WO2019151712A1 - Method for predicting effect of immunotherapy on cancer patient - Google Patents
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- WO2019151712A1 WO2019151712A1 PCT/KR2019/001037 KR2019001037W WO2019151712A1 WO 2019151712 A1 WO2019151712 A1 WO 2019151712A1 KR 2019001037 W KR2019001037 W KR 2019001037W WO 2019151712 A1 WO2019151712 A1 WO 2019151712A1
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Definitions
- the present invention relates to a method for predicting the effects of immunotherapy in cancer patients, and more particularly, to a method for predicting the effects of anti-PD-1 immune chemotherapy.
- Immune anticancer drugs mean drugs that enhance the body's own immune system to fight against cancer. There is a fundamental difference from the concept of looking at conventional cancer treatment in that it inhibits cancer by the inherent power of the human body. Immune anticancer drugs make up for the shortcomings of conventional cancer therapies. If a first-generation chemotherapy agent directly attacks cancer cells, and a second-generation target anticancer agent attacks cancer-related genes, then an anti-cancer drug called third-generation anticancer drugs enhances immunity. It will cure cancer.
- Anti-PD-1 antibody is a therapeutic agent that blocks the binding of PD-1, a protein on the surface of activated T cells (immune cells) and PD-L1, PD-L2, proteins on the surface of cancer cells .
- 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 the cancer cells cannot be attacked. Therefore, anti-PD-1 immunocancer agent adheres to PD-1 receptor of T cells and inhibits the binding of PD-L1, PD-L2 of cancer cells and PD-1 of T cells, thereby inhibiting T cell activity on cancer cells. And inhibit the avoidance of cancer cells.
- Kitruda and Obdibo have been released as anti-PD-1 anticancer drugs, they are being used in cancer treatment in the medical field.However, there is no way to distinguish between the patients who are effective and the ineffective patients. Thus, the ineffective patient group is losing the cost and time for anti-PD-1 immunotherapy treatment.
- the present invention relates to a method for predicting the effect of immunotherapy in cancer patients, and according to the method of the present invention, it is possible to distinguish between a patient group having an anti-PD-1 immunocancer drug and an ineffective patient group, It is expected to be used greatly.
- immune anticancer agent refers to a therapeutic agent that helps to cure cancer by increasing the immunity of a patient instead of a drug or a drug that directly attacks cancer cells such as radiation or an anticancer agent.
- T cells immune cells
- the immune system in the body plays a role in activating the attack on cancer cells, it can be applied to a variety of cancers and can also reduce side effects such as indigestion, vomiting, leukopenia, and hair loss.
- Various antibodies that block immune checkpoint proteins are currently being used or are being tested.
- Anti-PD-1 antibodies include 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 has Yervoy (ipilimumab) (Topalian et al., 2015 Cancer Cell 27: 450-461; Alsaab et al., 2017 Front Pharmacol 23: 561).
- PD-1 means PD-1 (also referred to as CD279) is a 55 KD receptor protein associated with the CD28 / CTLA4 co-stimulatory / inhibitory receptor family.
- CD279 is a 55 KD receptor protein associated with the CD28 / CTLA4 co-stimulatory / inhibitory receptor family.
- the extracellular domain consists of 1-167 amino acid residues and the cytoplasmic C-terminal tail comprises 191-288 residues, which are two hypothetical immune-modulating motifs, an immunoreceptor tyrosine based inhibitory motif (ITIM; Vivier et al. , 1997 Immunol Today 18: 286-291) and immunoreceptor tyrosine switch motifs (ITSM; Chemnitz et al., 2004 J Immunol 173: 945-954).
- ITIM immunoreceptor tyrosine based inhibitory motif
- ITSM immunoreceptor tyrosine switch motifs
- PD-1 PD-1 protein-binding protein-1
- T-cells T-cells
- B-cells B-cells
- monocytes NK cells
- PD-1 expression is often associated with the activity of immune cells.
- PHA phytohaemagglutinin
- phorbol ester (12-O-tetradecanoylphorbol-13-acetate or TPA)
- TPA phytohaemagglutinin
- TPA phorbol ester
- the expression of PD-1 is upward as shown in Western blot. Regulated (Vibharka et al., 1997 Exp Cell Res 232: 25-28).
- TILs tumor-infiltrating lymphocytes
- PD-1 ligand expression of tumor cells have been reported, and other types of tissues and organs such as lung (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), kidneys (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.
- TILs tumor-infiltrating lymphocytes
- Upregulation of PD-1 signaling leads to cancer proliferation of immune tolerance, as well as to viral infection and expansion in humans.
- Pandemic 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 to viral infection (Boni et al. , 2007 J Virol 81: 4215-4225; Golden-Mason et al., 2008 J Immunol 180: 3637-3641).
- HIV infection frequently evades the human immune system with similar mechanisms.
- Therapeutic regulation of PD-1 signaling by antagonistic molecules can restore immune cells from tolerance and can be reactivated 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).
- the T cell number provides a method for predicting the effects of immuno-cancer drug treatment in cancer patients, characterized in that measuring the T cell number in T cell number, T cell activation secretion, and T cell activation degree, It provides a method characterized in that the T cell number is measured by Ki-67 expression value, the expression value is a gene expression value, or a protein expression value, the measured value of step (c) is (a And 2.8 times or more than the measured value of step), the method comprising the step of predicting that the anti-cancer drug treatment effect of the subject is high, the measured value of step (c) is the measured value of step (a) In the case of more than 2.8 times, it provides a method comprising the step of predicting that the prognosis of the subject is good, when the measured value of step (c) is less than 2.8 times the measured value of step (a), Immunity of Subject A method comprising the step of predicting that the cancer drug treatment effect is low, and when the measured value of step (c) is less than 2.8 times
- the cancer may be breast cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colon cancer, pancreatic cancer, gastric cancer, gallbladder cancer, ovarian cancer, lymphoma, osteosarcoma, uterine cancer,
- a method for predicting the effects of an anticancer drug in cancer patients of at least one selected from the group consisting of oral cancer, bronchial cancer, nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, thyroid cancer, parathyroid cancer, ureter cancer, adenocarcinoma, and thymic cancer To provide a method, wherein the cancer is lung cancer or thymic cancer.
- the immuno-cancer agent provides an anti-PD-1 antibody, an anti-PD-L1 antibody, or an anti-CTLA-4 antibody.
- step (c) provides a method characterized in that performed in 1 day to 14 days from step (b), the T cell number provides a method characterized in that measured by Ki-67 expression value
- the method comprising the step of determining the immune anticancer drug candidates for cancer treatment, 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,
- FIG. 1 is a diagram showing a time diagram of drug administration and blood collection in an anti-PD-1 treated thymic epithelial tumor patient, according to an embodiment of the present invention.
- 3A and 3B show the effects of anti-PD-1 treatment based on antigen specificity of T cells in an anti-PD-1 treated thymic epithelial tumor patient, according to an embodiment of the invention.
- FIG. 5 is a diagram showing the predictive response of treatment response of 135 parameters after anti-PD-1 treatment in an anti-PD-1 treated thymic epithelial tumor patient according to an embodiment of the present invention.
- Figure 6 is a change in Ki-67 expression in PD-1 + CD8 + T cells according to the treatment response of patients in anti-PD-1 treated thymic epithelial tumors according to an embodiment of the present invention (Ki-67 D7 / D0 ).
- FIG. 7 shows disease control rate and disease progression-free survival rate of patients whose Ki-67 D7 / D0 cutoff values are divided based on 2.8 in anti-PD-1 treated thymic epithelial tumor patients according to an embodiment of the present invention. Is a diagram showing.
- FIG. 9 shows PD-1 + CD8 + T cells before, after 1 week and after 3 weeks of anti-PD-1 treatment in anti-PD-1 treated non-small cell lung cancer patients according to an embodiment of the present invention. It is a figure which shows the frequency change of Ki-67 + .
- Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) in the disease control control group were 90.9%, 75.0%, 93.8%, and 66.7%, respectively.
- High sensitivity and negative predictiveness mean that Ki-67 D7 / D0 can accurately predict patients who are not expected to benefit from anti-PD-1 treatment.
- Patients with a Ki-67 D7 / D0 cutoff value of less than 2.8 were found to have low disease control rates. These patients also had very low disease progression-free survival (PFS).
- stage 4 non-small cell lung cancer who received pembrolizumab (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 verify the predictive markers found in the TET cohort. In some patients blood collection was performed on days 0, 7, and 21 to monitor changes in the immune response.
- PBMCs Peripheral blood mononuclear cells
- Tumor response was assessed every nine weeks by computed tomography or magnetic resonance imaging according to the solid tumor response evaluation criteria (RECIST, version 1.1.). Objective responses were categorized as complete or partial response, and Durable disease control was divided into partial or stable cancer patients lasting more than 6 months.
- pembrolizumab or nivolumab human IgG4 agents to PD-1 expressing cells interferes with PD-1 staining in the sample after treatment
- anti-human IgG4 Fc staining is performed with anti-PD-1 staining. It was. Live / dead cell identification was performed using a red-fluorescent reactive dye (Invitrogen, Carlsbad, Calif.), And intracellular staining for Ki-67, granzyme B, FoxP3, and CTLA-4 was performed using FoxP3 transcription factor staining. Buffer kit (eBioscience) was used.
- Tumor antigen specific CD8 T cells were detected using PE-conjugated MHC I dextramer NY-ESO-1 157-165 (SLLMWITQV / HLA-A * 0201, Immudex, Copenhagen, Denmark) and CD8 T specific for HCMV 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 cytometer (BD Biosciences) and data analyzed with FlowJo software (Treestar, San Carlos, CA). Gate strategies of flow cytometry used in the present invention are shown in FIG. 2. Specifically, (a) shows the gating strategy of PD-1 + CD8 T cells, (b) multimer-positive CD8 T cells.
- 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 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 nonnormal 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 the data were abnormally distributed. Correlation between the two parameters was evaluated using the Pearson correlation coefficient.
- AUC area under curve
- ROC receiver operating characteristic curve
- TET patients underwent treatment with Pembrolizumab median 8 ranges (cycles 1-22). Six patients had an objective tumor response and the disease control control group consisted of 11 patients. Thirteen patients received pembrolizumab and 16 patients received nivolumab in a validation cohort of NSCLC patients. Eight of them presented an objective tumor response, and ten patients represented disease control controls. The median nivolumab or pembrolizumab administration cycle was 4 cycles (1-32 cycles).
- tumor-specific NY-ESO-1 + CD8 + T cells showed a significant increase in the frequency of Ki-67 + and HLA-DR + CD38 + but without HCMV-specific pp65 + CD8 + T cells showed minimal increase (FIG. 3B).
- FIG. 3B The results are shown in FIG. An increase in the frequency of Ki-67 + and HLA-DR + CD38 + was also observed in PD-1 + CD8 + T cells, and the results are shown in FIG. 4.
- the results indicate that anti-PD-1 treatment activates PD-1 + CD8 + T cells, and this phenomenon increases and decreases over time after drug administration.
- 135 derived from multicolor flow cytometry, including frequency and phenotypic markers of PD-1 + CD8 + T cells, CD8 + T cells, and CD4 + T cells. Dog parameters were analyzed together. The detailed analysis values are shown in Tables 4 and 5.
- Ki-67 D7 / D0 is a progressive disease states ( The measurements were higher in the group of patients with partial response (PR) than those with progressive disease (PD).
- Immune anticancer drugs mean drugs that enhance the body's own immune system to fight against cancer.
- Anti-PD-1 antibody the most representative immune anticancer agent, is a therapeutic agent that blocks the binding of PD-1, a protein on the surface of activated T cells (immune cells) and PD-L1, PD-L2, proteins on the surface of cancer cells .
- PD-1 a protein on the surface of activated T cells
- PD-L1, PD-L2 proteins on the surface of cancer cells
- the present invention relates to a method for predicting the effects of immunotherapy in cancer patients, and according to the method of the present invention, it is possible to distinguish between a patient group in which an anti-PD-1 immunocancer drug is effective and an ineffective patient group. It is expected to be used.
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Abstract
The present invention relates to a method for predicting the effect of immunotherapy on cancer patients and, more specifically, to a method for predicting the therapeutic effect of anti-PD-1 immunotherapy agents. Although Keytruda and Opdivo, as the anti-PD-1 immunotherapy agents, which are the most prominent cancer immunotherapy agents, have been released and are being used to treat cancer in the medical field, there is no way to distinguish in advance between a patient group having an effect from the anti-PD-1 immunotherapy agents and a patient group having no effect thereof, and thus the unaffected patient group lost time and money from anti-PD-1 immunotherapy agent treatment. Therefore, the present invention relates to the method for predicting the effects of the immunotherapy on cancer patients, and according to the method of the present invention, it is possible to distinguish in advance between a patient group having an effect from the anti-PD-1 immunotherapy agents and a patient group having no effect thereof, and thus, the present invention is expected to be widely used in medicine.
Description
본 발명은 암 환자에서 면역 치료 효과를 예측하는 방법에 관한 것으로, 보다 구체적으로항 PD-1 면역항암제 치료 효과를 예측하는 방법에 관한 것이다.The present invention relates to a method for predicting the effects of immunotherapy in cancer patients, and more particularly, to a method for predicting the effects of anti-PD-1 immune chemotherapy.
종래의 암 치료는 수술, 방사선 요법, 및 화학요법으로 최대한 암세포를 환자에게서 제거해 내는 방법이었다. 그러나 수술 및 방사선 요법은 비교적 초기 암으로 전이가 되지 않은 상황에서 완전히 암세포를 제거하였을 때 치료 효과가 있다. 즉, 수술에 의해 암 조직을 제거하더라도 소수의 암세포가 신체의 다른 부위로 전이된 경우에는 재발 위험이 높다. 또한 화학요법은 광범위하게 암 치료에 사용할 수 있으나, 대부분의 고형암에서 치료율이 좋지 않고, 빠르게 분열하는 정상적인 세포들을 함께 사멸시키므로 여러 가지 부작용이 나타나는 단점이 있었다. 이러한 종래의 문제점을 개선하기 위해서, 최근에는 면역항암제를 이용한 면역 치료가 대두되고 있다.Conventional cancer treatment has been a method of removing cancer cells from patients as much as possible by surgery, radiation therapy, and chemotherapy. Surgery and radiation therapy, however, have a therapeutic effect when cancer cells are completely removed in a situation where cancer has not metastasized to a relatively early stage. In other words, even if the cancer tissue is removed by surgery, the risk of recurrence is high when a few cancer cells have spread to other parts of the body. In addition, chemotherapy can be widely used for cancer treatment, but in most solid cancers, the treatment rate is not good, and it causes various side effects due to the killing of rapidly dividing normal cells together. In order to improve such a conventional problem, the immunotherapy using an immune anticancer agent is emerging recently.
면역항암제란 인체가 가지고 있는 고유의 면역계를 강화시켜 암에 대항력을 높여 주는 약물을 의미한다. 인체가 가진 본연의 힘으로 암을 저지한다는 점에서 종래의 암치료를 바라보는 개념과 근본적인 차이가 있다. 면역항암제는 기존의 암 치료법의 단점을 보완하였는데, 1세대 화학항암제가 암세포를 직접 공격하고, 2세대 표적 항암제가 암 관련 유전자를 공격하는 기능을 한다면, 3세대 항암제라 불리는 면역항암제는 면역력을 강화해 암을 치료하게 된다.Immune anticancer drugs mean drugs that enhance the body's own immune system to fight against cancer. There is a fundamental difference from the concept of looking at conventional cancer treatment in that it inhibits cancer by the inherent power of the human body. Immune anticancer drugs make up for the shortcomings of conventional cancer therapies.If a first-generation chemotherapy agent directly attacks cancer cells, and a second-generation target anticancer agent attacks cancer-related genes, then an anti-cancer drug called third-generation anticancer drugs enhances immunity. It will cure cancer.
가장 대표적인 면역항암제인 항 PD-1 항체는 활성화된 T세포(면역세포)의 표면에 있는 단백질인 PD-1과 암세포의 표면에 있는 단백질인 PD-L1, PD-L2의 결합을 막아주는 치료제이다. 암세포의 표면에 있는 PD-L1 과 PD-L2가 T세포의 표면에 있는 단백질인 PD-1과 결합하면, T세포의 기능이 저하되고 암세포를 공격하지 못한다. 따라서 항 PD-1 면역항암제는 T세포의 PD-1 수용체에 달라붙어, 암세포의 PD-L1, PD-L2와 T세포의 PD-1이 결합하는 것을 억제함으로서, 암 세포에 대한 T세포의 활성을 유지하도록 하고 암세포의 회피 기능을 억제한다. 항 PD-1 면역항암제로 키트루다와 옵디보가 출시되어 의학 분야에서 암 치료에 이용되고 있으나, 항 PD-1 면역항암제가 효과가 있는 환자군과 효과가 없는 환자군을 사전에 구분할 수 있는 방법이 부재하여, 효과가 없는 환자군의 경우 항 PD-1 면역항암제 치료에 대한 비용과 시간을 손실하고 있는 실정이다.Anti-PD-1 antibody, the most representative immune anticancer agent, is a therapeutic agent that blocks the binding of PD-1, a protein on the surface of activated T cells (immune cells) and PD-L1, PD-L2, proteins 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 the cancer cells cannot be attacked. Therefore, anti-PD-1 immunocancer agent adheres to PD-1 receptor of T cells and inhibits the binding of PD-L1, PD-L2 of cancer cells and PD-1 of T cells, thereby inhibiting T cell activity on cancer cells. And inhibit the avoidance of cancer cells. Although Kitruda and Obdibo have been released as anti-PD-1 anticancer drugs, they are being used in cancer treatment in the medical field.However, there is no way to distinguish between the patients who are effective and the ineffective patients. Thus, the ineffective patient group is losing the cost and time for anti-PD-1 immunotherapy treatment.
따라서 본 발명은 암 환자에서 면역 치료 효과를 예측하는 방법에 관한 것으로, 본 발명의 방법에 따르면 항 PD-1 면역항암제가 효과가 있는 환자군과 효과가 없는 환자군을 사전에 구분 가능하므로, 의학 분야에서 크게 이용될 것으로 기대된다.Accordingly, the present invention relates to a method for predicting the effect of immunotherapy in cancer patients, and according to the method of the present invention, it is possible to distinguish between a patient group having an anti-PD-1 immunocancer drug and an ineffective patient group, It is expected to be used greatly.
본 발명은 상기와 같은 종래의 기술상의 문제점을 해결하기 위해 안출된 것으로, 암 환자에서 면역 치료 효과를 예측하는 방법, 보다 구체적으로 항 PD-1 면역항암제 치료 효과를 예측하는 방법에 관한 것이다.The present invention has been made to solve the above-mentioned problems in the prior art, and relates to a method for predicting the effects of immunotherapy in cancer patients, and more particularly, to a method for predicting the effects of anti-PD-1 immune chemotherapy.
그러나 본 발명이 이루고자 하는 기술적 과제는 이상에서 언급한 과제에 제한되지 않으며, 언급되지 않은 또 다른 과제들은 아래의 기재로부터 당 업계에서 통상의 지식을 가진 자에게 명확하게 이해될 수 있을 것이다.However, the technical problem to be achieved by the present invention is not limited to the above-mentioned problem, another task that is not mentioned will be clearly understood by those skilled in the art from the following description.
이하, 본원에 기재된 다양한 구체예가 도면을 참조로 기재된다. 하기 설명에서, 본 발명의 완전한 이해를 위해서, 다양한 특이적 상세사항, 예컨대, 특이적 형태, 조성물 및 공정 등이 기재되어 있다. 그러나, 특정의 구체예는 이들 특이적 상세 사항 중 하나 이상 없이, 또는 다른 공지된 방법 및 형태와 함께 실행될 수 있다. 다른 예에서, 공지된 공정 및 제조 기술은 본 발명을 불필요하게 모호하게 하지 않게 하기 위해서, 특정의 상세사항으로 기재되지 않는다. "한 가지 구체예" 또는 "구체예"에 대한 본 명세서 전체를 통한 참조는 구체예와 결부되어 기재된 특별한 특징, 형태, 조성 또는 특성이 본 발명의 하나 이상의 구체예에 포함됨을 의미한다. 따라서, 본 명세서 전체에 걸친 다양한 위치에서 표현된 "한 가지 구체예에서" 또는 "구체예"의 상황은 반드시 본 발명의 동일한 구체예를 나타내지는 않는다. 추가로, 특별한 특징, 형태, 조성, 또는 특성은 하나 이상의 구체예에서 어떠한 적합한 방법으로 조합될 수 있다.Hereinafter, various embodiments described herein are described with reference to the drawings. In the following description, for a thorough understanding of the present invention, various specific details are set forth, such as specific forms, compositions, processes and the like. However, certain embodiments may be practiced without one or more of these specific details, or in conjunction with other known methods and forms. In other instances, well known processes and manufacturing techniques have not been described in particular detail in order to not unnecessarily obscure the present invention. Reference throughout this specification to "one embodiment" or "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 “embodiment” expressed at various places throughout this specification does not necessarily represent the same embodiment of the invention. In addition, particular features, forms, compositions, or properties may be combined in any suitable manner in one or more embodiments.
명세서에서 특별한 정의가 없으면 본 명세서에 사용된 모든 과학적 및 기술적인 용어는 본 발명이 속하는 기술분야에서 당업자에 의하여 통상적으로 이해되는 것과 동일한 의미를 가진다.Unless otherwise defined, all scientific and technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
본 발명의 일 구체예에서 "면역항암제"란, 방사선, 항암제 등 암세포를 직접적으로 공격하는 치료나 약물 대신 환자의 면역력을 키워 암을 치료할 수 있도록 돕는 치료제를 말한다. 주로 면역 체크포인트 단백질(PD-1, PD-L1, CTLA-4)을 찾아 암세포의 면역회피 기능을 마비시켜 T세포(면역세포)가 암세포를 파괴하는 것을 도와주는 방식이다. 몸속의 면역체계가 암세포를 공격하는 성질을 활성화시키는 역할을 하기 때문에 다양한 암에 적용할 수 있으며 소화불량, 구토, 백혈구 감소증, 탈모 등의 부작용도 줄일 수 있다. 현재 면역 체크포인트 단백질을 막는 다양한 항체들이 임상적으로 사용되거나 임상시험중에 있으며 항 PD-1 항체는 opdivo(nivolumab), keytruda(pembrolizumab), MEDI0680, pidizilumab가 있고 항 PD-L1 항체는 tecentriq(atezolizumab), Imfinzi(duvuralumab), Bavencio(Avelumab), MDX-1105 이 있으며, 항 CTLA-4 항체는 Yervoy(ipilimumab)이 있다(Topalian et al., 2015 Cancer Cell 27:450-461; Alsaab et al.,2017 Front Pharmacol 23:561). In one embodiment of the present invention, "immune anticancer agent" refers to a therapeutic agent that helps to cure cancer by increasing the immunity of a patient instead of a drug or a drug that directly attacks cancer cells such as radiation or an anticancer agent. Mainly find immune checkpoint proteins (PD-1, PD-L1, CTLA-4) to paralyze the cancer cells' immune evasion function to help T cells (immune cells) to destroy cancer cells. Because the immune system in the body plays a role in activating the attack on cancer cells, it can be applied to a variety of cancers and can also reduce side effects such as indigestion, vomiting, leukopenia, and hair loss. Various antibodies that block immune checkpoint proteins are currently being used or are being tested. Anti-PD-1 antibodies include 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 has Yervoy (ipilimumab) (Topalian et al., 2015 Cancer Cell 27: 450-461; Alsaab et al., 2017 Front Pharmacol 23: 561).
본 발명의 일 구체예에서 “PD-1”이란, PD-1(CD279로도 명명됨)은 CD28/CTLA4 공동 자극/억제 수용체 패밀리(co-stimulatory/inhibitory receptor family)와 관련된 55 KD의 수용체 단백질이다(Blank et al., 2005 Cancer Immunol Immunother 54:307-314). PD-1을 코딩하는 유전자 및 cDNA를 클로닝하여 마우스 및 인간에서의 특징을 살펴본 바 있다(Ishida et al., 1992 EMBO J 11:3887-3395; Shinohara et al., 1994 Genomics 23:704-706). 전장 PD-1은 288개의 아미노산 잔기 (NCBI accession number: NP_005009)를 포함한다. 세포외 도메인은 1-167 아미노산 잔기로 구성되고, 세포질 C-말단 꼬리는 191-288 잔기를 포함하며, 이는 2개의 가설적 면역-조절 모티프인 면역수용체 티로신 기반 저해 모티프(ITIM; Vivier et al., 1997 Immunol Today 18:286-291) 및 면역수용체 티로신 스위치 모티프(ITSM; Chemnitz et al., 2004 J Immunol 173:945-954)를 포함한다. 지금까지, 2개의 서열 관련 리간드 PD-L1(B7-H1), 및 PD-L2(B7-DC)는 PD-1과 특이적으로 상호작용하여 세포 내 신호전달을 유도하고, CD3 및 CD28 매개 T-세포 활성화를 저해하는 것으로 확인되었으며(Riley, 2009 Immunol Rev 229:114-125), 결국 T-세포 활성을 조절 예를 들어, 기타 성장 인자 및 싸이토카인 분비뿐 아니라, 세포 성장, IL-2 및 IFN-γ 분비를 감소시키는 것이다.In one embodiment of the invention “PD-1” means PD-1 (also referred to as CD279) is a 55 KD receptor protein associated with the CD28 / CTLA4 co-stimulatory / inhibitory receptor family. (Blank et al., 2005 Cancer Immunol Immunother 54: 307-314). Cloning genes and cDNAs encoding PD-1 have been characterized in mice and humans (Ishida et al., 1992 EMBO J 11: 3887-3395; Shinohara et al., 1994 Genomics 23: 704-706) . 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 comprises 191-288 residues, which are two hypothetical immune-modulating motifs, an immunoreceptor tyrosine based inhibitory motif (ITIM; Vivier et al. , 1997 Immunol Today 18: 286-291) and immunoreceptor tyrosine switch motifs (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, and CD3 and CD28 mediated T Has been shown to inhibit cell activation (Riley, 2009 Immunol Rev 229: 114-125), which eventually regulates T-cell activity, eg cell growth, IL-2 and IFN, as well as other growth factors and cytokine secretion -γ secretion is reduced.
PD-1의 발현은 T-세포, B-세포, 단핵세포 및 자연살해(NK) 세포와 같은 면역세포에서 빈번하게 확인된다. 기타 인간 조직, 예를 들어 근육, 상피, 신경 조직 등에서는 거의 발현되지 않는다. 또한, 고레벨의 PD-1 발현은 종종 면역세포의 활성과 관련이 있다. 예를 들어, 인간 T-세포주인 Jurkat이 PHA(phytohaemagglutinin) 또는 포르볼 에스테르 (12-O-tetradecanoylphorbol-13-acetate 또는 TPA)에 의해 활성화되면, 웨스턴 블랏에서 보이는 바와 같이 PD-1의 발현이 상향 조절되었다(Vibharka et al., 1997 Exp Cell Res 232:25-28). 항-CD3 항체의 자극에 의해, 자극된 마우스 T- 및 B-림프구와 1차 인간 CD4+ T 세포에서 동일한 현상이 관찰되었다(Agata et al., 1996 Int Immunol 8:765-772; Bennett et al., 2003 J Immunol 170:711-118). PD-1 발현 증가에 의해 효과 T세포를 자극하고, 활성화된 효과 T세포를 고갈 및 감소된 면역활성 방향으로 다시 안내한다. 따라서, PD-1 매개 저해 신호는 면역 관용에 중요한 역할을 한다(Bour-Jordan et al., 2011 Immunol Rev 241:180-205).Expression of PD-1 is frequently found 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, epithelial, nervous tissue, and the like. In addition, high levels of PD-1 expression are often associated with the activity of immune cells. For example, if the human T-cell line Jurkat is activated by phytohaemagglutinin (PHA) or phorbol ester (12-O-tetradecanoylphorbol-13-acetate or TPA), the expression of PD-1 is upward as shown in Western blot. Regulated (Vibharka et al., 1997 Exp Cell Res 232: 25-28). By stimulation of anti-CD3 antibodies, the same phenomenon was observed in stimulated mouse T- and B-lymphocytes and primary human CD4 + T cells (Agata et al., 1996 Int Immunol 8: 765-772; Bennett et al) , 2003 J Immunol 170: 711-118). Increased PD-1 expression stimulates effect T cells and directs activated effect T cells in the direction of depletion and reduced immunoactivity. Thus, PD-1 mediated signaling plays an important role in immune tolerance (Bour-Jordan et al., 2011 Immunol Rev 241: 180-205).
다양한 암에서 종양 침윤 림프구(tumor-infiltrating lymphocytes: TILs)의 PD-1 발현 및 종양 세포의 PD-1 리간드 발현 증가가 보고되었고, 다른 유형의 조직 및 기관 예를 들어 폐(Konishi et al., 2004 Clin Cancer Res 10:5094-5100), 간(Shi et al., 2008 Int J Cancer 128:887-896; Gao et al., 2009 Clin Cancer Res 15:971-979), 위(Wu et al., 2006 Acta Histochem 108:19-24), 신장(Thompson et al., 2004 Proc Natl Acad Sci 101:17174-17179; Thompson et al., 2007 Clin Cancer Res 13:1757-1761), 유방 (Ghebeh et al., 2006 Neoplasia 8:190-198), 난소(Hamanishi et al. 2007 Proc Natl Acad Sci 104:3360-3365), 췌장(Nomi et al., 2007 Clin Cancer Res 13:2151-2157), 멜라노사이트(Hino et al., 2010 Cancer 116:1757-1766), 및 식도(Ohigashi et al., 2005 Clin Cancer Res 11:2947-2953)가 포함된다. 더욱 빈번하게, 이러한 암에서 PD-1 및 PD-L1의 발현은 환자 생존 결과에 대한 좋지 못한 예후와 연관된다. PD-1 유전자를 낙아웃하여 이종이식(Xenograft) 암 세포 성장을 억제한 형질전환 마우스를 통해, 암 제거 또는 관용을 위한 면역 시스템 조절에서의 PD-1 신호전달에 대한 중요성을 더욱 자세히 설명하였다(Zhang et al., 2009 Blood 114:1545-1552).In various cancers, increased PD-1 expression of tumor-infiltrating lymphocytes (TILs) and PD-1 ligand expression of tumor cells have been reported, and other types of tissues and organs such as lung (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), kidneys (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), melanosite (Hino et al., 2010 Cancer 116: 1757-1766), and 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. Transgenic mice that knocked out the PD-1 gene and inhibited Xenograft cancer cell growth further explained the importance of PD-1 signaling in immune system regulation for cancer removal or tolerance. Zhang et al., 2009 Blood 114: 1545-1552).
PD-1 신호전달의 상향 조절에 의해 면역 관용의 암 증식으로 이어질 뿐 아니라, 인간의 바이러스 감염 및 확장으로도 이어진다. 유행성 간 감염 바이러스 HBV 및 HCV는 간세포에서 PD-1 리간드의 과발현을 유도하고 효과 T세포에서 PD-1 신호전달을 활성화하여, 바이러스 감염에 대한 T-세포 고갈 및 관용을 야기한다(Boni et al., 2007 J Virol 81:4215-4225; Golden-Mason et al., 2008 J Immunol 180:3637-3641). 마찬가지로, HIV 감염은 유사한 기작으로 인간 면역 시스템을 빈번하게 회피한다. 길항 분자에 의해 PD-1 신호전달을 치료적으로 조절하여 관용으로부터 면역세포를 회복할 수 있고, 재활성시켜 암 및 만성 바이러스 감염을 제거할 수 있다(Blank et al., 2005 Cancer Immunol Immunother 54:307-314; Okazaki et al., 2007 Int Immunol 19:813-824).Upregulation of PD-1 signaling leads to cancer proliferation of immune tolerance, as well as to viral infection and expansion in humans. Pandemic 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 to viral infection (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 similar mechanisms. Therapeutic regulation of PD-1 signaling by antagonistic molecules can restore immune cells from tolerance and can be reactivated 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).
본 발명의 일 구체예에서, (a) 피검체의 T 세포수를 측정하는 단계; (b) 상기 피검체에게 면역항암제를 투여하는 단계; (c) 상기 피검체의 T 세포수를 1차 재측정하는 단계; (d) (a)단계에서의 측정값보다 (c)단계에서의 측정값이 증가하는 것을 확인하는 단계;를 포함하는 암 환자에 있어서 면역항암제 치료 효과를 예측하는 방법을 제공하고, 상기 방법은 (e) 상기 피검체의 T 세포수를 2차 재측정하는 단계;를 추가로 포함하는 방법을 제공하며, 상기 (c)단계는 (b)단계로부터 1일 내지 14일에 수행하는 것을 특징으로 하는 방법을 제공하며, 상기 방법은 (f) (c)단계에서의 측정값보다 (e)단계에서의 측정값이 감소하는 것을 확인하는 단계;를 추가로 포함하는 방법을 제공하며, 상기 (e)단계는 (b)단계로부터 15일 내지 21일에 수행하는 것을 특징으로 하는 방법을 제공한다.In one embodiment of the invention, (a) measuring the T cell number of the subject; (b) administering an immune anticancer agent to the subject; (c) first re-measuring the T cell number of the subject; (d) confirming that the measured value in step (c) is greater than the measured value in step (a); and providing a method for predicting the effects of immuno-cancer drugs in cancer patients, the method comprising (e) re-measuring the second T cell number of the subject; provides a method comprising the step (c) is characterized in that performed in 1 to 14 days from step (b) It provides a method, the method further comprises the step of (f) confirming that the measured value in step (e) is less than the measured value in step (c); Step) provides a method, characterized in that performed on day 15 to 21 from step (b).
또한 상기의 T 세포수는 T 세포수, T 세포의 활성화 분비물, 및 T 세포 활성화도 중에서 T 세포수를 측정하는 것을 특징으로 하는 암 환자에 있어서 면역항암제 치료 효과를 예측하는 방법을 제공하고, 상기 T 세포수는 Ki-67 발현값으로 측정하는 것을 특징으로 하는 방법을 제공하며, 상기 발현값은 유전자 발현값, 또는 단백질 발현값인 방법을 제공하며, 상기 (c)단계의 측정값이 (a)단계의 측정값보다 2.8배 이상일 경우에, 상기 피검체의 면역항암제 치료 효과가 높을 것으로 예측하는 단계를 포함하는 방법을 제공하며, 상기 (c)단계의 측정값이 (a)단계의 측정값보다 2.8배 이상일 경우에, 상기 피검체의 예후가 좋을 것으로 예측하는 단계를 포함하는 방법을 제공하며, 상기 (c)단계의 측정값이 (a)단계의 측정값보다 2.8배 미만일 경우에, 상기 피검체의 면역항암제 치료 효과가 낮을 것으로 예측하는 단계를 포함하는 방법을 제공하며, 상기 (c)단계의 측정값이 (a)단계의 측정값보다 2.8배 미만일 경우에, 상기 피검체의 예후가 나쁠 것으로 예측하는 단계를 포함하는 방법을 제공한다.In addition, the T cell number provides a method for predicting the effects of immuno-cancer drug treatment in cancer patients, characterized in that measuring the T cell number in T cell number, T cell activation secretion, and T cell activation degree, It provides a method characterized in that the T cell number is measured by Ki-67 expression value, the expression value is a gene expression value, or a protein expression value, the measured value of step (c) is (a And 2.8 times or more than the measured value of step), the method comprising the step of predicting that the anti-cancer drug treatment effect of the subject is high, the measured value of step (c) is the measured value of step (a) In the case of more than 2.8 times, it provides a method comprising the step of predicting that the prognosis of the subject is good, when the measured value of step (c) is less than 2.8 times the measured value of step (a), Immunity of Subject A method comprising the step of predicting that the cancer drug treatment effect is low, 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 worse It provides a method comprising the steps.
또한 상기의 암은 유방암, 자궁경부암, 신경교종, 뇌암, 흑색종, 폐암, 방광암, 전립선암, 백혈병, 신장암, 간암, 대장암, 췌장암, 위암, 담낭암, 난소암, 임파종, 골육종, 자궁암, 구강암, 기관지암, 비인두암, 후두암, 피부암, 혈액암, 갑상선암, 부갑상선암, 요관암, 선암, 및 흉선암으로 구성되는 그룹으로부터 선택되는 어느 하나 이상인 암 환자에 있어서 면역항암제 치료 효과를 예측하는 방법을 제공하고, 상기 암은 폐암 또는 흉선암인 방법을 제공한다.In addition, the cancer may be breast cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colon cancer, pancreatic cancer, gastric cancer, gallbladder cancer, ovarian cancer, lymphoma, osteosarcoma, uterine cancer, A method for predicting the effects of an anticancer drug in cancer patients of at least one selected from the group consisting of oral cancer, bronchial cancer, nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, thyroid cancer, parathyroid cancer, ureter cancer, adenocarcinoma, and thymic cancer To provide a method, wherein the cancer is lung cancer or thymic cancer.
또한 상기의 면역항암제는 항 PD-1 항체, 항 PD-L1 항체, 또는 항 CTLA-4 항체인, 암 환자에 있어서 면역항암제 치료 효과를 예측하는 방법을 제공한다.In addition, the immuno-cancer agent provides an anti-PD-1 antibody, an anti-PD-L1 antibody, or an anti-CTLA-4 antibody.
본 발명의 다른 구체예에서, (a) 피검체의 T 세포수를 측정하는 단계; (b) 상기 피검체에게 후보물질을 투여하는 단계; (c) 상기 피검체의 T 세포수를 1차 재측정하는 단계; (d) (a)단계에서의 측정값보다 (c)단계에서의 측정값이 증가하는 것을 확인하는 단계;를 포함하는 암 환자에 있어서 면역항암제 후보물질의 암 치료 효과를 스크리닝하는 방법을 제공하고, 상기 (c)단계는 (b)단계로부터 1일 내지 14일에 수행하는 것을 특징으로 하는 방법을 제공하며, 상기 T 세포수는 Ki-67 발현값으로 측정하는 것을 특징으로 하는 방법을 제공하며, 상기 (c)단계의 측정값이 (a)단계의 측정값보다 2.8배 이상일 경우에, 상기 면역항암제 후보물질을 암 치료용 물질인 것으로 판단하는 단계를 포함하는 방법을 제공하며, 상기 암은 유방암, 자궁경부암, 신경교종, 뇌암, 흑색종, 폐암, 방광암, 전립선암, 백혈병, 신장암, 간암, 대장암, 췌장암, 위암, 담낭암, 난소암, 임파종, 골육종, 자궁암, 구강암, 기관지암, 비인두암, 후두암, 피부암, 혈액암, 갑상선암, 부갑상선암, 요관암, 선암, 및 흉선암으로 구성되는 그룹으로부터 선택되는 어느 하나 이상인 방법을 제공하며, 상기 암은 폐암 또는 흉선암인 방법을 제공한다.In another embodiment of the invention, (a) measuring the T cell number of the subject; (b) administering a candidate substance to the subject; (c) first re-measuring the T cell number of the subject; (d) confirming that the measured value in step (c) is greater than the measured value in step (a); and providing a method for screening the cancer therapeutic effect of an immuno-cancer drug candidate in a cancer patient comprising the , The step (c) provides a method characterized in that performed in 1 day to 14 days from step (b), the T cell number provides a method characterized in that measured by Ki-67 expression value When the measured value of step (c) is 2.8 times or more than the measured value of step (a), the method comprising the step of determining the immune anticancer drug candidates for cancer treatment, 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.
이하 상기 본 발명을 단계별로 상세히 설명한다.Hereinafter, the present invention will be described in detail step by step.
본 발명은 암 환자에서 면역 치료 효과를 예측하는 방법에 관한 것으로, 본 발명의 방법에 따르면 PD-1 면역항암제가 효과가 있는 환자군과 효과가 없는 환자군을 사전에 구분 가능하므로, 의학 분야에서 크게 이용될 것으로 기대된다.The present invention relates to a method for predicting the effects of immunotherapy in cancer patients, and according to the method of the present invention, PD-1 immunocancer drugs can be distinguished between the effective patient group and the ineffective patient group, and thus are widely used in the medical field. It is expected to be.
도 1은 본 발명의 일 실시예에 따른, anti-PD-1 치료된 흉선상피종양 환자에서 약물투여, 및 혈액 채취의 시간적 도식을 나타낸 도이다.1 is a diagram showing a time diagram of drug administration and blood collection in an anti-PD-1 treated thymic epithelial tumor patient, according to an embodiment of the present invention.
도 2a와 도 2b는 본 발명의 일 실시예에 따른, 본 발명에서 사용한 유세포분석의 게이트 전략(gating strategies)을 나타낸 도이다.2A and 2B are diagrams illustrating gate strategies of flow cytometry used in the present invention, according to an embodiment of the present invention.
도 3a와 도 3b는 본 발명의 일 실시예에 따른, anti-PD-1 치료된 흉선상피종양 환자에서 T 세포의 항원 특이성에 근거한 anti-PD-1 치료의 효과를 나타낸 도이다.3A and 3B show the effects of anti-PD-1 treatment based on antigen specificity of T cells in an anti-PD-1 treated thymic epithelial tumor patient, according to an embodiment of the invention.
도 4는 본 발명의 일 실시예에 따른, anti-PD-1 치료된 흉선상피종양 환자에서 anti-PD-1 치료 후 PD-1+CD8+ T세포의 Ki-67+과 HLA-DR+CD38+ 빈도 변화를 나타낸 도이다.4 shows Ki-67 + and HLA-DR + CD38 of PD-1 + CD8 + T cells after anti-PD-1 treatment in anti-PD-1 treated thymic epithelial tumor patients according to an embodiment of the present invention. + Is a diagram showing a frequency change.
도 5은 본 발명의 일 실시예에 따른, anti-PD-1 치료된 흉선상피종양 환자에서 anti-PD-1 치료 이후에 135개 파라미터들의 치료반응응 예측력을 나타낸 도이다.5 is a diagram showing the predictive response of treatment response of 135 parameters after anti-PD-1 treatment in an anti-PD-1 treated thymic epithelial tumor patient according to an embodiment of the present invention.
도 6은 본 발명의 일 실시예에 따른, anti-PD-1 치료된 흉선상피종양 환자에서 환자의 치료반응에 따른 PD-1+CD8+ T세포에서 Ki-67 발현의 변화(Ki-67D7/D0)를 나타낸 도이다.Figure 6 is a change in Ki-67 expression in PD-1 + CD8 + T cells according to the treatment response of patients in anti-PD-1 treated thymic epithelial tumors according to an embodiment of the present invention (Ki-67 D7 / D0 ).
도 7는 본 발명의 일 실시예에 따른, anti-PD-1 치료된 흉선상피종양 환자에서 Ki-67D7/D0 컷오프값이 2.8을 기준으로 구분한 환자들의 질병통제율, 및 질병 무진행 생존율을 나타낸 도이다.FIG. 7 shows disease control rate and disease progression-free survival rate of patients whose Ki-67 D7 / D0 cutoff values are divided based on 2.8 in anti-PD-1 treated thymic epithelial tumor patients according to an embodiment of the present invention. Is a diagram showing.
도 8a 내지 도 8c는 본 발명의 일 실시예에 따른, anti-PD-1 치료된 비소세포폐암 환자에서 약물투여 및 혈액 채취의 시간적 도식, 및 실험 결과를 나타낸 도이다.8A to 8C are diagrams showing time-schematic diagrams of drug administration and blood collection and experimental results in patients with anti-PD-1 treated NSCLC, according to an embodiment of the present invention.
도 9은 본 발명의 일 실시예에 따른, anti-PD-1 치료된 비소세포폐암 환자에서 anti-PD-1 치료 전, 치료 1주 후, 치료 3주 후의 PD-1+CD8+ T세포에서 Ki-67+의 빈도 변화를 나타낸 도이다.9 shows PD-1 + CD8 + T cells before, after 1 week and after 3 weeks of anti-PD-1 treatment in anti-PD-1 treated non-small cell lung cancer patients according to an embodiment of the present invention. It is a figure which shows the frequency change of Ki-67 + .
도 10a 내지 도 10c는 본 발명의 일 실시예에 따른, anti-PD-1 치료된 비소세포폐암 환자에서 Ki-67D7/D0 컷오프값이 2.8을 기준으로 구분한 환자들의 질병통제율, 질병 무진행 생존율, 및 전체 생존기간을 나타낸 도이다.10A to 10C are disease control rate and disease free of patients whose Ki-67 D7 / D0 cutoff values are divided based on 2.8 in anti-PD-1 treated non-small cell lung cancer patients according to an embodiment of the present invention. A diagram showing progression survival and overall survival.
펨브롤리주맙(pembrolizumab) 약물 투여 후 치료 반응을 예측 가능한 바이오 마커를 발굴하기 위해서, PD-1+CD8+ T세포, CD8+ T세포, 및 CD4+ T세포의 빈도 및 표현형 표지자를 포함하여 멀티칼라 유세포 분석으로부터 도출된 135개 파라미터를 통합 분석하였다. anti-PD-1 치료 이후에 디자인된 파라미터들의 변화까지도 평가한 결과, ROC 곡선으로부터 2.8을 최적 컷오프값으로 결정하였고, 상기 컷오프값에 따라 환자를 이분화하였다.In order to identify biomarkers predictable for treatment response after administration of pembrolizumab drug, multicolor including frequency and phenotypic markers of PD-1 + CD8 + T cells, CD8 + T cells, and CD4 + T cells 135 parameters derived from flow cytometry were integrated analysis. Even the changes in the designed parameters after anti-PD-1 treatment were evaluated. As a result, 2.8 was determined as the optimal cutoff value from the ROC curve, and the patients were divided according to the cutoff value.
질병통제대조군에서의 감도, 특이도, 음성 예측도(NPV), 및 양성 예측도(PPV)는 각각 90.9%, 75.0%, 93.8%, 및 66.7%로 나타났다. 높은 민감도와 음성 예측도는 Ki-67D7/D0 가 anti-PD-1 치료로 효과를 얻지 못할 것으로 예상되는 환자를 정확하게 예측할 수 있다는 것을 의미한다. Ki-67D7/D0 컷오프값이 2.8 미만인 환자들은 질병통제율(durable disease control rates)이 낮은 것으로 나타났다. 또한 이 환자들은 질병 무진행 생존율 (progression-free survival; PFS)이 매우 낮은 것으로 나타났다.Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) in the disease control control group were 90.9%, 75.0%, 93.8%, and 66.7%, respectively. High sensitivity and negative predictiveness mean that Ki-67 D7 / D0 can accurately predict patients who are not expected to benefit from anti-PD-1 treatment. Patients with a Ki-67 D7 / D0 cutoff value of less than 2.8 were found to have low disease control rates. These patients also had very low disease progression-free survival (PFS).
이하, 실시예를 통하여 본 발명을 더욱 상세히 설명하고자 한다. 이들 실시예는 오로지 본 발명을 보다 구체적으로 설명하기 위한 것으로서, 본 발명의 요지에 따라 본 발명의 범위가 이들 실시예에 의해 제한되지 않는다는 것은 당업계에서 통상의 지식을 가진 자에게 있어서 자명할 것이다.Hereinafter, the present invention will be described in more detail with reference to Examples. These examples are only for illustrating 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. .
실시예Example
환자 및 샘플 수집Patient and Sample Collection
2016년 3월부터 2016년 6월까지 펨브롤리주맙(pembrolizumab)을 투여(200mg/ 3주)하여 효과를 평가하는 2기 임상 시험(NCT02607631)에 등록된 환자 중 적어도 1회 이상의 백금계 항암제를 이용한 항암요법을 받은 4기 흉선상피종양(thymic epithelial tumor; TET) 환자들 중 기관 검사위원회(Institutional Review Boards)에 따라 혈액 수집에 동의한 환자 31명으로부터 첫번째 펨브롤리주맙 투여 전(Day 0)과 투여 7일 후(Day 7) 말초 혈액을 채취하였다. 상기 약물투여 및 혈액 채취의 시간적 도식을 도 1에 나타내었다. 상기와 같은 방법으로, 2016년 4월부터 2017년 4월까지 펨브롤리주맙(pembrolizumab, 200mg/ 3주) 또는 또는 니볼루맙(nivolumab, 2 mg/Kg/ 2주)을 투여받은 4기 비소세포폐암(non-small cell lung cancer; NSCLC) 환자 29명에게서도 혈액을 채취하였다. 이들의 자료는 TET 코호트에서 발견된 예측 마커의 검증에 사용하였다. 일부 환자에게서는 0일, 7일, 및 21일째에 혈액 수집을 실시하여 면역 반응의 변화를 모니터링하였다.Using at least one platinum-based anticancer agent among patients enrolled in Phase 2 clinical trials (NCT02607631) assessing effectiveness by administering pembrolizumab (200 mg / 3 weeks) from March 2016 to June 2016. Before the first fembrolizumab treatment (Day 0) and from 31 patients with chemotherapy for stage 4 thymic epithelial tumor (TET) who agreed to blood collection according to the Institutional Review Boards Peripheral blood was collected 7 days later (Day 7). A time schematic of the drug administration and blood collection is shown in FIG. 1. In the same manner, stage 4 non-small cell lung cancer who received pembrolizumab (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 verify the predictive markers found in the TET cohort. In some patients blood collection was performed on days 0, 7, and 21 to monitor changes in the immune response.
수집 혈액으로부터 Ficoll-Paque(GE Healthcare, Uppsala, Sweden) 밀도 구배 원심 분리법으로 말초 혈액 단핵세포(PBMCs)를 분리하였고, 실험에 사용하기 전까지 냉동 보존하였다.Peripheral blood mononuclear cells (PBMCs) were isolated from collected blood by Ficoll-Paque (GE Healthcare, Uppsala, Sweden) density gradient centrifugation and cryopreserved until use in experiments.
종양 반응은 고체 종양의 반응 평가 기준(RECIST, 버전 1.1.)에 따라 컴퓨터 단층 촬영 또는 자기 공명 영상으로 9주마다 평가하였다. 객관적 반응은 완전 반응 또는 부분 반응으로 구분하였고, 질병통제대조군(Durable disease control)은 6 개월 이상 지속되는 부분적 또는 안정적인 암환자군으로 구분하였다.Tumor response was assessed every nine weeks by computed tomography or magnetic resonance imaging according to the solid tumor response evaluation criteria (RECIST, version 1.1.). Objective responses were categorized as complete or partial response, and Durable disease control was divided into partial or stable cancer patients lasting more than 6 months.
유세포 분석Flow cytometry
멀티칼라 유세포 분석에 사용된 형광 접합 항체 종류를 하기 표 1에 기재하였다.The types of fluorescent conjugated antibodies used for multicolor flow cytometry are listed in Table 1 below.
항체명Antibody Name | 비고Remarks |
anti-CD8anti-CD8 | (SK1 and RPA-T8), BD Biosciences, San Jose, CA(SK1 and RPA-T8), BD Biosciences, San Jose, CA |
anti-CD3anti-CD3 | (UCTH1 or SK7), BD Biosciences, San Jose, CA(UCTH1 or SK7), BD Biosciences, San Jose, CA |
anti-CD45RAanti-CD45RA | (HI100), BD Biosciences, San Jose, CA(HI100), BD Biosciences, San Jose, CA |
anti-CD4anti-CD4 | (SK3), BD Biosciences, San Jose, CA(SK3), BD Biosciences, San Jose, CA |
anti-ICOSanti-ICOS | (DX29), BD Biosciences, San Jose, CA(DX29), BD Biosciences, San Jose, CA |
anti-CD25anti-CD25 | (M-A251), BD Biosciences, San Jose, CA(M-A251), BD Biosciences, San Jose, CA |
anti-CD28anti-CD28 | (CD28.2), BD Biosciences, San Jose, CA(CD28.2), BD Biosciences, San Jose, CA |
anti-Granzyme Banti-Granzyme B | (GB11), BD Biosciences, San Jose, CA(GB11), BD Biosciences, San Jose, CA |
anti-PD-1anti-PD-1 | (EH.12.2H7), Biolegend, San Diego, CA(EH.12.2H7), Biolegend, San Diego, CA |
anti-Ki-67anti-Ki-67 | (Ki-67), Biolegend, San Diego, CA(Ki-67), Biolegend, San Diego, CA |
anti-CD38anti-CD38 | (HB7), Biolegend, San Diego, CA(HB7), Biolegend, San Diego, CA |
anti-CTLA-4anti-CTLA-4 | (L3D10), Biolegend, San Diego, CA(L3D10), Biolegend, San Diego, CA |
anti-CD127anti-CD127 | (A019D5), Biolegend, San Diego, CA(A019D5), Biolegend, San Diego, CA |
anti-GITRanti-GITR | (108-17), Biolegend, San Diego, CA(108-17), Biolegend, San Diego, CA |
anti-TIGITanti-TIGIT | (MBSA43), eBioscience, San Diego, CA(MBSA43), eBioscience, San Diego, CA |
anti-HLA-DRanti-HLA-DR | (LN3), eBioscience, San Diego, CA(LN3), eBioscience, San Diego, CA |
anti-CCR4anti-CCR4 | (D8SEE), eBioscience, San Diego, CA(D8SEE), eBioscience, San Diego, CA |
amti-FoxP3amti-FoxP3 | (PCH101), eBioscience, San Diego, CA(PCH101), eBioscience, San Diego, CA |
anti-CD14anti-CD14 | (61D3), eBioscience, San Diego, CA(61D3), eBioscience, San Diego, CA |
anti-CD19anti-CD19 | (HIB19), eBioscience, San Diego, CA(HIB19), eBioscience, San Diego, CA |
anti-CD57anti-CD57 | (TBO1), eBioscience, San Diego, CA(TBO1), eBioscience, San Diego, CA |
anti-CCR7anti-CCR7 | (FAB197F), R&D Systems(FAB197F), R & D Systems |
anti-human IgG4 Fcanti-human IgG4 Fc | (HP6025), Southern Biotech(HP6025), Southern Biotech |
PD-1 발현 세포에 대한 펨브롤리주맙 또는 니볼루맙(human IgG4) 약제의 결합이 치료 후 시료에서 PD-1 염색을 방해하기 때문에, anti-PD-1 염색과 함께 anti-human IgG4 Fc 염색을 수행하였다. 생존/사멸(Live/dead) 세포 식별은 적색-형광 반응성 염료(Invitrogen, Carlsbad, CA)를 사용하였고, Ki-67, granzyme B, FoxP3, 및 CTLA-4에 대한 세포내 염색은 FoxP3 전사 인자 염색 완충액 키트(eBioscience)를 사용하였다. 종양 항원에 특이적인 CD8 T 세포는 PE-conjugated MHC I dextramer NY-ESO-1157-165(SLLMWITQV/HLA-A*0201, Immudex, Copenhagen, Denmark)를 사용하여 검출하였고, HCMV에 특이적인 CD8 T 세포는 PE-conjugated MHC I pentamer HCMV pp65 495-504(NLVPMVATV/HLA-A*0201, Proimmune, Oxford, UK)를 사용하여 검출하였다. 모든 유세포분석은 LSR II 유동 세포 계측기(BD Biosciences)로 수행하였고, FlowJo 소프트웨어(Treestar, San Carlos, CA)로 데이터 분석하였다. 본 발명에서 사용한 유세포분석의 게이트 전략(gating strategies)을 도 2에 나타내었다. 구체적으로, (a)는 PD-1+ CD8 T 세포, (b)는 multimer-positive CD8 T 세포의 게이팅 전략을 나타낸다.Since binding of pembrolizumab or nivolumab (human IgG4) agents to PD-1 expressing cells interferes with PD-1 staining in the sample after treatment, anti-human IgG4 Fc staining is performed with anti-PD-1 staining. It was. Live / dead cell identification was performed using a red-fluorescent reactive dye (Invitrogen, Carlsbad, Calif.), And intracellular staining for Ki-67, granzyme B, FoxP3, and CTLA-4 was performed using FoxP3 transcription factor staining. Buffer kit (eBioscience) was used. Tumor antigen specific CD8 T cells were detected using PE-conjugated MHC I dextramer NY-ESO-1 157-165 (SLLMWITQV / HLA-A * 0201, Immudex, Copenhagen, Denmark) and CD8 T specific for HCMV 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 cytometer (BD Biosciences) and data analyzed with FlowJo software (Treestar, San Carlos, CA). Gate strategies of flow cytometry used in the present invention are shown in FIG. 2. Specifically, (a) shows the gating strategy of PD-1 + CD8 T cells, (b) multimer-positive CD8 T cells.
통계 처리Statistical processing
마커의 발현량 변화는 7일 시료의 양성 세포 빈도를 0일 시료의 빈도를 기준으로 환산하여 분석하였다. 범주형 변수는 chi-square test, 또는 Fisher's exact test를 사용하여 비교하였다. 정규 분포 연속 변수의 경우에는 Student's t-test와 paired t-test를 사용하여, 정규 분포가 아닌 경우에는 Mann-Whitney U-test와 Wilcoxon signed-rank 검정을 사용하여 쌍을 이루지 않은 값과 쌍을 이루는 값을 각각 비교하였다. 2개 이상의 그룹을 비교할 때에는 One-way analysis of variance(ANOVA) 분석을 사용하였고, 데이터가 비정상적으로 분포되었을 때에는 Kruskal-Wallis test를 이용하였다. 두 매개 변수 사이의 상관 관계는 Pearson 상관 계수를 사용하여 평가하였다. 바이오 마커의 효용성을 평가하기 위해서는 receiver operating characteristic curve (ROC)로부터 area under curve(AUC)를 평가하였고, Youden 지수가 최대화된 지점에서 최적의 cutoff point를 추출하였다. 생존 곡선은 Kaplan-Meier 방법을 사용하여 생성하였고, 비교는 log-rank test로 수행하였으며, 생존율과 관련하여 발견된 바이오 마커의 독립적인 중요성을 평가하기 위해 나이, 성별, 조직학적 데이터, 이전 화학 요법 병력, 및 전이 부위 수를 조정한 다변수 cox 회귀 분석을 수행하였다. 모든 통계 분석에서 0.05 미만의 양면 P 값을 통계적으로 유의하다고 간주하였고, Prism software version 6.0(GraphPad, La Jolla, CA), 및 R statistical software(version 3.2.2, The R Foundation for Statistical Computing, Vienna, Austria)를 이용하여 분석하였다.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 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 nonnormal 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 the data were abnormally distributed. 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 by log-rank test, and age, sex, histological data, previous chemotherapy to assess the independent significance of the biomarkers found in relation to survival. Multivariate cox regression analysis was performed to adjust the history and number of metastasis sites. In all statistical analyzes, double-sided P values less than 0.05 were considered statistically significant, and 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).
환자의 임상적 특징 분석Clinical characteristics analysis of the patient
TET 환자의 임상 병리학적 특징과 NSCLC 환자의 임상 병리학적 특징을 각각 표 2와 표 3에 나타내었다.The clinical pathological characteristics of TET patients and the clinical pathological characteristics of NSCLC patients are shown in Tables 2 and 3, respectively.
CharacteristicCharacteristic | Discovery cohort (n = 31)Discovery cohort (n = 31) | |
Age, median (range), yrsAge, median (range), yrs | 5858 | (26-78)(26-78) |
Sex, n (%)Sex, n (%) | ||
Male Male | 2020 | (64.5)(64.5) |
Female Female | 1111 | (35.5)(35.5) |
Histology, n (%)Histology, n (%) | ||
Thymoma Thymoma | 66 | (19.4)(19.4) |
Thymic carcinoma Thymic carcinoma | 2525 | (80.6)(80.6) |
Prior chemotherapy line, median (range)Prior chemotherapy line, median (range) | 22 | (1-5)(1-5) |
Tumor burden, median (range), cmTumor burden, median (range), cm | 12.412.4 | (1.7-27.0)(1.7-27.0) |
Study drug, n (%)Study drug, n (%) | ||
Pembrolizumab Pembrolizumab | 3131 | (100.0)(100.0) |
Nivolumab Nivolumab | 00 | (0)(0) |
Best overall response, n (%)Best overall response, n (%) | ||
Complete response Complete response | 00 | (0)(0) |
Partial response Partial response | 66 | (19.4)(19.4) |
Stable disease Stable disease | 1818 | (58.1)(58.1) |
Progressive disease Progressive disease | 77 | (22.6)(22.6) |
CharacteristicCharacteristic | Validation cohort (n = 29)Validation cohort (n = 29) | |
Age, median (range), yrsAge, median (range), yrs | 6565 | (35-82)(35-82) |
Sex, n (%)Sex, n (%) | ||
Male Male | 2121 | (72.4)(72.4) |
Female Female | 88 | (27.6)(27.6) |
Histology, n (%)Histology, n (%) | ||
Adenocarcinoma Adenocarcinoma | 1515 | (51.7)(51.7) |
Squamous cell carcinoma Squamous cell carcinoma | 1010 | (34.5)(34.5) |
Others Other | 44 | (13.8)(13.8) |
Prior chemotherapy, median (range)Prior chemotherapy, median (range) | 33 | (1-9)(1-9) |
Tumor burden, median (range), cmTumor burden, median (range), cm | 7.57.5 | (2.0-15.7)(2.0-15.7) |
Study drug, n (%)Study drug, n (%) | ||
Pembrolizumab Pembrolizumab | 1313 | (44.8)(44.8) |
Nivolumab Nivolumab | 1616 | (55.2)(55.2) |
Best overall response, n (%)Best overall response, n (%) | ||
Complete response Complete response | 1One | (3.4)(3.4) |
Partial response Partial response | 77 | (24.2)(24.2) |
Stable disease Stable disease | 88 | (27.6)(27.6) |
Progressive disease Progressive disease | 1313 | (44.8)(44.8) |
TET 환자들에게 펨브롤리주맙 중앙값 8 주기(range, 1-22 주기) 치료를 수행하였다. 그 중 6명의 환자들이 객관적인 종양 반응을 나타내었고, 질병통제대조군은 11명의 환자로 구성되었다. NSCLC 환자를 대상으로 한 검증 코호트에서 펨브롤리주맙을 투여받은 환자는 13명이었고, 니볼루맙을 투여받은 환자는 16명이었다. 그 중 8명의 환자들이 객관적인 종양 반응을 나타내었고, 10명의 환자들이 질병통제대조군으로 나타내었다. 니볼루맙 또는 펨브롤리주맙 투여 주기의 중간값은 4 주기(1-32 주기)였다.TET patients underwent treatment with Pembrolizumab median 8 ranges (cycles 1-22). Six patients had an objective tumor response and the disease control control group consisted of 11 patients. Thirteen patients received pembrolizumab and 16 patients received nivolumab in a validation cohort of NSCLC patients. Eight of them presented an objective tumor response, and ten patients represented disease control controls. The median nivolumab or pembrolizumab administration cycle was 4 cycles (1-32 cycles).
TET 환자에서 anti-PD-1 치료 후 말초 CD8 T세포의 반응 확인Response of Peripheral CD8 T Cells after Anti-PD-1 Treatment in TET Patients
약물 투여 0일, 및 7일째에 채혈된 말초 혈액에서 CD8 T세포의 T세포 활성화(CD38, HLA-DR) 및 증식(Ki-67) 정도를 표현형 마커로 확인하였다. 여섯명의 비소세포폐암 환자로 부터 얻은 선행결과상 7일째 PD-1+CD8+ T세포에서 Ki-67+ 빈도의 증가가 보였으나 21 일째에는 유의하게 감소하는 것을 확인하였고 이를 도 9에 나타내었다. 그래서 치료 전과 치료 7일 째의 검체를 분석하였다. 먼저, 항원 특이성에 근거한 anti-PD-1 치료의 효과를 도 3에 나타내었다. NY-ESO-1 mRNA가 상향 조절된 환자와 잠재적인 HCMV 감염이 있는 환자를 평가하였다(도 3a). 평가 결과, 약물 투여 7 일째 종양 특이적 NY-ESO-1+CD8+ T세포는 Ki-67+과 HLA-DR+CD38+ 빈도의 유의한 증가를 보였으나, 종양 특이성이 없는 HCMV-specific pp65+CD8+ T세포는 최소 증가를 보였다(도 3b). 그 결과를 도 3에 나타내었다. Ki-67+과 HLA-DR+CD38+ 빈도의 증가는 PD-1+CD8+ T세포에서도 관찰되었고, 그 결과를 도 4에 나타내었다. 상기 결과로부터 anti-PD-1 치료가 PD-1+CD8+ T세포를 활성화시키고, 이러한 현상이 약물 투여 후 시간이 지남에 따라 증가 후 감소한다는 것을 알 수 있었다. The degree of T cell activation (CD38, HLA-DR) and proliferation (Ki-67) of CD8 T cells in the peripheral blood withdrawn on day 0 and day 7 of drug administration was confirmed by phenotypic markers. In the previous results obtained from six patients with non-small cell lung cancer, Ki-67 + frequency was increased in PD-1 + CD8 + T cells at 7 days, but significantly decreased at 21 days. Thus, 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 (FIG. 3A). As a result, 7 days after drug administration, tumor-specific NY-ESO-1 + CD8 + T cells showed a significant increase in the frequency of Ki-67 + and HLA-DR + CD38 + but without HCMV-specific pp65 + CD8 + T cells showed minimal increase (FIG. 3B). The results are shown in FIG. An increase in the frequency of Ki-67 + and HLA-DR + CD38 + was also observed in PD-1 + CD8 + T cells, and the results are shown in FIG. 4. The results indicate 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+ T세포의 초기 증식 반응으로 TET 환자에서 anti-PD-1 치료에 대한 종양 반응과 예후를 예측 가능하다.Early proliferative responses of PD-1 + CD8 + T cells can predict tumor response and prognosis for anti-PD-1 therapy in TET patients.
약물 투여 후 치료 반응을 예측 가능한 바이오 마커를 발굴하기 위해서, PD-1+CD8+ T세포, CD8+ T세포, 및 CD4+ T세포의 빈도 및 표현형 표지자를 포함하여 멀티칼라 유세포 분석으로부터 도출된 135개 파라미터를 통합 분석하였다. 상기 상세 분석값을 표 4와 표 5에 나타내었다.To identify biomarkers predictable for treatment response following drug administration, 135 derived from multicolor flow cytometry, including frequency and phenotypic markers of PD-1 + CD8 + T cells, CD8 + T cells, and CD4 + T cells. Dog parameters were analyzed together. The detailed analysis values are shown in Tables 4 and 5.
Time pointTime point | ParameterParameter | log2ratiolog2ratio | AUCAUC | PP |
Day 0Day 0 | CD3+ / lymphocyteCD3 + / lymphocyte | -0.0821-0.0821 | 0.545 0.545 | 0.680 0.680 |
Day 7Day 7 | CD3+ / lymphocyteCD3 + / lymphocyte | -0.1880-0.1880 | 0.575 0.575 | 0.496 0.496 |
Fold changeFold change | CD3+ / lymphocyteCD3 + / lymphocyte | -0.0431-0.0431 | 0.591 0.591 | 0.408 0.408 |
Day 0Day 0 | CD8+ / CD3+CD8 + / CD3 + | -0.2217-0.2217 | 0.698 0.698 | 0.072 0.072 |
Day 7Day 7 | CD8+ / CD3+CD8 + / CD3 + | -0.2372-0.2372 | 0.650 0.650 | 0.173 0.173 |
Fold changeFold change | CD8+ / CD3+CD8 + / CD3 + | 0.05600.0560 | 0.548 0.548 | 0.664 0.664 |
Day 0Day 0 | CD4+ / CD3+CD4 + / CD3 + | 0.18340.1834 | 0.668 0.668 | 0.127 0.127 |
Day 7Day 7 | CD4+ / CD3+CD4 + / CD3 + | 0.10690.1069 | 0.691 0.691 | 0.083 0.083 |
Fold changeFold change | CD4+ / CD3+CD4 + / CD3 + | -0.0589-0.0589 | 0.530 0.530 | 0.788 0.788 |
Day 0Day 0 | CD8+ / CD4+CD8 + / CD4 + | -0.4021-0.4021 | 0.693 0.693 | 0.079 0.079 |
Day 7Day 7 | CD8+ / CD4+CD8 + / CD4 + | -0.3626-0.3626 | 0.666 0.666 | 0.132 0.132 |
Fold changeFold change | CD8+ / CD4+CD8 + / CD4 + | 0.05550.0555 | 0.523 0.523 | 0.836 0.836 |
Day 0Day 0 | Naive (CCR7+CD45RA+) / CD8+Naive (CCR7 + CD45RA +) / CD8 + | -0.1616-0.1616 | 0.523 0.523 | 0.836 0.836 |
Day 7Day 7 | Naive (CCR7+CD45RA+) / CD8+Naive (CCR7 + CD45RA +) / CD8 + | 0.08630.0863 | 0.559 0.559 | 0.591 0.591 |
Fold changeFold change | Naive (CCR7+CD45RA+) / CD8+Naive (CCR7 + CD45RA +) / CD8 + | -0.1298-0.1298 | 0.518 0.518 | 0.869 0.869 |
Day 0Day 0 | Central memory (CCR7+CD45RA-) / CD8+Central memory (CCR7 + CD45RA-) / CD8 + | -0.0794-0.0794 | 0.582 0.582 | 0.457 0.457 |
Day 7Day 7 | Central memory (CCR7+CD45RA-) / CD8+Central memory (CCR7 + CD45RA-) / CD8 + | 0.05890.0589 | 0.532 0.532 | 0.773 0.773 |
Fold changeFold change | Central memory (CCR7+CD45RA-) / CD8+Central memory (CCR7 + CD45RA-) / CD8 + | 0.20310.2031 | 0.527 0.527 | 0.804 0.804 |
Day 0Day 0 | Effector memory (CCR7-CD45RA-) / CD8+Effector memory (CCR7-CD45RA-) / CD8 + | -0.3510-0.3510 | 0.550 0.550 | 0.650 0.650 |
Day 7Day 7 | Effector memory (CCR7-CD45RA-) / CD8+Effector memory (CCR7-CD45RA-) / CD8 + | -0.4528-0.4528 | 0.559 0.559 | 0.591 0.591 |
Fold changeFold change | Effector memory (CCR7-CD45RA-) / CD8+Effector memory (CCR7-CD45RA-) / CD8 + | -0.0426-0.0426 | 0.505 0.505 | 0.967 0.967 |
Day 0Day 0 | Effector memory RA (CCR7-CD45RA+) / CD8+Effector memory RA (CCR7-CD45RA +) / CD8 + | 0.46830.4683 | 0.595 0.595 | 0.386 0.386 |
Day 7Day 7 | Effector memory RA (CCR7-CD45RA+) / CD8+Effector memory RA (CCR7-CD45RA +) / CD8 + | 0.49110.4911 | 0.595 0.595 | 0.386 0.386 |
Fold changeFold change | Effector memory RA (CCR7-CD45RA+) / CD8+Effector memory RA (CCR7-CD45RA +) / CD8 + | 0.05890.0589 | 0.564 0.564 | 0.563 0.563 |
Day 0Day 0 | PD-1+ / CD8+PD-1 + / CD8 + | -0.2510-0.2510 | 0.593 0.593 | 0.397 0.397 |
Day 7Day 7 | PD-1+ / CD8+PD-1 + / CD8 + | -0.1152-0.1152 | 0.564 0.564 | 0.563 0.563 |
Fold changeFold change | PD-1+ / CD8+PD-1 + / CD8 + | 0.20650.2065 | 0.591 0.591 | 0.409 0.409 |
Day 0Day 0 | TIGIT+ / CD8+TIGIT + / CD8 + | 0.28170.2817 | 0.523 0.523 | 0.836 0.836 |
Day 7Day 7 | TIGIT+ / CD8+TIGIT + / CD8 + | -0.2226-0.2226 | 0.509 0.509 | 0.934 0.934 |
Fold changeFold change | TIGIT+ / CD8+TIGIT + / CD8 + | -0.1333-0.1333 | 0.607 0.607 | 0.331 0.331 |
Day 0Day 0 | PD-1+TIGIT+ / CD8+PD-1 + TIGIT + / CD8 + | -0.8299-0.8299 | 0.568 0.568 | 0.536 0.536 |
Day 7Day 7 | PD-1+TIGIT+ / CD8+PD-1 + TIGIT + / CD8 + | -0.6407-0.6407 | 0.564 0.564 | 0.563 0.563 |
Fold changeFold change | PD-1+TIGIT+ / CD8+PD-1 + TIGIT + / CD8 + | 0.26300.2630 | 0.507 0.507 | 0.951 0.951 |
Day 0Day 0 | HLA-DR+CD38+ / CD8+HLA-DR + CD38 + / CD8 + | 0.37000.3700 | 0.677 0.677 | 0.107 0.107 |
Day 7Day 7 | HLA-DR+CD38+ / CD8+HLA-DR + CD38 + / CD8 + | 0.53560.5356 | 0.595 0.595 | 0.386 0.386 |
Fold changeFold change | HLA-DR+CD38+ / CD8+HLA-DR + CD38 + / CD8 + | -0.1349-0.1349 | 0.659 0.659 | 0.148 0.148 |
Day 0Day 0 | Granzyme B+ / CD8+Granzyme B + / CD8 + | 0.26390.2639 | 0.677 0.677 | 0.107 0.107 |
Day 7Day 7 | Granzyme B+ / CD8+Granzyme B + / CD8 + | 0.22930.2293 | 0.648 0.648 | 0.180 0.180 |
Fold changeFold change | Granzyme B+ / CD8+Granzyme B + / CD8 + | -0.0270-0.0270 | 0.600 0.600 | 0.363 0.363 |
Day 0Day 0 | Ki-67+ / CD8+Ki-67 + / CD8 + | -0.2722-0.2722 | 0.518 0.518 | 0.869 0.869 |
Day 7Day 7 | Ki-67+ / CD8+Ki-67 + / CD8 + | 0.09500.0950 | 0.514 0.514 | 0.901 0.901 |
Fold changeFold change | Ki-67+ / CD8+Ki-67 + / CD8 + | -0.2188-0.2188 | 0.605 0.605 | 0.342 0.342 |
Day 0Day 0 | CD28- / CD8+CD28- / CD8 + | 0.37150.3715 | 0.664 0.664 | 0.137 0.137 |
Day 7Day 7 | CD28- / CD8+CD28- / CD8 + | 0.18600.1860 | 0.650 0.650 | 0.173 0.173 |
Fold changeFold change | CD28- / CD8+CD28- / CD8 + | 0.02770.0277 | 0.584 0.584 | 0.444 0.444 |
Day 0Day 0 | CD57+ / CD8+CD57 + / CD8 + | 0.50140.5014 | 0.757 0.757 | 0.020 0.020 |
Day 7Day 7 | CD57+ / CD8+CD57 + / CD8 + | 0.56840.5684 | 0.755 0.755 | 0.021 0.021 |
Fold changeFold change | CD57+ / CD8+CD57 + / CD8 + | 0.00000.0000 | 0.520 0.520 | 0.852 0.852 |
Day 0Day 0 | CD28-CD57+ / CD8+CD28-CD57 + / CD8 + | 0.49290.4929 | 0.627 0.627 | 0.248 0.248 |
Day 7Day 7 | CD28-CD57+ / CD8+CD28-CD57 + / CD8 + | 0.54700.5470 | 0.682 0.682 | 0.099 0.099 |
Fold changeFold change | CD28-CD57+ / CD8+CD28-CD57 + / CD8 + | 0.05660.0566 | 0.555 0.555 | 0.620 0.620 |
Day 0Day 0 | HLA-DR+CD38+ / PD-1+CD8+HLA-DR + CD38 + / PD-1 + CD8 + | -0.4546-0.4546 | 0.700 0.700 | 0.069 0.069 |
Day 7Day 7 | HLA-DR+CD38+ / PD-1+CD8+HLA-DR + CD38 + / PD-1 + CD8 + | 0.37110.3711 | 0.514 0.514 | 0.901 0.901 |
Fold changeFold change | HLA-DR+CD38+ / PD-1+CD8+HLA-DR + CD38 + / PD-1 + CD8 + | 0.80600.8060 | 0.727 0.727 | 0.039 0.039 |
Day 0Day 0 | Granzyme B+ / PD-1+CD8+Granzyme B + / PD-1 + CD8 + | -0.1890-0.1890 | 0.577 0.577 | 0.483 0.483 |
Day 7Day 7 | Granzyme B+ / PD-1+CD8+Granzyme B + / PD-1 + CD8 + | -0.1106-0.1106 | 0.566 0.566 | 0.549 0.549 |
Fold changeFold change | Granzyme B+ / PD-1+CD8+Granzyme B + / PD-1 + CD8 + | 0.04060.0406 | 0.514 0.514 | 0.901 0.901 |
Day 0Day 0 | Ki-67+ / PD-1+CD8+Ki-67 + / PD-1 + CD8 + | -1.0305-1.0305 | 0.791 0.791 | 0.008 0.008 |
Day 7Day 7 | Ki-67+ / PD-1+CD8+Ki-67 + / PD-1 + CD8 + | 0.29750.2975 | 0.536 0.536 | 0.741 0.741 |
Fold changeFold change | Ki-67+ / PD-1+CD8+Ki-67 + / PD-1 + CD8 + | 1.01581.0158 | 0.859 0.859 | 0.001 0.001 |
Day 0Day 0 | CD28- / PD-1+CD8+CD28- / PD-1 + CD8 + | -0.1944-0.1944 | 0.536 0.536 | 0.741 0.741 |
Day 7Day 7 | CD28- / PD-1+CD8+CD28- / PD-1 + CD8 + | -0.2530-0.2530 | 0.573 0.573 | 0.509 0.509 |
Fold changeFold change | CD28- / PD-1+CD8+CD28- / PD-1 + CD8 + | -0.1255-0.1255 | 0.516 0.516 | 0.885 0.885 |
Day 0Day 0 | CD57+ / PD-1+CD8+CD57 + / PD-1 + CD8 + | -0.1578-0.1578 | 0.648 0.648 | 0.180 0.180 |
Day 7Day 7 | CD57+ / PD-1+CD8+CD57 + / PD-1 + CD8 + | -0.3650-0.3650 | 0.664 0.664 | 0.137 0.137 |
Fold changeFold change | CD57+ / PD-1+CD8+CD57 + / PD-1 + CD8 + | -0.1178-0.1178 | 0.666 0.666 | 0.131 0.131 |
Day 0Day 0 | CD28-CD57+ / PD-1+CD8+CD28-CD57 + / PD-1 + CD8 + | -0.1275-0.1275 | 0.582 0.582 | 0.457 0.457 |
Day 7Day 7 | CD28-CD57+ / PD-1+CD8+CD28-CD57 + / PD-1 + CD8 + | -0.3139-0.3139 | 0.584 0.584 | 0.445 0.445 |
Fold changeFold change | CD28-CD57+ / PD-1+CD8+CD28-CD57 + / PD-1 + CD8 + | -0.2572-0.2572 | 0.573 0.573 | 0.508 0.508 |
Time pointTime point | ParameterParameter | log2ratiolog2ratio | AUCAUC | PP |
Day 0Day 0 | Treg (CD25hiCD127loFoxP3+) / CD4+Treg (CD25hiCD127loFoxP3 +) / CD4 + | -0.2853 -0.2853 | 0.700 0.700 | 0.069 0.069 |
Day 7Day 7 | Treg / CD4+Treg / CD4 + | -0.6635 -0.6635 | 0.764 0.764 | 0.017 0.017 |
Fold changeFold change | Treg / CD4+Treg / CD4 + | 0.0529 0.0529 | 0.559 0.559 | 0.591 0.591 |
Day 0Day 0 | Fr.I Treg (FoxP3loCD45RA+) / CD4+Fr.I Treg (FoxP3loCD45RA +) / CD4 + | -0.4381 -0.4381 | 0.664 0.664 | 0.137 0.137 |
Day 7Day 7 | Fr.I Treg / CD4+Fr.I Treg / CD4 + | -0.8074 -0.8074 | 0.627 0.627 | 0.247 0.247 |
Fold changeFold change | Fr.I Treg / CD4+Fr.I Treg / CD4 + | 0.1459 0.1459 | 0.514 0.514 | 0.901 0.901 |
Day 0Day 0 | Fr.II Treg (FoxP3hiCD45RA-) / CD4+Fr.II Tregs (FoxP3hiCD45RA-) / CD4 + | -0.9687 -0.9687 | 0.705 0.705 | 0.063 0.063 |
Day 7Day 7 | Fr.II Treg / CD4+Fr.II Tregs / CD4 + | -0.7892 -0.7892 | 0.743 0.743 | 0.027 0.027 |
Fold changeFold change | Fr.II Treg / CD4+Fr.II Tregs / CD4 + | 0.0703 0.0703 | 0.511 0.511 | 0.918 0.918 |
Day 0Day 0 | Fr.III Treg (FoxP3loCD45RA-) / CD4+Fr.III Tregs (FoxP3loCD45RA-) / CD4 + | -0.0411 -0.0411 | 0.611 0.611 | 0.312 0.312 |
Day 7Day 7 | Fr.III Treg / CD4+Fr.III Tregs / CD4 + | -0.1913 -0.1913 | 0.695 0.695 | 0.076 0.076 |
Fold changeFold change | Fr.III Treg / CD4+Fr.III Tregs / CD4 + | -0.2361 -0.2361 | 0.607 0.607 | 0.332 0.332 |
Day 0Day 0 | Ki-67+ / TregKi-67 + / Treg | -0.4407 -0.4407 | 0.650 0.650 | 0.173 0.173 |
Day 7Day 7 | Ki-67+ / TregKi-67 + / Treg | 0.0000 0.0000 | 0.564 0.564 | 0.563 0.563 |
Fold changeFold change | Ki-67+ / TregKi-67 + / Treg | 0.4639 0.4639 | 0.677 0.677 | 0.107 0.107 |
Day 0Day 0 | CCR4+ / TregCCR4 + / Tregs | -0.8524 -0.8524 | 0.664 0.664 | 0.137 0.137 |
Day 7Day 7 | CCR4+ / TregCCR4 + / Tregs | -0.6169 -0.6169 | 0.691 0.691 | 0.083 0.083 |
Fold changeFold change | CCR4+ / TregCCR4 + / Tregs | -0.2810 -0.2810 | 0.618 0.618 | 0.283 0.283 |
Day 0Day 0 | CTLA-4+ / TregCTLA-4 + / Tregs | 0.0638 0.0638 | 0.575 0.575 | 0.496 0.496 |
Day 7Day 7 | CTLA-4+ / TregCTLA-4 + / Tregs | -0.0235 -0.0235 | 0.614 0.614 | 0.302 0.302 |
Fold changeFold change | CTLA-4+ / TregCTLA-4 + / Tregs | -0.0388 -0.0388 | 0.500 0.500 | 1.000 1.000 |
Day 0Day 0 | ICOS+ / TregICOS + / Tregs | -0.3842 -0.3842 | 0.664 0.664 | 0.137 0.137 |
Day 7Day 7 | ICOS+ / TregICOS + / Tregs | -0.5940 -0.5940 | 0.686 0.686 | 0.091 0.091 |
Fold changeFold change | ICOS+ / TregICOS + / Tregs | 0.0731 0.0731 | 0.527 0.527 | 0.804 0.804 |
Day 0Day 0 | PD-1+ / TregPD-1 + / Tregs | 0.1712 0.1712 | 0.518 0.518 | 0.869 0.869 |
Day 7Day 7 | PD-1+ / TregPD-1 + / Tregs | 0.5267 0.5267 | 0.636 0.636 | 0.215 0.215 |
Fold changeFold change | PD-1+ / TregPD-1 + / Tregs | 0.2996 0.2996 | 0.607 0.607 | 0.331 0.331 |
Day 0Day 0 | TIGIT+ / TregTIGIT + / Tregs | -0.3268 -0.3268 | 0.595 0.595 | 0.386 0.386 |
Day 7Day 7 | TIGIT+ / TregTIGIT + / Tregs | -0.2409 -0.2409 | 0.686 0.686 | 0.090 0.090 |
Fold changeFold change | TIGIT+ / TregTIGIT + / Tregs | -0.0759 -0.0759 | 0.525 0.525 | 0.820 0.820 |
Day 0Day 0 | Ki-67+ / Fr. II TregKi-67 + / Fr. II Tregs | -0.0636 -0.0636 | 0.536 0.536 | 0.741 0.741 |
Day 7Day 7 | Ki-67+ / Fr. II TregKi-67 + / Fr. II Tregs | 0.4462 0.4462 | 0.727 0.727 | 0.039 0.039 |
Fold changeFold change | Ki-67+ / Fr. II TregKi-67 + / Fr. II Tregs | 0.6301 0.6301 | 0.732 0.732 | 0.035 0.035 |
Day 0Day 0 | CCR4+ / Fr. II TregCCR4 + / Fr. II Tregs | -0.6595 -0.6595 | 0.623 0.623 | 0.265 0.265 |
Day 7Day 7 | CCR4+ / Fr. II TregCCR4 + / Fr. II Tregs | -0.3735 -0.3735 | 0.677 0.677 | 0.107 0.107 |
Fold changeFold change | CCR4+ / Fr. II TregCCR4 + / Fr. II Tregs | -0.2397 -0.2397 | 0.630 0.630 | 0.239 0.239 |
Day 0Day 0 | CTLA-4+ / Fr. II TregCTLA-4 + / Fr. II Tregs | -0.0724 -0.0724 | 0.614 0.614 | 0.302 0.302 |
Day 7Day 7 | CTLA-4+ / Fr. II TregCTLA-4 + / Fr. II Tregs | -0.0275 -0.0275 | 0.502 0.502 | 0.984 0.984 |
Fold changeFold change | CTLA-4+ / Fr. II TregCTLA-4 + / Fr. II Tregs | 0.0976 0.0976 | 0.670 0.670 | 0.121 0.121 |
Day 0Day 0 | ICOS+ / Fr. II TregICOS + / Fr. II Tregs | 0.2493 0.2493 | 0.557 0.557 | 0.606 0.606 |
Day 7Day 7 | ICOS+ / Fr. II TregICOS + / Fr. II Tregs | -0.0883 -0.0883 | 0.564 0.564 | 0.563 0.563 |
Fold changeFold change | ICOS+ / Fr. II TregICOS + / Fr. II Tregs | 0.2717 0.2717 | 0.557 0.557 | 0.606 0.606 |
Day 0Day 0 | PD-1+ / Fr. II TregPD-1 + / Fr. II Tregs | 0.0756 0.0756 | 0.509 0.509 | 0.934 0.934 |
Day 7Day 7 | PD-1+ / Fr. II TregPD-1 + / Fr. II Tregs | 0.2174 0.2174 | 0.577 0.577 | 0.483 0.483 |
Fold changeFold change | PD-1+ / Fr. II TregPD-1 + / Fr. II Tregs | 0.7370 0.7370 | 0.634 0.634 | 0.222 0.222 |
Day 0Day 0 | TIGIT+ / Fr. II TregTIGIT + / Fr. II Tregs | -0.0702 -0.0702 | 0.573 0.573 | 0.509 0.509 |
Day 7Day 7 | TIGIT+ / Fr. II TregTIGIT + / Fr. II Tregs | -0.1026 -0.1026 | 0.627 0.627 | 0.248 0.248 |
Fold changeFold change | TIGIT+ / Fr. II TregTIGIT + / Fr. II Tregs | 0.0135 0.0135 | 0.525 0.525 | 0.820 0.820 |
Day 0Day 0 | Ki-67+ / Fr. III TregKi-67 + / Fr. III Tregs | -0.0081 -0.0081 | 0.518 0.518 | 0.869 0.869 |
Day 7 |
Ki-67+ / Fr. III TregKi-67 + / Fr. III Tregs | 0.7903 0.7903 | 0.623 0.623 | 0.265 0.265 |
Fold changeFold change | Ki-67+ / Fr. III TregKi-67 + / Fr. III Tregs | 0.6147 0.6147 | 0.627 0.627 | 0.248 0.248 |
Day 0 |
CCR4+ / Fr. III TregCCR4 + / Fr. III Tregs | -0.4385 -0.4385 | 0.614 0.614 | 0.302 0.302 |
Day 7 |
CCR4+ / Fr. III TregCCR4 + / Fr. III Tregs | -0.9978 -0.9978 | 0.659 0.659 | 0.148 0.148 |
Fold changeFold change | CCR4+ / Fr. III TregCCR4 + / Fr. III Tregs | -0.3426 -0.3426 | 0.652 0.652 | 0.166 0.166 |
Day 0 |
CTLA-4+ / Fr. III TregCTLA-4 + / Fr. III Tregs | 0.1780 0.1780 | 0.627 0.627 | 0.248 0.248 |
Day 7 |
CTLA-4+ / Fr. III TregCTLA-4 + / Fr. III Tregs | 0.0604 0.0604 | 0.570 0.570 | 0.522 0.522 |
Fold changeFold change | CTLA-4+ / Fr. III TregCTLA-4 + / Fr. III Tregs | -0.0132 -0.0132 | 0.509 0.509 | 0.934 0.934 |
Day 0 |
ICOS+ / Fr. III TregICOS + / Fr. III Tregs | -0.0509 -0.0509 | 0.527 0.527 | 0.804 0.804 |
Day 7 |
ICOS+ / Fr. III TregICOS + / Fr. III Tregs | -0.6131 -0.6131 | 0.661 0.661 | 0.143 0.143 |
Fold changeFold change | ICOS+ / Fr. III TregICOS + / Fr. III Tregs | -0.3117 -0.3117 | 0.573 0.573 | 0.509 0.509 |
Day 0 |
PD-1+ / Fr. III TregPD-1 + / Fr. III Tregs | 0.1841 0.1841 | 0.511 0.511 | 0.918 0.918 |
Day 7 |
PD-1+ / Fr. III TregPD-1 + / Fr. III Tregs | -0.1886 -0.1886 | 0.532 0.532 | 0.773 0.773 |
Fold changeFold change | PD-1+ / Fr. III TregPD-1 + / Fr. III Tregs | -0.0780 -0.0780 | 0.527 0.527 | 0.804 0.804 |
Day 0 |
TIGIT+ / Fr. III TregTIGIT + / Fr. III Tregs | -0.1227 -0.1227 | 0.514 0.514 | 0.901 0.901 |
Day 7 |
TIGIT+ / Fr. III TregTIGIT + / Fr. III Tregs | -0.1896 -0.1896 | 0.648 0.648 | 0.180 0.180 |
Fold changeFold change | TIGIT+ / Fr. III TregTIGIT + / Fr. III Tregs | -0.0399 -0.0399 | 0.566 0.566 | 0.549 0.549 |
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 5, 표 4, 및 표 5에 나타내었다.In addition to the measurements on days 0 and 7, the changes in the parameters designed after anti-PD-1 treatment were evaluated. Patients with disease control for more than 6 months (n = 11) and patients with disease control for less than 6 months (n = 20) were compared, and the volcano plot was expressed as the log2 ratio 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 FIG. 5, Table 4, and Table 5.
분석 결과, 소수의 파라미터만이 질병통제 되거나 되지 않은 환자간에 차이를 나타내었는데, PD-1+CD8+ T세포에서 Ki-67 발현의 변화(Ki-67D7/D0)가 파라미터들 중에서 가장 큰 변화가 있었다(P <0.05). 또한 Ki-67D7/D0 는 질병통제대조군에서도 가장 높은 예측 정확성을 보였고(AUC = 0.86, 95% CI 0.71-1.00, P = 0.001), 특히 안정된 질병상태(stable disease; SD), 또는 진행성 질병상태(progressive disease; PD)에 비해서 부분 반응(partial response; PR)을 보이는 환자군에서 더 측정값이 높게 나타났다. 상기 결과를 도 6에 나타내었다.As a result, only a few parameters showed differences among patients with or without disease control. Ki-67 expression in PD-1 + CD8 + T cells (Ki-67 D7 / D0 ) was the largest change among the parameters. (P <0.05). Ki-67 D7 / D0 also showed the highest predictive accuracy in the disease control control group (AUC = 0.86, 95% CI 0.71-1.00, P = 0.001), particularly stable disease (SD), or progressive disease status. The measurement was higher in the patient group with partial response (PR) compared to progressive disease (PD). The results are shown in FIG. 6.
ROC 곡선으로부터 2.8을 최적 컷오프값으로 결정하였고, 상기 컷오프값에 따라 환자를 이분화하였다. 질병통제대조군에서의 감도, 특이도, 음성 예측도(NPV), 및 양성 예측도(PPV)는 각각 90.9%, 75.0%, 93.8%, 및 66.7%로 나타났다. 높은 민감도와 음성 예측도는 Ki-67D7/D0 가 anti-PD-1 치료로 효과를 얻지 못할 것으로 예상되는 환자를 정확하게 예측할 수 있다는 것을 의미한다. Ki-67D7/D0 컷오프값이 2.8 미만인 환자들은 질병통제율(durable disease control rates)이 낮은 것으로 나타났다. 또한 이 환자들은 질병 무진행 생존율 (progression-free survival; PFS)이 매우 낮은 것으로 나타났다. 상기 결과를 도 7에 나타내었다. 또한 다양한 임상병리학적 요인들을 반영하여 조정한 다변수 분석에서도 Ki-67D7/D0 가 질병 무진행 생존율의 독립적으로 유의미한 요소인 것을 확인하였다(adjusted hazard ratio; aHR 0.20, 95% confidence interval; CI 0.07-0.56, P=0.002). 이를 하기 표 6에 나타내었다.2.8 was determined as the optimal cutoff value from the ROC curve and the patients were divided according to the cutoff value. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) in the disease control control group were 90.9%, 75.0%, 93.8%, and 66.7%, respectively. High sensitivity and negative predictiveness mean that Ki-67 D7 / D0 can accurately predict patients who are not expected to benefit from anti-PD-1 treatment. Patients with a Ki-67 D7 / D0 cutoff value of less than 2.8 were found to have low disease control rates. These patients also had very low disease progression-free survival (PFS). The results are shown in FIG. Multivariate analysis adjusted to reflect various clinicopathological factors also confirmed that Ki-67 D7 / D0 was independently a significant factor in disease-free survival (adjusted hazard ratio; aHR 0.20, 95% confidence interval; CI 0.07). -0.56, P = 0.002). This is shown in Table 6 below.
UnivariateUnivariate | MultivariateMultivariate | ||||||
VariableVariable | CategoryCategory | HRHR | 95% CI95% CI | PP | aHRaHR | 95% CI95% CI | PP |
Ki-67D7/D0 Ki-67 D7 / D0 | < 2.8<2.8 | 1.001.00 | 0.0300.030 | 1.001.00 | 0.0020.002 | ||
≥ 2.8≥ 2.8 | 0.400.40 | 0.17-0.910.17-0.91 | 0.200.20 | 0.07-0.560.07-0.56 | |||
AgeAge | < 60 yrs<60 yrs | 1.001.00 | 0.4570.457 | 1.001.00 | 0.0280.028 | ||
≥ 60 yrs≥ 60 yrs | 1.341.34 | 0.62-2.920.62-2.92 | 2.952.95 | 1.12-7.781.12-7.78 | |||
SexSex | MaleMale | 1.001.00 | 0.0860.086 | 1.001.00 | 0.0110.011 | ||
FemaleFemale | 2.032.03 | 0.90-4.560.90-4.56 | 3.303.30 | 1.31-8.281.31-8.28 | |||
HistologyHistology | ThymomaThymoma | 1.001.00 | 0.9860.986 | 1.001.00 | 0.6070.607 | ||
Thymic ca.Thymic ca. | 0.990.99 | 0.37-2.650.37-2.65 | 1.321.32 | 0.46-3.730.46-3.73 | |||
Prior chemotherapyPrior chemotherapy | < 3rd line<3 rd line | 1.001.00 | 0.0980.098 | 1.001.00 | 0.8760.876 | ||
≥ 3rd line≥ 3 rd line | 2.002.00 | 0.88-4.540.88-4.54 | 0.930.93 | 0.40-2.200.40-2.20 | |||
Tumor burdenTumor burden | < 12.5 cm<12.5 cm | 1.001.00 | 0.9260.926 | 1.001.00 | 0.1180.118 | ||
≥ 12.5 cm≥ 12.5 cm | 1.041.04 | 0.48-2.250.48-2.25 | 2.132.13 | 0.83-5.470.83-5.47 |
anti-PD-1 치료 후 PD-1+ CD8 T세포에서 Ki-67 발현 변화는 비소세포폐암 환자의 종양 반응 및 예후와 관련이 있다.Changes in Ki-67 expression in PD-1 + CD8 T cells after anti-PD-1 treatment are associated with tumor response and prognosis in patients with NSCLC.
예측 바이오 마커로서 Ki-67D7/D0 의 가치를 검증하기 위해, anti-PD-1 치료된 비소세포폐암 환자의 독립적 검증 코호트를 사용하였다. 그 모식도를 도 8a에, 결과를 도 8b와 도 8c에 나타내었다. 실험 결과, TET 환자와 유사하게, PD-1-CD8+ T세포와 비교하여 PD-1+CD8+ T세포에서 Ki-67 발현이 더 크게 증가하였고, Ki-67D7/D0 는 진행성 질병상태(progressive disease; PD) 환자보다 부분 반응(partial response; PR)을 보이는 환자군에서 더 측정값이 높게 나타났다. 이 코호트 조사에서, 컷오프값 2.8을 기준으로 암의 진행이 없는 질병통제대조군의 감도, 특이도, 음성 예측도(NPV), 및 양성 예측도(PPV)는 각각 80.0%, 73.7%, 87.5%, 및 61.5%로 나타났다. 구체적으로, Ki-67D7/D0 ≥2.8인 환자의 경우 질병통제율이 현저히 낮게 나타났고(도 10a), 질병 무진행 생존율(PFS)과 전체 생존율(OS)이 유의하게 높았다(도 10b, 및 도 10c). 또한 다양한 임상병리학적 요인들을 반영하여 조정한 다변수 분석에서도 Ki-67D7/D0 가 질병 무진행 생존율(aHR 0.14, 95% CI 0.04-0.46, P = 0.001), 및 전체 생존율(aHR 0.14, 95% CI 0.03-0.63, P = 0.010)의 독립적인 상관 관계를 확인하였다(표 7, 표 8 참조).To verify the value of Ki-67 D7 / D0 as a predictive biomarker, an independent validation cohort of patients with anti-PD-1 treated non-small cell lung cancer was used. The schematic diagram is shown to FIG. 8A, and the result is shown to FIG. 8B and FIG. 8C. 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 ( The measurements were higher in the group of patients with partial response (PR) than those with progressive disease (PD). In this cohort study, 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%, 87.5%, And 61.5%. Specifically, patients with Ki-67 D7 / D0 ≥2.8 showed significantly lower disease control rates (FIG. 10A), and disease free progression survival (PFS) and overall survival (OS) were significantly higher (FIG. 10B, and 10c). In the multivariate analysis adjusted for various clinicopathological factors, Ki-67 D7 / D0 showed disease-free survival (aHR 0.14, 95% CI 0.04-0.46, P = 0.001), and overall survival (aHR 0.14, 95). Independent correlations of% CI 0.03-0.63, P = 0.010) were confirmed (see Table 7, Table 8).
UnivariateUnivariate | MultivariateMultivariate | ||||||
VariableVariable | CategoryCategory | HRHR | 95% CI95% CI | PP | aHRaHR | 95% CI95% CI | PP |
Ki-67D7/D0 Ki-67 D7 / D0 | < 2.8<2.8 | 1.001.00 | 0.0130.013 | 1.001.00 | 0.0010.001 | ||
≥ 2.8≥ 2.8 | 0.190.19 | 0.05-0.700.05-0.70 | 0.140.14 | 0.04-0.460.04-0.46 | |||
AgeAge | < 60 yrs<60 yrs | 1.001.00 | 0.2360.236 | 1.001.00 | 0.8530.853 | ||
≥ 60 yrs≥ 60 yrs | 0.450.45 | 0.12-1.690.12-1.69 | 1.121.12 | 0.32-5.660.32-5.66 | |||
SexSex | MaleMale | 1.001.00 | 0.4440.444 | 1.001.00 | 0.6420.642 | ||
FemaleFemale | 0.590.59 | 0.15-2.300.15-2.30 | 1.311.31 | 0.42-4.030.42-4.03 | |||
HistologyHistology | AdenocarcinomaAdenocarcinoma | 1.001.00 | 0.3880.388 | 1.001.00 | 0.5140.514 | ||
Squamous cell ca.Squamous cell ca. | 0.310.31 | 0.05-1.720.05-1.72 | 1.091.09 | 0.34-3.520.34-3.52 | |||
OtherOther | 0.850.85 | 0.15-4.760.15-4.76 | 2.452.45 | 0.53-11.380.53-11.38 | |||
Prior chemotherapyPrior chemotherapy | < 3rd line<3 rd line | 1.001.00 | 0.0700.070 | 1.001.00 | 0.0950.095 | ||
≥ 3rd line≥ 3 rd line | 4.314.31 | 0.89-20.940.89-20.94 | 3.013.01 | 0.83-10.950.83-10.95 | |||
Tumor burdenTumor burden | < 7.5 cm<7.5 cm | 1.001.00 | 0.5790.579 | 1.001.00 | 0.8910.891 | ||
≥ 7.5 cm≥ 7.5 cm | 1.281.28 | 0.54-3.040.54-3.04 | 1.081.08 | 0.36-3.220.36-3.22 | |||
Anti-PD-1 agentAnti-PD-1 agent | NivolumabNivolumab | 1.001.00 | 0.8050.805 | 1.001.00 | 0.7220.722 | ||
PembrolizumabPembrolizumab | 0.900.90 | 0.38-2.130.38-2.13 | 0.830.83 | 0.30-2.300.30-2.30 |
UnivariateUnivariate | MultivariateMultivariate | ||||||
VariableVariable | CategoryCategory | HRHR | 95% CI95% CI | P-valueP-value | aHRaHR | 95% CI95% CI | P-valueP-value |
Ki-67D7/D0 Ki-67 D7 / D0 | < 2.8<2.8 | 1.001.00 | 0.0150.015 | 1.001.00 | 0.0100.010 | ||
≥ 2.8≥ 2.8 | 0.240.24 | 0.08-0.760.08-0.76 | 0.140.14 | 0.03-0.630.03-0.63 | |||
AgeAge | < 60 yrs<60 yrs | 1.001.00 | 0.4600.460 | 1.001.00 | 0.7940.794 | ||
≥ 60 yrs≥ 60 yrs | 0.670.67 | 0.23-1.940.23-1.94 | 1.201.20 | 0.30-4.870.30-4.87 | |||
SexSex | MaleMale | 1.001.00 | 0.4280.428 | 1.001.00 | 0.6540.654 | ||
FemaleFemale | 0.650.65 | 0.23-1.880.23-1.88 | 0.750.75 | 0.21-2.690.21-2.69 | |||
HistologyHistology | AdenocarcinomaAdenocarcinoma | 1.001.00 | 0.7500.750 | 1.001.00 | 0.5590.559 | ||
Squamous cell ca.Squamous cell ca. | 0.670.67 | 0.22-2.030.22-2.03 | 0.530.53 | 0.12-2.370.12-2.37 | |||
OtherOther | 0.700.70 | 0.15-3.280.15-3.28 | 1.591.59 | 0.26-9.830.26-9.83 | |||
Prior chemotherapyPrior chemotherapy | < 3rd line<3 rd line | 1.001.00 | 0.1230.123 | 1.001.00 | 0.0990.099 | ||
≥ 3rd line≥ 3 rd line | 2.272.27 | 0.80-6.410.80-6.41 | 3.423.42 | 0.79-14.720.79-14.72 | |||
Tumor burdenTumor burden | < 7.5 cm<7.5 cm | 1.001.00 | 0.2500.250 | 1.001.00 | 0.5320.532 | ||
≥ 7.5 cm≥ 7.5 cm | 1.811.81 | 0.66-5.000.66-5.00 | 1.481.48 | 0.43-5.040.43-5.04 | |||
Anti-PD-1 agentAnti-PD-1 agent | NivolumabNivolumab | 1.001.00 | 0.6180.618 | 1.001.00 | 0.8330.833 | ||
PembrolizumabPembrolizumab | 0.770.77 | 0.28-2.130.28-2.13 | 0.880.88 | 0.27-2.870.27-2.87 |
상기 실시예 및 결과로부터, anti-PD-1 치료 후에 PD-1+ CD8+ T세포의 초기 증식 반응으로부터 anti-PD-1에 대한 치료 효과가 없을 것으로 예상되는 음성 예측도(NPV) 고위 환자군을 예측하여 선별할 수 있음을 알 수 있었다. 이는 PD-1 면역항암제가 효과가 있는 환자군이 PD-1 면역항암제 치료를 받는 동안에 감수해야 하는 경제적, 시간적 손실을 사전에 방지할 수 있으므로, 의학 분야에서 크게 이용될 것으로 기대된다.From the above examples and results, a group of patients with negative predictive value (NPV) who were 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 It can be seen that the prediction can be selected. This is expected to be widely used in the medical field because a group of patients in which PD-1 immunosuppressive agent is effective can prevent the economic and time loss to be suffered while receiving PD-1 immunosuppressive agent.
면역항암제란 인체가 가지고 있는 고유의 면역계를 강화시켜 암에 대항력을 높여 주는 약물을 의미한다. 가장 대표적인 면역항암제인 항 PD-1 항체는 활성화된 T세포(면역세포)의 표면에 있는 단백질인 PD-1과 암세포의 표면에 있는 단백질인 PD-L1, PD-L2의 결합을 막아주는 치료제이다. 그러나 항 PD-1 면역항암제가 효과가 있는 환자군과 효과가 없는 환자군을 사전에 구분할 수 있는 방법이 부재하여, 효과가 없는 환자군의 경우 항 PD-1 면역항암제 치료에 대한 비용과 시간을 손실하고 있는 실정이다.Immune anticancer drugs mean drugs that enhance the body's own immune system to fight against cancer. Anti-PD-1 antibody, the most representative immune anticancer agent, is a therapeutic agent that blocks the binding of PD-1, a protein on the surface of activated T cells (immune cells) and PD-L1, PD-L2, proteins on the surface of cancer cells . However, there is no way to distinguish between the effective and ineffective groups of anti-PD-1 anticancer drugs, and the ineffective patients group is losing the cost and time for anti-PD-1 immunotherapy. It is true.
본 발명은 암 환자에서 면역 치료 효과를 예측하는 방법에 관한 것으로, 본 발명의 방법에 따르면 항 PD-1 면역항암제가 효과가 있는 환자군과 효과가 없는 환자군을 사전에 구분 가능하므로, 의학 분야에서 크게 이용될 것으로 기대된다.The present invention relates to a method for predicting the effects of immunotherapy in cancer patients, and according to the method of the present invention, it is possible to distinguish between a patient group in which an anti-PD-1 immunocancer drug is effective and an ineffective patient group. It is expected to be used.
Claims (21)
- (a) 피검체의 T 세포수를 측정하는 단계;(a) measuring the T cell number of the subject;(b) 상기 피검체에게 면역항암제를 투여하는 단계;(b) administering an immune anticancer agent to the subject;(c) 상기 피검체의 T 세포수를 1차 재측정하는 단계; (c) first re-measuring the T cell number of the subject;(d) (a)단계에서의 측정값보다 (c)단계에서의 측정값이 증가하는 것을 확인하는 단계;를 포함하는, 암 환자에 있어서 면역항암제 치료 효과를 예측하는 방법.(d) confirming that the measured value in step (c) is greater than the measured value in step (a).
- 제 1항에 있어서,The method of claim 1,상기 방법은 (e) 상기 피검체의 T 세포수를 2차 재측정하는 단계;를 추가로 포함하는, 방법The method further comprises (e) re-measuring the T cell number of the subject;
- 제 1항에 있어서,The method of claim 1,상기 (c)단계는 (b)단계로부터 1일 내지 14일에 수행하는 것을 특징으로 하는, 방법.Step (c) is characterized in that performed on day 1 to 14 from step (b).
- 제 3항에 있어서,The method of claim 3, wherein상기 방법은 (f) (c)단계에서의 측정값보다 (e)단계에서의 측정값이 감소하는 것을 확인하는 단계;를 추가로 포함하는, 방법.The method further comprises (f) confirming that the measured value in step (e) is less than the measured value in step (c).
- 제 2항에 있어서,The method of claim 2,상기 (e)단계는 (b)단계로부터 15일 내지 21일에 수행하는 것을 특징으로 하는, 방법.Step (e) is characterized in that it is carried out 15 to 21 days from step (b).
- 제 1항에 있어서,The method of claim 1,상기 T 세포수는 T 세포수, T 세포의 활성화 분비물, 및 T 세포 활성화도 중에서 T 세포수를 측정하는 것을 특징으로 하는, 방법.The T cell number is characterized in that for measuring the T cell number in T cell number, T cell activation secretion, and T cell activation degree.
- 제 6항에 있어서,The method of claim 6,상기 T 세포수는 Ki-67 발현값으로 측정하는 것을 특징으로 하는, 방법.The T cell number, characterized in that measured by Ki-67 expression value.
- 제 7항에 있어서,The method of claim 7, wherein상기 발현값은 유전자 발현값, 또는 단백질 발현값인, 방법.Wherein said expression value is a gene expression value or a protein expression value.
- 제 7항에 있어서,The method of claim 7, wherein상기 (c)단계의 측정값이 (a)단계의 측정값보다 2.8배 이상일 경우에, 상기 피검체의 면역항암제 치료 효과가 높을 것으로 예측하는 단계를 포함하는, 방법.If the measured value of step (c) is 2.8 times or more than the measured value of step (a), comprising the step of predicting that the anti-cancer drug treatment effect of the subject is high.
- 제 7항에 있어서,The method of claim 7, wherein상기 (c)단계의 측정값이 (a)단계의 측정값보다 2.8배 이상일 경우에, 상기 피검체의 예후가 좋을 것으로 예측하는 단계를 포함하는, 방법.Predicting that the subject's prognosis will be good if the measured value of step (c) is at least 2.8 times greater than the measured value of step (a).
- 제 7항에 있어서,The method of claim 7, wherein상기 (c)단계의 측정값이 (a)단계의 측정값보다 2.8배 미만일 경우에, 상기 피검체의 면역항암제 치료 효과가 낮을 것으로 예측하는 단계를 포함하는, 방법.If the measured value of the step (c) is less than 2.8 times the measured value of the step (a), the method comprising the step of predicting that the anti-cancer drug treatment effect is low.
- 제 7항에 있어서,The method of claim 7, wherein상기 (c)단계의 측정값이 (a)단계의 측정값보다 2.8배 미만일 경우에, 상기 피검체의 예후가 나쁠 것으로 예측하는 단계를 포함하는, 방법.Predicting that the prognosis of the subject is worse when the measured value of step (c) is less than 2.8 times than the measured value of step (a).
- 제 1항에 있어서,The method of claim 1,상기 암은 유방암, 자궁경부암, 신경교종, 뇌암, 흑색종, 폐암, 방광암, 전립선암, 백혈병, 신장암, 간암, 대장암, 췌장암, 위암, 담낭암, 난소암, 임파종, 골육종, 자궁암, 구강암, 기관지암, 비인두암, 후두암, 피부암, 혈액암, 갑상선암, 부갑상선암, 요관암, 선암, 및 흉선암으로 구성되는 그룹으로부터 선택되는 어느 하나 이상인, 방법.The cancer may be breast cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colon cancer, pancreatic cancer, gastric cancer, gallbladder cancer, ovarian cancer, lymphoma, osteosarcoma, uterine cancer, oral cancer, And at least one selected from the group consisting of bronchial cancer, nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, thyroid cancer, parathyroid cancer, ureter cancer, adenocarcinoma, and thymic cancer.
- 제 13항에 있어서,The method of claim 13,상기 암은 폐암 또는 흉선암인, 방법.The cancer is lung cancer or thymic cancer.
- 제 1항에 있어서,The method of claim 1,상기 면역항암제는 항 PD-1 항체, 항 PD-L1 항체, 또는 항 CTLA-4 항체인, 방법.Wherein said immunocancer agent is an anti PD-1 antibody, an anti PD-L1 antibody, or an anti CTLA-4 antibody.
- (a) 피검체의 T 세포수를 측정하는 단계;(a) measuring the T cell number of the subject;(b) 상기 피검체에게 면역항암제 후보물질을 투여하는 단계;(b) administering an immune anticancer agent candidate to the subject;(c) 상기 피검체의 T 세포수를 1차 재측정하는 단계; (c) first re-measuring the T cell number of the subject;(d) (a)단계에서의 측정값보다 (c)단계에서의 측정값이 증가하는 것을 확인하는 단계;를 포함하는, 암 환자에 있어서 면역항암제 후보물질의 암 치료 효과를 스크리닝하는 방법.(d) confirming that the measured value in step (c) is greater than the measured value in step (a). The method for screening a cancer therapeutic effect of an immunocancer drug candidate in a cancer patient.
- 제 16항에 있어서,The method of claim 16,상기 (c)단계는 (b)단계로부터 1일 내지 14일에 수행하는 것을 특징으로 하는, 방법.Step (c) is characterized in that performed on day 1 to 14 from step (b).
- 제 16항에 있어서,The method of claim 16,상기 T 세포수는 Ki-67 발현값으로 측정하는 것을 특징으로 하는, 방법.The T cell number, characterized in that measured by Ki-67 expression value.
- 제 16항에 있어서,The method of claim 16,상기 (c)단계의 측정값이 (a)단계의 측정값보다 2.8배 이상일 경우에, 상기 면역항암제 후보물질을 효과적인 면역항암제로 판단하는 단계를 포함하는, 방법.If the measured value of step (c) is 2.8 times or more than the measured value of step (a), comprising the step of judging the immuno-cancer drug candidates as an effective immuno-cancer.
- 제 16항에 있어서,The method of claim 16,상기 암은 유방암, 자궁경부암, 신경교종, 뇌암, 흑색종, 폐암, 방광암, 전립선암, 백혈병, 신장암, 간암, 대장암, 췌장암, 위암, 담낭암, 난소암, 임파종, 골육종, 자궁암, 구강암, 기관지암, 비인두암, 후두암, 피부암, 혈액암, 갑상선암, 부갑상선암, 요관암, 선암, 및 흉선암으로 구성되는 그룹으로부터 선택되는 어느 하나 이상인, 방법.The cancer may be breast cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colon cancer, pancreatic cancer, gastric cancer, gallbladder cancer, ovarian cancer, lymphoma, osteosarcoma, uterine cancer, oral cancer, And at least one selected from the group consisting of bronchial cancer, nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, thyroid cancer, parathyroid cancer, ureter cancer, adenocarcinoma, and thymic cancer.
- 제 20항에 있어서,The method of claim 20,상기 암은 폐암 또는 흉선암인, 방법.The cancer is lung cancer or thymic cancer.
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