Immunological biomarker for predicting clinical effect of cancer immunotherapy

Abstract

[Technical Problem] This invention relates to predicting an individual's response to cancer immunotherapy based on an individual's T-cell composition, and a therapeutic method for using cancer immunotherapy based on this prediction. The invention also provides a method for improving or maintaining an individual's response to cancer immunotherapy. [Solution to the Problem] Response to cancer immunotherapy is predicted by determining the relative values ​​of CD4+ T-cell subsets, dendritic cell subsets, and / or CD8+ T-cell subsets associated with dendritic cell stimulation in an antitumor immune response derived from an individual. A composition for treating or preventing cancer is also provided, comprising cells such as CD62L lowCD4+ T cells.

Description

[Technical Field]

[0001] This invention relates to the field of cancer immunotherapy. More specifically, this invention relates to predicting an individual's response to cancer immunotherapy based on an individual's T-cell composition, and a therapeutic method for using cancer immunotherapy based on that prediction. In another embodiment, this invention provides a method for improving or maintaining an individual's response to cancer immunotherapy. [Previous Technology]

[0002] Compared to conventional anticancer therapies that target the metabolism of cancer cells or similar substances (alkylating agents, platinum-modified compounds, antimetabolites, topoisomerase inhibitors, microtubule polymerization inhibitors, microtubule depolymerization inhibitors, and similar substances), cancer immunotherapy has attracted attention in recent years due to its fewer side effects and greater efficacy. Among cancer immunotherapies, anti-PD-1 immune checkpoint inhibition has garnered particular attention. Anti-PD-1 antibodies (nivolumab) have shown significantly superior performance over all survival periods compared to docetaxel (a known standard of care, such as secondary therapy for non-small cell lung cancer), thus becoming a standard therapy under a Grade A recommendation in the Lung Cancer Society Guidelines (Brahmer J et al., N Engl J Med 2015; 373: 123-135). Pembrolizumab (also an anti-PD-1 antibody) has shown superior survival compared to cytotoxic anticancer agents (note: in patients with PD-L1 expression on tumor cells of 50% or greater), which are known standard of care. This therapy is expected to become the standard of care for non-small cell lung cancer in the future. The efficacy of anti-PD-1 antibodies is not limited to lung cancer. This efficacy will be demonstrated in kidney cancer, head and neck cancer, gastrointestinal cancer, gynecological cancers, malignant lymphomas, and breast cancer. Kidney cancer is covered by insurance in Japan. Coverage for head and neck cancer, gastrointestinal cancer, and malignant lymphomas is expected the following year. Despite the apparent significant clinical efficacy of anti-PD-1 antibodies, they actually have a number of problems. An "ineffective cohort" (whose symptoms worsened within three months in almost all anti-PD-1 antibody clinical trials) has been found in progression-free survival (PFS) data. Meanwhile, in the group where anti-PD-1 antibodies were effective for one year or longer, almost no symptom worsening was observed thereafter, revealing a near-healing state. This indicates the existence of three distinct subgroups in terms of clinical efficacy: the "ineffective group," the "highly effective group," and the "intermediate group," but the predictive biomarkers are unknown. Administering anti-PD-1 antibodies (which are expected to be the standard of care in almost all cancers and tumors) to the approximately 40% ineffective group would be not only a medical problem but also a medical economic one. [List of Citations] [Non-Patent Literature] [NPL 1] Brahmer J et al., N Engl J Med 2015; 373: 123-135 [Summary of the Invention]

[0003] [Solution to the Problem] This invention provides a method for using a composition of an individual's CD4+ T cells as an indicator for predicting an individual's response to cancer immunotherapy. This invention also provides a method for using a composition of an individual's dendritic cells and / or CD8+ T cells as an indicator for predicting an individual's response to immunotherapy. This invention is partly based on the inventors' discovery that response to cancer immunotherapy is associated with a composition of T cells and / or dendritic cells within an individual, and that this response can be used as a biomarker. The biomarker of this invention has a higher level of sensitivity and specificity compared to conventionally studied biomarkers. The inventors have discovered that three groups of responses to cancer immunotherapy (e.g., anti-PD-1 therapy or anti-PD-L1 therapy) – namely, progressive disease (PD), stable disease (SD), and response (complete response (CR) + partial response (PR)) – each exhibit different immunopathological characteristics. Some embodiments of the invention provide a method for predicting the response to cancer immunotherapy as progressive disease (PD), stable disease (SD), or response (complete response (CR) + partial response (PR)) when cancer immunotherapy is applied to an individual. In this invention, it should be noted that a group of individuals including both the complete response (CR) and partial response (PR) groups, or a group including the complete response (CR) group but without the partial response (PR) group, can be identified as being identical to the partial response (PR) group. One embodiment of the present invention is a method for using the relative abundance of CD4+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses as an indicator for predicting an individual's response to cancer immunotherapy. Examples of CD4+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses include (but are not limited to): CD4+ T cell subsets with reduced expression of homing molecules in peripheral lymphoid organs, CD4+ T cell subsets presensitized by effector T cells, CD4+ T cell subsets presensitized by antigen recognition, and regulatory T cell subsets.The relative proportions of CD4+ T cell subsets are selected from the following groups: the ratio of CD62LlowCD4+ T cells in CD4+ T cells; the ratio of CCR7-CD4+ T cells in CD4+ T cells; the ratio of CD45RA-CD4+ T cells in CD4+ T cells; the ratio of CD45RO+CD4+ T cells in CD4+ T cells; the ratio of LAG-3+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells; the ratio of ICOS+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells; the ratio of CD62LhighCD25+CD4+ T cells in CD4+ T cells; and the ratio of CCR4+CD25+CD4+ T cells in CD4+ T cells. The invention provides, for example, a method for using the ratio of CD127+CD25+CD4+ T cell subsets among CD4+ T cells as an indicator of an individual's response to cancer immunotherapy. In one embodiment, the method includes determining the ratio of CD62LlowT cells among CD4+ T cells in a sample derived from an individual. A ratio higher than a threshold (ineffectiveness threshold) indicates that the individual is not part of the ineffectiveness cohort with respect to cancer immunotherapy. Another embodiment of the invention uses the relative amount of dendritic cell subsets associated with dendritic cell stimulation in antitumor immune responses as an indicator of an individual's response to cancer immunotherapy. Examples of dendritic cell subsets associated with dendritic cell stimulation in antitumor immune responses include (but are not limited to): increased dendritic cell subsets attributable to an increase in cell subsets with reduced expression of homing molecules in the CD4+ T cell population, and increased dendritic cell subsets attributable to an increase in CD4+ T cell subsets pre-sensitized by effector T cells in the CD4+ T cell population. Examples of dendritic cell subsets include (but are not limited to): HLA-DR+ dendritic cell subsets, CD80+ dendritic cell subsets, CD86+ dendritic cell subsets, and PD-L1+ dendritic cell subsets. Examples of dendritic cells include (but are not limited to): bone marrow dendritic cells (mDC, CD141+CD11c+ dendritic cells) and plasmacytoid dendritic cells (pDC, CD123+CD11c+ dendritic cells). Another embodiment of the present invention is a method for using the relative amount of CD8+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses as an indicator for predicting an individual’s response to cancer immunotherapy.Examples of CD8+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses include (but are not limited to): CD8+ T cell subsets increased due to an increase in cell subsets with reduced expression of homing molecules in the CD4+ T cell population; CD8+ T cell subsets increased due to an increase in CD4+ T cell subsets presensitized by effector T cells in the CD4+ T cell population; CD8+ T cell subsets increased due to an increase in CD4+ T cell subsets presensitized by antigen recognition in the CD4+ T cell population; CD8+ T cell subsets increased due to an increase in HLA-DR+ dendritic cell subsets in the dendritic cell population; CD8+ T cell subsets increased due to an increase in CD80+ T cell subsets in the dendritic cell population; and CD8+ T cell subsets increased due to an increase in PD-L1+ dendritic cell subsets in the dendritic cell population. Additionally, examples of CD8+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses include (but are not limited to): CD62LlowCD8+ T cell subsets, CD137+CD8+ T cell subsets, and CD28+CD62LlowCD8+ T cell subsets. One embodiment of the invention uses an individual's subset selected from the following: the amount of CD4+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses; the amount of dendritic cell subsets associated with CD4+ T cell stimulation in antitumor immune responses; the amount of CD8+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses; the amount of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; and the amount of ICOS+CD62LlowCD4+ T cell subsets; as a method for predicting an individual's response to cancer immunotherapy as a function variable (indicator).In one embodiment, the variables (X, Y) of the present invention are each selected from the following groups: the amount of CD62LlowCD4+ T cell subset; the amount of CCR7-CD4+ T cell subset; the amount of LAG-3+CD62LlowCD4+ T cell subset; the amount of ICOS+CD62LlowCD4+ T cell subset; the amount of CD45RA-CD4+ T cell subset; the amount of CD45RO+CD4+ T cell subset; the amount of CCR4+CD25+CD4+ T cell subset; the amount of CD62LhighCD25+CD4+ T cell subset; the amount of CD127+CD25+CD4+ T cell subset; the amount of CD45RA-Foxp3+CD4+ T cell subset; the amount of Foxp3+CD25+CD4+ T cell subset; the amount of HLA-DR+ dendritic cell subset; and the amount of CD80+ dendritic cell subset. The amounts of CD86+ dendritic cell subsets; PD-L1+ dendritic cell subsets; CD62LlowCD8+ T cell subsets; CD137+CD8+ T cell subsets; and CD28+CD62LlowCD8+ T cell subsets. In this invention, (X) can be, for example, a value selected from the group consisting of: the amount of CD4+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses; the amount of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in antitumor immune responses; and the amount of CD8+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses. The method of this invention can also calculate variables (X, Y) selected from the values ​​of the following groups: the amount of CD62LlowCD4+ T cell subsets; the amount of CCR7-CD4+ T cell subsets; the amount of LAG-3+CD62LlowCD4+ T cell subsets; the amount of ICOS+CD62LlowCD4+ T cell subsets; the amount of CD45RA-CD4+ T cell subsets; the amount of CD45RO+CD4+ T cell subsets; the amount of HLA-DR+ dendritic cell subsets; the amount of CD80+ dendritic cell subsets; the amount of CD86+ dendritic cell subsets; the amount of PD-L1+ dendritic cell subsets; the amount of CD62LlowCD8+ T cell subsets; the amount of CD137+CD8+ T cell subsets; and the amount of CD28+CD62LlowCD8+ T cell subsets; as (X). For example, the method of the present invention can calculate variables (X, Y), with the amount of regulatory T cell subsets or the amount of CD4+ T cell subsets associated with regulatory T cells as (Y).The method of this invention can also calculate variables (X, Y) selected from the following groups: the amount of CCR4+CD25+CD4+ T cell subsets; the amount of CD62LhighCD25+CD4+ T cell subsets; the amount of CD127+CD25+CD4+ T cell subsets; the amount of CD45RA-Foxp3+CD4+ T cell subsets; and the amount of CD4+Foxp3+CD25+ T cell subsets; as (Y). The method of this invention can use, for example, a comparison of the relative values ​​of X and Y with a threshold value (ineffectiveness threshold value) (including measuring the amount of CD4+CD62Llow T cells (X) and measuring the amount of CD4+Foxp3+CD25+ T cells (Y)) as an indicator to predict whether an individual is not part of the ineffectiveness group for cancer immunotherapy. The amount of regulatory T cell subsets or the amount or ratio of CD4+ T cell subsets associated with regulatory T cells can be used as (Y). Specifically, the ratio of CD62LlowCD4+ T cells to regulatory T cells has not been reported as a biomarker to date, but the inventors have found this ratio to be very suitable as a biomarker for predicting response to cancer immunotherapy. The method of this invention can use, for example, a comparison of the relative values ​​of X and Y with a threshold (ineffectiveness threshold) (including the measurement of the amount of CD80+ dendritic cells (X) and the measurement of the amount of CD28+CD62LlowCD8+ T cells (Y)) as an indicator to predict whether an individual is not part of the ineffectiveness group in relation to cancer immunotherapy. Since the inventors have found that multiple indicators independently demonstrate a correlation with response, multiple indicators can be combined as response indicators. When two or more indicators are combined as response indicators, indicators expressed by a function using any number of variables can be used. When using multiple indicators (X1, X2, X3, ... Xn), examples of response indicators include, but are not limited to, the following: F = a1X1b1 + a2X2b2 + a3X3b3... + anXnbn F = X1c1 × X2c2 × X3c3... × Xncn where each of a, b, and c is any real number. The response can be predicted by comparing the value calculated by such a function (indicator) with a threshold value. Multivariate analysis (e.g., logistic regression assessment) using discriminant analysis of the novel indicators discovered by the inventors can determine the coefficients used as indicators of an individual's response to cancer immunotherapy. Typically, the response can be predicted by a function F(X, Y) using two indicators (X, Y) disclosed herein as variables. In a particular embodiment, this function is a relative value of X and Y. Any function of X and Y (F(X, Y)) can be used as a relative value of X and Y. Specifically, when X is considered to be positively correlated with the response and Y is negatively correlated with the response, any function of X and Y (F(X, Y)) (which monotonically increases with respect to X and monotonically decreases with respect to Y) can be used, but is not limited to this.A function indicating a response, having two or more variables representing the response, can be derived by regression analysis to calculate the weight of each variable on the response. Examples of functions indicating responses, F(X, Y), include, but are not limited to, the following: F = aXr + bYs F = Xr × Ys where a, b, r, and s are any real numbers. For simplification, integers can be used as r and s. In some embodiments, examples of the relative values ​​of X and Y include (but are not limited to) Xn / Ym (where n and m are any real numbers, such as any integers), such as X / Y and X² / Y. Such combinations of indicators can make response predictions more accurate when factors X and Y each represent responses to therapy in different institutions. The inventors' research indicates that using functions with r and s in the range of -5 to 5 can more accurately predict an individual's response to cancer immunotherapy. Another aspect of the invention provides a method for further predicting individuals who have demonstrated a partial response (complete response (CR) + partial response (PR)) among those who are not part of the nullipopulation, by using a composition of an individual's CD4+ T cells as an indicator of response to cancer immunotherapy. One embodiment of the invention uses the ratio of Foxp3+CD25+ T cells, ICOS+CD62LlowCD4+ T cells, LAG-3+CD62LlowCD4+ T cell subsets, or PD-1+CD62LlowCD4+ T cell subsets within an individual (who has demonstrated a partial response to cancer immunotherapy) as an indicator of their response. The percentages of Foxp3+CD25+ T cells among CD4+ T cells, the percentages of ICOS+CD62LlowCD4+ T cell subsets among CD62LlowCD4+ T cells, the percentages of LAG-3+CD62LlowCD4+ T cell subsets among CD62LlowCD4+ T cells, or the percentages of PD-1+CD62LlowCD4+ T cell subsets among CD62LlowCD4+ T cells exceeding the null threshold indicate that the individual is not part of the reactive population. To determine whether an individual is part of the reactive population, it is necessary to determine that the individual is not part of the null population. Such determination of whether an individual is part of the null population can be achieved by the methods disclosed herein. Another embodiment of the invention provides a method for identifying the reactive population (PR) and stable population (SD) within a population of individuals determined not to be part of the null population using the above (X, Y).In methods for identifying the reactive (PR) and stable (SD) populations, variables (Z, W) can be calculated, with the amount of ICOS+CD62LlowCD4+ T cell subsets as (Z) and values ​​selected from the following subsets as (W) to predict whether an individual is part of the reactive (PR) or stable (SD) population. Typically, methods for identifying the reactive (PR) and stable (SD) populations use the amount of ICOS+CD62LlowCD4+ T cell subsets as (Z), the amount of CD4+Foxp3+CD25+ T cell subsets as (W), and the W5×Z value as indicators to determine the PR and SD populations. Threshold values ​​can be determined by considering sensitivity and specificity. Sensitivity and specificity can refer to the sensitivity and specificity for detecting the null population, the reactive population, or the stable population. In one embodiment, a threshold value where both sensitivity and specificity are 100% can be set for use with the biomarker of the present invention. For this reason, null cases can be selected very accurately, making this technically significant. When using two or more indicators disclosed as biomarkers of the present invention, threshold values ​​can be determined for each indicator, and these threshold values ​​can be used and, as needed, classified into first, second, third, and fourth threshold values. A threshold value can be determined such that the sensitivity for detecting the invalid population, the response population, or the stable population exceeds approximately 90%. In another embodiment, a threshold value can be determined such that the sensitivity for detecting the invalid population, the response population, or the stable population is approximately 100%. In yet another embodiment, a threshold value can be determined such that the specificity for detecting the invalid population, the response population, or the stable population exceeds approximately 90%. In yet another embodiment, a threshold value can be determined such that the specificity for detecting the invalid population, the response population, or the stable population is approximately 100%. In one embodiment, the composition of an individual's T cells is a composition of T cells from a sample obtained from the individual. Preferably, the sample is a peripheral blood sample. The biomarkers provided in this invention can be measured using peripheral blood samples, giving the biomarkers significant advantages in clinical applications, such as non-invasiveness, low cost, and long-term feasibility. In one embodiment, cancer immunotherapy includes the administration of an immune checkpoint inhibitor. Specifically, the biomarkers of this invention can accurately predict an individual's response to such cancer immunotherapies.In one embodiment, the immune checkpoint inhibitor comprises a PD-1 inhibitor or a PD-L1 inhibitor. Examples of PD-1 inhibitors include (but are not limited to) anti-PD-1 antibodies that inhibit the interaction (e.g., adhesion) between PD-1 and PD-L1, such as anti-PD-1 antibodies (nivolumab and pembrolizumab). Examples of PD-L1 inhibitors include (but are not limited to) anti-PD-L1 antibodies that inhibit the interaction (e.g., adhesion) between PD-1 and PD-L1, such as anti-PD-L1 antibodies (durvalumab, atezolizumab, and avelumab). Another aspect of the invention provides a method for predicting an individual's response to cancer immunotherapy using a composition of an individual's T cells to treat an individual with cancer. Alternatively, a method is provided for treating cancer within an individual with a specific composition of T cells or for such a composition. Cancer immunotherapy (especially immune checkpoint inhibitory therapy) is known to exhibit significant individual variability in response. By using the biomarkers of this invention to select individuals for cancer immunotherapy, the likelihood of achieving therapeutic effects such as tumor regression can be significantly increased. One embodiment of the invention provides a method for treating an individual with cancer, comprising: (1) determining the relative amount of selected populations consisting of: a relative amount of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; a relative amount of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; and a relative amount of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response, in CD4+ T cells derived from the individual; and (2) if the relative amount is higher than a threshold value (ineffectiveness threshold value), determining that the individual is not part of the ineffectiveness population in terms of response to cancer immunotherapy, and if the individual is determined not to be part of the ineffectiveness population, then applying cancer immunotherapy to the individual. Another embodiment of the present invention provides a method for treating an individual suffering from cancer, comprising applying cancer immunotherapy to the individual if the individual is determined not to be part of an invalid population by determining a relative quantity, wherein the relative quantity is selected from the following groups: a relative quantity of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; a relative quantity of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; and a relative quantity of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response, in CD4+ T cells derived from the individual, and the relative quantity is determined to be higher than a threshold value (invalid population threshold value).In this method, the relative proportions are selected from the following groups: the ratio of CD62LlowCD4+ T cells in CD4+ T cells; the ratio of CCR7-CD4+ T cells in CD4+ T cells; the ratio of CD45RA-CD4+ T cells in CD4+ T cells; the ratio of CD45RO+CD4+ T cells in CD4+ T cells; the ratio of LAG-3+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells; the ratio of ICOS+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells; the ratio of CCR4+CD25+CD4+ T cells in CD4+ T cells; the ratio of CD62LhighCD25+CD4+ T cells in CD4+ T cells; and the ratio of CD127+CD25+CD4+ T cells in CD4+ T cells. The percentages of CD4+ T cells, including CD45RA-Foxp3+CD4+ T cells, Foxp3+CD25+CD4+ T cells, HLA-DR+ dendritic cells, CD80+ dendritic cells, CD86+ dendritic cells, PD-L1+ dendritic cells, CD62LlowCD8+ T cells, CD137+CD8+ T cells, and CD28+CD62LlowCD8+ T cells.Preferably, the relative proportions are selected from the following groups: the ratio of CD62LlowCD4+ T cells in CD4+ T cells; the ratio of CCR7-CD4+ T cells in CD4+ T cells; the ratio of CD45RA-CD4+ T cells in CD4+ T cells; the ratio of CD45RO+CD4+ T cells in CD4+ T cells; the ratio of LAG-3+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells; the ratio of ICOS+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells; the ratio of HLA-DR+ dendritic cells in dendritic cells; the ratio of CD80+ dendritic cells in dendritic cells; the ratio of CD86+ dendritic cells in dendritic cells; and the ratio of PD-L1+ dendritic cells in dendritic cells. The proportions of CD62LlowCD8+ T cells, CD137+CD8+ T cells, and CD28+CD62LlowCD8+ T cells in CD8+ T cells are determined. Another embodiment of the invention provides a method for treating an individual with cancer, comprising: determining the proportion of Foxp3+CD25+ T cells in CD4+ T cells from a sample derived from the individual; if the proportion of Foxp3+CD25+ T cells in CD4+ T cells is below a threshold (ineffectiveness threshold), then determining that the individual is not part of the ineffectiveness cohort in terms of response to cancer immunotherapy; and if the individual is determined not to be part of the ineffectiveness cohort, then applying cancer immunotherapy to the individual. Another embodiment of the present invention provides a method for treating an individual with cancer, comprising: applying cancer immunotherapy to an individual determined not to be part of the invalid population in terms of response to cancer immunotherapy, by: determining the ratio of Foxp3+CD25+ T cells in CD4+ T cells from a sample derived from the individual; and if the ratio of Foxp3+CD25+ T cells in CD4+ T cells is below a threshold value (invalid population threshold value), then determining that the individual is not part of the invalid population in terms of response to cancer immunotherapy.Another embodiment of the present invention provides a method for treating an individual with cancer, comprising: (1) determining the amount (X, Y) of a subset selected from the following: a relative amount of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; a relative amount of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; a relative amount of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; an amount of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; and an amount of ICOS+CD62LlowCD4+ T cell subsets; (2) using a comparison of the relative values ​​of X and Y with an invalidity threshold to determine whether the individual is part of an invalidity group in relation to the response to cancer immunotherapy; and (3) if the individual is determined not to be part of an invalidity group, then applying cancer immunotherapy to the individual. Another embodiment of the present invention provides a method for treating an individual with cancer, comprising applying cancer immunotherapy to an individual determined not to be part of an ineffective population in relation to the response to cancer immunotherapy, by means of: (1) determining the amount (X, Y) of a subset selected from the following: a relative amount of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; a relative amount of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; a relative amount of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; an amount of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; and an amount of ICOS+CD62LlowCD4+ T cell subsets; and (2) using a comparison of the relative values ​​of X and Y with an ineffective population threshold to determine whether the individual is part of an ineffective population in relation to the response to cancer immunotherapy.For example, the quantities (X) and (Y) mentioned above are selected from the following subsets: CD62LlowCD4+ T cell subset; CCR7-CD4+ T cell subset; LAG-3+CD62LlowCD4+ T cell subset; CD45RA-CD4+ T cell subset; CD45RO+CD4+ T cell subset; HLA-DR+ dendritic cell subset; CD80+ dendritic cell subset; CD86+ dendritic cell subset; PD-L1+ dendritic cell subset; CD137+CD8+ T cell subset; CD62LlowCD8+ T cell subset; CD28+CD62LlowCD8+ T cell subset; Foxp3+CD25+CD4+ T cell subset; CD62LhighCD25+CD4+ T cell subset. The amounts of CD45RA-Foxp3+CD4+ T cell subsets; the amounts of CCR4+CD25+CD4+ T cell subsets; and the amounts of CD127+CD25+CD4+ T cell subsets. For example, the method of the present invention may use values ​​selected from the following groups: the amount of CD4+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses; the amount of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in antitumor immune responses; and the amount of CD8+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses; as (X). The method of this invention can also calculate variables (X, Y) selected from the values ​​of the following groups: the amount of CD62LlowCD4+ T cell subsets; the amount of CCR7-CD4+ T cell subsets; the amount of LAG-3+CD62LlowCD4+ T cell subsets; the amount of ICOS+CD62LlowCD4+ T cell subsets; the amount of CD45RA-CD4+ T cell subsets; the amount of CD45RO+CD4+ T cell subsets; the amount of HLA-DR+ dendritic cell subsets; the amount of CD80+ dendritic cell subsets; the amount of CD86+ dendritic cell subsets; the amount of PD-L1+ dendritic cell subsets; the amount of CD62LlowCD8+ T cell subsets; the amount of CD137+CD8+ T cell subsets; and the amount of CD28+CD62LlowCD8+ T cell subsets; as (X). For example, the method of the present invention can calculate variables (X, Y), with the amount of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells as (Y).The method of the present invention can also calculate variables (X, Y) to be selected from the values ​​of the following groups: the amount of CCR4+CD25+CD4+ T cell subsets; the amount of CD62LhighCD25+CD4+ T cell subsets; the amount of CD127+CD25+CD4+ T cell subsets; the amount of CD45RA-Foxp3+CD4+ T cell subsets; and the amount of CD4+Foxp3+CD25+ T cell subsets; as (Y). Another embodiment of the present invention provides a method for treating an individual suffering from cancer, comprising: determining the quantity (X, Y) of a subset selected from the following: the quantity of a CD4+ T cell subset associated with dendritic cell stimulation in an antitumor immune response; the quantity of a dendritic cell subset associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; the quantity of a CD8+ T cell subset associated with dendritic cell stimulation in an antitumor immune response; the quantity of regulatory T cells or a CD4+ T cell subset associated with regulatory T cells; and the quantity of an ICOS+CD62LlowCD4+ T cell subset. The relative values ​​of X and Y are compared with the threshold (ineffectiveness threshold) to determine whether the individual belongs to the ineffective population in terms of response to cancer immunotherapy. If the individual is determined not to be part of the ineffective population, and the ratios of Foxp3+CD25+ T cells, ICOS+CD62LlowCD4+ T cell subsets, LAG-3+CD62LlowCD4+ T cell subsets, or PD-1+CD62LlowCD4+ T cell subsets are higher than the threshold (effectiveness threshold), then the individual is determined to be part of the effective population in terms of response to cancer immunotherapy. If the individual is determined to be part of the effective population, then cancer immunotherapy is applied to the individual.Another embodiment of the present invention provides a method for treating an individual with cancer, comprising applying cancer immunotherapy to an individual determined not to be part of an ineffective population in terms of response to cancer immunotherapy, by determining the amount (X, Y) of a subset selected from the following: the amount of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; the amount of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; the amount of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; the amount of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; and the amount of ICOS+CD62LlowCD4+ T cell subsets. The relative values ​​of X and Y are compared with the threshold value (ineffectiveness threshold value) to determine whether the individual belongs to the ineffective population in terms of response to cancer immunotherapy. If the individual is determined not to be part of the ineffective population, and the ratio of Foxp3+CD25+ T cells, ICOS+CD62LlowCD4+ T cell subsets, LAG-3+CD62LlowCD4+ T cell subsets, or PD-1+CD62LlowCD4+ T cell subsets are higher than the threshold value (effectiveness threshold value), then the individual is determined to be part of the effective population in terms of response to cancer immunotherapy. For example, the method of the present invention may use values ​​selected from the following groups: the amount of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; the amount of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; and the amount of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; as (X). The method of this invention can also calculate variables (X, Y) selected from the values ​​of the following groups: the amount of CD62LlowCD4+ T cell subsets; the amount of CCR7-CD4+ T cell subsets; the amount of LAG-3+CD62LlowCD4+ T cell subsets; the amount of ICOS+CD62LlowCD4+ T cell subsets; the amount of CD45RA-CD4+ T cell subsets; the amount of CD45RO+CD4+ T cell subsets; the amount of HLA-DR+ dendritic cell subsets; the amount of CD80+ dendritic cell subsets; the amount of CD86+ dendritic cell subsets; the amount of PD-L1+ dendritic cell subsets; the amount of CD62LlowCD8+ T cell subsets; and the amount of CD137+CD8+ T cell subsets; the amount of CD28+CD62LlowCD8+ T cell subsets; as (X). For example, the method of the present invention can calculate variables (X, Y), with the amount of regulatory T cell subsets or CD4+ T cell subsets associated with regulatory T cells as (Y).The method of the present invention can also calculate variables (X, Y) to be selected from the values ​​of the following groups: the amount of CCR4+CD25+CD4+ T cell subsets; the amount of CD62LhighCD25+CD4+ T cell subsets; the amount of CD127+CD25+CD4+ T cell subsets; the amount of CD45RA-Foxp3+CD4+ T cell subsets; and the amount of CD4+Foxp3+CD25+ T cell subsets; as (Y). Another aspect of the present invention provides a method for identifying the response group (PR) and the stable group (SD) in a population of individuals that have been determined not to be part of the invalid group using the above (X, Y). The method for identifying the reactive (PR) and stable (SD) populations involves calculating variables (Z, W), where the amount of the ICOS+CD62LlowCD4+ T cell subset is used as (Z), and the values ​​of the following constituent populations are chosen freely: the amount of the CD4+CD25+ T cell subset; the amount of the CD4+Foxp3+ T cell subset; the amount of the CD4+Foxp3+CD25+ T cell subset; the amount of the CD62LhighCD25+CD4+ T cell subset; the amount of the CD45RA-Foxp3+CD4+ T cell subset; the amount of the CCR4+CD25+CD4+ T cell subset; and the amount of the CD127+CD25+CD4+ T cell subset; as (W) to predict whether an individual is part of the reactive (PR) or stable (SD) population. Another embodiment of the present invention provides a kit for predicting an individual's response to cancer immunotherapy, comprising reagents for detecting one or more cell surface markers selected from CD4, CD25, CD62L, Foxp3, and similar markers, such as combinations of markers selected from the following groups: * Combinations of CD4 and CD62L; * Combinations of CD4, CD45RA, and CCR7; * Combinations of CD4, CD45RO, and CCR7; * Combinations of CD4, CD62L, and LAG-3; * Combinations of CD4, CD62L, and ICOS; * Combinations of CD4, CD62L, and CD25; * Combinations of CD4, CD127, and CD25; * Combinations of CD4, CD45RA, and Foxp3; * Combinations of CD4, CD45RO, and Foxp3; * Combinations of CD4, CD25, and Foxp3; * Combinations of CD11c, CD141, and HLA-DR. * Combinations of CD11c, CD141, and CD80; * Combinations of CD11c, CD123, and HLA-DR; * Combinations of CD11c, CD123, and CD80; * Combinations of CD8 and CD62L; * Combinations of CD8 and CD137; and * Combinations of CD28, CD62L, and CD8. Preferably, the kit includes reagents for detecting each of CD4 and CD62L. Such combinations of detectors can be used to determine the combination of an individual's T cells.Such kits can be used to measure the ratio of specific T cell subsets within an individual, which are novel biomarkers disclosed herein. One embodiment of the invention comprises reagents for detecting cell surface markers used to predict an individual's response to cancer immunotherapy. The inventors have found that these cell surface markers expressed by an individual's T cells are relevant to the individual's response to cancer immunotherapy. It should be understood that, in view of the foregoing, kits comprising reagents for detecting such cell surface markers are suitable for predicting responses to cancer immunotherapy. The kit preferably comprises reagents for detecting CD4 and CD62L. The kit more preferably comprises reagents for detecting CD4, CD25, CD62L, and Foxp3. In one embodiment, the detector is an antibody. Preferably, the antibody facilitates the detection of appropriately labeled markers. Another embodiment of the invention is a composition for treating cancer within an individual, comprising an immune checkpoint inhibitor. One embodiment of the present invention is a composition comprising an immune checkpoint inhibitor for treating cancer in an individual, wherein the individual has relative amounts selected from the following groups: a relative amount of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; a relative amount of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; and a relative amount of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; wherein the relative amounts are equal to or greater than a threshold value (ineffectiveness threshold value).For example, relative proportions are typically selected from the following groups: the ratio of CD62LlowCD4+ T cells in CD4+ T cells; the ratio of CCR7-CD4+ T cells in CD4+ T cells; the ratio of LAG-3+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells; the ratio of ICOS+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells; the ratio of CD62LhighCD25+CD4+ T cells in CD4+ T cells; the ratio of CD127+CD25+CD4+ T cells in CD4+ T cells; the ratio of CD45RA-Foxp3+CD4+ T cells in CD4+ T cells; the ratio of Foxp3+CD25+CD4+ T cells in CD4+ T cells; and the ratio of HLA-DR+ dendritic cells in dendritic cells. The ratios of CD80+ dendritic cell subsets in dendritic cells; the ratios of CD86+ dendritic cell subsets in dendritic cells; the ratios of PD-L1+ dendritic cell subsets in dendritic cells; the ratios of CD62LlowCD8+ T cell subsets in CD8+ T cells; the ratios of CD137+CD8+ T cell subsets in CD8+ T cells; and the ratios of CD28+CD62LlowCD8+ T cell subsets in CD62LlowCD8+ T cells.Another embodiment of the present invention is a composition comprising an immune checkpoint inhibitor for treating cancer in an individual, wherein the individual is selected by comparing a threshold value (ineffectiveness threshold value) with the relative values ​​of X and Y, wherein the quantities (X, Y) are selected from the following groups derived from a sample of the individual: the quantity of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; the quantity of dendritic cell subsets associated with dendritic cell stimulation in an antitumor immune response; and the quantity of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; the quantity of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; and the quantity of ICOS+CD62LlowCD4+ T cell subsets; the quantities (X, Y) are generally selected from the following groups: the quantity of CD62LlowCD4+ T cell subsets; and the quantity of CCR7-CD4+ T cell subsets; Amounts of LAG-3+CD62LlowCD4+ T cell subsets; Amounts of ICOS+CD62LlowCD4+ T cell subsets; Amounts of CCR4+CD25+CD4+ T cell subsets; Amounts of CD45RA-CD4+ T cell subsets; Amounts of CD45RO+CD4+ T cell subsets; Amounts of CD62LhighCD25+CD4+ T cell subsets; Amounts of CD127+CD25+CD4+ T cell subsets; Amounts of CD45RA-Foxp3+CD4+ T cell subsets; Amounts of Foxp3+CD25+CD4+ T cell subsets; Amounts of HLA-DR+ dendritic cell subsets; Amounts of CD80+ dendritic cell subsets; Amounts of CD86+ dendritic cell subsets; Amounts of PD-L1+ dendritic cell subsets; Amounts of CD62LlowCD8+ T cell subsets. The amount of CD137+CD8+ T cell subsets; and the amount of CD28+CD62LlowCD8+ T cell subsets. For example, the method of the present invention may use values ​​selected from the following groups: the amount of CD4+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses; the amount of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in antitumor immune responses; and the amount of CD8+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses; as (X).The method of this invention can also calculate variables (X, Y) selected from the values ​​of the following groups: the amount of CD62LlowCD4+ T cell subsets; the amount of CCR7-CD4+ T cell subsets; the amount of LAG-3+CD62LlowCD4+ T cell subsets; the amount of ICOS+CD62LlowCD4+ T cell subsets; the amount of CD45RA-CD4+ T cell subsets; the amount of CD45RO+CD4+ T cell subsets; the amount of HLA-DR+ dendritic cell subsets; the amount of CD80+ dendritic cell subsets; the amount of CD86+ dendritic cell subsets; the amount of PD-L1+ dendritic cell subsets; the amount of CD62LlowCD8+ T cell subsets; the amount of CD137+CD8+ T cell subsets; and the amount of CD28+CD62LlowCD8+ T cell subsets; as (X). For example, the method of the present invention can calculate the variable (X, Y) as (Y) using the amount of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells. The method of the present invention can also calculate the variable (X, Y) as a value selected from the following groups: the amount of CCR4+CD25+CD4+ T cell subsets; the amount of CD62LhighCD25+CD4+ T cell subsets; the amount of CD127+CD25+CD4+ T cell subsets; the amount of CD45RA-Foxp3+CD4+ T cell subsets; and the amount of CD4+Foxp3+CD25+ T cell subsets; as (Y). For example, the method of the present invention can use the relative value of X and Y to a threshold value (invalid population threshold value) (including measuring the amount of CD4+CD62LlowT cells (X) and measuring the amount of CD4+Foxp3+CD25+T cells (Y)) as an indicator to predict whether an individual is not part of the invalid population for cancer immunotherapy. The amount or ratio of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells can be used as (Y).Another embodiment of the present invention is a composition comprising an immune checkpoint inhibitor for treating cancer in an individual, wherein: the individual is selected by comparing a threshold value (ineffectiveness threshold value) with a relative value of a quantity (X, Y), the quantity being selected from the following groups derived from a sample of the individual: the quantity of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; the quantity of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; the quantity of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; the quantity of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; and the quantity of ICOS+CD62LlowCD4+ T cell subsets. And the ratio of Foxp3+CD25+ T cell subsets in CD4+ T cells that are equal to or greater than the threshold value (effective population threshold value) or the ratio of ICOS+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells. The amounts (X) and (Y) are typically selected from the following subsets: CD62LlowCD4+ T cell subset; CCR7-CD4+ T cell subset; LAG-3+CD62LlowCD4+ T cell subset; ICOS+CD62LlowCD4+ T cell subset; CCR4+CD25+CD4+ T cell subset; CD45RA-CD4+ T cell subset; CD45RO+CD4+ T cell subset; CD62LhighCD25+CD4+ T cell subset; CD127+CD25+CD4+ T cell subset; CD45RA-Foxp3+CD4+ T cell subset; Foxp3+CD25+CD4+ T cell subset; HLA-DR+ dendritic cell subset; CD80+ dendritic cell subset. The amounts of CD86+ dendritic cell subsets; PD-L1+ dendritic cell subsets; CD62LlowCD8+ T cell subsets; CD137+CD8+ T cell subsets; and CD28+CD62LlowCD8+ T cell subsets. For example, the object of administration of the composition of the present invention may be an individual characterized by variables (X, Y) selected from the group of values ​​of: the amount of CD4+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses; the amount of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in antitumor immune responses; and the amount of CD8+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses; as (X).The method of this invention can also calculate variables (X, Y) to select values ​​from the following groups: the amount of CD62LlowCD4+ T cell subsets; the amount of CCR7-CD4+ T cell subsets; the amount of LAG-3+CD62LlowCD4+ T cell subsets; the amount of ICOS+CD62LlowCD4+ T cell subsets; the amount of CD45RA-CD4+ T cell subsets; the amount of CD45RO+CD4+ T cell subsets; the amount of HLA-DR+ dendritic cell subsets; the amount of CD80+ dendritic cell subsets; the amount of CD86+ dendritic cell subsets; the amount of PD-L1+ dendritic cell subsets; the amount of CD62LlowCD8+ T cell subsets; the amount of CD137+CD8+ T cell subsets; and the amount of CD28+CD62LlowCD8+ T cell subsets; as (X) to target individuals characterized by variables (X, Y). For example, for the compositions of the present invention, the variable (X, Y) can be calculated as (Y) the amount of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells. The method of the present invention can also calculate the variable (X, Y) to a value selected from the following groups: the amount of CCR4+CD25+CD4+ T cell subsets; the amount of CD62LhighCD25+CD4+ T cell subsets; the amount of CD127+CD25+CD4+ T cell subsets; the amount of CD45RA-Foxp3+CD4+ T cell subsets; and the amount of CD4+Foxp3+CD25+ T cell subsets; as (Y) to be targeted to individuals characterized by the variable (X, Y). For example, individuals predicted not to be part of the ineffective population for cancer immunotherapy can be targeted by comparing the relative values ​​of X and Y (ineffective population threshold) of the amount of CD4+CD62LlowT cells (X) and the amount of CD4+Foxp3+CD25+T cells (Y) to a threshold value. The amount or ratio of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells can be used as (Y). The compositions of the present invention can be used in combination with any other reagents. In one embodiment, the composition comprises a PD-1 inhibitor. Examples of PD-1 inhibitors include anti-PD-1 antibodies that inhibit the adhesion between PD-L1 and PD-1, such as nivolumab or pembrolizumab. In another embodiment, the composition comprises a PD-L1 inhibitor. Examples of PD-L1 inhibitors include anti-PD-L1 antibodies that inhibit the adhesion between PD-L1 and PD-1, such as durvalumab, atezolizumab, and avelumab. Compositions containing such immune checkpoint inhibitors are understood to achieve therapeutic effects with a particularly high probability when administered to individuals selected using the biomarkers of this invention. Another aspect of the invention provides a method for improving or maintaining an individual's response to cancer immunotherapy.Cells selected from the following groups were found to be important for an individual's response to cancer immunotherapy: CD62LlowCD4+ T cells; CCR7-CD4+ T cells; LAG-3+CD62LlowCD4+ T cells; ICOS+CD62LlowCD4+ T cells; CCR4+CD25+CD4+ T cells; CD45RA-CD4+ T cells; CD45RO+CD4+ T cells; CD62LhighCD25+CD4+ T cells; CD127+CD25+CD4+ T cells; CD45RA-Foxp3+CD4+ T cells; Foxp3+CD25+CD4+ T cells; HLA-DR+ dendritic cells; CD80+ dendritic cells; CD86+ dendritic cells; PD-L1+ dendritic cells; CD62LlowCD8+ T cells; CD137+CD8+ T cells; and CD28+CD62LlowCD8+ T cells. It should be understood that the use of such T cells can improve or maintain an individual's response to cancer immunotherapy. One embodiment of the invention is a composition comprising CD62LlowCD4+ T cells. CD62LlowCD4+ T cells or compositions comprising them are suitable for the treatment or prevention of cancer and can be used in combination with cancer immunotherapy. In yet another embodiment, in addition to CD62LlowCD4+ T cells or similar cells, the composition may comprise CD62LlowCD8+ T cells. One embodiment of the present invention provides a method for using cells to make cancer immunotherapy effective in individuals for whom cancer immunotherapy is predicted to be ineffective, the cells being selected from the following groups: CD62LlowCD4+ T cells; CCR7-CD4+ T cells; LAG-3+CD62LlowCD4+ T cells; ICOS+CD62LlowCD4+ T cells; CCR4+CD25+CD4+ T cells; CD45RA-CD4+ T cells; CD45RO+CD4+ T cells; CD62LhighCD25+CD4+ T cells; CD127+CD25+CD4+ T cells; CD45RA-Foxp3+CD4+ T cells; Foxp3+CD25+CD4+ T cells; HLA-DR+ dendritic cells; CD80+ dendritic cells; CD86+ dendritic cells; PD-L1+ dendritic cells; CD62LlowCD8+ T cells; CD137+CD8+ T cells; and CD28+CD62LlowCD8+ T cells; or combinations thereof. Another embodiment of the present invention provides a method for maintaining the effects of cancer immunotherapy by using, for example, CD62LlowCD4+ T cells or combinations thereof.The inventors' findings are novel discoveries that elucidate the role of peripheral blood CD62LlowCD4+ T cells as promoters of anti-tumor immune responses in cancer immunotherapy (especially anti-PD-1 antibody therapy and / or anti-PD-L1 antibody therapy), providing a new and revolutionary approach to cancer therapy. The inventors have also discovered that cells selected from the following groups are: CCR7-CD4+ T cells; LAG-3+CD62LlowCD4+ T cells; ICOS+CD62LlowCD4+ T cells; CCR4+CD25+CD4+ T cells; CD45RA-CD4+ T cells; CD45RO+CD4+ T cells; CD62LhighCD25+CD4+ T cells; CD127+CD25+CD4+ T cells; CD45RA-Foxp3+CD4+ T cells; Foxp3+CD25+CD4+ T cells; HLA-DR+ dendritic cells; CD80+ dendritic cells; CD86+ dendritic cells; PD-L1+ dendritic cells; CD62LlowCD8+ T cells; CD137+CD8+ T cells; and CD28+CD62LlowCD8+ T cells. As disclosed above, CD62LlowCD4+ T cells promote anti-tumor immune responses. In another embodiment of the invention, CD62LlowCD8+ T cells may be used in addition to CD62LlowCD4+ T cells. Some embodiments of the invention provide methods for purifying or refining CD62LlowCD4+ T cells or methods for manufacturing compositions comprising CD62LlowCD4+ T cells. In some embodiments, CD62LlowCD4+ T cells are isolated from human-derived samples. Some embodiments provide methods for preparing CD62LlowCD4+ T cells isolated from an individual for infusion into that individual and methods for infusing such cells into that individual. One embodiment of the invention is a composition containing CD62LlowCD4+ T cells derived from an individual to which the composition has been administered. Yet another embodiment of the invention provides methods for purifying or refining CD62LlowCD8+ T cells or methods for manufacturing compositions comprising CD62LlowCD8+ T cells.Using the same principles, this invention prepares cells selected from the following groups in a similar manner: CCR7-CD4+ T cells; LAG-3+CD62LlowCD4+ T cells; ICOS+CD62LlowCD4+ T cells; CCR4+CD25+CD4+ T cells; CD45RA-CD4+ T cells; CD45RO+CD4+ T cells; CD62LhighCD25+CD4+ T cells; CD127+CD25+CD4+ T cells; CD45RA-Foxp3+CD4+ T cells; Foxp3+CD25+CD4+ T cells; HLA-DR+ dendritic cells; CD80+ dendritic cells; CD86+ dendritic cells; PD-L1+ dendritic cells; CD62LlowCD8+ T cells; CD137+CD8+ T cells; and CD28+CD62LlowCD8+ T cells. A method for manufacturing a composition comprising CD62LlowCD4+ T cells may include purifying CD62LlowCD4+ T cells from a human T cell population. Purification may include removing CD62L-high expressing cells from the T cell population (negative selection). It is preferable to purify CD62LlowCD4+ T cells by using antibodies and / or magnetic beads and / or affinity columns for negative selection or similar methods, because impurities such as antibodies or magnetic beads do not remain on the cells to be used. The present invention also provides a method for manufacturing a composition comprising CD62LlowCD8+ T cells. One embodiment of the invention is a kit comprising a substance specifically binding to CD62L for purifying CD62LlowCD4+ T cells. Examples of substances specifically binding to CD62L include (but are not limited to) antibodies specific to CD62L. (Biomarkers of the Invention) The biomarkers of the present invention are understood to assess the overall balance of antitumor immune responses, including CD4+ T cells, dendritic cells, and / or CD8+ T cells, to assess overall tumor immunity. For this reason, the methods of the present invention can be considered effective against a wide range of cancers and tumors. The present invention assesses the overall antitumor immune response, thus it is also anticipated to be effective not only against PD-1 / PD-L1 immune checkpoint inhibitors, but also against anticancer therapies acting on other immune checkpoints. The present invention may also use markers indicating effector T cells such as CCR7-, or other than or excluding CD62Llow. Alternatively, CD45RA- and / or CD45RO+ may be used. For example, the ratio of CD45RA-CD4+ T cell subsets and / or the ratio of CD45RO+CD4+ T cell subsets in CD4+ T cells may also be used. It has been shown that the expression of LAG3 and ICOS can also be used (added to or substituted for) in a manner similar to CD62Llow.Similarly, it was found that CCR4 expression can be used (added to or replaced) in a manner similar to CD62Llow. The number / ratio of cells expressing HLA-DR and / or CD80 and / or CD86 in the bone marrow dendritic cell (mDC) and / or plasmacytoid dendritic cell (pDC) populations used in the alternative (or other) use examples can also be used as indicators. PD-L1 on dendritic cells should also be understood as a marker for this invention. Additionally, the number / ratio of cells expressing 4-1BB in the CD8+ T cells (CD62LlowCD4+ T cells) used in the alternative (or other) use examples can also be used as indicators. (Mechanism of the Invention) Although not wishing to be bound by any theory, the antitumor immune response phenomenon at the local tumor site proposed by the inventors is schematically illustrated in Figure 21. Figure 21 shows the cells observable in peripheral blood, namely CD62LlowCD4+ T cells, bone marrow dendritic cells (mDCs), plasmacytoid dendritic cells (pDCs), and CD62LlowCD8+ T cells, and the marker molecules expressed in these cells, namely LAG-3, ICOS, HLA-DR, CD80, and CD137. PD-L1 is expressed in dendritic cells, and PD-1 is expressed in both CD62LlowCD4+ T cells and CD62LlowCD8+ T cells. The composition of T cells is considered important in antitumor immune responses. For example, the stimulation of dendritic cells by CD62LlowCD4+ T cells is important. If CD62LlowCD4+ T cells (e.g., the balance between effector T cells and native T cells tends to favor native T cells) are insufficient, dendritic cells cannot be adequately stimulated even with the administration of immune checkpoint inhibitors. Therefore, the antitumor immune response is insufficient. For this reason, the ratio of CD62LlowCD4+ T cells among CD4+ T cells is used as an indicator to predict the anti-tumor effect induced by immune checkpoint inhibitors. Like CD62L, the ratio of CD45RA-CCR7- T cells among CD4+ T cells also indicates the balance between effector T cells and native T cells, making such ratios usable as indicators in this invention. Dendritic cells are stimulated by CD4+ T cells via HLA-DR. Therefore, when the ratio of HLA-DR+ cells in dendritic cells is reduced, dendritic cells cannot be adequately stimulated even with the administration of immune checkpoint inhibitors. Consequently, the anti-tumor immune response is insufficient. For this reason, the ratio of HLA-DR+ cells in dendritic cells is used as an indicator to predict the anti-tumor effect induced by immune checkpoint inhibitors. Dendritic cells stimulated by CD4+ T cells stimulate CD8+ T cells, and the stimulated CD8+ T cells ultimately exert anti-tumor activity.CD8+ T cells are generated from dendritic cells via CD80 / CD86 expressed on dendritic cells and CD137 expressed on CD8+ T cells. Therefore, both the ratio of CD80+ cells in dendritic cells and the ratio of CD137+ cells in CD8+ T cells can be indicators for predicting the antitumor effect induced by immune checkpoint inhibitors. In addition to the biomarkers shown in the mechanisms disclosed above, LAG-3, ICOS, and CCR4 in CD4+ T cells have also been found to be indicators for predicting the antitumor effect induced by immune checkpoint inhibitors as disclosed in the examples. For example, the present invention provides the following item: (Item 1) A method for using the relative amount of a CD4+ T cell subset associated with dendritic cell stimulation in an antitumor immune response as an indicator for predicting an individual's response to cancer immunotherapy, comprising: determining the relative amount of a CD4+ T cell subset in a sample derived from an individual, the relative amount being higher than an invalidity threshold indicating that the individual is not part of the invalidity group for cancer immunotherapy. (Project 2) As in Project 1, the relative proportions of CD4+ T cell subsets are selected from the following groups: the ratio of CD62LlowCD4+ T cell subsets in CD4+ T cells; the ratio of CCR7-CD4+ T cell subsets in CD4+ T cells; the ratio of LAG-3+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells; the ratio of ICOS+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells; the ratio of CCR4+CD25+CD4+ T cell subsets in CD4+ T cells; the ratio of CD62LhighCD25+CD4+ T cell subsets in CD4+ T cells; and the ratio of CD127+CD25+CD4+ T cell subsets in CD4+ T cells. The ratio of CD45RA-Foxp3+CD4+ T cell subsets in CD4+ T cells; and the ratio of Foxp3+CD25+CD4+ T cell subsets in CD4+ T cells. (Item 3) A method for using the ratio of CD62LlowT cells in an individual's CD4+ T cells as an indicator for predicting an individual's response to cancer immunotherapy, comprising: determining the ratio of CD62LlowT cells in CD4+ T cells in a sample derived from the individual, the ratio being higher than the null threshold indicating that the individual is not part of the null threshold for cancer immunotherapy. (Item 4) A method for using the relative amount of dendritic cell subsets associated with dendritic cell stimulation in an antitumor immune response as an indicator for predicting an individual's response to cancer immunotherapy, comprising: determining the ratio of dendritic cell subsets in dendritic cells in a sample derived from the individual, the ratio being higher than the null threshold indicating that the individual is not part of the null threshold for cancer immunotherapy.(Item 5) The method of Item 4, wherein the dendritic cell subset is selected from the following groups: HLA-DR+ dendritic cell subset; CD80+ dendritic cell subset; CD86+ dendritic cell subset; and PD-L1+ dendritic cell subset. (Item 6) A method for using the relative amount of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response as an indicator for predicting an individual's response to cancer immunotherapy, comprising: determining the ratio of CD8+ T cell subsets in a sample of CD8+ T cells derived from an individual, wherein a ratio higher than the null threshold indicates that the individual is not part of the null threshold for cancer immunotherapy. (Item 7) The method of Item 6, wherein the CD8+ T cell subset is the CD62LlowCD8+ T cell subset, the CD137+CD8+ T cell subset, or the CD28+CD62LlowCD8+ T cell subset. (Item 8) A method for using a relative value of (X, Y) as an indicator for predicting an individual's response to cancer immunotherapy, the relative value being selected from the following groups: the amount of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; the amount of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; the amount of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; the amount of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; and the amount of ICOS+CD62LlowCD4+ T cell subsets; The method comprises: measuring X; and measuring Y; wherein the relative value of X and Y is compared with an invalidity threshold value as an indicator for predicting whether an individual is not part of the invalidity threshold for cancer immunotherapy. (Item 9) As in Item 8, where quantities (X) and (Y) are selected from the following subsets: CD62LlowCD4+ T cell subset; CCR7-CD4+ T cell subset; LAG-3+CD62LlowCD4+ T cell subset; ICOS+CD62LlowCD4+ T cell subset; Foxp3+CD25+CD4+ T cell subset; HLA-DR+ dendritic cell subset; CD80+ dendritic cell subset; CD86+ dendritic cell subset; PD-L1+ dendritic cell subset; CD62LlowCD8+ T cell subset; CD137+CD8+ T cell subset; and CD28+CD62LlowCD8+ T cell subset. (Item 10) As in Item 8 or 9, where the relative value is X / Y. (Item 11) As in Item 8 or 9, where the relative value is X2 / Y.(Item 12) Using the method of any of Items 1 to 11, further using the ratios of Foxp3+CD25+ T cell subsets in CD4+ T cells, the ratio of ICOS+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells, the ratio of LAG-3+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells, or the ratio of PD-1+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells in individuals who are shown not to be part of the null population, as indicators of an individual's response to cancer immunotherapy, wherein the ratios of Foxp3+CD25+ T cell subsets in CD4+ T cells and the ratios of ICOS+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells are... The ratio of LAG-3+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells, or the ratio of PD-1+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells, exceeding the reactive threshold indicates a portion of the reactive population of the system. (Item 13) The method of any of Items 1 to 12, wherein the reactive threshold is determined by considering the sensitivity and specificity of detecting reactive populations. (Item 14) The method of any of Items 1 to 13, wherein the reactive threshold is determined so that the sensitivity for detecting reactive populations exceeds approximately 90%. (Item 15) The method of any of Items 1 to 13, wherein the reactive threshold is determined so that the specificity for detecting reactive populations exceeds approximately 90%. (Item 16) The method of any of Items 1 to 15, wherein the sample is a peripheral blood sample. (Item 17) The method of any one of Items 1 to 16, wherein cancer immunotherapy includes administration of an immune checkpoint inhibitor. (Item 18) The method of Item 17, wherein the immune checkpoint inhibitor is selected from the group consisting of PD-1 inhibitors and PD-L1 inhibitors. (Item 19) The method of Item 18, wherein the PD-1 inhibitor is an anti-PD-1 antibody that inhibits the interaction between PD-1 and PD-L1. (Item 20) The method of Item 18, wherein the PD-L1 inhibitor is an anti-PD-L1 antibody that inhibits the interaction between PD-1 and PD-L1. (Item 21) The method of Item 18, wherein the PD-1 inhibitor or PD-L1 inhibitor comprises nivolumab, pembrolizumab, durvalumab, atezolizumab, or avelumab.(Item 22) A method for treating an individual with cancer, comprising: (1) determining a relative amount of a selected group consisting of: a relative amount of a subset of CD4+ T cells associated with dendritic cell stimulation in an antitumor immune response; a relative amount of a subset of dendritic cells associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; and a relative amount of a subset of CD8+ T cells associated with dendritic cell stimulation in an antitumor immune response; (2) if the relative amount is higher than the null threshold, determining that the individual is not part of the null threshold in relation to the response to cancer immunotherapy; and (3) if the individual is determined not to be part of the null threshold, applying cancer immunotherapy to the individual. (Item 23) As in Item 22, wherein the relative quantities are selected from the following groups: the ratio of CD62LlowCD4+ T cell subsets in CD4+ T cells; the ratio of CCR7-CD4+ T cell subsets in CD4+ T cells; the ratio of LAG-3+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells; the ratio of ICOS+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells; the ratio of CCR4+CD25+CD4+ T cell subsets in CD4+ T cells; the ratio of CD62LhighCD25+CD4+ T cell subsets in CD4+ T cells; the ratio of CD127+CD25+CD4+ T cell subsets in CD4+ T cells; and the ratio of CD45RA-Foxp3+CD4+ T cell subsets in CD4+ T cells. The proportions of Foxp3+CD25+CD4+ T cells in CD4+ T cells; the proportion of HLA-DR+ dendritic cells in dendritic cells; the proportion of CD80+ dendritic cells in dendritic cells; the proportion of CD86+ dendritic cells in dendritic cells; the proportion of PD-L1+ dendritic cells in dendritic cells; the proportion of CD62LlowCD8+ T cells in CD8+ T cells; the proportion of CD137+CD8+ T cells in CD8+ T cells; and the proportion of CD28+CD62LlowCD8+ T cells in CD62LlowCD8+ T cells.(Item 24) A method for treating an individual with cancer, comprising: determining the quantity (X, Y) of a subset selected from the following: the quantity of a CD4+ T cell subset associated with dendritic cell stimulation in an antitumor immune response; the quantity of a dendritic cell subset associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; the quantity of a CD8+ T cell subset associated with dendritic cell stimulation in an antitumor immune response; the quantity of regulatory T cells or a CD4+ T cell subset associated with regulatory T cells; and the quantity of an ICOS+CD62LlowCD4+ T cell subset; using a comparison of the relative values ​​of X and Y with an invalidity threshold to determine whether the individual is part of an invalidity population in relation to a response to cancer immunotherapy; and if the individual is determined not to be part of an invalidity population, then applying cancer immunotherapy to the individual. (Item 25) As in Item 24, wherein quantities (X) and (Y) are selected from the following groups: quantity of CD62-LlowCD4+ T cell subset; quantity of CCR7-CD4+ T cell subset; quantity of LAG-3+CD62-LlowCD4+ T cell subset; quantity of ICOS+CD62-LlowCD4+ T cell subset; quantity of Foxp3+CD25+CD4+ T cell subset; quantity of HLA-DR+ dendritic cell subset; quantity of CD80+ dendritic cell subset; quantity of CD86+ dendritic cell subset; quantity of PD-L1+ dendritic cell subset; quantity of CD62-LlowCD8+ T cell subset; quantity of CD137+CD8+ T cell subset; and quantity of CD28+CD62-LlowCD8+ T cell subset.(Item 26) A method for treating an individual with cancer, comprising: determining the quantity (X, Y) of a subset selected from the following: the quantity of a CD4+ T cell subset associated with dendritic cell stimulation in an antitumor immune response; the quantity of a dendritic cell subset associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; the quantity of a CD8+ T cell subset associated with dendritic cell stimulation in an antitumor immune response; the quantity of regulatory T cells or a CD4+ T cell subset associated with regulatory T cells; and the quantity of an ICOS+CD62LlowCD4+ T cell subset; and using a comparison of the relative values ​​of X and Y with an invalidity threshold to determine whether the individual is part of an invalidity population in terms of response; If an individual is determined not to be part of the ineffective population, and the percentages of the Foxp3+CD25+ T cell subset, ICOS+CD62LlowCD4+ T cell subset, LAG-3+CD62LlowCD4+ T cell subset, or PD-1+CD62LlowCD4+ T cell subset are higher than the threshold for the response population, then the individual is determined to be part of the response population in relation to cancer immunotherapy; and if an individual is determined to be part of the response population, then cancer immunotherapy is applied to that individual. (Item 27) A kit for predicting an individual's response to cancer immunotherapy, comprising reagents for detecting combinations of markers selected from the group consisting of: * CD4 and CD62L; * CD4 and CCR7; * CD4, CD62L, and LAG-3; * CD4, CD62L, and ICOS; * CD4, CD62L, and CD25; * CD4, CD127, and CD25; * CD4, CD45RA, and Foxp3; * CD4, CD25, and Foxp3; * CD11c, CD141, and HLA-DR; * CD11c, CD141, and CD80; * CD11c, CD123, and HLA-DR; * CD11c, CD123, and CD80; * CD8 and CD62L. *A combination of CD8 and CD137; and *A combination of CD28, CD62L, and CD8. (Item 28) As in Item 27, this kit contains reagents for detecting CD4 and CD62L. (Item 29) As in Item 27, this kit contains reagents for detecting CD4, CD25, CD62L, and Foxp3. (Item 30) As in any of Items 27 to 29, the detector is an antibody.(Item 31) A composition comprising an immune checkpoint inhibitor for treating cancer in an individual, wherein the individual has relative amounts selected from the following groups: a relative amount of a CD4+ T cell subset associated with dendritic cell stimulation in an antitumor immune response; a relative amount of a dendritic cell subset associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; and a relative amount of a CD8+ T cell subset associated with dendritic cell stimulation in an antitumor immune response; wherein the relative amounts are equal to or greater than the null threshold. (Item 32) The composition of Item 31, wherein the relative amounts are selected from the following groups: the ratio of CD62LlowCD4+ T cell subsets in CD4+ T cells; the ratio of CCR7-CD4+ T cell subsets in CD4+ T cells; the ratio of LAG-3+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells; the ratio of ICOS+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells; the ratio of CCR4+CD25+CD4+ T cell subsets in CD4+ T cells; the ratio of CD62LhighCD25+CD4+ T cell subsets in CD4+ T cells; the ratio of CD127+CD25+CD4+ T cell subsets in CD4+ T cells; and the ratio of CD45RA-Foxp3+CD4+ T cell subsets in CD4+ T cells. The proportions of Foxp3+CD25+CD4+ T cells in CD4+ T cells; the proportion of HLA-DR+ dendritic cells in dendritic cells; the proportion of CD80+ dendritic cells in dendritic cells; the proportion of CD86+ dendritic cells in dendritic cells; the proportion of PD-L1+ dendritic cells in dendritic cells; the proportion of CD62LlowCD8+ T cells in CD8+ T cells; the proportion of CD137+CD8+ T cells in CD8+ T cells; and the proportion of CD28+CD62LlowCD8+ T cells in CD62LlowCD8+ T cells. (Item 33) A composition comprising an immune checkpoint inhibitor for treating cancer in an individual, wherein the individual is selected by comparing the relative values ​​of an ineffective threshold and an amount (X, Y), the amount being selected from the following groups: the amount of a CD4+ T cell subset associated with dendritic cell stimulation in an antitumor immune response; the amount of a dendritic cell subset associated with dendritic cell stimulation in an antitumor immune response; and the amount of a CD8+ T cell subset associated with dendritic cell stimulation in an antitumor immune response; the amount of regulatory T cells or a CD4+ T cell subset associated with regulatory T cells; and the amount of an ICOS+CD62LlowCD4+ T cell subset.(Item 34) A composition comprising an immune checkpoint inhibitor for treating cancer in an individual, wherein: the individual is selected by comparing the relative values ​​of an ineffective threshold and a quantity (X, Y), the quantity being selected from the following groups: the quantity of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; the quantity of dendritic cell subsets associated with dendritic cell stimulation in an antitumor immune response; and the quantity of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; the quantity of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; and the quantity of ICOS+CD62LlowCD4+ T cell subsets, and in a sample derived from the individual, the ratio of Foxp3+CD25+ T cell subsets among CD4+ T cells having a value above the responder threshold; and the ratio of ICOS+CD62LlowCD4+ T cell subsets among CD62LlowCD4+ T cells. The ratio of LAG-3+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells; or the ratio of PD-1+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells. (Item 35) A composition of any one of items 31 to 34, wherein the immune checkpoint inhibitor is selected from the group consisting of PD-1 inhibitors and PD-L1 inhibitors. (Item 36) A composition of item 35, wherein the PD-1 inhibitor is an anti-PD-L1 antibody that inhibits the interaction between PD-L1 and PD-1. (Item 37) A composition of item 35, wherein the PD-L1 inhibitor is an anti-PD-L1 antibody that inhibits the interaction between PD-1 and PD-L1. (Item 38) A composition of item 35, wherein the PD-1 inhibitor or PD-L1 inhibitor is nivolumab, pembrolizumab, durvalumab, atezolizumab, or avelumab. (Item 39) A composition for treating or preventing cancer, comprising cells selected from the following groups: CD62LlowCD4+ T cells; CCR7-CD4+ T cells; LAG-3+CD62LlowCD4+ T cells; ICOS+CD62LlowCD4+ T cells; CCR4+CD25+CD4+ T cells; CD62LhighCD25+CD4+ T cells; CD127+CD25+CD4+ T cells; CD45RA-Foxp3+CD4+ T cells; Foxp3+CD25+CD4+ T cells; HLA-DR+ dendritic cells; CD80+ dendritic cells; CD86+ dendritic cells; PD-L1+ dendritic cells; CD62LlowCD8+ T cells; CD137+CD8+ T cells; and CD28+CD62LlowCD8+ T cells.(Item 40) The composition of Item 39, used in conjunction with cancer immunotherapy. (Item 41) The composition of Item 39, wherein the composition is administered in combination with an immune checkpoint inhibitor. (Item 42) The composition of Item 41, wherein the immune checkpoint inhibitor is selected from the group consisting of PD-1 inhibitors and PD-L1 inhibitors. (Item 43) The composition of Item 42, wherein the PD-1 inhibitor is an anti-PD-1 antibody that inhibits the interaction between PD-1 and PD-L1. (Item 44) The composition of Item 42, wherein the PD-L1 inhibitor is an anti-PD-L1 antibody that inhibits the interaction between PD-1 and PD-L1. (Item 45) The composition of Item 42, wherein the PD-1 inhibitor or PD-L1 inhibitor comprises nivolumab, pembrolizumab, durvalumab, atezolizumab, or avelumab. (Item 46) The composition of any one of Items 39 to 45, further comprising CD62LlowCD8+ T cells. (Item 47) A composition of any one of items 39 to 46, used to make cancer immunotherapy effective in individuals for whom cancer immunotherapy is predicted to be ineffective. (Item 48) A composition of any one of items 39 to 47, used to maintain the effect of cancer immunotherapy. (Item 49) A composition of any one of items 39 to 48, wherein the CD62LlowCD4+ T cells are derived from an individual to whom the composition is administered. (Item 50) A method of manufacturing a composition comprising CD62LlowCD4+ T cells for the treatment or prevention of cancer, comprising purifying CD62LlowCD4+ T cells from a human-derived T cell population. (Item 51) The method of item 50, wherein purification comprises removing CD62L-high-expressing cells from the T cell population. (Item 52) A kit comprising a substance specifically binding to CD62L for purifying CD62LlowCD4+ T cells. (Item A1) A method for using the ratio of CD62LlowT cells among an individual's CD4+ T cells as an indicator of an individual's response to cancer immunotherapy, comprising determining that the ratio of CD62LlowT cells among CD4+ T cells in a sample derived from the individual is higher than an invalidity threshold indicating that the individual is not part of an invalidity group for cancer immunotherapy. (Item A2) A method for using the ratio of Foxp3+CD25+ T cells among an individual's CD4+ T cells as an indicator of an individual's response to cancer immunotherapy, comprising determining that the ratio of Foxp3+CD25+ T cells among CD4+ T cells in a sample derived from the individual is lower than an invalidity threshold indicating that the individual is not part of an invalidity group for cancer immunotherapy.(Item A3) A method for predicting an individual's response to cancer immunotherapy using the relative value of an individual's CD4+CD62LlowT cell count (X) and CD4+Foxp3+CD25+T cell count (Y), the method comprising: measuring X; and measuring Y; wherein the comparison of the relative value of X and Y with an invalidity threshold is used as an indicator to predict whether an individual is not part of the invalidity threshold for cancer immunotherapy. (Item A4) The method of Item A3, wherein the relative value is X / Y. (Item A5) The method of Item A3, wherein the relative value is X² / Y. (Item A6) The method of any of items A1 to A5 further uses the ratio of Foxp3+CD25+T cells among CD4+ T cells in an individual who is not part of the null population as an indicator of that individual's response to cancer immunotherapy, wherein a ratio of Foxp3+CD25+CD4+T cells among CD4+ T cells exceeding the response threshold indicates that the individual's response population is part of the system's response population. (Item A7) The method of any of items A1 to A6, wherein the null population threshold is determined by considering the sensitivity and specificity of null population detection. (Item A8) The method of any of items A1 to A7, wherein the null population threshold is determined so that the sensitivity for null population detection exceeds approximately 90%. (Item A9) The method of any of items A1 to A8, wherein the null population threshold is determined so that the specificity for null population detection exceeds approximately 90%. (Item A10) The method of any of items A1 to A9, wherein the sample is a peripheral blood sample. (Item A11) The method of any of items A1 to A10, wherein cancer immunotherapy includes administration of an immune checkpoint inhibitor. (Item A12) The method of item A11, wherein the immune checkpoint inhibitor is selected from the group consisting of PD-1 inhibitors and PD-L1 inhibitors. (Item A13) The method of item A12, wherein the PD-1 inhibitor is an anti-PD-1 antibody that inhibits the interaction between PD-1 and PD-L1. (Item A14) The method of item A12, wherein the PD-L1 inhibitor is an anti-PD-L1 antibody that inhibits the interaction between PD-1 and PD-L1. (Item A15) The method of item A12, wherein the PD-1 inhibitor or PD-L1 inhibitor includes nivolumab, pembrolizumab, durvalumab, atezolizumab, or avelumab.(Item A16) A method for treating an individual with cancer, comprising: determining the ratio of CD62LlowT cells among CD4+ T cells in a sample derived from the individual; if the ratio of CD62LlowT cells among CD4+ T cells is higher than the null threshold, then determining that the individual is not part of the null threshold in terms of response to cancer immunotherapy; and if the individual is determined not to be part of the null threshold, then applying cancer immunotherapy to the individual. (Item A17) A method for treating an individual with cancer, comprising: determining the ratio of Foxp3+CD25+ T cells among CD4+ T cells in a sample derived from the individual; if the ratio of Foxp3+CD25+ T cells among CD4+ T cells is lower than the null threshold, then determining that the individual is not part of the null threshold in terms of response to cancer immunotherapy; and if the individual is determined not to be part of the null threshold, then applying cancer immunotherapy to the individual. (Item A18) A method for treating an individual with cancer, comprising: determining the amount of CD4+CD62LlowT cells (X); determining the amount of CD4+Foxp3+CD25+T cells (Y); using a comparison of the relative values ​​of X and Y with an invalidity threshold to determine whether the individual is part of an invalidity cohort in relation to the response to cancer immunotherapy; and if the individual is determined not to be part of an invalidity cohort, applying cancer immunotherapy to the individual. (Item A19) A method for treating an individual with cancer, comprising: determining the amount of CD4+CD62LlowT cells (X); determining the amount of CD4+Foxp3+CD25+T cells (Y); using a comparison of the relative values ​​of X and Y with an invalidity threshold to determine whether the individual is part of an invalidity cohort in relation to a response to cancer immunotherapy; if the individual is determined not to be part of an invalidity cohort and the ratio of Foxp3+CD25+T cells is higher than a response threshold, then the individual is determined to be part of a response to cancer immunotherapy; and if the individual is determined to be part of a response cohort, then cancer immunotherapy is applied to the individual. (Item A20) A kit for predicting an individual's response to cancer immunotherapy, comprising reagents for detecting one or more cell surface markers selected from CD4, CD25, CD62L, and Foxp3. (Item A21) The kit of Item A20, comprising reagents for detecting CD4 and CD62L. (Item A22) Kits such as those in Items A20 or A21, which contain reagents for detecting CD4, CD25, CD62L, and Foxp3. (Item A23) Kits such as any of Items A20 to A22, wherein the detection reagent is an antibody.(Item A24) A composition comprising an immune checkpoint inhibitor for treating cancer in an individual, wherein the individual has a ratio of CD62LlowT cells to CD4+ cells in a sample derived from the individual, wherein the ratio is equal to or greater than the null threshold. (Item A25) A composition comprising an immune checkpoint inhibitor for treating cancer in an individual, wherein the individual has a ratio of Foxp3+CD25+T cells to CD4+ cells in a sample derived from the individual, wherein the ratio is equal to or less than the null threshold. (Item A26) A composition comprising an immune checkpoint inhibitor for treating cancer in an individual, wherein the individual is selected by comparing the null threshold with the relative values ​​of the amount of CD4+CD62LlowT cells (X) and the amount of CD4+Foxp3+CD25+T cells (Y) in a sample derived from the individual. (Item A27) A composition comprising an immune checkpoint inhibitor for treating cancer in an individual, wherein the individual is selected by comparing the relative values ​​of the amount of CD4+CD62LlowT cells (X) and the amount of CD4+Foxp3+CD25+T cells (Y) in a sample derived from the individual, and wherein the sample derived from the individual has a ratio of Foxp3+CD25+T cell subsets of CD4+T cells equal to or higher than the reactive population threshold. (Item A28) A composition of any one of items A24 to A27, wherein the immune checkpoint inhibitor is selected from the group consisting of PD-1 inhibitors and PD-L1 inhibitors. (Item A29) A composition of item A28, wherein the PD-1 inhibitor is an anti-PD-1 antibody that inhibits the interaction between PD-1 and PD-L1. (Item A30) A composition of item A28, wherein the PD-L1 inhibitor is an anti-PD-L1 antibody that inhibits the interaction between PD-1 and PD-L1. (Item A31) A composition of Item A28, wherein the PD-1 inhibitor or PD-L1 inhibitor comprises nivolumab, pembrolizumab, durvalumab, atezolizumab, or avelumab. (Item A32) A composition for the treatment or prevention of cancer comprising CD62LlowCD4+ T cells. (Item A33) A composition of Item A32, used in conjunction with cancer immunotherapy. (Item A34) A composition of Item A32, wherein the composition is administered in combination with an immune checkpoint inhibitor. (Item A35) A composition of Item A34, wherein the immune checkpoint inhibitor is selected from the group consisting of PD-1 inhibitors and PD-L1 inhibitors. (Item A36) A composition of Item A35, wherein the PD-1 inhibitor is an anti-PD-1 antibody that inhibits the interaction between PD-1 and PD-L1.(Item A37) A composition of item A35, wherein the PD-L1 inhibitor is an anti-PD-L1 antibody that inhibits the interaction between PD-1 and PD-L1. (Item A38) A composition of item A35, wherein the PD-1 inhibitor or PD-L1 inhibitor comprises nivolumab, pembrolizumab, durvalumab, atezolizumab, or avelumab. (Item A39) A composition of any of items A32 to A38, further comprising CD62LlowCD8+ T cells. (Item A40) A composition of any of items A32 to A39, used to make cancer immunotherapy effective in individuals for whom cancer immunotherapy is predicted to be ineffective. (Item A41) A composition of any of items A32 to A40, used to maintain the effect of cancer immunotherapy. (Item A42) A composition of any of items A32 to A41, wherein the CD62LlowCD4+ T cells are derived from an individual to whom the composition is administered. (Item A43) A method for manufacturing a composition comprising CD62LlowCD4+ T cells for the treatment or prevention of cancer, comprising purifying CD62LlowCD4+ T cells from a human T cell population. (Item A44) The method of Item A43, wherein purification comprises removing CD62L-high expressing cells from the T cell population. (Item A45) A kit comprising a substance specifically binding to CD62L for purifying CD62LlowCD4+ T cells.

Implementation Method

[0005] The invention is disclosed herein by way of illustrative examples and with reference to the accompanying drawings as needed. Throughout this specification, unless specifically indicated, singular expressions are to be understood to encompass their concepts in the plural form. Furthermore, unless specifically indicated, the terms used herein are to be understood to have the meaning commonly used in the art. Thus, unless defined, all terms and scientific techniques used herein have the same meaning as commonly understood by one skilled in the art to which this invention relates. In cases of conflict, this specification (including definitions) shall prevail. (Definitions) As used herein, "biomarker" means a characteristic that can be objectively measured and evaluated as an indicator of a normal biological process, pathological process, or pharmacological response to a therapeutic intervention. As used herein, "cancer" means a highly atypical, rapidly proliferating malignant tumor that can destructively infiltrate or metastasize to surrounding tissues or to its presence. In this invention, cancer includes (but is not limited to) solid cancers and hematopoietic tumors. As used herein, "cancer immunotherapy" refers to a method of treating tumors using biological defense mechanisms (such as the immune mechanisms of an organism). As used herein, "anti-tumor immune response" refers to any immune response against a tumor in a living organism. As used herein, "dendritic cell stimulation in an anti-tumor immune response" refers to any phenomenon in which dendritic cells are stimulated during an immune response against a tumor in a living organism. Such stimulation can be a direct or indirect factor inducing an anti-tumor immune response. Although not limited to the following, dendritic cell stimulation in an anti-tumor immune response is typically applied by CD4+ T cells (e.g., effector T cells), thereby causing dendritic cells to stimulate CD8+ T cells, and the stimulated CD8+ T cells exert an anti-tumor effect. As used herein, "correlation" refers to two things that are statistically significantly correlated. For example, "the relative amount of B related to A" means that when A occurs, the relative amount of B is statistically significantly affected (e.g., increased or decreased). As used herein, a “cell subpopulation” refers to a group of cells that constitute a portion of the whole cell population. As used herein, the term “relative quantity” and “ratio” are used interchangeably with the term “ratio”. Generally, the terms “relative quantity” and “ratio” refer to the ratio of the number of cells constituting a given cell subpopulation (e.g., CD62LlowCD4+ T cells) to the number of cells constituting a specific cell population (e.g., CD4+ T cell population). As used herein, “sensitivity” refers to the ratio of the number of individuals with the specified characteristic selected from an individual population to the total number of individuals with the specified characteristic in the individual population, i.e., (number of individuals with the specified characteristic in the selected target) / (total number of individuals with the specified characteristic in the individual population).As used herein, "specificity" refers to the ratio of the number of individuals with the specified characteristic among the selected targets to the total number of selected targets when selecting individuals with the specified characteristic from a population of individuals; that is, (number of individuals with the specified characteristic among the selected targets) / (total number of selected targets). As used herein, "ineffective cohort" refers to a group of individuals classified as progressive (PD, progressive disease) according to RECIST version 1.1 for treatment efficacy assessment after cancer therapy from the start of treatment until approximately 9 weeks prior. The ineffective cohort is also referred to as the PD cohort, progressive cohort, or NR (non-responder), and these terms are used interchangeably herein. As used herein, "partial response cohort" refers to a group of individuals classified as partially responding (PR, partial response) according to RECIST version 1.1 for treatment efficacy assessment after cancer therapy. The partial response cohort is also referred to as the PR cohort, and these terms are used interchangeably herein. As used herein, the "stable cohort" refers to a group of individuals who are classified as stable (SD, stable disease) according to RECIST version 1.1 for treatment response after cancer therapy, up to approximately nine weeks after the start of treatment. The "stable cohort" is also referred to as the SD cohort or the intermediate cohort, and these terms are used interchangeably throughout this text. Additionally, if this cohort transitions to disease progression approximately one year after disease control, it is referred to as the IR (intermediate responder). Since the majority of this cohort is classified as SD approximately nine weeks after treatment initiation, the "stable cohort" can also be used interchangeably with the IR (intermediate responder) cohort. As used herein, the "complete response cohort" refers to a group of individuals who are classified as having a complete response (CR, complete response) according to RECIST version 1.1 for treatment response after cancer therapy. The "complete response cohort" is also referred to as the CR cohort, and these terms are used interchangeably throughout this text. This invention detects cases where an individual population includes a complete response population (CR) other than a partial response population (PR), and cases where an individual population includes a complete response population (CR) but does not include a partial response population (PR), which is the same as a partial response population (PR). As used herein, "partial response population" and "complete response population" are collectively referred to as "response population". This is also referred to as a "highly effective population". In addition, a population with long-term disease control lasting more than 1 year after the start of treatment is referred to as a GR (good responder). However, since most of this population is identified as a "partial response population" or "complete response population" 9 weeks after the start of treatment, "response population" can also be used interchangeably with the GR (good responder) population. As used herein, "relative value" refers to a value obtained by calculating another value when using one value as a baseline for comparison. As used herein, the term "detector" generally refers to all reagents capable of detecting substances of interest (e.g., cell surface markers or the like). As used in this article, the “quantity” of a cell subpopulation encompasses the relative quantity of the absolute number and proportion of certain cells within a cell population.As used herein, a "critical threshold" refers to a value that determines a variable value, whereby the value has a specific meaning when the variable value is greater than or less than the threshold. The critical threshold is also referred to herein as a cutoff value. As used herein, a "null threshold" refers to a threshold value used to identify null groups and stable + reactive groups within a specified population of individuals. The null threshold is selected to obtain predetermined sensitivity and specificity when selecting null groups within a specified population of individuals. As used herein, a "reactive group threshold" refers to a threshold value used to identify stable and reactive groups within a specified population of individuals, or within a specified population of individuals after removing null groups using the null threshold. The reactive threshold is selected to obtain predetermined sensitivity and specificity when selecting reactive groups within a specified population of individuals, or within a specified population of individuals after removing null groups using the null threshold. The term "about" is used to define numerical values ​​used herein to mean that the described values ​​cover a range of values ​​up to ±10%. As used herein, "flow cytometry" refers to a technique for measuring the number of cells, individual or other biological particles suspended in a liquid or in a specific physical / chemical / biological environment. (Cancer Immunotherapy) Cancer immunotherapy is a method of treating cancer using the biological defense mechanisms of an organism. Cancer immunotherapy can be broadly classified into cancer immunotherapy that enhances anti-cancer immune function and cancer immunotherapy that inhibits cancer's immune evasion mechanisms. Cancer immunotherapy further includes active immunotherapy for activating immune function in the body and passive immunotherapy for returning immune cells activated or expanded in vitro to the body. Understanding the biomarkers of this invention to assess the overall balance of CD4+ T cell immunity and thus assess overall tumor immunity itself allows for a general prediction of the therapeutic effects of all cancer immunotherapies. Examples of cancer immunotherapies include: nonspecific immune enhancers, intercytokine therapy, cancer vaccine therapy, dendritic cell therapy, acceptor immunotherapy, nonspecific lymphocyte therapy, cancer antigen-specific T cell therapy, antibody therapy, immune checkpoint inhibition therapy, and the like. Examples in this specification demonstrate that the biomarkers of the present invention accurately predict therapeutic efficacy, particularly (but not limited to) immune checkpoint inhibitory therapies. PD-1 inhibitors are representative examples of immune checkpoint inhibitors. Examples of PD-1 inhibitors include (but are not limited to) anti-PD-1 antibodies, nivolumab (sold as Opdivo™), and pembrolizumab. In a preferred embodiment, nivolumab may be selected. While not wishing to be bound to any particular therapy, one reason for the preference for nivolumab is that examples demonstrate that the biomarkers of the present invention can clearly identify reactive and non-reactive individuals, and in particular, that responses and non-responses can be clearly distinguished by specific threshold values. Of course, it should be understood that the biomarkers of the present invention can achieve the same level of efficacy against other PD-1 inhibitors. The present invention can also use PD-L1 inhibitors to achieve the same level of efficacy as PD-1 inhibitors.It should be understood that anti-PD-1 antibodies achieve their anti-cancer effects by releasing PD-1 signals to inhibit T cell activation. It should also be understood that anti-PD-L1 antibodies achieve their anti-cancer effects by releasing PD-L1 signals to inhibit T cell activation. Although the mechanism by which PD-1 inhibits T cell function is not fully elucidated, it should be understood that the interaction between PD-1 (planned death 1) and PD-L1 or PD-L2 recruits tyrosine phosphatase, SHP-1, or SHP-2 to the cytoplasmic domain of PD-1, thereby preventing the activation of the T cell receptor signaling protein ZAP70 and inhibiting T cell activation (Okazaki, T., Chikuma, S., Iwai, Y. et al.: A rheostat for immune responses: the unique properties of PD-1 and their advantages for clinical application. Nat. Immunol., 14, 1212-1218 (2013)). This should be understood as being caused by the recruitment of SHP-1 or SHP-2 to a portion known as the ITSM motif, which dephosphorylates proximal signaling kinases of nearby T cell receptors. In other words, the memory of "antigen stimulation" is erased from T cells that have already been stimulated by the antigen. PD-1 is expressed at high levels in killer T cells and natural killer cells that have infiltrated cancer tissue. It should be understood that the immune response mediated by PD-1 signaling is mitigated by PD-L1 on the tumor. Although the immune response mediated by PD-1 signaling is mitigated by PD-L1, the effect of enhancing the anti-tumor immune response is achieved by inhibiting the interaction between PD-1 and PD-L1 and / or by signaling induced by the interaction with anti-PD-1 antibodies. PD-L1 inhibitors (e.g., anti-PD-L1 antibodies, avelumumab, durvalumab, and atezolizumab) are another example of immune checkpoint inhibitors. PD-L1 inhibitors bind to and inhibit the aforementioned PD-1 pathway to the PD-L1 side to suppress the interaction between PD-1 and PD-L1 and / or the signaling induced by this interaction, thereby inducing an anti-tumor immune response. While not wishing to be bound by any particular therapy, given the results shown in the examples, individuals who respond to or do not respond to therapies that inhibit the PD-1 pathway (e.g., anti-PD-1 antibodies or anti-PD-L1 antibodies) can be clearly identified by using the biomarkers of this invention. CTLA-4 inhibitors (e.g., anti-CTLA-4 antibodies, ipilimumab, or tremelimumab) are another example of immune checkpoint inhibitors.CTLA-4 inhibitors activate T cells to induce an anti-tumor immune response. T cells are activated by the interaction of CD28 with CD80 or CD86 on their surface. However, it should be understood that even if T cells are already activated, the surface-expressed CTLA-4 (cytotoxic T-lymphocyte-associated antigen 4) preferentially interacts with CD80 or CD86, which have a higher affinity than CD20, to inhibit activation. CTLA-4 inhibitors induce an anti-tumor immune response by inhibiting CTLA-4 to prevent inhibition of the interaction between CD20 and CD80 or CD86. In another embodiment, immune checkpoint inhibitors may target immune checkpoint proteins such as TIM-3 (a T-cell immunoglobulin and mucin containing protein 3), LAG-3 (lymphocyte activation gene 3), B7-H3, B7-H4, B7-H5 (VISTA), or TIGIT (a T-cell immune receptor with Ig and ITIM domains). It should be understood that the aforementioned immune checkpoints also suppress immune responses against autologous tissues, but when antigens such as viruses are present in the body for extended periods, the number of immune checkpoints on T cells increases. It should also be understood that tumor tissue is an antigen that is present in the body for extended periods, allowing anti-tumor immune responses to evade these immune checkpoints. The aforementioned immune checkpoint inhibitors disable this evasion function to achieve an anti-tumor effect. While not wishing to be bound by any particular therapy, it should be understood that the biomarkers of this invention assess the balance of the overall anti-tumor immune response in humans, making them usable as indicators for accurately predicting the therapeutic effects of such immune checkpoint inhibitors. One embodiment of the invention provides a composition comprising an immune checkpoint inhibitor. The composition comprising an immune checkpoint inhibitor can be administered to individuals selected by evaluation using the biomarkers of this invention to achieve significant therapeutic effects with a high probability. The composition comprising the immune checkpoint inhibitor of this invention is typically administered systemically or locally, either orally or non-enterally. Dosage varies depending on age, weight, symptoms, treatment effect, method of administration, duration of treatment, or similar factors, but is generally administered orally once or several times a day in the range of 0.1 mg to 100 mg per dose per adult, or non-enterally (preferably intravenously) once or several times a day in the range of 0.01 mg to 30 mg per dose per adult, or continuously intravenously over a period of 1 hour to 24 hours per day. Of course, dosage varies depending on the condition, such that amounts less than the above doses may be sufficient or amounts exceeding the range may be desired. Regarding administration, compositions containing immune checkpoint inhibitors may have dosage forms such as solid or liquid formulations for oral administration or injections, topical formulations, or suppositories for non-enteral administration. Examples of solid formulations for oral administration include tablets, pills, capsules, powders, granules, and the like. Capsules include hard capsules and soft capsules.The compositions of this invention comprise one or more active ingredients (e.g., antibodies against immune checkpoint proteins), which may be used directly or in combination with excipients (lactose, mannitol, glucose, microcrystalline cellulose, starch, or the like), binders (hydroxypropyl cellulose, polyvinylpyrrolidone, magnesium aluminum metasilicate, or the like), disintegrants (calcium cellulose glycolate, or the like), lubricants (magnesium stearate, or the like), stabilizers, and solubilizers (glutamic acid, aspartic acid, or the like), formulated according to known methods for use. The compositions may also be coated with a coating agent (refined sugar, gelatin, hydroxypropyl cellulose, hydroxypropyl methylcellulose phthalate, or the like) or, as desired, with two or more layers. Capsules made from absorbable substances (such as gelatin) are also included. When formulated as a liquid for oral administration, the compositions of this invention comprise pharmaceutically acceptable aqueous solutions, suspensions, emulsions, syrups, elixirs, or the like. In such liquid formulations, one or more active ingredients are dissolved, suspended, or emulsified in a commonly used diluent (purified water, ethanol, mixtures thereof, or the like). Such liquid formulations may also contain wetting agents, suspending agents, emulsifiers, sweeteners, flavoring agents, aroma agents, preservatives, buffers, or the like. Examples of injectable formulations intended for non-enteric administration include solutions, suspensions, latexes, and solid injections used by dissolving or suspending them in a solvent at the time of use. Injectable formulations are used by dissolving, suspending, or emulsifying one or more active ingredients in a solvent. Examples of solvents used include distilled water, physiological saline, vegetable oils, propylene glycol, polyethylene glycol, alcohols such as ethanol, combinations thereof, and the like for injection. Such injectable formulations may also contain: stabilizers, solubilizers (glutamic acid, aspartic acid, polysorbate 80™, or the like), suspending agents, emulsifiers, analgesics, buffers, preservatives, or the like. They are prepared by sterilization or aseptic processing in a final step. Sterile solid dosage forms, such as frozen products, can also be manufactured, dissolved in sterile or sterile distilled water for injection or other solvents before use. (Cancer) Examples of target cancers in this invention include (but are not limited to): melanoma (malignant melanoma), non-small cell lung cancer, renal cell carcinoma, malignant lymphoma (Hodgkin's or non-Hodgkin's lymphoma), head and neck cancer, urinary tract cancer (bladder cancer, urethral carcinoma, and prostate cancer), small cell lung cancer, thymic carcinoma, gastric cancer, esophageal cancer, esophagogastric junction cancer, liver cancer (hepatocellular carcinoma, intrahepatic bile duct carcinoma), primary brain tumors (glioblastoma and primary central nervous system lymphoma), malignant pleural mesothelioma, gynecological cancers (ovarian cancer, cervical cancer, and uterine cancer), soft tissue sarcoma, bile duct carcinoma, multiple myeloma, breast cancer, colon cancer, and the like. (Biomarkers)This invention provides a novel biomarker for predicting the therapeutic effect of cancer immunotherapy. In one embodiment, an individual's T-cell composition is used as an indicator for predicting the therapeutic effect of cancer immunotherapy. In one embodiment, an indicator of an individual's T-cell composition at or above a response threshold indicates that the individual is part of a response to cancer immunotherapy. In another embodiment, an indicator of an individual's T-cell composition at or below a response threshold indicates that the individual is part of a response to cancer immunotherapy. In yet another embodiment, an indicator of an individual's T-cell composition at or above a null threshold indicates that the individual is part of an ineffective response to cancer immunotherapy. In yet another embodiment, an indicator of an individual's T-cell composition at or below a null threshold indicates that the individual is part of an ineffective response to cancer immunotherapy. Those skilled in the art can determine the appropriate threshold for each of these indicators. Those skilled in the art can predict an individual's response to cancer immunotherapy at indicated sensitivity and / or specificity by using the thresholds disclosed herein (null threshold and / or response threshold). Regarding the indicators disclosed herein, those skilled in the art can appropriately determine the threshold values ​​for achieving the desired sensitivity and specificity based on the results of evaluating the efficacy of cancer immunotherapy on a reference population. The population of individuals demonstrated in the examples of this specification can be considered as a reference population. In other words, those skilled in the art can determine the threshold values ​​based on the experimental results disclosed in the examples or determine new threshold values ​​based on the results of practicing this invention on a reference population. Sensitivity refers to the ratio of the number of individuals with the specified characteristics in the selected target population to the total number of individuals with the specified characteristics in the population. For example, when all individuals with the specified characteristics in the population are selected, the sensitivity is 100%. When half of the individuals with the specified characteristics in the population are selected, the sensitivity is 50%. When none of the individuals with the specified characteristics in the population are selected, the sensitivity is 0%. Sensitivity is determined, for example, as (number of individuals with the specified characteristics in the selected target population) / (total number of individuals with the specified characteristics in the population). High sensitivity means that when searching for individuals with a particular condition (e.g., those who are ineffective with cancer immunotherapy), such individuals are likely to be definitively identified as having that condition. The biomarkers of this invention, capable of high sensitivity, are well-suited for ensuring the identification of ineffective groups for a particular therapy. Threshold values ​​can also be selected based on such targets to achieve higher sensitivity.Specificity refers to the ratio of the number of individuals possessing a specified characteristic to the total number of selected individuals when choosing individuals with that characteristic from a population. For example, specificity is 100% when all candidates from the population possess the specified characteristic. Specificity is 50% when half of the candidates from the population possess the specified characteristic. Specificity is 0% when none of the candidates from the population possess the specified characteristic. Specificity is determined, for example, as (number of individuals with the specified characteristic among the selected individuals) / (total number of selected individuals). High specificity means a lower probability of incorrectly classifying an individual who is not in a certain condition (e.g., a response group to cancer immunotherapy) as not in such a condition (e.g., a response group to cancer immunotherapy). The biomarkers of the present invention, capable of high specificity, are applicable, for example, to avoiding incorrectly classifying a response group to a particular therapy as an ineffective group for discontinuous therapy. Threshold values ​​can also be selected based on such targets to achieve higher specificity. For example, when an increase in a marker is correlated with the effectiveness of cancer immunotherapy, individuals are identified as part of an invalid population whose markers are at or below a certain threshold (invalid population threshold). Individuals not identified as part of the invalid population (i.e., stable or reactive population) are still part of the invalid population, but their sensitivity decreases at a higher threshold (increased sensitivity), while individuals identified as part of the invalid population, although not part of the invalid population (e.g., stable or reactive population), show an increase (decreased specificity). Conversely, individuals identified as part of the invalid population, although not part of the invalid population (i.e., stable or reactive population), show a decrease (increased specificity) at a lower threshold (increased specificity), while individuals not identified as part of the invalid population (i.e., stable or reactive population) show an increase (decreased sensitivity). Regarding the biomarkers of this invention, thresholds can be set and used to achieve very high specificity and / or sensitivity, making the biomarkers of this invention unprecedented and advantageous markers for predicting the therapeutic effects of cancer immunotherapy. Those skilled in this technique can also appropriately set threshold values ​​within such a range where both specificity and sensitivity are very high, based on the target. It should be understood that, as long as the determination of interest can be performed, even if a specific value is presented as an instance of a threshold value, a value close to that value can be used. The ratio of CD62LlowT cells among an individual's CD4+ T cells can be used as an indicator to predict an individual's response to cancer immunotherapy, for example, as an indicator for selecting the invalid population. The inventors have discovered that a ratio of CD62LlowT cells among CD4+ T cells higher than the invalid population threshold can predict with very high accuracy that an individual in this situation will not be part of the invalid population for cancer immunotherapy.The null threshold for the ratio of CD62LlowT cells to CD4+ T cells can be appropriately determined by a person skilled in this technique based on the reference or threshold values ​​(cutoff values) shown in Figure 6 that can be used as null thresholds. It should be noted that the ratio may be expressed as a percentage (%) below. For example, when 19.4 is used as the null threshold in the results of Figure 6, it should be understood that the ratio of CD62LlowT cells to CD4+ T cells can be used as a biomarker to determine whether an individual is part of a null cluster with 92.9% sensitivity and 96.7% specificity. When a value of 14.45 or less (e.g., 14.45, 13.8, 13.3, 12.3, or 10.9) is used in a similar manner as the null threshold for the ratio of CD62LlowT cells to CD4+ T cells in the results of Figure 6, the null cluster can be predicted using this ratio as a biomarker with 100% specificity. When a value of 22.55 or greater (e.g., 23.1, 24.1, 24.8, 25.05, 25.45, 25.95, 27, 28.75, or similar) is used as the null threshold for the ratio of CD62LlowT cells to CD4+ T cells, the null threshold can be predicted using that ratio as a biomarker with 100% sensitivity. In other words, the null threshold for the ratio of CD62LlowT cells to CD4+ T cells can be in the range of about 10 to about 30 (%). Examples of such null thresholds include about 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, and 30 (%). The ratio of Foxp3+CD25+ T cells in an individual's CD4+ T cells can be used as an indicator to predict an individual's response to cancer immunotherapy (such as an invalidity threshold). The inventors have discovered that a ratio of Foxp3+CD25+ T cells in CD4+ T cells in an individual-derived sample below the invalidity threshold indicates that the individual is not part of the invalidity population with respect to cancer immunotherapy. The invalidity threshold for the ratio of Foxp3+CD25+ T cells in an individual's CD4+ T cells can be appropriately determined by a person skilled in the art based on a reference individual. Such an invalidity threshold can range from about 2 to about 4 (%). Examples of threshold values ​​include approximately 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, or 4.0 (%). The indicators of the T-cell compositions disclosed herein are preferably used in combination. Since the inventors have found that multiple indicators independently demonstrate a correlation with response, it should be understood that combining multiple indicators and using them as indicators of response can further improve the accuracy of predictions.When two or more indicators are combined as indicators of response, indicators can be used that are represented by functions using any number of variables. Examples of response indicators when using multiple indicators (X1, X2, X3… Xn) include (but are not limited to) the following: F = a1X1b1 + a2X2b2 + a3X3b3… + anXnbn F = X1c1*X2c2*X3c3… *Xncn where each of a, b, and c is any real number. Responses can be predicted based on the values ​​of indicators calculated using such functions. Multivariate analysis, such as logistic regression or discriminant analysis, can be performed on the novel indicators discovered by the inventors to determine the coefficients used as indicators of an individual's response to cancer immunotherapy. Although the combined indicators are not limited, the inventors have discovered indicators such as: the amount of CD4+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses, the amount of dendritic cell subsets associated with dendritic cell stimulation in antitumor immune responses, the amount of CD8+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses, the amount of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells, and the amount of ICOS+CD62LlowCD4+ T cell subsets. Indicators predicting responses through different mechanisms can be combined to demonstrate indicators with a strong correlation to the response, and this correlation is not a false correlation. For example, two or more indicators, such as three, four, five or more indicators, are selected from the following groups: the amount of CD62LlowCD4+ T cell subsets; the amount of CCR7-CD4+ T cell subsets; the amount of LAG-3+CD62LlowCD4+ T cell subsets; the amount of ICOS+CD62LlowCD4+ T cell subsets; the amount of CD45RA-CD4+ T cell subsets; the amount of CD45RO+CD4+ T cell subsets; the amount of HLA-DR+ dendritic cell subsets; the amount of CD80+ dendritic cell subsets; the amount of CD86+ dendritic cell subsets; the amount of PD-L1+ dendritic cell subsets; the amount of CD62LlowCD8+ T cell subsets; the amount of CD137+CD8+ T cell subsets; and the amount of CD28+CD62LlowCD8+ T cell subsets. The amounts of CCR4+CD25+CD4+ T cell subsets; CD62LhighCD25+CD4+ T cell subsets; CD127+CD25+CD4+ T cell subsets; CD45RA-Foxp3+CD4+ T cell subsets; and CD4+Foxp3+CD25+ T cell subsets can be used in combination.Using a combined index as a measure of response to cancer immunotherapy can reveal whether an individual is part of the ineffective population relative to the therapy. Similarly, using a combined index as a measure of response to cancer immunotherapy can determine whether an individual is part of the responding population in terms of their response to the therapy. Typically, the response can be predicted by a function F(X, Y) using two indices (X, Y) as variables as described herein. In some cases, the function is a relative value of X and Y. Any function of X and Y (F(X, Y)) can be used as a relative value of X and Y. It should be understood that, especially when X is positively correlated with the response and Y is negatively correlated with the response, any function of X and Y (F(X, Y)) (which monotonically increases relative to X and monotonically decreases relative to Y) can be used, but the function is not limited to this. In cases where two or more variables represent the response, a function representing the response can be obtained by logistic regression or similar regression by calculating the contribution of each variable to the response. Examples of F(X, Y) representing a response include (but are not limited to) the following: F = aXr + bYs F = Xr * Ys where a, b, r, and s are any real numbers. For simplicity, integers can be used as r and s. In some embodiments, examples of relative values ​​of X and Y include (but are not limited to) Xn / Ym (n and m are any integers), such as X / Y and X² / Y. Combinations of such indicators can make response predictions more accurate when each of factors X and Y indicates the response of different institutions to therapy. The inventors' research shows that functions with r and s in the range of -5 to 5 can be used to accurately predict an individual's response to cancer immunotherapy. In one embodiment, by using the amount of T cells associated with dendritic cell stimulation in an antitumor immune response as X and the amount of regulatory T cells or a subset of CD4+ T cells associated with regulatory T cells as Y, it can be shown that an individual is not part of the ineffective population relative to cancer immunotherapy. In this context, it has been demonstrated that functions with r and s in the range of -5 to 5 can accurately predict an individual's response to cancer immunotherapy. Examples of such functions include: X / Y, X2 / Y, X3 / Y, X4 / Y, X5 / Y, X / Y2, X2 / Y2, X3 / Y2, X4 / Y2, X5 / Y2, X / Y3, X2 / Y3, X3 / Y3, X4 / Y3, X5 / Y3, X / Y4, X2 / Y4, X3 / Y4, X4 / Y4, X5 / Y4, X / Y5, X2 / Y5, X3 / Y5, X4 / Y5, X5 / Y5, and similar functions. The examples in this manual demonstrate that F = X2.475 / Y can be used as an indicator by logistic regression of the combination of the number of CD62LlowT cells (X) and the number of Foxp3+CD25+T cells (Y) in an individual's CD4+ T cells. However, those skilled in this technique can appropriately derive different combinations or functions for the indicators disclosed herein through similar analysis.In regression analysis, sample results greater than the combined variable value + 1 can be used to calculate the coefficients in the function of the variable combination. When a functional form in a combination of two indicators is found through regression analysis, the regression analysis is performed using results from at least four samples. Preferably, the regression analysis is performed using results from 20 or more samples. More preferably, the regression analysis is performed using results from 30 or more samples. Regression analysis with a larger number of samples is advantageous because it yields a more accurate combination of indicators that predicts an individual's response. In one embodiment, the number of CD62LlowT cells (X) and the number of Foxp3+CD25+T cells (Y) in an individual's CD4+ T cells can be used as null thresholds for the combined indicators. For example, the relative value of X and Y can be used as an indicator to predict an individual's response to cancer immunotherapy. For example, the present invention can calculate the variable (X, Y) to select the value of the following constituent groups: the amount of CD62LlowCD4+ T cell subset; the amount of CCR7-CD4+ T cell subset; the amount of LAG-3+CD62LlowCD4+ T cell subset; the amount of ICOS+CD62LlowCD4+ T cell subset; the amount of CD45RA-CD4+ T cell subset; the amount of CD45RO+CD4+ T cell subset; the amount of HLA-DR+ dendritic cell subset; the amount of CD80+ dendritic cell subset; the amount of CD86+ dendritic cell subset; the amount of PD-L1+ dendritic cell subset; the amount of CD62LlowCD8+ T cell subset; the amount of CD137+CD8+ T cell subset; and the amount of CD28+CD62LlowCD8+ T cell subset; as (X). The method of the present invention can also calculate the variable (X, Y), with the amount of regulatory T cell subsets or CD4+ T cell subsets associated with regulatory T cells as (Y). The method of the present invention can also calculate the variable (X, Y) with a value selected from the following groups: the amount of CCR4+CD25+CD4+ T cell subsets; the amount of CD62LhighCD25+CD4+ T cell subsets; the amount of CD127+CD25+CD4+ T cell subsets; the amount of CD45RA-Foxp3+CD4+ T cell subsets; and the amount of CD4+Foxp3+CD25+ cell subsets; as (Y). The present invention further provides a method for identifying the response cohort (PR) and stable cohort (SD) within an individual population that has been determined not to be part of the invalid cohort using the above-described (X, Y). The method for identifying the reactive (PR) and stable (SD) populations can predict whether an individual belongs to either the PR or SD population by calculating the variable (Z, W), where the amount of the ICOS+CD62LlowCD4+ T cell subset is used as (Z), and the value is selected from the following components:The amounts of CD4+CD25+ T cell subsets; CD4+Foxp3+ T cell subsets; CD4+Foxp3+CD25+ T cell subsets; CD62LhighCD25+CD4+ T cell subsets; CD45RA-Foxp3+CD4+ T cell subsets; CCR4+CD25+CD4+ T cell subsets; and CD127+CD25+CD4+ T cell subsets; are taken as (W). The relative values ​​of X and Y are not particularly restricted, but any function of X and Y (F(X, Y)) can be used, whose relation increases monotonically with respect to X and decreases monotonically with respect to Y. For example, such a function could be F(X, Y) = G(X) / H(Y); or F(X, Y) = G(X)-H(Y), where G(X) and H(Y) can be functions that increase monotonically with respect to X and Y, respectively. For example, G(X) can be XR, logRX, RX, or similar, where R is any real number satisfying the condition, preferably a positive integer. For example, H(Y) can be YR, logRY, RY, or similar, where R is any real number satisfying the condition, preferably a positive integer. In this form, the accuracy of the prediction can be improved by using a combination of a positive prediction X of the therapeutic effect of cancer immunotherapy and a negative prediction Y of cancer immunotherapy as an indicator. Examples of relative values ​​of X and Y include (but are not limited to) Xn / Ym (where n and m are any positive real numbers), such as X / Y and X2 / Y. Such combinations of indicators can make the prediction of response more accurate when each of factors X and Y indicates the response of different institutions to the therapy. There are no particular restrictions on the use of functions of Z and W. Any function of Z and W (J(Z, W)) can be used. Examples of such functions could be J(Z,W) = K(Z) * L(W); or J(Z, W) = K(Z) + L(W), where K(Z) and L(W) are typically functions that monotonically increase with respect to Z and W, respectively. For example, K(Z) could be ZR, logRZ, RZ, or similar, where R is any real number satisfying the condition, preferably a positive integer. For example, L(W) could be WR, logRW, RW, or similar, where R is any real number satisfying the condition, preferably a positive integer. Based on J(Z, W), the accuracy of determining the response group (PR) and stable group (SD) in the invalid group can be improved. Examples of relative values ​​of Z and W include (but are not limited to) Zn × Wm (where n and m are any real numbers), such as W5 × Z. When each of the factors Z and W indicates the response of different institutions to the therapy, such combinations of indicators can make the prediction of response more accurate.When the number of CD62LlowT cells in CD4+ T cells is X and the number of Foxp3+CD25+ T cells in CD4+ T cells is Y, the X / Y ratio can be used as an indicator to predict an individual's response to cancer immunotherapy. The inventors have found that individuals with a high X / Y ratio have not been shown to be part of the invalid population in relation to cancer immunotherapy. Therefore, the X / Y value can be used as a threshold value for the invalid population. The threshold value for the invalid population of X / Y can be appropriately determined by a person skilled in the art based on reference values, or the value shown in Figure 8 (cutoff value) can be used as the threshold value for the invalid population. When 7.35 is used as the threshold value for the invalid population of X / Y, the X / Y ratio can be used as a biomarker for determining whether an individual is part of the invalid population to predict the invalid population with a sensitivity of 71.4% and a specificity of 100%. When a value of 7.35 or less (e.g., 7.35, 6.83, 6.31, 5.64, 5.01, or similar) is used as the null threshold for X / Y, it can be used as a biomarker to predict nulls with 100% specificity. When a value of 9.305 or greater (e.g., 9.895, 10.19, 11.71, 12.07, 12.32, 12.42, or similar) is used as the null threshold for X / Y, it can be used as a biomarker to predict nulls with 100% sensitivity. In other words, the null threshold for X / Y can range from about 5 to about 13. Examples of null thresholds for X / Y include about 5, 6, 7, 8, 9, 10, 11, 12, and 13. Additionally, X² / Y, as a relative value of X and Y, can be used as the null threshold, which is an indicator for predicting an individual's response to cancer immunotherapy. The inventors have discovered that individuals with a high X2 / Y ratio are highly unlikely to be part of the invalid population in relation to cancer immunotherapy. The invalid population threshold for X2 / Y can be appropriately determined by a person skilled in the art based on reference values, or the threshold values ​​(cutoff values) shown in Figure 10 can be used as invalid population thresholds. When 174.3 is used as the invalid population threshold for X2 / Y, X2 / Y can be used as a biomarker to predict whether an individual is part of the invalid population with both 100% sensitivity and specificity. 110.6, 118.2, 134.9, 151.6, 157.4, 174.3, 194.2, 202.3, 208.3, and similar values ​​can be used as invalid population thresholds for X2 / Y. In other words, the invalid population threshold for X2 / Y can be in the range of approximately 110 to approximately 210. Examples of invalid group thresholds for X² / Y include approximately 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, and 210. Those skilled in this technique can use other relative values ​​of X and Y by setting appropriate thresholds from at least the data disclosed herein.The results of calculations (e.g., multiplication) of two or more indicators (biomarkers, BM) of the present invention can also be used to distinguish between PR and SD (showing a portion of the system response population). In one embodiment, the value of Zn × Wm (n and m are positive real numbers) (the first biomarker is referred to as "Z" and the second biomarker as "W") can be used to distinguish between PR and SD, but is not limited thereto. The results of calculations (e.g., addition and / or multiplication) of three or more biomarkers can also be used to distinguish between PR and SD. For example, when using the product W × Z of the ratio of CD25+Foxp3+CD4+ T cells in CD4+ T cells (W) and the ratio of ICOS+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells (Z) to distinguish between PR and SD populations, the threshold value of W × Z of 1.816 is shown to be usable as a biomarker with 80% sensitivity and 89.5% specificity (bottom middle plot of Figure 21). Alternatively, Z × W5 has been shown to be usable as a biomarker with 54.55% sensitivity and 100% specificity. Additionally, any function of Z and W (J(Z, W)) can be used as disclosed above. For example, functions such as J = Zr × Ws with r and s in the range of -5 to 6 can be used to accurately predict an individual's response to cancer immunotherapy. Examples of such functions include Z × W, Z2 × W, Z3 × W, Z4 × W, Z5 × W, Z6 × W, Z × W2, Z2 × W2, Z3 × W2, Z4 × W2, Z5 × W2, Z6 × W2, Z × W3, Z2 × W3, Z3 × W3, Z4 × W3, Z5 × W3, Z6 × W3, Z × W4, Z2 × W4, Z3 × W4, Z4 × W4, Z5 × W4, Z6 × W4, Z × W5, Z2 × W5, Z3 × W5, Z4 × W5, Z5 × W5, Z6 × W5, Z × W6, Z2 × W6, Z3 × W6, Z4 × W6, Z5 × W6, Z6 × W6, and similar functions. The examples in this specification demonstrate that Z × W5 can be used as a superior predictive function, combining the ratio of CD25+Foxp3+CD4+ T cells (W) to CD4+ T cells and the ratio of ICOS+CD62LlowCD4+ T cells to CD62LlowCD4+ T cells (Z) using logistic regression or similar methods (Figures 25 and 26). Furthermore, those skilled in this technique can appropriately derive different combinations or functions for the indicators disclosed herein using similar analyses. This specification further provides indicators that can be used to distinguish between the response cohort (fully responding + partially responding) and the stable cohort (intermediate cohort) within a population of individuals deemed part of the invalid cohort.The ratio of Foxp3+CD25+ T cells among CD4+ T cells can be used as an indicator to predict the response to cancer immunotherapy in individuals who have been predicted not to be part of the nullipopulation. The inventors have discovered that a high ratio of Foxp3+CD25+ T cells among CD4+ T cells in individuals who have not been part of the nullipopulation indicates that such individuals are very likely to be part of the responding population in response to cancer immunotherapy. CD4+Foxp3+CD25+ T cells are regulatory T cells with immunosuppressive properties; therefore, it has been unexpectedly found that individuals with a high ratio of such cells are very likely to respond to cancer immunotherapy. The ratio of the LAG-3+CD62LlowCD4+ T cell subset among CD62LlowCD4+ T cells or the PD-1+CD62LlowCD4+ T cell subset among CD62LlowCD4+ T cells can be used as an indicator to predict the response to cancer immunotherapy in individuals who have been predicted not to be part of the nullipopulation. The inventors have discovered that these cell subpopulations can be used to distinguish between a reactive population (complete response + partial response) and a stable population (intermediate population). The method of this invention can use, for example, a comparison of the relative values ​​of X and Y with threshold values ​​(invalid population threshold values) (including measuring the amount of CD80+ dendritic cells (X) and measuring the amount of CD28+CD62LlowCD8+ T cells (Y)) as an indicator to predict whether an individual is part of the invalid population relative to cancer immunotherapy. Any combination of any biomarker disclosed herein with any invalid population threshold value can be used to predict whether an individual is part of the invalid population. Alternatively, threshold values ​​determined for such indicators can be used as invalid population threshold values ​​to predict invalid populations, and the threshold value of the ratio of Foxp3+CD25+ T cells among CD4+ T cells can be used as a reactive population threshold value to predict whether an individual population (preferably excluding the invalid population) is part of the reactive population relative to cancer immunotherapy. Alternatively, a method is provided for identifying the Response Group (PR) and the Stable Group (SD) within a population of individuals that are determined not to be part of the invalid population. The method for identifying the PR and SD can predict whether an individual is part of the PR or SD by calculating variables (Z, W), using the amount of the ICOS+CD62LlowCD4+ T cell subset as (Z), and selecting values ​​from the following subsets: the amount of the CD4+CD25+ T cell subset; the amount of the CD4+Foxp3+ T cell subset; the amount of the CD4+Foxp3+CD25+ T cell subset; the amount of the CD62LhighCD25+CD4+ T cell subset; the amount of the CD45RA-Foxp3+CD4+ T cell subset; the amount of the CCR4+CD25+CD4+ T cell subset; and the amount of the CD127+CD25+CD4+ T cell subset as (W).The threshold value for the response population of the ratio of Foxp3+CD25+ T cells in CD4+ T cells can be appropriately determined by a person skilled in this technique based on reference values, or the results shown in Figure 12 can be appropriately selected as the threshold value for the response population. It should be noted that the ratio may be expressed as a percentage (%) in the following text. When 2.05 is used as the threshold value for the response population of the ratio of Foxp3+CD25+ T cells in CD4+ T cells, the ratio of Foxp3+CD25+ T cells in CD4+ T cells can be used as a biomarker to predict whether an individual is part of a response population with a sensitivity of 52.6% and a specificity of 100%. When a value of 2.05 or less (e.g., 2.05, 1.895, 1.76, 1.7, 1.61, or similar) is used as the threshold value for the response population of the ratio of Foxp3+CD25+ T cells in CD4+ T cells, this can be used as a biomarker to predict the response population with 100% specificity. When a value of 3.35 or greater (e.g., 3.35, 3.63, 4.365, or similar) is used as the response threshold for the ratio of Foxp3+CD25+ T cells to CD4+ T cells, this can be used as a biomarker to predict the response population with 100% sensitivity. In other words, the response threshold for the ratio of Foxp3+CD25+ T cells to CD4+ T cells can range from about 1.6 to about 4.4 (%). Examples of response thresholds for the ratio of Foxp3+CD25+ T cells to CD4+ T cells include approximately 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, and 4.4 (%). Another aspect of the invention provides a method for applying cancer immunotherapy to an individual selected from any of the biomarkers (preferably combinations of biomarkers) disclosed above. One embodiment provides a method for administering an immune checkpoint inhibitor to an individual whose biomarker is in a state indicated by any of the thresholds disclosed herein. (Cell Grading / Separation) Samples for T cell grading / separation can be appropriately collected from the individual using known methods. For example, this can be collected from the individual's peripheral blood, bone marrow, tumor tissue, hematopoietic tissue, spleen, normal tissue, lymph, or similar sources. Samples collected from peripheral blood are advantageous due to their simplicity and non-invasiveness.The composition of T cells in an individual's sample can be measured by a person skilled in the art using known methods. Typically, the number of cells positive for a marker (e.g., CD4) of a specific cell subpopulation in a given sample can be measured using flow cytometry or similar methods. Some embodiments of the invention include measuring the number of CD62LlowT cells (X) and / or the number of FoxP3+CD25+T cells (Y) among CD4+ T cells. While flow cytometry is typically used to measure the composition of cell populations, methods such as antibody arrays or immunostaining on a sample containing cells, protein expression analysis (e.g., Western blot, mass spectrometry, HPLC, or similar) on a sample containing cells, mRNA expression analysis (microarray or next-generation sequencing) on ​​a sample containing cells, or similar methods can be used. To measure cell counts in various cell subsets, such as the CD62LlowCD4+ T cell subset and the CD4+CD25+CD4+Foxp3+CD25+ T cell subset, measurements can be obtained by experimentally removing cells from all cell populations except for those subsets. Kits exist for their specific applications. For example, when using the human CD4+ effector memory T cell isolation kit (Militenyi Biotech), cells corresponding to the CD4+CD62LlowT cell subset can be isolated from peripheral blood without the use of CD4 or CD62L antibodies. This is achieved by counting and recording the total live cell count, and also by counting and recording the number of cells obtained using this kit. When using the human CD4+CD25+ regulatory T cell isolation kit (Militenyi Biotech), cell counts corresponding to the CD4+CD25+CD4+Foxp3+CD25+ T cell subset can be obtained without the use of anti-FoxP3 antibodies. Since FoxP3 is located in the cell nucleus, this has the advantage of removing molecular staining from the nucleus. Alternatively, human CD4+CD25+CD127dim / - regulatory T cell separation kits (Militenyi Biotech) or human CD25+CD49d- regulatory T cell separation kits (Militenyi Biotech) can be selected as similar kits.Antibodies are not required. Antibodies are prepared that specifically recognize and bind to molecules expressed on a single cell, causing them to emit color when they bind to molecules expressed on or inside the cell. The antibodies are then detected to measure the number of cells emitting color. Since the molecules expressed on or inside the cell are proteins, the mRNA encoding the protein is also formed in the cell when the protein is expressed. In other words, examining the mRNA in a single cell is sufficient to check the presence / absence of the mRNA encoding the protein of interest. This can be achieved through single-cell gene expression analysis, i.e., single-cell-level mRNA analysis. Examples of single-cell gene expression analysis include: 1) a next-generation sequencing method using Quartz-Seq; 2) a method for isolating cells using the Fluidigm C1 system or ICELL8 single-cell system and preparing isolated cells with a SMART-Seq v4 function library; 3) a method for isolating cells using a cell classifier and measuring cells using the Ambion single-cell to CT kit for quantitative PCR; 4) the CyTOF system (Helios), and similar methods. In other words, blood is obtained, live cells are counted, and cells are isolated using a cell sorter or similar method. For example, the expression levels of specific genes can be measured using quantitative PCR equipment on individual isolated cells using an Ambion single-cell to CT kit. Based on this result, individual cells are examined to determine which subset of the T cell population—CD62LlowCD4+ or CD4+Foxp3+CD25+—the cells fall into, and the number of cells falling into each subset is counted. The ratio of these numbers (x to y) is then calculated. Examples of candidate genes whose expression has been examined include: αβTCR, CD3, CD4, CD25, CTLA4, GITR, FoxP3, STAT5, FoxO1, FoxO3, IL-10, TGFβ, IL-35, SMAD2, SMAD3, SMAD4, CD62Llow, CD44, IL-7R (CD127), IL-15R, CCR7low, BLIMP1, and similar genes. As illustrated in the examples in this specification, compared to CD62LhighCD4+ T cells, CD62LlowCD4+ T cells show increased expression of the following genes: AURAKA, CCL17, CD101, CD24, FOXF1, GZMA, GZMH, IL18RAP, IL21, IL5RA, ND2, SMAD5, SMAD7, and VEGFA (Figure 34a). The quantity and / or ratio of cell subsets can be determined by measuring the expression of these genes, thereby determining which T cell subset the obtained T cells belong to.As illustrated in the examples of this specification, compared to CD62LlowCD4+ T cells, the genes with increased expression in CD62LhighCD4+ T cells include: BACH2, CCL28, CCR7, CD27, CD28, CD62L, CSNK1D, FOXP1, FOXP3, IGF1R, IL16, IL27RA, IL6R, LEF1, MAL, and TCF7 (Figure 34a). The quantity and / or ratio of cell subpopulations can be determined by measuring the expression of these genes, thereby determining which T cell subpopulation the obtained T cells belong to. The measurement of the ratio of cell subpopulations in this invention, or the comparison with a threshold value, can use a reference sample with a limiting signal. The signal of a reference material prepared to induce a fluorescence signal corresponding to a given cell subpopulation (e.g., particles to which fluorescent pigments are attached) can be compared with that of a sample containing the cell population to measure the quantity and ratio of the cell population in the sample by comparison with the reference material. The signal can also be compared between a reference material prepared to induce a fluorescence signal corresponding to a predetermined threshold (e.g., particles to which fluorescent pigments are attached) and a sample containing a cell population, to determine the presence / absence of the marker of the present invention in the T cell composition of the sample by comparison with the reference material. When determining whether a particular marker is high (high expression) or low (low expression) in the present invention, those skilled in the art can use a classification baseline of expression intensity commonly used in the art. For example, when using a PE-labeled anti-human CD62L antibody as a boundary, CD62L can be classified into CD62Llow and CD62Lhigh using a signal intensity corresponding to a 10E2 signal. In one embodiment, CD62L can be determined as high (high expression) or low (low expression) as follows: Prepare an antibody as a negative control of the anti-CD62L antibody isotype. The antibody used as a negative control should not recognize (bind) any antigen on T cells, but can adsorb nonspecifically. For example, an antibody sold as an isotype control is used. The same fluorescence label is used for the anti-CD62L antibody and the negative control. After preparation, their respective fluorescence patterns were applied. In the typical pattern, the isotype control showed a peak in the low fluorescence region, while the anti-CD62L antibody showed a peak in the high fluorescence region, with the fluorescence intensity gradually decreasing in the low fluorescence region (Fig. 28). In Fig. 28, the purple line represents the staining pattern of the negative control, and the area below the light blue line represents the staining pattern of the anti-CD62L antibody. The two patterns were compared. Although some areas had the same fluorescence intensity as the negative control, it was determined that all peaks of the negative control were shifted to the right (= stained areas). Generally, this indicates that almost all cells were stained with the antibody.The determination of the low and high boundaries on the x-axis (FL4-H) is now discussed. The right side of the figure shows a schematic diagram assuming the low and high peaks are separated. The high peak exhibits horizontal symmetry, but the low peak has a composite peak that cannot be considered horizontally symmetrical. The high peak is located at approximately 400 on FL4-H. The maximum value (=A) of the high peak in FL4-H is approximately 2,000. If the high peak is considered horizontally symmetrical, then the inherent minimum value (=B) of the high peak, separated from the peak at the same distance from FL4-H to A and located on the opposite side of the peak at A, is approximately 90. There should be overlap with the low peak in this region. Although the high peak is horizontally symmetrical, the horizontal symmetry disappears near D. In other words, D can be inferred to be the inherent maximum value of the low peak, meaning that a low peak exists up to the vicinity of D. Ultimately, the high and low quanta can be separated at point C, the center of D (which is the maximum value of the low quanta) and B (which is the minimum value of the high quanta). This value corresponds to 10E2. In other words, the range of the high quanta can be from C to A, and the range of the low quanta can be from E to C. The regions formed by the peaks and ranges correspond to cell counts. The position of C on BD should vary depending on the ratio of the size of the high and low peaks, the sharpness of the peaks, or similar factors, but the difference is considered small when C is located at the center of BD. Figure 29 shows a histogram of CD62L according to FACS analysis. It should be understood that CD62Llow can be very clearly separated by a boundary of 10E2. As used herein, "flow cytometry" refers to a technique for measuring the number of cells, individual or other biological particles suspended in a liquid and in an individual entity / chemical / biological property. Various cell types are analyzed using flow cytometry. Specifically, the differentiation of blood cells can be determined using flow cytometry. Such distinctions began to be used in diagnostics outside of research. Examples of the advantages of flow cytometry include: easy determination of embryonic cell ratios; high specificity and sensitivity; high reproducibility; analysis of most cells; short processing time; and similar advantages. The device using this technique is called a "flow cytometer." A flow cytometer is a device used to measure the optical properties of suspended matter (cells) in a homogeneous suspension of cells. Cells pass through the focal point of a laser beam on the liquid flow. As they pass, the optical properties of individual cells can be measured simultaneously at 500 to 4000 cells per second, using forward-scattered light, side-scattered light, and one or more fluorescence wavelengths, to rapidly and accurately measure biological characteristics such as cell size, internal structure, and the amount of nucleic acids or various antigens present in the cell membrane, cytoplasm, or nucleus.Scattered light is the light that is scattered into the environment after the laser hits the cell. Forward scattering (FSC) is detected at the front relative to the laser axis. The intensity of scattered light is proportional to the cell's surface area. In other words, a relatively large FSC value indicates a large cell, and a small FSC value indicates a small cell. Lateral scattering (SSC) is detected at a position 90° (perpendicular) to the laser axis. The intensity of scattered light is proportional to the intracellular structure or cellular particle composition. In other words, a relatively large SSC value indicates a complex internal cell structure, and a small SSC value indicates a simple internal cell structure. The results of flow cytometry are typically represented as a dot plot, with FSC as the X-axis and SSC as the Y-axis. Each cell is represented by a single dot / point in the plot. The cell's position is determined by the relative values ​​of FSC and SSC. Relatively small lymphocytes with simple internal structures are shown in the lower left section, large granulocytes with internal granules are shown in the upper right section, and large but structurally simple monocytes are shown in separate groups between the lymphocytes and granulocytes. Fluorescence refers to the light produced when fluorescent pigments labeling cells are excited by a laser beam and release energy. Flow cytometers (e.g., Becton & Dickinson FACSCalibur) typically emit single-wavelength laser beams of 488 nm and 635 nm. Cells themselves also have the property of emitting weak fluorescence (autofluorescence). However, when attempting to specifically detect cellular molecules using fluorescence, it is necessary to pre-bind the fluorescent pigment to the cell or its molecules in some form. For example, FITC (fluorescent isothiocyanate) absorbs 488 nm excitation light and primarily emits 530 nm fluorescence (green). Pre-labeling antibodies with FITC causes differences in the amount of antibody bound based on the amount of antigen on the cell surface, resulting in differences in the fluorescence intensity of FITC. Therefore, the amount of antigen present on the cell surface can be assessed. FACSCalibur, used as an example, is equipped with four fluorescence detectors capable of detecting different fluorescence wavelengths. If multiple fluorescent pigments emitting light of different wavelengths are prepared, up to four different antigens can be detected simultaneously. For example, in addition to FITC, which is excited by a 488 nm single-wavelength laser beam, PE (phycoerythrin) mainly emits fluorescence at 585 nm, while PerCP (polydinophytin-chlorophyll protein) and PE-Cy5 (carbonylcyanin 5) mainly emit fluorescence at 670 nm. APC (allophycocyanin), which is a fluorescent pigment excited by a 635 nm single-wavelength laser beam, mainly emits fluorescence at 670 nm. These fluorescent pigments are combined with various antibodies and used for double or triple staining of cells. CD4, CD8, CD62L, CD25, Foxp3 molecules and similar molecules expressed on the surface of T lymphocytes can be detected using monoclonal antibodies that specifically react with them.Strictly speaking, there are two types of flow cytometers: those that analyze only cells and those that sort analyzed cells. The latter is called "FACS". As used herein, "FACS" is an abbreviation for fluorescence-activated cell sorter and refers to a device used in methods for sorting specific cells by the presence / absence of surface antigens on free cells such as lymphocytes using laser beam analysis. Results of flow cytometry can be displayed in histograms, dot plots, or similar formats. As used herein, a "histogram" is a graph used in flow cytometry to represent the intensity of optical signals for various parameters on the X-axis and the cell count on the Y-axis in fluorescence measurements. Tens of thousands or more cells can be counted in this format. As used herein, a "dot plot" is a graph plotting the fluorescence intensity of two types of fluorescent pigments on the X and Y axes. In dual or triple staining analysis, this can be achieved by placing individual fluorescence intensities on the X or Y axis, with each cell corresponding to a point on a two-dimensional graph. For example, peripheral blood or bone marrow fluid is collected, and red blood cells are removed by hemolysis or hydrocentrifugation. The residue is then reacted with fluorescently labeled antibodies (antibodies against the antigen of interest and their control antibodies) and thoroughly washed for observation using flow cytometry. The detected scattered light or fluorescence is converted into electrical signals and analyzed by a computer. The results can be used to differentiate lymphocytes, monocytes, and granulocytes by representing FSC intensity as cell size and SSC intensity as intracellular structure. Subsequently, the cell population of interest can be gated as needed to examine antigen expression patterns within the cells. In practicing the method of this invention, those skilled in the art can appropriately identify surface markers on the displayed cells to classify or count them. In an international workshop, consensus was reached on CD antigens, primarily based on the biochemical characteristics (especially molecular weight) of the antigens they recognize, classifying them into clusters. This is known as CD classification. Various types of monoclonal antibodies recognizing specific leukocyte differentiation antigens are thus named under a unified protocol, using the CD followed by a numerical value, i.e., CD numbers (i.e., D1, CD2, and similar). Typical examples of cell surface markers, including those labeled with CD used in this paper, are explained below. CD4 (6.2): ​​A class II MHC molecule that binds to antigen-presenting cells and acts as a co-receptor for the T-lymphocyte antigen receptor complex. CD4 is expressed in MHC class II restricted helper T lymphocytes. CD8 (6.4): A dimeric protein with α and β SS bonds. CD8 binds to class I MHC molecules on antigen-presenting cells and acts as a co-receptor for the T-lymphocyte antigen receptor complex. CD8 is expressed in MHC class I restricted killer T lymphocytes. CD25: CD25 is a 55 kDa glycoprotein, also known as the low-affinity interleukin-2 receptor α chain.(IL-2Rα). CD25 is also expressed in activated T cells, B cells, macrophages, and some inactivated CD4+ T cells that act as regulatory T cells. Therefore, CD25 is used as a marker for regulatory T cells. CD62L: CD62L (L-selectin) is a molecule required for the recognition and homing of high endothelial venules (HEVs) specifically present in lymphoid organs. Native T cells possess this molecule in preparation for circulation via lymphoid organs and antigen presentation. After recognizing an antigen presented by dendritic cells in a lymphoid organ, native T cells lose the homing molecule recognized by T cell receptors and activated by effector T cells. Therefore, effector T cells that have been antigen-recognized, activated, selected, and expanded have the CD62Llow phenotype. Foxp3: Foxp3 is a major transcription factor for regulatory T cells (Tregs), that is, a transcription factor that plays an essential role in all differentiation / functional expression / maintaining the differentiation state of Tregs. Because its expression is almost specific to Tregs, Foxp3 is commonly used as a marker molecule for recognizing Tregs. Foxp3 increases CD25 or CTLA4 expression while inhibiting the production of effector intercytokines (IL-2, IFNγ, IL-4, IL-17, and similar). PD-1 is closely associated with T cell exhaustion. In short, this is the attenuation response of T cells to large amounts of antigens that persist for a long time. Even if native T cells become T cells with high-level effector function due to activation by antigen-presenting cells, once T cells are exposed to large amounts of antigens for a long time, they will express immune checkpoint molecules PD-1→LAG-3→CD244, lose function, and eventually lead to apoptosis. Because cancer cells are present "largely" and "for a long time," it is understood that this system is effective. (Effects of preventing / treating cancer by infusing CD62LlowCD4+ T cells) Another aspect of the present invention is a method for improving or maintaining / sustaining the therapeutic effects of cancer immunotherapy by infusing specific cells or combinations thereof. CD62LlowCD4+ T cells have been recognized as important in an individual's response to cancer immunotherapy. It should be understood that the use of such T cells can improve or maintain an individual's response to cancer immunotherapy. One embodiment of the invention is a composition comprising CD62LlowCD4+ T cells. CD62LlowCD4+ T cells or compositions comprising them are suitable for use in conjunction with cancer immunotherapy. While not wishing to be bound by any theory, the therapeutic effect attributable to patients for whom CD62LlowCD4+ T cell infusion has not achieved adequate prevention / treatment of cancer with PD-1 inhibitors and / or PD-L1 inhibitors can be understood as follows.When PD-L1, expressed on the surface of cancer cells, binds to PD-1, expressed on the surface of T cells, the anti-tumor effect of T cells is suppressed (an immune evasion mechanism of cancer cells). Anti-PD-1 antibodies are antibody molecules that inhibit this binding between PD-L1 and PD-1 and block the immune evasion mechanism of cancer cells, allowing T cells to exert their anti-tumor effects. Therefore, it should be understood that the anti-tumor effect attributed to the inhibition of PD-1 / PD-L1 binding mainly works during the effector phase of T cell invasion of the tumor, but its effect is low during the T cell activation phase. In other words, PD-1 or PD-L1 inhibitors are unlikely to exert their anti-tumor effects unless T cell activation and sufficient effector T cells are present. Anti-PD-1 antibodies exert their maximum anti-tumor effect in approximately 20% to 30% of cancer patients, but the T cell immune status required for the anti-tumor effect of anti-PD-1 antibodies and methods for assessing such immune status are unknown. CD62L (L-selectin) is a "homing receptor" for lymphocytes. CD62L is expressed on the cell surface of native T cells and promotes their migration to lymph nodes. When native T cells in lymph nodes are stimulated by antigen-presenting cells, these cells are activated into effector T cells, while CD62L expression decreases (CD62Llow) and they differentiate into CD4+ T cells (helper T cells) or CD8+ T cells (cytotoxic T cells). The inventors have found that CD62Llow T cells are highly effective as a method for recognizing T cells activated by cancer antigens in environments where cancer antigens are unknown and cancer antigen-specific T cells cannot be recognized. In mouse models, receptive infusion of CD62Llow T cells isolated from tumor-bearing lymph nodes can cure mice carrying cancer. Using effector T cells isolated in this manner, a greater anti-tumor effect is obtained by introducing CD62LlowCD4+ T cells (Example 4 and Figure 14). Most cancer cells in the tumor systems used in this experiment do not express MHC class II antigens. Therefore, it should be understood that the high anti-tumor effect of CD62LlowCD4+ T cells is not achieved by affecting direct cytotoxic function, but by affecting the function of antigen-presenting cells such as dendritic cells, thereby coordinating the immunity of all T cells. Excellent anti-tumor effects are also obtained when CD62LlowCD8+ T cells are used in conjunction with CD62LlowCD4+ T cells (Example 4 and Figure 14). The inventors have discovered that the ratio of CD62LlowCD4+ T cells among all T cells is clearly related to the anti-tumor effect of anti-PD-1 antibodies; that is, it essentially includes CD62LlowCD4+ T cells (which are the T cells that exert anti-tumor effects) to exert anti-tumor effects in the presence of anti-PD-1 antibodies.While not wishing to be bound by any theory, this discovery should be understood as follows: While T-cell immunity is generally sufficient to exert anti-tumor effects, this immunity is attributed to evasion due to the attenuation of PD-1 / PD-L1 antigen recognition signals in cancer patients containing a large number of CD62LlowCD4+ T cells. CD62LlowCD4+ T cells activate antigen-presenting cells, such as dendritic cells, to activate the activation phase. Additionally, activated CD8+ T cells require antigen presentation from locally activated antigen-presenting cells of already effector CD4+ T cells to acquire cytotoxic function. In this regard, it should be understood that PD-1 / PD-L1 binding inhibitors, which primarily restore the attenuation of antigen recognition signals during the effector phase, are complementary to the function of effector CD4+ T cells. It should be understood that even if the immune evasion mechanism is blocked by anti-PD-1 antibodies in patients without a large number of CD62LlowCD4+ T cells, the function of antigen-presenting cells that should present cancer antigens is still suppressed, resulting in unsatisfactory anti-tumor effects. In light of the foregoing, it should be understood that administration of CD62LlowCD4+ T cells can exert the anti-tumor effect of anti-PD-1 antibodies in patients with low CD62LlowCD4+ T cell counts, thereby rendering the anti-PD-1 antibodies ineffective in their anti-tumor effect. (Including the manufacture and use of cells comprising the composition) A method for manufacturing a composition comprising CD62LlowCD4+ T cells may include purifying CD62LlowCD4+ T cells from a human T cell population. Purification may include removing CD62L-high expressing cells from the T cell population (negative selection). It is preferable to purify the CD62LlowCD4+ T cell line by using antibodies and / or magnetic beads or similar negative selection, as impurities such as antibodies or magnetic beads will not remain on the cells to be used. One embodiment of the invention is a kit comprising substances specifically binding to CD62L for purifying CD62LlowCD4+ T cells. Examples of substances specifically binding to CD62L include (but are not limited to) antibodies specific to CD62L. Those skilled in this art can isolate and expand the specific T cell subsets disclosed herein using the methods described herein (e.g., flow cytometry). In one embodiment, the composition disclosed herein provides CD4+CD62LlowT cells. T lymphocytes can be collected using known techniques and concentrated or drained using known techniques such as flow cytometry and / or antibody affinity binding (e.g., immunomagnetic selection). Following the concentration and / or draining steps, the desired in vitro expansion of T lymphocytes can be performed using readily known techniques (including, but not limited to, those disclosed in US 6040177 by Riddell et al.) or variations thereof, as described herein by those skilled in this art.For example, a desired T cell population or subset can be expanded by adding a first T lymphocyte population to an in vitro culture medium, followed by adding feeder cells to the medium (e.g., such that the resulting cell population contains at least about 5, 10, 20, 40 or more feeder cells per T lymphocyte in the first population to be expanded), and culturing the culture (e.g., for a sufficient time to increase the number of T cells). The culture is typically cultured under conditions suitable for expanding T lymphocytes, such as temperature. For the growth of human T lymphocytes, the temperature is typically, for example, at least about 25°C, preferably at least about 30°C, and more preferably about 37°C. Cells can be isolated and / or expanded, and then stored as needed, and subsequently administered to individuals according to the methods disclosed herein or methods well known in the art. The amount of cells of interest (e.g., CD62LlowCD4+ T cells) in a composition containing cells of the present invention can be appropriately determined by those skilled in the art to achieve the desired effect, and for human administration, it may be at least 2 × 10⁸, preferably at least 6 × 10⁸, and more preferably at least 2 × 10⁹.Compositions containing the cells disclosed herein may contain pharmaceutically acceptable carriers or excipients other than the cells of interest (e.g., CD62LlowCD4+ T cells). As used herein, "pharmaceutically acceptable" means approved by a government regulatory authority or listed in a pharmacopoeia or other generally accepted pharmacopoeia for use in animals or more specifically in humans. As used herein, "carrier" refers to a culture, infusion solution, perfusion fluid, diluent, adjuvant, excipient, or mediator administered with a therapeutic agent. Compositions containing the cells of the present invention contain cells as a principal component, such that the carrier can better maintain cells such as in a culture, infusion solution, or perfusion fluid. For example, when a pharmaceutical composition is administered intravenously, physiological saline and aqueous glucose are preferred carriers. Preferably, aqueous solutions of physiological saline and aqueous solutions of glucose and glycerol are used as liquid carriers for injectable solutions. When a drug is administered orally, water is a preferred carrier. Examples of suitable excipients include: light anhydrous silica, crystalline cellulose, mannitol, starch, glucose, lactose, sucrose, gelatin, malt, rice, wheat flour, chalk, silica gel, sodium stearate, glyceryl monostearate, talc, sodium chloride, powdered skim milk, glycerin, propylene glycol, water, ethanol, calcium carboxymethyl cellulose, sodium carboxymethyl cellulose, hydroxypropyl cellulose, hydroxypropyl methyl cellulose, polyvinyl acetal diethylamino acetate, polyvinylpyrrolidone, gelatin, medium-chain fatty acid triglycerides, polyethylene oxide hydrogenated castor oil 60, refined sugar, carboxymethyl cellulose, corn starch, inorganic salts, and the like. When desired, the composition may also contain small amounts of wetting agents, emulsifiers, or pH buffers. These compositions may be in the form of solutions, suspensions, emulsions, tablets, pills, capsules, powders, sustained-release formulations, or the like. The composition can also be formulated into suppositories using conventional binders and carriers such as triglycerides. Oral formulations may also contain standard carriers such as medical mannitol, lactose, starch, magnesium stearate, sodium saccharin, cellulose, or magnesium carbonate. Examples of suitable carriers are disclosed in EW Martin, Remington's Pharmaceutical Sciences (Mark Publishing Company). Such compositions contain a therapeutically effective amount of a preferred purified form of the therapeutic agent and a suitable amount of carrier in a form suitable for administration to the patient. The formulation must be suitable for the administration method. The formulation may additionally contain, for example, surfactants, excipients, colorants, flavoring agents, preservatives, stabilizers, buffers, solubilizers, isotonic agents, binders, disintegrants, lubricants, flow aids, flavoring agents, or the like. (Preferred Embodiment) One embodiment of the invention is the use of an amount selected from within the individual of: an amount of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response;The quantity of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in antitumor immune responses; the quantity of CD8+ T cell subsets associated with dendritic cell stimulation in antitumor immune responses; the quantity of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; and the quantity of ICOS+CD62LlowCD4+ T cell subsets; these are methods used as variables (indicators) in a function to predict an individual's response to cancer immunotherapy. In one embodiment, the variables (indicators) used to predict an individual's response to cancer immunotherapy are selected from the following subsets: the amount of CD62LlowCD4+ T cell subsets; the amount of CCR7-CD4+ T cell subsets; the amount of CD45RA-CD4+ T cell subsets; the amount of CD45RO+CD4+ T cell subsets; the amount of LAG-3+CD62LlowCD4+ T cell subsets; the amount of ICOS+CD62LlowCD4+ T cell subsets; the amount of CCR4+CD25+CD4+ T cell subsets; the amount of CD62LhighCD25+CD4+ T cell subsets; the amount of CD127+CD25+CD4+ T cell subsets; the amount of CD45RA-Foxp3+CD4+ T cell subsets; the amount of CD4+CD25+ T cell subsets; the amount of CD4+Foxp3+ T cell subsets; and the amount of Foxp3+CD25+CD4+ T cell subsets. The amounts of HLA-DR+ dendritic cell subsets; CD80+ dendritic cell subsets; CD86+ dendritic cell subsets; PD-L1+ dendritic cell subsets; CD62LlowCD8+ T cell subsets; CD137+CD8+ T cell subsets; and CD28+CD62LlowCD8+ T cell subsets. One embodiment of the present invention uses a method for predicting an individual's response to cancer immunotherapy using relative amounts selected from the following: relative amounts of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; relative amounts of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; relative amounts of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; relative amounts of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; and relative amounts of ICOS+CD62LlowCD4+ T cell subsets; as variables (indicators) for predicting an individual's response to cancer immunotherapy. In one embodiment, the relative amounts of variables (indicators) for predicting an individual's response to cancer immunotherapy are selected from the group consisting of: the ratio of CD62LlowCD4+ cell subsets among CD4+ T cells; and the ratio of CCR7-CD4+ T cell subsets among CD4+ T cells.The percentages of CD45RA-CD4+ T cells in CD4+ T cells; the percentages of CD45RO+CD4+ T cells in CD4+ T cells; the percentages of LAG-3+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells; the percentages of ICOS+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells; the percentages of CCR4+CD25+CD4+ T cells in CD4+ T cells; the percentages of CD62LhighCD25+CD4+ T cells in CD4+ T cells; the percentages of CD127+CD25+CD4+ T cells in CD4+ T cells; the percentages of CD45RA-Foxp3+CD4+ T cells in CD4+ T cells; the percentages of CD4+CD25+ T cells in CD4+ T cells; and the percentages of CD4+Foxp3+ T cells in CD4+ T cells. The proportions of Foxp3+CD25+CD4+ T cells in CD4+ T cells; the proportion of HLA-DR+ dendritic cells in dendritic cells; the proportion of CD80+ dendritic cells in dendritic cells; the proportion of CD86+ dendritic cells in dendritic cells; the proportion of PD-L1+ dendritic cells in dendritic cells; the proportion of CD62LlowCD8+ T cells in CD8+ T cells; the proportion of CD137+CD8+ T cells in CD8+ T cells; and the proportion of CD28+CD62LlowCD8+ T cells in CD62LlowCD8+ T cells. One embodiment of the present invention uses an individual's selection of the following quantities: the quantity of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; the quantity of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; the quantity of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; and the quantity of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; as a method for predicting the individual's response to cancer immunotherapy as a function of a variable (indicator), wherein an indicator function above a threshold (ineffectiveness threshold) indicates that the individual is not part of the ineffectiveness group for cancer immunotherapy. In one embodiment, the variables (indicators) used to predict an individual's response to cancer immunotherapy are selected from the following subsets: the amount of CD62LlowCD4+ T cell subset; the amount of LAG-3+CD62LlowCD4+ T cell subset; the amount of CCR7-CD4+ T cell subset; the amount of CD45RA-CD4+ T cell subset; the amount of CD45RO+CD4+ T cell subset; the amount of HLA-DR+ dendritic cell subset; and the amount of CD80+ dendritic cell subset.The levels of CD86+ dendritic cell subsets; PD-L1+ dendritic cell subsets; CD62LlowCD8+ T cell subsets; CD137+CD8+ T cell subsets; CD28+CD62LlowCD8+ T cell subsets; CD4+Foxp3+CD25+ T cell subsets; CD62LhighCD25+CD4+ T cell subsets; CD45RA-Foxp3+CD4+ T cell subsets; CCR4+CD25+CD4+ T cell subsets; and CD127+CD25+CD4+ T cell subsets; where the indicator function is above the threshold value (ineffectiveness threshold value), it indicates that the individual is not part of the ineffectiveness group for cancer immunotherapy. One embodiment of the present invention is a method for using a relative amount selected from the following within an individual: a relative amount of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; a relative amount of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; a relative amount of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; and a relative amount of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; as a variable (indicator) for predicting the individual's response to cancer immunotherapy, wherein an indicator function higher than a threshold value (ineffectiveness threshold value) indicates that the individual is not part of the ineffectiveness group for cancer immunotherapy. In one embodiment, the relative amounts of variables (indicators) used as functions for predicting an individual's response to cancer immunotherapy are selected from the following groups: the ratio of CD62LlowCD4+ T cell subsets in CD4+ T cells; the ratio of LAG-3+CD62LlowCD4+ T cell subsets in CD62LlowCD4+ T cells; the ratio of CCR7-CD4+ T cell subsets in CD4+ T cells; the ratio of CD45RA-CD4+ T cell subsets in CD4+ T cells; the ratio of CD45RO+CD4+ T cell subsets in CD4+ T cells; the ratio of HLA-DR+ dendritic cell subsets in dendritic cells; the ratio of CD80+ dendritic cell subsets in dendritic cells; the ratio of CD86+ dendritic cell subsets in dendritic cells; the ratio of PD-L1+ dendritic cell subsets in dendritic cells; and the ratio of CD62LlowCD8+ T cell subsets in CD8+ T cells. The ratio of CD137+CD8+ T cell subsets in CD8+ T cells; the ratio of CD28+CD62LlowCD8+ T cell subsets in CD62LlowCD8+ T cells; the ratio of CD4+Foxp3+CD25+ T cell subsets in CD4+ T cells.The percentages of CD62LhighCD25+CD4+ T cells in CD4+ T cells; the percentages of CD45RA-Foxp3+CD4+ T cells in CD4+ T cells; the percentages of CCR4+CD25+CD4+ T cells in CD4+ T cells; and the percentages of CD127+CD25+CD4+ T cells in CD4+ T cells; where the indicator function being above the threshold (ineffectiveness threshold) indicates that the individual is not part of the ineffectiveness group for cancer immunotherapy. One embodiment of the present invention is a method for predicting an individual's response to cancer immunotherapy using a quantity (X, Y) as a variable (indicator) in the function F(X, Y), wherein the quantity is selected from: the quantity of CD4+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; the quantity of dendritic cell subsets associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; the quantity of CD8+ T cell subsets associated with dendritic cell stimulation in an antitumor immune response; and the quantity of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; wherein a value higher than the threshold (ineffectiveness threshold) in the function F(X, Y) indicates that the individual is not part of the ineffectiveness group for cancer immunotherapy. In one embodiment, the function F(X, Y) can be calculated using variables (indicators) of a function that predict an individual's response to cancer immunotherapy. These variables are values ​​of (X) selected from the following subsets: the amount of CD62LlowCD4+ T cells; the amount of LAG-3+CD62LlowCD4+ T cells; the amount of CCR7-CD4+ T cells; the amount of CD45RA-CD4+ T cells; the amount of CD45RO+CD4+ T cells; the amount of HLA-DR+ dendritic cells; the amount of CD80+ dendritic cells; the amount of CD86+ dendritic cells; the amount of PD-L1+ dendritic cells; the amount of CD62LlowCD8+ T cells; the amount of CD137+CD8+ T cells; and the amount of CD28+CD62LlowCD8+ T cells. In one embodiment, F(X, Y) can be calculated as a function (indicator) predicting an individual's response to cancer immunotherapy, where the variable is a value of (Y) selected from the following subsets: the amount of CD4+Foxp3+CD25+ T cells; the amount of CD62LhighCD25+CD4+ T cells; the amount of CD45RA-Foxp3+CD4+ T cells; the amount of CCR4+CD25+CD4+ T cells; and the amount of CD127+CD25+CD4+ T cells. A value above the threshold (ineffectiveness threshold) for F(X, Y) indicates that the individual is not part of the ineffectiveness group for cancer immunotherapy.One embodiment of the present invention is a method for predicting an individual's response to cancer immunotherapy using an individual's in-unit quantity (X, Y) as a variable (indicator) of the function F(X, Y). This quantity is selected from: the relative quantity of a CD4+ T cell subset associated with dendritic cell stimulation in an antitumor immune response; the relative quantity of a dendritic cell subset associated with dendritic cells stimulated by CD4+ T cells in an antitumor immune response; the relative quantity of a CD8+ T cell subset associated with dendritic cell stimulation in an antitumor immune response; and the relative quantity of regulatory T cells or a CD4+ T cell subset associated with regulatory T cells. Wherein, a value above the threshold (ineffectiveness threshold) in the function F(X, Y) indicates that the individual is not part of the ineffectiveness group for cancer immunotherapy. In one embodiment, the function F(X, Y) can be calculated using variables (indicators) of a function that predict an individual's response to cancer immunotherapy. These variables are values ​​selected as (X) from the following subsets: the ratio of CD62LlowCD4+ T cells in CD4+ T cells; the ratio of LAG-3+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells; the ratio of CCR7-CD4+ T cells in CD4+ T cells; the ratio of CD45RA-CD4+ T cells in CD4+ T cells; the ratio of CD45RO+CD4+ T cells in CD4+ T cells; the ratio of HLA-DR+ dendritic cells in dendritic cells; the ratio of CD80+ dendritic cells in dendritic cells; the ratio of CD86+ dendritic cells in dendritic cells; and the ratio of PD-L1+ dendritic cells in dendritic cells. The ratios of CD62LlowCD8+ T cells in CD8+ T cells; the ratios of CD137+CD8+ T cells in CD8+ T cells; and the ratios of CD28+CD62LlowCD8+ T cells in CD62LlowCD8+ T cells. In one embodiment, the function F(X, Y) can be calculated using variables (indicators) that predict an individual's response to cancer immunotherapy. These variables are values ​​of (Y) selected from the following subsets: the ratio of CD4+Foxp3+CD25+ T cells in CD4+ T cells; the ratio of CD62LhighCD25+CD4+ T cells in CD4+ T cells; the ratio of CD45RA-Foxp3+CD4+ T cells in CD4+ T cells; the ratio of CCR4+CD25+CD4+ T cells in CD4+ T cells; and the ratio of CD127+CD25+CD4+ T cells in CD4+ T cells. A value above the threshold (ineffectiveness threshold) indicates that the individual is not part of the ineffectiveness group for cancer immunotherapy.Another embodiment of the present invention is a method for predicting an individual's response to cancer immunotherapy, wherein a function F(X, Y) above the threshold (ineffectiveness threshold) indicates that the individual is not part of the ineffectiveness group for cancer immunotherapy. The function F(X, Y) can be calculated using variables (indicators) that predict an individual's response to cancer immunotherapy. These variables are values ​​for (X) selected from the following subsets: the amount of CD62LlowCD4+ T cells; the amount of LAG-3+CD62LlowCD4+ T cells; the amount of CCR7-CD4+ T cells; the amount of CD45RA-CD4+ T cells; the amount of CD80+ dendritic cells; the amount of CD62LlowCD8+ T cells; the amount of CD137+CD8+ T cells; and the amount of CD28+CD62LlowCD8+ T cells. The variable F(Y) is a subset selected from the following subset: the amount of CD4+Foxp3+CD25+ T cells. The amounts of CD62LhighCD25+CD4+ T cell subsets; and the amounts of CD45RA-Foxp3+CD4+ T cell subsets. Another embodiment of the invention is a method for predicting an individual's response to cancer immunotherapy, wherein a function F(X, Y) above a threshold value (ineffectiveness threshold value) indicates that the individual is not part of the ineffectiveness group for cancer immunotherapy. The function F(X, Y) can be used to predict an individual's response to cancer immunotherapy by calculating the following variables (indicators), which are values ​​of (X) selected from the following groups: the ratio of CD62LlowCD4+ T cells in CD4+ T cells; the ratio of LAG-3+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells; the ratio of CCR7-CD4+ T cells in CD4+ T cells; the ratio of CD45RA-CD4+ T cells in CD4+ T cells; the ratio of CD80+ dendritic cells in dendritic cells; the ratio of CD62LlowCD8+ T cells in CD8+ T cells; the ratio of CD137+CD8+ T cells in CD8+ T cells; and the ratio of CD28+CD62LlowCD8+ T cells in CD62LlowCD8+ T cells. And the ratio of CD4+Foxp3+CD25+ T cell subsets in CD4+ T cells as (Y); the ratio of CD62LhighCD25+CD4+ T cell subsets in CD4+ T cells; and the ratio of CD45RA-Foxp3+CD4+ T cell subsets in CD4+ T cells. Any function of X and Y that monotonically increases relative to X and monotonically decreases relative to Y (F(X, Y)) can be used as the above function F(X, Y). Examples of functions F(X, Y) indicating a response include F =F(X, Y) is expressed as Xr × Ys, where r and s are any real numbers. Preferably, r is positive and s is negative when X is positively correlated with the response and Y is negatively correlated with the response. For simplicity, integers can be used for r and s. For example, F(X, Y) can be represented as Xn × Ym, where n and m are any integers. The inventors' research has demonstrated that functions with r and s in the range of -3 to 3 can accurately predict an individual's response to cancer immunotherapy. Examples of preferred forms of the function include (but are not limited to): X / Y, X² / Y, X × Y, and the like. One embodiment of the present invention is a method for using a variable (indicator) selected from the following within an individual who has been determined not to be part of an invalid population: the amount of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; the amount of ICOS+CD62LlowCD4+ T cell subsets; the amount of LAG-3+CD62LlowCD4+ T cell subsets; and the amount of PD-1+CD62LlowCD4+ T cell subsets; as a function to predict the individual's response to cancer immunotherapy, wherein an indicator function above a threshold (response population threshold) indicates that the individual is part of a response population to cancer immunotherapy. In one embodiment, the variables (indicators) used to predict an individual's response to cancer immunotherapy are selected from the following groups: the amount of ICOS+CD62LlowCD4+ T cell subsets; the amount of CD4+CD25+ T cell subsets; the amount of CD4+Foxp3+ T cell subsets; the amount of CD4+Foxp3+CD25+ T cell subsets; the amount of CD62LhighCD25+CD4+ T cell subsets; the amount of CD45RA-Foxp3+CD4+ T cell subsets; the amount of CCR4+CD25+CD4+ T cell subsets; the amount of CD127+CD25+CD4+ T cell subsets; the amount of LAG-3+CD62LlowCD4+ T cell subsets; and the amount of PD-1+CD62LlowCD4+ T cell subsets. One embodiment of the present invention is a method for using a relative quantity selected from the following within an individual who has been determined not to be part of an invalid population: a relative quantity of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; a relative quantity of ICOS+CD62LlowCD4+ T cell subsets; a relative quantity of LAG-3+CD62LlowCD4+ T cell subsets; and a relative quantity of PD-1+CD62LlowCD4+ T cell subsets; as a variable (indicator) for predicting the individual's response to cancer immunotherapy, wherein an indicator function above a threshold (response population threshold) indicates that the individual is part of a response population to cancer immunotherapy. In one embodiment, the variable (indicator) for predicting the individual's response to cancer immunotherapy is selected from the following group:The percentages of ICOS+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells; the percentages of CD4+CD25+ T cells in CD4+ T cells; the percentages of CD4+Foxp3+ T cells in CD4+ T cells; the percentages of CD4+Foxp3+CD25+ T cells in CD4+ T cells; the percentages of CD62LhighCD25+CD4+ T cells in CD4+ T cells; the percentages of CD45RA-Foxp3+CD4+ T cells in CD4+ T cells; the percentages of CCR4+CD25+CD4+ T cells in CD4+ T cells; and the percentages of CD127+CD25+CD4+ T cells in CD4+ T cells. The ratio of LAG-3+CD62LlowCD4+ T cell subsets in CD4+ T cells; and the ratio of PD-1+CD62LlowCD4+ T cell subsets in CD4+ T cells. One embodiment of the invention is a method for using a quantity (W, Z) selected from the following in an individual determined not to be part of an invalid population: the quantity of regulatory T cells or CD4+ T cell subsets associated with regulatory T cells; and the quantity of ICOS+CD62LlowCD4+ T cell subsets; as a variable (indicator) for predicting the individual's response to cancer immunotherapy, wherein a value above the threshold (response population threshold) indicates that the individual is part of a response population to cancer immunotherapy. In one embodiment, the function J(W, Z) can be calculated using variables (indicators) that predict an individual's response to cancer immunotherapy. These variables are (Z) the quantity of the ICOS+CD62LlowCD4+ T cell subset, and (W) a value selected from the following subsets: CD4+CD25+ T cell subset; CD4+Foxp3+ T cell subset; CD4+Foxp3+CD25+ T cell subset; CD62LhighCD25+CD4+ T cell subset; CD45RA-Foxp3+CD4+ T cell subset; CCR4+CD25+CD4+ T cell subset; and CD127+CD25+CD4+ T cell subset. A value above the threshold (response threshold) for J(W, Z) indicates that the individual's response to cancer immunotherapy is part of a response population. One embodiment of the present invention is the use of an amount (W, Z) selected from the following in an individual determined not to be part of an invalid population: a relative amount of regulatory T cells or a subset of CD4+ T cells associated with regulatory T cells; and a relative amount of an ICOS+CD62LlowCD4+ T cell subset; as a function J(W, Z) for predicting the individual's response to cancer immunotherapy.A method for calculating the variable (indicator) J(W, Z), wherein a value above the threshold (response threshold) indicates that the individual is part of a response population to cancer immunotherapy. In one embodiment, the function J(W, Z) can be used to calculate the variable (indicator) of an individual's response to cancer immunotherapy, which is the ratio of the ICOS+CD62LlowCD4+ T cell subset in CD62LlowCD4+ T cells as (Z), and the value of (W) selected from the following components: the ratio of the CD4+CD25+ T cell subset in CD4+ T cells; the ratio of the CD4+Foxp3+ T cell subset in CD4+ T cells; the ratio of the CD4+Foxp3+CD25+ T cell subset in CD4+ T cells; the ratio of the CD62LhighCD25+CD4+ T cell subset in CD4+ T cells; and the ratio of the CD45RA-Foxp3+CD4+ T cell subset in CD4+ T cells. The ratio of the CCR4+CD25+CD4+ T cell subset within CD4+ T cells; and the ratio of the CD127+CD25+CD4+ T cell subset within CD4+ T cells. A function J(W, Z) above the threshold (response threshold) indicates that the individual is part of the response cohort to cancer immunotherapy. Another embodiment of the invention is a method for predicting the response to cancer immunotherapy in individuals determined not to be part of the invalid population, wherein a function J(W, Z) above the threshold (response threshold) indicates that the individual is part of the response cohort to cancer immunotherapy. The function J(W, Z) can be calculated using variables (indicators) to predict an individual's response to cancer immunotherapy. These variables are (Z) the quantity of the ICOS+CD62LlowCD4+ T cell subset, and (W) a value selected from the following subsets: the quantity of the CD4+CD25+ T cell subset; the quantity of the CD4+Foxp3+ T cell subset; the quantity of the CD4+Foxp3+CD25+ T cell subset; the quantity of the CD62LhighCD25+CD4+ T cell subset; and the quantity of the CD45RA-Foxp3+CD4+ T cell subset. Another embodiment of the invention is a method for predicting the response to cancer immunotherapy in individuals who are determined not to be part of the ineffective population, wherein a value above the threshold (response population threshold) indicates that the individual is part of the response population to cancer immunotherapy. The function J(W, Z) can be calculated using variables (indicators) that predict an individual's response to cancer immunotherapy. These variables are (Z) the ratio of the ICOS+CD62LlowCD4+ T cell subset among CD62LlowCD4+ T cells; and (W) the value of the subset selected from the following components: the ratio of the CD4+CD25+ T cell subset among CD4+ T cells.The ratios of CD4+Foxp3+ T cell subsets in CD4+ T cells; the ratios of CD4+Foxp3+CD25+ T cell subsets in CD4+ T cells; the ratios of CD62LhighCD25+CD4+ T cell subsets in CD4+ T cells; and the ratios of CD45RA-Foxp3+CD4+ T cell subsets in CD4+ T cells. Any function of Z and W that increases monotonically with respect to Z and W (J(Z, W)) can be used as the above function J(Z, W). Examples of functions indicating a response J(Z, W) include J = Zr × Ws, where r and s are any real numbers. When Z is positively correlated with the response and W is negatively correlated with the response, r is preferably positive and s is negative. To simplify the function, integers can be used for r and s. For example, J(Z, W) can be expressed as Zn × Wm, where n and m are any integers. The inventors' research has demonstrated that functions with r and s in the range of -5 to 6 can be used to accurately predict an individual's response to cancer immunotherapy. Examples of preferred forms of the functions include (but are not limited to): Z / W, Z2 / W, Z × W, Z × W5, and the like. The methods disclosed herein (including those disclosed above) can be used to apply cancer immunotherapy to individuals indicated to be not part of the ineffective group and / or part of the reactive group relative to cancer immunotherapy. Any cancer immunotherapy disclosed herein can be used. One embodiment provides a composition for treating cancer in an individual indicated to be not part of the ineffective group and / or part of the reactive group relative to cancer immunotherapy by using the methods disclosed herein (including those disclosed above). The composition may contain any of the active ingredients disclosed herein and have any of the components disclosed herein. (General Techniques) The molecular biological, biochemical, and microbiological methods used in this paper are well-known, and conventional methods in this field are disclosed, for example, in Sambrook J. et al. (1989). Molecular Cloning: A Laboratory Manual, Cold Spring Harbor and its 3rd edition (2001); Ausubel, FM (1987). Current Protocols in Molecular Biology, Greene pub. Associates and Wiley-Interscience; Ausubel, FM (1989). Short Protocols in Molecular Biology: A Compendium of Methods from Current Protocols in Molecular Biology,Greene Pub. Associates and Wiley-Interscience; Innis, MA (1990). PCR Protocols: A Guide to Methods and Applications, Academic Publishing; Ausubel, FM (1992). Short Protocols in Molecular Biology: A Compendium of Methods from Current Protocols in Molecular Biology, Greene Pub. Associates; Ausubel, FM (1995). Methods from Current Protocols in Molecular Biology, Greene Pub. Associates; Innis, MA et al. (1995). PCR Strategies, Academic Publishing; Ausubel, FM (1999). Short Protocols in Molecular Biology: A Compendium of Methods from Current Protocols in Molecular Biology, Wiley, and manual update; Sninsky, JJ et al. (1999). PCR Applications: Protocols for Functional Genomics, Academic Published by Bessatsu Jikken Igaku [Experimental Medicine, Supplemental Volume], Idenshi Donyu Oyobi Hatsugen Kaiseki Jikken Ho [Experimental Methods for Transgenesis & Expression Analysis], Yodosha, 1997, and similar sources. Relevant portions (which may be all) of the above documents are incorporated herein by reference.As used herein, "or" is used when "at least one or more" of the things listed in the sentence may be used. When explicitly described herein as a "range" of "two values," that range also includes the two values ​​themselves. References such as scientific literature, patents, and patent applications cited herein are incorporated herein by reference, and the entire contents of each document are described specifically to the same degree. As disclosed above, the invention is disclosed while showing preferred embodiments to aid understanding. The invention is disclosed below based on examples. The above description and the following examples are not intended to limit the invention, but are for illustrative purposes only. Therefore, the scope of the invention is not limited to the embodiments and examples specifically described herein, but is limited only to the scope of the claims. [Examples] The invention is illustrated by the following examples herein. (1) [Examples demonstrating the realization of the labeling invention] Example 1: Therapeutic effect of anti-PD-1 antibody and T cell population composition This example shows that analysis of CD62LlowCD4+ T cells and Foxp3+CD25+CD4+ T cells using peripheral blood can predict the therapeutic effect of therapy using anti-PD-1 antibodies. (2) [Examples demonstrating the realization of the cell infusion invention] Example 2: Hypothesis example of CD62Llow cell infusion Example 3: Follow-up observation Example shows that the anti-tumor immune response of patients is related to the T cell composition, that is, the increase of CD62Llow cells enhances the anti-tumor immune response of patients undergoing anti-PD-1 therapy to reduce tumors. Example 4: Cell infusion into mice Example shows that the percentage of CD62LlowCD4+ T cells is increased by infusing CD62LlowCD4+ T cells into mice. The increase in the percentage of CD62LlowCD4+ T cells and Tregs also enhances the anti-tumor immune response in mice. Example 5: Isolation / Expansion of CD62Llow Cells. This example demonstrates the successful isolation and expansion of CD62Llow cells. (Example 1: Therapeutic Efficacy of Anti-PD-1 Antibody and T Cell Population Combination) 1-1. Objective The objective of this example is to demonstrate that analysis of CD62LlowCD4+ T cells and Foxp3+CD25+CD4+ T cells using peripheral blood can predict the therapeutic efficacy of anti-PD-1 antibody therapy. The relationship between the therapeutic efficacy of anti-PD-1 antibody and T cell population combination will be investigated. 1-2. Materials and Methods The efficacy of nivolumab therapy in patients with non-small cell lung cancer was studied according to the methods shown in Figure 2. Peripheral blood was collected from non-small cell lung cancer patients who self-treated the day before nivolumab therapy. CT scans were performed to assess the effect from week 8 of nivolumab therapy initiation. Partial response (PR), stable disease (SD), and progressive disease (PD) were assessed at this time. The assessment criteria were based on RECIST version 1.1. Table 1 below shows the patient characteristics. [Table 1] Table 1. Patient characteristics (n = 44) age Median 67 scope 51-84 Gender - Number (%) male 30 (68) female 14 (32) Histological diagnosis - number (%) scaly 12 (27) Non-scaly 32 (73) Smoking history - number (%) Current or former smokers 33 (75) No history of smoking 11 (25) Pathology semesters - number (%) Phase III clinical trials 9 (20) Clinical Phase IV 26 (59) Recurrence after surgery 9 (20) EGFR status - number (%) wild type 37 (84) Mutation (exon 19 deletion or L858R) 7 (16) The following is a composition of the peripheral blood T cell population of the individual analyzed. (1) Blood collection: Collect 8 ml of blood in a blood collection tube for monocyte separation (product name: BD Vacutainer® CPTTM, BD Japan), gently invert and mix at room temperature. (2) Centrifugation (monocyte separation by specific gravity centrifugation): After blood collection, centrifuge BD Vacutainer® CPTTM at 1500 to 1800 × g for 15 minutes (centrifuge name / manufacturer: Kubota). (3) Collection: Aspirate approximately half of the plasma layer to avoid disturbing the cell layer above the gel barrier. Collect the cell layer above the gel barrier using a Pasteur pipette and transfer it to a 50 ml tube (Falcon tube or similar). Add phosphate-buffered saline (10% FBS PBS) supplemented with 10% fetal bovine serum to make a mixture of 30 ml or more. Centrifuge the mixture (4°C, 400 to 450 g × 5 min) and wash twice. (4) Cell counting: After the first wash / centrifugation, add 10 ml of PBS supplemented with 10% FBS (inactive within 30 min at 56°C) to the resuspended cells. Collect 50 μl of cell suspension from the centrifuge tube. Stir 50 μl of 0.1% trypan blue solution and cell suspension. Place the cells in a modified Neubauer hemocytometer for cell counting. (5) Freezing: After the second wash / centrifugation, use CELLBANKER™ 2 (Takara Bio). Resuspend the cells at 5 × 10⁵ to 5 × 10⁶ / ml and transfer them to a 2.0 ml low-temperature flask (Corning). Immediately after treatment, freeze the cells in a Panasonic cryostat at -80°C. Transfer the cells to liquid nitrogen (in liquid phase) within 24 hours and one week after the above treatment. (6) Cultures: Frozen cells were adjusted to 1 to 5 × 10⁵ / ml in RPMI 1640 medium (10% FBS) and cultured at 37°C for 24 to 36 hours in T-25 cell culture flasks with 5% CO₂. (7) Cell Adjustment: Cell cultures were collected in 15 ml centrifuge tubes and centrifuged at 1500 rpm for 10 minutes to allow cells to aggregate at the bottom of the tube. After centrifugation, the supernatant was removed. 10 ml of FACS buffer was added to the cell aggregates to resuspend the cells using a pipette. The cells were centrifuged again at 1500 rpm for 10 minutes, and the supernatant was then aspirated. The cells were counted and adjusted to a final concentration of 1.0 × 10⁶ cells / ml. FACS buffer: 2% FBS, PBS containing 0.05% azide compound.(8) Antibody Reaction: Place a suspension of peripheral blood mononuclear cells (5 × 10⁵ cells per tube) in 0.5 ml of each FACS tube. Centrifuge the tubes at 1500 rpm for 5 minutes. Retain the cell aggregates and aspirate and remove only the supernatant layer. *Tube 1: 20 μl FITC-labeled anti-human CD4 antibody (25 μg / ml) 20 μl PE-labeled anti-human CD62L antibody (5 μg / ml) 20 μl PE-Cy5-labeled anti-human CD8 antibody (5 μg / ml) Stir and mix the antibody solution and cell suspension. Maintain the tubes at 4°C. After 30 minutes, add 1 ml of FACS buffer to each tube using a Komagome pipette, and centrifuge the mixture at 1500 rpm for 5 minutes. Aspirate and remove the supernatant layer. Add 0.5 ml of 1% paraformaldehyde to each tube, and aspirate the supernatant from the tube, leaving only the cell aggregates to suspend the cells. *Tuber 2: 20 μl FITC-labeled anti-human CD4 antibody (25 μg / ml) 20 μl PE-Cy5-labeled anti-human CD25 antibody (5 μg / ml) Stir and mix the antibody solution and cell suspension. Maintain the tubes at 4°C. After 30 minutes, add 1 ml of FACS buffer to each tube, and centrifuge the mixture at 1500 rpm for 5 minutes. Aspirate and remove the supernatant. Intracellular Fixation and Permeabilization buffer set™ (eBioscience) is used for cytoplasmic staining with 3 μl PE-labeled anti-human FOXP3 antibody (500 μg / ml). Add 0.5 ml of 1% paraformaldehyde to each test tube, and aspirate the supernatant from the test tube, leaving only the cell aggregates to suspend the cells. (9) Analyze the fluorescence of test tubes 1 and 2 using flow cytometry (product name: FACS Calibur™; BD Japan). Incorporate analysis data for 30,000 cells. Step 1: Analyze test tube 1 using FSC or SSC to identify lymphocyte regions using two-dimensional analysis. Further gate the cells in the lymphocyte region relative to the CD4+ fraction to obtain a histogram of CD62L (cell count in the blue region). Step 2: Analyze test tube 2 using Foxp3 and CD25 gated in both the lymphocyte and CD4+ regions to obtain two-dimensional analysis data. (Cell count in the orange area) Step 3 Calculate the ratio function of CD62LlowCD4+ / Foxp3+CD25+CD4+. Cell count in Step 1 / Cell count in Step 2. Figure 1 shows an example of cell fractionation results in flow cytometry.Note that mRNA is measured using a microarray between CD62Llow and CD62Lhigh. Although this invention uses flow cytometry to grade cells, other separation methods may also be used. (Judgment) If below a predetermined value, the drug is predicted to be ineffective for progressive disease. If above a predetermined value, proceed to step 4. Step 4: Cell count in the orange area of ​​the function / Cell counts of R1 and R2 in step 1 × 100 (%) (Judgment) If below a predetermined value, stable disease (SD) is predicted. If above a predetermined value, partial response (PR) is predicted. Statistical analysis was performed on the relationship between the obtained T cell population composition and the observed treatment effect. 1-3. Results Table 2 below shows the observed treatment effect on patients. [Table 2] Table 2. Response to nivolumab. Objective responses over 8 weeks - number (%) Complete or partial reaction 11 (25) Stablize 19 (43) Progressive 14 (32) Confirmed complete and partial responses were assessed by test subjects according to the response assessment criteria for solid tumors (version 1.1). The rate of treatment efficacy observed in this example is approximately the same as the response rate obtained in the Phase III clinical trial known as checkmate 017. Therefore, it should be understood that there is no bias in response to nivolumab. As shown in Figure 3, there were no significant differences in peripheral blood white blood cell count, lymphocyte count, CD4+ cell percentage, or CD8+ cell percentage between the PR+ SD and PD groups. The individual population in this example does not include the complete response (CR) group. If a CR group is present, it will be identified as part of the PR group of this invention. The results show that the percentage of CD62low cells among CD8 cells is significantly lower in the PD group (Figure 4A). However, the percentages of the PR+ SD and PD groups overlap extensively, with P = 0.0138 in the important trial. Conversely, the percentage of CD62low cells among CD4+ cells is completely different between the PR+ SD and PD groups, with almost no overlap (Figure 4B). Meanwhile, the results showed that the percentage of CD25+Foxp3+ cells (which are regulatory T cells among CD4+ cells) was significantly higher in the PD population (Figure 4C). The CD25+Foxp3+CD4+ cell fractionation can be considered a consensus in the field of regulatory T cell fractionation. Furthermore, Figures D and E of Figure 4 show the results of the analysis of correlations among the three T cell subsets and the differences between the PD population and the PR+SD population. A strong correlation was found between the percentage of CD62LlowCD8+ and the percentage of CD62LlowCD4+ (Figure 4D). Due to biological significance, it is suggested that CD8+ effector counts are regulated by CD4+ effectors. This demonstrates that it is preferable to use only one of them as a biomarker. It has been shown that using the percentage of CD62LlowCD4+ with a very small p-value as a biomarker for effectors is very useful in predicting the therapeutic effect of immune checkpoint inhibitors. In addition, no correlation was found between regulatory T cells and the percentage of CD62LlowCD4+. This indicates that individual cell counts are regulated by different mechanisms. It should be understood that the accuracy of predicting treatment efficacy can be improved by using both as biomarkers in combination. Figures 5 through 12 show the results of further examination of the parameters that can be used as biomarkers. Even by using only the percentage of CD62LlowCD4+, which has a large difference, excellent results of 92.6% sensitivity and 96.7% specificity were obtained with a threshold of 19.4% (Figure 5). Sensitivity and specificity for various thresholds are shown in Figure 6. The accuracy of predictions using the relative number of regulator T cells and the percentage of CD62LlowCD4+ was examined.Figure 7 shows the results of the ratio (X / Y) of the two factors shifting differently in the numerator and denominator in the PD population when the percentage of CD62LlowCD4+ is used as X and the percentage of CD25+Foxp3+CD4+ is used as Y. It should be understood that this indicator can clearly distinguish patients in which the increase in regulatory T cells is so significant that no further antitumor effect is observed. Figure 8 shows the sensitivity and specificity for various threshold values. It should be understood that a threshold value of 7.35 yields a marker with 100% specificity and 71.4% sensitivity. For functions using this combination of factors, logistic regression was used to detect suitable functions from the results of a sample N = 40, taking into account the weights of these factors regarding their impact on treatment efficacy. The logistic regression model was used to find coefficients that led to the function X².475 / Y (Figure 27). It should be understood that by using functions with coefficients in their vicinity (X² - 3 / Y), the response can be accurately predicted. For example, it should be understood that functions such as X2 / Y and X3 / Y can be used. Specifically, Figures 9 and 10 show the results of squaring the percentage of CD62LlowCD4+ and, in particular, using X2 / Y as the relative value of X and Y. It should be understood that this can be used as an excellent biomarker with 100% sensitivity and specificity. Figure 10 shows the sensitivity and specificity for various threshold values. It should be understood that when using this value with a threshold of 174.3, this can be used as an excellent biomarker with 100% sensitivity and specificity. Figure 11 shows the results of detecting biomarkers that can predict PR and SD after determining the PD population. Unexpectedly, there was no difference in the percentage of CD62LlowCD4+, but there was a difference in the percentage of CD25+Foxp3+CD4+ cells. Since CD25+Foxp3+CD4+ cells are Tregs with immunosuppressive activity, the higher percentage of CD25+Foxp3+CD4+ cells in the PR group, which exhibits a stronger anti-tumor immune response, is an unexpected result. PR and SD were identified with a sensitivity of 52.8% and a specificity of 100% at a threshold of 2.05% for the percentage of CD25+Foxp3+CD4+ cells (Figure 11). Figure 12 illustrates the sensitivity and specificity at various thresholds. While not wishing to be bound by any theory, the mechanism for predicting the clinical efficacy of the cancer immunotherapy of this invention can be understood as follows. *It should be understood that CD4+ T cells transmit instructions to dendritic cells via MHC class I molecules, and the dendritic cells receiving these instructions stimulate CD8+ T cells via MHC class II molecules. These CD4+ T cells encompass effector T cells (e.g., CD62LlowCD4+ T cells) and regulatory T cells (e.g., Foxp3+CD25+ T cells).Simultaneously, this invention predicts the clinical efficacy of cancer immunotherapy by assessing the balance between CD62LlowCD4+ T cells and Foxp3+CD25+ T cells. CD62L (L-selectin) is a molecule used to recognize and specifically hom to high endothelial venules (HEVs) in lymphoid organs. Since native T cells are activated by effector T cells upon stimulation by antigen-presenting cells, CD62L expression is reduced, and homing is no longer performed by effector T cells. Examples of markers for effector T cells that activate native T cells include CCR7, such as CD62L. Due to activation, the expression level of CCR7 is reduced. Therefore, CCR7 can be used instead of CD62Llow. For example, CCR7lowCD4+ T cells and / or CCR7-CD4+ T cells can be used instead of (or otherwise) CD62LlowCD4+ T cells. Examples of cell subsets that can be used as indicators of effector T cells include (but are not limited to) subsets selected from the following groups: CD62LlowCD4+ T cell subset, CCR7-CD4+ T cell subset, LAG-3+CD62LlowCD4+ T cell subset, ICOS+CD62LlowCD4+ T cell subset, CD45RA-CD4+ T cell subset, CD45RO+CD4+ T cell subset, HLA-DR+ dendritic cell subset, CD80+ dendritic cell subset, CD86+ dendritic cell subset, PD-L1+ dendritic cell subset, CD62LlowCD8+ T cell subset, and CD137+CD8+ T cell subset. The quantity (absolute quantity) and / or ratio (relative quantity) of these cell subsets can be used as indicators of effector T cells. Examples of cell subsets that can be used as indicators of regulatory T cells include (but are not limited to) cell subsets selected from the following composition: the amount of CCR4+CD25+CD4+ T cells; the amount of CD62LhighCD25+CD4+ T cells; the amount of CD127+CD25+CD4+ T cells; the amount of CD45RA-Foxp3+CD4+ T cells; the amount of CD4+Foxp3+CD25+ T cells; and the amount of ICOS+CD62LlowCD4+ T cells. The amount (absolute quantity) and / or ratio (relative quantity) of these cell subsets can be used as indicators of regulatory T cells. (Example 2: Cell therapy for improving or maintaining the therapeutic effect of cancer immunotherapy) Before starting cancer immunotherapy, CD62LlowCD4+ T cells are isolated from and stored in a peripheral blood sample from an individual. In vitro expansion of isolated CD62LlowCD4+ T cells (“In vitro expansion” in Figure 13). Isolated CD62LlowCD4+ T cells can be cryopreserved and stored.When an individual is determined not to be part of the nullipopulation by the steps shown in Example 1 or similar, and the ratio of CD62LlowCD4+ T cells to CD25+Foxp3+CD4+ T cells is high, cancer immunotherapy, such as therapy using anti-PD-1 antibodies (e.g., nivolumab), is applied, as shown in the top appendix of Figure 13. During treatment, the individual's CD4+ T cell composition is monitored using the methods disclosed in Example 1. In this regard, when indicators (such as the percentage of CD62LlowCD4+ in the individual's CD4+ T cell composition to the percentage of CD25+Foxp3+CD4+ cells) decrease to the nullipopulation immunopathology, in vitro expanded CD62LlowCD4+ T cells can be infused to restore the initial immunopathology and sustain the effect of cancer immunotherapy. Storage / culture costs can be minimized by culturing and infusing only CD62LlowCD4+ T cells. This is more economical than continuing only immune checkpoint inhibitors such as anti-PD-1 antibodies every two weeks. Individuals with low markers (such as the percentage of CD62LlowCD4+ cells / CD25+Foxp3+CD4+ cells in their CD4+ T-cell composition) and deemed to be part of the nullipopulation (e.g., a lower ratio of CD62LlowCD4+ T cells / CD25+Foxp3+CD4+ T cells in the bottom panel of Figure 13) were infused with CD62LlowCD4+ T cells (isolated from the individual and expanded in vitro) to change their immunopathological condition into a reactive population, followed by cancer immunotherapy with an anti-PD-1 antibody such as nivolumab. This can induce an antitumor immune response to cancer immunotherapy, even in individuals who have not yet benefited from it. (Example 3: Follow-up) Seven patients were followed up to observe the percentage of CD62LlowCD4+ T cells. Peripheral blood monocytes were analyzed every four weeks. The results are shown in Table 3 below. Each of 1 to 7 represents the outcome for a different patient. [Table 3] Table 3. CD4 T cells % CD62Llow CD4 1 29.6 23.6 32.5 14.9 2 60.5 25.8 54.4 39.9 3 40.4 43.7 43.3 39.8 44.8 39.7 41.2 34.4 4 30.3 25 33.3 28.4 31.9 24.3 26.4 28.5 5 39 30.2 34.5 39.3 6 36 24.6 33.8 32.6 7 34.3 24.6 24.7 30.2 CD4 T cells % CD62Llow CD4 1 29.6 23.6 32.5 14.9 2 60.5 25.8 54.4 39.9 3 40.4 43.7 43.3 39.8 44.8 39.7 41.2 34.4 4 30.3 25 33.3 28.4 31.9 24.3 26.4 28.5 5 39 30.2 34.5 39.3 6 36 24.6 33.8 32.6 7 34.3 24.6 24.7 30.2 In the early stages of nivolumab therapy, tumor regression was observed in patient 1, but swelling in the cervical lymph nodes persisted for a period. Despite suspected progressive disease (PD), the swelling decreased on CT scans at 8 weeks, leading to a partial response (PR). The percentage of CD62LlowCD4+ T cells decreased when tumor size increased. The percentage of CD62LlowCD4+ T cells increased again when the tumor regressed. All other individuals maintained a high percentage of CD62LlowCD4+ T cells prior to treatment, resulting in a PR or stable disease (SD). The results in Case 1 should be understood to indicate that individuals will not respond when the percentage of CD62LlowCD4+ is less than 19.4%, but will respond when the percentage of CD62LlowCD4+ T cells recovers. (Example 4: Cell infusion in mice) Tumor model mice were infused with cells containing a combination of 2 × 10⁶ CD62LlowCD4+ / 5 × 10⁶ CD62LlowCD8+ (Fig. 14A "●"), 5 × 10⁶ CD62LlowCD8+ (Fig. 14A "Δ"), and 1 × 10⁶ CD62LlowCD4+ (Fig. 14B "●"). Tumor size was then observed to increase over time. The ratio of (CD62LlowCD4+ cells among CD62LlowCD8+ cells (Fig. 14A "●") or 5 × 106 CD62LlowCD8+ cells (Fig. 14A "Δ")) in the spleen was measured on day 13 post-tumor infusion in both infused and cell-free infusion cohorts (Fig. 14A "○"). The ratio of (CD62Llow cells among CD4+ T cells) to (CD62LhighCD25+ cells among CD4+ T cells) in the spleen was also measured over time in a cohort infused with 1 × 106 CD62LlowCD4+ cells (Fig. 14B "●"). Analysis of peripheral blood T cells in mice is challenging. As an alternative example, standard spleen cell analysis was used. T cell analysis of mouse spleen was considered equivalent to human PBMCs. The CD4+CD62LhighCD25+ T cell fraction included regulatory T cells (Tregs). Figure 14 shows the results.In the analysis of the T cell composition on day 13, the ratio of (CD62Llow cells among CD4+ T cells) to (CD62LhighCD25+ cells among CD4+ T cells) was 10.6 in the cohort infused with 2 × 10⁶ CD62LlowCD4+ / 5 × 10⁶ CD62LlowCD8+ cells, 2.94 in the cohort infused with 5 × 10⁶ CD62LlowCD8+ cells, and 2.70 in the cohort without cell infusion. It should be understood that infusion of CD62LlowCD4+ cells increases the percentage of CD62LlowCD4+ cells in the T cell composition. Furthermore, significant tumor regression was observed in a population with a high ratio of (CD62LlowCD4+ cells among CD4+ T cells) to (CD62LhighCD25+ cells among CD4+ T cells) infused with 2 × 10⁶ CD62LlowCD4+ cells and 5 × 10⁶ CD62LlowCD8+ cells (Fig. 14A "●"). These results demonstrate that the antitumor effect is achieved through the infusion of CD62LlowCD4+ cells and through the infusion of a mixture of CD62LlowCD4+ and CD62LlowCD8+ cells. It was observed that in a population infused with 1 × 10⁶ CD62LlowCD4+ cells, the ratio of CD62Llow cells to CD62LhighCD25+ cells among CD4+ T cells was high, attributed to cell infusion during the phase where tumor regression had ceased (3.70 → 9.09). However, when the tumor re-enlarged, the ratio of (CD62Llow cells among CD4+ T cells) to (CD62LhighCD25+ cells among CD4+ T cells) decreased (4.55). This result demonstrates that the tumor regression effect is achieved by CD62Llow cells in the CD4+ T cell population, rather than CD62L high-expressing cells (such as CD62LhighCD25+ cells), and that it is preferable to remove CD62L high-expressing cells from cells containing the composition that achieves the anti-tumor effect. (Example 5: Isolation and expansion of CD62Llow cells) CD62L staining patterns were observed for different races and mice. Figure 15 shows the results. Figure A shows the FACS of lymph nodes drained using a tumor vaccine from a Caucasian individual. Simultaneous gated lymphocyte regions of CD62L were observed. Figure C shows a similar observation of CD62L in monocytes derived from peripheral blood of a Japanese individual. Figure D shows the CD62L staining pattern in mouse lymphocytes. It should be understood that similar staining patterns are observed in human races / biological species. This exhibits a bimodal distribution with a fluorescence intensity of 102 as the boundary. Figure B shows the purity of the FACS after isolating only CD62Llow cells from the cell population of the individual in Figure A using magnetic beads.Cell populations with fluorescence intensities exceeding 10² can be almost completely removed. After cell isolation, virtual TCR stimulation is applied, and cells are cultured at low concentrations of IL-2. It is possible to expand cell counts by 1000-fold or more. (Example 6: Using markers expressed on dendritic cells) 6-1. Objective To study the relationship between the therapeutic effect of anti-PD-1 antibodies and markers expressed on dendritic cells. To detect whether markers expressed on dendritic cells can be used to predict the clinical efficacy of the cancer immunotherapy of this invention. 6-2. Materials and Methods The materials and methods are the same as in Example 1. The antibody shown in Figure 23 was used to detect HLA-DR and CD80 / CD86 expressed on dendritic cells. The determination method is the same as in Example 1. 6-3. Results Figure 16 shows the results. The ratios of HLA-DR+ cells and CD80 cells in bone marrow dendritic cells (mDC, CD141+CD11c+ dendritic cells) and plasmacytoid dendritic cells (pDC, CD123+CD11c+ dendritic cells) are excellent indicators for identifying PD, SD, and PR. When using HLA-DR, CD80 in pDC, HLA-DR in mDC, and CD80 in mDC to determine PD and PR+SD, the p-values ​​were 0.0008735, 0.002689351, 6.87852×10-6, and 0.003033095, respectively, which are considered excellent values. As shown in Figure 17, the results of these markers in mDC are correlated with the ratio of CD62LlowCD4+ T cells. Based on the above results, the number / ratio of cells expressing HLA-DR and / or CD80 and / or CD86 in the bone marrow dendritic cell (mDC) and / or plasmacytoid dendritic cell (pDC) populations can be used as an indicator, instead of (or otherwise) using CD4+ T cells (CD62LlowCD4+ T cells) as an indicator. (Example 7: Using markers expressed on CD8+ T cells) 7-1. Objective To study the relationship between the therapeutic effect of anti-PD-1 antibodies and markers expressed on CD8+ T cells. To detect whether markers expressed on CD8+ T cells can be used to predict the clinical efficacy of the cancer immunotherapy of this invention. 7-2. Materials and Methods The materials and methods are the same as in Example 1. The antibody shown in Figure 23 was used to detect 4-1BB (CD137) expressed on CD8+ T cells. The determination method is the same as in Example 1. 7-3. Results Figure 18 shows the results. The ratio of HLA-DR+ cells and CD80 cells in bone marrow dendritic cells (mDC, CD141+CD11c+ dendritic cells) is associated with the marker 4-1BB (CD137) expressed on CD62LlowCD8+ T cells.As shown in the results of Example 6, the results of Example 7 demonstrate that the number / ratio of 4-1BB cells in CD62LlowCD8+ T cells can be used to predict the clinical efficacy of the cancer immunotherapy of the present invention in the same way as the number / ratio of CD62LlowCD4+ T cells. While not wishing to be bound by any theory, it should be understood that (1) CD4+ T cells transmit instructions to dendritic cells via MHC class I molecules, thereby increasing the number of dendritic cells expressing HLA-DR and / or CD80 and / or CD86, and (2) the dendritic cells receiving these instructions stimulate CD8+ T cells via MHC class II molecules, thereby increasing CD62LlowCD137 (4-1BB)+CD8+ T cells. The number / ratio of CD62LlowCD137 (4-1BB)+CD8+ T cells and dendritic cells expressing HLA-DR and / or CD80 and / or CD86 can be used to predict the clinical efficacy of the cancer immunotherapy in this invention in a manner similar to the number / ratio of CD62LlowCD4+ T cells. Furthermore, the exhaustion of this series of anti-tumor mechanisms is restored due to anti-PD-1 and anti-PD-L1 antibodies, but PD-1 expression on T cells is ineffective in predicting the efficacy of this invention (data not shown). Given these results, it should be understood that PD-L1 expression on dendritic cells can also be used to predict the clinical efficacy of the cancer immunotherapy of this invention. (Example 8: Using other markers expressed on CD4+ T cells) 8-1. Target detection: Whether markers other than CD62L expressed on CD4+ T cells can be used to predict treatment efficacy. 8-2. Materials and methods: The materials and methods are the same as in Example 1. The antibodies shown in Figure 23 are used to detect various markers expressed on CD4+ T cells. The determination method is the same as in Example 1. 8-3. Results: Figures 19 and 20 show the results. It should be understood that each of LAG3, ICOS, and CCR4 expressed on CD4+ T cells can be more effective in predicting the clinical efficacy of the cancer immunotherapy of this invention than CXCR3, CCR6, and CXCR5. (Example 9: Other Markers for Separating PR and SD) 9-1. Target Example 1 shows that the percentage of CD25+Foxp3+CD4+ cells is an excellent marker for separating PR and SD. Detection of other markers for separating PR and SD. 9-2. Materials and Methods The materials and methods are the same as in Example 1. Antibodies were used to detect the following markers of ICOS expressed on CD4+ T cells, equivalent to the antibodies used in Example 8. The interpretation method is the same as in Example 1. 9-3. Results As the results shown in Figure 21 clearly demonstrate, ICOS expressed on CD4+ T cells is considered a better marker than Foxp3+CD25+.Furthermore, when the ratio of CD25+Foxp3+CD4+ T cells in CD4+ T cells (W) and the ratio of ICOS+CD62LlowCD4+ T cells in CD62LlowCD4+ T cells (Z) are combined and used as the product Z × W to distinguish between the PR and SD populations, a threshold value of 1.816 for Z × W indicates that this can be used as a marker with 80% sensitivity and 89.5% specificity (Figure 21). This result also demonstrates that PR and SD can be distinguished using the results of calculating (e.g., multiplying) two or more W values ​​of this invention. A non-limiting example can distinguish PR from SD by using a variable (Z, W) such as Z × W or Zn × Wn, where n and m are positive real numbers, with the amount of the ICOS+CD62LlowCD4+ T cell subset as (Z), and the values ​​of the following subsets selected for their composition: the amount of the CD4+CD25+ T cell subset; the amount of the CD4+Foxp3+ T cell subset; the amount of the CD4+Foxp3+CD25+ T cell subset; the amount of the CD62LhighCD25+CD4+ T cell subset; the amount of the CD45RA-Foxp3+CD4+ T cell subset; the amount of the CCR4+CD25+CD4+ T cell subset; and the amount of the CD127+CD25+CD4+ T cell subset as (W). PR and SD can also be distinguished by calculating (e.g., adding and / or multiplying) the results of three or more biomarkers. To derive more detailed functions for the indicators, logistic regression was performed to further examine the functions of the ratio of CD25+Foxp3+CD4+ T cells (W) in combined CD4+ T cells and the ratio of ICOS+CD62LlowCD4+ T cells (Z) in CD62LlowCD4+ T cells. As shown in Figures 24 and 25, the function J = Z × W5 was derived from the results of a sample N = 32. When the function J = Z × W5 was used as an indicator to separate PR and SD, ROC analysis showed improved efficacy compared to each of Z and W (Figure 26). Furthermore, it should be understood that PR and SD can be successfully separated using another function of similar form, J = Z × W4-6. Since CD4+ T cells are important for predicting NR in PD-1 / PD-L1 blockade therapy, the inventors then examined, in addition to detecting ICOS expression, whether PD-1 and LAG-3 expression on CD4+ T cells could serve as markers for distinguishing between the PR and SD populations. The lymphocyte activation gene 3 (LAG-3) protein, expressed on activated T cells, interacts with PD-1 to maintain T cell exhaustion.LAG-3 binds to MHC class II antigens and regulates the size of the expanding effector T cell population following antigen activation (28-30 Hui, E. et al., T cell costimulatory receptor CD28 is a primary target for PD-1-mediated inhibition. Science 355, 1428-1433 (2017); Baixeras, E. et al., Characterization of the lymphocyte activation gene 3-encoded protein. A new ligand for human leukocyte antigen class II antigens. J Exp Med 176, 327-337 (1992); Workman, CJ et al., Lymphocyte activation gene-3 (CD223) regulates the size of the expanding T cell population following antigen activation in vivo. J Immunol 172, 5450-5455 (2004)). The inventors thereby detected the expression of PD-1, LAG-3, and ICOS on gated CD62Lhigh and CD62LlowCD4+ T cells. The results are shown in Figure 32. These molecules are expressed on CD62LlowCD4+ T cells, but are detected minimally on CD62LhighCD4+ T cells (Figures 32d-32e). Notably, post-hoc experiments using one-way variance analysis (ANOVA) showed that IR (SD) had a significantly lower percentage of PD-1+, LAG-3+, and ICOS+ cells in the total CD62LlowCD4+ T cell population compared to GR (PR) and NR (Figures 32a-32c). It is possible that IR has a different CD4+ T cell immune status than GR. Therefore, the amount of these cell subsets can be used to distinguish between PR and SD. It should be understood that, based on the results of this example, the quantity / ratio of LAG-3+CD62LlowCD4+ T cells and PD-1+CD62LlowCD4+ T cell subsets, in addition to ICOS+CD62LlowCD4+ T cells, can be used as markers to distinguish between PR and SD.(Example 10: Survival Analysis) 10-1. Overview To demonstrate the prediction of treatment efficacy using a predictive function (X² / Y, where X = the percentage of CD62LlowT cells in the CD4+ T cell population (%) and Y = the percentage of CD25+FOXP3+ T cells in the CD4+ T cell population (%)), survival duration was analyzed in the discovery group (a portion of the individual patient population in Example 1) where treatment outcomes (PD, SD, CR) had been determined. Additionally, in a separate validation group of 41 patients who continued treatment (whose predictive function was analyzed prior to assessment of tumor response), the predictive function was tested to determine whether it could distinguish between NR (PD). 10-2. Materials and Methods The characteristics of the patient populations in the discovery and validation groups are shown in the table below. The predictive function values ​​for each patient were calculated according to the steps described in Example 1. [Table 4] a. Patient Characteristics. Discovery group; n = 40 Validation group; n = 41 Age - years Age - years Median 67 Median 71 scope 51-84 scope 38-85 Gender - Number (%) Gender - Number (%) male 26 (65) male 35 (85.4) female 14(35) female 6(14.6) Histology - Number (%) Histology - Number (%) scaly 10(25) scaly 10 (24.4) Non-scaly 30 (75) Non-scaly 31 (75.6) Smoking history - number (%) Smoking history - number (%) Current or former smokers 29 (72.5) Current or former smokers 38 (92.7) Never smokes 11 (27.5) Never smokes 3 (7.3) Disease Stage - Number (%) Disease Stage - Number (%) Phase III clinical trials 9 (22.5) Phase III clinical trials 9 (22.0) Clinical Phase IV 22 (55) Clinical Phase IV 25 (61.0) Recurrence after surgery 9 (22.5) Recurrence after surgery 7(17.1) EGFR status - number (%) EGFR status - number (%) wild type 33 (82.5) wild type 41(100) Mutation (exon 19 deletion or L858R) 7 (17.5) Mutation (exon 19 deletion or L858R) 0(0) 10-3. Results Figure 30a (P < 0.0047, t = 3.004, df = 38) shows the predictive function (X2 / Y, where X = the percentage of CD62LlowT cells in the CD4+ T cell population and Y = the percentage of CD25+FOXP3+ T cells in the CD4+ T cell population) values ​​for each patient in the discovery group. Figure 30b shows the receiver operating characteristic (ROC) curve analysis of the predictive function for detecting NR at 8 weeks in the discovery group. The sensitivity and specificity were 85.7% and 100%, respectively, when the predictive function threshold was 192. Figures 30c and 30d show the progression-free survival (PFS) and overall survival (OS) curves for patients diagnosed with responder type (X2 / Y >= 192) and NR type (X2 / Y < 192) according to PBMC prior to nivolumab treatment. The responders and NR types in the discovery group (limit value = 192) differed significantly in PFS and OS (P < 0.0001). Next, the inventors investigated whether the prediction function limit value (X² / Y < 192) could distinguish NR in an independent validation group of 41 consecutive patients. Peripheral blood samples were collected as the discovery group prior to nivolumab therapy but analyzed before tumor response assessment. As shown in Figure 30e, the prediction function value was significantly higher in the response validation group (P = 0.00068, t = 3.693, df = 39). The sensitivity and specificity of the prediction in the NR validation group were 90.9% and 89.5%, respectively, at the <192 limit value. Responder-type PFS was significantly longer in the validation group than in the NR-type (Figure 30f; P < 0.0001). Although the median follow-up time was only 195 days, responder-type patients also had significantly longer overall survival (OS) (Figure 30g; P = 0.0022). The target response in each group at week 8 is as follows. [Table 5] b. Response to nivolumab Discovery group: n = 40 Validation group: n = 41 Week 8 Target Response - Number (%) Week 8 Target Response - Number (%) Complete or partial reaction 11 (27.5) Complete or partial reaction 7(17.1) stable disease 15 (37.5) stable disease 12 (29.3) progressive disease 14 (35) progressive disease 22 (53.7) Abbreviations: Sq, squamous; c-stage, clinical stage; del, deletion. The results in this example demonstrate that the method for predicting response to the cancer immunotherapy described herein also accurately predicts response to cancer immunotherapy in promising studies. Furthermore, the prediction of response to cancer immunotherapy also provides a direct prediction of the patient's overall treatment response (overall survival (OS) or progression-free survival (PFS)). (Example 11: Availability of CD28+ cell subsets as markers) In recent years, CD28 (rather than the T cell receptor (TCR)) has been confirmed as the primary target of PD-1-dependent signaling inhibition. Therefore, the inventors examined whether the percentage of CD28+ cells in the total population of CD8+ T cells correlated with the predictive function value. The inventors derived a prediction function (X² / Y), where X = the percentage of CD62LlowT cells in the CD4+ T cell population (%) and Y = the percentage of CD25+FOXP3+ T cells in the CD4+ T cell population (%). The values ​​of these functions were significantly correlated with the percentage of CD28+ cells in the total CD62LlowCD8+ T cell population (P = 0.0045) (Figure 31). Based on the results of this example, it should be understood that the quantity and / or percentage of CD28+CD62LlowCD8+ T cells in the CD62LlowCD8+ T cell population, along with the prediction function value, can be used to predict patient response to cancer immunotherapy. (Example 12: CD62LlowCD4+ T cell gene expression in different patient groups) 12-1. Overview: PBMC flow cytometry (FCM) analysis showed that the quantity and quality of CD62LlowCD4+ T cells play an important role in anti-tumor immunity and in determining response to PD-1 blockade therapy. The inventors performed microarray analysis to observe differences in CD62LlowCD4+ T cells at the molecular level among GR, IR, and NR patients in this example. The inventors first elucidated the differences in gene expression between CD62LhighCD4+ and CD62LlowCD4+ T cells. Then, they investigated the differentially expressed genes on CD62LlowCD4+ T cells from each patient group. 12-2. Materials and Methods Total RNA was isolated from CD62LhighCD4+ and CD62LlowCD4+ T cells purified from both PBMCs of each responder type using TRIzol reagent (Thermo Fisher Science, Waltham, MA). cDNA and cRNA synthesis and single-stranded cDNA (ssDNA) labeling reactions were performed using the WT add-on kit (Thermo Fisher Scientific) according to the manufacturer's instructions. Total RNA (0.5 μg) was reverse transcribed into cDNA and subsequently synthesized into cRNA.ssDNA was reverse transcribed from 15 μg cRNA and then labeled; 1.8 μg of labeled ssDNA was prepared using a human microarray Clariom S analysis (Thermo Fisher Scientific) in a gene chip mixing oven 645. The mixing array was scanned using a GCS3000 7G system (Thermo Fisher Scientific). The gene expression data was registered under ID GSE103157. To identify gene expression markers from two sets of gene expression data, the inventors evaluated the gene expression differences between the two sets as follows. First, the inventors performed outlier tests on all probe values, and then calculated the z-score of each probe using the mean and variance of the probe values ​​excluding outliers. To compare the z-scores of the two gene sets, the z-scores of each gene were converted into probabilities, and then the difference pd in the gene probabilities between the two sets was calculated; that is, [Equation 1] where the k-th gene is compared between the two gene sets (a and b). In this analysis, the inventors used the following as gene markers to select genes: [Equation 2]. 12-3. Results The inventors first elucidated the differences in gene expression between CD62LhighCD4+ and CD62LlowCD4+ T cells. CD62LhighCD4+ T cells and CD62LlowCD4+ T cells had independent gene expression curves (Figures 33a and 34a). Consistent with previous reports, the majority of CD62LhighCD4+ T cells were considered native T cells due to the high expression of CC chemokine receptor type 7 (CCR7), CD28, and transcription factor 7 (TCF7) genes in CD62LhighCD4+ T cells in all GR, IR, and NR patients. A minority of CD62LhighCD4+ T cells were considered regulatory T cells due to higher foxp3 expression. Next, the inventors combined genes (genes 1884, 1826, 1410, 1167, and 1513, respectively) (totaling 3484) from markers comparing cells from GR and IR, GR and NR, IR and NR, GR+IR and NR, and GR and IR+NR (Figure 33b). Of these, 30 of the 53 genes known to be associated with T-cell immunity were expressed in relation to nivolumab treatment response (Figure 34b).This indicates that CXC chemokine receptor type 3 (CXCR3), interleukin-23 receptor (IL23R), interleukin-13 receptor subunit α-2 (IL13RA2), PD-1 ligand 2 (PDL2), CD80, C-type lectin domain family 2 member A (CLEC2A), interleukin 7 (IL7), transforming growth factor β receptor 3 (TGFBR3), and histone deacetase 9 (HDAC9) are better expressed in CD62LlowCD4+ T cells derived from GR and / or IR. As can be seen from the results of this example, the cell subpopulation to which the obtained cells belong can be determined by examining the expression of differentially expressed genes between CD62LhighCD4+ and CD62LlowCD4+ T cells, and the quantity and / or ratio of the cell subpopulation can be measured. Furthermore, it should be understood that differentiation of patient populations can be achieved by examining the expression of differentially expressed genes in CD62LlowCD4+ T cells from each patient population. (Example 13: Cell Metastasis Experiment) CD62LlowCD4+ T cells were prepared from tumor drainage lymph nodes. 1.5 × 10⁶ B16BL6 melanoma cells (in HBSS) were subcutaneously inoculated into B6 mice. Inguinal lymph nodes were obtained after 9 to 10 days. Based on the obtained lymph nodes, CD4+ T cells were isolated using a CD4+ T cell separation mantle + LS column. CD62LlowCD4+ T cells were purified by negative selection using CD62L microbeads (LS column). These CD62LlowCD4+ T cells were used for intravenous metastasis. Tumor model: HBSS containing 3 × 10⁶ B16BL6 melanoma cells was subcutaneously inoculated into the midline of the abdomen of B6 mice. Mice were divided into (1) control group (N = 10), (2) antibody group (N = 17) and (3) antibody + cell group (N = 4). No treatment was given to the control group. Following inoculation of tumor cells into the antibody + cell group, the aforementioned CD62LlowCD4+ T cells (1 × 10⁶ cells) metastasized on day 4 or 5 post-inoculation, and anti-PD-1 antibody (RMP1-14 BioXcell 250 µg) was administered intraperitoneally (on the day of cell administration, 3 days after cell administration, and 6 days after cell administration). For the antibody group, anti-PD-1 antibody (RMP1-14 BioXcell 250 µg) was administered intraperitoneally (on the day of cell administration, 3 days after cell administration, and 6 days after cell administration). The survival rate of mice in each group was monitored. Results showed that on day 16 after administration of antibodies and / or T cells, all subjects in the control group died, while the survival rate of the antibody + cell group was 50%, which was higher than that of the antibody group (Figure 35). These results indicate that metastasis of CD62LlowCD4+ T cells can enhance the efficacy of anti-PD-1 antibodies.[Industry Availability] Anti-PD-1 / PD-L1 antibodies are considered a fundamental therapy for almost all progressive cancer treatments. However, the Ministry of Health, Labour and Welfare has warned that the high cost of these drugs could potentially increase social security costs, necessitating an increase in the incremental cost-effectiveness ratio (the ratio of increased treatment efficacy to increased drug costs). The biomarker provided in this invention is medically and socially essential because it allows for a simple, low-cost, and accurate prediction of the efficacy of anti-PD-1 / PD-L1 antibodies. This invention is understood as a technology needed for all cancers and tumors worldwide, thus possessing very high market value. [Simplified Explanation of the Diagram]

[0004] [Figure 1] Figure 1 is a diagram showing the results of T cell grading in peripheral blood samples obtained from an individual using flow cytometry. The top left diagram identifies lymphocyte regions using two-dimensional analysis of FSC and SSC. The top right diagram shows the grading of CD8 and CD4 expression. The bottom left diagram shows the grading of CD25+FoxP3+. The bottom right diagram is a histogram of CD62L expression. The CD62L low expression (CD62Llow) cell grading shows a bimodal distribution. [Figure 2] Figure 2 is a schematic diagram showing the steps of measuring the treatment effect in Example 1. [Figure 3] Figure 3 is a diagram comparing cell counts and T cell composition between the ineffective group and other groups. Figure A (WBC) compares peripheral blood white blood cell (WBC) counts. Figure B (Lym) compares lymphocyte counts. Figure C compares the percentage of CD4+ cells. Figure D compares the percentage of CD8+ cells. No significant differences were found in these parameters between the null cohort and other cohorts. [Figure 4] Figure 4 is a plot comparing the T cell composition between the null cohort and other cohorts. Figure A compares the percentage of CD62Llow cells in CD8+ T cells. This was significantly lower in the PD cohort. P = 0.0138. Figure B compares the percentage of CD62Llow cells in CD4+ T cells. A more significant reduction was observed in the PD cohort compared to the percentage of CD62Llow cells in CD8+ T cells. P = 5.32 × 10-7. Figure C compares the percentage of CD25+FoxP3+ cells in CD4+ T cells. This was significantly higher in the PD cohort. P = 0.0132. Figure D is a scatter plot of the percentages of CD62Llow cells in CD8+ T cells and CD62Llow cells in CD4+ T cells. A weak correlation was found between these values. Figure E is a scatter plot of the percentage of CD62Llow cells in CD4+ T cells and the percentage of CD25+FoxP3+ cells in CD4+ T cells. No correlation was found between these values. It should be understood that they independently promote the response to cancer immunotherapy. [Figure 5] Figure 5 shows the efficacy of the percentage of CD62Llow cells in CD4+ T cells as an indicator for distinguishing between PR+ SD and PD populations. The right panel depicts the sensitivity and specificity after changing the threshold value. The area below the plotted point is 0.974. Therefore, this is considered an excellent marker. [Figure 6] Figure 6 shows the sensitivity and specificity of the percentage of CD62Llow cells in CD4+ T cells for distinguishing between PR+ SD and PD populations after changing the threshold value.[Figure 7] Figure 7 shows the efficacy of the relative value (X / Y) of the percentage of CD62Llow cells (X) in CD4+ T cells and the percentage of CD25+FoxP3+ cells (Y) in CD4+ T cells as an indicator for distinguishing between PR+SD and PD populations. The right figure depicts the sensitivity and specificity after changing the threshold value. The area below the plotted point is 0.961. Therefore, this is considered an excellent marker. [Figure 8] Figure 8 shows the sensitivity and specificity of using the relative value of X / Y, such as the percentage of CD62Llow cells (X) in CD4+ T cells and the percentage of CD25+FoxP3+ cells (Y) in CD4+ T cells, to distinguish between PR+SD and PD populations after changing the threshold value. [Figure 9] Figure 9 shows the efficacy of the relative value (X² / Y) of the percentage of CD62Llow cells (X) in CD4+ T cells and the percentage of CD25+FoxP3+ cells (Y) in CD4+ T cells as an indicator for distinguishing between PR+ SD and PD populations. The right figure shows the sensitivity and specificity curves after changing the threshold value. The area below the plotted point is 1.0, which shows that this indicator is a very favorable marker, allowing for determination at 100% sensitivity and specificity. [Figure 10] Figure 10 shows the sensitivity and specificity of using the relative value (X² / Y) of the percentage of CD62Llow cells (X) in CD4+ T cells and the percentage of CD25+FoxP3+ cells (Y) in CD4+ T cells to distinguish between PR+ SD and PD populations after changing the threshold value. [Figure 11A, Figure 11B] Figure 11 shows the efficacy of the percentage of CD25+FoxP3+ cells in CD4+ T cells as an indicator for distinguishing between PR and SD populations. The right panel shows the sensitivity and specificity curves after changing the threshold value. The area below the plotted point is 0.773. [Figure 12] Figure 12 shows the sensitivity and specificity of the percentage of CD25+FoxP3+ cells in CD4+ T cells for distinguishing between PR and SD populations after changing the threshold value. [Figure 13] Figure 13 is a schematic diagram illustrating an example of a method for improving or maintaining an individual's response to cancer immunotherapy. [Figure 14A, Figure 14B] Figure 14 is a graph showing the relationship between the therapeutic effect of mice receiving T cell infusion and the ratio of (CD62Llow cells in CD4+ T cells) / (CD62LhighCD25+ cells in CD4+ T cells). The horizontal axis indicates the number of days since tumor inoculation in mice. Composition of cells infused with markers. The vertical axis represents tumor size (mm). In the left figure, the ratio of (CD62Llow cells among CD4+ T cells) to (CD62LhighCD25+ cells among CD4+ T cells) in the spleen of mice that received cell infusion was measured up to the time indicated by the arrow.This ratio has each of the values ​​indicated in the bottom column. In the right figure, the ratio of (CD62Llow cells among CD4+ T cells) / (CD62LhighCD25+ cells among CD4+ T cells) in the spleen of mice was measured over time. This value was changed as indicated in the bottom column. [Figure 15] Figure 15 is a schematic diagram showing CD62L staining patterns in different human races and mice. Figure A shows FACS using lymph node drainage Caucasian tumor vaccine. Gated lymphocyte regions and CD62L were observed. C is a similar observation of CD62L in peripheral blood-derived monocytes from Japanese individuals. Figure D shows the CD62L staining pattern in mouse lymphocytes. It should be understood that similar staining patterns are present throughout the human race / biological species. This has a bimodal distribution with a fluorescence intensity of 102 as the boundary. Figure B shows the purity of FACS after CD62Llow cells were isolated from the cell population of the individual in Figure A using magnetic beads. [Figure 16] Figure 16 shows the correlation between the ratio of CD80 cells (top right) and HLA-DR+ cells (top left) in bone marrow dendritic cells (mDC) and PD, SD, and PR, and the correlation between the ratio of CD80 cells (bottom right) and HLA-DR+ cells (bottom left) in plasmacytoid dendritic cells (pDC) and PD, SD, and PR. [Figure 17] Figure 17 shows the correlation between the ratio of CD80 cells (top right) and HLA-DR+ cells (top left) in bone marrow dendritic cells (mDC) and CD62LlowCD4+ T cells, and the correlation between the ratio of CD80 cells (bottom right) and HLA-DR+ cells (bottom left) in plasmacytoid dendritic cells (pDC) and X2 / Y (i.e., (amount of CD62LlowCD4+ T cells)2 / (CD4+Foxp3+CD25+ T cells)). [Figure 18] Figure 18 shows the results of the ratio of CD80 cells (top right) and HLA-DR+ cells (top left) in bone marrow dendritic cells (mDC) and the ratio of CD137+CD62LlowCD8+ T cells to CD62LlowCD8+ T cells. [Figures 19A and 19B] Figure 19 shows the correlation between the ratio of ICOS+ cells (right) and LAG3+ cells (left) in CD62LlowCD4+ T cells and PD and PR+SD. [Figures 20A to 20D] Figure 20 shows the correlation between the ratio of CXCR3+ cells (top left), CCR4+ cells (top right), CCR6+ cells (bottom left), and CXCR5+ cells (bottom right) in CD62LlowCD4+ T cells and PD and PR+SD.Only CCR4 showed a correlation sufficient to serve as a marker (p = 0.0250). [Figure 21] Figure 21 is a graph showing the correlation between CD25+Foxp3+CD4+ T cells (top left) in CD4+ T cells or ICOS+CD62LlowCD4+ T cells (top right) in CD62LlowCD4+ T cells and PR and SD. The figure below shows the sensitivity and specificity of using the product W × Z of the ratio of CD25+Foxp3+CD4+ T cells (W) in CD4+ T cells and the ratio of ICOS+CD62LlowCD4+ T cells (Z) in CD62LlowCD4+ T cells to distinguish between PR and SD populations after changing the threshold value of W × Z. [Figure 22] Figure 22 is a schematic diagram illustrating the mechanism associated with the present invention. [Figure 23] Figure 23 is a table showing the antibodies used in the examples. [Figure 24] Figure 24 shows the logistic regression diagram when using only the biomarkers exhibited to determine the response cohort. [Figure 25] Figure 25 shows the logistic regression diagram for deriving a suitable combination function for determining the ratio of CD25+Foxp3+CD4+ T cells (W) in CD4+ T cells and the ratio of ICOS+CD62LlowCD4+ T cells (Z) in CD62LlowCD4+ T cells. [Figure 26] Figure 26 shows the ROC analysis results obtained by logistic regression when using a combination function of the ratio of CD25+Foxp3+CD4+ T cells (W) in CD4+ T cells and the ratio of ICOS+CD62LlowCD4+ T cells (Z) in CD62LlowCD4+ T cells. It shows that the response cohort can be determined with higher accuracy than individual biomarkers by using this function. [Figure 27] Figure 27 is a logistic regression diagram illustrating the derivation of a suitable combination function for determining the percentage of CD62Llow cells (X) and the percentage of CD25+FoxP3+ cells (Y) in the CD4+ T cell population of the null cohort. [Figure 28] Figure 28 is a diagram illustrating an example of a method for setting threshold values ​​for CD62Llow and CD62Lhigh. [Figure 29] Figure 29 is a histogram of CD62L expression levels, showing the clear separation of CD62L-low cells. [Figure 30] Figure 30 is a diagram illustrating the prediction of treatment outcomes in the discovery and validation groups. (a) Prediction function values ​​in patients in the patient discovery group. The prediction function (X² / Y) is based on the percentage of CD62Llow cells (X) and the percentage of CD25+FOXP3+ cells (Y) in the total CD4+ cell population. (b) Receiver operating characteristic curve of the prediction function for predicting non-responders (n = 40) in the discovery group.The sensitivity and specificity parameters at the threshold of the predictive function (192) were 85.7% and 100%, respectively (P < 0.0001). (c) Progression-free survival (PFS) curves for patients diagnosed as non-responders or responders based on the threshold of the predictive function (192). (d) Overall survival (OS) curves for the detection group. (e) Values ​​of the predictive function in the validation group of patients. In these patients, peripheral blood mononuclear cells were examined prior to CT evaluation. (f) PFS curves for patients in the validation group. (g) OS curves for patients in the validation group. In Figures a and e, data are presented as mean ± standard error of mean, with the sign indicating values ​​from individual patients. Statistical significance of differences was assessed by Student's two-tailed t-test (a, e) or logarithmic rank test (b to d, f, g). [Figure 31] Figure 31 is a graph showing the correlation between the percentage of CD28+ cells in the total CD62LlowCD8+ T cell population and the prediction function (X² / Y, where X = the percentage of CD62LlowT cells in the CD4+ T cell population (%) and Y = the percentage of CD25+FOXP3+ T cells in the CD4+ T cell population (%) (N = 12). [Figures 32a to 32f] Figure 32 shows the differences in the percentage of T cell subsets and prediction function values ​​among non-small cell lung cancer patients with different treatment outcomes. FACS (Fluorescence-Activated Cell Classification) results were obtained from peripheral blood samples from three subgroups (good responders (GR), intermediate responders (IR), and non-responders (NR)) of patients (total N = 81) during the first tumor response assessment period 8 weeks after nivolumab treatment. The percentages of PD-1+, LAG-3+, and ICOS+ cells in the total populations of CD62LlowCD4+ and CD62LhighCD4+ cells are indicated in d to f, respectively. Data are presented as mean ± standard error of mean. The sign indicates values ​​from individual patients. Statistical significance of differences was assessed by one-way variance analysis and subsequent post-hoc analysis (Benjamini, Krieger, and Yekutieli's two-stage step-up method). [Figure 33] Figure 33 is a graph showing the gene expression responsible for a good response to nivolumab treatment. Figure 33a shows the labels obtained by comparing gene expression data of CD62LhighCD4+ T cells and CD62LlowCD4+ T cells in good responders (GR), intermediate responders (IR), and non-responders (NR). In Figure 33b, 29 of the 39 well-known genes associated with antitumor immunity in the above-mentioned tags are shown in terms of nivolumab treatment response.The degree of expression of these genes in CD62LlowCD4+ T cells is shown (depicted), indicating relatively high gene expression in GR compared to IR and NR, and relatively high gene expression in GR and IR compared to NR. [Figure 34] Figure 34a shows immune-related genes that typically exhibit different expressions between CD62LlowCD4+ T cells and CD62LhighCD4+ T cells in good-responding, intermediate-responding, and non-responding patients. Considering that these genes can be used to differentiate cell subpopulations. Figure 34b shows 53 genes that exhibit different expressions in CD62LlowCD4+ T cells associated with response to nivolumab. It should be understood that these genes can be used as markers to differentiate patient populations by examining their expression on CD62LlowCD4+ T cells. Good responders: GR, intermediate-responders: IR, and non-responders: NR. [Figure 35] Figure 35 is a diagram showing the changes in survival rates in (1) the control group, (2) the antibody group, and (3) the antibody + cell group.

Claims

1. Use of a CD62LlowCD4+ T cell for manufacturing a pharmaceutical agent for a method of treating or preventing cancer, characterized in that the pharmaceutical agent is administered in combination with an immune checkpoint inhibitor, wherein the pharmaceutical agent comprises the CD62LlowCD4+ T cell, wherein the immune checkpoint inhibitor comprises a PD-1 inhibitor or a PD-L1 inhibitor, wherein the PD-1 inhibitor is an anti-PD-1 antibody that inhibits the interaction between PD-1 and PD-L1, and wherein the PD-L1 inhibitor is an anti-PD-L1 antibody that inhibits the interaction between PD-1 and PD-L1.

2. As claimed in claim 1, wherein the agent further comprises Foxp3+CD25+CD4+ T cells.

3. As requested in paragraph 1 or 2, wherein the agent is used in conjunction with cancer immunotherapy.

4. As claimed in claim 1 or 2, wherein the immune checkpoint inhibitor further comprises a CTLA-4 inhibitor.

5. As claimed in claim 1, wherein the PD-1 inhibitor or PD-L1 inhibitor comprises nivolumab, pembrolizumab, durvalumab, atezolizumab, or avelumab.

6. As claimed in claim 1 or 2, wherein the agent further comprises CD62LlowCD8+ T cells.

7. As claimed in claim 1 or 2, wherein the agent is intended to make cancer immunotherapy effective in individuals for whom cancer immunotherapy is predicted to be ineffective.

8. As requested in paragraph 1 or 2, it is for maintaining the effectiveness of cancer immunotherapy.

9. As requested in claim 1 or 2, wherein the CD62LlowCD4+ T cell line is derived from an individual to whom the drug was administered.

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