Methods of Treating Cancer with CD40 Agonists

Abstract

The present disclosure provides a method for identifying a subpopulation of cancer patients suitable for combination therapy with a CD40 agonist and one or more chemotherapy drugs, and treating the subpopulation of cancer patients with the combination therapy, which may include the steps of: (a) determining a MYC gene signature from a test biological sample and one or more reference biological samples from the subject, wherein the reference biological samples of the one or more reference biological samples are collected from each individual in a cohort of subjects having the same cancer, and the subject is part of the cohort.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63 / 196,676, filed June 3, 2021, which is hereby incorporated by reference in its entirety.

[0002] Field The present disclosure relates to methods for identifying subpopulations of cancer patients suitable for combination therapy with a CD40 agonist and one or more chemotherapy drugs. [Background technology]

[0003] background Full T cell activation requires two separate but synergistic signals. The first signal, through the T cell antigen receptor, is provided by the antigen and MHC complex in the APC and is responsible for the specificity of the immune response. The second or costimulatory signal, through the interaction of CD28 with B7-1 (CD80) / B7-2 (CD86) and CD40 with CD40L, is required for the implementation of a full T cell response. In the absence of costimulatory signals, T cells during antigen stimulation may become unaffected (anergy) or enter programmed cell death (apoptosis).

[0004] CD40, a member of the TNF receptor superfamily (TNFR), is predominantly expressed on B cells and other antigen-presenting cells (APCs), such as dendritic cells and macrophages. CD40 ligand (CD40L) is expressed primarily by activated T cells.

[0005] The interaction of CD40 with CD40L functions as a costimulatory signal for the activation of T cells. The formation of the CD40-CD40L complex on resting cells induces proliferation, immunoglobulin class switching, antibody secretion, and also plays a role in germinal center development and memory B cell survival, all of which are important for humoral immune responses. Binding of CD40L to CD40 on dendritic cells (DCs) induces DC maturation, as evidenced by an increase in the expression of costimulatory molecules, such as the B7 family of molecules (CD80, CD86), and an increase in the production of proinflammatory cytokines, such as interleukin 12. This results in a strong T cell response.

[0006] CD40 signaling activates several pathways, including NFκB (nuclear factor kV), MAPK (mitogen-activated protein kinase) and STAT3 (signal transducer and activator of transcription-3), which regulate gene expression through the activator proteins c-Jun, ATF2 (activator of transcription-2) and the transcription factor Rel. Adaptive proteins TNF receptor (TNFR) associated factors (e.g., TRAF1, TRAF2, TRAF3, TRAF5 and TRAF6) interact with this receptor and mediate signal transduction. Depending on the particular cell type, CD40 activation results in the expression of a specific set of genes. Genes activated in response to transduction of signals from CD40 include numerous cytokines and chemokines (IL-1, IL-6, IL-8, IL-10, IL-12, TNF-alpha and macrophage-1 inflammatory protein alpha (MIP1α)). In some cell types, activation of CD40 can lead to the production of cytotoxic radicals (COX-2 (cyclooxygenase-2)) and NO (nitric oxide) production.

[0007] CD40 is overexpressed on a wide range of malignant cells. The role of CD40 in inhibiting tumors and stimulating the immune system makes it an attractive target for antibody-based immunotherapy. Anti-CD40 antibodies can act against tumor cells through several mechanisms: (i) antibody effector functions, such as ADCC, (ii) direct cytotoxic effects on tumor cells, and (iii) activation of anti-tumor immune responses. However, there is a significant need to develop methods to identify subsets of patients who may experience superior clinical benefits from anti-CD40 treatment. Summary of the Invention [Means for solving the problem]

[0008] Abstract In at least one embodiment, the disclosure provides a method for aiding in the treatment of a subject having cancer, the method comprising: (a) determining a MYC gene signature from a test biological sample from the subject and one or more reference biological samples, the one or more reference biological samples being collected from each individual in a cohort of subjects having the same cancer, the subject being a part of the cohort; (b) calculating a MYC gene signature score for the subject; (c) calculating a MYC gene signature score for the cohort; and (d) aiding in the treatment of the subject with a combination of anti-CD40 therapy and chemotherapy if the MYC gene signature score for the subject is lower than the MYC gene signature score for the cohort.

[0009] In at least one embodiment, the disclosure provides a method for treating a subject having cancer, the method comprising: (a) determining a MYC gene signature of a test biological sample and one or more reference biological samples, the one or more reference biological samples being collected from each individual in a cohort of subjects having the same cancer, the subject being a part of the cohort; (b) calculating a MYC gene signature score of the subject; (c) calculating a MYC gene signature score of the cohort; and (d) treating the subject with a combination of anti-CD40 therapy and chemotherapy if the MYC gene signature score of the subject is lower than the MYC gene signature score of the cohort.

[0010] In at least one embodiment, the MYC gene signature score is calculated by averaging the log-normalized expression values ​​for each gene in the MYC gene set. In at least one embodiment, the MYC gene set includes one or more genes known to be regulated by MYC version 1 (V1). In at least one embodiment, the one or more genes are selected from the group consisting of tumor suppressor genes, oncogenes, translocated oncogenes, protein kinase genes, cell differentiation marker genes, homeodomain protein genes, transcription factor genes, cytokine genes, and growth factor genes. In at least one embodiment, the one or more genes are selected from the group consisting of ABCE1, ACP1, AIMP2, AP3S1, APEX1, BUB3, C1QBP, CAD, CANX, CBX3, CCNA2, CCT2, CCT3, CCT4, CCT5, CCT7, CDC20, CDC45, CDK2, CDK4, CLNS1A, CNBP, COPS5, COX5A, CSTF2, CTPS1, CUL1 , CYC1, DDX18, DDX21, DEK, DHX15, DUT, EEF1B2, EIF1AX, EIF2S1, EIF2S2, EIF3B, EIF3D, EIF3J, EIF4A1, EIF4 E, EIF4G2, EIF4H, EPRS1, ERH, ETF1, EXOSC7, FAM120A, FBL, G3BP1, GLO1, GNL3, GOT2, GSPT1, H2AZ1, HDAC2, H DDC2, HDGF, HNRNPA1, HNRNPA2B1, HNRNPA3, HNRNPC, HNRNPD, HNRNPR, HNRNPU, HPRT1, HSP90AB1, HSPD1, HSP E1, IARS1, IFRD1, ILF2, IMPDH2, KARS1, KPNA2, KPNB1, LDHA, LSM2, LSM7, MAD2L1, MCM2, MCM4, MCM5, MCM6, MC M7, MRPL23, MRPL9, MRPS18B, MYC, NAP1L1, NCBP1, NCBP2, NDUFAB1, NHP2, NME1, NOLC1, NOP16, NOP56, NPM1, O DC1, ORC2, PA2G4, PABPC1, PABPC4, PCBP1, PCNA, PGK1, PHB, PHB2, POLD2, POLE3, PPIA, PPM1G, PRDX3, PRDX4,PRPF31, PRPS2, PSMA1, PSMA2, PSMA4, PSMA6, PSMA7, PSMB2, PSMB3, PSMC4, PSMC6, PSMD1, PSMD14, PSMD3, PSMD7, PSMD8, PTGES3, PWP1, RACK1, RAD23B, RAN, RA NBP1, RFC4, RNPS1, RPL14, RPL18, RPL22, RPL34, RPL6, RPLP0, RPS10, RPS2, RPS3, RPS5, RPS6, RRM1, RRP9, RSL1D1, RUVBL2, SERBP1, SET, SF3A1, SF3B3, SLC25A 3, SMARCC1, SNRPA, SNRPA1, SNRPB2, SNRPD1, SNRPD2, SNRPD3, SNRPG, SRM, SRPK1, SRSF1, SRSF2, SRSF3, SRSF7, SSB, SSBP1, STARD7, SYNCRIP, TARDBP, TCP1, TFDP1, TOMM70, TRA2B, TRIM28, TUFM, TXNL4A, TYMS, U2AF1, UBA2, UBE2E1, UBE2L3, USP1, VBP1, VDAC1, VDAC3, XPO1, XPOT, XRCC6, YWHAE and YWHAQ.

[0011] In at least one embodiment, the disclosure provides a method of treating a subject having cancer, comprising: (a) measuring circulating CD244 in a test biological sample and one or more reference biological samples. - Effector Memory CD4 + T cells and total effector memory CD4 + (b) enumerating T cells, wherein the one or more reference biological samples are collected from each individual in a cohort of subjects having the same cancer, and the subjects are part of the cohort; (b) enumerating the circulating CD244 T cells in the test biological sample; + Effector Memory CD4 + a first number of said total effector memory CD4 T cells; + (c) determining a first ratio to a second number of circulating CD244 T cells in said one or more reference biological samples; + Effector Memory CD8 +The third number of T cells, total effector memory CD4 + determining a second ratio to a fourth number of T cells; and (d) if the first ratio in (c) of the subject is lower than the second ratio in (d) of the cohort, treating the subject with a combination of anti-CD40 therapy and chemotherapy.

[0012] In at least one embodiment, the effector memory CD4 + T cells are CD45RA - CD27 - It is.

[0013] In at least one embodiment, the disclosure provides a method of treating a subject having cancer, comprising: (a) detecting circulating CXCR5 in a test biological sample and one or more reference biological samples; + Effector Memory CD8 + T cells and total effector memory CD8 + (b) enumerating the circulating CXCR5 T cells in the test biological sample, wherein the one or more reference biological samples are collected from each individual in a cohort of subjects having the same cancer, and the subjects are part of the cohort; + Effector Memory CD8 + a first number of said total effector memory CD8 T cells; + (c) determining a first ratio to a second number of circulating CXCR5 T cells in said one or more reference biological samples. + Effector Memory CD8 + The third number of T cells, total effector memory CD8 + determining a second ratio to a fourth number of T cells; and (d) if the first ratio in (c) of the subject is lower than the second ratio in (d) of the cohort, treating the subject with a combination of anti-CD40 therapy and chemotherapy.

[0014] In at least one embodiment, the effector memory CD8+ T cells are CD45RA - CD27 + It is.

[0015] In at least one embodiment, the test biological sample and the one or more reference biological samples are tumor samples.

[0016] In at least one embodiment, the test biological sample and the one or more reference biological samples are blood samples. In at least one embodiment, peripheral blood mononuclear cells (PBMCs) are isolated from the blood.

[0017] In at least one embodiment, said test biological sample and said one or more reference biological samples are obtained prior to the initiation of any cancer treatment.

[0018] In at least one embodiment, the cancer is selected from the group consisting of pancreatic cancer, endometrial cancer, non-small cell lung cancer (NSCLC), renal cell carcinoma, urothelial carcinoma, head and neck cancer, melanoma, bladder cancer, hepatocellular carcinoma, breast cancer, ovarian cancer, gastric cancer, colorectal cancer, glioblastoma, biliary tract cancer, glioma, Merkel cell carcinoma, Hodgkin lymphoma, non-Hodgkin lymphoma, cervical cancer, advanced or refractory solid tumors, small cell lung cancer, non-squamous non-small cell lung cancer, desmoplastic melanoma, pediatric advanced solid tumors or lymphomas, mesothelin-positive pleural mesothelioma, esophageal cancer, anal cancer, salivary gland cancer, prostate cancer, carcinoid tumor, primitive neuroectodermal tumor (pNET) and thyroid cancer. In at least one embodiment, the cancer is pancreatic cancer.

[0019] In at least one embodiment, the anti-CD40 therapy comprises an anti-CD40 antibody or antigen-binding fragment thereof. In at least one embodiment, the anti-CD40 antibody or antigen-binding fragment thereof is selected from the group consisting of sotigalimab, celiclerumab, ChiLob7 / 4, ADC-1013, SEA-CD40, CP-870,893, dacetuzumab, and CDX-1140. In at least one embodiment, the anti-CD40 antibody is sotigalimab.

[0020] In at least one embodiment, the chemotherapy is selected from the group consisting of gemcitabine, nab-paclitaxel, forfirinox, nitrogen mustard / oxyazaphosphorines, nitrosoureas, triazenes, and alkyl sulfonates, anthracycline antibiotics such as doxorubicin and daunorubicin, taxanes such as Taxol™ and docetaxel, vinca alkaloids such as vincristine and vinblastine, 5-fluorouracil (5-FU), leucovorin, irinotecan, idarubicin, mitomycin C, oxaliplatin, raltitrexed, pemetrexed, tamoxifen, cisplatin, carboplatin, methotrexate, tinomycin D, mitoxantrone, blenoxane, mithramycin, methotrexate, paclitaxel, 2-methoxyestradiol, prinomastat, batimastat, BAY 12-9656, carboxamide triazole, CC-1088, dextromethorphan acetate, dimethylxanthenone acetate, endostatin, IM-862, marimastat, penicillamine, PTK787 / ZK 222584, RPI.4610, squalamine lactate, SU5416, thalidomide, combretastatin, tamoxifen, COL-3, neovastat, BMS-275291, SU6668, anti-VEGF antibody, Med-522 (vitaxin II), CAI, interleukin-12, IM862, amiloride, angiostatin, angiostatin Kl-3, angiostatin Kl-5, captopril, DL-α-difluoromethylornithine, DL-α-difluoromethylornithine HCl, endostatin, fumagillin, herbimycin A, 4-hydroxyphenylretinamide, juglone, laminin, laminin hexapeptide, laminin pentapeptide, lavendustin A, med The chemotherapy is selected from the group consisting of roxyprogesterone, minocycline, placental ribonuclease inhibitor, suramin, thrombospondin, antibodies targeting pro-angiogenic factors, topoisomerase inhibitors, microtubule inhibitors, low molecular weight tyrosine kinase inhibitors of pro-angiogenic growth factors, GTPase inhibitors, histone deacetylase inhibitors, AKT kinase or ATPase inhibitors, Win (Wnt) signal inhibitors, E2F transcription factor inhibitors, mTOR inhibitors, alpha, beta and gamma interferon, IL-12, matrix metalloproteinase inhibitors, ZD6474, SU1248, vitaxin, PDGFR inhibitors, NM3 and 2-ME2, and cilengitide. In at least one embodiment, the chemotherapy is a combination of gemcitabine and nab-paclitaxel.

[0021] In at least one embodiment, the present disclosure provides a reagent capable of binding to a gene involved in MYC signaling; circulating CD244 + Effector Memory CD4 + Total effector memory CD4 T cells + and circulating CXCR5 + Effector Memory CD8 + Total effector memory CD8 T cells + A system is provided that includes a reagent capable of determining the ratio to T cells.

[0022] The present disclosure also provides a method of treating cancer in a human subject in need thereof, comprising: (a) determining the level (cell count) of circulating cross-presenting dendritic cells (DCs) in a biological sample from said subject; and (b) administering to said subject a CD40 agonist in combination with a chemotherapeutic agent if said level (cell count) of circulating cross-presenting DCs is increased compared to a control or reference.

[0023] In at least one embodiment, the cross-presenting DCs are CD1C+CD141+, and the method comprises the steps of (a) determining the level (cell count) of CD1C+CD141+DCs in the subject; and (b) administering the CD40 agonist in combination with the chemotherapeutic agent to the subject if the level (cell count) of CD1C+CD141+DCs is increased compared to the control or reference.

[0024] The present disclosure also provides a method of treating cancer in a human subject in need thereof, comprising: (a) determining the level (cell count) of circulating HLA-DR+CCR7+ B cells in a biological sample from said subject; and (b) administering to said subject a CD40 agonist in combination with a chemotherapeutic agent if said level (cell count) of circulating HLA-DR+CCR7+ B cells is increased compared to a control or reference.

[0025] The present disclosure further provides a method of treating cancer in a human subject in need thereof, comprising: (a) determining a level (cell count) of at least one of circulating PD-1+ T cells, circulating TCF-1+ T cells, and / or circulating Tbet+ T cells in a biological sample from the subject; and (b) administering to the subject a CD40 agonist in combination with a chemotherapeutic agent if the level (cell count) of at least one of circulating PD-1+ T cells, circulating TCF-1+ T cells, and / or circulating Tbet+ T cells is increased compared to a control or reference.

[0026] Also provided is a method of treating cancer in a human subject in need thereof, comprising: (a) determining the level (cell count) of circulating 2B4+CD4 T cells in a biological sample from the subject; and (b) administering to the subject a CD40 agonist in combination with a chemotherapeutic agent if the level (cell count) of circulating 2B4+CD4 T cells is decreased compared to a control or reference.

[0027] In another aspect, the present disclosure provides a method for treating cancer in a human subject in need thereof, the method comprising: (a) determining the level (cell count) of circulating T helper cells in a biological sample from said subject; and (b) administering to said subject a CD40 agonist in combination with a chemotherapeutic agent if said level (cell count) of circulating T helper cells is increased compared to a control or reference.

[0028] The present disclosure also provides a method for treating cancer in a human subject in need thereof, comprising: (a) determining an E2F gene signature in a biological sample from said subject and calculating an E2F signature score; and (b) administering to said subject a CD40 agonist in combination with a chemotherapeutic agent when said E2F gene signature score is decreased compared to a control or reference.

[0029] In one embodiment, the method comprises calculating the E2F gene signature score by averaging log-normalized expression values ​​for each gene in an E2F gene set. In one embodiment, the E2F gene set includes ABCE1, ACP1, AIMP2, AP3S1, APEX1, BUB3, C1QBP, CAD, CANX, CANX, CBX3, CCNA2, CCT2, CCT3, CCT4, CCT5, CCT7, CDC20, CDC45, CDK2, CDK4, CLNS1A, CNBP, COPS5, COX5A, CSTF2, CTPS1, CUL1, CYC1, DDX18, DDX21, DEK, DHX15, DUT, EEF1B2, EIF1AX, EIF2S1, EIF2S 2, EIF3B, EIF3D, EIF3J, EIF4A1, EIF4E, EIF4G2, EIF4H, EPRS1, ERH, ETF1, EXOSC7, FAM120A, FBL, G3BP1, GLO1, GNL3, GOT2, GSPT1, H2AZ1, HD AC2, HDDC2, HDGF, HNRNPA1, HNRNPA2B1, HNRNPA3, HNRNPC, HNRNPD, HNRNPR, HNRNPU, HPRT1, HSP90AB1, HSPD1, HSPE1, IARS1, IFRD1, ILF2, IM PDH2, KARS1, KPNA2, KPNB1, LDHA, LSM2, LSM2, LSM7, MAD2L1, MCM2, MCM4, MCM5, MCM6, MCM7, MRPL23, MRPL23, MRPL9, MRPS18B, MYC, NAP1L1, NCBP1, NCBP2, NDUFAB1, NHP2, NME1, NOLC1, NOP16, NOP56, NPM1, ODC1, ORC2, PA2G4, PABPC1, PABPC4, PCBP1, PCNA, PGK1, PHB, PHB2, POLD2, P OLE3, PPIA, PPM1G, PRDX3, PRDX4, PRPF31, PRPS2, PSMA1, PSMA2, PSMA4, PSMA6, PSMA7, PSMB2, PSMB3, PSMC4, PSMC4, PSMC6, PSMD1, PSMD14, P SMD3, PSMD7, PSMD8, PTGES3, PWP1, RACK1, RAD23B, RAN, RANBP1, RFC4, RNPS1, RPL14, RPL18, RPL22, RPL34, RPL6, RPLP0, RPS10, RPS2, RPS3,The gene includes one or more genes selected from the group consisting of RPS5, RPS6, RRM1, RRP9, RSL1D1, RUVBL2, SERBP1, SET, SF3A1, SF3B3, SLC25A3, SMARCC1, SNRPA, SNRPA1, SNRPB2, SNRPD1, SNRPD2, SNRPD3, SNRPG, SRM, SRPK1, SRSF1, SRSF2, SRSF3, SRSF7, SSB, SSBP1, SSBP1, STARD7, SYNCRIP, TARDBP, TCP1, TFDP1, TOMM70, TRA2B, TRIM28, TUFM, TXNL4A, TYMS, U2AF1, UBA2, UBE2E1, UBE2L3, USP1, VBP1, VDAC1, VDAC3, XPO1, XPOT, XRCC6, YWHAE, YWHAE, and YWHAQ.

[0030] In a further aspect, the present disclosure provides a method for treating cancer in a human subject in need thereof, the method comprising: (a) determining an IFN-γ gene signature in a biological sample from said subject and calculating an IFN-γ gene signature score; and (b) administering to said subject a CD40 agonist in combination with a chemotherapeutic agent if said IFN-γ gene signature score is increased compared to a control or reference.

[0031] In one embodiment, the method further comprises calculating said IFN-γ gene signature score by averaging the log-normalized expression value for each gene in the IFN-γ gene set. In one embodiment, the IFN-γ gene set comprises one or more genes selected from the group consisting of CD8A, CD274, LAG3 and STAT1. In one embodiment, the biological sample is a liquid biopsy, optionally a blood or serum sample, a surgical sample, or other biopsy sample obtained from said subject. In one embodiment, the method further comprises performing step (a) before starting treatment with said CD40 agonist. In one embodiment, the cancer is selected from pancreatic cancer, endometrial cancer, non-small cell lung cancer (NSCLC), renal cell carcinoma, urothelial carcinoma, head and neck cancer, melanoma, bladder cancer, hepatocellular carcinoma, breast cancer, ovarian cancer, gastric cancer, colorectal cancer, glioblastoma, biliary tract cancer, glioma, Merkel cell carcinoma, Hodgkin's lymphoma, non-Hodgkin's lymphoma, cervical cancer, advanced or refractory solid tumors, small cell lung cancer, non-squamous non-small cell lung cancer, desmoplastic melanoma, pediatric advanced solid tumors or lymphomas, mesothelin-positive pleural mesothelioma, esophageal cancer, anal cancer, salivary gland cancer, prostate cancer, carcinoid tumors, primitive neuroectodermal tumors (pNETs), and thyroid cancer. In one embodiment, the cancer is pancreatic cancer, optionally pancreatic ductal adenocarcinoma (PDAC). In one embodiment, the CD40 agonist is an antibody or antigen-binding fragment thereof that specifically binds and agonizes human CD40. In one embodiment, the antibody or antigen-binding fragment thereof is selected from the group consisting of sotigalimab, celiclerumab, ChiLob7 / 4, ADC-1013, SEA-CD40, CP-870,893, dacetuzumab, and CDX-1140. In one embodiment, the chemotherapeutic agent is gemcitabine, nab-paclitaxel, forfirinox, nitrogen mustard / oxyazaphosphorines, nitrosoureas, triazenes, and alkyl sulfonates, anthracycline antibiotics such as doxorubicin and daunorubicin, taxanes such as Taxol and docetaxel, vinca alkaloids such as vincristine and vinblastine, 5-fluorouracil (5-FU), leucovorin, irinotecan, idarubicin, mitomycin C,Oxaliplatin, Raltitrexed, Pemetrexed, Tamoxifen, Cisplatin, Carboplatin, Methotrexate, Actinomycin D, Mitoxantrone, Blenoxane, Mithramycin, Methotrexate, Paclitaxel, 2-Methoxyestradiol, Prinomasat, Batimastat, BAY 12-9656, Carboxamidetriazole, CC-1088, Dextromethorphan Acetate, Dimethylxanthenone Acetate, Endostatin, IM-862, Marimastat, Penicillamine, PTK787 / ZK 222584, RPI 4610, squalamine lactate, SU5416, thalidomide, combretastatin, tamoxifen, COL-3, neovastat, BMS-275291, SU6668, anti-VEGF antibody, Med-522 (vitaxin II), CAI, interleukin-12, IM862, amiloride, angiostatin, angiostatin Kl-3, angiostatin Kl-5, captopril, DL-α-difluoromethylaminobutyric acid, DL-α-difluoromethylaminobutyric acid HCI, endostatin, fumagillin, herbimycin A, 4-hydroxyphenylretinamide, juglone, laminin, laminin hexapeptide, laminin pentapeptide, lavendustin A, medlo The chemotherapeutic agent is selected from the group consisting of oxyprogesterone, minocycline, placental ribonuclease inhibitor, suramin, thrombospondin, antibodies targeting pro-angiogenic factors, topoisomerase inhibitors, microtubule inhibitors, low molecular weight tyrosine kinase inhibitors of pro-angiogenic growth factors, GTPase inhibitors, histone deacetylase inhibitors, AKT kinase or ATPase inhibitors, Win (Wnt) signal inhibitors, E2F transcription factor inhibitors, mTOR inhibitors, alpha, beta and gamma interferon, IL-12, matrix metalloproteinase inhibitors, Z06474, SU1248, vitaxin, POGFR inhibitors, NM3 and 2-ME2, and cilengitide. In one embodiment, the chemotherapeutic agent is a combination of gemcitabine and nab-paclitaxel.

[0032] Each of the aspects and embodiments described herein can be used together unless excluded, either explicitly or obviously, from the context of that embodiment or aspect.

[0033] The features of the present disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings, in which: [Brief description of the drawings]

[0034] [Figure 1] FIG. 1 shows the treatment cohorts and analysis populations of the Phase 2 trials described in Examples 1-6.

[0035] [Diagram 2] FIG. 2 shows the percent change in total target lesions in the efficacy studies in the Phase 2 trials described in Examples 1-6.

[0036] [Diagram 3] FIG. 3 shows the overall survival (OS) rates of the cohorts in the Phase 2 trials described in Examples 1-6.

[0037] [Figure 4A] Figure 4A shows immune profiling of peripheral blood mononuclear cells (PBMCs) demonstrating an increase in activated effector memory (EM) T cells (Ki67+CD8+) in all three cohorts, with cohort A1 (nivolumab + chemotherapy) showing the most pronounced effect.

[0038] [Figure 4B] Figure 4B shows immune profiling of peripheral blood mononuclear cells (PBMCs) showing an increase in activated myeloid dendritic cells (CD86+mDCs) in cohorts B2 and C2. Cohort A1 showed a predominant decrease.

[0039] [Figure 5A]Figure 5A shows tumor multiplex IHC analysis of all three cohorts showing a decrease in the percentage of tumor cells expressing PD-L1 in cohorts A1 and C2, while cohort B2 showed mixed changes in PD-L1 expression.

[0040] [Figure 5B] FIG. 5B shows tumor multiple IHC analysis of all three cohorts showing an increase in tumor CD80+M1 macrophages in cohort B2, while cohorts A1 and C2 showed a decrease.

[0041] [Figure 6] FIG. 6 shows microbiome profiling of fecal samples of all three cohorts showing that cohort A1 had increased bacteroidia and decreased clostridia, while cohort B2 showed the opposite.

[0042] [Figure 7A] FIG. 7A shows patient survival stratified by baseline immune profiling of CXCR5+ effector memory CD8+ T cells for all three cohorts.

[0043] [Figure 7B] FIG. 7B shows patient survival stratified by baseline immune profiling of CD244+ effector memory CD4+ T cells for all three cohorts.

[0044] [Figure 8A] FIG. 8A shows patient survival stratified by baseline TNFα tumor gene expression profiling from RNA-Seq analysis of all three cohorts.

[0045] [Figure 8B] FIG. 8B shows patient survival stratified by baseline MYC tumor gene expression profiling from RNA-Seq analysis of all three cohorts.

[0046] [Figure 9] Figure 9 shows the CyTOF gating strategy. The gating strategy used to define immune cell populations by CyTOF analysis is shown. Representative flow plots are shown.

[0047] [Figure 10-1] Figure 10 shows the T cell phenotyping gating strategy. The gating strategy used to define T cell populations by flow cytometry analysis is shown. Representative flow plots are shown. [Figure 10-2] Figure 10 shows the T cell phenotyping gating strategy. The gating strategy used to define T cell populations by flow cytometry analysis is shown. Representative flow plots are shown.

[0048] [Figure 11-1] Figure 11 shows single marker controls for mIF. Equivalence of single marker optimized antibody immunohistochemistry (IHC) developed with 3,3'-diaminobenzidine (DAB) on human tonsil tissue (top row) with corresponding multiplexed immunofluorescence (mIF) on tonsils (bottom row). Immunofluorescence images show individual marker positions within the performed 7-color assay. [Figure 11-2] Figure 11 shows single marker controls for mIF. Equivalence of single marker optimized antibody immunohistochemistry (IHC) developed with 3,3'-diaminobenzidine (DAB) on human tonsil tissue (top row) with corresponding multiplexed immunofluorescence (mIF) on tonsils (bottom row). Immunofluorescence images show individual marker positions within the performed 7-color assay.

[0049] [Figure 12A]Figures 12A-12B show the PRINCE study design and CONSORT diagrams. Figure 12A shows that PRINCE was a seamless Phase 1b / 2 study, with the Phase 2 portion randomizing patients to treatment with nivo / chemo, sotiga / chemo, or sotiga / nivo / chemo. Figure 12B is a CONSORT diagram for the Phase 2 portion of the study. Patients enrolled in cohorts B2 and C2 during Phase 1b were included in the safety and / or efficacy analyses of the Phase 2 portion. [Figure 12B] Figures 12A-12B show the PRINCE study design and CONSORT diagrams. Figure 12A shows that PRINCE was a seamless Phase 1b / 2 study, with the Phase 2 portion randomizing patients to treatment with nivo / chemo, sotiga / chemo, or sotiga / nivo / chemo. Figure 12B is a CONSORT diagram for the Phase 2 portion of the study. Patients enrolled in cohorts B2 and C2 during Phase 1b were included in the safety and / or efficacy analyses of the Phase 2 portion.

[0050] [Figure 13A-13B]Figures 13A-13B show the increase in activated T cell frequency with nivo / chemo treatment. Figure 13A shows the frequency of circulating CD38+CD8 (left panel) and CD4 (right panel) non-naive T cells as a fraction of total non-naive CD8 or CD4 T cells, respectively, in patients from each cohort before and during treatment. Shown as fold change compared to C1D1 (before treatment) and plotted on a pseudo-log scale. The dark line indicates the median and the error bars indicate the 95% CI. The p-value indicates the probability of a non-zero slope for the line of best fit along the entire series. Figure 13B shows the frequency of circulating CD39+ non-naive CD8 (left panel) and CD4 (right panel) T cells as a fraction of total non-naive CD8 or CD4 T cells, respectively, in patients from each cohort before and during treatment. Shown as fold change compared to C1D1 (before treatment) and plotted on a pseudo-log scale. The dark line indicates the median and the error bars indicate the 95% CI. The p-values ​​indicate the probability of a non-zero slope for the line of best fit along the entire series. **See Table 7 for the number of samples in the applicable analyses.

[0051] [Figure 14A]Figures 14A-14E show that biomarker signatures in blood and tumors reveal specific immune mechanisms of activation in response to nivo / chemo and sotiga / chemo treatment in patients with mPDAC. Figure 14A shows the frequency of circulating Ki-67+ non-naive CD8 (left panel) and CD4 (right panel) T cells as a fraction of total non-naive CD8 or CD4 T cells, respectively, in patients from each cohort pre- and on-treatment. Presented as fold change compared to C1D1 (pre-treatment) and plotted on a pseudo-log scale. Dark lines indicate median values ​​and error bars indicate 95% CI. p-values ​​indicate the probability of a non-zero slope for the line of best fit along the entire series. Figure 14B shows representative flow plots using PBMC samples over time from patients in the nivo / chemo treatment arm showing an increase in Ki-67+ non-naive CD8 (upper panel) and CD4 (lower panel) T cells. Figure 14C shows volcano plots indicating circulating proteins significantly up- or downregulated from C1D1 (pre-treatment) to C1D15 (left) or C2D1 (right) for each of the three cohorts. Dotted lines indicate FDR values ​​of 0.05 and fold changes of 2 in Log2 protein expression. Proteins of interest related to immune mechanisms are highlighted. Figures 14D and 14E show the frequency of PD-L1+ tumor cells (Figure 14D, left panel) and intratumoral iNOS+CD80+ (Figure 14E, left panel) macrophages from multiplex IHC of on-treatment biopsies (C2D1 when feasible, see Methods for details) shown as fold changes compared to pre-treatment biopsies for each cohort. p-values ​​indicate Wilcoxon signed rank test between pre-treatment and (non-normalized) on-treatment cell percentages. Representative images from patients in the nivo / chemo cohort (PD-L1+ tumor cells) (FIG. 14D, right panel) and the sotiga / chemo arm (iNOS+CD80+ macrophages) (FIG. 14E, right panel) are shown. **See Table 7 for number of samples in applicable analyses. [Figure 14B]Figures 14A-14E show that biomarker signatures in blood and tumors reveal specific immune mechanisms of activation in response to nivo / chemo and sotiga / chemo treatment in patients with mPDAC. Figure 14A shows the frequency of circulating Ki-67+ non-naive CD8 (left panel) and CD4 (right panel) T cells as a fraction of total non-naive CD8 or CD4 T cells, respectively, in patients from each cohort pre- and on-treatment. Presented as fold change compared to C1D1 (pre-treatment) and plotted on a pseudo-log scale. Dark lines indicate median values ​​and error bars indicate 95% CI. p-values ​​indicate the probability of a non-zero slope for the line of best fit along the entire series. Figure 14B shows representative flow plots using PBMC samples over time from patients in the nivo / chemo treatment arm showing an increase in Ki-67+ non-naive CD8 (upper panel) and CD4 (lower panel) T cells. Figure 14C shows volcano plots indicating circulating proteins significantly up- or downregulated from C1D1 (pre-treatment) to C1D15 (left) or C2D1 (right) for each of the three cohorts. Dotted lines indicate FDR values ​​of 0.05 and fold changes of 2 in Log2 protein expression. Proteins of interest related to immune mechanisms are highlighted. Figures 14D and 14E show the frequency of PD-L1+ tumor cells (Figure 14D, left panel) and intratumoral iNOS+CD80+ (Figure 14E, left panel) macrophages from multiplex IHC of on-treatment biopsies (C2D1 when feasible, see Methods for details) shown as fold changes compared to pre-treatment biopsies for each cohort. p-values ​​indicate Wilcoxon signed rank test between pre-treatment and (non-normalized) on-treatment cell percentages. Representative images from patients in the nivo / chemo cohort (PD-L1+ tumor cells) (FIG. 14D, right panel) and the sotiga / chemo arm (iNOS+CD80+ macrophages) (FIG. 14E, right panel) are shown. **See Table 7 for number of samples in applicable analyses. [Figure 14C]Figures 14A-14E show that biomarker signatures in blood and tumors reveal specific immune mechanisms of activation in response to nivo / chemo and sotiga / chemo treatment in patients with mPDAC. Figure 14A shows the frequency of circulating Ki-67+ non-naive CD8 (left panel) and CD4 (right panel) T cells as a fraction of total non-naive CD8 or CD4 T cells, respectively, in patients from each cohort pre- and on-treatment. Presented as fold change compared to C1D1 (pre-treatment) and plotted on a pseudo-log scale. Dark lines indicate median values ​​and error bars indicate 95% CI. p-values ​​indicate the probability of a non-zero slope for the line of best fit along the entire series. Figure 14B shows representative flow plots using PBMC samples over time from patients in the nivo / chemo treatment arm showing an increase in Ki-67+ non-naive CD8 (upper panel) and CD4 (lower panel) T cells. Figure 14C shows volcano plots indicating circulating proteins significantly up- or downregulated from C1D1 (pre-treatment) to C1D15 (left) or C2D1 (right) for each of the three cohorts. Dotted lines indicate FDR values ​​of 0.05 and fold changes of 2 in Log2 protein expression. Proteins of interest related to immune mechanisms are highlighted. Figures 14D and 14E show the frequency of PD-L1+ tumor cells (Figure 14D, left panel) and intratumoral iNOS+CD80+ (Figure 14E, left panel) macrophages from multiplex IHC of on-treatment biopsies (C2D1 when feasible, see Methods for details) shown as fold changes compared to pre-treatment biopsies for each cohort. p-values ​​indicate Wilcoxon signed rank test between pre-treatment and (non-normalized) on-treatment cell percentages. Representative images from patients in the nivo / chemo cohort (PD-L1+ tumor cells) (FIG. 14D, right panel) and the sotiga / chemo arm (iNOS+CD80+ macrophages) (FIG. 14E, right panel) are shown. **See Table 7 for number of samples in applicable analyses. [Figure 14D]Figures 14A-14E show that biomarker signatures in blood and tumors reveal specific immune mechanisms of activation in response to nivo / chemo and sotiga / chemo treatment in patients with mPDAC. Figure 14A shows the frequency of circulating Ki-67+ non-naive CD8 (left panel) and CD4 (right panel) T cells as a fraction of total non-naive CD8 or CD4 T cells, respectively, in patients from each cohort pre- and on-treatment. Presented as fold change compared to C1D1 (pre-treatment) and plotted on a pseudo-log scale. Dark lines indicate median values ​​and error bars indicate 95% CI. p-values ​​indicate the probability of a non-zero slope for the line of best fit along the entire series. Figure 14B shows representative flow plots using PBMC samples over time from patients in the nivo / chemo treatment arm showing an increase in Ki-67+ non-naive CD8 (upper panel) and CD4 (lower panel) T cells. Figure 14C shows volcano plots indicating circulating proteins significantly up- or downregulated from C1D1 (pre-treatment) to C1D15 (left) or C2D1 (right) for each of the three cohorts. Dotted lines indicate FDR values ​​of 0.05 and fold changes of 2 in Log2 protein expression. Proteins of interest related to immune mechanisms are highlighted. Figures 14D and 14E show the frequency of PD-L1+ tumor cells (Figure 14D, left panel) and intratumoral iNOS+CD80+ (Figure 14E, left panel) macrophages from multiplex IHC of on-treatment biopsies (C2D1 when feasible, see Methods for details) shown as fold changes compared to pre-treatment biopsies for each cohort. p-values ​​indicate Wilcoxon signed rank test between pre-treatment and (non-normalized) on-treatment cell percentages. Representative images from patients in the nivo / chemo cohort (PD-L1+ tumor cells) (FIG. 14D, right panel) and the sotiga / chemo arm (iNOS+CD80+ macrophages) (FIG. 14E, right panel) are shown. **See Table 7 for number of samples in applicable analyses. [Figure 14E]Figures 14A-14E show that biomarker signatures in blood and tumors reveal specific immune mechanisms of activation in response to nivo / chemo and sotiga / chemo treatment in patients with mPDAC. Figure 14A shows the frequency of circulating Ki-67+ non-naive CD8 (left panel) and CD4 (right panel) T cells as a fraction of total non-naive CD8 or CD4 T cells, respectively, in patients from each cohort pre- and on-treatment. Presented as fold change compared to C1D1 (pre-treatment) and plotted on a pseudo-log scale. Dark lines indicate median values ​​and error bars indicate 95% CI. p-values ​​indicate the probability of a non-zero slope for the line of best fit along the entire series. Figure 14B shows representative flow plots using PBMC samples over time from patients in the nivo / chemo treatment arm showing an increase in Ki-67+ non-naive CD8 (upper panel) and CD4 (lower panel) T cells. Figure 14C shows volcano plots indicating circulating proteins significantly up- or downregulated from C1D1 (pre-treatment) to C1D15 (left) or C2D1 (right) for each of the three cohorts. Dotted lines indicate FDR values ​​of 0.05 and fold changes of 2 in Log2 protein expression. Proteins of interest related to immune mechanisms are highlighted. Figures 14D and 14E show the frequency of PD-L1+ tumor cells (Figure 14D, left panel) and intratumoral iNOS+CD80+ (Figure 14E, left panel) macrophages from multiplex IHC of on-treatment biopsies (C2D1 when feasible, see Methods for details) shown as fold changes compared to pre-treatment biopsies for each cohort. p-values ​​indicate Wilcoxon signed rank test between pre-treatment and (non-normalized) on-treatment cell percentages. Representative images from patients in the nivo / chemo cohort (PD-L1+ tumor cells) (FIG. 14D, right panel) and the sotiga / chemo arm (iNOS+CD80+ macrophages) (FIG. 14E, right panel) are shown. **See Table 7 for number of samples in applicable analyses.

[0052] [Figure 15A]Figures 15A-15H show an increase in activated T cells and type 1 immune responses with nivo / chemo treatment, while proteins important for helper and innate immune responses are increased with sotiga / chemo treatment in patients with mPDAC. Figure 15A shows the frequency of circulating HLA-DR+ non-naive CD4 (left panel) or CD8 (right panel) T cells in patients from each cohort pre- and during treatment. Figure 15B shows representative flow plots from PBMC samples over time from patients in the nivo / chemo treatment arm showing an increase in HLA-DR+ non-naive CD8 (top) and CD4 (bottom) T cells. Figures 15C-15F show the fold change in Log2 expression values ​​from each cohort plotted on a pseudo-log scale from pre-treatment (C1D1) for circulating IFNγ (Figure 15C), PD-1 (Figure 15D), CXCL9 (Figure 15E), and CXCL10 (Figure 15F) during treatment. Time series plots in a-f show median values ​​in thick lines and individual patient values ​​in thin lines, with error bars indicating 95% confidence intervals. p-values ​​on time series plots indicate p-values ​​of non-zero slope for the line of best fit along the entire series. Figures 15G-15H show DIABLO Circos plots (Figure 15G) and correlation matrices (Figure 15H) showing factors from CyTOF, X50 flow cytometry, Olink, protein mass spectrometry that were significantly associated with treatment, and correlations between these factors. In the circus plot (Figure 15G), the outer line of the circle indicates the magnitude and direction of the treatment association. The inner line of the plot indicates positive and negative correlations between biomarker factors. In the correlation plot (Figure 15H), the color of the text indicates the treatment association of the biomarker. For all cell populations shown, frequencies are of the parent population. **See Table 7 for the number of samples in the applicable analysis. [Figure 15B]Figures 15A-15H show an increase in activated T cells and type 1 immune responses with nivo / chemo treatment, while proteins important for helper and innate immune responses are increased with sotiga / chemo treatment in patients with mPDAC. Figure 15A shows the frequency of circulating HLA-DR+ non-naive CD4 (left panel) or CD8 (right panel) T cells in patients from each cohort pre- and during treatment. Figure 15B shows representative flow plots from PBMC samples over time from patients in the nivo / chemo treatment arm showing an increase in HLA-DR+ non-naive CD8 (top) and CD4 (bottom) T cells. Figures 15C-15F show the fold change in Log2 expression values ​​from each cohort plotted on a pseudo-log scale from pre-treatment (C1D1) for circulating IFNγ (Figure 15C), PD-1 (Figure 15D), CXCL9 (Figure 15E), and CXCL10 (Figure 15F) during treatment. Time series plots in a-f show median values ​​in thick lines and individual patient values ​​in thin lines, with error bars indicating 95% confidence intervals. p-values ​​on time series plots indicate p-values ​​of non-zero slope for the line of best fit along the entire series. Figures 15G-15H show DIABLO Circos plots (Figure 15G) and correlation matrices (Figure 15H) showing factors from CyTOF, X50 flow cytometry, Olink, protein mass spectrometry that were significantly associated with treatment, and correlations between these factors. In the circus plot (Figure 15G), the outer line of the circle indicates the magnitude and direction of the treatment association. The inner line of the plot indicates positive and negative correlations between biomarker factors. In the correlation plot (Figure 15H), the color of the text indicates the treatment association of the biomarker. For all cell populations shown, frequencies are of the parent population. **See Table 7 for the number of samples in the applicable analysis. [Fig. 15C-15D]Figures 15A-15H show an increase in activated T cells and type 1 immune responses with nivo / chemo treatment, while proteins important for helper and innate immune responses are increased with sotiga / chemo treatment in patients with mPDAC. Figure 15A shows the frequency of circulating HLA-DR+ non-naive CD4 (left panel) or CD8 (right panel) T cells in patients from each cohort pre- and during treatment. Figure 15B shows representative flow plots from PBMC samples over time from patients in the nivo / chemo treatment arm showing an increase in HLA-DR+ non-naive CD8 (top) and CD4 (bottom) T cells. Figures 15C-15F show the fold change in Log2 expression values ​​from each cohort plotted on a pseudo-log scale from pre-treatment (C1D1) for circulating IFNγ (Figure 15C), PD-1 (Figure 15D), CXCL9 (Figure 15E), and CXCL10 (Figure 15F) during treatment. Time series plots in a-f show median values ​​in thick lines and individual patient values ​​in thin lines, with error bars indicating 95% confidence intervals. p-values ​​on time series plots indicate p-values ​​of non-zero slope for the line of best fit along the entire series. Figures 15G-15H show DIABLO Circos plots (Figure 15G) and correlation matrices (Figure 15H) showing factors from CyTOF, X50 flow cytometry, Olink, protein mass spectrometry that were significantly associated with treatment, and correlations between these factors. In the circus plot (Figure 15G), the outer line of the circle indicates the magnitude and direction of the treatment association. The inner line of the plot indicates positive and negative correlations between biomarker factors. In the correlation plot (Figure 15H), the color of the text indicates the treatment association of the biomarker. For all cell populations shown, frequencies are of the parent population. **See Table 7 for the number of samples in the applicable analysis. [Fig. 15E-15F]Figures 15A-15H show an increase in activated T cells and type 1 immune responses with nivo / chemo treatment, while proteins important for helper and innate immune responses are increased with sotiga / chemo treatment in patients with mPDAC. Figure 15A shows the frequency of circulating HLA-DR+ non-naive CD4 (left panel) or CD8 (right panel) T cells in patients from each cohort pre- and during treatment. Figure 15B shows representative flow plots from PBMC samples over time from patients in the nivo / chemo treatment arm showing an increase in HLA-DR+ non-naive CD8 (top) and CD4 (bottom) T cells. Figures 15C-15F show the fold change in Log2 expression values ​​from each cohort plotted on a pseudo-log scale from pre-treatment (C1D1) for circulating IFNγ (Figure 15C), PD-1 (Figure 15D), CXCL9 (Figure 15E), and CXCL10 (Figure 15F) during treatment. Time series plots in a-f show median values ​​in thick lines and individual patient values ​​in thin lines, with error bars indicating 95% confidence intervals. p-values ​​on time series plots indicate p-values ​​of non-zero slope for the line of best fit along the entire series. Figures 15G-15H show DIABLO Circos plots (Figure 15G) and correlation matrices (Figure 15H) showing factors from CyTOF, X50 flow cytometry, Olink, protein mass spectrometry that were significantly associated with treatment, and correlations between these factors. In the circus plot (Figure 15G), the outer line of the circle indicates the magnitude and direction of the treatment association. The inner line of the plot indicates positive and negative correlations between biomarker factors. In the correlation plot (Figure 15H), the color of the text indicates the treatment association of the biomarker. For all cell populations shown, frequencies are of the parent population. **See Table 7 for the number of samples in the applicable analysis. [Figure 15G]Figures 15A-15H show an increase in activated T cells and type 1 immune responses with nivo / chemo treatment, while proteins important for helper and innate immune responses are increased with sotiga / chemo treatment in patients with mPDAC. Figure 15A shows the frequency of circulating HLA-DR+ non-naive CD4 (left panel) or CD8 (right panel) T cells in patients from each cohort pre- and during treatment. Figure 15B shows representative flow plots from PBMC samples over time from patients in the nivo / chemo treatment arm showing an increase in HLA-DR+ non-naive CD8 (top) and CD4 (bottom) T cells. Figures 15C-15F show the fold change in Log2 expression values ​​from each cohort plotted on a pseudo-log scale from pre-treatment (C1D1) for circulating IFNγ (Figure 15C), PD-1 (Figure 15D), CXCL9 (Figure 15E), and CXCL10 (Figure 15F) during treatment. Time series plots in a-f show median values ​​in thick lines and individual patient values ​​in thin lines, with error bars indicating 95% confidence intervals. p-values ​​on time series plots indicate p-values ​​of non-zero slope for the line of best fit along the entire series. Figures 15G-15H show DIABLO Circos plots (Figure 15G) and correlation matrices (Figure 15H) showing factors from CyTOF, X50 flow cytometry, Olink, protein mass spectrometry that were significantly associated with treatment, and correlations between these factors. In the circus plot (Figure 15G), the outer line of the circle indicates the magnitude and direction of the treatment association. The inner line of the plot indicates positive and negative correlations between biomarker factors. In the correlation plot (Figure 15H), the color of the text indicates the treatment association of the biomarker. For all cell populations shown, frequencies are of the parent population. **See Table 7 for the number of samples in the applicable analysis. [Figure 15H]Figures 15A-15H show an increase in activated T cells and type 1 immune responses with nivo / chemo treatment, while proteins important for helper and innate immune responses are increased with sotiga / chemo treatment in patients with mPDAC. Figure 15A shows the frequency of circulating HLA-DR+ non-naive CD4 (left panel) or CD8 (right panel) T cells in patients from each cohort pre- and during treatment. Figure 15B shows representative flow plots from PBMC samples over time from patients in the nivo / chemo treatment arm showing an increase in HLA-DR+ non-naive CD8 (top) and CD4 (bottom) T cells. Figures 15C-15F show the fold change in Log2 expression values ​​from each cohort plotted on a pseudo-log scale from pre-treatment (C1D1) for circulating IFNγ (Figure 15C), PD-1 (Figure 15D), CXCL9 (Figure 15E), and CXCL10 (Figure 15F) during treatment. Time series plots in a-f show median values ​​in thick lines and individual patient values ​​in thin lines, with error bars indicating 95% confidence intervals. p-values ​​on time series plots indicate p-values ​​of non-zero slope for the line of best fit along the entire series. Figures 15G-15H show DIABLO Circos plots (Figure 15G) and correlation matrices (Figure 15H) showing factors from CyTOF, X50 flow cytometry, Olink, protein mass spectrometry that were significantly associated with treatment, and correlations between these factors. In the circus plot (Figure 15G), the outer line of the circle indicates the magnitude and direction of the treatment association. The inner line of the plot indicates positive and negative correlations between biomarker factors. In the correlation plot (Figure 15H), the color of the text indicates the treatment association of the biomarker. For all cell populations shown, frequencies are of the parent population. **See Table 7 for the number of samples in the applicable analysis.

[0053] [Figure 16A]Figure 16A-H shows that pre-treatment non-immunosuppressive tumor microenvironment and activated circulating CD8 T cells are associated with survival in mPDAC patients treated with nivo / chemo. Figure 16A is a heat map of gene expression signatures significantly associated with survival outcomes in response to nivo / chemo treatment between higher (above median) and lower (below median) values ​​in pre-treatment tumor samples. Individual patients are shown in columns and annotated to indicate association by survival status at 1 year. Figure 16B shows Kaplan-Meier (KM) curves for overall survival stratified by TNFα through NFκB hallmark pathway signatures above and below median signature values ​​across all patients in all cohorts. FIG. 16C is a KM curve for overall survival stratified by the percentage of iNOS+ intratumoral macrophages among total macrophages from mIF of pretreatment biopsies above and below the median percentage across all patients in all cohorts (FIG. 16C, top panel). Representative pretreatment tumor mIF images from two patients (FIG. 16C, bottom panel). FIG. 16D shows the percentage of tumor cells expressing PD-L1 in pretreatment biopsies by mIF stratified by overall survival status at 1 year. p-values ​​are Wilcoxon signed rank test. FIG. 16E shows a correlation matrix of immune percentages and gene expression signatures in pretreatment tumor biopsies with labels color-coded by association with survival outcome. FIG. 16F is a heatmap of median fluorescence intensity of proteins on CD38+ effector memory CD8 T cell populations from pretreatment PBMC samples across patients in the nivo / chemo cohort. Figure 16G shows KM curves for overall survival stratified by the frequency of circulating CD38+CD8 effector memory T cells among total CD8 T cells at baseline above and below the median frequency. Frequency of CD38+CD8 T cells among total CD8 T cells in pre-treatment and on-treatment PBMC samples (C1D15, C2D1, C4D1) separated by patient survival status at 1 year.p-values ​​represent Wilcoxon signed rank test between time points indicating an increase in cell proportions during treatment. Figure 16H shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, and the position is determined by the reduced dimensionality across all tumor and circulating biomarkers. For all cell populations shown, frequencies are those of the parent population. For all KM curves, p-values ​​are from log-rank tests between groups, and shaded areas indicate 95% CI. **See Table 7 for the number of samples in applicable analyses. [Figure 16B]Figure 16A-H shows that pre-treatment non-immunosuppressive tumor microenvironment and activated circulating CD8 T cells are associated with survival in mPDAC patients treated with nivo / chemo. Figure 16A is a heat map of gene expression signatures significantly associated with survival outcomes in response to nivo / chemo treatment between higher (above median) and lower (below median) values ​​in pre-treatment tumor samples. Individual patients are shown in columns and annotated to indicate association by survival status at 1 year. Figure 16B shows Kaplan-Meier (KM) curves for overall survival stratified by TNFα through NFκB hallmark pathway signatures above and below median signature values ​​across all patients in all cohorts. FIG. 16C is a KM curve for overall survival stratified by the percentage of iNOS+ intratumoral macrophages among total macrophages from mIF of pretreatment biopsies above and below the median percentage across all patients in all cohorts (FIG. 16C, top panel). Representative pretreatment tumor mIF images from two patients (FIG. 16C, bottom panel). FIG. 16D shows the percentage of tumor cells expressing PD-L1 in pretreatment biopsies by mIF stratified by overall survival status at 1 year. p-values ​​are Wilcoxon signed rank test. FIG. 16E shows a correlation matrix of immune percentages and gene expression signatures in pretreatment tumor biopsies with labels color-coded by association with survival outcome. FIG. 16F is a heatmap of median fluorescence intensity of proteins on CD38+ effector memory CD8 T cell populations from pretreatment PBMC samples across patients in the nivo / chemo cohort. Figure 16G shows KM curves for overall survival stratified by the frequency of circulating CD38+CD8 effector memory T cells among total CD8 T cells at baseline above and below the median frequency. Frequency of CD38+CD8 T cells among total CD8 T cells in pre-treatment and on-treatment PBMC samples (C1D15, C2D1, C4D1) separated by patient survival status at 1 year.p-values ​​represent Wilcoxon signed rank test between time points indicating an increase in cell proportions during treatment. Figure 16H shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, and the position is determined by the reduced dimensionality across all tumor and circulating biomarkers. For all cell populations shown, frequencies are those of the parent population. For all KM curves, p-values ​​are from log-rank tests between groups, and shaded areas indicate 95% CI. **See Table 7 for the number of samples in applicable analyses. [Figure 16C]Figure 16A-H shows that pre-treatment non-immunosuppressive tumor microenvironment and activated circulating CD8 T cells are associated with survival in mPDAC patients treated with nivo / chemo. Figure 16A is a heat map of gene expression signatures significantly associated with survival outcomes in response to nivo / chemo treatment between higher (above median) and lower (below median) values ​​in pre-treatment tumor samples. Individual patients are shown in columns and annotated to indicate association by survival status at 1 year. Figure 16B shows Kaplan-Meier (KM) curves for overall survival stratified by TNFα through NFκB hallmark pathway signatures above and below median signature values ​​across all patients in all cohorts. FIG. 16C is a KM curve for overall survival stratified by the percentage of iNOS+ intratumoral macrophages among total macrophages from mIF of pretreatment biopsies above and below the median percentage across all patients in all cohorts (FIG. 16C, top panel). Representative pretreatment tumor mIF images from two patients (FIG. 16C, bottom panel). FIG. 16D shows the percentage of tumor cells expressing PD-L1 in pretreatment biopsies by mIF stratified by overall survival status at 1 year. p-values ​​are Wilcoxon signed rank test. FIG. 16E shows a correlation matrix of immune percentages and gene expression signatures in pretreatment tumor biopsies with labels color-coded by association with survival outcome. FIG. 16F is a heatmap of median fluorescence intensity of proteins on CD38+ effector memory CD8 T cell populations from pretreatment PBMC samples across patients in the nivo / chemo cohort. Figure 16G shows KM curves for overall survival stratified by the frequency of circulating CD38+CD8 effector memory T cells among total CD8 T cells at baseline above and below the median frequency. Frequency of CD38+CD8 T cells among total CD8 T cells in pre-treatment and on-treatment PBMC samples (C1D15, C2D1, C4D1) separated by patient survival status at 1 year.p-values ​​represent Wilcoxon signed rank test between time points indicating an increase in cell proportions during treatment. Figure 16H shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, and the position is determined by the reduced dimensionality across all tumor and circulating biomarkers. For all cell populations shown, frequencies are those of the parent population. For all KM curves, p-values ​​are from log-rank tests between groups, and shaded areas indicate 95% CI. **See Table 7 for the number of samples in applicable analyses. [Figure 16D]Figure 16A-H shows that pre-treatment non-immunosuppressive tumor microenvironment and activated circulating CD8 T cells are associated with survival in mPDAC patients treated with nivo / chemo. Figure 16A is a heat map of gene expression signatures significantly associated with survival outcomes in response to nivo / chemo treatment between higher (above median) and lower (below median) values ​​in pre-treatment tumor samples. Individual patients are shown in columns and annotated to indicate association by survival status at 1 year. Figure 16B shows Kaplan-Meier (KM) curves for overall survival stratified by TNFα through NFκB hallmark pathway signatures above and below median signature values ​​across all patients in all cohorts. FIG. 16C is a KM curve for overall survival stratified by the percentage of iNOS+ intratumoral macrophages among total macrophages from mIF of pretreatment biopsies above and below the median percentage across all patients in all cohorts (FIG. 16C, top panel). Representative pretreatment tumor mIF images from two patients (FIG. 16C, bottom panel). FIG. 16D shows the percentage of tumor cells expressing PD-L1 in pretreatment biopsies by mIF stratified by overall survival status at 1 year. p-values ​​are Wilcoxon signed rank test. FIG. 16E shows a correlation matrix of immune percentages and gene expression signatures in pretreatment tumor biopsies with labels color-coded by association with survival outcome. FIG. 16F is a heatmap of median fluorescence intensity of proteins on CD38+ effector memory CD8 T cell populations from pretreatment PBMC samples across patients in the nivo / chemo cohort. Figure 16G shows KM curves for overall survival stratified by the frequency of circulating CD38+CD8 effector memory T cells among total CD8 T cells at baseline above and below the median frequency. Frequency of CD38+CD8 T cells among total CD8 T cells in pre-treatment and on-treatment PBMC samples (C1D15, C2D1, C4D1) separated by patient survival status at 1 year.p-values ​​represent Wilcoxon signed rank test between time points indicating an increase in cell proportions during treatment. Figure 16H shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, and the position is determined by the reduced dimensionality across all tumor and circulating biomarkers. For all cell populations shown, frequencies are those of the parent population. For all KM curves, p-values ​​are from log-rank tests between groups, and shaded areas indicate 95% CI. **See Table 7 for the number of samples in applicable analyses. [Figure 16E]Figure 16A-H shows that pre-treatment non-immunosuppressive tumor microenvironment and activated circulating CD8 T cells are associated with survival in mPDAC patients treated with nivo / chemo. Figure 16A is a heat map of gene expression signatures significantly associated with survival outcomes in response to nivo / chemo treatment between higher (above median) and lower (below median) values ​​in pre-treatment tumor samples. Individual patients are shown in columns and annotated to indicate association by survival status at 1 year. Figure 16B shows Kaplan-Meier (KM) curves for overall survival stratified by TNFα through NFκB hallmark pathway signatures above and below median signature values ​​across all patients in all cohorts. FIG. 16C is a KM curve for overall survival stratified by the percentage of iNOS+ intratumoral macrophages among total macrophages from mIF of pretreatment biopsies above and below the median percentage across all patients in all cohorts (FIG. 16C, top panel). Representative pretreatment tumor mIF images from two patients (FIG. 16C, bottom panel). FIG. 16D shows the percentage of tumor cells expressing PD-L1 in pretreatment biopsies by mIF stratified by overall survival status at 1 year. p-values ​​are Wilcoxon signed rank test. FIG. 16E shows a correlation matrix of immune percentages and gene expression signatures in pretreatment tumor biopsies with labels color-coded by association with survival outcome. FIG. 16F is a heatmap of median fluorescence intensity of proteins on CD38+ effector memory CD8 T cell populations from pretreatment PBMC samples across patients in the nivo / chemo cohort. Figure 16G shows KM curves for overall survival stratified by the frequency of circulating CD38+CD8 effector memory T cells among total CD8 T cells at baseline above and below the median frequency. Frequency of CD38+CD8 T cells among total CD8 T cells in pre-treatment and on-treatment PBMC samples (C1D15, C2D1, C4D1) separated by patient survival status at 1 year.p-values ​​represent Wilcoxon signed rank test between time points indicating an increase in cell proportions during treatment. Figure 16H shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, and the position is determined by the reduced dimensionality across all tumor and circulating biomarkers. For all cell populations shown, frequencies are those of the parent population. For all KM curves, p-values ​​are from log-rank tests between groups, and shaded areas indicate 95% CI. **See Table 7 for the number of samples in applicable analyses. [Fig. 16F-16G]Figure 16A-H shows that pre-treatment non-immunosuppressive tumor microenvironment and activated circulating CD8 T cells are associated with survival in mPDAC patients treated with nivo / chemo. Figure 16A is a heat map of gene expression signatures significantly associated with survival outcomes in response to nivo / chemo treatment between higher (above median) and lower (below median) values ​​in pre-treatment tumor samples. Individual patients are shown in columns and annotated to indicate association by survival status at 1 year. Figure 16B shows Kaplan-Meier (KM) curves for overall survival stratified by TNFα through NFκB hallmark pathway signatures above and below median signature values ​​across all patients in all cohorts. FIG. 16C is a KM curve for overall survival stratified by the percentage of iNOS+ intratumoral macrophages among total macrophages from mIF of pretreatment biopsies above and below the median percentage across all patients in all cohorts (FIG. 16C, top panel). Representative pretreatment tumor mIF images from two patients (FIG. 16C, bottom panel). FIG. 16D shows the percentage of tumor cells expressing PD-L1 in pretreatment biopsies by mIF stratified by overall survival status at 1 year. p-values ​​are Wilcoxon signed rank test. FIG. 16E shows a correlation matrix of immune percentages and gene expression signatures in pretreatment tumor biopsies with labels color-coded by association with survival outcome. FIG. 16F is a heatmap of median fluorescence intensity of proteins on CD38+ effector memory CD8 T cell populations from pretreatment PBMC samples across patients in the nivo / chemo cohort. Figure 16G shows KM curves for overall survival stratified by the frequency of circulating CD38+CD8 effector memory T cells among total CD8 T cells at baseline above and below the median frequency. Frequency of CD38+CD8 T cells among total CD8 T cells in pre-treatment and on-treatment PBMC samples (C1D15, C2D1, C4D1) separated by patient survival status at 1 year.p-values ​​represent Wilcoxon signed rank test between time points indicating an increase in cell proportions during treatment. Figure 16H shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, and the position is determined by the reduced dimensionality across all tumor and circulating biomarkers. For all cell populations shown, frequencies are those of the parent population. For all KM curves, p-values ​​are from log-rank tests between groups, and shaded areas indicate 95% CI. **See Table 7 for the number of samples in applicable analyses. [Figure 16H]Figure 16A-H shows that pre-treatment non-immunosuppressive tumor microenvironment and activated circulating CD8 T cells are associated with survival in mPDAC patients treated with nivo / chemo. Figure 16A is a heat map of gene expression signatures significantly associated with survival outcomes in response to nivo / chemo treatment between higher (above median) and lower (below median) values ​​in pre-treatment tumor samples. Individual patients are shown in columns and annotated to indicate association by survival status at 1 year. Figure 16B shows Kaplan-Meier (KM) curves for overall survival stratified by TNFα through NFκB hallmark pathway signatures above and below median signature values ​​across all patients in all cohorts. FIG. 16C is a KM curve for overall survival stratified by the percentage of iNOS+ intratumoral macrophages among total macrophages from mIF of pretreatment biopsies above and below the median percentage across all patients in all cohorts (FIG. 16C, top panel). Representative pretreatment tumor mIF images from two patients (FIG. 16C, bottom panel). FIG. 16D shows the percentage of tumor cells expressing PD-L1 in pretreatment biopsies by mIF stratified by overall survival status at 1 year. p-values ​​are Wilcoxon signed rank test. FIG. 16E shows a correlation matrix of immune percentages and gene expression signatures in pretreatment tumor biopsies with labels color-coded by association with survival outcome. FIG. 16F is a heatmap of median fluorescence intensity of proteins on CD38+ effector memory CD8 T cell populations from pretreatment PBMC samples across patients in the nivo / chemo cohort. Figure 16G shows KM curves for overall survival stratified by the frequency of circulating CD38+CD8 effector memory T cells among total CD8 T cells at baseline above and below the median frequency. Frequency of CD38+CD8 T cells among total CD8 T cells in pre-treatment and on-treatment PBMC samples (C1D15, C2D1, C4D1) separated by patient survival status at 1 year.p-values ​​represent Wilcoxon signed rank test between time points indicating an increase in cell proportions during treatment. Figure 16H shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, and the position is determined by the reduced dimensionality across all tumor and circulating biomarkers. For all cell populations shown, frequencies are those of the parent population. For all KM curves, p-values ​​are from log-rank tests between groups, and shaded areas indicate 95% CI. **See Table 7 for the number of samples in applicable analyses.

[0054] [Figure 17] Figure 17 shows PD-L1 expression on pre-treatment tumor cells in line with longer survival in mPDAC patients treated with nivo / chemo. Percentage of tumor cells expressing PD-L1 in pre-treatment biopsies by multiplex IHC stratified by overall survival status at 1 year. p-values ​​are Wilcoxon signed rank test.

[0055] [Figure 18A]Figures 18A-18F show that antigen-experienced non-naive T cells and follicular helper T cells in the periphery are associated with survival in mPDAC patients treated with nivo / chemo. Figure 18A shows the KM curves for overall survival stratified by the frequency of circulating PD-1+CD39+ effector memory 1 CD4 T cells above and below the median across all patients in all cohorts. Figure 18B is a heat map of the median fluorescence intensity of proteins present on pre-treatment PD-1+CD39+ effector memory 1 CD4 T cells across all patients in the nivo / chemo cohort. Figure 18C shows the frequency of PD-1+CD39+ effector memory 1 CD4 T cells before and during treatment (C1D15, C2D1, C4D1). FIG. 18D shows KM curves for overall survival stratified by the frequency of circulating T follicular helper (CXCR5+PD-1+CD4+) T cells above and below the median across all patients in all cohorts. FIG. 18E is a heat map of median fluorescence intensity of proteins present on pre-treatment T follicular helper T cells across all patients in the nivo / chemo cohort. FIG. 18F shows the frequency of T follicular helper T cells before and during treatment (C1D15, C2D1, C4D1). For all cell populations shown, frequencies are of the parent population. Time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. p-values ​​for the time series show Wilcoxon signed rank test between survival groups at each time point. On the KM curves, p-values ​​are from the log-rank test between groups and shaded areas show 95% CI. [Fig. 18B-18C]Figures 18A-18F show that antigen-experienced non-naive T cells and follicular helper T cells in the periphery are associated with survival in mPDAC patients treated with nivo / chemo. Figure 18A shows the KM curves for overall survival stratified by the frequency of circulating PD-1+CD39+ effector memory 1 CD4 T cells above and below the median across all patients in all cohorts. Figure 18B is a heat map of the median fluorescence intensity of proteins present on pre-treatment PD-1+CD39+ effector memory 1 CD4 T cells across all patients in the nivo / chemo cohort. Figure 18C shows the frequency of PD-1+CD39+ effector memory 1 CD4 T cells before and during treatment (C1D15, C2D1, C4D1). FIG. 18D shows KM curves for overall survival stratified by the frequency of circulating T follicular helper (CXCR5+PD-1+CD4+) T cells above and below the median across all patients in all cohorts. FIG. 18E is a heat map of median fluorescence intensity of proteins present on pre-treatment T follicular helper T cells across all patients in the nivo / chemo cohort. FIG. 18F shows the frequency of T follicular helper T cells before and during treatment (C1D15, C2D1, C4D1). For all cell populations shown, frequencies are of the parent population. Time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. p-values ​​for the time series show Wilcoxon signed rank test between survival groups at each time point. On the KM curves, p-values ​​are from the log-rank test between groups and shaded areas show 95% CI. [Figure 18D]Figures 18A-18F show that antigen-experienced non-naive T cells and follicular helper T cells in the periphery are associated with survival in mPDAC patients treated with nivo / chemo. Figure 18A shows the KM curves for overall survival stratified by the frequency of circulating PD-1+CD39+ effector memory 1 CD4 T cells above and below the median across all patients in all cohorts. Figure 18B is a heat map of the median fluorescence intensity of proteins present on pre-treatment PD-1+CD39+ effector memory 1 CD4 T cells across all patients in the nivo / chemo cohort. Figure 18C shows the frequency of PD-1+CD39+ effector memory 1 CD4 T cells before and during treatment (C1D15, C2D1, C4D1). FIG. 18D shows KM curves for overall survival stratified by the frequency of circulating T follicular helper (CXCR5+PD-1+CD4+) T cells above and below the median across all patients in all cohorts. FIG. 18E is a heat map of median fluorescence intensity of proteins present on pre-treatment T follicular helper T cells across all patients in the nivo / chemo cohort. FIG. 18F shows the frequency of T follicular helper T cells before and during treatment (C1D15, C2D1, C4D1). For all cell populations shown, frequencies are of the parent population. Time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. p-values ​​for the time series show Wilcoxon signed rank test between survival groups at each time point. On the KM curves, p-values ​​are from the log-rank test between groups and shaded areas show 95% CI. [Fig. 18E-18F]Figures 18A-18F show that antigen-experienced non-naive T cells and follicular helper T cells in the periphery are associated with survival in mPDAC patients treated with nivo / chemo. Figure 18A shows the KM curves for overall survival stratified by the frequency of circulating PD-1+CD39+ effector memory 1 CD4 T cells above and below the median across all patients in all cohorts. Figure 18B is a heat map of the median fluorescence intensity of proteins present on pre-treatment PD-1+CD39+ effector memory 1 CD4 T cells across all patients in the nivo / chemo cohort. Figure 18C shows the frequency of PD-1+CD39+ effector memory 1 CD4 T cells before and during treatment (C1D15, C2D1, C4D1). FIG. 18D shows KM curves for overall survival stratified by the frequency of circulating T follicular helper (CXCR5+PD-1+CD4+) T cells above and below the median across all patients in all cohorts. FIG. 18E is a heat map of median fluorescence intensity of proteins present on pre-treatment T follicular helper T cells across all patients in the nivo / chemo cohort. FIG. 18F shows the frequency of T follicular helper T cells before and during treatment (C1D15, C2D1, C4D1). For all cell populations shown, frequencies are of the parent population. Time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. p-values ​​for the time series show Wilcoxon signed rank test between survival groups at each time point. On the KM curves, p-values ​​are from the log-rank test between groups and shaded areas show 95% CI.

[0056] [Figure 19A]Figures 19A-19C show that antigen-experienced non-naive central memory T cells and follicular helper T cells in the periphery are associated with survival in mPDAC patients treated with nivo / chemo. Figure 19A shows the KM curves for overall survival stratified by the frequency of circulating PD-1+CD39+ central memory CD4 T cells above and below the median across all patients in all cohorts. Figure 19B is a heat map of the median fluorescence intensity of proteins present on pre-treatment PD-1+CD39+ central memory CD4 T cells across all patients in the nivo / chemo cohort. Figure 19C shows the frequency of PD-1+CD39+ central memory CD4 T cells before and during treatment (C1D15, C2D1, C4D1). **See Table 7 for the number of samples in the applicable analysis. [Figure 19B-19C] Figures 19A-19C show that antigen-experienced non-naive central memory T cells and follicular helper T cells in the periphery are associated with survival in mPDAC patients treated with nivo / chemo. Figure 19A shows the KM curves for overall survival stratified by the frequency of circulating PD-1+CD39+ central memory CD4 T cells above and below the median across all patients in all cohorts. Figure 19B is a heat map of the median fluorescence intensity of proteins present on pre-treatment PD-1+CD39+ central memory CD4 T cells across all patients in the nivo / chemo cohort. Figure 19C shows the frequency of PD-1+CD39+ central memory CD4 T cells before and during treatment (C1D15, C2D1, C4D1). **See Table 7 for the number of samples in the applicable analysis.

[0057] [Figure 20A]Figures 20A-20G show helper signatures and proliferating CD4 T cells in tumors associated with survival in patients undergoing sotiga / chemo treatment. Figure 20A is a heat map of gene expression signatures significantly associated with survival in response to sotiga / chemo treatment between higher (above median) and lower (below median) values ​​in pretreatment tumor biopsies. Individual patients are shown in columns, with labels corresponding to 1-year overall survival status. Figures 20B-20D show KM curves for overall survival stratified by Th1 (Figure 20B), IFNγ (Figure 20C), and E2F (Figure 20D) gene expression signatures above and below the median. FIG. 20E shows KM curves for overall survival stratified by Ki-67-Foxp3-CD4 T cells from mIF for pretreatment tumor samples above and below the median in relation to patient survival values ​​(FIG. 20E, top panel), as well as representative images from tumor samples with high and low Ki-67-Foxp3-CD4 T cells (FIG. 20E, bottom panel). FIG. 20F shows a correlation matrix of immune infiltrates and gene expression signatures in pretreatment tumor biopsies colored by association with overall survival outcomes. FIG. 20G is a DIABLO Circos plot showing factors from RNAseq(gx) and Vectra imaging significantly associated with survival status at 1 year, and the correlations between these factors. The outer lines of the circles indicate the magnitude and direction of the treatment association. The inner lines of the plots indicate positive and negative correlations between biomarker factors. On all KM curves, p values ​​are from the log-rank test between groups, and the shaded areas indicate the 95% CI. **See Table 7 for numbers of samples in applicable analyses. [Figure 20B]Figures 20A-20G show helper signatures and proliferating CD4 T cells in tumors associated with survival in patients undergoing sotiga / chemo treatment. Figure 20A is a heat map of gene expression signatures significantly associated with survival in response to sotiga / chemo treatment between higher (above median) and lower (below median) values ​​in pretreatment tumor biopsies. Individual patients are shown in columns, with labels corresponding to 1-year overall survival status. Figures 20B-20D show KM curves for overall survival stratified by Th1 (Figure 20B), IFNγ (Figure 20C), and E2F (Figure 20D) gene expression signatures above and below the median. FIG. 20E shows KM curves for overall survival stratified by Ki-67-Foxp3-CD4 T cells from mIF for pretreatment tumor samples above and below the median in relation to patient survival values ​​(FIG. 20E, top panel), as well as representative images from tumor samples with high and low Ki-67-Foxp3-CD4 T cells (FIG. 20E, bottom panel). FIG. 20F shows a correlation matrix of immune infiltrates and gene expression signatures in pretreatment tumor biopsies colored by association with overall survival outcomes. FIG. 20G is a DIABLO Circos plot showing factors from RNAseq(gx) and Vectra imaging significantly associated with survival status at 1 year, and the correlations between these factors. The outer lines of the circles indicate the magnitude and direction of the treatment association. The inner lines of the plots indicate positive and negative correlations between biomarker factors. On all KM curves, p values ​​are from the log-rank test between groups, and the shaded areas indicate the 95% CI. **See Table 7 for numbers of samples in applicable analyses. [Figure 20C]Figures 20A-20G show helper signatures and proliferating CD4 T cells in tumors associated with survival in patients undergoing sotiga / chemo treatment. Figure 20A is a heat map of gene expression signatures significantly associated with survival in response to sotiga / chemo treatment between higher (above median) and lower (below median) values ​​in pretreatment tumor biopsies. Individual patients are shown in columns, with labels corresponding to 1-year overall survival status. Figures 20B-20D show KM curves for overall survival stratified by Th1 (Figure 20B), IFNγ (Figure 20C), and E2F (Figure 20D) gene expression signatures above and below the median. FIG. 20E shows KM curves for overall survival stratified by Ki-67-Foxp3-CD4 T cells from mIF for pretreatment tumor samples above and below the median in relation to patient survival values ​​(FIG. 20E, top panel), as well as representative images from tumor samples with high and low Ki-67-Foxp3-CD4 T cells (FIG. 20E, bottom panel). FIG. 20F shows a correlation matrix of immune infiltrates and gene expression signatures in pretreatment tumor biopsies colored by association with overall survival outcomes. FIG. 20G is a DIABLO Circos plot showing factors from RNAseq(gx) and Vectra imaging significantly associated with survival status at 1 year, and the correlations between these factors. The outer lines of the circles indicate the magnitude and direction of the treatment association. The inner lines of the plots indicate positive and negative correlations between biomarker factors. On all KM curves, p values ​​are from the log-rank test between groups, and the shaded areas indicate the 95% CI. **See Table 7 for numbers of samples in applicable analyses. [Figure 20D]Figures 20A-20G show helper signatures and proliferating CD4 T cells in tumors associated with survival in patients undergoing sotiga / chemo treatment. Figure 20A is a heat map of gene expression signatures significantly associated with survival in response to sotiga / chemo treatment between higher (above median) and lower (below median) values ​​in pretreatment tumor biopsies. Individual patients are shown in columns, with labels corresponding to 1-year overall survival status. Figures 20B-20D show KM curves for overall survival stratified by Th1 (Figure 20B), IFNγ (Figure 20C), and E2F (Figure 20D) gene expression signatures above and below the median. FIG. 20E shows KM curves for overall survival stratified by Ki-67-Foxp3-CD4 T cells from mIF for pretreatment tumor samples above and below the median in relation to patient survival values ​​(FIG. 20E, top panel), as well as representative images from tumor samples with high and low Ki-67-Foxp3-CD4 T cells (FIG. 20E, bottom panel). FIG. 20F shows a correlation matrix of immune infiltrates and gene expression signatures in pretreatment tumor biopsies colored by association with overall survival outcomes. FIG. 20G is a DIABLO Circos plot showing factors from RNAseq(gx) and Vectra imaging significantly associated with survival status at 1 year, and the correlations between these factors. The outer lines of the circles indicate the magnitude and direction of the treatment association. The inner lines of the plots indicate positive and negative correlations between biomarker factors. On all KM curves, p values ​​are from the log-rank test between groups, and the shaded areas indicate the 95% CI. **See Table 7 for numbers of samples in applicable analyses. [Figure 20E]Figures 20A-20G show helper signatures and proliferating CD4 T cells in tumors associated with survival in patients undergoing sotiga / chemo treatment. Figure 20A is a heat map of gene expression signatures significantly associated with survival in response to sotiga / chemo treatment between higher (above median) and lower (below median) values ​​in pretreatment tumor biopsies. Individual patients are shown in columns, with labels corresponding to 1-year overall survival status. Figures 20B-20D show KM curves for overall survival stratified by Th1 (Figure 20B), IFNγ (Figure 20C), and E2F (Figure 20D) gene expression signatures above and below the median. FIG. 20E shows KM curves for overall survival stratified by Ki-67-Foxp3-CD4 T cells from mIF for pretreatment tumor samples above and below the median in relation to patient survival values ​​(FIG. 20E, top panel), as well as representative images from tumor samples with high and low Ki-67-Foxp3-CD4 T cells (FIG. 20E, bottom panel). FIG. 20F shows a correlation matrix of immune infiltrates and gene expression signatures in pretreatment tumor biopsies colored by association with overall survival outcomes. FIG. 20G is a DIABLO Circos plot showing factors from RNAseq(gx) and Vectra imaging significantly associated with survival status at 1 year, and the correlations between these factors. The outer lines of the circles indicate the magnitude and direction of the treatment association. The inner lines of the plots indicate positive and negative correlations between biomarker factors. On all KM curves, p values ​​are from the log-rank test between groups, and the shaded areas indicate the 95% CI. **See Table 7 for numbers of samples in applicable analyses. [Figure 20F]Figures 20A-20G show helper signatures and proliferating CD4 T cells in tumors associated with survival in patients undergoing sotiga / chemo treatment. Figure 20A is a heat map of gene expression signatures significantly associated with survival in response to sotiga / chemo treatment between higher (above median) and lower (below median) values ​​in pretreatment tumor biopsies. Individual patients are shown in columns, with labels corresponding to 1-year overall survival status. Figures 20B-20D show KM curves for overall survival stratified by Th1 (Figure 20B), IFNγ (Figure 20C), and E2F (Figure 20D) gene expression signatures above and below the median. FIG. 20E shows KM curves for overall survival stratified by Ki-67-Foxp3-CD4 T cells from mIF for pretreatment tumor samples above and below the median in relation to patient survival values ​​(FIG. 20E, top panel), as well as representative images from tumor samples with high and low Ki-67-Foxp3-CD4 T cells (FIG. 20E, bottom panel). FIG. 20F shows a correlation matrix of immune infiltrates and gene expression signatures in pretreatment tumor biopsies colored by association with overall survival outcomes. FIG. 20G is a DIABLO Circos plot showing factors from RNAseq(gx) and Vectra imaging significantly associated with survival status at 1 year, and the correlations between these factors. The outer lines of the circles indicate the magnitude and direction of the treatment association. The inner lines of the plots indicate positive and negative correlations between biomarker factors. On all KM curves, p values ​​are from the log-rank test between groups, and the shaded areas indicate the 95% CI. **See Table 7 for numbers of samples in applicable analyses. [Figure 20G]Figures 20A-20G show helper signatures and proliferating CD4 T cells in tumors associated with survival in patients undergoing sotiga / chemo treatment. Figure 20A is a heat map of gene expression signatures significantly associated with survival in response to sotiga / chemo treatment between higher (above median) and lower (below median) values ​​in pretreatment tumor biopsies. Individual patients are shown in columns, with labels corresponding to 1-year overall survival status. Figures 20B-20D show KM curves for overall survival stratified by Th1 (Figure 20B), IFNγ (Figure 20C), and E2F (Figure 20D) gene expression signatures above and below the median. FIG. 20E shows KM curves for overall survival stratified by Ki-67-Foxp3-CD4 T cells from mIF for pretreatment tumor samples above and below the median in relation to patient survival values ​​(FIG. 20E, top panel), as well as representative images from tumor samples with high and low Ki-67-Foxp3-CD4 T cells (FIG. 20E, bottom panel). FIG. 20F shows a correlation matrix of immune infiltrates and gene expression signatures in pretreatment tumor biopsies colored by association with overall survival outcomes. FIG. 20G is a DIABLO Circos plot showing factors from RNAseq(gx) and Vectra imaging significantly associated with survival status at 1 year, and the correlations between these factors. The outer lines of the circles indicate the magnitude and direction of the treatment association. The inner lines of the plots indicate positive and negative correlations between biomarker factors. On all KM curves, p values ​​are from the log-rank test between groups, and the shaded areas indicate the 95% CI. **See Table 7 for numbers of samples in applicable analyses.

[0058] [Figure 21A-21B]Figures 21A-21B show overall survival and tumor response. Figure 21A shows overall survival of patients in the efficacy population. Figure 21B shows the maximum percentage change from baseline in the sum of the diameters of the target lesions for each patient using post-baseline tumor assessments. Four patients in the nivo / chemo arm, one patient in the sotiga / chemo arm, and three patients in the sotiga / nivo / chemo arm did not have any post-baseline tumor assessments. A confirmed complete response (CR) or partial response (PR) is defined as two consecutive tumor assessments with an overall response of complete / partial response.

[0059] [Figure 22A]Figures 22A-22L show circulating cross-presenting activated APCs and type 1 helper T cells associated with survival in patients receiving sotiga / chemo treatment. Figure 22A shows a force-directed graph visualization of unsupervised clustering of cells from CyTOF across all patients and time points showing specific populations of dendritic cells associated with survival and tracked with gating analysis in additional panels. Figure 22B shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D1 frequency of circulating CD1c+ cross-presenting DCs (CD141+) above and below the median at C1D1. Figure 22C shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22D shows the time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating CD1c- cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22E shows the time series (top) and KM curves (bottom) for overall survival stratified by C2D1 frequency of circulating conventional DCs above and below the median at C2D1. Figure 22F shows the KM curves for overall survival stratified by the frequency of pretreatment circulating PD-1+Tbet+ non-naive CD4 T cells. Figure 22G is a heat map of pretreatment mean fluorescence intensity of proteins present on PD-1+Tbet+ non-naive CD4 T cells across all patients. Figure 22H shows the frequency of PD-1+Tbet+ non-naive CD4 T cells pre- and on-treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22I shows the KM curves for overall survival stratified by the frequency of pre-treatment circulating Tbet+Eomes+ non-naive CD4 T cells. Figure 22J is a heat map of pre-treatment mean fluorescence intensity of proteins present on Tbet+Eomes+ non-naive CD4 T cells across all patients.Figure 22K shows the frequency of Tbet+Eomes+ non-naive CD4 T cells before and during treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22L shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, with the location determined by the reduced dimensionality across all tumor and circulating biomarkers. For dendritic cell populations, the frequency is among total white blood cells. For T cell populations, the frequency is among parents. The time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. The p-values ​​for the time series show the Wilcoxon signed rank test between survival groups at each time point. On the KM curves, the p-values ​​are from the log-rank test between groups, and the shaded areas show the 95% CI. **For numbers of samples in applicable analyses, see Supplementary Table 19. [Fig. 22B-22C]Figures 22A-22L show circulating cross-presenting activated APCs and type 1 helper T cells associated with survival in patients receiving sotiga / chemo treatment. Figure 22A shows a force-directed graph visualization of unsupervised clustering of cells from CyTOF across all patients and time points showing specific populations of dendritic cells associated with survival and tracked with gating analysis in additional panels. Figure 22B shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D1 frequency of circulating CD1c+ cross-presenting DCs (CD141+) above and below the median at C1D1. Figure 22C shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22D shows the time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating CD1c- cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22E shows the time series (top) and KM curves (bottom) for overall survival stratified by C2D1 frequency of circulating conventional DCs above and below the median at C2D1. Figure 22F shows the KM curves for overall survival stratified by the frequency of pretreatment circulating PD-1+Tbet+ non-naive CD4 T cells. Figure 22G is a heat map of pretreatment mean fluorescence intensity of proteins present on PD-1+Tbet+ non-naive CD4 T cells across all patients. Figure 22H shows the frequency of PD-1+Tbet+ non-naive CD4 T cells pre- and on-treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22I shows the KM curves for overall survival stratified by the frequency of pre-treatment circulating Tbet+Eomes+ non-naive CD4 T cells. Figure 22J is a heat map of pre-treatment mean fluorescence intensity of proteins present on Tbet+Eomes+ non-naive CD4 T cells across all patients.Figure 22K shows the frequency of Tbet+Eomes+ non-naive CD4 T cells before and during treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22L shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, with the location determined by the reduced dimensionality across all tumor and circulating biomarkers. For dendritic cell populations, the frequency is among total white blood cells. For T cell populations, the frequency is among parents. The time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. The p-values ​​for the time series show the Wilcoxon signed rank test between survival groups at each time point. On the KM curves, the p-values ​​are from the log-rank test between groups, and the shaded areas show the 95% CI. **For numbers of samples in applicable analyses, see Supplementary Table 19. [Fig. 22D-22E]Figures 22A-22L show circulating cross-presenting activated APCs and type 1 helper T cells associated with survival in patients receiving sotiga / chemo treatment. Figure 22A shows a force-directed graph visualization of unsupervised clustering of cells from CyTOF across all patients and time points showing specific populations of dendritic cells associated with survival and tracked with gating analysis in additional panels. Figure 22B shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D1 frequency of circulating CD1c+ cross-presenting DCs (CD141+) above and below the median at C1D1. Figure 22C shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22D shows the time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating CD1c- cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22E shows the time series (top) and KM curves (bottom) for overall survival stratified by C2D1 frequency of circulating conventional DCs above and below the median at C2D1. Figure 22F shows the KM curves for overall survival stratified by the frequency of pretreatment circulating PD-1+Tbet+ non-naive CD4 T cells. Figure 22G is a heat map of pretreatment mean fluorescence intensity of proteins present on PD-1+Tbet+ non-naive CD4 T cells across all patients. Figure 22H shows the frequency of PD-1+Tbet+ non-naive CD4 T cells pre- and on-treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22I shows the KM curves for overall survival stratified by the frequency of pre-treatment circulating Tbet+Eomes+ non-naive CD4 T cells. Figure 22J is a heat map of pre-treatment mean fluorescence intensity of proteins present on Tbet+Eomes+ non-naive CD4 T cells across all patients.Figure 22K shows the frequency of Tbet+Eomes+ non-naive CD4 T cells before and during treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22L shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, with the location determined by the reduced dimensionality across all tumor and circulating biomarkers. For dendritic cell populations, the frequency is among total white blood cells. For T cell populations, the frequency is among parents. The time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. The p-values ​​for the time series show the Wilcoxon signed rank test between survival groups at each time point. On the KM curves, the p-values ​​are from the log-rank test between groups, and the shaded areas show the 95% CI. **For numbers of samples in applicable analyses, see Supplementary Table 19. [Figure 22F]Figures 22A-22L show circulating cross-presenting activated APCs and type 1 helper T cells associated with survival in patients receiving sotiga / chemo treatment. Figure 22A shows a force-directed graph visualization of unsupervised clustering of cells from CyTOF across all patients and time points showing specific populations of dendritic cells associated with survival and tracked with gating analysis in additional panels. Figure 22B shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D1 frequency of circulating CD1c+ cross-presenting DCs (CD141+) above and below the median at C1D1. Figure 22C shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22D shows the time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating CD1c- cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22E shows the time series (top) and KM curves (bottom) for overall survival stratified by C2D1 frequency of circulating conventional DCs above and below the median at C2D1. Figure 22F shows the KM curves for overall survival stratified by the frequency of pretreatment circulating PD-1+Tbet+ non-naive CD4 T cells. Figure 22G is a heat map of pretreatment mean fluorescence intensity of proteins present on PD-1+Tbet+ non-naive CD4 T cells across all patients. Figure 22H shows the frequency of PD-1+Tbet+ non-naive CD4 T cells pre- and on-treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22I shows the KM curves for overall survival stratified by the frequency of pre-treatment circulating Tbet+Eomes+ non-naive CD4 T cells. Figure 22J is a heat map of pre-treatment mean fluorescence intensity of proteins present on Tbet+Eomes+ non-naive CD4 T cells across all patients.Figure 22K shows the frequency of Tbet+Eomes+ non-naive CD4 T cells before and during treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22L shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, with the location determined by the reduced dimensionality across all tumor and circulating biomarkers. For dendritic cell populations, the frequency is among total white blood cells. For T cell populations, the frequency is among parents. The time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. The p-values ​​for the time series show the Wilcoxon signed rank test between survival groups at each time point. On the KM curves, the p-values ​​are from the log-rank test between groups, and the shaded areas show the 95% CI. **For numbers of samples in applicable analyses, see Supplementary Table 19. [Fig. 22G-22H]Figures 22A-22L show circulating cross-presenting activated APCs and type 1 helper T cells associated with survival in patients receiving sotiga / chemo treatment. Figure 22A shows a force-directed graph visualization of unsupervised clustering of cells from CyTOF across all patients and time points showing specific populations of dendritic cells associated with survival and tracked with gating analysis in additional panels. Figure 22B shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D1 frequency of circulating CD1c+ cross-presenting DCs (CD141+) above and below the median at C1D1. Figure 22C shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22D shows the time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating CD1c- cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22E shows the time series (top) and KM curves (bottom) for overall survival stratified by C2D1 frequency of circulating conventional DCs above and below the median at C2D1. Figure 22F shows the KM curves for overall survival stratified by the frequency of pretreatment circulating PD-1+Tbet+ non-naive CD4 T cells. Figure 22G is a heat map of pretreatment mean fluorescence intensity of proteins present on PD-1+Tbet+ non-naive CD4 T cells across all patients. Figure 22H shows the frequency of PD-1+Tbet+ non-naive CD4 T cells pre- and on-treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22I shows the KM curves for overall survival stratified by the frequency of pre-treatment circulating Tbet+Eomes+ non-naive CD4 T cells. Figure 22J is a heat map of pre-treatment mean fluorescence intensity of proteins present on Tbet+Eomes+ non-naive CD4 T cells across all patients.Figure 22K shows the frequency of Tbet+Eomes+ non-naive CD4 T cells before and during treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22L shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, with the location determined by the reduced dimensionality across all tumor and circulating biomarkers. For dendritic cell populations, the frequency is among total white blood cells. For T cell populations, the frequency is among parents. The time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. The p-values ​​for the time series show the Wilcoxon signed rank test between survival groups at each time point. On the KM curves, the p-values ​​are from the log-rank test between groups, and the shaded areas show the 95% CI. **For numbers of samples in applicable analyses, see Supplementary Table 19. [Figure 22I]Figures 22A-22L show circulating cross-presenting activated APCs and type 1 helper T cells associated with survival in patients receiving sotiga / chemo treatment. Figure 22A shows a force-directed graph visualization of unsupervised clustering of cells from CyTOF across all patients and time points showing specific populations of dendritic cells associated with survival and tracked with gating analysis in additional panels. Figure 22B shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D1 frequency of circulating CD1c+ cross-presenting DCs (CD141+) above and below the median at C1D1. Figure 22C shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22D shows the time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating CD1c- cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22E shows the time series (top) and KM curves (bottom) for overall survival stratified by C2D1 frequency of circulating conventional DCs above and below the median at C2D1. Figure 22F shows the KM curves for overall survival stratified by the frequency of pretreatment circulating PD-1+Tbet+ non-naive CD4 T cells. Figure 22G is a heat map of pretreatment mean fluorescence intensity of proteins present on PD-1+Tbet+ non-naive CD4 T cells across all patients. Figure 22H shows the frequency of PD-1+Tbet+ non-naive CD4 T cells pre- and on-treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22I shows the KM curves for overall survival stratified by the frequency of pre-treatment circulating Tbet+Eomes+ non-naive CD4 T cells. Figure 22J is a heat map of pre-treatment mean fluorescence intensity of proteins present on Tbet+Eomes+ non-naive CD4 T cells across all patients.Figure 22K shows the frequency of Tbet+Eomes+ non-naive CD4 T cells before and during treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22L shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, with the location determined by the reduced dimensionality across all tumor and circulating biomarkers. For dendritic cell populations, the frequency is among total white blood cells. For T cell populations, the frequency is among parents. The time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. The p-values ​​for the time series show the Wilcoxon signed rank test between survival groups at each time point. On the KM curves, the p-values ​​are from the log-rank test between groups, and the shaded areas show the 95% CI. **For numbers of samples in applicable analyses, see Supplementary Table 19. [Fig. 22J-22K]Figures 22A-22L show circulating cross-presenting activated APCs and type 1 helper T cells associated with survival in patients receiving sotiga / chemo treatment. Figure 22A shows a force-directed graph visualization of unsupervised clustering of cells from CyTOF across all patients and time points showing specific populations of dendritic cells associated with survival and tracked with gating analysis in additional panels. Figure 22B shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D1 frequency of circulating CD1c+ cross-presenting DCs (CD141+) above and below the median at C1D1. Figure 22C shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22D shows the time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating CD1c- cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22E shows the time series (top) and KM curves (bottom) for overall survival stratified by C2D1 frequency of circulating conventional DCs above and below the median at C2D1. Figure 22F shows the KM curves for overall survival stratified by the frequency of pretreatment circulating PD-1+Tbet+ non-naive CD4 T cells. Figure 22G is a heat map of pretreatment mean fluorescence intensity of proteins present on PD-1+Tbet+ non-naive CD4 T cells across all patients. Figure 22H shows the frequency of PD-1+Tbet+ non-naive CD4 T cells pre- and on-treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22I shows the KM curves for overall survival stratified by the frequency of pre-treatment circulating Tbet+Eomes+ non-naive CD4 T cells. Figure 22J is a heat map of pre-treatment mean fluorescence intensity of proteins present on Tbet+Eomes+ non-naive CD4 T cells across all patients.Figure 22K shows the frequency of Tbet+Eomes+ non-naive CD4 T cells before and during treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22L shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, with the location determined by the reduced dimensionality across all tumor and circulating biomarkers. For dendritic cell populations, the frequency is among total white blood cells. For T cell populations, the frequency is among parents. The time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. The p-values ​​for the time series show the Wilcoxon signed rank test between survival groups at each time point. On the KM curves, the p-values ​​are from the log-rank test between groups, and the shaded areas show the 95% CI. **For numbers of samples in applicable analyses, see Supplementary Table 19. [Figure 22L]Figures 22A-22L show circulating cross-presenting activated APCs and type 1 helper T cells associated with survival in patients receiving sotiga / chemo treatment. Figure 22A shows a force-directed graph visualization of unsupervised clustering of cells from CyTOF across all patients and time points showing specific populations of dendritic cells associated with survival and tracked with gating analysis in additional panels. Figure 22B shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D1 frequency of circulating CD1c+ cross-presenting DCs (CD141+) above and below the median at C1D1. Figure 22C shows a time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22D shows the time series (top) and KM curves (bottom) for overall survival stratified by C1D15 frequency of circulating CD1c- cross-presenting DCs (CD141+) above and below the median at C1D15. Figure 22E shows the time series (top) and KM curves (bottom) for overall survival stratified by C2D1 frequency of circulating conventional DCs above and below the median at C2D1. Figure 22F shows the KM curves for overall survival stratified by the frequency of pretreatment circulating PD-1+Tbet+ non-naive CD4 T cells. Figure 22G is a heat map of pretreatment mean fluorescence intensity of proteins present on PD-1+Tbet+ non-naive CD4 T cells across all patients. Figure 22H shows the frequency of PD-1+Tbet+ non-naive CD4 T cells pre- and on-treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22I shows the KM curves for overall survival stratified by the frequency of pre-treatment circulating Tbet+Eomes+ non-naive CD4 T cells. Figure 22J is a heat map of pre-treatment mean fluorescence intensity of proteins present on Tbet+Eomes+ non-naive CD4 T cells across all patients.Figure 22K shows the frequency of Tbet+Eomes+ non-naive CD4 T cells before and during treatment (C1D1, C1D15, C2D1 and C4D1) colored by survival status at 1 year. Figure 22L shows multi-omic dimensionality reduction of circulating factor and tumor data using independent component analysis, where each dot represents a single patient colored by survival status at 1 year, with the location determined by the reduced dimensionality across all tumor and circulating biomarkers. For dendritic cell populations, the frequency is among total white blood cells. For T cell populations, the frequency is among parents. The time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. The p-values ​​for the time series show the Wilcoxon signed rank test between survival groups at each time point. On the KM curves, the p-values ​​are from the log-rank test between groups, and the shaded areas show the 95% CI. **For numbers of samples in applicable analyses, see Supplementary Table 19.

[0060] [Figure 23A] Figures 23A-23B show that soluble molecules associated with dendritic cell maturation are associated with survival during treatment (C1D15) in mPDAC patients treated with sotiga / chemo. Figure 23A shows Kaplan-Meier (KM) curves for overall survival stratified by soluble CD83 protein expression above and below the median signature value across all patients in all cohorts at C1D15. Figure 23B shows Kaplan-Meier (KM) curves for overall survival stratified by soluble ICOSL protein expression above and below the median signature value across all patients in all cohorts at C1D15. **See Table 7 for number of samples in applicable analyses. [Figure 23B]Figures 23A-23B show that soluble molecules associated with dendritic cell maturation are associated with survival during treatment (C1D15) in mPDAC patients treated with sotiga / chemo. Figure 23A shows Kaplan-Meier (KM) curves for overall survival stratified by soluble CD83 protein expression above and below the median signature value across all patients in all cohorts at C1D15. Figure 23B shows Kaplan-Meier (KM) curves for overall survival stratified by soluble ICOSL protein expression above and below the median signature value across all patients in all cohorts at C1D15. **See Table 7 for number of samples in applicable analyses.

[0061] [Figure 24A]Figures 24A-24E show that higher frequencies of specific B cell populations and lower concentrations of 2B4+ T cells are associated with survival in patients treated with sotiga / chemo. Figure 24A shows a graphical visualization of the power of unsupervised clustering of cells from CyTOF across all patients and time points showing specific populations of B cells associated with survival and tracked with gating analysis in additional panels. Figure 24B shows KM curves for overall survival stratified by the frequency of pretreatment circulating HLA-DR+CCR7+ B cells among total white blood cells above and below the median frequency. Figure 24C shows KM curves for overall survival stratified by the frequency of pretreatment circulating 2B4+ non-naive CD4 T cells among total non-naive CD4 T cells. Figure 24D shows a heat map of pretreatment mean fluorescence intensity of proteins present on 2B4+ non-naive CD4 T cells across all patients. Figure 24E shows the frequency of 2B4+ non-naive CD4 T cells before and during treatment (C1D1, C1D15, C2D1 and C4D1). The plot shows box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. p-values ​​show Wilcoxon signed rank test between time points showing increase during treatment. On all KM curves, p-values ​​are from log-rank test between groups and shaded areas show 95% CI. **See Table 7 for number of samples in applicable analysis. [Figure 24B]Figures 24A-24E show that higher frequencies of specific B cell populations and lower concentrations of 2B4+ T cells are associated with survival in patients treated with sotiga / chemo. Figure 24A shows a graphical visualization of the power of unsupervised clustering of cells from CyTOF across all patients and time points showing specific populations of B cells associated with survival and tracked with gating analysis in additional panels. Figure 24B shows KM curves for overall survival stratified by the frequency of pretreatment circulating HLA-DR+CCR7+ B cells among total white blood cells above and below the median frequency. Figure 24C shows KM curves for overall survival stratified by the frequency of pretreatment circulating 2B4+ non-naive CD4 T cells among total non-naive CD4 T cells. Figure 24D shows a heat map of pretreatment mean fluorescence intensity of proteins present on 2B4+ non-naive CD4 T cells across all patients. Figure 24E shows the frequency of 2B4+ non-naive CD4 T cells before and during treatment (C1D1, C1D15, C2D1 and C4D1). The plot shows box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. p-values ​​show Wilcoxon signed rank test between time points showing increase during treatment. On all KM curves, p-values ​​are from log-rank test between groups and shaded areas show 95% CI. **See Table 7 for number of samples in applicable analysis. [Figure 24C]Figures 24A-24E show that higher frequencies of specific B cell populations and lower concentrations of 2B4+ T cells are associated with survival in patients treated with sotiga / chemo. Figure 24A shows a graphical visualization of the power of unsupervised clustering of cells from CyTOF across all patients and time points showing specific populations of B cells associated with survival and tracked with gating analysis in additional panels. Figure 24B shows KM curves for overall survival stratified by the frequency of pretreatment circulating HLA-DR+CCR7+ B cells among total white blood cells above and below the median frequency. Figure 24C shows KM curves for overall survival stratified by the frequency of pretreatment circulating 2B4+ non-naive CD4 T cells among total non-naive CD4 T cells. Figure 24D shows a heat map of pretreatment mean fluorescence intensity of proteins present on 2B4+ non-naive CD4 T cells across all patients. Figure 24E shows the frequency of 2B4+ non-naive CD4 T cells before and during treatment (C1D1, C1D15, C2D1 and C4D1). The plot shows box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. p-values ​​show Wilcoxon signed rank test between time points showing increase during treatment. On all KM curves, p-values ​​are from log-rank test between groups and shaded areas show 95% CI. **See Table 7 for number of samples in applicable analysis. [Fig. 24D-24E]Figures 24A-24E show that higher frequencies of specific B cell populations and lower concentrations of 2B4+ T cells are associated with survival in patients treated with sotiga / chemo. Figure 24A shows a graphical visualization of the power of unsupervised clustering of cells from CyTOF across all patients and time points showing specific populations of B cells associated with survival and tracked with gating analysis in additional panels. Figure 24B shows KM curves for overall survival stratified by the frequency of pretreatment circulating HLA-DR+CCR7+ B cells among total white blood cells above and below the median frequency. Figure 24C shows KM curves for overall survival stratified by the frequency of pretreatment circulating 2B4+ non-naive CD4 T cells among total non-naive CD4 T cells. Figure 24D shows a heat map of pretreatment mean fluorescence intensity of proteins present on 2B4+ non-naive CD4 T cells across all patients. Figure 24E shows the frequency of 2B4+ non-naive CD4 T cells before and during treatment (C1D1, C1D15, C2D1 and C4D1). The plot shows box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. p-values ​​show Wilcoxon signed rank test between time points showing increase during treatment. On all KM curves, p-values ​​are from log-rank test between groups and shaded areas show 95% CI. **See Table 7 for number of samples in applicable analysis.

[0062] [Figure 25-1] Figure 25 shows biomarkers of survival after nivo / chemo and sotiga / chemo and their overlap. Venn diagram of circulating biomarkers in broad categories (top). The left circle shows biomarkers of survival after sotiga / chemo, the right circle shows biomarkers of survival after nivo / chemo, and the center shows overlapping biomarkers associated with survival in both treatment groups. Color indicates the direction of association, with blue for higher values ​​associated with longer survival and red for higher values ​​associated with shorter survival. The same structure is shown for tumor biomarkers (bottom). [Figure 25-2] Figure 25 shows biomarkers of survival after nivo / chemo and sotiga / chemo and their overlap. Venn diagram of circulating biomarkers in broad categories (top). The left circle shows biomarkers of survival after sotiga / chemo, the right circle shows biomarkers of survival after nivo / chemo, and the center shows overlapping biomarkers associated with survival in both treatment groups. Color indicates the direction of association, with blue for higher values ​​associated with longer survival and red for higher values ​​associated with shorter survival. The same structure is shown for tumor biomarkers (bottom).

[0063] [Figure 26A] Figures 26A-26E show that lower frequency of circulating CD38+ non-naive T cells is associated with longer survival in patients treated with sotiga / nivo / chemo. a, b, KM curves for overall survival stratified by frequency of circulating CD38+ non-naive CD4 (Figure 26A) and CD8 (Figure 26B) T cells at baseline above and below the median frequency values. c, Heatmap of median fluorescence intensity of pre-treatment proteins present on CD38+ non-naive CD4 and CD8 T cells across all patients. Figures 26D-26E show frequency of CD38+ non-naive CD4 (Figure 26D) and CD8 (Figure 26E) T cells shown before and during treatment. For all cell populations shown, frequencies are parental frequencies. Time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. For time series, p-values ​​represent Wilcoxon signed-rank test between time points. On KM curves, p-values ​​are from log-rank test between groups and shaded areas represent 95% CI. **See Table 7 for number of samples in applicable analyses. [Figure 26B]Figures 26A-26E show that lower frequency of circulating CD38+ non-naive T cells is associated with longer survival in patients treated with sotiga / nivo / chemo. a, b, KM curves for overall survival stratified by frequency of circulating CD38+ non-naive CD4 (Figure 26A) and CD8 (Figure 26B) T cells at baseline above and below the median frequency values. c, Heatmap of median fluorescence intensity of pre-treatment proteins present on CD38+ non-naive CD4 and CD8 T cells across all patients. Figures 26D-26E show frequency of CD38+ non-naive CD4 (Figure 26D) and CD8 (Figure 26E) T cells shown before and during treatment. For all cell populations shown, frequencies are parental frequencies. Time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. For time series, p-values ​​represent Wilcoxon signed-rank test between time points. On KM curves, p-values ​​are from log-rank test between groups and shaded areas represent 95% CI. **See Table 7 for number of samples in applicable analyses. [Fig. 26C-26E]Figures 26A-26E show that lower frequency of circulating CD38+ non-naive T cells is associated with longer survival in patients treated with sotiga / nivo / chemo. a, b, KM curves for overall survival stratified by frequency of circulating CD38+ non-naive CD4 (Figure 26A) and CD8 (Figure 26B) T cells at baseline above and below the median frequency values. c, Heatmap of median fluorescence intensity of pre-treatment proteins present on CD38+ non-naive CD4 and CD8 T cells across all patients. Figures 26D-26E show frequency of CD38+ non-naive CD4 (Figure 26D) and CD8 (Figure 26E) T cells shown before and during treatment. For all cell populations shown, frequencies are parental frequencies. Time series plots show box plots with median and quartiles in bold and individual patient values ​​in thin lines colored by survival status at 1 year. For time series, p-values ​​represent Wilcoxon signed-rank test between time points. On KM curves, p-values ​​are from log-rank test between groups and shaded areas represent 95% CI. **See Table 7 for number of samples in applicable analyses.

[0064] [Figure 27A]Figures 27A-27D show that survival in response to sotiga, nivo and chemo combination therapy can be influenced by circulating regulatory B cells. Figure 27A shows the frequency of circulating CCR7+CD11b+CD27- B cells in patients from each treatment arm pre- and on-treatment (C1D15, C2D1, C4D1) shown as fold change compared to C1D1 and plotted on a pseudo-log scale. Figure 27B shows the KM curves for overall survival stratified by CCR7+CD11b+CD27- B cells above and below the median frequency value. Figure 27C is a heat map of the median fluorescence intensity of different proteins present on CCR7+CD11b++CD27- B cells on-treatment (C1D15) across all patients. Figure 27D shows the frequency of CCR7+CD11b+CD27- B cells pre- and on-treatment (C1D15, C2D1, C4D1) stratified by overall survival status at 1 year for each treatment arm. For all cell populations shown, frequencies are among total white blood cells. Time series plots show median in bold, individual patient values ​​in thin, and error bars are 95% confidence intervals. p-values ​​for time series show Wilcoxon signed rank test between survival groups at each time point. On KM curves, p-values ​​are from log-rank test between groups, and shaded areas show 95% CI. **See Table 7 for number of samples in applicable analyses. [Figure 27B]Figures 27A-27D show that survival in response to sotiga, nivo and chemo combination therapy can be influenced by circulating regulatory B cells. Figure 27A shows the frequency of circulating CCR7+CD11b+CD27- B cells in patients from each treatment arm pre- and on-treatment (C1D15, C2D1, C4D1) shown as fold change compared to C1D1 and plotted on a pseudo-log scale. Figure 27B shows the KM curves for overall survival stratified by CCR7+CD11b+CD27- B cells above and below the median frequency value. Figure 27C is a heat map of the median fluorescence intensity of different proteins present on CCR7+CD11b++CD27- B cells on-treatment (C1D15) across all patients. Figure 27D shows the frequency of CCR7+CD11b+CD27- B cells pre- and on-treatment (C1D15, C2D1, C4D1) stratified by overall survival status at 1 year for each treatment arm. For all cell populations shown, frequencies are among total white blood cells. Time series plots show median in bold, individual patient values ​​in thin, and error bars are 95% confidence intervals. p-values ​​for time series show Wilcoxon signed rank test between survival groups at each time point. On KM curves, p-values ​​are from log-rank test between groups, and shaded areas show 95% CI. **See Table 7 for number of samples in applicable analyses. [Fig. 27C-27D]Figures 27A-27D show that survival in response to sotiga, nivo and chemo combination therapy can be influenced by circulating regulatory B cells. Figure 27A shows the frequency of circulating CCR7+CD11b+CD27- B cells in patients from each treatment arm pre- and on-treatment (C1D15, C2D1, C4D1) shown as fold change compared to C1D1 and plotted on a pseudo-log scale. Figure 27B shows the KM curves for overall survival stratified by CCR7+CD11b+CD27- B cells above and below the median frequency value. Figure 27C is a heat map of the median fluorescence intensity of different proteins present on CCR7+CD11b++CD27- B cells on-treatment (C1D15) across all patients. Figure 27D shows the frequency of CCR7+CD11b+CD27- B cells pre- and on-treatment (C1D15, C2D1, C4D1) stratified by overall survival status at 1 year for each treatment arm. For all cell populations shown, frequencies are among total white blood cells. Time series plots show median in bold, individual patient values ​​in thin, and error bars are 95% confidence intervals. p-values ​​for time series show Wilcoxon signed rank test between survival groups at each time point. On KM curves, p-values ​​are from log-rank test between groups, and shaded areas show 95% CI. **See Table 7 for number of samples in applicable analyses. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0065] The following description and examples set forth embodiments of the present disclosure in detail.

[0066] It is understood that the present disclosure is not limited to the specific embodiments described herein, as such may vary. Those skilled in the art will recognize that there are variations and modifications of the present disclosure that are encompassed within its scope.

[0067] All terms are intended to be understood as understood by one of ordinary skill in the art. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0068] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0069] Although various features of the present disclosure may be described in the context of a single embodiment, these features may also be provided separately or in any suitable combination. Conversely, although the present disclosure may be described herein for clarity in the context of separate embodiments, the present disclosure may also be implemented in a single embodiment.

[0070] Detailed Description Unless otherwise defined, all technical terms, notations and other scientific terms or terminology used herein are intended to have the meaning commonly understood by those skilled in the art to which this disclosure belongs.In some cases, terms with commonly understood meanings are defined herein for clarity and / or ready reference, and the inclusion of such definitions herein should not necessarily be interpreted as indicating substantial differences beyond those commonly understood in the art.Many of the techniques and procedures described and referred to herein are well understood and commonly used by those skilled in the art using conventional methodology.

[0071] The singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. For example, the term "a cell" includes one or more cells, including mixtures thereof. "A and / or B" is used herein to include all of the following options: "A," "B," "A or B," and "A and B."

[0072] A composition or method that "comprising" or "including" one or more recited elements, or any grammatical variant thereof, can include other elements not specifically recited. For example, a composition that includes an antibody can contain the antibody alone or in combination with other components.

[0073] Certain ranges are indicated herein using numbers preceded by the term "about". The term "about" as used herein has its original meaning of approximately, providing literal support for the exact number it precedes, as well as numbers that are near or approximately the number it precedes. When determining whether a number is near or approximately a specifically recited number, a nearby or close number that is not recited may be a number that provides a substantial equivalent to the specifically recited number in the context in which it is presented. For example, if the degree of approximation is not otherwise clear from the context, "about" means either within plus or minus 10% of the provided value, or rounded to the nearest significant figure, and in all cases includes the provided value. When ranges are provided, they include the boundaries.

[0074] The term "treatment" of cancer or any grammatical variant thereof, as used herein, refers to administering a combination therapy of a CD40 agonist, such as an anti-CD40 antibody (e.g., sotigalimab) and one or more chemotherapy drugs to a subject having or diagnosed with cancer to achieve at least one positive therapeutic effect, such as a reduced number of cancer cells, a reduced tumor size, a reduced rate of cancer cell infiltration into peripheral organs, or a reduced rate of tumor metastasis or tumor growth. A positive therapeutic effect in cancer can be measured in several ways (see WA Weber, J. Nucl. Med. 50: 1S-10S (2009)). Treatment regimens for the disclosed combinations that are effective for treating cancer patients can vary according to factors such as the disease state, age and weight of the patient, and the ability of the treatment to elicit an anti-cancer response in the subject. The treatment methods, medicaments and uses disclosed may not be effective to achieve a positive therapeutic effect in all subjects, but they should be effective in a statistically significant number of subjects as determined by any statistical test known in the art.

[0075] The term "antibody" includes intact antibodies and binding fragments thereof that specifically bind to a single antigen or that specifically bind to multiple antigens (e.g., multispecific antibodies, e.g., bispecific antibodies, trispecific antibodies, etc.). Thus, any reference to an antibody should be understood to refer to the antibody or binding fragment in intact form, unless the context requires otherwise.

[0076] The term "binding fragment," which may be used interchangeably with "antigen-binding fragment," as used herein refers to an antibody fragment formed from a portion of an antibody that contains one or more CDRs, or any other antibody fragment that specifically binds to an antigen but does not contain an intact native antibody structure. Examples of antigen-binding fragments include, without limitation, diabodies, Fab, Fab', F(ab') 2 , F(ab) c, Fv fragment, disulfide stabilized Fv fragment (dsFv), (dsFv) 2 , bispecific dsFv (dsFv-dsFv'), disulfide stabilized diabodies (ds diabodies), triabodies, tetrabodies, single chain antibody molecules (scFv), scFv dimers, multispecific antibodies, camelized single domain antibodies, nanobodies, minibodies, domain antibodies, bivalent domain antibodies, IgNAR, V-NAR and hcIgG. Binding fragments can be produced by recombinant DNA techniques or by enzymatic or chemical separation of intact immunoglobulins.

[0077] With respect to antibodies, "Fab" refers to the portion of an antibody that consists of a single light chain (both the variable and constant regions) linked by disulfide bonds to the variable region and first constant region of a single heavy chain.

[0078] "Fab'" refers to a Fab fragment that includes a portion of the hinge region.

[0079] "F(ab') 2 " refers to a Fab' dimer.

[0080] With respect to an antibody, "Fc" refers to the portion of an antibody that consists of the second and third constant regions of a first heavy chain linked via disulfide bonds to the second and third constant regions of a second heavy chain. The Fc portion of an antibody is responsible for various effector functions, such as ADCC and CDC, but does not function in antigen binding.

[0081] With respect to an antibody, "Fv" refers to the smallest fragment of an antibody that contains a complete antigen-binding site. The Fv fragment consists of the variable region of a single light chain bound to the variable region of a single heavy chain.

[0082] A "single-chain Fv antibody" or "scFv" refers to an engineered antibody consisting of a light chain variable region and a heavy chain variable region connected to each other directly or via a peptide linker sequence (Huston JS et al., Proc Natl Acad Sci USA, 85:5879(1988)).

[0083] "Single chain Fv-Fc antibody" or "scFv-Fc" refers to an engineered antibody consisting of an scFv connected to the Fc region of an antibody.

[0084] "Camelized single domain antibodies", "heavy chain antibodies" or "HCAbs" are antibodies that consist of two V H It refers to antibodies that contain heavy chains and no light chains (Riechmann L. and Muyldermans S., J Immunol Methods. December 10; 231(1-2): 25-38 (1999); Muyldermans S., J Biotechnol. June; 74(4):277-302 (2001); WO94 / 04678; WO94 / 25591; U.S. Patent No. 6,005,079). Heavy chain antibodies were originally derived from camelids (camels, dromedaries and llamas). Although lacking light chains, camelized antibodies have a bona fide antigen-binding repertoire (Hamers-Casterman C. et al., Nature. June 3; 363(6428):446-8 (1993); Nguyen VK et al. "Heavy-chain antibodies in Camelidae; a case of evolutionary innovation", Immunogenetics. April; 54(1):39-47 (2002); Nguyen VK et al. Immunology. May; 109(1):93-101 (2003)). The variable domains of heavy-chain antibodies (VHH domains) represent the smallest known antigen-binding units generated by the adaptive immune response (Koch-Nolte F. et al., FASEB J. November; 21(13):3490-8. Epub 2007 Jun. 15 (2007)).

[0085] "Nanobody" refers to an antibody fragment that consists of a VHH domain derived from a heavy chain antibody and two constant domains, CH2 and CH3.

[0086] "Diabody" refers to small antibody fragments that have two antigen-binding sites, but these fragments are not bound to each other in the same polypeptide chain. L V connected to the domain H Domain (V H -V L or V L -V H ) (see, e.g., Holliger P. et al., Proc Natl Acad Sci USA. July 15; 90(14):6444-8 (1993); EP404097; WO93 / 11161). By using a linker that is too short to allow pairing between the two domains on the same chain, the domains are forced to pair with complementary domains on another chain, thereby creating two antigen-binding sites. These antigen-binding sites may target the same or different antigens (or epitopes).

[0087] A "domain antibody" refers to an antibody fragment that contains only the variable region of a heavy chain or the variable region of a light chain. In certain cases, two or more V H The domains are covalently linked with a peptide linker to create a bivalent or multivalent domain antibody. H The domains may target the same or different antigens.

[0088] In certain embodiments, "(dsFv) 2 " comprises three peptide chains: two V L Two V H portion.

[0089] In certain embodiments, a "bispecific ds diabody" isH1 and V L1 V via a disulfide bridge between L1 -V H2 V bound to (which is also linked by a peptide linker) H1 -V L2 (linked by a peptide linker).

[0090] In certain embodiments, a "bispecific dsFv" or "dsFv-dsFv'" comprises three peptide chains: V L1 Part and V L2 A V heavy chain linked by a peptide linker (e.g., a long flexible linker) attached to the V H1 -V H2 Each disulfide-paired heavy and light chain has a different antigen specificity.

[0091] In certain embodiments, an "scFv dimer" is a dimer of another V H -V L The V dimerized with the H -V L (linked by a peptide linker), such that the V of one part H is the V of the other part L and form two binding sites that can target the same antigen (or epitope) or different antigens (or epitopes). In another embodiment, an "scFv dimer" is L1 -V H2 (also linked by a peptide linker) H1 -V L2 (linked by a peptide linker), such that V H1 and V L1 In cooperation with V H2 and V L2 and each linked pair has a different antigen specificity.

[0092] The term "biological sample" or "sample" refers to any solid or liquid sample isolated from an individual or subject. For example, it can refer to any solid (e.g., tissue sample) or liquid sample (e.g., blood) isolated from an animal (e.g., human), such as, without limitation, a biopsy (e.g., solid tissue sample) or blood (e.g., whole blood). Such samples can be, for example, fresh, fixed (e.g., formalin, alcohol, or acetone fixed), paraffin-embedded, or frozen prior to analysis. In an embodiment, the biological sample is obtained from a tumor (e.g., pancreatic cancer). A "test biological sample" is a biological sample that has been the subject of analysis, monitoring, or observation. A "reference biological sample" containing the same type of biological sample (e.g., the same type of tissue or cell) is a control for the test biological sample.

[0093] The term "gene signature" refers to the hallmark gene signature publicly accessible through the Molecular Signatures Database (MSigDB) (V7.4) for gene set enrichment analysis (GSEA). Hallmark gene sets are coherently expressed signatures obtained by aggregating many MSigDB gene sets to represent a well-defined biological state or process. For example, the MYC hallmark gene set includes genes belonging to tumor suppressors, oncogenes, translocated oncogenes, protein kinases, cell differentiation markers, homeodomain proteins, transcription factors, and cytokines and growth factors.

[0094] As used herein, "individual" or "subject" includes animals, such as humans (e.g., human individuals) and non-human animals. In some embodiments, an "individual" or "subject" is a patient under the care of a physician. Thus, a subject can be a human patient or individual who has, is at risk of, or is suspected of having a disease of interest (e.g., cancer) and / or one or more symptoms of the disease. A subject can also be an individual who has been diagnosed as being at risk for a condition of interest at the time of diagnosis or at a later time. The term "non-human animal" includes all vertebrates, such as mammals, such as rodents, such as mice, non-human primates, and other mammals, such as sheep, dogs, cows, chickens, and non-mammals, such as amphibians, reptiles, and the like.

[0095] It is understood that certain features of the present disclosure that are described in terms of separate embodiments for clarity may also be provided in combination in a single embodiment. Conversely, various features of the present disclosure that are described in terms of a single embodiment for brevity may also be provided separately or in any suitable subcombination. All combinations of the embodiments related to the present disclosure are specifically embraced by the present disclosure and are disclosed herein as if each and every combination were individually and expressly disclosed. Moreover, all subcombinations of the various embodiments and elements thereof are also specifically embraced by the present disclosure and are disclosed herein as if each and every such subcombination were individually and expressly disclosed herein.

[0096] II. Methods for identifying subsets of cancer patients In particular, methods are provided herein for identifying subsets of cancer patients for treatment with CD40 agonists, such as anti-CD40 antibodies (e.g., sotigalimab). In some embodiments, the treatment is combined with one or more chemotherapy drugs (e.g., gemcitabine and nab-paclitaxel). Methods are provided herein for treating cancer in the identified subsets of cancer patients, as well as methods of aiding in cancer treatment. Systems and / or kits are also provided herein for identifying subsets of cancer patients suitable for treatment.

[0097] The following methods can be used to assess whether a subject will respond effectively to a combination therapy comprising a CD40 agonist (e.g., an anti-CD40 antibody, such as sotigalimab, or a CD40 ligand fusion protein) and chemotherapy (e.g., gemcitabine and nab-paclitaxel), or to assess continued treatment with this combination therapy.

[0098] The term "CD40 agonist" refers to an agent that specifically binds to CD40 to activate CD40, similar to the binding of CD40 ligand. CD40 agonists can include compounds that bind to CD40 to activate the receptor. CD40 agonists can also be compounds that mimic CD40 ligand to bind to and activate CD40. CD40 agonists can be antibodies against CD40, such as monoclonal antibodies or antigen-binding fragments thereof. When a specific biologic name is mentioned herein, it can also include its biosimilars as well as reference product biologics. Exemplary antibodies or antigen-binding fragments thereof include, without limitation, sotigalimab, selicrelumab, ChiLob7 / 4, ADC-1013, SEA-CD40, CP-870,893, dacetuzumab, and CDX-1140.

[0099] Test biological samples (e.g., bulk tumor tissue and / or blood) and one or more reference or control biological samples can be obtained from a test subject having a particular type of cancer and one or more reference subjects having the same type of cancer as the test subject, both before and after administration of the combination therapy. Exemplary biological samples for use in the methods of the present disclosure include, without limitation, tumor samples, blood samples, serum samples, surgical samples, and biopsy samples. In one example, bulk tissue samples can be subjected to whole exome and transcriptome analysis using any of the techniques known in the art (e.g., ImmunoID NeXT platform, Personalis, Inc.). The obtained data can be used for gene expression quantification. Whole transcriptome sequencing results can be aligned, for example, using STAR, and normalized expression values ​​in transcripts per million (TPM) can be calculated, for example, using Personalis' ImmunoID NeXT tool, Expressionist. Hallmark gene signature score (e.g., TNFα gene signature score, E2F gene signature score, IFN-γ gene signature score or MYC gene signature score) can be calculated, for example, for MYC, E2F or IFN-γ, by averaging the log-normalized expression value for each gene in the MYC, E2F or IFN-γ hallmark gene set. For survival analysis, subjects are stratified based on the value of this MYC, E2F or IFN-γ gene signature, with "high" vs. "low" defined by the median signature value across all subjects, including test subjects and one or more reference subjects. In some embodiments, for example, a lower normalized expression value of a set of genes in the MYC hallmark gene set can be significantly associated with longer overall survival, for example, in metastatic pancreatic cancer patients treated with, for example, sotigalimab in combination with gemcitabine+nab-paclitaxel.In other embodiments, for example, a lower normalized expression value of a set of genes in the E2F gene set may be significantly associated with a longer overall survival, for example, in metastatic pancreatic cancer patients treated with sotigalimab in combination with gemcitabine+nab-paclitaxel. In some embodiments, for example, an increased normalized expression value of a set of genes in the IFNγ gene set may be significantly associated with a longer overall survival, for example, in metastatic pancreatic cancer patients treated with sotigalimab in combination with gemcitabine+nab-paclitaxel.

[0100] Biological samples, e.g., peripheral blood samples, can be obtained from the test subject and one or more reference subjects both before and after administration of the combination therapy. Peripheral blood mononuclear cells (PBMCs) can be isolated from the peripheral blood samples. The isolated PBMCs can be subjected to immune profiling, e.g., using the X50 Platform. For example, a multiplex flow panel designed to evaluate T cell phenotype and function can be utilized. In some embodiments, PBMCs are subjected to live CD45 + Patient PBMCs can be classified into different immune cell populations based on the presence of surface markers: CD8 + T cells are differentiated by the presence of CD3 and CD8 surface markers, and are identified as CD45 + The cells may be selected from CD4 + T cells are differentiated by the presence of CD3 and CD8 surface markers, and are identified as CD45 + The cells may be selected from CD8 + and CD4 + T cells can be further subdivided into multiple T cell subsets, such as effector memory type 1 (EM1) cells. EM1 T cells are classically CD45RA - CD27 + This cell population can be defined as CCR7- by CXCR5 expression. + population or by CD244 expression + (also called 2B4) group. + Population and / or CD244+ The ratio of circulating CXCR5 T cell counts in a population to the total EM1 T cell population count can be shown to correlate with overall survival. + Effector Memory (CD45RA - CD27 + )CD8 + Total effector memory (CD45RA - CD27 + )CD8 + A lower ratio of circulating CD244 to T cells may be significantly associated with longer overall survival, for example in metastatic pancreatic cancer patients treated with sotigalimab in combination with gemcitabine + nab-paclitaxel. + Effector Memory (CD45RA - CD27 + )CD4 + Total effector memory (CD45RA - CD27 + )CD4 + A lower ratio to T cells may be significantly associated with longer overall survival, for example in metastatic pancreatic cancer patients treated with sotigalimab in combination with gemcitabine plus nab-paclitaxel.

[0101] In other embodiments, CD4 T cells can be further characterized into type I helper CD4 T cells and antigen-experienced CD4 T cells. Type I helper CD4 T cells can be identified by the expression of Tbet+, Eomes+ and PD-1+, while antigen-experienced CD4 T cells can be identified by the expression of PD-1+, Tbet+ and TCF-1+. Both of these populations can be significantly associated with longer overall survival in, for example, metastatic pancreatic cancer patients treated with, for example, sotigalimab in combination with gemcitabine+nab-paclitaxel.

[0102] B cell phenotype and function can also be analyzed. B cells can be identified based on CD19 expression, and further differentiated into memory vs. naive vs. plasmablasts based on CD38 vs. CD27 expression. In some embodiments, the cell count of circulating HLA-DR+CCR7+ B cells can be determined from a biological sample from a subject. The cell count of this circulating B cell population can be compared to a control or reference sample, and in some embodiments, an increased cell count of HLA-DR+CCR7+ B cells can be significantly associated with a longer overall survival, for example, in metastatic pancreatic cancer patients treated with sotigalimab in combination with gemcitabine+nab-paclitaxel.

[0103] In some embodiments, the cell count of circulating cross-presenting dendritic cells (DCs) can be determined from a biological sample from a subject. As used herein, a cross-presenting dendritic cell can be any dendritic cell that acquires exogenous antigens for presentation on MHC class I molecules. A cross-presenting dendritic cell can be identified, for example, as described in the following examples. In some embodiments, a cross-presenting dendritic cell can be identified by HLA-DR+CD14-CD16-CD11c+CD141+ markers. In some embodiments, a cross-presenting DC is CD1C+CD141+. The cell count of circulating cross-presenting DCs is compared to a control or reference sample, and in some embodiments, an increased cell count of cross-presenting DCs or CD1C+CD141+DCs can be significantly associated with longer overall survival, for example, in metastatic pancreatic cancer patients treated with sotigalimab in combination with gemcitabine+nab-paclitaxel.

[0104] In the above method, the pre-treatment biological sample can be obtained at any time before the treatment with the combination therapy of CD40 agonist (e.g., sotigalimab) + chemotherapy (e.g., gemcitabine and nab-paclitaxel). For example, the pre-treatment biological sample can be obtained minutes, hours, days, weeks or months before the start of treatment, or at substantially the same time as the start of treatment. The post-treatment biological sample can also be obtained from the subject at any time after the start of treatment. For example, the post-treatment biological sample can be obtained minutes, hours, days, weeks or months after the treatment with the combination therapy of CD40 agonist (e.g., sotigalimab) + chemotherapy (e.g., gemcitabine and nab-paclitaxel). Non-limiting examples of time points at which post-treatment biological samples are obtained include, but are not limited to, 1 week to 24 months, 1 week to 18 months, 1 week to 12 months, 1 week to 9 months, 1 week to 6 months, 1 week to 3 months, 1 week to 9 weeks, 1 week to 8 weeks, 1 week to 6 weeks, 1 week to 4 weeks, or 1 week to 2 weeks after the start of treatment with a combination therapy of a CD-40 agonist (e.g., sotigalimab) + chemotherapy (e.g., gemcitabine and nab-paclitaxel). The time points at which post-treatment biological samples can be obtained are determined based on the cycle of the combination therapy. Non-limiting examples of such time points are: after the 1st, 2nd, 3rd, 4th, 5th, 6th, 7th, 8th, 9th, 10th, 12th, 16th, 18th, 20th, 24th, 30th, or 32nd cycle.

[0105] A subject diagnosed with cancer may be determined to have responded to a combination therapy comprising a CD40 agonist (e.g., an anti-CD40 antibody, e.g., sotigalimab) and chemotherapy if the subject shows a partial response after treatment with the therapy. "Partial response" refers to at least a 30% decrease in the sum of the longest diameters (LD) of target lesions, with reference to baseline total LD. A subject may also be determined to have responded to a combination therapy if the subject shows tumor shrinkage after treatment with the therapy. A subject may also be determined to have responded to a combination therapy if the subject shows progression-free survival. "Progression-free survival" (PFS) refers to the period from the start of treatment to the last day before entering progressive disease (PD) status. "PD" refers to at least a 20% increase in the sum of the LD of target lesions, with reference to the smallest total LD ​​recorded since the start of treatment, or the appearance of one or more new lesions.

[0106] The biological sample may be from a subject, e.g., pancreatic cancer, endometrial cancer, non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), e.g., clear cell RCC, non-clear cell RCC), urothelial carcinoma, head and neck cancer (e.g., head and neck squamous cell carcinoma), melanoma (e.g., advanced melanoma, e.g., high risk melanoma, stage III-IV melanoma, unresectable or metastatic melanoma), bladder cancer, hepatocellular carcinoma, breast cancer (e.g., triple negative breast cancer, ER cancer, + / HER2 -breast cancer), ovarian cancer, gastric cancer (e.g., metastatic gastric cancer or esophagogastric junction adenocarcinoma), colorectal cancer, glioblastoma, biliary tract cancer, glioma (e.g., recurrent malignant glioma with a hypermutator phenotype), Merkel cell carcinoma (e.g., advanced or metastatic Merkel cell carcinoma), Hodgkin's lymphoma, non-Hodgkin's lymphoma (e.g., primary mediastinal B-cell lymphoma (PMBCL)), cervical cancer, advanced or refractory solid tumors The cancer may be obtained from a subject having, suspected of having, or at risk of developing a cancer selected from, but not limited to, small cell lung cancer (e.g., stage IV non-small cell lung cancer), non-squamous non-small cell lung cancer, desmoplastic melanoma, pediatric advanced solid tumors or lymphomas, mesothelin-positive pleural mesothelioma, esophageal cancer, anal cancer, salivary gland cancer, prostate cancer, carcinoid tumors, primitive neuroectodermal tumors (pNETs), and thyroid cancer.

[0107] III. Methods of Treating Cancer The methods provided herein may allow for the assessment of a subject's responsiveness to a combination therapy that includes a CD40 agonist (e.g., an anti-CD40 antibody, e.g., sotigalimab) and chemotherapy. Subjects likely to respond to the combination therapy may be administered, for example, sotigalimab and at least one chemotherapy drug (e.g., gemcitabine and nab-paclitaxel).

[0108] The disclosed method may also allow for classification of subjects into groups of subjects more likely to benefit from treatment with a combination therapy of CD40 agonist and chemotherapy, and groups of subjects less likely to benefit. The ability to select such subjects from a pool of subjects for whom a combination therapy including a CD40 agonist (e.g., an anti-CD40 antibody, e.g., sotigalimab) and chemotherapy is being considered is beneficial for effective treatment.

[0109] The methods provided herein involve administering this treatment over a short period of time and measuring MYC gene signature score, E2F gene signature, IFN-γ gene signature, post-treatment vs. pre-treatment circulating CXCR5.+ Effector Memory CD8 + Total effector memory CD8 T cells + The present invention may also be used to determine whether to continue a combination therapy comprising a CD40 agonist (e.g., an anti-CD40 antibody, e.g., sotigalimab) and chemotherapy after determining whether this treatment is more or less likely to benefit the patient based on the ratio of CD40 agonists to T cells, the baseline level of exhausted CD244+ effector memory CD4+ T cells, the baseline level of CXCR5+ effector memory CD8+ T cells, the baseline level of circulating cross-presenting dendritic cells, the baseline level of CD1C+CD141+ dendritic cells, baseline circulating HLA-DR+CCR7+ B cells, baseline circulating PD-1+ T cells, circulating TCF-1+ T cells and / or circulating Tbet+ T cells, the level of circulating T helper cells, or any combination thereof.

[0110] If the subject is more likely to respond to a combination therapy including a CD40 agonist (e.g., an anti-CD40 antibody, e.g., sotigalimab) and chemotherapy, the subject may be administered an effective amount of one or more chemotherapy drugs (e.g., gemcitabine, nab-paclitaxel) and a CD40 agonist (e.g., a CD40 antibody, e.g., sotigalimab). The effective amount of each chemotherapy drug and CD40 agonist may be appropriately determined by a medical practitioner, for example, taking into account the patient's characteristics (e.g., age, sex, weight, race, etc.), disease progression, and previous exposure to the drug.

[0111] In some embodiments, the CD40 agonist is an anti-CD40 antibody. In some embodiments, the anti-CD40 antibody is selected from the group consisting of sotigalimab, celiclerumab, ChiLob7 / 4, ADC-1013, SEA-CD40, CP-870,893, dacetuzumab, and CDX-1140. In some embodiments, the anti-CD40 antibody is sotigalimab.

[0112] In some embodiments, the methods may include administering 240 mg of sotigalimab to the patient about every two weeks.

[0113] In some embodiments, the one or more chemotherapy drugs are gemcitabine, nab-paclitaxel, folfirionx, nitrogen mustard / oxazaphosphorine, nitrosoureas, triazenes, and alkyl sulfonates, anthracycline antibiotics such as doxorubicin and daunorubicin, taxanes such as Taxol™ and docetaxel, vinca alkaloids such as vincristine and vinblastine, 5-fluorouracil (5-FU), leucovorin, irinotecan, idarubicin, mitomycin C, oxaliplatin, raltitrexed, pemetrexed, or the like. , tamoxifen, cisplatin, carboplatin, methotrexate, tinomycin D, mitoxantrone, brenoxane, mithramycin, methotrexate, paclitaxel, 2-methoxyestradiol, purinomastert, batimastat, BAY12-9656, carboxamidotriazole, CC-1088, dextromethorphan acetate, dimethylxanthenone acetate, endostatin, IM-862, marimastat, penicillamine, PTK787 / ZK 222584, RPI.4610, squalamine lactate, SU5416, thalidomide, combretastatin, tamoxifen, COL-3, neobasstat, BMS-275291, SU6668, anti-VEGF antibody, Med-522 (Vitaxin II), CAI, interleukin 12, IM862, amiloride, angiostatin, angiostatin Kl-3, angiostatin Kl-5, captopril, DL-α-difluoromethylornithine, DL-α-difluoromethylornithine HCl, endostatin, fumagillin, herbimycin A, 4-hydroxyphenylretinamide, juglone, laminin, laminin hexapeptide, laminin pentapeptide, labendustin The therapeutic agent may be selected from the group consisting of: sucrose A, medroxyprogesterone, minocycline, placental ribonuclease inhibitor, suramin, thrombospondin, antibodies targeting pro-angiogenic factors, topoisomerase inhibitors, microtubule inhibitors, low molecular weight tyrosine kinase inhibitors of pro-angiogenic growth factors, GTPase inhibitors, histone deacetylase inhibitors, AKT kinase or ATPase inhibitors, Win (Wnt) signal inhibitors, E2F transcription factor inhibitors, mTOR inhibitor agents, alpha, beta and gamma interferon, IL-12, matrix metalloproteinase inhibitors, ZD6474, SU1248, vitaxin, PDGFR inhibitors, NM3 and 2-ME2, and sirengitide. In some embodiments, the one or more chemotherapy drugs may be a combination of gemcitabine and nab-paclitaxel.

[0114] After classifying or selecting a subject based on whether the subject is more or less likely to respond to a combination therapy that includes a CD40 agonist (e.g., an anti-CD40 antibody, e.g., sotigalimab) and chemotherapy, a medical practitioner (e.g., a physician) can administer an appropriate treatment modality to the subject. Methods for administering anti-CD40 antibodies (e.g., sotigalimab, celiclerumab, ChiLob7 / 4, ADC-1013, SEA-CD40, CP-870,893, dacetuzumab and CDX-1140) are well known in the art, for example, as described in their product labels.

[0115] It is understood that any of the treatments described herein (e.g., combination therapies including a CD40 agonist (e.g., an anti-CD40 antibody, e.g., sotigalimab) and chemotherapy, or treatments other than a combination therapy) may include one or more additional therapeutic agents. That is, any of the treatments described herein may be co-administered (administered in combination with) one or more additional anti-tumor agents. Additionally, any of the treatments described herein may include one or more agents for treating, for example, pain, nausea, and / or one or more side effects of a combination therapy including a CD40 agonist (e.g., an anti-CD40 antibody, e.g., sotigalimab) and chemotherapy.

[0116] The combination therapy comprising a CD40 agonist (e.g., an anti-CD40 antibody, e.g., sotigalimab) and chemotherapy can be, for example, simultaneous or sequential. For example, the one or more chemotherapy drugs and the anti-CD40 antibody can be administered at the same time, or the one or more chemotherapy drugs can be administered first in time and the anti-CD40 antibody can be administered second in time, or vice versa. The dosing frequency of the one or more chemotherapy drugs and the anti-CD40 antibody can be different or the same. In one embodiment, the dosing frequency is different. An exemplary dosing frequency of the combination therapy comprising a CD40 agonist (e.g., an anti-CD40 antibody, e.g., sotigalimab) and chemotherapy can be once every few weeks, for example, once every 1, 2, 3, 4 or 1 month, or once every 6 weeks.

[0117] IV. Therapeutic application A. Antibody Administration The antibody described herein is administered in an effective regimen, which means a dosage, route of administration and frequency of administration that delays onset, reduces severity, inhibits further progression, and / or ameliorates at least one sign or symptom of the disorder.If the subject is already suffering from the disorder, the regimen can be called a therapeutically effective regimen.If the subject is at increased risk of the disorder compared to the general population, but has not yet experienced symptoms, the regimen can be called a prophylactically effective regimen.In some cases, therapeutic or prophylactic efficacy can be observed in an individual subject compared to historical controls, or past experience in the same subject.In other cases, therapeutic or prophylactic efficacy can be demonstrated in preclinical or clinical trials in a population of treated subjects compared to a control population of untreated subjects.

[0118] In some examples, the subject is identified as having PD-L1 positive, CD40 positive, a lower baseline level of exhausted CD244+ effector memory CD4+ T cells, a lower baseline level of CXCR5+ effector memory CD8+ T cells, a higher baseline level of circulating cross-presenting dendritic cells, a higher baseline level of CD1C+CD141+ dendritic cells, a higher baseline level of circulating HLA-DR+CCR7+ B cells, a higher baseline level of circulating PD-1+ T cells, circulating TCF-1+ T cells and / or circulating Tbet+ T cells, a higher level of circulating T helper cells, or any combination thereof. In some embodiments, the patient is selected for treatment with the antibodies described herein based on a lower baseline expression of one or more genes from the MYC gene set or the E2F gene set compared to a reference population. In some examples, the one or more genes are ABCE1, ACP1, AIMP2, AP3S1, APEX1, BUB3, C1QBP, CAD, CANX, CBX3, CCNA2, CCT2, CCT3, CCT4, CCT5, CCT7, CDC20, CDC45, CDK2, CDK4, CLNS1A, CNBP, COPS5, COX5A, CSTF2, CTPS1, CUL1, CYC1, DDX18, DDX21, DEK, DHX15, DUT, EEF1B2, EIF1AX, EIF2S1, EIF2S2, EIF3B, EIF3D, EIF3J, EIF4A1, EIF4E, EIF4G2, EIF4H, EPRS1, ERH, ETF1, EXOSC7, FAM120 A.FBL LF2, IMPDH2, KARS1, KPNA2, KPNB1, LDHA, LSM2, LSM7, MAD2L1, MCM2, MCM4, MCM5, MCM6, MCM7, MRPL23, MRPL9, MRPS18B, MYC, NAP1L1, NCBP1, NCBP2, NDUFAB1, NHP2, NME1, NOLC1, NOP16,NOP56, NPM1, ODC1, ORC2, PA2G4, PABPC1, PABPC4, PCBP1, PCNA, PGK1, PHB, PHB2, POLD2, POLE3, PPIA, PPM1G, PRDX3, PRDX4, PRPF31, PRPS2, PSMA1, PSMA2, PSMA4, PSMA6, PSMA7, PSMB2, PSMB3 , PSMC4, PSMC6, PSMD1, PSMD14, PSMD3, PSMD7, PSMD8, PTGES3, PWP1, RACK1, RAD23B, RAN, RANBP1, RFC4, RNPS1, RPL14, RPL18, RPL22, RPL34, RPL6, RPLP0, RPS10, RPS2, RPS3, RPS5, RPS6, RRM 1, RRP9, RSL1D1, RUVBL2, SERBP1, SET, SF3A1, SF3B3, SLC25A3, SMARCC1, SNRPA, SNRPA1, SNRPB2, SNRPD1, SNRPD2, SNRPD3, SNRPG, SRM, SRPK1, SRSF1, SRSF2, SRSF3, SRSF7, SSB, SSBP1, STARD7, SYNCRIP, TARDBP, TCP1, TFDP1, TOMM70, TRA2B, TRIM28, TUFM, TXNL4A, TYMS, U2AF1, UBA2, UBE2E1, UBE2L3, USP1, VBP1, VDAC1, VDAC3, XPO1, XPOT, XRCC6, YWHAE and YWHAQ. In some embodiments, patients are selected for treatment with the antibodies described herein based on high baseline expression of one or more genes from the IFNγ gene set compared to a reference population. In some examples, the one or more genes are selected from the group consisting of CD8A, CD274, LAG3 and STAT1.

[0119] In some examples, the set of genes further includes E2F1-3.

[0120] In some embodiments, any of the methods described herein include administration of a therapeutically effective amount of one or more of the anti-CD40 antibodies described herein to a subject in need thereof. As used herein, a "therapeutically effective amount" or "therapeutically effective dosage" of an anti-cancer therapy (e.g., any of the anti-CD40 antibodies described herein) is an amount sufficient to produce a beneficial or desired result. For therapeutic use, beneficial or desired results include, but are not limited to, clinical results, such as reducing one or more symptoms resulting from cancer, increasing the quality of life of a subject afflicted with cancer, reducing the dose of other drug therapy required to treat cancer, enhancing the effect of another drug therapy, e.g., via targeting, delaying disease progression, and / or prolonging survival. An effective dosage may be administered in one or more administrations. For purposes of this disclosure, an effective dosage of an anti-cancer therapy is an amount sufficient to achieve therapeutic or prophylactic treatment, either directly or indirectly. As understood in the clinical context, a therapeutically effective dosage of an anti-cancer therapy may or may not be achieved in conjunction with another anti-cancer therapy.

[0121] Exemplary dosages for any of the antibodies described herein include, as fixed dosages, about 0.1 to 20 mg / kg or 0.5 to 5 mg / kg body weight (e.g., about 0.5 mg / kg, 1 mg / kg, 2 mg / kg, 3 mg / kg, 4 mg / kg, 5 mg / kg, 6 mg / kg, 7 mg / kg, 8 mg / kg, 9 mg / kg, 10 mg / kg, 11 mg / kg, 12 mg / kg, 13 mg / kg, 14 mg / kg, 15 mg / kg, 16 mg / kg, 17 mg / kg, 18 mg / kg, 19 mg / kg, or 20 mg / kg) or 10 to 1600 mg (e.g., less than 10 mg, less than 20 mg, inclusive of any values ​​in between these numbers). less than, 30 mg, 40 mg, 50 mg, 60 mg, 70 mg, 80 mg, 90 mg, 100 mg, 150 mg, 200 mg, 250 mg, 300 mg, 350 mg, 400 mg, 450 mg, 500 mg, 550 mg, 600 mg, 650 mg, 700 mg, 750 mg, 800 mg, 850 mg, 900 mg, 950 mg, 1000 mg, 1100 mg, 1200 mg, 1300 mg, 1400 mg, 1500 mg, or 1600 mg or more). In one embodiment, the antibodies described herein are given every three weeks in an amount of about 300-1500 mg. In another embodiment, the antibodies described herein are given every four weeks in an amount of about 300-1800 mg. Dosage will depend, among other factors, on the subject's condition and response to previous treatment, if any, whether the treatment is prophylactic or therapeutic, and whether the disorder is acute or chronic.

[0122] Administration can be parenteral, intravenous, oral, subcutaneous, intraarterial, intracranial, intrathecal, intraperitoneal, intratumoral, topical, intranasal, or intramuscular. In some embodiments, administration into the systemic circulation is by intravenous or subcutaneous administration. Intravenous administration can be by infusion over a period of time, such as, for example, 30 to 90 minutes.

[0123] The frequency of administration depends on, among other factors, the half-life of the antibody in circulation, the condition of the subject, and the route of administration. The frequency can be once a day, once a week, once a month, four times a year, or at irregular intervals in response to changes in the condition of the subject or the progression of the disorder being treated. In an embodiment, the frequency can be a two-week cycle. In another embodiment, the frequency can be a three-week cycle. In another embodiment, the frequency is a four-week cycle. In another embodiment, the frequency is a six-week cycle. An exemplary frequency for intravenous administration is between once a week and four times a year over the continuous course of treatment, although more frequent or less frequent dosing is also possible. For subcutaneous administration, an exemplary dosing frequency is once a day to once a month, although more frequent or less frequent dosing is also possible.

[0124] The number of dosages administered depends on whether the disorder is acute or chronic and the response of the disorder to treatment.For acute disorder or acute exacerbation of chronic disorder, between 1 and 10 doses are often sufficient.Sometimes, a single bolus dose, optionally divided, is sufficient for acute disorder or acute exacerbation of chronic disorder.Treatment can be repeated for recurrence or acute exacerbation of acute disorder.For chronic disorder, antibody can be administered at regular intervals, for example, once a week, every two weeks, once a month, four times a year, every six months, for at least 1, 5 or 10 years, or for the life of the subject.

[0125] Treatment comprising an anti-CD40 antibody increases the median progression-free survival or overall survival time of subjects with cancer by at least about 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%, 121%, 122%, 123%, 124%, 125%, 126%, 127%, 128%, 129%, 130%, 131%, 132%, 133%, 134%, 135%, 136%, 137%, 138%, 139%, 1 Disease can be alleviated by an increase of 1%, 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 even 100%, or any of these times can be increased by 2 weeks, 1, 2 or 3 months, or 4 or 6 months, or even 9 months or 1 year. Additionally or alternatively, treatment comprising an anti-CD40 antibody may increase a subject's complete response rate, partial response rate, or objective response rate (complete + partial) by at least about 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%, 109%, 109%. , 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 even 100%. The control subjects receive the same treatment as the subjects receiving the anti-CD40 antibody, except for the anti-CD40 antibody. Thus, the control subjects can receive a placebo alone, or a combination of a placebo and some chemotherapeutic agent other than the anti-CD40 antibody, if it is also received by the subjects receiving the anti-CD40 antibody.

[0126] The anti-CD40 antibodies disclosed herein can enhance the number of activated effector memory T cells (Ki67+CD8+) compared to the amount of effector memory T cells (Ki67+CD8+) in the absence of one of the anti-CD40 antibodies disclosed herein. The anti-CD40 antibodies disclosed herein can also enhance the number of activated myeloid dendritic cells (CD86+) compared to the amount of activated myeloid dendritic cells (CD86+) in the absence of one of the anti-CD40 antibodies disclosed herein. The anti-CD40 antibodies disclosed herein can further increase the amount of tumor CD80+M1 macrophages.

[0127] Anti-CD40 antibodies can also reduce bacteroidia and increase clostridia as well as gammaproteobacteria in fecal samples from subjects compared to control subjects.

[0128] Typically, in clinical trials (e.g., Phase II, Phase II / III or Phase III trials), the increase in median progression-free survival and / or response rate of subjects treated with anti-CD40 antibodies compared to control subjects is statistically significant, e.g., at a level of p=0.05 or 0.01 or even 0.001. Complete and partial response rates are determined by objective criteria commonly used in clinical trials for cancer, e.g., as listed or accepted by the National Cancer Institute and / or the Food and Drug Administration, and may include, for example, tumor volume, tumor number, metastasis, survival time, and quality of life measures, among others.

[0129] Pharmaceutical compositions for parenteral administration may be sterile and substantially isotonic and may be manufactured under GMP conditions. Pharmaceutical compositions may be provided in unit dosage form (i.e., dosages for a single administration). Pharmaceutical compositions may be formulated using one or more physiologically acceptable carriers, diluents, excipients or adjuvants. The formulation will depend on the route of administration selected. For injection, the antibody may be formulated in an aqueous solution, e.g., in a physiologically compatible buffer, e.g., Hank's solution, Ringer's solution, or saline or acetate buffer (to reduce discomfort at the site of injection). The solution may contain a formulatory agent, e.g., a suspending agent, a stabilizing agent and / or a dispersing agent. Alternatively, the antibody may be in lyophilized form for constitution with a suitable vehicle, e.g., sterile pyrogen-free water, prior to use. The concentration of the antibody in the liquid formulation may vary, e.g., from about 10 to 150 mg / ml. In some formulations, the concentration is about 20 to 80 mg / ml.

[0130] B. Combination Therapy The present disclosure contemplates the use of anti-CD40 antibodies alone or in combination with one or more active therapeutic agents. The additional active therapeutic agent may be a small chemical molecule; a macromolecule, such as a protein, an antibody, a peptibody, a peptide, DNA, RNA, or a fragment of such a macromolecule; or a cell or gene therapy. Combination therapy may target different but complementary mechanisms of action, and thus have a synergistic therapeutic or preventive effect against the underlying disease, disorder, or condition. Additionally or alternatively, combination therapy may allow for a dose reduction of one or more of the drugs, thereby ameliorating, reducing, or eliminating the adverse effects associated with one or more of the drugs.

[0131] The active therapeutic agents in such combination therapy can be formulated as a single composition or as separate compositions. When administered separately, each therapeutic agent in the combination can be given at or about the same time, or at different times. Furthermore, therapeutic agents are administered "in combination" even if they have different forms of administration (e.g., oral capsule and intravenous), they are given at different dosage intervals, one therapeutic agent is given in a fixed dosage regimen, while another therapeutic agent is increased, decreased, or discontinued, or each therapeutic agent in the combination is independently increased, decreased, increased, or reduced in dosage, or discontinued and / or resumed during the course of treatment of the patient. When the combination is formulated as separate compositions, in some embodiments, the separate compositions are provided together in a kit.

[0132] In certain embodiments, any of the anti-CD40 antibodies disclosed herein are administered or applied sequentially with one or more of the additional active therapeutic agents, e.g., where one or more of the additional active therapeutic agents are administered before or after administration of the anti-CD40 antibody according to the present disclosure. In other embodiments, the antibody is administered simultaneously with one or more of the additional active therapeutic agents, e.g., where the anti-CD40 antibody is administered at or about the same time as one or more of the additional therapeutic agents; the anti-CD40 antibody and one or more of the additional therapeutic agents may be in two or more separate formulations or may be combined into a single formulation (i.e., co-formulated). Regardless of whether the additional agent(s) are administered sequentially or simultaneously with the anti-CD40 antibody, they are considered to be administered in combination for the purposes of this disclosure.

[0133] The antibody of the present disclosure may be used in combination with at least one other (active) agent in any manner appropriate under the circumstances. In one embodiment, treatment with at least one active agent and at least one anti-CD40 antibody of the present disclosure is maintained for a certain period of time. In another embodiment, treatment with at least one active agent is reduced or discontinued (e.g., if the subject is stable) while treatment with the anti-CD40 antibody of the present disclosure is maintained at a certain dosing regimen. In a further embodiment, treatment with at least one active agent is reduced or discontinued (e.g., if the subject is stable) while treatment with the anti-CD40 antibody of the present disclosure is reduced (e.g., lower dose, less frequent dosing, or shorter treatment regimen). In yet another embodiment, treatment with at least one active agent is reduced or discontinued (e.g., if the subject is stable) while treatment with the anti-CD40 antibody of the present disclosure is increased (e.g., higher dose, more frequent dosing, or longer treatment regimen). In yet another embodiment, treatment with the at least one active agent is maintained and treatment with an anti-CD40 antibody of the present disclosure is reduced or discontinued (e.g., a lower dose, less frequent dosing, or a shorter treatment regimen). In yet another embodiment, treatment with the at least one active agent and treatment with an anti-CD40 antibody of the present disclosure are reduced or discontinued (e.g., a lower dose, less frequent dosing, or a shorter treatment regimen).

[0134] Treatment with the antibody of the present disclosure may be combined with other treatments that are effective against the disorder being treated.When used in treating a proliferative condition, cancer, tumor, or precancerous disease, disorder, or condition, the antibody of the present disclosure may be combined with chemotherapy, radiation (e.g., localized or total body radiation therapy), stem cell treatment, surgery, or treatment with other biologics.

[0135] The antibodies of the present disclosure may be administered with a vaccine to elicit an immune response against cancer. Such immune response is enhanced by the antibodies of the present disclosure. The vaccine may include an antigen expressed on the surface of tumor cancer cells and / or fragments thereof, optionally linked to a carrier molecule, effective to induce an immune response.

[0136] In some embodiments, one or more of the additional therapeutic agents are immunomodulators.Suitable immunomodulators that can be used in the present disclosure include CD40L, B7 and B7RP1; activating monoclonal antibodies (mAbs) against stimulatory receptors, such as anti-CD38, anti-ICOS and 4-IBB ligand; dendritic cell antigen loading (in vitro or in vivo); anti-cancer vaccines, such as dendritic cell cancer vaccines; cytokines / chemokines, such as IL1, IL2, IL12, IL18, ELC / CCL19, SLC / CCL21, MCP-1, IL-4, IL-18, TNF, IL-15, MDC, IFNα / β, M-CSF, IL-3, GM-CSF, IL-13 and anti-IL-10; bacterial lipopolysaccharide (LPS); indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors and immunostimulatory oligonucleotides.

[0137] In certain embodiments, the present disclosure provides a method for the inhibition of tumor growth, comprising administering an anti-CD40 antibody described herein in combination with a signal transduction inhibitor (STI) to achieve additive or synergistic inhibition of tumor growth. As used herein, the term "signal transduction inhibitor" refers to an agent that selectively inhibits one or more steps in a signal transduction pathway. Signal transduction inhibitors (STIs) contemplated by the present disclosure include: (i) bcr / abl kinase inhibitors (e.g., imatinib mesylate, GLEEVEC®); (ii) epidermal growth factor (EGF) receptor inhibitors, including kinase inhibitors (e.g., gefitinib, erlotinib, afatinib, and osimertinib) and antibodies; (iii) her-2 / neu receptor inhibitors (e.g., HERCEPTIN®); (iv) Akt family kinase or Akt pathway inhibitors (e.g., rapamycin); (v) cell cycle kinase inhibitors (e.g., flavopiridol); and (vi) phosphatidylinositol kinase inhibitors. Agents involved in immune modulation may also be used in combination with the anti-TIGIT antibodies described herein to inhibit tumor growth in cancer patients.

[0138] In some embodiments, one or more of the additional therapeutic agents is a chemotherapeutic agent. Examples of chemotherapeutic agents include gemcitabine, nab-paclitaxel, forfirinox, nitrogen mustard / oxyazaphosphorines, nitrosoureas, triazenes, and alkyl sulfonates, anthracycline antibiotics such as doxorubicin and daunorubicin, taxanes such as Taxol™ and docetaxel, vinca alkaloids such as vincristine and vinblastine, 5-fluorouracil (5-FU), leucovorin, irinotecan, idarubicin, mitomycin C, oxaliplatin, raltitrexed, pemetrexed, tamoxifen, cisplatin, carboplatin, methotrexate, tinomycin D, mitoxantrone, blenoxane, mithramycin, methotrexate, paclitaxel, 2-methoxyestradiol, prinomastat, batimastat, BAY 12-9656, carboxamide triazole, CC-1088, dextromethorphan acetate, dimethylxanthenone acetate, endostatin, IM-862, marimastat, penicillamine, PTK787 / ZK 222584, RPI.4610, squalamine lactate, SU5416, thalidomide, combretastatin, tamoxifen, COL-3, neovastat, BMS-275291, SU6668, anti-VEGF antibody, Med-522 (vitaxin II), CAI, interleukin-12, IM862, amiloride, angiostatin, angiostatin Kl-3, angiostatin Kl-5, captopril, DL-α-difluoromethylornithine, DL-α-difluoromethylornithine HCl, endostatin, fumagillin, herbimycin A, 4-hydroxyphenylretinamide, juglone, laminin, laminin hexapeptide, laminin pentapeptide, lavendustin A, medroxyprogesterone, minocycline , placental ribonuclease inhibitors, suramin, thrombospondin, antibodies targeting pro-angiogenic factors, topoisomerase inhibitors, microtubule inhibitors, low molecular weight tyrosine kinase inhibitor agents of pro-angiogenic growth factors, GTPase inhibitors, histone deacetylase inhibitors, AKT kinase or ATPase inhibitors, Win (Wnt) signal inhibitors, E2F transcription factor inhibitors, mTOR inhibitor agents, alpha, beta and gamma interferon, IL-12, matrix metalloproteinase inhibitors, ZD6474, SU1248, vitaxin, PDGFR inhibitors, NM3 and 2-ME2, and cilengitide; and pharma- ceutically acceptable salts, acids or derivatives of any of the above.

[0139] Chemotherapeutic agents also include anti-hormonal agents that act to regulate or inhibit hormone action on tumors, such as antiestrogens, including tamoxifen, raloxifene, aromatase-inhibiting 4(5)-imidazole, 4-hydroxytamoxifen, trioxifene, keoxifene, onapristone and toremifene; and anti-androgens, such as abiraterone, enzalutamide, apalutamide, darolutamide, flutamide, nilutamide, bicalutamide, leuprolide and goserelin; and pharmacologic acceptable salts, acids or derivatives of any of the above. In certain embodiments, combination therapy includes chemotherapy regimens that include one or more chemotherapy agents. In certain embodiments, combination therapy includes the administration of hormones or related hormonal agents.

[0140] Additional treatment modalities that may be used in combination with anti-CD40 antibodies include radiation therapy, antibodies against tumor antigens, antibody-toxin conjugates, T cell adjuvants, bone marrow transplantation, or antigen-presenting cells containing TLR agonists used to stimulate such antigen-presenting cells (e.g., dendritic cell therapy).

[0141] In certain embodiments, the present disclosure contemplates the use of anti-CD40 antibodies described herein in combination with RNA interference-based therapy to silence gene expression.RNAi begins with the cleavage of longer double-stranded RNA into small interfering RNA (siRNA).One strand of siRNA is incorporated into a ribonucleoprotein complex known as RNA-induced silencing complex (RISC), which is then used to identify mRNA molecules that are at least partially complementary to the incorporated siRNA strand.RISC can bind to or cleave mRNA, both of which inhibit translation.

[0142] In certain embodiments, the present disclosure contemplates the use of the anti-CD40 antibodies described herein in combination with agents that modulate the levels of adenosine. Such therapeutic agents may act on ectonucleotides that catalyze the conversion of ATP to adenosine, including ectonucleoside triphosphate diphosphohydrolase 1 (ENTPD1, ​​also known as CD39 or cluster of differentiation 39), which hydrolyzes ATP to ADP and ADP to AMP, and 5'-nucleotidase, ecto (NT5E or 5NT, also known as CD73 or cluster of differentiation 73), which converts AMP to adenosine. In one embodiment, the present disclosure contemplates combination with CD73 inhibitors, such as those described in WO2017 / 120508, WO2018 / 094148, and WO2018 / 067424. In one embodiment, the CD73 inhibitor is AB680. In another approach, adenosine A2a and A2b receptors are targeted.Combination with A2a and / or A2b receptor antagonists is also contemplated.In one embodiment, the present disclosure contemplates combination with adenosine receptor antagonists described in WO / 2018 / 136700 or WO2018 / 204661.In one embodiment, the adenosine receptor antagonist is AB928 (etrumadenant).

[0143] In certain embodiments, the present disclosure contemplates the use of the anti-CD40 antibodies described herein in combination with inhibitors of phosphatidylinositol 3-kinase (PI3K), particularly the PI3K gamma isoform. PI3K gamma inhibitors can stimulate anti-cancer immune responses through modulation of myeloid cells, for example, by inhibiting suppressive myeloid cells, by attenuating immunosuppressive tumor-infiltrating macrophages, or by stimulating macrophages and dendritic cells to make cytokines that contribute to effective T cell responses resulting in reduced cancer development and spread. Exemplary PI3K gamma inhibitors that can be combined with the anti-CD40 antibodies described herein include those described in WO2020 / 0247496A1. In one embodiment, the PI3K gamma inhibitor is IPI-549.

[0144] In certain embodiments, the present disclosure contemplates the use of anti-CD40 antibodies described herein in combination with inhibitors of arginase, which has been shown to be either responsible for or involved in proinflammatory immune dysfunction, tumor immune escape, immunosuppression, and immunopathology of infectious diseases. Exemplary arginase compounds can be found, for example, in PCT / US2019 / 020507 and WO / 2020 / 102646.

[0145] In certain embodiments, the present disclosure contemplates the use of anti-CD40 antibodies according to the present disclosure with inhibitors of HIF-2α, which plays an essential role in cellular response to low oxygen availability.Under hypoxic conditions, hypoxia-inducible factor (HIF) transcription factors can activate the expression of genes that regulate metabolism, angiogenesis, cell proliferation and survival, immune evasion, and inflammatory response.HIF-2α overexpression has been associated with poor clinical outcomes in patients with various cancers; hypoxia is also common in many acute and chronic inflammatory disorders, such as inflammatory bowel disease and rheumatoid arthritis.

[0146] The present disclosure also contemplates the combination of anti-CD40 antibody described herein with one or more RAS signaling inhibitors.Oncogenic mutations in RAS family genes, such as HRAS, KRAS and NRAS, are associated with various cancers.For example, the mutations of G12C, G12D, G12V, G12A, G13D, Q61H, G13C and G12S, among others, in KRAS family genes have been observed in multiple tumor types.Direct and indirect inhibition strategies have been investigated for the inhibition of mutant RAS signaling.Indirect inhibitors target effectors other than RAS in RAS signaling pathway, including but not limited to inhibitors of RAF, MEK, ERK, PI3K, PTEN, SOS (e.g., SOS1), mTORC1, SHP2 (PTPN11) and AKT. Non-limiting examples of indirect inhibitors in development include RMC-4630, RMC-5845, RMC-6291, RMC-6236, JAB-3068, JAB-3312, TNO155, RLY-1971, BI1701963. Direct inhibitors of RAS mutants are also being explored, generally targeting KRAS-GTP or KRAS-GDP complexes. Exemplary direct RAS inhibitors in development include, but are not limited to, sotorasib (AMG510), MRTX849, mRNA-5671, and ARS1620. In some embodiments, the one or more RAS signaling inhibitors are selected from the group consisting of RAF inhibitors, MEK inhibitors, ERK inhibitors, PI3K inhibitors, PTEN inhibitors, SOS1 inhibitors, mTORC1 inhibitors, SHP2 inhibitors, and AKT inhibitors. In other embodiments, the one or more RAS signaling inhibitors directly inhibit RAS mutants.

[0147] In some embodiments, the present disclosure relates to a combination of an anti-CD40 antibody according to the present disclosure with one or more inhibitors of anexelekto (i.e., AXL). The AXL signaling pathway is associated with tumor growth and metastasis and is believed to mediate resistance to various cancer treatments. There are various AXL inhibitors in development that also inhibit other kinases in the TAM family (i.e., TYRO3, MERTK), as well as other receptor tyrosine kinases, including MET, FLT3, RON, and AURORA, among others. Exemplary multikinase inhibitors include gilteritinib, merestinib, cabozantinib, BMS777607, and foretinib. AXL-specific inhibitors, such as SGI-7079, TP-0903 (i.e., dubermatinib), BGB324 (i.e., bemcentinib), and DP3975, have also been developed.

[0148] In certain embodiments, the present disclosure contemplates the use of the anti-TIGIT antibodies described herein in combination with adoptive cell therapy, a new and promising form of personalized immunotherapy in which immune cells with anti-tumor activity are administered to cancer patients. Adoptive cell therapy has been explored, for example, using tumor-infiltrating lymphocytes (TILs) and T cells engineered to express chimeric antigen receptors (CARs) or T cell receptors (TCRs). Adoptive cell therapy generally involves collecting T cells from an individual, genetically modifying them to target specific antigens or enhance their anti-tumor effects, amplifying them to sufficient numbers, and injecting the genetically modified T cells into a cancer patient. T cells can be collected from the patient (e.g., autologous) where the expanded cells are later reinfused, or collected from a donor patient (e.g., allogeneic).

[0149] T cell-mediated immunity involves multiple sequential steps, each of which is regulated by balancing stimulatory and inhibitory signals to optimize the response. Nearly all inhibitory signals in immune responses ultimately modulate intracellular signaling pathways, but many are initiated through membrane receptors, whose ligands are either membrane-bound or soluble (cytokines). The costimulatory and inhibitory receptors and ligands that regulate T cell activation are not frequently overexpressed in cancer compared to normal tissues, but the inhibitory ligands and receptors that regulate T cell effector functions in tissues are commonly overexpressed on tumor cells or on non-transformed cells associated with the tumor microenvironment. The function of soluble and membrane-bound receptors (ligand immune checkpoints) can be modulated using agonistic antibodies (for costimulatory pathways) or antagonistic antibodies (for inhibitory pathways). Thus, in contrast to most antibodies currently approved for cancer therapy, antibodies that block or agonize immune checkpoints do not directly target tumor cells, but rather target lymphocyte receptors or their ligands to enhance intrinsic anti-tumor activity [see Pardoll, (April 2012) Nature Rev. Cancer 12:252-64].

[0150] Examples of immune checkpoints (ligands and receptors) that are candidates for blockade, some of which are selectively upregulated in various types of tumor cells, include PD-1 (programmed cell death protein 1); PD-L1 (programmed cell death 1 ligand 1); BTLA (B and T lymphocyte attenuator); CTLA4 (cytotoxic T lymphocyte-associated antigen 4); TIM-3 (T cell immunoglobulin mucin protein 3); LAG-3 (lymphocyte activation gene 3); TIGIT (T cell immunoreceptor with Ig and ITIM domains); and killer inhibitory receptors, which can be divided into two classes based on their structural features: i) killer cell immunoglobulin-like receptors (KIRs), and ii) C-type lectin receptors (members of the type II transmembrane receptor family). Other less well-defined immune checkpoints have been described in the literature, including both receptors (e.g., the 2B4 (also known as CD244) receptor) and ligands (e.g., certain B7 family inhibitory ligands, e.g., B7-H3 (also known as CD276) and B7-H4 (also known as B7-S1, B7x, and VCTN1)) [see Pardoll, (April 2012) Nature Rev. Cancer 12:252-64].

[0151] The present disclosure contemplates the use of the anti-CD40 antibodies described herein in combination with inhibitors of the immune checkpoint receptors and ligands described above, as well as immune checkpoint receptors and ligands yet to be described. Certain modulators of immune checkpoints are currently approved, and many others are in development. When it was approved for the treatment of melanoma in 2011, the fully humanized CTLA4 monoclonal antibody ipilimumab (e.g., YERVOY®; Bristol Myers Squibb) became the first immune checkpoint inhibitor to receive regulatory approval in the United States. A fusion protein containing CTLA4 and an antibody (CTLA4-Ig; abatcept (e.g., ORENCIA®; Bristol Myers Squibb)) has been used for the treatment of rheumatoid arthritis, and other fusion proteins have been shown to be effective in kidney transplant patients sensitized to the Epstein-Barr virus. The next class of immune checkpoint inhibitors to receive regulatory approval were those against PD-1 and its ligands PD-L1 and PD-L2. Approved anti-PD-1 antibodies include nivolumab (e.g., OPDIVO®; Bristol Myers Squibb) and pembrolizumab (e.g., KEYTRUDA®; Merck) for a variety of cancers, including squamous cell carcinoma, classical Hodgkin lymphoma, and urothelial carcinoma. Approved anti-PD-L1 antibodies include avelumab (e.g., BAVENCIO®; EMD Serono & Pfizer), atezolizumab (e.g., TECENTRIQ®; Roche / Genentech), and durvalumab (e.g., IMFINZI®; AstraZeneca) for certain cancers, including urothelial carcinoma.In some combinations provided herein, the immune checkpoint inhibitor is selected from MEDI-0680, nivolumab, pembrolizumab, avelumab, atezolizumab, budigalimab, BI-754091, camrelizumab, cosibelimab, durvalumab, dostarlimab, cemiplimab, sintilimab, tislelizumab, toripalimab, retifanlimab, sasanlimab, and zimberelimab (AB122). In some embodiments, the immune checkpoint inhibitor is MEDI-0680 (AMP-514; WO2012 / 145493) or pidilizumab (CT-011). Another approach to target the PD-1 receptor is a recombinant protein composed of the extracellular domain of PD-L2 (B7-DC) fused to the Fc portion of IgG1, called AMP-224. In one embodiment, the present disclosure contemplates the use of an anti-CD40 antibody according to the present disclosure with a PD-1 antibody. In one particular embodiment, the PD-1 antibody is nivolumab.

[0152] In another aspect, the present disclosure contemplates combination with cytokines that inhibit T cell activation (e.g., IL-6, IL-10, TGF-B, VEGF, and other immunosuppressive cytokines) or cytokines that stimulate T cell activation to stimulate an immune response.

[0153] In yet another embodiment, T cell responses can be stimulated by a combination of the disclosed anti-CD40 antibodies with one or more of the following: (i) proteins that inhibit T cell activation (e.g., immune checkpoint inhibitors), such as CTLA-4, PD-1, PD-L1, PD-L2, LAG-3, TIM-3, PVRIG, Galectin 9, CEACAM-1, BTLA, CD69, Galectin-1, CD113, GPR5. (ii) antagonists of 6, VISTA, 2B4, CD48, GARP, PD1H, LAIR1, TIM-1 and TIM-4, and / or (ii) agonists of proteins that stimulate T cell activation, such as B7-1, B7-2, CD28, 4-1BB (CD137), 4-1BBL, ICOS, ICOS-L, OX40, OX40L, GITR, GITRL, CD70, CD27, CD40, DR3 and CD2. Other agents that can be combined with the anti-CD40 antibodies of the present disclosure for the treatment of cancer include antagonists of inhibitory receptors on NK cells or agonists of activating receptors on NK cells. For example, the anti-CD40 antibodies described herein can be combined with antagonists of KIR, such as lirilumab.

[0154] Still other agents for combination therapy include agents that inhibit or deplete macrophages or monocytes, including, but not limited to, CSF-1R antagonists, e.g., CSF-1R antagonist antibodies, including RG7155 (WO11 / 70024, WO11 / 107553, WO11 / 131407, WO13 / 87699, WO13 / 119716, WO13 / 132044) or FPA-008 (WO11 / 140249; WO13169264; WO14 / 036357).

[0155] In another embodiment, the disclosed anti-CD40 antibodies may be combined with one or more of the following: agonistic agents that ligate positive costimulatory receptors, blocking agents that attenuate signaling through inhibitory receptors, antagonists, and one or more agents that systemically increase the frequency of anti-tumor T cells, agents that overcome distinct immunosuppressive pathways within the tumor microenvironment (e.g., blocking inhibitory receptor engagement (e.g., PD-Ll / PD-1 interactions), agents that deplete or inhibit Tregs (e.g., using anti-CD25 monoclonal antibodies (e.g., daclizumab) or by ex vivo anti-CD25 bead depletion), or agents that reverse / prevent T cell anergy or exhaustion), and agents that induce innate immune activation and / or inflammation at the tumor site.

[0156] In one embodiment, the cancer immunology agent is a CTLA-4 antagonist, e.g., an antagonistic CTLA-4 antibody. Suitable CTLA-4 antibodies include, for example, ipilimumab (e.g., YERVOY®; Bristol Myers Squibb) or tremelimumab. In another embodiment, the cancer immunology agent is a PD-Ll antagonist, e.g., an antagonistic PD-Ll antibody. Suitable PD-Ll antibodies include, for example, atezolizumab (MPDL3280A; WO2010 / 077634) (e.g., TECENTRIQ®; Roche / Genentech), durvalumab (MEDI4736), BMS-936559 (WO2007 / 005874) and MSB0010718C (WO2013 / 79174). In another embodiment, the cancer immunological agent is a LAG-3 antagonist, for example, an antagonistic LAG-3 antibody. Suitable LAG-3 antibodies include, for example, BMS-986016 (WO10 / 19570, WO14 / 08218), or IMP-731 or IMP-321 (WO08 / 132601, WO09 / 44273). In another embodiment, the cancer immunological agent is a CD137 (4-1BB) agonist, for example, an agonistic CD137 antibody. Suitable CD137 antibodies include, for example, urelumab and PF-05082566 (WO12 / 32433). In another embodiment, the cancer immunological agent is a GITR agonist, for example, an agonistic GITR antibody. Suitable GITR antibodies include, for example, BMS-986153, BMS-986156, TRX-518 (WO06 / 105021, WO09 / 009116) and MK-4166 (WO11 / 028683). In another embodiment, the cancer immunology agent is an OX40 agonist, for example, an agonistic OX40 antibody. Suitable OX40 antibodies include, for example, MEDI-6383 or MEDI-6469. In another embodiment, the cancer immunology agent is an OX40L antagonist, for example, an antagonistic OX40 antibody. Suitable OX40L antagonists include, for example, RG-7888 (WO06 / 029879). In another embodiment, the cancer immunology agent is a CD27 agonist, for example, an agonistic CD27 antibody.Suitable CD27 antibodies include, for example, varlilumab. In another aspect, the cancer immunology agent is MGA271 (against B7H3) (WO11 / 109400). In yet another embodiment, a combination of an anti-CD40 antibody according to the present disclosure with an agent against Trop-2, such as the antibody drug conjugate sacituzumab govitecan-hziy, is contemplated. In yet another embodiment, a combination of an anti-CD40 antibody described herein with an agent that inhibits the CD47-SIRPα pathway is contemplated. An example of an anti-CD47 antibody is magrolimab.

[0157] In some embodiments, the combination is a combination of the antibody of the present disclosure and a second antibody against a surface antigen preferentially expressed on cancer cells compared to control normal tissue.Some examples of antibodies that can be administered in combination therapy with the antibody of the present disclosure for the treatment of cancer include Herceptin (Trastuzumab) against HER2 antigen, Avastin (Bevacizumab) against VEGF, or antibodies against EGF receptor, such as (Erbitux, Cetuximab) and Vectibix (Panitumumab).Other agents that can be administered include antibodies or other inhibitors of PD-1, PD-L1, CTLA-4, 4-1BB, BTLA, PVRIG, VISTA, TIM-3 and LAG-3; or other downstream signaling inhibitors, such as mTOR and GSK3β inhibitors; and cytokines, such as interferon-γ, IL-2 and IL-15. Some specific examples of additional agents include: ipilimumab, pazopanib, sunitinib, dasatinib, pembrolizumab, INCR024360, dabrafenib, trametinib, atezolizumab (MPDL3280A), erlotinib (e.g., TARCEVA®), cobimetinib, nivolumab, and zimberelimab. The choice of the second antibody or other agent for combination therapy depends on the cancer being treated. If necessary, the cancer is tested for expression or preferential expression of the antigen to guide the selection of the appropriate antibody. In some embodiments, the isotype of the second antibody is human IgG1 to promote effector functions, such as ADCC, CDC, and phagocytosis.

[0158] The present disclosure includes pharma- ceutically acceptable salts, acids, or derivatives of any of the above.

[0159] V.Kit The antibody against CD40 may be combined with any of the second antibodies or agents described for use in co-therapy as a component of a kit. The disclosure disclosed herein provides one or more kits including one or more of the antibodies disclosed herein and one or more pharma- ceutically acceptable excipients or carriers (e.g., without limitation, phosphate buffered saline solution, water, sterile water, polyethylene glycol, polyvinylpyrrolidone, lecithin, peanut oil, sesame oil, emulsions, e.g., oil / water emulsions or water / oil emulsions, microemulsions, nanocarriers, and various types of wetting agents). Additives, e.g., alcohol, oil, glycol, preservatives, flavoring agents, coloring agents, suspending agents, and the like, may also be included in the kits of the present disclosure along with the carriers, diluents, or excipients. In one embodiment, the pharma-ceutically acceptable carriers suitable for use in the antibody compositions disclosed herein are sterile, pathogen-free, and / or otherwise safe for administration to subjects without associated risks of infection and other undue harmful side effects. In the kit, each agent may be provided in a separate vial with instructions for the combination to be administered or instructions for separate administration. The kit may also include written instructions for the proper handling and storage of any of the anti-CD40 antibodies disclosed herein.

[0160] Every maximum numerical limitation given throughout this specification is intended to include every lower numerical limitation, as if such lower numerical limitations were expressly written herein. Every minimum numerical limitation given throughout this specification includes every higher numerical limitation, as if such higher numerical limitations were expressly written herein. Every numerical range given throughout this specification includes every narrower numerical range that falls within such broader numerical range, as if such narrower numerical ranges were all expressly written herein.

[0161] All patent applications, websites, other publications, accession numbers, etc. cited above or below are incorporated by reference in their entirety for all purposes to the same extent as if each individual item was specifically and individually indicated to be so incorporated by reference. Where different versions of a sequence are associated with one accession number at different times, the version associated with that accession number at the effective filing date of this application is meant. Effective filing date means the actual filing date or, if applicable, the filing date of the priority application that references that accession number, whichever is earlier. Similarly, where different versions of publications, websites, etc. are published at different times, the version most recently published at the effective filing date of this application is meant, unless otherwise indicated. Any feature, step, element, embodiment, or aspect of this disclosure may be used in combination with any other feature, step, element, embodiment, or aspect, unless otherwise specifically indicated.

[0162] Although the present disclosure has been described in some detail by way of illustration and example, for purposes of clarity and understanding, it will be apparent that certain changes and modifications may be practiced within the scope of the appended claims. EXAMPLES

[0163] These examples are provided for illustrative purposes only and do not limit the scope of the claims provided herein.

[0164] Example 1 Clinical trial phase 2 study design Results from a phase 1b study evaluating gemcitabine and nab-paclitaxel with or without sotigalimab demonstrated promising clinical activity in patients with untreated metastatic pancreatic ductal adenocarcinoma (mPDAC) (O'Hara et al. Lancet Oncol. 2021;22(1):118-131). The phase 1b study was a dose-ranging study to evaluate safety and clinical activity and determine a recommended phase 2 dose of sotigalimab in combination with gemcitabine (Gem) and nab-paclitaxel (NP) with or without nivolumab. Presented herein are results from a subsequent randomized phase 2 study (NCT03214250) evaluating gemcitabine and nab-paclitaxel with or without sotigalimab.

[0165] The first 12 participants were randomized 4:1:1 to A1 (Gem+NP+nivolumab), B2 (Gem+NP+sotigalimab 0.3mg / kg) or C2 (Gem+NP+nivolumab+sotigalimab 0.3mg / kg). The remaining participants were randomized 1:1:1. Twelve dose-limiting toxicity (DLT)-evaluable participants from Phase 1b (6 in B2 and 6 in C2) were included in the Phase 2 efficacy analysis (Figure 1).

[0166] The primary endpoint was 1-year overall survival (OS) rate compared to the historical rate of 35% for Gem+NP (Von Hoff et al. N Engl J Med. 2013;369(18):1691-1703). Secondary endpoints were safety (adverse events [AEs], treatment-related adverse events [TRAEs]), objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and duration of response (DOR). Exploratory endpoints were immune pharmacodynamics, associations between immune biomarkers and clinical outcomes, and baseline and on-treatment microbiome profiles.

[0167] Participants were eligible for enrollment if they had a histologic or cytologic diagnosis of metastatic pancreatic adenocarcinoma and Eastern Cooperative Oncology Group (ECOG) status of 0 or 1; no prior treatment for metastatic disease was permitted, nor were any prior CD40, PD-1, PD-L1, or CTLA-4 treatments in any setting. The enrollment period for phase 2 was August 30, 2018 to June 10, 2019.

[0168] The dosing schedule was chemotherapy on days 1, 8, and 15 of each 28-day cycle. Gemcitabine (1000 mg / m 2 ) + nab-paclitaxel (125 mg / m 2 ) were administered on days 1 and 15. Both were starting doses. For cohorts A1 and C2, nivolumab 240 mg was administered on days 1 and 15.

[0169] Tumor biopsies were collected at screening and on day 4 (cohort with sotigalimab) or day 8 (cohort without sotigalimab) of cycle 2 and at end of treatment (as appropriate). Blood, tumor tissue, and fecal samples were collected at baseline (day 1 of cycle 1 or at screening) and during treatment and analyzed for tumor and immune biomarkers using various techniques known in the art. The planned enrollment of 35 patients / arm provided 81% power to test the alternative of OS rate of 58% versus 35% using a one-sided one-sample Z-test with a 5% type I error. The study was not adequately powered for cross-arm comparisons.

[0170] Example 2 Clinical trial phase 2 study population All participants had a minimum follow-up of 15 months at the time of the data snapshot (March 2021), shown as Table 1 . [Table 1]

[0171] Baseline characteristics were generally balanced across arms, including tumor burden, presence of liver metastases (25 [73.5%], 28 [75.7%], 27 [73.0%] for A1, B2, and C2, respectively), and stage at initial diagnosis (stage 1–3 vs. stage 4 [stage 4: 27 (79.4%), 28 (75.5%), 27 (73.0%) for A1, B2, and C2, respectively]) (Table 1 ).

[0172] Example 3 Effectiveness The median time on treatment was 5.2, 5.1 and 4.7 months for cohorts A1, B2 and C2, respectively. The 1-year OS rates were 57.3% for A1 (one-sided p=0.007, 95%CI lower limit=41%), 48.1% for B2 (p=0.062, 95%CI lower limit=34%) and 41.3% for C2 (p=0.236, 95%CI lower limit=27%), versus a historical rate of 35%. A single MSI-H patient in A1 had an OS of 249 days and therefore does not meaningfully affect the interpretation of the primary endpoint. The median OS and secondary endpoints are listed in Table 2. [Table 2]

[0173] FIG. 2 shows the percentage change in total target lesions and FIG. 3 shows the OS.

[0174] Example 3 safety Rates of treatment-related adverse events (TRAEs) were similar overall and consistent with the Phase 1b portion of the study across cohorts. Eight participants (7%) experienced adverse events (AEs) leading to treatment discontinuation, seven from A1 (peripheral neuropathy, myocarditis, pneumonitis, thrombotic microangiography (2), and hyperbilirubinemia), one from B2 (pneumonitis), and one from C2 (fever). 98.1% of participants experienced a TRAE, with at least one having a grade 3 or 4 event (66.7%, 86.5%, and 80.0% for A1, B2, and C2, respectively). The top five TRAEs occurring in 10% or more of participants by preferred term are shown in Table 3. [Table 3]

[0175] Thirty-nine participants (36%) experienced serious TRAEs (13, 15, and 11 in A1, B2, and C2, respectively) and two participants died due to TRAEs; one each in B2 (probably all-study drug related acute liver failure) and C2 (probably all-study drug related intracranial hemorrhage). Cytokine release syndrome occurred in 0, 9 (24.3%), and 12 (34.3%) participants in A1, B2, and C2, respectively, and grade 3-4 occurred in 0, 3 (8.1%), and 2 (5.7%) participants in A1, B2, and C2, respectively.

[0176] Example 4 Pharmacodynamic effects Immunopharmacodynamic effects consistent with an immunotherapeutic mechanism of action were observed in blood, tumor, and feces upon treatment (Figures 4A, 4B, 5A, and 5B). All three cohorts showed increased expression of activated effector memory (EM) T cells (Ki67+CD8 + Cells (Figure 4A) / CD4 +The expected pharmacodynamic effect of sotigalimab was an increase in activated myeloid dendritic cells (CD86 EM cells (data not shown)), with nivolumab plus chemotherapy (cohort A1) inducing the most significant effect. + + mDC) occurred in the majority of participants in cohort B2 (sotigalimb + chemotherapy) and frequently in cohort C2 (nivolumab + sotigalimab + chemotherapy), whereas nivolumab + chemotherapy (A1) treatment predominantly produced decreases (Figure 4B). A decrease in the percentage of tumor cells expressing PD-L1 was observed in most tumors in response to treatment with nivolumab (A1, n=5; and C2, n=6), whereas sotigalimab + chemotherapy (B2, n=3) showed mixed changes in PD-L1 expression (Figure 5A). Sotigalimab + chemotherapy (B2, n=2) treatment significantly increased tumor CD80 + In M1 macrophages, nivolumab-containing treatments increased Bacteriodia and decreased Clostridia (A1, n=2; and C2, n=1) (Figure 5B). Nivolumab + chemotherapy (A1) treatment increased Bacteriodia and decreased Clostridia, whereas sotigalimab + chemotherapy (B2) showed the opposite effect. All three treatment arms showed an increase in Gammaproteobacteria, consistent with a chemotherapy effect (Figure 6).

[0177] Example 5 Baseline immune and tumor biomarkers associated with clinical outcomes Baseline blood, tumor and fecal biomarkers defined distinct subsets of PDAC participants that were associated with improved overall survival with nivolumab + chemotherapy and / or sotigalimab + chemotherapy treatment, but not with the immunotherapy combination. + EM CD8 + Higher baseline levels of T cells (Figure 7A) were associated with improved survival in response to nivolumab + chemotherapy (A1) treatment, whereas lower baseline levels were associated with improved survival with sotigalimab + chemotherapy (B2), but not with the nivolumab + sotigalimab combination (C2). +)EM CD4 + Lower baseline levels of T cells were associated with improved survival in response to sotigalimab + chemotherapy (B2) treatment, but no difference in survival outcomes was observed in the cohort containing nivolumab treatment (Figure 7B). Lower baseline levels of inflammatory gene signature (TNFα) were associated with improved survival in response to nivolumab + chemotherapy (A1, n=17) treatment, but no difference in survival outcomes was observed in the sotigalimab-containing arm (B2, n=12; C2, n=12) (Figure 8A). Lower levels of MYC gene signature (Figure 8B) were associated with improved survival in response to sotigalimab + chemotherapy (B2, n=12) treatment, but no difference in survival outcomes was observed in the nivolumab-containing arm (A1, n=17; C2, n=12).

[0178] The primary endpoint of 1-year OS rate >35% was achieved in A1 (nivolumab + chemotherapy), in contrast to previously reported data in this setting (O'Hara et al. Lancet Oncol. 2021;22(1):118-131). The primary endpoint was not achieved in B2 or C2, but moderate clinical activity was observed in B2 (sotigalimb + chemotherapy). The safety profile of IO + chemotherapy treatment across the three cohorts was manageable and consistent with previously reported phase 1b data. Comprehensive multi-omic analysis of pre- and intra-treatment blood, tissue and fecal samples revealed the expected pharmacodynamic effects and immune activation in A1 and B2. Furthermore, biomarker signatures associated with patient subsets with clinical benefit in response to nivolumab + chemotherapy (A1) do not overlap with signatures associated with benefit to sotigalimab + chemotherapy (B2). Such signatures were associated with the use of immunotherapy but not chemotherapy. The combination of sotigalimab, nivolumab and chemotherapy treatment (C2) showed mixed pharmacodynamic effects with no clear biomarker subsets showing benefit, raising the potential hypothesis of IO-IO drug antagonism in this setting. Given the observed clinical activity and hypothesis-generating biomarker results, further exploration and prospective testing of baseline biomarkers is warranted to improve the clinical accuracy of IO+chemotherapy in PDAC, and a platform study (REVOLUTION, NCT04787991) has been initiated to build on these data.

[0179] Example 6 Identification of circulating immune cells and hallmark gene signature analysis Whole-exome and RNA sequencing of bulk tumor tissue For each patient, single paired formalin-fixed, paraffin-embedded or fresh frozen tumor and normal peripheral blood mononuclear cell (PBMC) samples were collected and profiled using the ImmunoID NeXT platform (Personalis, Inc.) for whole-exome and transcriptome analysis. The resulting data were used for gene expression quantification. Whole-transcriptome sequencing results were aligned using STAR, and normalized expression values ​​in transcripts per million (TPM) were calculated using the Personalis ImmunoID NeXT tool, Expressionist. Immune profiling of patient PBMCs using the X50 platform

[0180] Peripheral blood was collected into EDTA vacutainer tubes via venipuncture and PBMC samples were processed at baseline C1D1 (pre-treatment). A multiplex flow panel designed to assess T cell phenotype and function was utilized. All samples were thawed, stained for viability and antibodies, and run under a unified protocol at the University of Pennsylvania.

[0181] Whole-exome and RNA sequencing of bulk tumor tissue For each patient, single paired formalin-fixed, paraffin-embedded or fresh frozen tumor and normal peripheral blood mononuclear cell (PBMC) samples were collected and profiled using the ImmunoID NeXT platform (Personalis, Inc.) for whole-exome and transcriptome analysis. The resulting data were used for gene expression quantification. Whole-transcriptome sequencing results were aligned using STAR, and normalized expression values ​​in transcripts per million (TPM) were calculated using the Personalis ImmunoID NeXT tool, Expressionist.

[0182] Immune profiling of patient PBMCs using the X50 platform Peripheral blood was collected into EDTA vacutainer tubes via venipuncture and PBMC samples were processed at baseline C1D1 (pre-treatment). A multiplex flow panel designed to assess T cell phenotype and function was utilized. All samples were thawed, stained for viability and antibodies, and run under a unified protocol at the University of Pennsylvania.

[0183] Identification of circulating immune cells Patient peripheral blood mononuclear cells (PBMCs) were cultured using live CD45 + Patient PBMCs were classified into different immune cell populations based on the presence of surface markers: CD8 + T cells are CD45-specific by the presence of CD3 and CD8 surface markers. + Selected from cells. CD4 + T cells are identified as CD45 by the presence of CD3 and CD4 surface markers. + Selected from cells. CD8 + T cells are further subdivided into multiple T cell subsets, such as effector memory type 1 (EM1) cells. EM1 T cells classically express CD45RA - CD27 + This cell population is defined as CXCR5 + The CXCR5 + The ratio of CD4 cell counts to the total EM1 T cell population count was shown to be associated with overall survival. + T cells are further subdivided into multiple T cell subsets, such as effector memory type 3 (EM3) cells. EM3 T cells classically express CD45RA - CD27 - This cell population is defined as CD244 + This CD244 was further categorized into groups. + The ratio of cell counts in the population to the total EM3 T cell population count was shown to correlate with overall survival.

[0184] For overall survival analysis, patients were classified into EM1 CD8 + Stratification was based on the percentage of CXCR5 expression on T cells, with "high" vs. "low" frequency defined by the median ratio across all subjects in the cohort. Overall survival analysis was performed for CXCR5 + EM1 CD8 + Total EM1 CD8 + We showed that a lower ratio to nivolumab was associated with longer survival in patients treated with nivolumab in combination with gemcitabine + nab-paclitaxel and shorter survival in patients treated with sotigalimab in combination with gemcitabine + nab-paclitaxel (Figures 4-8).

[0185] Hallmark gene signature analysis The hallmark gene signatures are publicly accessible via the Molecular Signatures Database (V7.4) for Gene Set Enrichment Analysis (GSEA). The following gene sets include genes belonging to the following gene families: (1) tumor suppressors; (2) oncogenes; (3) translocated oncogenes; (4) protein kinases; (5) cell differentiation markers; (6) homeodomain proteins; (7) transcription factors; and (8) cytokines and growth factors.

[0186] The MYC hallmark gene set is composed of a total of 200 genes known to be regulated by MYC. A total "score" was calculated for MYC by averaging the log-normalized expression values ​​for each gene in the gene set and determining the median. For survival analysis, patients were stratified based on the value of this MYC gene signature, with "high" vs. "low" defined by the median signature value across all patients in all cohorts. The individual gene list for the MYC hallmark gene signature is listed on Table 4. [Table 4-1] [Table 4-2]

[0187] Methods for Examples 7-15. Study Design and Safety Monitoring The following examples show further analysis of the data obtained in Examples 1-6. In this Phase Ib / II study, patients aged 18 years or older with mPDAC were enrolled from seven US academic hospitals that are part of the Parker Institute for Cancer Immunotherapy pancreatic cancer consortium. No prior treatment for metastatic disease was permitted, but prior adjuvant and neoadjuvant chemo / radiotherapy was permitted if completed >4 months prior to enrollment. Patients were required to have an archival or fresh tumor specimen available prior to treatment or be able to undergo biopsy to obtain tissue. Further key eligibility criteria included an Eastern Cooperative Oncology Group (ECOG) performance status score of 0-1, adequate organ function, and the presence of at least one measurable lesion according to Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v.1.1). Patients were excluded if they had previous exposure to agonistic CD40, anti-PD-1, anti-PD-L1 monoclonal antibodies or any other immunomodulatory anticancer agents. Patients were also excluded if they had an active or recent autoimmune disease requiring systemic immunosuppressive treatment, had undergone a solid organ transplant, or had a concurrent cancer unless it was indolent or considered life-threatening (e.g., basal cell carcinoma).

[0188] The phase Ib trial was an open-label, multicenter, 4-cohort dose-ranging study aimed to identify a recommended phase 2 dose (RP2D) of anti-CD40 sotigalimab (sotiga) in combination with chemo (gemcitabine [gem] and nab-paclitaxel [NP]) with or without anti-PD1 nivolumab (nivo). 13The Phase II trial was a randomized, open-label, multicenter, 3-arm study of nivo, sotiga, or chemo in combination with both immunomodulators. The RP2D for sotiga at 0.3 mg / kg was defined during the Phase Ib portion of the study by a Data Review Team (DRT) composed of the investigators and sponsor clinical staff. During Phase II, the DRT met on a quarterly basis to review all safety data for each study arm. Bayesian termination rules were used to monitor toxicity and determine whether enrollment or dosing in the study arm(s) needed to be stopped.

[0189] The protocol and all amendments were approved by the lead Institutional Review Board of the University of Pennsylvania and were accepted at all participating sites. The study was conducted in accordance with the principles of the Declaration of Helsinki and the International Conference on Harmonisation Good Clinical Practice guidelines. All patients provided written informed consent prior to enrollment.

[0190] Randomization and blinding The phase II study was open-label without blinding. Patients were randomly assigned to one of three arms: nivo / chemo, sotiga / chemo, or sotiga / nivo / chemo. Twelve dose-limiting toxicity (DLT)-evaluable patients from phase Ib (six each of sotiga / chemo and sotiga / nivo / chemo) were included in the phase II efficacy analysis (see the statistical analysis section for details on the definition of the analysis population). To achieve balance in the total number of patients enrolled in each arm, the first 12 patients enrolled in phase II were randomly assigned in a 4:1:1 ratio to nivo / chemo, sotiga / chemo, or sotiga / nivo / chemo, respectively (nivo / chemo did not accrue patients in phase Ib, so more patients needed to be enrolled in that arm). The remaining patients were randomly assigned in a 1:1:1 ratio. Randomization was administered by the Parker Institute for Cancer Immunotherapy using an interactive voice / web response system (IxRS). A permuted block design with no stratification by baseline patient or tumor characteristics was used for randomization. Patients who were randomized but did not receive any of the study drugs were substituted through the randomization of further patients.

[0191] procedure 1,000 mg / m for each 28-day cycle 2 and 125 mg / m 2gem / NP were administered intravenously (iv) on days 1, 8, and 15 for each arm. Nivo was administered at 240 mg iv on days 1 and 15. Sotiga was administered at 0.3 mg / kg iv on day 3, 2 days after chemo. Alternatively, sotiga may be administered on day 10 if not administered on day 3, provided the patient received chemo on day 8. Investigators were also given the option to utilize a 21-day chemo cycle, in which case the day 15 dose was not administered. Up to two dose reductions were allowed for sotiga and gem, and up to three dose reductions were allowed for NP for management of toxicity. Nivo was allowed to be withheld, but no dose reductions were allowed. A maximum interruption of 4 weeks was allowed per protocol before study discontinuation was required.

[0192] Patients were radiographically evaluated every 8 weeks for the first year and every 3 months thereafter, regardless of dose delays. Disease assessments were collected until radiographic progression or initiation of subsequent treatment, whichever occurred first. Patients were followed for survival. Safety assessments included vital signs, physical examination, electrocardiogram, and laboratory tests. Adverse events were graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.03. Adverse event terms were coded using the Medical Dictionary for Regulatory Activities (MedDRA), version 23.0.

[0193] Blood samples for isolation of peripheral blood mononuclear cells (PBMCs) were collected longitudinally at participating clinical sites, shipped overnight, processed on a ficoll gradient at a central location (Infinity Biologix, Piscataway, NJ, USA), and stored frozen. Serum was processed within 2 h of collection at each site, immediately frozen at -80°C, and then shipped in batches to a central biorepository. Blood sampling for immune biomarkers was performed during screening, days 1 and 15 of cycle 1, days 1 of cycles 2-4, and at treatment discontinuation. Samples were not collected if patients started any new anticancer therapy before their end-of-treatment (EOT) visit. For patients who continued treatment for at least 1 year, blood was collected at 1 year and every 6 months thereafter.

[0194] Baseline or archival as well as post-treatment tumor specimens were collected for biomarker analysis. Fresh tumor biopsies were immediately flash frozen or formalin fixed and paraffin embedded. Any medically feasible post-treatment tumor specimen was accepted; however, depending on the assigned treatment arm, specimens after the second dose of sotiga or the third dose of nivo during cycle 2 were preferred. Further biopsies were permitted for patients with extended stable disease, defined as more than two consecutive disease assessments demonstrating response via RECIST v1.1, as well as at the time of disease progression. Ad hoc biopsy collection was permitted with medical monitor approval.

[0195] Outcome The primary endpoint was the historical rate of 35% for gem / NP. 14 The 1-year OS rate in each treatment arm was compared with that in the control group. Secondary endpoints were progression-free survival (PFS), duration of response (DOR), objective response rate (ORR), disease control rate (DCR), and incidence of adverse events. Key exploratory endpoints included evaluation of immune pharmacodynamic (PD) effects and tumor and immune biomarker analysis.

[0196] statistical analysis This study did not include a gem / NP(chemo) control arm. Therefore, 1-year OS rates for each arm were estimated and were estimated using the historical value of 35%. 14 The study was not adequately powered for statistical comparisons between arms, and adjustments for multiple comparisons were not made for clinical endpoints.

[0197] The null hypothesis was a 1-year OS rate of 35% and the alternative hypothesis was a 1-year OS rate of 55%. Planned enrollment was 105 patients (35 per arm), which included 12 DLT-evaluable patients from Phase Ib. A sample size of 35 patients per arm provided 81% power to test this hypothesis using a one-sample Z-test with a type I error rate of 5%.

[0198] Efficacy analyses were performed on the efficacy population defined as follows: (1) all patients randomized in Phase II who received at least one dose of any study drug, and (2) the 12 DLT-evaluable patients enrolled in Phase Ib ( sotiga Six people from / chemo and sotiga / nivo 6 patients with chemo / chemo; defined as experiencing dose-limiting toxicity or receiving at least 2 doses of chemo and 1 dose of sotiga during cycle 1) 13 For efficacy analyses, patients were grouped according to the treatment arm assigned at randomization. Safety analyses were performed on all Phase Ib (DLT-evaluable and DLT-non-evaluable) and Phase II patients (defined as the safety population) who received at least one dose of any study drug at RP2D. For safety analyses, patients were grouped according to the study treatment actually received. Two Phase II patients were randomized to sotiga / nivo / chemo, but they only received chemo and nivo doses (i.e., they did not receive sotiga); these patients were grouped as sotiga / nivo / chemo for efficacy analyses and as nivo / chemo for safety and biomarker analyses.

[0199] OS was defined as the time from treatment initiation to death due to any cause. Patients who were not reported as dead at the time of analysis were censored at the nearest date of contact. OS was estimated by the Kaplan-Meier method for each treatment arm. One-year OS rates and corresponding one-sided 95% confidence intervals (CIs) were calculated to determine whether the lower limit of the CI excluded the historical value assumed to be 35%. p-values ​​were calculated using a one-sided one-sample Z-test for a historical proportion of 35%. ORR was defined as the proportion of patients with investigator-assessed partial response (PR) or complete response (CR) according to RECIST version 1.1 - confirmation of response was not required; DCR as the proportion of patients with PR, CR or stable disease lasting at least 7 weeks as best response; DOR as the time from first tumor assessment demonstrating response to the date of radiographic disease progression; and PFS as the time from treatment initiation to radiographic disease progression or death (whichever occurred first). Confidence intervals (CIs) for ORR were calculated using the Clopper-Pearson method. DOR and PFS and corresponding CIs were estimated using the Kaplan-Meier method. Safety and tolerability were summarized narratively for adverse events. Statistical analyses were performed using R version 4.1.0 or later.

[0200] interim analysis Two prespecified interim analyses (IA) of the Phase II clinical data were conducted. These IAs were strictly meant to support decision-making for future studies. No adaptations to study design or conduct were planned based on the interim results, and type I error control was not applied to any of the endpoints at the interim or final analyses. The IAs were conducted by the Parker Institute for Cancer Immunotherapy, and the results were shared with the study investigators and pharmaceutical partners (Apexigen and Bristol Myers Squibb).

[0201] The first IA was performed approximately 4 months after the last patient was randomized in Phase II, and the second IA was performed approximately 9 months after the last patient was randomized. Both IAs evaluated safety and all efficacy endpoints (ORR, DCR, DOR, OS, PFS) for patients enrolled in Phase Ib. In addition, the first IA included phase II analyses of ORR and DCR, and the second IA included phase II analyses of all efficacy endpoints except OS (i.e., ORR, DCR, DOR, PFS). Phase II OS data was not analyzed during either IA.

[0202] Immunophenotyping by Mass Cytometry Time of Flight (CyTOF) A broad immunophenotyping panel was used on cryopreserved PBMCs by CyTOF analysis performed under a unified protocol (PMID: 31315057) at Primity Bio (Fremont, CA, USA) in a blinded fashion. Cryopreserved PBMCs were thawed in pre-warmed RPMI-1640 at 37°C containing 10% FBS and 25 U / mL benzonase. Samples were washed once more in RPMI-1640 containing 10% FBS and 25 U / mL benzonase and a third time in pre-warmed RPMI-1640 at 37°C containing 10% FBS. Samples were resuspended in 1000 nM cisplatin prepared in PBS containing 0.1% BSA for 5 min at room temperature for viability determination and then washed with staining buffer. Human BD Fc block was added to the cells for 10 min at 4°C, followed by the addition of surface antibody cocktail. The surface staining cocktail was incubated for 30 min at 4°C. The samples were washed twice with staining buffer to remove the dye. The cells were then resuspended in FoxP3 transcription factor 1x Fix / Perm buffer (eBioscience) for 1 h at room temperature to prepare the cells for intracellular staining. Fixation was then followed by washing in 1x permeabilization buffer. The intracellular staining cocktail was prepared in permeabilization buffer, added to the samples and incubated at room temperature for 1 h. After intracellular staining, the samples were washed twice with permeabilization buffer and once with staining buffer. Prior to CyTOF acquisition, the samples were resuspended in iridium (Ir) intercalating solution for at least 24 h and stored at 4°C. On the day of acquisition, the samples were washed five times in cell culture grade water (HyClone) and run on a CyTOF Helios instrument (Fluidigm). Details about the CyTOF panel are shown in Table 5. [Table 5-1] [Table 5-2] Data was analyzed using CellEngine™ cloud-based flow cytometry analysis software (CellCarta, Montreal, Quebec, Canada).

[0203] Supervised gating was performed manually by scientists without reference to clinical outcomes. High-level gates were adjusted for each sample. Single marker gates were drawn uniformly for analysis across patients and time points. An example gating strategy is provided in FIG. 9.

[0204] After gating on live singlets, immune populations were defined as follows, as shown in FIG. 9. CD4 and CD8 T naive, effector and memory populations were identified based on CD45RA, CD27 and CCR7 expression. Tregs were identified based on Foxp3, CD25 and CD127 expression. B cells were identified based on CD19 expression and further differentiated into memory vs. naive vs. plasmablasts based on CD38 vs. CD27 expression. NK cells were identified based on CD56 expression and further sub-divided based on CD56 vs. CD16 expression. Monocytes were identified based on CD14 and HLA-DR expression and further sub-divided into classical, non-classical and intermediate based on CD14 vs. CD16 expression. Dendritic cells were defined as HLA-DR+CD14-CD16- non-lymphoid cells and further differentiated between myeloid and plasmacytoid based on CD11c vs. CD123 expression, respectively. Myeloid dendritic cells were further sub-divided into conventional dendritic cells type 1 (cDC1; CD141+) and conventional dendritic cells type 2 (cDC2; CD141-) based on their expression of CD141 expression.

[0205] In addition to manual gating of defined populations, the data were analyzed in an unsupervised fashion. To do this, all samples for all patients and all time points were combined together and run through a clustering algorithm. 35、36 After clustering, the clusters are visualized using force-based graph layout. 35、36, and colored by association with overall survival. This visualization was used to identify clusters of interest and the associated populations were then added to the manual gating hierarchy. All time series and survival analyses shown in the results are derived from gated populations, whether discovered by manual gating or by unsupervised analysis.

[0206] High-parameter flow cytometry of T lymphocytes Cryopreserved PBMC samples for fluorescence flow cytometry were analyzed on a broadly pre-approved 28-color BD Symphony A5 cytometer (BD Biosciences) at the Translational Cytometry Laboratory of the Penn Cytomics and Cell Sorting Shared Resource (University of Pennsylvania, Philadelphia, PA, USA). Staff were blinded to treatment cohorts and clinical outcomes. At the time of analysis, cryopreserved PBMC samples were thawed in pre-warmed RPMI-1640 medium (Gibco) containing 10% FBS and 100 U / ml penicillin-streptomycin (Gibco) at 37°C. Samples were washed, counted, and resuspended in medium containing 1 mg / mL DNase I (Roche) and 5 mM magnesium chloride and incubated for 1 h at 37°C. After resting, cells were washed with PBS (Corning) without additives and transferred to staining tubes. PBMCs were incubated with 1 uL (0.2 μg) of 0.2 mg / mL nivolumab antibody (Selleck Chemicals) for 5 min at RT, followed by the addition of Fixable Viability Stain 510 for 10 min at RT in the dark. Cells were then washed twice with FACS wash buffer (PBS, 1% BSA, 2 mM EDTA). A surface antibody cocktail (T cell phenotyping antibody panel, Table 6) was prepared daily and used to culture up to 1 × 10 T cells per tube. 7 Cells were stained. [Table 6] Cells were incubated for 20 min at RT and then washed twice with FACS staining buffer. Cells were resuspended in FoxP3 transcription factor staining buffer Fix / Perm solution (eBiosciences) and incubated for 1 h at RT to prepare cells for intracellular staining. After fixation, samples were washed with Foxp3 permeabilization buffer. Freshly prepared cytoplasmic / intracellular staining cocktail master mix was added to the samples and incubated overnight at 4°C. The following day, samples were washed with permeabilization buffer and resuspended in FACS wash buffer. Cells were stored in the dark at 4°C and acquired within 2 h. After daily QC, the instrument was standardized by placing tightly dyed beads (BD Biosciences, Cytometer Setup and Tracking Beads (CS&T)) in the designated target channel. Compensation controls (Invitrogen UltraComp eBeads, or cells for live / dead staining) were prepared daily along with frozen PBMC treatment controls. Compensation matrices were calculated with Diva software (BD Biosciences) and used only for that day's run. Data was analyzed using CellEngine™ cloud-based flow cytometry analysis software (CellCarta, Montreal, Quebec, Canada). High-level gates were adjusted for each patient across all time points by at least two investigators blinded to patient outcomes. Single marker gates were drawn uniformly for analysis across patients and time points. A representative gating strategy is provided in Figure 10.

[0207] After gating on live cells and the CD3+ population, T cell populations were defined as follows, as shown in FIG. 10: using a combination of CD45RA, CD27 and CCR7 expression on CD4+ and CD8+ T cells, naïve (CD45RA+CD27+CCR7+), T central memory (CM; CD45RA-CD27+CCR7+), T effector memory 1 (EM1; CD45RA-CD27+CCR7-), T effector memory 2 (EM2; CD45RA-CD27-CCR7+), T effector memory 3 (EM3; CD45RA-CD27-CCR7-) and terminally differentiated effector memory (EMRA) (CD45RA+CD27 - CCR7- subpopulation. CD4+ regulatory T cells are Foxp3+CD25 hi CD127 - / low The non-naive CD4+ and CD8+ T cell populations used in the time course and survival analyses included the defined effector memory, central memory and TEMRA populations defined above. Expression of further differentiation, activation and inhibitory markers was assessed within each of these compartments.

[0208] In addition to manual gating of defined populations, the data were analyzed in an unsupervised manner. To do this, all samples for all patients and all time points were combined together and 35、36 The clustering was performed via a clustering algorithm as described in. After clustering, the clusters were visualized using force-driven graph layout. 35、36 , and colored by association with overall survival. This visualization was used to identify clusters of interest and the associated populations were then added to the manual gating hierarchy. All time series and survival analyses shown in the results are derived from gated populations, whether discovered by manual gating or by unsupervised analysis.

[0209] Serum proteomic profiling Serum proteins were analyzed according to the manufacturer's instructions and as previously described.37 , and quantification was performed using an Olink multiplex proximity extension assay (PEA) panel (Olink Proteomics; www.olink.com). The assays were performed at the Olink Analysis Service Center (Boston, MA, USA). The basis of PEA is a dual recognition immunoassay in which two matched antibodies, labeled with unique DNA oligonucleotides, bind simultaneously to a target protein in solution. This brings the two antibodies into close proximity and hybridizes their DNA oligonucleotides, which serve as a template for a DNA polymerase-dependent extension step. This creates a double-stranded DNA "barcode" that is unique for a particular antigen and quantitatively proportional to the initial concentration of the target protein. Hybridization and extension are immediately followed by PCR amplification, and then the amplicons are finally quantified by microfluidic qPCR using a Fluidigm BioMark HD system (Fluidigm Corporation, South San Francisco, CA). Data were normalized using internal, interplate and negative controls in every single sample as well as correction factors and expressed as a Log2 scale proportional to protein concentration. Final assay readout is reported as normalized protein expression (NPX) value, which is an arbitrary unit on the log2 scale, with higher values ​​corresponding to higher protein expression. A difference of 1 NPX is equal to twice the protein concentration. Two Olink panels (Target96 Immuno-Oncology and Target96 Immune Response) consisting of 172 unique analytes were used in this study. Further details on analytes, detection range, data normalization and standardization are available at https: / / www.olink.com / resources-support / document-download-center / .

[0210] Unbiased mass spectrometry serum profiling Serum samples were profiled using a high-throughput quantitative proteomics workflow for over 1600 quantifiable proteins at Biognosys (Schlieren-Zurich, Switzerland). All samples were handled and thawed equally. During aliquoting, a small amount of each sample was pooled and used as a quality control sample for subsequent library generation and to assess quality and batch effects throughout sample preparation and acquisition. The three processing batches were randomized blocks with respect to treatment and location (samples from one patient were kept within but randomized across the same batch). An automated depletion pipeline consisting of sequential depletion, parallel digestion and liquid chromatography (LC)-mass spectrometry (MS) acquisition was performed as previously reported. 38 The analysis was performed as follows. Quality control samples were depleted within each processing batch. Both data-independent acquisition (DIA) LC-MS measurements and data-dependent acquisition (DDA) LC-MS / MS measurements were acquired. DIA and DDA mass spectrometry data were analyzed using the software SpectroMine (version 3.0.2101115.47784, Biognosys) using default settings including 1% false discovery rate control at the level of PSMs, peptides and proteins, allowing for two failed cleavages and variable modifications (N-terminal acetylation and methionine oxidation). For library generation, the human UniProt.fasta database (Homo sapiens, 2020-01-01, 20,367 entries) was used, and default settings were used.

[0211] The raw mass spectrometry data were analyzed using the software Spectronaut (version 14.7.201007.47784, Biognosys) with default settings, but Q-value sparse filtering was enabled using a global imputing strategy as well as a hybrid library that included all DIA and DDA runs performed in this study. 39Default settings included 1% false discovery rate control at peptide and protein level, and cross-run normalization using global normalization with respect to the median. Protein-wise mean normalization based on the 80% quantile of QC samples between batches 1 and 2-3 removed batch effects identified both by principal component analysis (PCA, "stats" R-package) or hierarchical clustering.

[0212] Whole-exome and transcriptome sequencing FFPE tumor and normal PBMC samples were profiled using ImmunoID NeXT (Personalis, Inc., Menlo Park, CA, USA); an extended exome / transcriptome platform and analysis pipeline that generates comprehensive tumor mutation information, gene expression quantification, neoantigen characterization, HLA typing and allele-specific HLA loss of heterozygosity data (HLA LOH), TCR repertoire profiling and tumor microenvironment profiling. Whole exome library preparation and sequencing were performed as previously described. 40 DNA extracted from tumors and PBMCs was used to generate whole-exome capture libraries using the KAPA HyperPrep Kit and Agilent's SureSelect Target Enrichment Kit according to the manufacturer's recommendations with the following modifications: 1) Targeted probes were used to enhance coverage of biomedically and clinically relevant genes. 2) The protocol was modified to obtain an average library insert length of approximately 250 bp. 3) KAPA HiFi DNA polymerase (Kapa Biosystems) was used instead of Herculase II DNA polymerase (Agilent). Paired-end sequencing was performed on a NovaSeq instrumentation (Illumina, San Diego, CA, USA). Paired-end sequencing was performed on a NovaSeq instrumentation (Illumina, San Diego, CA, USA).

[0213] Whole-transcriptome sequencing results were aligned using STAR. 41 Normalized expression values ​​in transcripts per million (TPM) were calculated using the ImmunoID NeXT tool, Expressionist, in Personalis. For RNA sequencing and alignment quality control, the following metrics were assessed: average read length, average mapped read-pair length, percentage of uniquely mapped reads, number of splice sites, mismatch rate per base, deletion / insertion rate per base, average deletion / insertion length, and aberrant read-pair alignments, including interchromosomal and orphan reads. The ImmunoID NeXT DNA and RNA analysis pipeline aligns reads to the hs37d5 reference genome structure. The pipeline performs alignment, duplicate removal, and base quality score recalibration using best practices outlined by the Broad Institute. 42、43 The pipeline removes duplicates using Picard and improves sequence alignments using the Genome Analysis Toolkit (GATK) to correct base quality scores (BQSRs). Aligned sequence data are returned in BAM format following the SAM specification. Raw read counts from were also normalized using R to obtain a weighted trimmed mean of log expression ratios (trimmed mean of M values ​​(TMM)).

[0214] To calculate gene expression signature for a given gene set, score was determined through the geometric mean of normalized counts of each gene signature.Patient tumor samples were collected from a range of primary tumors and metastatic sites.Due to the influence of tissue of origin on bulk RNAseq, we chose to limit all gene expression analysis to liver metastasis, which is the most common biopsy site, and constituted approximately 64% of biopsies.Therefore, all gene expression analysis includes only biopsies from liver metastasis.See Table 7 for counts. [Table 7]

[0215] Multiple tissue staining and imaging Tumor tissues were collected prior to treatment (fresh baseline biopsy or archival tissue), during treatment (during cycle 2), and at the end of treatment as required. Tissues were fixed in formalin and then paraffin embedded. All tissue imaging was performed under the guidance of an experienced pathologist (TJH) at the Advanced Immunomorphology Platform Laboratory at Memorial Sloan Kettering Cancer Center (New York, NY). Primary antibody staining conditions were optimized using standard immunohistochemical staining on a Leica Bond RX automated research stainer (Leica Bond Polymer Refine Detection DS9800) with DAB detection. Serial antibody titrations on 4 μm tissue sections and control tonsil tissue were used to determine optimal antibody concentrations, then moved to a 7-color multiplex assay with parity (see FIG. 11 for control staining). Four antibody panels were utilized for staining. Panels A1 and B1 were used for tissues collected in phase 1b. Panels A2 and B2 were further optimized for distribution of cellular markers and used on tissues collected in Phase 2. Multiplex assay antibodies and conditions are described in Table 8. [Table 8]

[0216] Seven-color multiplex imaging assay. FFPE tissue sections were baked at 62°C for 3 hours in vertical slide orientation, followed by subsequent deparaffinization on a Leica Bond RX, followed by antigen retrieval for 30 minutes on a Leica Bond ER2, followed by six sequential staining cycles, each round including a 30-minute combined block and primary antibody incubation (Akoya antibody diluent / block). For Ki67 and pan-CK, detection was performed using a secondary horseradish peroxidase (HRP)-conjugated polymer (Akoya Opal polymer HRP Ms+Rb; 10-minute incubation). Detection of all other primary antibodies was performed using a goat anti-mouse poly-HRP or goat anti-rabbit poly-HRP secondary antibody (Invitrogen; 10-minute incubation). HRP-conjugated secondary antibody polymers were detected using fluorescent tyramide signal amplification using Opal dyes 520, 540, 570, 620, 650 and 690 (Akoya Biosciences, Marlborough, MA). The covalent tyramide reaction was followed by heat-induced stripping of the primary / secondary antibody complexes using Akoya AR9 buffer and Leica Bond ER2 (90% AR9 and 10% ER2) at 100°C for 20 minutes prior to the next cycle. After six sequential rounds of staining, sections were stained with Hoechst 33342 (Invitrogen) to visualize nuclei and mounted with ProLong Gold antifade reagent mounting medium (Invitrogen).

[0217] Multispectral imaging and spectral unmixing. Seven-color multi-stained slides were imaged using a Vectra Multispectral Imaging System version 3 (Akoya). Scans were performed at 20× (final magnification of 200×). Filter cubes used for multispectral imaging were DAPI, FITC, Cy3, Texas Red and Cy5. A spectral library containing the emission spectral peaks of the fluorophores in this study was created using Vectra image analysis software (Akoya). Using multispectral images from single-stained slides for each marker, the spectral library was used to separate each multispectral cube into its individual components (spectral unmixing), allowing identification of the seven marker channels of interest using Inform 2.4 image analysis software.

[0218] mIF image analysis. Individual region of interest (ROI) images were exported to TIFF files and run through a pipeline for multiplexed imaging quality control and processing under the supervision of an experienced pathologist. A machine learning cell segmentation algorithm was used to segment entire individual cells along the nuclear as well as membrane boundaries using multiple membrane markers, allowing boundaries to be drawn for all cell types. For each cell segment, pixel values ​​within each region were averaged to obtain a single intensity value per cell and per marker. Using these single cell intensity values, cell type assignment was performed manually by a scientist who determined the cutoff point for positive marker expression for each sample. To perform this manual thresholding, the distribution of single cell marker values ​​and the appearance of fluorescence on the image itself were simultaneously inspected using CellEngine™ software (CellCarta) along with Mantis Viewer (http: / / doi.org / 10.5281 / zenodo.4009579), a custom in-house open source software used for fluorescence image visualization, and thresholds for each marker were drawn for each sample. Using these individual marker thresholds, cell types were defined by the positivity of the combined relevant markers in the panel, as described in Table 9. [Table 9] Once cell types were defined, the percentage of total cells and the percentage of parental populations were calculated for each ROI. Then, for each sample, median values ​​across the ROI were taken for the percentage of total cells, the percentage of parental populations, and sometimes the percentage of other relevant populations. Analysis of all data for associations with survival and pharmacodynamic changes

[0219] Data storage and structure. All processed biomarker data are combined with cleaned clinical data to create the Cancer Data & Evidence Library (CANDEL). 44The data was loaded into a proprietary in-house database called CANDEL. CANDEL uses database technology Datomic™ (www.datomic.com) and a suite of tools built to enable storage and rapid querying and visualization of molecular and clinical data from the R programming language.

[0220] Data analysis in R. All molecular data were analyzed using the R programming language (R Foundation for Statistical Computing) with the packages and versions listed in Table 10. 45 were used to analyze associations with outcomes and procedures. [Table 10]

[0221] Associations with survival were analyzed for cell population percentages, protein values, and gene expression signatures by separating patients into two groups based on median values ​​across all patients in all cohorts. Kaplan-Meier plots were generated between these two groups and for each cohort, and log-rank p-value significance was determined using the survminer and survival packages. Ggplot2 and base R plotting were used to visualize differences between any defined groups or to visualize changes during treatment. Wilcoxon signed-rank tests with a significance cutoff of p=0.05 were used to determine differences between pre- and mid-treatment values, as well as between survival groups (greater than and less than 1 year) at any given time point. Median log fold changes were calculated to determine any additional pharmacodynamic differences seen from pre- to mid-treatment. Heatmaps and circus plots for multi-omic analysis were generated using the DIABLO method in the mixOmics R package. Heatmaps were generated using pheatmap and correlations across data types were calculated using the Spearman method.

[0222] Associative analysis of mass spectrometry data. Due to the large number of proteins analyzed, additional methods were used for the mass spectrometry data. Initial univariate candidate filtering was performed using pairwise Wilcoxon tests applied protein-by-protein across the cohort with Holmes-Bonferroni correction (within groups). Proteins with p-values ​​below or equal to 0.05 from a randomly selected 80% of the observations were used for further optimization using a sparse partial least squares discriminant analysis (sPLS-DA) approach with zero as a threshold for absolute feature importance. 46 Ratios between C1D1 and C1D15, C2D1 and C3D1 were calculated and further used downstream. A randomly selected 80% of the observations were used for sPLS-DA for all cohort arms. A leave-one-out algorithm was used for optimal component and protein selection. sPLSDA training and testing were performed using the R-package "mixOmics". 47 The remaining 20% ​​of the observations were used for validation. The accuracy of prediction for all three groups C1D15, C2D1 and C3D1 was calculated as the ratio of the sum of true positives and negatives to all observations (R-package "caret"). Unsupervised hierarchical analysis was performed on centered and normalized data using the R-package "ComplexHeatmap".

number

[0223] Example 7 Baseline demographic and disease characteristics From Aug. 30, 2018 to June 10, 2019, 99 patients were randomized to one of three treatment arms (N=37, 31, and 31 for nivo / chemo, sotiga / chemo, sotiga / nivo / chemo, respectively; Figure 12). Six patients (N=3, 1, and 2, respectively) were randomized but not dosed and were excluded from the analysis. Figure 12. Efficacy was evaluated in 105 patients (N=34, 36, 35), including 93 patients randomized and dosed in the Phase II and Phase Ib studies. 13 Twelve DLT-evaluable patients from Phase 1b were included (6 each of sotiga / chemo and sotiga / nivo / chemo). Safety was evaluated in 108 patients (N=36, 37, 35, respectively), including 105 patients evaluated for efficacy plus 3 non-DLT-evaluable patients from Phase 1b. Clinical snapshot data for analysis was March 24, 2021.

[0224] Baseline characteristics for the efficacy population were generally balanced across arms, including age, sex, race / ethnicity, primary pancreatic tumor location, site of metastatic spread, stage at diagnosis, and tumor burden (Table 11, Table 12). [Table 11] [Table 12]

[0225] A higher percentage of sotiga / chemo patients had an ECOG score of 0 at screening (44%, 56% and 43% in nivo / chemo, sotiga / chemo and sotiga / nivo / chemo, respectively). Across arms, 74-79% of patients had de novo stage IV disease.

[0226] Pretreatment PD-L1+ tumor percentages by multiplex immunofluorescence imaging (mIF) were similar between the nivo / chemo and sotiga / nivo / chemo arms, but lower in the sotiga / chemo arm. Table 13. [Table 13]

[0227] Seventy-four (70%) patients had pretreatment tumor tissue of sufficiently high quality for whole exome sequencing (WES) available. By WES, the treatment arms were balanced for oncogene frequency in KRAS, BRCA1 / 2, SMAD4, and TP53 in mPDAC (Supplementary Table 2). Most patients (62%) had KRAS mutant tumors. Tumor tissue for one patient (in nivo / chemo) was microsatellite instability-high. Seven patients (3 in nivo / chemo, 1 in sotiga / chemo, 3 in sotiga / nivo / chemo) had BRCA mutations detected in the tumor. Furthermore, the arms were relatively balanced for gene expression signatures in pretreatment tumor tissue and had similar baseline frequencies of immune cell populations in circulation.

[0228] Example 8 Tracking Drug Exposure At the time of analysis, the median duration of follow-up for patients in the efficacy population was 24.2 months (interquartile range [IQR] 20.5-26.3), with a minimum follow-up of 15 months. Two patients, one each on sotiga / chemo and sotiga / nivo / chemo, continue treatment. Median time on treatment was similar across the three arms (median (IQR), months: 5.2 (1.9-8.1), 5.1 (3.4-8.9), and 4.7 (2.4-7.9) months for nivo / chemo, sotiga / chemo, and sotiga / nivo / chemo, respectively). Exposure to each drug in the combination was also similar across the three arms (Table 14). [Table 14]

[0229] Example 9 clinical activity The primary endpoint was a historical control rate of 35%. 14 The 1-year OS rate was 1.0 vs. 1.2 vs. 1.0. The study was not adequately powered to compare the arms.

[0230] For nivo / chemo, the 1-year OS rate was 57.7% (one-sided p=0.006; one-sided 95% confidence limit=41.7%), and the median OS was 16.7 months (95% CI: 9.8-18.4) (Figure 21 Figure 1). The median progression-free survival (PFS) was 6.4 months (95% CI: 5.2-8.8), the investigator-assessed objective response rate (ORR) was 50.0% (95% CI: 32.4-67.6), the disease control rate (DCR) was 73.5% (95% CI: 55.6-87.1), and the median duration of response (DOR) was 7.4 months (95% CI: 2.1-not estimable) (Figure 13, Table 15). [Table 15]

[0231] For sotiga / chemo, the 1-year OS rate was 48.1% (one-sided p=0.062; one-sided 95% lower confidence limit=33.7%), with a median OS of 11.4 months (95% CI: 7.2-20.1). The median PFS was 7.3 months (95% CI: 5.4-9.2), investigator-assessed ORR was 33.3% (95% CI: 18.6-51.0), DCR was 77.8% (95% CI: 60.9-89.9), and median DOR was 5.6 months (95% CI: 3.8-8.0).

[0232] For sotiga / nivo / chemo, the 1-year OS rate was 41.3% (one-sided p=0.233; lower confidence limit=27.0%), with a median OS of 10.1 months (95% CI: 7.9-13.2). The median PFS was 6.7 months (95% CI: 4.2-9.8), investigator-assessed ORR was 31.4% (95% CI: 16.9-49.3), DCR was 68.6% (95% CI: 50.7-83.2), and median DOR was 7.9 months (95% CI: 1.9-not estimable).

[0233] Example 10 Adverse events The spectrum, frequency, and severity of treatment-related adverse events (TRAEs) were similar across arms and were 13 The safety profile was consistent with that observed in . Overall, 106 (98%) patients reported at least one TRAE. The most common non-hematopoietic TRAEs of any grade were nausea, fatigue, fever, and chills (Table 16). [Table 16]

[0234] The most common grade 3-4 TRAEs were hematologic and generally transient in nature. Adverse events of special interest (AESIs) were observed in 90 (83%) patients, including cytokine release syndrome (CRS), infusion reactions, thrombocytopenia, and elevated liver function tests (LFTs) (Table 17). [Table 17]

[0235] CRS was observed in 0, 9 (24%) and 12 (34%) patients in nivo / chemo, sotiga / chemo and sotiga / nivo / chemo, respectively, with 5 events rated as grade 3 (3 in sotiga / chemo and 2 in sotiga / nivo / chemo). No grade 4 or 5 CRS was observed. Infusion-related reactions were observed in 2 (6%), 5 (14%) and 5 (14%) patients, respectively. Low platelet counts occurred in 18 (50%), 21 (57%) and 22 (63%) patients, respectively. Elevated LFTs were observed in 24 (67%), 30 (81%) and 26 (74%) patients, respectively. Six (17%) patients on nivo / chemo, one (3%) patient on sotiga / chemo, and one (3%) patient on sotiga / nivo / chemo discontinued all study drug due to AEs (Table 18). [Table 18]

[0236] Two patients died due to adverse events: acute liver failure with sotiga / chemo (causality could not be determined and therefore considered possibly related to all study drugs) and intracranial hemorrhage with sotiga / nivo / chemo (again, possibly related to all study drugs).

[0237] Example 11 Pharmacodynamic effects To understand the pharmacodynamic effects in each arm, we performed multi-omic profiling of serial patient blood samples and tumor biopsies obtained before and during treatment. In all three arms, longitudinal profiling of patient peripheral blood mononuclear cells (PBMCs) (see Methods) revealed an increase in proliferative (Ki-67+) non-naive (Table 19; defined immune cell populations) CD8 and CD4 T cells during treatment (Figures 14A-14B). [Table 19]

[0238] This increase was strongest and observed earlier in the nivo / chemo arm and to a lesser extent in the sotiga / chemo arm; comparatively, the effect was numerically reduced in the sotiga / nivo / chemo arm, possibly indicating a reduced systemic immune activation of T cell expansion or altered kinetics of T cell modulation that were not captured in the time series analysis. Circulating activated (HLA-DR+, CD38+ or CD39+) non-naive CD4 and CD8 T cells were also increased in all three arms, especially in the nivo-containing arm (Figure 15A Figure 15B and Figure 13). Among the array of 172 serum proteins studied, patients in all three treatment arms had treatment-induced decreases in known biomarkers prognostic for pancreatic cancer, such as K1C19 (a pancreatic ductal protein), consistent with tumor regression measurements (Figure 14C and Table 20). [Table 20] Patients in all three treatment arms also had reductions in the immunosuppressive molecules IL-8 and MMP-12 (Table 20, Table 21), while patients treated with nivo / chemo showed early (C1D15) decreased levels of the immunosuppressive protein arginase 1 and the costimulatory ligand CD40L (Figure 14C). [Table 21-1] [Table 21-2]

[0239] Patients treated with nivo / chemo had an early (C1D15) increase in cytokines associated with T cell activation and type 1 immunity, most notably soluble PD-1, type 1 skewing chemokines (CXCL9, CXCL10), type II interferons, and IL-18 (Figure 14C, Table 20). In contrast, patients treated with sotiga / chemo showed an early (C1D15) increase in proteins associated with dendritic cell maturation and activation, e.g., LAMP3 and CXCL11 (Figure 14C, Table 21), followed by a later (C1D15, C3D1) upregulation of proteins associated with T cell activation, e.g., type II interferons and IL-15 (Figure 14C, Figure 15C, Figure 15CD, Figure 15E, Figure 15F, and Table 21). Increases in IL-15 in C3D1 were uniquely observed with sotiga / chemo treatment (Table 21). All treatment-related changes in circulating proteins with sotiga / nivo / chemo were also observed to change in the individual nivo / chemo or sotiga / chemo arms, but with different kinetics. For example, patients treated with sotiga / nivo / chemo had earlier (C1D15) increases in LAMP3, CXCL11, CXCL10, and soluble PD-1 at C1D15, and later (C2D1) increases in type 11 interferon and CXCL9 (Figure 14C, Figure 15C, Figure 15CD, Figure 15E, Figure 15F, and Table 22). [Table 22-1] [Table 22-2]

[0240] Patient sera were then profiled using unbiased mass spectrometry combined with sparse PLS discriminant analysis to identify important circulating proteins not identified by the targeted approach (Figure 14C). Patients treated with nivo / chemo had increases in proteins associated with immune cell migration and T cell activation (GKN1, B3GN2 and PGRP1) and decreases in the chemokine CXCL7 compared to pre-treatment levels (Table 23). [Table 23]

[0241] Patients treated with sotiga / chemo had increases in a soluble protein important for helper T / B cell activation (CCL15) and a soluble protein important for monocyte activation (GSHB) (Table 24). [Table 24]

[0242] An integrated analysis of biomarkers measured during treatment (C2D1) was performed. In the nivo / chemo-treated arm, increased serum levels of chemokines and cytokines related to type 1 immunity (CXCL9, CXCL10, CXCL11 and IFN-γ) positively correlated with activated T cells (HLA-DR+, CD38+) (Figure 15G and Figure 15H). In the sotiga / chemo-treated arm, molecules reported to be related to innate and adaptive immune cell migration increased during treatment (GKN1 and CCL15) and proteins related to DC maturation, e.g., LAMP3 and CXCL11, or positively correlated with activated non-naive T cells (CD38+ or CD39+) and CCR7+ B cells (Figure 15G and Figure 15H).

[0243] To assess pharmacodynamic effects in tumors, tumor tissues were profiled using mIF before and during treatment (approximately C2D1, see Methods). Analysis of paired biopsies from individual patients revealed that nivo / chemo treatment resulted in a numerically reduced percentage of tumor cells expressing PD-L1 in all samples measured (n=5). The change in the percentage of PD-L1 positive tumor cells was heterogeneous, decreasing in one sample and increasing in the other two. The combination of sotiga / nivo / chemo produced a decrease in PD-L1 positive tumor cells in five of six patient samples analyzed (Figure 14D). For sotiga / chemo treatment, two of three patients with paired biopsies showed an increase in tumor-infiltrating iNOS+CD80+CD68+ macrophages, an effect that was not observed for paired biopsies from patients treated with nivo / chemo or sotiga / nivo / chemo (Figure 14E).

[0244] Example 12 Biomarkers associated with survival benefit To identify subsets of patients more likely to demonstrate longer survival from a particular combination treatment, we performed an exploratory analysis using comprehensive multi-omic multi-parameter immune and tumor biomarker data. An approach focused on biological signals observed across multiple assays helped to identify causal biology signals with the greatest robustness in the context of a small Phase II study. This deep integrated analytical approach provided a comprehensive view of the tumor and immune makeup and identified multiple biomarkers associated with survival benefit in each arm.

[0245] Example 13 Biomarkers and immunobiology associated with survival benefit after nivo / chemo Total RNA sequencing and mIF were analyzed to examine the tumor microenvironment (TME) of patients before treatment. Oxidative phosphorylation, fatty acid metabolism, xenobiotic metabolism, and bile acid metabolism gene expression signatures were associated with longer survival, whereas TGF-β signaling signatures were associated with shorter survival (Figure 16A). Lower expression of hallmark gene expression signatures, IL-6, TNF-α signaling through NFκB, and lower frequency of iNOS+ macrophages by mIF were associated with longer survival in patients treated with nivo / chemo (Figure 16B, Figure 16C, Figure 16D). Higher frequency of PD-L1+ tumor cells before treatment, as measured by mIF, was weakly associated with survival longer than 1 year (Figure 17), but was not significantly associated with longer overall survival by Kaplan-Meier analysis. Furthermore, circulating immunosuppressive factors were associated with shorter survival (Table 25). Lower levels of nitric oxide synthase 3 and arginase-1 were associated with longer survival in patients treated with nivo / chemo (Table 25). [Table 25-1] [Table 25-2]

[0246] The survival benefit after nivo / chemo was associated with diverse immunocompetent circulating T cell responses prior to treatment. CD4 and CD8 T cells were classified as effector memory (EM) (Figure 10) or central memory (CM) (Figure 10). Effector memory T cells were further sub-divided based on CCR7 expression: EM1, EM2 and EM3 (Figure 10). Higher frequencies of activated (CD38+) EM CD8 T cells (Figure 16E), antigen-experienced (PD-1+CD39+) EM1 (Figure 18A) and CM CD4 T cells (Figure 19A), as well as T follicular helper cells (CD4+PD-1+CXCR5+) (Figure 18D) were all associated with longer survival in patients treated with nivo / chemo. Activated (CD38+) EM CD8 T cells also co-expressed PD-1 and the type 1 transcription factor Tbet (Figure 16F). Activated (CD38+) EM CD8 T cells increased over time, but only pretreatment levels were associated with 1-year survival status (Figure 16G). Antigen-experienced (PD-1+CD39+) EM1 and CM CD4 T cells co-expressed CTLA-4 and ICOS (Figures 18B and 19B). During treatment, this cell phenotype continued to be associated with better survival (Figures 18C and 19C). Higher on-treatment abundance of T follicular helper cells, which had high expression of TCF-1 and ICOS, was associated with survival at 1 year (Figures 18E and 18F). Multi-omic dimensionality reduction analysis of both circulating and tumor factors summarized these findings, revealing principal axes of independent variance in the data and showing separation between patients with survival longer than 1 year and those with survival shorter than 1 year (Figure 16H). Overall, patients with longer survival after nivo / chemo treatment had lower pretreatment levels of immunosuppressive molecules and higher pretreatment frequencies of activated type 1 T cells compared to patients with shorter survival (Figure 16H).

[0247] Example 14 Biomarkers and immunobiology associated with survival benefit following sotiga / chemo Different TME biomarkers were associated with survival benefit after sotiga / chemo vs. nivo / chemo treatment. Patients with longer survival after sotiga / chemo treatment had pretreatment tumor profiles with diverse CD4 helper T cell infiltrates and lower levels of gene expression signatures and immune cell types associated with immunosuppression. CD4 T cell gene expression signatures associated with longer survival included Th1 and Th2 responses, and IFN-γ signaling (Figures 20A-20C, Table 26). [Table 26]

[0248] Patients with longer survival after sotiga / chemo treatment also had higher frequencies of tumor-infiltrating non-proliferating (Ki-67-) normal CD4 T cells and regulatory (Foxp3+) CD4 T cells by mIF (Figure 20E and Figure 20F, Table 26) and lower frequencies of infiltrating proliferating (Ki-67+) CD4 T cells (Figure 20F, Table 26). Patients whose tumors had high E2F signaling signatures also had shorter survival (Figure 20D, Table 26). By cross-platform analysis using DIABLO (see Methods), E2F signaling signatures positively correlated with glycolysis and hypoxia gene expression signatures and infiltrating iNOS-macrophages, which were also associated with shorter survival after sotiga / chemo treatment (Figure 20F and Figure 20G).

[0249] Preclinical data suggests that CD40 agonism results in antigen-presenting cell (APC) activation, and therefore we hypothesized that patients who experienced survival benefit after sotiga / chemo would have evidence of this in the circulation. Therefore, we performed immune profiling of pre- and post-treatment PBMCs using CyTOF and flow cytometry (see Methods). Using unsupervised clustering analysis (Figure 22A, see Methods), we identified multiple circulating dendritic cell (DC) subsets associated with survival before and after treatment with sotiga / chemo (Figure 22A, Table 27). [Table 27-1] [Table 27-2]

[0250] Patients with longer overall survival had a higher frequency of CD1c+CD141+DCs before treatment (Figure 22B, Table 27). A higher frequency of CD141+DCs, with reduced CD1c co-expression during treatment, was associated with longer survival (C1D15, Figure 22C and Figure 22D). A higher frequency of conventional DCs (cDCs; Figure 9 and Table 19) during treatment (C2D1) was also associated with longer survival (Figure 22E Figure 5e, Table 27). Consistent with DC maturation, higher concentrations of soluble CD83 and soluble ICOSL during treatment were also associated with longer survival in patients treated with sotiga / chemo (Figure 23). Patients with longer survival after sotiga / chemo also had a higher frequency of circulating HLA-DR+CCR7+B cells before treatment (Figure 24A, Figure 24B, Table 27). Overall, in contrast to patients with longer survival after nivo / chemo treatment, patients with longer survival after sotiga / chemo treatment had higher frequencies of circulating DCs and B cells in the circulation before treatment, along with phenotypic changes in the APC compartment.

[0251] In addition to the activated APC compartment, we also found that the pretreatment frequency of key CD4 T cell populations was associated with survival benefit after sotiga / chemo treatment. Higher pretreatment frequencies of circulating type 1 helper (Tbet+Eomes+) and antigen-experienced (PD1+Tbet+) non-naive CD4 T cells were associated with longer survival in patients treated with sotiga / chemo (Figure 22F and Figure 22G, Table 27). PD-1+Tbet+ non-naive CD4 T cells expressed high levels of TCF-1, whereas Tbet+Eomes+ non-naive CD4 T cells expressed high levels of PD-1 (Figure 22G and Figure 22J). Higher frequencies of both populations of non-naive CD4 T cells pretreatment were associated with survival benefit, and the frequency of this phenotype remained relatively consistent throughout treatment (Figure 22H and Figure 22K). Lower frequencies of circulating non-naive CD4 T cells expressing 2B4 before treatment were also associated with longer survival (Figure 24C, Table 26). 2B4 expression on CD8 T cells was associated with an exhausted phenotype, and these cells co-expressed other molecules associated with an exhausted or anti-inflammatory phenotype; PD-1, CTLA-4, LAG-3, and did not express Ki-67 (Figure 24D). Moreover, the frequency of this phenotype increased during treatment (C4D1) (Figure 24E). Overall, circulating type 1 (Tbet+) CD4 T cells before treatment were associated with a survival benefit after sotiga / chemo, whereas higher levels of potentially dysfunctional 2B4+ CD4 T cells were associated with shorter survival.

[0252] Thus, the pretreatment biomarker profiles associated with survival benefit following sotiga / chemo and nivo / chemo treatment in both tissue and blood were distinct (Figure 25). Because all patients received the same chemotherapy backbone, it is suggested that these predictive markers are not simply related to prognosis or chemotherapy treatment. This conclusion is supported by the strong mechanistic association of each set of biomarkers with the CD40 and PD-1 axes.

[0253] Example 15 sotiga / nivoBiomarkers and immunobiology associated with survival benefit after chemotherapy In this study, sotiga / nivo / chemo treatment did not result in a survival benefit over historical controls from chemo alone (Figure 21A). In a multi-omic biomarker analysis, we found that biomarkers associated with longer survival after sotiga / chemo and nivo / chemo were individually not predictive for sotiga / nivo / chemo treatment (Table 28). [Table 28]

[0254] However, we identified several unique cell populations associated with longer survival after sotiga / nivo / chemo treatment, including lower frequencies of activated CD38+ non-naive CD4 (Figure 26A, Table 28) and CD8 (Figure 26B, Table 28) T cells. The CD38+ non-naive CD4 T cell population also expressed high levels of TCF-1, and activation markers including CTLA-4, PD-1, ICOS, whereas the CD38+ non-naive CD8 T cell population expressed high levels of PD-1, Tbet, Eomes, TCF-1, and 2B4 (Figure 26C). The frequency of this cell phenotype increased during treatment but did not continue to be associated with shorter survival (Figures 26D-26E). In the nivo / chemo treated arm, a higher frequency of similar activated T cell populations was associated with longer survival, whereas such an association was not observed in the sotiga / chemo treated arm (Figure 16F, Figure 16G, Figure 26A, Figure 26B, Table 25). In addition to the CD38+ T cell population, a lower frequency of CCR7+CD11b+CD27- B cells during treatment (C1D15) was associated with longer survival after sotiga / nivo / chemo treatment (Figure 27B). Importantly, the frequency of this phenotype increased during treatment in the sotiga / nivo / chemo arm, but decreased in the other arms (Figure 27A). These cells co-expressed CD40, HLA-DR, CD11c and CD38, and were associated with worse survival during treatment (C1D15) (Figure 27C and Figure 27D). Taken together, these data suggest that a higher frequency of chronically activated T cells before and during treatment, as well as the presence of CCR7+CD11b+CD27- B cells during treatment, may be associated with shorter survival after treatment with sotiga / nivo / chemo.

[0255] Consideration This multicenter, randomized Phase II clinical study, known as PRINCE, evaluated the efficacy and mechanism of sotiga ± nivo ± chemo in patients with mPDAC in the first-line treatment setting. The Phase Ib portion of the study demonstrated that sotiga / chemo ± nivo was tolerable, clinically active, and a potentially novel chemo-immunotherapy combination for this disease. 13 The randomized design produced relatively balanced demographic and baseline disease characteristics across the three treatment arms. Although not adequately powered to compare across arms, enrolling three treatment arms allowed for concurrent assessment of the relative contributions of sotiga and nivo in combination with chemo.

[0256] The nivo / chemo arm achieved the primary endpoint of an increase in 1-year OS rate (57.3%, p=0.007) versus a historical control of 35% for the gemcitabine / nab-paclitaxel chemo regimen. The sotiga / chemo arm approached significance (48.1%, p=0.062), but the sotiga / nivo / chemo arm did not demonstrate an improvement in overall survival (41.3%, p=0.223). ORR was highest for nivo / chemo (50%); however, many of the responses observed in this arm were of short duration and were not confirmed by subsequent scans.

[0257] All combination treatments were well tolerated and the safety profile was consistent with previous studies. 12、13 The toxicity was similar to that observed in the sotiga / nivo / chemo arm. Specifically, no additive toxicity was observed in the sotiga / nivo / chemo arm. This study followed standard chemo treatment guidelines that allow for continuous treatment until progression. Some accumulation of toxicity may be attributable to this prolonged exposure to chemo; therefore, future studies should consider response-based chemo withdrawal.

[0258] One limitation of this study is the lack of a chemo-control arm, which hinders our ability to evaluate survival benefit versus contemporary control patients. Second, this study enrolled patients across a small number of tertiary care cancer centers. We assessed overall survival according to the criteria for the initial definitive study of gemcitabine / nab-paclitaxel, but subsequent phase III studies have reported higher 1-year overall survival rates of approximately 40-45%. 15、16 However, these rates are numerically lower than the 1-year overall survival rates observed for nivo / chemo and sotiga / chemo.

[0259] We performed a multi-omic, multi-parametric biomarker analysis to better understand the immune pharmacodynamic effects and mechanisms of response and resistance to the chemo-immunotherapy combination. Overall, circulating cell, protein and tumor tissue biomarker findings were consistent with the expected mechanisms of action of either PD-1 blockade and / or CD40 activation. We observed some pharmacodynamic patterns evident across all cohorts that were likely related to the identical chemotherapy regimens used in this study, although many patterns were specific to certain cohorts and therefore not chemotherapy specific. We found that the pharmacodynamic patterns were consistent with previously published reports on the mechanisms of action for nivo and sotiga in other diseases. 17、18 Consistent with this, we observed an increase in proliferating non-naive CD4 and CD8 T cells during treatment in all three treatment arms. This increase in proliferating T cells was, as expected, greater in the two arms containing nivo. 18However, unique immunopharmacodynamic effects were identified for nivo / chemo and sotiga / chemo individually in T cell populations, as well as circulating proteins and tumor tissue samples. These data indicate that the immunotherapies evaluated herein have distinct activities that exceed or surpass the effects of chemotherapy in a subset of patients. Many of these pharmacodynamic effects were somewhat attenuated in the sotiga / nivo / chemo arm, potentially indicating a reduced or antagonistic effect when all dual immunotherapies / chemo are used in combination.

[0260] In addition to pharmacodynamic effects, we tested biomarkers associated with survival. The multi-omic, multi-parameter, exploratory translational analyses in this study revealed that both nivo / chemo and sotiga / chemo demonstrated benefit in subsets of patients that could be identified by various tumor and circulating predictive biomarkers. Furthermore, these analyses revealed that the tumor and circulating response signatures identified for sotiga / chemo were unique compared to those identified for nivo / chemo, reflecting distinct mechanisms of response to each immune intervention. It is important to note that this retrospective analysis is meant to be hypothesis-generating for future studies, and prospective studies are required to demonstrate that these biomarkers are truly predictive for survival after these regimens.

[0261] We identified factors associated with improved survival after nivo / chemo treatment from pre-treatment tumor samples, including lower expression of immunosuppressive and inflammatory signatures, and lower frequency of inflammatory macrophages. Lower frequencies of circulating cytokines associated with suppressive adaptive immune function were associated with longer overall survival. Survival after nivo / chemo was also associated with a diverse circulating T cell compartment, composed of antigen-experienced and activated type 1-skewing (Tbet+) CD4 and CD8 T cells, as well as higher frequencies of circulating THF cells, which may represent cells that modulate the TME. 19、20Despite the feasibility of translating biomarkers into assays for patient selection, especially due to the time frame it relates to from biopsy to biomarker analysis to enable clinical decision making, many of these biomarkers could potentially be used as pre-treatment patient selection criteria for future studies. For example, circulating activation antigen-experienced (PD-1+CD39+) T cell populations could constitute attractive biomarkers, since these populations are abundant in the blood and easily and rapidly measured.

[0262] In the sotiga / chemo arm, survival benefit was associated with extensive helper T cell infiltration in the tumor microenvironment. We identified an association between higher frequencies of both non-proliferating conventional and regulatory CD4+ T cells in the tumor and longer survival. Furthermore, in line with previous reports 21 Consistent with this, a pretreatment glycolytic or hypoxic TME gene signature was associated with shorter survival. This metabolic phenotype positively correlated with high tumor expression of E2F and MYC signaling. Increased E2F and myc signaling within the TME has been reported to impede both CD4+ T cell infiltration and responses to agonistic CD40 antibodies in preclinical models. 22 A higher frequency of circulating, pre-treatment antigen-experienced, type 1-skewed CD4+ T cells was associated with survival to sotiga / chemo. Furthermore, a higher frequency of circulating HLA-DR+CCR7+ B cells pre-treatment, which may indicate the presence of germinal centers, was associated with survival to sotiga / chemo. 23) was associated with longer survival in response to sotiga / chemo treatment. Consistent with an agonistic CD40 mechanism of action, multiple DC subsets were also strongly associated with longer survival in response to sotiga / chemo treatment. Circulating levels of CD1c+CD141+ cross-presenting dendritic cells before treatment were associated with longer survival. However, CD1c+ DCs during treatment were not associated with survival. Instead, a higher frequency of CD1c-CD141+ cross-presenting DCs was associated with survival. Loss of CD1c expression has been reported to be associated with stronger cross-presentation, and previous studies have suggested that agonistic CD40 treatment may induce cross-presenting DCs and promote epitope spreading. 24~26 Furthermore, several immunosuppressive signatures were associated with poorer outcomes on sotiga / chemo treatment. These included higher frequencies of m-MDSCs, "exhausted-like" CD4 T cells, and chemokines / cytokines associated with suppressive function, which were associated with shorter survival, suggesting that these immune features may subvert successful responses to sotiga / chemo. From a practical standpoint, baseline assessment of CD4 T cells may provide the most tractable biomarker for patient selection for sotiga / chemo in subsequent studies.

[0263] For sotiga / nivo / chemo, a relatively small number of tumor and circulating immune biomarkers were associated with survival, and in particular, biomarkers associated with longer survival in the sotiga / chemo and nivo / chemo monotherapy immunotherapy treatment arms were not associated after sotiga / nivo / chemo. We hypothesize that this arm results in a systemic overactivation of the immune system, resulting in a less functional immune state and therefore reduced antitumor immunity. Indeed, specific populations of CD38+CD4 and CD8 T cells were associated with shorter survival in response to sotiga / nivo / chemo combination treatment. The immunological phenotype of these cells suggests that excessive T cell activation may result in a terminally exhausted state unique to this chemo-immunotherapy combination. 27Furthermore, during treatment (C4D1), the dual immunotherapy combination of sotiga / nivo / chemo treatment had an increase in circulating CCR7+CD11b+CD27- B cells, consistent with shorter survival both at this time point and earlier during treatment (C1D15). Expression of CD11b on B cells has been associated with tolerogenic or regulatory responses in the lupus setting. 28 , which may have a dampening effect on antitumor immunity. Moreover, these findings are consistent with recent preclinical studies in gliomas suggesting that agonistic CD40 impairs responses to PD-1 blockade, in part through the induction of regulatory B cells. 29 Therefore, we hypothesize that regulatory B cells may play an additional role in the suppressed immunity following dual immunotherapy combination in mPDAC. However, mechanistic studies need to be performed to further understand these findings in the mPDAC setting.

[0264] Finally, the data presented in this study raise an intriguing hypothesis regarding the role of T cells in mediating immunity against pancreatic cancer and also suggest that characterizing the immune status of patients prior to treatment may help direct different immunotherapy-based treatment approaches. Prior to treatment, higher frequencies of circulating and infiltrating T cells, as well as increased activated CD8 T cells, were associated with improved survival, especially with nivo / chemo treatment. Notably, data reported from other solid tumors indicate that T cells are more likely to be involved in the immune response to pancreatic cancer than T cells. 30~33 Unlike mPDAC, circulating antigen-experienced CD8 T cells or infiltrating CD8 T cells were not associated with survival, suggesting that other immune cell types are more prominent in mPDAC. In contrast, the association between survival and circulating antigen-experienced T cells was observed primarily in type 1 (Tbet+) CD4 T cells. Furthermore, infiltrating T cells in all tumor samples were predominantly CD4 T cells, and surprisingly, very few patient tumor samples had increased CD8 T cell infiltration (Supplementary Figure X). Recent clinical studies suggest that CD4 tumor-infiltrating lymphocytes may be important for antitumor immunity. 34We hypothesize that the CD4 T cell compartment may have an important role in the response to chemoimmunotherapeutic treatment in mPDAC, a finding not yet reported in other solid cancer types.

[0265] This randomized Phase II study is the first to suggest a benefit of first-line chemoimmunotherapy in patients with mPDAC. Previous studies with nivo / chemo have shown that first-line 12 failed to show clinical benefit in patients; however, steroids were tolerated in that study, although steroid use is not recommended in PRINCE. This study deployed an unprecedented scale of multi-omic, multi-parameter biomarker analysis to identify potential mechanisms of response and resistance to chemoimmunotherapy regimens in mPDAC. Using this approach, we identify novel biomarkers for these immune mechanisms in mPDAC. These multi-omic analyses revealed complex interactions of the TME and immune system, previously poorly understood in patients with mPDAC, consistent with previous preclinical studies. These results may aid in the design of clinical trials and refined approaches for first-line nivo / chemo and sotiga / chemo treatments in selected patients with mPDAC. We plan to further evaluate the biomarker profile presented herein in prospective biomarker-selected patient cohorts in upcoming clinical trials in this disease.

[0266] While the present disclosure has been particularly shown and described with reference to specific embodiments, some of which are preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail can be made in those embodiments without departing from the spirit and scope of the present disclosure disclosed herein. References 1. Rahib, L., et al. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res 74, 2913-2921 (2014). 2. Sharma, P., et al. The Next Decade of Immune Checkpoint Therapy. Cancer Discov 11, 838-857 (2021). 3. O'Reilly, E.M., et al. Durvalumab With or Without Tremelimumab for Patients With Metastatic Pancreatic Ductal Adenocarcinoma: A Phase 2 Randomized Clinical Trial. JAMA Oncol 5, 1431-1438 (2019). 4. Royal, R.E., et al. Phase 2 trial of single agent Ipilimumab (anti-CTLA-4) for locally advanced or metastatic pancreatic adenocarcinoma. J Immunother 33, 828-833 (2010). 5. Patnaik, A., et al. Phase I Study of Pembrolizumab (MK-3475; Anti-PD-1 Monoclonal Antibody) in Patients with Advanced Solid Tumors. Clin Cancer Res 21, 4286-4293 (2015). 6. Brahmer, J.R., et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl J Med 366, 2455-2465 (2012). 7. Balachandran, V.P., et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551, 512-516 (2017). 8. Balli, D., Rech, A.J., Stanger, B.Z. & Vonderheide, R.H. Immune Cytolytic Activity Stratifies Molecular Subsets of Human Pancreatic Cancer. Clin Cancer Res 23, 3129-3138 (2017). 9. Stromnes, I.M., Hulbert, A., Pierce, R.H., Greenberg, P.D. & Hingorani, S.R. T-cell Localization, Activation, and Clonal Expansion in Human Pancreatic Ductal Adenocarcinoma. Cancer Immunol Res 5, 978-991 (2017). 10. Byrne, K.T. & Vonderheide, R.H. CD40 Stimulation Obviates Innate Sensors and Drives T Cell Immunity in Cancer. Cell Rep 15, 2719-2732 (2016). 11. Winograd, R., et al. Induction of T-cell Immunity Overcomes Complete Resistance to PD-1 and CTLA-4 Blockade and Improves Survival in Pancreatic Carcinoma. Cancer Immunol Res 3, 399-411 (2015). 12. Wainberg, Z.A., et al. Open-label, Phase I Study of Nivolumab Combined with nab-Paclitaxel Plus Gemcitabine in Advanced Pancreatic Cancer. Clin Cancer Res 26, 4814-4822 (2020). 13. O'Hara, M.H., et al. CD40 agonistic monoclonal antibody APX005M (sotigalimab) and chemotherapy, with or without nivolumab, for the treatment of metastatic pancreatic adenocarcinoma: an open-label, multicentre, phase 1b study. Lancet Oncol 22, 118-131 (2021). 14. Von Hoff, D.D., et al. Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. N Engl J Med 369, 1691-1703 (2013). 15. Tempero, M., et al. Ibrutinib in combination with nab-paclitaxel and gemcitabine for first-line treatment of patients with metastatic pancreatic adenocarcinoma: phase III RESOLVE study. Ann Oncol 32, 600-608 (2021). 16. Van Cutsem, E., et al. Randomized Phase III Trial of Pegvorhyaluronidase Alfa With Nab-Paclitaxel Plus Gemcitabine for Patients With Hyaluronan-High Metastatic Pancreatic Adenocarcinoma. J Clin Oncol 38, 3185-3194 (2020). 17. Filbert, E.L., Bjorck, P.K., Srivastava, M.K., Bahjat, F.R. & Yang, X. APX005M, a CD40 agonist antibody with unique ...

Claims

1. A composition comprising a CD40 agonist and / or a chemotherapeutic agent for use in a method of treating cancer in a human subject, wherein (i) the composition comprises the CD40 agonist, wherein the CD40 agonist is administered in combination with a chemotherapeutic agent; or (ii) the composition comprises the chemotherapeutic agent, and the chemotherapeutic agent is administered in combination with a CD40 agonist; wherein the method comprises: (a) determining the level (cell count) of at least one circulating peripheral blood mononuclear cell (PBMC) type selected from the group consisting of HLA-DR+CCR7+ B cells, cross-presenting dendritic cells (DCs), CD1C+CD141+ cross-presenting DCs, PD-1+ T cells, TCF-1+ T cells, Tbet+ T cells, T helper cells, and 2B4+ CD4 T cells in a biological sample from the subject; or determining a gene signature in a biological sample from the subject and calculating a signature score, wherein the gene signature is a MYC gene signature or an E2F gene signature; and (b) administering the composition to the subject if the level (cell count) of the at least one circulating PBMC type selected from the group consisting of HLA-DR+CCR7+ B cells, cross-presenting dendritic cells (DCs), CD1C+CD141+ cross-presenting DCs, PD-1+ T cells, TCF-1+ T cells, Tbet+ T cells, and T helper cells is increased compared to a control or reference, or if the level (cell count) of circulating 2B4+ CD4 T cells is decreased compared to a control or reference, or if the gene signature score is decreased compared to a control or reference. A composition comprising:

2. The composition described in claim 1, wherein the control or reference is a sample obtained from one or more reference subjects having the same type of cancer as the test subject.

3. The method comprising: (a) determining the level (cell count) of HLA-DR+CCR7+ B cells, cross-presenting dendritic cells (DCs), CD1C+CD141+ cross-presenting DCs, PD-1+ T cells, TCF-1+ T cells, Tbet+ T cells, or T helper cells in the subject; and (b) administering the composition to the subject if the level (cell count) of the HLA-DR+CCR7+ B cells, cross-presenting dendritic cells (DCs), CD1C+CD141+ cross-presenting DCs, PD-1+ T cells, TCF-1+ T cells, Tbet+ T cells, or T helper cells is increased compared to the control or reference. The composition of claim 1 , comprising:

4. The composition of claim 1, wherein the circulating PBMCs are HLA-DR+CCR7+ B cells.

5. The composition of claim 4, wherein the HLA-DR+CCR7+ B cells are identified based on CD19 expression and further screened based on CD38 vs. CD27 expression.

6. The composition of any one of claims 1 to 3, wherein the circulating PBMC types are at least one of the following: cross-presenting DCs, CD1C+CD141+ cross-presenting DCs, PD-1+ T cells, TCF-1+ T cells, and Tbet+ T cells.

7. The composition of claim 6, wherein the circulating Tbet+ cells are at least one of the following: PD1+ Tbet+ cells and Tbet+Eomes+ cells.

8. The method according to claim 1, (a) determining a gene signature in a biological sample from the subject and calculating a signature score, wherein the gene signature is a MYC gene signature or an E2F gene signature; and (b) if the gene signature score is decreased compared to a control or reference, administering the composition to the subject. The composition of claim 1 or 2, comprising:

9. The method of claim 8, wherein the gene signature is a MYC gene signature, (a) the subject is an individual in a cohort of subjects having the same cancer, and the subject is a part of the cohort; (b) the gene signature score for each subject in the cohort is calculated; and (c) the control or reference is calculated using the gene signature scores of the subjects of the cohort; The composition of claim 8.

10. 10. The composition of claim 9, wherein the MYC gene signature score is calculated by averaging the log-normalized expression values for each gene in the MYC gene set.

11. The composition of claim 10, wherein the MYC gene set comprises one or more genes known to be regulated by MYC version 1 (V1).

12. The composition of claim 11, wherein the one or more genes are selected from the group consisting of tumor suppressor genes, oncogenes, translocated oncogenes, protein kinase genes, cell differentiation marker genes, homeodomain protein genes, transcription factor genes, cytokine genes and growth factor genes.

13. The one or more genes are ABCE1, ACP1, AIMP2, AP3S1, APEX1, BUB3, C1QBP, CAD, CANX, CBX3, CCNA2, CCT2, CCT3, CCT4, CCT5, CCT7, CDC20, CDC45, CDK2, CDK4, CLNS1A, CNBP, COPS5, COX5A, CSTF2, CTPS1, CUL1, CYC1, DDX18, DDX21, DEK, DHX15, DUT, EEF1B2, EIF1AX, EIF2S1, EIF2S2, EIF3B, EIF3D, EIF3J, EIF4A1, EI F4E, EIF4G2, EIF4H, EPRS1, ERH, ETF1, EXOSC7, FAM120A, FBL, G3BP1, GLO1, GNL3, GOT2, GSPT1, H2AZ1, HDAC2, HDDC2, HDGF, HNRNPA1, HNRNPA2B1, HNRNPA 3, HNRNPC, HNRNPD, HNRNPR, HNRNPU, HPRT1, HSP90AB1, HSPD1, HSPE1, IARS1 , IFRD1, ILF2, IMPDH2, KARS1, KPNA2, KPNB1, LDHA, LSM2, LSM7, MAD2L1, MCM2 , MCM4, MCM5, MCM6, MCM7, MRPL23, MRPL9, MRPS18B, MYC, NAP1L1, NCBP1, NCB P2, NDUFAB1, NHP2, NME1, NOLC1, NOP16, NOP56, NPM1, ODC1, ORC2, PA2G4, PA BPC1, PABPC4, PCBP1, PCNA, PGK1, PHB, PHB2, POLD2, POLE3, PPIA, PPM1G, PR DX3, PRDX4, PRPF31, PRPS2, PSMA1, PSMA2, PSMA4, PSMA6, PSMA7, PSMB2, PSMB 3, PSMC4, PSMC6, PSMD1, PSMD14, PSMD3, PSMD7, PSMD8, PTGES3, PWP1, RACK1 , RAD23B, RAN, RANBP1, RFC4, RNPS1, RPL14, RPL18, RPL22, RPL34, RPL6, RPLP 0, RPS10, RPS2, RPS3, RPS5, RPS6, RRM1, RRP9, RSL1D1, RUVBL2, SERBP1, SET , SF3A1, SF3B3, SLC25A3, SMARCC1, SNRPA, SNRPA1, SNRPB2, SNRPD1, SNRPD2,12. The composition of claim 11, wherein the nucleotide sequence is selected from the group consisting of SNRPD3, SNRPG, SRM, SRPK1, SRSF1, SRSF2, SRSF3, SRSF7, SSB, SSBP1, STARD7, SYNCRIP, TARDBP, TCP1, TFDP1, TOMM70, TRA2B, TRIM28, TUFM, TXNL4A, TYMS, U2AF1, UBA2, UBE2E1, UBE2L3, USP1, VBP1, VDAC1, VDAC3, XPO1, XPOT, XRCC6, YWHAE and YWHAQ.

14. The composition described in claim 8, wherein the gene signature is the E2F gene signature.

15. The method of claim 1, wherein the E2F gene signature score is calculated by averaging log-normalized expression values for each gene in an E2F gene set, the E2F gene set preferably comprising: ABCE1, ACP1, AIMP2, AP3S1, APEX1, BUB3, C1QBP, CAD, CANX, CANX, CBX3, CCNA2, CCT2, CCT3, CCT4, CCT5, CCT7, CDC20, CDC45, CDK2, CDK4, CLNS1A, CNBP, COPS5, COX5A, CSTF2, CTPS1, CUL1, CYC1, DDX18, DD X21, DEK, DHX15, DUT, EEF1B2, EIF1AX, EIF2S1, EIF2S2, EIF3B, EIF3D, EIF3 J, EIF4A1, EIF4E, EIF4G2, EIF4H, EPRS1, ERH, ETF1, EXOSC7, FAM120A, FBL, G3BP1, GLO1, GNL3, GOT2, GSPT1, H2AZ1, HDAC2, HDDC2, HDGF, HNRNPA1, HNRN PA2B1, HNRNPA3, HNRNPC, HNRNPD, HNRNPR, HNRNPU, HPRT1, HSP90AB1, HSPD1 , HSPE1, IARS1, IFRD1, ILF2, IMPDH2, KARS1, KPNA2, KPNB1, LDHA, LSM2, LS M2, LSM7, MAD2L1, MCM2, MCM4, MCM5, MCM6, MCM7, MRPL23, MRPL23, MRPL9, MR PS18B, MYC, NAP1L1, NCBP1, NCBP2, NDUFAB1, NHP2, NME1, NOLC1, NOP16, NOP 56, NPM1, ODC1, ORC2, PA2G4, PABPC1, PABPC4, PCBP1, PCNA, PGK1, PHB, PHB2 , POLD2, POLE3, PPIA, PPM1G, PRDX3, PRDX4, PRPF31, PRPS2, PSMA1, PSMA2, P SMA4, PSMA6, PSMA7, PSMB2, PSMB3, PSMC4, PSMC4, PSMC6, PSMD1, PSMD14, PS MD3, PSMD7, PSMD8, PTGES3, PWP1, RACK1, RAD23B, RAN, RANBP1, RFC4, RNPS1 , RPL14, RPL18, RPL22, RPL34, RPL6, RPLP0, RPS10, RPS2, RPS3, RPS5, RPS6,RRM1, RRP9, RSL1D1, RUVBL2, SERBP1, SET, SF3A1, SF3B3, SLC25A3, SMARCC1, SNRPA, SNRPA1, SNRPB2, SNRPD 1, SNRPD2, SNRPD3, SNRPG, SRM, SRPK1, SRSF1, SRSF2, SRSF3, SRSF7, SSB, SSBP1, SSBP1, STARD7, SYNCRIP, T 15. The composition of claim 14, comprising one or more genes selected from the group consisting of ARDBP, TCP1, TFDP1, TOMM70, TRA2B, TRIM28, TUFM, TXNL4A, TYMS, U2AF1, UBA2, UBE2E1, UBE2L3, USP1, VBP1, VDAC1, VDAC3, XPO1, XPOT, XRCC6, YWHAE, YWHAE, and YWHAQ.

16. A combination comprising a CD40 agonist and a chemotherapeutic agent for use in a method of treating cancer in a human subject, the method comprising: (a) determining the level (cell count) of at least one circulating peripheral blood mononuclear cell (PBMC) type selected from the group consisting of HLA-DR+CCR7+ B cells, cross-presenting dendritic cells (DCs), CD1C+CD141+ cross-presenting DCs, PD-1+ T cells, TCF-1+ T cells, Tbet+ T cells, T helper cells, and 2B4+ CD4 T cells in a biological sample from the subject; or determining a gene signature in a biological sample from the subject and calculating a signature score, wherein the gene signature is a MYC gene signature or an E2F gene signature; and (b) administering the combination to the subject if the level (cell count) of the at least one circulating PBMC type selected from the group consisting of HLA-DR+CCR7+ B cells, cross-presenting dendritic cells (DCs), CD1C+CD141+ cross-presenting DCs, PD-1+ T cells, TCF-1+ T cells, Tbet+ T cells, and T helper cells is increased compared to a control or reference, or if the level (cell count) of circulating 2B4+ CD4 T cells is decreased compared to a control or reference, or if the gene signature score is decreased compared to a control or reference. A combination comprising: