EP4792135A1 - Methods of cancer detection, prediction of response to therapy, and treatment - Google Patents

Abstract

Methods of cancer detection in a subject, and methods of predicting a response to a PD- 1/PD-L1 blockade cancer therapy in a cancer patient prior to and/or during the use of the therapy, as well as methods of treatment, are provided, wherein peripheral blood monocytes are analyzed for a unique subset of classical monocytes that express a transcriptional RNA-based signature associated with sensitivity to PD-1/PD-L1 blockade cancer therapy.

Description

METHODS OF CANCER DETECTION, PREDICTION OF RESPONSE TO THERAPY, AND TREATMENT

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Patent Application No. 63/543.820, filed on October 12, 2023 and to Polish Patent Application No. P.446354 filed on October 11, 2023, the disclosures of which are incorporated by reference herein in their entirety.

BACKGROUND

[0002] Cancer treatment protocols involve, for example, chemotherapy and radiotherapy, both of which suppress tumors by directly killing malignant cells, and immune checkpoint blockade (ICB)-based therapy, which rescues the antitumor activity of T cells through the targeted blockade of checkpoints (i.e., membrane proteins). Such proteins include programmed cell death protein 1 (PD-1) and its ligand PD-L1 (also known as CD274).

[0003] PD-1 and PD-Ll inhibitors are a group of checkpoint inhibitor anticancer drugs that block the association of PD-1 and PD-L1. In the cancer disease state, the interaction of PD- L1 on the tumor cells with PD-1 on a T-cell reduces T-cell function signals to prevent the immune system from attacking the tumor cells. Use of an inhibitor that blocks the interaction of PD-L1 with the PD-1 receptor can prevent the cancer from evading the immune system. Exemplary PD- 1 inhibitors include cemiplimab, nivolumab, and pembrolizumab, and exemplary PD-L1 inhibitors include atezolizumab, avelumab, and durvalumab.

[0004] While the use of PD-1/PD-L1 blockade cancer therapies has proven to be successful in the treatment of many different kinds of cancers, not all patients respond to such therapies. Typically, only about 30% of patients with relapsed or refractory disease achieve complete response to PD-1/PD-L1 blockade therapy.

[0005] There are no broadly accepted approaches to predict the effectiveness of PD-l/PD- L1 blockade therapy. Experimental approaches include multiplex imaging of the intact tumor microenvironment, which requires biopsies that are often difficult to obtain. Thus, there is a need for a more robust and easily executed and interpreted assay to identify patients who are unlikely to respond to a cancer treatment protocol that includes PD-1/PD-L1 inhibitors, preferably before actually applying the treatment protocol or at an early timepoint during therapy.

SUMMARY

[0006] There are provided methods related to cancer detection, prognosis, and treatment - - methods of detection of cancer in a subject, methods of predicting a response to a PD-1/PD-L1 blockade cancer therapy in a cancer patient prior to and/or during the use of the therapy, and methods of treatment of a cancer patient. These methods are based on the analysis of “Cluster 0” peripheral blood monocytes, identified herein, which may or may not be isolated from other monocytes in peripheral blood. Such methods are robust and generally easy to execute and interpret.

[0007] The number of “Cluster 0” monocytes is shown herein to be a sensitive indicator of disease activity in multiple lymphomas and solid tumors; and the transcriptional signature of these cells is predictive of a response to PD-1/PD-L1 blockade. The methods described herein, based on a robust peripheral blood assay that analyzes, captures, and preferably quantifies the transcriptional signature of Cluster 0 monocytes, have broad utility in the diagnosis, response monitoring, and treatment selection in patients with a variety of cancers.

[0008] One such method of predicting a response to a PD-1/PD-L1 blockade cancer therapy in a cancer patient prior to and/or during use of the therapy includes: analyzing monocytes isolated from peripheral blood mononuclear cells (PBMCs) of the cancer patient for expression of a transcriptional RNA-based signature associated with sensitivity to the PD-1/PD-L1 blockade cancer therapy; wherein if the transcriptional RNA-based signature comprises a set of genes that are upregulated compared to the same set of genes in a control group, the cancer patient will experience an inferior response to the PD-1/PD-L1 blockade cancer therapy.

[0009] Another method of predicting a response to a PD-1/PD-L1 blockade cancer therapy in a cancer patient prior to and/or during use of the therapy includes: analyzing classical monocytes for a subset of Cluster 0 monocytes, wherein the Cluster 0 monocytes express one or more cytokines/chemokines selected from interleukin 1 beta (sometimes referred to herein as IL 1 beta, IL1B, IL1B, or IL10), CXCL8, CXCL2, CXCL3, and CCL2; and analyzing the Cluster 0 monocytes for expression of a transcriptional RNA-based signature associated with sensitivity to the PD-1/PD-L1 blockade cancer therapy; wherein if the transcriptional RNA-based signature comprises a set of genes that are upregulated compared to the same set of genes in a control group, the cancer patient will experience an inferior response to the PD- 1/PD-L 1 blockade cancer therapy.

[0010] Significantly, the presence of Cluster 0 monocytes in the blood of a subject can be used as an indicator of the presence of cancer, often before other symptoms appear. Thus, there is provided a method of predicting the presence of cancer in a subject, the method comprising: analyzing monocytes for a subset of Cluster 0 monocytes, wherein the Cluster 0 monocytes express one or more cytokines/chemokines selected from IL IB, CXCL8, CXCL2, CXCL3, and CCL2, and wherein the presence of cluster 0 monocytes is indicative of the presence of cancer.

[0011] Also provided are methods of treating a cancer patient that include: predicting a response to a PD- 1/PD-L 1 blockade cancer therapy in a cancer patient prior to and/or during use of the therapy as described herein; and treating the cancer patient with a treatment protocol, which may include a PD- 1/PD-L 1 blockade cancer therapy alone, or in combination with another therapy, if the patient is predicted to have a positive response to the PD-1/PD-L1 blockade cancer therapy, or a treatment protocol other than, or in addition to, a PD- 1/PD-L 1 blockade cancer therapy if the patient is predicted to have an inferior response to the PD-1/PD-L1 blockade cancer therapy.

The above summary is not intended to describe each disclosed embodiment or every implementation. The description that follows more particularly exemplifies illustrative embodiments. In several places throughout the application, guidance is provided through lists of examples, which examples may be used in various combinations. In each instance, the recited list serves only as a representative group and should not be interpreted as an exclusive list. Thus, the scope of the present disclosure should not be limited to the specific illustrative embodiments described herein, but rather extends at least to the embodiments described by the language of the claims, and the equivalents of those embodiments. Any of the elements that are positively recited in this specification as alternatives may be explicitly included in the claims or excluded from the claims, in any combination as desired. Although various theories and possible mechanisms may have been discussed herein, in no event should such discussions serve to limit the claimable subject matter.

BRIEF DESCRIPTION OF THE FIGURES

[0012] Embodiments will be described, by way of example, with reference to the following drawings.

[0013] FIG. 1 A is an annotated Uniform Manifold Approximation and Projection (UMAP) of myeloid clusters, wherein each cluster is labeled with a distinct shade and unique number.

[0014] FIG. IB is a bar chart showing the relative abundance of myeloid clusters between healthy donors (HD) and patients with newly diagnosed (ND) Classical Hodgkin lymphoma (cHL).

[0015] FIG. 1C-I and 1C-II are dot plots showing the relative expression of selected genes in myeloid clusters. Block IV (FIG. 1C-II) includes genes that are upregulated in Cluster 0 monocytes.

[0016] FIG. ID shows cellular phenotype maps of the representative RNAscope images from patients with ND cHL (medium gray, Hodgkin Reed Sternberg (HRS) cells; dark gray, Cluster 0-like macrophages CXCL2⁺ (left), CXCL3⁺ (middle), IL1B⁺ (right); light gray, other macrophages CXCL27CXCL37IL1B ) (all images) and associated proximity plots for the indicated CXCL2⁺, CXCL3⁺, and ILB⁺ Cluster 0-like macrophages and HRS cells.

[0017] FIG. 2A is a dot plot showing the differential expression analysis of response- related Cluster 0 transcripts in patients with relap sed/refractory (R/R) cHL who achieved a complete response (CR) or progressed on PD-1 blockade (i.e., progressive disease (PD)).

[0018] FIG. 2B-I to 2B-III show pathway enrichment analysis of response-related differentially expressed genes in Cluster 0 using Molecular Signatures Database (MSigDB) gene sets: REACTOME gene sets (FIG. 2B-I); WIKI PATHWAYS gene sets (FIG. 2B-II); and IMMUNESIGDB C7 gene sets (FIG. 2B-III), as described in the Examples Section.

[0019] FIG. 2C shows the AUCell signature score in circulating Cluster 0 monocytes from healthy donors (HDs) and patients with ND or R/R cHL at cycle 1 day 1 (C1D1) and cycle 4 day 1 (C4D1) at the single cell (upper panel of violin plots) and patient (lower panel) levels. [0020] FIG. 2D shows the AUCell signature score in all monocytes from HDs and patients with cHL at the single cell (upper panel of violin plots) and patient (lower panel) levels.

[0021] FIG. 2E shows the AUCell signature score in all circulating monocytes from patients with R/R Renal cell carcinoma (RCCa) at baseline (C1D1), stratified by the subsequent response to PD-L1 blockade, at the single cell (upper panel) and patient (lower panel) levels.

[0022] FIG. 3-1 and 3-II show the pathway enrichment analysis of response-related differentially expressed genes in Cluster 0 using Molecular Signatures Database (MSigDB) and REACTOME gene sets.

[0023] FIG. 4-1 and 4-II show the pathway enrichment analysis of response-related differentially expressed genes in Cluster 0 using Molecular Signatures Database (MSigDB) and WIKI PATHWAYS gene sets.

[0024] FIG. 5-1 and 5-II show the pathway enrichment analysis of response-related differentially expressed genes in Cluster 0 using Molecular Signatures Database (MSigDB) and IMMUNESIGDB C7 gene sets.

DETAILED DESCRIPTION

Definitions

[0025] Unless otherwise defined herein, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs and as commonly used in the art to which this application belongs. In case of conflict, the present specification, including definitions, will control.

[0026] Generally, nomenclature used in connection with, and techniques of, cell and tissue culture, molecular biology, virology, immunology, microbiology, genetics, analytical chemistry, synthetic organic chemistry, medicinal and pharmaceutical chemistry, and protein and nucleic acid chemistry and hybridization described herein are those well-known and commonly used in the art. See, e.g., www.informatics.jax.org/mgihome/nomen/gene.shtml and www.genenames.org/. Purification techniques are performed according to manufacturer’s specifications, or as described herein. Further, unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular. [0027] Herein, PD-l/PD-L 1 blockade cancer therapy involves the use of a PD-1 inhibitor such as cemiplimab, nivolumab, and pembrolizumab, or a PD-L1 inhibitor such as atezolizumab, avelumab, and durvalumab.

[0028] Herein, “predicting” a response to a PD-1/PD-L1 blockade cancer therapy includes predicting the likelihood of a response before the patient has received such therapy, as well as monitoring the effectiveness of such therapy during the course of treatment to predict future effectiveness. In this way, a suboptimal treatment can be ceased or adjusted as necessary.

[0029] Herein, “patient” refers to any human being receiving or who may receive medical treatment and is used interchangeably herein with the term “subject.” A “cancer patient” is a patient who has been diagnosed with at least one of a variety of cancers, such as a lymphoid malignancy (e.g., classical Hodgkin’s lymphoma), or a solid tumor (e.g., renal cell carcinoma).

[0030] Herein, “inferior” means an ineffective response to the PD-l/PD-L blockade cancer therapy, wherein the cancer patient has less than a complete response to the therapy (e.g., partial response or progressive disease), according to the 2007 International Working Group response criteria (see the Experimental Section). In contrast, a “positive” response to the PD-l/PD-L 1 blockade cancer therapy refers to a complete response to the therapy according to the same criteria.

[0031] Herein, a “control group” includes cancer patients having the same cancer as the cancer patient, that have had a complete response to a PD-l/PD-L 1 blockade cancer therapy. Optionally, the control group may further include healthy donors.

[0032] Herein, “prior to and/or during” PD-1/PD-L1 blockade cancer therapy means before any inhibitory compounds are administered, or during the process of delivering the therapy.

[0033] Herein, a “signature” encompasses genes whose expression profde or whose occurrence is associated with a specific cell type, subtype, or cell state of a specific cell type or subtype within a population of monocytes. Often in the art, the terms “signature,” “expression profile,” and “expression program” are used interchangeably. A “gene signature” as used herein, may refer to any set of up- and/or down-regulated genes that are representative of a cell type or subtype. A gene signature as used herein, may also refer to any set of up- and/or down-regulated genes between different cells or cell (sub)populations derived from a gene-expression profile. For example, a gene signature may comprise a list of genes differentially expressed in a distinction of interest.

[0034] Herein, it will be understood by one of skill in the art that “treating” encompasses enhancing treatment, or improving treatment efficacy. Treatment may include inhibition of tumor regression as well as inhibition of tumor growth, metastasis, or tumor cell proliferation, or inhibition or reduction of otherwise deleterious effects associated with a tumor. Efficacy of treatment may be determined by any known method for diagnosing or treating the particular cancer. It will be appreciated that, although not precluded, “treating” does not require that the symptoms associated with the condition being treated are completely eliminated. As used herein “treatment” or “treating,” includes any beneficial or desirable effect on the symptoms or pathology of the condition being treated and may include even minimal reductions in one or more measurable markers of the conditions.

[0035] The phrase “therapeutically effective amount” as used herein refers to a sufficient amount of a drug, agent, or compound to provide a desired therapeutic effect. When an effective amount or therapeutically effective amount is indicated, the precise amount of the indicated agent(s) to be administered may be determined by a physician with consideration of the potency of the agent(s) and the age, weight, the extent of progression of the cancer, and/or other condition(s) of the patient (subject).

[0036] The term “administer,” “administering,” or “administration” refers to the act of the attending physician or caregiver, prescribing the agent for administration and thereby causing the application of an agent to a subject, through ingestion, inhalation, infusion, injection, or any other means, whether self-administered or administered by a clinician or other qualified care giver.

[0037] Herein, “antibody” includes intact antibodies, fragments of antibodies, e.g., Fab, F(ab')2 fragments, and intact antibodies and fragments that have been mutated either in their constant and/or variable region (e.g., mutations to produce chimeric, partially humanized, or fully humanized antibodies, as well as to produce antibodies with a desired trait, e.g., enhanced binding and/or reduced FcR binding). Unless otherwise stated, it is intended that the term “antibody” encompass any Ig class or any Ig subclass (e.g., the IgGl, IgG2, IgG3, and IgG4 subclasses of IgG) obtained from any source (e.g., humans and non-human primates, and in rodents, lagomorphs, caprines, bovines, equines, ovines, etc.). [0038] In this application, terms such as “a,” “an,” and “the” are not intended to refer to only a singular entity, but include the general class of which a specific example may be used for illustration. The terms “a,” “an,” and “the” are used interchangeably with the term “at least one ” The phrases “at least one of’ and “comprises at least one of’ followed by a list refer to any one of the items in the list and any combination of two or more items in the list.

[0039] As used herein, the term “or” is generally employed in its usual sense including “and/or” unless the content clearly dictates otherwise. The term “and/or” means one or all of the listed elements or a combination of any two or more of the listed elements. Thus, the term “and/or” as used in a phrase such as “A, B, and/or C” is intended to include “A and/or B and/or C” and to thus encompass each of the following aspects: A, B, and C; A, B, or C; A or C; A or B; B or C; A and C; A and B; B and C; A (alone); B (alone); and C (alone).

[0040] The words “preferred” and “preferably” refer to embodiments of the disclosure that may afford certain benefits, under certain circumstances. However, other claims may also be preferred, under the same or other circumstances. Furthermore, the recitation of one or more preferred claims does not imply that other claims are not useful, and is not intended to exclude such other claims from the scope of the disclosure.

[0041] Herein, any of the elements or combinations of elements that are recited in this specification in open-ended language (e.g., comprise and derivatives thereof) are considered to additionally be recited in closed-ended language (e.g., consist and derivatives thereof) and in partially closed-ended language (e.g., consist essentially, and derivatives thereof). That is, it is understood that wherever aspects are described herein with the language “comprising,” otherwise analogous aspects described in terms of “consisting of’ and/or “consisting essentially of’ are also contemplated. In other words, if an embodiment comprises A, B and C, embodiments consisting essentially of A, B and C are also contemplated, as are embodiments consisting of A, B and C.

[0042] Groupings of alternative elements or embodiments disclosed herein are not to be construed as limitations. Each group member may be referred to and claimed individually or in any combination with other members of the group or other elements found therein. It is anticipated that one or more members of a group may be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified, thus fulfilling the written description of all Markush groups used in the appended claims.

[0043] Reference throughout this specification to “one embodiment,” “an embodiment,” “certain embodiments,” or “some embodiments,” etc., means that a particular feature, configuration, composition, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of such phrases in various places throughout this specification are not necessarily referring to the same embodiment of the invention. Furthermore, the particular features, configurations, compositions, or characteristics may be combined in any suitable manner in one or more embodiments.

[0044] The term “about” is understood as within a range of normal tolerance in the art, for example within 2 standard deviations of the mean. “About” may be understood as within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of the stated value. All numerical values provided herein are modified by the term about.

[0045] Ranges provided herein are understood to be shorthand for all values within the range, including fractions/decimals. For example, a range of 1 to 50 is understood to include any number, combination of numbers, or sub-range from the group consisting of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50.

[0046] Classical Hodgkin lymphomas (cHLs) are composed of rare malignant Hodgkin Reed-Sternberg (HRS) cells embedded within an extensive T-cell and macrophage-rich inflammatory /immune cell infiltrate. HRS cells exhibit near-universal copy gains of chromosome 9p24. MCD274 (PD-L1) I PDCD1LG2 (PD-L2) and copy number-dependent increased expression of these PD-1 ligands. This genetic basis for enhanced PD-1 signaling prompted clinical evaluation of PD-1 blockade in patients with relapsed/refractory (R/R) cHL.

[0047] The cHL Tumor Immune MicroEnvironment (TIME) is CD4⁺ T-cell rich with concomitant expansion of Th 1 -polarized regulatory T cells and PD-1⁺ T effector cells. In intact cHLs, the malignant HRS cells are in close proximity to PD-1⁺ CD4⁺ T cells and PD-L1⁺ macrophages, which form a localized immunoprotective microenvironment. [0048] Analysis of the circulating immune signature in patients with cHL revealed the importance of recently expanded, clonally diverse CD4⁺ T cells and incompletely characterized innate effectors, including monocyte subsets, in the response to PD-1 blockade (Cader, F.Z., et al., Nat Med, 26, 1468-1479 (2020)). Herein, single-cell RNA sequencing (scRNA-seq) and T-cell receptor (TCR) and B-cell receptor (BCR) clonotype assessment has been utilized to comprehensively define the circulating immune signature in patients with cHL and healthy donors.

[0049] The interplay between previously unidentified CD4⁺ T-cell, B-cell, and innate cell subsets in the response to PD-1 blockade has been analyzed and has led to the surprising and unexpected identification of a previously unappreciated and unidentified monocyte population, referred to herein as “Cluster 0” monocytes. It has further been surprisingly discovered that these Cluster 0 monocytes are important for predicting a response to a PD-1/PD-L1 blockade cancer therapy. Thus, identifying and optionally quantifying the presence of Cluster 0 monocytes in the blood of a subject forms the basis of the methods described herein.

[0050] More specifically, methods related to cancer detection, prognosis, and treatment - methods of detection of cancer in a subject, methods of predicting a response to a PD-1/PD-L1 blockade cancer therapy in a cancer patient prior to and/or during the use of the therapy, and methods of treatment of a cancer patient are provided herein.

[0051] The methods are based on analyzing a cancer patient’s blood, specifically the monocytes in peripheral blood. These methods may have broad utility in the diagnosis, prognosis, response monitoring, treatment selection, and determination of treatment efficacy in patients with a variety of cancers.

[0052] Advantageously, these methods do not directly analyze tumor cells, which can be difficult to obtain; rather, they analyze monocytes that result from an immune response to the cancer. Thus, these methods provide a simple yet elegant assay that is robust and generally easy to execute and interpret.

[0053] Historically, monocytes have been segregated into broad categories termed Ml and M2; however, such characterization does not capture the heterogeneity and cell-specific functions with respect to immunosuppression and tumor promotion. Thus, the segregation of monocytes into Ml and M2 categories may not be capable of providing determinative specificity in diagnosing cancer, in cancer therapy response monitoring, or in treatment selection in patients with a variety of cancers.

[0054] The methods described herein apply to cancer patients having a variety of cancers, such as a lymphoid malignancy (e.g., classical Hodgkins lymphoma), or a solid tumor (e.g., renal cell carcinoma). These methods also typically apply to cancer patients having relapsed or refractory cancer or newly diagnosed cancer.

[0055] Significantly, identifying the presence of Cluster 0 monocytes in the blood of a subject can be an indicator of the presence of cancer, often before other symptoms appear. Such identification can thus allow for early detection and more treatment options that have less of an impact or no impact on a patient’s quality of life or lifestyle. Prophylactic treatments may also be prescribed that prevent or delay the onset of the cancer or conditions ancillary thereto. Thus, the present disclosure provides a method of determining the presence of cancer in a subject, the method comprising: analyzing monocytes isolated from peripheral blood mononuclear cells (PBMCs) of the subject for a subset of Cluster 0 monocytes, wherein the Cluster 0 monocytes express one or more cytokines/chemokines selected from IL1B, CXCL8, CXCL2, CXCL3, and CCL2. Preferably, analyzing monocytes includes first isolating monocytes from a sample of PBMCs of the subject. The method may also involve quantifying the Cluster 0 monocytes. Generally, the number of Cluster 0 monocytes correlates to the disease activity, wherein the larger the number of Cluster 0 monocytes, the more robust the cancer. Other aspects of the following description of methods of predicting a response to aPD-l/PD-Ll blockade cancer therapy (e.g., specific methods of isolation and analyzing) apply to this method of detecting cancer in a subject.

[0056] A method of predicting a response to a PD-1/PD-L1 blockade cancer therapy in a cancer patient prior to and/or during the use of the therapy may be used to predict the effectiveness of such therapy before administering any PD-1 or PD-L1 inhibitor, whether administered alone or in combination with another therapy, to the cancer patient. Alternatively, a method of predicting a response to a PD-1/PD-L1 blockade cancer therapy may be used to monitor the effectiveness of such therapy during the process of using any PD-1 or PD-L1 inhibitor (e.g., between administration of doses of a PD-1 or PD-L1 inhibitor), whether such therapy is being used alone or in combination with another therapy. In certain embodiments, a method of predicting a response to a PD-l/PD- LI blockade cancer therapy can be carried out at least once, or at least twice (i.e., at 2, 3, 4, 5, or more time points), during a course of treatment.

[0057] Specifically, in one embodiment, a method of predicting a response to a PD-l/PD- L1 blockade cancer therapy in a cancer patient prior to and/or during use of the therapy is provided, wherein the method includes: analyzing monocytes isolated from peripheral blood mononuclear cells (PBMCs) for expression of a transcriptional RNA-based signature associated with sensitivity to the PD-1/PD-L1 blockade cancer therapy; wherein if the transcriptional RNA-based signature comprises a set of genes that are upregulated compared to the same set of genes in a control group, the cancer patient will experience an inferior response to the PD- 1/PD-L 1 blockade cancer therapy. Preferably, analyzing monocytes first involves isolating monocytes from a sample of PBMCs of the cancer patient.

[0058] In some embodiments of such methods, a sample of a cancer patient’s whole blood is obtained, which typically includes few, if any, circulating tumor cells. A sample of PBMCs (e.g., lymphocytes including T cells, B cells, and NK cells, monocytes, and dendritic cells) is isolated from the whole blood, and the monocytes are isolated from the PBMCs for further analysis. In some embodiments, the monocytes are analyzed further for classical monocytes, which are generally found in both healthy donors and cancer patients.

[0059] In some embodiments, the classical monocytes are analyzed further for “Cluster 0” monocytes, which are identified herein as being present only in PBMC samples of cancer patients (see Figure lb). That is, whereas classical monocytes are found in both healthy donors and cancer patients, cancer patients have a very large population of these Cluster 0 monocytes. For example, in Hodgkin’s Lymphoma, over 30% of the circulating monocytes are Cluster 0 monocytes, which are the most abundant population of monocytes in the blood of such patients. Typically, healthy donors do not have any Cluster 0 monocytes.

[0060] In some embodiments, classical monocytes are isolated from all the circulating peripheral blood monocytes in a sample of blood. Preferably, isolating classical monocytes includes selecting CD14⁺⁺ and CD 16" monocytes (i.e., classical monocytes) from all the monocytes, or directly from the PBMCs (without first isolating all the monocytes), to form a population of classical monocytes. [0061] Classical monocytes are typically negatively selected, preferably using flow cytometry, although other known techniques may be used involving magnetic beads or other known sorting or bead-based purification techniques. Herein, negatively selecting the classical monocytes preferably includes using monoclonal anti-human antibodies that are preferably biotin- conjugated. Examples of antibodies used for negative selection include antibodies against cellsurface antigens comprising CD3 (T cells), CD7 (T and NK cells), CD 16 (NK cells and non- classical monocytes), CD 19 (B cells), CD56 (NK cells), CD 123 (plasmacytoid dendritic cells), CLEC10A/CD301 (conventional dendritic cells), or CD235a (erythrocytes). Specific techniques for negatively selecting classical monocytes using flow cytometry and biotin-conjugated antibodies are well known to those of skill in the art. Other methods for negatively selecting classical monocytes that are well known to those of skill in the art include column-based separation methods.

[0062] In some embodiments, the classical monocytes can be further analyzed for circulating Cluster 0 peripheral blood monocytes (i.e., “Cluster 0” monocytes), which have been identified herein as important for predicting a response to a PD-1/PD-L1 blockade cancer therapy. That is, such circulating Cluster 0 monocytes are associated with a response to PD-1/PD-L1 blockade in patients with cancer. Cluster 0 monocytes are identified as a subset of classical monocytes that overexpress one or more, preferably multiple, immunosuppressive and tumorpromoting cytokines/chemokines including, for example, IL1B, CXCL8, CXCL2, CXCL3, and CCL2. Preferably, the Cluster 0 monocytes express both IL1B and CCL2, which combination is expressed highly and selectively in the Cluster 0 monocytes. As shown herein, patients who do not respond effectively to PD-1/PD-L1 blockade have significantly higher levels of the circulating Cluster 0 response-related transcriptional (RNA-based) signature. Similarly, tumor-infiltrating Cluster 0 monocytes (i.e., macrophages) with similar features can be identified.

[0063] Cluster 0 monocytes may be isolated, and preferably quantified, from the classical monocytes (i.e., CD14⁺⁺ and CD16' monocytes) using positive selection and flow cytometry, although other known techniques may be used involving magnetic beads or other known sorting or bead-based purification techniques. Herein, positively selecting the Cluster 0 monocytes preferably includes using antibodies against the one or more cytokines/chemokines of the Cluster 0 monocytes, preferably antibodies against two of the cytokines/chemokines, which are preferably IL1B and CCL2. [0064] Particularly advantageous methods described herein include analysis of Cluster 0 monocytes. That is, in methods of predicting a response to a PD-1/PD-L1 blockade cancer therapy in a cancer patient prior to and/or during use of the therapy, the following steps are included: analyzing classical monocytes isolated from PBMCs of the cancer patient for Cluster 0 monocytes, wherein the Cluster 0 monocytes express one or more cytokines/chemokines selected from IL1B, CXCL8, CXCL2, CXCL3, and CCL2; and analyzing the Cluster 0 monocytes for expression of a transcriptional RNA-based signature associated with sensitivity to the PD-1/PD-L1 blockade cancer therapy; wherein if the transcriptional RNA-based signature comprises a set of genes that are upregulated compared to the same set of genes in a control group, the cancer patient will experience an inferior response to the PD-1/PD-L1 blockade cancer therapy. Preferably, analyzing classical monocytes includes first isolating classical monocytes from a sample of PBMCs of the cancer patient.

[0065] Thus, in some embodiments, all monocytes in a sample are analyzed for expression of a transcriptional RNA-based signature associated with sensitivity to the PD-1/PD-L1 blockade cancer therapy. See, for example, the results shown in Figure 2d. In other embodiments, only the Cluster 0 monocytes are analyzed. See, for example, the results shown in Figure 2c.

[0066] Single-cell (sc) analyses of the peripheral immune transcriptional RNA-based signature (techniques of which are well known to those of skill in the art) of the Cluster 0 monocytes in cancer patients reveal previously unappreciated mechanisms of inflammation and immune evasion. Thus, the methods described herein involve analyzing the monocytes, preferably the Cluster 0 monocytes, for expression of a transcriptional RNA-based signature associated with sensitivity to the PD-1/PD-L1 blockade cancer therapy; wherein if the transcriptional RNA-based signature comprises a set of genes that are upregulated compared to the same set of genes in a control group, the cancer patient will experience an inferior response to the PD-1/PD-L1 blockade cancer therapy.

[0067] Analyzing the Cluster 0 monocytes for expression of a transcriptional RNA-based signature involves using an assay that evaluates the relative abundance of a set of genes of biological significance, e.g., a set of genes that are more abundant in cancer patients who do not respond to PD-1/PD-L1 cancer therapy. An example of such an assay is commercially available from Nanostring Technologies, Seattle, WA, under the tradename nCounter, although any platform that allows one to assess the transcriptional abundance of a set of genes may be used.

[0068] This set of genes may be further analyzed using a computational method that expresses the transcriptional RNA-based signature of the monocytes as a computational signature score, which is an objective single parameter. That is, the RNA-based transcriptional signature of a set of genes may be reduced to a score that allows comparison to a control. A suitable computational method is one that identifies cells with overexpression of a pre-determined set of genes (a gene set) in a single-cell RNA sequencing dataset. The program determines whether a significant subset of the gene set is enriched within all of the expressed genes in an examined cell using area under the curve (AUC). The distribution of computational scores across all examined cells provides an objective metric for the relative expression of the gene set in cells from patients categorized on the basis of a clinical parameter. One such example of a computational method is by AUCell, for example.

[0069] AUCell is a method that allows identifying cells with active gene regulatory networks in single-cell RNA-seq data. These gene sets are the regulons, consisting of the transcription factors and their putative targets. AUCell calculates the enrichment of the regulon as an area under the recovery curve (AUC) across the ranking of all genes in a particular cell, whereby genes are ranked by their expression value. This method is therefore independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets (e.g., subsetting the expression matrix if needed). In brief, the scoring method is based on a recovery analysis, where the x-axis is the ranking of all genes based on expression level (genes with the same expression value, e.g. 'O', are randomly sorted); and the y-axis is the number of genes recovered from the input set. AUCell then uses the “Area Under the Curve” (AUC) to calculate whether a critical subset of the input gene set is enriched at the top of the ranking for each cell. In this way, the AUC represents the proportion of expressed genes in the signature and their relative expression value compared to the other genes within the cell. The output of this step is a matrix with the AUC score for each gene-set in each cell. Either the AUC scores (across regulons) were used directly as continuous values to cluster single-cells, or a binary matrix was generated using a cutoff of the AUC score for each regulon. These cutoffs are determined either automatically, or manually adjusted by inspecting the distribution of the AUC scores. [0070] Herein, the AUCell method is utilized to assess the relative expression of Cluster 0 genes in Cluster 0 monocytes from patients with cHL who exhibited a partial or complete response upon treatment with PD-1 blockade therapy. In addition to comparing the AUCell scores for all individual Cluster 0 monocytes in responsive and non-responsive patients with cHL, the aggregate AUCell score for all Cluster 0 monocytes in an individual patient in each category (responsive versus non-responsive) is explored. The same method is applied to assess the relative expression of the response-related Cluster 0 genes in all monocytes from responsive and non-responsive patients with cHL and, separately, patients with relapsed/ refractory renal cell carcinoma treated with PD-Ll blockade.

[0071] Thus, the methods described herein use such techniques in analyzing the Cluster 0 monocytes for expression of a transcriptional RNA-based signature associated with sensitivity to the PD-1/PD-L1 blockade cancer therapy; wherein if the transcriptional RNA-based signature comprises a set of genes that are upregulated compared to the same set of genes in a control group, the cancer patient will experience an inferior response to the PD- 1/PD-L 1 blockade cancer therapy.

[0072] It is noted that any set of genes or genetic loci may be analyzed so long as their expression distinguishes among cells that are associated with sensitivity to the PD-1/PD-L1 blockade cancer therapy. A preferred set of genes identified in this analysis includes at least the top genes (l)-(50) listed in Table la. A more preferred set of genes includes at least the top genes (l)-(100) listed in Tables la and lb. An even more preferred set of genes includes the full set of 368 genes listed in Tables la-ld. These genes in Tables la-ld are listed in order of upregulation from greatest (1) upregulation to least (368) upregulation relative to the same set of genes in a control group.

Table la

Table Id

[0073] All gene name symbols refer to the gene as commonly known in the art. Gene symbols may be those referred to by the HUGO Gene Nomenclature Committee (HGNC) or National Center for Biotechnology Information (NCBI).

[0074] The set of genes listed in Tables la-ld is of biological significance, as evidenced by the information presented in Figures 2b and 3-5. That is, the set of genes identified in this analysis include genes included in one or more of the biological pathways listed in Figure 2b, and Figures 3-5, which shows the genes included in the pathways highlighted in Figure 2b. Preferably, the one or more pathways is associated with a response to Lipopolysaccharides (LPS), IL1, TNFa, Toll-like receptor (TLR), and prostaglandin signaling, inflammasome activation and modulation by Triggering Receptor Expressed on Myeloid cells 1 (TREM1), an Ig-like immunoregulatory receptor, or amplifier of innate immune responses.

[0075] There are also provided methods of treating a cancer patient. The methods of treating are based on the methods of predicting a response to a PD-1/PD-L1 blockade cancer therapy described herein, which may be predictive for the effectiveness of PD-1/PD-L1 blockade cancer therapy used alone or in combination therapies. Accordingly, the treatment protocols may include use of PD-1/PD-L1 blockade cancer therapy, alone or in combination with other known therapies, or therapies other than PD-1/PD-L1 blockade cancer therapy.

[0076] The methods include: predicting a response to a PD-1/PD-L1 blockade cancer therapy in a cancer patient prior to and/or during use of the therapy according to one of the methods described herein; and treating the cancer patient with a treatment protocol comprising a PD-l/PD- L1 blockade cancer therapy alone, or in combination with another (second or different) therapy, if the patient is predicted to have a positive response to the PD-1/PD-L1 blockade cancer therapy, or a treatment protocol other than, or in addition to, a PD-1/PD-L1 blockade cancer therapy if the patient is predicted to have an inferior response to the PD-1/PD-L1 blockade cancer therapy. [0077] Thus, the treatment protocol may include a PD-1/PD-L1 blockade cancer therapy alone, or in combination with another (second or different) therapy, if the transcriptional RNA- based signature of the Cluster 0 monocytes is associated with a positive response to the PD-l/PD- L1 blockade cancer therapy. In this context, a cancer patient who has a “positive response” to a PD-1/PD-L1 blockade cancer therapy is one who possesses Cluster 0 monocytes having a transcriptional RNA-based computational signature score less than, or equal to, that of the control group. Such “positive response” to the PD-l/PD-L 1 blockade cancer therapy refers to a cancer patient having a complete response to the therapy according to the 2007 International Working Group response criteria (see the Experimental Section).

[0078] In other embodiments, the treatment protocol may include a PD-1/PD-L1 blockade cancer therapy other than, or in addition to, a PD-1/PD-L1 blockade cancer therapy, if the transcriptional RNA-based signature of the monocytes is associated with an inferior response to the PD-1/PD-L1 blockade cancer therapy. In this context, a cancer patient who has an “inferior response” to a PD-1/PD-L1 blockade cancer therapy is one who possesses Cluster 0 monocytes having a transcriptional RNA-based computational signature score greater than that of the control group. Such “inferior response” to the PD-l/PD-L blockade cancer therapy refers to a cancer patient having less than a complete response to the therapy (e.g., partial response or progressive disease), according to the 2007 International Working Group response criteria (see the Experimental Section).

[0079] A PD-1/PD-L1 blockade cancer therapy involves administering an effective amount of a PD-1 inhibitor such as cemiplimab, nivolumab, and pembrolizumab, or a PD-L1 inhibitor such as atezolizumab, avelumab, and durvalumab. Therapeutic amounts of PD-1 and PD-L1 inhibitors are readily known or determined by those of ordinary skill in the art, as are appropriate treatment protocols.

[0080] In some embodiments, a second/different therapy may be administered to the patient, as an alternative to, or in addition to, a PD-l/PD-L 1 blockade cancer therapy. In such embodiments, such other (i.e., second/different) therapy may typically be one that modulates the cancer patient’s immune response. Or, such treatment protocol may be directed at reducing the number of Cluster 0 monocytes, e.g., as by inhibiting the conversion of classical monocytes to Cluster 0 monocytes. [0081] Alternatively, any second or different therapy may be directed at inhibiting aspects of the Cluster 0 monocytes, including blockade of IL-1 receptor signaling (which is known to synergize with PD-1 blockade in murine models of RCCa and breast cancer), neutralizing CXCL8 (IL8) antibodies or CXCR2 antagonists (which are known to block multiple cytokines/chemokines including IL-8, CXCL1, CXCL2, and CXCL3, suppress inflammasome activation, myeloid cell recruitment, tumor growth and angiogenesis, and enhance the efficacy of PD-1 blockade in certain solid tumors), or blocking CCL2 activity (which decreases macrophage infiltration, tumor proliferation, and metastasis in inflammatory breast cancer, in part via downstream activation of CCL3). Therapeutically effective amounts and appropriate treatment protocols will depend upon at least the second or different therapy being administered and patient information, and are readily determined by those of ordinary skill in the art.

[0082] The therapy or treatment protocol according to the disclosure may be performed alone or in conjunction with another therapy, and may be provided at home, the doctor’s office, a clinic, a hospital’s outpatient department, or a hospital. Treatment generally begins at a hospital so that the doctor can observe the therapy’s effects closely and make any adjustments that are needed. The duration of the therapy depends on the age and condition of the patient, the stage of the cancer, and how the patient responds to the treatment.

ILLUSTRATIVE EMBODIMENTS

[0083] Embodiment 1 is a method of predicting a response to a PD-1/PD-L1 blockade cancer therapy in a cancer patient prior to and/or during use of the therapy, the method comprising: analyzing monocytes isolated from peripheral blood mononuclear cells (PBMCs) of the cancer patient for expression of a transcriptional RNA-based signature associated with sensitivity to the PD-1/PD-L1 blockade cancer therapy; wherein if the transcriptional RNA-based signature comprises a set of genes that are upregulated compared to the same set of genes in a control group, the cancer patient will experience an inferior response to the PD- 1/PD-L 1 blockade cancer therapy.

[0084] Embodiment 2 is the method of embodiment 1, wherein the monocytes comprise classical monocytes (selected for CD14⁺⁺ and CD16"). Embodiment 3 is the method of embodiment 2, wherein the classical monocytes comprise Cluster 0 monocytes, wherein the Cluster 0 monocytes express one or more cytokines/chemokines selected from IL IB, CXCL8, CXCL2, CXCL3, and CCL2. Embodiment 4 is the method of embodiment 3, wherein the Cluster 0 monocytes express both IL1B and CCL2, wherein such combination is expressed highly and selectively in the Cluster 0 monocytes.

[0085] Embodiment 5 is a method of predicting a response to a PD-1/PD-L1 blockade cancer therapy in a cancer patient prior to and/or during use of the therapy, the method comprising: analyzing classical monocytes for a subset of Cluster 0 monocytes, wherein the Cluster 0 monocytes express one or more cytokines/chemokines selected from IL IB, CXCL8, CXCL2, CXCL3, and CCL2; and analyzing the Cluster 0 monocytes for expression of a transcriptional RNA-based signature associated with sensitivity to the PD-1/PD-L1 blockade cancer therapy; wherein if the transcriptional RNA-based signature comprises a set of genes that are upregulated compared to the same set of genes in a control group, the cancer patient will experience an inferior response to the PD-1/PD-L1 blockade cancer therapy.

[0086] Embodiment 6 is the method of embodiment 5, wherein analyzing the classical monocytes comprises: isolating classical monocytes from peripheral blood mononuclear cells (PBMCs) of the cancer patient; and analyzing the classical monocytes for a subset of Cluster 0 monocytes, wherein the Cluster 0 monocytes express one or more cytokines/chemokines selected from IL1B, CXCL8, CXCL2, CXCL3, and CCL2.

[0087] Embodiment 7 is the method of any preceding embodiment, wherein the control group comprises cancer patients having the same cancer as the cancer patient and having a complete response to a PD-1/PD-L1 blockade cancer therapy. Embodiment 8 is the method of embodiment 7, wherein the control group further comprises healthy donors (i.e., donors without cancer).

[0088] Embodiment 9 is the method of any preceding embodiment, wherein isolating the classical monocytes comprises: isolating peripheral blood mononuclear cells (PBMCs) from the cancer patient; and selecting CD14⁺⁺ and CD16' monocytes (i.e., classical monocytes) from the PBMCs to form a population of classical monocytes. Embodiment 10 is the method of embodiment 9, wherein selecting CD14⁺⁺ and CD16' monocytes comprises negatively selecting the classical monocytes. Embodiment 11 is the method of embodiment 10, wherein negatively selecting the classical monocytes comprises using flow cytometry. Embodiment 12 is the method of embodiment 10 or 11, wherein negatively selecting comprises using monoclonal anti-human antibodies, preferably that are biotin-conjugated. Embodiment 13 is the method of embodiment 12, wherein the negative selection method includes antibodies directed against cell-surface antigens comprising CD3 (T cells), CD7 (T and NK cells), CD 16 (NK cells and non-classical monocytes), CD19 (B cells), CD56 (NK cells), CD123 (plasmacytoid dendritic cells), CLEC10A/CD301 (conventional dendritic cells), or CD235a (erythrocytes).

[0089] Embodiment 14 is the method of any one of embodiments 9 to 13, as dependent on embodiment 9, wherein isolating PBMCs from the cancer patient comprises: obtaining a sample of whole blood from the cancer patient; and isolating PBMCs from the whole blood.

[0090] Embodiment 15 is the method of any one of embodiments 5 to 14, wherein the Cluster 0 monocytes express IL1B and CCL2. Embodiment 16 is the method of any one of embodiments 5 to 15, wherein analyzing classical monocytes for Cluster 0 monocytes comprises isolating the Cluster 0 monocytes. Embodiment 17 is the method of any one of embodiments 5 to 16, wherein analyzing classical monocytes for Cluster 0 monocytes comprises quantifying the Cluster 0 monocytes. Embodiment 18 is the method of any one of embodiments 5 to 17, wherein analyzing classical monocytes for Cluster 0 monocytes comprises using flow cytometry.

[0091] Embodiment 19 is the method of any one of embodiments 5 to 18, wherein analyzing classical monocytes for Cluster 0 monocytes comprises selecting the Cluster 0 monocytes using antibodies against the one or more cytokines/chemokines of the Cluster 0 monocytes. Embodiment 20 is the method of embodiment 19, wherein selecting the Cluster 0 monocytes comprises using antibodies against two of the cytokines/chemokines. Embodiment 21 is the method of embodiment 20, wherein selecting the Cluster 0 monocytes comprises using antibodies against IL IB and CCL2.

[0092] Embodiment 22 is the method of any one of embodiments 1 to 21, wherein analyzing the monocytes, preferably Cluster 0 monocytes, for expression of a transcriptional RNA- based signature comprises using an assay that evaluates the relative abundance of a set of genes of biological significance. Embodiment 23 is the method of any one of embodiments 1 to 22, wherein analyzing the monocytes, preferably Cluster 0 monocytes, for expression of a transcriptional RNA- based signature comprises using a computational method that expresses the transcriptional RNA- based signature of the monocytes as a computational signature score. Embodiment 24 is the method of embodiment 23, wherein if the computational signature score is higher compared to that of a control group, the cancer patient will experience an inferior response to the PD-1/PD-L1 blockade cancer therapy.

[0093] Embodiment 25 is the method of any one of embodiments 1 to 24, wherein the set of genes comprise at least genes numbered (l)-(50) listed in Table la. Embodiment 26 is the method of embodiment 25, wherein the set of genes comprise at least genes numbered (l)-(100) listed in Tables la and lb. Embodiment 27 is the method of embodiment 26, wherein the set of genes comprise the 368 genes listed in Tables la-ld. Embodiment 28 is the method of any one of embodiments 1 to 27, wherein the set of genes comprise genes included in one or more of the pathways listed in Figure 2b. Embodiment 29 is the method of embodiment 28, wherein the one or more pathways is associated with a response to Lipopolysaccharides (LPS), IL1, TNFa, Tolllike receptor (TLR), and prostaglandin signaling, inflammasome activation and modulation by Triggering Receptor Expressed on Myeloid cells 1 (TREM1), an Ig-like immunoregulatory receptor, o r amplifier of innate immune responses.

[0094] Embodiment 30 is the method of any preceding embodiment, wherein the cancer patient has a lymphoid malignancy or a solid tumor. Embodiment 31 is the method of embodiment 30, wherein the cancer patient has renal cell carcinoma. Embodiment 32 is the method of embodiment 30, wherein the cancer patient has classical Hodgkins lymphoma. Embodiment 33 is the method of anyone of one embodiments 1 to 32, wherein the cancer patient has a newly diagnosed cancer. Embodiment 34 is the method of any preceding embodiment, wherein the cancer patient has a relapsed or refractory cancer.

[0095] Embodiment 35 is a method of determining the presence of cancer in a subject, the method comprising: analyzing monocytes for a subset of Cluster 0 monocytes, wherein the Cluster 0 monocytes express one or more cytokines/chemokines selected from IL IB, CXCL8, CXCL2, CXCL3, and CCL2.

[0096] Embodiment 36 is the method of embodiment 35, wherein analyzing monocytes comprises: isolating monocytes from peripheral blood mononuclear cells (PBMCs); and analyzing monocytes for a subset of Cluster 0 monocytes. Embodiment 37 is the method of embodiment 35 or 36, wherein the Cluster 0 monocytes express IL IB and CCL2.

[0097] Embodiment 38 is the method of any preceding embodiment, wherein the subject has a lymphoid malignancy or a solid tumor. Embodiment 39 is the method of embodiment 38, wherein the subject has renal cell carcinoma. Embodiment 40 is the method of embodiment 38, wherein the subject has classical Hodgkins lymphoma.

[0098] Embodiment 41 is the method of any preceding embodiment, wherein analyzing monocytes for Cluster 0 monocytes comprises quantifying the Cluster 0 monocytes. Embodiment 42 is the method of any preceding embodiment, wherein the monocytes comprise classical monocytes. Embodiment 43 is the method of any preceding embodiment, wherein analyzing monocytes for Cluster 0 monocytes comprises using flow cytometry.

[0099] Embodiment 44 is the method of any preceding embodiment, wherein analyzing monocytes for Cluster 0 monocytes comprises selecting the Cluster 0 monocytes using antibodies against the one or more cytokines/chemokines of the Cluster 0 monocytes. Embodiment 45 is the method of embodiment 44, wherein analyzing monocytes for Cluster 0 monocytes comprises selecting the Cluster 0 monocytes using antibodies against two of the cytokines/chemokines. Embodiment 46 is the method of embodiment 45, wherein analyzing monocytes for Cluster 0 monocytes comprises selecting the Cluster 0 monocytes using antibodies against IL1B and CCL2.

[0100] Embodiment 47 is a method of treating a cancer patient, the method comprising: predicting a response to a PD-1/PD-L1 blockade cancer therapy in a cancer patient prior to and/or during use of the therapy according to the method of any preceding embodiment, and treating the cancer patient with a treatment protocol comprising a PD-1/PD-L1 blockade cancer therapy, if the cancer patient is predicted to have a positive response to the PD-1/PD-L1 blockade cancer therapy, or a treatment protocol other than, or in addition to, a PD-1/PD-L1 blockade cancer therapy if the patient is predicted to have an inferior response to the PD-1/PD-L1 blockade cancer therapy.

[0101] Embodiment 48 is the method of embodiment 47, wherein the treatment protocol comprises a PD-1/PD-L1 blockade cancer therapy, alone or in combination with another second/ different therapy, if the transcriptional RNA-based signature of the monocytes is associated with a positive response to the PD-1/PD-L1 blockade cancer therapy. Embodiment 49 is the method of embodiment 47, wherein the treatment protocol comprises other than, or in addition to, a PD-1/PD-L1 blockade cancer therapy, if the transcriptional RNA-based signature of the monocytes is associated with an inferior response to the PD-1/PD-L1 blockade cancer therapy. Embodiment 50 is the method of embodiment 49, wherein treating the cancer patient comprises treating with a treatment protocol other than a PD-1/PD-L1 blockade cancer therapy. Embodiment 51 if the method of embodiment 49, wherein treating the cancer patient comprises treating with a treatment protocol comprising a PD-1/PD-L1 blockade cancer therapy and a second/different therapy that modulates the cancer patient’s immune response.

[0102] Embodiment 52 is the method of any preceding embodiment that includes treating with the PD-1/PD-L1 therapy, wherein the PD-1/PD-L1 blockade cancer therapy comprises administering a PD-1 inhibitor or a PD-L1 inhibitor. Embodiment 53 is the method of embodiment 52, wherein the PD-1 inhibitor comprises cemiplimab, nivolumab, or pembrolizumab. Embodiment 54 is the method of embodiment 52, wherein the PD-L1 inhibitor comprises atezolizumab, avelumab, or durvalumab.

[0103] Embodiment 55 is the method of any preceding embodiment, wherein the treatment protocol comprises a second/different therapy other than a PD-1/PD-L1 blockade cancer therapy. Embodiment 56 is the method of embodiment 55, wherein the second/different therapy comprises one that modulates the cancer patient’s immune response. Embodiment 57 is the method of embodiment 56, wherein the second/different therapy comprises reducing the number of Cluster 0 monocytes. Embodiment 58 is the method of embodiment 57, wherein reducing the number of Cluster 0 monocytes comprises inhibiting the conversion of classical monocytes to Cluster 0 monocytes. Embodiment 59 is the method of any one preceding embodiment, wherein the second/different therapy comprises inhibiting components of a Cluster 0 transcriptional program. Embodiment 60 is the method of embodiment 59, wherein inhibiting components of the Cluster 0 transcriptional program comprises inhibiting IL-1 receptor signaling, neutralizing CXCL8 (IL8) antibodies or CXCR2 antagonists, or blocking CCL2 activity.

EXAMPLES

[0104] The examples of this section are merely for illustrative purposes and are not meant to be overly limiting on the scope of the appended claims. Notwithstanding that the numerical ranges and parameters setting forth the broad scope are approximations, the numerical values set forth in the specific examples are reported as precisely as possible.

Methods [0105] Patient samples. Cryopreserved peripheral blood mononuclear cells (PBMCs) were obtained from: normal healthy donors; patients with newly diagnosed, previously untreated cHL; and patients with relapsed/refractory cHL who received single-agent nivolumab in the CheckMate 205 phase II trial (Ansell SM, et al., Blood Adv, 2023 Aug 2, bloodadvances.2023010334. doi: 10.1182/bloodadvances.202301334. Online ahead of print) with institutional review board (IRB) approval. Baseline (cycle 1 day 1) and on-treatment (cycle 4 day 1) PBMCs were obtained from CheckMate 205 trial patients who received nivolumab 3 mg/kg every 2 weeks until disease progression or unacceptable toxicity. An independent review committee (IRC) used the 2007 International Working Group response criteria (Cheson BD, et al., J Clin Oncol, 25:579-86, 2007) to assess best overall response (BOR) to therapy - complete response (CR), partial response (PR), or progressive disease (PD).

[0106] Formalin-fixed, paraffin-embedded (FFPE) tumor biopsies from patients with newly diagnosed cHL were obtained from the archives of Brigham & Women’s Hospital, Boston, MA, with IRB approval.

[0107] Isolation of CD3⁺ and CD3 peripheral blood mononuclear cells Unmanipulated CD3⁺ and CD3' cells were purified from cryopreserved bulk PBMCs by negative selection with Miltenyi Biotec separation kits (Miltenyi Biotec North America, Gaithersburg, MD). CD3⁺ cells were isolated using the Pan T-cell isolation kit, which included CD14, CD15, CD16, CD19, CD34, CD36, CD56, CD123, and CD235a biotin-conjugated monoclonal antibodies (no. 130-096-535). CD3" cells were selected by positively depleting CD3⁺cells with CD3 -conjugated MicroBeads (no. 130-050-101), as previously described (Cader FZ, et al., Nat Med, 26:1468-1479, 2020).

[0108] Single-cell RNA (scRNAseq) and Single-cell T-cell Receptor (scTCRseq) sequencing. Viable CD3⁺ and CD3' cells were resuspended in PBS with 0.04% BSA at a concentration of 1000 cells/pL and 17,000 cells were loaded onto a 10x Genomics CHROMIUM instrument (10x Genomics, Boston, MA) according to the manufacturer’s instructions. The scRNAseq libraries were processedusing CHROMIUM single cell 5' library & gel bead kit (10x Genomics). Quality controls for amplified cDNA libraries and final sequencing libraries were performed using Bioanalyzer High Sensitivity DNA Kit (Agilent Scientific Instruments, Santa Clara, CA). For CD3⁺ samples, 2 pL of post cDNA amplification material was also used to prepare scTCRseq libraries. The sequencing libraries for scRNAseq and scTCRseq were normalized to 4 nM concentration, pooled using a volume ratio of 4: 1 and sequenced on Illumina NovaSeq S4 300 cycle platform (Illumina Inc., San Diego, CA).

[0109] Upstream scRNAseq analytical pipeline. The sequencing data were demultiplexed and raw single-cell RNAseq reads were aligned to GRCh38-3.0.0 using Cell Ranger version 3.1.0 pipeline (10x Genomics). The count matrix for each sample was read using Seurat V3.0 (Stuart T, et al., Cell, 177: 1888-1902 e21, 2019) and made into a Seurat object. All of the Seurat objects were merged and batch corrected with Harmony (Korsunsky I, et al., Nat Methods, 16:1289-1296, 2019) for CD3⁺ cells and CD3" cells, respectively. The Seurat objects were processed by the standard Seurat workflow. Briefly, cells that expressed fewer than 200 genes, or greater than 5000 genes, or had >20% mitochondrial reads were removed. The count matrix was log normalized and the top 2000 most variable genes were calculated by the vst method in Seurat. The UMAP was generated using the first 50 dimensions of the Harmony coordinates. To find clusters, the shared nearest neighbor (SNN) graph was built on the first 50 dimensions of the Harmony coordinates followed by the Louvain community detection algorithm with a resolution of 1.5. Cells expressing either CD8A or CD8B were computationally removed from the CD3⁺ Seurat object to generate an additional CD3⁺CD8‘ (CD4⁺ enriched) Seurat object.

[0110] QC cluster inclusion/exclusion criteria and annotation. Based on the Seurat clustering at the resolution 1.5, the mean number of sequenced cells per sample was calculated. Clusters that were present in at least 10% of the specimens at a level equal to or greater than 1% of the mean number of sequenced cells per sample were included in the downstream analyses. BioTuring Browser v2 software was used to analyze the data and annotate the clusters that met the inclusion criteria. Two additional clusters were excluded from the downstream analysis because their features (canonical marker genes) were those of contaminating red blood cells or platelets.

[0111] Derivation and display of cluster marker features. Marker genes for each cluster were determined using the function wilcoxauc in the presto R package (github.com/immunogenomics/presto). Padj<0.05 and FC>1.75 were used to filter the top marker genes for each cluster and those genes were used to generate a heatmap using ComplexHeatmap (Gu Z, et al., Bioinformatics, 32:2847-9, 2016). Dot plot visualization was used to show the differences in the expression of selected top marker genes across clusters. [0112] Differential expression analysis. Tib A Bioconductor package called Distinct (Tiberi S, et al., bioRxiv:2020.11.24.394213, 2022) was used to perform comparative multisample, multi-group single-cell RNAseq differential expression analyses. The Seurat objects were converted to SingleCellExperiment objects for analysis with Distinct.

[0113] Gene set enrichment analysis (GSEA). Pathway enrichment analyses were carried out using the following Molecular Signatures Database (MSigDB) gene sets: CP:REACTOME: Reactomegene sets; CP: WIKIPATHWAYS: WikiPathways gene sets; and C7: immunologic gene sets (Subramanian A, et al., Proceedings of the National Academy of Sciences, 102: 15545-15550, 2005; Liberzon A, etal., Cell Syst, 1 :417-425, 2015).

[0114] Development of the AUCell signature. AUCell was used to quantify the enrichment of a specific input gene signature in the cellular subsets (Aibar S, et al., Nat Methods, 14:1083-1086, 2017). Specifically, AUCell_buildRankings were used to rank genes in each cell by expression level, and AUCell_calcAUC was used to calculate a gene set enrichment score per cell using the most highly expressed 5% of the genes in that cell.

[0115] The codes used to analyze the single cell RNA seq data are available at github.com/crazyhottommy/hodgkin_lymphoma_publication_scRNAseq_analysis.

Imaging the intact cHL tumor immune microenvironment

[0116] RNAscope Multiplex Fluorescent Assay. FFPE tissue sections (4pm) from patients with newly diagnosed cHL were selected for hybridization and simultaneous visualization of four RNA targets using the RNAscope LS Multiplex Fluorescent Assay and automated Leica Biosystems’ BONDRX System. RNA-specific probes were hybridized to target transcripts (set I: CD68-channel 1, ILlB-channel 2, PD-Ll-channel3, CD30-channel 4; set II: CD68-channel 1, CXCL2-channel 2, PD-Ll-channel 3, CD30-channel 4; set 3: CD68-channel 1, CXCL3-channel 2, PD-Ll-channel 3, CD30-channel 4) followed by staining with nuclear counterstain/4’,6- diamidino-2-phenylindole. Signals were amplified and detected using the OPAL dyes specific for each channel (channel 1 -Opal 520, channel 2 - Opal 570, channel 3 - Opal 620, channel 4 - Opal 690). Slides were mounted with Prolong Gold Anti-fade mounting medium and whole slide tissue scans were imaged and digitized using the VECTRA Multispectral Imaging Systems. [0117] Digitized whole slide scans were analyzed with HALO and HALO Al software (HALO Image Analysis Platform version 3.5.3577 and HALO Al version 3.5.3577, Indica Labs, Inc., Albuquerque, NM). Tissue scans were first evaluated for RNA quality, and regions without RNA signals were excluded from the analysis. The Al-custom nuclear segmentation algorithm was then trained and developed for cell segmentation. Cell phenotyping was subsequently performed based on the combination of marker genes expression and HALO spatial plots were generated. Proximity analyses were utilized to identify specific cell subsets in the immediate vicinity of malignant HRS cells (<75pm from an HRS cell) and illustrated with proximity histograms.

Development of the AUCell signature

[0118] External scRNA-seq datasets. An external PBMC scRNAseq dataset from patients with renal cell carcinoma who were treated with PD-L1 blockade (Yuen KC, et al., Nat Med, 26:693-698, 2020) was also used to evaluate the Cluster 0 monocyte/macrophage AUCell signature. The count matrix for this study was obtained from GEO, converted into Seurat objects and normalized using NormalizeData in Seurat and batch corrected with Harmony. A UMAP was generated using the first 20 dimensions of the Harmony coordinates. Cells were clustered using an SNN graph and Louvain algorithm at a resolution of 0.5 based on the batch-corrected reduction. Clusters were manually annotated and inspected in the BioTuring browser v2.

[0119] Arecently published integrated dataset of 178,651 mononuclear phagocytes from 13 tissues across 41 single-cell RNAseq datasets (Mulder K, et al., Immunity, 54:1883-1900 e5, 2021) was used to generate a compendium of conserved monocyte/macrophage gene programs. A cell type label transfer method was used that has been previously described and implemented in Seurat (Stuart T, et al, Cell, 177:1888-1902 e21, 2019) to assess the presence of the cHL Cluster 0 monocytes transcriptional program in specific subsets of cancer-associated monocytes in other solid tumors. Briefly, cell type label transfer was performed by projecting the PCA structure of the reference dataset onto the query dataset. First, common anchors were identified between the dataset and the “MoMac- VERSE” dataset (Mulder K, et al., Immunity, 54:1883-1900 e5, 2021) using FindTransferAnchors, and then TransferData was used to classify the cells in the MoMac- VERSE dataset based on the cell type labels of the dataset. Principal component analysis was used as the dimensionality reduction method, and the first 50 principal components were used for this analysis. Statistical significance was assessed by performing a Chi-square test for enrichment of Cluster 0-like cells in the IL1B+ monocyte cluster versus all other cell types in the Mulder et al. dataset.

[0120] Statistical analyses. Two-group comparisons including comparisons between cell abundances and AUCell gene signature score distributions were measured by Wilcoxan rank sum tests. Nominal one-sided p-values were reported (p-values < 0.05) and adjusted for multiple comparisons using the Benjamini -Hochberg method (FDR<0.1).

Results

[0121] Analysis of the circulating immune signature in patients with cHL and control healthy donors. Single cell RNA sequencing (scRNA-seq) of CD3⁺ and CD3" peripheral blood mononuclear cells (PBMCs) from 20 patients with R/R cHL who were treated with single-agent PD-1 blockade (nivolumab) (Ansell, S.M., et al., Blood Adv (2023 Aug 2, bloodadvances.2023010334. doi: 10.1182/bloodadvances.202301334. Online ahead of print)) was performed at two timepoints, baseline (cycle 1 day 1 (C1D1)) and on therapy (cycle 4 day 1 (C4D1)). These samples were annotated for the patients’ best overall response to PD-1 blockade: complete response (CR), 9 patients; partial response (PR), 5 patients; and progressive disease (PD), 6 patients. PBMCs from 11 patients with newly diagnosed, previously untreated cHL and 13 control healthy donors were similarly sequenced.

[0122] Characterization of circulating CD3 immune cell subsets - NK cells, B cells, and myeloid cells. Clusters that were present in at least 10% of the specimens at a level equal to or greater than 1% of the mean number of sequential cells per sample were included in the analysis. Using these inclusion criteria, 229,670 cells in 24 clusters were identified. Cell clusters were annotated using a knowledge-guided manual approach based on differentially expressed genes and prototypical lineage markers. Three broad categories of CD3' cells defined by their classic markers -NK cells (NKG7⁺, CD16 (FCGR3A)⁺ , G7MB a^PRFl⁺y, B cells CD19 CD20 (MS4A1) and 7MA5⁺); and myeloid cells, including monocytes (CD33⁺, CD68⁺, and CD14⁺ conventional dendritic cells (eDCs) (CD33⁺an CLECI0A ), and plasmacytoid dendritic cells (pDCs) (CD123 IL3RA ) - were identified. [0123] Characterization of circulating monocyte and dendritic cell subsets. Circulating classical, intermediate, and non-classical monocytes were characterized using the canonical markers, CD14 and CD16 (FCGR3A), and additional subtype-specific transcripts: classical, CD14⁺, CD16' (Clusters 1 and 2); intermediate, CD14⁺, CD16 (Cluster 4); and non- classical, CD14-, CD16 RHOC C1Q and SIGLEC10⁺ (Clusters 7 and 22) (FIG. 1A-1C). Circulating monocytes with features of prior type I IFN stimulation (Cluster 14), type-2 conventional dendritic cells (eDCs) (Cluster 10, CD33 CLEC10A⁺, CDlc⁺, and MHC class II^hlgh) and plasmacytoid dendritic cells (pDCs) (Cluster 18, CLEC4C⁺, CD123⁺, NRP1⁺, and CDllc (ITGAX)') were also identified (FIG. 1A-1C).

[0124] FIG. 1A shows an annotated UMAP of myeloid clusters, wherein each cluster is labeled with a distinct shade and unique number. FIG. IB shows the relative abundance of myeloid clusters between HD and patients with ND cHL. Differences between HD and patients with ND cHL were assessed by the Wilcoxon rank-sum test. P values that remained significant after Benjamini-Hochberg correction with an FDR<0.1 are noted are dot plots with relative expression of selected genes in myeloid clusters. Displayed genes were curated from the top differentially expressed genes that defined a cluster (adj p <0.05, FC >1.75) and supplemented with relevant markers based on a priori knowledge. The size of the dot indicates the percentage of marker-expressing cells in each cluster and the z-score reflects mean marker expression across the clusters.

[0125] Identification of an abundant cancer-associated monocyte/macrophage population, Cluster 0, in the periphery and intact TIME. Cluster 0 monocytes were the most abundant subset of circulating monocytes detected in patients with cHL and were virtually absent in healthy donors (FIG. IB). The cluster 0 monocytes exhibited markedly increased expression (relative to classical monocytes) of multiple likely immunosuppressive and tumor-promoting cytokines/chemokines, including IL IB, CXCL8, CXCL2. CXCL3, and CCL2. m PD- i (CD274) and SIRPA (FIG. 1C).

[0126] It was postulated that the circulating Cluster 0 monocytes in patients with cHL might differentiate into tissue-infdtrating macrophages. For this reason, tumor-associated macrophages (TAMs) in intact biopsies of newly diagnosed cHLs were evaluated for expression of Cluster 0 markers using RNAscope. With this 4-plex in situ hybridization approach, primary HRS cells were defined by their expression of CD30 (TNFRSF8) and PD-L1 and CD68⁺ TAMs were assessed for their expression of the individual Cluster 0 markers, CXCL2, CXCL3, and IL1B (FIG. 1C). Tumor-infiltrating CD68⁺ monocytes/macrophages with Cluster 0 features CXCL2, CXCL3, and IL1B expression) in the intact cHL TIME and the immediate proximity of CD30⁺PD- Ll⁺ HRS cells were identified. See FIG. ID (top panel), which are cellular phenotype maps of the representative RNAscope images from patients with ND cHL (medium gray, HRS cells; dark gray, Cluster 0-like macrophages CXCL2⁺ (left), CXCL3⁺ (middle), IL1B⁺ (right); light gray, other macrophages CXCL27CXCL37IL1B") (all images). Proximity analysis revealed that the majority of Cluster 0-like macrophages (83% of CD68 CXCL2 \ 88% of CD68⁺CXCL3⁺, and 88% of (T)68 IL1B cells) were localized within 75pm of the CD30⁺PD-LF HRS cells (FIG. ID, bottom panel, proximity plots). Herein, “Cluster 0-like” means features (major aspects of the transcriptional program) are shared with the newly described Cluster 0 monocytes.

[0127] After identifying Cluster 0-like IL 10⁺ monocytes/macrophages in the cHL TIME, it was asked whether similar cells were detectable in other cancers. A single-cell compendium of monocytes and macrophages from multiple solid tumors was interrogated, and a cell type label transfer (Stuart, T., et al, Cell, 177, 1888-1902 el821 (2019)) using the CD3' scRNA-seq dataset described herein as a reference was performed. In this publicly available compendium of tumor- associated monocytes and macrophages (60,691 total cells), 5,783 cells with a transcriptional profile similar to lhe Cluster 0 monocytes were identified. Of interest, 81% (4,682/5,783) of the cells with a Cluster 0 transcriptional signature were also previously identified as IL1B⁺ monocytes in the compendium. There was a highly significant enrichment of Cluster 0-like cells in the previously designated IL1B cluster (4,682 Cluster 0-like cells/7,985 IL1B cells) compared to the rest of the dataset (1,102 Cluster 0-like cells/52,706 Other cells, ²=25707.926 p<0.0001, Chi-squared test with Yates correction). The identification of Cluster O/IL1B⁺ tumor-infiltrating monocytes/macrophages in this solid tumor compendium suggests that similarly programmed inflammatory monocytes/macrophages are detectable in multiple cancers.

[0128] Response-related circulating Cluster 0 transcriptional signature in patients with cHL. The clinical significance of circulating Cluster 0 monocytes in patients with R/R cHL who were treated with PD-1 blockade was also evaluated. As a first step, a differential expression analysis of response-related Cluster 0 transcripts in patients who achieved a CR or progressed (PD) on PD-1 blockade was performed. In comparison with complete responders, those who progressed on nivolumab (PD) had significant upregulation of an extended Cluster 0 transcriptional signature at C4Dl. See FIG. 2A, which shows the results of the differential expression analysis; Adj p <0.1, FC >1.5 were used to filter the differentially expressed Cluster 0 genes in patients who achieved a CR or progressed on therapy at C4D1. The top 50 of 368 differentially expressed genes are shown for patients by response, at C4D1 and baseline C1D1. See also, Tables la-ld, which provide a list of genes included in the AUCell signature.

[0129] Gene set enrichment analysis (GSEA) of the response-related Cluster 0 signature revealed highly significant upregulation of pathways associated with response to Lipopolysaccharides (LPS), IL1, TNFa, Toll-like receptor (TLR), and prostaglandin signaling, inflammasome activation and modulation by Triggering Receptor Expressed on Myeloid cells 1 (TREM1), an Ig-like immunoregulatory receptor, and amplifier of innate immune responses. See FIG. 2B, which shows the results of the pathway enrichment analysis of response-related differentially expressed genes in Cluster 0 using Molecular Signatures Database (MSigDB) gene sets: CP:REACTOME: Reactome gene sets (FIG. 2B-I);CP:WIKIPATHWAYS: WikiPathways gene sets (FIG. 2B-II); and C7: Immunologic gene sets (FIG. 2B-III). Gene Ratio represents the number of genes in the AUCell signature over the total number of genes in a given gene set; FDR significant <0.05; Count represents the number of cells in the overlap between theAUCell gene signature and given gene set. See also, FIG. 3-1, FIG. 3 -II, FIG. 4-1, FIG. 4-II, FIG. 5-1, and FIG. 5-II, which show the AUCell genes included in the pathways highlighted in FIG. 2B-I, FIG. 2B- II, and FIG. 2B-III.

[0130] Together, these findings highlighted the likely role of TLR engagement by damage- associated molecular patterns (DAMPs), inflammasome activation, and orchestrated IL1B- dependent downstream signaling in Cluster 0 monocytes/ macrophages.

[0131] The response-related Cluster 0 genes (FIG. 2A) were then used to develop a transcriptional (AUCell) signature score associated with lack of response to PD-1 blockade at C4D1 and predictive for unfavorable outcome at baseline (C1D1). See FIG. 2C, which shows AUCell signature score in circulating Cluster 0 monocytes from healthy donors (HDs) and patients with newly diagnosed (ND) or relapsed/refractory (R/R) cHL at CID 1 and C4D1 at the single cell (upper panel) and patient (lower panel) levels. On both a single-cell and per-patient level, the Cluster 0 AUCell score was significantly higher in patients with cHL who did not respond to PD- 1 blockade than in complete responders (FIG. 2C, upper and lower panels, respectively). Response-related differences in the AUCell score were also detected when all circulating monocytes, rather than pre-selected Cluster 0 cells, were analyzed. See FIG 2D, which shows the AUCell signature score in all monocytes from HDs and patients with cHL at the single cell (upper panel) and patient (lower panel) levels.

[0132] Response-related circulating Cluster 0 transcriptional signature in patients with another solid tumor. After defining the response-related Cluster 0 AUCell signature in patients with R/RcHL (FIG. 2C and FIG. 2D), the performance of the signature in an additional series of patients with a solid tumor (R/R renal cell carcinoma [RCCa]) who were treated with PD- L1 blockade and had publicly available PBMC scRNA-seq data (data not shown) was assessed. In this RCCa series, circulating monocytes from patients who did not respond to PD-L1 blockade had significantly higher baseline AUCell signature scores than those who responded to this treatment. See FIG. 2E, which shows AUCell signature score in all circulating monocytes from patients with R/R Renal cellcarcinoma (RCCa) at baseline (C1D1), stratified by the subsequent response to PD- L1 blockade, at the single cell (upper panel) and patient (lower panel) levels.

[0133] Therefore, circulating Cluster 0-like monocytes were also detectable and associated with inferior responses to blockade of the PD-1 pathway in an additional solid tumor.

Discussion

[0134] Via scRNA-seq analysis, a major population of circulating Cluster 0 monocytes were identified with a proinflammatory, tumor-promoting cytokine/chemokine signature was detected in patients with cHL, but not in healthy donors, and associated with lack of response to PD-1/PD-L1 blockade. These same Cluster 0 monocytes were also found localized in proximity to HRS cells in intact cHLs and detected in additional solid tumors. Additionally, circulating Cluster 0 monocytes were associated with a lack of response to PD-L1 blockade in another solid tumor (RCCa), underscoring the broad relevance of these findings.

[0135] The coordinated upregulation of cytokine/chemokines, including IL IB, CXCL2, CXCL3, CXCL8,and CCL2, and the additional response-related factors IL-6, CXCL1, and CCL3, in Cluster 0 monocytes/macrophages identifies candidate targets for combination therapies with PD-1 blockade and suggests associated treatment strategies. In previous studies, inhibition of IL- 1 receptor signaling abrogated the recruitment of monocytes into the TIME, reduced immune suppression and tumor progression, and synergized with PD-1 blockade in murine models of RCCa and breast cancer. Additionally, neutralizing CXCL8 (IL8) antibodies and CXCR2 antagonists, which block multiple Cluster 0 cytokines/chemokines including IL-8, CXCL1, CXCL2, and CXCL3, suppress inflammasome activation, myeloid cell recruitment, tumor growth and angiogenesis, and enhance the efficacy of PD-1 blockade in certain solid tumors. Furthermore, CCL2 blockade decreases macrophage infiltration, tumor proliferation, and metastasis in inflammatory breast cancer, in part via downstream activation of CCL3. Alternative therapeutic strategies may target the Cluster 0 monocytes themselves.

[0136] The complete disclosures of the patents, patent documents, and publications cited herein are incorporated by reference in their entirety as if each were individually incorporated. To the extent that there is any conflict or discrepancy between this specification as written and the disclosure in any document that is incorporated by reference herein, this specification as written will control. Various modifications and alterations to this disclosure will become apparent to those skilled in the art without departing from the scope and spirit of this disclosure. It should be understood that this disclosure is not intended to be unduly limited by the illustrative embodiments and examples set forth herein and that such examples and embodiments are presented by way of example only with the scope of the disclosure intended to be limited only by the claims set forth herein as follows.

Claims

Claims

1. A method of predicting a response to a PD-1/PD-L1 blockade cancer therapy in a cancer patient prior to and/or during use of the therapy, the method comprising: analyzing monocytes isolated from peripheral blood mononuclear cells (PBMCs) of the cancer patient for expression of a transcriptional RNA-based signature associated with sensitivity to the PD-1/PD-L1 blockade cancer therapy; wherein if the transcriptional RNA-based signature comprises a set of genes that are upregulated compared to the same set of genes in a control group, the cancer patient will experience an inferior response to the PD-1/PD-L1 blockade cancer therapy.

2. The method of claim 1, wherein the monocytes comprise classical monocytes (selected for CD14 ^; and CD 16"), optionally wherein the classical monocytes comprise Cluster 0 monocytes, wherein the Cluster 0 monocytes express one or more cytokines/chemokines selected from IL IB, CXCL8, CXCL2, CXCL3, and CCL2.

3. The method of claim 2, wherein the Cluster 0 monocytes express both IL1B and CCL2, wherein such combination is expressed highly and selectively in the Cluster 0 monocytes.

4. The method of claim 2 or claim 3, wherein analyzing the classical monocytes comprises: isolating classical monocytes from peripheral blood mononuclear cells (PBMCs) of the cancer patient; and analyzing the classical monocytes for a subset of Cluster 0 monocytes, wherein the Cluster 0 monocytes express one or more cytokines/chemokines selected from IL1, CXCL8, CXCL2, CXCL3, and CCL2, preferably wherein the Cluster 0 monocytes express IL IB and CCL2.

5. The method of any one of claims 1 4, wherein the control group comprises cancer patients having the same cancer as the cancer patient and having a complete response to a PD- 1/PD- 1 blockade cancer therapy, and optionally wherein the control group further comprises healthy donors (i.e., donors without cancer).

6. The method of claim 4 or 5, wherein isolating the classical monocytes comprises: isolating PBMCs from the cancer patient; and selecting CD14⁺⁺ and CD16" monocytes (i.e., classical monocytes) from the PBMCs to form a population of classical monocytes.

7. The method of claim 6, wherein selecting CD14⁺⁺ and CD16' monocytes comprises negatively selecting the classical monocytes, optionally using flow cytometry, optionally using antibodies directed against cell-surface antigens such as CD3 (T cells), CD7 (T and NK cells), CD16 (NK cells and non-classical monocytes), CD19 (B cells), CD56 (NK cells), CD123 (plasmacytoid dendritic cells), CLEC10A/CD301 (conventional dendritic cells), or CD235a (erythrocytes).

8. The method of any one of claims 3 to 7, wherein analyzing classical monocytes for Cluster 0 monocytes comprises isolating the Cluster 0 monocytes, quantifying the Cluster 0 monocytes, using flow cytometry, or selecting the Cluster 0 monocytes using antibodies against the one or more cytokines/chemokines of the Cluster 0 monocytes.

9. The method of claim 8, wherein selecting the Cluster 0 monocytes comprises using antibodies against two of the cytokines/chemokines, such as antibodies against IL IB and CCL2.

10. The method of any one of claims 1 to 9, wherein analyzing the monocytes, preferably Cluster 0 monocytes, for expression of a transcriptional RNA-based signature comprises using a computational method that expresses the transcriptional RNA-based signature of the monocytes as a computational signature score.

11. The method of any one of claims 1 to 10, wherein the set of genes comprise at least genes numbered (l)-(50) listed in Table la, such as genes numbered (l)-(100) listed in Tables la and lb or all 368 genes listed in Tables la-ld, or wherein the set of genes comprises genes included in one or more of the pathways listed in Figure 2b.

12. The method of claim 11, wherein the one or more pathways is associated with a response to Lipopolysaccharides (LPS), IL1, TNFa, Toll-like receptor (TLR), and prostaglandin signaling, inflammasome activation and modulation by Triggering Receptor Expressed on Myeloid cells 1 (TREM1), an Ig-like immunoregulatory receptor, o r amplifier of innate immune responses.

13. The method of any one of claims 1 to 12, wherein the cancer patient has a lymphoid malignancy or a solid tumor, such as renal cell carcinoma or classical Hodgkins lymphoma, optionally wherein the cancer patient has a newly diagnosed cancer, a relapsed cancer, or a refractory cancer.

14. A method of determining the presence of cancer in a subject, the method comprising: analyzing monocytes for a subset of Cluster 0 monocytes, wherein the Cluster 0 monocytes express one or more cytokines/chemokines selected from IL IB, CXCL8, CXCL2, CXCL3, and CCL2, preferably wherein the Cluster 0 monocytes express IL1B and CCL2.

15. The method of claim 14, wherein the subject has a lymphoid malignancy or a solid tumor, such as renal cell carcinoma or classical Hodgkins lymphoma.

16. The method of claim 14 or 15, wherein analyzing monocytes for Cluster 0 monocytes comprises quantifying the Cluster 0 monocytes, using flow cytometry, selecting the Cluster 0 monocytes using antibodies against the one or more cytokines/chemokines of the Cluster 0 monocytes, selecting the Cluster 0 monocytes using antibodies against two of the cytokines/chemokines, and/or selecting the Cluster 0 monocytes using antibodies against IL1B and CCL2.

17. A method of treating a cancer patient, the method comprising: predicting a response to a PD-1/PD-L1 blockade cancer therapy in a cancer patient prior to and/or during use of the therapy according to the method of any one of claims 1 to 16; and treating the cancer patient with a treatment protocol comprising: a PD-1/PD-L1 blockade cancer therapy, if the cancer patient is predicted to have a positive response to the PD-1/PD-L1 blockade cancer therapy, or a treatment protocol other than, or in addition to, a PD-1/PD-L1 blockade cancer therapy if the patient is predicted to have an inferior response to the PD- 1/PD-L1 blockade cancer therapy.

18. The method of claim 17, wherein the treatment protocol comprises: a PD-1/PD-L1 blockade cancer therapy, alone or in combination with another second/different therapy, if the transcriptional RNA-based signature of the monocytes is associated with a positive response to the PD-1/PD-L1 blockade cancer therapy; optionally wherein the PD-1/PD-L1 blockade cancer therapy comprises administering a PD-1 inhibitor, such as cemiplimab, nivolumab, or pembrolizumab, or a PD-L1 inhibitor, such as atezolizumab, avelumab, or durvalumab.

19. The method of claim 18, wherein treating the cancer patient comprises treating with a treatment protocol other than a PD-1/PD-L1 blockade cancer therapy, such as a second/different therapy that modulates the cancer patient’s immune response.

20. The method of claim 19, wherein the second/different therapy comprises one that modulates the cancer patient’s immune response, such as reducing the number of Cluster 0 monocytes, optionally wherein reducing the number of Cluster 0 monocytes comprises inhibiting the conversion of classical monocytes to Cluster 0 monocytes; optionally wherein the second/different therapy comprises inhibiting components of a Cluster 0 transcriptional program; and optionally wherein inhibiting components of the Cluster 0 transcriptional program comprises inhibiting IL-1 receptor signaling, neutralizing CXCL8 (IL8) antibodies or CXCR2 antagonists, or blocking CCL2 activity.