Cell localization signature and immunotherapy

Abstract

The present disclosure provides methods for identifying a subject suitable for anti-PD-1 / PD-L1 antagonist therapy, the method comprising measuring CD8 localization and PD-L1 expression in a tumor sample obtained from the subject. In some embodiments, the method further comprises administering to the subject identified as having a tumor exhibiting a negative CD8 localization phenotype (i) anti-PD-1 / PD-L1 antagonist therapy or (ii) combination therapy of an anti-PD-1 / PD-L1 antagonist and an anti-CTLA-4 antagonist, wherein the tumor is PD-L1 negative.

Description

[Technical Field]

[0001] Cross-reference of previously filed applications This PCT application claims the benefit of priority of U.S. Provisional Application No. 63 / 072,651, filed on 31 August 2020, which is incorporated herein by reference in its entirety.

[0002] Areas of disclosure This disclosure provides a method for treating a subject affected by a tumor using immunotherapy. [Background technology]

[0003] Human cancers possess numerous genetic and epigenetic alterations, giving rise to nascent antigens that can be recognized by the immune system (Sjoblom et al., Science (2006) 314(5797):268-274). The adaptive immune system, composed of T and B lymphocytes, has potent anti-cancer capabilities and possesses broad ability and sophisticated specificity to respond to diverse tumor antigens. Furthermore, the immune system exhibits considerable plasticity and memory. By successfully utilizing all these characteristics of the adaptive immune system, immunotherapy is considered to be unparalleled among all modes of cancer treatment.

[0004] Over the past decade, intensive efforts to develop specific immune checkpoint pathway inhibitors are beginning to yield new immunotherapeutic approaches to treat cancer, including the development of antibodies that block the suppressive programmed death 1 (PD-1) / programmed death ligand 1 (PD-L1) pathway, such as nivolumab and pembrolizumab (formerly lambrolizumab, USAN Council Statement, 2013), which specifically bind to the PD-1 receptor, as well as atezolizumab, durvalumab, and avelumab, which specifically bind to PD-L1.

[0005] Responses to the immune system and immunotherapy have been shown to be complex. Additionally, the effectiveness of anticancer agents can vary based on the individual patient's characteristics. Therefore, there is a need for targeted treatment strategies that identify patients who are more likely to respond to a particular anticancer agent and, by doing so, improve the clinical outcomes of patients diagnosed with cancer. SUMMARY OF THE INVENTION MEANS FOR SOLVING THE PROBLEM

[0006] Certain embodiments of the present disclosure are pharmaceutical compositions comprising an anti-PD-1 / PD-L1 antagonist for use in a method of treating a human subject afflicted with a tumor, wherein a tumor sample obtained from the subject exhibits (i) an excluded CD8 localization phenotype, and (ii) a negative PD-L1 expression status. In some embodiments, the anti-PD-1 / PD-L1 antagonist is administered to the subject in combination with an anticancer agent. In some embodiments, the anti-PD-1 / PD-L1 antagonist is administered to the subject in combination with an anti-CTLA-4 antagonist.

[0007] In some embodiments, the tumor sample is a tumor tissue biopsy sample. In some embodiments, the tumor sample is formalin-fixed paraffin-embedded tumor tissue or fresh frozen tumor tissue.

[0008] In some embodiments, CD8 localization is measured by staining the tumor sample with an antibody that binds to CD8 or an antigen-binding portion thereof. In some embodiments, the tumor sample is imaged after staining with the antibody.

[0009] In some embodiments, PD-L1 expression is measured by staining a tumor sample with an antibody or antigen-binding portion thereof that specifically binds to PD-L1. In some embodiments, a negative PD-L1 expression state is characterized by a tumor sample in which less than about 1% of the tumor cells express PD-L1. In some embodiments, PD-L1 expression is measured using an IHC assay. In some embodiments, the IHC assay includes an automated IHC assay. In some embodiments, CD8 localization is measured by performing IHC and then classifying CD8 localization in the tumor sample.

[0010] In some embodiments, the classification is performed by a method that includes receiving, by at least one processor of a computing device, a plurality of histological images of tumor samples in a plurality of patients; performing, by the at least one processor, image analysis of the plurality of histological images to obtain an abundance of CD8+ T cells in the tumor parenchyma and stroma in each of the plurality of histological images; training, by the at least one processor, a machine learning algorithm using the results of the image analysis and the abundance of CD8+ T cells in the tumor parenchyma and stroma; generating, by the at least one processor, a machine learning feature space that includes a plurality of classifications based on the training; and identifying, by the at least one processor, boundaries between the plurality of classifications in the machine learning feature space.

[0011] Certain aspects of the present disclosure relate to a pharmaceutical composition comprising an anti-PD-1 / PD-L1 antagonist for use in a method for identifying human subjects suitable for anti-PD-1 / PD-L1 antagonist therapy, the method comprising (i) measuring the expression of PD-L1 in tumor samples obtained from a subject, and (ii) measuring the CD8 localization in the tumor samples, wherein the CD8 localization is measured by staining the tumor samples with an antibody or antigen-binding moiety that binds to CD8, and classifying the CD8 localization in the tumor samples, the classification being performed by at least one processor of a computing device to receive multiple histological images of tumor samples from multiple patients, performing image analysis of the multiple histological images by at least one processor to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma in each of the multiple histological images, and at least one processor to obtain the results of the image analysis and the abundance of CD8+ T cells in the tumor parenchyma and stroma The present invention relates to a pharmaceutical composition, which is prepared by a method comprising: training a machine learning algorithm using the abundance of T cells; generating a machine learning feature space containing multiple classifications based on the training using at least one processor; and identifying boundaries between the multiple classifications in the machine learning feature space using at least one processor.

[0012] In some embodiments, performing image analysis of multiple histological images involves applying an artificial neural network to multiple histological images. In some embodiments, the machine learning algorithm includes a random forest classifier algorithm. In some embodiments, the abundance of CD8+ T cells includes a graphical representation of the relationship between the percentage of stromal CD8+ T cells and the percentage of parenchymal CD8+ T cells relative to the total number of T cells present in each of the multiple histological images. In some embodiments, the pharmaceutical composition for use further includes applying a polar coordinate transformation of the graphical representation by at least one processor of a computing device to produce a polar plot, and training a machine learning algorithm using the polar plot. In some embodiments, the multiple classifications include inflammation, desert, excluded, or balanced.

[0013] In some embodiments, the pharmaceutical composition for use further includes determining a classification for each of a plurality of histological images based on a machine learning feature space. In some embodiments, the pharmaceutical composition for use further includes validating the results from the machine learning feature space by comparing the labels for each of a plurality of histological images obtained by at least one pathologist with the classification for each of the plurality of histological images. In some embodiments, the pharmaceutical composition for use further includes receiving additional histological images by at least one processor of a computing device, performing additional image analysis of the additional histological images to obtain the abundance of additional CD8+ T cells in the tumor parenchyma and stroma in the additional histological images, applying a machine learning algorithm to the results from the additional image analysis and the abundance of additional CD8+ T cells, and determining a classification for the additional histological images based on a machine learning feature space.

[0014] In some embodiments, CD8 localization is measured by measuring the expression of a panel of genes in tumor samples obtained from the subject.

[0015] In some embodiments, subjects identified as having an exclusionary CD8 localized phenotype and PD-L1-negative tumors are treated with a therapy including an anti-PD-1 / PD-L1 antagonist. In some embodiments, subjects identified as having an exclusionary CD8 localized phenotype and PD-L1-negative tumors are treated with a therapy including an anti-PD-1 / PD-L1 antagonist and an anti-CTLA-4 antagonist.

[0016] In some embodiments, the anti-PD-1 / PD-L1 antagonist comprises an antibody or its antigen-binding fragment ("anti-PD-1 antibody" or "anti-PD-L1 antibody") that specifically binds to a target protein selected from programmed death 1 (PD-1) or programmed death ligand 1 (PD-L1). In some embodiments, the anti-PD-1 / PD-L1 antagonist comprises an anti-PD-1 antibody. In some embodiments, the anti-PD-1 antibody comprises nivolumab or pembrolizumab.

[0017] In some embodiments, the anti-PD-1 / PD-L1 antagonist comprises an anti-PD-L1 antibody. In some embodiments, the anti-PD-L1 antibody comprises avelumab, atezolizumab, or durvalumab.

[0018] In some embodiments, the anti-CTLA-4 antagonist comprises an antibody or its antigen-binding fragment ("anti-CTLA-4 antibody") that specifically binds to cytotoxic T lymphocyte-associated protein 4 (CTLA-4). In some embodiments, the anti-CTLA-4 antibody comprises ipilimumab.

[0019] Certain embodiments of the present disclosure relate to a method for treating cancer in a human subject, comprising administering an anti-PD-1 / anti-PD-L1 antagonist to the subject, wherein the subject has been identified as having a tumor exhibiting (i) an exclusionary CD8 localization phenotype and (ii) a negative PD-L1 expression status. In some embodiments, the method further comprises administering an anti-CTLA-4 antagonist.

[0020] In some embodiments, the exclusionary CD8 localization phenotype is measured by detecting CD8 expression in tumor samples obtained from subjects. In some embodiments, the exclusionary CD8 localization phenotype is measured by staining tumor samples with an antibody that binds to CD8 or its antigen-binding moiety. In some embodiments, CD8 localization is measured by staining tumor samples with an antibody that binds to CD8 or its antigen-binding moiety, and then classifying the CD8 localization in the tumor samples, the classification being performed by a method comprising: receiving multiple histological images of tumor samples from multiple patients by at least one processor of a computing device; performing image analysis of the multiple histological images by at least one processor to obtain the abundance of CD8+ T cells (cCD8+ T-cells) in the tumor parenchyma and stroma in each of the multiple histological images; training a machine learning algorithm using the results of the image analysis and the abundance of CD8+ T cells in the tumor parenchyma and stroma by at least one processor; generating a machine learning feature space containing multiple classifications based on the training by at least one processor; and identifying the boundaries between the multiple classifications in the machine learning feature space by at least one processor.

[0021] Certain aspects of the present disclosure are methods for identifying human subjects suitable for anti-PD-1 / PD-L1 antagonist therapy, comprising (i) measuring the expression of PD-L1 in tumor samples obtained from the subjects, and (ii) measuring the CD8 localization in the tumor samples, wherein the CD8 localization is measured by staining the tumor samples with an antibody or antigen-binding moiety that binds to CD8, and then classifying the CD8 localization in the tumor samples, wherein the classification is performed by a method comprising: receiving multiple histological images of tumor samples from multiple patients by at least one processor of a computing device; performing image analysis of the multiple histological images by at least one processor to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma in each of the multiple histological images; training a machine learning algorithm by at least one processor using the results of the image analysis and the abundance of CD8+ T cells in the tumor parenchyma and stroma; generating a machine learning feature space containing multiple classifications based on the training by at least one processor; and identifying boundaries between the multiple classifications in the machine learning feature space by at least one processor.

[0022] In some embodiments, image analysis of multiple histological images includes applying an artificial neural network to multiple histological images. In some embodiments, the machine learning algorithm includes a random forest classifier algorithm. In some embodiments, the abundance of CD8+ T cells includes a graphical representation of the relationship between the percentage of stromal CD8+ T cells and the percentage of parenchymal CD8+ T cells relative to the total number of T cells present in each of the multiple histological images. In some embodiments, the method further includes applying a polar coordinate transformation of the graphical representation by at least one processor of a computing device to produce a polar plot, and training a machine learning algorithm using the polar plot. In some embodiments, the multiple classifications include inflammatory, desert, exclusion, or balanced types.

[0023] In some embodiments, the method further includes determining a classification for each of a plurality of histological images based on a machine learning feature space. In some embodiments, the method further includes validating the results from the machine learning feature space by comparing the labels for each of a plurality of histological images obtained by at least one pathologist with the classification for each of the plurality of histological images. In some embodiments, the method further includes receiving additional histological images by at least one processor of a computing device, performing additional image analysis of the additional histological images to obtain the abundance of additional CD8+ T cells in the tumor parenchyma and stroma in the additional histological images, applying a machine learning algorithm to the results from the additional image analysis and the abundance of additional CD8+ T cells, and determining a classification for the additional histological images based on a machine learning feature space.

[0024] In some embodiments, the method further includes administering an anti-PD-1 / PD-L1 antagonist to subjects identified as having an exclusionary CD8 localized phenotype and a PD-L1-negative tumor. In some embodiments, the method further includes administering an anti-CTLA-4 antagonist.

[0025] In some embodiments, the anti-PD-1 / PD-L1 antagonist comprises an antibody or its antigen-binding fragment that specifically binds to a target protein selected from programmed death 1 (PD-1) or programmed death ligand 1 (PD-L1) ("anti-PD-1 antibody" or "anti-PD-L1 antibody"). In some embodiments, the anti-PD-1 / PD-L1 antagonist is an anti-PD-1 antibody. In some embodiments, the anti-PD-1 antibody comprises nivolumab or pembrolizumab. In some embodiments, the anti-PD-1 / PD-L1 antagonist comprises an anti-PD-L1 antibody. In some embodiments, the anti-PD-L1 antibody comprises avelumab, atezolizumab, or durvalumab. In some embodiments, the anti-CTLA-4 antagonist comprises an antibody or its antigen-binding fragment that specifically binds to cytotoxic T lymphocyte-associated protein 4 (CTLA-4) ("anti-CTLA-4 antibody"). In some embodiments, the anti-CTLA-4 antibody contains ipilimumab.

[0026] In some embodiments, the tumor originates from a cancer selected from the group consisting of hepatocellular carcinoma, gastroesophageal cancer, melanoma, bladder cancer, lung cancer, kidney cancer, head and neck cancer, colon cancer, pancreatic cancer, prostate cancer, ovarian cancer, urothelial carcinoma, colorectal cancer, and any combination thereof. In some embodiments, the tumor is recurrent. In some embodiments, the tumor is resistant to treatment. In some embodiments, the tumor is locally advanced. In some embodiments, the tumor is metastatic.

[0027] In some embodiments, the administration treats the tumor. In some embodiments, the administration reduces the size of the tumor. In some embodiments, the tumor size is reduced by at least about 10%, about 20%, about 30%, about 40%, or about 50% compared to the tumor size before administration. In some embodiments, subjects show progression-free survival of at least about 1 month, at least about 2 months, at least about 3 months, at least about 4 months, at least about 5 months, at least about 6 months, at least about 7 months, at least about 8 months, at least about 9 months, at least about 10 months, at least about 11 months, at least about 1 year, at least about 18 months, at least about 2 years, at least about 3 years, at least about 4 years, or at least about 5 years after the first administration.

[0028] In some embodiments, the subjects show disease stabilization after administration. In some embodiments, the subjects show a partial response after administration. In some embodiments, the subjects show a complete response after administration.

[0029] Certain embodiments of this disclosure relate to a kit for treating a tumor-affected subject, comprising (a) an anti-PD-1 / PD-L1 antagonist, and (b) instructions for using the anti-PD-1 / PD-L1 antagonist in accordance with the methods disclosed herein. In some embodiments, the anti-PD-1 / PD-L1 antagonist comprises an anti-PD-1 antibody. In some embodiments, the anti-PD-1 / PD-L1 antagonist comprises an anti-PD-L1 antibody. In some embodiments, the kit further comprises an anti-CTLA-4 antagonist. In some embodiments, the anti-CTLA-4 agonist comprises an anti-CTLA-4 antibody.

[0030] In some embodiments, subjects exhibit milder adverse events compared to subjects that do not exhibit the elimination CD8 localized phenotype. In some embodiments, subjects do not exhibit adverse events more severe than Grade 1, more severe than Grade 2, or more severe than Grade 3. In some embodiments, subjects exhibit Grade 3 or more severe adverse events at a lower frequency compared to subjects that do not exhibit the elimination CD8 localized phenotype. [Brief explanation of the drawing]

[0031] [Figure 1] Figure 1 illustrates exemplary images of tumor tissue samples of various classifications, which were subsequently imaged using CD8+ immunostaining according to an exemplary embodiment. [Figure 2] Figure 2 is an exemplary diagram illustrating a method for an image analysis and machine learning-based approach to training a model for tumor topology classification, according to an exemplary embodiment. [Figure 3] Figure 3 is another exemplary diagram illustrating a method for classifying tumor topology using image analysis and machine learning-based approaches, according to an exemplary embodiment. [Figure 4] Figure 4 is a flowchart illustrating the process for training a machine learning algorithm for classifying CD8 tumor topologies, according to an exemplary embodiment. [Figure 5] Figure 5 is a flowchart illustrating the process for classifying the CD8 tumor topology of histological images using a trained machine learning algorithm, according to an exemplary embodiment. [Figure 6] Figure 6 is a block diagram of exemplary components of a device according to an exemplary embodiment. [Figure 7] Figures 7A–7C are graphical representations of overall survival (OS) in patients with PD-L1-negative (PD-L1 expression <1%) melanoma (Figures 7A–7B) or urothelial carcinoma (Figure 7C) tumors after treatment with either anti-PD-1 antibody (Figures 7A and 7C) or a combination of anti-PD-1 antibody and anti-CTLA-4 antibody (Figure 7B). Patients were stratified by CD8 topology as having either an exclusionary CD8 phenotype (Figures 7A–7C), an inflammatory CD8 phenotype (Figures 7A–7C), or a desert CD8 phenotype (Figure 7C), as measured using immunohistochemistry and subsequent machine learning analysis as described herein. Patients at risk in each group are shown in Figures 7A–7B. [Modes for carrying out the invention]

[0032] Certain aspects of the present disclosure relate to a method for treating a human subject with a tumor, comprising administering an anti-PD-1 / PD-L1 antagonist to the subject, wherein a tumor sample obtained from the subject exhibits (i) an exclusionary CD8 localization phenotype and (ii) a negative PD-L1 expression status ("PD-L1 negative").

[0033] Other aspects of this disclosure relate to a method for identifying subjects suitable for immuno-oncology (IO) therapy, such as anti-PD-1 / PD-L1 antagonist therapy alone or in combination with anti-CTLA-4 antagonist therapy. In some aspects, the method comprises (i) measuring PD-L1 expression in tumor samples obtained from subjects, and (ii) measuring CD8 expression in tumor samples, where CD8 expression is measured by immunostaining and imaging, and then classifying localized CD8 expression in tumor samples using a machine learning algorithm. In some aspects, the method further comprises administering an anti-PD-1 / PD-L1 antagonist to subjects identified as having tumor samples exhibiting (i) an exclusionary CD8 localization phenotype and (ii) a negative PD-L1 expression status ("PD-L1 negative").

[0034] In some embodiments, the method further includes administering an additional anticancer agent. In some embodiments, the method further includes administering an anti-CTLA-4 antagonist.

[0035] I. Terminology To make this disclosure more easily understandable, we first define some specific terms. Where used in this application, unless otherwise expressly provided herein, each of the following terms shall have the meanings set forth below. Other definitions are provided throughout this application.

[0036] Whenever an aspect is described using the word “including” in this specification, it is understood that other similar aspects are also provided, which are described using the terms “consisting of” and / or “essentially consisting of.”

[0037] Certain embodiments disclosed herein can be implemented in hardware (e.g., circuitry), firmware, software, or any combination thereof. Some embodiments can also be implemented as instructions stored in a machine-readable medium that can be read and executed by one or more processors. A machine-readable medium can include any mechanism for storing or transmitting information in a format readable by a machine (e.g., a computing device). For example, a machine-readable medium can include read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, electrical, optical, acoustic, or other forms of propagating signals (e.g., carrier waves, infrared signals, digital signals, etc.), and others. Furthermore, firmware, software, routines, and instructions may also be described herein as performing certain operations. However, such descriptions are for convenience only, and it should be understood that such operations actually result from computing devices, processors, controllers, or other devices that perform the firmware, software, routines, instructions, etc. Furthermore, any variation of the implementation can be executed by a general-purpose computer as described herein.

[0038] For the purposes of this study, any reference to the term “module” should be understood to include at least one of the following: software, firmware, or hardware (e.g., one or more of circuits, microchips, and devices, or any combination thereof), and any combination thereof. Furthermore, each module may contain one or more components within an actual device, and it will be understood that each component forming part of a described module may function in cooperation with or independently of any other components forming part of the module. Conversely, multiple modules described herein may represent a single component within an actual device. Furthermore, components within a module may reside within a single device or may be distributed between multiple devices in a wired or wireless manner.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art in the field relating to this disclosure. For example, *Concise Dictionary of Biomedicine and Molecular Biology*, Juo, Pei-Show, 2nd ed., 2002, CRC Press, *The Dictionary of Cell and Molecular Biology*, 3rd ed., 1999, Academic Press, and *the Oxford Dictionary of Biochemistry and Molecular Biology*, Revised, 2000, Oxford University Press provide those skilled in the art with a general dictionary of many of the terms used herein.

[0040] Units, prefixes, and symbols are expressed in the form recognized by the International System of Units (SI). Numerical ranges include the numerical values ​​that define the range. Where a range of values ​​is given, each intervening integer value between the given upper and lower bounds of that range, and each fraction thereof, should be understood to be specifically disclosed along with each subrange between such values. The upper and lower bounds of any range can independently be included in or excluded from that range, and each range that includes either, neither, or both of those limits is also included within the scope of this disclosure. Thus, ranges given herein should be understood to be abbreviations of all values ​​within the range, including the given endpoint. For example, the range 1–10 should be understood to include any number, combination of numbers, or subrange from the group consisting of 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10.

[0041] Where a value is explicitly given, it should be understood that values ​​that are approximately the same quantity or amount as the given value are also within the scope of this disclosure. Where a combination is disclosed, each subcombination of the elements of that combination is also specifically disclosed and is within the scope of this disclosure. Conversely, where different elements or groups of elements are disclosed individually, their combinations are also disclosed. Where any element of the disclosure is disclosed as having multiple options, examples of that disclosure in which each option is excluded, either individually or in any combination with other options, are also disclosed herein. Multiple elements of the disclosure may have such exclusions, and all combinations of elements having such exclusions are disclosed herein.

[0042] As used herein, the terms “CD8 localization” and “CD8 topology” are interchangeable and refer to CD8 in a sample, e.g., a tumor sample obtained by a subject using the methods disclosed herein. + This refers to the general compartmentalization distribution of cells. The "exclusionary" or "stromal" CD8 localization phenotype is CD8 + This refers to a sample in which the majority or all of the cells are located outside the tumor parenchyma. The "inflammatory type" or "parenchymal" CD8 localization phenotype is characterized by a large number of CD8 cells. +This refers to a sample in which cells are located within the tumor parenchyma. The "cold" or "desert" type of CD8 localization phenotype is CD8 + This refers to a sample in which no cells are detected. CD8 is CD8 + CD8 is a marker of T cells, and therefore, in some aspects, CD8 localization is an indicator of the immune response to tumors.

[0043] "Administer" refers to the physical introduction of a composition containing a therapeutic agent into a subject using any of the various methods and delivery systems known to those skilled in the art. Preferred routes of administration for immunotherapies, such as those using anti-PD-1 antibodies or anti-PD-L1 antibodies, include, for example, intravenous, intramuscular, subcutaneous, intraperitoneal, spinal, or other intestinal avoidance routes of administration by injection or infusion. As used herein, the term "intestinal avoidance administration" means a mode of administration other than enteral and topical administration, usually by injection, and includes, but is not limited to, intravenous, intramuscular, intra-arterial, intrathecal, intralymphatic, intralesional, intracapsular, intraorbital, intracardiac, intradermal, intraperitoneal, transtracheal, subcutaneous, subepidermal, intra-articular, subcapsular, subarachnoid, intraspinal, epidural, and intrasternal injections and infusions, as well as in vivo electroporation. Other non-intestinal routes of administration include oral, topical, cutaneous, or mucosal routes of administration, such as intranasal, intravaginal, intrarectal, sublingual, or topical administration. Administration may also be carried out, for example, once, multiple times, and / or over one or more extended periods.

[0044] As used herein, “adverse event” (AE) is any undesirable, generally unintended, or unwanted sign (including abnormal laboratory findings), symptom, or disease associated with the use of a medical procedure. For example, an adverse event may be associated with the activation of the immune system or proliferation of immune system cells (e.g., T cells) in response to a procedure. A medical procedure may have one or more associated AEs, each AE may have the same or different levels of severity. References to methods that can “modify adverse events” mean treatment regimens that reduce the incidence and / or severity of one or more AEs associated with the use of different treatment regimens. In some embodiments, the methods disclosed herein identify subjects having an elimination-type CD8 localization phenotype, and the subjects exhibit milder adverse events after administration of a composition containing an anti-PD-1 / PD-L1 antagonist compared to subjects not exhibiting an elimination-type CD8 localization phenotype. In some embodiments, the subjects do not exhibit adverse events more severe than Grade 1, more severe than Grade 2, or more severe than Grade 3. In some embodiments, subjects exhibit grade 3 or more severe adverse events at a lower frequency compared to subjects not exhibiting the elimination CD8 localized phenotype. In some embodiments, subjects exhibit grade 2 or more severe adverse events at a lower frequency compared to subjects not exhibiting the elimination CD8 localized phenotype. The specific nature of each AE grade level depends on the indication and / or condition. The application of the AE grading system can be found in the Common Terminology Criteria for Adverse Events (CTCAE) v5.0 published by the National Cancer Institute, which is available at ctep.cancer.gov / protocolDevelopment / electronic_applications / ctc.htm#ctc_60 and is incorporated herein by reference in its entirety.

[0045] "Antibody" (Ab) includes, but is not limited to, a glycoprotein immunoglobulin that specifically binds to an antigen and contains at least two heavy (H) chains and two light (L) chains interconnected by disulfide bonds, or those containing the antigen-binding portion thereof. Each H chain contains a heavy-chain variable region (abbreviated herein as V H ), and a heavy-chain constant region. The heavy-chain constant region contains three constant domains, C H1 , C H2 , and C H3 . Each light chain contains a light-chain variable region (abbreviated herein as V L ), and a light-chain constant region. The light-chain constant region contains one constant domain, C L . The V H region and the V L region can be further subdivided into hypervariable regions called complementarity-determining regions (CDRs) intervened by more conserved regions called framework regions (FRs). Each V H and V L contains three CDRs and four FRs, and are arranged in the following order from the amino terminus to the carboxy terminus: FR1, CDR1, FR2, CDR2, FR3, CDR3, and FR4. The variable regions of the heavy and light chains contain a binding domain that interacts with the antigen. The constant region of the antibody can mediate the binding of the immunoglobulin to host tissues or factors, including various cells of the immune system (e.g., effector cells) and the first component (C1q) of the classical complement system. Thus, the term "anti-PD-1 antibody" includes a full antibody having two heavy chains and two light chains that specifically bind to PD-1, as well as the antigen-binding portion of the full antibody. Non-limiting examples of the antigen-binding portion are shown elsewhere in this specification.

[0046] Immunoglobulins may be derived from any of the commonly known isotypes, including, but not limited to, IgA, secretory IgA, IgG, and IgM. IgG subclasses are also well known to those skilled in the art, and these include, but not limited to, human IgG1, IgG2, IgG3, and IgG4. “Isotype” refers to an antibody class or subclass (e.g., IgM or IgG1) encoded by a heavy chain constant region gene. The term “antibody” includes, by example, both naturally occurring and non-naturally occurring antibodies, monoclonal and polyclonal antibodies, chimeric and humanized antibodies, human or non-human antibodies, totally synthetic antibodies, and single-chain antibodies. Non-human antibodies can be humanized by recombinant methods to reduce their immunogenicity in humans. Unless expressly stated otherwise and the context indicates otherwise, the term “antibody” also includes antigen-binding fragments or parts of any of the immunoglobulins described above, including monovalent and bivalent fragments or parts, and single-chain antibodies.

[0047] "Isolated antibodies" refer to antibodies that substantially contain no other antibodies with different antigen specificities (for example, an isolated antibody that specifically binds to PD-1 substantially contains no antibodies that specifically bind to antigens other than PD-1). However, an isolated antibody that specifically binds to PD-1 may cross-react to other antigens, such as PD-1 molecules from different species. Furthermore, isolated antibodies may substantially contain no other cellular material and / or chemical substances.

[0048] The term "monoclonal antibody" (mAb) refers to a preparation of an antibody molecule that does not exist in nature and has a single molecular composition; that is, an antibody molecule whose primary sequence is essentially identical and which exhibits a single binding specificity and affinity for a particular epitope. Monoclonal antibodies are an example of isolated antibodies. Monoclonal antibodies can be produced by hybridoma, recombinant, transgenic, or other techniques known to those skilled in the art.

[0049] A “human antibody” (HuMAb) refers to an antibody having a variable region in which both the framework region and the CDR region are derived from a human germline immunoglobulin sequence. Furthermore, if the antibody includes a constant region, the constant region is also derived from a human germline immunoglobulin sequence. The human antibodies of this disclosure may include amino acid residues not encoded by a human germline immunoglobulin sequence (e.g., mutations introduced by random or site-directed mutagenesis in vitro, or by somatic mutation in vivo). However, as used herein, the term “human antibody” is not intended to include antibodies in which a CDR sequence derived from the germline of another mammalian species, such as mouse, has been transplanted against a human framework sequence. The terms “human antibody” and “full human antibody” are used synonymously.

[0050] A "humanized antibody" refers to an antibody in which some, almost all, or all of the amino acids outside the CDR of a non-human antibody are replaced with corresponding amino acids derived from human immunoglobulin. In one aspect of the humanized form of an antibody, some, almost all, or all of the amino acids outside the CDR are replaced with amino acids derived from human immunoglobulin, while some, almost all, or all of the amino acids within one or more CDRs remain unchanged. Small additions, deletions, insertions, substitutions, or modifications of amino acids are acceptable as long as they do not invalidate the antibody's ability to bind to a particular antigen. A "humanized antibody" retains similar antigen specificity to that of the original antibody.

[0051] A "chimeric antibody" refers to an antibody in which the variable region originates from one species and the constant region originates from another species; for example, an antibody in which the variable region originates from a mouse antibody and the constant region originates from a human antibody.

[0052] An "anti-antigen antibody" refers to an antibody that specifically binds to an antigen. For example, an anti-PD-1 antibody specifically binds to PD-1, an anti-PD-L1 antibody specifically binds to PD-L1, and an anti-CTLA-4 antibody specifically binds to CTLA-4.

[0053] The "antigen-binding portion" (also called the "antigen-binding fragment") of an antibody refers to one or more fragments of the antibody that retain the ability to specifically bind to the antigen to which the entire antibody binds. It has been shown that the antigen-binding function of an antibody can be performed by fragments of a full-length antibody. Examples of binding fragments that fall within the scope of the term "antigen-binding portion" of an antibody, e.g., anti-PD-1 antibody or anti-PD-L1 antibody as described herein, include (i) Fab fragments (fragments from papain cleavage) or V L , V H (ii) a similar monovalent fragment consisting of LC and CH1 domains, (ii) a similar divalent fragment containing an F(ab')2 fragment (fragment from pepsin cleavage) or two Fab fragments linked by disulfide bridges in the hinge region, (iii) V H and an Fd fragment consisting of the CH1 domain, (iv) V of a single arm of the antibody L and V H Fv fragment consisting of domains, (v)V H The fragment includes (vi) a dAb fragment consisting of domains (Ward et al., (1989) Nature 341:544-546), (vi) an isolated complementarity-determining region (CDR), and (vii) a combination of two or more isolated CDRs which may be linked by a synthetic linker. Furthermore, it includes two domains of the Fv fragment, V L and V H These are encoded by separate genes, but they can be recombined using a recombination method. L and V HThe regions can be linked by synthetic linkers, which allow them to be created as a single protein chain forming a monovalent molecule (known as single-chain Fv (scFv); see, e.g., Bird et al. (1988) Science 242:423-426 and Huston et al. (1988) Proc. Natl. Acad. Sci. USA 85:5879-5883). Such single-chain antibodies are also intended to be included within the scope of the term “antigen-binding moiety” of an antibody. These antibody fragments are obtained using conventional techniques known to those skilled in the art, and the fragments are screened for utility in the same manner as intact antibodies. Antigen-binding moieties can be produced by recombinant DNA techniques or by enzymatic or chemical cleavage of intact immunoglobulins.

[0054] Antibodies useful in the methods and compositions described herein include inducible T cell costimulator (ICOS), CD137 (4-1BB), CD134 (OX40), NKG2A, CD27, CD96, glucocorticoid-inducible TNFR-related protein (GITR), and herpesvirus entry mediator (HVEM), programmed death-1 (PD-1), programmed death ligand-1 (PD-L1), cytotoxic T lymphocyte antigen-4 (CTLA-4), B lymphocyte and T lymphocyte attenuator (BTLA), T cell immunoglobulin and mucin domain-3 (TIM-3), lymphocyte activation gene-3 (LAG-3), and adenosine A2a receptor. This includes, but is not limited to, antibodies and their antigen-binding moieties that specifically bind to proteins selected from the group consisting of (A2aR), killer cell lectin-like receptor G1 (KLRG-1), natural killer cell receptor 2B4 (CD244), CD160, T cell immune receptors having Ig and ITIM domains (TIGIT), and the receptor for the V-domain Ig suppressor of T cell activation (VISTA), KIR, TGFβ, IL-10, IL-8, IL-2, B7-H4, Fas ligand, CXCR4, CSF1R, mesothelin, CEACAM-1, CD52, HER2, MICA, MICB, CSF1R, and any combination thereof.

[0055] "Cancer" refers to a broad group of diseases characterized by the uncontrolled proliferation of abnormal cells in the body. This uncontrolled cell division and proliferation leads to the formation of malignant tumors, which can invade neighboring tissues and metastasize to distant parts of the body via the lymphatic system or bloodstream.

[0056] The term “immunotherapy” refers to the treatment of a subject who is suffering from, at risk of suffering from, or experiencing a relapse of, a disease, by means of inducing, enhancing, suppressing, or otherwise modifying the immune response. The “treatment” or “therapy” of a subject refers to any type of intervention or process performed on the subject, or administration of an activator to the subject, with the aim of reversing, reducing, improving, inhibiting, delaying, or preventing the onset, progression, development, severity, or relapse of symptoms, complications, or conditions, or biochemical signs associated with the disease.

[0057] "Programmed death-1" (PD-1) refers to an immunosuppressive receptor belonging to the CD28 family. PD-1 is primarily expressed on already activated T cells in vivo and binds to two ligands, PD-L1 and PD-L2. As used herein, the term "PD-1" includes human PD-1 (hPD-1), variants, isoforms, and species homologs of hPD-1, as well as analogs having at least one common epitope with hPD-1. The complete hPD-1 sequence can be found under GenBank accession number U64863.

[0058] Programmed death ligand-1 (PD-L1) is one of two cell surface glycoprotein ligands for PD-1 (the other being PD-L2), and when it binds to PD-1, it downregulates T cell activation and cytokine secretion. As used herein, the term "PD-L1" includes human PD-L1 (hPD-L1), variants, isoforms, and species homologs of hPD-L1, as well as analogs having at least one common epitope with hPD-L1. The complete hPD-L1 sequence can be found under GenBank accession number Q9NZQ7. The human PD-L1 protein is encoded by the human CD274 gene (NCBI Gene ID: 29126).

[0059] As used herein, “PD-L1 negative” can be used interchangeably with “PD-L1 expression of less than approximately 1%.” PD-L1 expression can be measured by any method known in the art. In some embodiments, PD-L1 expression is measured by automated immunohistochemistry (IHC). In some embodiments, a PD-L1 negative tumor may therefore have less than approximately 1% of tumor cells expressing PD-L1, as measured by automated IHC. In some embodiments, a PD-L1 negative tumor does not have tumor cells expressing PD-L1.

[0060] As used herein, PD-1 or PD-L1 “inhibitor” refers to any molecule that can block, reduce, or otherwise restrict the interaction between PD-1 and PD-L1 and / or the activity of PD-1 and / or PD-L1. In some embodiments, the inhibitor is an antibody or an antigen-binding fragment of an antibody. In other embodiments, the inhibitor includes a small molecule.

[0061] "Subject" includes any human or non-human animal. The term "non-human animal" includes, but is not limited to, vertebrates, e.g., non-human primates, sheep, dogs, and rodents, e.g., mice, rats, and guinea pigs. In a preferred embodiment, the subject is human. The terms "subject" and "patient" are used interchangeably herein.

[0062] The “therapeutic effective dose” or “therapeutic effective dosage” of a drug or therapeutic agent is any amount of the drug, when used alone or in combination with another therapeutic agent, that protects a subject from the onset of the disease or promotes disease regression, as demonstrated by a reduction in the severity of disease symptoms, an increase in the frequency and duration of disease-free periods, or the prevention of disability or impairment due to the distress of the disease. The ability of a therapeutic agent to promote disease regression can be evaluated using various methods known to those skilled in the art, for example, by assaying the activity of the drug in human subjects during clinical trials, in animal model systems to predict efficacy in humans, or in in vitro assays.

[0063] For example, “anti-cancer agents” promote the regression of cancer in a subject. In a preferred embodiment, a therapeutically effective dose of the drug promotes cancer regression to the point of eliminating the cancer. “Promoting cancer regression” means that, by administering an effective dose of the drug alone or in combination with an anti-tumor agent, a reduction in tumor growth or size, tumor necrosis, a decrease in the severity of at least one disease symptom, an increase in the frequency and duration of disease-free periods, or prevention of disability or impairment due to the distress of the disease. Furthermore, the terms “effective” and “efficacy” in relation to a treatment include both pharmacological efficacy and physiological safety. Pharmacological efficacy refers to the ability of a drug to promote cancer regression in a patient. Physiological safety refers to the level of toxicity or other harmful physiological effects (adverse effects) at the cellular, organ and / or biological level resulting from the administration of the drug.

[0064] As used herein, “immuno-oncology” therapy or “IO” therapy refers to a therapy that utilizes the immune response to target and treat a tumor in a subject. Therefore, as used herein, IO therapy is a type of anti-cancer therapy. In some embodiments, IO therapy includes the administration of an antibody or its antigen-binding fragment to a subject. In some embodiments, IO therapy includes the administration of immune cells, e.g., T cells, e.g., modified T cells, e.g., T cells modified to express a chimeric antigen receptor or a specific T cell receptor. In some embodiments, IO therapy includes the administration of a therapeutic vaccine to a subject. In some embodiments, IO therapy includes the administration of a cytokine or chemokine to a subject. In some embodiments, IO therapy includes the administration of an interleukin to a subject. In some embodiments, IO therapy includes the administration of an interferon to a subject. In some embodiments, IO therapy includes the administration of a colony-stimulating factor to a subject.

[0065] As an example of tumor treatment, a therapeutically effective dose of an anticancer agent preferably inhibits cell proliferation or tumor growth by at least about 20%, more preferably at least about 40%, even more preferably at least about 60%, and even more preferably at least about 80% compared to an untreated subject. In other preferred embodiments of the present disclosure, tumor regression can be observed and continued for at least about 20 days, more preferably at least about 40 days, or even more preferably at least about 60 days. Despite these final measurements of therapeutic efficacy, immune-related response patterns must also be taken into consideration when evaluating immunotherapeutic agents.

[0066] "Immune response," as understood in the art, generally refers to the biological response in vertebrates to foreign substances or abnormal cells, such as cancer cells, which protect the organism from these substances and the diseases they cause. The immune response is mediated by the action of one or more cells of the immune system (e.g., T lymphocytes, B lymphocytes, natural killer (NK) cells, macrophages, eosinophils, mast cells, dendritic cells, or neutrophils) and soluble macromolecules (including antibodies, cytokines, and complement) produced by any of these cells or the liver, resulting in the selective targeting, binding, damage, destruction, and / or removal from the vertebrate body of invading pathogens, pathogen-infected cells or tissues, cancer cells or other abnormal cells, or, in the case of autoimmune or pathological inflammation, normal human cells or tissues. Immune responses include, for example, T cells, e.g., effector T cells, Th cells, CD4 + cells, CD8 + This includes activation or inhibition of T cells or Treg cells, or activation or inhibition of any other cells of the immune system, such as NK cells.

[0067] The "immune-associated response pattern" refers to the clinical response patterns frequently observed in cancer patients treated with immunotherapeutic agents that produce antitumor effects by inducing cancer-specific immune responses or by modifying innate immune processes. This response pattern is characterized by beneficial therapeutic effects followed by an initial increase in tumor volume or the appearance of new lesions, which in conventional chemotherapy evaluations are classified as disease progression and are synonymous with drug failure. Therefore, proper evaluation of immunotherapeutic agents may require long-term monitoring of their effects on the target disease.

[0068] As used herein, the terms “to treat,” “to treat,” and “to treat” refer to any type of intervention or process performed on a subject, or the administration of an activator to a subject, for the purpose of reversing, reducing, improving, inhibiting, delaying, or preventing the progression, onset, severity, or recurrence of disease-related symptoms, complications, conditions, or biochemical signs, or enhancing overall survival. A treatment may be performed on a subject with the disease or on a subject without the disease (e.g., for preventative purposes).

[0069] The term “effective dose” or “effective dosage” is defined as the amount sufficient to achieve, or at least partially achieve, the desired effect. The “therapeutic effective dose” or “therapeutic effective dosage” of a drug or therapeutic agent is any amount of the drug, when used alone or in combination with another therapeutic agent, that promotes disease regression, as demonstrated by a reduction in the severity of disease symptoms, an increase in the frequency and duration of disease-free periods, an increase in overall survival (the length of time a patient diagnosed with a disease, such as cancer, is still alive from either the date of diagnosis or the date of initiation of treatment), or the prevention of disability or impairment due to the distress of the disease. The therapeutic effective dose or dosage of a drug includes the “preventive effective dose” or “preventive effective dosage,” which is any amount of the drug, when administered alone or in combination with another therapeutic agent, to a subject at risk of developing or experiencing a relapse of the disease, that inhibits the onset or relapse of the disease. The ability of therapeutic agents to promote disease regression or inhibit the onset or recurrence of disease can be evaluated using various methods known to those skilled in the art, for example, by assaying the activity of the active agent in human subjects during clinical trials, in animal model systems to predict efficacy in humans, or in in vitro assays.

[0070] For example, anticancer drugs are drugs that promote the regression of cancer in a subject. In some embodiments, a therapeutically effective dose of the drug promotes cancer regression to the point of eliminating the cancer. "Promoting cancer regression" means that, by administering an effective dose of the drug alone or in combination with an antitumor drug, a reduction in tumor growth or size, tumor necrosis, a decrease in the severity of at least one disease symptom, an increase in the frequency and duration of disease-free periods, an increase in overall survival, prevention of disability or impairment due to the distress of the disease, or improvement in another mode of disease symptoms in the patient. In addition, the terms "effective" and "efficacy" in relation to treatment include both pharmacological efficacy and physiological safety. Pharmacological efficacy refers to the ability of a drug to promote cancer regression in a patient. Physiological safety refers to the level of toxicity or other harmful physiological effects (adverse effects) at the cellular, organ and / or biological level resulting from the administration of the drug.

[0071] As an example of tumor treatment, a therapeutically effective dose or dosage of the drug inhibits cell proliferation or tumor growth by at least about 20%, at least about 40%, at least about 60%, or at least about 80% compared to an untreated subject. In some embodiments, a therapeutically effective dose or dosage of the drug completely inhibits cell proliferation or tumor growth, i.e., 100% inhibition of cell proliferation or tumor growth. The ability of a compound to inhibit tumor growth can be evaluated using the assays described herein. Alternatively, this property of a composition can be evaluated by examining the ability of a compound to inhibit cell proliferation, and such inhibition can be measured in vitro by assays known to those skilled in the art. In some embodiments described herein, tumor regression can be observed and continued for at least about 20 days, at least about 40 days, or at least about 60 days.

[0072] As used herein, the term “biological sample” refers to a biomaterial isolated from a subject. A biological sample may contain any biomaterial suitable for determining target gene expression by, for example, sequencing nucleic acids in a tumor (or circulating tumor cells) and identifying genomic alterations in the sequenced nucleic acids. A biological sample may be any suitable biological tissue or fluid, such as tumor tissue, blood, plasma, and serum. In one embodiment, the sample is a tumor sample. In some embodiments, the tumor sample may be obtained from a tumor tissue biopsy sample, such as formalin-fixed paraffin-embedded (FFPE) tumor tissue or fresh-frozen tumor tissue. In another embodiment, the biological sample is a liquid biopsy sample, which in some embodiments includes one or more of blood, serum, plasma, circulating tumor cells, exoRNA, ctDNA, and cfDNA.

[0073] As used herein, “tumor sample” refers to a biological sample containing tumor tissue. In some embodiments, the tumor sample is a tumor biopsy sample. In some embodiments, the tumor sample includes tumor cells and one or more non-tumor cells present in the tumor microenvironment (TME). For the purposes of this disclosure, the TME consists of at least two regions. The tumor “parenchyma” is a region of the TME that primarily contains tumor cells, e.g., a portion of the TME containing the majority of tumor cells. The tumor parenchyma does not necessarily consist solely of tumor cells, but may also contain other cells, e.g., stromal cells and / or lymphocytes. The “stromal” region of the TME includes adjacent non-tumor cells. In some embodiments, the tumor sample includes all or part of the tumor parenchyma and one or more cells of the stroma. In some embodiments, the tumor sample is obtained from the parenchyma. In some embodiments, the tumor sample is obtained from the stroma. In other embodiments, the tumor sample is obtained from both the parenchyma and the stroma.

[0074] The use of alternative options (e.g., "or") should be understood to mean one, both, or any combination thereof of the alternative options. Where used herein, the indefinite article "a" or "an" should be understood to refer to "one or more" of any constituent elements listed or enumerated.

[0075] The terms “approximately” or “essentially include” refer to a value or composition that falls within the tolerance range of a particular value or composition as determined by those skilled in the art, and this is considered to depend in part on how the value or composition is measured or determined, i.e., on the limits of the measuring system. For example, “approximately” or “essentially include” may also mean within 1 or a standard deviation greater than 1, according to the practice of the art. Alternatively, “approximately” or “essentially include” may mean a range of up to 10%. Furthermore, particularly with respect to biological systems or processes, this term may mean up to one order of magnitude or up to five times the value. Where a particular value or composition is provided in this application and claims, unless otherwise stated, the meaning of “approximately” or “essentially include” should be assumed to be within the tolerance range of that particular value or composition.

[0076] Where used herein, any range of concentration, percentage, ratio, or integer should be understood, unless otherwise indicated, to include any integer value within the range and, where appropriate, fractions thereof (such as one-tenth and one-hundredth of an integer).

[0077] Various aspects of this disclosure are described in further detail in the following subsections.

[0078] II. Method of Disclosure PD-L1 expression has been identified as a biomarker of responsiveness to anti-PD-1 antibody therapy. Surprisingly, this disclosure finds that a subpopulation of PD-L1-negative tumors is responsive to therapies targeting PD-1 signaling. This was observed with both anti-PD-1 antibody monotherapy and combination therapies including anti-PD-1 and anti-CTLA-4 antibodies.

[0079] Certain aspects of the present disclosure relate to a method for treating a human subject with a tumor, comprising administering an anti-PD-1 / PD-L1 antagonist to the subject, wherein a tumor sample obtained from the subject exhibits (i) an exclusionary CD8 localization phenotype and (ii) a negative PD-L1 expression status ("PD-L1 negative").

[0080] Other aspects of this disclosure relate to a method for identifying subjects suitable for immuno-oncology (IO) therapy, such as anti-PD-1 / PD-L1 antagonist therapy alone or in combination with anti-CTLA-4 antagonist therapy. In some aspects, the method comprises (i) measuring PD-L1 expression in tumor samples obtained from subjects, and (ii) measuring CD8 expression in tumor samples, where CD8 expression is measured by immunostaining and imaging, and then classifying localized CD8 expression in tumor samples using a machine learning algorithm. In some aspects, the method further comprises administering an anti-PD-1 / PD-L1 antagonist to subjects identified as having tumor samples exhibiting (i) an exclusionary CD8 localization phenotype and (ii) a negative PD-L1 expression status ("PD-L1 negative").

[0081] In some embodiments, the method further includes administering an additional anticancer agent. In some embodiments, the method further includes administering an anti-CTLA-4 antagonist.

[0082] In some embodiments, the tumor sample obtained from the subject includes a tumor biopsy sample. In some embodiments, the tumor sample is formalin-fixed, paraffin-embedded tumor tissue. In some embodiments, the tumor sample is fresh-frozen tumor tissue.

[0083] II.A. Measurement of CD8 and PD-L1 expression CD8 localization and / or PD-L1 expression in tumor samples can be measured using any method known in the art. In some embodiments, CD8 expression is measured using a first method and PD-L1 expression is measured using a second method, where the first and second methods are different. In some embodiments, CD8 expression and PD-L1 expression are measured in the same tumor sample. In some embodiments, CD8 expression and PD-L1 expression are measured in two different tumor samples obtained from the same subject. In some embodiments, CD8 expression and PD-L1 expression are measured in two different tumor samples obtained from the same subject, where the two different tumor samples are two sections of the same tumor. In some embodiments, CD8 expression and PD-L1 expression are measured in two different tumor samples obtained from the same subject, where the two different tumor samples are two adjacent sections of the same tumor.

[0084] II.A.1.CD8 localization CD8 localization can be determined using any method known in the art. In some embodiments, the method involves directly measuring the localization of CD8 expression in a tumor sample obtained from a subject, for example, the location of CD8-expressing cells. In certain embodiments, CD8 localization involves measuring the CD8 protein in the tumor sample. In some embodiments, the CD8 protein is measured by contacting the tumor sample with an antibody that binds to CD8 or its antigen-binding moiety. In some embodiments, CD8 localization is measured using an immunohistochemical assay. In some embodiments, the assay includes an automated immunohistochemical assay. In other embodiments, CD8 localization involves measuring the CD8 mRNA in the tumor sample. In some embodiments, CD8 localization is measured using an RNA in situ hybridization assay. In other embodiments, CD8 localization is measured by isolating RNA from the tumor sample or a partial section and measuring CD8 expression by a reverse transcriptase PCR (RT-PCR) assay.

[0085] In certain embodiments, CD8 localization is measured by staining a tumor sample with an antibody or its antigen-binding moiety that binds to CD8. In some embodiments, CD8 localization is measured by staining a tumor sample with an antibody or its antigen-binding moiety that binds to CD8 and imaging the tumor sample, for example, by preparing one or more histological images of the tumor sample. Imaging of the tumor sample can be performed by a human or it can be automated, for example, by machine computation (competed). In some embodiments, the histological images are analyzed by a human, for example, a pathologist, and CD8 expression is characterized by a human. In other embodiments, the histological images are analyzed by a machine, for example, a computer via machine learning, and CD8 expression is characterized by a machine.

[0086] In some embodiments, CD8 localization is measured using immunohistochemical and imaging assays. In some embodiments, the assay results are not analyzed by humans, e.g., pathologists, and CD8 expression is not characterized by humans. In some embodiments, the assay results are analyzed by machines, e.g., computers, via machine learning, and CD8 expression is characterized by machines.

[0087] In certain embodiments, CD8 localization is measured by immunohistochemical staining and imaging, followed by classification of CD8 localization in tumor samples. CD8 localization classification can be performed using any method known in the art. In some embodiments, CD8 localization classification is not performed by a human. In some embodiments, CD8 localization classification is not performed by a pathologist. In some embodiments, CD8 localization classification is performed by a computing device.

[0088] Some aspects of this disclosure relate to a method for identifying subjects suitable for therapy comprising an anti-PD-1 / PD-L1 antagonist, comprising: receiving multiple histological images of tumor samples from multiple patients by at least one processor of a computing device; performing image analysis of the multiple histological images by at least one processor to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma in each of the multiple histological images; training a machine learning algorithm using the results of the image analysis and the abundance of CD8+ T cells in the tumor parenchyma and stroma by at least one processor; generating a machine learning feature space comprising multiple classifications based on the training by at least one processor; and identifying boundaries between the multiple classifications in the machine learning feature space by at least one processor. In some aspects, performing image analysis of multiple histological images comprises applying an artificial neural network to the multiple histological images. In some aspects, the abundance of CD8+ T cells comprises a graphical representation of the relationship between the percentage of stromal CD8+ T cells and the percentage of parenchymal CD8+ T cells relative to the total number of T cells present in each of the multiple histological images.

[0089] In some embodiments, the method further includes applying a polar coordinate transformation of the graph display to at least one processor of a computing device to produce a polar plot, and training a machine learning algorithm using the polar plot. In some embodiments, the multiple classifications include inflammatory, desert, exclusion, or balanced. In some embodiments, the machine learning algorithm includes a random forest classifier algorithm. In some embodiments, the method further includes determining a classification for each of the multiple histological images based on a machine learning feature space. In some embodiments, the method further includes validating the results from the machine learning feature space by comparing the labels for each of the multiple histological images obtained by at least one pathologist with the classification for each of the multiple histological images. In some embodiments, the method further includes receiving additional histological images, performing additional image analysis of the additional histological images to obtain additional CD8+ T cell abundances in the tumor parenchyma and stroma in the additional histological images, applying a machine learning algorithm to the results from the additional image analysis and the additional CD8+ T cell abundances, and determining a classification for the additional histological images based on a machine learning feature space.

[0090] Other aspects of this disclosure relate to a system including memory and a processor coupled to memory, wherein the processor is configured to receive multiple histological images of tumor samples from multiple patients, perform image analysis of the multiple histological images to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma in each of the multiple histological images, train a machine learning algorithm using the results of the image analysis and the abundance of CD8+ T cells in the tumor parenchyma and stroma, generate a machine learning feature space including multiple classifications based on the training, identify boundaries between the multiple classifications in the machine learning feature space, and store data relating to the machine learning feature space and boundaries in memory. In some aspects, performing image analysis of multiple histological images includes applying an artificial neural network to the multiple histological images, and the machine learning algorithm includes a random forest classifier algorithm. In some aspects, the abundance of CD8+ T cells includes a graphical representation of the relationship between the percentage of stromal CD8+ T cells and the percentage of parenchymal CD8+ T cells relative to the total number of T cells present in each of the multiple histological images. In some embodiments, the processor is configured to further receive additional histological images, perform additional image analysis on the additional histological images to obtain the abundance of additional CD8+ T cells in the tumor parenchyma and stroma in the additional histological images, apply a machine learning algorithm to the results from the additional image analysis and the abundance of additional CD8+ T cells, and determine a classification for the additional histological images based on the machine learning feature space. In some embodiments, multiple classifications include inflammatory, desert, exclusionary, or balanced types.

[0091] Other aspects of this disclosure relate to a non-temporary computer-readable medium on which instructions are stored, and which, by execution of the instructions by one or more processors of the device, causes one or more processors to perform operations including receiving multiple histological images of tumor samples from multiple patients, performing image analysis of the multiple histological images to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma in each of the multiple histological images, training a machine learning algorithm using the results of the image analysis and the abundance of CD8+ T cells in the tumor parenchyma and stroma, generating a machine learning feature space containing multiple classifications based on the training, and identifying the boundaries between the multiple classifications in the machine learning feature space. In some aspects, performing image analysis of multiple histological images includes applying an artificial neural network to the multiple histological images. In some aspects, the machine learning algorithm includes a random forest classifier algorithm. In some aspects, the abundance of CD8+ T cells includes a graphical representation of the relationship between the percentage of stromal CD8+ T cells and the percentage of parenchymal CD8+ T cells relative to the total number of T cells present in each of the multiple histological images. In some embodiments, the operation further includes receiving additional histological images, performing additional image analysis of the additional histological images to obtain the abundance of additional CD8+ T cells in the tumor parenchyma and stroma in the additional histological images, applying a machine learning algorithm to the results from the additional image analysis and the abundance of additional CD8+ T cells, and determining the classification of the additional histological images based on a machine learning feature space. In some embodiments, the classifications include inflammatory, desert, exclusionary, or balanced types.

[0092] In other embodiments, CD8 localization is measured by assaying the expression of one or more additional biomarkers. In some embodiments, the expression profiles of one or more additional biomarkers indicate whether high levels of CD8 localization are present in a tumor (e.g., an inflammatory CD8 localization phenotype) or stroma (e.g., an exclusionary CD8 localization phenotype). In some embodiments, CD8 localization is measured using a genome expression profiling (GEP) assay. Any method known in the art for measuring the expression of a particular gene or a panel of genes may be used in the methods of this disclosure. In some embodiments, the expression of one or more inflammatory genes in a panel of inflammatory genes is determined by detecting the presence of mRNA transcribed from the inflammatory gene, the presence of a protein encoded by the inflammatory gene, or both.

[0093] However, in any method involving the measurement of CD8 in test tissue samples, it should be understood that the step of providing test tissue samples obtained from patients is an appropriate step.

[0094] II.A.2. PD-L1 Expression In some embodiments, test tissue samples can be obtained from patients in need of therapy to evaluate PD-L1 expression. In other embodiments, evaluation of PD-L1 expression can be achieved without obtaining test tissue samples. In some embodiments, selecting a suitable patient includes (i) providing, as appropriate, test tissue samples obtained from patients having cancerous tissue, which include tumor cells and / or tumor-infiltrating inflammatory cells, and (ii) evaluating the percentage of cells expressing PD-L1 on the cell surface in the test tissue samples, based on the evaluation that the percentage of cells expressing PD-L1 on the cell surface in the test tissue samples is higher than a predetermined threshold level.

[0095] However, in any method involving the measurement of PD-L1 in a test tissue sample, it should be understood that the step of providing a test tissue sample obtained from a patient is an optional step. It should also be understood that in certain embodiments, the “measurement” or “evaluation” step to identify cells expressing PD-L1 in a test tissue sample (e.g., PD-L1 expression on the cell surface) or to determine their number or proportion may be performed by performing a modified assay for PD-L1 expression, such as a reverse transcriptase polymerase chain reaction (RT-PCR) assay or an IHC assay. In certain other embodiments, the modified step is not included, and PD-L1 expression is evaluated, for example, by examining a report of test results from a laboratory. In certain embodiments, the steps of the method up to and including the evaluation of PD-L1 expression provide an interim result that may be provided to a physician or other healthcare provider for use in selecting a suitable candidate for an anti-PD-1 antibody or anti-PD-L1 antibody therapy. In certain embodiments, the step of providing an interim result is performed by a physician or a person acting under the direction of a physician. In other embodiments, these steps are performed by an independent laboratory or by an independent person, such as a laboratory technician.

[0096] In certain embodiments of any of these methods, the percentage of cells expressing PD-L1 is assessed by performing an assay to determine the presence of PD-L1 RNA. In further embodiments, the presence of PD-L1 RNA is determined by RT-PCR, in situ hybridization, or RNase protection. In other embodiments, the percentage of cells expressing PD-L1 is assessed by performing an assay to determine the presence of PD-L1 polypeptide. In further embodiments, the presence of PD-L1 polypeptide is determined by immunohistochemistry (IHC), enzyme-linked immunosorbent assay (ELISA), in vivo imaging, or flow cytometry. In some embodiments, PD-L1 expression is assayed by IHC. In other embodiments of all these methods, cell surface expression of PD-L1 is assayed using, for example, IHC or in vivo imaging.

[0097] Imaging techniques have provided crucial tools in cancer research and treatment. Recent advances in molecular imaging systems, including positron emission tomography (PET), single-photon emission computed tomography (SPECT), fluorescence reflection imaging (FRI), fluorescence-mediated tomography (FMT), bioluminescence imaging (BLI), laser scanning confocal microscopy (LSCM), and multiphoton microscopy (MPM), suggest that the use of these techniques in cancer research is likely to expand further. Some of these molecular imaging systems not only allow clinicians to see where tumors are located in the body, but also to visualize specific molecules, cellular expression and activity, and biological processes that influence tumor behavior and / or responsiveness to therapeutics (Condeelis and Weissleder, "In vivo imaging in cancer," Cold Spring Harb. Perspect. Biol. 2(12):a003848 (2010)). ImmunoPET imaging is particularly attractive for monitoring and assaying antigen expression in tissue samples due to the combination of antibody specificity and the sensitivity and resolution of PET imaging (McCabe and Wu, "Positive progress in immunoPET—not just a coincidence," Cancer Biother. Radiopharm. 25(3):253-61 (2010); Olafsen et al., "ImmunoPET imaging of B-cell lymphoma using 124I-anti-CD20 scFv dimers (diabodies)," Protein Eng. Des. Sel. 23(4):243-9 (2010)). In certain embodiments of this method, PD-L1 expression is assayed by immunoPET imaging. In certain embodiments of this method, the percentage of cells in a test tissue sample expressing PD-L1 is assessed by performing an assay to determine the presence of PD-L1 polypeptides on the surface of cells in the test tissue sample. In certain embodiments, the test tissue sample is an FFPE tissue sample.In other embodiments, the presence of PD-L1 polypeptide is determined by an IHC assay. In further embodiments, the IHC assay is performed using an automated process. In some embodiments, the IHC assay is performed using an anti-PD-L1 monoclonal antibody that binds to the PD-L1 polypeptide.

[0098] In one aspect of the present invention, an automated IHC method is used to assay the expression of PD-L1 on the surface of cells in an FFPE tissue specimen. In some aspects, the immunostained, e.g., IHC images are further analyzed using a machine learning algorithm. In some aspects, the immunostained, e.g., IHC images are analyzed by a pathologist. The disclosure provides a method for detecting the presence of human PD-L1 antigen in a test tissue sample or for quantifying the level of human PD-L1 antigen or the percentage of cells expressing the antigen in a sample, comprising contacting the test sample and a negative control sample with a monoclonal antibody that specifically binds to human PD-L1 under conditions that allow for the formation of a complex between the antibody or a portion thereof and human PD-L1. In certain aspects, the test tissue sample and the control tissue sample are FFPE samples. The formation of the complex is then detected, and the presence of human PD-L1 antigen in the sample is indicated by a difference in complex formation between the test sample and the negative control sample. Various methods are used to quantify PD-L1 expression.

[0099] In a particular embodiment, the automated IHC method includes (a) deparaffinizing and rehydrating mounted tissue sections in an automated staining apparatus; (b) recovering the antigen by heating at 110°C for 10 minutes using a declocking chamber and pH 6 buffer; (c) setting reagents in the automated staining apparatus; and (d) operating the automated staining apparatus to neutralize endogenous peroxidase in the tissue specimen, block nonspecific protein binding sites on the slide, incubate the slide with a primary antibody, incubate with a post-primary blocking agent, incubate with a NovoLink polymer, add a chromogenic substrate to develop color, and counterstain with hematoxylin.

[0100] To evaluate PD-L1 expression in tumor tissue samples, in some embodiments, a pathologist examines the number of membrane PD-L1+ tumor cells in each field of view under a microscope, mentally estimates the percentage of positive cells, and then averages them to obtain the final percentage. Differences in staining intensity are defined as 0 / negative, 1+ / weak, 2+ / medium, and 3+ / strong. Typically, the percentage values ​​are assigned first to the 0 and 3+ buckets, followed by the intermediate 1+ and 2+ intensities. For highly heterogeneous tissues, the specimen is divided into multiple zones, each zone is scored separately, and then combined to obtain a single set of percentage values. The percentages of negative and positive cells for different staining intensities are determined from each region, and the median is assigned to each zone. The final percentage values ​​are assigned to the tissue for each staining intensity category, i.e., negative, 1+, 2+, and 3+. The sum of all staining intensities must be 100%. In one embodiment, the threshold number of cells that need to be PD-L1 positive is at least about 100 cells, at least about 125 cells, at least about 150 cells, at least about 175 cells, or at least about 200 cells. In a particular embodiment, the threshold number of cells that need to be PD-L1 positive is at least about 100 cells. In some embodiments, artificial intelligence can be used instead of a pathologist.

[0101] Staining is also evaluated in tumor-infiltrating inflammatory cells, such as macrophages and lymphocytes. In most cases, macrophages serve as internal positive controls, as staining is observed in the majority of macrophages. While staining with a 3+ intensity is not required, it should be considered that any technical failure should be ruled out if macrophage staining is absent. Macrophages and lymphocytes are evaluated for plasma membrane staining, and for all samples, only positive or negative for each cell category is recorded. Staining is also characterized by the designation of tumor immune cells as external / internal. "Internal" means that the immune cells are located within the tumor tissue and / or at the boundary of the tumor region, without physical interposition between tumor cells. "External" means that there is no physical connection to the tumor, and the immune cells are found peripherally, associated with connective tissue or any relevant adjacent tissue.

[0102] In certain embodiments of these scoring methods, the sample is scored by two independently working pathologists, and the scores are then combined. In certain other embodiments, the identification of positive and negative cells is scored using appropriate software.

[0103] The histological score (also known as the H-score) is used as a more quantitative measure of IHC data. The histological score is calculated as follows: Hist score = [(% tumor × 1 (low intensity)) + (% tumor × 2 (medium intensity)) + (% tumor × 3 (high intensity)]

[0104] To determine the histscore, the pathologist estimates the percentage of stained cells in each intensity category within the specimen. Since the expression of most biomarkers is heterogeneous, the histscore is a more accurate representation of overall expression. The final histscore ranges from 0 (no expression) to 300 (maximum expression).

[0105] An alternative method for quantifying PD-L1 expression in IHC test tissue samples is to determine the adjusted inflammation score (AIS) score, which is defined as the inflammation density multiplied by the percentage of PD-L1 expression by tumor-infiltrating inflammatory cells (Taube et al., "Colocalization of inflammatory response with B7-h1 expression in human melanocytic lesions supports an adaptive resistance mechanism of immune escape," Sci. Transl. Med. 4(127):127ra37 (2012)).

[0106] II.B. Treatment Methods Certain embodiments of this disclosure relate to methods for identifying suitable targets for therapy and subsequently administering the therapy to suitable targets. Methods for identifying suitable targets as described herein can be used prior to any immuno-oncology (IO) therapy. In some embodiments, the suitable targets are administered and / or subsequently administered antibodies or antigen-binding fragments that specifically bind to proteins selected from PD-1, PD-L1, CTLA-4, LAG-3, TIGIT, TIM3, CSF1R, NKG2a, OX40, ICOS, CD137, KIR, TGFβ, IL-10, IL-8, IL-2, CD96, VISTA, B7-H4, Fas ligand, CXCR4, mesothelin, CD27, GITR, MICA, MICB, and any combination thereof.

[0107] In some embodiments, an anti-PD-1 / PD-L1 antagonist is administered to a suitable subject, and / or administered thereafter. In certain embodiments, the anti-PD-1 / PD-L1 antagonist is an anti-PD-1 or anti-PD-L1 antibody. In some embodiments, an antibody or its antigen-binding fragment that specifically binds to PD-1 is administered to a suitable subject, and / or administered thereafter. In some embodiments, an antibody or its antigen-binding fragment that specifically binds to PD-L1 is administered to a suitable subject, and / or administered thereafter.

[0108] In some embodiments, the subject is further administered an anti-CTLA-4 agonist and / or subsequently administered. In some embodiments, a preferred subject is administered an antibody or antigen-binding fragment that specifically binds to CTLA-4 and / or subsequently administered.

[0109] In some embodiments, a suitable subject is administered and / or subsequently administered two or more antibodies or antigen-binding fragments disclosed herein. In some embodiments, a suitable subject is administered and / or subsequently administered at least two antibodies or antigen-binding fragments. In some embodiments, a suitable subject is administered and / or subsequently administered at least three antibodies or antigen-binding fragments. In certain embodiments, a suitable subject is administered and / or subsequently administered an antibody or antigen-binding fragment that specifically binds to PD-1 and an antibody or antigen-binding fragment that specifically binds to CTLA-4. In certain embodiments, a suitable subject is administered and / or subsequently administered an antibody or antigen-binding fragment that specifically binds to PD-L1 and an antibody or antigen-binding fragment that specifically binds to CTLA-4.

[0110] In certain embodiments, the therapy is administered to a suitable subject after CD8 localization and PD-L1 expression have been assayed. In some embodiments, the therapy is administered at least about 1 day, at least about 2 days, at least about 3 days, at least about 4 days, at least about 5 days, at least about 6 days, at least about 7 days, at least about 8 days, at least about 9 days, at least about 10 days, at least about 11 days, at least about 12 days, at least about 13 days, or at least about 14 days after CD8 localization and PD-L1 expression have been assayed.

[0111] Some aspects of the present disclosure relate to a method for treating cancer in a human subject, comprising administering an anti-PD-1 / anti-PD-L1 antagonist to the subject, wherein the subject is identified as having a tumor exhibiting (i) an exclusionary CD8 localization phenotype and (ii) a negative PD-L1 expression status ("PD-L1 negative"). Some aspects of the present disclosure relate to a method for identifying a suitable subject.

[0112] II.C. Anti-PD-1 / PD-L1 / CTLA-4 antagonist Certain aspects of this disclosure relate to methods for treating preferred subjects determined according to the methods disclosed herein using anti-PD-1 / PD-L1 antagonist therapy. Some aspects of this disclosure relate to methods for treating preferred subjects determined according to the methods disclosed herein using anti-PD-1 / PD-L1 antagonist and anti-CTLA-4 antagonist therapy. Any anti-PD-1 / PD-L1 / CTLA-4 antagonist known in the art may be used in the methods described herein. In some aspects, the anti-PD-1 antagonist includes an anti-PD-1 antibody.

[0113] In some embodiments, the subject receives monotherapy with a single anti-PD-1 / PD-L1 antagonist. In some embodiments, the subject receives monotherapy with an anti-PD-1 antibody. In some embodiments, the subject receives monotherapy with an anti-PD-L1 antibody. In some embodiments, the subject receives combination therapy including a first anti-PD-1 / PD-L1 antagonist and additional anticancer therapy. In some embodiments, the additional anticancer agent includes a second IO therapy, chemotherapy, standard treatment, or any combination thereof.

[0114] In certain embodiments, the subject is treated with combination therapy including an anti-PD-1 antibody and a second anticancer agent. In certain embodiments, the subject is treated with combination therapy including an anti-PD-1 antibody and an anti-CTLA-4 antibody. In certain embodiments, the subject is treated with combination therapy including an anti-PD-L1 antibody and an anti-CTLA-4 antibody.

[0115] II.C.1. Anti-PD-1 antibodies useful for the present disclosure Anti-PD-1 antibodies known in the art can be used in the compositions and methods described herein. Various human monoclonal antibodies that bind specifically to PD-1 with high affinity are disclosed in U.S. Patent No. 8,008,449. The anti-PD-1 human antibodies disclosed in U.S. Patent No. 8,008,449 have been demonstrated to exhibit one or more of the following characteristics: (a) determination by surface plasmon resonance using a Biacore biosensor system, 1 × 10⁻¹⁶ -7 K below M D (b) binds to human PD-1, (c) substantially does not bind to human CD28, CTLA-4, or ICOS, (d) increases T cell proliferation in a mixed lymphocyte reaction (MLR) assay, (e) increases interferon-γ production in an MLR assay, (f) binds to human PD-1 and cynomolgus monkey PD-1, (g) inhibits the binding of PD-L1 and / or PD-L2 to PD-1, (h) stimulates an antigen-specific memory response, (i) stimulates an antibody response, and (j) inhibits tumor cell proliferation in vivo. Anti-PD-1 antibodies available in this disclosure include monoclonal antibodies that specifically bind to human PD-1 and exhibit at least one, and in some embodiments, at least five, of the above features.

[0116] Other anti-PD-1 monoclonal antibodies include, for example, U.S. Patent Nos. 6,808,710, 7,488,802, 8,168,757, and 8,354,509, U.S. Patent Application Publication No. 2016 / 0272708, and PCT Publications WO2012 / 145493 and WO2008 / 15 6712, WO2015 / 112900, WO2012 / 145493, WO2015 / 112800, WO2014 / 206107, WO2015 / 35606, WO2015 / 085847, WO2014 / 179664, WO2017 / 020291, WO2017 / 020858, WO201 6 / 197367, WO2017 / 024515, WO2017 / 025051, WO2017 / 123557, WO2016 / 106159, WO 2014 / 194302, WO2017 / 040790, WO2017 / 133540, WO2017 / 132827, WO2017 / 024465, This is described in WO2017 / 025016, WO2017 / 106061, WO2017 / 19846, WO2017 / 024465, WO2017 / 025016, WO2017 / 132825, and WO2017 / 133540 (each of these being incorporated in whole by reference).

[0117] In some embodiments, the anti-PD-1 antibody is nivolumab (also known as OPDIVO®, 5C4, BMS-936558, MDX-1106, and ONO-4538), pembrolizumab (Merck; also known as KEYTRUDA®, lambrolizumab, and MK-3475; see WO2008 / 156712), PDR001 (Novartis; see WO2015 / 112900), MEDI-0680 (AstraZeneca; also known as AMP-514; see WO2012 / 145493), semiprimab (Regeneron; also known as REGN-2810; see WO2015 / 112800), JS001 (TAIZHOU JUNSHI PHARMA; also known as tripalimab; Si-Yang Liu et al., J. See Hematol. Oncol. 10:136 (2017), BGB-A317 (Beigene; also known as tislerizumab; see WO2015 / 35606 and U.S. Patent Application No. 2015 / 0079109), INCSHR1210 (Jiangsu Hengrui Medicine; also known as SHR-1210; see WO2015 / 085847; see Si-Yang Liu et al., J. Hematol. Oncol. 10:136 (2017)), TSR-042 (Tesaro Biopharmaceutical; also known as ANB011; see WO2014 / 179664), GLS-010 (Wuxi / Harbin Gloria Pharmaceuticals; also known as WBP3055; see Si-Yang Liu et al., J. Hematol. Oncol. See 10:136 (2017), AM-0001 (Armo), STI-1110 (Sorrento Therapeutics; see WO2014 / 194302), AGEN2034 (Agenus; see WO2017 / 040790), MGA012 (Macrogenics; see WO2017 / 19846), BCD-100 (Biocad; see Kaplon et al.The selection is made from the group consisting of mAbs 10(2):183-203 (2018) and IBI308 (see Innovent; WO2017 / 024465, WO2017 / 025016, WO2017 / 132825, and WO2017 / 133540).

[0118] In one embodiment, the anti-PD-1 antibody is nivolumab. Nivolumab is a fully human IgG4(S228P)PD-1 immune checkpoint inhibitor antibody that selectively blocks interaction with PD-1 ligands (PD-L1 and PD-L2), thereby blocking the downregulation of antitumor T cell function (U.S. Patent No. 8,008,449; Wang et al., 2014 Cancer Immunol Res. 2(9):846-56).

[0119] In another embodiment, the anti-PD-1 antibody is pembrolizumab. Pembrolizumab is a humanized monoclonal IgG4 (S228P) antibody against the human cell surface receptor PD-1 (programmed death-1 or programmed cell death-1). Pembrolizumab is described, for example, in U.S. Patent Nos. 8,354,509 and 8,900,587.

[0120] The anti-PD-1 antibodies usable in the disclosed compositions and methods also include isolated antibodies that specifically bind to human PD-1 and cross-compete with any anti-PD-1 antibodies disclosed herein for binding to human PD-1, such as nivolumab (see, e.g., U.S. Patent Nos. 8,008,449 and 8,779,105; WO2013 / 173223). In some embodiments, the anti-PD-1 antibody binds to the same epitope as any of the anti-PD-1 antibodies described herein, such as nivolumab. The ability of antibodies to cross-compete for binding to the antigen indicates that these monoclonal antibodies bind to the same epitope region of the antigen and sterically prevent the binding of other cross-competing antibodies to that particular epitope region. These cross-competing antibodies are expected to have functional properties very similar to the reference antibody, such as nivolumab, for their binding to the same epitope region of PD-1. Cross-competing antibodies can be readily identified based on their ability to cross-compete with nivolumab in standard PD-1 binding assays, such as Biacore analysis, ELISA assays, or flow cytometry (see, e.g., WO2013 / 173223).

[0121] In certain embodiments, an antibody that cross-competes with human PD-1 antibody, nivolumab, for binding to human PD-1, or an antibody that binds to the same epitope region of human PD-1 antibody, nivolumab, is a monoclonal antibody. For administration to human subjects, these cross-competing antibodies are chimeric antibodies, engineered antibodies, or humanized or human antibodies. Such chimeric antibodies, engineered antibodies, humanized or human monoclonal antibodies can be prepared and isolated by methods well known in the art.

[0122] The anti-PD-1 antibodies usable in the compositions and methods of this disclosure also include the antigen-binding moiety of the above-mentioned antibody. It has been well demonstrated that the antigen-binding function of the antibody can be performed by fragments of a full-length antibody.

[0123] The anti-PD-1 antibodies suitable for use in the disclosed compositions and methods are antibodies that bind to PD-1 with high specificity and affinity, block the binding of PD-L1 and / or PD-L2, and inhibit the immunosuppressive effect of the PD-1 signaling pathway. In any of the compositions or methods disclosed herein, the anti-PD-1 "antibody" includes an antigen-binding moiety or fragment that exhibits similar functional properties to the whole antibody in that it binds to the PD-1 receptor and inhibits ligand binding to upregulate the immune system. In certain embodiments, the anti-PD-1 antibody or its antigen-binding moiety cross-competes with nivolumab for binding to human PD-1.

[0124] In some embodiments, the anti-PD-1 antibody is administered once every 2, 3, 4, 5, 6, 7, or 8 weeks in a dose ranging from 0.1 mg / kg to 20.0 mg / kg body weight, for example, once every 2, 3, or 4 weeks in a dose ranging from 0.1 mg / kg to 10.0 mg / kg body weight. In other embodiments, the anti-PD-1 antibody is administered once every 2 weeks in a dose of approximately 2 mg / kg, approximately 3 mg / kg, approximately 4 mg / kg, approximately 5 mg / kg, approximately 6 mg / kg, approximately 7 mg / kg, approximately 8 mg / kg, approximately 9 mg / kg, or 10 mg / kg body weight. In other embodiments, the anti-PD-1 antibody is administered once every three weeks at a dose of approximately 2 mg / kg, 3 mg / kg, 4 mg / kg, 5 mg / kg, 6 mg / kg, 7 mg / kg, 8 mg / kg, 9 mg / kg, or 10 mg / kg body weight. In one embodiment, the anti-PD-1 antibody is administered once every three weeks at a dose of approximately 5 mg / kg body weight. In another embodiment, the anti-PD-1 antibody, for example, nivolumab, is administered once every two weeks at a dose of approximately 3 mg / kg body weight. In yet another embodiment, the anti-PD-1 antibody, for example, pembrolizumab, is administered once every three weeks at a dose of approximately 2 mg / kg body weight.

[0125] Anti-PD-1 antibodies useful for this disclosure can be administered as fixed doses. In some embodiments, anti-PD-1 antibodies are administered in fixed doses of approximately 100-1000 mg, 100-900 mg, 100-800 mg, 100-700 mg, 100-600 mg, 100-500 mg, 200-1000 mg, 200-900 mg, 200-800 mg, 200-700 mg, 200-600 mg, 200-500 mg, 200-480 mg, or 240-480 mg. In one embodiment, the anti-PD-1 antibody is administered in amounts of at least approximately 200 mg, at least approximately 220 mg, at least approximately 240 mg, at least approximately 260 mg, at least approximately 280 mg, at least approximately 300 mg, at least approximately 320 mg, at least approximately 340 mg, at least approximately 360 mg, at least approximately 380 mg, at least approximately 400 mg, at least approximately 420 mg, at least approximately 440 mg, at least approximately 460 mg, at least approximately 480 mg, at least approximately 500 mg, and less than In another embodiment, the anti-PD-1 antibody is administered as a fixed dose of approximately 520 mg, at least approximately 540 mg, at least approximately 550 mg, at least approximately 560 mg, at least approximately 580 mg, at least approximately 600 mg, at least approximately 620 mg, at least approximately 640 mg, at least approximately 660 mg, at least approximately 680 mg, at least approximately 700 mg, or at least approximately 720 mg, with administration intervals of approximately 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 weeks.

[0126] In some embodiments, the anti-PD-1 antibody is administered once every three weeks at a fixed dose of approximately 200 mg. In other embodiments, the anti-PD-1 antibody is administered once every two weeks at a fixed dose of approximately 200 mg. In other embodiments, the anti-PD-1 antibody is administered once every two weeks at a fixed dose of approximately 240 mg. In some embodiments, the anti-PD-1 antibody is administered once every four weeks at a fixed dose of approximately 480 mg.

[0127] In some embodiments, nivolumab is administered once every two weeks at a fixed dose of approximately 240 mg. In some embodiments, nivolumab is administered once every three weeks at a fixed dose of approximately 240 mg. In other embodiments, nivolumab is administered once every three weeks at a fixed dose of approximately 360 mg. In some embodiments, nivolumab is administered once every four weeks at a fixed dose of approximately 480 mg.

[0128] In some embodiments, pembrolizumab is administered once every two weeks at a fixed dose of approximately 200 mg. In some embodiments, pembrolizumab is administered once every three weeks at a fixed dose of approximately 200 mg. In some embodiments, pembrolizumab is administered once every four weeks at a fixed dose of approximately 400 mg.

[0129] In some embodiments, the PD-1 inhibitor is a small molecule. In some embodiments, the PD-1 inhibitor comprises miramolecule. In some embodiments, the PD-1 inhibitor comprises a macrocyclic peptide. In some embodiments, the PD-1 inhibitor comprises BMS-986189. In some embodiments, the PD-1 inhibitor comprises the inhibitor disclosed in International Publication WO2014 / 151634, which is incorporated herein by reference in its entirety. In some embodiments, the PD-1 inhibitor comprises INCMGA00012 (Insight Pharmaceuticals). In some embodiments, the PD-1 inhibitor comprises a combination of an anti-PD-1 antibody and a PD-1 small molecule inhibitor disclosed herein.

[0130] II.C.2. Anti-PD-L1 antibodies useful in this disclosure In certain embodiments, an anti-PD-L1 antibody is used in place of an anti-PD-1 antibody in any of the methods disclosed herein. Anti-PD-L1 antibodies known in the art can be used in the compositions and methods of this disclosure. Examples of anti-PD-L1 antibodies useful in the compositions and methods of this disclosure include the antibody disclosed in U.S. Patent No. 9,580,507. The anti-PD-L1 human monoclonal antibody disclosed in U.S. Patent No. 9,580,507 has been demonstrated to exhibit one or more of the following characteristics: (a) 1 × 10⁻¹⁶ as determined by surface plasmon resonance using a Biacore biosensor system. -7 K below M D (b) binds to human PD-L1, (c) increases T cell proliferation in a mixed lymphocyte reaction (MLR) assay, (d) increases interferon-γ production in an MLR assay, (e) stimulates an antibody response, and (f) reverses the effect of regulatory T cells on T cell effector cells and / or dendritic cells. Anti-PD-L1 antibodies available in this disclosure include monoclonal antibodies that specifically bind to human PD-L1 and exhibit at least one, and in some embodiments, at least five, of the above features.

[0131] In certain embodiments, the anti-PD-L1 antibody is BMS-936559 (also known as 12A4, MDX-1105; see, for example, U.S. Patent No. 7,943,743 and WO2013 / 173223), and atezolizumab (Roche; also known as TECENTRIQ®; MPDL3280A, RG7446; see, for example, U.S. Patent No. 8,217,149; also, Herbst et al. (2013) J Clin Oncol See also 31(suppl):3000), durvalumab (AstraZeneca; IMFINZI®, also known as MEDI-4736; see WO2011 / 066389), avelumab (Pfizer; BAVENCIO®, also known as MSB-0010718C; see WO2013 / 079174), STI-1014 (Sorrento; see WO2013 / 181634), CX-072 (Cytomx; see WO2016 / 149201), KN035 (3D Med / Alphamab; see Zhang et al., Cell Discov. 7:3 (March 2017)), LY3300054 (Eli Lilly The selection is made from the group consisting of Co. (see, for example, WO2017 / 034916), BGB-A333 (BeiGene; see Desai et al., JCO 36 (15suppl):TPS3113 (2018)), and CK-301 (Checkpoint Therapeutics; see Gorelik et al., AACR:Abstract 4606 (Apr 2016)).

[0132] In certain embodiments, the PD-L1 antibody is atezolizumab (TECENTRIQ®). Atezolizumab is a fully humanized IgG1 monoclonal anti-PD-L1 antibody.

[0133] In certain embodiments, the PD-L1 antibody is durvalumab (IMFINZI®). Durvalumab is a human IgG1κ monoclonal anti-PD-L1 antibody.

[0134] In certain embodiments, the PD-L1 antibody is avelumab (BAVENCIO®). Avelumab is a human IgG1λ monoclonal anti-PD-L1 antibody.

[0135] The anti-PD-L1 antibodies available for use in the disclosed compositions and methods also include isolated antibodies that specifically bind to human PD-L1 and cross-compete with any of the anti-PD-L1 antibodies disclosed herein, e.g., atezolizumab, durvalumab, and / or avelumab, for binding to human PD-L1. In some embodiments, the anti-PD-L1 antibody binds to the same epitope as any of the anti-PD-L1 antibodies described herein, e.g., atezolizumab, durvalumab, and / or avelumab. The ability of antibodies to cross-compete for binding to an antigen indicates that these antibodies bind to the same epitope region of the antigen and sterically prevent the binding of other cross-competing antibodies to that particular epitope region. These cross-competing antibodies are expected to have functional properties very similar to the functional properties of the reference antibody, e.g., atezolizumab and / or avelumab, for their binding to the same epitope region of PD-L1. Cross-competing antibodies can be readily identified based on their ability to cross-compete with atezolizumab and / or avelumab in standard PD-L1 binding assays, e.g., Biacore analysis, ELISA assay, or flow cytometry (see, e.g., WO2013 / 173223).

[0136] In certain embodiments, antibodies that cross-compete with atezolizumab, durvalumab, and / or avelumab for binding to human PD-L1, or that bind to the same epitope region of a human PD-L1 antibody, are monoclonal antibodies. For administration to human subjects, these cross-competing antibodies are chimeric antibodies, engineered antibodies, or humanized or human antibodies. Such chimeric antibodies, engineered antibodies, humanized or human monoclonal antibodies can be prepared and isolated by methods well known in the art.

[0137] The anti-PD-L1 antibodies usable in the disclosed compositions and methods also include the antigen-binding moiety of the above-mentioned antibody. It has been well demonstrated that the antigen-binding function of the antibody can be performed by fragments of a full-length antibody.

[0138] The anti-PD-L1 antibodies suitable for use in the disclosed compositions and methods are antibodies that bind to PD-L1 with high specificity and affinity, block PD-1 binding, and inhibit the immunosuppressive effect of the PD-1 signaling pathway. In any of the compositions or methods disclosed herein, the anti-PD-L1 "antibody" includes an antigen-binding moiety or fragment that exhibits similar functional properties to the whole antibody in that it binds to PD-L1 and inhibits receptor binding, thereby upregulating the immune system. In certain embodiments, the anti-PD-L1 antibody or its antigen-binding moiety cross-competes with atezolizumab, durvalumab, and / or avelumab for binding to human PD-L1.

[0139] The anti-PD-L1 antibody useful in this disclosure may be any PD-L1 antibody that specifically binds to PD-L1, for example, an antibody that cross-competes with durvalumab, avelumab, or atezolizumab for binding to human PD-1, for example, an antibody that binds to the same epitope as durvalumab, avelumab, or atezolizumab. In one particular embodiment, the anti-PD-L1 antibody is durvalumab. In another embodiment, the anti-PD-L1 antibody is avelumab. In some embodiments, the anti-PD-L1 antibody is atezolizumab.

[0140] In some embodiments, the anti-PD-L1 antibody is administered once every 2, 3, 4, 5, 6, 7, or 8 weeks in doses ranging from approximately 0.1 mg / kg to approximately 20.0 mg / kg body weight, or approximately 2 mg / kg, 3 mg / kg, 4 mg / kg, 5 mg / kg, 6 mg / kg, 7 mg / kg, 8 mg / kg, 9 mg / kg, 10 mg / kg, 11 mg / kg, 12 mg / kg, 13 mg / kg, 14 mg / kg, 15 mg / kg, 16 mg / kg, 17 mg / kg, 18 mg / kg, 19 mg / kg, or 20 mg / kg.

[0141] In some embodiments, the anti-PD-L1 antibody is administered at a dose of approximately 15 mg / kg body weight once every three weeks. In other embodiments, the anti-PD-L1 antibody is administered at a dose of approximately 10 mg / kg body weight once every two weeks.

[0142] In other embodiments, the anti-PD-L1 antibody useful for this disclosure is a fixed dose. In some embodiments, the anti-PD-L1 antibody is administered as a fixed dose of approximately 200 mg to 1600 mg, approximately 200 mg to 1500 mg, approximately 200 mg to 1400 mg, approximately 200 mg to 1300 mg, approximately 200 mg to 1200 mg, approximately 200 mg to 1100 mg, approximately 200 mg to 1000 mg, approximately 200 mg to 900 mg, approximately 200 mg to 800 mg, approximately 200 mg to 700 mg, approximately 200 mg to 600 mg, approximately 700 mg to 1300 mg, approximately 800 mg to 1200 mg, approximately 700 mg to 900 mg, or approximately 1100 mg to 1300 mg. In some embodiments, the anti-PD-L1 antibody is administered as a fixed dose of at least approximately 240 mg, at least approximately 300 mg, at least approximately 320 mg, at least approximately 400 mg, at least approximately 480 mg, at least approximately 500 mg, at least approximately 560 mg, at least approximately 600 mg, at least approximately 640 mg, at least approximately 700 mg, at least approximately 720 mg, at least approximately 800 mg, at least approximately 840 mg, at least approximately 880 mg, at least approximately 900 mg, at least 960 mg, at least approximately 1000 mg, at least approximately 1040 mg, at least approximately 1100 mg, at least approximately 1120 mg, at least approximately 1200 mg, at least approximately 1280 mg, at least approximately 1300 mg, at least approximately 1360 mg, or at least approximately 1400 mg, with administration intervals of approximately 1, 2, 3, or 4 weeks. In some embodiments, the anti-PD-L1 antibody is administered as a fixed dose of approximately 1200 mg once every approximately 3 weeks. In other embodiments, the anti-PD-L1 antibody is administered once every two weeks at a fixed dose of approximately 800 mg. In other embodiments, the anti-PD-L1 antibody is administered once every two weeks at a fixed dose of approximately 840 mg.

[0143] In some embodiments, atezolizumab is administered once every three weeks at a fixed dose of approximately 1200 mg. In some embodiments, atezolizumab is administered once every two weeks at a fixed dose of approximately 800 mg. In some embodiments, atezolizumab is administered once every two weeks at a fixed dose of approximately 840 mg.

[0144] In some configurations, avelumab is administered once every two weeks at a fixed dose of approximately 800 mg.

[0145] In some embodiments, durvalumab is administered at a dose of approximately 10 mg / kg once every two weeks. In some embodiments, durvalumab is administered at a fixed dose of approximately 800 mg / kg once every two weeks. In some embodiments, durvalumab is administered at a fixed dose of approximately 1200 mg / kg once every three weeks.

[0146] In some embodiments, the PD-L1 inhibitor is a small molecule. In some embodiments, the PD-L1 inhibitor comprises miramolecule. In some embodiments, the PD-L1 inhibitor comprises a macrocyclic peptide. In some embodiments, the PD-L1 inhibitor comprises BMS-986189.

[0147] In some embodiments, the PD-L1 inhibitor is of formula (I):

[0148] [ka] It includes a miramolecule having the formula described above, in which R 1 ~R 13 R is an amino acid side chain. a -R n is either hydrogen, methyl, or forms a ring with an adjacent R group, R 14 is -C(O)NHR 15 And here R 15is either hydrogen or a glycine residue optionally substituted with additional glycine residues and / or tails that can improve pharmacokinetic properties. In some embodiments, PD-L1 inhibitors include compounds disclosed in International Publication WO2014 / 151634, which is incorporated herein by reference in whole. In some embodiments, the PD-L1 inhibitors include compounds disclosed in International Publications WO2016 / 039749, WO2016 / 149351, WO2016 / 077518, WO2016 / 100285, WO2016 / 100608, WO2016 / 126646, WO2016 / 057624, WO2017 / 151830, WO2017 / 176608, WO2018 / 085750, WO2018 / 237153, or WO2019 / 070643, each of which is incorporated herein by reference in whole.

[0149] In certain embodiments, PD-L1 inhibitors include small molecule PD-L1 inhibitors disclosed in International Publications WO2015 / 034820, WO2015 / 160641, WO2018 / 044963, WO2017 / 066227, WO2018 / 009505, WO2018 / 183171, WO2018 / 118848, WO2019 / 147662, or WO2019 / 169123, each of which is incorporated herein by reference in whole.

[0150] In some embodiments, the PD-L1 inhibitor includes a combination of an anti-PD-L1 antibody and a PD-L1 small molecule inhibitor as disclosed herein.

[0151] II.C.3. Anti-CTLA-4 antibody Anti-CTLA-4 antibodies known in the art can be used in the compositions and methods of the present disclosure. The anti-CTLA-4 antibodies of the present disclosure bind to human CTLA-4 and disrupt the interaction between CTLA-4 and the human B7 receptor. Since the interaction between CTLA-4 and B7 transmits signals that lead to the inactivation of T cells harboring the CTLA-4 receptor, disruption of the interaction effectively induces, enhances, or prolongs the activation of such T cells, thereby inducing, enhancing, or prolonging an immune response.

[0152] A human monoclonal antibody that binds specifically to CTLA-4 with high affinity is disclosed in U.S. Patent No. 6,984,720. Other anti-CTLA-4 monoclonal antibodies are described, for example, in U.S. Patents No. 5,977,318, No. 6,051,227, No. 6,682,736, and No. 7,034,121, and in International Publications WO2012 / 122444, WO2007 / 113648, WO2016 / 196237, and WO2000 / 037504, each of which is incorporated herein by reference in its entirety. The anti-CTLA-4 human monoclonal antibody disclosed in U.S. Patent No. 6,984,720 has been demonstrated to exhibit one or more of the following characteristics: (a) determined by Biacore analysis, at least about 10 7 M -1 , or about 10 9 M -1 , or about 10 10 M -1 ~10 11 M -1 or a higher equilibrium coupling constant (K a (b) specifically binds to human CTLA-4 with binding affinity reflected in (b) at least about 10 3 , about 10 4 , or about 10 5 m -1 s -1 The dynamic association constant (k a ), (c) at least about 10 3 , about 10 4 , or about 10 5 m -1 s -1The dynamic dissociation constant (k d ), and (d) inhibit the binding of CTLA-4 to B7-1 (CD80) and B7-2 (CD86). Anti-CTLA-4 antibodies useful in this disclosure include monoclonal antibodies that specifically bind to human CTLA-4 and exhibit at least one, at least two, or at least three of the above features.

[0153] In certain embodiments, the CTLA-4 antibody is selected from the group consisting of ipilimumab (YERVOY®, also known as MDX-010, 10D1; see U.S. Patent No. 6,984,720), MK-1308 (Merck), AGEN-1884 (Agenus Inc.; see WO2016 / 196237), and tremelimumab (AstraZeneca; also known as tisilimmab, CP-675,206; see WO2000 / 037504 and Ribas, Update Cancer Ther. 2(3): 133-39 (2007)). In certain embodiments, the anti-CTLA-4 antibody is ipilimumab.

[0154] In certain embodiments, the CTLA-4 antibody is ipilimumab for use in the compositions and methods disclosed herein. Ipilimumab is a fully human IgG1 monoclonal antibody that blocks the binding of CTLA-4 to its B7 ligand, thereby stimulating T cell activation and improving overall survival (OS) in patients with advanced melanoma.

[0155] In certain embodiments, the CTLA-4 antibody is tremelimumab.

[0156] In certain embodiments, the CTLA-4 antibody is MK-1308.

[0157] In certain embodiments, the CTLA-4 antibody is AGEN-1884.

[0158] The anti-CTLA-4 antibodies usable in the disclosed compositions and methods also include isolated antibodies that specifically bind to human CTLA-4 and cross-compete with any of the anti-CTLA-4 antibodies disclosed herein, e.g., ipilimumab and / or tremelimumab, for binding to human CTLA-4. In some embodiments, the anti-CTLA-4 antibody binds to the same epitope as any of the anti-CTLA-4 antibodies described herein, e.g., ipilimumab and / or tremelimumab. The ability of antibodies to cross-compete for binding to an antigen indicates that these antibodies bind to the same epitope region of the antigen and sterically prevent the binding of other cross-competing antibodies to that particular epitope region. These cross-competing antibodies are expected to have functional properties very similar to the reference antibody, e.g., ipilimumab and / or tremelimumab, for their binding to the same epitope region of CTLA-4. Cross-competing antibodies can be readily identified based on their ability to cross-compete with ipilimumab and / or tremelimumab in standard CTLA-4 binding assays, e.g., Biacore analysis, ELISA assay, or flow cytometry (see, e.g., WO2013 / 173223).

[0159] In certain embodiments, an antibody that cross-competes with a human CTLA-4 antibody, such as ipilimumab and / or tremelimumab, for binding to human CTLA-4, or an antibody that binds to the same epitope region of a human CTLA-4 antibody, is a monoclonal antibody. For administration to human subjects, these cross-competing antibodies are chimeric antibodies, engineered antibodies, or humanized or human antibodies. Such chimeric antibodies, engineered antibodies, humanized or human monoclonal antibodies can be prepared and isolated by methods well known in the art.

[0160] The anti-CTLA-4 antibodies available for use in the compositions and methods of the disclosed disclosure also include the antigen-binding moiety of the antibody described above. It has been well demonstrated that the antigen-binding function of the antibody can be performed by fragments of a full-length antibody.

[0161] The anti-CTLA-4 antibodies suitable for use in the disclosed methods or compositions are antibodies that bind to CTLA-4 with high specificity and affinity, block the activity of CTLA-4, and disrupt the interaction of CTLA-4 with the human B7 receptor. In any of the compositions or methods disclosed herein, the anti-CTLA-4 “antibody” includes an antigen-binding moiety or fragment that exhibits similar functional properties to the whole antibody in that it binds to CTLA-4, inhibits the interaction of CTLA-4 with the human B7 receptor, and upregulates the immune system. In certain embodiments, the anti-CTLA-4 antibody or its antigen-binding moiety cross-competes with ipilimumab and / or tremelimumab for binding to human CTLA-4.

[0162] In some embodiments, the anti-CTLA-4 antibody or its antigen-binding moiety is administered once every 2, 3, 4, 5, 6, 7, or 8 weeks at a dose ranging from 0.1 mg / kg to 10.0 mg / kg body weight. In some embodiments, the anti-CTLA-4 antibody or its antigen-binding moiety is administered once every 3, 4, 5, or 6 weeks at a dose of 1 mg / kg or 3 mg / kg body weight. In one embodiment, the anti-CTLA-4 antibody or its antigen-binding moiety is administered once every 2 weeks at a dose of 3 mg / kg body weight. In another embodiment, the anti-PD-1 antibody or its antigen-binding moiety is administered once every 6 weeks at a dose of 1 mg / kg body weight.

[0163] In some embodiments, the anti-CTLA-4 antibody or its antigen-binding portion is administered as a fixed dose. In some embodiments, the anti-CTLA-4 antibody is administered in fixed doses of approximately 10 to 1000 mg, approximately 10 mg to 900 mg, approximately 10 mg to 800 mg, approximately 10 mg to 700 mg, approximately 10 mg to 600 mg, approximately 10 mg to 500 mg, approximately 100 mg to 1000 mg, approximately 100 mg to 900 mg, approximately 100 mg to 800 mg, approximately 100 mg to 700 mg, approximately 100 mg to 100 mg, approximately 100 mg to 500 mg, approximately 100 mg to 480 mg, or approximately 240 mg to 480 mg. In one embodiment, the anti-CTLA-4 antibody or its antigen-binding portion is present in amounts of at least approximately 60 mg, at least approximately 80 mg, at least approximately 100 mg, at least approximately 120 mg, at least approximately 140 mg, at least approximately 160 mg, at least approximately 180 mg, at least approximately 200 mg, at least approximately 220 mg, at least approximately 240 mg, at least approximately 260 mg, at least approximately 280 mg, at least approximately 300 mg, at least approximately 320 mg, at least approximately 340 mg, at least approximately 360 mg, at least approximately 380 mg, at least approximately 400 mg, at least approximately 420 mg, at least approximately 440 mg, at least approximately 460 mg, at least approximately 480 mg, at least approximately 500 mg, and at least approximately 520 mg. In another embodiment, the anti-CTLA-4 antibody or its antigen-binding moiety is administered as a fixed dose of at least approximately 540 mg, at least approximately 550 mg, at least approximately 560 mg, at least approximately 580 mg, at least approximately 600 mg, at least approximately 620 mg, at least approximately 640 mg, at least approximately 660 mg, at least approximately 680 mg, at least approximately 700 mg, or at least approximately 720 mg.

[0164] In some embodiments, ipilimumab is administered at a dose of approximately 3 mg / kg once every three weeks. In some embodiments, ipilimumab is administered at a dose of approximately 10 mg / kg once every three weeks. In some embodiments, ipilimumab is administered at a dose of approximately 10 mg / kg once every 12 weeks. In some embodiments, ipilimumab is administered four times.

[0165] II.D. Additional anti-cancer therapy In some aspects of this disclosure, the methods disclosed herein further include administering an anti-PD-1 / PD-L1 antagonist, e.g., an anti-PD-1 antibody or an anti-PD-L1 antibody, and one or more additional anticancer therapies. In certain aspects, the method includes (i) administering a first anti-PD-1 / PD-L1 antagonist (e.g., an anti-PD-1 antibody or an anti-PD-L1 antibody), and (ii) administering one or more additional anticancer therapies. In certain aspects, the method includes (i) administering a first anti-PD-1 / PD-L1 antagonist (e.g., an anti-PD-1 antibody or an anti-PD-L1 antibody), (ii) administering an anti-CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), and (iii) administering one or more additional anticancer therapies.

[0166] Additional anticancer therapies may include any therapy known in the art for the treatment of tumors in a subject and / or any standard treatment, as disclosed herein. In some embodiments, additional anticancer therapies may include surgery, radiotherapy, chemotherapy, immunotherapy, or any combination thereof. In some embodiments, additional anticancer therapies may include chemotherapy, including any chemotherapy disclosed herein.

[0167] The methods disclosed herein may use any chemotherapy known in the art. In some embodiments, the chemotherapy is a platinum-based chemotherapy. The platinum-based chemotherapy is a platinum coordination complex. In some embodiments, the platinum-based chemotherapy is a platinum doublet chemotherapy. In some embodiments, the chemotherapy is administered at doses approved for a specific indication. In other embodiments, the chemotherapy is administered at any dose disclosed herein. In some embodiments, the platinum-based chemotherapy is cisplatin, carboplatin, oxaliplatin, satraplatin, picoplatin, nedaplatin, triplatin, lipoplatin, or a combination thereof. In certain embodiments, the platinum-based chemotherapy is any other platinum-based chemotherapy known in the art. In some embodiments, the chemotherapy is the nucleotide analog gemcitabine. In one embodiment, the chemotherapy is a folate antagonist. In one embodiment, the folate antagonist is pemetrexed. In certain embodiments, the chemotherapy is a taxane. In other embodiments, taxane is paclitaxel. In some embodiments, chemotherapy is any other chemotherapy known in the art. In certain embodiments, at least one, at least two or more chemotherapeutic agents are administered in combination with the IO therapy. In some embodiments, the IO therapy is administered in combination with gemcitabine and cisplatin. In some embodiments, the IO therapy is administered in combination with pemetrexed and cisplatin. In certain embodiments, the IO therapy is administered in combination with gemcitabine and pemetrexed. In one embodiment, the IO therapy is administered in combination with paclitaxel and carboplatin. In one embodiment, the IO therapy is further administered.

[0168] In some embodiments, additional anticancer therapy includes immunotherapy (IO therapy). In some embodiments, additional anticancer therapy includes administration of antibodies or antigen-binding moieties that specifically bind to LAG-3, TIGIT, TIM3, NKG2a, CSF1R, OX40, ICOS, MICA, MICB, CD137, KIR, TGFβ, IL-10, IL-8, B7-H4, Fas ligand, CXCR4, mesothelin, CD27, GITR, or any combination thereof.

[0169] II.C.1. Anti-LAG-3 antibody The anti-LAG-3 antibody of this disclosure binds to human LAG-3. Antibodies that bind to LAG-3 are disclosed in International Publication WO2015 / 042246 and U.S. Patent Applications Publications 2014 / 0093511 and 2011 / 0150892, each of which is incorporated herein by reference in whole.

[0170] An exemplary LAG-3 antibody useful in this disclosure is 25F7 (described in U.S. Patent Application Publication No. 2011 / 0150892). A further exemplary LAG-3 antibody useful in this disclosure is BMS-986016. In one embodiment, the anti-LAG-3 antibody useful in the composition cross-competes with 25F7 or BMS-986016. In another embodiment, the anti-LAG-3 antibody useful in the composition binds to the same epitope as 25F7 or BMS-986016. In yet another embodiment, the anti-LAG-3 antibody comprises six CDRs of 25F7 or BMS-986016. In another embodiment, the anti-LAG-3 antibody is IMP731(H5L7BW), MK-4280(28G-10), REGN3767, humanized BAP050, IMP-701(LAG-5250), TSR-033, BI754111, MGD013, or FS-118. These and other anti-LAG-3 antibodies useful in the claimed invention include, for example, WO2016 / 028672, WO2017 / 106129, WO2017 / 062888, WO2009 / 044273, WO2018 / 069500, WO2016 / 126858, WO2014 / 179664, WO2016 / 200782, WO2015 / 200119, WO2017 / 019846, WO2017 / 198741, WO2017 / 220555, WO2017 / 220569, WO201 These can be found in 8 / 071500, WO2017 / 015560, WO2017 / 025498, WO2017 / 087589, WO2017 / 087901, WO2018 / 083087, WO2017 / 149143, WO2017 / 219995, US2017 / 0260271, WO2017 / 086367, WO2017 / 086419, WO2018 / 034227, and WO2014 / 140180 (each of these is incorporated herein by reference in its entirety).

[0171] II.C.2. Anti-CD137 antibody Anti-CD137 antibodies specifically bind to and activate immune cells expressing CD137, stimulating an immune response against tumor cells, particularly a cytotoxic T cell response. Antibodies that bind to CD137 are disclosed in U.S. Patent Application Publication No. 2005 / 0095244 and U.S. Patents Nos. 7,288,638, 6,887,673, 7,214,493, 6,303,121, 6,569,997, 6,905,685, 6,355,476, 6,362,325, 6,974,863 and 6,210,669, each of which is incorporated herein by reference in whole.

[0172] In some embodiments, the anti-CD137 antibody is urelumab (BMS-663513) (20H4.9-IgG4 [10C7 or BMS-663513]) as described in U.S. Patent No. 7,288,638. In some embodiments, the anti-CD137 antibody is BMS-663031 (20H4.9-IgG1) as described in U.S. Patent No. 7,288,638. In some embodiments, the anti-CD137 antibody is 4E9 or BMS-554271 as described in U.S. Patent No. 6,887,673. In some embodiments, the anti-CD137 antibody is an antibody disclosed in U.S. Patent Nos. 7,214,493, 6,303,121, 6,569,997, 6,905,685, or 6,355,476. In some embodiments, the anti-CD137 antibody is 1D8 or BMS-469492, 3H3 or BMS-469497, or 3E1 as described in U.S. Patent No. 6,362,325. In some embodiments, the anti-CD137 antibody is the antibody disclosed in issued U.S. Patent No. 6,974,863 (e.g., 53A2). In some embodiments, the anti-CD137 antibody is the antibody disclosed in issued U.S. Patent No. 6,210,669 (e.g., 1D8, 3B8, or 3E1). In some embodiments, the antibody is Pfizer's PF-05082566 (PF-2566). In other embodiments, the anti-CD137 antibody useful for the methods disclosed herein cross-competes with the anti-CD137 antibodies disclosed herein. In some embodiments, the anti-CD137 antibody binds to the same epitope as the anti-CD137 antibodies disclosed herein. In other embodiments, the anti-CD137 antibodies useful in this disclosure include the six CDRs of anti-CD137 antibodies disclosed herein.

[0173] II.C.3. Anti-KIR antibody Antibodies that specifically bind to KIRs block the interaction between killer cell immunoglobulin-like receptors (KIRs) on NK cells and their ligands. Blocking these receptors promotes NK cell activation, potentially facilitating the latter's destruction of tumor cells. Examples of anti-KIR antibodies are disclosed in International Publications WO2014 / 055648, WO2005 / 003168, WO2005 / 009465, WO2006 / 072625, WO2006 / 072626, WO2007 / 042573, WO2008 / 084106, WO2010 / 065939, WO2012 / 071411, and WO2012 / 160448, each of which is incorporated herein by reference in its entirety.

[0174] One anti-KIR antibody useful in this disclosure is lirirumab (also known as BMS-986015, IPH2102, or the S241P variant of 1-7F9), first described in International Publication WO2008 / 084106. A further anti-KIR antibody useful in this disclosure is 1-7F9 (also known as IPH2101), described in International Publication WO2006 / 003179. In one embodiment, the anti-KIR antibody for the composition of the present invention cross-competes with lirirumab or I-7F9 for binding to KIR. In another embodiment, the anti-KIR antibody binds to the same epitope as lirirumab or I-7F9. In yet another embodiment, the anti-KIR antibody contains six CDRs of lirirumab or I-7F9.

[0175] II.C.4. Anti-GITR antibody Anti-GITR antibodies useful in the methods disclosed herein include any anti-GITR antibody that specifically binds to human GITR targets and activates glucocorticoid-induced tumor necrosis factor receptor (GITR). GITR is a member of the TNF receptor superfamily expressed on the surface of multiple types of immune cells, including regulatory T cells, effector T cells, B cells, natural killer (NK) cells, and activated dendritic cells ("anti-GITR agonist antibodies"). Specifically, GITR activation enhances the proliferation and function of effector T cells and neutralizes the suppression induced by activated regulatory T cells. In addition, GITR stimulation promotes anti-tumor immunity by enhancing the activity of other immune cells, such as NK cells, antigen-presenting cells, and B cells. Examples of anti-GITR antibodies are disclosed in International Publications WO2015 / 031667, WO2015 / 184,099, WO2015 / 026,684, WO11 / 028683 and WO2006 / 105021, U.S. Patents 7,812,135 and 8,388,967, and U.S. Patent Application Publications 2009 / 0136494, 2014 / 0220002, 2013 / 0183321 and 2014 / 0348841, each of which is incorporated herein by reference in its entirety.

[0176] In some embodiments, the anti-GITR antibody useful in this disclosure is TRX518 (e.g., described in Schaer et al. Curr Opin Immunol. (2012) Apr; 24(2): 217-224 and WO2006 / 105021). In other embodiments, the anti-GITR antibody is selected from MK4166, MK1248, and antibodies described in WO11 / 028683 and U.S. Patent No. 8,709,424, and includes, for example, a VH chain containing SEQ ID NO: 104 and a VL chain containing SEQ ID NO: 105 (where SEQ ID NOs are derived from WO11 / 028683 or U.S. Patent No. 8,709,424). In certain embodiments, the anti-GITR antibody is an anti-GITR antibody disclosed in WO2015 / 031667, for example, an antibody comprising VH CDR1-3 including SEQ ID NOs. 31, 71, and 63 of WO2015 / 031667, and an antibody comprising VL CDR1-3 including SEQ ID NOs. 5, 14, and 30 of WO2015 / 031667. In certain embodiments, the anti-GITR antibody is an anti-GITR antibody disclosed in WO2015 / 184099, for example, an antibody Hum231#1 or Hum231#2, or their CDRs, or their derivatives (e.g., pab1967, pab1975, or pab1979). In certain embodiments, the anti-GITR antibody is an anti-GITR antibody disclosed in JP2008278814, WO09 / 009116, WO2013 / 039954, U.S. Patent Application Publications 20140072566, 20140072565, 20140065152, or WO2015 / 026684, or INBRX-110 (INHIBRx), LKZ-145 (Novartis), or MEDI-1873 (MedImmune). In certain embodiments, the anti-GITR antibody is an anti-GITR antibody described in PCT / US2015 / 033991 (e.g., an antibody containing the variable region 28F3, 18E10, or 19D3).

[0177] In certain embodiments, the anti-GITR antibody cross-competes with anti-GITR antibodies described herein, such as TRX518, MK4166, or antibodies containing amino acid sequences of the VH and VL domains described herein. In some embodiments, the anti-GITR antibody binds to the same epitope as the anti-GITR antibodies described herein, such as TRX518 or MK4166. In certain embodiments, the anti-GITR antibody contains six CDRs of TRX518 or MK4166.

[0178] II.C.5. Anti-TIM3 antibody Any anti-TIM3 antibody or antigen-binding fragment known in the art can be used in the method described herein. In some embodiments, anti-TIM3 antibodies are internationally published as WO2018013818, WO2015 / 117002 (e.g., MGB453, Novartis), WO2016 / 161270 (e.g., TSR-022, Tesaro / AnaptysBio), WO2011155607, WO2016 / 144803 (e.g., STI-600, Sorrento Therapeutics), WO2016 / 071448, WO17055399, WO17055404, WO17178493, WO18036561, WO18039020 (e.g., Ly-3221367, Eli Selected from the anti-TIM3 antibodies disclosed in Lilly, WO2017205721, WO17079112, WO17079115, WO17079116, WO11159877, WO13006490, WO2016068802, WO2016068803, WO2016 / 111947, and WO2017 / 031242, each of which is incorporated herein by reference in its entirety.

[0179] II.C.6. Anti-OX40 antibody Any antibody or antigen-binding fragment that specifically binds to OX40 (also known as CD134, TNFRSF4, ACT35, and / or TXGP1L) may be used in the methods disclosed herein. In some embodiments, the anti-OX40 antibody is BMS-986178 (Bristol-Myers Squibb Company), as described in International Publication WO20160196228. In some embodiments, the anti-OX40 antibody is selected from the anti-OX40 antibodies described in International Publications WO95012673, WO199942585, WO14148895, WO15153513, WO15153514, WO13038191, WO16057667, WO03106498, WO12027328, WO13028231, WO16200836, WO17063162, WO17134292, WO17096179, WO17096281, and WO17096182, each of which is incorporated herein by reference in whole.

[0180] II.C.7. Anti-NKG2A antibody Any antibody or antigen-binding fragment that specifically binds to NKG2A can be used in the methods disclosed herein. NKG2A is a member of the type C lectin receptor family expressed on subsets of natural killer (NK) cells and T lymphocytes. Specifically, NKG2A is expressed primarily on tumor-infiltrating innate immune effector NK cells, as well as on some CD8+ T cells. Its natural ligand, human leukocyte antigen E (HLA-E), is expressed on solid tumors and hematological malignancies. NKG2A is an inhibitory receptor that suppresses the action of HLA-E.

[0181] In some embodiments, the anti-NKG2A antibody may be BMS-986315, a human monoclonal antibody that blocks the interaction between NKG2A and its ligand HLA-E, thus enabling activation of an anti-tumor immune response. In some embodiments, the anti-NKG2A antibody is a checkpoint inhibitor that activates T cells, NK cells, and / or tumor-infiltrating immune cells. In some embodiments, the anti-NKG2A antibody is, for example, WO2006 / 070286 (Innate Pharma SA; University of Genova), U.S. Patent No. 8,993,319 (Innate Pharma SA; University of Gennova), WO2007 / 042573 (Innate Pharma S / A; Novo Nordisk A / S; University of Gennova); U.S. Patent No. 9,447,185 (Innate Pharma S / A; Novo Nordisk A / S; University of Gennova); WO2008 / 009545 (Novo Nordisk A / S); U.S. Patent No. 8,206,709; U.S. Patent No. 8,901,283; U.S. Patent No. 9,683,041 (Novo Nordisk A / S); WO2009 / 092805 (Novo Nordisk Selected from anti-NKG2A antibodies described in U.S. Patent Nos. 8,796,427 and 9,422,368 (Novo Nordisk A / S); WO2016 / 134371 (Ohio State Innovation Foundation); WO2016 / 032334 (Janssen); WO2016 / 041947 (Innate); WO2016 / 041945 (Academisch Ziekenhuis Leiden HODNLUMC); WO2016 / 041947 (Innate Pharma); and WO2016 / 041945 (Innate Pharma), each of which is incorporated herein by reference in its entirety.

[0182] II.C.8. Anti-ICOS antibody Any antibody or antigen-binding fragment that specifically binds to ICOS can be used in the methods disclosed herein. ICOS is an immune checkpoint protein that is a member of the CD28 superfamily. ICOS is a 55-60 kDa type I transmembrane protein that is expressed on T cells after T cell activation and co-stimulates T cell activation after binding to its ligand, ICOS-L(B7H2). ICOS is also known as the inducible T cell co-stimulator, CVID1, AILIM, inducible co-stimulator, CD278, activation-inducible lymphocyte immunomodulatory molecule, and CD278 antigen.

[0183] In some embodiments, the anti-ICOS antibody is BMS-986226, a humanized IgG monoclonal antibody that binds to and stimulates human ICOS. In some embodiments, anti-ICOS antibodies are, for example, WO2016 / 154177 (Jounce Therapeutics, Inc.), WO2008 / 137915 (MedImmune), WO2012 / 131004 (INSERM, French National Institute of Health and Medical Research), EP3147297 (INSERM, French National Institute of Health and Medical Research), WO2011 / 041613 (Memorial Sloan Kettering Cancer Center), EP2482849 (Memorial Sloan Kettering Cancer Center), WO1999 / 15553 (Robert Koch Institute), U.S. Patent Nos. 7,259,247 and 7,722,872 (Robert Koch Institute), WO1998 / 038216 (Japan Tobacco Selected from anti-ICOS antibodies described in U.S. Patent Nos. 7,045,615, 7,112,655 and 8,389,690 (Japan Tobacco Inc.), U.S. Patent Nos. 9,738,718 and 9,771,424 (GlaxoSmithKline), and WO2017 / 220988 (Kymab Limited), each of which is incorporated herein by reference in its entirety.

[0184] II.C.9. Anti-TIGIT antibody Any antibody or antigen-binding fragment thereof that specifically binds to TIGIT can be used in the methods disclosed herein. In some embodiments, the anti-TIGIT antibody is BMS-986207. In some embodiments, the anti-TIGIT antibody is clone 22G2 as described in WO2016 / 106302. In some embodiments, the anti-TIGIT antibody is MTIG7192A / RG6058 / RO7092284 as described in WO2017 / 053748, or clone 4.1D3. In some embodiments, the anti-TIGIT antibody is selected from, for example, the anti-TIGIT antibodies described in WO2016 / 106302 (Bristol-Myers Squibb Company) and WO2017 / 053748 (Genentech).

[0185] II.C.10. Anti-CSF1R antibody Any antibody or antigen-binding fragment that specifically binds to CSF1R can be used in the methods disclosed herein. In some embodiments, the anti-CSF1R antibody is an antibody species disclosed in any of the International Publications WO2013 / 132044, WO2009 / 026303, WO2011 / 140249, or WO2009 / 112245, for example, kabilizumab, RG7155 (emactuzumab), AMG820, SNDX 6352 (UCB The anti-CSF1R antibody in this method may be replaced with an anti-CSF1R inhibitor or anti-CSF1 inhibitor, such as BLZ-945, pexidartinib (PLX3397, PLX108-01), AC-708, PLX-5622, PLX7486, ARRY-382, or PLX-73086.

[0186] II.E. Tumors In some embodiments, the tumor originates from a cancer selected from the group consisting of hepatocellular carcinoma, gastroesophageal cancer, melanoma, bladder cancer, lung cancer, kidney cancer, head and neck cancer, colon cancer, and any combination thereof. In certain embodiments, the tumor originates from hepatocellular carcinoma and has a high inflammatory signature score. In certain embodiments, the tumor originates from gastroesophageal cancer and has a high inflammatory signature score. In certain embodiments, the tumor originates from melanoma and has a high inflammatory signature score. In certain embodiments, the tumor originates from bladder cancer and has a high inflammatory signature score. In some embodiments, the tumor originates from lung cancer and has a high inflammatory signature score. In some embodiments, the tumor originates from kidney cancer and has a high inflammatory signature score. In some embodiments, the tumor originates from head and neck cancer and has a high inflammatory signature score. In some embodiments, the tumor originates from colon cancer and has a high inflammatory signature score.

[0187] In certain embodiments, the subject has received one, two, three, four, five or more prior cancer treatments. In other embodiments, the subject is untreated. In certain embodiments, the subject has progressed after other cancer treatments. In certain embodiments, prior cancer treatments include immunotherapy. In other embodiments, prior cancer treatments include chemotherapy. In some embodiments, the tumor has recurred. In some embodiments, the tumor is metastatic. In other embodiments, the tumor is not metastatic. In some embodiments, the tumor is locally advanced.

[0188] In some embodiments, the subject has received prior therapy to treat the tumor, and the tumor is recurrent or treatment-resistant. In certain embodiments, at least one prior therapy includes standard treatment. In some embodiments, at least one prior therapy includes surgery, radiotherapy, chemotherapy, immunotherapy, or any combination thereof. In some embodiments, at least one prior therapy includes chemotherapy. In some embodiments, the subject has received prior immuno-oncology (IO) therapy to treat the tumor, and the tumor is recurrent or treatment-resistant. In some embodiments, the subject has received two or more prior therapies to treat the tumor, and the subject is recurrent or treatment-resistant. In some embodiments, the subject is receiving either anti-PD-1 antibody therapy or anti-PD-L1 antibody therapy.

[0189] In some embodiments, the preceding line of therapy includes chemotherapy. In some embodiments, the chemotherapy includes a platinum-based therapy. In some embodiments, the platinum-based therapy includes a platinum-based anti-cancer agent selected from the group consisting of cisplatin, carboplatin, oxaliplatin, nedaplatin, triplatin tetranitrate, phenanthriplatin, picoplatin, satraplatin, and any combination thereof. In certain embodiments, the platinum-based therapy includes cisplatin. In certain embodiments, the platinum-based therapy includes carboplatin.

[0190] In some embodiments, at least one prior therapy is selected from therapies comprising the administration of anticancer agents selected from the group consisting of platinum-based drugs (e.g., cisplatin, carboplatin), taxanes (e.g., paclitaxel, albumin-bound paclitaxel, docetaxel), vinorelbine, vinblastine, etoposide, pemetrexed, gemcitabine, bevacizumab (AVASTIN®), erlotinib (TARCEVA®), crizotinib (XALKORI®), cetuximab (ERBITUX®), and any combination thereof. In certain embodiments, at least one prior therapy comprises platinum-based doublet chemotherapy.

[0191] In some embodiments, the subject has experienced disease progression after at least one prior therapy. In certain embodiments, the subject has received at least two prior therapies, at least three prior therapies, at least four prior therapies, or at least five prior therapies. In certain embodiments, the subject has received at least two prior therapies. In one embodiment, the subject has experienced disease progression after at least two prior therapies. In certain embodiments, at least two prior therapies include a first prior therapy and a second prior therapy, and the subject has experienced disease progression after the first prior therapy and / or the second prior therapy, and the first prior therapy includes surgery, radiotherapy, chemotherapy, immunotherapy, or any combination thereof, and the second prior therapy includes surgery, radiotherapy, chemotherapy, immunotherapy, or any combination thereof. In some embodiments, the first prior therapy includes platinum-based doublet chemotherapy, and the second prior therapy includes monotherapy. In some embodiments, the monotherapy includes docetaxel.

[0192] II.F. Pharmaceutical Compositions and Dosages The therapeutic agents of this disclosure may be configured as a pharmaceutical composition containing, for example, an antibody and / or cytokine and a pharmaceutically acceptable carrier. As used herein, “pharmaceutically acceptable carrier” includes any and all physiologically compatible solvents, dispersion media, coatings, antimicrobial and antifungal agents, isotonic agents and absorption retarders, etc. Preferably, carriers for antibody-containing compositions are suitable for intravenous, intramuscular, subcutaneous, enteric, spinal, or epidermal administration (e.g., by injection or infusion), while carriers for antibody and / or cytokine-containing compositions are suitable for non-enteric, for example, oral administration. In some embodiments, subcutaneous injection is based on Halozyme Therapeutics’ ENHANZE® drug delivery technology (see U.S. Patent No. 7,767,429, which is incorporated herein by reference in whole). ENHANZE® uses a co-formulation of an antibody and recombinant human hyaluronidase enzyme (rHuPH20) to overcome conventional limitations on the volume of biologics and drugs that can be delivered subcutaneously due to the extracellular matrix (see U.S. Patent No. 7,767,429). The pharmaceutical compositions of this disclosure may include one or more pharmaceutically acceptable salts, antioxidants, aqueous and non-aqueous carriers, and / or adjuvants such as preservatives, wetting agents, emulsifiers and dispersants. Thus, in some embodiments, the pharmaceutical compositions of this disclosure may further include recombinant human hyaluronidase enzyme, for example, rHuPH20.

[0193] Higher doses of nivolumab monotherapy up to 10 mg / kg every two weeks were achieved without reaching the maximum tolerated dose (MTD); however, significant toxicity reported in other trials of combination therapy with checkpoint inhibitors and anti-angiogenic therapies (see, e.g., Johnson et al., 2013; Rini et al., 2011) support the selection of nivolumab doses less than 10 mg / kg.

[0194] Treatment is continued as long as a clinical benefit is observed or until unacceptable toxicity or disease progression occurs. However, in certain embodiments, the antibodies disclosed herein are administered at doses significantly lower than the approved dose of the drug, i.e., sub-therapeutic doses. The antibodies may be administered at the dose that has shown to produce the highest efficacy as monotherapy in clinical trials, for example, a single dose of approximately 3 mg / kg of nivolumab every three weeks (Topalian et al., 2012a; Topalian et al., 2012), or at significantly lower doses, i.e., sub-therapeutic doses.

[0195] Dosage and frequency vary depending on the half-life of the antibody in the subject. Generally, human antibodies exhibit the longest half-lives, followed by humanized antibodies, chimeric antibodies, and non-human antibodies. Dosage and frequency of administration may differ depending on whether the treatment is prophylactic or therapeutic. In prophylactic applications, relatively low doses are typically administered at relatively infrequent intervals over a long period. Some patients continue treatment for the rest of their lives. In therapeutic applications, relatively high doses at relatively short intervals may be required until disease progression slows or ends, preferably until the patient shows partial or complete improvement of disease symptoms. A prophylactic regimen may then be administered to the patient.

[0196] The actual dose levels of the active ingredient in the pharmaceutical compositions of this disclosure can be varied to obtain an amount of the active ingredient that is effective in achieving the desired therapeutic response for a particular patient, composition, and mode of administration without being excessively toxic to the patient. The selected dose level depends on a variety of pharmacokinetic factors, including the activity of the particular composition of this disclosure being employed, the route of administration, the time of administration, the elimination rate of the particular compound being employed, the duration of treatment, other drugs, compounds, and / or materials used in combination with the particular composition being employed, the age, sex, weight, condition, general health, and prior medical history of the patient being treated, and similar factors well known in the medical field. The compositions of this disclosure can be administered via one or more routes of administration using one or more of the various methods well known in the art. As will be understood by those skilled in the art, the route and / or mode of administration is considered to vary depending on the desired outcome.

[0197] III. Kit Furthermore, (a) kits containing an anti-PD-1 antibody or an anti-PD-L1 antibody for therapeutic use are also included within the scope of this disclosure. A kit typically includes a label indicating the intended use of the kit's contents and instructions for use. The term “label” includes any document or recorded material on or provided with the kit, or otherwise accompanying the kit. Accordingly, this disclosure provides a kit for treating a tumor-affected subject, comprising (a) an anti-PD-1 antibody in a dose ranging from 0.1 to 10 mg / kg body weight, or an anti-PD-L1 antibody in a dose ranging from 0.1 to 20 mg / kg body weight, and (b) instructions for using the anti-PD-1 antibody or anti-PD-L1 antibody in a manner disclosed herein. The Disclosure further provides a kit for treating a tumor-affected subject, comprising (a) an anti-PD-1 antibody in a dose ranging from about 4 mg to about 500 mg, or an anti-PD-L1 antibody in a dose ranging from about 4 mg to about 2000 mg, and (b) instructions for using the anti-PD-1 antibody or anti-PD-L1 antibody in a method disclosed herein. In some embodiments, the Disclosure provides a kit for treating a tumor-affected subject, comprising (a) an anti-PD-1 antibody in a dose ranging from 200 mg to 800 mg, or an anti-PD-L1 antibody in a dose ranging from 200 mg to 1800 mg, and (b) instructions for using the anti-PD-1 antibody or anti-PD-L1 antibody in a method disclosed herein.

[0198] In certain embodiments for treating human patients, the kit comprises an anti-human PD-1 antibody disclosed herein, for example, nivolumab or pembrolizumab. In certain embodiments for treating human patients, the kit comprises an anti-human PD-L1 antibody disclosed herein, for example, atezolizumab, durvalumab, or avelumab.

[0199] In some embodiments, the kit further comprises an anti-CTLA-4 antibody. In certain embodiments for treating human patients, the kit comprises an anti-human CTLA-4 antibody disclosed herein, for example, ipilimumab, tremelimumab, MK-1308, or AGEN-1884.

[0200] In some embodiments, the kit further comprises a gene panel assay as disclosed herein. In some embodiments, the kit further comprises instructions for administering an anti-PD-1 antibody or an anti-PD-L1 antibody to a preferred subject according to a method disclosed herein.

[0201] All references cited above, and all references cited herein, are incorporated herein by reference in their entirety.

[0202] The following examples are provided for illustrative purposes only and are not intended to limit them.

[0203] IV. Exemplary Embodiments of Artificial Intelligence and Machine Learning Evaluation of tumor topology Inflammation of the tumor microenvironment (TME), characterized by CD8+ T cell infiltration, is associated with improved clinical outcomes across multiple tumor types. Since parenchymal CD8+ T cell infiltration is associated with improved survival rates with immuno-oncology (IO) treatment, and intratumoral localization also impacts outcomes, the importance of spatial analysis of CD8+ T cells within the TME is strongly emphasized. CD8+ T cell patterns within tumors, as assessed by immunohistochemical staining of histological images, are diverse and can be classified as follows: (i) immunodesert type (very minimal T cell infiltration), (ii) immunoexclusion type (T cells confined to the tumor stroma or invasion margins), or (iii) immunoinflammatory type (T cells infiltrating the tumor parenchyma and located in close proximity to tumor cells). Artificial intelligence (AI)-based image analysis can be used to characterize the compartments of tumor parenchyma and stroma within the TME.

[0204] Figure 1 illustrates exemplary images of tumor tissue samples with various classifications, using CD8+ histological images obtained by immunohistochemistry, according to an exemplary embodiment. The tumor images show various classifications of CD8+ T cell patterns within the TME. The images in the top row of Figure 1 show immunodesert and immunoexclusion classifications, while the images in the bottom row of Figure 1 show immunoinflammatory classifications.

[0205] The immunodesert classification indicates that T cells are present in very small numbers or no at all in the tumor mesoplasm (TME). In some embodiments, the immunodesert classification may be referred to herein as “desert type” or “cold.” The immunoexclusion classification indicates that T cells accumulate in the tumor stroma without efficient infiltration of the tumor parenchyma. In some embodiments, the immunoexclusion classification may be referred to herein as “stromal type.” The immunoinflammatory classification indicates that T cells infiltrate the tumor parenchyma. In some embodiments, the immunoinflammatory classification may be referred to herein as “parenchymal type.”

[0206] In some embodiments, different levels (e.g., first and second elimination levels, first, second, and third inflammation levels) may exist within the immunoexclusionary and immunoinflammatory classifications, depending on the progression of T cells migrating within the TME. In some embodiments, the third inflammation level may indicate a greater number of T cells infiltrating the parenchyma than the first inflammation level. Although not shown in Figure 1, an intermediate classification may exist between the eliminationary and inflammationary types, referred to herein as "balanced." The term "balanced" refers to an intermediate classification level between the eliminationary and inflammationary types, in which case the number of T cells accumulated in the tumor stroma and the T cells accumulated in the tumor parenchyma may be similar.

[0207] In some embodiments, the tumor specimen in the histological image obtained by immunohistochemistry may be obtained by tissue biopsy and / or excision of tumor tissue. In some embodiments, the tumor specimen is a tumor tissue biopsy specimen. In some embodiments, the tumor specimen is formalin-fixed paraffin-embedded tumor tissue or fresh-frozen tumor tissue. In some embodiments, the tumor specimen is obtained from the tumor stroma. In some embodiments, the histological image obtained by immunohistochemistry may be referred to herein as a histological image.

[0208] In some embodiments, the CD8 topology method is not standardized, which can result in inter-reviewer variability among different pathologists reviewing histological images. Interpretation of CD8 topology from histological images may be confounded by various factors, such as different tumor types, limited tumor structure due to biopsy or sampling, and heterogeneity of inflammation within the tumor sample.

[0209] To address these problems in the art, embodiments described herein present solutions that provide a standardized and scalable approach using image analysis and machine learning techniques to facilitate the examination and evaluation of the CD8 topology of tumor tissue in patients.

[0210] Figure 2 is an exemplary diagram illustrating a method of an image analysis and machine learning-based approach for training a model for tumor topology classification, according to an exemplary embodiment. In particular, Figure 2 shows three different stages of the method, including image analysis, polar coordinate transformation, and machine learning. The training data may include histological images obtained by immunostaining showing CD8+ T cell patterns within the TME for multiple patients. These training images may be labeled to be classified into various categories by a trained topology model. In some embodiments, the classification categories are “desert type,” “exclusion type,” and “stromal type.” In some embodiments, the classification category includes “balanced type.”

[0211] In the first stage, training data is processed to extract information from each histological image. In some embodiments, the image analysis process identifies and outputs various parameters for each image. In some embodiments, the image parameters are known, and the image analysis process selects a subset of parameters for further analysis. Such parameters may include, for example, the number of stromal CD8+ T cells, the number of parenchymal CD8+ T cells, and the total number of CD8+ T cells in each image. Other parameters may include the density of stromal CD8+ T cells and the density of parenchymal CD8+ T cells in each image, which may be particularly useful when the total number of all CD8+ T cells is unknown or cannot be determined.

[0212] In some embodiments, image analysis can be used to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma in each histological image. In some embodiments, the abundance of CD8+ T cells may include a graphical representation of the relationship between the percentage of stromal CD8+ T cells and the percentage of parenchymal CD8+ T cells relative to the total number of T cells present in each of multiple histological images, as shown by the "Image Analysis Read" plot in Figure 2. In some embodiments, the graphical representation may show the density, percentage, and / or quantity of stromal CD8+ T cells and parenchymal CD8+ T cells in each image. In some embodiments, the image analysis may include any image recognition, processing, and / or analysis algorithms. In some embodiments, the image analysis can be performed by applying an artificial neural network (e.g., a convolutional neural network) to multiple histological images.

[0213] In the second stage, a polar coordinate transformation can be performed on the results from the image analysis to convert the image analysis read graph into a polar plot with polar coordinates. In some embodiments, the polar coordinate transformation may include a mathematical transformation of the features derived during the image analysis into a polar feature space.

[0214] In the third stage, the transformation results of the image analysis, as well as the abundance of CD8+ T cells in the tumor parenchyma and stroma, can be used to train a machine learning algorithm. In some embodiments, polar coordinate transformation is omitted, and the machine learning algorithm is trained using the results of the image analysis process without polar coordinate transformation. In some embodiments, the machine learning algorithm may include any type of classification algorithm, e.g., a random forest classifier. In some embodiments, the machine learning algorithm can be trained using the same training data used to train the image analysis algorithm. In some embodiments, the random forest classifier can be trained using manipulated features (e.g., features derived from image analysis) and a pathologist-defined CD8+ topology. In some embodiments, the random forest classifier can be trained using labeled histological images (e.g., histological images previously labeled by at least one pathologist's classification) to provide classifications for additional histological images received. In some embodiments, the classifications may include inflammatory, desert, exclusionary, or balanced. In some embodiments, the machine learning algorithm may be referred to as a predictive model trained to predict classifications in histological images of tumors. In some embodiments, recommendations for immunotherapy or treatment for a patient's tumor can be generated based on determining the classification of at least one histological image of the patient's tumor using a trained machine learning algorithm.

[0215] Figure 3 is another exemplary diagram illustrating a method for classifying tumor topologies using an image analysis and machine learning-based approach according to an exemplary embodiment. In some embodiments, Figure 3 illustrates further details for embodiments of the method shown in Figure 2. Figure 3 illustrates four stages for training one or more machine learning algorithms for tumor topology classification and classifying new images using the trained algorithms, where the stages include image analysis, feature extraction, machine learning, and prediction.

[0216] First, as shown in Figure 3-(1), image analysis can be performed to identify CD8-positive cells and the division between parenchymal and stromal compartments in histological images of the tumor. In some embodiments, image analysis may include applying a neural network (e.g., a convolutional neural network) to multiple histological images to evaluate CD8+ T cells in different parts of the tumor (e.g., tumor epithelium, stroma, and parenchyma) in each image. The image analysis tool may result in the identification of values ​​for several different parameters for each image in multiple histological images. In some embodiments, two parameters (e.g., the number of stromal CD8+ T cells and the number of parenchymal CD8+ T cells) can be selected for further analysis. In some embodiments, the abundance of CD8+ T cells in the tumor parenchyma and stroma can be obtained from image analysis for multiple histological images.

[0217] Next, as shown in Figure 3-(2), feature extraction can be performed by applying a mathematical transformation of the features derived from the image analysis to transform the data into a polar coordinate feature space. In some embodiments, feature extraction may be part of an image analysis process to identify relationships between stromal CD8+ T cells and parenchymal CD8+ T cells.

[0218] Following the mathematical transformation, a machine learning algorithm (e.g., a random forest classifier) ​​can be trained using the manipulated features and the pathologist-defined CD8 topology, as shown in Figure 3-(3). In some embodiments, training the machine learning algorithm may involve generating a machine learning feature space containing multiple classifications (e.g., inflammatory, desert, exclusionary, or balanced). The machine learning algorithm can also identify the boundaries between the multiple classifications in the machine learning feature space.

[0219] Once a machine learning algorithm is trained, as shown in Figure 3-(4), the trained algorithm can classify the CD8 topology in new histological images as inflammatory, desert, elimination, or balanced. Such classifications for a given patient image can then be used to diagnose the patient's condition, determine the patient's immune response, and / or recommend or exclude treatment options for that patient.

[0220] Figure 4 is a flowchart illustrating the process for training a machine learning algorithm for classifying CD8 tumor topologies, according to an exemplary embodiment. Method 400 may be carried out by processing logic which may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed on a processing device), or a combination thereof. It should be understood that not all operations are required to carry out the disclosures provided herein. Furthermore, as will be understood by those skilled in the art, some operations may be performed simultaneously or in a different order than that shown in Figure 4.

[0221] In operation 402, multiple histological images of tumor samples from multiple patients can be received by at least one processor of the computing device. In some embodiments, the histological images may include tumor tissue samples showing CD8+ T cell patterns within the TME for multiple patients, obtained using CD8+ immunostaining techniques.

[0222] Operation 404 allows for image analysis of multiple histological images to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma in each of the multiple histological images. In some embodiments, image analysis of multiple histological images includes applying an artificial neural network (e.g., a convolutional neural network) to the multiple histological images. In some embodiments, the abundance of CD8+ T cells in the tumor parenchyma and stroma may include a graphical representation of the relationship between the percentage of stromal CD8+ T cells and the percentage of parenchymal CD8+ T cells relative to the total number of T cells present in each of the multiple histological images.

[0223] In operation 406, a machine learning algorithm can be trained using the results of image analysis as well as the abundance of CD8+ T cells in the tumor parenchyma and stroma. In some embodiments, a polar coordinate transformation can be applied to a graphical representation of the relationship between stromal CD8+ T cells and parenchymal CD8+ T cells, and the resulting polar coordinate plot can be used to train the machine learning algorithm. In some embodiments, the machine learning algorithm includes a random forest classifier algorithm.

[0224] Operation 408 can generate a machine learning feature space containing multiple classifications based on training. In some embodiments, the multiple classifications may include inflammatory, desert, exclusionary, or balanced types.

[0225] Operation 410 can identify the boundaries between multiple classifications in the machine learning feature space. In some embodiments, data about the machine learning feature space and the boundaries between multiple classifications in the machine learning feature space may be stored in the memory of a computing device or computer system.

[0226] Figure 5 is a flowchart illustrating a process for classifying the CD8 tumor topology of histological images using a trained machine learning algorithm, according to an exemplary embodiment. Method 500 may be carried out by processing logic which may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed on a processing device), or a combination thereof. It should be understood that not all operations are required to carry out the disclosures provided herein. Furthermore, as will be understood by those skilled in the art, some operations may be carried out simultaneously or in a different order than that shown in Figure 5.

[0227] In operation 502, a new histological image of the patient's tumor sample can be received by at least one processor of the computing device. In some embodiments, the new histological image may include a tumor tissue sample showing a CD8+ T cell pattern within the TME, obtained using CD8+ immunostaining techniques.

[0228] In operation 504, image analysis of new histological images is performed to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma in the new histological images. This image analysis can be performed, for example, using the same image analysis algorithm as operation 404 in Figure 4.

[0229] In operation 506, the trained machine learning algorithm can be applied to the results of image analysis as well as the abundance of CD8+ T cells in the tumor parenchyma and stroma. In some embodiments, the trained machine learning algorithm can be generated by method 400 in Figure 4. In some embodiments, the trained machine learning algorithm may include a machine learning feature space that includes different classifications of CD8 topology (e.g., inflammatory, desert, exclusionary, or balanced).

[0230] In operation 508, a machine learning feature space can be used to determine the classification of a new histological image. In some embodiments, the machine learning algorithm may be able to determine where the patterns of stromal CD8+ T cells and parenchymal CD8+ T cells in the new histological image fall within the boundaries of multiple classifications in the machine learning feature space. Based on this mapping, the machine learning algorithm can output a classification of the new histological image.

[0231] Figure 6 is a block diagram of exemplary components of computer system 600. One or more computer systems 600 can be used to implement, for example, any of the embodiments discussed herein, as well as combinations and partial combinations thereof. In some embodiments, one or more computer systems 600 can be used to implement methods 400 and 500 shown in Figures 4 and 5, respectively. Computer system 600 may include one or more processors (also called central processing devices or CPUs), for example, processor 604. Processor 604 may be connected to a communication infrastructure or bus 606.

[0232] The computer system 600 may also include a user input / output interface 602, such as a monitor, keyboard, pointing device, etc., which can communicate with the communication infrastructure 606 via the user input / output interface 603.

[0233] One or more processors 604 may be graphics processing units (GPUs). In one embodiment, the GPU may be a processor that is a specialized electronic circuit designed to handle mathematically intensive applications. The GPU may have a parallel structure that is efficient for parallel processing of large data blocks, such as mathematically intensive data common to computer graphics applications, images, videos, etc.

[0234] The computer system 600 may also include main memory or primary memory 608, such as random access memory (RAM). Main memory 608 may include one or more levels of cache. Main memory 608 may store control logic (i.e., computer software) and / or data within it.

[0235] The computer system 600 may also include one or more secondary storage devices or memories 610. The secondary memory 610 may include, for example, a hard disk drive 612 and / or a removable storage drive 614.

[0236] The removable storage drive 614 can interact with the removable storage unit 618. The removable storage unit 618 may include a computer-usable or readable storage device that stores computer software (control logic) and / or data. The removable storage unit 618 may be a program cartridge and cartridge interface (e.g., those found in video game devices), a removable memory chip (e.g., EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface. The removable storage drive 614 can read from and / or write to the removable storage unit 618.

[0237] The secondary memory 610 may include other means, devices, components, means, or other approaches to enable computer programs and / or other instructions and / or data to be accessed by the computer system 600. Such means, devices, components, means, or other approaches may include, for example, removable storage units 622 and interfaces 620. Examples of removable storage units 622 and interfaces 620 may include program cartridges and cartridge interfaces (e.g., those found in video game devices), removable memory chips (e.g., EPROM or PROM) and associated sockets, memory sticks and USB ports, memory cards and associated memory card slots, and / or any other removable storage units and associated interfaces.

[0238] The computer system 600 may further include a communication interface or network interface 624. The communication interface 624 can enable the computer system 600 to communicate and interact with any combination of external devices, external networks, external entities, etc. (referenced individually or collectively by reference number 628). For example, the communication interface 624 can enable the computer system 600 to communicate with an external or remote device 628 via a communication path 626, which may be wired and / or wireless (or a combination thereof) and may include any combination of LAN, WAN, Internet, etc. Control logic and / or data can be exchanged with the computer system 600 via the communication path 626.

[0239] The computer system 600 may also be, to give some non-limiting examples, a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet, a smartphone, a smartwatch or other wearable, an appliance, part of the Internet of Things, and / or an embedded system, or any combination thereof.

[0240] The computer system 600 may be a client or server that accesses or hosts any application and / or data via any delivery paradigm, including but not limited to remote or distributed cloud computing solutions, local or on-premises software ("on-premises" cloud-based solutions), "as a service" models (e.g., Content as a Service (CaaS), Digital Content as a Service (DCaaS), Software as a Service (SaaS), Management Software as a Service (MSaaS), Platform as a Service (PaaS), Desktop as a Service (DaaS), Framework as a Service (FaaS), Backend as a Service (BaaS), Mobile Backend as a Service (MBaaS), Infrastructure as a Service (IaaS), etc.), and / or hybrid models that include any combination of the above examples or other service or delivery paradigms.

[0241] Any applicable data structures, file formats, and schemas within the computer system 600 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extensible Markup Language (XML), Yet Another Markup Language (YAML), Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or any other functionally similar representations, either alone or in combination. Alternatively, proprietary data structures, formats, or schemas may be used exclusively or in combination with known or open standards.

[0242] In some embodiments, a tangible, non-temporary device or product including a tangible, non-temporary computer usable or readable medium storing control logic (software) may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, tangible products embodying the computer system 600, main memory 608, secondary memory 610, and removable storage units 618 and 622, and any combination thereof. When such control logic is executed by one or more data processing devices (e.g., the computer system 600), such data processing devices can be made to operate as described herein.

[0243] References in the detailed description such as "one exemplary embodiment," "an exemplary embodiment," or "an exemplary embodiment" indicate that the described exemplary embodiments may include certain features, structures, or characteristics, but not all exemplary embodiments necessarily include certain features, structures, or characteristics. Furthermore, such phrases do not necessarily refer to the same exemplary embodiment. Moreover, if certain features, structures, or characteristics are described in relation to an exemplary embodiment, it is within the knowledge of those skilled in the art that such features, structures, or characteristics will be affected in relation to other exemplary embodiments, whether or not they are explicitly stated.

[0244] The exemplary embodiments described herein are provided for illustrative purposes only and are not limiting. Other exemplary embodiments are possible and modifications can be made to the exemplary embodiments within the spirit and scope of this disclosure. Therefore, the detailed description is not intended to limit this disclosure. Rather, the scope of this disclosure is defined only in accordance with the claims and their equivalents.

[0245] The embodiments can be implemented in hardware (e.g., circuitry), firmware, software, or any combination thereof. Alternatively, the embodiments can be implemented as instructions stored in a machine-readable medium that can be read and executed by one or more processors. The machine-readable medium may include any mechanism for storing or transmitting information in a format readable by a machine (e.g., a computing device). For example, the machine-readable medium may include read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, electrical, optical, acoustic, or other forms of propagating signals (e.g., carrier waves, infrared signals, digital signals, etc.), and others. Furthermore, firmware, software, routines, and instructions may be described herein as performing certain operations. However, such descriptions are merely for convenience, and it should be understood that such operations actually arise from computing devices, processors, controllers, or other devices that execute the firmware, software, routines, instructions, etc. Furthermore, any variation of the implementation can be executed by a general-purpose computer, as described above.

[0246] Exemplary embodiments of artificial intelligence and machine learning described herein for use in the identification of CD8 topology can be applied to measure the expression of any biomarker known in the art, including any tumor biomarker. In some embodiments, tumor biomarkers to be analyzed and / or characterized using the methods disclosed herein include, but are not limited to, PD-L1, PD-1, LAG3, CLTA-4, TIGIT, TIM3, NKG2a, CSF1R, OX40, ICOS, MICA, MICB, CD137, KIR, TGFβ, IL-10, IL-8, B7-H4, Fas ligand, CXCR4, mesothelin, CD27, GITR, and any combination thereof. Markers may also include those morphologically identified without the use of staining antibodies, such as lymphocytes, fibroblasts, macrophages, neutrophils, eosinophils, or any combination thereof. Similarly, although the examples described herein are related to tumors, the machine learning-based methods described herein can also be applied to other histological types in various therapeutic applications, such as fibrosis, cardiac, gastrointestinal, and other oncological and non-oncological therapeutic fields. This disclosure relates, for example, to the following: [Section 1] A pharmaceutical composition comprising an anti-PD-1 / PD-L1 antagonist for use in a method of treating a human subject with tumors, wherein a tumor sample obtained from the subject is (i) Exclusionary CD8 localization phenotype, and (ii) Negative PD-L1 expression status A pharmaceutical composition that exhibits the following characteristics. [Section 2] A pharmaceutical composition for use as described in item 1, wherein an anti-PD-1 / PD-L1 antagonist is administered in combination with an anticancer agent. [Section 3] A pharmaceutical composition for use according to item 1 or 2, wherein an anti-PD-1 / PD-L1 antagonist is administered in combination with an anti-CTLA-4 antagonist. [Section 4] A pharmaceutical composition for use according to any one of items 1 to 3, wherein the tumor sample is a tumor tissue biopsy sample. [Section 5] A pharmaceutical composition for use according to any one of claims 1 to 4, wherein the tumor sample is formalin-fixed paraffin-embedded tumor tissue or fresh frozen tumor tissue. [Section 6] A pharmaceutical composition for use according to any one of claims 1 to 5, wherein CD8 localization is measured by staining a tumor sample with an antibody or antigen-binding moiety that binds to CD8. [Section 7] The pharmaceutical composition according to item 6, wherein a tumor sample is imaged after being stained with an antibody. [Section 8] A pharmaceutical composition for use according to any one of claims 1 to 6, wherein PD-L1 expression is measured by staining a tumor sample with an antibody or its antigen-binding moiety that specifically binds to PD-L1. [Section 9] A pharmaceutical composition for use according to any one of claims 1 to 8, wherein the negative PD-L1 expression status is characterized by a tumor sample in which less than approximately 1% of tumor cells express PD-L1. [Section 10] A pharmaceutical composition for use as described in item 7, wherein PD-L1 expression is measured using an IHC assay. [Section 11] A pharmaceutical composition for use as described in item 10, comprising an automated IHC assay. [Section 12] A pharmaceutical composition for use according to any one of items 1 to 11, wherein CD8 localization is measured by performing IHC and then classifying the CD8 localization in a tumor sample. [Section 13] The classification is Receiving multiple histological images of tumor samples from multiple patients by at least one processor of a computing device, At least one processor performs image analysis of multiple histological images to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma for each of the multiple histological images. At least one processor is used to train a machine learning algorithm using the results of image analysis as well as the abundance of CD8+ T cells in the tumor parenchyma and stroma. To generate a machine learning feature space containing multiple classifications based on training using at least one processor, and Identifying the boundaries between multiple classifications in a machine learning feature space using at least one processor. A pharmaceutical composition for use as described in item 12, carried out by a method comprising: [Section 14] A pharmaceutical composition comprising an anti-PD-1 / PD-L1 antagonist for use in a method for identifying human subjects suitable for anti-PD-1 / PD-L1 antagonist therapy, the method comprising (i) measuring the expression of PD-L1 in tumor samples obtained from a subject, and (ii) measuring the CD8 localization in tumor samples, CD8 localization is measured by staining tumor samples with antibodies that bind to CD8 or their antigen-binding moieties, and by classifying the CD8 localization in tumor samples. The classification is Receiving multiple histological images of tumor samples from multiple patients by at least one processor of a computing device, At least one processor performs image analysis of multiple histological images to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma for each of the multiple histological images. At least one processor is used to train a machine learning algorithm using the results of image analysis as well as the abundance of CD8+ T cells in the tumor parenchyma and stroma. To generate a machine learning feature space containing multiple classifications based on training using at least one processor, and Identifying the boundaries between multiple classifications in a machine learning feature space using at least one processor. A pharmaceutical composition produced by a method including the following. [Section 15] A pharmaceutical composition for use according to item 13 or 14, wherein image analysis of multiple histological images includes applying an artificial neural network to multiple histological images. [Section 16] A pharmaceutical composition for use as described in item 15, wherein the machine learning algorithm includes a random forest classifier algorithm. [Section 17] A pharmaceutical composition for use according to any one of claims 13 to 16, comprising a graphical representation of the relationship between the abundance of CD8+ T cells and the percentage of stromal CD8+ T cells relative to the total number of T cells present in each of several histological images. [Section 18] Applying a polar coordinate transformation to a graph display using at least one processor of a computing device to generate a polar plot, and using the polar plot to train a machine learning algorithm. A pharmaceutical composition for use as described in item 17, further comprising: [Section 19] A pharmaceutical composition for use according to any one of claims 13 to 18, wherein multiple classifications include inflammatory, desert, elimination, or balanced types. [Section 20] A pharmaceutical composition for use according to any one of items 13 to 19, further comprising determining a classification for each of multiple histological images based on a machine learning feature space. [Section 21] A pharmaceutical composition for use according to item 20, further comprising verifying the results from a machine learning feature space by comparing the labels for each of the multiple histological images obtained by at least one pathologist with the classifications for each of the multiple histological images. [Section 22] A pharmaceutical composition for use according to any one of claims 13 to 21, further comprising: receiving additional histological images by at least one processor of a computing device; performing additional image analysis of the additional histological images to obtain the abundance of additional CD8+ T cells in the tumor parenchyma and stroma in the additional histological images; applying a machine learning algorithm to the results from the additional image analysis and the abundance of additional CD8+ T cells; and determining a classification for the additional histological images based on a machine learning feature space. [Section 23] A pharmaceutical composition for use according to any one of claims 1 to 22, wherein CD8 localization is measured by measuring the expression of a panel of genes in tumor samples obtained from a subject. [Section 24] A pharmaceutical composition for use according to any one of claims 1 to 23, wherein a subject identified as having an exclusionary CD8 localized phenotype and a PD-L1 negative tumor is administered therapy comprising an anti-PD-1 / PD-L1 antagonist. [Section 25] A pharmaceutical composition for use according to any one of claims 1 to 23, wherein a subject identified as having an exclusionary CD8 localized phenotype and a PD-L1 negative tumor is administered a therapy comprising an anti-PD-1 / PD-L1 antagonist and an anti-CTLA-4 antagonist. [Section 26] A pharmaceutical composition for use according to any one of claims 1 to 24, comprising an anti-PD-1 / PD-L1 antagonist which is an antibody or antigen-binding fragment thereof ("anti-PD-1 antibody" or "anti-PD-L1 antibody") that specifically binds to a target protein selected from programmed death 1 (PD-1) or programmed death ligand 1 (PD-L1). [Section 27] A pharmaceutical composition for use according to any one of claims 1 to 26, comprising an anti-PD-1 / PD-L1 antagonist and an anti-PD-1 antibody. [Section 28] A pharmaceutical composition for use as described in item 26 or 27, comprising an anti-PD-1 antibody, nivolumab, or pembrolizumab. [Section 29] A pharmaceutical composition for use according to any one of claims 1 to 26, comprising an anti-PD-1 / PD-L1 antagonist and an anti-PD-L1 antibody. [Section 30] A pharmaceutical composition for use as described in item 29, comprising an anti-PD-L1 antibody, avelumab, atezolizumab, or durvalumab. [Section 31] A pharmaceutical composition for use according to any one of claims 3 to 13 and 15 to 30, comprising an anti-CTLA-4 antagonist which is an antibody or an antigen-binding fragment thereof that specifically binds to cytotoxic T lymphocyte-associated protein 4 (CTLA-4) ("anti-CTLA-4 antibody"). [Section 32] A pharmaceutical composition for use as described in item 31, comprising an anti-CTLA-4 antibody and ipilimumab. [Section 33] A method for treating cancer in human subjects, comprising administering an anti-PD-1 / anti-PD-L1 antagonist to the subject, (i) Exclusionary CD8 localization phenotype, and (ii) Negative PD-L1 expression status A method for identifying a tumor exhibiting the following characteristics. [Section 34] The method according to item 33, further comprising administering an anti-CTLA-4 antagonist. [Section 35] The method according to item 33 or 34, wherein the exclusionary CD8 localization phenotype is measured by detecting CD8 expression in tumor samples obtained from the subject. [Section 36] The method according to any one of claims 33 to 35, wherein the exclusionary CD8 localization phenotype is measured by staining a tumor sample with an antibody that binds to CD8 or its antigen-binding moiety. [Section 37] CD8 localization is measured by staining tumor samples with an antibody that binds to CD8 or its antigen-binding portion, and then classifying the CD8 localization in the tumor samples. The classification is Receiving multiple histological images of tumor samples from multiple patients by at least one processor of a computing device, At least one processor performs image analysis of multiple histological images to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma for each of the multiple histological images. At least one processor is used to train a machine learning algorithm using the results of image analysis as well as the abundance of CD8+ T cells in the tumor parenchyma and stroma. To generate a machine learning feature space containing multiple classifications based on training using at least one processor, and Identifying the boundaries between multiple classifications in a machine learning feature space using at least one processor. The method described in any one of paragraphs 33 to 36, which is carried out by a method including the following: [Section 38] A method for identifying human subjects suitable for anti-PD-1 / PD-L1 antagonist therapy, comprising (i) measuring the expression of PD-L1 in tumor samples obtained from the subjects, and (ii) measuring the localization of CD8 in tumor samples, CD8 localization is measured by staining tumor samples with an antibody that binds to CD8 or its antigen-binding portion, and then classifying the CD8 localization in the tumor samples. The classification is Receiving multiple histological images of tumor samples from multiple patients by at least one processor of a computing device, At least one processor performs image analysis of multiple histological images to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma for each of the multiple histological images. At least one processor is used to train a machine learning algorithm using the results of image analysis as well as the abundance of CD8+ T cells in the tumor parenchyma and stroma. To generate a machine learning feature space containing multiple classifications based on training using at least one processor, and Identifying the boundaries between multiple classifications in a machine learning feature space using at least one processor. A method that includes a method. [Section 39] The method according to paragraph 37 or 38, wherein image analysis of multiple histological images includes applying an artificial neural network to multiple histological images. [Section 40] The method according to Section 39, wherein the machine learning algorithm includes a random forest classifier. [Section 41] The method according to any one of items 37 to 40, wherein the abundance of CD8+ T cells includes a graphical representation of the relationship between the percentage of stromal CD8+ T cells and the percentage of parenchymal CD8+ T cells relative to the total number of T cells present in each of several histological images. [Section 42] The method according to paragraph 41, further comprising applying a polar coordinate transformation of a graph display to at least one processor of a computing device to produce a polar plot, and using the polar plot to train a machine learning algorithm. [Section 43] The method according to any one of paragraphs 37 to 42, wherein the classification includes multiple types: inflammatory, desert, exclusion, or balanced. [Section 44] The method according to any one of items 37 to 47, further comprising determining a classification for each of multiple histological images based on a machine learning feature space. [Section 45] The method according to paragraph 44, further comprising validating the results from a machine learning feature space by comparing the labels for each of the multiple histological images obtained by at least one pathologist with the classifications for each of the multiple histological images. [Section 46] The method according to any one of paragraphs 37 to 45, further comprising: receiving additional histological images by at least one processor of a computing device; performing additional image analysis of the additional histological images to obtain the abundance of additional CD8+ T cells in the tumor parenchyma and stroma in the additional histological images; applying a machine learning algorithm to the results from the additional image analysis and the abundance of additional CD8+ T cells; and determining a classification for the additional histological images based on a machine learning feature space. [Section 47] The method according to any one of claims 38 to 47, further comprising administering an anti-PD-1 / PD-L1 antagonist to a subject identified as having an elimination-type CD8 localized phenotype and a PD-L1-negative tumor. [Section 48] The method according to item 47, further comprising administering an anti-CTLA-4 antagonist. [Section 49] The method according to any one of claims 33 to 48, wherein the anti-PD-1 / PD-L1 antagonist comprises an antibody or antigen-binding fragment thereof ("anti-PD-1 antibody" or "anti-PD-L1 antibody") that specifically binds to a target protein selected from programmed death 1 (PD-1) or programmed death ligand 1 (PD-L1). [Section 50] The method according to any one of items 33 to 49, wherein the anti-PD-1 / PD-L1 antagonist is an anti-PD-1 antibody. [Section 51] The method according to item 49 or 50, wherein the anti-PD-1 antibody comprises nivolumab or pembrolizumab. [Section 52] The method according to any one of items 33 to 49, wherein the anti-PD-1 / PD-L1 antagonist comprises an anti-PD-L1 antibody. [Section 53] The method according to item 52, wherein the anti-PD-L1 antibody comprises avelumab, atezolizumab, or durvalumab. [Section 54] The method according to any one of claims 34-37 and 39-53, wherein the anti-CTLA-4 antagonist comprises an antibody or antigen-binding fragment thereof that specifically binds to cytotoxic T lymphocyte-associated protein 4 (CTLA-4) ("anti-CTLA-4 antibody"). [Section 55] The method according to item 54, wherein the anti-CTLA-4 antibody comprises ipilimumab. [Section 56] A pharmaceutical composition for use according to any one of claims 1 to 32, or the method according to any one of claims 33 to 55, wherein the tumor is derived from a cancer selected from the group consisting of hepatocellular carcinoma, gastroesophageal cancer, melanoma, bladder cancer, lung cancer, kidney cancer, head and neck cancer, colon cancer, pancreatic cancer, prostate cancer, ovarian cancer, urothelial carcinoma, colorectal cancer, and any combination thereof. [Section 57] A pharmaceutical composition for use according to any one of claims 1 to 32 and 56, or the method according to any one of claims 33 to 56, for a tumor that is recurrent. [Section 58] A pharmaceutical composition for use according to any one of claims 1 to 32 and 56, or the method according to any one of claims 33 to 56, for tumors that are resistant to treatment. [Section 59] A pharmaceutical composition for use according to any one of claims 1-32 and 56-58, or the method according to any one of claims 33-58, for locally progressive tumors. [Section 60] A pharmaceutical composition for use according to any one of claims 1-32 and 56-58, or the method according to any one of claims 33-58, wherein the tumor is metastatic. [Section 61] A pharmaceutical composition for use according to any one of claims 1-32 and 56-60, or the method according to any one of claims 33-60, wherein the tumor is treated by administration. [Section 62] A pharmaceutical composition for use according to any one of claims 1-32 and 56-61, or the method according to any one of claims 33-61, wherein administration reduces the size of a tumor. [Section 63] The pharmaceutical composition or method according to item 62, wherein the size of the tumor is reduced by at least about 10%, about 20%, about 30%, about 40%, or about 50% compared to the tumor size before administration. [Section 64] A pharmaceutical composition for use as described in any one of claims 1 to 32 and 56 to 63, or a method as described in any one of claims 33 to 63, wherein the subject exhibits a progression-free survival of at least approximately 1 month, at least approximately 2 months, at least approximately 3 months, at least approximately 4 months, at least approximately 5 months, at least approximately 6 months, at least approximately 7 months, at least approximately 8 months, at least approximately 9 months, at least approximately 10 months, at least approximately 11 months, at least approximately 1 year, at least approximately 18 months, at least approximately 2 years, at least approximately 3 years, at least approximately 4 years, or at least approximately 5 years after the first administration. [Section 65] A pharmaceutical composition for use according to any one of claims 1-32 and 56-64, or the method according to any one of claims 33-64, wherein the subject exhibits disease stabilization after administration. [Section 66] A pharmaceutical composition for use according to any one of claims 1-32 and 56-64, or the method according to any one of claims 33-64, wherein the subject exhibits a partial response after administration. [Section 67] A pharmaceutical composition for use according to any one of claims 1-32 and 56-66, or the method according to any one of claims 33-66, wherein the subject shows a complete response after administration. [Section 68] A kit for treating subjects suffering from tumors, (a) Anti-PD-1 / PD-L1 antagonists, and (b) Instructions for using an anti-PD-1 / PD-L1 antagonist in accordance with any one of the methods described in paragraphs 34-69. A kit that includes this. [Section 69] The kit described in item 68, wherein the anti-PD-1 / PD-L1 antagonist contains an anti-PD-1 antibody. [Section 70] The kit described in item 68, wherein the anti-PD-1 / PD-L1 antagonist contains an anti-PD-L1 antibody. [Section 71] A kit as described in any one of sections 68-70, further comprising an anti-CTLA-4 antagonist. [Section 72] The kit described in item 71, comprising an anti-CTLA-4 agonist and an anti-CTLA-4 antibody. [Section 73] A pharmaceutical composition for use according to any one of claims 1-32 and 56-66, or the method according to any one of claims 33-66, wherein the subject exhibits milder adverse events compared to a subject that does not exhibit the exclusionary CD8 localization phenotype. [Section 74] A pharmaceutical composition for use as described in any one of the claims 1-32 and 56-66, or a method as described in any one of the claims 33-66, wherein the subject does not exhibit an adverse event more severe than Grade 1, an adverse event more severe than Grade 2, or an adverse event more severe than Grade 3. [Section 75] A pharmaceutical composition for use according to any one of claims 1-32 and 56-66, or the method according to any one of claims 33-66, wherein the subject exhibits a lower frequency of grade 3 or more severe adverse events compared to a subject that does not exhibit the elimination type of CD8 localization phenotype. [Examples]

[0247] [Example 1]

[0248] A random forest AI classifier was trained to predict inflammatory, elimination, and cold patterns assigned by pathologists to CD8 immunostaining using parenchymal and stromal CD8 measurements from a deep learning platform. In retrospective analyses of all-marker-evaluable clinical baseline CD8 immunostaining in CA209-067 melanoma (MEL-NIVO+IPI arm, n=102); (MEL-NIVO arm, n=107) and CA209-275 urothelial carcinoma (UC-NIVO, n=263), AI-defined CD8 topology was independently compared to survival.

[0249] The PD-L1<1% / CD8 exclusion subset showed longer median overall survival (mOS) and lower hazard ratios (HR) in all trial arms compared to the PD-L1<1% / CD8 inflammation population: [MEL-NIVO+IPI: mOS>50 months (n=20) vs. 10.1 months (n=12), HR=0.23 (95%CI:0.09~0.61); MEL-NIVO: mOS>50 months (n=20) vs. 25.8 months (n=15), HR=0.68 (95%CI:0.27~1.7); UC-NIVO: mOS=9.0 months (n=87) vs. 3.1 months (n=24), HR=0.62 (95%CI:0.38~1.00)] (Figures 7A~7C).

[0250] The CD8 exclusion pattern shows superior survival compared to the CD8 inflammatory pattern in the context of PD-L1-negative tumors, and a combined immunohistochemical approach combining CD8 topology and PD-L1 has the potential to improve patient selection across multiple tumor indications and treatment situations. Further research is underway to identify the mechanisms underlying these findings. [Example 2]

[0251] As described in Example 1, a random forest classifier was trained to predict CD8 topology using parenchymal and stromal CD8+ immunocytometry derived from a deep learning platform. For model validation, pathologists manually classified CD8 immunohistochemistry in melanoma samples into inflammatory (CD8+ cells in tumor parenchyma), elimination (CD8+ cells confined to the stroma), and desert (CD8+ cell deficiency) patterns. The association with overall survival (OS) was investigated in a subset of untreated metastatic melanoma patients who received nivolumab + ipilimumab (NIVO+IPI, n=102) or NIVO alone (n=107) in a Phase 3 clinical trial. Retrospective analysis of AI-defined CD8 topology at baseline was performed alone and in combination with manual scoring of programmed death ligand 1 (PD-L1) expression on tumor cells.

[0252] The classifier model's predictions were consistent with manual scoring (determined by pathologist consensus) and non-inferior to the agreement between two pathologists, with Cohen's kappa coefficients of k=0.79 and k=0.65, respectively. Within the PD-L1 ≥1% population, there was no statistically significant difference in outcomes between the CD8-excluded and CD8-inflammatory phenotypes. However, patients with PD-L1 <1% / CD8-excluded tumors showed a longer median OS compared to patients with PD-L1 <1% / CD8-inflammatory tumors (Table 1). 38% (40 / 104) of tumors with PD-L1 <1% were CD8-excluded. Among tumors with PD-L1 <1%, patients with an exclusionary phenotype also showed a lower frequency of severe adverse events (grade 3 or higher) after treatment than patients with an inflammatory phenotype: NIVO+IPI, 75% (n=20) vs. 91% (n=11); NIVO, 61% (n=18) vs. 80% (n=15). Compared to PD-L1 status, the combined biomarker (CD8 exclusionary + PD-L1 ≥1% in the AI ​​classification) identified a larger group of patients who showed greater survival benefits with NIVO+IPI or NIVO alone (Table 2).

[0253] Table 1. Immunotherapy outcomes by CD8+ topology in melanoma with PD-L1 <1% [Table 1]

[0254] Table 2. Composite biomarker outcomes in the study [Table 2] The hazard ratio represents a comparison between patients with PD-L1 expression of 1% or more and patients with PD-L1 expression of less than 1%, or between patients with PD-L1 expression of 1% or more and a CD8-excluding phenotype and patients with PD-L1 expression of less than 1% and a non-CD8-excluding phenotype.

[0255] This study combined AI-based CD8 topology classification and PD-L1 expression as composite biomarkers related to immunotherapy response. In melanoma patients with PD-L1 <1%, median OS as measured by NIVO+IPI was significantly longer in patients with CD8-exclusionary tumors than in patients with inflammatory phenotypes.

Claims

1. A computer implementation method for identifying human subjects suitable for immunotherapy to treat human tumors, wherein the method is performed on data processing hardware that causes the operation to take place. Receiving histological images of tumor samples from human subjects; To perform image analysis of histological images and obtain image analysis results that show the abundance of CD8+ T cells in the tumor parenchyma and stroma; Processing the results of image analysis obtained by performing image analysis on histological images using a trained tumor topology classification model that includes a machine learning feature space containing boundaries for multiple possible classifications of CD8 localization, to determine the classification of CD8 localization in a tumor sample from the boundaries of the machine learning feature space for multiple possible classifications of CD8 localization; and To generate recommendations for treatment options for human subjects based on the classification of CD8 localization in tumor samples. Computer implementation methods, including those mentioned above.

2. The method according to claim 1, wherein possible classifications of multiple CD8 localizations include inflammatory, desert, exclusion, and balanced types.

3. The method according to claim 2, wherein the classification of CD8 localization in a tumor sample includes exclusionary types.

4. The operation is, To determine if the tumor sample exhibits negative PD-L1 expression. It further includes, Generating recommendations for immunotherapy is based further on the determination that tumor samples exhibit negative PD-L1 expression status. The method according to claim 1.

5. A trained tumor topology classification model, Receiving multiple training histological images of tumor samples from multiple patients; For each of the training histological images in multiple training histological images, image analysis of the training histological images is performed to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma in the training histological images; To train a tumor topology classification model using the results of image analysis in each of multiple training histological images, as well as the abundance of CD8+ T cells in the tumor parenchyma and stroma; Generating a machine learning feature space containing multiple classifications based on training; and Identifying the boundaries between possible classifications of multiple CD8 localizations. The method according to claim 1, which is trained by a training process including the following:

6. The method according to claim 5, wherein the abundance of CD8+ T cells includes a graphical representation of the relationship between the percentage of stromal CD8+ T cells and the percentage of parenchymal CD8+ T cells relative to the total number of T cells present in a histological image.

7. The operation is, Applying polar coordinate transformation to the graph display to generate polar plots, It further includes, The method according to claim 6, wherein training a tumor topology classification model is further based on using polar plots.

8. Each of the training histological images is obtained by at least one pathologist and includes a label that provides classification for the CD8 localization in the training histological image; and Training a tumor topology classification model involves validating results from the machine learning feature space by comparing labels on multiple training histological images. The method according to claim 7.

9. The method according to claim 1, wherein the treatment options include immunotherapy.

10. The method according to claim 1, wherein the immunotherapy comprises anti-PD-1 / PD-L1 antagonist therapy.

11. The method according to claim 10, wherein the anti-PD-1 / PD-L1 antagonist comprises an antibody ("anti-PD-1 antibody" or "anti-PD-L1 antibody") or an antigen-binding fragment thereof that specifically binds to a target protein selected from programmed death 1 (PD-1) or programmed death ligand 1 (PD-L1).

12. The method according to claim 10, wherein the anti-PD-1 / PD-L1 antagonist comprises an anti-PD-1 antibody.

13. The method according to claim 12, wherein the anti-PD-1 antibody comprises nivolumab or pembrolizumab.

14. The method according to claim 10, wherein the anti-PD-1 / PD-L1 antagonist comprises an anti-PD-L1 antibody.

15. The method according to claim 14, wherein the anti-PD-L1 antibody comprises avelumab, atezolizumab, or durvalumab.

16. Data processing hardware; and Memory hardware that communicates with data processing hardware and, when executed on the data processing hardware, stores instructions that cause the data processing hardware to perform actions to identify human subjects suitable for immunotherapy to treat human tumors. Includes, The operation is, Receiving histological images of tumor samples from human subjects; To perform image analysis of histological images and obtain image analysis results that show the abundance of CD8+ T cells in the tumor parenchyma and stroma; Processing the results of image analysis obtained by performing image analysis on histological images using a trained tumor topology classification model that includes a machine learning feature space containing boundaries for multiple possible classifications of CD8 localization, to determine the classification of CD8 localization in a tumor sample from the boundaries of the machine learning feature space for multiple possible classifications of CD8 localization; and To generate recommendations for treatment options for human subjects based on the classification of CD8 localization in tumor samples. A system that includes this.

17. The system according to claim 16, wherein the possible classifications of multiple CD8 localizations include inflammatory, desert, exclusion, and balanced types.

18. The system according to claim 17, wherein the classification of CD8 localization in tumor samples includes exclusionary types.

19. The operation is, To determine if the tumor sample exhibits negative PD-L1 expression. It further includes, Generating recommendations for immunotherapy is based further on the determination that tumor samples exhibit negative PD-L1 expression status. The system according to claim 16.

20. A trained tumor topology classification model, Receiving multiple training histological images of tumor samples from multiple patients; For each of the training histological images in multiple training histological images, image analysis of the training histological images is performed to obtain the abundance of CD8+ T cells in the tumor parenchyma and stroma in the training histological images; To train a tumor topology classification model using the results of image analysis in each of multiple training histological images, as well as the abundance of CD8+ T cells in the tumor parenchyma and stroma; Generating a machine learning feature space containing multiple classifications based on training; and Identifying the boundaries between possible classifications of multiple CD8 localizations. The system according to claim 16, which is trained by a training process including the following:

21. The system according to claim 20, wherein the abundance of CD8+ T cells includes a graphical representation of the relationship between the percentage of stromal CD8+ T cells and the percentage of parenchymal CD8+ T cells relative to the total number of T cells present in a histological image.

22. The operation is, Applying polar coordinate transformation to the graph display to generate polar plots, It further includes, The system according to claim 21, wherein training a tumor topology classification model is further based on using polar plots.

23. Each of the training histological images is obtained by at least one pathologist and includes a label that provides classification for the CD8 localization in the training histological image; and Training a tumor topology classification model involves validating results from the machine learning feature space by comparing labels on multiple training histological images. The system according to claim 22.

24. The system according to claim 16, wherein the treatment options include immunotherapy.

25. The system according to claim 16, wherein the immunotherapy comprises anti-PD-1 / PD-L1 antagonist therapy.

26. The system according to claim 25, wherein the anti-PD-1 / PD-L1 antagonist comprises an antibody ("anti-PD-1 antibody" or "anti-PD-L1 antibody") or an antigen-binding fragment thereof that specifically binds to a target protein selected from programmed death 1 (PD-1) or programmed death ligand 1 (PD-L1).

27. The system according to claim 25, wherein the anti-PD-1 / PD-L1 antagonist comprises an anti-PD-1 antibody.

28. The system according to claim 27, comprising an anti-PD-1 antibody, nivolumab or pembrolizumab.

29. The system according to claim 25, wherein the anti-PD-1 / PD-L1 antagonist comprises an anti-PD-L1 antibody.

30. The system according to claim 29, wherein the anti-PD-L1 antibody comprises avelumab, atezolizumab, or durvalumab.

Images