGACTTAGAGGACCTCCCCCTCACTCTGGA
B-TRBV10-2/3-F4(SEQ ID NO.16)
GAACGACATGGCTACGATCCGACTTAGCCTCACTCTGGAGTCMGCTACC
B-TRBV11-1/2/3-F4(SEQ ID NO.17)
GAACGACATGGCTACGATCCGACTTAGGCAGAGAGGCTCAAAGGAGTAGACT
B-TRBV12-3.2/5.2(SEQ ID NO.18)
GAACGACATGGCTACGATCCGACTTAGCTGAAGATCCAGCCCTCAGAACCC
B-TRBV12-3.1/4/5.1-F(SEQ ID NO.19)
GAACGACATGGCTACGATCCGACTTAGAAGATCCAGCCCTCAGAACCCAG
B-TRBV13-F4(SEQ ID NO.20)
GAACGACATGGCTACGATCCGACTTAGGATCGATTCTCAGCTCAACAGT
B-TRBV14F(SEQ ID NO.21)
GAACGACATGGCTACGATCCGACTTAGCTGGAGGGACGTATTCTACTCTGAA
B-TRBV15F(SEQ ID NO.22)
GAACGACATGGCTACGATCCGACTTAGCTTTCTTGACATCCGCTCACCA
B-TRBV16-F2(SEQ ID NO.23)
GAACGACATGGCTACGATCCGACTTAGCCTGTAGCCTTGAGATCCAGGCTACG
B-TRBV18-F4(SEQ ID NO.24)
GAACGACATGGCTACGATCCGACTTAGCACCCTGTAGCCTTGAGATCCAGGCT
B-TRBV19F(SEQ ID NO.25)
GAACGACATGGCTACGATCCGACTTAGAATCCTTTCCTCTCACTGTGACATC
B-TRBV20-1-F4(SEQ ID NO.26)
GAACGACATGGCTACGATCCGACTTAGTCAACCATGCAAGCCTGACC
B-TRBV24-1-F2(SEQ ID NO.27)
GAACGACATGGCTACGATCCGACTTAGTTCTCCCTGTCCCTAGAGTCTGCC
B-TRBV25-1F(SEQ ID NO.28)
GAACGACATGGCTACGATCCGACTTAGGGCCCTCACATACCTCTCAGTACCT
B-TRBV27-1(SEQ ID NO.29)
GAACGACATGGCTACGATCCGACTTAGGATCCTGGAGTCGCCCAGC
B-TRBV28(SEQ ID NO.30)
GAACGACATGGCTACGATCCGACTTAGATTCTGGAGTCCGCCAGC
B-TRBV29-1-F4(SEQ ID NO.31)
GAACGACATGGCTACGATCCGACTTAGAACTCTGACTGTGAGCAACATGAG
B-TRBV30-F5(SEQ ID NO.32)
GAACGACATGGCTACGATCCGACTTAGCAGATCAGCTCTGAGGTGCCCCA
B-TRBJ1.1-R2(SEQ ID NO.33)
GTCTTCCTAAGACCGCTTGGCCTCCGACTTCTTACCTACAACTGTGAGTCTGGTG
B-TRBJ1.2R(SEQ ID NO.34)
GTCTTCCTAAGACCGCTTGGCCTCCGACTTCTTACCTACAACGGTTAACCTGGTC
B-TRBJ1.3R(SEQ ID NO.35)
GTCTTCCTAAGACCGCTTGGCCTCCGACTTCTTACCTACAACAGTGAGCCAACTT
B-TRBJ1-4(SEQ ID NO.36)
GTCTTCCTAAGACCGCTTGGCCTCCGACTTAAGACAGAGAGCTGGGTTCCACT
B-TRBJ1.5R(SEQ ID NO.37)
GTCTTCCTAAGACCGCTTGGCCTCCGACTTCTTACCTAGGATGGAGAGTCGAGTC
B-TRBJ1.6R(SEQ ID NO.38)
GTCTTCCTAAGACCGCTTGGCCTCCGACTTCATACCTGTCACAGTGAGCCTG
B-TRBJ2.1R(SEQ ID NO.39)
GTCTTCCTAAGACCGCTTGGCCTCCGACTTCCTTCTTACCTAGCACGGTGA
B-TRBJ2.2R(SEQ ID NO.40)
GTCTTCCTAAGACCGCTTGGCCTCCGACTTCTTACCCAGTACGGTCAGCCT
B-TRBJ2.3R(SEQ ID NO.41)
GTCTTCCTAAGACCGCTTGGCCTCCGACTTCCGCTTACCGAGCACTGTCAG
B-TRBJ2-4(SEQ ID NO.42)
GTCTTCCTAAGACCGCTTGGCCTCCGACTTAGCACTGAGAGCCGGGTCC
B-TRBJ2.5-R2(SEQ ID NO.43)
GTCTTCCTAAGACCGCTTGGCCTCCGACTTCGAGCACCAGGAGCCGCGT
B-TRBJ2.6R(SEQ ID NO.44)
GTCTTCCTAAGACCGCTTGGCCTCCGACTTCTCGCCCAGCACGGTCAGCCT
B-TRBJ2.7-R2(SEQ ID NO.45)
GTCTTCCTAAGACCGCTTGGCCTCCGACTTCTTACCTGTGACCGTGAGCCTG
Reference to the literature
[1]Braun DA,Burke KP,Van Allen EM.Genomic approaches to understanding response and resistance to immunotherapy.Clin Cancer Res:an official journal of the American Association for Cancer Research.2016；22(23):5642–50。
[2]Wang X,Teng F,Kong L,Yu J.PD-L1 expression in human cancers and its association with clinical outcomes.Onco Targets Ther.2016Aug 12；9:5023-39。
[3]Snyder,A.et al.Genetic basis for clinical response to CTLA-4 blockade in melanoma.N.Engl.J.Med.371.2189–2199(2014)。
[4]Van Allen,E.M.et al.Genomic correlates of response to CTLA4 blockade in metastatic melanoma.Science 350,207–211(2015)。
Sequence listing
<110> Beijing Jikina science and technology Co., ltd
Beijing Jiyin medical laboratory Co., ltd
<120> a predictive index for tumor immunotherapy
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<170> SIPOSequenceListing 1.0
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<400> 1
gaacgacatg gctacgatcc gacttaggga tcaaatttca ctctgaagat cc 52
<210> 2
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<212> DNA
<213> Artificial sequence (Artificial Sequence)
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gaacgacatg gctacgatcc gacttagaaa cagttccaaa tcgmttctca c 51
<210> 3
<211> 49
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 3
gaacgacatg gctacgatcc gacttagcaa gtcgcttctc acctgaatg 49
<210> 4
<211> 50
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 4
gaacgacatg gctacgatcc gacttaggcc agttctctaa ctctcgctct 50
<210> 5
<211> 51
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 5
gaacgacatg gctacgatcc gacttagctc aggtctccag ttccctaatt a 51
<210> 6
<211> 50
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 6
gaacgacatg gctacgatcc gacttagccc tcacgttggc gtctgctgta 50
<210> 7
<211> 50
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 7
gaacgacatg gctacgatcc gacttagcag gctggtgtcg gctgctccct 50
<210> 8
<211> 50
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 8
gaacgacatg gctacgatcc gacttagccg ctcaggctgg agtcagctgc 50
<210> 9
<211> 50
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 9
gaacgacatg gctacgatcc gacttagagt cgcttgctgt accctctcag 50
<210> 10
<211> 50
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 10
gaacgacatg gctacgatcc gacttagggg gttggagtcg gctgctccct 50
<210> 11
<211> 51
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 11
gaacgacatg gctacgatcc gacttagggg atccgtctcc actctgamga t 51
<210> 12
<211> 51
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 12
gaacgacatg gctacgatcc gacttagggg atccgtctct actctgaaga t 51
<210> 13
<211> 51
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 13
gaacgacatg gctacgatcc gacttagtga gggatccgtc tccactctga a 51
<210> 14
<211> 53
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 14
gaacgacatg gctacgatcc gacttagccc tgacttgcac tctgaactaa acc 53
<210> 15
<211> 49
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 15
gaacgacatg gctacgatcc gacttagagg acctccccct cactctgga 49
<210> 16
<211> 49
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 16
gaacgacatg gctacgatcc gacttagcct cactctggag tcmgctacc 49
<210> 17
<211> 52
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 17
gaacgacatg gctacgatcc gacttaggca gagaggctca aaggagtaga ct 52
<210> 18
<211> 51
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 18
gaacgacatg gctacgatcc gacttagctg aagatccagc cctcagaacc c 51
<210> 19
<211> 50
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 19
gaacgacatg gctacgatcc gacttagaag atccagccct cagaacccag 50
<210> 20
<211> 49
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 20
gaacgacatg gctacgatcc gacttaggat cgattctcag ctcaacagt 49
<210> 21
<211> 52
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 21
gaacgacatg gctacgatcc gacttagctg gagggacgta ttctactctg aa 52
<210> 22
<211> 49
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 22
gaacgacatg gctacgatcc gacttagctt tcttgacatc cgctcacca 49
<210> 23
<211> 53
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 23
gaacgacatg gctacgatcc gacttagcct gtagccttga gatccaggct acg 53
<210> 24
<211> 53
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 24
gaacgacatg gctacgatcc gacttagcac cctgtagcct tgagatccag gct 53
<210> 25
<211> 52
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 25
gaacgacatg gctacgatcc gacttagaat cctttcctct cactgtgaca tc 52
<210> 26
<211> 47
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 26
gaacgacatg gctacgatcc gacttagtca accatgcaag cctgacc 47
<210> 27
<211> 51
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 27
gaacgacatg gctacgatcc gacttagttc tccctgtccc tagagtctgc c 51
<210> 28
<211> 52
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 28
gaacgacatg gctacgatcc gacttagggc cctcacatac ctctcagtac ct 52
<210> 29
<211> 46
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 29
gaacgacatg gctacgatcc gacttaggat cctggagtcg cccagc 46
<210> 30
<211> 45
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 30
gaacgacatg gctacgatcc gacttagatt ctggagtccg ccagc 45
<210> 31
<211> 51
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 31
gaacgacatg gctacgatcc gacttagaac tctgactgtg agcaacatga g 51
<210> 32
<211> 50
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 32
gaacgacatg gctacgatcc gacttagcag atcagctctg aggtgcccca 50
<210> 33
<211> 55
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 33
gtcttcctaa gaccgcttgg cctccgactt cttacctaca actgtgagtc tggtg 55
<210> 34
<211> 55
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 34
gtcttcctaa gaccgcttgg cctccgactt cttacctaca acggttaacc tggtc 55
<210> 35
<211> 55
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 35
gtcttcctaa gaccgcttgg cctccgactt cttacctaca acagtgagcc aactt 55
<210> 36
<211> 53
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 36
gtcttcctaa gaccgcttgg cctccgactt aagacagaga gctgggttcc act 53
<210> 37
<211> 55
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 37
gtcttcctaa gaccgcttgg cctccgactt cttacctagg atggagagtc gagtc 55
<210> 38
<211> 52
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 38
gtcttcctaa gaccgcttgg cctccgactt catacctgtc acagtgagcc tg 52
<210> 39
<211> 51
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 39
gtcttcctaa gaccgcttgg cctccgactt ccttcttacc tagcacggtg a 51
<210> 40
<211> 51
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 40
gtcttcctaa gaccgcttgg cctccgactt cttacccagt acggtcagcc t 51
<210> 41
<211> 51
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 41
gtcttcctaa gaccgcttgg cctccgactt ccgcttaccg agcactgtca g 51
<210> 42
<211> 49
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 42
gtcttcctaa gaccgcttgg cctccgactt agcactgaga gccgggtcc 49
<210> 43
<211> 49
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 43
gtcttcctaa gaccgcttgg cctccgactt cgagcaccag gagccgcgt 49
<210> 44
<211> 51
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 44
gtcttcctaa gaccgcttgg cctccgactt ctcgcccagc acggtcagcc t 51
<210> 45
<211> 52
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 45
gtcttcctaa gaccgcttgg cctccgactt cttacctgtg accgtgagcc tg 52
Claims (3)
1. A system for predicting efficacy of a PD-1/PD-L1 inhibitor immunotherapy, the system comprising:
a sequencing information acquisition unit for acquiring tumor-infiltrating lymphocytes and CD8 of baseline peripheral blood of the subject + PD1 + Sequencing data of CDR3 beta sequences of T cells;
a statistics unit for counting tumor-infiltrating lymphocytes and CD8 of the subject + PD1 + A CDR3 β sequence class common to T cells;
a calculation unit for calculating: the number of the shared CDR3 beta sequences in the tumor-infiltrating lymphocytes accounts for the proportion of the number of all CDR3 beta sequences in the tumor-infiltrating lymphocytes, and the average number of the proportion is calculated according to the number of the types of the shared CDR3 beta sequences obtained by the statistical unit and is recorded as an IR index;
and the judging unit is used for judging the immunotherapy curative effect of the PD-1/PD-L1 inhibitor through the IR index, wherein the larger the IR index is, the better the immunotherapy curative effect of the PD-1/PD-L1 inhibitor is.
2. The system according to claim 1,
the statistical unit is used for carrying out CDR3 beta sequence characteristic recognition on the sequencing data of the CDR3 beta sequence to obtain the CDR3 beta sequence type, and counting tumor infiltrating lymphocytes of the subject and the CD8 + PD1 + A CDR3 β sequence class common to T cells;
the calculation unit is used for calculating the average proportion of the common CDR3 beta type in tumor infiltrating lymphocytes, namely IR index:
wherein,
t is the CDR3 beta sequence class of tumor infiltrating lymphocytes;
p is CD8 in peripheral blood + PD1 + CDR3 beta sequence species of T cell samples;
n is the number of common CDR3 β sequence species contained by both the tumor infiltrating lymphocytes and cd8+pd1+ T cells of the baseline peripheral blood;
Tfreq i is the ratio of the number of CDR3 beta sequences in the shared CDR3 beta class i to the number of all CDR3 beta sequences in tumor infiltrating lymphocytes.
3. The system according to claim 1 or 2, the sequencing data of the CDR3 β sequence acquired by the sequencing information acquisition unit is a sequencing read length sequence, the system further comprising: and the sequence splicing unit is used for selecting the sequencing read length of the primer-containing sequence, and splicing the selected read length to form a plurality of splicing sequences for the statistical unit and the calculation unit.
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| CN106987631A (en) * | 2017-04-01 | 2017-07-28 | 武汉赛云博生物科技有限公司 | A kind of immune group sequencing technologies for the adjoint diagnosis of PD 1/PD L1 blocking treatments |
| CN111094977A (en) * | 2017-07-13 | 2020-05-01 | 古斯塔夫·鲁西研究所 | Imaging tools based on imaging omics to monitor tumor lymphocyte infiltration and prognosis in anti-PD-1/PD-L1 treated tumor patients |
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| CN106987631A (en) * | 2017-04-01 | 2017-07-28 | 武汉赛云博生物科技有限公司 | A kind of immune group sequencing technologies for the adjoint diagnosis of PD 1/PD L1 blocking treatments |
| CN111094977A (en) * | 2017-07-13 | 2020-05-01 | 古斯塔夫·鲁西研究所 | Imaging tools based on imaging omics to monitor tumor lymphocyte infiltration and prognosis in anti-PD-1/PD-L1 treated tumor patients |
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