Methods of treating a patient with a PD-1 antagonist

Abstract

The invention relates to a method of treating cancer in a patient in need thereof comprising co-expression of PD-L1 and / or PD-L2. In one aspect, the present disclosure provides a method of treating cancer in a patient comprising determine if a sample from a tumor from the patient expresses a level of PD-L1 or is predicted to express a level of PD-L1 that exceeds a first pre-determined threshold and expresses a level of PD-L2 or is predicted to express a level of PD-L2 that exceeds a second pre-determined threshold; and administering a PD-1 antagonist to the patient if the level or the predicted level of PD-L1 exceeds the first pre-determined threshold and the level or the predicted level of PD-L2 exceeds the second pre-determined threshold.

Description

METHODS OF TREATING A PATIENT WITH A PD-1 ANTAGONISTCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 611,867 filed December 19, 2023, the entire contents of which are incorporated by reference herein.REFERENCE TO SEQUENCE LISTING SUBMITTED ELECTRONICALLY

[0002] The contents of the electronic sequence listing (25889-SL.xml; Size: 36,864 bytes; and Date of Creation: July 11, 2024) are herein incorporated by reference in their entirety.FIELD OF THE INVENTION

[0003] The invention relates generally to a method of treating a patient having a level of PD-L1 and / or PD-L2 that exceed pre-determined thresholds, and selection of a patient for treatment with a PD-1 antagonist where the patient has a level of PD-L1 and / or PD-L2 that exceed predetermined thresholds. Also provided is a method of correlating PD-L1 and / or PD-L2 mRNA data to IHC data and selection of a patient for treatment with a PD-1 antagonist based on the mRNA data and correlation to response rate.BACKGROUND OF THE INVENTION

[0004] PD-1 is recognized as an important player in immune regulation and the maintenance of peripheral tolerance. PD-1 is moderately expressed on naive T, B and NKT cells and up- regulated by T / B cell receptor signaling on lymphocytes, monocytes and myeloid cells (Sharpe et al., The function of programmed cell death 1 and its ligands in regulating autoimmunity and infection. Nature Immunology, 8:239-245 (2007)).

[0005] Two known ligands for PD-1, PD-L1 (B7-H1) and PD-L2 (B7-DC), are expressed in human cancers arising in various tissues. In large sample sets of e.g., ovarian, renal, colorectal, pancreatic, liver cancers and melanoma, it was shown that PD-L1 expression correlated with poor prognosis and reduced overall survival irrespective of subsequent treatment (Dong et al., Nat Med. 8(8):793-800 (2002); Yang et al. Invest Ophthalmol Vis Sci. 49: 2518-2525 (2008); Ghebeh et al. Neoplasia 8: 190-198 (2006); Hamanishi et al., Proc. Natl. Acad. Sci. USA 104: 3360-3365 (2007); Thompson et al.. Cancer 5: 206-211 (2006) ; Nomi et al., Clin. Cancer Research 13:2151-2157 (2007); Ohigashi et al., Clin. Cancer Research 11: 2947-2953 (2005); Inman et al., Cancer 109: 1499-1505 (2007); Shimauchi et al. Int. J. Cancer 121:2585-2590(2007); Gao et al. Clin. Cancer Research 15: 971-979 (2009); Nakanishi J. Cancer Immunol Immunother. 56: 1173- 1182 (2007); and Hino et al., Cancer 00: 1-9 (2010)).

[0006] Similarly, PD-1 expression on tumor infiltrating lymphocytes was found to mark dysfunctional T cells in breast cancer and melanoma (Ghebeh et al., BMC Cancer. 8:5714-15 (2008); Ahmadzadeh et al., Blood 114: 1537-1544 (2009)) and to correlate with poor prognosis in renal cancer (Thompson et al.. Clinical Cancer Research 15: 1757-1761 (2007)). Thus, it has been proposed that PD-L1 expressing tumor cells interact with PD-1 expressing T cells to attenuate T cell activation and evasion of immune surveillance, thereby contributing to an impaired immune response against the tumor.

[0007] Immune checkpoint therapies targeting the PD-1 axis have resulted in groundbreaking improvements in clinical response in multiple human cancers (Brahmer et al., N Engl J Med2012, 366: 2455-65; Garon et al. N Engl J Med 2015, 372: 2018-28; Hamid et al., N Engl J Med2013, 369: 134-44; Robert et al.. Lancet 2014, 384: 1109-17; Robert et al., N Engl J Med 2015, 372: 2521-32; Robert et al., N Engl J Med 2015, 372: 320-30; Topalian et al., N Engl J Med 2012, 366: 2443-54; Topalian et al., J Clm Oncol 2014, 32: 1020-30; Wolchok et al., N Engl J Med 2013, 369: 122-33). Immune therapies targeting the PD-1 axis include monoclonal antibodies directed to the PD-1 receptor (KEYTRUDA® (pembrolizumab), Merck Sharp & Dohme LLC., Rahway, NJ, USA; OPDIVO® (nivolumab), Bristol-Myers Squibb Company, Princeton, NJ, USA, and LIBTAYO® (cemiplimab), Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA) and also those that bind to the PD-L1 ligand (MPDL3280A;TECENTRIQ® (atezolizumab), Genentech, San Francisco, CA, USA; IMFINZI® (durvalumab), AstraZeneca Pharmaceuticals LP, Wilmington, DE; BAVENCIO® (avelumab). Merck KGaA, Darmstadt. Germany; JEMPERL1* (dostarlimab). GlaxoSmithKline Biologies LLC.Philadelphia, PA, USA). Both therapeutic approaches have demonstrated anti-tumor effects in numerous cancer types.SUMMARY

[0008] The present disclosure provides a method of treating cancer in a patient in need thereof comprising co-expression of PD-L1 and / or PD-L2. In one aspect, the present disclosure provides a method of treating cancer in a patient comprising determine if a sample from a tumor from the patient expresses a level of PD-L1 or is predicted to express a level of PD-L1 that exceeds a first pre-determined threshold and expresses a level of PD-L2 or is predicted to express a level of PD- L2 that exceeds a second pre-determined threshold; and administering a PD-1 antagonist to thepatient if the level or the predicted level of PD-L1 exceeds the first pre-determined threshold and the level or the predicted level of PD-L2 exceeds the second pre-determined threshold.

[0009] In another aspect, the present disclosure provides a method of identifying a patient with a PD-1 antagonist comprising (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer and / or (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer; using the first correlation to predict the level of PD-L1 mRNA expression and / or using the second correlation to predict the level of PD-L2 mRNA in the patient’s tumor; and identify ing the patient as a candidate for treatment with the PD-1 antagonist using the predicted PD-L1 mRNA expression and the predicted PD-L2 mRNA to predict the response rate of the patient.

[0010] The summary of the technology' described above is non-limiting and other features and advantages of the technology’ will be apparent from the following detailed description, and from the claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] FIG 1 is a scatterplot showing PD-L2 IHC data versus PD-L1 IHC for responders (•) and non-responders (o) in head and neck cancers.

[0012] FIG. 2 is a scatterplot showing PD-L1 mRNA data versus PD-L1 IHC for responders (•) and non-responders (o) in head and neck cancers.

[0013] FIG. 3 is a scatterplot showing PD-L2 mRNA data versus PD-L2 IHC for responders (•) and non-responders (o) in head and neck cancers.

[0014] FIG. 4 shows a line graph for the ROC analysis of mRNA PD-L1 versus IHC PD-L1+ / - in head and neck cancers.

[0015] FIG. 5 shoyvs a line graph for the ROC analysis of mRNA PD-L2 versus IHC PD-L2+ / - in head and neck cancers.

[0016] FIG. 6 is a scatterplot showing PD-L2 mRNA data versus PD-L1 mRNA for responders (•) and non-responders (o) in head and neck cancers.

[0017] FIG. 7 shoyvs PD-L2 mRNA data versus PD-L1 mRNA for responders (•) and non- responders (c ) in non-head and neck cancers

[0018] FIG 8 is a line graph shoyving IHC PD-L1 data versus mRNA PD-L1 data from KN012 head and neck cancers.

[0019] FIG 9 is a line graph showing IHC PD-L2 data versus mRNA PD-L2 data from KN012 head and neck cancers.

[0020] FIG. 10 is a scatterplot showing PD-L2 mRNA data versus PD-L1 mRNA for responders (•) and non-responders (o) in non-head and neck cancers.

[0021] FIG. 11 is a scatterplot showing PD-L2 mRNA data versus PD-L1 mRNA for responders (•) and non-responders (o) in non-head and neck cancers.

[0022] FIG. 12 shows relative variable importance for each variable.

[0023] FIG. 13 is a boxplot that shows mechanism of action (PD-1 versus PD-L1) versus ORR percent for overall tumor types and key tumor types.

[0024] FIG. 14 shows the ORR difference percent by Strata for PD-1 versus PD-L1 in various tumor indications. FIG. 14 includes data points from biomarker enriched trials.

[0025] FIG. 15 shows the ORR difference percent by Strata for PD-1 versus PD-L1 in various tumor indications. FIG. 15 excludes data points from biomarker enriched trials.

[0026] FIG. 16 shows the approved treatment versus ORR percent by tumor ty pes and overall tumor type.

[0027] FIG 17A-D shows the ORR difference by strata for pembrolizumab versus other approved PD-1 / PD-L1 treatments.

[0028] FIG. 18 shows the ORR difference percent by strata for PD-1 versus PD-L1 in various tumor indications. FIG. 18 uses the data from secondary' analysis evaluation ORR(SOC) using all data points.

[0029] FIG. 19 shows the ORR difference percent by strata for PD-1 versus PD-L1 in various tumor indications. FIG. 19 uses the data from secondary analysis evaluation ORR(SOC) excluding data points from biomarker enriched trials.

[0030] FIG. 20 shows PD-1 / PD-L1 therapy versus ORR(Trt)-ORR(SOC) percent for overall and by key tumor types.

[0031] FIG. 21 A-D shows ORR difference percent by7Strata for pembrolizumab versus various other approved PD-1 / PD-L1 based treatments.DETAILED DESCRIPTION

[0032] In one aspect, the present disclosure provides a method of treating cancer in a patient in need thereof comprising: a. determining if a sample from a tumor from the patient expresses a level of PD-L1 or is predicted to express a level of PD-L1 that exceeds a first pre-determined threshold and expresses a level of PD-L2 or is predicted to express a level of PD- L2 that exceeds a second pre-determined threshold; andb. administering a PD-1 antagonist to the patient if the level or the predicted level of PD-L1 exceeds the first pre-determined threshold and the level or the predicted level of PD-L2 exceeds the second pre-determined threshold.

[0033] In one aspect, the present disclosure provides a method of treating cancer in a patient in need thereof comprising: a. determining if a sample from a tumor from the patient expresses a level of PD-L1 or is predicted to express a level of PD-L1 that exceeds a pre-determined threshold; and b. administering a PD-1 antagonist to the patient if the level or the predicted level of PD-L1 exceeds the pre-determined threshold.

[0034] In one aspect, the present disclosure provides a method of treating cancer in a patient in need thereof comprising: a. determining if a sample from a tumor from the patient expresses a level of PD-L2 or is predicted to express a level of PD-L2 that exceeds a pre-determined threshold; and b. administering a PD-1 antagonist to the patient if the level or the predicted level of PD-L2 exceeds the pre-determined threshold.

[0035] In one aspect, the first pre-determined threshold and the second pre-determined threshold are the same. In a different aspect, the first pre-determined threshold and the second pre-determined threshold are different.

[0036] In one aspect, the level of PD-L1 and the level of PD-L2 are determined using immunohistochemistry .

[0037] In one aspect, the level of PD-L1 and the level of PD-L2 are predicted using a method comprising: a. measuring the level of PD-L1 mRNA in the tumor sample and the level of PD-L2 mRNA in the tumor sample; and b. using a first correlation to predict the level of PD-L1 in the tumor sample from the level of PD-L1 mRNA and using a second correlation to predict the level of PD- L2 in the tumor sample from the level of PD-L2 mRNA.

[0038] In one aspect, the level of PD-L1 is predicted using a method comprising: a. measuring the level of PD-L1 mRNA in the tumor sample; and b. using a correlation to predict the level of PD-L 1 in the tumor sample from the level of PD-L 1 mRNA.

[0039] In one aspect, the level of PD-L2 is predicted using a method comprising:a. measuring the level of PD-L2 mRNA in the tumor sample; and b. using a correlation to predict the level of PD-L2 in the tumor sample from the level of PD-L2 mRNA.

[0040] In one aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer; b. determining the level of PD-L1 mRNA and the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the first correlation to predict the level of PD-L1 and using the second correlation to predict the level of PD-L2 in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist using the predicted PD-L1 mRNA expression and the predicted PD-L2 mRNA to predict the response rate of the patient.

[0041] In another aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer; b. determining the level of PD-L1 mRNA and the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the first correlation to predict the level of PD-L1 and using the second correlation to predict the level of PD-L2 in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L1 exceeds a first pre-determined threshold and the predicted level of PD-L2 exceeds a second pre-determined threshold.

[0042] In another aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising:a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer; b. using the first correlation to predict the level of PD-L1 mRNA expression and using the second correlation to predict the level of PD-L2 mRNA in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L1 exceeds a first pre-determined threshold and the predicted level of PD-L2 exceeds a second pre-determined threshold.

[0043] In a further aspect, the present disclosure provides a method of identify ing a patient for treatment with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden’s index, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index; b. using the first correlation to predict the level of PD-L1 mRNA expression and using the second correlation to predict the level of PD-L2 mRNA in the patient’s tumor; and c. identifying the patient as a candidate for treatment w ith the PD-1 antagonist using the predicted PD-L1 mRNA expression and the predicted PD-L2 mRNA to predict the response rate of the patient.

[0044] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer-using Youden’s index, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index; b. determining the level of PD-L1 mRNA and the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the first correlation to predictthe level of PD-L1 and using the second correlation to predict the level of PD-L2 in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist using the predicted PD-L1 mRNA expression and the predicted PD-L2 mRNA to predict the response rate of the patient.

[0045] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden’s index, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index; b. determining the level of PD-L1 mRNA and the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the first correlation to predict the level of PD-L1 and using the second correlation to predict the level of PD-L2 in the patient’s tumor; and d. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L1 exceeds a first pre-determined threshold and the predicted level of PD-L2 exceeds a second pre-determined threshold.

[0046] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden’s index, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index; b. using the first correlation to predict the level of PD-L1 mRNA expression and using the second correlation to predict the level of PD-L2 mRNA in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L1 exceeds a first pre-determined threshold and the predicted level of PD-L2 exceeds a second pre-determined threshold.

[0047] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising:a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index; b. using the first correlation to predict the level of PD-L1 mRNA expression and using the second correlation to predict the level of PD-L2 mRNA in the patient’s tumor; and c. identifying the patient as a candidate for treatment w ith the PD-1 antagonist using the predicted PD-L1 mRNA expression and the predicted PD-L2 mRNA to predict the response rate of the patient.

[0048] In one aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer; b. using the first correlation to predict the level of PD-L1 mRNA expression and using the second correlation to predict the level of PD-L2 mRNA in the patient’s tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L1 mRNA expression and the predicted PD-L2 mRNA to predict the response rate of the patient.

[0049] In one aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer; b. determining the level of PD-L1 mRNA and the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the first correlation to predictthe level of PD-L1 and using the second correlation to predict the level of PD-L2 in the patient’s tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L1 mRNA expression and the predicted PD-L2 mRNA to predict the response rate of the patient.

[0050] In one aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer; b. determining the level of PD-L1 mRNA and the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the first correlation to predict the level of PD-L1 and using the second correlation to predict the level of PD-L2 in the patient’s tumor; and c. treating the patient with the PD-1 antagonist if the predicted level of PD-L1 exceeds a first pre-determined threshold and the predicted level of PD-L2 exceeds a second pre-determined threshold.

[0051] In one aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer; b. using the first correlation to predict the level of PD-L1 mRNA expression and using the second correlation to predict the level of PD-L2 mRNA in the patient’s tumor; and c. treating the patient with the PD-1 antagonist if the predicted level of PD-L1 exceeds a first pre-determined threshold and the predicted level of PD-L2 exceeds a second pre-determined threshold.

[0052] In a further aspect, the present disclosure provides a method of treating a patient for treatment with a PD-1 antagonist comprising:a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index; b. using the first correlation to predict the level of PD-L1 mRNA expression and using the second correlation to predict the level of PD-L2 mRNA in the patient’s tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L1 mRNA expression and the predicted PD-L2 mRNA to predict the response rate of the patient.

[0053] In a further aspect, the present disclosure provides a method of treating a patient for treatment with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index; b. determining the level of PD-L1 mRNA and the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the first correlation to predict the level of PD-L1 and using the second correlation to predict the level of PD-L2 in the patient's tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L1 mRNA expression and the predicted PD-L2 mRNA to predict the response rate of the patient.

[0054] In a further aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index; b. determining the level of PD-L1 mRNA and the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the first correlation to predictthe level of PD-L1 and using the second correlation to predict the level of PD-L2 in the patient’s tumor; and c. treating the patient with the PD-1 antagonist if the predicted level of PD-L1 exceeds a first pre-determined threshold and the predicted level of PD-L2 exceeds a second pre-determined threshold.

[0055] In a further aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden’s index, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index; b. using the first correlation to predict the level of PD-L1 mRNA expression and using the second correlation to predict the level of PD-L2 mRNA in the patient’s tumor; and c. treating the patient with the PD-1 antagonist if the predicted level of PD-L1 exceeds a first pre-determined threshold and the predicted level of PD-L2 exceeds a second pre-determined threshold.

[0056] In a further aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden’s index, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index; b. using the first correlation to predict the level of PD-L1 mRNA expression and using the second correlation to predict the level of PD-L2 mRNA in the patient’s tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L1 mRNA expression and the predicted PD-L2 mRNA to predict the response rate of the patient.In a further aspect, the present disclosures provides a method of treating a patient with a PD-1 antagonist when the response rate in (c) is above a pre-determined threshold. In yet a further aspect, the present disclosures provides a method of treating a patient with a PD-1 antagonistwhen the response rate in (c) is compared to the standard of care treatment. In yet another aspect, the present disclosures provides a method of treating a patient with a PD-1 antagonist when the response rate in (c) is compared to the standard of care treatment response rate.In one aspect, the PD-L1 expression measured by IHC and PD-L2 expression measured by IHC correlates to a response rate.

[0057] In one aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a level of PD-L 1 mRNA and a level of PD-L2 mRNA in a sample of a tumor from a patient having cancer; b. using a first correlation to predict the level of PD-L 1 in the patient’s tumor from the level of PD-L1 mRNA expression and using the second correlation to predict the level of PD-L2 in the patient's tumor from the level of PD-L2 mRNA expression; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L 1 exceeds a first pre-determined threshold and the predicted level of PD-L2 exceeds a second pre-determined threshold.

[0058] In a further aspect, the first correlation was determined by comparing PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in a population of patients having cancer using Youden’s index, and the second correlation was determined by comparing PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index.

[0059] In one aspect, the present disclosure provides a method of treating cancer in a patient in need thereof comprising: a. determining a level of PD-L1 mRNA and a level of PD-L2 mRNA in a sample of a tumor from the patient; b. using a first correlation to predict the level of PD-L 1 in the patient’s tumor from the level of PD-L1 mRNA expression and using the second correlation to predict the level of PD-L2 in the patient’s tumor from the level of PD-L2 mRNA expression; and c. administering to the patient a PD-1 antagonist if the predicted level of PD-L 1 exceeds a first pre-determined threshold and the predicted level of PD-L2 exceeds a second pre-determined threshold or administering to the patient a different anticancer treatment if the predicted level of PD-L 1 does not exceed the first pre-determined threshold and / or the predicted level of PD-L2 does not exceed the second pre-determined threshold.

[0060] In a further aspect, the first correlation was determined by comparing PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in a population of patients having cancer using Youden’s index, and the second correlation was determined by comparing PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index.

[0061] In one aspect, wherein the first pre-determined threshold and the second pre-determined threshold are the same. In a different aspect, the first pre-determined threshold and the second pre-determined threshold are different.

[0062] In a further aspect, the cancer is selected from the group consisting of: melanoma, nonsmall cell lung cancer (NSCLC), head and neck squamous cell cancer, classical Hodgkin lymphoma, primary mediastinal large B-cell lymphoma, urothelial carcinoma, microsatellite instability -high or mismatch repair deficient cancer; microsatellite instability-high or mismatch repair deficient colorectal cancer, gastric cancer, esophageal cancer, cervical cancer, hepatocellular carcinoma, biliary tract cancer, Merkel cell carcinoma, renal cell carcinoma, endometrial carcinoma; tumor mutational burden-high (TMB-H) cancer, cutaneous squamous cell carcinoma and triple-negative breast cancer. In yet a further aspect, the cancer is classical Hodgkin lymphoma.

[0063] In one aspect, the PD-1 antagonist is nivolumab, atezolizumab, durvalumab, cemiplimab, avelumab, or dostarlimab. In another aspect, the PD-1 antagonist is pembrolizumab or a pembrolizumab variant. In a further aspect, the PD-1 antagonist is pembrolizumab.

[0064] In another aspect, the present disclosure provides a method of selecting a PD-1 or PD- L1 treatment for a patient in need thereof, by analyzing relative variable importance. In another aspect, the present disclosure provides a method of selecting a PD-1 or PD-L1 treatment for a patient in need thereof, by analyzing variables including at least one of the following variables: indication, line of therapy, treatment, biomarker population, MO A. trial size, phase, biomarker type, arm description, prior PD-1 / PD-L1, pediatric, arm type. In yet another aspect, the present disclosure provides a method of selecting a PD-1 or PD-L1 treatment for a patient in need thereof by analyzing variables including indication, line of therapy, and treatment.PD-L1

[0065] In one aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising:a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. determining the level of PD-L1 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L1 in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist using the predicted PD-L1 mRNA expression to predict the response rate of the patient.

[0066] In another aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. determining the level of PD-L1 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L1 in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L1 exceeds a pre-determined threshold.

[0067] In another aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. using the correlation to predict the level of PD-L1 mRNA in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L1 exceeds a pre-determined threshold.

[0068] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden’s index; b. using the correlation to predict the level of PD-L1 mRNA expression in the patient’s tumor; andc. identifying the patient as a candidate for treatment with the PD-1 antagonist using the predicted PD-L1 mRNA expression to predict the response rate of the patient.

[0069] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index; b. determining the level of PD-L1 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L1 in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist using the predicted PD-L1 mRNA expression to predict the response rate of the patient.

[0070] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index; b. determining the level of PD-L 1 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L1 in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L 1 exceeds a pre-determined threshold.

[0071] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden’s index; b. using the first correlation to predict the level of PD-L 1 mRNA expression in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L 1 exceeds a pre-determined threshold.

[0072] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising:a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index; b. using the correlation to predict the level of PD-L1 mRNA expression in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist using the predicted PD-L1 mRNA expression to predict the response rate of the patient.

[0073] In one aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. using the correlation to predict the level of PD-L1 mRNA expression in the patient’s tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L1 mRNA expression to predict the response rate of the patient.

[0074] In one aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. determining the level of PD-L1 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L1 to predict the level of PD-L2 in the patient’s tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L1 mRNA expression to predict the response rate of the patient.

[0075] In one aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. determining the level of PD-L1 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L1 in the patient’s tumor; andc. treating the patient with the PD-1 antagonist if the predicted level of PD-L1 exceeds a pre-determined threshold.

[0076] In one aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. using the correlation to predict the level of PD-L1 mRNA expression in the patient’s tumor; and c. treating the patient with the PD-1 antagonist if the predicted level of PD-L1 exceeds a pre-determined threshold.

[0077] In a further aspect, the present disclosure provides a method of treating a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index; b. using the correlation to predict the level of PD-L1 mRNA expression in the patient’s tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L1 mRNA expression to predict the response rate of the patient.

[0078] In a further aspect, the present disclosure provides a method of treating a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index; b. determining the level of PD-L1 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L1 in the patient’s tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L1 mRNA expression to predict the response rate of the patient.

[0079] In a further aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising:a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index; b. determining the level of PD-L1 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L1 in the patient’s tumor; and c. treating the patient with the PD-1 antagonist if the predicted level of PD-L1 exceeds a pre-determined threshold.

[0080] In a further aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index; b. using the correlation to predict the level of PD-L 1 mRNA expression in the patient’s tumor; and c. treating the patient with the PD-1 antagonist if the predicted level of PD-L1 exceeds a pre-determined threshold.

[0081] In a further aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. determining a correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index; b. using the correlation to predict the level of PD-L1 mRNA expression in the patient’s tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L1 mRNA expression to predict the response rate of the patient.

[0082] In one aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a level of PD-L1 mRNA in a sample of a tumor from a patient having cancer; b. using a correlation to predict the level of PD-L 1 in the patient’s tumor from the level of PD-L 1 mRNA expression; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L 1 exceeds a pre-determined threshold.

[0083] In a further aspect, the first correlation was determined by comparing PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in a population of patients having cancer using Youden’s index.

[0084] In one aspect, the present disclosure provides a method of treating cancer in a patient in need thereof comprising: a. determining a level of PD-L1 mRNA in a sample of a tumor from the patient; b. using a correlation to predict the level of PD-L1 in the patient’s tumor from the level of PD-Ll mRNA expression; and c. administering to the patient a PD-1 antagonist if the predicted level of PD-L1 exceeds a pre-determined threshold or administering to the patient a different anticancer treatment if the predicted level of PD-L1 does not exceed the first predetermined threshold.

[0085] In a further aspect, the first correlation was determined by comparing PD-L1 mRNA expression and PD-LI expression measured by immunohistochemistry (IHC) in a population of patients having cancer using Youden’s index.

[0086] In a further aspect, the cancer is selected from the group consisting of: melanoma, nonsmall cell lung cancer (NSCLC), head and neck squamous cell cancer, classical Hodgkin lymphoma, primary mediastinal large B-cell lymphoma, urothelial carcinoma, microsatellite instability -high or mismatch repair deficient cancer; microsatellite instability-high or mismatch repair deficient colorectal cancer, gastric cancer, esophageal cancer, cervical cancer, hepatocellular carcinoma, biliary tract cancer, Merkel cell carcinoma, renal cell carcinoma, endometrial carcinoma; tumor mutational burden-high (TMB-H) cancer, cutaneous squamous cell carcinoma and triple-negative breast cancer. In yet a further aspect, the cancer is classical Hodgkin lymphoma.

[0087] In one aspect, the PD-1 antagonist is nivolumab, atezolizumab, durvalumab, cemiplimab, avelumab, or dostarlimab. In another aspect, the PD-1 antagonist is pembrolizumab or a pembrolizumab variant. In a further aspect, the PD-1 antagonist is pembrolizumab.

[0088] In another aspect, the present disclosure provides a method of selecting a PD-1 or PD- LI treatment for a patient in need thereof, by analyzing relative variable importance. In another aspect, the present disclosure provides a method of selecting a PD-1 or PD-LI treatment for a patient in need thereof, by analyzing variables including at least one of the following variables: indication, line of therapy, treatment, biomarker population, MO A, trial size, phase, biomarker type, arm description, prior PD-1 / PD-L1, pediatric, arm type. In yet another aspect, the presentdisclosure provides a method of selecting a PD-1 or PD-L1 treatment for a patient in need thereof by analyzing variables including indication, line of therapy, and treatment.PD-L2

[0089] In one aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. determining the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L2 in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist using the predicted PD-L2 mRNA expression to predict the response rate of the patient.

[0090] In another aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. determining the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L2 in the patient's tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L2 exceeds a pre-determined threshold.

[0091] In another aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. using the correlation to predict the level of PD-L2 mRNA in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L2 exceeds a pre-determined threshold.

[0092] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden’s index; b. using the correlation to predict the level of PD-L2 mRNA expression in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist using the predicted PD-L2 mRNA expression to predict the response rate of the patient.

[0093] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden’s index; b. determining the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L2 in the patient’s tumor; and c. identifying the patient as a candidate for treatment w ith the PD-1 antagonist using the predicted PD-L2 mRNA expression to predict the response rate of the patient.

[0094] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden’s index; b. determining the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L2 in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L2 exceeds a pre-determined threshold.

[0095] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index;b. using the first correlation to predict the level of PD-L2 mRNA expression in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L2 exceeds a pre-determined threshold.

[0096] In a further aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden’s index; b. using the correlation to predict the level of PD-L2 mRNA expression in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist using the predicted PD-L2 mRNA expression to predict the response rate of the patient.

[0097] In one aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. using the correlation to predict the level of PD-L2 mRNA expression in the patient’s tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L2 mRNA expression to predict the response rate of the patient.

[0098] In one aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. determining the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L2 to predict the level of PD-L2 in the patient’s tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L2 mRNA expression to predict the response rate of the patient.

[0099] In one aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising:a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. determining the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L2 in the patient’s tumor; and c. treating the patient with the PD-1 antagonist if the predicted level of PD-L2 exceeds a pre-determined threshold.

[0100] In one aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer; b. using the correlation to predict the level of PD-L2 mRNA expression in the patient’s tumor; and c. treating the patient with the PD-1 antagonist if the predicted level of PD-L2 exceeds a pre-determined threshold.

[0101] In a further aspect, the present disclosure provides a method of treating a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index; b. using the correlation to predict the level of PD-L2 mRNA expression in the patient’s tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L2 mRNA expression to predict the response rate of the patient.

[0102] In a further aspect, the present disclosure provides a method of treating a patient for treatment with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden’s index; b. determining the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L2 in the patient’s tumor; andc. treating the patient with the PD-1 antagonist using the predicted PD-L2 mRNA expression to predict the response rate of the patient.

[0103] In a further aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden’s index; b. determining the level of PD-L2 mRNA in a sample of a tumor from a patient having cancer and using the correlation to predict the level of PD-L2 in the patient’s tumor; and c. treating the patient with the PD-1 antagonist if the predicted level of PD-L2 exceeds a pre-determined threshold.

[0104] In a further aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index; b. using the correlation to predict the level of PD-L2 mRNA expression in the patient’s tumor; and c. treating the patient with the PD-1 antagonist if the predicted level of PD-L2 exceeds a pre-determined threshold.

[0105] In a further aspect, the present disclosure provides a method of treating a patient with a PD-1 antagonist comprising: a. determining a correlation between PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in population of patients having cancer using Youden's index; b. using the correlation to predict the level of PD-L2 mRNA expression in the patient’s tumor; and c. treating the patient with the PD-1 antagonist using the predicted PD-L2 mRNA expression to predict the response rate of the patient.

[0106] In one aspect, the present disclosure provides a method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a level of PD-L2 mRNA in a sample of a tumor from a patient having cancer;b. using a correlation to predict the level of PD-L2 in the patient's tumor from the level of PD-L2 mRNA expression; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L2 exceeds a pre-determined threshold.

[0107] In a further aspect, the first correlation was determined by comparing PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in a population of patients having cancer using Youden’s index.

[0108] In one aspect, the present disclosure provides a method of treating cancer in a patient in need thereof comprising: a. determining a level of PD-L2 mRNA in a sample of a tumor from the patient; b. using a correlation to predict the level of PD-L2 in the patient’s tumor from the level of PD-L2 mRNA expression; and c. administering to the patient a PD-1 antagonist if the predicted level of PD-L2 exceeds a pre-determined threshold or administering to the patient a different anticancer treatment if the predicted level of PD-L2 does not exceed the first predetermined threshold.

[0109] In a further aspect, the first correlation was determined by comparing PD-L2 mRNA expression and PD-L2 expression measured by immunohistochemistry (IHC) in a population of patients having cancer using Youden’s index.

[0110] In a further aspect, the cancer is selected from the group consisting of: melanoma, nonsmall cell lung cancer (NSCLC), head and neck squamous cell cancer, classical Hodgkin lymphoma, primary mediastinal large B-cell lymphoma, urothelial carcinoma, microsatellite instability -high or mismatch repair deficient cancer; microsatelhte instability-high or mismatch repair deficient colorectal cancer, gastric cancer, esophageal cancer, cervical cancer, hepatocellular carcinoma, biliary tract cancer, Merkel cell carcinoma, renal cell carcinoma, endometrial carcinoma; tumor mutational burden-high (TMB-H) cancer, cutaneous squamous cell carcinoma and triple-negative breast cancer. In yet a further aspect, the cancer is classical Hodgkin lymphoma.[OHl] In one aspect, the PD-1 antagonist is nivolumab, atezolizumab, durvalumab, cemiplimab, avelumab, or dostarlimab. In another aspect, the PD-1 antagonist is pembrolizumab or a pembrolizumab variant. In a further aspect, the PD-1 antagonist is pembrolizumab.

[0112] In another aspect, the present disclosure provides a method of selecting a PD-1 or PD- L1 treatment for a patient in need thereof, by analyzing relative variable importance. In another aspect, the present disclosure provides a method of selecting a PD-1 or PD-L1 treatment for apatient in need thereof, by analyzing variables including at least one of the following variables: indication, line of therapy, treatment, biomarker population, MOA, trial size, phase, biomarker type, arm description, prior PD-1 / PD-L1, pediatric, arm type. In yet another aspect, the present disclosure provides a method of selecting a PD-1 or PD-L1 treatment for a patient in need thereof by analyzing variables including indication, line of therapy, and treatment.Definitions

[0113] Listed below are definitions of various terms used herein. These definitions apply to the terms as they are used throughout this specification and claims, unless otherwise limited in specific instances, either individually or as part of a larger group.

[0114] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Generally, the nomenclature used herein and the laboratory procedures in cell culture, molecular genetics, organic chemistry, and peptide chemistry are those well-known and commonly employed in the art.

[0115] As used herein, the articles “a” and “an” refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element. Furthermore, use of the term “including” as well as other forms, such as “include,” “includes,” and “included,” is not limiting.

[0116] As used herein, the term “about” in quantitative terms refers to plus or minus 10% of the value it modifies (rounded up to the nearest whole number if the value is not sub-dividable, such as a number of molecules or nucleotides).

[0117] All ranges disclosed herein are inclusive of the recited endpoint and independently combinable (for example, the range of “from 50 mg to 500 mg” is inclusive of the endpoints, 50 mg and 500 mg, and all the intermediate values). The endpoints of the ranges and any values disclosed herein are not limited to the precise range or value; they are sufficiently imprecise to include values approximating these ranges and / or values.

[0118] As used herein, the term “comprising” may include the embodiments “consisting of’ and “consisting essentially of.” The terms “comprise(s),” “include(s),” “having,” “has,” “may,” “contain(s),” and variants thereof, as used herein, are intended to be open-ended transitional phrases, terms, or words that require the presence of the named ingredients / steps and permit the presence of other ingredients / steps. However, such description should be construed as also describing compositions or processes as “consisting of’ and “consisting essentially of’ the enumerated components, which allows the presence of only the named components orcompounds, along with any acceptable earners or fluids, and excludes other components or compounds.

[0119] As used herein, the terms “at least one” item or “one or more” item each include a single item selected from the list as well as mixtures of two or more items selected from the list.

[0120] The terms “administration” or “administer” refers to the act of injecting or otherwise physically delivering a substance as it exists outside the body (e.g., an anti-PD-1 antibody) into a patient or subject, such as by oral, mucosal, intradermal, intravenous, subcutaneous, intramuscular delivery, and / or any other methods of physical delivery described herein or known in the art.

[0121] The term “subject” (alternatively “patient”) as used herein refers to a mammal that has been the object of treatment, observation, or experiment. The mammal may be male or female. The mammal may be one or more selected from the group consisting of humans, bovine (e.g., cows), porcine (e.g, pigs), ovine (e.g., sheep), capra (e.g, goats), equine (e.g, horses), canine (e.g. domestic dogs), feline (e.g, house cats), lagomorph (e.g. rabbits), rodent (e.g.. rats or mice), and Procyon lotor (e.g, raccoons). In particular embodiments, the subject is human.

[0122] The term “subject in need thereof’ as used herein refers to a subject diagnosed with or suspected of having cancer as defined herein.

[0123] As used herein, the term “antibody” refers to any form of immunoglobulin molecule that exhibits the desired biological or binding activity. Thus, it is used in the broadest sense and specifically covers, but is not limited to, monoclonal antibodies (including full length monoclonal antibodies), polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies), humanized, fully human antibodies, and chimeric antibodies, and may include post-translational modifications thereof (e.g., C-terminal Lysine clipping in the heavy chain, conversion of glutamine or glutamic acid to pyroglutamate) that may occur when an antibody is recombinantly expressed in host cells (e.g., CHO cells), or during purification / storage. “Parental antibodies” are antibodies obtained by exposure of an immune system to an antigen prior to modification of the antibodies for an intended use. such as humanization of an antibody for use as a human therapeutic. As used herein, the term “antibody” encompasses not only intact polyclonal or monoclonal antibodies, but also, unless otherwise specified, fusion proteins comprising an antigen binding fragment thereof that competes with the intact antibody for specific.

[0124] In general, the basic antibody structural unit comprises a tetramer. Each tetramer includes two identical pairs of polypeptide chains, each pair having one “light” (about 25 kDa) and one “heavy” chain (about 50-70 kDa). The amino-terminal portion of each chain includes a variable region of about 100 to 110 or more amino acids primarily responsible for antigenrecognition. The variable regions of each hght / heavy chain pair form the antibody binding site. Thus, in general, an intact antibody has two binding sites. The carboxy-terminal portion of the heavy chain may define a constant region primarily responsible for effector function. Typically, human light chains are classified as kappa and lambda light chains. Furthermore, human heavy' chains are typically classified as mu, delta, gamma, alpha, or epsilon, and define the antibody’s isotype as IgM, IgD, IgG, IgA, and IgE, respectively. Within light and heavy chains, the variable and constant regions are joined by a “J” region of about 12 or more amino acids, with the heavy chain also including a “D” region of about 10 more amino acids. See generally, Fundamental Immunology Ch. 7 (Paul, W., ed., 2nd ed. Raven Press, N.Y. (1989).

[0125] “Variable regions” or “V region” or “V chain” as used herein means the segment of IgG chains which is variable in sequence between different antibodies. A “variable region” of an antibody refers to the variable region of the antibody light chain or the variable region of the antibody heavy chain, either alone or in combination. The variable region of the heavy chain may be referred to as “VH.” The variable region of the light chain may be referred to as “VL.”

[0126] Typically, the variable regions of both the heavy and light chains comprise three hypervariable regions, also called complementarity determining regions (CDRs), which are located within relatively conserved framework regions (FR). The CDRs are usually aligned by the framework regions, enabling binding to a specific epitope. In general, from N-terminal to C- terminal, both light and heavy chains variable domains comprise FR1, CDR1, FR2, CDR2, FR3, CDR3, and FR4. As referred to herein the light chain CDRs are CDRL1, CDRL2 and CDRL3, respectively, and the heavy chain CDRs are CDRH1, CDRH2 and CDRH3, respectively. The assignment of amino acids to each domain is, generally, in accordance with the definitions of Sequences of Proteins of Immunological Interest. Kabat, et al. ; National Institutes of Health. Bethesda, Md.; 5th ed.; NIH Publ. No. 91-3242 (1991); Kabat (1978) Adv. Prot. Chem. 32: 1-75; Kabat, etal., (1977) J. Biol. Chem. 252:6609-6616; Chothia, et l., (1987) J Mol. Biol. 196:901- 917 or Chothia, et al., (1989) Nature 342:878-883.

[0127] A “CDR” refers to one of three hypervariable regions (Hl, H2, or H3) within the nonframework region of the antibody VH f>-sheet framework, or one of three hypervariable regions (LI, L2, or L3) within the non-framework region of the antibody VL 0-sheet framework. Accordingly, CDRs are variable region sequences interspersed within the framework region sequences. CDR regions are well known to those skilled in the art and have been defined by. for example, Kabat as the regions of most hypervariability within the antibody variable domains. CDR region sequences also have been defined structurally by Chothia as those residues that are not part of the conserved (5 -sheet framework, and thus are able to adapt to differentconformations. Both terminologies are well recognized in the art. CDR region sequences have also been defined by AbM, Contact, and IMGT. The positions of CDRs within a canonical antibody variable region have been determined by comparison of numerous structures (Al- Lazikani et al., 1997, J. Mol. Biol. 273:927-48; Morea et a / ., 2000, Methods 20:267-79). Because the number of residues within a hypervariable region varies in different antibodies, additional residues relative to the canonical positions are conventionally numbered with a, b, c and so forth next to the residue number in the canonical variable region numbering scheme (Al-Lazikani et al., supra). Such nomenclature is similarly well known to those skilled in the art. Correspondence between the numbering system, including, for example, the Kabat numbering and the IMGT unique numbering system, is well known to one skilled in the art and shown below in Table 1. In some embodiments, the CDRs are as defined by the Kabat numbering system. In other embodiments, the CDRs are as defined by the IMGT numbering system. In yet other embodiments, the CDRs are as defined by the AbM numbering system. In still other embodiments, the CDRs are as defined by the Chothia numbering system. In yet other embodiments, the CDRs are as defined by the Contact numbering system.Table 1. Correspondence between the CDR Numbering Systems

[0128] "‘Chimeric antibody” refers to an antibody in which a portion of the heavy and / or light chain contains sequences derived from a particular species (e.g, human) or belonging to a particular antibody class or subclass, while the remainder of the chain(s) is derived from another species (e.g, mouse) or belonging to another antibody class or subclass, as well as fragments of such antibodies, so long as they exhibit the desired biological activity.

[0129] “Human antibody” refers to an antibody that comprises human immunoglobulin protein sequences or derivatives thereof. A human antibody may contain murine carbohydrate chains ifproduced in a mouse, in a mouse cell, or in a hybridoma derived from a mouse cell. Similarly, “mouse antibody” or “rat antibody” refer to an antibody that comprises only mouse or rat immunoglobulin sequences or derivatives thereof, respectively.

[0130] “Humanized antibody” refers to forms of antibodies that contain sequences from nonhuman (e.g., murine) antibodies as well as human antibodies. Such antibodies contain minimal sequence derived from non-human immunoglobulin. In general, the humanized antibody will comprise substantially all of at least one, and typically two, variable domains, in which all or substantially all of the hypervariable loops correspond to those of a non-human immunoglobulin and all or substantially all of the FR regions are those of a human immunoglobulin sequence. The humanized antibody optionally also will comprise at least a portion of an immunoglobulin constant region (Fc), typically that of a human immunoglobulin. The prefix “hum”, “hu” or “h” may be added to antibody clone designations when necessary to distinguish humanized antibodies from parental rodent antibodies. The humanized forms of rodent antibodies will generally comprise the same CDR sequences of the parental rodent antibodies, although certain amino acid substitutions may be included to increase affinity, increase stability of the humanized antibody, or for other reasons.

[0131] “Monoclonal antibody” or “mAb” or “Mab”. as used herein, refers to a population of substantially homogeneous antibodies, z.e., the antibody molecules comprising the population are identical in amino acid sequence except for possible naturally occurring mutations that may be present in minor amounts. In contrast, conventional (polyclonal) antibody preparations typically include a multitude of different antibodies having different amino acid sequences in their variable domains, particularly their CDRs, which are often specific for different epitopes. The modifier “monoclonal” indicates the character of the antibody as being obtained from a substantially homogeneous population of antibodies, and is not to be construed as requiring production of the antibody by any particular method. For example, the monoclonal antibodies to be used in accordance with the present disclosure may be made by the hybridoma method first described by Kohler et al. (1975) Nature 256: 495, or may be made by recombinant DNA methods {see. e.g., U.S. Pat. No. 4,816,567). The “monoclonal antibodies” may also be isolated from phage antibody libraries using the techniques described in Clackson et al. (1991) Nature 352: 624-628 and Marks et al. (1991) J. Mol. Biol. 222: 581-597, for example. See also Presta (2005) J. Allergy Clin. Immunol. 116:731.

[0132] As used herein, unless otherwise indicated, “antibody fragment” or “antigen binding fragment” refers to a fragment of an antibody that retains the ability' to bind specifically to the antigen, e.g., fragments that retain one or more CDR regions and the ability to bind specificallyto the antigen. An antibody that “specifically binds to7’ PD-1 is an antibody that exhibits preferential binding to PD-1 (as appropriate) as compared to other proteins, but this specificity does not require absolute binding specificity. An antibody is considered “specific” for its intended target if its binding is determinative of the presence of the target protein in a sample, e.g., without producing undesired results such as false positives. Antibodies, or binding fragments thereof, will bind to the target protein with an affinity that is at least two-fold greater, preferably at least ten times greater, more preferably at least 20-times greater, and most preferably at least 100-times greater than the affinity with non-target proteins.

[0133] Antigen binding portions include, for example, Fab, Fab’, F(ab’)2, Fd, Fv, fragments including CDRs, and single chain variable fragment antibodies (scFv), and polypeptides that contain at least a portion of an immunoglobulin that is sufficient to confer specific antigen binding to the antigen (e.g., PD-1). An antibody includes an antibody of any class, such as IgG, IgA, or IgM (or sub-class thereof), and the antibody need not be of any particular class. Depending on the antibody amino acid sequence of the constant region of its heavy chains, immunoglobulins can be assigned to different classes. There are five major classes of immunoglobulins: IgA, IgD, IgE, IgG, and IgM, and several of these may be further divided into subclasses (isotypes), e.g, IgGl, IgG2, IgG3, IgG4, IgAl, and IgA2. The heavy -chain constant regions that correspond to the different classes of immunoglobulins are called alpha, delta, epsilon, gamma, and mu, respectively. The subunit structures and three-dimensional configurations of different classes of immunoglobulins are well known.

[0134] An "antigen" is a structure to which an antibody can selectively bind. A target antigen may be a polypeptide, carbohydrate, nucleic acid, lipid, hapten, or other naturally occurring or synthetic compound. In some embodiments, the target antigen is a polypeptide. In certain embodiments, an antigen is associated with a cell, for example, is present on or in a cell, for example, a cancer cell.

[0135] An "intact" antibody is one comprising an antigen-binding site as well as a CL and at least heavy chain constant regions, CHI, CH2 and CH3. The constant regions may include human constant regions or amino acid sequence variants thereof. In certain embodiments, an intact antibody has one or more effector functions.

[0136] As used herein, the term “immune response” relates to any one or more of the following: specific immune response, non-specific immune response, both specific and nonspecific response, innate response, primary immune response, adaptive immunity, secondary immune response, memory immune response, immune cell activation, immune cell-proliferation, immune cell differentiation, and cytokine expression.

[0137] The therapeutic agents and compositions provided by the present disclosure can be administered via any suitable enteral route or parenteral route of administration. The term “enteral route” of administration refers to the administration via any part of the gastrointestinal tract. Examples of enteral routes include oral, mucosal, buccal, and rectal route, or intragastric route. “Parenteral route” of administration refers to a route of administration other than enteral route. Examples of parenteral routes of administration include intravenous, intramuscular, intradermal, intraperitoneal, intratumor, intravesical, intraarterial, intrathecal, intracapsular, intraorbital, intracardiac, transtracheal, intraarticular, subcapsular, subarachnoid, intraspinal, epidural and intrastemal. subcutaneous, or topical administration. The therapeutic agents and compositions of the disclosure can be administered using any suitable method, such as by oral ingestion, nasogastric tube, gastrostomy tube, injection, infusion, implantable infusion pump, and osmotic pump. A suitable route and method of administration may vary depending on a number of factors such as the specific therapeutic agent being used, the rate of absorption desired, specific formulation or dosage form used, type or severity of the disorder being treated, the specific site of action, and conditions of the patient, and can be readily selected by a person skilled in the art.

[0138] “Chemotherapeutic agent” is a chemical compound useful in the treatment of cancer. Classes of chemotherapeutic agents include, but are not limited to: alkylating agents, antimetabolites, kinase inhibitors, spindle poison plant alkaloids, cytoxic / antitumor antibiotics, topoisomerase inhibitors, photosensitizers, anti-estrogens and selective estrogen receptor modulators (SERMs), anti-progesterones, estrogen receptor down-regulators (ERDs), estrogen receptor antagonists, leutinizing hormone-releasing hormone agonists, anti-androgens, aromatase inhibitors, EGFR inhibitors. VEGF inhibitors, and anti-sense oligonucleotides that inhibit expression of genes implicated in abnormal cell proliferation or tumor growth. Chemotherapeutic agents useful in the treatment methods of the invention include cytostatic and / or cytotoxic agents.

[0139] The term “variant” when used in relation to an antibody (e g., an anti-PD-1 antibody) or an amino acid region within the antibody may refer to a peptide or polypeptide comprising one or more (such as, for example, about 1 to about 25, about 1 to about 20, about 1 to about 15, about 1 to about 10, or about 1 to about 5) amino acid sequence substitutions, deletions, and / or additions as compared to a native or unmodified sequence. For example, a variant of an anti-PD-1 antibody may result from one or more (such as, for example, about 1 to about 25, about 1 to about 20. about 1 to about 15, about 1 to about 10, or about 1 to about 5) changes to an amino acid sequence of a native or previously unmodified anti-PD-1 antibody. Variants may be naturally occurring or may be artificially constructed. Polypeptide variants may be prepared from thecorresponding nucleic acid molecules encoding the variants. In specific embodiments, an antibody variant (e.g, an anti-PD-1 antibody variant) at least retains the antibody functional activity. In some embodiments, an anti-PD-1 antibody variant binds to PD-1 and / or is antagonistic to PD-1 activity.

[0140] “Conservatively modified variants’7or “conservative substitution” refers to substitutions of amino acids in a protein with other amino acids having similar characteristics (e.g, charge, side-chain size, hydrophobicity / hydrophilicity, backbone conformation and rigidity', etc.), such that the changes can frequently be made without altering the biological activity or other desired property of the protein, such as antigen affinity and / or specificity. Those of skill in this art recognize that, in general, single amino acid substitutions in non-essential regions of a polypeptide do not substantially alter biological activity' (see, e.g., Watson et al. (1987) Molecular Biology of the Gene, The Benjamin / Cummings Pub. Co., p. 224 (4th Ed.)). In addition, substitutions of structurally or functionally similar amino acids are less likely to disrupt biological activity. Exemplary conservative substitutions are set forth in Table 2 below.Table 2. Exemplary Conservative Amino Acid Substitutions

[0141] "‘Homology” refers to sequence similarity between two polypeptide sequences when they are optimally aligned. When a position in both of the two compared sequences is occupied by the same amino acid monomer subunit, e.g., if a position in a light chain CDR of two different Abs is occupied by alanine, then the two Abs are homologous at that position. The percent of homology is the number of homologous positions shared by the two sequences divided by the total number of positions compared * 100. For example, if 8 of 10 of the positions in two sequences are matched when the sequences are optimally aligned then the two sequences are 80% homologous. Generally, the comparison is made when two sequences are aligned to give maximum percent homology. For example, the comparison can be performed by a BLAST algorithm wherein the parameters of the algorithm are selected to give the largest match between the respective sequences over the entire length of the respective reference sequences.

[0142] The following references relate to BLAST algorithms often used for sequence analysis: BLAST ALGORITHMS: Altschul, S.F., etal., (1990) J. Mol. Biol. 215:403-410; Gish, W„ et al., (1993) Nature Genet. 3:266-272; Madden. T.L., et al., (1996) Meth. Enzymol. 266: 131-141; Altschul. S.F., et al.. (1997) Nucleic Acids Res. 25:3389-3402; Zhang, J., et al., (1997) Genome Res. 7:649-656; Wootton, I.C., et al., (1993) Comput. Chem. 17: 149-163; Hancock, J.M. et al., (1994) Comput. Appl. Biosci. 10:67-70; ALIGNMENT SCORING SYSTEMS: Dayhoff, M.O., et al., “A model of evolutionary change in proteins.” in Atlas of Protein Sequence and Structure. (1978) vol. 5, suppl. 3. M.O. Dayhoff (ed.), pp. 345-352, Natl. Biomed. Res. Found.. Washington, DC; Schwartz, R.M., et al., “Matrices for detecting distant relationships.” in Atlas of Protein Sequence and Structure, (1978) vol. 5, suppl. 3.” M.O. Dayhoff (ed.), pp. 353-358, Natl. Biomed. Res. Found., Washington, DC; Altschul, S.F., (1991) J. Mol. Biol. 219:555-565; States, D.J., et al., (1991) Methods 3:66-70; Henikoff, S., et al.. (1992) Proc. Natl. Acad. Sci. USA 89: 10915-10919; Altschul, S.F., et al., (1993) J. Mol. Evol. 36:290-300; ALIGNMENT STATISTICS: Karlin, S„ et al., (1990) Proc. Natl. Acad. Sci. USA 87:2264-2268; Karlin, S„ et al., (1993) Proc. Natl. Acad. Sci. USA 90:5873-5877; Dembo, A., et al., (1994) Ann. Prob. 22:2022-2039; and Altschul. S.F. “Evaluating the statistical significance of multiple distinct localalignments.” in Theoretical and Computational Methods in Genome Research (S. Suhai. ed.), (1997) pp. 1-14, Plenum, New York.

[0143] “RECIST 1. 1 Response Criteria” as used herein means the definitions set forth in Eisenhauer, E.A. et al., Eur. J. Cancer 45:228-247 (2009) for target lesions or nontarget lesions, as appropriate based on the context in which response is being measured.

[0144] “Sustained response” means a sustained therapeutic effect after cessation of treatment as described herein. In some embodiments, the sustained response has a duration that is at least the same as the treatment duration, or at least 1.5, 2.0, 2.5 or 3 times longer than the treatment duration.

[0145] “Non-responder patient”, when referring to a specific anti-tumor response to a treatment described herein, means the patient did not exhibit an anti-tumor response.

[0146] “Responder patient” when referring to a specific anti-tumor response to a treatment described herein, means the patient exhibited an anti-tumor response.

[0147] “Treat” or “treating” cancer as used herein means to administer an anti-human PD-1 monoclonal antibody or antigen binding fragment thereof, to a subject having cancer or diagnosed with cancer to achieve at least one positive therapeutic effect, such as, for example, reduced number of cancer cells, reduced tumor size, reduced rate of cancer cell infiltration into peripheral organs, or reduced rate of tumor metastasis or tumor growth, comprising administration by oral, mucosal, intradermal, intravenous, subcutaneous, intramuscular delivery, and / or any other methods of physical delivery described herein or known in the art. Typically, the agent(s) of the treatment method are administered in an amount effective to alleviate one or more disease symptoms in the treated subject or population, whether by inducing the regression of or inhibiting the progression of such symptom(s) by any clinically measurable degree. The amount of the agent(s) of the treatment method that is effective to alleviate any particular disease symptom may vary according to factors such as the disease state, age, and weight of the patient, and the ability of the therapeutic combination to elicit a desired response in the subject. Whether a disease symptom has been alleviated can be assessed by any clinical measurement typically used by physicians or other skilled healthcare providers to assess the severity or progression status of that symptom. “Treatment” may include one or more of the following: inducing / increasing an antitumor immune response, decreasing the number of one or more tumor markers, halting or delaying the growth of a tumor or blood cancer or progression of disease such as cancer, stabilization of disease, inhibiting the growth or survival of tumor cells, eliminating or reducing the size of one or more cancerous lesions or tumors, decreasing the level of one or more tumor markers, ameliorating or abrogating the clinical manifestations of disease, reducing theseverity or duration of the clinical symptoms, prolonging the survival or patient relative to the expected survival in a similar untreated patient, and inducing complete or partial remission of a cancerous condition, wherein the disease is cancer, more specifically, non-small cell lung cancer.

[0148] As used herein, “adjuvant treatment'’ means adjunct therapy, adjuvant care, or augmentation therapy, is a therapy that is given in addition to the primary or initial therapy to maximize its effectiveness. Adjuvant treatment is treatment given after the main treatment to reduce the chance of cancer.

[0149] As used herein, “neoadjuvant treatment” is a treatment given as a first step to provide immediate disease control by killing cancer cells at the primary tumor site and those that have metastasized from it. An added benefit of neoadjuvant treatment may be a decrease in the size of the tumor to be resected, the main treatment.

[0150] The amount of a therapeutic agent that is effective to alleviate any particular disease symptom may vary according to factors such as the disease state, age, and weight of the patient, and the ability of the drug to elicit a desired response in the subject. Whether a disease symptom has been alleviated can be assessed by any clinical measurement typically used by physicians or other skilled healthcare providers to assess the severity or progression status of that symptom.

[0151] Positive therapeutic effects in cancer can be measured in a number of ways (See, W. A. Weber. J. Nucl. Med. 50: 1S-10S (2009)). For example, with respect to tumor growth inhibition, according to NCI standards, a T / C 42% is the minimum level of anti-tumor activity. A T / C < 10% is considered a high anti-tumor activity7level, with T / C (%) = Median tumor volume of the treated / Median tumor volume of the control x 100. In some embodiments, the treatment achieved by a therapy of the disclosure is any of PR, CR, OR, PFS. DFS. and OS. PFS, also referred to as “Time to Tumor Progression” indicates the length of time during and after treatment that the cancer does not grow, and includes the amount of time patients have experienced a CR or PR, as well as the amount of time patients have experienced SD. DFS refers to the length of time during and after treatment that the patient remains free of disease. OS refers to a prolongation in life expectancy as compared to naive or untreated individuals or patients. In some embodiments, response to a therapy of the disclosure is any of PR, CR, PFS, DFS, or OR that is assessed using RECIST 1.1 response criteria. The treatment regimen for a therapy of the disclosure that is effective to treat a cancer patient may vary according to factors such as the disease state, age, and weight of the patient, and the ability of the therapy to elicit an anti-cancer response in the subject. While an embodiment of any of the aspects of the disclosure may not be effective in achieving a positive therapeutic effect in every subject, it should do so in a statistically significant number of subjects as determined by any statistical test known in the art such as the Student’s t-test, thechi2-test. the U-test according to Mann and Whitney, the Kruskal-Wallis test (H-test), Jonckheere-Terpstra-test and the Wilcoxon-test.

[0152] “PD-1 antagonist” means any chemical compound or biological molecule that blocks binding of PD-L1 expressed on a cancer cell to PD-1 expressed on an immune cell (T cell, B cell or NKT cell) and preferably also blocks binding of PD-L2 expressed on a cancer cell to the immune-cell expressed PD-1. Alternative names or synonyms for PD-1 and its ligands include: PDCD1, PD1, CD279 and SLEB2 for PD-1; PDCD1L1, PDL1, B7H1, B7-4, CD274 and B7-H for PD-L1; and PDCD1L2, PDL2, B7-DC, Btdc and CD273 for PD-L2. In any of the treatment methods, medicaments and uses of the invention in which a human individual is being treated, the PD-1 antagonist blocks binding of human PD-L1 to human PD-1, and preferably blocks binding of both human PD-L1 and PD-L2 to human PD-1. Human PD-1 amino acid sequences can be found in NCBI Locus No.: NP 005009. Human PD-L1 and PD-L2 amino acid sequences can be found in NCBI Locus No.: NP_054862 and NP_079515, respectively.

[0153] “Pembrolizumab” (formerly known as MK-3475, SCH 900475 and lambrolizumab) alternatively referred to herein as “pembro,” is a humanized IgG4 mAh with the structure described in WHO Drug Information, Vol. 27, No. 2, pages 161-162 (2013) and which comprises the heavy and light chain amino acid sequences and CDRs described in Table 3. Pembrolizumab has been approved by the U.S. FDA as described in the Prescribing Information for KEYTRUDA" (Merck & Co., Inc., Rahway, NJ USA; initial U.S. approval 2014, updated February 2023). The term pembrolizumab includes mAb’s having a structure as described above (L / .) but which do not include the C-terminal lysine in the heavy chain.

[0154] "‘Pembrolizumab variant” as used herein means a monoclonal antibody that comprises heavy chain and light chain sequences that are identical to those in pembrolizumab, except for having three, two or one conservative amino acid substitutions at positions that are located outside of the light chain CDRs and six, five, four, three, two or one conservative amino acid substitutions that are located outside of the heavy chain CDRs, e.g., the variant positions are located in the FR regions or the constant region, and optionally has a deletion of the C-terminal lysine residues of the heavy chain. In other words, pembrolizumab and a pembrolizumab variant comprise identical CDR sequences, but differ from each other due to having a conservative amino acid substitution at no more than three or six other positions in their full length light and heavy chain sequences, respectively. A pembrolizumab variant is substantially the same as pembrolizumab with respect to the following properties: binding affinity to PD-1 and ability to block the binding of each of PD-L1 and PD-L2 to PD-1.

[0155] ‘'Platinum-containing chemotherapy’7(also known as platins) refers to the use of chemotherapeutic agent(s) used to treat cancer that are coordination complexes of platinum. Platinum-containing chemotherapeutic agents are alkydating agents that crosslink DNA, resulting in ineffective DNA mismatch repair and generally leading to apoptosis. Examples of platins include cisplatin, carboplatin, and oxaliplatin.PD-1 antagonists or anti-human PD-1 monoclonal antibodies useful in the invention

[0156] Examples of mAbs that bind to human PD-1, useful in the treatment methods, compositions, and uses of the invention, are described in US 7,521.051, US 8,008,449. and US 8,354,509. Specific anti -human PD-1 mAbs useful as the PD-1 antagonist in the treatment methods, compositions, and uses of the invention include: pembrolizumab (formerly known as MK-3475, SCH 900475 and lambrolizumab), a humanized IgG4 mAb with the structure described in WHO Drug Information, Vol. 27, No. 2, pages 161-162 (2013) and which comprises the heavy and light chain amino acid sequences shown in Figure 7. and the humanized antibodies h409Al l, h409A16 and h409A17, which are described in WO 2008 / 156712.

[0157] Provided herein are PD-1 antagonists or anti -human PD-1 monoclonal antibodies that can be used in any of the methods, compositions, kits, and uses disclosed herein, including any chemical compound or biological molecule that blocks binding of PD-L1 to PD-1 and preferably also blocks binding of PD-L2 to PD-1.

[0158] Any monoclonal antibodies that bind to a PD-1 polypeptide, a PD-1 polypeptide fragment, a PD-1 peptide, or a PD-1 epitope and block the interaction between PD-1 and its ligand PD-L1 or PD-L2 can be used. In some embodiments, the anti -human PD-1 monoclonal antibody binds to a PD-1 polypeptide, a PD-1 polypeptide fragment, a PD-1 peptide, or a PD-1 epitope and blocks the interaction between PD-1 and PD-L1. In other embodiments, the antihuman PD-1 monoclonal antibody binds to a PD-1 polypeptide, a PD-1 polypeptide fragment, a PD-1 peptide, or a PD-1 epitope and blocks the interaction between PD-1 and PD-L2. In yet other embodiments, the anti -human PD-1 monoclonal antibody binds to a PD-1 polypeptide, a PD-1 polypeptide fragment, a PD-1 peptide, or a PD-1 epitope and blocks the interaction between PD-1 and PD-L1 and the interaction between PD-1 and PD-L2.

[0159] Any monoclonal antibodies that bind to a PD-L1 polypeptide, a PD-L1 polypeptide fragment, a PD-L1 peptide, or a PD-L1 epitope and block the interaction between PD-L1 and PD-1 can also be used.

[0160] In certain embodiments, the anti-human PD-1 monoclonal antibody is selected from the group consisting of pembrolizumab, nivolumab, cemiplimab, dostarlimab, pidilizumab (U.S. Pat.No. 7,332,582), AMP-514 (Medlmmune LLC, Gaithersburg. MD). PDR001 (U.S. Pat. No. 9,683,048), BGB-A317 (U.S. Pat. No. 8,735,553), and MGA012 (MacroGenics, Rockville, MD). In one embodiment, the anti -human PD-1 monoclonal antibody is pembrolizumab. In another embodiment, the anti-human PD-1 monoclonal antibody is nivolumab. In another embodiment, the anti -human PD-1 monoclonal antibody is cemiplimab. In one embodiment, the anti -human PD-1 monoclonal antibody is dostarlimab. In yet another embodiment, the anti-human PD-1 monoclonal antibody is pidilizumab. In one embodiment, the anti -human PD-1 monoclonal antibody is AMP-514. In another embodiment, the anti-human PD-1 monoclonal antibody is PDR001. In yet another embodiment, the anti-human PD-1 monoclonal antibody is BGB-A317. In still another embodiment, the anti-human PD-1 monoclonal antibody is MGA012.

[0161] In some embodiments, an anti -human PD-1 antibody or antigen binding fragment thereof for use in the methods and uses of the invention comprises three light chain CDRs of CDRL1, CDRL2 and CDRL3 and / or three heavy chain CDRs of CDRH1, CDRH2 and CDRH3.

[0162] In one embodiment of the invention, CDRL1 has the amino acid sequence as set forth in SEQ ID NO: 1 or a variant of the amino acid sequence as set forth in SEQ ID NO: 1, CDRL2 has the amino acid sequence as set forth in SEQ ID NO: 2 or a variant of the amino acid sequence as set forth in SEQ ID NO:2, and CDRL3 has the amino acid sequence as set forth in SEQ ID NO:3 or a variant of the amino acid sequence as set forth in SEQ ID NO:3.

[0163] In one embodiment, CDRH1 has the amino acid sequence as set forth in SEQ ID NO:6 or a variant of the amino acid sequence as set forth in SEQ ID NO:6, CDRH2 has the amino acid sequence as set forth in SEQ ID NO:7 or a variant of the amino acid sequence as set forth in SEQ ID NO:7. and CDRH3 has the amino acid sequence as set forth in SEQ ID NO: 8 or a variant of the amino acid sequence as set forth in SEQ ID NO:8.

[0164] In one embodiment, the three light chain CDRs have the amino acid sequences as set forth in SEQ ID NO:1, SEQ ID NO:2, and SEQ ID NO:3 and the three heavy chain CDRs have the amino acid sequences as set forth in SEQ ID NO:6, SEQ ID NO:7 and SEQ ID NO:8.

[0165] In an alternative embodiment of the invention. CDRL1 has the amino acid sequence as set forth in SEQ ID NO: 11 or a variant of the amino acid sequence as set forth in SEQ ID NO: 11, CDRL2 has the amino acid sequence as set forth in SEQ ID NO: 12 or a variant of the amino acid sequence as set forth in SEQ ID NO: 12, and CDRL3 has the amino acid sequence as set forth in SEQ ID NO: 13 or a variant of the amino acid sequence as set forth in SEQ ID NO: 13.

[0166] In one embodiment, CDRH1 has the amino acid sequence as set forth in SEQ ID NO: 16 or a variant of the amino acid sequence as set forth in SEQ ID NO: 16, CDRH2 has the amino acid sequence as set forth in SEQ ID NO: 17 or a variant of the amino acid sequence as set forthin SEQ ID NO: 17. and CDRH3 has the amino acid sequence as set forth in SEQ ID NO: 18 or a variant of the amino acid sequence as set forth in SEQ ID NO: 18.

[0167] In one embodiment, the three light chain CDRs have the amino acid sequences as set forth in SEQ ID NO: 1, SEQ ID NO:2, and SEQ ID NO:3 and the three heavy chain CDRs have the amino acid sequences as set forth in SEQ ID NO:6, SEQ ID NO:7 and SEQ ID NO:8.

[0168] In an alternative embodiment, the three light chain CDRs have the amino acid sequences as set forth in SEQ ID NO: 11, SEQ ID NO: 12, and SEQ ID NO: 13 and the three heavy chain CDRs have the amino acid sequences as set forth in SEQ ID NO: 16, SEQ ID NO: 17 and SEQ ID NO: 18.

[0169] In a further embodiment of the invention, CDRL1 has the amino acid sequence as set forth in SEQ ID NO:21 or a variant of the amino acid sequence as set forth in SEQ ID NO:21, CDRL2 has the amino acid sequence as set forth in SEQ ID NO:22 or a variant of the amino acid sequence as set forth in SEQ ID NO:22, and CDRL3 has the amino acid sequence as set forth in SEQ ID NO:23 or a variant of the amino acid sequence as set forth in SEQ ID NO:23.

[0170] In yet another embodiment, CDRH1 has the amino acid sequence as set forth in SEQ ID NO:24 or a variant of the amino acid sequence as set forth in SEQ ID NO:24, CDRH2 has the amino acid sequence as set forth in SEQ ID NO: 25 or a variant of the amino acid sequence as set forth in SEQ ID NO:25, and CDRH3 has the amino acid sequence as set forth in SEQ ID NO:26 or a variant of the amino acid sequence as set forth in SEQ ID NO:26.

[0171] In another embodiment, the three light chain CDRs have the amino acid sequences as set forth in SEQ ID NO:21, SEQ ID NO:22, and SEQ ID NO:23 and the three heavy chain CDRs have the amino acid sequences as set forth in SEQ ID NO:24. SEQ ID NO:25 and SEQ ID NO:26.

[0172] Some anti-human PD-1 antibody and antigen binding fragments of the invention comprise a light chain variable region and a heavy chain variable region. In some embodiments, the light chain variable region comprises the amino acid sequence as set forth in SEQ ID NO:4 or a variant of the amino acid sequence as set forth in SEQ ID NO: 4, and the heavy chain variable region comprises the amino acid sequence as set forth in SEQ ID NO: 9 or a variant of the amino acid sequence as set forth in SEQ ID NO:9. In further embodiments, the light chain variable region comprises the amino acid sequence as set forth in SEQ ID NO: 14 or a variant of the amino acid sequence as set forth in SEQ ID NO: 14, and the heavy chain variable region comprises the amino acid sequence as set forth in SEQ ID NO: 19 or a variant of the amino acid sequence as set forth in SEQ ID NO: 19. In further embodiments, the heavy chain variable region comprises the amino acid sequence as set forth in SEQ ID NO:27 or a variant of the amino acid sequence as setforth in SEQ ID NO:27 and the light chain variable region comprises the amino acid sequence as set forth in SEQ ID NO:28 or a variant of the amino acid sequence as set forth in SEQ ID NO:28, the amino acid sequence as set forth in SEQ ID NO: 29 or a variant of the amino acid sequence as set forth in SEQ ID NO:29, or the amino acid sequence as set forth in SEQ ID NO:30 or a variant of the amino acid sequence as set forth in SEQ ID NO:30. In such embodiments, a light chain variable region or heavy chain variable region sequence is identical to the reference sequence except having one, two, three, four or five amino acid substitutions. In some embodiments, the substitutions are in the framework region (z.e.. outside of the CDRs). In some embodiments, one, two, three, four or five of the amino acid substitutions are conservative substitutions.

[0173] In one embodiment of the methods, kits or uses of the invention, the anti-human PD-1 antibody or antigen binding fragment comprises a light chain variable region comprising or consisting of the amino acid sequence as set forth in SEQ ID NON and a heavy chain variable region comprising or consisting of the amino acid sequence as set forth in SEQ ID NO: 9. In a further embodiment, the anti -human PD-1 antibody or antigen binding fragment comprises a light chain variable region comprising or consisting of the amino acid sequence as set forth in SEQ ID NO: 14 and a heavy chain variable region comprising or consisting of the amino acid sequence as set forth in SEQ ID NO: 19. In one embodiment of the methods of the invention, the anti -human PD-1 antibody or antigen binding fragment comprises a light chain variable region comprising or consisting of the amino acid sequence as set forth in SEQ ID NO:28 and a heavy chain variable region comprising or consisting of the amino acid sequence as set forth in SEQ ID NO:27. In a further embodiment, the anti-human PD-1 antibody or antigen binding fragment comprises a light chain variable region comprising or consisting of the amino acid sequence as set forth in SEQ ID NO: 29 and a heavy chain variable region compnsing or consisting of the amino acid sequence as set forth in SEQ ID NO:27. In another embodiment, the antibody or antigen binding fragment comprises a light chain variable region comprising or consisting of the amino acid sequence as set forth in SEQ ID NO:30 and a heavy chain variable region comprising or consisting of the amino acid sequence as set forth in SEQ ID NO:27.

[0174] In another embodiment, the methods, kits or uses of the invention comprise an antihuman PD-1 antibody or antigen binding protein that has a VL domain and / or a VH domain with at least 99%, 98%, 97%, 96%, 95%, 90%, 85%, 80%, 75% or 50% sequence homology to one of the VL domains or VH domains described above, and exhibits specific binding to PD-1. In another embodiment, the anti-human PD-1 antibody or antigen binding protein of the methods the invention comprises VL and VH domains having up to 1, 2, 3, 4, or 5 or more amino acid substitutions, and exhibits specific binding to PD-1.

[0175] In any of the embodiments above, the PD-1 antagonist may be a full-length anti-PD-1 antibody or an antigen binding fragment thereof that specifically binds human PD- 1. In certain embodiments, the PD-1 antagonist is a full-length anti-PD-1 antibody selected from any class of immunoglobulins, including IgM, IgG, IgD, IgA, and IgE. Preferably, the antibody is an IgG antibody. Any isotype of IgG can be used, including IgGi, IgG2, IgGv, and IgG4. Different constant domains may be appended to the VL and VH regions provided herein. For example, if a particular intended use of an antibody (or fragment) of the invention were to call for altered effector functions, a heavy chain constant domain other than IgGl may be used. Although IgGl antibodies provide for long half-life and for effector functions, such as complement activation and antibody-dependent cellular cytotoxicity, such activities may not be desirable for all uses of the antibody. In such instances an IgG4 constant domain, for example, may be used.

[0176] In embodiments of the invention, the PD-1 antagonist is an anti-PD-1 antibody comprising a light chain comprising or consisting of a sequence of amino acid residues as set forth in SEQ ID NO:5 and a heavy chain comprising or consisting of a sequence of amino acid residues as set forth in SEQ ID NO: 10. In alternative embodiments, the PD-1 antagonist is an anti-PD-1 antibody comprising a light chain comprising or consisting of a sequence of amino acid residues as set forth in SEQ ID NO: 15 and a heavy chain comprising or consisting of a sequence of amino acid residues as set forth in SEQ ID NO:20. In further embodiments, the PD-1 antagonist is an anti-PD-1 antibody comprising a light chain comprising or consisting of a sequence of amino acid residues as set forth in SEQ ID NO:32 and a heavy7chain comprising or consisting of a sequence of amino acid residues as set forth in SEQ ID NO:31. In additional embodiments, the PD-1 antagonist is an anti-PD-1 antibody comprising a light chain comprising or consisting of a sequence of amino acid residues as set forth in SEQ ID NO:33 and a heavy chain comprising or consisting of a sequence of amino acid residues as set forth in SEQ ID NO:31. In yet additional embodiments, the PD-1 antagonist is an anti-PD-1 antibody comprising a light chain comprising or consisting of a sequence of amino acid residues as set forth in SEQ ID NO: 34 and a heavy chain comprising or consisting of a sequence of amino acid residues as set forth in SEQ ID NO:31. In some methods of the invention, the PD-1 antagonist is pembrolizumab or a pembrolizumab biosimilar. In some methods of the invention, the PD-1 antagonist is nivolumab or anivolumab biosimilar.

[0177] Ordinarily, amino acid sequence variants of the anti-PD-1 antibodies and antigen binding fragments of the invention will have an amino acid sequence having at least 75% amino acid sequence identity with the amino acid sequence of a reference antibody or antigen binding fragment (e.g. heavy chain, light chain, VH, VL, or humanized sequence), more preferably at least80%. more preferably at least 85%, more preferably at least 90%. and most preferably at least 95, 98, or 99%. Identity or homology with respect to a sequence is defined herein as the percentage of amino acid residues in the candidate sequence that are identical with the anti-PD-1 residues, after aligning the sequences and introducing gaps, if necessary, to achieve the maximum percent sequence identity, and not considering any conservative substitutions as part of the sequence identity. None ofN-terminal, C-terminal, or internal extensions, deletions, or insertions into the antibody sequence shall be construed as affecting sequence identity or homology.

[0178] Sequence identity refers to the degree to which the amino acids of two polypeptides are the same at equivalent positions when the two sequences are optimally aligned. Sequence identity can be determined using a BLAST algorithm wherein the parameters of the algorithm are selected to give the largest match between the respective sequences over the entire length of the respective reference sequences. The following references relate to BLAST algorithms often used for sequence analysis: BLAST ALGORITHMS: Altschul, S.F., et al., (1990) J. Mol. Biol. 215:403-410; Gish, W„ et al., (1993) Nature Genet. 3:266-272; Madden. T.L.. et al.. (1996) Meth. Enzymol. 266: 131-141; Altschul, S.F., et al., (1997) Nucleic Acids Res. 25:3389-3402; Zhang, J., et al., (1997) Genome Res. 7:649-656; Wootton, J.C., et al., (1993) Comput. Chem. 17: 149-163; Hancock, J.M. et al., (1994) Comput. Appl. Biosci. 10:67-70; ALIGNMENT SCORING SYSTEMS: Dayhoff, M.O., et al.. "A model of evolutionary change in proteins." in Atlas of Protein Sequence and Structure, (1978) vol. 5, suppl. 3. M.O. Dayhoff (ed.), pp. 345- 352, Natl. Biomed. Res. Found., Washington, DC; Schwartz, R.M., et al., "Matrices for detecting distant relationships." in Atlas of Protein Sequence and Structure, (1978) vol. 5, suppl. 3." M.O. Dayhoff (ed.), pp. 353-358, Natl. Biomed. Res. Found., Washington, DC; Altschul, S.F.. (1991) J. Mol. Biol. 219:555-565; States, D.J., et al., (1991) Methods 3:66-70; Hemkoff, S.. et al., (1992) Proc. Natl. Acad. Sci. USA 89: 10915-10919; Altschul, S.F., et al., (1993) J. Mol. Evol.36:290-300; ALIGNMENT STATISTICS: Karlin, S„ et al., (1990) Proc. Natl. Acad. Sci. USA 87:2264-2268; Karlin, S„ et al., (1993) Proc. Natl. Acad. Sci. USA 90:5873-5877; Dembo, A., et al.. (1994) Ann. Prob. 22:2022-2039; and Altschul, S.F. "Evaluating the statistical significance of multiple distinct local alignments." in Theoretical and Computational Methods in Genome Research (S. Suhai, ed.), (1997) pp. 1-14, Plenum, New York.

[0179] Likewise, either class of light chain can be used in the compositions and methods herein. Specifically, kappa, lambda, or variants thereof are useful in the present compositions and methods of the invention.Table 3. Exemplary PD-1 Antibody SequencesTable 4. Additional PD-1 Antibodies and Antigen Binding Fragments Useful in the Methods and Uses of the Invention.Anti-PD-1 antibody dosing

[0180] In some embodiments, the anti-PD-1 antibody (e.g., anti-PD-1 monoclonal antibody) or antigen binding fragment thereof is administered subcutaneously or intravenously, on a weekly, biweekly, triweekly, every 4 weeks, every 5 weeks, every 6 weeks, monthly, bimonthly, or quarterly basis at about 10, about 20, about 50, about 80, about 100, about 200. about 300. about 400, about 500, about 1000 or about 2500 mg / subject.

[0181] In some specific methods, the dose of the anti-PD-1 antibody (e.g., anti-PD-1 monoclonal antibody) or antigen binding fragment thereof is from about 0.01 mg / kg to about 50 mg / kg, from about 0.05 mg / kg to about 25 mg / kg, from about 0. 1 mg / kg to about 10 mg / kg, from about 0.2 mg / kg to about 9 mg / kg, from about 0.3 mg / kg to about 8 mg / kg, from about 0.4 mg / kg to about 7 mg / kg, from about 0.5 mg / kg to about 6 mg / kg, from about 0.6 mg / kg to about 5 mg / kg, from about 0.7 mg / kg to about 4 mg / kg, from about 0.8 mg / kg to about 3 mg / kg, from about 0.9 mg / kg to about 2 mg / kg. from about 1.0 mg / kg to about 1.5 mg / kg. from about 1.0 mg / kg to about 2.0 mg / kg. from about 1.0 mg / kg to about 3.0 mg / kg. or from about 2.0 mg / kg to about 4.0 mg / kg.

[0182] In some specific methods, the dose of the anti-PD-1 antibody (e.g., anti-PD-1 monoclonal antibody) or antigen binding fragment thereof is from about 10 mg to about 500 mg, from about 25 mg to about 500 mg, from about 50 mg to about 500 mg, from about 100 mg to about 500 mg, from about 200 mg to about 500 mg, from about 150 mg to about 250 mg, from about 175 mg to about 250 mg, from about 200 mg to about 250 mg, from about 150 mg to about 240 mg, from about 175 mg to about 240 mg, or from about 200 mg to about 240 mg. In some embodiments, the dose of the anti-PD-1 antibody (e.g., anti-PD-1 monoclonal antibody) or antigen binding fragment thereof is about 50 mg, about 75 mg, about 100 mg, about 125 mg. about 150 mg, about 175 mg, about 200 mg, about 225 mg, about 240 mg, about 250 mg, about 300 mg, about 400 mg, or about 500 mg.

[0183] In another embodiment of the invention, the PD-1 antagonist in the therapy is pembrolizumab, or a pembrolizumab variant, which is administered in a liquid medicament at a dose selected from the group consisting of 1 mg / kg Q2W, 2 mg / kg Q2W, 3 mg / kg Q2W, 5 mg / kg Q2W, 10 mg / kg Q2W, 1 mg / kg Q3W, 2 mg / kg Q3W, 3 mg / kg Q3W, 5 mg / kg Q3W, or 10 mg / kg Q3W. In other embodiments, the PD-1 antagonist in the therapy is pembrolizumab, or a pembrolizumab variant, which is administered in a liquid medicament at a flat dose such as 200 mg Q3W or 400 mg Q6W.

[0184] In some embodiments of the methods, compositions, kits and uses described herein, the anti -human PD-1 antibody (e.g., anti-PD-1 monoclonal antibody) or antigen binding fragmentthereof is pembrolizumab. and the human patient is administered about 200 mg, about 240 mg, about 400 mg, about 480 mg, or about 2 mg / kg pembrolizumab once every three or six weeks. In one embodiment, the human patient is administered about 200 mg pembrolizumab once every three weeks. In one embodiment, the human patient is administered about 240 mg pembrolizumab once even’ three weeks. In one embodiment, the human patient is administered 2 mg / kg pembrolizumab once every three weeks. In one embodiment, the human patient is administered 400 mg pembrolizumab once every three weeks.

[0185] In certain embodiments of the methods, compositions, kits and uses described herein, the anti -human PD-1 monoclonal antibody or antigen binding fragment thereof is pembrolizumab and the human patient is administered 400 mg pembrolizumab once every six weeks.

[0186] In some embodiments of the methods, compositions, kits and uses described herein, the anti -human PD-1 monoclonal antibody or antigen binding fragment thereof is pembrolizumab, and the human patient is administered about 200 mg, about 240 mg, about 400 mg. about 480 mg, or about 2 mg / kg pembrolizumab once every six weeks. In one embodiment, the human patient is administered about 200 mg pembrolizumab once every six weeks. In one embodiment, the human patient is administered about 240 mg pembrolizumab once every’ six weeks. In one embodiment, the human patient is administered about 400 mg pembrolizumab once every six weeks. In one embodiment, the human patient is administered 480 mg pembrolizumab once every’ six weeks. In one embodiment, the human patient is administered 2 mg / kg pembrolizumab once every six weeks.

[0187] In some embodiments, pembrolizumab is provided as a liquid medicament that comprises 25 mg / ml pembrolizumab, 7% (w / v) sucrose, 0.02% (yv / v) polysorbate 80 in 10 mM histidine buffer pH 5.5. In other embodiments, pembrolizumab is provided as a liquid medicament that comprises about 125 to about 200 mg / rnL of pembrolizumab, or an antigen binding fragment thereof; about 10 mM histidine buffer; about 10 mM L-methionine, or a pharmaceutically acceptable salt thereof; about 7% (w / v) sucrose; and about 0.02 % (w / v) polysorbate 80.

[0188] In certain embodiments of the methods, compositions, kits and uses described herein, the anti -human PD-1 monoclonal antibody or antigen binding fragment thereof is pembrolizumab, and the human patient is administered about 200 mg pembrolizumab once every' three weeks. In certain embodiments of the methods, compositions, kits and uses described herein, the anti -human PD-1 monoclonal antibody or antigen binding fragment thereof is pembrolizumab, and the human patient is administered about 400 mg pembrolizumab once every six weeks.

[0189] In some embodiments, the selected dose of pembrolizumab is administered by IV infusion. In one embodiment, the selected dose of pembrolizumab is administered by IV infusion over a time period of between 25 and 40 minutes, or about 30 minutes. In other embodiments, the selected dose of pembrolizumab is administered by subcutaneous injection.

[0190] In some embodiments, the selected dose of pembrolizumab is administered subcutaneously. In embodiments of the invention, the amount of pembrolizumab administered subcutaneously to the patient is from 320 mg to 420 mg, from 340 mg to 420 mg, from 345 mg to 415 mg, from 350 mg to 410 mg, from 355 mg to 405 mg, from 360 mg to 400 mg, from 365 mg to 395 mg, from 370 mg to 390 mg, from 375 mg to 385 mg, or from 379 mg to 381 mg. In one embodiment, pembrolizumab is administered by subcutaneous injection at a dose of about 280 mg to about 450 mg. In a further embodiment of the invention, pembrolizumab is administered by subcutaneous injection at a dose of about 300 mg to about 450 mg. In yet a further embodiment of the invention, pembrolizumab is administered subcutaneously at a dose of about 320 mg to about 450 mg.

[0191] In embodiments of the invention, pembrolizumab is administered subcutaneously to the patient, wherein the pembrolizumab is part of a composition and is present in the composition at a concentration of 130 mg / mL. In embodiments of the invention, pembrolizumab administered subcutaneously to the patient, wherein the pembrolizumab is part of a composition and is present in the composition at a concentration of 165 mg / mL. In embodiments of the invention, pembrolizumab is administered subcutaneously to the patient in two injections. In embodiments of the invention, the amount of pembrolizumab administered subcutaneously to the patient is 380 mg in one pre-filled syringe. In embodiments of the invention, the amount of pembrolizumab administered subcutaneously to the patient is 380 mg in two pre-filled syringes.

[0192] In one embodiment, the selected dose of pembrolizumab is administered by subcutaneous injection at a dose that is at least about 1.6 times higher than a 200 mg or a 2 mg / kg dose. In one embodiment, the subcutaneous dose is administered once every three weeks. In one embodiment, the subcutaneous dose is administered once every six weeks. In one embodiment, the bioavailability of the pembrolizumab subcutaneous dose is at least 63%. In one embodiment, the bioavailability of the pembrolizumab subcutaneous dose is at least 64%. In one embodiment, the bioavailability7of the pembrolizumab subcutaneous dose is at least 66%.

[0193] In other embodiments of the methods, compositions, kits and uses described herein, the anti-human PD-1 monoclonal antibody or antigen binding fragment thereof is nivolumab, the human patient is administered about 240 mg or about 3 mg / kg nivolumab, and nivolumab is administered once every two weeks. In one specific embodiment, the human patient isadministered about 240 mg mvolumab once every two weeks. In one specific embodiment, the human patient is administered about 3 mg / kg nivolumab once every tw o weeks. In other embodiments of the methods, compositions, kits and uses described herein, the anti-human PD-1 monoclonal antibody or antigen binding fragment thereof is nivolumab, the human patient is administered about 480 mg nivolumab once every four weeks.

[0194] In yet other embodiments of the methods, compositions, kits and uses described herein, the anti-human PD-1 monoclonal antibody or antigen binding fragment thereof is cemiplimab, the human patient is administered about 350 mg cemiplimab once every three weeks.EXAMPLES

[0195] The following examples are meant to be illustrative and should not be construed as further limiting. The contents of the figures and all references, patents, and published patent applications cited throughout this application are expressly incorporated herein by reference.

[0196] The disclosed subject matter is not to be limited in scope by the specific embodiments and examples described herein. Indeed, various modifications of the disclosure in addition to those described will become apparent to those skilled in the art from the foregoing description and accompanying figures. Such modifications are intended to fall within the scope of the appended claims.

[0197] All references (e.g., publications or patents or patent applications) cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual reference (e.g., publication or patent or patent application) was specifically and individually indicated to be incorporated by reference in its entirety for all purposes. Other embodiments are within the following claims.EXAMPLE 1: Overall response rate by PD-L1 and PD-L2 status and use of mRNA to differentiate anti-PD-1 versus anti-PD-Ll agents and evaluate the relationship of PD-L2 expression.

[0198] PD -L2 is most frequently co-expressed with PD-L1, however, it may also be expressed in isolation. Co-expression appears to be associated with highest response rates to pembrolizumab across numerous types. PD-L2 expression appears to be associated with comparable response rates to those for patients who are PD-L1+.Table 5: Overall Response Rate by PD-L1 and PD-L2 Status

[0199] As shown in Table 5, the overall response rate by PD-L1 and PD-L2 status is 27.5% when PD-L1 and PD-L2 status is > 1%. The overall response when PD-L1 and PD-L2 is 0% is 5.9%. When PD-L1 status is > 1% and PD-L2 status is 0%, the ORR is 1 1.4%. When PD-L1 status is 0% and PD-L2 is > 1%, the ORR is 0%. The logistic regression suggests that PD-L2 expression is associated with higher overall response rate after adjusting for PD-L1.

[0200] PD-L2 remains positively associated with longer PFS after adjusting for impact of PD- L1 status. ORR vs PD-L1 / PD-L2 in Keytruda trials shows a strong correlation with both PD-L1 and PD-L2. The higher the median PD-L1 expression, the more similar anti-PD-1 and anti-PD- L1 behave.Use of mRNA to differentiate anti-PD-1 versus anti-PD-Ll agents and evaluate the relationship of PD-L2 expression.

[0201] An assessment was completed assessing the potential to differentiate anti-PD-1 versus anti-PD-Ll agents using IHC and mRNA data from KN001, KN012, and KN028 clinical trials.

[0202] Head and neck patient data was used to map PD-L1 and PD-L2 IHC levels to mRNA. To establish mRNA cutoffs, samples from the KN012 clinical trial (head and neck arm) are used. The IHC data is used to establish a relationship between IHC and mRNA. The mRNA PD-L1 and PD-L2 data in KNOOlb, KN012 (non-H&N) and KN028 was used to estimate the PD- L1 / PD-L2 association with response.

[0203] There are 114 patients from KN012 trial head and neck indication with IHC PD-L1 and PD-L2 data, where 87 patients in full analysis set (FAS) population with available best observed response (BOR) data by central review. There are 112 patients from KN012 trial head and neck indication with both IHC and mRNA PD-L1 and PD-L2 data, where 87 patients are in the FAS population with available BOR data by central review. There are 844 patients from KN001, KN012 and KN028 have mRNA PD-L1 and PD-L2 data available, where 703 patients are in the FAS population with available BOR data by central review.

[0204] The response rates by IHC PD-L1 / PD-L2 category for head and neck cancer are shown in Table 6, and FIG 1. The Pearson correlation is 0.59 (p value < 0.0001). The Spearmancorrelation is 0.60 (p value < 0.0001). The overall agreement of IHC PD-L1 / PD-L2 categorization is 74.7%.Table 6: Response Rates by IHC PD-L1 / PD-L2 category for head and neck cancer.

[0205] The association of IHC and mRNA with response in head and neck cancers is shown in FIG. 2. and FIG. 3. The PD-L1 IHC data is shown in Table 7 (below). The PD-L2 IHC data is shown in Table 8 (below). The analysis used IHC and mRNA as continuous variables in a logistic regression model.Table 7: PD-L1 IHC dataTable 8: PD-L2 IHC data

[0206] Receiver Operating Characteristic Curve (ROC) analysis of mRNA PD-L1 versus IHC PD-L1+ in head and neck cancer was performed. Youden Index (YI) for PD-L1 by mRNA versus IHC + / - = -0.91. The results are summarized in Table 9 and Table 10 and are displayed in FIG. 4.

[0207] The response rates by IHC PD-Ll / mRNA PD-L1 category are shown in the Table 10 below. The overall agreement of IHC / mRNA PD-L1 categorization is 88.5%.Table 9: ROC analysis of mRNA PD-L1 versus IHC PD-L1+ in head and neck cancerTable 10: Response Rates by IHC PD-Ll / mRNA PD-L1 category

[0208] The ROC analysis of mRNA PD-L2 versus IHC PD-L2+ / - in head and neck cancers was performed. Youden Index (YI) for PD-L2 by mRNA versus IHC+ / - = -0.69. The results are summarized in Table 11 and Table 12 and are displayed in FIG. 5.

[0209] The response rates by IHC PD-L2 / mRNA PD-L2 category are shown in Table 12 below. The overall agreement of IHC / mRNA PD-L2 categorization = 69.0%.Table 11: ROC analysis of mRNA PD-L1 versus IHC PD-L1+ in head and neck cancerTable 12: Response Rates by IHC PD-Ll / mRNA PD-L1 category

[0210] PD-L1 and PD-L2 by mRNA in head and neck cancers were compared. The cut points are based on mRNA versus IHC + / - ROC analyses in head and neck cancer. The response rates are shown in Table 13 below. The overall agreement of mRNA PD-L1 / PD-L2 categorization = 80.5%. The responders and non-responders are graphed in FIG 6 showing PD-L2 mRNA versus PD-L1 mRNA. The Pearson correlation is 0.73 (p value < 0.0001). The Spearman correlation is 0.71 (p value < 0.0001).Table 13: Response Rates by mRNA PD-L1 / PD-L2 category7for head and neck cancers

[0211] PD-L1 and PD-L2 by mRNA outside of head and neck cancer indications were compared. The cut points are based on mRNA versus IHC + / - ROC analyses in head and neck cancer.

[0212] The response rates are shown in Table 14 for PD-L1 and PD-L2 by mRNA outside of head and neck cancer. The overall agreement of mRNA PD-L1 and PD-L2 categorization is 55.2%. The responders and non-responders are graphed in FIG 7. showing PD-L2 mRNA versus PD-L1 mRNA outside of head and neck cancer indication. The Pearson correlation is 0.59 (pvalue < 0.0001). The Spearman correlation is 0.58 (p value < 0.0001). The results are shown inFIG. 7.Table 14: Response rates by mRNA PD-L1 / PD-L2 category for non-head and neck cancer

[0213] A comparison of percentage of patients and response rate by PD-L1 / PD-L2 status was performed as shown in Table 15.Table 15: Response Rates by PD-L1 / PD-L2 Status

[0214] Overall, the tumor microenvironment conducive to pembrolizumab response is characterized by IFNy-induced upregulation of both PD-L1 and PD-L2. KN012 head and neck cancer patients were used to map PD-L1 and PD-L2 IHC levels to mRNA. There are consistent findings between ICH and mRNA with the highest response rates observed among those exceeding cut point for both PD-L1 and PD-L2. There are slightly lower response rates among those with high PD-L I / low PD-L2. There is no response among the small group with low PD-L1 and high PD-L2.

[0215] It appears the PD-L2 expression is present in many tumors and may be extensive. PD- L2 is most frequently co-expressed with PD-L1 but may also be expressed in isolation. Coexpression of PD-L1 and PD-L2 appears to be associated with highest response rates to Keytruda across numerous tumor types. PD-L2 expression in patients negative for PD-L1 is not common but appears to be associated with comparable response rates to those patients who are PD-L1+.Example IB: BOR analysis

[0216] The analysis was performed with two methods. The first method (Example 1A) was identifying the cut point (based on Youden’s index) of mRNA PD-L1 / PD-L2 predicting IHC PD- L1 / PD-L2 positivity using patients from head and neck cancer indication. The cut point was applied to the patients outside head and neck cancer indication and the association was estimated between PD-L1 / PD-L2 status and response. The second method (Example IB) included identify ing the cut point (based on Youden’s index) of mRNA PD-Ll / PD-L2s to predict BOR (central review- RECIST 1.1) using patients from head and neck cancer indication. The results were very' similar to method 1.

[0217] The ROC analysis of IHC PD-L1 and mRNA PD-L1 versus BOR in KN012 head and neck cancer indication was performed. The results are shown in Table 16, 17 and FIG. 8. The Youden Index (YI) for PD-L1 by mRNA vs BOR = -0.89. The overall agreement of IHC / mRNA PD-L1 categorization = 86.2%Table 16: ROC analysis of IHC PD-L1 and mRNA PD-L1 versus BOR in KN012 head and neck cancer indication.Table 17: Response rates by IHC PD-L1 mRNA PD-L1 categorization for PD-L1 / PD-L2 category for non-head and neck cancer.

[0218] The ROC analysis of IHC PD-L2 and mRNA PD-L2 versus BOR in KN012 head and neck cancer indication was performed. The results are shown in Table 18, Table 19 and FIG 9. The Youden Index (YI) for PD-L2 by mRNA vs BOR = -0.72. The overall agreement of IHC / mRNA PD-L2 categorization = 67.8%.Table 18: ROC analysis of IHC PD-L2 and mRNA PD-L2 versus BOR in KN012 head and neck cancer indication.Table 19: Response rates by IHC PD-L2 / mRNA PD-L2 categorization

[0219] PD-L1 and PD-L2 by mRNA in KN012 head and neck cancer indication was compared. The cut points are based on individual IHC and mRNA versus BOR ROC analyses in KN012 head and neck cancer indication.

[0220] The response rates are shown in Table 20 for PD-L1 and PD-L2 by mRNA outside of head and neck cancer. The Pearson correlation is 0.73 (p value < 0.0001). The Spearman correlation is 0.71 (p value < 0.0001). The overall agreement of mRNA PD-L1 / PD-L2 categorization = 81.6%. The results are shown in FIG. 10.Table 20: Response rates by mRNA PD-L1 / PD-L2 positivity.

[0221] PD-L1 and PD-L2 by mRNA outside of KN012 head and neck cancer indication was compared. The cut points are based on individual IHC and mRNA versus BOR ROC analyses in KN012 head and neck cancer indication.

[0222] The response rates are shown in Table 21 for PD-L1 and PD-L2 by mRNA outside of head and neck cancer. The Pearson correlation is 0.59 (p value < 0.0001). The Spearman correlation is 0.58 (p value < 0.0001). The overall agreement of mRNA PD-L1 / PD-L2 categorization = 60. 1%. The results are shown in FIG. 11.Table 21: Response rates by mRNA PD-L1 / PD-L2 positivity.Example 2: PD-1 / PD-L1 Monotherapy ORR analysisExample 2A: Analysis of ORR for PD-1 vs. PD-L1 Based Treatments

[0223] Blocking the PD-1 receptor of the PD-1 pathway blocks ligand interactions with both PD-L1 and PD-L2, whereas anti-PD-Ll therapeutic antibodies only block the PD-L1 ligand (Rozali et al 2012, Yearley et al 2017). PD-1-PD-L1 interaction surfaces on the proteins themselves differ among the approved anti-PD-1 therapeutic molecules (Lee et al 2016, Tan et al 2016, Tan et al 2018). These differences can give rise to differential interactions with the immune system in pre-clinical studies (De Sousa Linhares et al 2019, Maute et al 2015, Tang & Kim 2019). These structural and functional differences suggest the potential for clinical efficacy to vary among PD-1 / PD-L1 immune checkpoint inhibitors. Meta-analyses comparing the efficacy of PD-1 versus PD-L1 as well as pembrolizumab versus nivolumab in treating non-small cell lung cancer have pointed to a potential superiority of pembrolizumab over nivolumab, and PD-1 inhibitors in general over PD-L1 inhibitors (You et al 2018, Yu et al 2022, Zhang et al 2018). Head-to-head comparisons in randomized clinical trials have not been performed to compare the various immune checkpoint inhibitors. Consequently, clinical efficacy can currently only be compared using systematic reviews of cross trial efficacy of the PD-1 / PD-L1 immune checkpoint inhibitors.

[0224] All clinical trials in clinicaltrials.gov involving monotherapy administration of PD-1 or PD-L1 immune checkpoint inhibitors through December 2021 were prospectively identified. There were 1487 trials identified through December 2021. 3,443 arms of data were identified since June 2012, which contains data from 199,680 subjects. The data fields include trial phase, biomarker selection or stratification, biological characterization of agent, indication includinghistologic subtypes, population classifiers (brain metastases and pediatrics). The endpoints collected include ORR, DOR, PFS, and OS. The arms are broken out for dose comparisons as well as indications are annotated for comparison to standard of care therapies, FDA approval.

[0225] 441 trials containing monotherapy efficacy data for 73 PD-1 or PD-L1 inhibitors were identified. Taken together, a propensity adjusted estimate shows a 2.7% ORR advantage for pembrolizumab over nivolumab and a 6.2%, 11 .4% and 5.0% ORR advantages comparing pembrolizumab to atezolizumab, avelumab and durvalumab respectively. This advantage remains for specific tumor types and in particular the pembrolizumab ORR advantage is 5.0%, 8.2%, 8,8% and 7.5% in non-small cell lung cancer versus nivolumab, atezolizumab, avelumab and durvalumab respectively. The trial characteristics that best predict ORR are indication, line of therapy and treatment respectively. The ORR advantage for PD-1 therapies versus PD-L1 vary across indications and are most marked for Hodgkin’s lymphoma. PD-L1 / PD-L2 amplifications on chromosome 9p24. 1 are a defining feature of the disease (Roemer et al 2016) identifying a potential indication clinical advantage for PD-1 inhibitors over PD-L1 inhibitors in that and potentially additional indications. Previous work has shown expression of PD-L2 in all tumor types, but where both PD-L1 and PD-L2 were expressed together, patients responded better to pembrolizumab (Yearley et al 2017).All Data Points

[0226] 754 monotherapy data-points for PD-1 or PD-L1 compounds were extracted from PDx clinical trial database (Patient N = 54,495). For analyses, N=729 data-points were considered after removing trials without ORR, removing Phase 4 trial data, and removing OL trial data. The average ORR (%) is calculated by a simple unadjusted mean. However, this does not account for potential confounding such as trial-level difference (indication, phase, line, etc.). Trial-level variables include indication, design phase, line of therapy, PD-1 / PD-L1 treatment, biomarker population (Y / N), MO A (PD-1 vs PD-L1), trial N, biomarker type, arm description (text), prior PD-1 / PD-L1, pediatric population (Y / N), and arm type (treatment vs. control).Table 22: Overview of Data for PD-1 / PD-L1 monotherapy

[0227] The data was analyzed to determine which trial characteristic predicts ORR. FIG 12. shows relative importance (compared to important variable, tumor indication). The main cofounding variables appear to be indication, line of therapy, PD-1 / PD-L1 treatment, biomarker population. MO A, trial data points (N), and trial phase.

[0228] The ORR percentage by mechanism of action (PD-1 vs. PD-L1) was analyzed in the overall data point, and additional within the NSCLC, bladder, and melanoma indications.

[0229] The boxplot of FIG. 13 shows the observed ORR % by PD-1 vs. PD-L1. Table X below shows adjusted estimates (adjusts for phase of trial, line of therapy, biomarker population, and tumor indication (for overall).Table 23: Adjusted estimates for overall set of data-points, NSCLC, Bladder, and Melanoma indications

[0230] An analysis was performed comparing the ORR difference percentage of PD-1 and PD- L1 based treatments for all data points. All data points were used (729 data points) and the adjusted ORR difference estimates [95% CI] were used. Within each indication, treatment difference estimates [95% CI] are calculated through a mixed model approach (unless convergence failure, then fixed effects). For NSCLC, melanoma, bladder and overall, estimates are adjusted for phase, line of therapy, biomarker population, and tumor indication [for overall]). If a given indication has <30 total subjects (in either treatment arm), they were not included on this plot due to low sample size.

[0231] As shown in Figure 14, compared to PD-L1 based treatments, PD-1 treatments tend to show higher ORR across a variety of tumor types. CHL, MCC. CRC. endometrial, bladder,mesothelioma, melanoma, overall, gastric, breast. SCLC. RCC. ovarian, HCC. anal, prostate, PTCL, biliary, HNC, NSCLC, STS all favor PD-1 based treatments over PD-L1 when analyzing the ORR. esophageal, cervical, GBM, CSCC all favor PD-L1 based treatments when analyzing ORR of PD-1 and PD-L1 based treatments.Data Points Excluding Biomarker Enriched Trials

[0232] An analysis was performed comparing the ORR difference percentage of PD-1 and PD- L1 based treatments for all data points that did not include a biomarker enriched trial (N = 423 data points).

[0233] The adjusted ORR difference estimates [95% CI] were used. Within each indication, treatment difference estimates [95% CI] are calculated through a mixed model approach (unless convergence failure, then fixed effects). For NSCLC, melanoma, bladder and overall, estimates are adjusted for phase, line of therapy, biomarker population, and tumor indication [for overall]). If a given indication has <30 total subjects (in either treatment arm), they were not included on this plot due to low sample size.

[0234] As shown in Figure 15, compared to PD-L1 based treatments, PD-1 treatments tend to show higher ORR across a variety of tumor types. CHL, MCC, melanoma, bladder, mesothelioma, gastric, overall, RCC, HCC, SCLC, breast, ovarian, anal, PTCL, HNC, STS all favor PD-1 based treatments over PD-L1 when analyzing the ORR. esophageal, cervical, biliary, GBM, NSCLC, CSCC all favor PD-L1 based treatments when analyzing ORR of PD-1 and PD- L1 based treatments.Example 2B: Pembrolizumab analysis compared to other approved treatments with ORR analysis.

[0235] An analysis was performed on pembrolizumab compared to other approved PD-l / PD- L1 treatments (atezolizumab, avelumab, durvalumab, nivolumab). The approved treatment was compared by ORR percentage by tumor type (NSCLC, bladder, melanoma) and by overall data points. Table 24 shows adjusted treatment difference estimates [95% CI] calculated through a mixed model approach (unless convergence failure, then fixed effects). For NSCLC, melanoma, bladder and overall, estimates are adjusted for phase, line of therapy, biomarker population, and tumor indication [for overall]).Table 24: shows adjusted ORR % difference for overall indications, NSCLC, Bladder and Melanoma indications.

[0236] Compared to other PD-L1 based treatments, pembrolizumab tends to show higher ORR across a variety of tumor types. Within each indication, treatment difference estimates [95% CI] are calculated through a mixed model approach (unless convergence failure, then fixed effects). For NSCLC, melanoma, bladder and overall, estimates are adjusted for phase, line of therapy, biomarker population, and tumor indication [for overall]). If a given indication has <30 total subjects (in either treatment arm), they were not included on this plot due to low sample size. FIG. 17 A shows PCNSL, NSCLC, SCLC, bladder, RCC, gastric, HNC, HCC, overall, ovarian, and NPC tumor indications favor pembrolizumab as compared to nivolumab comparing ORR difference % by Strata. FIG. 17 B shows CRC, bladder, NSCLC, overall, biliary, RCC, STS, HCC, melanoma favor pembrolizumab over atezolizumab comparing ORR difference % by Strata. FIG. 17 C shows endometrial, CHL, MCC, RCC, bladder, gastric, NSCLC, melanoma, HCC, HNC, breast, mesothelioma, overall, ovarian, anal all favor pembrolizumab compared to avelumab comparing ORR difference % by Strata. FIG. 17 D shows endometrial, gastric, NSCLC, overall, bladder, HCC, biliary, HNC indications all favor pembrolizumab compared to durvalumab when comparing ORR difference % by Strata.Example 2C - Secondary analysis evaluating ORR(SOC)All Data Points

[0237] Secondary analysis evaluated the difference in ORR-ORR(SOC) for PD-1 vs. PD-L1.The current PDx database has SOC ORR and the difference in differences between PD-1 vs. PD-L1 (or other comparisons) was calculated. For a given data point, based on the SOC ORR estimate, the number of responders / non-responders based on the observed N of the PD-1 / PD-L1therapy was determined. For example, responders = N*(SOC ORR %). A mixed effect modeling approach was the used for analysis.

[0238] Adjusted ORR-SOC difference estimates (95 % CI) were used to compare PD-1 and PD-L1 treatment. All data points were used (N=582 data points). Within each indication, treatment difference estimates [95% CI] are calculated through a mixed model approach (unless convergence failure, then fixed effects). For NSCLC, melanoma, bladder and overall, estimates are adjusted for phase, line of therapy, biomarker population, and tumor indication [for overall]). If a given indication has <30 total subjects (in either treatment arm), they were not included on this plot due to low sample size. Shown in FIG 18, PD-1 treatments tend to show higher ORR- SOC across a variety of tumor types when compared to PD-L1 treatments. Specifically, CHL, prostate, SCLC, MCC, RCC, NPC, melanoma, endometrial, bladder, overall, HCC, anal, mesothelioma, pancreas, NSCLC, ovarian, esophageal, cervical, and gastric tumor indications all favor PD-1 compared to PD-L1 treatments when comparing ORR difference % by Strata.Data Points Excluding Biomarker Enriched Trials

[0239] An analysis was performed comparing the ORR difference percentage of PD-1 and PD- L1 based treatments for all data points that did not include a biomarker enriched trial (N = 454 data points). Within each indication, treatment difference estimates [95% CI] are calculated through a mixed model approach (unless convergence failure, then fixed effects). For NSCLC, melanoma, bladder and overall, estimates are adjusted for phase, line of therapy, biomarker population, and tumor indication [for overall]). If a given indication has <30 total subjects (in either treatment arm), they were not included on this plot due to low sample size.

[0240] Shown in FIG 19. PD-1 treatments tend to show higher ORR-SOC across a variety of tumor types when compared to PD-L1 treatments. Specifically, CHL, SCLC, MCC, NPC, RCC, gastric, melanoma, overall, bladder, mesothelioma, HCC, ovarian, NSCLC, anal, esophageal, and cervical indications all favor PD-1 compared to PD-L1 treatments when comparing ORR difference % by Strata.Example 2D: Pembrolizumab analysis compared to other approved treatments with ORR(SOC) analysis.

[0241] An analysis was performed on pembrolizumab compared to other approved PD-l / PD- L1 treatments (atezolizumab, avelumab, druvalumab, nivolumab). The approved treatment was compared by ORR(SOC) percentage by tumor type (NSCLC, bladder, melanoma) and by overalldata points. Figure 20 shows PD-1 / PD-L1 therapy versus ORR(Trt)-ORR(SOC) percent for overall and by key tumor types.

[0242] Within each indication, treatment difference estimates [95% CI] are calculated through a mixed model approach (unless convergence failure, then fixed effects). For NSCLC, melanoma, bladder and overall, estimates are adjusted for phase, line of therapy, biomarker population, and tumor indication [for overall]). If a given indication has <30 total subjects (in either treatment arm), they were not included on this plot due to low sample size.

[0243] Within each indication, treatment difference estimates [95% CI] are calculated through a mixed model approach (unless convergence failure, then fixed effects). For NSCLC, melanoma, bladder and overall, estimates are adjusted for phase, line of therapy, biomarker population, and tumor indication [for overall]). If a given indication has <30 total subjects (in either treatment arm), they were not included on this plot due to low sample size. Plot is truncated to [-40, 60] for visualization purposes.

[0244] As shown in FIG. 21 A-D, pembrolizumab tends to show higher ORR across a variety of tumor types as compared to other PD-L1 based treatments.

[0245] For monotherapy, PD-1 and PD-L1 treatments, PD-1 treatments tend to have higher ORR with an estimated ORR advantage of -5% across all trials for overall indications. Similar results observed for differences in differences analyses for ORR(SOC).

[0246] Among the approved PD-1 / PD-L1 treatments (durvalumab, avelumab, atezolizumab, nivolumab, pembrolizumab), pembrolizumab has an estimated ORR advantage of -1.8% (compared to nivolumab) to 8.9% (compared to durvalumab) after adjusting for key cofounding variables (tumor types, line of therapy, phase 3 vs. phase 1 / 2, biomarker enrichment). Within NSCLC indication, pembrolizumab has an estimated ORR advantage of -10-12%.

Claims

WHAT IS CLAIMED IS:

1. A method of treating cancer in a patient in need thereof comprising: a. determining if a sample from a tumor from the patient expresses a level of PD-L1 or is predicted to express a level of PD-L1 that exceeds a first pre-determined threshold and expresses a level of PD-L2 or is predicted to express a level of PD-L2 that exceeds a second pre-determined threshold; and b. administering a PD-1 antagonist to the patient if the level or the predicted level of PD-L1 exceeds the first pre-determined threshold and the level or the predicted level of PD-L2 exceeds the second pre-determined threshold.

2. The method of claim 1 , wherein the first pre-determined threshold and the second predetermined threshold are the same.

3. The method of claim 1 , wherein the first pre-determined threshold and the second predetermined threshold are different.

4. The method of any one of claims 1-3, wherein the cancer is selected from the group consisting of: melanoma, non-small cell lung cancer (NSCLC), head and neck squamous cell cancer, classical Hodgkin lymphoma, primary mediastinal large B-cell lymphoma, urothelial carcinoma, microsatellite instability-high or mismatch repair deficient cancer; microsatellite instability -high or mismatch repair deficient colorectal cancer, gastric cancer, esophageal cancer, cervical cancer, hepatocellular carcinoma, biliary tract cancer, Merkel cell carcinoma, renal cell carcinoma, endometrial carcinoma; tumor mutational burden-high (TMB-H) cancer, cutaneous squamous cell carcinoma and triple-negative breast cancer.

5. The method of claim 4, wherein the cancer is classical Hodgkin lymphoma.

6. The method of any one of claims 1-5, wherein the PD-1 antagonist is pembrolizumab or a pembrolizumab variant.

7. The method of any one of claims 1-5, wherein the PD-1 antagonist is pembrolizumab.

8. The method of any one of claims 1-5, wherein the PD-1 antagonist is nivolumab. atezolizumab, durvalumab, cemiplimab, avelumab, or dostarlimab.

9. The method of any one of claims 1-8, wherein the level of PD-L1 and the level of PD-L2 are determined using immunohistochemistry.

10. The method of any one of claims 1-8, wherein the level of PD-L1 and the level of PD-L2 are predicted using a method comprising: a. measuring the level of PD-L1 mRNA in the tumor sample and the level of PD-L2 mRNA in the tumor sample; and b. using a first correlation to predict the level of PD-L1 in the tumor sample from the level of PD-L1 mRNA and using a second correlation to predict the level of PD- L2 in the tumor sample from the level of PD-L2 mRNA.

11. A method of identifying a patient for treatment with a PD-1 antagonist comprising: a. (i) determining a first correlation between PD-L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in population of patients having cancer, and (ii) determining a second correlation between PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer; b. using the first correlation to predict the level of PD-L1 mRNA expression and using the second correlation to predict the level of PD-L2 mRNA in the patient’s tumor; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist using the predicted PD-L1 mRNA expression and the predicted PD-L2 mRNA to predict the response rate of the patient.

12. The method of claim 11, wherein the PD-1 antagonist is pembrolizumab or a pembrolizumab variant.

13. The method of claim 11, wherein the PD-1 antagonist is pembrolizumab.

14. The method of claim 1 1, wherein the PD-1 antagonist is nivolumab, atezolizumab, durvalumab, cemiplimab, avelumab, or dostarlimab.

15. The method of any one of claims 11-14, wherein the cancer is selected from the group consisting of: melanoma, non-small cell lung cancer (NSCLC), head and neck squamous cell cancer, classical Hodgkin lymphoma, primary mediastinal large B-cell lymphoma, urothelial carcinoma, microsatellite instability-high or mismatch repair deficient cancer; microsatellite instability-high or mismatch repair deficient colorectal cancer, gastric cancer, esophageal cancer, cervical cancer, hepatocellular carcinoma, biliary tract cancer, Merkel cell carcinoma, renal cell carcinoma, endometrial carcinoma; tumor mutational burden-high (TMB-H) cancer, cutaneous squamous cell carcinoma and triple-negative breast cancer.

16. A method of identifying a patient for treatment with a PD-1 antagonist comprising: a. determining a level of PD-L1 mRNA and a level of PD-L2 mRNA in a sample of a tumor from a patient having cancer; b. using a first correlation to predict the level of PD-L1 in the patient’s tumor from the level of PD-L 1 mRNA expression and using the second correlation to predict the level of PD-L2 in the patient’s tumor from the level of PD-L2 mRNA expression; and c. identifying the patient as a candidate for treatment with the PD-1 antagonist if the predicted level of PD-L 1 exceeds a first pre-determined threshold and the predicted level of PD-L2 exceeds a second pre-determined threshold.

17. The method of claim 16, wherein the first correlation was determined by comparing PD- L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in a population of patients having cancer using Youden’s index, and the second correlation was determined by comparing PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index.

18. A method of treating cancer in a patient in need thereof comprising: a. determining a level of PD-L 1 mRNA and a level of PD-L2 mRNA in a sample of a tumor from the patient; b. using a first correlation to predict the level of PD-L1 in the patient’s tumor from the level of PD-L 1 mRNA expression and using the second correlation to predict the level of PD-L2 in the patient’s tumor from the level of PD-L2 mRNA expression; c. administering to the patient a PD-1 antagonist if the predicted level of PD- L1 exceeds a first pre-determined threshold and the predicted level of PD-L2 exceeds asecond pre-determined threshold or administering to the patient a different anti-cancer treatment if the predicted level of PD-L1 does not exceed the first pre-determined threshold and / or the predicted level of PD-L2 does not exceed the second pre-determined threshold.

19. The method of claim 18, wherein the first correlation was determined by comparing PD- L1 mRNA expression and PD-L1 expression measured by immunohistochemistry (IHC) in a population of patients having cancer using Youden’s index, and the second correlation was determined by comparing PD-L2 mRNA expression and PD-L2 expression measured by IHC in a population of patients having cancer using Youden’s index.

20. The method of claim 18 or claim 19, wherein the first pre-determined threshold and the second pre-determined threshold are the same.

21. The method of claim 18 or claim 19, wherein the first pre-determined threshold and the second pre-determined threshold are different.

22. The method of any one of claims 18-21, wherein the cancer is selected from the group consisting of: melanoma, non-small cell lung cancer (NSCLC), head and neck squamous cell cancer, classical Hodgkin lymphoma, primary mediastinal large B-cell lymphoma, urothelial carcinoma, microsatellite instability-high or mismatch repair deficient cancer; microsatellite instability -high or mismatch repair deficient colorectal cancer, gastric cancer, esophageal cancer, cervical cancer, hepatocellular carcinoma, biliary tract cancer, Merkel cell carcinoma, renal cell carcinoma, endometrial carcinoma; tumor mutational burden-high (TMB-H) cancer, cutaneous squamous cell carcinoma and triple-negative breast cancer.

23. The method of any one of claims 18-21, wherein the cancer is classical Hodgkin lymphoma.

24. The method of any one of claims 18-23, wherein the PD-1 antagonist is pembrolizumab or a pembrolizumab variant.

25. The method of any one of claims 18-23, wherein the PD-1 antagonist is pembrolizumab.

26. The method of any one of claims 18-23. wherein the PD-1 antagonist is nivolumab, atezolizumab, durvalumab, cemiplimab, avelumab, or dostarlimab.

27. The method of any one of claims 18-21, wherein the cancer is classical Hodgkin lymphoma, and the PD-1 antagonist is an anti-PD-1 monoclonal antibody.

28. The method of claim 27, wherein the anti-PD-1 monoclonal antibody is pembrolizumab, nivolumab, cemiplimab, or dostarlimab.

29. The method of claim 27, wherein the anti-PD-1 monoclonal antibody is pembrolizumab.

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