WO2020246846A1 - Procédé à base de tox pour prédire une réponse thérapeutique à une immunothérapie anticancéreuse - Google Patents

Procédé à base de tox pour prédire une réponse thérapeutique à une immunothérapie anticancéreuse Download PDF

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WO2020246846A1
WO2020246846A1 PCT/KR2020/007335 KR2020007335W WO2020246846A1 WO 2020246846 A1 WO2020246846 A1 WO 2020246846A1 KR 2020007335 W KR2020007335 W KR 2020007335W WO 2020246846 A1 WO2020246846 A1 WO 2020246846A1
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tox
cells
expression
expression level
predicting
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Korean (ko)
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이인석
김경수
하상준
박세연
김혜련
김가민
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연세대학교 산학협력단
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer

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  • the present invention relates to a method for predicting a treatment response to an immune anticancer therapy, and more specifically, to a method for predicting the treatment response of an immune anticancer therapy to non-small cell lung cancer and melanoma by using a biomarker.
  • Lung cancer is one of the most common cancers in both sexes.
  • non small lung cancer (NSLC) is a type of carcinoma and refers to all epithelial lung cancers, not small lung cancer.
  • Such non-small cell lung cancer occupies a high proportion in the incidence of total lung cancer.
  • non-small cell lung cancer is divided into several sub-types according to the size, shape, and chemical composition of cancer cells, and representatively, adenocarcinoma, squamous cell carcinoma, and large cell carcinoma.
  • Adenocarcinoma is found in the outer region of the lung and tends to progress more slowly than other lung cancers, but shows a high tendency to metastasize at an early stage and also shows high radiation resistance.
  • Squamous cell carcinoma starts in the early version of the cells that make up the airway, and it has a high incidence mainly in smokers.
  • large-cell cancer can develop anywhere in the lung, and its treatment is still rising as a challenge since its progression is fast enough to be similar to that of small cell lung cancer.
  • Non-small cell lung cancer may include persistent cough, chest pain, weight loss, nail damage, joint pain, and shortness of breath.
  • non-small cell lung cancer progresses more slowly than other cancers, it hardly shows any symptoms at the beginning. Therefore, early detection and treatment of non-small cell lung cancer is difficult, and it is highly likely to be detected after metastasis to the whole body such as bone, liver, small intestine, and brain. Accordingly, when the diagnosis of non-small cell lung cancer, more than half of the patients are in a state that they cannot perform surgery, so early treatment is practically difficult.
  • prior surgery such as radical resection is performed, but only about 30% of cases can perform radical resection.
  • the majority of all patients who underwent radical resection appear to recur and die from more aggressive disease after surgical resection.
  • PD-1 programmed cell death-1
  • PD-L1 programmed cell death ligand-1
  • tumor PD-L1 expression by immunohistochemistry can be used as the best predictive biomarker for PD-1 blockade at present.
  • IHC immunohistochemistry
  • the accuracy of predicting the treatment response of PD-L1 dependent on tumor PD-L1 expression is not high enough to confirm drug efficacy. More specifically, PD-L1 expression negative patients may respond to PD-1 blockade, and PD-L1 expression positive patients may not respond to PD-1 blockade. Furthermore, some responding patients without PD-L1 may have similar duration of response if they are positive for PD-L1 in clinical trial Checkmate 057. Moreover, PD-L1 expression is dynamic and can change temporally and spatially. This change in PD-L1 expression may be adaptive immune resistance exerted by tumors.
  • the inventors of the present invention noted that the tumor induces a transcriptional network to exhaust T cells (T-cell exhaustion). Furthermore, due to T cell exhaustion, immune checkpint molecules such as PD-1, CTLA-4, LAG-3 and TIM-3 are expressed, and the functioning functions of T cells are gradually lost due to immune checkpoint molecules. I could recognize that I was in a state of dysfunction.
  • T cell exhaustion can be overcome and effective anti-tumor responses can be restored by blocking a transcription factor that promotes T cell exhaustion in the tumor microenvironment.
  • the pathogens of the present invention were able to discover TOX, a T cell-specific intra T cell transcription factor that promotes T cell exhaustion in the tumor microenvironment.
  • the inventors of the present invention suppress the expression of TOX in T cells specific to T cells existing in the tumor microenvironment, thereby expressing immune checkpoint molecules such as PD-1, CTLA-4 and TIM-3 expressed by T cell exhaustion. It could be recognized that it could be suppressed. Furthermore, it was found that it can improve the effect of immune chemotherapy and a method of predicting the treatment response to PD-1 blockade, an immune chemotherapy that suppresses these immune checkpoint molecules.
  • the inventors of the present invention improve the effectiveness of immuno-anticancer treatment systems and methods of predicting therapeutic response to PD-1 blockade, in particular, based on the expression of TOX in T cells specific to T cells in the tumor microenvironment. It has come to develop a treatment system that can be used.
  • the problem to be solved by the present invention is to measure the expression level of TOX with respect to a biological sample isolated from an individual, and based on this, immune anticancer therapy, in particular, configured to predict a therapeutic response to PD-1 blockade, It is to provide a method of predicting a treatment response to therapy,
  • Another problem to be solved by the present invention is to provide a kit for predicting a therapeutic response to an immune anticancer therapy, configured to include an agent measuring the expression level of TOX with respect to a biological sample isolated from an individual.
  • kits for predicting a therapeutic response to an immune anticancer therapy configured to include an agent for measuring the expression level of TOX in T cells specific to T cells present in a tumor microenvironment with respect to a biological sample.
  • measuring the expression level of TOX for a biological sample isolated from the subject and predicting the treatment response of the immunological anticancer therapy to the subject based on the measured expression level of TOX.
  • a method of predicting a treatment response to an immune anticancer therapy comprising it is provided.
  • measuring the expression level of TOX in a biological sample may be a step of measuring the expression level of TOX in T cells specific to T cells existing in the tumor microenvironment.
  • tumor microenvironment refers to a physicochemical environment in direct contact with a tumor, and due to the composition of the microenvironment, tumor generation, growth, and metastasis are smooth, and immune cells Can be avoided from Meanwhile, the tumor microenvironment composition may include factors such as normal epithelial cells, dendritic cells, cancer stem cells, lymphocytes, normal blood vessels, fibroblasts, vascular endothelial progenitor cells, granulocytes, and monocyte cancer cells, but is not limited thereto. Furthermore, the heterogeneity of cancer increases due to factors present in the microenvironment of these tumors.
  • the individual is a non-small cell lung cancer and melanoma suspected individual
  • the biological sample may include at least one selected from the group consisting of peripheral blood, serum, and plasma.
  • the immune anticancer therapy may preferably be an anti-PD-1 treatment, but is not limited thereto.
  • non-small cell lung cancer is a type of epithelial cancer and refers to all epithelial lung cancers other than small lung cancer.
  • anti-PD-1 treatment may be used, but is not limited thereto, and anti-CTLA-4 treatment, anti-CD28 treatment, anti-KIR treatment, anti-TCR treatment, anti-LAG- 3 treatment, anti TIM-3 treatment, anti TIGIT treatment, anti A2aR treatment, anti ICOS treatment, anti OX40 treatment, anti 4-1BB treatment, and anti-GITR treatment.
  • melanoma refers to a tumor of melanocytes, which is a cell originating from a neural tube.
  • anti-PD-1 treatment may be used, but is not limited thereto, and anti-CTLA-4 treatment, anti-CD28 treatment, anti-KIR treatment, anti-TCR treatment, anti-LAG-3 treatment , Anti TIM-3 treatment, anti TIGIT treatment, anti A2aR treatment, anti ICOS treatment, anti OX40 treatment, anti 4-1BB treatment, and anti-GITR treatment.
  • the immune anticancer therapy may be an anti-PD-1 treatment.
  • the anti-PD-1 therapy can be applied as an anti-cancer therapy to individuals suspected of various types of cancer.
  • individuals who want to predict the treatment response to anti-PD-1 treatment are non-small cell lung cancer, skin melanoma, head and neck cancer, stomach cancer, liver cancer, bone cancer, pancreatic cancer, skin cancer, uterine cancer, ovarian cancer, rectal cancer, and colon Cancer, colon cancer, breast cancer, uterine sarcoma, fallopian tube carcinoma, endometrial carcinoma, cervical carcinoma, vaginal carcinoma, vulvar carcinoma, esophageal cancer, laryngeal cancer, small intestine cancer, thyroid cancer, parathyroid cancer, soft tissue sarcoma, urethral cancer, penile cancer, prostate Cancer, chronic or acute leukemia, childhood solid tumor, differentiated lymphoma, bladder cancer, kidney cancer, renal cell carcinoma, renal pelvic carcinoma, primary central nervous system lymphom
  • the individual who wants to predict the response to the anti-PD-1 treatment of the present invention may be an individual having non-small cell lung cancer and melanoma, but is not limited thereto, and cancer responding to anti-PD-1 therapy It can be a variety of individuals.
  • anti-PD-1 treatment may be a therapy configured to block a mechanism in which T cells cannot attack cancer cells. More specifically, anti-PD-1 treatment may be based on blocking the binding of PD-L1, the surface proteins of cancer cells, and PD-L2, to PD-1, which is a protein on the surface of T cells. For example, when an immune anticancer agent binds to the PD-1 receptor of T cells, it is possible to inhibit the evasion function of T cells against cancer cells. Thus, in the present specification, “anti-PD-1 treatment” may be used in the same meaning as "PD-1 blocking".
  • the step of predicting a therapeutic response to an immune anticancer therapy comprises predicting a positive therapeutic response to an anti-PD-1 treatment when the measured expression level of TOX is less than a predetermined level.
  • a method of predicting a treatment response to an immune anti-cancer therapy can be provided.
  • kits for predicting a therapeutic response to an immune anticancer therapy comprising an agent measuring the expression of TOX in a biological sample isolated from an individual.
  • the formulation for measuring the expression level of TOX in a biological sample may be a formulation measuring the expression level of TOX in T cells specific for T cells present in the tumor microenvironment.
  • the present invention has an effect of providing a novel biomarker capable of predicting a treatment response to PD-1 blockade.
  • the present invention has the effect of predicting a treatment response to PD-1 blockade based on the expression of TOX. Accordingly, the present invention uses the expression of TOX to predict an early treatment response to PD-1 blockade for an individual, thereby providing information to quickly determine whether to proceed with anti-PD-1 treatment. have.
  • the present invention can distinguish between a patient who can be effective anti-PD-1 treatment and a patient who does not, so that it can be helpful to maximize the therapeutic effect when applied to the clinic.
  • FIG. 1 is an exemplary view showing a procedure of a method for predicting a treatment response to an immuno-cancer therapy according to an embodiment of the present invention.
  • 2A to 2G show results of confirming candidate genes related to T cell exhaustion using T cell-derived single cell transcriptome data.
  • 3A to 3C show the results of activity of immune checkpoint molecules according to the expression level of TOX in tumor tissues of patients with non-small cell lung cancer and melanoma.
  • 4A to 4D show the results of the activity of immune checkpoint molecules according to the expression level of TOX in tumor tissues of non-small cell lung cancer and melanoma mouse models.
  • FIG. 5 shows the expression results of immune checkpoint molecules in cancer tissue-derived T cells by knocking down TOX mRNA and the increase in the number of cells expressing IFN-gamma and TNF-alpha, which are inflammatory reaction derivatives.
  • 6A to 6C show comparison of the expression level of TOX by cell and the evaluation results of survival rate of patients with non-small cell lung cancer and melanoma according to the TOX level.
  • FIG. 1 is an exemplary view showing a procedure of a method for predicting a treatment response to an immuno-cancer therapy according to an embodiment of the present invention.
  • the expression level of TOX is measured for a biological sample isolated from an individual (S110), and the measured expression level of TOX It is configured to predict the treatment response of the immune chemotherapy for the individual based on (S120).
  • the measurement of the expression level of TOX in the step (S110) of measuring the expression level of TOX for a biological sample isolated from an individual is the expression of TOX in T cells specific to T cells in the tumor microenvironment. You can measure your level.
  • the expression level of TOX measured in the step (S120) of predicting a treatment response to an immune anticancer therapy is less than a predetermined level, it may be determined as a positive treatment response to the anti-PD-1 treatment.
  • the individual is a suspected non-small cell lung cancer individual and a suspected melanoma individual
  • the biological sample may include immune T cells and blood cells derived from cancer tissue.
  • the immune anticancer therapy may be an anti-PD-1 treatment. However, it is not limited thereto.
  • the method for predicting a treatment response provides information to early predict a treatment response to an individual immuno-anticancer therapy, particularly anti-PD-1, by measuring the levels of various markers. Can provide.
  • Example 1 Biomarker for predicting the therapeutic response of immune chemotherapy for non-small cell lung cancer and melanoma patients and target setting for therapeutic agent
  • 2A and 2B show the results of confirming a candidate gene related to T cell exhaustion using T cell-derived single cell transcriptome data.
  • CD8+ T cells distribution of CD8+ T cells according to the expression level of PDCD1 (PD-1 coding gene), which is a T cell exhaustion marker, is shown.
  • PDCD1 PD-1 coding gene
  • CD8+ T cells include heterogeneous cells in the tumor microenvironment.
  • CD8+ T cells are divided into high PDCD1 cells and low PDCD1 cell groups by the median expression level of PDCD1.
  • the expression levels of differentially expressed genes (DEGs) in high PDCD1 T cells and low PDCD1 T cell groups are shown in a violin plot.
  • the violin plot is a method of expressing the distribution density in the box and beard data in a symmetric way.
  • High PDCD1 T cells and low PDCD1 T cell groups have different shapes of violin plots, which may mean that the expression of differential expression genes is different according to the expression level of PDCD1.
  • FIG. 2A(d) a result of visualizing the distribution of CD8+ T cells according to the level of the differential expression gene in a two-dimensional map is shown. It appears that the highly differentially expressed T cells are distributed in the upper part of the distribution map.
  • differential expression genes with different distributions and patterns in the population divided according to the level of PDCD1 expression appear as potential candidate genes related to T cell exhaustion.
  • single-cell transcript data was obtained from tumor samples of melanoma and non-small cell lung cancer using single-cell RNA sequencing, and Wilcoxon rank sum test was performed.
  • the results of identifying the transcription factors associated with T cell exhaustion in T cells based on adjusted p ⁇ 0.05 are shown.
  • Transcription factors in melanoma were selected from IRF8, ETV1, TSC22D1, BATF, CALCOCO1, AATF, NFATC1, HCFC1, TOX, NAB1, ZNF638, PRDM1 and FAIM3, and transcription factors in non-small cell lung cancer were TOX, IVNS1ABP, SNRPBM.
  • IRF7 and BIN1 were selected.
  • TOX a common factor among transcription factors of melanoma and non-small cell lung cancer, was selected as the transcription factor involved in final T cell exhaustion.
  • FIG. 2C a two-dimensional map of the distribution according to the expression level of the immune checkpoint molecule gene and the transcription factor TOX in CD8+ T cells of a melanoma patient is shown.
  • the high-expression group and the low-expression group in which the immune checkpoint molecule genes PDCD1, HAVCR2, CTLA4 and TIGIT and the transcription factor TOX were highly expressed and low-expressed group showed different expression patterns. More specifically, it appears that the high expression group is distributed at the lower part of the map, and the low expression group is distributed at the upper part of the map.
  • the immune checkpoint molecule gene, the high PDCD1 T cells and low PDCD1 T cells groups had different shapes of violin plots, and the distribution between the groups showed significant differences (p ⁇ 0.001). More specifically, the high PDCD1 T cell group appears to have a high distribution density in the section where the CTLA4 expression level is high, and the low PDCD1 T cell group appears to have a high distribution density in the section where the CTLA4 expression level is low.
  • the immune checkpoint molecule gene the groups of high PDCD1 T cells and low PDCD1 T cells had different shapes of violin plots, and the distribution between the groups showed significant differences (p ⁇ 0.001). More specifically, the high PDCD1 T cell group appears to have a high distribution density in the section where the TIGIT expression level is high, and the low PDCD1 T cell group appears to have a high distribution density in the section where the TIGIT expression level is low.
  • the high and low PDCD1 T cells groups had different shapes of violin plots, and the distribution between the groups showed significant differences (p ⁇ 0.001). More specifically, the high PDCD1 T cell group appears to have a high distribution density in the section where the expression level of TOX is high, and the low PDCD1 T cell group appears to have a high distribution density in the section where the TOX expression level is low.
  • FIG. 2D (a) a two-dimensional map of the distribution according to the expression level of the immune checkpoint molecule gene and the transcription factor TOX in CD8+ T cells of a patient with non-small cell lung cancer is shown.
  • the high-expression group and the low-expression group in which the immune checkpoint molecule genes PDCD1, HAVCR2, CTLA4 and TIGIT and the transcription factor TOX were highly expressed and low-expressed group showed different expression patterns. More specifically, it appears that the high expression group is distributed at the upper part of the map, and the low expression group is distributed at the lower part of the map.
  • the immune checkpoint molecule gene, the high PDCD1 T cells and low PDCD1 T cells groups had different shapes of violin plots, and the distribution between the groups showed significant differences (p ⁇ 0.001). More specifically, the high PDCD1 T cell group appears to have a high distribution density in the section where the CTLA4 expression level is high, and the low PDCD1 T cell group appears to have a high distribution density in the section where the CTLA4 expression level is low.
  • the immune checkpoint molecule gene the groups of high PDCD1 T cells and low PDCD1 T cells had different shapes of violin plots, and the distribution between the groups showed significant differences (p ⁇ 0.001). More specifically, the high PDCD1 T cell group appears to have a high distribution density in the section where the TIGIT expression level is high, and the low PDCD1 T cell group appears to have a high distribution density in the section where the TIGIT expression level is low.
  • the high and low PDCD1 T cells groups had different shapes of violin plots, and the distribution between the groups showed significant differences (p ⁇ 0.001). More specifically, the high PDCD1 T cell group appears to have a high distribution density in the section where the expression level of TOX is high, and the low PDCD1 T cell group appears to have a high distribution density in the section where the TOX expression level is low.
  • FIG. 2e (a) shows the trajectory configured according to the state of the CD8 + T cells. Each is shown in three branches, and each branch appears to have a dominant cell type.
  • FIG. 2F the expression kinetics of immune checkpoint molecule genes and transcription factor TOX according to T cell status are shown.
  • the immune checkpoint molecule genes appear to have a tendency to increase in expression levels compared to when CD8+ T cells are exhausted.
  • FIG. 2G a result of a structural analysis of linking the states of the immune checkpoint molecule genes and the transcription factor TOX is shown.
  • the expression level of each immune checkpoint molecule gene showed a tendency to increase as T cells progressed from the running state to the exhausted state, and decreased as the T cell progressed from the running state to the memory state. This appears to be the same trend in the transcription factor TOX. Accordingly, it appears that transcription factor TOX and immune checkpoint molecule genes are associated with each other.
  • TOX a transcription factor in T cells present in the tumor microenvironment
  • the expression level of immune checkpoint molecule genes is related to exhaustion of T cells. It was confirmed that the expression level was also related.
  • the expression level of TOX in T cell-specific T cells existing in the tumor microenvironment can be used as a biomarker for predicting a therapeutic response to immuno-anticancer therapy according to various embodiments of the present invention.
  • TOX is also associated with the mechanism of immune checkpoint molecule genes, it can also be used as a therapeutic agent for lowering the expression of immune checkpoint molecules by using a targeted therapeutic agent that inhibits TOX.
  • Example 2 Expression of TOX according to expression of immune checkpoint molecule in tumor and method for predicting treatment response based thereon
  • 3A to 3C show the expression results of TOX according to the expression of immune checkpoint molecules in tumors of non-small cell lung cancer patients and squamous cell cancer patients.
  • 3A shows the results of analysis of tumor T cells of patients with non-small cell lung cancer and squamous cell cancer according to the expression of immune checkpoint molecules and TOX.
  • the expression of the immune checkpoint molecule when the expression of the immune checkpoint molecule is increased in the tumor of a patient with non-small cell lung cancer, the expression of TOX is shown to be increased. More specifically, the values of the first and third quadrants tend to increase proportionally. In addition, when the expression of the immune checkpoint molecule is increased in the tumor of the patient with squamous cell carcinoma, the expression of TOX appears to be increased. More specifically, the values of the first and third quadrants tend to increase proportionally.
  • 3B shows the results of the number of positive cells expressing TOX according to the expression of an immune checkpoint molecule in tumors of non-small cell lung cancer patients and squamous cell cancer patients.
  • 3C shows the expression of TOX according to the expression of immune checkpoint molecules PD-1 and TIM-3 in tumors of non-small cell lung cancer patients and squamous cell cancer patients.
  • FIG. 3C the results of analyzing tumor T cells of patients with non-small cell lung cancer and squamous cell cancer patients according to PD-1 expression and TIM-3 expression are shown.
  • the first quadrant region was classified as PD-1(+) positive-TIM-3(+) positive cells
  • the third quadrant region was classified as PD-1(-) negative-TIM-3(-) negative cells
  • the quadrant area was classified as PD-1(+) positive-TIM-3(-) negative cells.
  • the results shown in the histogram plot for TOX of cells classified according to the expression of PD-1 and TIM-3 are shown.
  • the fluorescence intensity for TOX is indicated in parentheses.
  • the red PD-1 positive-TIM-3 positive cell group had a large amount of TOX expression, and the fluorescence intensity was also the highest at 1577.
  • the red PD-1 positive-TIM-3 positive cell group had a large amount of TOX expression, and the fluorescence intensity was also the highest at 4970.
  • FIG. 3C (b) the results of TOX expression of cells classified according to PD-1 expression and TIM-3 expression are shown.
  • PD-1 positive-TIM-3 positive cells expressed significantly higher TOX than PD-1 positive-TIM-3 negative cells and PD-1 negative-TIM-3 negative cells. Indicates the amount.
  • PD-1 positive-TIM-3 positive cells had significantly higher TOX than PD-1 positive-TIM-3 negative cells and PD-1 negative-TIM-3 negative cells. It indicates the expression level.
  • 4A shows the results of analyzing tumor T cells of the MC37 mouse model according to PD-1 expression and TOX expression.
  • 4B shows the results of the number of cells expressing positive TOX according to the expression of PD-1 molecules in CT26, TC1 and LLC1 mouse models.
  • Figure 4c shows the expression of TOX according to the expression of PD-1 and TIM-3 in the tumor of the MC38 mouse model.
  • FIG. 4C the results of analyzing tumor T cells of the MC38 mouse model according to PD-1 expression and TIM-3 expression are shown.
  • the red quadrant area is classified as PD-1 positive-TIM-3 positive cells
  • the orange quadrant area is classified as PD-1 negative-TIM-3 negative cells
  • the blue quadrant area is classified as PD-1 negative-TIM-3 negative cells. They were classified as PD-1 positive-TIM-3 negative cells.
  • the cells classified according to the expression of PD-1 and TIM-3 are shown in the histogram plot for TOX.
  • the fluorescence intensity for TOX is indicated in parentheses. In the tumor of the MC38 mouse model, the red PD-1 positive-TIM-3 positive cell group had a large amount of TOX expression, and the fluorescence intensity was also the highest at 1048.
  • FIG. 4C (c) the results of TOX expression of cells classified according to PD-1 expression and TIM-3 expression are shown.
  • PD-1 positive-TIM-3 positive cells showed significantly higher TOX expression than PD-1 positive-TIM-3 negative cells and PD-1 negative-TIM-3 negative cells.
  • PD-1 positive-TIM-3 positive cells are PD-1 positive-TIM-3 negative cells and PD-1 negative-TIM in tumors of CT26, TC1 and LLC1 mouse models.
  • -3 shows a significantly higher expression of TOX than negative cells.
  • FIG. 5 shows the expression results of immune checkpoint molecules of cancer tissue-derived T cells by knocking down TOX mRNA.
  • FIG. 5 (a) the results of the analysis of the T cells knocked down TOX mRNA according to the expression of the immune checkpoint molecule and TOX is shown.
  • the expression of PD-1 when the expression of PD-1 is increased, the expression of TIGIT, TIM-3 and TOX appears to be increased. More specifically, the values of the first and third quadrants tend to increase proportionally.
  • the expression of TIGIT, TIM-3 and TOX did not increase as PD-1 expression increased.
  • the immune checkpoint molecule and the number of TOX-expressing cells in T cells and control T cells in which TOX mRNA was knocked down are shown.
  • Example 2 it appears that the expression of the transcription factor TOX and the expression of immune checkpoint molecules have a proportional relationship. That is, it was confirmed that immune checkpoint molecules were promoted by the expression of TOX. Accordingly, the expression level of TOX can predict the expression of conventional immune checkpoint molecules, and can predict a therapeutic response to an immune anticancer therapy according to various embodiments of the present invention.
  • Example 3 Anti-PD-1 treatment response prediction based on the expression level of TOX_Non-small cell lung cancer and melanoma
  • 6A shows the results of comparing the expression distribution of TOX in each cell derived from a tumor of a melanoma patient.
  • the distribution of TOX expression in individual cells derived from tumors of melanoma patients appears to be more expressed in T cells than in other immune cells or cancer cells.
  • 6B shows the results of evaluating the survival rate of patients with non-small cell lung cancer and melanoma according to the TOX level.
  • FIG. 6B (a) the evaluation results of prediction of anti-PD-1 treatment response according to the expression level of TOX in melanoma are shown.
  • FIG. 6C(a) the difference in anti-PD-1 treatment results of actual patients according to the expression level of TOX in melanoma is shown. Further referring to (b) and (c) of Figure 6c, the difference in anti-PD-1 treatment results of actual patients according to the level of expression of TOX in non-small cell lung cancer is shown. More specifically, in all three cases, patients who responded to anti-PD-1 treatment appeared to be more distributed when the expression level of TOX was low. In particular, according to the AUC analysis result of FIG. 6C (d), in all three cases including YCC, a cohort applied to various examples of the present invention, the distribution of the expression level of TOX between the responder and the non-responder group was significant. Appears to have a difference (AUC> 0.65).
  • Example 3 it may mean that the expression level of TOX in T cells-specific T cells may be an excellent marker in predicting anti-PD-1 treatment response. Furthermore, it may mean that the survival rate can be increased by inhibiting the expression of TOX in T cell-specific T cells. As a result, an inhibitor that suppresses the expression of TOX in T cell-specific T cells has an effect of increasing the survival rate.

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Abstract

La présente invention concerne un procédé de prédiction d'une réponse thérapeutique à une immunothérapie anticancéreuse, le procédé comprenant les étapes consistant à mesurer un niveau d'expression de TOX par rapport à un échantillon biologique isolé d'un sujet, et à prédire, sur la base du niveau d'expression mesuré de TOX, une réponse thérapeutique à une immunothérapie anticancéreuse concernant le sujet.
PCT/KR2020/007335 2019-06-05 2020-06-05 Procédé à base de tox pour prédire une réponse thérapeutique à une immunothérapie anticancéreuse WO2020246846A1 (fr)

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KR10-2019-0066986 2019-06-05
KR20190066986 2019-06-05
KR10-2020-0067675 2020-06-04
KR1020200067675A KR102461058B1 (ko) 2019-06-05 2020-06-04 Tox에 기초한 면역 항암 요법에 대한 치료 반응 예측 방법

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