KR102089241B1 - A method for prediction of the immunotherapy effects to cancer patients - Google Patents

Abstract

The present invention relates to a method for predicting the effect of immunotherapy in cancer patients, and more specifically, to a method for predicting the effect of treatment with anti-PD-1 immunoanticancer agents. Immune anti-cancer drugs are drugs that strengthen the body's own immune system and increase its resistance against cancer. Immune anti-cancer drugs complement the shortcomings of existing cancer treatments. If the first-generation chemotherapeutic agents attack cancer cells directly and the second-generation target anti-cancer drugs function to attack cancer-related genes, the immuno-cancer drugs called third-generation anticancer drugs enhance immunity To treat cancer. Kitruda and Opdivo are released as PD-1 immuno-cancer drugs, which are the most representative immuno-cancer drugs. They are used in cancer treatment in the medical field, but PD-1 immuno-cancer drugs are effective in distinguishing patients from those that are effective and those who are not. In the absence of a method, in the case of the ineffective patient group, the cost and time for the treatment of the PD-1 immuno-cancer agent are being lost.
Therefore, the present invention relates to a method for predicting an immunotherapy effect in a cancer patient, and according to the method of the present invention, it is possible to distinguish between a patient group in which a PD-1 immune anticancer agent is effective and an ineffective patient group in advance. It is expected to be used.

Description

A method for prediction of the immunotherapy effects to cancer patients}

The present invention relates to a method for predicting an immunotherapy effect in a cancer patient, and more particularly, to a method for predicting an anti-PD-1 immunoanticancer treatment effect.

Conventional cancer treatment has been a method of removing cancer cells from a patient as much as possible by surgery, radiation therapy, and chemotherapy. However, surgery and radiation therapy have a therapeutic effect when cancer cells are completely removed in a situation where metastasis to a relatively early cancer has not occurred. That is, even if cancer tissue is removed by surgery, the risk of recurrence is high when a small number of cancer cells have spread to other parts of the body. In addition, chemotherapy can be widely used for the treatment of cancer, but in most solid cancers, the treatment rate is poor, and it kills normal cells that divide rapidly. In order to improve these conventional problems, in recent years, immunotherapeutics using immuno-cancer agents have emerged.

Immune anti-cancer drugs are drugs that strengthen the body's own immune system and increase its resistance against cancer. There is a fundamental difference from the concept of looking at conventional cancer treatment in that it stops cancer with the power of the human body. Immune anti-cancer drugs complement the shortcomings of existing cancer treatments. If the first-generation chemotherapeutic agents attack cancer cells directly and the second-generation target anti-cancer drugs function to attack cancer-related genes, the immuno-cancer drugs called third-generation anticancer drugs enhance immunity. To treat cancer.

Anti-PD-1 antibody, the most representative immuno-cancer drug, is a therapeutic agent that prevents the binding of the protein PD-1 on the surface of activated T cells (immune cells) and the proteins PD-L1 and PD-L2 on the surface of cancer cells. . When PD-L1 and PD-L2 on the surface of cancer cells bind to PD-1, a protein on the surface of T cells, the function of T cells is reduced and cancer cells cannot be attacked. Therefore, the anti-PD-1 immune anti-cancer agent adheres to the PD-1 receptor of T cells and inhibits the binding of PD-L1, PD-L2 of cancer cells to PD-1 of T cells, thereby activating T cells against cancer cells. And inhibits the evasion function of cancer cells. Although Kitruda and Opdivo are released as anti-PD-1 immuno-cancer drugs, they are used in cancer treatment in the medical field, but there is no way to distinguish between the effective and ineffective patient groups. Therefore, in the case of the ineffective patient group, it is a situation that is losing the cost and time for the treatment of the anti-PD-1 immune anti-cancer agent.

Therefore, the present invention relates to a method for predicting an immunotherapy effect in a cancer patient, and according to the method of the present invention, it is possible to distinguish between an effective patient group and an ineffective patient group according to the method of the present invention in the medical field. It is expected to be used greatly.

The present invention has been devised to solve the problems of the prior art as described above, and relates to a method for predicting an immunotherapy effect in a cancer patient, and more specifically, a method for predicting an anti-PD-1 immunoanticancer treatment effect.

However, the technical problem to be achieved by the present invention is not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

Hereinafter, various embodiments described herein are described with reference to the drawings. In the following description, for the full understanding of the present invention, various specific details, such as specific forms, compositions and processes, have been described. However, certain embodiments may be practiced without one or more of these specific details, or in combination with other known methods and forms. In other instances, well-known processes and manufacturing techniques are not described in specific details in order not to unnecessarily obscure the present invention. Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, form, composition or characteristic described in connection with the embodiment is included in one or more embodiments of the invention. Thus, the context of “in one embodiment” or “an embodiment” expressed in various places throughout this specification does not necessarily represent the same embodiment of the invention. Additionally, special features, shapes, compositions, or properties can be combined in any suitable way in one or more embodiments.

Unless otherwise specified in the specification, all scientific and technical terms used herein have the same meaning as commonly understood by a person skilled in the art to which the present invention pertains.

In one embodiment of the present invention, "immune anti-cancer agent" refers to a therapeutic agent that helps to treat cancer by increasing the immunity of the patient instead of treatment or drugs that directly attack cancer cells, such as radiation and anti-cancer agents. It is a method that helps T cells (immune cells) destroy cancer cells by paralyzing the immune evasion function of cancer cells by mainly looking for immune checkpoint proteins (PD-1, PD-L1, CTLA-4). Since the body's immune system plays a role in activating the properties of attacking cancer cells, it can be applied to various cancers and can also reduce side effects such as indigestion, vomiting, leukopenia, and hair loss. Various antibodies that block the immune checkpoint protein are currently in clinical use or in clinical trials, and anti-PD-1 antibodies are opdivo (nivolumab), keytruda (pembrolizumab), MEDI0680, pidizilumab, and anti-PD-L1 antibodies are tecentriq (atezolizumab) , Imfinzi (duvuralumab), Bavencio (Avelumab), MDX-1105, and the anti-CTLA-4 antibody is Yervoy (ipilimumab) (Topalian et al., 2015 Cancer Cell 27: 450-461; Alsaab et al., 2017 Front Pharmacol 23: 561).

In one embodiment of the present invention, “PD-1”, also called CD279, is a receptor protein of 55 KD associated with the CD28 / CTLA4 co-stimulatory / inhibitory receptor family (Blank et al. , 2005 Cancer Immunol Immunother 54: 307-314). Genes in PD-1 and cDNA were cloned to examine characteristics in mice and humans (Ishida et al., 1992 EMBO J 11: 3887-3395; Shinohara et al., 1994 Genomics 23: 704-706) . The full-length PD-1 contains 288 amino acid residues (NCBI accession number: NP_005009). The extracellular domain consists of 1-167 amino acid residues, and the cytoplasmic C-terminal tail contains 191-288 residues, which are two hypothetical immuno-regulatory motifs, the immunoreceptor tyrosine-based inhibitory motif (ITIM; Vivier et al. , 1997 Immunol Today 18: 286-291) and the immunoreceptor tyrosine switch motif (ITSM; Chemnitz et al., 2004 J Immunol 173: 945-954). To date, two sequence-related ligands PD-L1 (B7-H1), and PD-L2 (B7-DC) specifically interact with PD-1 to induce intracellular signaling, CD3 and CD28 mediated T It has been shown to inhibit cell activation (Riley, 2009 Immunol Rev 229: 114-125), which eventually modulates T-cell activity, for example cell growth, IL-2 and IFN, as well as other growth factors and cytokine secretion It is to reduce -γ secretion.

Expression of PD-1 is frequently confirmed in immune cells such as T-cells, B-cells, monocytes and natural killer (NK) cells. It is rarely expressed in other human tissues, such as muscle, epithelium, and nerve tissues. In addition, high levels of PD-1 expression are often associated with the activity of immune cells. For example, when the human T-cell line Jurkat is activated by phytohaemagglutinin (PHA) or porbol ester (12-O-tetradecanoylphorbol-13-acetate or TPA), the expression of PD-1 is elevated as seen in Western blots. Was regulated (Vibharka et al., 1997 Exp Cell Res 232: 25-28). The same phenomenon was observed in stimulated mouse T- and B-lymphocytes and primary human CD4 ⁺ T cells by stimulation of anti-CD3 antibodies (Agata et al., 1996 Int Immunol 8: 765-772; Bennett et al ., 2003 J Immunol 170: 711-118). The effect T cells are stimulated by increased PD-1 expression, and the activated effect T cells are depleted and guided back to the reduced immune activity direction. Thus, PD-1 mediated inhibition signals play an important role in immune tolerance (Bour-Jordan et al., 2011 Immunol Rev 241: 180-205).

Increased PD-1 expression of tumor-infiltrating lymphocytes (TILs) in various cancers and PD-1 ligand expression in tumor cells has been reported, and other types of tissues and organs such as lungs (Konishi et al., 2004 Clin Cancer Res 10: 5094-5100), liver (Shi et al., 2008 Int J Cancer 128: 887-896; Gao et al., 2009 Clin Cancer Res 15: 971-979), stomach (Wu et al., 2006 Acta Histochem 108: 19-24), kidney (Thompson et al., 2004 Proc Natl Acad Sci 101: 17174-17179; Thompson et al., 2007 Clin Cancer Res 13: 1757-1761), breast (Ghebeh et al. , 2006 Neoplasia 8: 190-198), ovary (Hamanishi et al. 2007 Proc Natl Acad Sci 104: 3360-3365), pancreas (Nomi et al., 2007 Clin Cancer Res 13: 2151-2157), melanocytes (Hino et al., 2010 Cancer 116: 1757-1766), and the esophagus (Ohigashi et al., 2005 Clin Cancer Res 11: 2947-2953). More frequently, the expression of PD-1 and PD-L1 in these cancers is associated with poor prognosis for patient survival outcomes. The importance of PD-1 signaling in immune system regulation for cancer elimination or tolerance was described in more detail through transgenic mice that knocked out the PD-1 gene and inhibited Xenograft cancer cell growth ( Zhang et al., 2009 Blood 114: 1545-1552).

Up-regulation of PD-1 signaling not only leads to cancer proliferation of immune tolerance, but also to human viral infection and expansion. Epidemic liver infection viruses HBV and HCV induce overexpression of PD-1 ligand in hepatocytes and activate PD-1 signaling in effect T cells, causing T-cell depletion and tolerance for viral infections (Boni et al. , 2007 J Virol 81: 4215-4225; Golden-Mason et al., 2008 J Immunol 180: 3637-3641). Likewise, HIV infection frequently evades the human immune system with a similar mechanism. PD-1 signaling can be modulated therapeutically by antagonist molecules to recover immune cells from tolerance and re-activated to eliminate cancer and chronic viral infections (Blank et al., 2005 Cancer Immunol Immunother 54: 307-314; Okazaki et al., 2007 Int Immunol 19: 813-824).

In one embodiment of the present invention, (a) measuring the T cell number of the subject; (b) administering an anti-cancer agent to the subject; (c) primary re-measurement of the number of T cells in the subject; (d) confirming that the measured value in step (c) is increased than the measured value in step (a); and provides a method for predicting the therapeutic effect of an immuno-cancer drug in a cancer patient, the method comprising: (e) a second re-measurement of the T cell number of the subject; provides a method further comprising, the step (c) is characterized in that it is carried out from 1 to 14 days from step (b) It provides a method, wherein the method (f) (c) step of confirming that the measured value in step (e) decreases than the measured value; provides a method further comprising, Step) provides a method characterized in that it is performed on the 15th to 21st days from step (b).

In addition, the T cell number provides a method for predicting the therapeutic effect of an immunoanticancer agent in a cancer patient, characterized in that the T cell number is measured in the T cell number, T cell activation secretion, and T cell activation degree. The number of T cells provides a method characterized by measuring the Ki-67 expression value, and the expression value provides a method that is a gene expression value or a protein expression value, and the measured value in step (c) is (a). ) If the 2.8 times or more than the measured value of the step, provides a method comprising the step of predicting that the therapeutic effect of the immune anti-cancer agent of the subject is high, the measured value of step (c) is the measured value of step (a) If it is more than 2.8 times, provides a method comprising the step of predicting that the prognosis of the subject is good, if the measured value of step (c) is less than 2.8 times the measured value of step (a), the Immunity of the subject Providing a method comprising the step of predicting that the cancer drug treatment effect is low, if the measured value of step (c) is less than 2.8 times the measured value of step (a), predicting that the prognosis of the subject is poor Provides a method comprising steps.

In addition, the cancer is breast cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colon cancer, pancreatic cancer, stomach cancer, gallbladder cancer, ovarian cancer, lymphoma, osteosarcoma, uterine cancer, Method for predicting the therapeutic effect of immunocancer drug therapy in patients with at least one cancer selected from the group consisting of oral cancer, bronchial cancer, nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, thyroid cancer, parathyroid cancer, ureteral cancer, adenocarcinoma, and thymic cancer And the cancer is lung cancer or thymic cancer.

In addition, the immunoanti-cancer agent provides an anti-PD-1 antibody, an anti-PD-L1 antibody, or an anti-CTLA-4 antibody.

In another embodiment of the present invention, (a) measuring the T cell number of the subject; (b) administering a candidate substance to the subject; (c) primary re-measurement of the number of T cells in the subject; (d) confirming that the measured value in step (c) is higher than the measured value in step (a); and providing a method for screening the cancer treatment effect of an immunocancer drug candidate in a cancer patient comprising a , The step (c) provides a method characterized in that it is performed from 1 to 14 days from step (b), and the T cell number provides a method characterized by measuring with Ki-67 expression value, , When the measured value of step (c) is 2.8 times or more than the measured value of step (a), it provides a method comprising determining the candidate candidate for the anti-cancer agent as a cancer treatment substance, wherein the cancer is Breast cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colon cancer, pancreatic cancer, stomach cancer, gallbladder cancer, ovarian cancer, lymphoma, osteosarcoma, uterine cancer, oral cancer, bronchial cancer, Nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, Service sangseonam, part thyroid cancer, ureteral cancer, any one or more methods selected from the group consisting of cancer, and thymic cancer, and the cancer is lung cancer or thymus cancer provides methods.

Hereinafter, the present invention will be described in detail step by step.

The present invention relates to a method for predicting an immunotherapy effect in a cancer patient, and according to the method of the present invention, it is possible to distinguish between a patient group in which a PD-1 immune anticancer agent is effective and an ineffective patient group, so it is widely used in the medical field. It is expected to be.

1 is a diagram showing a time diagram of drug administration and blood collection in an anti-PD-1 treated thyroid epithelial tumor patient, according to an embodiment of the present invention.
FIG. 2 is a diagram showing a gate strategy of flow cytometry analysis used in the present invention according to an embodiment of the present invention.
3 is a view showing the effect of anti-PD-1 treatment based on antigen specificity of T cells in an thymic epithelial tumor patient treated with anti-PD-1, according to an embodiment of the present invention.
Figure 4, in accordance with an embodiment of the present invention, after the anti-PD-1 treatment in patients with anti-PD-1 treated thymic epithelial tumor, PD-1 ⁺ CD8 ⁺ Ki-67 ⁺ and HLA-DR ⁺ CD38 of T cells ⁺ Diagram showing frequency change.
5 is a view showing the predictive power of response response of 135 parameters after anti-PD-1 treatment in an anti-PD-1 treated thyroid epithelial tumor patient, according to an embodiment of the present invention.
6 is a change of Ki-67 expression in PD-1 ⁺ CD8 ⁺ T cells according to a treatment response of a patient in an anti-PD-1 treated thyroid epithelial tumor patient according to an embodiment of the present invention (Ki-67 _{D7 / D0} ).
7 is a disease control rate and disease-free survival rate of patients with a Ki-67 _{D7 / D0} cutoff value based on 2.8 in an anti-PD-1 treated thyroid epithelial tumor patient, according to an embodiment of the present invention. It is a diagram showing.
8 is a view showing a time diagram of drug administration and blood collection in a non-small cell lung cancer patient treated with anti-PD-1 according to an embodiment of the present invention, and experimental results.
9 is PD-1 ⁺ CD8 ⁺ T cells after anti-PD-1 treatment, 1 week after treatment, and 3 weeks after treatment in a non-small cell lung cancer patient treated with anti-PD-1 according to an embodiment of the present invention. It is a diagram showing the frequency change of Ki-67 ⁺ in.
10 is a disease control rate, disease-free survival rate of patients with a Ki-67 _{D7 / D0} cutoff value based on 2.8 in an anti-PD-1 treated non-small cell lung cancer patient, according to an embodiment of the present invention. And overall survival.

Hereinafter, the present invention will be described in more detail through examples. These examples are only intended to illustrate the present invention in more detail, and it will be apparent to those skilled in the art that the scope of the present invention is not limited by these examples according to the gist of the present invention. .

Example

Patient and sample collection

From March 2016 to June 2016, at least one platinum-based anticancer drug used in at least one of the patients enrolled in the Phase 2 clinical trial (NCT02607631) evaluating the effect by administering pembrolizumab (200 mg / 3 weeks) Of 31 patients with thymic epithelial tumor (TET) who received chemotherapy, 31 patients who agreed to collect blood according to the Institutional Review Boards before and after the first administration of Pembrolizumab (Day 0) Peripheral blood was collected 7 days later (Day 7). The time diagram of the drug administration and blood collection is shown in FIG. 1. In the same manner as described above, stage 4 non-small cell lung cancer received pembrolizumab (200 mg / 3 weeks) or nivolumab (2 mg / Kg / 2 weeks) from April 2016 to April 2017 Blood was also collected from 29 patients with non-small cell lung cancer (NSCLC). Their data were used to validate predictive markers found in the TET cohort. Blood collection was performed on days 0, 7, and 21 in some patients to monitor changes in the immune response.

Peripheral blood mononuclear cells (PBMCs) were isolated from the collected blood by Ficoll-Paque (GE Healthcare, Uppsala, Sweden) density gradient centrifugation and cryopreserved until use in the experiment.

Tumor response was evaluated every 9 weeks by computed tomography or magnetic resonance imaging according to the response evaluation criteria of solid tumors (RECIST, version 1.1.). The objective response was divided into a complete response or a partial response, and the disease control control was divided into a partial or stable cancer patient group that lasted more than 6 months.

Flow cytometry

Table 1 shows the types of fluorescent conjugated antibodies used for multi-color flow cytometry.

Antibody name Remark anti-CD8 (SK1 and RPA-T8), BD Biosciences, San Jose, CA anti-CD3 (UCTH1 or SK7), BD Biosciences, San Jose, CA anti-CD45RA (HI100), BD Biosciences, San Jose, CA anti-CD4 (SK3), BD Biosciences, San Jose, CA anti-ICOS (DX29), BD Biosciences, San Jose, CA anti-CD25 (M-A251), BD Biosciences, San Jose, CA anti-CD28 (CD28.2), BD Biosciences, San Jose, CA anti-Granzyme B (GB11), BD Biosciences, San Jose, CA anti-PD-1 (EH.12.2H7), Biolegend, San Diego, CA anti-Ki-67 (Ki-67), Biolegend, San Diego, CA anti-CD38 (HB7), Biolegend, San Diego, CA anti-CTLA-4 (L3D10), Biolegend, San Diego, CA anti-CD127 (A019D5), Biolegend, San Diego, CA anti-GITR (108-17), Biolegend, San Diego, CA anti-TIGIT (MBSA43), eBioscience, San Diego, CA anti-HLA-DR (LN3), eBioscience, San Diego, CA anti-CCR4 (D8SEE), eBioscience, San Diego, CA amti-FoxP3 (PCH101), eBioscience, San Diego, CA anti-CD14 (61D3), eBioscience, San Diego, CA anti-CD19 (HIB19), eBioscience, San Diego, CA anti-CD57 (TBO1), eBioscience, San Diego, CA anti-CCR7 (FAB197F), R & D Systems anti-human IgG4 Fc (HP6025), Southern Biotech

Since binding of pembrolizumab or nivolumab (human IgG4) drug to PD-1 expressing cells interferes with PD-1 staining in samples after treatment, anti-human IgG4 Fc staining is performed together with anti-PD-1 staining Did. For live / dead cell identification, red-fluorescent reactive dyes (Invitrogen, Carlsbad, CA) were used, and intracellular staining for Ki-67, granzyme B, FoxP3, and CTLA-4 stained FoxP3 transcription factor A buffer kit (eBioscience) was used. CD8 T cells specific for tumor antigens were detected using PE-conjugated MHC I dextramer NY-ESO-1 _157-165 (SLLMWITQV / HLA-A * 0201, Immudex, Copenhagen, Denmark) and CD8 specific for HCMV T cells were detected using PE-conjugated MHC I pentamer HCMV pp65 _495-504 (NLVPMVATV / HLA-A * 0201, Proimmune, Oxford, UK). All flow cytometry was performed with LSR II flow cytometry (BD Biosciences) and data analysis with FlowJo software (Treestar, San Carlos, CA). The gate strategies of the flow cytometry analysis used in the present invention are shown in FIG. 2. Specifically, (A) shows the gating strategy of PD-1 ⁺ CD8 T cells, and (B) multimer-positive CD8 T cells.

Statistics processing

The change in the expression level of the marker was analyzed by converting the positive cell frequency of the 7-day sample based on the frequency of the 0-day sample. Categorical variables were compared using the chi-square test, or Fisher's exact test. Paired with unpaired values using Student's t-test and paired t-test for normal distribution continuous variables, and Mann-Whitney U-test and Wilcoxon signed-rank test for non-normal distribution The values were compared respectively. One-way analysis of variance (ANOVA) analysis was used to compare two or more groups, and the Kruskal-Wallis test was used when data were abnormally distributed. The correlation between the two parameters was evaluated using the Pearson correlation coefficient. To evaluate the effectiveness of the biomarker, the area under curve (AUC) was evaluated from the receiver operating characteristic curve (ROC), and the optimal cutoff point was extracted at the point where the Youden index was maximized. Survival curves were generated using the Kaplan-Meier method, comparisons were performed with a log-rank test, and age, gender, histological data, and previous chemotherapy were used to evaluate the independent significance of the biomarkers found in relation to survival rates. A multivariate cox regression analysis was performed that adjusted the history and number of metastatic sites. A double-sided P value of less than 0.05 was considered statistically significant in all statistical analyzes, Prism software version 6.0 (GraphPad, La Jolla, CA), and R statistical software (version 3.2.2, The R Foundation for Statistical Computing, Vienna, Austria).

Analysis of patient's clinical characteristics

Table 2 and Table 3 show the clinical pathological characteristics of TET patients and the clinical pathological characteristics of NSCLC patients, respectively.

Characteristic Discovery cohort (n = 31) Age, median (range), yrs 58 (26-78) Sex, n (%)    Male 20 (64.5)    Female 11 (35.5) Histology, n (%)    Thymoma 6 (19.4)    Thymic carcinoma 25 (80.6) Prior chemotherapy line, median (range) 2 (1-5) Tumor burden, median (range), cm 12.4 (1.7-27.0) Study drug, n (%)    Pembrolizumab 31 (100.0)    Nivolumab 0 (0) Best overall response, n (%)    Complete response 0 (0)    Partial response 6 (19.4)    Stable disease 18 (58.1)    Progressive disease 7 (22.6)

Characteristic Validation cohort (n = 29) Age, median (range), yrs 65 (35-82) Sex, n (%)    Male 21 (72.4)    Female 8 (27.6) Histology, n (%)    Adenocarcinoma 15 (51.7)    Squamous cell carcinoma 10 (34.5)    Others 4 (13.8) Prior chemotherapy, median (range) 3 (1-9) Tumor burden, median (range), cm 7.5 (2.0-15.7) Study drug, n (%)    Pembrolizumab 13 (44.8)    Nivolumab 16 (55.2) Best overall response, n (%)    Complete response One (3.4)    Partial response 7 (24.2)    Stable disease 8 (27.6)    Progressive disease 13 (44.8)

TET patients were treated with pembrolizumab median 8 cycles (range 1-22 cycles). Six patients had an objective tumor response, and the disease control group consisted of 11 patients. In the validation cohort of NSCLC patients, 13 patients received pembrolizumab and 16 patients received nivolumab. Eight patients showed an objective tumor response, and 10 patients showed a disease control control. The median of the Nivolumab or Pembrolizumab administration cycle was 4 cycles (1-32 cycles).

TET  Peripheral CD8 after anti-PD-1 treatment in patients T cell  Check reaction

The degree of T cell activation (CD38, HLA-DR) and proliferation (Ki-67) of CD8 T cells in peripheral blood collected on day 0 and 7 of drug administration was confirmed by phenotypic markers. Prior results obtained from six non-small cell lung cancer patients showed an increase in Ki-67 ⁺ frequency in PD-1 ⁺ CD8 ⁺ T cells on the 7th day, but it was confirmed to decrease significantly on the 21st day. . Therefore, samples were analyzed before and 7 days after treatment. First, the effect of anti-PD-1 treatment based on antigen specificity is shown in FIG. 3. Patients with up-regulated NY-ESO-1 mRNA and patients with potential HCMV infection were evaluated. As a result of the evaluation, on the 7th day of drug administration, tumor-specific NY-ESO-1 ⁺ CD8 ⁺ T cells showed a significant increase in Ki-67 ⁺ and HLA-DR ⁺ CD38 ⁺ frequency, but HCMV-specific pp65 ⁺ without tumor specificity CD8 ⁺ T cells showed minimal increase. The results are shown in FIG. 3. Ki-67 ⁺ and HLA-DR ⁺ CD38 ⁺ increase in frequency was observed in PD-1 ⁺ CD8 ⁺ T cells, and the results are shown in FIG. 4. From the above results, it was found that anti-PD-1 treatment activates PD-1 ⁺ CD8 ⁺ T cells, and this phenomenon increases and decreases over time after drug administration.

PD-1 ⁺ CD8 ⁺ T cell  With initial proliferative TET  The tumor response and prognosis for anti-PD-1 treatment in patients is predictable.

135 derived from multicolor flow cytometry analysis, including the frequency and phenotypic markers of PD-1 ⁺ CD8 ⁺ T cells, CD8 ⁺ T cells, and CD4 ⁺ T cells, in order to discover biomarkers predictable treatment response after drug administration The dog parameters were analyzed in integrated. Table 4 shows the detailed analysis values.

0일과 7일째의 측정값뿐만 아니라, anti-PD-1 치료 이후에 디자인된 파라미터들의 변화까지도 평가하였다. 6개월 이상 질병통제된 종양 환자(n=11)와 6개월 미만으로 질병통제된 종양 환자(n=20)를 비교하였고, 화산 도표(Volcano plot)는 반응자와 비반응자 사이의 log2 비율로 나타내었다. x축은 135개 파라미터의 log2 비율, y축은 p값(Mann-Whitney U-test)을 나타낸다. 상기 결과를 도 5와 표 4에 나타내었다.

The measured values on day 0 and 7, as well as changes in parameters designed after anti-PD-1 treatment were evaluated. Patients with disease control over 6 months (n = 11) and those with disease control under 6 months (n = 20) were compared, and the volcano plot was expressed as the ratio of log2 between responders and non-responders. . The x-axis represents the log2 ratio of 135 parameters, and the y-axis represents the p-value (Mann-Whitney U-test). The results are shown in Figure 5 and Table 4.

delete

As a result of analysis, only a small number of parameters showed a difference between patients with or without disease control. The change in Ki-67 expression (Ki-67 _{D7 / D0} ) in PD-1 ⁺ CD8 ⁺ T cells was the largest among the parameters. There was (P <0.05). In addition, Ki-67 _{D7 / D0} showed the highest predictive accuracy in the disease control control group (AUC = 0.86, 95% CI 0.71-1.00, P = 0.001), especially stable disease (SD), or progressive disease state Compared with (progressive disease; PD), the patient group with partial response (PR) showed higher measurement value. The results are shown in FIG. 6.

2.8 was determined as the optimal cutoff value from the ROC curve, and the patient was divided according to the cutoff value. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) in the disease control control were 90.9%, 75.0%, 93.8%, and 66.7%, respectively. The high sensitivity and negative predictability means that Ki-67 _{D7 / D0} can accurately predict patients who are not expected to benefit from anti-PD-1 treatment. Patients with Ki-67 _{D7 / D0} cutoff values less than 2.8 were found to have low durable disease control rates. The patients also had very low progression-free survival (PFS). The results are shown in FIG. 7. In addition, in multivariate analysis adjusted to reflect various clinical and pathological factors, it was confirmed that Ki-67 _{D7 / D0} is an independently significant factor of disease-free survival (adjusted hazard ratio; aHR 0.20, 95% confidence interval; CI 0.07 -0.56, P = 0.002). It is shown in Table 5 below.

Univariate Multivariate Variable Category HR 95% CI P aHR 95% CI P Ki - 67 _{D7 / D0} <2.8 1.00 0.030 1.00 0.002 ≥2.8 0.40 0.17-0.91 0.20 0.07-0.56 Age <60 yrs 1.00 0.457 1.00 0.028 ≥60 yrs 1.34 0.62-2.92 2.95 1.12-7.78 Sex Male 1.00 0.086 1.00 0.011 Female 2.03 0.90-4.56 3.30 1.31-8.28 Histology Thymoma 1.00 0.986 1.00 0.607 Thymic ca. 0.99 0.37-2.65 1.32 0.46-3.73 Prior chemotherapy <3 ^rd line 1.00 0.098 1.00 0.876 ≥3 ^rd line 2.00 0.88-4.54 0.93 0.40-2.20 Tumor burden <12.5 cm 1.00 0.926 1.00 0.118 ≥12.5 cm 1.04 0.48-2.25 2.13 0.83-5.47

PD-1 after anti-PD-1 treatment ⁺  CD8 In T cells Ki -67 changes in expression Non-small cell lung cancer  It is related to the patient's tumor response and prognosis.

To verify the value of Ki-67 _{D7 / D0} as a predictive biomarker, an independent validation cohort of anti-PD-1 treated non-small cell lung cancer patients was used. The schematic diagram and results are shown in FIG. 8. Experimental results, TET analogy to patients, PD-1 ^- CD8 ⁺ in comparison with the T cell was the Ki-67 expression for a larger increase in PD-1 ⁺ CD8 ⁺ T cells, Ki-67 _{D7 / D0} is a progressive disease states ( Patients with partial response (PR) showed higher measurements than patients with progressive disease (PD). In this cohort investigation, the sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) of the disease control control group without cancer progression based on the cutoff value of 2.8 were 80.0%, 73.7%, and 87.5%, respectively. And 61.5%. Specifically, in patients with Ki-67 _{D7 / D0} ≥2.8, the disease control rate was significantly lower, and disease-free survival (PFS) and overall survival (OS) were significantly higher. The results are shown in FIG. 10. In addition, Ki-67 _{D7 / D0} is disease-free survival rate (aHR 0.14, 95% CI 0.04-0.46, P = 0.001), and overall survival rate (aHR 0.14, 95) in multivariate analysis adjusted to reflect various clinical and pathological factors. % CI 0.03-0.63, P = 0.010) was confirmed independent correlation (see Table 6, Table 7).

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From the above examples and results, a negative predictive value (NPV) senior patient group expected to have no therapeutic effect on anti-PD-1 from the initial proliferative response of PD-1 ⁺ CD8 ⁺ T cells after anti-PD-1 treatment was obtained. It was found that it can be predicted and selected. This is expected to be greatly used in the medical field, since it is possible to prevent the economic and temporal loss that a patient group in which a PD-1 immunocancer agent is effective undergoes while undergoing PD-1 immunocancer drug treatment in advance.

Claims (21)

(a) measuring the number of T cells in the subject;
(b) administering an anti-cancer agent to the subject;
(c) primary re-measurement of the number of T cells in the subject; And
(d) confirming that the measured value in step (c) is increased than the measured value in step (a).
The step (c) is characterized in that it is performed on the 1st to 14th days from the step (b),
The method of claim 1, wherein the criterion of the measured value in step (c) for predicting the treatment effect is “2.8 times the measured value in step (a)”.
According to claim 1,
The method further comprises (e) a second re-measurement of the T cell number of the subject; further comprising
delete According to claim 2,
The method further comprises (f) confirming that the measured value in step (e) decreases from the measured value in step (c).
According to claim 2,
The step (e) is characterized in that it is carried out on the 15th to 21st days from the step (b).
According to claim 1,
The T cell number is characterized by measuring the T cell number in the T cell number, T cell activation secretion, and T cell activation.
The method of claim 6,
The T cell number is characterized in that measured by the expression value of Ki-67, method.
The method of claim 7,
The expression value is a gene expression value, or a protein expression value.
According to claim 1,
And when the measured value of step (c) is 2.8 times or more than the measured value of step (a), predicting that the therapeutic effect of the immune anti-cancer agent of the subject is high.
According to claim 1,
And when the measured value in step (c) is 2.8 times or more than the measured value in step (a), predicting that the prognosis of the subject is good.
According to claim 1,
And when the measured value of step (c) is less than 2.8 times the measured value of step (a), predicting that the therapeutic effect of the immune anti-cancer agent of the subject is low.
According to claim 1,
And when the measured value of step (c) is less than 2.8 times the measured value of step (a), predicting that the prognosis of the subject is poor.
According to claim 1,
The cancer is breast cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colon cancer, pancreatic cancer, stomach cancer, gallbladder cancer, ovarian cancer, lymphoma, osteosarcoma, uterine cancer, oral cancer, The method of any one or more selected from the group consisting of bronchial cancer, nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, thyroid cancer, parathyroid cancer, ureteral cancer, adenocarcinoma, and thymic cancer.
The method of claim 13,
The method is lung cancer or thymic cancer.
According to claim 1,
The immunoanticancer agent is an anti-PD-1 antibody, an anti-PD-L1 antibody, or an anti-CTLA-4 antibody.
(a) measuring the number of T cells in the subject;
(b) administering a candidate anti-cancer drug to the subject;
(c) primary re-measurement of the number of T cells in the subject; And
(d) confirming that the measured value in step (c) is increased than the measured value in step (a); comprising a method for screening the cancer treatment effect of an immunocancer drug candidate in a cancer patient,
The step (c) is characterized in that it is performed on the 1st to 14th days from the step (b),
The method of claim 1, wherein the criterion of the measured value in step (c) for predicting the treatment effect is “2.8 times the measured value in step (a)”.
delete The method of claim 16,
The T cell number is characterized in that measured by the expression value of Ki-67, method.
The method of claim 16,
And when the measured value in step (c) is 2.8 times or more than the measured value in step (a), determining the candidate candidate for the immunocancer agent as an effective immunocancer agent.
The method of claim 16,
The cancer is breast cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colon cancer, pancreatic cancer, stomach cancer, gallbladder cancer, ovarian cancer, lymphoma, osteosarcoma, uterine cancer, oral cancer, The method of any one or more selected from the group consisting of bronchial cancer, nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, thyroid cancer, parathyroid cancer, ureteral cancer, adenocarcinoma, and thymic cancer.
The method of claim 20,
The method is lung cancer or thymic cancer.

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