Methods for treating glioblastoma

Abstract

The present disclosure provides a novel treatment method by identifying a glioblastoma patient population that can be effectively treated by immunotherapy. Also provided is a treatment method that can be combined with immune checkpoint therapy (ICB) to enhance the effectiveness of treatment. An aspect of the present disclosure relates to a method for treating glioblastoma in a subject, comprising administering immune checkpoint blockade (ICB) therapy to the subject after determining that the subject has low CD73 expression in a biological sample derived from the subject. TIFF2023510113000019.tif32170

Description

[Technical Field]

[0001] This application claims priority to U.S. Provisional Patent Application No. 62 / 950,509, filed December 19, 2019, which is incorporated herein by reference in its entirety.

[0002] II. FIELD OF THE INVENTION The present invention relates to the fields of biotechnology and therapeutic treatments. [Background technology]

[0003] III. Background Over the past decade, significant advances in cancer treatment have been achieved through the use of targeted therapies and immunotherapy. Checkpoint blockade immunotherapy rescues T lymphocytes from key inhibitory signals by blocking immunosuppressive ligand-receptor interactions involving CTLA-4 and PD-1, thereby enhancing underlying T cell-mediated antitumor immune activity. However, ubiquitous rescue of inhibitory signals can also activate T lymphocytes that react to self-antigens, leading to loss of self-tolerance and immune-related adverse events. Patients who experience severe toxicity typically require temporary or permanent treatment interruption and may require prolonged, heavy immunosuppression to manage toxicity. The high frequency of severe to life-threatening toxicities and the unpredictability of patient response to anti-CTLA-4 and / or anti-PD-1 therapy limit clinicians' prescribing of this form of treatment.

[0004] Although several factors associated with patient response to immune checkpoint blockade therapy have been discovered, there is a need in the art for predictors of immune checkpoint blockade therapy toxicity and predictors of responders to immune checkpoint blockade therapy. Stratifying patients into those likely to respond to checkpoint blockade therapy and those unlikely to respond based on one or more biomarkers would provide more effective and therapeutic treatment for patients, as the most effective treatment could be provided to patients before further spread of the disease. Summary of the Invention

[0005] The present disclosure provides a novel treatment method by identifying a glioblastoma patient population that can be effectively treated by immunotherapy.Also provided is a treatment that can be combined with immune checkpoint therapy (ICB) to enhance the effectiveness of treatment.Therefore, an aspect of the present disclosure relates to a method for treating glioblastoma in a subject, comprising administering immune checkpoint blockade (ICB) therapy to the subject after determining that the subject has low CD73 expression in a biological sample from the subject.A further aspect relates to a method for treating glioblastoma in a subject, comprising administering to the subject an agent selected from a CD73 inhibitor, a CD39 inhibitor, or an A2AR antagonist after determining that the subject has high CD73 expression in a biological sample from the subject.

[0006] A further aspect is a method of predicting response to ICB therapy in a subject having glioblastoma, comprising: (a) determining an expression level of CD73 in a sample from the subject; (b) comparing the expression level of CD73 in the sample from the subject to a control; and (c) after (i) detecting an expression level of CD73 in the biological sample from the subject that is reduced compared to a control representing an expression level of CD73 in a biological sample from a subject determined not to respond to ICB therapy; or (ii) detecting an expression level of CD73 that is reduced or not significantly different compared to a control representing an expression level of CD73 in a biological sample from a subject determined to respond to ICB therapy. or (d) predicting that the subject will respond to ICB therapy after an expression level of CD73 is detected in a biological sample from the subject; or (d) predicting that the subject will not respond to ICB therapy after (i) an expression level of CD73 is detected in a sample from the subject that is increased compared to a control representing the expression level of CD73 in a biological sample from a subject who has been determined to respond to ICB therapy; or (ii) an expression level of CD73 is detected in a biological sample from the subject that is increased or does not significantly differ compared to a control representing the expression level of CD73 in a biological sample from a subject who has been determined not to respond to ICB therapy.

[0007] Yet a further aspect relates to a method comprising detecting CD73 in a biological sample from a subject with glioblastoma. In some embodiments, a low level of CD73 expression is detected. In some embodiments, a high level of CD73 expression is detected.

[0008] In some embodiments, the biological sample comprises isolated immune cells. In some embodiments, the biological sample comprises isolated macrophages. In some embodiments, the biological sample comprises a serum sample. In some embodiments, the biological sample comprises an isolated fraction of immune cells. In some embodiments, the biological sample comprises a biopsy. In some embodiments, the biological sample comprises a sample comprising tissue cells and immune cells. In some embodiments, the tissue comprises cells from a glioblastoma tumor. In some embodiments, CD73 expression is determined to be low in immune cells compared to a control. In some embodiments, CD73 expression is determined to be high in immune cells compared to a control. In some embodiments, a high or low CD73 expression level is determined in a biological sample from a subject by comparing the expression level of CD73 in the biological sample from the subject to a control. In some embodiments, a low expression level refers to a lower number of CD73+ immune cells detected in a biological sample from a subject compared to a control. In some embodiments, a high expression level refers to a higher number of CD73+ immune cells detected in a biological sample from a subject compared to a control. For example, low expression can refer to a lower number of CD73+ immune cells detected in a biological sample, such as a biopsy, compared to a reference, baseline, or control (the reference, baseline, or control representing the number of CD73+ immune cells detected in a biological sample from a subject determined to respond to immunotherapy), or within 0.5, 1, 2, or 3 standard deviations of the control, or not significantly different from the control. Similarly, high expression can refer to a higher number of CD73+ immune cells detected in a biological sample, such as a biopsy, compared to a reference, baseline, or control (the reference, baseline, or control representing the number of CD73+ immune cells detected in a biological sample from a subject determined to respond to immunotherapy), or at least 1.5, 2, 3, 4, 5, 6, 10, 20, 100, 500, or 1000 times higher than the control. In some embodiments, a biological sample from a subject may be fractionated to isolate immune cells from other cells.In some embodiments, the biological sample is fractionated to isolate immune cells from tumor cells, and the CD73 expression level or amount of CD73+ cells is determined in the isolated fraction. In some embodiments, the biological sample is tumor cell-free, essentially tumor cell-free, or an immune cell-enriched and tumor cell-depleted fraction.

[0009] In some embodiments, the ICB therapy includes monotherapy or combination ICB therapy. In some embodiments, the subject has been determined to be a candidate for ICB therapy. In some embodiments, the subject is currently being treated with ICB therapy or has received ICB therapy at least once. In some embodiments, the subject has never been treated with ICB therapy. In some embodiments, the subject has been determined to be non-responsive to previous treatment.

[0010] In some embodiments of the present disclosure, the method comprises or further comprises treating the subject with ICB therapy. In some embodiments, the subject is predicted to respond to ICB therapy based on the detected level of CD73 in a biological sample from the subject. In some embodiments, the ICB therapy comprises an inhibitor of PD-1, PDL1, PDL2, CTLA-4, B7-1, and / or B7-2. In some embodiments, the ICB therapy comprises an anti-PD-1 monoclonal antibody and / or an anti-CTLA-4 monoclonal antibody. In some embodiments, the ICB therapy comprises one or more of nivolumab, pembrolizumab, pidilizumab, ipilimumab, or tremelimumab.

[0011] In some embodiments, the method further comprises administering at least one additional anti-cancer treatment. In some embodiments, the at least one additional anti-cancer treatment is surgical therapy, chemotherapy, radiation therapy, hormone therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biological therapy. In some embodiments, the method further comprises administering ICB therapy to the subject.

[0012] In some embodiments, the control comprises a cutoff value or a normalized value. In some embodiments, the expression level comprises a normalized expression level. In some embodiments, CD73 expression is detected by immunoassay. In some embodiments, a low expression level comprises a normalized expression level determined to be decreased compared to the control. In some embodiments, a low expression level comprises a normalized expression level determined to be increased compared to the control.

[0013] In some embodiments, a CD73 inhibitor, a CD39 inhibitor, or an A2AR antagonist is administered before ICB therapy. In some embodiments, ICB therapy and a CD73 inhibitor, a CD39 inhibitor, or an A2AR antagonist are administered simultaneously. In some embodiments, a CD73 inhibitor, a CD39 inhibitor, or an A2AR antagonist is administered at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours, days, or weeks (or any derivable range therein) before ICB therapy. In some embodiments, the CD73 inhibitor, CD39 inhibitor, or A2AR antagonist is administered within at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours, days, or weeks (or any derivable range therein) of administration of ICB therapy.

[0014] In some embodiments, the CD73 inhibitor or CD39 inhibitor comprises an anti-CD73 antibody or an anti-CD39 antibody, respectively. In some embodiments, the antibody comprises a blocking antibody and / or induces antibody-dependent cellular cytotoxicity. In some embodiments, the A2AR antagonist comprises ATL-444, istradefylline (KW-6002), MSX-3, preladenant (SCH-420,814), SCH-58261, SCH-412,348, SCH-442,416, ST-1535, caffeine, VER-6623, VER-6947, VER-7835, bipadenant (BIIB-014), ZM-241,385, or a combination thereof.

[0015] In some embodiments, the method further comprises comparing the detected CD73 expression level with a control. In some embodiments, the control comprises a biological sample from a subject who does not respond to ICB therapy. In some embodiments, the control comprises a biological sample from a subject who responds to ICB therapy. In some embodiments, the subject is determined to have a higher expression level than the control. In some embodiments, the subject is determined to have a lower expression level than the control. In some embodiments, the subject is determined to have an expression level that is not significantly different from the control.

[0016] Throughout this application, the term "about" is used in accordance with its plain and ordinary meaning in the field of cellular and molecular biology to indicate that a value includes the standard deviation of error for the device or method being employed to determine the value.

[0017] The use of the singular indefinite articles ("a" and "an") used with the term "comprising" can mean "one," but it is also consistent with the meaning of "one or more," "at least one," and "one or more than one."

[0018] As used herein, the terms "or" and "and / or" are used to describe multiple components in combination with or excluding each other. For example, "x, y, and / or z" can refer to "x" alone, "y" alone, "z" alone, "x, y, and z," "(x and y) or z," "x or (y and z)," or "x or y or z." It is specifically contemplated that x, y, or z may be specifically excluded from an embodiment.

[0019] The terms "comprise" (and any form of comprise, e.g., "comprise"), "have" (and any form of have, e.g., "have"), "include" (and any form of include, e.g., "includes"), "characterized" (and any form of characterized, e.g., "characterized") or "contain" (and any form of contain, e.g., "contains") are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.

[0020] Compositions and methods for their use can "comprise," "consist essentially of," or "consist" of any of the components or steps disclosed throughout this application. The phrase "consisting of" excludes any unspecified elements, steps, or ingredients. The phrase "consisting essentially of" limits the scope of the described subject matter to the specified materials or steps and materials or steps that do not materially affect its basic and novel characteristics. It is contemplated that embodiments described in connection with the term "comprising" may also be realized in the context of the terms "consisting of" or "essentially consisting of."

[0021] Specifically, it is contemplated that any limitation stated with respect to one embodiment of the invention may also apply to any other embodiment of the invention. Furthermore, any composition of the invention may be used in any method of the invention, and any method of the invention may be used to make or utilize any composition of the invention. Aspects of an embodiment described in an example may also be realized with respect to embodiments described elsewhere in a different example or elsewhere in this application, for example, in the Summary of the Invention, Detailed Description of the Embodiments, Claims, and Figure Legends.

[0022] [The present invention 1001] 1. A method of treating glioblastoma in a subject, comprising administering immune checkpoint blockade (ICB) therapy to the subject after the subject is determined to have low CD73 expression in a biological sample from the subject. [The present invention 1002] 1001. The method of claim 1001, wherein said expression is lower than a control. [The present invention 1003] The method of any one of claims 1001 to 1002, wherein the biological sample comprises isolated immune cells. [The present invention 1004] The method of claim 1003, wherein the biological sample comprises isolated macrophages. [The present invention 1005] The method of any of claims 1001 to 1004, wherein the biological sample comprises a serum sample, a biopsy sample, or an isolated fraction of immune cells. [The present invention 1006] The method of any of claims 1003 to 1005, wherein expression of CD73 is determined to be low in immune cells. [The present invention 1007] The method of any of claims 1001 to 1006, wherein the ICB therapy comprises monotherapy or combination ICB therapy. [The present invention 1008] The method of any of claims 1001 to 1007, wherein the ICB therapy comprises an inhibitor of PD-1, PDL1, PDL2, CTLA-4, B7-1 and / or B7-2. [The present invention 1009] The method of any of claims 1001 to 1008, wherein the ICB therapy comprises an anti-PD-1 monoclonal antibody and / or an anti-CTLA-4 monoclonal antibody. [The present invention 1010] 1009. The method of claim 1009, wherein the ICB therapy comprises one or more of nivolumab, pembrolizumab, pidilizumab, ipilimumab, or tremelimumab. [The present invention 1011] The method of any of claims 1001 to 1008, further comprising administering at least one additional anti-cancer treatment. [The present invention 1012] The method of claim 1011, wherein the at least one additional anti-cancer treatment is surgical therapy, chemotherapy, radiation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biological therapy. [The present invention 1013] 1013. The method of any of claims 1002 to 1012, wherein the control comprises a cutoff value or a normalized value. [The present invention 1014] The method of any of claims 1001 to 1013, wherein the low expression level comprises a normalized expression level that is determined to be decreased relative to a control. [The present invention 1015] The method of any of claims 1001 to 1014, wherein CD73 expression is detected by immunoassay. [The present invention 1016] A method of treating glioblastoma in a subject, comprising administering to the subject an agent selected from a CD73 inhibitor, a CD39 inhibitor, or an A2AR antagonist after the subject is determined to have high CD73 expression in a biological sample from the subject. [The present invention 1017] The method of claim 1016, wherein said expression is determined to be higher compared to a control. [The present invention 1018] The method of any one of claims 1016 to 1017, further comprising administering ICB therapy to the subject. [The present invention 1019] The method of claim 1018, wherein a CD73 inhibitor, a CD39 inhibitor, or an A2AR antagonist is administered prior to ICB therapy. [The present invention 1020] The method of claim 1018, wherein the ICB therapy and the CD73 inhibitor, CD39 inhibitor, or A2AR antagonist are administered simultaneously. [The present invention 1021] 1021. The method of any one of claims 1016 to 1020, wherein the CD73 inhibitor or CD39 inhibitor comprises an anti-CD73 antibody or an anti-CD39 antibody, respectively. [The present invention 1022] The method of claim 1021, wherein the antibody comprises a blocking antibody and / or induces antibody-dependent cellular cytotoxicity. [The present invention 1023] Any of the methods of claims 1016 to 1022, wherein the A2AR antagonist comprises ATL-444, istradefylline (KW-6002), MSX-3, preladenant (SCH-420,814), SCH-58261, SCH-412,348, SCH-442,416, ST-1535, caffeine, VER-6623, VER-6947, VER-7835, bipadenant (BIIB-014), ZM-241,385, or a combination thereof. [The present invention 1024] 1024. The method of any of claims 1016 to 1023, wherein the biological sample comprises isolated immune cells. [The present invention 1025] The method of claim 1024, wherein the biological sample comprises isolated macrophages. [The present invention 1026] 1026. The method of any of claims 1016 to 1025, wherein the biological sample comprises a serum sample, a biopsy sample, or an isolated fraction of immune cells. [The present invention 1027] 1027. The method of any of claims 1016 to 1026, wherein expression of CD73 is determined to be high in immune cells. [The present invention 1028] 1028. The method of any of claims 1024 to 1027, wherein the ICB therapy comprises monotherapy or combination ICB therapy. [The present invention 1029] 1029. The method of any of claims 1018 to 1028, wherein the ICB therapy comprises an inhibitor of PD-1, PDL1, PDL2, CTLA-4, B7-1 and / or B7-2. [The present invention 1030] 1029. The method of any of claims 1018 to 1029, wherein the ICB therapy comprises an anti-PD-1 monoclonal antibody and / or an anti-CTLA-4 monoclonal antibody. [The present invention 1031] The method of claim 1030, wherein the ICB therapy comprises one or more of nivolumab, pembrolizumab, pidilizumab, ipilimumab, or tremelimumab. [The present invention 1032] 1029. The method of any of claims 1016 to 1029, further comprising administering at least one additional anti-cancer treatment. [The present invention 1033] The method of claim 1032, wherein the at least one additional anti-cancer treatment is surgical therapy, chemotherapy, radiation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biological therapy. [The present invention 1034] Any of the methods of claims 1017 to 1033, wherein the control comprises a cutoff value or a normalized value. [This invention 1035] The method of any of claims 1016 to 1034, wherein the elevated expression level comprises a normalized expression level that is determined to be elevated relative to a control. [The present invention 1036] 1036. The method of any of claims 1016 to 1035, wherein CD73 expression is detected by immunoassay. [This invention 1037] A method for predicting response to ICB therapy in a subject with glioblastoma, comprising the steps of: (a) determining the expression level of CD73 in a sample from the subject; (b) comparing the expression level of CD73 in the sample from the subject to a control; and (c)(i) after detecting a decreased expression level of CD73 in a biological sample from a subject determined to be non-responsive to ICB therapy compared to a control representing the expression level of CD73 in a biological sample from the subject; or (ii) detecting an expression level of CD73 in a biological sample from a subject that is reduced or not significantly different from a control that represents the expression level of CD73 in a biological sample from a subject that has been determined to respond to ICB therapy; predicting that the subject will respond to ICB therapy; or (d)(i) after detecting an increased expression level of CD73 in a biological sample from a subject determined to be responsive to ICB therapy compared to a control representing the expression level of CD73 in a biological sample from the subject; or (ii) after detecting an expression level of CD73 in a biological sample from a subject that is increased or not significantly different from a control representing the expression level of CD73 in a biological sample from a subject that has been determined not to respond to ICB therapy; predicting that the subject will not respond to ICB therapy. [The present invention 1038] The method of claim 1037, further comprising treating a subject predicted to respond to ICB therapy with ICB therapy. [This invention 1039] The method of any one of claims 1037 to 1038, wherein the biological sample comprises isolated immune cells. [The present invention 1040] The method of claim 1039, wherein the biological sample comprises isolated macrophages. [The present invention 1041] The method of any of claims 1037 to 1040, wherein the biological sample comprises a serum sample, a biopsy sample, or an isolated fraction of immune cells. [The present invention 1042] 1037-1041. The method of any of claims 1037-1041, wherein the ICB therapy comprises monotherapy or combination ICB therapy. [This invention 1043] The method of any of claims 1037 to 1042, wherein the ICB therapy comprises an inhibitor of PD-1, PDL1, PDL2, CTLA-4, B7-1 and / or B7-2. [This invention 1044] The method of any of claims 1037 to 1043, wherein the ICB therapy comprises an anti-PD-1 monoclonal antibody and / or an anti-CTLA-4 monoclonal antibody. [This invention 1045] The method of claim 1044, wherein the ICB therapy comprises one or more of nivolumab, pembrolizumab, pidilizumab, ipilimumab, or tremelimumab. [The present invention 1046] Any of the methods of claims 1037 to 1045, further comprising administering ICB therapy to a subject predicted to respond to ICB therapy. [This invention 1047] Any of the methods of claims 1037 to 1045, further comprising administering a CD73 inhibitor, a CD39 inhibitor or an A2AR antagonist to a subject predicted not to respond to ICB therapy. [This invention 1048] The method of claim 1047, further comprising administering ICB therapy to the subject. [This invention 1049] The method of claim 1048, wherein a CD73 inhibitor, a CD39 inhibitor, or an A2AR antagonist is administered prior to ICB therapy. [The present invention 1050] The method of claim 1048, wherein the ICB therapy and the CD73 inhibitor, CD39 inhibitor, or A2AR antagonist are administered simultaneously. [This invention 1051] The method of any of claims 1047 to 1050, wherein the CD73 inhibitor or CD39 inhibitor comprises an anti-CD73 antibody or an anti-CD39 antibody, respectively. [This invention 1052] The method of claim 1051, wherein the antibody comprises a blocking antibody and / or induces antibody-dependent cellular cytotoxicity. [This invention 1053] Any of the methods of claims 1047 to 1052, wherein the A2AR antagonist comprises ATL-444, istradefylline (KW-6002), MSX-3, preladenant (SCH-420,814), SCH-58261, SCH-412,348, SCH-442,416, ST-1535, caffeine, VER-6623, VER-6947, VER-7835, bipadenant (BIIB-014), ZM-241,385, or a combination thereof. [This invention 1054] The method of any of claims 1037 to 1053, further comprising administering at least one additional anti-cancer treatment. [This invention 1055] The method of claim 1054, wherein the at least one additional anti-cancer treatment is surgical therapy, chemotherapy, radiation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biological therapy. [This invention 1056] Any of the methods of claims 1037 to 1055, wherein the control comprises a cutoff value or a normalized value. [This invention 1057] 1056. The method of any one of claims 1037 to 1056, wherein the expression level comprises a normalized expression level. [This invention 1058] The method of any of claims 1037 to 1057, wherein CD73 expression is detected by immunoassay. [This invention 1059] A method comprising detecting CD73 in a biological sample from a subject having glioblastoma. [The present invention 1060] The method of any one of claims 1059 to 1109, wherein the biological sample comprises isolated immune cells. [This invention 1061] The method of claim 1060, wherein the biological sample comprises isolated macrophages. [This invention 1062] 1062. The method of any of claims 1059 to 1061, wherein the biological sample comprises a serum sample, a biopsy sample, or an isolated fraction of immune cells. [This invention 1063] 1063. The method of any of claims 1059 to 1062, wherein the control comprises a cutoff value or a normalized value. [This invention 1064] The method of any of claims 1059 to 1063, wherein the expression level comprises a normalized expression level. [This invention 1065] 1065. The method of any of claims 1059 to 1064, wherein CD73 expression is detected by immunoassay. [The present invention 1066] The method of any of claims 1059 to 1065, wherein the subject has been determined to be a candidate for ICB therapy. [This invention 1067] Any of the methods of claims 1059 to 1066, wherein the subject is currently being treated with ICB therapy, has received ICB therapy at least once, or has never been treated with ICB therapy. [The present invention 1068] The method of any of claims 1059 to 1067, further comprising the step of comparing the detected expression level of CD73 with a control. [The present invention 1069] The method of claim 1068, wherein the control comprises a biological sample from a subject who does not respond to ICB therapy. [The present invention 1070] The method of claim 1068, wherein the control comprises a biological sample derived from a subject that responds to ICB therapy. [This invention 1071] The method of any one of claims 1069 to 1070, wherein the subject is determined to have a higher expression level than the control. [This invention 1072] The method of any one of claims 1069 to 1070, wherein the subject is determined to have a lower expression level than the control. [This invention 1073] The method of any one of claims 1069 to 1070, wherein the subject is determined to have an expression level that is not significantly different from the control. [This invention 1074] The method of any of claims 1066 to 1073, wherein the ICB therapy comprises monotherapy or combination ICB therapy. [This invention 1075] The method of any of claims 1066 to 1074, wherein the ICB therapy comprises an inhibitor of PD-1, PDL1, PDL2, CTLA-4, B7-1 and / or B7-2. [This invention 1076] 1076. The method of any of claims 1066 to 1075, wherein the ICB therapy comprises an anti-PD-1 monoclonal antibody and / or an anti-CTLA-4 monoclonal antibody. [This invention 1077] 1076. The method of claim 1076, wherein the ICB therapy comprises one or more of nivolumab, pembrolizumab, pidilizumab, ipilimumab, or tremelimumab. Other objects, features, and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description and specific examples, while indicating particular embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description. [Brief explanation of the drawings]

[0023] The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. A better understanding of the invention may be obtained by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.

[0024] [Figure 1A]Figure 1A-F. Identification of tumor-infiltrating leukocyte phenotypes. TILs were analyzed by CyTOF and identified for viable CD45+ cells using the PhenoGraph algorithm. A: Boxplots (n = 66) showing the frequency of CD3, CD4, CD8, or CD68-positive cells and CD4+FoxP3+ cells from live singlets obtained by manual gating of mass cytometry data. In all boxplots shown, the boxes indicate the interquartile range, the central bar indicates the median, and the whiskers indicate the range. Individual patients are represented by dots. p-values ​​were calculated by the Mann-Whitney test (two-tailed). q-values ​​were calculated using the p.adjust function. A q<0.05 was considered statistically significant. B: Heatmap showing the normalized expression of various immune markers by our PhenoGraph-based clustering method for CD45+ cells obtained from NSCLC (n = 11), RCC (n = 11), CRC (n = 11), PCa (n = 5), and GBM (n = 7) patients. The color bar on the right indicates the leukocyte lineage of each metacluster (myeloid: CD3-CD68+; T cells: CD3+; NK cells: CD3-CD56+). The bar graph on the right indicates the relative frequency of each metacluster. C: Boxplot showing the Shannon entropy of the distribution of tumor types in the immune metaclusters. Shannon entropy was calculated for the empirical distribution of tumors among 1000 cells. This procedure was repeated 1000 times per cluster to bootstrap the cluster-size-corrected standard error of entropy (n = 1000). Boxplots of entropy values ​​within each cluster are sorted by mean entropy. The dashed line indicates the expected entropy value if the tumor type distribution within a cluster matches the tumor type distribution of all cells in the dataset. D: Boxplot showing the frequency of each CD4 and CD8 T cell metacluster across tumor types (number of patients: GBM = 7, NSCLC = 11, RCC = 11, CRC = 11, PCa = 5). A Kruskal-Wallis test was performed on the 14 metaclusters and corrected for multiple comparisons using the Benjamini-Hochberg (BH) method. E: Stacked bar graph visualizing metacluster frequencies in individual patients using the color code shown on the right.The dendrogram on the left shows hierarchical clustering of patient metacluster frequencies. The black frame highlights patient subgroups identified by this clustering method. The color bar on the left indicates individual patient tumor types using the color code shown below. F: Boxplots showing T-cell metacluster frequencies among the patient subgroups identified in E: Group I = 11, Group II = 8, Group III = 9. In all boxplots shown, the boxes indicate the interquartile range, the central bars indicate the median, and the whiskers indicate the range. Individual patients are represented by dots. Comparisons between subgroups were performed using the Kruskal-Wallis test. The Mann-Whitney test was used for pairwise comparisons. Significant pairwise comparisons are indicated (FDR = 5%). [Figure 1B] See legend to Figure 1A. [Figure 1C] See legend to Figure 1A. [Figure 1D] See legend to Figure 1A. [Figure 1E] See legend to Figure 1A. [Figure 1F] See legend to Figure 1A. [Figure 2A]Figure 2A-E. Figure 2: CD73hi macrophages are specifically present in GBM. Myeloid cells (CD3-CD68+) were analyzed by CyTOF and further characterized by sc-RNA seq in GBM in patients with multiple tumor types. A: Boxplots showing the frequency of the L1, L5, L8, and L17 metaclusters across tumor types (patient numbers: NSCLC = 11, RCC = 11, CRC = 11, PCa = 5, GBM = 7). q-values ​​were calculated using the Kruskal-Wallis test (between different tumor types) and the Benjamini-Hochberg method. Pairwise comparisons were performed using the Mann-Whitney U test and corrected for multiple comparisons using Benjamini-Hochberg. Significant pairwise comparisons are shown (FDR = 5%). B: TILs from untreated GBM tumors (n = 4) were analyzed by sc-RNA seq and identified using the MAGIC algorithm. Heatmap showing normalized expression of selected markers in leukocyte clusters identified by MAGIC. Black arrows indicate CD73hi myeloid cell clusters. C: Upper top panel: t-SNE map with color legend on the right showing cluster phenotype and relative expression levels of CD73 at the single-cell level. Oval areas highlight CD73hi macrophage clusters (R3, R7, R14, R17). Lower bottom panel: t-SNE map with color legend on the right showing relative expression levels of blood-derived macrophage and microglial gene signatures at the single-cell level (n=4). D: Heatmap showing normalized expression of chemokine receptors on CD73hi macrophage clusters identified by MAGIC. Black arrows indicate CD73hi myeloid cell clusters. E: Upper panel: t-SNE map showing relative expression levels of immunosuppressive and immunostimulatory gene signatures at the single-cell level. Lower bottom panel: t-SNE map showing the relative expression levels of the hypoxia-induced gene signature (n=4). [Figure 2B] See legend to Figure 2A. [Figure 2C] See legend to Figure 2A. [Figure 2D] See legend to Figure 2A. [Figure 2E] See legend to Figure 2A. [Figure 3A] Figure 3A-G. Figure 3: CD73hi myeloid cells persist after anti-PD-1 treatment and correlate with shorter overall survival in the TCGA-GBM cohort. CD73hi macrophage gene signature of differentially expressed genes (z>3.0, 45 genes) (Supplementary Table 3). Heatmap showing normalized expression (z-score >2.0) of the top differentially expressed genes in CD73hi macrophages identified by MAGIC. B: Kaplan-Meier plot showing overall survival of GBM patients from the TCGA database with expression above (blue = high expression, n=263 patients) or below (red = low expression, n=262 patients) the median expression of the 45-gene signature derived in A. Log-rank p-values ​​(two-tailed) and hazard ratios (HRs) are displayed. Leukocyte phenotypes in single-cell suspensions of tumors from immune checkpoint therapy-naïve (naïve) and pembrolizumab-treated (pembro) patients were analyzed by mass cytometry and identified using the PhenoGraph algorithm for viable CD45+ cells. C: t-SNE maps showing the phenotypic similarity of GBM-infiltrating leukocytes at the single-cell level in pembrolizumab-treated (n=5) or untreated (n=7) patients. D: TILs from GBM tumors treated with pembrolizumab (n=5) or ICT-naïve GBM patients (n=7) were analyzed by mass cytometry and identified using the PhenoGraph algorithm for viable CD45+ cells. Heatmaps showing the normalized expression of selected markers on the CD45+ metaclusters identified by PhenoGraph. E–F: Stacked bar graphs showing the frequency of CD73hi myeloid metaclusters and T cell clusters in pembrolizumab-treated and untreated GBM patients. G: Representative heatmap of transcriptome profiling using GSEA of tumor specimens from untreated patients (n = 6) and anti-PD-1 treated patients (n = 4) using a customized 739-gene Nanostring panel. [Figure 3B] See legend to Figure 3A. [Figure 3C] See legend to Figure 3A. [Figure 3D] See legend to Figure 3A. [Figure 3E] See legend to Figure 3A. [Figure 3F] See legend to Figure 3A. [Figure 3G] See legend to Figure 3A. [Figure 4A] Figure 4A-D. Absence of CD73 enhances the efficacy of ICT in a mouse model of GBM. A: Representative MRI images 14 days after orthotopically inoculating CD73- / - and wild-type mice with the GL-261 tumor line with and without ICT treatment. Figure represents three independent experiments. B: Kaplan-Meier plot showing overall survival of wild-type and CD73- / - mice (n ~10 mice) treated with anti-PD-1 alone, anti-PD-1 and anti-CTLA-4, or untreated mice orthotopically injected with GL-261 glioma. p values ​​were calculated using the log-rank test (two-tailed). See Supplementary Table 2 for details. C: Heatmap showing intratumoral CD45+ immune populations determined by FlowSOM in both WT and CD73- / - mice bearing GBM tumors. The color code in the upper right indicates z-scored expression values. The legend in the lower right indicates the cell type of each colored cluster. D: Boxplots showing the abundance ratios of leukocyte subsets (five animals per group). Data are representative of two independent experiments. Boxplot data are means ± SEM. For pairwise comparisons, p values ​​were calculated using the Mann-Whitney U test (two-tailed). [Figure 4B] See legend to Figure 4A. [Figure 4C] See legend to Figure 4A. [Figure 4D] See legend to Figure 4A. [Figure 5] Gating strategy for identification of immune cell subsets by manual gating. Contour plots show the gating strategy used to define the manually gated CD3, CD4, CD8, and FoxP3 positive populations in Figure 1a. [Figure 6] Figure 6A-D. Heterogeneity of tumor-infiltrating leukocytes. A: Scatter plots showing the absolute number of viable CD45+ singlets in mass cytometry samples used for multi-tumor comparisons. The dashed line indicates the 600-cell threshold for sample inclusion. B: Stacked bar graphs (left) showing the distribution of identified metacluster frequencies within various tumor types, color-coded as follows: t-SNE maps of 10,000 randomly selected cells per tumor type, color-coded by tumor type (upper right panel) (color legend shown on the right) or by metacluster (lower right panel) (color legend shown on the left panel). C: Boxplots showing CD45+ immune metacluster frequencies across tumor types from the PhenoGraph-based clustering approach in Figure 1 (number of patients: GBM = 7, NSCLC = 11, RCC = 11, CRC = 11, and PCa = 5). In all boxplots shown, the boxes indicate the interquartile range, the central bars indicate the median, and the whiskers indicate the range. Individual patients are represented by dots. D: Histogram showing the expression of immune markers on each metacluster shown on the left in relation to Figure 1d. [Figure 7A]Figure 7A-C. PD-1hi T cells expand during immune checkpoint therapy in clinical responders. T cell phenotypes in PBMC suspensions from renal cell carcinoma (RCC) patients receiving combination ICT with ipilimumab and nivolumab were analyzed by mass cytometry and identified using the PhenoGraph algorithm on viable CD45+ cells (n=14). A: Heatmap showing the normalized expression of selected markers on CD45+ metaclusters identified by PhenoGraph. B: Frequency of CD4+ T cell cluster P33 and CD8+ T cell cluster P24 in responders (n=7) and non-responders (n=7) before (T0) and after two (T2) or four (T4) cycles of treatment with combination ICT. p-values ​​were calculated using a Mann-Whitney U test (two-tailed). q-values ​​were calculated using the output p-values. C: Heatmap displaying the correlation matrix of clusters from PBMC samples and TILs. Pearson correlation coefficients were calculated between each RCC PBMC cluster (above the threshold described in Methods) and each TIL cluster (for each RCC PBMC and TIL cluster, to account for separate normalization) using z-scored values ​​across all 29 channels shared between each experiment. [Figure 7B] See legend to Figure 7A. [Figure 7C] See legend to Figure 7A. [Figure 8A]Figure 8A-C. Distribution of T cell phenotypes across tumor types. T cell phenotypes in single-cell suspensions of tumors from immune checkpoint therapy-naïve patients were analyzed by mass cytometry and identified using the PhenoGraph algorithm for viable CD45+CD3+ cells. A: Scatter plot showing the absolute number of viable CD45+CD3+ singlets in tumor single-cell suspensions from immune checkpoint therapy-naïve patients (n=37). The dashed line indicates the 600-cell threshold for sample inclusion (see Methods). B: Boxplot showing the frequency of selected T cell metaclusters across tumor types (patient numbers: NSCLC n=10, RCC n=11, CRC n=9). Samples are identical to those used in Figures 2, 3, and 5. C: Histogram showing immune marker expression on each CD4 and CD8 T cell metacluster shown on the left. Related to Figure 1F. [Figure 8B] See legend to Figure 8A. [Figure 8C] See legend to Figure 8A. [Figure 9-1]Figure 9A-H. Characterization of myeloid metaclusters. A: Histograms showing immune marker expression on each metacluster shown on the left and in Figure 2A. B: Contour plots showing the gating strategy used to manually define myeloid cells phenotypically similar to the L8 metacluster identified by PhenoGraph. All cells were gated on live CD45+ cells according to the gating strategy outlined in Figure 5. C: Boxplots showing manually gated L8 subset frequencies as a percentage of live CD45+ cells (number of patients: GBM = 7, NSCLC = 11, RCC = 11, pCRC = 7, mCRC = 4, PCa = 5). For pairwise comparisons, p-values ​​were calculated using the Mann-Whitney test. q-values ​​were calculated using the Benjamini-Hochberg method with the output p-values. D: Histogram overlay of CD73 expression of CD68+ cells in normal donor PBMCs (blue) and GBM-TILs (red) by CyTOF. E: Representative IHC images of GBM patient samples. F: Boxplots showing the density of CD3+, CD8+, and CD68+ cells (cells / mm2) in IHC sections of GBM patient samples (n=7). G: Representative images of multicolor IF in GBM tumor samples (n=6). H: Boxplots showing the percentage of CD68+ and CD68+CD73+ cells among all nucleated cells (n=6). [Figure 9-2] See description of Figure 9-1. [Figure 10]Figure 10A-B. Similarity of tumor-infiltrating leukocyte phenotype between the first and second cohorts of untreated GBM patients. Leukocyte phenotype in single-cell suspensions of tumors from immune checkpoint therapy-naïve patients was analyzed by mass cytometry and identified using the PhenoGraph algorithm for viable CD45+ cells. A: Grouped boxplots showing the frequency of indicated CD45+ cells in single-cell suspensions of tumors from untreated Cohort 1 patients (n=7) and untreated Cohort 2 patients (n=9). Boxes indicate the interquartile range, the central bar indicates the median, and the whiskers indicate the range. Individual patients are represented by dots. B: Heatmap showing the normalized expression of selected markers on the CD45+ metaclusters identified by PhenoGraph from Cohort 2 patients. [Figure 11] Figure 11A-B. Distribution of tumor-infiltrating leukocyte phenotypes in pembrolizumab-treated and -untreated GBM patients. Leukocyte phenotypes in single-cell suspensions of tumors from immune checkpoint therapy-naive (naive) and pembrolizumab-treated (pembro) patients were analyzed by mass cytometry and identified using the PhenoGraph algorithm for viable CD45+ cells. A: Scatter plot showing the absolute number of viable CD45+ singlets in single-cell suspensions of tumors from untreated patients (n=8) and pembrolizumab-treated patients (n=5). B: Grouped boxplot showing CD45+ immune metacluster frequencies identified by PhenoGraph in untreated tumors (n=7) and pembrolizumab-treated tumors (n=5). [Figure 12A]Figure 12A-E. Phenotypic distribution of tumor-infiltrating leukocytes from orthotopically injected GL-261 gliomas in untreated wild-type and CD73- / - mice. CD73- / - and WT mice were inoculated intracranially with GL-261 gliomas. A: Left: Boxplot showing tumor size determined by MRI in WT (blue) and CD73- / - mice (red). Data represent two independent experiments (five mice per group). p-values ​​were calculated using the Mann-Whitney U test. Boxes indicate the interquartile range, the central bar indicates the median, and the whiskers indicate the range. Right: Representative MRI images are shown 14 days after tumor cell inoculation. Arrows indicate tumor size. B: Kaplan-Meier plot showing overall survival of untreated wild-type or CD73- / - mice (n = 10) orthotopically injected with GL-261 gliomas. p-values ​​were calculated using the log-rank test (two-tailed). Data shown represent two experiments. C: Representative heatmap showing intratumoral CD11b+ immune clusters in both WT and CD73- / - mice bearing GBM tumors by FlowSOM analysis. D: The cluster on the right indicates a cluster showing significant changes. p values ​​were calculated using a Mann-Whitney U test (two-tailed) and corrected for multiple comparisons using the Benjamini-Hochberg method. Data represent two independent experiments (five mice per group). E: Bar graph showing the CD45+ immune cluster frequency identified by the heatmap (five mice per group). Boxes indicate the interquartile range, the central bar indicates the median, and the whiskers indicate the range. Individual mice are represented by dots. [Figure 12B] See legend to Figure 12A. [Figure 12C] See legend to Figure 12A. [Figure 12D] See legend to Figure 12A. [Figure 12E] See legend to Figure 12A. DETAILED DESCRIPTION OF THE INVENTION

[0025] Detailed Description of the Invention Immune checkpoint therapy (ICT) with anti-CTLA-4 and anti-PD-1 / PD-L1 has revolutionized the treatment of many solid tumors. However, the clinical efficacy of ICT is limited to a subset of patients with specific tumor types (1, 2). Although multiple clinical trials using combinatorial immune checkpoint strategies are ongoing, the mechanistic principles for tumor-specific targeting of immune checkpoints remain elusive. To gain insight into tumor-specific immune regulatory targets, we analyzed tumors (N = 94) representing five different cancer types, including those that respond relatively well to ICT, such as glioblastoma (GBM), prostate cancer (PCa), and colorectal cancer (CRC). Through mass cytometry and single-cell RNA sequencing, we identified a unique population of CD73hi macrophages in GBM that persisted after anti-PD-1 treatment. To test whether targeting CD73 is critical for the success of combination strategies in GBM, we performed a reverse translation study using CD73- / - mice. We found that the absence of CD73 improved survival in a mouse model of GBM treated with anti-CTLA-4 and anti-PD-1. Our data identify CD73 as a specific immunotherapeutic target for improving antitumor immune responses to ICT in GBM and demonstrate that comprehensive human and reverse translational studies can be used to rationally design combinatorial immune checkpoint strategies.

[0026] IV. Immunotherapy In some embodiments, the method includes the administration of cancer immunotherapy. Cancer immunotherapy (sometimes called immuno-oncology, abbreviated IO) is the use of the immune system to treat cancer. Immunotherapies can be classified as active, passive, or hybrid (active and passive). These approaches take advantage of the fact that cancer cells often have molecules on their surface known as tumor-associated antigens (TAAs) that can be detected by the immune system. Such molecules are often proteins or other macromolecules (e.g., carbohydrates). Active immunotherapy directs the immune system to attack tumor cells by targeting TAAs. Passive immunotherapy enhances existing anti-tumor responses and includes the use of monoclonal antibodies, lymphocytes, and cytokines. Immunotherapies are known in the art, and some are described below.

[0027] Immune checkpoint blockade therapy Aspects of the present disclosure may include the administration of immune checkpoint blockade therapy, which is further described below.

[0028] PD-1, PDL1, and PDL2 inhibitors PD-1 can act within the tumor microenvironment where T cells encounter infection or tumors. Activated T cells upregulate PD-1 and continue to express it in peripheral tissues. Cytokines such as IFN-gamma induce PDL1 expression on epithelial cells and tumor cells. PDL2 is expressed on macrophages and dendritic cells. The primary role of PD-1 is to limit the activity of effector T cells in the periphery and prevent excessive damage to tissues during immune responses. The inhibitors disclosed herein can block one or more functions of PD-1 and / or PDL1 activity.

[0029] Other names for "PD-1" include CD279 and SLEB2. Other names for "PDL1" include B7-H1, B7-4, CD274, and B7-H. Other names for "PDL2" include B7-DC, Btdc, and CD273. In some embodiments, PD-1, PDL1, and PDL2 are human PD-1, PDL1, and PDL2.

[0030] In some embodiments, the PD-1 inhibitor is a molecule that inhibits the binding of PD-1 to its ligand binding partner. In certain aspects, the PD-1 ligand binding partner is PDL1 and / or PDL2. In another embodiment, the PDL1 inhibitor is a molecule that inhibits the binding of PDL1 to its ligand binding partner. In certain aspects, the PDL1 binding partner is PD-1 and / or B7-1. In another embodiment, the PDL2 inhibitor is a molecule that inhibits the binding of PDL2 to its ligand binding partner. In certain aspects, the PDL2 binding partner is PD-1. The inhibitor can be an antibody, antigen-binding fragment thereof, immunoadhesin, fusion protein, or oligopeptide. Exemplary antibodies are described in U.S. Patent Nos. 8,735,553, 8,354,509, and 8,008,449, all of which are incorporated herein by reference. Other PD-1 inhibitors for use in the methods and compositions provided herein are known in the art, such as those described in U.S. Patent Application Nos. 2014 / 0294898, 2014 / 022021, and 2011 / 0008369, all of which are incorporated herein by reference.

[0031] In some embodiments, the PD-1 inhibitor is an anti-PD-1 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody). In some embodiments, the anti-PD-1 antibody is selected from the group consisting of nivolumab, pembrolizumab, and pidilizumab. In some embodiments, the PD-1 inhibitor is an immunoadhesin (e.g., an immunoadhesin comprising an extracellular or PD-1-binding portion of PDL1 or PDL2 fused to a constant region (e.g., an Fc region of an immunoglobulin sequence). In some embodiments, the PDL1 inhibitor comprises AMP-224. Nivolumab, also known as MDX-1106-04, MDX-1106, ONO-4538, BMS-936558, and OPDIVO®, is an anti-PD-1 antibody described in WO2006 / 121168. Pembrolizumab, also known as MK-3475, Merck 3475, lambrolizumab, KEYTRUDA®, and SCH-900475, is an anti-PD-1 antibody described in WO2009 / 114335. Pidilizumab, also known as CT-011, hBAT, or hBAT-1, is an anti-PD-1 antibody described in WO2009 / 101611. AMP-224, also known as B7-DCIg, is a PDL2-Fc fusion soluble receptor described in WO2010 / 027827 and WO2011 / 066342. Additional PD-1 inhibitors include MEDI0680, also known as AMP-514, and REGN2810.

[0032] In some embodiments, the ICB therapy includes a PDL1 inhibitor, such as durvalumab, also known as MEDI4736, atezolizumab, also known as MPDL3280A, avelumab, also known as MSB00010118C, MDX-1105, BMS-936559, or a combination thereof. In certain aspects, the ICB therapy includes a PDL2 inhibitor, such as rHIgM12B7.

[0033] In some embodiments, the inhibitor comprises the heavy and light chain CDRs or VRs of nivolumab, pembrolizumab, or pidilizumab. Thus, in one embodiment, the inhibitor comprises the CDR1, CDR2, and CDR3 domains of the VH region of nivolumab, pembrolizumab, or pidilizumab and the CDR1, CDR2, and CDR3 domains of the VL region of nivolumab, pembrolizumab, or pidilizumab. In another embodiment, the antibody competes for binding to and / or binds to the same epitope on PD-1, PDL1, or PDL2 as the above-mentioned antibody. In another embodiment, the antibody has at least about 70, 75, 80, 85, 90, 95, 97, or 99% (or any derivable range therein) of variable region amino acid sequence identity with the above-mentioned antibody.

[0034] 2. CTLA-4, B7-1, and B7-2 Another immune checkpoint that can be targeted in the methods provided herein is cytotoxic T lymphocyte-associated protein 4 (CTLA-4), also known as CD152. The complete cDNA sequence of human CTLA-4 has Genbank accession number L15006. CTLA-4 is found on the surface of T cells and acts as an "off" switch upon binding to B7-1 (CD80) or B7-2 (CD86) on the surface of antigen-presenting cells. CTLA4 is a member of the immunoglobulin superfamily that is expressed on the surface of helper T cells and transmits inhibitory signals to T cells. CTLA4 is similar to the T cell costimulatory protein CD28; both molecules bind to B7-1 and B7-2 on antigen-presenting cells. CTLA-4 transmits inhibitory signals to T cells, while CD28 transmits stimulatory signals. Intracellular CTLA-4 is also found in regulatory T cells and may be important for their function. T cell activation via the T cell receptor and CD28 leads to increased expression of CTLA-4, an inhibitory receptor for the B7 molecule. The inhibitors of the present disclosure can block one or more functions of CTLA-4, B7-1, and / or B7-2 activity. In some embodiments, the inhibitors block the interaction between CTLA-4 and B7-1. In some embodiments, the inhibitors block the interaction between CTLA-4 and B7-2.

[0035] In some embodiments, the ICB therapy comprises an anti-CTLA-4 antibody (eg, a human, humanized, or chimeric antibody), an antigen-binding fragment thereof, an immunoadhesin, a fusion protein, or an oligopeptide.

[0036] Anti-human CTLA-4 antibodies (or VH and / or VL domains derived therefrom) suitable for use in the present method can be produced using methods well known in the art. Alternatively, art-recognized anti-CTLA-4 antibodies can be used. For example, the anti-CTLA-4 antibodies disclosed in U.S. Patent No. 8,119,129, WO01 / 14424, WO98 / 42752; WO00 / 37504 (CP675,206, also known as tremelimumab; formerly ticilimumab), U.S. Patent No. 6,207,156; Hurwitz et al., 1998 can be used in the methods disclosed herein. The teachings of each of the aforementioned publications are incorporated herein by reference. Antibodies that compete with any of these art-recognized antibodies for binding to CTLA-4 can also be used. For example, humanized CTLA-4 antibodies are described in International Publication Nos. WO2001 / 014424, WO2000 / 037504 and US Pat. No. 8,017,144, all of which are incorporated herein by reference.

[0037] An additional anti-CTLA-4 antibody useful as ICB therapy in the methods and compositions of the disclosure is ipilimumab (also known as 10D1, MDX-010, MDX-101, and Yervoy®) or antigen-binding fragments and variants thereof (see, e.g., WO 01 / 14424).

[0038] In some embodiments, the inhibitor comprises the heavy and light chain CDRs or VRs of tremelimumab or ipilimumab. Thus, in one embodiment, the inhibitor comprises the CDR1, CDR2, and CDR3 domains of the VH region of tremelimumab or ipilimumab and the CDR1, CDR2, and CDR3 domains of the VL region of tremelimumab or ipilimumab. In another embodiment, the antibody competes for binding to and / or binds to the same epitope on PD-1, B7-1, or B7-2 as the above-mentioned antibody. In another embodiment, the antibody has at least about 70, 75, 80, 85, 90, 95, 97, or 99% (or any derivable range therein) of variable region amino acid sequence identity with the above-mentioned antibody.

[0039] B. Inhibition of Costimulatory Molecules In some embodiments, the immunotherapy comprises an inhibitor of a costimulatory molecule. In some embodiments, the inhibitor comprises an inhibitor of B7-1 (CD80), B7-2 (CD86), CD28, ICOS, OX40 (TNFRSF4), 4-1BB (CD137; TNFRSF9), CD40L (CD40LG), GITR (TNFRSF18), and combinations thereof. Inhibitors include inhibitory antibodies, polypeptides, compounds, and nucleic acids.

[0040] C. Dendritic Cell Therapy Dendritic cell therapy uses dendritic cells to present tumor antigens to lymphocytes, which then activate and prime the lymphocytes to elicit an antitumor response and kill other cells that present the antigens. Dendritic cells are antigen-presenting cells (APCs) in the mammalian immune system. In cancer treatment, they assist in cancer antigen targeting. One example of a dendritic cell-based cellular cancer therapy is Sipuleucel-T.

[0041] One method of inducing dendritic cells to present tumor antigens is by inoculation with autologous tumor lysates or short peptides (small portions of proteins corresponding to protein antigens on cancer cells). These peptides are often administered in combination with adjuvants (highly immunogenic substances) to enhance immune and antitumor responses. Other adjuvants include proteins or other chemicals that attract and / or activate dendritic cells, such as granulocyte-macrophage colony-stimulating factor (GM-CSF).

[0042] Dendritic cells can also be activated in vivo by causing tumor cells to express GM-CSF, which can be achieved by genetically engineering tumor cells to produce GM-CSF or by infecting tumor cells with an oncolytic virus that expresses GM-CSF.

[0043] Another strategy involves extracting dendritic cells from the patient's blood and activating them ex vivo. The dendritic cells are activated in the presence of tumor antigens, which can be single tumor-specific peptides / proteins or tumor cell lysates (a solution of disassembled tumor cells). These cells (with optional adjuvants) are injected to induce an immune response.

[0044] Dendritic cell therapy involves the use of antibodies that bind to receptors on the surface of dendritic cells. An antigen is added to the antibody to induce dendritic cells to mature and provide immunity against tumors. Dendritic cell receptors such as TLR3, TLR7, TLR8, or CD40 have been used as antibody targets.

[0045] D. CAR-T cell therapy Chimeric antigen receptors (CARs, also known as chimeric immune receptors, chimeric T cell receptors, or artificial T cell receptors) are engineered receptors that combine new specificities with immune cells to target cancer cells. Typically, these receptors transfer the specificity of a monoclonal antibody onto a T cell. The receptors are called chimeric because parts from different sources are fused together. CAR-T cell therapy refers to the use of such transformed cells for cancer treatment.

[0046] The basic principle of CAR-T cell design involves a recombinant receptor that combines antigen-binding and T-cell activation functions. The general premise of CAR-T cells is to engineer T cells that target markers found on cancer cells. Scientists can extract T cells from humans, genetically modify them, and then inject them back into the patient to attack cancer cells. Once engineered into CAR-T cells, T cells act as "living drugs." CAR-T cells create a link between their extracellular ligand-recognition domain and an intracellular signaling molecule, which then activates the T cell. The extracellular ligand-recognition domain is typically a single-chain variable fragment (scFv). A key aspect of the safety of CAR-T cell therapy is ensuring that only cancerous tumor cells, and not normal cells, are targeted. CAR-T cell specificity is determined by the choice of the targeted molecule.

[0047] Exemplary CAR-T therapies include Tisagenlecleucel (Kymriah) and Axicabtagene ciloleucel (Yescarta). In some embodiments, the CAR-T therapy targets CD19.

[0048] E. Cytokine Therapy Cytokines are proteins produced by many types of cells present in tumors. Cytokines can modulate the immune response. Tumors often use cytokines to promote their own growth and dampen the immune response. This immunomodulatory effect allows cytokines to be used as drugs to induce an immune response. Two commonly used cytokines are interferons and interleukins.

[0049] Interferons are produced by the immune system. They are typically involved in antiviral responses but are also used in cancer treatment. Interferons are classified into three groups: type I (IFNα and IFNβ), type II (IFNγ), and type III (IFNλ).

[0050] Interleukins have numerous immune system effects, with IL-2 being an exemplary interleukin cytokine therapy.

[0051] F. Adoptive T cell therapy Adoptive T cell therapy is a form of passive immunization through the infusion of T cells (adoptive cell transfer). T cells are found in the blood and tissues and are typically activated when they encounter a foreign pathogen. Specifically, T cells are activated when their surface receptors encounter cells that display a portion of a foreign protein on their surface antigen. Such cells can be either infected cells or antigen-presenting cells (APCs). T cells are found in normal and tumor tissues, where they are known as tumor-infiltrating lymphocytes (TILs). T cells are activated by the presence of APCs, such as dendritic cells, that present tumor antigens. Although these cells can attack tumors, the environment within the tumor is highly immunosuppressive, preventing immune-mediated tumor death.

[0052] Several methods have been developed to generate and obtain tumor-targeting T cells. Tumor antigen-specific T cells can be extracted from tumor samples (TIL) or filtered from the blood. Subsequent activation and culture are performed ex vivo, and the resulting product is then reinfused. Activation can be achieved through gene therapy or by exposing T cells to tumor antigens.

[0053] It is contemplated that cancer treatment may exclude any of the cancer treatments described herein. Furthermore, aspects of the present disclosure include patients who have previously been treated with a therapy described herein, patients who are currently being treated with a therapy described herein, or patients who have not been treated with a therapy described herein. In some aspects, the patient is a patient who has been determined to be resistant to a therapy described herein. In some aspects, the patient is a patient who has been determined to be sensitive to a therapy described herein.

[0054] V. Additional treatments The current method and composition of the present disclosure can comprise one or more additional therapeutic methods known in the art and / or as described herein.In some embodiments, the additional therapeutic method comprises additional cancer treatment.Examples of such treatment are described herein, such as immunotherapy as described herein or the additional therapeutic type described below.

[0055] Oncolytic viruses In some embodiments, the additional treatment comprises an oncolytic virus. An oncolytic virus is a virus that preferentially infects and kills cancer cells. When infected cancer cells are destroyed by oncolysis, they release new infectious virus particles or virions to help destroy the remaining tumor. Oncolytic viruses are thought to not only cause direct destruction of tumor cells, but also stimulate host anti-tumor immune responses for long-term immunotherapy.

[0056] B. Polysaccharides In some embodiments, the additional treatment comprises polysaccharides.Certain compounds found in mushrooms, mainly polysaccharides, can upregulate the immune system and may have anti-cancer properties.For example, beta-glucans such as lentinan have been shown in laboratory studies to stimulate macrophages, NK cells, T cells and immune system cytokines, and are being investigated in clinical trials as immune adjuvants.

[0057] C. Neoantigens In some embodiments, the additional treatment involves neoantigen administration. Many tumors express mutations. These mutations potentially create new targetable antigens (neoantigens) for use in T cell immunotherapy. The presence of CD8+ T cells in cancer lesions, as identified using RNA sequencing data, is higher in tumors with high mutational burden. The levels of transcripts associated with the cytolytic activity of natural killer cells and T cells are positively correlated with the mutational burden in many human tumors.

[0058] D. Chemotherapy In some embodiments, the additional treatment comprises chemotherapy. Suitable classes of chemotherapeutic agents include (a) alkylating agents, such as nitrogen mustards (e.g., mechlorethamine, cyclophosphamide, ifosfamide, melphalan, chlorambucil), ethylenimines and methylmelamines (e.g., hexamethylmelamine, thiotepa), alkylsulfonates (e.g., busulfan), nitrosoureas (e.g., carmustine, lomustine, chlorozoticin, streptozocin), and triazines (e.g., dacarbazine); (b) antimetabolites, such as folic acid analogs (e.g., methotrexate), pyrimidine analogs (e.g., 5-fluorouracil, floxuridine, cytarabine, azauridine), and purine analogs and related substances (e.g., 6-mercaptopurine, 6-thioguanine, pentostatin), (c) natural products, such as vinca alkaloids (e.g., vinblastine, vincristine), epipodophyllotoxins (e.g., etoposide, teniposide), antibiotics (e.g., dactinomycin, daunorubicin, doxorubicin, bleomycin, plicamycin, and mitoxantrone), enzymes (e.g., L-asparaginase), and biological response modifiers (e.g., interferon-α), and (d) various other agents, such as platinum coordination complexes (e.g., cisplatin, carboplatin), substituted ureas (e.g., hydroxyurea), methylhydrazine derivatives (e.g., procarbazine), and adrenocortical hormone synthesis inhibitors (e.g., taxol and mitotane). In some embodiments, cisplatin is a particularly suitable chemotherapeutic agent.

[0059] Cisplatin has been widely used to treat cancers, such as metastatic testicular or ovarian cancer, advanced bladder cancer, head and neck cancer, cervical cancer, lung cancer, or other tumors. Cisplatin is not absorbed orally and must therefore be delivered via other routes, such as intravenous, subcutaneous, intratumoral, or intraperitoneal injection. Cisplatin can be used alone or in combination with other agents. In certain embodiments, effective doses for clinical use include about 15 mg / m² to about 20 mg / m² for 5 days every 3 weeks for a total of 3 courses. In some embodiments, the amount of cisplatin delivered to cells and / or subjects with a construct comprising an Egr-1 promoter operably linked to a polynucleotide encoding a therapeutic polypeptide is less than the amount delivered when cisplatin is used alone.

[0060] Other suitable chemotherapeutic agents include microtubule inhibitors, such as paclitaxel ("taxol") and doxorubicin hydrochloride ("doxorubicin"). The combination of an Egr-1 promoter / TNFα construct delivered via an adenoviral vector with doxorubicin has been determined to be effective in overcoming resistance to chemotherapy and / or TNFα, suggesting that combined treatment with the construct and doxorubicin overcomes resistance to both doxorubicin and TNFα.

[0061] Doxorubicin is poorly absorbed and is preferably administered intravenously. In certain embodiments, suitable intravenous dosages for adults include about 60 mg / m2 to about 75 mg / m2 at intervals of about 21 days, or about 25 mg / m2 to about 30 mg / m2 on each of two or three consecutive days (repeated at intervals of about 3 to about 4 weeks), or about 20 mg / m2 once weekly. In elderly patients, the lowest dose should be used if there is previous bone marrow suppression caused by previous chemotherapy or neoplastic bone marrow infiltration, or if the drug is used in combination with other myelopoiesis-suppressing drugs.

[0062] Nitrogen mustards are another suitable chemotherapeutic agent useful in the methods of the present disclosure. Nitrogen mustards include, but are not limited to, mechlorethamine (HN2), cyclophosphamide and / or ifosfamide, melphalan (L-sarcolysin), and chlorambucil. Cyclophosphamide (CYTOXAN®) is commercially available from Mead Johnson, and NEOSTAR® (available from Adria) is another suitable chemotherapeutic agent. Suitable oral dosages for adults include, for example, about 1 mg / kg / day to about 5 mg / kg / day, and intravenous dosages include, for example, an initial dose of about 40 mg / kg to about 50 mg / kg divided over a period of about 2 to about 5 days, or about 10 mg / kg to about 15 mg / kg every about 7 to about 10 days, or about 3 mg / kg to about 5 mg / kg twice weekly, or about 1.5 mg / kg / day to about 3 mg / kg / day. Due to adverse gastrointestinal effects, the intravenous route is preferred. Drugs may also be administered intramuscularly, by infiltration, or into body cavities.

[0063] Suitable additional chemotherapeutic agents include pyrimidine analogs, such as cytarabine (cytosine arabinoside), 5-fluorouracil (5-FU), and floxuridine (fluorodeoxyuridine; FudR). 5-FU can be administered to a subject at a dose anywhere from about 7.5 to about 1000 mg / m². Furthermore, the 5-FU administration schedule can be for a variety of periods, for example, up to 6 weeks, or as determined by one of ordinary skill in the art to which this disclosure pertains.

[0064] Another suitable chemotherapeutic agent, gemcitabine diphosphate (GEMZAR®, Eli Lilly & Co., "gemcitabine"), is recommended for the treatment of advanced and metastatic pancreatic cancer and is therefore useful in the present disclosure for these cancers.

[0065] The amount of chemotherapeutic agent delivered to a patient can vary. In one suitable embodiment, the chemotherapeutic agent can be administered in an amount effective to cause cancer arrest or regression in the host when the chemotherapy is administered in conjunction with the construct. In other embodiments, the chemotherapeutic agent can be administered in an amount that is anywhere from 2 to 10,000 times lower than the chemotherapeutic effective amount of the chemotherapeutic agent. For example, the chemotherapeutic agent can be administered in an amount that is about 20, about 500, or even about 5,000 times lower than the effective amount of the chemotherapeutic agent. The chemotherapeutic agents of the present disclosure can be tested in vivo for the desired therapeutic activity when used in combination with the construct and to determine effective dosages. For example, such compounds can be tested in appropriate animal model systems, such as rats, mice, chickens, cows, monkeys, and rabbits, before testing in humans. In vitro testing can also be used to determine appropriate combinations and dosages, as described in the Examples.

[0066] E. Radiation Therapy In some embodiments, the additional or previous therapy comprises radiation, such as ionizing radiation. As used herein, "ionizing radiation" refers to radiation that contains particles or photons that have sufficient energy to cause ionization (gain or loss of electrons) or that can generate sufficient energy through nuclear interactions to cause ionization (gain or loss of electrons). An exemplary and preferred ionizing radiation is X-rays. Means for delivering X-rays to target tissues or cells are well known in the art.

[0067] In some embodiments, the amount of ionizing radiation is greater than 20 Gray (Gy) and is administered in a single dose, hi some embodiments, the amount of ionizing radiation is 18 Gy and is administered in three doses. In some embodiments, the amount of ionizing radiation is at least 2, 4, 6, 8, 10, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 18, 19, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 40 Gy (or any derivable range therein), at most 2, 4, 6, 8, 10, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 18, 19, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 or 40 Gy (or any derivable range therein) or exactly 2, 4, 6, 8, 10, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 18, 19, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 or 40 Gy (or any derivable range therein). In some embodiments, ionizing radiation is administered in at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 doses (or any derivable range therein), at most 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 doses (or any derivable range therein), or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 doses (or any derivable range therein). If more than one dose is administered, the doses may be separated by a time of about 1, 4, 8, 12, or 24 hours, or 1, 2, 3, 4, 5, 6, 7, or 8 days, or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, or 16 weeks (or any derivable range therein).

[0068] In some embodiments, the amount of IR can be presented as a total dose of IR, which is then administered in fractionated doses. For example, in some embodiments, the total dose is 50 Gy, administered in 10 fractionated doses of 5 Gy each. In some embodiments, the total dose is 50-90 Gy, administered in 20-60 fractionated doses of 2-3 Gy each. In some embodiments, the total dose of IR is at least 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 125, 130, 135, 140, or 150 (or any derivable range therein), at most 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 0, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100 , 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 125, 130, 135, 140, or 150 (or any derivable range therein) or about 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 125, 130, 135, 140 or 150 (or any derivable range therein). In some embodiments, the total dose is administered in fractionated doses of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 20, 25, 30, 35, 40, 45, or 50 Gy (or any derivable range therein), at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 20, 25, 30, 35, 40, 45, or 50 Gy (or any derivable range therein), or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 20, 25, 30, 35, 40, 45, or 50 Gy (or any derivable range therein). In some embodiments, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100 (or any derivable range therein), at most 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100 (or any derivable range therein) or exactly 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 , 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100 (or any derivable range therein) fractionated doses are administered. In some embodiments, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 (or any derivable range therein) fractionated doses, at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 (or any derivable range therein) fractionated doses, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 (or any derivable range therein) fractionated doses are administered per day. In some embodiments, the number of amino acids present in the nucleotide sequence is at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 (or any derivable range therein), at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 (or any derivable range therein), or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,22, 23, 24, 25, 26, 27, 28, 29, or 30 (or any derivable range therein) fractionated doses are administered per week.

[0069] F. Surgery Approximately 60% of cancer patients undergo some type of surgery, including preventive, diagnostic or staging, curative, and palliative surgery. Curative surgery involves resection, which physically removes, excises, and / or destroys all or part of cancerous tissue, and may be combined with other therapies, such as the treatment of this embodiment, chemotherapy, radiation therapy, hormone therapy, gene therapy, immunotherapy, and / or alternative therapies. Tumor resection refers to the physical removal of at least part of the tumor. In addition to tumor resection, surgical treatments include laser surgery, cryosurgery, electrosurgery, and microsurgical surgery (Mohs surgery).

[0070] Removal of part or all of the cancerous cells, tissues, or tumors may result in the formation of a cavity in the body. Treatment can be achieved by perfusion, direct injection, or local application of additional anti-cancer treatment to the area. Such treatments may be repeated, for example, every 1, 2, 3, 4, 5, 6, or 7 days, or every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months. These treatments may also be at various dosages.

[0071] G. Other Agents It is contemplated that other agents may be combined with certain aspects of this embodiment to improve the therapeutic efficacy of treatment. These additional agents include agents that affect the upregulation of cell surface receptors and gap junctions, cytostatic and differentiation agents, inhibitors of cell adhesion, agents that sensitize hyperproliferative cells to apoptosis-inducing agents, or other biological agents. Increasing intercellular signaling by increasing the number of gap junctions will enhance the anti-hyperproliferative effect on adjacent hyperproliferative cell populations. In other embodiments, cytostatic or differentiation agents may be combined with certain aspects of this embodiment to improve the anti-hyperproliferative efficacy of treatment. Inhibitors of cell adhesion are contemplated to improve the efficacy of this embodiment. Examples of cell adhesion inhibitors are focal adhesion kinase (FAK) inhibitors and lovastatin. Furthermore, it is contemplated that other agents that sensitize hyperproliferative cells to apoptosis, such as the antibody c225, may also be combined with certain aspects of this embodiment to improve the efficacy of treatment.

[0072] VI. Sample Preparation In certain aspects, the method includes collecting a sample from a subject. Collection methods provided herein may include biopsy methods, such as fine needle aspiration, core needle biopsy, vacuum-assisted biopsy, incisional biopsy, excision biopsy, punch biopsy, shave biopsy, or skin biopsy. In certain embodiments, the sample is collected from a biopsy specimen from esophageal tissue by any of the aforementioned biopsy methods. In other embodiments, the sample may be collected from any of the tissues provided herein, including non-cancerous or cancerous tissues and non-cancerous or cancerous tissues from serum, gallbladder, mucosa, skin, heart, lung, breast, pancreas, blood, liver, muscle, kidney, smooth muscle, bladder, colon, intestine, brain, prostate, esophageal, or thyroid tissue. Alternatively, the sample may be collected from any other source, including blood, sweat, hair follicles, oral tissue, tears, menstrual secretions, feces, or saliva. In certain aspects of the current method, any medical professional, such as a doctor, nurse, or medical technician, may collect the biological sample for testing. Additionally, biological samples can be collected without the assistance of a medical professional.

[0073] The sample may include, but is not limited to, a subject's tissue, cells, or biological material derived from cells. The biological sample may be a heterogeneous or homogeneous population of cells or tissues. The biological sample may be collected using any method known in the art that can provide a sample suitable for the analytical methods described herein. The sample may be collected by non-invasive methods, including skin or cervical scraping, oral mucosal swabbing, saliva collection, urine collection, feces collection, menstrual secretions, tears, or semen collection.

[0074] Samples may be collected by methods known in the art. In certain embodiments, samples are collected by biopsy. In other embodiments, samples are collected by swabbing, endoscopy, scraping, phlebotomy, or any other method known in the art. In some cases, samples may be collected, stored, or transported using components of the kit of the present method. In some cases, multiple samples, such as multiple esophageal samples, may be collected for diagnosis by the methods described herein. In other cases, multiple samples, for example, one or more samples from one tissue type (e.g., esophagus) and one or more samples from another specimen (e.g., serum), may be collected for diagnosis by the present method. In some cases, multiple samples, for example, one or more samples from one tissue type (e.g., esophagus) and one or more samples from another specimen (e.g., serum), may be collected at the same or different times. Samples may be collected at different times and stored and / or analyzed by different methods. For example, samples may be collected and analyzed by conventional staining or any other cytological analysis method.

[0075] In some embodiments, the sample comprises a fractionated sample, such as a blood sample fractionated by centrifugation or other fractionation techniques. The sample may be enriched for white blood cells or red blood cells. In some embodiments, the sample may be fractionated or enriched for white blood cells or lymphocytes. In some embodiments, the sample comprises a whole blood sample.

[0076] In some embodiments, the biological sample can be collected by a doctor, nurse, or other medical professional, such as a medical technician, endocrinologist, cytologist, phlebotomist, radiologist, or pulmonologist.The medical professional can recommend the appropriate test or assay to be performed on the sample.In certain aspects, a molecular profiling company may provide an opinion on which assay or test is most appropriate.In a further aspect of the method, the patient or subject can collect the biological sample for testing without the assistance of a medical professional, such as by collecting a whole blood sample, a urine sample, a fecal sample, an oral sample, or a saliva sample.

[0077] In other cases, samples are obtained by invasive procedures, including biopsy, needle aspiration, endoscopy, or phlebotomy. Methods of needle aspiration may also include fine needle aspiration, core needle biopsy, vacuum-assisted biopsy, or large core biopsy. In some embodiments, multiple samples may be obtained by the methods herein to ensure a sufficient amount of biological material.

[0078] Also, general methods for collecting biological samples are known in the art. Publications such as Ramzy, Ibrahim Clinical Cytopathology and Aspiration Biopsy 2001, which is incorporated herein by reference in its entirety, describe general methods for biopsy and cytological methods. In one embodiment, the sample is a fine needle aspirate of the esophagus or suspected esophageal tumor or neoplasm. In some cases, the fine needle aspirate sampling procedure may be guided by the use of ultrasound, X-ray or other imaging devices.

[0079] In some aspects of the method, the molecular profiling provider may collect the biological sample directly from the subject, from a medical professional, from a third party, or from a kit provided by the molecular profiling provider or a third party. In some cases, the subject, medical professional, or third party may obtain the biological sample and send it to the molecular profiling provider, which then collects the biological sample. In some cases, the molecular profiling provider may provide appropriate containers and excipients for storing and transporting the biological sample to the molecular profiling provider.

[0080] In some embodiments of the methods described herein, a medical professional need not be involved in the initial diagnosis or sample acquisition. Alternatively, an individual may collect a sample using an over-the-counter (OTC) kit. The OTC kit may include a means for collecting the sample as described herein, a means for storing the sample in preparation for testing, and instructions for proper use of the kit. In some cases, molecular profiling services are included in the purchase price of the kit. In other cases, molecular profiling services are charged separately. A sample suitable for use by a molecular profiling service can be any material containing tissue, cells, nucleic acids, genes, gene fragments, expression products, gene expression products, or gene expression product fragments from the individual being tested. Methods for determining the suitability and / or appropriateness of a sample are provided.

[0081] In some embodiments, the subject may be referred to a specialist, such as an oncologist, surgeon, or endocrinologist.The specialist may also collect a biological sample for testing, or refer the individual to a testing center or research facility to submit a biological sample.In some cases, a medical professional may refer the subject to a testing center or research facility to submit a biological sample.In other cases, the subject may provide the sample.In some cases, a molecular profiling company may collect the sample.

[0082] VII. Cancer Monitoring In certain aspects, if a patient is determined to be at high risk of recurrence or have a poor prognosis based on the expression of the above biomarkers, such as expression levels and / or the presence of CD73-positive macrophages in a biological sample from the subject, the methods of the present disclosure may be more frequently combined with one or more other cancer diagnostic or screening tests.

[0083] In some embodiments, the method of the present disclosure further comprises one or more monitoring tests. The monitoring protocol can include any method known in the art. In particular, the monitoring includes taking samples and examining the samples for diagnosis. For example, the monitoring can include endoscopy, biopsy, endoscopic ultrasound, X-ray, barium swallow, CT scan, MRI, PET scan, laparoscopy or HER2 test. In some embodiments, the monitoring test includes radiological imaging. Examples of radiological imaging useful in the method of the present disclosure include liver ultrasound, computed tomography (CT) abdominal scan, liver magnetic resonance imaging (MRI), whole-body CT scan and whole-body MRI.

[0084] VIII.ROC analysis In statistics, a receiver operating characteristic (ROC), or ROC curve, is a graphical plot that shows the performance of a binary classification system as the discrimination threshold is varied. The curve is constructed by plotting the true positive rate against the false positive rate at various threshold settings. (The true positive rate is also known as sensitivity in biomedical informatics or recall in machine learning. The false positive rate is also known as fallout and can be calculated as 1 - specificity.) Thus, the ROC curve is sensitivity as a function of fallout. In general, if the probability distributions of both detection and false alarm are known, an ROC curve can be constructed by plotting the cumulative distribution function of the detection probability (the area under the probability distribution from -infinity to +infinity) on the y-axis against the cumulative distribution function of the false alarm probability on the x-axis.

[0085] ROC analysis provides a tool for selecting the most likely optimal model and discarding suboptimal models, independent of (and before specifying) the cost context or class distribution. ROC analysis relates in a direct and natural way to cost-benefit analysis of diagnostic decision-making.

[0086] ROC curves were originally developed by electrical engineers and radar technicians during World War II to detect enemy targets on the battlefield, and were soon introduced into psychology to explain the perceptual detection of stimuli. Since then, ROC analysis has been used for decades in medicine, radiology, biometrics, and other fields, and is increasingly used in machine learning and data mining research.

[0087] ROC is also known as relative operating characteristic curve, because it is a comparison of two operating characteristics (TPR and FPR) when the standard changes. ROC analysis curve is known in the art and is described in Metz CE (1978) Basic principles of ROC analysis. Seminars in Nuclear Medicine 8:283-298; Youden WJ (1950) An index for rating diagnostic tests. Cancer 3:32-35; Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry 39:561-577; and Greiner M, Pfeiffer D, Smith RD (2000) Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Preventive Veterinary Medicine 45:23-41, which are incorporated herein in their entirety. ROC analysis can be used to create cutoff values ​​for prognostic and / or diagnostic purposes.

[0088] IX. Nucleic Acid Assays Aspects of the method include assaying nucleic acids to determine the expression or activity level and / or presence of CD73-expressing cells in a biological sample. Arrays can be used to detect differences between two samples. Specifically contemplated applications include identifying and / or quantifying differences between RNA from a normal sample and a non-normal sample, between a cancerous state and a non-cancerous state, between one cancerous state, such as cells with a fast doubling time, and another cancerous state, such as cells with a slow doubling time, or between samples that have undergone two different treatments. RNA may also be compared between a sample believed to be susceptible to a particular disease or condition and a sample believed not to be susceptible or resistant to that disease or condition. A non-normal sample is one that exhibits a phenotypic trait of the disease or condition or is considered abnormal with respect to that disease or condition. It may also be compared to cells that are normal with respect to that disease or condition. Phenotypic traits include symptoms of or susceptibility to a disease or condition, the components of which may or may not be genetic, or may or may not be caused by hyperproliferative or neoplastic cells.

[0089] Arrays may be used to determine the expression level of biomarkers. Arrays include solid supports to which nucleic acid probes are attached. Arrays typically include multiple different nucleic acid probes attached to the surface of a substrate at different known locations. These arrays, also referred to as "microarrays" or colloquially as "chips," are generally described in the art, for example, U.S. Patent Nos. 5,143,854, 5,445,934, 5,744,305, 5,677,195, 6,040,193, 5,424,186, and Fodor et al., 1991, each of which is incorporated herein by reference in its entirety for all purposes. Techniques for synthesizing these arrays using mechanical synthesis methods are described, for example, in U.S. Patent No. 5,384,261, which is incorporated herein by reference in its entirety for all purposes. While planar array surfaces are used in certain aspects, arrays may be fabricated on surfaces of virtually any shape or on multiple surfaces. The array can be nucleic acids on beads, gels, polymer surfaces, fibers such as fiber optics, glass, or any other suitable substrate. See U.S. Patent Nos. 5,770,358, 5,789,162, 5,708,153, 6,040,193, and 5,800,992, which are incorporated by reference in their entirety for all purposes.

[0090] Additional assays useful for determining biomarker expression include, but are not limited to, nuclear amplification, polymerase chain reaction, quantitative PCR, RT-PCR, in situ hybridization, Northern hybridization, hybridization protection assay (HPA) (GenProbe), branched DNA (bDNA) assay (Chiron), rolling circle amplification (RCA), single molecule hybridization detection (US Genomics), Invader assay (ThirdWave Technologies), and / or Bridge Litigation Assay (Genaco).

[0091] Another useful assay for quantifying and / or identifying nucleic acids, including those containing biomarker genes, is RNA sequencing (RNA-seq). RNA-seq, also known as whole-transcriptome shotgun sequencing, uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment. RNA-seq is used to analyze the constantly changing cellular transcriptome. Specifically, RNA-seq facilitates the ability to view alternatively spliced ​​transcripts, post-transcriptional modifications, gene fusions, mutations / SNPs, and changes in gene expression. In addition to mRNA transcripts, RNA-seq can look at various populations of RNA, including total RNA, small RNAs (e.g., miRNAs), tRNAs, and ribosomal profiling. RNA-seq can also be used to determine exon / intron boundaries and verify or correct previously annotated 5' and 3' gene boundaries.

[0092] X. Protein Assay To determine the biomarker expression level, various techniques can be used to measure the expression levels of polypeptides and proteins in biological samples. Examples of such formats include, but are not limited to, enzyme immunoassay (EIA), radioimmunoassay (RIA), Western blot analysis, and enzyme-linked immunosorbent assay (ELISA). Those skilled in the art can easily adapt known protein / antibody detection methods for use in determining the protein expression level of a biomarker.

[0093] In one embodiment, antibodies or antibody fragments or derivatives can be used in methods such as Western blot, ELISA, flow cytometry, or immunofluorescence techniques to detect biomarker expression and / or the presence of cell surface markers such as CD73. In some embodiments, either the antibody or the protein is immobilized on a solid support. Suitable solid supports or carriers include any support capable of binding an antigen or antibody. Well-known supports or carriers include glass, polystyrene, polypropylene, polyethylene, dextran, nylon, amylase, natural and modified cellulose, polyacrylamide, gabbro, and magnetite.

[0094] Those skilled in the art will know many other carriers suitable for binding antibodies or antigens and will be able to adapt such supports for use with the present disclosure.The support is then washed with an appropriate buffer, followed by treatment with a detectably labeled antibody.The solid support can then be washed again with a buffer to remove unbound antibody.The amount of label bound to the solid support can then be detected by conventional means.

[0095] Immunohistochemistry is also suitable for detecting the expression level of biomarkers. In some embodiments, expression can be detected using antibodies or antisera, such as polyclonal antisera, and monoclonal antibodies specific to each marker. Antibodies can be detected by directly labeling the antibody itself, for example, with a radioactive label, a fluorescent label, a hapten label such as biotin, or an enzyme such as horseradish peroxidase or alkaline phosphatase. Alternatively, an unlabeled primary antibody is used in conjunction with a labeled secondary antibody, including an antiserum, a polyclonal antiserum, or a monoclonal antibody specific to the primary antibody. Immunohistochemistry protocols and kits are well known in the art and are commercially available.

[0096] Immunological methods that use either specific polyclonal or monoclonal antibodies to detect and measure complex formation as a measure of protein expression are known in the art. Examples of such techniques include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), fluorescence-activated cell sorting (FACS), and antibody arrays. Such immunoassays typically involve measuring complex formation between a protein and its specific antibody. These assays and their quantification against purified, labeled standards are well known in the art. Two-site, monoclonal-based immunoassays or competitive binding assays utilizing antibodies reactive to two non-interfering epitopes may also be used.

[0097] Numerous labels are available and generally known in the art. Radioisotope labels include, for example, 36S, 14C, 1251, 3H, and 131I. Antibodies can be labeled with radioisotopes using techniques known in the art. Fluorescent labels include, for example, rare earth chelates (europium chelates) or labels such as fluorescein and its derivatives, rhodamine and its derivatives, dansyl, Lissamine, phycoerythrin, and Texas Red. Fluorescent labels can be conjugated to antibody variants using techniques known in the art. Fluorescence can be quantified using a fluorometer. A variety of enzyme-substrate labels are available, and U.S. Patent Nos. 4,275,149 and 4,318,980 provide reviews of some of these. Enzymes generally catalyze the chemical alteration of a chromogenic substrate, which can be measured using various techniques. For example, enzymes can catalyze a color change in a substrate, which can be measured spectrophotometrically. Alternatively, the enzyme may alter the fluorescence or chemiluminescence of the substrate. Techniques for quantifying the change in fluorescence have been described above. The chemiluminescent substrate becomes electronically excited by a chemical reaction and may then emit light that can be measured (e.g., using a chemiluminometer) or donate energy to a fluorescent acceptor. Examples of enzyme labels include luciferases (e.g., firefly luciferase and bacterial luciferase; U.S. Pat. No. 4,737,456), luciferin, 2,3-dihydrophthalazinediones, malate dehydrogenase, urease, peroxidases such as horseradish peroxidase (HRPO), alkaline phosphatase, beta-galactosidase, glucoamylase, lysozyme, saccharide oxidases (e.g., glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase), heterocyclic oxidases (e.g., uricase and xanthine oxidase), lactoperoxidase, microperoxidase, and the like.Techniques for conjugating enzymes to antibodies are described in O'Sullivan et al., Methods for the Preparation of Enzyme-Antibody Conjugates for Use in Enzyme Immunoassay, in Methods in Enzymology (Ed. J. Langone & H. Van Vunakis), Academic Press, New York, 73:147-166 (1981).

[0098] XI. Administration of Therapeutic Compositions The therapy provided herein can include the administration of a combination of therapeutic agents, such as a first cancer therapy and a second cancer therapy.The therapy can be administered in any suitable manner known in the art.For example, the first and second cancer treatments can be administered sequentially (at different times) or concurrently (at the same time).In some embodiments, the first and second cancer treatments are administered as separate compositions.In some embodiments, the first and second cancer treatments are in the same composition.

[0099] The embodiments of the present disclosure relate to compositions and methods, including therapeutic compositions. Different treatments can be administered in one composition or more than one composition, for example, two compositions, three compositions or four compositions. Various combinations of active substances can also be used.

[0100] The therapeutic substances of the present disclosure can be administered by the same or different administration routes. In some embodiments, cancer treatment is administered intravenously, intramuscularly, subcutaneously, topically, orally, transdermally, intraperitoneally, intraorbitally, by implantation, by inhalation, intrathecally, intracerebroventricularly, or intranasally. In some embodiments, antibiotics are administered intravenously, intramuscularly, subcutaneously, topically, orally, transdermally, intraperitoneally, intraorbitally, by implantation, by inhalation, intrathecally, intracerebroventricularly, or intranasally. The appropriate dosage can be determined based on the type of disease being treated, the severity and course of the disease, the individual's clinical condition, the individual's medical history and response to treatment, and the discretion of the attending physician.

[0101] Treatments can include various "unit doses." A unit dose is defined as containing a predetermined amount of a therapeutic composition. The amount to be administered and the specific route and formulation are within the ability of those skilled in the clinical arts to determine. A unit dose need not be administered as a single injection, but may include continuous infusion over a period of time. In some embodiments, a unit dose comprises a single administrable dose.

[0102] The amount administered, both in terms of the number of treatments and the unit dose, depends on the desired therapeutic effect. An effective dose is understood to refer to the amount required to achieve a specific effect. In certain embodiments, it is believed that a dose ranging from 10 mg / kg to 200 mg / kg can affect the protective capacity of these agents. Thus, dosages are contemplated to include about 0.1, 0.5, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, and 200, 300, 400, 500, 1000 μg / kg, mg / kg, μg / day, mg / day, or any range derivable therein. Furthermore, such doses can be administered multiple times per day and / or over multiple days, weeks, or months.

[0103] In certain embodiments, an effective dose of the pharmaceutical composition is one that can provide blood levels of about 1 μM to 150 μM. In other embodiments, an effective dose provides blood levels of about 4 μM to 100 μM; or about 1 μM to 100 μM; or about 1 μM to 50 μM; or about 1 μM to 40 μM; or about 1 μM to 30 μM; or about 1 μM to 20 μM; or about 1 μM to 10 μM; or about 10 μM to 150 μM; or about 10 μM to 100 μM; or about 10 μM to 50 μM; or about 25 μM to 150 μM; or about 25 μM to 100 μM; or about 25 μM to 50 μM; or about 50 μM to 150 μM; or about 50 μM to 100 μM (or any derivable range therein).In other aspects, the dose can provide the following blood levels of the agent resulting from the therapeutic agent being administered to a subject: about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100 μM, at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46 , 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100 μM or at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100 μM or any derivable range therein.In certain embodiments, a therapeutic agent administered to a subject is metabolized in the body to a metabolic therapeutic agent, in which case the blood levels may refer to the amount of that therapeutic agent. Alternatively, to the extent that the therapeutic agent is not metabolized by the subject, the blood levels described herein may refer to the unmetabolized therapeutic agent.

[0104] Precise amounts of the therapeutic composition also depend on the judgment of the practitioner and are peculiar to each individual. Factors affecting dosage include the physical and clinical condition of the patient, the route of administration, the intended goal of treatment (alleviation of symptoms versus cure), and the potency, stability, and toxicity of the particular therapeutic agent or other treatments the subject may be receiving.

[0105] Those skilled in the art will understand and appreciate that dosage units of μg / kg body weight or mg / kg body weight can also be converted to and expressed in equivalent concentration units of μg / ml or mM (blood levels), such as 4 μM to 100 μM. It will also be understood that uptake is species and organ / tissue dependent. Applicable conversion factors and physiological assumptions made regarding uptake and concentration measurements are well known and will enable those skilled in the art to convert one concentration measurement to another and make reasonable comparisons and conclusions regarding the doses, efficacies, and results described herein.

[0106] XII. Treatment Methods Provided herein are methods for treating or slowing the progression of cancer in a subject through the administration of a therapeutic composition.

[0107] In some aspects, the treatment produces a durable response in the individual after cessation of treatment. The methods described herein may find use in treating conditions where enhanced immunogenicity is desired, such as increasing tumor immunogenicity in the treatment of cancer.

[0108] In some embodiments, the individual has a cancer that is resistant (demonstrated to be resistant) to one or more anti-cancer treatments. In some embodiments, the resistance to anti-cancer treatment includes the recurrence of cancer or refractory cancer. Recurrence can refer to the reappearance of cancer at the original site or a new site after treatment. In some embodiments, the resistance to anti-cancer treatment includes the progression of cancer during treatment with the anti-cancer treatment. In some embodiments, the cancer is an early-stage cancer or a late-stage cancer.

[0109] In some embodiments of the methods of the present disclosure, the cancer has a low level of T cell infiltration. In some embodiments, the cancer has no detectable T cell infiltration. In some embodiments, the cancer is a non-immunogenic cancer (e.g., non-immunogenic colorectal cancer and / or ovarian cancer). Without being bound by theory, the combination treatment may increase T cell (e.g., CD4+ T cells, CD8+ T cells, memory T cells) priming, activation, proliferation, and / or infiltration compared to before administration of the combination.

[0110] The cancer may be a solid tumor, a metastatic cancer, or a non-metastatic cancer. In certain aspects, the cancer may originate in the bladder, blood, bone, bone marrow, brain, breast, urinary tract, cervix, esophagus, duodenum, small intestine, large intestine, colon, rectum, anus, gums, head, kidney, liver, lung, nasopharynx, neck, ovary, prostate, skin, stomach, testicle, tongue, or uterus.

[0111] The cancer may specifically be of the following tissue types, but is not limited to: neoplasia, malignant; carcinoma; undifferentiated, bladder, blood, bone, brain, breast, urinary, esophageal, thymoma, duodenum, colon, rectum, anus, gums, head, kidney, soft tissue, liver, lung, nasopharynx, neck, ovary, prostate, skin, stomach, testis, tongue, uterus, thymus, cutaneous squamous cell, non-colorectal gastrointestinal, colorectal, melanoma, Merkel cell, renal cell, cervix, hepatocellular, urothelial, non-small cell lung, head and neck, endometrium, esophagogastric junction, small cell lung mesothelioma, ovary, esophagogastric junction, glioblastoma, adrenal cortex, uvea, pancreas, reproductive cell Alveolar, giant cell, and spindle cell carcinoma;Small cell carcinoma;Papillary carcinoma;Squamous cell carcinoma;Lymphoepithelial carcinoma;Basal cell carcinoma;Calcifying epithelioma;Transitional cell carcinoma;Papillary transitional cell carcinoma;Adenocarcinoma;Gastrinoma, malignant;Choleduct carcinoma;Hepatocellular carcinoma;Mixed hepatocellular carcinoma and cholangiocarcinoma;Follicle tumor;Adenoid cystic carcinoma;Adenomatous intrapolypoid adenocarcinoma;Adenocarcinoma, familial polyposis coli;Solid tumor;Carcinoid tumor, malignant;Bronchioloalveolar adenocarcinoma;Papillary adenocarcinoma;Chromophobe carcinoma;Eosinophilic carcinoma;Eosinophilic adenocarcinoma;Basophilic carcinoma;Clear cell adenocarcinoma;Granular cell carcinoma;Follicle adenocarcinoma;Papillary adenocarcinoma and follicular adenocarcinoma Adenocarcinoma;Non-encapsulated sclerosing carcinoma;Adrenal cortical carcinoma;Endometrioid carcinoma;Cutaneous adnexal carcinoma;Apocrine gland carcinoma;Sebaceous gland carcinoma;Earwax gland carcinoma;Mucoepidermoid carcinoma;Cystadenocarcinoma;Papillary cystadenocarcinoma;Papillary serous cystadenocarcinoma;Mucinous cystadenocarcinoma;Mucinous adenocarcinoma;Signet ring cell carcinoma;Invasive ductal carcinoma;Medullary carcinoma;Lobular carcinoma;Inflammatory carcinoma;Paget's disease of the breast;Acinic cell carcinoma;Adenosquamous carcinoma;Adenocarcinoma with squamous metaplasia;Thymoma, malignant;Ovarian stromal tumor, malignant;Theca cell tumor, malignant;Granulosa cell tumor, malignant;Androblastoma, malignant;Sertoli cell tumor;Leydig cell tumor, malignant;Lipocyte tumor, Malignant; Paraganglioma, malignant; Extramammary paraganglioma, malignant; Pheochromocytoma; Hemangiosarcoma; Malignant melanoma; Amelanotic melanoma; Superficial spreading melanoma; Malignant melanoma in giant pigmented nevus; Epithelioid cell melanoma; Cutaneous melanoma, blue nevus, malignant; Sarcoma; Fibrosarcoma; Fibrous histiocytoma, malignant; Myxosarcoma; Liposarcoma; Leiomyosarcoma; Rhabdomyosarcoma; Embryonic rhabdomyosarcoma; Alveolar rhabdomyosarcoma; Stromal sarcoma; Mixed tumor, malignant; Mullerian mixed tumor; Nephroblastoma; Hepatoblastoma; Carcinosarcoma; Mesenchymoma, malignant; Brenner tumor, malignant; Phyllodes tumor, malignant; Synovial sarcoma; Malignant; Dysgerminoma; Embryonic carcinoma;Teratoma, malignant; Ovarian goiter, malignant; Choriocarcinoma; Mesonephroma, malignant; Angiosarcoma; Hemangioendothelioma, malignant; Kaposi's sarcoma; Hemangiopericytoma, malignant; Lymphangiosarcoma; Osteosarcoma; Parosteal osteosarcoma; Chondrosarcoma; Chondroblastoma, malignant; Mesenchymal chondrosarcoma; Giant cell tumor of bone; Ewing's sarcoma; Odontogenic tumor, malignant; Ameloblastic odontosarcoma; Ameloblastoma, malignant; Ameloblastic fibrosarcoma; Pinealoma, malignant; Chordoma; Glioma, malignant; Ependymoma; Astrocytoma; Protoplasmic astrocytoma; Fibrous astrocytoma; Astroblastoma; Oligodendroglioma; Oligodendroglioma; Primitive neuroectodermal; Cerebellar sarcoma; Ganglioblastoma; Neuroblastoma; Retinal blastoma Cell tumor; Olfactory nerve tumor; Meningioma, malignant; Neurofibrosarcoma; Schwannoma, malignant; Granular cell tumor, malignant; Malignant lymphoma; Hodgkin's disease; Hodgkin's lymphoma; Lateral granuloma; Malignant lymphoma, small lymphocytic; Malignant lymphoma, large cell diffuse; Malignant lymphoma, follicular; Mycosis fungoides; Other specified non-Hodgkin's lymphoma; Malignant histiocytosis; Multiple myeloma; Mast cell sarcoma; Immunoproliferative small intestinal disease; Leukemia; Lymphocytic leukemia; Plasma cell leukemia; Erythroleukemia; Lymphocytic leukemia; Myeloid leukemia; Basophilic leukemia; Eosinophilic leukemia; Monocytic leukemia; Mast cell leukemia; Megakaryoblastic leukemia; Myeloid sarcoma; and Hairy cell leukemia.

[0112] In some embodiments, the cancer comprises cutaneous squamous cell carcinoma, non-colorectal and colorectal gastrointestinal cancer, Merkel cell carcinoma, anal cancer, cervical cancer, hepatocellular carcinoma, urothelial carcinoma, melanoma, lung cancer, non-small cell lung cancer, small cell lung cancer, head and neck cancer, kidney cancer, bladder cancer, Hodgkin's lymphoma, pancreatic cancer, or skin cancer.

[0113] In some embodiments, the cancer comprises lung cancer, pancreatic cancer, metastatic melanoma, kidney cancer, bladder cancer, head and neck cancer, or Hodgkin's lymphoma.

[0114] The methods can include determining, administering, or selecting an appropriate cancer "management regimen" and predicting the outcome. As used herein, the phrase "management regimen" refers to a management plan that defines the type of testing, screening, diagnosis, monitoring, care, and treatment (e.g., dosage, schedule, and / or duration of treatment) to be provided to a subject in need thereof (e.g., a subject diagnosed with cancer).

[0115] The term "treatment" or "treating" refers to any treatment for disease in a mammal, including (i) preventing the disease, i.e., preventing the clinical symptoms of the disease from appearing by administration of a protective composition prior to the induction of the disease; (ii) suppressing the disease, i.e., preventing the clinical symptoms of the disease from appearing by administration of a protective composition after an inductive event but prior to the clinical appearance or reappearance of the disease; (iii) inhibiting the disease, i.e., preventing the appearance of clinical symptoms by administration of a protective composition after their initial appearance; and / or (iv) ameliorating the disease, i.e., causing the regression of clinical symptoms by administration of a protective composition after their initial appearance. In some embodiments, treatment may exclude disease prevention.

[0116] In certain aspects, further cancer or metastasis testing or screening or further diagnostics, such as contrast-enhanced computed tomography (CT), positron emission tomography-CT (PET-CT), and magnetic resonance imaging (MRI), may be performed to detect cancer or cancer metastasis in patients determined to have a particular gut microbiota composition.

[0117] XIII. Kit Certain aspects of the present invention also relate to kits comprising the compositions of the present invention or compositions for carrying out the methods of the present invention. In some embodiments, the kits can be used to assess the expression level and / or presence of cell surface markers. In certain embodiments, the kits comprise: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 100, 500, 1,000 or more, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 100, 500, 1,000 or more, or at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 100, 500, 1,000 or more probes, primers or primer sets, synthetic molecules, detection agents, antibodies or inhibitors, or any value or range and combination derivable therein. In some embodiments, there are kits for assessing the expression level and / or cell surface expression of a biomarker in a cell.

[0118] The kit may include components that may be individually packaged or placed in containers such as tubes, bottles, vials, syringes, or other suitable container means.

[0119] Individual components may also be provided in concentrated amounts in the kit. In some embodiments, components are provided individually at the same concentration as when they are in solution with other components. Component concentrations may be provided as 1x, 2x, 5x, 10x, or 20x or higher.

[0120] Kits for using the probes, synthetic nucleic acids, non-synthetic nucleic acids, and / or inhibitors of the present disclosure for prognostic or diagnostic applications are included as part of this disclosure. Specifically, any such molecules corresponding to any of the biomarkers identified herein are contemplated, including nucleic acid primers / primer sets and probes that are identical to or complementary to all or a portion of the biomarker, which may include non-coding sequences of the biomarker as well as coding sequences of the biomarker.

[0121] In certain aspects, negative and / or positive control nucleic acids, probes, and inhibitors are included in some kit embodiments. In addition, the kits may include samples that are negative or positive controls for biomarker expression levels.

[0122] It is contemplated that any method or composition described herein can be implemented with respect to any other method or composition described herein, and that various aspects may be combined. The originally filed claims are contemplated to encompass claims that are multiple dependent on any filed claim or combination of filed claims.

[0123] Embodiments of the present disclosure include kits for analyzing pathological samples by assessing the biomarker expression profile of the sample, comprising two or more probes or detection agents in suitable container means, wherein the probes or detection agents detect one or more of the markers identified herein. [Example]

[0124] XIV. Working Examples The following examples are included to demonstrate preferred embodiments of the invention. It should be understood by those of skill in the art that the techniques disclosed in the examples below represent techniques discovered by the inventors to function well in the practice of the invention, and therefore can be considered to constitute preferred modes for its practice. However, those of skill in the art, in light of the present disclosure, should understand that numerous changes can be made in the specific embodiments disclosed and still obtain like or similar results without departing from the spirit and scope of the invention.

[0125] Example 1: Immune profiling of human tumors identifies CD73 as a combinatorial target in glioblastoma A. Results ICT provides antitumor responses in subsets of patients with specific tumor types (3-9). Recently, independent studies have provided detailed single-cell analyses of tumor-infiltrating leukocytes (TILs) from individual tumors, namely, renal cell carcinoma (RCC), hepatocellular carcinoma (HCC), non-small cell lung cancer (NSCLC), and melanoma (10-13). While these studies provide new insights and validate previous findings regarding immune infiltrates in various cancers, heterogeneity of responses across cancer types may be the result of tumor-type-specific immune checkpoint expression patterns, necessitating comprehensive comparison of TIL phenotypes across multiple tumors. To address this need, we applied mass cytometry (CyTOF) to profile immune cell subsets in 85 patients with five different tumor types: NSCLC (n = 15), RCC (n = 25), MSI-stable colorectal cancer (CRC) (n = 11), prostate cancer (PCa) (n = 21), and glioblastoma multiforme (GBM) (n = 13) (Supplementary Table 1). This is the first CyTOF dataset to evaluate immune cell subsets across various human tumor types.

[0126] We first compared the major immune infiltrates present in each tumor type (Figure 5). We found that NSCLC, RCC, and CRC tumors were CD3 + T cell-rich, CD4+ FoxP3 + We found that CD3 cells were most frequently found in CRC (Figure 1A). Both PCa and GBM were CD3 + Although GBM was poorly infiltrated by T cells, it had higher CD68 + The tumors had a high abundance of myeloid cells (Figure 1A). To identify shared phenotypes among various tumor types, we performed PhenoGraph clustering of CD45+ cells and identified six CD68 + Eight were CD3, including a myeloid cluster and one NK cell metacluster. + T cell metacluster, 10 of which are CD3 - We identified 18 metaclusters (L1-18) that are metaclusters (Figure 1B and Figures 6A-B). We identified a group of six immune metaclusters present in all five tumor types. These clusters exhibited high Shannon entropy, a measure of greater uniformity in their distribution across tumor types. We also identified eight immune metaclusters that exhibited low Shannon entropy values, indicative of tumor-specific distribution (Figure 1C).

[0127] Analysis of the frequency of various T cell clusters in NSCLC, RCC, and CRC reveals that CD3 + CD4 + PD-1 hi and CD3 + CD8 + PD-1 hi We identified metaclusters (L3 and L6, respectively) (Figure 1D and Figure 6C-D). Analyzing PBMC samples from the RCC cohort, we identified T cell subsets (P33 and P24) that correlated with the L3 and L6 clusters, respectively. Interestingly, the P33 and P24 clusters were found to be expanded in responders compared to non-responders to ICT (Figure 7A-C). We also found that CD4 T cells in CRC and PCa, respectively, potentially contribute to the lack of response to ICT (14, 15). + FoxP3 hi Regulatory T cells (L12) and CD8+ VISTA + We noted a higher abundance of (L14) cells (Figure 1D, Figure 6D). PhenoGraph clustering of all CD3-gated cells from 30 samples across three T cell-infiltrated tumor types (NSCLC, RCC, and CRC) revealed 17 metaclusters (Figure 8A-B). We performed hierarchical clustering of all 30 patient samples based on T cell metacluster frequency and identified three major subgroups (I, II, and III) (Figure IE). T cell metacluster T1 (PD-1 hi ICOS + CD4 + T cell-like L3) and T4 (PD-1 hi CD8 + T cell-like L6) were more frequently observed in subgroup II, which mainly included the two tumor types that responded well to ICT, NSCLC and RCC (Figure 1F). Subgroup III consisted of metacluster T2 (CD4 + T cells) and T3 (CD8 + Subgroup I contained a higher frequency of T cells (T cells), while subgroup II displayed intermediate frequencies of various T cell subsets with both high and low immune checkpoint expression (Figure 8C).

[0128] Next, we investigated the CD45 expression across different tumor types. + CD3 identified from PhenoGraph clustering of cells - CD68 + A detailed analysis of the myeloid cluster was performed. We identified two PD-L1 - subset (L5 and L17) and two PD-L1 + L5 was a subset of the VISTA-like lesions (L1 and L8) (Fig. 2A and Fig. 9A). + L17 was identified as a subset and was present at a higher frequency in CRC compared to NSCLC and PCa. +L1 was identified as a myeloid subset shared by all tumor types, but was found only in CRC.

[0129] Metacluster L8 was a unique subset found exclusively in GBM, which was further verified by manual gating (Figure 2A and Figure 9A-C). L8 expressed high levels of CD73 in addition to other co-inhibitory molecules such as VISTA and PD-1 (Figure 9D). Furthermore, IHC and IF studies demonstrated that human GBM tumors co-express CD68, which co-expresses CD73. + The GBMs were found to have a high density of macrophages (Fig. 9E-H). To validate these findings regarding leukocyte infiltration in GBM, we analyzed macrophage and T cell infiltration by CyTOF in an independent cohort of nine GBM patients (Fig. 10). Compared to the initial GBM cohort, we found a similarly high frequency of CD73 hi Macrophages and low T cell numbers were found.

[0130] CD73 is an ectonucleotidase that cooperates with its upstream signaling molecule CD39 to convert extracellular ATP to adenosine (16). CD73 has been shown to promote tumor progression and induce immunosuppression in GBM (16-20). Furthermore, it has recently been shown that kynurenine produced by murine GBM cells can upregulate CD39 in macrophages (19). hi To gain a deeper understanding of genes that may define myeloid cells, we performed single-cell RNA sequencing (sc-RNA seq) on four additional GBM tumors (Supplementary Table 1). This analysis revealed 17 clusters, four of which were CD3 + T cell clusters, 10 of which are CD3 - CD68 + Of the 10 myeloid clusters, four (R7, R14, R3, and R17) expressed CD73 hi (Figure 2B, indicated by arrows).hi We found that the myeloid cluster had high expression of genes suggestive of a blood-derived macrophage signature, as opposed to a microglial signature (21) (Figure 2C). hi Macrophages were found to express CCR5, CCR2, ITGAV / ITGB5, and CSF1R, and CD73 hi These findings suggest that macrophages are likely recruited to the GBM tumor microenvironment by these factors (22-26) (Figure 2D). We also investigated the relationship between CD73 and the expression of immunostimulatory or immunosuppressive genes. hi Evaluate myeloid cells and CD73 hi We found that myeloid cells had high expression of immunosuppressive and hypoxia-related genes (Fig. 2E).

[0131] Next, we investigated the four CD73 hi Clusters (R7, R14, R3, and R17) were used to identify CD73 hi We derived a macrophage-specific gene signature (Fig. 3; see Methods). MARCO, TGFB, and several SIGLECs express CD73 hi To understand the significance of the gene signature, we examined CD73 for potential correlation with survival. hi To perform this analysis, we used the TCGA-GBM cohort (N=525). We investigated the relationship between overall survival (OS) and CD73 in the TCGA-GBM cohort. hi We found a significant negative correlation (Figure 3B, p=0.013, HR=1.268) between high expression of the gene signature CD73. hiBased on the potential immunosuppressive function of myeloid cells, we evaluated GBM samples from patients treated with anti-PD-1 to determine whether the predominance of these cells might correlate with a lack of response to treatment. We used a cohort of five GBM patients enrolled in a Phase II trial evaluating the effect of pembrolizumab in patients with recurrent GBM (NCT02337686, "Methods"). PhenoGraph clustering of seven untreated tumors and a cohort of five GBM patients treated with pembrolizumab revealed CD3 - CD68 + Twelve subsets were characterized as myeloid subsets, two CD3 + T cell subsets and one NK cell CD3 - CD56 + Seventeen clusters of CD68 subsets were identified (Figures 3C-D; Figure 11). + Among the myeloid subsets, three CD73 hi Myeloid clusters were observed (Figure 3D; G2, G8, G11 indicated by red arrows). Comparing untreated GBM samples with anti-PD-1-treated GBM samples, we found that these three CD73 hi We found that the myeloid clusters persisted (Figure 3E). Evaluation of the remaining myeloid clusters, which were CD73-low or CD73-negative, also persisted despite treatment with ICT, consistent with a previous study in which no changes in myeloid markers were observed after anti-PD-1 treatment (27). Notably, two T cell clusters, representing CD4 and CD8 T cells, respectively, were identified (Figure 3D; G3 and G6 indicated by blue arrows), and they showed no significant differences between untreated and anti-PD-1-treated GBM tumors (Figure 3F). GSEA analysis of untreated and anti-PD-1-treated tumors revealed higher expression of IFN-γ-responsive genes in anti-PD-1-treated patients (Figure 3G), consistent with a recent study suggesting a modest clinical benefit of anti-PD-1 treatment in the neoadjuvant setting (28). These findings suggest that anti-PD-1, despite inducing a moderate immune response, likely in TILs, may be a key factor in determining the expression of CD73 T cells. hiThese findings suggest that CD73 does not significantly alter the GBM TME, which is characterized by its high content of myeloid cells. hi It is possible that the predominance of myeloid cells contributes to the lack of T-cell infiltration, thereby leading to poor clinical outcomes.

[0132] To test the hypothesis that targeting CD73 is important for the success of the combination strategy in GBM, we compared wild-type (WT) and CD73 GBM tumor cells orthotopically inoculated with GL-261 GBM tumor cells. - / - We performed a reverse translation study using mice. In the absence of CD73, intracranial tumor growth was hindered (Fig. 12A), and mice showed improved survival, confirming the immunosuppressive role of CD73 in GBM (p=0.01) (Fig. 12B). To understand the effects of CD73 within the tumor microenvironment, we performed comparative immune profiling of the tumor microenvironment and compared the tumor microenvironment of WT and CD73 mice using CyTOF. - / - We assessed differences in immune infiltrates between mice (Fig. 12C). The absence of CD73 has been shown to increase intratumoral T cell abundance in mouse tumor models such as B16-F10 melanoma and MC-38 colon carcinoma (29), whereas the absence of CD45 + Clustering of gated cells was performed using WT mice and CD73 - / - In the GBM model, the inventors found no significant changes in T cell subsets between GBM tumor-bearing mice and those with CD73 compared to WT mice. - / - Immunosuppressive CD206 in mice + Arg1 + VISTA + PD-1 + CD115 + Myeloid (CD11b), including a decrease in myeloid cluster (Gmm20, p=0.0079) + F4 / 80 + ) subsets were noted (Fig. 12D). Interestingly, we also observed a significant difference in the CD73 - / - iNOS in mice +A concomitant increase in the myeloid cluster (Gmm13, p=0.0159) was observed (Figure 12D-E). This data supports a role for CD73 in macrophage polarization. Overall, the data indicate that the absence of CD73 in a murine GBM tumor model improves survival by regulating intratumoral myeloid subsets.

[0133] Next, we evaluated whether CD73-mediated changes in macrophage phenotype could affect the efficacy of ICT. We treated GBM tumor-bearing mice with anti-PD-1 antibody or a combination of anti-PD-1 and anti-CTLA-4 antibodies. Figure 4A shows representative MRI images of GBM tumors from untreated and ICT-treated mice. Compared to untreated controls, WT and CD73 macrophage tumors treated with the combination of anti-PD-1 and anti-CTLA-4 were significantly increased. - / - A significant improvement in survival was observed in mice (p<0.0001) (Figure 4B). Importantly, after treatment with anti-PD-1 and anti-CTLA-4 in combination, CD73 - / - Mice showed improved survival compared to WT GBM tumor-bearing mice (p=0.03, Figure 4B). - / - We did not find a significant survival benefit from anti-PD-1 treatment in mice (Fig. 4B). + iNOS on immunosuppressive macrophages + The ratio of immune-stimulated macrophages was significantly higher in CD73 mice compared to WT mice. - / - It was noted that CD206 was significantly higher in tumor-bearing mice treated with the combination therapy. + Granzyme B on immunosuppressive macrophages + The ratio of CD8 T cells was significantly higher in the CD73 - / - The T cell infiltration rate was significantly higher in mice and became even more pronounced after combination therapy (Figure 4C-D). Therefore, these data suggest that the increased T cell infiltration using combination ICT is due to the CD73 - / - Combined with the polarization of macrophages towards an immunostimulatory phenotype in mice, this suggests that it plays an important role in determining the response to ICT.

[0134] Although multiple immune checkpoints exist (30-32), data suggest that the dynamic interactions of immune checkpoints in the tumor microenvironment are tumor-type specific. Clinical trials with combination immunotherapies are progressing at an unprecedented pace. However, our comprehensive understanding of tumor-immune interactions remains limited, preventing us from designing rational combination therapies in a tumor-specific manner. This study combined detailed human tumor analysis with mouse reverse translation studies to generate a combination strategy for future clinical trials in GBM. Overall, this study highlights the crucial importance of reverse translation studies for testing relevant hypotheses generated from human datasets for precision immunotherapy.

[0135] In this study, we provide immune profiling data from 1) multiple different human tumors and 2) anti-PD-1 clinical trials in GBM patients. We demonstrate that CD73, specifically present in GBM, persists even after treatment with anti-PD-1 therapy. hi Furthermore, we identified a myeloid population, CD73, which negatively correlated with OS in the TCGA-GBM cohort. hi A gene signature was derived from the myeloid cell cluster. scRNA sequencing identified CD73 hi We showed that myeloid cells are enriched in immunosuppressive genes and have a distinct signature from the endogenous microglial signature. hi Myeloid cells are further characterized by higher expression of chemokines / chemokine receptors, such as CCR5, CCR2, ITGAV / ITGB5, and CSF1R. Several clinical trials are testing the utility of targeting these individual chemokine receptors in patients with advanced solid tumors, including GBM, but CD73 hiThe cumulative expression of these receptors on myeloid cells suggests that CD73 itself is a more appropriate target, as it is highly expressed on the majority of cells that express all of these receptors. For example, clinical trials targeting CSF1R have demonstrated limited clinical efficacy, which may be due to the continued presence of myeloid populations that express other immunosuppressive markers (33, 34).

[0136] This data supports the immunosuppressive role of CD73 in GBM patients receiving anti-PD-1 therapy. hi We demonstrate the persistence of myeloid subsets and the therapeutic benefit of immune checkpoint inhibitors in a CD73-deficient mouse model. Based on this data and previous studies, we propose a combination therapy strategy that targets CD73 and dually blocks PD-1 and CTLA-4. Anti-CD73 antibodies have shown promising results in preclinical and early clinical studies (35, 36). Therefore, these data have clinical application through the rapid translation of combination therapy with currently available anti-CD73 antibodies for GBM.

[0137] B. Method 1. Patients and surgical samples Patients with recurrent glioblastoma multiforme were treated with pembrolizumab every 3 weeks in MDACC Clinical Protocol 2014-0820 (NCT02337686) and consented to PA13-0291. Clinical characteristics of individual patients are shown in Supplementary Table 1.

[0138] 2. Cell lines and tumor models The mouse glioblastoma cancer cell line (GL-261) was obtained from the National Cancer Institute (Rockville, MD, USA). Cells were harvested in logarithmic phase and washed twice with PBS immediately before tumor injection. 50,000 cells were injected intracerebrally into mice (5 or 10 per group) as previously described (37). Anti-CTLA-4 (clone 9H10) and anti-PD-1 (RMP1-14) antibodies were purchased from BioXcell (West Lebanon, NH). Anti-PD-1 and a combination of anti-PD-1 and anti-CTLA-4 were injected intraperitoneally into mice on days 7 (200 μg / mouse), 10 (100 μg / mouse), and 13 (100 μg / mouse) after tumor inoculation.

[0139] 3. Mass cytometry (CyTOF) Patient PBMCs were isolated from blood by density gradient centrifugation, resuspended in 90% AB serum and 10% DMSO, and stored in liquid nitrogen until analysis. Fresh tumor tissue was isolated using the GentleMACS system (Miltenyi Biotec; Bergisch Gladbach, Germany) according to the manufacturer's instructions and cultured overnight in 96-well plates with RPMI 1640 medium supplemented with 10% human AB serum, 10 mM Hepes, 50 μM β-ME, penicillin / streptomycin / l-glumacrophagesine. For mouse experiments, freshly harvested tumors were dissociated with Liberase / DNAse solution and incubated at 37°C for 30 minutes before single cell isolation. Cells were stained with up to 36 antibodies. Antibodies were either purchased pre-conjugated from Fluidigm or purified and conjugated in-house using the MaxPar X8 Polymer Kit (Fluidigm) according to the manufacturer's instructions (see Supplementary Table 4). Briefly, samples were stained with cell surface antibodies in phosphate-buffered saline (PBS) containing 5% goat serum and 30% BSA for 30 minutes at 4°C. Optimal antibody concentrations were determined by serial dilution staining of human PBMCs. After viability staining with 5 μM cisplatin (Fluidigm) in PBS containing 30% BSA, samples were washed in PBS containing 30% BSA, fixed, and permeabilized according to the manufacturer's instructions using the FoxP3 Staining Buffer Set (eBioscience), and then incubated with intracellular antibodies in permeabilization buffer for 30 minutes at 4°C. Samples were washed, incubated in Ir intercalator (Fluidigm), and stored at 4°C until acquisition, typically within 12 hours. Immediately prior to acquisition, samples were washed and resuspended in water containing EQ 4 element beads (Fluidigm). Samples were acquired on a Helios mass cytometer (Fluidigm).

[0140] 4. Mass cytometry analysis Using CyTOF, we analyzed four separate cohorts of human patient samples (after excluding samples with too few cells for analysis, as described separately below for the different datasets): 1) 66 samples from TILs extracted from five different tumor types; 2) five additional GBM TIL samples extracted from tumors resected from patients after treatment with pembrolizumab; 3) a validation cohort of nine additional immune checkpoint therapy-naive GBM TIL samples; and 4) 14 matched PBMC samples from 14 separate RCC patients, both before and after two and / or four cycles of ipilimumab and nivolumab combination treatment. While the panels used for the multi-tumor and GBM cohorts were identical (with the exception of one difference regarding which channel was used for HLA-DR in some samples, as described below), a separate panel was used for the RCC PBMC cohort, which was analyzed completely separately (Supplementary Table 1). For the most part, the various analyses using these different cohorts proceeded in similar, if not identical, ways, with differences explicitly noted below.

[0141] First, the files (fcs) were uploaded to Cytobank and normalized using bead-based normalization software for mass cytometry data (R package premessa, Parker Institute for Cancer Immunotherapy) (Amir el, AD, et al. viSNE enables visualization of high-dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nature biotechnology 31, 545-552 (2013)). Because RCC PBMC samples (cohort 3 above) were labeled using MassTag cell barcoding for each sample from a given patient, they were further demultiplexed using the strategy outlined in Zunder et al. (2015) (Levine, JH, et al. Data-Driven Phenotypic Dissection of AML Reveals Progenitor-Like Cells that Correlate with Prognosis. Cell 162, 184-197 (2015)) before bead-based normalization across patients. For the initial TIL (Cohort 1) and additional post-treatment GBM (Cohort 2) samples, we used antibodies against HLA-DR conjugated to either 174Yb or 209Bi for different samples, and therefore merged the signals for 174Yb and 209Bi isotopes into a single channel for HLA-DR.

[0142] Samples were then manually gated in FlowJo by event length, live / dead discrimination, and population of interest using lineage markers (CD45 and CD3) for separate analysis. Data were then exported as .fcs files to Matlab or R for downstream analysis and arcsinh transformed using a factor of 5 (x_transformed=arsinh(x / 5)). Samples with fewer than 600 events in the final gate (e.g., CD45 + cells or CD3 + cells) were excluded due to insufficient cells for clustering, dimensionality reduction, and other analyses. For GBM-specific TIL analysis, we randomly selected 4300 cells (chosen as this was the minimum number of viable cells after gating from all but one sample) from each of the 11 samples because file 1814 contained 1170 cells and was not subsampled, so all 1170 cells were included in the analysis.

[0143] To visualize high-dimensional data in two dimensions, we applied the t-SNE dimensionality reduction algorithm (Van Gassen, S., et al. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636-645 (2015)) to the analysis of the multi-tumor TIL samples and, separately, to a total of 12 GBM samples (including five post-treatment samples along with seven initial samples). For the multi-tumor samples, we randomly selected 10,000 cells from each tumor type using all markers except CD326 (EPCAM) and markers used to manually gate populations of interest (e.g., CD45 and CD3). For GBM TIL analysis, subsampling was performed as described above. All t-SNE maps were created using the Barnes-Hut implementation of the algorithm in the R package Rtsne, and data were displayed using the ggplot2 R package (). For t-SNE plots where the expression of individual markers was overlaid, the arcsinh-transformed signal intensity of all values ​​was divided by the 99th percentile value of the intensity for that channel to derive a signal intensity ranging from 0 to 1 for each channel.

[0144] For mouse CyTOF samples, both preprocessing and normalization were performed identically (but using an entirely separate mouse panel). Clustering and other downstream analyses were performed differently, as described below.

[0145] 5. Mass cytometry clustering Clustering analysis was performed using a MATLAB implementation of the PhenoGraph clustering algorithm (Azizi, E., et al. Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment. Cell 174, 1293-1308 el236 (2018)). For clustering analysis of multiple tumor samples (Cohort 1), to reduce noise and other effects from batch and compress marker redundancy, data from individual patients were projected onto principal components accounting for 90% of the observed variance before clustering, using all markers except CD326 (EPCAM) and the markers used to manually gate the populations of interest (CD45 and CD3, respectively, and CD68 for separate T cell analysis when used as a negative gate). This approach was used to avoid capturing physiologically irrelevant populations and reduce residual noise not accounted for by bead normalization. In the space formed by these principal components, PhenoGraph was used for each sample to identify clusters. The parameter k for the number of nearest neighbors was uniquely selected for each sample using the formula k = minimum number (0.002 * cell number, 10). For each individual sample, pan-positive (expressing high levels of all markers, i.e., likely doublets) and pan-negative (expressing no markers) clusters were excluded from downstream meta-clustering and frequency analysis because they were likely artifacts. They accounted for less than 0.4% of each parent population.

[0146] For mouse CyTOF data, normalized data were clustered using the FlowSOM clustering method via Cytobank (van Dijk, D., et al. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion. Cell 174, 716-729 e727 (2018)).

[0147] To compare phenotypes between samples while accounting for batch effects, clusters from each sample were represented by their centroids across all non-abandoned channels, and CD45 + For TIL analysis, 794 clusters (across 45 samples) × 34 markers and CD3 + For TIL analysis, they were merged into a single matrix of size 486 clusters (across 30 samples) x 32 markers. PhenoGraph was run on both of these matrices separately with parameter k=10 to obtain CD45 + The analysis yielded 18 metaclusters, and CD3 + The analysis yielded 17 metaclusters.

[0148] To discover tumor-type-independent immune landscapes across tumor types, we calculated the frequency of cells belonging to each metacluster for each sample and tumor type in the multi-tumor TIL analysis. Samples were hierarchically clustered by their metacluster frequency using Ward's hierarchical clustering method and visualized in a dendrogram.

[0149] For the RCC PBMC analysis (Figure 7), barcoding reduced the need for an initial sample-specific clustering step and subsequent metaclustering. As a result, all cells from all pre- and post-treatment samples (over 1 million total cells would have been obtained without subsampling) were clustered simultaneously. For the clustering analysis of 12 pre- or post-treatment GBM samples (Figure 3A), the number of clusters obtained from each individual patient in this smaller set (approximately 200 in total) did not allow for stable and robust downstream metaclustering, so clustering was performed simultaneously on cells from all samples (subsampled identically to the tSNE section above: 4300 cells from each sample plus 1170 cells from sample 1814). This may have resulted in a mildly enhanced batch effect in this particular analysis, which should therefore be taken into account in interpretation. Also, in this analysis, one small pan-positive cluster of 147 cells (0.3% of the total) was excluded from downstream analysis. Additionally, all nine samples in the GBM validation cohort (Cohort 3) clustered together. In all of these analyses, PCA preprocessing was performed as described above.

[0150] For heatmap display of marker expression by either cluster or metacluster, depending on the analysis, expression was normalized by dividing by the maximum mean cluster value for each parameter and displayed in R with a custom-made script using the geom_tile function in the ggplot2 package. In all boxplots, the depicted box indicates the interquartile range, the central bar indicates the median, and the whiskers indicate the range.

[0151] 6. Statistical analysis Metacluster and subset frequencies were compared using a two-step approach. First, a Kruskal-Wallis test was performed on the 14 metaclusters from the multitumor CyTOF analysis, corrected for multiple comparisons using the Benjamini-Hochberg method. L2, L4, L15, and L18, as well as T12 and T13, were excluded from the multiple comparison correction because they were not expressed in the analyzed dataset, were expressed in only one patient, or were of unclear lineage, making them unsuitable for comparison. q values ​​were calculated using the p.adjust() function (R Studio Version 1.0.153), and a q value < 0.05 was considered statistically significant. Second, pairwise comparisons were performed using the Mann-Whitney test only for metaclusters / subsets with statistically significant variation between tumor types, corrected for multiple comparisons within each cluster using the Benjamini-Hochberg method, with a q value < 0.05 considered statistically significant.

[0152] To calculate the cell cluster frequency ratios in the mouse experiment (Figure 4D), three granzyme B-expressing CD8 T cell clusters were identified (clusters 19, 26, and 27) and their cell frequencies were added together. Similarly, four iNOS-expressing myeloid clusters (clusters 1, 2, 6, and 7) were identified and their cell frequencies were added together. Only one CD206-expressing myeloid cluster was identified (cluster 5), so it was collected separately. Granzyme B + Cumulative frequency of CD8 T cell clusters and iNOS + The cumulative frequency of myeloid clusters was calculated by CD206 + Statistics were obtained by dividing by the frequency of the myeloid cluster and plotting the ratio in GraphPad Prism 7. A summary of the statistical methods used for these analyses is included in Supplementary Table 2.

[0153] 7. Cluster Mixing Using bootstrap techniques, 18 CD45 genes were identified across six tumor types (including mCRC). +We estimated the mixture of immune metaclusters to account for a variety of cluster sizes, ranging from just over 1,800 cells to just over 180,000 cells. We calculated Shannon entropy for the empirical distribution of tumor types across 1,000 cells uniformly sampled with replacement from each cluster. This sampling procedure was repeated 1,000 times for each cluster, and the cluster-size-corrected standard error of entropy was bootstrapped. Figure 2C shows boxplots of the entropy values ​​within each cluster, ordered by mean entropy.

[0154] 8. Immunohistochemistry For IHC analysis, GBM tumor tissues were fixed in 10% formalin, embedded in paraffin, and sectioned transversely. Four-micrometer sections were stained with hematoxylin and eosin (H&E). IHC analysis was performed on paraffin-embedded tissue sections. Primary antibodies were used to detect CD3 (Dako, Cat# A0452), CD8 (Thermo Scientific, Cat# MS-457-S), and CD68 (Dako, Cat# M0876). Antibodies were detected with secondary antibodies, followed by peroxidase-conjugated avidin / biotin and 3,3'-diaminobenzidine (DAB) substrate (Leica Microsystem). All IHC slides were scanned and digitized using a Scanscope XT (Aperio / Leica Technologies) scanscope system. Quantitative analysis of IHC staining was performed using the provided image analysis software (ImageScope-Aperio / Leica). For analysis of the density of positive cells (number of positive cells / mm2), five random areas (at least 1 mm2 each) were selected using a customized algorithm for each specific marker.

[0155] 9. Multiplex immunofluorescence assay and multispectral analysis For multiplex staining, we followed the Opal protocol staining method (Finck, R., et al. Normalization of mass cytometry data with bead standards. Cytometry A 83, 483-494 (2013)) for the following markers: CD73 (1:200, Abcam, ab91086) followed by visualization with fluorescein Cy3 (1:50); CD163 (1:25, Leica Biosystems, NCL-L-CD163) followed by visualization with Cy5 (1:50); and CD68 (1:100, Dako, M0876) followed by visualization with Cy5.5 (1:50). Nuclei were then visualized with DAPI (1:2000). All sections were coverslipped using Vectashield H-1400 mounting medium. For multispectral analysis, we followed a detailed methodology previously described (Stack et al., 2014). Individually stained sections were used to establish a spectral library of fluorophores required for multispectral analysis. Slides were scanned under fluorescent conditions using a Vectra slide scanner (PerkinElmer). Then, for each marker, the mean fluorescence intensity per case was determined as a baseline from which positive cells could be established. Finally, a colocalization algorithm was used to determine the percentage of CD68, CD163, and CD73 staining.

[0156] 10. Single-cell RNA sequencing Single-cell RNA sequencing (sc-RNA seq) was performed using a 10x Genomics Chromium Single Cell Controller. Briefly, tumor cell single-cell suspensions were prepared as described above. Cells were resuspended in freezing medium containing 90% AB serum and 10% DMSO and stored in liquid nitrogen until analysis. For sc-RNA seq analysis, cells were thawed, washed, and analyzed for viability and CD45 expression using a BD FACSAria. +Cells were sorted. Cells were then droplet-separated using the Chromium™ Single Cell 3'v2 Reagent Kit in conjunction with a 10x Genomics Microfluidics System to create cDNA libraries with individual barcodes for each cell. Barcoded cDNA transcripts from GBM patients were pooled and sequenced using an Ilumina HiSeq 4000 Sequencing System.

[0157] 11. Single-cell RNA-sequencing clustering and statistical analysis For each of the four GBM sc-RNAseq samples, Illumina fastq files were preprocessed and converted into count matrices using the Sequence Quality Control (SEQC) package. Briefly, SEQC takes Illumina barcodes and genome sequence fastq or bcl files as input; merges them into a single fastq file containing alignable sequence and metadata; filters reads for common errors, including barcode substitution errors and low-complexity errors; aligns reads using STAR; resolves multiple alignments; and groups the error-reduced, filtered reads into count matrices by cellular, molecular, and gene annotations. It also outputs a set of QC metrics to assess library quality. The pipeline is described in detail in Azizi, et al. 2018.

[0158] These four separate count matrices were then merged into one large count matrix of size 13,263 cells (range 2,763–3,666 per patient) × 19,187 genes. The data were preprocessed in three sequential ways: first, normalized according to the median library size per cell, as is standard practice for sc-RNA-seq data; second, logarithmic transformation; and finally, principal component analysis (PCA) was applied to further reduce noise and maximize signal robustness while exploiting the inherent redundancy in gene expression (the so-called "intrinsic dimensionality"), ensuring that principal components accounted for 90% of the retained variance.

[0159] Next, the median number of unique molecules per cell (UMIs) was low across the four samples (1170, 1210, 1468, and 1592, respectively), resulting in a sparse data matrix, as is typical for sc-RNAseq data. Therefore, we used the imputation algorithm Markov affinity-based graph imputation of cells (MAGIC) to denoise the count matrix and correct for data sparsity and gene dropout. MAGIC exploits information shared between similar ("neighboring") cells via data diffusion to denoise the count matrix and, importantly, to fill in missing transcripts ("dropouts" or false negatives) that were likely present but lost due to sampling error. This is particularly important when investigating gene-gene relationships, such as in the case of coexpression patterns in important cell populations. One minor note: MAGIC also performs PCA as a preprocessing step, but returns a full (non-dimensionality-reduced) imputed count matrix. For downstream analyses (e.g., clustering), we applied the PCA preprocessing described above to this imputed count matrix. A complete detailed description of the intuitive, biological, and mathematical theory of MAGIC, as well as the algorithmic procedure, is provided in van Djik et al., 2018. For this analysis, we used an R implementation of MAGIC with the following parameter settings: all genes; k (number of nearest neighbors) of 10; alpha of 15; and an automatic ("t=auto") value for the exponent to which the diffusion operator should be raised, such that t is chosen according to the Procrustes disparity of the diffusion data (the value of t chosen in this way was 8).

[0160] t-SNE visualization of the sc-RNA-seq data was performed using the Barnes-Hut implementation of the algorithm, again using a reduced PCA space applied to all cells from all four patients, and signal intensity relative to maximum imputed expression of either individual markers or the mean expression of the multigene signature.

[0161] We performed clustering of the sc-RNA-seq data using PhenoGraph in the reduced PCA space of all cells, again with k set to 0.002 * number (cells) = 38. We identified one cluster of cells that did not express any standard immunotyping markers (CD45, CD3, CD8, CD4, CD14, CD68, etc.) at significant frequencies, totaling less than half of one percent. However, it did express high levels of several markers associated with neurons. We therefore conclude that it was probably a rare contaminant that was erroneously overlooked by the CD45-based sorting process and excluded from all analyses, and that it was outside the range of the immune population investigated in this study.

[0162] The hypoxia, anti-inflammatory ("immunosuppressive") and pro-inflammatory ("immunostimulatory") gene signatures were taken from Azizi, et al. 2018, and the microglial vs. myeloid-derived signature was taken from Muller et al., 2017. In all cases, the intensity of expression of the signature in question was calculated as the average expression of the genes included in the signature.

[0163] Of particular interest in this study is CD73 +To define a gene signature representing macrophage populations, we grouped four sc-RNA-seq PhenoGraph clusters expressing high levels of CD73 and various combinations of other immunosuppressive factors (R3, R7, R14, and R17) into one cluster (all cells from the four clusters were merged) and compared their differential expression to all cells not in any of the four clusters (i.e., belonging to any of 13 other clusters, including T cell, myeloid, and NK cell populations). Combined, there were 3,453 cells in one of these three clusters. While traditional bulk RNA-seq methods for differential expression rely on average expression and fold change between samples / cell populations, a crucial aspect of single-cell data is the ability to utilize the complete distribution (i.e., distribution, as opposed to point representation) of cells in a cell population (in terms of multidimensional gene expression). A method that takes full advantage of these complete distributions and is increasingly used in recent studies to assess differential expression between populations is Earth Mover's Distance (EMD). Physically speaking, EMD quantifies the minimum "cost" of converting one pile of some material (e.g., soil) into another pile of material, defined as the amount of material to be moved multiplied by the distance traveled. Thus, in probability theory, it measures the distance between two distributions (again, as opposed to simply the distance between, say, their means). In the case of a one-dimensional distribution (as in the case of the distribution of expression of a single gene in a group of cells), it can be conveniently and efficiently calculated as the L1 norm of the cumulative density functions of the two distributions. Therefore, we consider the L1 norm of the cumulative density functions of the two distributions of interest (four CD73 + We used this method to calculate the EMD for each gene between cells belonging to the cluster and cells belonging to all other clusters, and then ranked all 19,000+ genes by their EMD (the top gene was CD73 + (Genes that are differentially highly expressed in the cluster and vice versa for lower-ranked genes.) EMD values ​​across all genes and corresponding z-scores are provided for all genes in Supplementary Table 3. All genes with z-scores above 2.0 are shown in Figure 3A.

[0164] 12. NanoString Gene Expression Analysis RNA was isolated from formalin-fixed, paraffin-embedded (FFPE) tumor sections by dewaxing using a deparaffinization solution (Qiagen, Valencia, CA), and total RNA was extracted using the RecoverALL™ Total Nucleic Acid Isolation Kit (Ambion, Austin, TX) according to the manufacturer's instructions. RNA purity was assessed using an ND-Nanodrop 1000 spectrometer (Thermo Scientific, Wilmington, MA, USA). For the NanoString platform, 100 ng of RNA was used to detect immune gene expression using the nCounter PanCancer Immune Profiling panel with a custom CodeSet. Reporter probe counts were tabulated for each sample by the nCounter Digital Analyzer, and raw data output was imported into nSolver (available on the worldwide web at nanostring.com / products / nSolver). The nSolver data analysis package was used for normalization, and hierarchical clustering heat map analysis was performed with Qlucore Omics Explorer version 3.5 software (Qlucore, NY, USA).

[0165] 13. MRI Image Quantification MRI images were quantified using ImageJ software version 1.52a. First, images were imported and brightness / contrast adjusted. Then, image slices were scanned to identify tumor sections. A gate was drawn around the tumor in each section, and the area was measured. Image geometry indicated a slice thickness of 0.75 mm and a distance of 1 mm between the two sections. The tumor area in each section was multiplied by 0.75, the tumor area in the two sections was averaged, and multiplied by (1-0.75)0.25 (this gave the depth value). The volume of each tumor was calculated by multiplying the tumor area from the tumor-containing section by the depth. All values ​​were added to determine the tumor volume in cubic millimeters.

[0166] 14. Survival analysis Using this method, a gene expression signature was defined by taking the top 44 genes with a z-score greater than 3.0. Microarray panel-based gene expression data was downloaded from cBioportal (available on the World Wide Web at cbioportal.org / datasets, Glioblastoma Multiforme (TCGA, Provisional) as of November 7, 2018). In the analysis, we used 525 patients with primary tumors for which clinical data was available. In the provisional dataset, we used data from 201 patients published in Nature 2008, and data from 151 patients published in Cell 2013; because 9 genes were not found in the U133 microarray data, we used 35 of the 44 signature genes. Patients were sorted by the mean z-score of the signature genes and then divided into a high-expression group (n = 263) and a low-expression group (n = 262). The log-rank test showed a significant negative association between survival and the expression level of the signature genes (p = 0.013) (Figure 3B).

[0167] 15. Statistical Analysis of Mouse Experiments: All data represent at least two to three independent experiments using 5 to 10 mice in each in vivo experiment. Data are expressed as the mean ± standard error of the mean (SEM) and were analyzed using Prism 7.0 statistical analysis software (GraphPad Software, La Jolla, CA). Student's t-test (two-tailed), ANOVA, and Bonferroni multiple comparison tests were used to identify significant differences (p < 0.05) between treatment groups. Data from survival experiments were analyzed using the log-rank test.

[0168] C.Table (Supplementary Table 1) Patient characteristics for which correlation assays were performed ICT: immune checkpoint therapy, CT: chemotherapy, RT: radiotherapy, TT: targeted therapy, HT: hormone therapy TIFF0007772700000001.tif186136TIFF0007772700000002.tif186159TIFF0007772700000003.tif186159TIFF0007772700000004.tif186133

[0169] (Supplementary Table 2) Table summarizing statistical tests BH: Benjamini-Hochberg, CRC: colorectal cancer, GBM: glioblastoma multiforme, KO: CD73 knockout, KW: Kruskal-Wallis test, MW: Mann-Whitney test, WSR: Wilcoxon signed-rank test, n / a: not applicable; NSCLC: non-small cell lung cancer, PCa: prostate cancer, RCC: renal cell carcinoma, wt: CD73 wild-type TIFF0007772700000005.tif223135TIFF0007772700000006.tif223152TIFF0007772700000007.tif223154TIFF0007772700000008.tif22355

[0170] (Supplementary Table 3) EMD values ​​of genes and corresponding z-scores TIFF0007772700000009.tif214160

[0171] (Supplementary Table 4) Key Resources Table TIFF0007772700000010.tif242155TIFF0007772700000011.tif247155TIFF0007772700000012.tif235155TIFF0007772700000013.tif124165

[0172] All methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of the present invention have been described in terms of preferred embodiments, it will be apparent to those skilled in the art that changes can be made in the methods described herein and in the steps or in the order of steps of the methods described herein without departing from the concept, spirit, and scope of the invention. More specifically, it will be apparent that the same or similar results can be achieved by substituting certain chemically and physiologically related agents for the agents described herein. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope, and concept of the invention as defined by the appended claims.

[0173] References The following references and publications referenced throughout the specification, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein, are specifically incorporated herein by reference. TIFF0007772700000014.tif201160TIFF0007772700000015.tif245160TIFF0007772700000016.tif245160TIFF0007772700000017.tif26160

Claims

1. 1. A medicament for use in a method of treating glioblastoma in a subject, the medicament comprising an agent comprising: the agent is an anti-PD-1 or anti-PDL1 antibody, and the method comprises administering the antibody to the subject after the subject has been determined to have lower CD73 expression in a biological sample from the subject relative to a control, and the biological sample comprises macrophages. Medicine.

2. The pharmaceutical of claim 1, wherein the biological sample comprises a serum sample, a biopsy sample, or an isolated fraction of immune cells.

3. The pharmaceutical of claim 1 or 2, wherein the expression of CD73 is determined to be low in immune cells.

4. The method of any one of claims 1 to 3, wherein the method further comprises administering at least one additional anti-cancer treatment.

5. The pharmaceutical composition of claim 4, wherein the at least one additional anti-cancer treatment is surgical therapy, chemotherapy, radiation therapy, hormone therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biological therapy.

6. (a) the control comprises a cut-off value or a normalized value; and / or (b) the reduced expression level comprises a normalized expression level determined to be reduced relative to a control; and / or (c) the CD73 expression was detected by immunoassay. The pharmaceutical composition according to any one of claims 1 to 5.

7. 1. A method for predicting response to anti-PD-1 or anti-PDL1 therapy in a subject with glioblastoma, the method comprising: (a) determining the expression level of CD73 in a sample from the subject; and (b) comparing the expression level of CD73 in the sample from the subject with a control; and the method comprises (c) (i) after detecting a decreased expression level of CD73 in a biological sample from the subject relative to a control representing the expression level of CD73 in a biological sample from the subject who has been determined to be non-responsive to the therapy; or (ii) detecting an expression level of CD73 in a biological sample from the subject that is reduced or not significantly different from a control that represents the expression level of CD73 in a biological sample from the subject that has been determined to respond to the therapy; predicting that said subject will respond to said therapy; or (d) (i) after detecting an increased expression level of CD73 in a biological sample from the subject relative to a control representing the expression level of CD73 in a biological sample from the subject who has been determined to be responsive to the therapy; or (ii) detecting an expression level of CD73 in a biological sample from the subject that is increased or not significantly different from a control representing the expression level of CD73 in a biological sample from the subject who has been determined to not respond to the therapy; predicting that said subject will not respond to said therapy. further comprising the biological sample comprises macrophages; method.

8. 8. The method of claim 7, wherein the biological sample comprises a serum sample, a biopsy sample, or an isolated fraction of immune cells.

9. (a) the control comprises a cut-off value or a normalized value; and / or (b) the expression level comprises a normalized expression level; and / or (c) the CD73 expression was detected by immunoassay.

9. The method of claim 7 or 8.

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