WO2019117132A1

WO2019117132A1 - Biomarker for prognostic prediction of cancer immunotherapy

Info

Publication number: WO2019117132A1
Application number: PCT/JP2018/045465
Authority: WO
Inventors: 哲朗笹田; 規和松尾; 公一東; 星野　友昭
Original assignee: 地方独立行政法人神奈川県立病院機構; 学校法人久留米大学
Priority date: 2017-12-12
Filing date: 2018-12-11
Publication date: 2019-06-20
Also published as: JP7304558B2; JPWO2019117132A1

Abstract

The present disclosure provides a biomarker for prognostic prediction of cancer immunotherapy by an immune checkpoint inhibitory drug, the biomarker being one or more selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF, and MMP2.

Description

Biomarkers for prognosis prediction of cancer immunotherapy

This application claims priority to Japanese Patent Application No. 2017-237807, which is hereby incorporated by reference in its entirety.
The present invention relates to biomarkers for prognosis of cancer immunotherapy with immune checkpoint inhibitors.

Cancer immunotherapy with immune checkpoint inhibitors is only effective in certain patients and is expensive to treat. At present, for example, an anti-PD-1 antibody or an anti-PD-L1 antibody is used as an immune checkpoint inhibitor. Because PD-1 immunocheckpoint inhibitor therapy targets the immune system, the clinical outcome and features associated with treatment-related adverse events (AEs) appear to be very different from conventional therapies (NPL 1) -7). For example, PD-L1 expression on tumor cells or immune cells assessed by immunohistochemistry (IHC) has been shown in anti-PD-1 treated non-small cell lung cancer (NSCLC) patients It is reported that it is associated with the improved response rate (Non-patent Documents 3, 8 and 9). However, PD-L1 expression is not always a reliable marker, as it can be quite heterogeneous even within the same tumor, and can change dynamically and dramatically depending on the situation (non-patented) Literatures 8 and 9). In addition, biopsy of the tumor to assess IHC-based PD-L1 expression requires invasive procedures such as bronchoscopy or video-assisted thoracoscopy, which may be difficult depending on the size and location of the tumor being investigated There is. Other characteristics, such as treatment-related AE and neutrophil-lymphocyte ratio, have also been suggested as potential predictors of response to anti-PD-1 treatment (10-12). However, at the present time there are no clinically reliable and useful biomarkers for predicting response to anti-PD-1 treatment.

The problem to be solved by the present invention is to provide a biomarker for prognostic prediction of cancer immunotherapy with an immune checkpoint inhibitor. Also, by providing a kit for predicting the occurrence of prognosis or treatment-related adverse event based on the level of the biomarker, and using the biomarker for predicting prognosis or the occurrence of treatment-related adverse event. is there.

The present inventors have found biomarkers for prognostic prediction of cancer immunotherapy with an immune checkpoint inhibitor and completed the present invention. In particular, the present invention relates to granulocyte / monocyte colony stimulating factor (GM-CSF), chitinase 3-like 1 (CHI 3 L 1), C—X-C motif chemokine 2 for prognosis prediction of cancer immunotherapy with an immune checkpoint inhibitor. (CXCL2), one or more biomarkers selected from the group consisting of vascular endothelial growth factor (VEGF), interferon (IFN) α2 and matrix metalloproteinase 2 (MMP2).

In one aspect, the invention is a method of prognosticating cancer immunotherapy with an immune checkpoint inhibitor,
a) comparing the level of GM-CSF in the collected biological sample with a preset GM-CSF cutoff value,
b) comparing the level of CHI3L1 in the collected biological sample with a preset cutoff value of CHI3L1;
c) the levels of CXCL2, VEGF, IFNα2 and / or MMP2 in the biological sample collected after administration of said immune checkpoint inhibitor are the same as in the biological sample collected prior to said administration of said immune checkpoint inhibitor Comparing with the level of CXCL2, VEGF, IFNα2 and / or MMP2, or d) monitoring the level of CXCL2 in a biological sample taken after administration of said immune checkpoint inhibitor,
Provide a way that includes

In a further aspect, the present invention provides one or more biomarkers selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2, which are biomarkers for evaluating efficacy for the method. .

In a further aspect, the invention consists of a group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2 for the prognosis of cancer immunotherapy with an immune checkpoint inhibitor or prediction of the occurrence of treatment related adverse events. Providing the use of one or more biomarkers selected from

According to the present invention, one or more biomarkers selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF, and MMP2 enable prognosis prediction of cancer immunotherapy with an immune checkpoint inhibitor. .

FIG. 1A shows the association between clinical outcome and treatment related AE. The vertical axis is the largest percent change in target lesion tumor volume from baseline. FIG. 1B shows a Kaplan-Meier plot of progression free survival. FIG. 1C shows overall survival for patients who did and did not develop treatment-related AE. Differences were assessed by log rank test. FIG. 2A shows a representative flow cytometry plot of NSCLC patients. FIG. 2B shows the relative proportions of PD-1 + CD4 +, PD-1 + CD8 +, and FoxP3 + CD4 + lymphocytes in peripheral blood samples before and after administration from 20 nivolumab-treated patients. The differences were analyzed statistically by Wilcoxon signed rank test. Figure 3A shows a schedule of dosing and peripheral blood sampling. FIG. 3B shows Kaplan-Meier plots (n = 27) of progression free survival in the two groups divided by median plasma GM-CSF and CHI3L1 before dosing. Differences were assessed by log rank test. FIG. 3C shows a Kaplan-Meier plot (n = 20) of progression free survival in the two groups (decreased vs no decreased) divided by changes in plasma CXCL2, VEGF, IFNα2 and MMP2 levels after administration. Differences were assessed by log rank test. FIG. 4A shows CXCL2, VEGF, IFNα2 and MMP2 titers in plasma samples before and after dosing from 20 nivolumab-treated patients. Patients were classified according to objective tumor response (PR, SD, and PD). FIG. 4B shows the percent of objective tumor response (PR, SD, and PD) in nivolumab-treated patients with or without a decrease in plasma CXCL2, VEGF, IFNα2 and MMP2 levels after administration. FIG. 4C shows CXCL2 titers in plasma samples before and after dosing from 20 nivolumab-treated patients. Patients were classified according to treatment related AE. FIG. 4D shows the percentage of treatment-related AEs in nivolumab-treated patients with or without a decrease in plasma CXCL2 levels after administration. FIG. 4E shows a summary of changes in plasma CXCL2, VEGF, IFNα2 and MMP2 levels and the association of objective tumor response and treatment-related AE after administration in each patient. FIG. 5A shows time dependent changes in plasma CXCL2 levels from baseline. FIG. 5B shows time dependent changes in plasma VEGF levels from baseline. FIG. 5C shows time dependent changes in plasma IFNα2 levels from baseline. FIG. 5D shows time-dependent changes in plasma MMP2 levels from baseline.

In one embodiment, the invention relates to one or more selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2 for prognosis of cancer immunotherapy with an immune checkpoint inhibitor. It relates to a biomarker.

In a further aspect, the invention is a method of prognosticating cancer immunotherapy with an immune checkpoint inhibitor,
a) comparing the level of GM-CSF in the collected biological sample with a preset GM-CSF cutoff value,
b) comparing the level of CHI3L1 in the collected biological sample with a preset cutoff value of CHI3L1;
c) the levels of CXCL2, VEGF, IFNα2 and / or MMP2 in the biological sample collected after administration of said immune checkpoint inhibitor are the same as in the biological sample collected prior to said administration of said immune checkpoint inhibitor Comparing with the level of CXCL2, VEGF, IFNα2 and / or MMP2, or d) monitoring the level of CXCL2 in a biological sample taken after administration of said immune checkpoint inhibitor,
On the way, including.

In yet another aspect, the invention relates to a method of treating cancer, comprising
a) comparing the level of GM-CSF in the collected biological sample with a preset GM-CSF cutoff value,
b) comparing the level of CHI3L1 in the collected biological sample with a preset cutoff value of CHI3L1;
c) the level of CXCL2, VEGF, IFNα2 and / or MMP2 in the biological sample collected after administration of the immune checkpoint inhibitor, and the corresponding CXCL2 in the biological sample collected prior to the administration of the immune checkpoint inhibitor , Comparing with the levels of VEGF, IFNα2 and / or MMP2, or d) monitoring the level of CXCL2 in a biological sample taken after administration of the immune checkpoint inhibitor, and e) step a) B), c) or d), and administering an immune checkpoint inhibitor to a subject predicted to have a good prognosis based on the results obtained in b), c) or d).

In the present disclosure, as an immune checkpoint inhibitor, an anti-PD-1 antibody or an anti-PD-L1 antibody may be used. Immune checkpoint inhibitors do not act directly on cancer cells, but by enhancing the ability of the patient to respond to the immune response. The immune checkpoint inhibitor, anti-PD-1 antibody or anti-PD-L1 antibody, is said to inhibit the PD-1 / PD-L1 pathway. The anti-PD-1 antibody may be, but not limited to, nivolumab and pembrolizumab. Anti-PD-L1 antibodies may be, but are not limited to, averumab, atezolizumab and durvalumab.

In the present disclosure, treatment-related adverse events or adverse events are any undesirable and unintended signs (including abnormal laboratory test values), symptoms and diseases observed during treatment or treatment. There is no relationship between adverse events and treatment or treatment. Adverse events are evaluated, for example, according to the “Adverse event common term criteria” prepared by the National Cancer Institute.

In the present disclosure, prognosis means the medical prospect of the patient's progress or the patient's life expectancy after some treatment for a certain disease. For example, prognosis includes prediction of progression free survival, overall survival or objective tumor response. Favorable prognosis means, for example, that the clinical stage of the disease does not deteriorate or deteriorate slowly after treatment of the disease, and in the case of cancer, no or few tumor metastasis to lymph nodes is observed. , Infiltration of tumor cells into surrounding tissues does not occur or the level thereof is low, or recurrence does not occur or the time until recurrence is long. Thus, if the patient has a good prognosis or if the patient's prognosis is predicted to be good, then the patient will develop a treatment related adverse event or the patient will be expected to develop a treatment related adverse event. possible.

In the present disclosure, the comparing step may include a detecting or measuring step and the like. The term "comparison" may mean comparing information obtained by measuring numerical values, or comparing information obtained from different conditions. The information obtained from the biological sample can be compared, for example, by mobility, color tone, fluorescence intensity, emission intensity or gradation. The method of acquiring information is not particularly limited, but various commonly used methods can be used. As the method, immunological assays such as ELISA or radioimmunoassay (RIA), multiplex assay may be used.

In the present disclosure, the cutoff value can be appropriately selected and determined from methods available to those skilled in the art. For example, the cutoff value may be a median level of corresponding biomarkers in a biological sample in a cancer patient population where no immune checkpoint inhibitor has been administered. The GM-CSF cutoff value may be the median level of GM-CSF in a biological sample in a cancer patient population where no immune checkpoint inhibitor has been administered. The cutoff value of CHI3L1 may be the median level of CHI3L1 in a biological sample in a cancer patient population not receiving an immune checkpoint inhibitor. The cut-off value range of GM-CSF is, for example, about 10-30 pg / ml, about 15-25 pg / ml, about 17.5-22.5 pg / ml when the level of GM-CSF is measured by multiplex assay. It is ml. Preferably, the range of GM-CSF cut-off values is about 15-25 pg / ml, as GM-CSF levels are measured in a multiplex assay. The range of cutoff values of CHI3L1 is, for example, about 60 to 120 ng / ml, about 70 to 110 ng / ml, about 80 to 100 ng / ml, about 85 to 95 ng / ml when CHI3L1 levels are measured in a multiplex assay It is. Preferably, the range of CHI3L1 cutoff values is about 70-110 ng / ml when the level of CHI3L1 is measured in a multiplex assay.

As used herein, when a numerical value is accompanied by the term "about," it is intended to include the range of ± 10% of that value. The numerical range includes all numerical values between the end points and numerical values of the end points. "About" with respect to a range applies to the endpoints of the range. Thus, for example, “about 20 to 30” includes “20 ± 10% to 30 ± 10%”.

In the present disclosure, the administration mode, site of administration, and dosage of the immune checkpoint inhibitor are not particularly limited, and can be appropriately determined by those skilled in the art according to the condition of the subject. An example of a preferred mode of administration is intravenous administration. An example of a preferred site of administration is intravascular. An example of a preferred dose is 2-10 mg / kg body weight 2-3 weeks apart.

In one embodiment of the invention, the level of the biomarker is compared to a preset cutoff value. The biomarker of the present invention may be one or more biomarkers selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2. In this embodiment, preferably, the level of one or more biomarkers selected from the group consisting of GM-CSF and CHI3L1 is compared to a preset cutoff value. In one embodiment of the invention, the level of the biomarker in the biological sample collected prior to the start of administration of the immune checkpoint inhibitor is compared to a preset cutoff value.

In a further embodiment of the present invention, the prognosis predicts that the level of GM-CSF is equal to or higher than a preset GM-CSF cut-off value, or the level is previously determined It is characterized that it predicts that a prognosis is bad that it is less than the cut-off value of GM-CSF set. In another embodiment of the present invention, the prognosis predicts that the level of CHI3L1 is less than or equal to a preset CHI3L1 cut-off value, or the level is preset. It is characterized that it predicts that prognosis is bad that it is more than the cutoff value of CHI3L1.

In one embodiment of the invention, the levels of biomarkers before and after administration of the immune checkpoint inhibitor are compared. The biomarker of the present invention may be one or more biomarkers selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2. In this embodiment, the levels of one or more biomarkers, preferably selected from the group consisting of CXCL2, VEGF, IFNα2 and MMP2, are compared.

In a further embodiment of the invention, the prognosis is that the level of CXCL2, VEGF or IFNα2 after administration of the immune checkpoint inhibitor is the same as the level of the corresponding CXCL2, VEGF or IFNα2 prior to the administration of said immune checkpoint inhibitor The relative decrease is predicted to have a good prognosis, or the level of CXCL2, VEGF or IFNα2 after administration of the immune checkpoint inhibitor is corresponding before the administration of the immune checkpoint inhibitor The absence of a decrease compared to the levels of CXCL2, VEGF or IFNα2 is characterized as predicting a poor prognosis.

In a further embodiment of the invention, the prognosis is that the level of MMP2 after administration of the immune checkpoint inhibitor is increased compared to the level of MMP2 before administration of said immune checkpoint inhibitor. The prognosis is that the prognosis is predicted to be good or that the level of MMP2 after administration of the immune checkpoint inhibitor is not increased compared to the level of MMP2 in the biological sample collected before administration It is characterized in that it is predicted to be defective.

In one embodiment of the invention, the level of biomarker after administration of the immune checkpoint inhibitor is monitored. In the present disclosure, monitoring means observing change over time, and in this embodiment, before starting administration of the immune checkpoint inhibitor and at one or more time points after starting administration of the immune checkpoint inhibitor. The levels of the biomarkers in the biological sample collected at are compared. The biomarker of the present invention may be one or more biomarkers selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2. In this embodiment, preferably the level of biomarkers of CXCL2 is monitored.

In a further embodiment of the invention, the prognosis is good that the level of CXCL2 after administration of the immune checkpoint inhibitor is reduced compared to the level of CXCL2 before administration of the immune checkpoint inhibitor Predicting that there is a prognosis or that the level of CXCL2 after administration of the immune checkpoint inhibitor is increased relative to the level of CXCL2 before administration of the immune checkpoint inhibitor is predicted to be poor It features.

In one embodiment of the invention, the occurrence of treatment related adverse events is predicted based on the level of the biomarker. The biomarker of the present invention may be one or more biomarkers selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2. In this embodiment, the levels of one or more biomarkers, preferably selected from the group consisting of CXCL2 and GM-CSF, are compared. In one embodiment, the prediction of the occurrence of a treatment-related adverse event is that the level of CXCL2 after administration of the immune checkpoint inhibitor is decreased compared to CXCL2 before administration of the immune checkpoint inhibitor , And are expected to develop treatment-related adverse events. In another embodiment, predicting that the level of GM-CSF before administration of the immune checkpoint inhibitor is less than a preset GM-CSF cut-off value is predicted to cause a treatment related adverse event It features.

In the present disclosure, the biomarker is derived from a biological sample. The biological sample may, for example, be a blood sample. Collection of biological samples may be performed either invasively or non-invasively. The blood sample may be peripheral blood, peripheral blood mononuclear cells, plasma or serum. There is no particular limitation on the time to collect the biological sample. In one aspect of the invention, the biological sample is taken prior to the start of administration of the immune checkpoint inhibitor. In this case, the step of administering the immune checkpoint inhibitor is not necessarily involved. When the process is not included, “before the start of administration” is not limited to a specific time. In certain embodiments of the invention, the biological sample is from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, from the start of administration of the immune checkpoint inhibitor. Collected after 15, 16, 17, 18, 19 and / or 20 weeks. The biological sample may preferably be collected 6 weeks or more, more preferably 6-8 weeks or more, or 12-20 weeks or more from the start of administration of the immune checkpoint inhibitor.

In the present disclosure, the cancer includes, for example, leukemia, myelodysplastic syndrome, multiple myeloma, malignant lymphoma, esophagus cancer, gastric cancer, small intestine cancer, small intestine cancer, colon cancer, pancreatic cancer, lung cancer, breast cancer, germ cell cancer, liver cancer, gallbladder Cancer, head and neck cancer, skin cancer, sarcoma, kidney cancer, bladder cancer, prostate cancer, testicular cancer, testicular cancer, uterine cancer, cervical cancer, ovarian cancer, thyroid cancer, carcinoid, lung blastoma, brain tumor, or thymus cancer . The cancer may be non-small cell lung cancer, renal cell carcinoma, malignant melanoma, head and neck cancer, Hodgkin's disease or gastric cancer. The cancer may be advanced / recurrent non-small cell lung cancer.

In one embodiment of the invention, a kit for predicting the occurrence of a prognosis or treatment related adverse event based on the level of the biomarker is included. The biomarker of the present invention may be one or more biomarkers selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2. The kit can include various configurations to compare the levels of biomarkers. The composition of the kit may be any one that can detect and / or visualize the level of the biomarker in the biological sample. The kit further comprises a computer model or algorithm for analyzing the level of biomarkers in the sample. The kit can be, for example, a kit for an immunological assay such as ELISA or RIA, multiplex assay.

One embodiment of the present invention includes the use of a biomarker for prognostication or prediction of the occurrence of treatment-related adverse events. The biomarker of the present invention may be one or more biomarkers selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2. Use of a biomarker for predicting prognosis or the occurrence of treatment-related adverse events, for example, directly or indirectly detecting the level of the biomarker for prognosis or predicting the occurrence of treatment-related adverse events, Means to measure or compare.

In an exemplary embodiment, the present invention provides:
(1)
One or more biomarkers selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2 for prognosis prediction of cancer immunotherapy with an immune checkpoint inhibitor.
(2)
The biomarker according to (1), wherein the biomarker is GM-CSF or CHI3L1 and the level of the biomarker is compared to a preset cutoff value.
(3)
The biomarker according to (1), wherein the biomarker is CXCL2, VEGF, IFNα2 or MMP2, and the levels of the biomarker are compared before and after administration of the immune checkpoint inhibitor.
(4)
The biomarker according to (1), wherein the biomarker is CXCL2 and the level of the biomarker after administration of the immune checkpoint inhibitor is monitored.
(5)
The prognosis predicts that the level of GM-CSF is higher than or equal to the preset GM-CSF cut-off value, or the level is a preset GM-CSF cut. The biomarker according to (2), which is predicted to have a poor prognosis if it is less than the off value.
(6)
The prognosis predicts that the level of CHI3L1 is better than or equal to a preset CHI3L1 cutoff value, or that the level is greater than a preset CHI3L1 cutoff value The biomarker according to (2), wherein the biomarker is predicted to have a poor prognosis.
(7)
Prognostic value is that the level of CXCL2, VEGF or IFNα2 after administration of the immune checkpoint inhibitor is reduced compared to the level of the corresponding CXCL2, VEGF or IFNα2 before administration of the immune checkpoint inhibitor Predict that the prognosis is good or that the level of CXCL2, VEGF or IFNα2 after administration of the immune checkpoint inhibitor is that of the corresponding CXCL2, VEGF or IFNα2 prior to administration of the immune checkpoint inhibitor The biomarker according to (3), which is characterized in that it does not decrease compared to the level and predicts that the prognosis is poor.
(8)
Prognosis predicts that the prognosis is good that the level of MMP2 after administration of the immune checkpoint inhibitor is increased compared to the level of MMP2 before administration of the immune checkpoint inhibitor Or that the level of MMP2 after administration of the immune checkpoint inhibitor is not increased as compared to the level of MMP2 in the biological sample collected prior to administration, which predicts a poor prognosis The biomarker according to (3), characterized in that
(9)
Prognostic predicts that the prognosis is good that the level of CXCL2 after administration of the immune checkpoint inhibitor is reduced compared to the level of CXCL2 before administration of the immune checkpoint inhibitor, or An increase in the level of CXCL2 after administration of the immune checkpoint inhibitor as compared to the level of CXCL2 before administration of the immune checkpoint inhibitor is characterized by predicting that the prognosis is poor (4 The biomarker as described in).
(10)
The biomarker according to any one of (1) to (9), wherein the biomarker is derived from a biological sample.
(11)
The biomarker according to (10), wherein the biological sample is a blood sample.
(12)
The biomarker according to (11), wherein the blood sample is peripheral blood, peripheral blood mononuclear cells, plasma or serum.
(13)
The biomarker according to any one of (1) to (12), wherein the immune checkpoint inhibitor is an anti-PD-1 antibody or an anti-PD-L1 antibody.
(14)
Cancer includes leukemia, myelodysplastic syndrome, multiple myeloma, malignant lymphoma, esophagus cancer, gastric cancer, small intestine cancer, colon cancer, pancreatic cancer, lung cancer, breast cancer, germ cell cancer, liver cancer, gallbladder cancer, head and neck cancer, Skin cancer, sarcoma, kidney cancer, bladder cancer, prostate cancer, testicular cancer, testicular cancer, uterine cancer, cervical cancer, ovarian cancer, thyroid cancer, carcinoid, lung carcinoma, lung cancer, or thymus cancer, (1)-(13) The biomarker according to any one of the above.
(15)
The biomarker according to (14), wherein the cancer is non-small cell lung cancer, renal cell carcinoma, malignant melanoma, head and neck cancer, Hodgkin's disease, bladder cancer or gastric cancer.
(16)
The biomarker according to (15), wherein the cancer is advanced / recurrent non-small cell lung cancer.
(17)
Any of (1) to (16), wherein the biological sample collected after administration of the immune checkpoint inhibitor is a biological sample collected after 6-8 weeks from the start of administration of the immune checkpoint inhibitor The biomarker as described in.
(18)
The biomarker according to any of (1)-(17), wherein the prognosis is prediction of progression free survival, overall survival or objective tumor response.
(19)
That the biomarker is CXCL2 and that the level of CXCL2 after administration of the immune checkpoint inhibitor is reduced compared to CXCL2 before administration of the immune checkpoint inhibitor, thereby causing a treatment related adverse event The biomarker according to any of (3), (4), (7) or (9)-(18), which is further predicted to be expressed.
(20)
The biomarker is GM-CSF, which further predicts that a treatment-related adverse event will occur based on the level of GM-CSF, (1), (2), (5) or (10)-(18) The biomarker according to any one of the above.
(21)
(1)-a kit for predicting the occurrence of prognosis or treatment-related adverse event based on the level of the biomarker according to any of (20).
(22)
(1) Use of the biomarker according to any of (20) for prognostication or prediction of occurrence of treatment-related adverse event.
(23)
It is a prognosis prediction method of cancer immunotherapy with an immune checkpoint inhibitor,
a) comparing the level of GM-CSF in the collected biological sample with a preset GM-CSF cutoff value,
b) comparing the level of CHI3L1 in the collected biological sample with a preset cutoff value of CHI3L1;
c) the levels of CXCL2, VEGF, IFNα2 and / or MMP2 in the biological sample collected after administration of said immune checkpoint inhibitor are the same as in the biological sample collected prior to said administration of said immune checkpoint inhibitor Comparing with the level of CXCL2, VEGF, IFNα2 and / or MMP2, or d) monitoring the level of CXCL2 in a biological sample taken after administration of said immune checkpoint inhibitor,
Method, including.
(24)
In step a), it is predicted that the level is equal to or higher than a preset GM-CSF cut-off value, or the level is a preset GM-CSF cut-off value. The method according to (23), which is predicted to have a poor prognosis if less than.
(25)
In step b), it is predicted that the level is lower than or equal to a preset CHI3L1 cutoff value, or the level is greater than a preset CHI3L1 cutoff value. The method according to (23), wherein the prognosis is poor.
(26)
In step c), the levels of CXCL2, VEGF and / or IFNα2 in the biological sample taken after administration are compared to the levels of corresponding CXCL2, VEGF and / or IFNα2 in the biological sample taken prior to administration The decrease is predicted to be of good prognosis, or the level of CXCL2, VEGF and / or IFNα2 in the biological sample collected after administration is the same as the corresponding CXCL2 in the biological sample collected before administration The method according to (23), which is characterized by not having decreased in comparison with the level of VEGF and / or IFNα2, and predicting that the prognosis is poor.
(27)
In step c), it is predicted that the prognosis is good that the level of MMP2 in the biological sample collected after administration is increased compared to the level of MMP2 in the biological sample collected before administration That the level of MMP2 in the biological sample collected after or after administration is not increased as compared to the level of MMP2 in the biological sample collected prior to The method according to (23), characterized in that
(28)
In step d), the level of CXCL2 in the biological sample collected after administration of the immune checkpoint inhibitor is decreased compared to the level of CXCL2 before administration of the immune checkpoint inhibitor, and the prognosis is good There is a poor prognosis that the level of CXCL2 in a biological sample predicted to be present or taken after administration of the immune checkpoint inhibitor is increased compared to the level of CXCL2 before administration of the immune checkpoint inhibitor The method according to (23), wherein the method is predicted.
(29)
The method according to any of (23)-(28), wherein the biological sample is a blood sample.
(30)
The method according to (29), wherein the blood sample is peripheral blood, peripheral blood mononuclear cells, plasma or serum.
(31)
The method according to any of (23)-(30), wherein the immune checkpoint inhibitor is an anti-PD-1 antibody or an anti-PD-L1 antibody.
(32)
Cancer includes leukemia, myelodysplastic syndrome, multiple myeloma, malignant lymphoma, esophagus cancer, gastric cancer, small intestine cancer, colon cancer, pancreatic cancer, lung cancer, breast cancer, germ cell cancer, liver cancer, gallbladder cancer, head and neck cancer, Skin cancer, sarcoma, kidney cancer, bladder cancer, prostate cancer, testicular cancer, testicular cancer, uterine cancer, cervical cancer, ovarian cancer, thyroid cancer, carcinoid, lung carcinoma, lung cancer, or thymus cancer, (23)-(31) The method according to any one of the above.
(33)
The method according to (32), wherein the cancer is non-small cell lung cancer, renal cell carcinoma, malignant melanoma, head and neck cancer, Hodgkin's disease, bladder cancer or gastric cancer.
(34)
The method according to (33), wherein the cancer is advanced / recurrent non-small cell lung cancer.
(35)
Any of (23)-(34), wherein the biological sample collected after administration of the immune checkpoint inhibitor is a biological sample collected after 6-8 weeks from the start of administration of the immune checkpoint inhibitor The method described in.
(36)
The method according to any of (23)-(35), wherein the prognosis is prediction of progression free survival, overall survival or objective tumor response.
(37)
The level of CXCL2 after administration of the immune checkpoint inhibitor is reduced as compared to CXCL2 prior to administration of the immune checkpoint inhibitor, further predicting that treatment-related adverse events occur (23) -The method in any one of-(36).
(38)
The method according to any of (23)-(37), further predicting that a treatment related adverse event will occur based on the level of GM-CSF.
(39)
(23) A kit for use in the method according to any of (38).
(40)
One or more biomarkers selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2, which is a bio for evaluating efficacy for any of the methods according to (23)-(38) marker.

In another exemplary embodiment, the present invention also provides:
(1)
A method for treating cancer,
a) comparing the level of GM-CSF in the collected biological sample with a preset GM-CSF cutoff value,
b) comparing the level of CHI3L1 in the collected biological sample with a preset cutoff value of CHI3L1;
c) the levels of CXCL2, VEGF, IFNα2 and / or MMP2 in the biological sample collected after administration of said immune checkpoint inhibitor are the same as in the biological sample collected prior to said administration of said immune checkpoint inhibitor Comparing with the level of CXCL2, VEGF, IFNα2 and / or MMP2, or d) monitoring the level of CXCL2 in a biological sample taken after administration of said immune checkpoint inhibitor, and e) administering an immune checkpoint inhibitor to a subject predicted on the basis of the result obtained in a), b), c) or d) and predicted to have a good prognosis.
(2)
In step a), it is predicted that the level is equal to or higher than a preset GM-CSF cut-off value, or the level is a preset GM-CSF cut-off value. The method according to (1), wherein predicting the prognosis is poor is less than.
(3)
In step b), it is predicted that the level is lower than or equal to a preset CHI3L1 cutoff value, or the level is greater than a preset CHI3L1 cutoff value. The method according to (1), wherein the prognosis is predicted to be poor.
(4)
In step c), the levels of CXCL2, VEGF and / or IFNα2 in the biological sample taken after administration are compared to the levels of corresponding CXCL2, VEGF and / or IFNα2 in the biological sample taken prior to administration The decrease is predicted to be of good prognosis, or the level of CXCL2, VEGF and / or IFNα2 in the biological sample collected after administration is the same as the corresponding CXCL2 in the biological sample collected before administration The method according to (1), which is characterized by not having decreased in comparison with the level of VEGF and / or IFNα2, and predicting that the prognosis is poor.
(5)
In step c), it is predicted that the prognosis is good that the level of MMP2 in the biological sample collected after administration is increased compared to the level of MMP2 in the biological sample collected before administration That the level of MMP2 in the biological sample collected after or after administration is not increased as compared to the level of MMP2 in the biological sample collected prior to The method according to (1), characterized in that
(6)
In step d), the level of CXCL2 in the biological sample collected after administration of the immune checkpoint inhibitor is decreased compared to the level of CXCL2 before administration of the immune checkpoint inhibitor, and the prognosis is good There is a poor prognosis that the level of CXCL2 in a biological sample predicted to be present or taken after administration of the immune checkpoint inhibitor is increased compared to the level of CXCL2 before administration of the immune checkpoint inhibitor The method according to (1), wherein the method is predicted.
(7)
The method according to any one of (1) to (6), wherein the biological sample is a blood sample.
(8)
The method according to (7), wherein the blood sample is peripheral blood, peripheral blood mononuclear cells, plasma or serum.
(9)
The method according to any one of (1) to (8), wherein the immune checkpoint inhibitor is an anti-PD-1 antibody or an anti-PD-L1 antibody.
(10)
Cancer includes leukemia, myelodysplastic syndrome, multiple myeloma, malignant lymphoma, esophagus cancer, gastric cancer, small intestine cancer, colon cancer, pancreatic cancer, lung cancer, breast cancer, germ cell cancer, liver cancer, gallbladder cancer, head and neck cancer, Skin cancer, sarcoma, kidney cancer, bladder cancer, prostate cancer, testicular cancer, testicular cancer, uterine cancer, cervical cancer, ovarian cancer, thyroid cancer, carcinoid, lung blastoma, brain tumor, or thymus cancer, (1)-(9) The method according to any one of the above.
(11)
The method according to (10), wherein the cancer is non-small cell lung cancer, renal cell carcinoma, malignant melanoma, head and neck cancer, Hodgkin's disease, bladder cancer or gastric cancer.
(12)
The method according to (11), wherein the cancer is advanced / recurrent non-small cell lung cancer.
(13)
Any of (1) to (12), wherein the biological sample collected after administration of the immune checkpoint inhibitor is a biological sample collected after 6-8 weeks from the start of administration of the immune checkpoint inhibitor The method described in.
(14)
The method according to any of (1)-(13), wherein the prognosis is prediction of progression free survival, overall survival or objective tumor response.
(15)
The level of CXCL2 after administration of the immune checkpoint inhibitor is reduced as compared to CXCL2 before administration of the immune checkpoint inhibitor, further predicting that it will cause treatment related adverse events, (1) -The method in any one of-(14).
(16)
The method according to any of (1)-(15), which further predicts that a treatment-related adverse event will occur based on the level of GM-CSF.
(17)
A kit for use in the method according to any one of (1) to (16).
(18)
One or more biomarkers selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2, which is a biologic for evaluating efficacy according to any one of (1)-(16) marker.

Examples of the present invention will be shown below, but the present invention is not limited to the examples.

Materials and Methods Patients Progressive / recurrent NSCLC patients receiving nivolumab (nivolumab) at a single center (Kurume University Hospital, Kurume City, Japan) were included in the study in February-September 2016. Nivolumab (3 mg / kg) was administered every two weeks to eligible patients with progressive / recurrent NSCLC who had at least one regimen of chemotherapy history. Peripheral blood samples were collected from 27 cases prior to nivolumab administration. In addition, peripheral blood samples were collected from 20 cases showing progression-free survival (PFS) over 6 weeks after nivolumab administration (6-8 weeks after the first administration day). In addition, peripheral blood samples were collected every 6-12 weeks from patients continuing to receive nivolumab (FIG. 3A). Peripheral blood mononuclear cells (PBMC) and clots were used for biomarker analysis. This study was conducted in accordance with the provisions of the Helsinki Declaration and was approved by the Clinical Trials Review Board (IRB) of Kurume University Hospital. Informed consent was obtained from all patients who participated in the study after describing the nature of the study and the possible outcomes.

Immunohistochemistry (IHC) Analysis Paraffin-embedded tissue samples were sliced into 4 μm thick sections and mounted on coated glass slides. Thereafter, anti-PD-L1 antibody (× 100, clone E1L3N, Cell Signaling Technology, Danvers, Mass., USA) using a BOND-III autostainer (Leica Microsystems, Newcastle upon Tyne, UK) I did the processing by. Briefly, tissue sections were heat treated for 30 minutes with epitope retrieval solution 2 (pH 9.0), anti-PD-L1 antibody was added and incubated for 30 minutes. In this automated system, HRP (horseradish peroxidase) polymer was used as a secondary antibody, and a refine polymer detection system (Leica Microsystems) was used. The slides were visualized using 3,3'-diaminobenzidine (DAB) as chromogen.

Flow cytometric analysis 14 ml peripheral blood was collected before and after administration of nivolumab, and PBMCs were separated by density gradient centrifugation using Ficoll-Paque Plus (GE Healthcare, Uppsala, Sweden). PBMCs were suspended in PBS containing 20% human AB serum, appropriately diluted antibodies were added and incubated on ice for 30 minutes. The antibodies used in this study were anti-CD3-PE (clone OKT3) from BioLegend (San Diego, CA, USA) and anti-CD4-FITC (clone RPA-T4) from BD Biosciences (Franklin Lakes, NJ, USA). ), Anti-CD8-PerCP-Cy5.5 (clone RPA-T8), anti-CD279-APC (clone MIH4), anti-CD25-APC (clone M-A251), anti-FoxP3-PE (clone 259D / C7), and mice It was IgG1 (κ) -PE (clone MOPC-21, for negative control). Anti-human FoxP3 staining kit (BD Biosciences) was used according to the manufacturer's instructions. Stained cells were analyzed on BD FACS Canto II using FACS Diva software (BD Biosciences).

Measurement of soluble factors in clots Bead-based multiplex assays are used to comprehensively detect the levels of 88 different soluble factors (cytokines, chemokines, growth factors, etc.) in clots before and after nivolumab administration It was. In this assay, soluble factors in 100-μl of 2-fold diluted blood clots were measured using the Bio-Plex 200 system (Bio-Rad Laboratories, Hercules, CA, USA) according to the manufacturer's instructions. As a soluble factor, IL-1Rα, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, using a Bio-Rad Laboratories analysis kit IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-13, IL-15, IL-16, IL-17A, IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, IFN-α2, IFN-β, IFN-γ, TNF-α, GM-CSF, 6Ckine, BCA-1, CTACK, ENA-78, Eotaxin, Eotaxin-2, Eotaxin-3, Fractalkine, GCP-2, Gro-α, CXCL2 (Gro-β), IP-10, I-TAC, MIP-1 α MIP-1δ, MIP-3α, MIP-3β, MPIF-1, SCYB16, SDF-1 α + β, TARC, TECK, MCP-1, MCP-2, MCP-3, MCP-4, MDC, MIF, MIG, VEGF , APRIL, BAFF, sCD30, sCD163, CHI3L1, gp130, sIL-6Rα, LIGHT, MMP-1, MMP-2, MMP-3, osteocalcin, osteopontin, pentraxin 3, sTNF-R1, sTNF-R2, TSLP, TWEAK, VEGF, IgG1, IgG2, IgG3, IgG4, IgM and IgA were measured.

Statistical Analysis The severity of adverse events (AEs) was graded using the American National Cancer Institute Adverse Event Common Terminology Criteria Fourth Edition and the investigators determined the causal relationship between them and nivolumab treatment. The best overall effect is, from the day of initial administration of nivolumab, first objectively confirmed the exacerbation specified by the new guidelines (RECIST) 1.1 criteria (Non-patent document 13) for determining treatment effect on solid cancer It was defined as the qualification of the best response recorded by the day it was PFS was defined as the time from the first dose to the date of progression or death from any cause. Overall survival (OS) was defined as the time from the first dose to the date of death from any cause. Survival curves were derived using the Kaplan-Meier method and compared by log-rank test.

The association between the occurrence of treatment-related AEs and the best overall effect was analyzed by Fisher's exact test. The association between changes in soluble factor levels after administration and the objective tumor response or percentage of treatment related AE was also estimated by Fisher's exact test. The difference in percentage of lymphocyte subsets of PBMC before and after administration was analyzed by Wilcoxon signed rank test. Clinical characteristics, expression of PD-L1 on tumor cells, expression of PD-1 and FoxP3 on PBMC before administration, to evaluate which biomarkers are associated with the efficacy and safety of nivolumab treatment It was investigated whether soluble factors in blood clots before and after administration were associated with PFS and treatment related AE. Cox regression and univariate logistic regression were performed on all patients to investigate whether these factors were associated with PFS and treatment-related AE, respectively. Furthermore, it was investigated whether changes in the expression of PD-1 and FoxP3 on PBMC and soluble factor levels in blood clot at 6-8 weeks after the first administration day of nivolumab were associated with safety and efficacy. Cox regression and univariate logistic regression were performed on 20 patients with PFS> 6 weeks to investigate whether the changes were associated with PFS and treatment-related AE, respectively. Hazard ratios and odds ratios are shown with 95% confidence intervals (CI). Statistical significance was considered at P <0.05. All statistical analysis can be performed using JMP version 11 (SAS Institute Inc., Cary, NC, USA) or GraphPad Prism version 6.07 for Windows (GraphPad Software, La Jolla, CA, USA, www.graphpad.com) It carried out using.

Results Patient Background A total of 27 patients with progressive / recurrent NSCLC who received nivolumab therapy were included in this study. The median age of all patients was 69 years (53-82), and 20 cases (74%) were male. Seventeen patients (63%) had good general condition (PS) and the US Eastern Cooperative Oncology Group (ECOG) was classified 0 or 1. Eighteen cases (67%) and seven cases (26%) were adenocarcinoma and squamous cell carcinoma respectively, and seven cases (26%) and one case (4%) had EGFR mutation and ALK mutation respectively. Seven cases (26%) were smoke-free and 22 cases (81%) were stage IV or recurrent cancer (Table 1). All patients had at least one regimen of chemotherapy history. At the time of analysis, the objective response rate (ORR) and disease control rate (DCR) were 33.3% and 51.9%, respectively. The median follow-up was 198 days (64-330 days). Median PFS was 57 days (27-288 days) and median OS was not reached.

Impact of treatment-related AEs on response rates and PFS In NSCLC patients who received nivolumab, a potential correlation between immune-related AEs and clinical outcomes has been reported in the past (non-patent documents 10 and 11). The relationship between treatment-related AE and the efficacy of nivolumab was investigated. As shown in Table 2, all grades of treatment related AE occurred in 44% of patients. Treatment-related AEs leading to discontinuation occurred in 6 cases (22%), elevation of AST and ALT [2 cases (7%)], elevation of creatinine [1 case (4%)], adrenal insufficiency [1 case (4%)], hypothyroidism [1 case (4%)], pneumonia [1 case (4%)] were included. FIG. 1A shows the change in tumor volume from baseline to post treatment. Tumor burden decreased in 13 cases (48%), of which 10 developed treatment-related AEs. ORR and DCR in the group that expressed all grades of treatment related AE were significantly higher than the group that did not express (respectively ORR: 57% vs 7%, P = 0.003; DCR: 83% vs 26 %, P = 0.006). Furthermore, as shown in FIGS. 1B and 1C, both PFS and OS with nivolumab therapy were significantly longer in patients who developed treatment-related AEs (HR, 0.173; 95% CI, 0.066-0, respectively). P <0.001: HR, 0.218; 95% CI, 0.049-0.967; P = 0.045). These results suggested that a high incidence of treatment-related AEs in NSCLC patients receiving nivolumab correlates well with good clinical outcomes.

Clinicopathologic Characteristics Associated with PFS or Treatment-Related AE To investigate biomarkers that can predict the efficacy and safety of nivolumab treatment, pre-dose clinicopathologic characteristics correlate with PFS or treatment-related AE It was analyzed whether or not (Table 3). Whereas good PS and low body temperature before nivolumab treatment were associated with improvement in PFS (HR, 2.29; 95% CI, 1.339-3.915, respectively; P = 0.003: HR, 2. 395; 95% CI, 1.271-4.514; P = 0.007), other factors such as age, gender, histology, smoking status, stage, number of previous systemic treatments were not relevant . We also investigated whether the neutrophil-lymphocyte ratio was associated with PFS or AE, but such a significant relationship was not found. PD-L1 expression can be assessed in 19 of 27 (70%) tumor samples, of which 10 (53%) are fresh biopsy specimens and 9 (47%) specimens stored. Obtained as. Baseline PD-L1 expression was weakly positive (1-49% of tumor cells) in 4 cases (21%) and strongly positive (> 50% of tumor cells) in 4 cases (21%) Table 1). As shown in Table 3, PD-L1 expression did not correlate with PFS or treatment related AE (P = 0.331 and P = 0.845, respectively).

Abbreviations: PFS, progression-free survival; AE, adverse events; HR, hazard ratio; CI, confidence interval; PS, general status; Sq, squamous epithelium

Effect on Distribution Frequency of Lymphocyte Subsets Next, the distribution frequency of lymphocyte subsets was analyzed, including PD-1 + CD4 +, PD-1 + CD8 +, FoxP3 + CD4 + lymphocytes before and after nivolumab administration (FIG. 2A). The percentage of PD-1 + cells in CD4 + and CD8 + lymphocytes in blood samples before administration was significantly higher than in samples after administration (12.9 ± 7.6% vs 6.6 ± 4.6 respectively) %, P = 0.004; 12.1 ± 6.5% vs 5.9 ± 3.9%, P <0.001). In contrast, the percentage of FoxP3 + cells in CD4 + lymphocytes in blood samples before dosing was significantly lower than in samples after dosing (7.1 ± 3.8% vs 11.4 ± 8.5% , P = 0.024) (Figure 2B). Neither baseline rates of PD-1 + CD4 +, PD-1 + CD8 +, and FoxP3 + CD4 + lymphocytes before nivolumab administration nor changes in their rates after administration were significantly associated with PFS or treatment related AE (Table 3 and Table) 4).

Abbreviations: PFS, progression free survival; AE, adverse event; HR, hazard ratio; CI, confidence interval

Relationship between soluble factors in blood clot before administration and PFS or treatment related AE To clarify which soluble factors that mediate immune response are related to PFS or treatment related AE, in clot samples before administration The levels of 88 different soluble factors were investigated. Cox regression showed that blood clot levels of GM-CSF and CHI3L1 were significantly correlated with PFS (P = 0.005 and P = 0.007, respectively). To illustrate the effect of these factors, Kaplan-Meier plots of PFS in the two groups divided by median GM-CSF and CHI3L1 are shown in FIG. 3B. High pre-dose GM-CSF levels were significantly associated with improvement in PFS (median survival 186 days vs. 47 days, P = 0.013). Furthermore, low levels of CHI3L1 tended to be associated with improved PFS (median survival 126 days vs 55 days) but did not reach statistical significance (P = 0.238).

Changes in soluble factor levels in clots associated with PFS or treatment related AEs We also analyzed whether changes in levels of 88 different soluble factors in clots were associated with PFS or treatment related AEs. Cox regression showed that changes in levels of CXCL2, VEGF, IFNα2, and MMP2 significantly correlated with PFS (P <0.001, P = 0.019, P = 0.014, and P = respectively). 0.010). In addition, changes in levels of CXCL2 were also significantly associated with treatment related AE (P = 0.017) (Table 4). Based on these results, the patients were stratified into two groups according to the change of the selected soluble factor (ie "with reduction" or "without reduction"). The Kaplan-Meier plot of PFS in stratified groups is shown in FIG. 3C. Patients who showed a reduction in clotted CXCL2 levels had a significantly longer median PFS (median survival 252 days vs. 57 days, P = 0.003). Also, patients who showed decreased levels of clotted VEGF also had significantly longer PFS than those who did not (median survival 243 days vs. 57 days, P = 0.015). On the other hand, the groups stratified according to the level of IFNα2 and MMP2 in the blood showed no significant difference in PFS. FIG. 4A shows levels of CXCL2, VEGF, IFNα2, MMP2 before and after administration in selected groups according to objective tumor response. While changes in levels of CXCL2 and MMP2 were significantly associated with clinical response (P = 0.001 and P = 0.044 respectively), other factors such as VEGF and IFNα2 were not significantly associated (Figure 4B). Furthermore, FIG. 4C shows CXCL2 levels before and after administration in the group that expressed and not expressed treatment-related AE. As shown in FIG. 4D, patients who showed a decrease in CXCL2 levels had a relatively higher incidence of treatment-related AEs than those who did not (86% vs 38%, P = 0.07). It was noteworthy that the majority of patients (86%, 6 out of 7) who had decreased CXCL2 in the clot tended to simultaneously exhibit both PR and treatment-related AE (Figure 4E).

Longitudinal analysis of soluble factor levels in blood clots To further clarify how dynamic changes in levels of selected soluble factors can affect clinical outcome, as shown in FIG. I analyzed the time course. Of the 20 patients receiving nivolumab, 11 (55%) had discontinued due to exacerbation, and 9 (45%) had continued dosing at the time of analysis. In particular, in 9 of 11 patients with exacerbation (82%), relapses were closely matched with elevated levels of CXCL2 in blood clots above baseline. In addition, all six patients who maintained reduced levels of clotted CXCL2 during treatment showed sustained disease control and were able to continue nivolumab therapy. In contrast, VEGF, IFNα2, and MMP2 did not show a clear association of their time course of titer with clinical outcome.

According to the present invention, there is provided a biomarker for prognosis of cancer immunotherapy with an immune checkpoint inhibitor. Provision of the novel biomarker of the present invention makes it possible to predict the prognosis of cancer immunotherapy with an immune checkpoint inhibitor.

Claims

It is a prognosis prediction method of cancer immunotherapy with an immune checkpoint inhibitor,
a) comparing the level of GM-CSF in the collected biological sample with a preset GM-CSF cutoff value,
b) comparing the level of CHI3L1 in the collected biological sample with a preset cutoff value of CHI3L1;
c) the levels of CXCL2, VEGF, IFNα2 and / or MMP2 in the biological sample collected after administration of said immune checkpoint inhibitor are the same as in the biological sample collected prior to said administration of said immune checkpoint inhibitor Comparing with the level of CXCL2, VEGF, IFNα2 and / or MMP2, or d) monitoring the level of CXCL2 in a biological sample taken after administration of said immune checkpoint inhibitor,
Method, including.
In step a), it is predicted that the level is equal to or higher than a preset GM-CSF cut-off value, or the level is a preset GM-CSF cut-off value. The method according to claim 1, wherein the prediction is that the prognosis is poor.
In step b), it is predicted that the level is lower than or equal to a preset CHI3L1 cutoff value, or the level is greater than a preset CHI3L1 cutoff value. The method according to claim 1, wherein the prognosis is predicted to be poor.
In step c), the levels of CXCL2, VEGF and / or IFNα2 in the biological sample taken after administration are compared to the levels of corresponding CXCL2, VEGF and / or IFNα2 in the biological sample taken prior to administration The decrease is predicted to be of good prognosis, or the level of CXCL2, VEGF and / or IFNα2 in the biological sample collected after administration is the same as the corresponding CXCL2 in the biological sample collected before administration The method according to claim 1, characterized in that the decrease in level compared to the level of VEGF and / or IFNα2 predicts that the prognosis is poor.
In step c), it is predicted that the prognosis is good that the level of MMP2 in the biological sample collected after administration is increased compared to the level of MMP2 in the biological sample collected before administration That the level of MMP2 in the biological sample collected after or after administration is not increased as compared to the level of MMP2 in the biological sample collected prior to The method according to claim 1, characterized in that
In step d), the level of CXCL2 in the biological sample collected after administration of the immune checkpoint inhibitor is decreased compared to the level of CXCL2 before administration of the immune checkpoint inhibitor, and the prognosis is good There is a poor prognosis that the level of CXCL2 in a biological sample predicted to be present or taken after administration of the immune checkpoint inhibitor is increased compared to the level of CXCL2 before administration of the immune checkpoint inhibitor The method according to claim 1, wherein the method is predicted.
The method according to any one of claims 1-6, wherein the biological sample is a blood sample.
The method according to any one of claims 1-7, wherein the immune checkpoint inhibitor is an anti-PD-1 antibody or an anti-PD-L1 antibody.
The method according to any one of claims 1 to 8, wherein the cancer is non-small cell lung cancer, renal cell carcinoma, malignant melanoma, head and neck cancer, Hodgkin's disease, bladder cancer or gastric cancer.
The biological sample collected after administration of the immune checkpoint inhibitor is any biological sample collected after 6-8 weeks from the start of administration of the immune checkpoint inhibitor. The method described in.
3. The method of claim 1, wherein the level of CXCL2 after administration of the immune checkpoint inhibitor is reduced as compared to CXCL2 prior to administration of said immune checkpoint inhibitor, further predicting to develop a treatment related adverse event. The method according to any one of -10.
12. The method according to any one of the preceding claims, wherein the method further predicts that a treatment related adverse event will occur based on the level of GM-CSF.
A kit for use in the method of any one of claims 1-12.
One or more biomarkers selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2, which is a biologic for evaluating efficacy according to any one of claims 1-13. marker.
One or more selected from the group consisting of CXCL2, VEGF, CHI3L1, IFNα2, GM-CSF and MMP2 for prognosis of cancer immunotherapy with immune checkpoint inhibitors or prediction of the occurrence of treatment related adverse events Use of biomarkers.