WO2016197131A1

WO2016197131A1 - Method for predicting a clinical response to cancer immunotherapy

Info

Publication number: WO2016197131A1
Application number: PCT/US2016/036065
Authority: WO
Inventors: Arash Ash Alizadeh; Aaron M. NEWMAN; Ronald Levy
Original assignee: Leland Stanford Junior University
Current assignee: Leland Stanford Junior University
Priority date: 2015-06-04
Filing date: 2016-06-06
Publication date: 2016-12-08
Anticipated expiration: 2017-12-04

Abstract

Provided herein is a method of classifying a patient. An implementation of the present method may include the steps of i) determining, for a tumor sample obtained from a patient diagnosed with a B-cell malignancy, a) the proportion of activated memory CD4+ T-cells among a plurality of lymphocytes, b) the number of tyrosine residues in idiotype first complementarity determining region (CDR1) amino acid sequences, and c) the selection pressure on nucleotide sequences encoding idiotype CDRs, and ii) predicting whether the patient will exhibit a clinically beneficial response to an anti-tumor vaccine therapy based on an integrative evaluation of the proportion of T-cells, the number of tyrosine residues, and the selection pressure determined for the tumor sample.

Description

METHOD FOR PREDICTING A CLINICAL RESPONSE TO CANCER IMMUNOTHERAPY

Government Rights

[0001 ] This invention was made with Government support under contract W81XWH-12-1 - 0498 awarded by the U.S. Army Medical Research and Materiel Command. The Government has certain rights in the invention.

Cross-Reference to Related Applications

[0002] Pursuant to 35 U.S.C. § 1 19 (e), this application claims priority to the filing date of the United States Provisional Patent Application Serial No. 62/170,840 filed June 4, 2015, the disclosure of which application is incorporated herein by reference.

Introduction

[0003] Lymphomas represent about 4% of the new cases of cancer diagnosed in the United States each year, making them the fifth most common cancer diagnosis and a leading cause of cancer death. About 60,000 individuals are diagnosed with lymphoma every year, of which about 90% are Non-Hodgkin Lymphomas (NHLs), with the remainder being Hodgkin Lymphoma (HL). In fact, while the incidence of most cancers is decreasing, lymphoma is one of only two tumors increasing in frequency, although the cause for this increase is unknown.

[0004] Non-Hodgkin lymphomas are a heterogeneous group of disorders involving malignant monoclonal proliferation of lymphoid cells in lymphoreticular sites, including lymph nodes, bone marrow, the spleen, the liver, and the gastrointestinal tract. Presenting symptoms usually include peripheral lymphadenopathy. Compared with Hodgkin lymphoma, there is a greater likelihood of disseminated disease at the time of diagnosis. NHL can be formed from either B-cells or T-cells.

[0005] In B-cell lymphoma malignancies, a clonotypic surface immunoglobulin (Ig) expressed by malignant B-cells is known as an idiotype (Id) epitope. Id is a tumor-specific antigen and, therefore, provides a unique opportunity to target the tumor through a method of active immunotherapy, where the patient is vaccinated against the tumor-specific idiotype. Id proteins contain structures that can be recognized by antibodies, and can be isolated from autologous tumor cells and formulated into a custom-made therapeutic tumor vaccine. An approach for generating patient-specific Id vaccines involves fusion of individual patient's lymphoma cells with myeloma cells, yielding a 'rescue' hybridoma secreting large quantities of Id protein. The Id is then chemically conjugated to the highly immunogenic carrier protein keyhole limpet hemocyanin (KLH) rendering it more immunogenic. The resulting Id-KLH conjugate is then injected subcutaneously (s.c.) along with an immunologic adjuvant to evoke tumor-specific antibody and T cell responses.

Summary

[0006] Provided herein is a method of classifying a patient for treatment. An implementation of the present method may include the steps of i) determining, for a tumor sample obtained from a patient diagnosed with a B-cell malignancy, a) the proportion of activated memory CD4⁺ T-cells among a plurality of lymphocytes, b) the number of tyrosine residues in idiotype first complementarity determining region (CDR1) amino acid sequences, and c) the selection pressure on nucleotide sequences encoding idiotype CDRs, and ii) predicting whether the patient will exhibit a clinically beneficial response to an anti-tumor vaccine therapy based on an integrative evaluation of the proportion of T-cells, the number of tyrosine residues, and the selection pressure determined for the tumor sample. The methods can include (iii) treating a patient predicted as having a clinically beneficial response to an anti-tumor vaccine therapy with the anti-tumor vaccine therapy.

[0007] In any embodiment, the selection pressure may include a quantitative measure of a deviation of the frequency of non-synonymous mutations observed in the nucleotide sequences encoding idiotype CDR amino acid sequences relative to an expected frequency. In some embodiments, the selection pressure is quantified based on an analysis that includes a Bayesian model of the posterior probability of the non-synonymous mutation-to- synonymous mutation frequency in a nucleotide sequence encoding an idiotype CDR amino acid sequences that provides an estimate of the non-synonymous-to-synonymous mutation frequency for the nucleotide sequence encoding the idiotype CDR amino acid sequence, and a log odds ratio of the non-synonymous-to-synonymous mutation frequency in the nucleotide sequence encoding the idiotype CDR amino acid sequence normalized by its expected non-synonymous-to-synonymous mutation frequency in a paired germline nucleotide sequence encoding the immunoglobulin corresponding to the idiotype, wherein the log odds ratio is used to quantify the selection pressure.

[0008] In any embodiment, the predicting step may include predicting that the patient will exhibit the clinically beneficial response to the anti-tumor vaccine therapy based on an integrative evaluation that the proportion of T-cells is equal to or higher than a threshold proportion, the number of tyrosine residues is equal to or less than a threshold number of tyrosine residues, and the selection pressure is equal to or less than a threshold selection pressure. In some embodiments, the threshold number of tyrosine residues is 2 or less. In some embodiments, the threshold proportion of T-cells, the threshold number of tyrosine residues, and the threshold selection pressure are based on an analysis of a plurality of tumor samples from a plurality of patients diagnosed with the B-cell malignancy, wherein the plurality of patients is treated with the anti-tumor vaccine therapy. In some embodiments, the threshold proportion of T-cells is the median proportion of a plurality of proportions of activated memory CD4⁺ T-cells among a plurality of lymphocytes in at least a subset of the plurality of tumor samples, or higher. In some embodiments, the threshold number of tyrosine residues is the median number of a plurality of numbers of tyrosine residues in idiotype CDR1 amino acid sequences in at least a subset of the plurality of tumor samples, or less. In some embodiments, the threshold selection pressure, when used to dichotomize the plurality of tumor samples according to the plurality of selection pressures on nucleotide sequences encoding idiotype CDRs of the plurality of tumor samples, reduces a skew in the ratio between the number of tumor samples whose proportion of T-cells is equal to or higher than the threshold proportion and number of tyrosine residues is equal to or less than the threshold number, and the number of tumor samples whose proportion of T-cells is lower than the threshold proportion or the number of tyrosine residues is more than the threshold number. In some embodiments the threshold selection pressure is in the range of 0.3 to -0.3.

[0009] In any embodiment, the tumor sample may be a sample obtained prior to beginning the anti-tumor vaccine therapy and/or may be obtained from a patient who has previously not been treated for the B-cell malignancy with an immunotherapy.

[0010] In any embodiment, the determining step may include using one or more of flow cytometry, sequencing, and microarray analysis, on the tumor sample obtained from the patient. In some embodiments, the determining step i-a) includes using flow cytometry or analyzing a gene expression profile of the tumor sample to obtain the proportion of activated memory CD4⁺ T-cells among the plurality of lymphocytes. In some embodiments, the analyzing includes obtaining the gene expression profile from the tumor sample, and computing the proportion of activated memory CD4⁺ T-cells based on the tumor sample gene expression profile and a plurality of cell type-specific gene expression profiles. In some embodiments, the determining step i-c) includes sequencing a plurality of nucleotide sequences encoding a plurality of idiotype amino acid sequences, estimating the non- synonymous-to-synonymous mutation frequency for a nucleotide sequence encoding the idiotype CDR amino acid sequence using a Bayesian model of the posterior probability of the non-synonymous mutation-to-synonymous mutation frequency in the nucleotide sequence encoding an idiotype CDR amino acid sequences, and calculating a log odds ratio of the non-synonymous-to-synonymous mutation frequency in the nucleotide sequence encoding the idiotype CDR amino acid sequence normalized by its expected non- synonymous-to-synonymous mutation frequency in a paired germline nucleotide sequence encoding the immunoglobulin corresponding to the idiotype, thereby quantifying the selection pressure on a nucleotide sequence encoding the idiotype CDR. [0011] In any embodiment, the clinically beneficial response may be a stronger humoral anti-idiotype immunological response, a longer progression-free survival, a longer overall survival and/or a longer time to subsequent therapy compared to a control subject who has not received the anti-tumor vaccine therapy.

[0012] In any embodiment, the B-cell malignancy may be follicular lymphoma, chronic lymphocytic leukemia, lymphoblastic leukemia, small lymphocytic lymphoma, hairy cell leukemia, diffuse large B cell lymphoma , mucosa-Associated lymphatic tissue lymphoma (MALT); mantle cell lymphoma (MCL); Burkitt lymphoma; mediastinal large B cell lymphoma; Waldenstrom macroglobulinemia; nodal marginal zone B cell lymphoma (NMZL); splenic marginal zone lymphoma (SMZL); intravascular large B-cell lymphoma; primary effusion lymphoma; lymphomatoid granulomatosis; histologically transformed aggressive lymphomas; multiple myeloma; plasmacytoma; cutaneous B-cell lymphoma; or plasmablastic lymphoma.

[0013] In any embodiment, the anti-tumor vaccine therapy may be an idiotype vaccine therapy.

[0014] Also disclosed herein is a method for treating a patient diagnosed with a B-cell malignancy, including i) classifying a patient diagnosed with a B-cell malignancy according to any of the method described above, and ii) administering the anti-tumor vaccine to the patient, wherein the patient is classified as a patient predicted to exhibit a clinically beneficial response to the anti-tumor vaccine therapy. In some embodiments, the anti-tumor vaccine is an idiotype vaccine. In some embodiments, the method further includes administering a non- vaccine therapy to the patient, wherein the patient is classified as a patient predicted not to exhibit a clinically beneficial response to the anti-tumor vaccine therapy.

[0015] Also disclosed herein is a computer-implemented method of classifying a patient. An implementation of the computer-implemented method may include the steps of i) accessing one or more files on a computer system, wherein the one or more files comprise sequence- specific data of a plurality of genes expressed in a tumor sample obtained from a patient diagnosed with a B-cell malignancy, ii) analyzing the sequence-specific data to determine, for the sample, a) the proportion of activated memory CD4⁺ T-cells among a plurality of lymphocytes, b) the number of tyrosine residues in idiotype CDR1 amino acid sequences, and c) the selection pressure on nucleotide sequences encoding idiotype CDRs, iii) classifying whether the patient will exhibit a clinically beneficial response to an anti-tumor vaccine therapy based on an integrative evaluation of the proportion of T-cells, the number of tyrosine residues, and the selection pressure determined for the tumor sample, and iv) outputting the integrative evaluation and/or the classification. In some embodiments, the sequence-specific data include a gene expression profile of the tumor sample and nucleotide sequences containing sequences encoding idiotype CDR amino acid sequences. In some embodiments, the anti-tumor vaccine is an idiotype vaccine.

[0016] In some embodiments, the classifying step includes predicting that the patient will exhibit the clinically beneficial response to an anti-tumor vaccine therapy based on the integrative evaluation that the proportion of T-cells is equal to or higher than a threshold proportion, the number of tyrosine residues is equal to or lower than a threshold number, and the selection pressure is equal to or less than a threshold selection pressure. In some embodiments, the method includes accessing a reference file that contains reference data specifying one or more of the threshold proportion of T-cells, the threshold number of tyrosine residues, and the threshold selection pressure for classifying the patient, wherein the reference data is obtained from a plurality of patients diagnosed with the B-cell malignancy, wherein the plurality of patients diagnosed with the B-cell malignancy has been treated with the anti-tumor vaccine therapy.

Brief Description Of The Drawings

[0017] FIG. 1 is an image depicting clustered gene expression patterns of genes expressed in tumor samples taken from idiotype vaccine (Id) responders and non-responders.

[0018] FIG. 2 is a collection of images schematically depicting a workflow for using

CIBERSORT, according to an embodiment of the present disclosure.

[0019] FIG. 3A-3B are a collection of graphs showing benchmarking results of CIBERSORT against flow cytometry.

[0020] FIGs. 4A-4C are collection of graphs showing an analysis of leukocyte subsets in follicular lymphoma (FL) tumors associated with response to idiotypic vaccination, according to an embodiment of the present disclosure.

[0021] FIGs. 5A-5C are collection of a table and graphs showing an idiotype sequence determinant associated with response to idiotypic vaccination, according to an embodiment of the present disclosure.

[0022] FIG. 6 is a graphs showing progression free survival (PFS) in two stratified cohorts of patients receiving MyVax®.

[0023] FIGs. 7A-7B are a collection of graphs showing prediction of PFS in patients receiving MyVax® stratified according to an embodiment of the present disclosure.

[0024] FIG. 8 is an image showing CIBERSORT benchmarking experiments.

[0025] FIGs. 9A-9D are a collection of graphs showing deep deconvolution and enumeration of individual cell subsets using CIBERSORT, according to an embodiment of the present disclosure.

[0026] FIGs. 10A-10C are a collection of graphs showing PFS in stratified cohorts of patients receiving MyVax®, according to an embodiment of the present disclosure. [0027] FIG. 1 1 shows a table showing clinical features of randomized Genitope patients, according to embodiments of the present disclosure.

Definitions

[0028] The terms "polynucleotide", "nucleotide", "nucleotide sequence", "nucleic acid", "nucleic acid molecule", "nucleic acid sequence" and "oligonucleotide" are used interchangeably, and can also include plurals of each respectively depending on the context in which the terms are utilized. They refer to a polymeric form of nucleotides of any length, either deoxyribonucleotides (DNA) or ribonucleotides (RNA), or analogs thereof. Polynucleotides may have any three-dimensional structure, and may perform any function, known or unknown. The following are non-limiting examples of polynucleotides: coding or non-coding regions of a gene or gene fragment, loci (locus) defined from linkage analysis, exons, introns, messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA, ribozymes, small interfering RNA, (siRNA), microRNA (miRNA), small nuclear RNA (snRNA), cDNA, recombinant polynucleotides, branched polynucleotides, plasmids, vectors, isolated DNA (A, B and Z structures) of any sequence, PNA, locked nucleic acid (LNA), TNA (treose nucleic acid), isolated RNA of any sequence, nucleic acid probes, and primers. LNA, often referred to as inaccessible RNA, is a modified RNA nucleotide. The ribose moiety of an LNA nucleotide is modified with an extra bridge connecting the 2' and 4' carbons. The bridge "locks" the ribose in the 3'-endo structural conformation, which is often found in the A-form of DNA or RNA, which can significantly improve thermal stability.

[0029] As used herein, the terms "an oligonucleotide having a nucleotide sequence encoding a polypeptide," "polynucleotide having a nucleotide sequence encoding a polypeptide," "nucleic acid sequence encoding a peptide," or equivalents thereof, means a nucleic acid sequence including the coding region of a particular polypeptide. The coding region may be present in a cDNA, genomic DNA, or RNA form. When present in a DNA form, the oligonucleotide or polynucleotide may be single-stranded (i.e., the sense strand) or double-stranded. Suitable control elements such as enhancers/promoters, splice junctions, polyadenylation signals, etc. may be placed in close proximity to the coding region of the gene if needed to permit proper initiation of transcription and/or correct processing of the primary RNA transcript. Alternatively, the coding region utilized in the expression vectors of the present invention may contain endogenous enhancers/promoters, splice junctions, intervening sequences, polyadenylation signals, etc., or a combination of both endogenous and exogenous control elements.

[0030] The term "isolated" when used in relation to a nucleic acid, as in "an isolated oligonucleotide" or "isolated polynucleotide" refers to a nucleic acid sequence that is identified and separated from at least one contaminant nucleic acid with which it is ordinarily associated (e.g. host cell proteins).

[0031] As used herein, the terms "portion" when used in reference to a nucleotide sequence (as in "a portion of a given nucleotide sequence") refers to fragments of that sequence. The fragments may range in size from ten nucleotides to the entire nucleotide sequence minus one nucleotide (e.g., 10 nucleotides, 20, 30, 40, 50, 100, 200, etc.).

[0032] As used herein, the term "portion" when in reference to an amino acid sequence (as in "a portion of a given amino acid sequence") refers to fragments of that sequence. The fragments may range in size from six amino acids to the entire amino acid sequence minus one amino acid (e.g., 6 amino acids, 10, 20, 30, 40, 75, 200, etc.)

[0033] As used herein, the term "purified" or "to purify" refers to the removal of contaminants from a sample. For example, monoclonal antibodies reactive with a framework epitope of an immunoglobulin may be purified by removal of contaminating non-immunoglobulin proteins; they are also purified by the removal of immunoglobulins that do not bind to the same antigen. The removal of non-immunoglobulin proteins and/or the removal of immunoglobulins that do not bind the particular antigen results in an increase in the percentage of antigen specific immunoglobulins in the sample. In another example, recombinant antigen-specific polypeptides are expressed in bacterial host cells and the polypeptides are purified by the removal of host cell proteins; the percentage of recombinant antigen-specific polypeptides is thereby increased in the sample.

[0034] A "plurality" contains at least 2 members. In certain cases, a plurality may have at least 10, at least 100, at least 1000, at least 10,000, at least 100,000, at least 10⁶, at least 10⁷, at least 10⁸ or at least 10⁹ or more members.

[0035] As used herein, the term "patient" refers to any animal, such as a mammal like a dog, cat, bird, livestock, and including a human. The patient may be diagnosed with a disease, such as a B-cell malignancy.

[0036] As used herein, the terms "treat," "treatment," "treating," and the like, refer to obtaining a desired pharmacologic and/or physiologic effect. The effect may be prophylactic in terms of completely or partially preventing a disease or symptom thereof and/or may be therapeutic in terms of a partial or complete cure for a disease and/or adverse affect attributable to the disease. "Treatment," as used herein, covers any treatment of a disease in a mammal, particularly in a human, and includes: (a) preventing the disease from occurring in a subject which may be predisposed to the disease but has not yet been diagnosed as having it; (b) inhibiting the disease, i.e., arresting its development; and (c) relieving the disease, e.g., causing regression of the disease, e.g., to completely or partially remove symptoms of the disease. [0037] As used herein "idiotype" refers to an epitope in the hypervariable region of an immunoglobulin chain, including but not limited to an epitope formed by contributions from both the light chain and heavy chain CDRs. A "non-idiotypic portion" refers to an epitope located outside the hypervariable regions, such as the framework regions.

[0038] As used herein "immunoglobulin" refers to any of a group of large glycoproteins that are secreted by plasma cells and that function as antibodies in the immune response by binding with specific antigens. The specific antigen bound by an immunoglobulin may or may not be known. There are five classes of immunoglobulins: IgA, IgD, IgE, IgG, and IgM.

[0039] The term "antibody," as used herein, is intended to refer to immunoglobulin molecules containing four polypeptide chains, two heavy (H) chains and two light (L) chains (lambda or kappa) inter-connected by disulfide bonds. An antibody has a known specific antigen with which it binds. Each heavy chain of an antibody includes a heavy chain variable region (abbreviated herein as HCVR, HV or VH) and a heavy chain constant region. The heavy chain constant region includes three domains, CH1 , CH2 and CH3. Each light chain includes a light chain variable region (abbreviated herein as LCVR or VL or KV or LV to designate kappa or lambda light chains) and a light chain constant region. The light chain constant region includes one domain, CL. The VH and VL regions can be further subdivided into regions of hypervariability, termed complementarity determining regions (CDRs), interspersed with regions that are more conserved, termed framework regions (FR). Each variable region (VH or VL) contains 3 CDRs, designated CDR1 , CDR2 and CDR3. Each variable region also contains 4 framework sub-regions, designated FRI, FR2, FR3 and FR4.

[0040] As used herein, the term "antibody fragments" refers to a portion of an intact antibody. Examples of antibody fragments include, but are not limited to, linear antibodies, single-chain antibody molecules, Fv, Fab and F(ab').sub.2 fragments, and multispecific antibodies formed from antibody fragments. The antibody fragments may retain at least part of the heavy and/or light chain variable region.

[0041 ] As used herein, the terms "complementarity determining region" and "CDR" refer to the regions that are primarily responsible for antigen-binding. There are three CDRs in a light chain variable region (CDRL1 , CDRL2, and CDRL3), and three CDRs in a heavy chain variable region (CDRH1 , CDRH2, and CDRH3). The particular designation in the art for the exact location of the CDRs varies depending on what definition is employed. Preferably, the IMGT designations are used (see Brochet et al. (2008) Nucleic Acids Res. 36:W503-8, herein specifically incorporated by reference), which uses the following designations for both light and heavy chains: residues 27-38 (CDR1), residues 56-65 (CDR2), and residues 105- 1 16 (CDR3); see also Lefranc, MP, The Immunologist, 7: 132-136, 1999, herein incorporated by reference. [0042] The residues that make up the six CDRs have also been characterized by Kabat and Chothia as follows: residues 24-34 (CDRL1), 50-56 (CDRL2) and 89-97 (CDRL3) in the light chain variable region and 31 -35 (CDRH1), 50-65 (CDRH2) and 95-102 (CDRH3) in the heavy chain variable region; Kabat et al., (1991) Sequences of Proteins of Immunological Interest, 5th Ed. Public Health Service, National Institutes of Health, Bethesda, Md., herein incorporated by reference; and residues 26-32 (CDRL1), 50-52 (CDRL2) and 91 -96 (CDRL3) in the light chain variable region and 26-32 (CDRH1), 53-55 (CDRH2) and 96-101 (CDRH3) in the heavy chain variable region; Chothia and Lesk (1987) J. Mol. Biol. 196: 901 - 917, herein incorporated by reference. Unless otherwise specified, the terms "complementarity determining region" and "CDR" as used herein, include the residues that encompass IMGT, Kabat and Chothia definitions. Also, unless specified, as used herein, the numbering of CDR residues is according to IMGT.

[0043] As used herein, the term "framework" refers to the residues of the variable region other than the CDR residues as defined herein. There are four separate framework sub- regions that make up the framework: FR1 , FR2, FR3, and FR4. In order to indicate if the framework sub-region is in the light or heavy chain variable region, an "L" or "H" may be added to the sub-region abbreviation (e.g., "FRL1 " indicates framework sub-region 1 of the light chain variable region). Unless specified, the numbering of framework residues is according to IMGT.

[0044] As used herein, "antigen" refers to any substance that, when introduced into a body, e.g., of a patient or subject, stimulates an immune response such as the production of an antibody that recognizes the antigen.

[0045] As used herein, the term "vaccine" refers to a composition containing an antigen for use as a therapy or treatment to induce an immune response. Vaccines may be used both prophylactically (for prevention of disease) and therapeutically (for the treatment of existing disease). For example, with respect to cancer therapies, a therapeutic vaccine would generally be given to a cancer patient to induce an immune response to fight the cancer, e.g., by attacking the patient's malignant cells, while a prophylactic vaccine would generally be given to an individual who does not have a particular type of cancer to induce an immune response to prevent that type of cancer, e.g., by attacking viruses known to cause that type of cancer.

[0046] The term "passive immunotherapy" as used herein refers to therapeutic treatment of a subject or patient using immunological agents such as antibodies (e.g., monoclonal antibodies) produced outside a subject or patient, without the purpose of inducing the subject or patient's immune system to produce a specific immune response to the therapeutic agent. [0047] The term "active immunotherapy" as used herein refers to therapeutic treatment of a subject or patient to induce the subject or patient's immune system to produce a specific immune response, e.g., to a protein derived from a malignant cell. In some embodiments, the immunogenic composition used in active immunotherapy includes one or more antigens derived from a subject's malignant cells. In some embodiments, the immunogenic agent includes at least a portion of an immunoglobulin derived from a subject's malignant cell. It is understood by those of skill in the art that, as used in active immunotherapy, an immunoglobulin derived from a patient or subject's malignant cell is generally used as an antigen, not as an antibody intended to act as a therapeutic agent in passive immunotherapy.

[0048] The term "selection pressure," as used herein, refers to a quantitative estimate of the strength of selection acting on a population of replicating nucleic acids encoding a polypeptide, e.g., nucleic acids encoding idiotype amino acid sequences in a B cell tumor sample. Thus, the selective pressure may be a quantitative measure of a deviation of the frequency of non-synonymous mutations observed in a population of replicating nucleic acids encoding a polypeptide relative to an expected frequency. The selection pressure may be represented by a sigma value, which represents the log odds ratio of the non- synonymous-to-synonymous mutation frequency in the complementarity determining or framework regions of idiotype immunoglobulin sequences normalized by their expected non- synonymous-to-synonymous mutation frequency in the paired germline immunoglobulin sequence. The larger the selection pressure is, the more positive sigma is, and the higher the observed frequency of non-synonymous mutations relative to expectation in the nucleotide sequences, and vice versa. In case of nucleotide sequences encoding immunoglobulin variable region amino acid sequences, such as idiotype amino acid sequences, a higher frequency of non-synonymous mutations observed relative to expectation may indicate positive selection, while a reduced frequency of non-synonymous mutations observed relative to expectation may indicate negative selection (i.e., preservation of the B cell receptor).

[0049] "Predicting," as used herein, refers to the process of establishing that a specific event will, or is likely to, occur, or an outcome will be, or is likely to be, achieved, prior to the event or outcome taking place. In some cases, where the prediction is related to a patient's response to therapy, predicting the outcome of therapy may include establishing that the patient will respond in one way if the patient satisfies a set of patient-specific conditions, or that the patient will respond in another way if the patient does not satisfy at least some conditions in the set of patient-specific conditions. In some cases, predicting an outcome to therapy is done before the therapy is administered to the patient. [0050] Before the present invention is further described, it is to be understood that this invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0051] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges, and are also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0052] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice of the present disclosure, some exemplary methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited.

[0053] It must be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a tumor sample" includes a plurality of such tumor samples and reference to "the gene expression profile" includes reference to one or more gene expression profiles and equivalents thereof known to those skilled in the art, and so forth. It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as "solely," "only" and the like in connection with the recitation of claim elements, or use of a "negative" limitation.

[0054] It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination. All combinations of the embodiments pertaining to the invention are specifically embraced by the present invention and are disclosed herein just as if each and every combination was individually and explicitly disclosed. In addition, all sub-combinations of the various embodiments and elements thereof are also specifically embraced by the present invention and are disclosed herein just as if each and every such sub-combination was individually and explicitly disclosed herein.

[0055] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.

[0056] The practice of various embodiments of the present disclosure employs, unless otherwise indicated, conventional techniques of immunology, biochemistry, chemistry, molecular biology, microbiology, cell biology, genomics and recombinant DNA, which are within the skill of the art. See Green and Sambrook, MOLECULAR CLONING: A LABORATORY MANUAL, 4th edition (2012); CURRENT PROTOCOLS IN MOLECULAR BIOLOGY (F. M. Ausubel, et al. eds., (1987)); the series METHODS IN ENZYMOLOGY (Academic Press, Inc.): PCR 2: A PRACTICAL APPROACH (M. J. MacPherson, B. D. Hames and G. R. Taylor eds. (1995)), IMMUNOLOGY METHODS MANUAL (I. Lefkovits ed., Academic Press 1997); and CELL AND TISSUE CULTURE: LABORATORY PROCEDURES IN BIOTECHNOLOGY (Doyle & Griffiths, John Wiley & Sons 1998), the disclosures of which are incorporated herein by reference.

Detailed Description

[0057] As summarized above, the present disclosure provides a method of classifying a patient. Aspects of the present method may include the steps of i) determining, for a tumor sample obtained from a patient diagnosed with a B-cell malignancy, a) the proportion of activated memory CD4⁺ T-cells among a plurality of lymphocytes, b) the number of tyrosine residues in an idiotype first complementarity determining region (CDR1) amino acid sequence, and c) the selection pressure on nucleotide sequences encoding idiotype CDRs, and ii) predicting whether the patient will exhibit a clinically beneficial response to an antitumor vaccine therapy based on an integrative evaluation of the proportion of T-cells, the number of tyrosine residues, and the selection pressure determined for the tumor sample. Further detail of present method is now described.

Method of Classifying

[0058] In the present method, a clinical response to immunotherapy, e.g., anti-tumor vaccine therapy, administered to a patient diagnosed with a B cell malignancy is predicted using an integrative model that evaluates at least three patient-specific parameters of the tumor, including parameters related to the makeup of lymphocyte subtypes in the tumor sample, the amino acid composition of the tumor idiotype, and the selection pressure on nucleotide sequences encoding the tumor idiotype. The specific input parameters to the integrative model include the proportion of activated memory CD4⁺ T-cells among a plurality of lymphocytes, e.g., among the overall lymphocyte population in the tumor sample; the number of tyrosine residues in an idiotype first complementarity determining region (CDR1) amino acid sequence; and the selection pressure on nucleotide sequences encoding idiotype CDRs. Based on these input parameters, the model stratifies the patient according to predetermined criteria (e.g., whether a patient's particular parameter value is above or below a threshold value for that parameter, etc.) for clinically beneficial response to an immunotherapy, e.g., anti-tumor vaccine therapy. It may be predicted that the patient will exhibit a clinically beneficial response to the anti-tumor vaccine therapy based on the integrative evaluation that the patient's parameter values satisfy all of the predetermined criteria.

[0059] As the evaluation of the three patient-specific parameters is an integrative evaluation, evaluation of any individual parameter alone or any combination of two parameters does not provide sufficient statistical power to predict whether a patient diagnosed with a B cell malignancy will exhibit a clinically beneficial response to an anti-tumor vaccine therapy, e.g., an idiotype vaccine therapy. In certain embodiments, the integrative evaluation relies on comparing the patient-specific parameters to thresholds for each parameter, and one or more parameter thresholds may depend on other parameter thresholds, as described herein.

[0060] In some embodiments, if any one of the patient's parameter values does not satisfy the corresponding predetermined criteria in the integrative evaluation, it is predicted that the patient will not exhibit a clinically beneficial response to the immunotherapy, e.g., anti-tumor vaccine therapy.

[0061 ] The patient-specific parameter used in the present method may include the makeup of lymphocyte subtypes in the tumor sample. Lymphocyte subtypes of interest associated with the tumor sample may include any number of lymphocytes that are distinguishable using any method, as described herein, e.g., flow cytometry, sequence analysis, etc. In certain embodiments, the lymphocyte subtypes are classified based on the types of cell surface receptors expressed by a lymphocyte, e.g., using flow cytometry. In some embodiments, the lymphocyte subtypes are classified based on the gene expression profile (GEP) of the bulk tumor sample and deconvoluting the GEP using a computational algorithm that enables identification of the different lymphocyte subtypes in the sample, e.g., using CIBERSORT (Figs. 2-4), as described in Newman et al., Nat Methods. 2015 12:453; and U.S. Provisional Application Ser. No. 61/106,601 , filed on January 22, 2015, which are incorporated herein by reference. Lymphocyte subtypes of interest include naive B cells, memory B cells, Plasma cells, CD8T cells, naive CD4 T cells, CD4 memory RO unactivated T cells, CD4 memory RO activated T cells, follicular helper T cells, regulatory T cells, gamma delta T cells, unstimulated NK cells, stimulated NK cells, Monocytes, Macrophages MO, Macrophages M1 , Macrophages M2, unstimulated Dendritic cells, stimulated Dendritic cells, unstimulated Mast cells, stimulated Mast cells, Eosinophils, and Neutrophils. In some embodiments, the lymphocyte subtypes of interest may be combined into broader subgroups that include two or more of the subtypes. Thus in some embodiments, the lymphocyte subgroup may include naive B cells, memory B cells, CD8T cells, naive CD4 T cells, CD4 memory RO unactivated T cells, CD4 memory RO activated T cells, NK cells, and Monocytes. In some embodiments, the lymphocyte subgroup may include B cells, CD8T cells, regulatory T cells, and CD4 T cells. In some embodiments, a measured T cell type is activated CD4 memory T cells, e.g. CD4 memory T cells, memory T cells, or CD4 T cells.

[0062] In certain embodiments, the makeup of lymphocyte subtypes in the tumor sample used as an input to the integrative model is the proportion of activated memory CD4⁺ T-cells among the overall lymphocyte population in the tumor sample. Thus, the proportion may be a ratio between an estimate of the amount of biological material, e.g., individual cells or mRNA, attributable to activated memory CD4⁺ T-cells and the total amount of biological material, e.g., individual cells or mRNA, attributable to any lymphocyte in the tumor sample. The proportion of activated memory CD4⁺ T-cells among the overall lymphocyte population in a tumor sample may vary depending on the patient from which the tumor sample was obtained, and may be in the range of 0 to 15%, e.g., 0 to 12%, including 2 to 10%.

[0063] The proportion of activated memory CD4⁺ T-cells among the overall lymphocyte population may be positively correlated with a patient's clinical response to an anti-tumor vaccine therapy. Thus, in the present integrative evaluation of the tumor sample parameters, a patient that is predicted to exhibit a clinically beneficial response to an anti-tumor vaccine therapy may have a proportion of activated memory CD4⁺ T-cells among the overall lymphocyte population in the tumor sample that is equal to or higher than a threshold proportion. The threshold proportion may be a predetermined threshold proportion, as described further below.

[0064] The patient-specific parameter used in the integrative model of the present method may include the amino acid composition of the tumor idiotype, and in particular may include the number of tyrosine residues in idiotype first complementarity determining region (CDR1) amino acid sequences. The number of tyrosine residues may be obtained from light chain and/or heavy chain CDR1 amino acid sequences of an idiotype. The CDR1 tyrosine enumeration may be generated from a biological sample using any convenient protocol. The CDR1 region in a tumor-specific idiotype may be delineated on the immunoglobulin protein itself, or on a coding sequence generated from tumor cell mRNA, cDNA, chromosomal DNA, etc., usually cDNA obtained from reverse transcription of tumor sample mRNA. Any suitable methods of delineating the CDR1 region may be used, e.g., as described above. As is conventional in the art, an in-frame TAT or TAC codon provides for a tyrosine residue. Suitable methods for enumerating CDR1 tyrosine residues are described in, e.g., U.S. App. Pub. No. 20140220562, which is incorporated herein by reference. The total number of CDR1 (light and heavy chain) tyrosine residues in a tumor idiotype may range from 0 to 8, e.g., 0 to 6, including 0 to 5.

[0065] The number of tyrosine residues in idiotype CDR1 amino acid sequences may be negatively correlated with a patient's clinical response to an anti-tumor vaccine therapy. Thus, in the present integrative evaluation of the tumor sample parameters, a patient that is predicted to exhibit a clinically beneficial response to an anti-tumor vaccine therapy may have a number of tyrosine residues in idiotype CDR1 amino acid sequences that is equal to or less than a threshold number. The threshold number of tyrosine residues may be a predetermined threshold number, as described further below.

[0066] The patient-specific parameter used in the integrative model of the present method may include the selection pressure on nucleotide sequences encoding the tumor idiotype, and in particular the selection pressure on nucleotide sequences encoding idiotype CDRs. The selection pressure on nucleotide sequences encoding idiotype CDR amino acid sequences may be measured using any suitable method that provides a quantitative measure or estimate of the strength of selection acting on the nucleotide sequences encoding idiotype CDR amino acid sequences. In some cases, the selective pressure represents a quantitative measure of a deviation of the frequency of non-synonymous mutations observed in the nucleotide sequences encoding idiotype CDR amino acid sequences relative to an expected frequency. A suitable method is described in, e.g., Yaari et al., 2012 Nucleic Acids Res. 40:e134, which is incorporated herein by reference. In general terms, the selection pressure, represented by sigma, may be quantified by 1) identifying observed and expected number of mutations in nucleotide sequences encoding idiotype CDR amino acid sequences; 2) estimating the non-synonymous-to-synonymous mutation frequency for a nucleotide sequence encoding the idiotype CDR amino acid sequence using a Bayesian model of the posterior probability of the non-synonymous mutation-to-synonymous mutation frequency; and 3) calculating a log odds ratio of the non- synonymous-to-synonymous mutation frequency in the nucleotide sequence encoding the idiotype CDR amino acid sequence normalized by its expected non-synonymous-to- synonymous mutation frequency in a paired germline nucleotide sequence encoding the immunoglobulin corresponding to the idiotype. The analysis (e.g., steps 2) and 3) above) may be repeated on multiple nucleotide sequences encoding idiotype CDR amino acid sequences from a tumor sample. By aggregating the results of the analysis over multiple nucleotide sequences encoding idiotype CDR amino acid sequences, a higher confidence and statistical power for the calculated selection pressure may be achieved. [0067] In certain embodiments, a more positive sigma value denotes positive selection and a higher observed frequency of non-synonymous mutations relative to expectation, and a more negative sigma value denotes negative selection and a lower observed frequency of non-synonymous mutations relative to expectation. In certain embodiments, the selection pressure is estimated over all (light and heavy chain) CDRs, except for CDR3, within nucleotide sequences encoding idiotype amino acid sequences in a tumor sample.

[0068] The selection pressure on nucleotide sequences encoding idiotype CDRs may be negatively correlated with a patient's clinical response to an anti-tumor vaccine therapy. Thus, in the present integrative evaluation of the tumor sample parameters, a patient that is predicted to exhibit a clinically beneficial response to an anti-tumor vaccine therapy may have a selection pressure on nucleotide sequences encoding idiotype CDRs that is equal to or less than a threshold selection pressure. The threshold selection pressure may be a predetermined threshold selection pressure, as described further below.

[0069] In general terms, idiotype CDR amino acid sequences and nucleotide sequences encoding such may be obtained by selective amplification of nucleic acids containing nucleotide sequences encoding an idiotype from mRNA extracted from the patient's tumor sample. Alternatively, deep sequencing of the mRNA may provide the nucleotide sequences of nucleic acids encoding an idiotype. Any other suitable method may be used.

[0070] In classifying a patient diagnosed with a B cell malignancy, the patient-specific parameters, as described above, may be evaluated in an integrative model, wherein the parameter values are compared against predetermined criteria for beneficial clinical response to anti-tumor vaccine therapy for each of the parameters. The predetermined criteria may include a predetermined threshold value. In some embodiments, the threshold value is chosen based on an analysis of tumor samples from a reference population of patients, wherein the patients are diagnosed with the B-cell malignancy and are treated with the anti-tumor vaccine therapy. The reference population of patients may be patients who were part of a clinical trial for the anti-tumor vaccine. In certain instances, the tumor samples are obtained before the patients of the reference population are treated with the anti-tumor vaccine therapy. Thus, in some embodiments, the present method includes determining the threshold values by analyzing tumor samples from patients who are diagnosed with the B- cell malignancy and are then treated with the anti-tumor vaccine therapy after obtaining the tumor samples. In order to select the threshold values, the number of patients from whom tumor samples are obtained may vary, and may be 40 or more, e.g., 45 or more, 50 or more, 55 or more, 60 or more, or 70 or more, and may be 200 or less, e.g., 100 or less, 80 or less, or 60 or less. In order to select the threshold values, the number of patients from whom tumor samples are obtained to determine the threshold values may be in the range of 40 to 300, e.g., 45 to 150, 50 to 100, or 55 to 80. In certain embodiments, in order to select the threshold values, the patients from whom tumor samples are obtained are in the range of 20 to 85 years old, e.g., 25 to 80 years old, including 26 to 78 years old. In certain embodiments, in order to select the threshold values, the patients from which tumor samples are obtained have a median age in the range of 40 to 60 years old, e.g., 43 to 58 years old, including 45 to 55 years old. Other clinical features of the patient population from which tumor samples may be obtained to determine the threshold values are provided in Table 1 , provided in Fig. 1 1 . In some cases, in order to select the threshold values, the reference patient population from which tumor samples are obtained includes individuals belonging to the same demographic group as the patient who is to be classified by the present method. In some cases, in order to select the threshold values, the reference patient population from which tumor samples are obtained may include individuals belonging to the same ethnic group and/or racial group and/or nationality as the patient who is to be classified by the present method. In some embodiments, in order to select the threshold values, the reference patient population from which tumor samples are obtained does not include individuals belonging to a different ethnic group and/or racial group and/or nationality as the patient who is to be classified by the present method.

[0071 ] The proportion of activated memory CD4⁺ T-cells among the overall lymphocyte population in tumor samples from patients who are diagnosed with the B-cell malignancy and are treated with the anti-tumor vaccine therapy may be positively correlated with the patients' clinical response to the anti-tumor vaccine therapy. In certain embodiments, the threshold value for the proportion of activated memory CD4⁺ T-cells among the overall lymphocyte population for use in the present integrative evaluation of a patient is the median proportion of a plurality of proportions of activated memory CD4⁺ T-cells among the overall lymphocyte population in tumor samples from the plurality of patients who are diagnosed with the B-cell malignancy and are treated with the anti-tumor vaccine therapy.

[0072] The number of tyrosine residues in idiotype CDR1 amino acid sequences in tumor samples from patients who are diagnosed with the B-cell malignancy and are treated with the anti-tumor vaccine therapy may be negatively correlated with the patients' clinical response to the anti-tumor vaccine therapy. In certain embodiments, the threshold value for the number of tyrosine residues in idiotype CDR1 amino acid sequences for use in the present integrative evaluation of a patient is the median number of a plurality of numbers of tyrosine residues in idiotype CDR1 amino acid sequences in tumor samples from patients who are diagnosed with the B-cell malignancy and are treated with the anti-tumor vaccine therapy. In certain embodiments, the threshold value for the number of tyrosine residues in idiotype CDR1 amino acid sequences for use in the present integrative evaluation of a patient is 2 or less, e.g., 1 or less. [0073] The selection pressure on nucleotide sequences encoding idiotype CDRs in tumor samples from patients who are diagnosed with the B-cell malignancy and are treated with the anti-tumor vaccine therapy may be negatively correlated with the patients' clinical response to the anti-tumor vaccine therapy. In certain embodiments, the threshold value for the selection pressure for use in the present integrative evaluation of a patient is selected by first determining the relative number of patients within each subset of the population of patients who are diagnosed with the B-cell malignancy, are treated with the anti-tumor vaccine therapy and evaluated according to the criteria for the number of tyrosine residues in idiotype CDR1 amino acid sequences and the proportion of activated memory CD4⁺ T-cells among the overall lymphocyte population, as described above. Then the threshold for the selection pressure on nucleotide sequences encoding idiotype CDRs may be selected based on the relative number of patients within the subset of patients that satisfy the criteria for the number of tyrosine residues in idiotype CDR1 amino acid sequences and the proportion of activated memory CD4⁺ T-cells among the overall lymphocyte population, and the subset of patients that do not satisfy either one of the criteria. In some embodiments, the threshold for the selection pressure on nucleotide sequences encoding idiotype CDRs may be selected to reduce or compensate for the skew in the relative number of patients within the subset of patients who satisfy the criteria for the number of tyrosine residues in idiotype CDR1 amino acid sequences and the proportion of activated memory CD4⁺ T-cells among the overall lymphocyte population, and the subset of patients who do not satisfy either one of the criteria. For instance, if the relative number of patients that satisfy the criteria for the number of tyrosine residues in idiotype CDR1 amino acid sequences and the proportion of activated memory CD4⁺ T-cells among the overall lymphocyte population, and number of patients that do not satisfy either one of the criteria is 1 :2, then the threshold for the selection pressure may be chosen to be the selection pressure that would include 2/3^rds of the patients who are diagnosed with the B-cell malignancy and are treated with the anti-tumor vaccine therapy for use as the selection pressure criterion for a clinically beneficial response to the anti-tumor vaccine therapy.

[0074] Thus, in order to select the threshold selection pressure, an ordered list of a plurality of selection pressures on nucleotide sequences encoding idiotype CDRs, obtained from the plurality of tumor samples from patients who are diagnosed with the B-cell malignancy and are treated with the anti-tumor vaccine therapy, maybe divided in a manner that reduces, or compensates for, a skew in the ratio between the number of tumor samples whose proportion of T-cells is equal to or higher than the threshold proportion and number of tyrosine residues is equal to or less than the threshold number, and the number of tumor samples whose proportion of T-cells is lower than the threshold proportion or the number of tyrosine residues is more than the threshold number, as described above. In other words, the threshold selection pressure may be selected such that, when the threshold selection pressure is used to dichotomize the plurality of tumor samples according to the plurality of selection pressures on nucleotide sequences encoding idiotype CDRs of the plurality of tumor samples, the resulting distribution of the number of patients whose measured selection pressures on nucleotide sequences encoding idiotype CDRs are equal to or below the threshold selection pressure and the number of patients whose measured selection pressures on nucleotide sequences encoding idiotype CDRs are above the threshold selection pressure compensates for, or reduces the skew, in the ratio between the number of tumor samples whose proportion of T-cells is equal to or higher than the threshold proportion and number of tyrosine residues is equal to or less than the threshold number, and the number of tumor samples whose proportion of T-cells is lower than the threshold proportion or the number of tyrosine residues is more than the threshold number, as described above. The degree of compensation or reduction in the skew may vary, and may be in the range of 30% to 180%, e.g., 50% to 150%, 75% to 125 %, including 90% to 1 10%. The degree of compensation may be 40% or more, e.g., 50% or more, 75% or more, 90% or more, 100% or more , and may be 170% or less, 150% or less, 120% or less, or 1 10% or less.

[0075] In some embodiments, the threshold selective pressure, measured by sigma as described above, may be 0.5 or less, e.g., 0.3 or less, 0.2 or less, 0.1 or less, and may be - 0.5 or more, e.g., -0.4 or more, -0.3 or more, including -0.28 or more. In some embodiments, the threshold selective pressure, measured by sigma as described above, may be in the range of 0.5 to -0.5, e.g., 0.4 to -0.4, 0.3 to -0.3, 0.1 to -0.1 , or may be in the range of -0.1 to -0.4, e.g. -0.15 to -0.35, -0.2 to -0.3, including -0.22 to -0.29.

[0076] The B cell malignancy diagnosed in a patient may be any lymphocytic malignancy of the B lineage associated with cell-surface expression of immunoglobulin (e.g., idiotype) on the malignant cells. Such a B cell malignancy may be responsive to active immunotherapy, including vaccination with a tumor-specific idiotype immunogen. In certain embodiments, the B cell malignancy is Non-Hodgkin's lymphoma (NHL). In certain embodiments, the B cell malignancy is follicular lymphoma (FL). In some embodiments, the B cell malignancy is chronic lymphocytic leukemia (CLL), lymphoblastic leukemia, small lymphocytic lymphoma, hairy cell leukemia, diffuse large B cell lymphoma , mucosa-Associated lymphatic tissue lymphoma (MALT); mantle cell lymphoma (MCL); Burkitt lymphoma; mediastinal large B cell lymphoma; Waldenstrom's macroglobulinemia; nodal marginal zone B cell lymphoma (NMZL); splenic marginal zone lymphoma (SMZL); intravascular large B-cell lymphoma; primary effusion lymphoma; lymphomatoid granulomatosis; histologically transformed aggressive lymphomas; multiple myeloma; plasmacytoma; cutaneous B-cell lymphoma; or plasmablastic lymphoma, etc. [0077] The patient-specific parameter of the tumor may be obtained by analyzing a sample of the tumor obtained from the patient. Samples can be obtained from the tissues or fluids of an individual. For example, samples can be obtained from whole blood, lymph or bone marrow biopsy, etc. The samples are collected any time after an individual is suspected of or diagnosed to have a B cell malignancy or has exhibited symptoms diagnostic of such a disease. For example, for a B-cell lymphoma patient, suitable tumor samples may be obtained, e.g., by surgical biopsy of an enlarged lymph node (LN) or other extranodal tissue involved by lymphoma, by fine needle aspiration (FNA) of an enlarged LN, by phlebotomy or aspirate of a patient whose blood or other fluids contains greater than about 5x10⁶ lymphoma cells/mL (quantified by manual differential); or bone marrow (BM) aspiration when the patient's BM contains greater than about 30% involvement (percentage of total inter- trabecular space). In certain embodiments, the sample is a biopsy sample. In additional embodiments, the sample includes less than about 50% malignant cells. In further embodiments, the sample includes less than about 10% malignant cells.

[0078] As the present method for classifying a patient is a predictive method, the sample, e.g., tumor biopsy sample, from the patient who is to be classified according to the present method may be obtained before the patient starts an anti-timuor vaccine therapy, e.g., before administration of the first immunization dose of the anti-tumor vaccine, before development of a personalized idiotype vaccine, etc.

[0079] In certain embodiments, the patient who is to be classified according to the present method has not been previously treated with an immunotherapy, such as a passive immunotherapy. In some embodiments, the patient who is to be classified according to the present method has been treated previously with one or more chemotherapeutic agents for the B cell malignancy.

[0080] Analyzing the tumor sample may be done by any convenient method for obtaining the patient-specific parameter values, as described above. The tumor sample may be analyzed histologically using any suitable histology stain and/or immunohistochemical methods. The sample may be dissociated, e.g., using a protease such as trypsin, and individual cells of the sample may be analyzed, e.g., using flow cytometry. In certain embodiments, flow cytometry may be used to obtain the number of different types of lymphocytes, including activated memory CD4⁺ T-cells, present in the tumor sample, and hence the proportion of any given lymphocyte subset, e.g., activated memory CD4⁺ T-cells, among the overall lymphocyte population in the tumor sample. Methods of preparing a sample of cells for flow cytometry analysis is described in, e.g., U.S. Pat. Nos. 5,378,633, 5,631 , 165, 6,524,858, 5,266,269, 5,017,497 and 6,549,876; U.S. App. Pub. Nos. US20120178098, US20080153170, 20010006787, US20080158561 , US20100151472, US20100099074, US20100009364, US20090269800, US20080241820, US20080182262, US20070196870 and US20080268494; PCT publication W099/54494; Brown et al (Clin Chem. 2000 46: 1221 -9), McCoy et al (Hematol. Oncol. Clin. North Am. 2002 16:229-43) and Scheffold J. Clin. Immunol. 2000 20:400-7) and books such as Carey et al (Flow Cytometry in Clinical Diagnosis, 4^th Edition ASCP Press, 2007), Ormerod (Flow Cytometry— A practical approach 3rd Edition. Oxford University Press, Oxford, UK 2000), Ormerod (Flow Cytometry 2nd Edition. BIOS Scientific Publishers, Oxford, UK 1999) and Ormerod (Flow Cytometry— A basic introduction 2009 Cytometry Part A 75A, 2009), each of which are incorporated by reference herein.

[0081 ] In some embodiments, the tumor sample may be analyzed to determine the nucleotide sequence of nucleic acids in the tumor sample. Such methods may include reverse transcription and amplification of tumor sample mRNA, followed by sequence analysis, including Sanger sequencing, deep sequencing, microarray analysis, or sequencing by hybridization, etc. Sequencing may include whole transcriptome sequence and/or targeted sequencing. Suitable methods are described in, e.g., in U.S. Patent Application Nos. 2005-0079510 and 2006-0040297; in Mitra et al., (2003) Analytical Biochemistry 320: 55-65; Shendure et al., (2005) Science 309: 1728-1732; in Margulies et al., (2005) Nature 437:376-380; Bentley et al, Nature 2008, 456:53-59; and in Shendure et al., 2008 Nature Biotechnology 26: 1 135, which are incorporated by reference. In some embodiments, sequencing methods, as described herein, may be used to determine parameters related to the makeup of lymphocyte subtypes in the tumor sample, e.g., the proportion of activated memory CD4⁺ T-cells among the overall lymphocyte population, the amino acid composition of the tumor idiotype, e.g., the number of tyrosine residues in an idiotype first complementarity determining region (CDR1) amino acid sequence, and the selection pressure on nucleotide sequences encoding idiotype CDRs.

[0082] Where the immunoglobulin protein itself is analyzed, a biochemical method may be applied to separate the CDR1 from the rest of the sequence, following by western blotting or mass spectroscopy to enumerate the tyrosines. Alternatively a specific modification to the tyrosines within CDR1 (eg, sulfonation, nitrosylation, phosphorylation, glycosylation) may be detected with a monoclonal antibody, or manifested as a spectral shift on MALDI or SELDI TOF mass spectrometry.

[0083] In certain embodiments, the patient-specific parameters of the tumor, e.g., the proportion of activated memory CD4⁺ T-cells among a plurality of lymphocytes, the number of tyrosine residues in an idiotype first complementarity determining region (CDR1) amino acid sequence, and the selection pressure on nucleotide sequences encoding idiotype CDRs may be obtained for the tumor sample by accessing one or more databases that includes the patient-specific parameters from an earlier analysis of the tumor sample. Thus, in some cases, an analysis of a tumor sample obtained from a patient who is to be classified according to the present method may have occurred at an earlier time and the result of the analysis may be recorded, e.g, in a database, a report, etc. The earlier analysis may include flow cytometry analysis of lymphocyte cell types in the sample, deep sequencing of expressed genes in the sample, expression profiling of the sample, e.g., using microarrays, and/or targeted sequencing of idiotype sequences in the sample.

[0084] The immunotherapy to which a patient's response is predicted may be an active immunotherapy, e.g., anti-tumor vaccine therapy. In some cases, the anti-tumor vaccine therapy is anti-idiotype vaccine therapy. A suitable anti-idiotype vaccine therapy for use in the present method is described in, e.g., U.S. App. Pub. No. 20140220562, which is incorporated herein by reference.

[0085] The clinical response to an immunotherapy may include any suitable measure of clinical outcome/endpoint in response to immunotherapy. In some embodiments, the clinical outcome is measured by progression-free survival (PFS), time to progression (TTP), time to subsequent therapy, response rate (complete or partial response), overall survival, disease free survival, and/or an immunological response to the immunotherapy. Suitable clinical outcomes/endpoints are described in, e.g., Johnson et al. (2003) J. Clin. Oncol. 21 (7): 1404; U.S. Food and Drug Administration guidance titled "Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologies", published May 2007 (UCM071590.pdf); and Cheson et al., J Clin Oncol. 2007. 25:579, which are incorporated herein by reference. Progression of disease may be measured by any convenient method, including measuring the size of a tumor using positron emission tomography (PET) or computed tomography (CT) scan, magnetic resonance imaging (MRI), etc., lymph node biopsy, and bone marrow evaluation, as summarized in, e.g., Cheson et al., J Clin Oncol. 1999. 17: 1244, which is incorporated herein by reference. In cases where the immunotherapy is an anti-tumor vaccine therapy, e.g., anti-idiotype vaccine therapy, the immunological response may be measured by a humoral anti-idiotype immunological response. Measurements of immunological response to the immunotherapy may include serum antibody titer to the vaccine target, e.g. anti-idiotype antibody titer as measured by, e.g., ELISA, as described in, e.g., U.S. App. Pub. No. 20140220562, which is incorporated herein by reference.

[0086] In some embodiments, the clinically beneficial response of a patient who receives an immunotherapy, e.g., an anti-tumor vaccine therapy, may be a clinical outcome that is better than the clinical outcome of a patient who does not receive the anti-tumor vaccine therapy. Thus, in some embodiments, the clinically beneficial response of a patient who receives the immunotherapy, e.g., anti-tumor vaccine therapy, may be a progression-free survival of the treated patient that is longer than the progression-free survival of a patient who does not receive the anti-tumor vaccine therapy. Progression may be local progression, regional progression, locoregional progression, or metastatic progression. The clinically beneficial response may be a progression-free survival for the treated patient that is longer by 6 months or more, e.g., 9 months or more, 12 months or more, 24 months or more, 36 months or more, or 48 months or more, than the progression-free survival for the untreated patient. The clinically beneficial response may be a progression-free survival for the treated patient that is longer by a range of 6 months to 10 years, e.g., 9 months to 5 years, or 1 to 4 years, compared to the progression-free survival for the untreated patient.

[0087] In some embodiments, the clinically beneficial response of a patient who receives the immunotherapy, e.g., anti-tumor vaccine therapy, may be the time to subsequent therapy of the treated patient that is longer than the time to subsequent therapy of a patient who does not receive the anti-tumor vaccine therapy. The clinically beneficial response may be a time to subsequent therapy for the treated patient that is longer by 6 months or more, e.g., 9 months or more, 12 months or more, 24 months or more, 36 months or more, or 48 months or more, than the time to subsequent therapy for the untreated patient. The clinically beneficial response may be a time to subsequent therapy for the treated patient that is longer by a range of 6 months to 10 years, e.g., 9 months to 5 years, or 1 to 4 years, compared to the time to subsequent therapy for the untreated patient.

[0088] In some embodiments, the clinically beneficial response of a patient who receives the immunotherapy, e.g., anti-tumor vaccine therapy, may be the overall survival of the patient that is longer than the overall survival of a patient who does not receive the anti-tumor vaccine therapy. The clinically beneficial response may be the overall survival for the treated patient that is longer by 6 months or more, e.g., 9 months or more, 12 months or more, 24 months or more, 36 months or more, or 48 months or more, than the overall survival for the untreated patient. The clinically beneficial response may be the overall survival for the treated patient that is longer by a range of 6 months to 10 years, e.g., 9 months to 5 years, or 1 to 4 years, compared to the overall survival for the untreated patient.

[0089] In some embodiments, the clinically beneficial response of a patient who receives the immunotherapy, e.g., anti-idiotype vaccine therapy, may be an antibody titer of an antiidiotype antibody specific to the patient that is higher, as measured by ELISA, in a post- immunization serum sample than a pre-immunization serum sample from the patient by 3 fold or more, e.g., 10 fold or more, 20 fold or more, 50 fold or more, 100 fold or more, 500 fold or more, or 1000 fold or more.

[0090] In some embodiments, the clinical response to immunotherapy, e.g., anti-tumor vaccine therapy, of a first patient who is predicted to exhibit a clinically beneficial response to the anti-tumor vaccine therapy, according to the present method, is a superior clinical outcome than the clinical outcome of a second patient who does not receive the anti-tumor vaccine therapy, regardless of whether the second untreated patient is predicted to exhibit a clinically beneficial response to the anti-tumor vaccine therapy. Thus, in some cases, the clinical response to immunotherapy, e.g., anti-tumor vaccine therapy, of a first patient who is predicted to exhibit a clinically beneficial response to the anti-tumor vaccine therapy, according to the present method, is a superior clinical outcome than the clinical outcome of a second patient who is predicted to exhibit a clinically beneficial response to the anti-tumor vaccine therapy but does not receive the anti-tumor vaccine therapy.

[0091] In certain embodiments, the prognosis of the patient whose disease, e.g., B cell lymphoma, has been diagnosed may be known according to a prognostic index. In some embodiments, the patient may be associated with a risk group based on the prognostic index. A suitable prognostic index includes, e.g., the international progrnostic index (I PI) such as the Follicular Lymphoma International Prognostic Index (FLIPI), as described in Solal-Celingy et al., Blood. 2004.104:1258; and Perea et al., Ann Oncol. 2005. 16:1508, which are incorporated herein by reference. In some embodiments, the present method of classifying a patient diagnosed with a B cell malignancy predicts a clinically beneficial response to an immunotherapy, e.g., anti-tumor vaccine therapy, independently of the patient's association with a risk group according to a prognostic index, e.g., the FLIPI, and/or according to other factors, such as sex.

Method for Treating

[0092] An aspect of the present disclosure includes a method of treating a patient diagnosed with a B-cell malignancy by first classifying the patient whether the patient is predicted to exhibit a clinically beneficial response to an anti-tumor vaccine, e.g., an idiotype vaccine, using the method of classifying as described herein, and then administering the anti-tumor vaccine to the patient, if the patient is predicted to exhibit a clinically beneficial response to the anti-tumor vaccine therapy based on at least the three patient-specific parameters, as described above.

[0093] Treating a patient with an idiotype vaccine may be achieved in any suitable method, such as that describes in U.S. App. Pub. No. 20140220562, which is incorporated herein by reference. In general, the treating may include administering an immunogenic composition containing at least a portion of the immunoglobulin from the B lineage lymphoma cell surface (i.e., the idiotype). In certain embodiments, a patient is treated with immunogenic compositions to induce the patient's immune system to produce a specific immune response to a malignancy. In some embodiments, the immunogenic composition used in active immunotherapy includes one or more antigens derived from a patient's malignant cells. In some embodiments, the immunogenic composition includes at least an idiotypic portion of an immunoglobulin derived from a subject's own malignant cell(s). For example, B-cell lymphoma cells have on their surface particular immunoglobulins. These immunoglobulins, particularly the idiotypic portions ("idiotypic proteins") can be used as antigens in immunogenic compositions to produce patient-specific idiotypic vaccines. In certain embodiments, the idiotypic proteins are produced recombinantly. In some embodiments, particular individual recombinant idiotypic proteins are selected for use, while in other embodiments, multiple, tumor-specific idiotypic proteins are used in a multivalent composition (see, e.g., U.S. Pat. No. 5,972,334 to Denney, issued Oct. 25, 1999, incorporated by reference herein in its entirety). In certain embodiments, the idiotypic protein is a recombinant idiotype (Id) immunoglobulin (Ig) derived from a patient's B-cell lymphoma, for example an IgG with either a kappa (κ) or a lambda (λ) light chain, obtained from each patient. In some embodiments, the immunogenic composition includes the same heavy and light chain V region sequences expressed by the patient's tumor.

[0094] In certain embodiments, the idiotypic protein is conjugated to a carrier, e.g., a protein using any suitable techniques. Materials that are commonly chemically coupled to the antigens e.g., to enhance antigenicity, include keyhole limpet hemocyanin (KLH), thyroglobulin (THY), bovine serum albumin (BSA), ovalbumin (OVA), tetanus toxoid (TT), diphtheria toxoid, and tuberculin purified protein derivative. In some embodiments, KLH manufactured under Current Good Manufacturing Practice (cGMP) conditions is obtained from, e.g., Biosyn Arzneimittel GmbH and used for the preparation of Id-KLH conjugates. The immune response to injected Id-KLH can be further enhanced by co-injecting granulocyte-macrophage colony stimulating factor (GM-CSF), which functions as an immunological adjuvant. In phase II trials, patients mounting anti-Id humoral immune responses had longer freedom from progression and improved survival compared to patients who did not mount such responses.

[0095] In some embodiments, a cytokine is linked to the idiotypic protein. In certain embodiment, the immunogenic composition produced includes a fusion protein containing the idiotypic protein and a cytokine such as GM-CSF, interleukin-2 (IL-2) or IL-4 (see, e.g., PCT International Application PCT/US93/09895, Publication No. WO 94/08601 and Tao and Levy (1993) Nature 362:755 and Chen et al. (1994) J. Immunol. 153:4775; all of which are herein incorporated by reference). Generally in such fusion proteins, sequences encoding the desired cytokine are added to the 3' end of sequences encoding the idiotypic protein.

[0096] In some embodiments, the antibodies are conjugated to various radiolabels for both diagnostic and therapeutic purposes. Radiolabels allow "imaging" of tumors and other tissue, as well helping to direct radiation treatment to tumors. Exemplary radiolabels include, but are not limited to, ¹³¹ l, ¹²⁵l, ¹²³l, ⁹⁹Tc, ⁶⁷Ga, ¹¹¹ ln, ¹⁸⁸Re, ¹⁸⁶Re, and ⁹⁰Y.

[0097] In certain embodiments, the subject has measurable tumor burden prior to treatment and exhibits at least a 25% reduction in tumor burden after treatment (e.g. at least 25%, 30%, 40% or between 25-40%). In other embodiments, the subject has a measurable tumor burden prior to treatment and exhibits at least a 50% reduction in tumor burden after treatment (e.g. at least 50%, 60%, 70%, 80%, or 90%). In some embodiments, the treatment results in less than 25% depletion of normal B cells in the subject (e.g., less than 25%, less than 20%, less than 15%, less than 10% or less than 5%). In particular embodiments, the treatment results in less than 15% depletion of normal B cells in the subject.

[0098] Active immunotherapy, e.g., anti-tumor vaccine, may be administered as part of a chemotherapeutic program (e.g. CVP (cyclophosphamide, vincristine, prednisone) or CHOP (CVP plus doxorubicin), whether before or after. The active immunotherapy may also be administered before, after or with cytokines, GM-CSF, or IL-2 (See, e.g., U.S. Pat. No. 6,455,043, herein incorporated by reference).

[0099] The idiotypic immunogen may be administered by any suitable means, including parenteral, non-parenteral, subcutaneous, topical, intraperitoneal, intrapulmonary, intranasal, and intralesional administration (e.g., for local immunosuppressive treatment). Parenteral infusions include, but are not limited to, intramuscular, intravenous, intra-arterial, intraperitoneal, or subcutaneous administration. In addition, antibodies are suitably administered by pulse infusion, particularly with declining doses. In some instances, the dosing is given by injections, such as intravenous or subcutaneous injections, depending in part on whether the administration is brief or chronic.

[00100] Dosage regimens may be adjusted to provide the optimum desired response (e.g., a therapeutic or prophylactic response). For example, a single bolus may be administered, several divided doses may be administered over time or the dose may be proportionally reduced or increased as indicated by the exigencies of the therapeutic situation. Parenteral compositions may be formulated in dosage unit form for ease of administration and uniformity of dosage. The dosages of the idiotypic immunogens are generally dependent on (a) the unique characteristics of the active compound and the particular therapeutic or prophylactic effect to be achieved, and (b) the limitations inherent in the art of compounding such an active compound for the treatment of sensitivity in individuals.

[00101] An exemplary, non-limiting range for a therapeutically or prophylactically effective amount of an idiotypic immunogen is 0.01-20 mg, e.g., 0.1-10 mg, including 0.5 to 2 mg. It is to be noted that dosage values may vary with the type and severity of the condition to be alleviated. It is to be further understood that for any particular subject, specific dosage regimens should be adjusted over time according to the individual need and the professional judgment of the person administering or supervising the administration of the compositions, and that dosage ranges set forth herein are exemplary only and are not intended to limit the present invention. The dosage administered will, of course, vary depending upon known factors such as the pharmacodynamic characteristics of the particular agent, its mode and route of administration, the age, health, and weight of the recipient, the nature and extent of symptoms, the kind of concurrent treatment, the frequency of treatment, and the effect desired. For example, a dosage of active ingredient can be about 0.01 to 10 milligrams per kilogram of body weight every four weeks.

[00102] The immunogen can be incorporated into pharmaceutical compositions suitable for administration to a subject. For example, the pharmaceutical composition may include an immunogen and a pharmaceutically acceptable carrier. As used herein, "pharmaceutically acceptable carrier" includes solvents, dispersion media, coatings, antibacterial and antifungal agents, isotonic and absorption delaying agents, and the like that are physiologically compatible. Examples of pharmaceutically acceptable carriers include one or more of the following: water, saline, phosphate buffered saline, dextrose, glycerol, ethanol and the like, as well as combinations thereof. In some cases isotonic agents, for example, sugars, polyalcohols such as mannitol, sorbitol, or sodium chloride may be included in the composition. Pharmaceutically acceptable carriers may further contain minor amounts of auxiliary substances such as wetting or emulsifying agents, preservatives or buffers, which enhance the shelf life or effectiveness of the antibodies of the present invention.

[00103] The compositions may be in a variety of forms. These include, for example, liquid, semi-solid and solid dosage forms, such as liquid solutions (e.g., injectable and infusible solutions), dispersions or suspensions, tablets, pills, powders, liposomes and suppositories. The specific form depends on the intended mode of administration and therapeutic application. Compositions may be in the form of injectable or infusible solutions, such as compositions similar to those used for passive immunization of humans with other antibodies.

[00104] Therapeutic compositions typically are sterile and stable under the conditions of manufacture and storage. The composition can be formulated as a solution, microemulsion, dispersion, liposome, or other ordered structure suitable to high drug concentration. Sterile injectable solutions can be prepared by incorporating the active compound (i.e., antibody or antibody fragment) in the required amount in an appropriate solvent with one or a combination of ingredients enumerated above, as required, followed by sterile filtration. Generally, dispersions are prepared by incorporating the active compound into a sterile vehicle that contains a basic dispersion medium and the required other ingredients from those enumerated above. In the case of sterile powders for the preparation of sterile injectable solutions, the methods of preparation may include vacuum drying and freeze- drying that yield a powder of the active ingredient plus any additional desired ingredient from a previously sterile-filtered solution thereof. The proper fluidity of a solution can be maintained, for example, by the use of a coating such as lecithin, by the maintenance of the required particle size in the case of dispersion and by the use of surfactants. Prolonged absorption of injectable compositions can be brought about by including in the composition an agent that delays absorption, for example, monostearate salts and gelatin. [00105] In certain embodiments, the active compound may be prepared with a carrier that will protect the compound against rapid release, such as a controlled release formulation, including implants, transdermal patches, and microencapsulated delivery systems. Biodegradable, biocompatible polymers can be used, such as ethylene vinyl acetate, polyanhydrides, polyglycolic acid, collagen, polyorthoesters, and polylactic acid. Any suitable methods for the preparation of such formulations may be used (see, e.g., Sustained and Controlled Release Drug Delivery Systems, J. R. Robinson, ed., Marcel Dekker, Inc., New York, 1978).

[00106] The pharmaceutical compositions of the invention may include a "therapeutically effective amount" or a "prophylactically effective amount" of an immunogen. A "therapeutically effective amount" refers to an amount effective, at dosages and for periods of time necessary, to achieve the desired therapeutic result. A therapeutically effective amount of the antibody or antibody fragment may vary according to factors such as the disease state, age, sex, and weight of the individual, and the ability of the antibody or antibody fragment to elicit a desired response in the individual. A therapeutically effective amount is also one in which any toxic or detrimental effects of the antibody or antibody fragment are outweighed by the therapeutically beneficial effects. A "prophylactically effective amount" refers to an amount effective, at dosages and for periods of time necessary, to achieve the desired prophylactic result. Typically, since a prophylactic dose is used in subjects prior to or at an earlier stage of disease, the prophylactically effective amount will be less than the therapeutically effective amount.

[00107] In certain embodiments, the present method of treating a patient includes administering a non-vaccine therapy (e.g., chemotherapy, radiotherapy, passive immunotherapy and/or stem cell therapy) to the patient, if the patient is predicted not to exhibit a clinically beneficial response to an anti-tumor vaccine therapy, e.g., idiotype vaccine therapy. Exemplary chemotherapy may include CVP (cyclophosphamide, vincristine, prednisone), CHOP (CVP plus doxorubicin), FMC (fludarabine, mitoxantrone, and cyclophosphamide), bendamustine, or purine analogs (e.g., thalidomide), etc.; passive immunotherapy may include administering an anti-C20 monoclonal antibody (e.g., rituximab); stem cell therapy may include autologous stem cell transplantation; and combinations thereof. Suitable therapeutic regimens are described in, e.g., Maloney. N Engl J Med. 2012 366(21):2008; and Lim et al., Haematologica. 2010 95(1): 13, which are incorporated herein by reference. In some embodiments, the patient that is predicted not to exhibit a clinically beneficial response to an anti-tumor vaccine therapy belongs to a high risk group, according to a prognostic index, e.g., an IPI as discussed above, and the method includes administering a non-vaccine therapy to the patient. Computer-Implemented Method

[00108] Also provided here in is a computer implemented method for carrying out the method of classifying a patient, as described in detail above. In an implementation of the present computer implemented method, a user may select one or more files on a computer system to be analyzed, wherein the one or more files include sequence-specific data of a plurality of genes expressed in a tumor sample obtained from a patient diagnosed with a B-cell malignancy. The sequence-specific data may then be analyzed to determine at least three patient-specific parameters of the tumor sample, including the proportion of activated memory CD4⁺ T-cells among a plurality of lymphocytes, the number of tyrosine residues in idiotype CDR1 amino acid sequences, and the selection pressure on nucleotide sequences encoding idiotype CDRs. The patient is then classified as to whether the patient will exhibit a clinically beneficial response to an anti-tumor vaccine therapy based on an integrative evaluation of the patient-specific parameters, e.g., the proportion of T-cells, the number of tyrosine residues, and the selection pressure determined in the tumor sample, as discussed in detail above. The result of the integrative evaluation and/or classification is then provided to the user as an output.

[00109] The sequence-specific data may include any sequence-specific data that is suitable for determining the patient-specific parameters, as described above. In certain embodiments, the sequence-specific data includes a gene expression profile (GEP) of the bulk tumor sample and nucleotide sequences that contain sequences encoding idiotype CDR amino acid sequences. In some embodiments, analyzing the sequence-specific data includes using the GEP of the bulk tumor sample and deconvoluting the GEP using a computational algorithm that enables identification of the different lymphocyte subtypes in the sample, e.g., using CIBERSORT, as described above, to obtain the proportion of activated memory CD4⁺ T-cells among a plurality of lymphocytes.

[00110] In some embodiments, the classifying includes predicting that the patient will exhibit a clinically beneficial response to an anti-tumor vaccine therapy based on an integrative evaluation that the proportion of T-cells is equal to or higher than a threshold proportion, the number of tyrosine residues is equal to or lower than a threshold number, and the selection pressure is equal to or less than a threshold selection pressure. The threshold values (e.g., threshold proportion, threshold number of tyrosine residues, and the threshold selection pressure) may be predetermined threshold values. In certain embodiments the threshold number of tyrosine residues is 2 or less, e.g., 1 or less. In certain embodiments, the threshold values are specified in a reference file that contains one or more of the threshold proportion of T-cells, the threshold number of tyrosine residues, and the threshold selection pressure for classifying the patient, wherein the reference data are from a plurality of patients who are diagnosed with the B-cell malignancy and are treated with the anti-tumor vaccine therapy. In certain instances, the tumor samples are obtained before the patients are treated with the anti-tumor vaccine therapy.

[00111] In some embodiments, the threshold values are determined based on a reference data set containing the proportion of T-cells, the number of tyrosine residues, and the selection pressure determined in tumor samples from patients who are diagnosed with the B-cell malignancy and treated with the anti-tumor vaccine therapy. In some embodiments, the threshold values are determined based on a reference data set containing sequence- specific data that include a gene expression profile of tumor samples and nucleotide sequences containing sequences encoding idiotype CDR amino acid sequences for patients who are diagnosed with the B-cell malignancy and are treated with the anti-tumor vaccine therapy. In such cases, the present computer-implemented method may include analyzing the sequence-specific data to obtain the proportion of T-cells, the number of tyrosine residues, and the selection pressure determined in tumor samples from patients who are diagnosed with the B-cell malignancy and treated with the anti-tumor vaccine therapy. The threshold values may then be determined by identifying significant associations between each parameter and the outcome of the anti-tumor vaccine therapy for each patient, as described herein.

[00112] The result of the integrative evaluation and/or classification may be provided as an output to a user in any convenient form. In some embodiments, the output is provided on a user interface, a print out, in a database, etc. and the output may be in the form of a table, graph, a report etc.

[00113] A computer system for implementing the present computer-implemented method may include any arrangement of components as is commonly used in the art. The computer system may include a memory, a processor, input and ouput devices, a network interface, storage devices, power sources, and the like. The memory or storage device may be configured to store instructions that enable the processor to implement the present computer-implemented method by processing and executing the instructions stored in the memory or storage device.

[00114] Also provided herein is a non-transitory computer-readable storage medium containing instructions, executable by at least one processing device, that when executed cause the processing device to perform the computer-implemented method of classifying a patient, as described above.

Utility

[00115] The present methods find use in various applications where it is desirable to predict whether a patient whether a patient diagnosed with a B-cell malignancy will exhibit a clinically beneficial response to an anti-tumor vaccine therapy. The present method of classifying a patient may provide clinicians with a means to determine whether a patient diagnosed with a B cell malignancy will respond to an anti-tumor vaccine therapy before starting the therapy, without the need for additional intervention and time for assessment of response. This will aid the clinician in determining which treatment regimens among many would be most effective for the patient. The present methods may also find use in identifying patients who will most likely benefit from personalized idiotype vaccination and improve the precision of clinical trials for anti-tumor vaccines.

Examples

[001 16] The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention, and are not intended to limit the scope of what the inventors regard as their invention nor are they intended to represent that the experiments below are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to numbers used (e.g. amounts, temperature, etc.) but some experimental errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, molecular weight is weight average molecular weight, temperature is in degrees Celsius, and pressure is at or near atmospheric. Standard abbreviations may be used, e.g. , bp, base pair(s) ; kb, kilobase(s) ; pi, picoliter(s); s or sec, second(s); min, minute(s); h or hr, hour(s); aa, amino acid(s) ; kb, kilobase(s) ; bp, base pair(s); nt, nucleotide(s); i.m. , intramuscular(ly) ; i.p. , intraperitoneal(ly) ; s.c , subcutaneous(ly) ; and the like.

Example 1 : Genomic Signatures for Integrative Models of Clinical Heterogeneity in

Patients with Follicular Lymphoma

[001 17] Patients with Follicular Lymphoma (FL), the most common type of indolent Non- Hodgkin's Lymphoma (NHL) , exhibit striking heterogeneity in response to chemotherapy, immune responses to vaccination, propensity for disease progression, and risk of death from disease. Here, it was hypothesized that diverse cells of the tumor microenvironment, along with inherited traits of the host and mutations arising in FL tumors, including somatic variation in B-cell antigen receptors, interact and influence the clinical behavior of FL, including responses to various therapies. This hypothesis was tested by studying specimens from a large cohort of patients with previously untreated FL, who were part of a recently completed randomized phase I I I controlled clinical trial testing MyVax®, a novel idiotype- based therapy [NCT00017290], administered by Genitope Corporation. Recombinant idiotype (Id), a personalized vaccine derived from the immunoglobulin sequences, was made from each patient's tumor, conjugated to keyhole limpet hemocyanin (KLH) and mixed with granulocyte-macrophage colony-stimulating factor (GM-CSF). The trial compared this specific vaccine to non-specific immunotherapy, KLH, also mixed with GM-CSF. Although the clinical trial failed to meet its primary endpoint, patients that received a vaccine and mounted a specific immune response had significantly better outcomes than controls. Thus, with these specimens, models that integrate existing clinical indices with host and tumor genotypes (including immunoglobulin sequence determinants) and gene expression phenotypes that predict objective responses to idiotype vaccination and chemotherapy, and that influence survival were defined and validated. The results of the study enable identification of patients with FL most likely to benefit from chemotherapy and personalized idiotype vaccination, thereby increasing the precision of future FL clinical trials.

Identification of Predictive Signatures

[00118] To identify genetic determinants of response to immunotherapy, 204 of 287 randomized Genitope patients (Table 1 , provided in FIG. 11) whose tumors yielded total cellular RNA of sufficient quality and quantity for microarray analysis were selected. These 204 patients were subsequently divided into training (n=96) and validation cohorts (n=108) balanced for relevant clinical variables (Table 1 , provided in FIG. 1 1), and the former subset was hybridized to Affymetrix® HGU133 Plus 2.0 chipsets to generate Gene Expression Profiles (GEPs). Expression normalization was performed with the MAS5 algorithm (affy package v.1.26 of Bioconductor v.1.8 in R 2.15.1), using a custom CDF (Chip Definition File), which updates and maps array oligonucleotides to Entrez® gene identifiers. All analyses in this section relate to GEPs in the training set, which consists of 33 control patients receiving non-specific immunotherapy (KLH) and 57 patients receiving MyVax®, comprised of 23 responders and 34 non-responders, as determined by Id-specific humoral immune response.

[00119] To delineate genes distinguishing MyVax® responders and non-responders, standard differential expression analysis using a two-sided t-test with unequal variance, controlled for False Discovery Rate (FDR), was first performed. No significant differentially expressed genes were found at an FDR<10%, and only 1 gene, ANKFY1 , was found at an FDR<25%, suggesting that an approach to identify gene sets with skewed representation across the transcriptome might be more appropriate. Gene Set Enrichment Analysis (GSEA) was therefore used to analyze responding versus non-responding phenotypes using MSigDB collections, however no gene sets reached statistical significance (i.e., FDR<25%). These data indicated that meaningful expression differences, if any, between responders and non-responders, may reflect functional heterogeneity limited to subsets of patients and/or admixed contributions from distinct tumor-associated cell types.

[00120] To assess the former possibility, AutoSOME, an unsupervised clustering method that requires no user assumptions about cluster number or structure, was used. Application of AutoSOME to the vaccination arm identified a variety of expression clusters, primarily capturing genes associated with vascularization and stromal cells, cell proliferation and migration, and malignant B-cells (Fig. 1). However, because expression signatures defining these clusters were found in both responders and non-responders, it was hypothesized that their component genes primarily reflect tumor physiological heterogeneity, not factors related to Id response (Fig. 1).

[00121] Figure 1. Clustered gene expression patterns are heterogeneous and do not distinguish Id responders from non-responders. Selected clusters derived from patients in the microarray training cohort are rendered as a heat map, with orange denoting higher gene expression and blue lower expression relative to each gene. All gene expression values are median-centered across samples and depicted in log2 space. AutoSOME was used for unsupervised cluster analysis with a p-value threshold of 0.05 and 100 ensemble iterations. Prior to clustering, the data matrix was adjusted using unit variance normalization of samples, median centering of genes, and sum of squares=1 normalization applied to genes and samples. ToppFun was used for functional annotation.

[00122] To address this hypothesis, and to determine whether any gene exhibits expression patterns correlated with progression free survival (PFS) in response to Id vaccination, univariate Cox proportional-hazards regression was applied to each measured gene in the training cohort. To be considered a candidate biomarker for Id response, a gene was required to be significantly correlated with PFS in MyVax® patients, but not controls. While 635 of 14,893 evaluable genes satisfied this condition at a nominal p-value threshold of 0.05, no genes remained significant after correction for multiple hypothesis testing (FDR<25%). This suggested that cellular heterogeneity in FL tumor samples might be obscuring biological signals predictive of Id response and benefit, and that an approach to unmix tumor- associated cells, such as leukocytes, from bulk GEPs might be worth pursuing.

[00123] Indeed, the immune system represents an emerging hallmark of cancer, and infiltration of tumors by specific immune cell subsets can be linked with tumor growth, cancer progression and outcome. Since the immune system also determines response to vaccination, it was hypothesized that levels of specific tumor-associated leukocytes (TALs) might be predictive of FL therapeutic outcomes. Because TALs are admixed within FL bulk tumor biopsies, and may not be discernable by common GEP analytical techniques, a novel computational method, called CIBERSORT, was devised, which addresses limitations of previous approaches to estimate fractions of distinct TALs within bulk tumor expression data (Fig. 2). As input, CIBERSORT uses cell-specific "barcode" GEPs, whose widespread availability in the public domain allowed us to build a leukocyte signature matrix consisting of 22 diverse populations, including 7 T-cell types, naive and memory B-cells, plasma and NK cells, and 10 diverse myeloid populations (including M1/M2 macrophages). Testing and benchmarking showed that CIBERSORT has high sensitivity and specificity, and performs comparably to flow cytometry when applied to solid tissues, as demonstrated with lung adenocarcinoma and adjacent normal tissues (Fig. 3, panel a) and FL specimens (Fig. 3, panel b). Moreover, application of CIBERSORT across solid tumors and hematopoietic cancers, including FL, confirmed known associations between immune populations and outcomes, but also generated a variety of new predictions, a subset of which were confirmed experimentally (see also, Example 2, below).

[00124] Figure 2. Anecdotal workflow illustrating the use of CIBERSORT for deconvolving bulk tumor gene expression data to yield relative proportions of TALs, which are subsequently tested for association with cancer clinical outcomes.

[00125] Figure 3. CIBERSORT benchmarking experiments, (a) Comparison between flow cytometry (FCM) and GEP deconvolution (CIBERSORT) for enumeration of leukocyte populations in lung adenocarcinoma tumor and paired normal lung control specimens. Median fractions of CD4+, CD8+, CD19+, CD56+, and CD14+ populations measured by FCM were normalized by overall CD45+ content. For comparison with CIBERSORT, leukocyte signature matrix populations (see Identification of Predictive Signatures: SOW, Goal 2 in Body) were grouped into the same cluster of differentiation categories. 'Remaining' denotes unclassifiable signature matrix populations versus the remaining CD45+ fraction enumerated by FCM. Mean CIBERSORT-inferred fractions for lung adenocarcinoma and adjacent normal GEPs were determined using two publicly available microarray data sets, GSE7670 and GSE10072. (b) Paired analysis of 7 FL specimens from the Genitope trial comparing FCM to CIBERSORT for CD19+ and CD4+/CD8+ populations.

[00126] CIBERSORT was applied to the Genitope GEP training cohort (96 subjects) in order to enumerate levels of 22 leukocyte populations in bulk FL tumor specimens. While CIBERSORT correctly inferred high fractions of malignant Bcells in FL, no significant differences in relative TAL levels were observed between responders, non-responders, and controls (Fig. 4, panel a). Univariate Cox regression was therefore used to relate TAL levels to progression free survival (PFS) within each cohort, and distinct leukocyte subsets, namely γδ T cells, CD8 T cells, and activated CD4 memory T cells, were found to be significantly associated with survival in patients receiving personalized vaccination (Fig. 4b), but not controls. This was consistent with a predictive (and not simply a prognostic) relationship (Fig. 4, panel b). To further assess whether any of the 3 lymphocyte subsets presage a therapeutic benefit from MyVax®, the 3 lymphocyte subsets were dichotomized according to their respective median frequencies, and compared resulting Kaplan Meier survival curves across MyVax® responders and nonresponders, and controls (Fig. 4, panel c). While all 3 populations significantly stratified PFS, only activated CD4 memory T cells were associated with a considerable PFS benefit in Id responders (Fig. 4c, bottom panel). Thus, these initial data indicated that CD4 memory T cells may represent a promising biomarker for predicting anti-tumor benefit elicited by Id vaccination.

[00127] Figure 4. Analysis of 22 leukocyte subsets in FL tumors identifies candidates associated with response to idiotypic vaccination, (a) Estimated mRNA fractions of 22 leukocyte subsets across Id responders (MyVax® R), Id non-responders (MyVax® NR), and controls from the training cohort, pooled into 1 1 immune populations for clarity. Fractions are presented as means for each leukocyte subset, (b) Association between PFS and inferred levels of 22 leukocyte subsets across patients in the training cohort. Z-scores represent the standardized output of univariate Cox regression (Wald test), with negative Z-scores and positive Z-scores reflecting favorable and adverse associations with survival, respectively. Vertical dotted lines denote a significance threshold of 0.05 (i.e., |Z-score|>1.96). (c) Kaplan Meier plots showing survival curves for each patient group in panel A further binned into High and Low subgroups by the median fraction of the indicated cell type across all evaluable patients in the training cohort. Only leukocytes reaching significance in panel B are shown.

Characterization of B-cell Receptor Differences

[00128] Immunoglobulin somatic mutations that reflect differences in normal and pathological B-cells, and that are associated with FL therapeutic response were identified. Total RNA was isolated from Genitope tumor biopsies and cDNA was amplified using primers specific for the appropriate heavy chain or light chains. Reverse transcription and polymerase chain reaction (RT-PCR) were performed using family-specific Ig variable and constant region primers, and cDNAs were directly sequenced by Sanger methodology, and confirmed by repeat RT-PCR. The corresponding cloned sequences used for generating the monoclonal Id vaccines were also analyzed separately, and used to validate all results.

[00129] To quantify specific sequence features as they relate to clinical outcomes, Id cDNA sequences for heavy (IgH) and light (Igk or Igl) chains using the IMGT VQUEST tool with Junction Analysis were analyzed. The variable regions were partitioned into their seven structurally defined sub-regions (Framework regions 1 through 4 [FR1/2/3/4], and Complementarity Determining Regions 1 through 3 [CDR1/2/3]), and for each region, d the frequency of every possible amino acid were enumerated. Then, as a preliminary assessment, differential distribution in amino acid content between patients mounting and failing to mount an idiotype humoral immune response was tested for. For further consideration, a given amino acid was required to achieve statistical significance (i.e., P<0.05) among vaccinated patients in both training and validation cohorts. The former consisted of patients in the microarray training set (as described above) plus all additional Id- receiving patients not included in the microarray validation set due to low RNA quality and/or yield (n=14 responders and 36 non-responders). [00130] Among all 20 possible amino acids, a single sequence feature satisfied the significance criteria, namely the frequency of tyrosine content in the CDR1 region of the idiotype (combined heavy and light chains; training, P=0.03; validation, P=0.002; all evaluable patients, P=0.0008; twosided t test with unequal variance; Fig. 5, panel a). To correlate with PFS, tyrosine frequency in CDR1 was split by its median value (n=2) in the training set. Of note, the median ld-CDR1-Y content was also 2 in the validation cohort, and in a larger cohort of 707 lymphoma patients (including patients in this study [NCT00017290] and three Phase II studies [NCT00071955, NCT00302861 , NCT00004197]). Dichotomized ld-CDR1-Y was significantly associated with PFS in the vaccination arm, but not the control arm in both the training cohort (Figs. 5, panels b, c respectively) and validation cohort.

[00131] Figure 5. An idiotype sequence determinant associated with response to personalized vaccination, (a) For each amino acid residue (rows), its frequency was enumerated across the structurally defined CDR and Framework Regions (FR or FWR) in each patient's Id sequence (columns), considering the full patient-derived Id vaccine sequence (combining the heavy and light genes. Individual amino acids were then assessed for differential distribution among all patients mounting immune responses or failing to mount them. Statistical significance was estimated using a two-sided t test with unequal variance, and depicted as gradations of color corresponding to the negative logarithm of nominal p- values within the shaded table and inset key. (b) Higher CDR1 tyrosine frequency is associated with inferior progression-free survival (PFS) among patients in the training cohort receiving MyVax. Kaplan-Meier curves depict patients stratified by CDR1 tyrosine count relative to the population median (CDR1 Tyr High [3-5 tyrosines] versus CDR1 Tyr Low [0-2 tyrosines), with the log-rank test used to test significance of separation, (c) Among patients receiving control immunotherapy in the training cohort, CDR1 tyrosine frequency is not significantly associated with PFS.

[00132] Whether selection pressure differentially impacts patient Id sequences in relation to PFS and therapeutic response was also addressed. All patients with the same Vgene were pooled and BASELINe (http(colon)//selection(dot)med(dot)yale(dot)edu/baseline/), a Bayesian method for assessment of antigen driven selection in immunoglobulin sequences, was used. The tool statistically quantifies the evidence for either positive selection pressure (positive sigma) or for B cell receptor (BCR) preservation (i.e., no selection; negative sigma), returning both significance estimates (p-values) and selection magnitude (sigma).

[00133] Briefly, BASELINe employs a Bayesian framework to calculate the posterior probability of replacement frequency (pi) in the CDR and FWR regions of Ig sequences (i.e., fraction of non-synonymous base changes over all point mutations), and accounts for empirically defined rates of (i) relative mutability for each trinucleotide (ii) the probability that a mutation will fall in either CDR or FWR, and (iii) the probability that a base change will result in a nonsynonomous mutation. Selection strength (sigma) is then defined as the log odds ratio of the replacement to silent frequency in an immunoglobulin sequence (pi) normalized by its expected replacement to silent frequency in paired germline (pi hat). When sigma is positive, the replacement to silent frequency is higher than expected, indicating positive selection, whereas when sigma is negative, replacement to silent frequency is lower than expected, indicating negative selection (i.e., BCR preservation).

[00134] To determine sigma for the CDR of each immunoglobulin sequence, the following steps were performed: (i) define the related germline V-gene and allele using IMGT/V-Quest, (ii) run BASELINe on the V gene with paired germline using the following settings: FASTA input, ignore CDR3 (FR1-FR3), and otherwise default parameters.

[00135] Running BASELINe with default parameters (i.e., IMGT without CDR3), no evidence for either a continuous or dichotomous (median split) association between CDR selective pressure (measured by sigma) and PFS was found, regardless of how patients were stratified (i.e., all subjects, MyVax®, including responders versus non-responders, and KLH controls). Furthermore, no association with PFS was found even when restricting the analysis to Id sequences with a significant nominal p-value for selection pressure (<0.05). While BCR preservation was correlated with better PFS, the association was not significant. This suggests that BCR selection pressure alone is not a major independent factor influencing PFS in the randomized arm of the Genitope trial.

Integrative Modeling

[00136] Integration of the aforementioned candidate biomarkers was explored. Specifically, among patients in the CD4 memory T-cell high subset (defined by median split), those with low CDR1 tyrosine content (i.e., 0-2 tyrosines; also dichotomized by median frequency) were selected. These patients were designated "Mem T cell-Tyr" high, whereas all remaining patients were considered "Mem T cell- Tyr" low. Within the training set, this discrete model was significantly associated with PFS in patients receiving MyVax® (Fig. 6), but not control immunotherapy, and outperformed either index when assessed separately (data not shown). Intriguingly, patients estimated to have high levels of CD4 memory T-cells were found to have significantly lower tyrosine content than other patients (P<0.0001 ; two sided Wilcoxon rank sum test), suggesting a possible biological mechanism relating the two factors (see also, Example 3, below).

[00137] Figure 6. Kaplan Meier plot depicting patients in the vaccination arm of the GEP training cohort stratified by a model combining inferred activated CD4 memory T cell frequency and Id CDR1 tyrosine content (denoted "Mem T cell-Tyr"). All p-values were calculated using the log-rank test.

[00138] While a significant relationship between selection pressure and PFS was not found in univariate models, a modest relationship between BCR preservation and longer PFS in the MyVax® group was observed. Therefore, whether selection pressure might synergize with the other model components was tested. Owing to the 2:1 skew between low and high groups in the "Mem T cell-Tyr" model (due to intersecting 2 median splits), the degree of additional skew was minimized by using a 2:1 split for selection pressure. The range of sigmas calculated by BASELine (detailed above) in the original training cohort was analyzed to determine the threshold, and the negative selection group (2/3 of the training cohort) was intersected with the "Mem T cell-Tyr" high group, creating a new discrete integrative model, termed CD4-Y-SP ("Activated memory CD4 T cell high + Y low + Selection Pressure low).

[00139] The threshold for sigma was determined by ordering it in the training cohort from low to high (negative to positive selection), and taking the threshold that divides the list into two groups, respectively containing the lower 2/3 of sigma values containing only Igs under negative selection and the upper 1/3 of sigma values containing the remaining Igs, including those under positive selection. The resulting threshold was -0.274. Of note, while the validation set was not used to define this cut point, when the same 2:1 split was applied to the validation cohort, the sigma threshold was comparable (-0.24), indicating good balance between the two groups. Integration of selection pressure into a new 3-component composite predictor was performed by intersecting patients in the high group from the 2- component model (i.e., CD4 T cells and tyrosines) with patients whose sigma falls below the threshold established in the training set (e.g., -0.274). All remaining patients were assigned to the low group, including those patients who were in the high group of the 2-component model but whose sigma exceeded the training threshold.

[00140] Applied to the training cohort, the CD4-Y-SP binary model significantly stratified patients in the MyVax® but not control groups. Moreover, it outperformed the "Mem T cell- Tyr" model in the MyVax® cohort, achieving both a lower Logrank p-value (0.002 compared to 0.003, respectively), and a better hazard ratio (0.28 versus 0.39, respectively). To further evaluate CD4-Y-SP, a series of additional analyses were performed, detailed below.

Validation

[00141] To validate the "CD4-Y-SP" integrative model, its performance was tested on the GEP validation cohort. As observed for the training set, CD4-Y-SP was significantly associated with PFS in the MyVax® but not control arm in the validation cohort (Fig. 7, panel a). CD4-Y-SP also achieved both lower p-values and better hazard ratios than a two- component ("Mem T cell-Tyr") model. CD4-Y-SP was further tested by simulating a new risk- adapted clinical trial, in which patients were screened in silico by CD4-Y-SP prior to randomization. In this context, evaluable patients in the MyVax® group exhibited significantly longer PFS than those in the non-specific immunotherapy group (Fig. 7, panel a; MyVax® High versus Control High). Results were similar when assessing the training and validation sets together (Fig. 7, panel b). Notably CD4-Y-SP was also found to be a significant independent contributor to PFS in the MyVax® but not control arm in a multivariate analysis incorporating gender and the FL International Prognostic Index (FLIPI) (P<0.002, Log-rank test). Finally, despite a significant association with PFS in the vaccination but not control groups, the two-component (Mem T cell-Tyr) model did not reach significance in a simulated trial. These data indicate that the CD4-Y-SP model is superior to "Mem T cell- Tyr" and represents a potentially promising integrative biomarker for identifying FL patients likely to respond to and benefit from idiotypic vaccination.

[00142] Figure 7. An integrative model for predicting benefit from Id vaccination, (a) Kaplan Meier plot depicting patients in the vaccination and control arms of the GEP validation cohort stratified by a model combining inferred activated CD4 memory T cell frequency, Id CDR1 tyrosine content, and BCR preservation (denoted "CD4-Y-SP"). (b) Same as panel a, but applied to the entire GEP cohort. All p-values were calculated using the log-rank test and are calculated with respect to the MyVax High group.

Conclusion

[00143] A phase I I I clinical trial administered by Genitope Corporation tested antitumor vaccination for FL patients, but failed to meet its primary endpoint [NCT00017290]. While no difference in PFS was found between the specific arm receiving MyVax® and the nonspecific vaccine arm receiving KLH, those patients that received a vaccine and mounted a specific immune response had significantly better outcomes than patients in the control arm. These results strongly suggested that the specific immune responses were of therapeutic benefit, and not simply a non-specific prognostic biomarker that identifies patients already destined to have superior outcomes. Nonetheless, a biomarker predicting therapeutic benefit from idiotypic vaccination would be preferable if it did not require additional intervention and time for the assessment of an evoked response. A central goal of this research was to identify such a predictive biomarker.

[00144] Gene expression profiling and preliminary Id sequence analysis were used to build and internally validate an integrative model of FL therapeutic response. Because traditional methods relating gene expression to survival proved unsuccessful, a novel computational approach for inferring cellular composition of unpurified tumor GEPs, called CIBERSORT, was developed. This new method was largely independent of approaches relying on cell isolation, preservation, and reagent quality, all of which remain difficult to standardize across large numbers of tumors. CIBERSORT thus facilitates an unbiased systematic assessment of cell content and of host/tumor interactions across diverse tumors.

[00145] Using CIBERSORT, effector memory T-cells were identified as predictive of superior PFS in patients receiving active immunotherapy. Further, a significant relationship between the frequency of CDR1 tyrosines and immunotherapeutic response was identified, which points to a distinct biochemical feature of Ids that may influence their immunogenicity and therapeutic potential. A higher frequency of tyrosine residues in CDR1 appears to result in FL idiotypes that are less immunogenic as active immunotherapies. Accordingly, such idiotypes might also be less immunogenic prior to active vaccination and possibly less subject to immunosurveillance, suppressing infiltration of beneficial immune cells, such CD4 lymphocytes. For those patients whose tumors harbor high tyrosine frequency in CDR1 , a low tyrosine vaccine could be engineered using targeted mutagenesis.

[00146] In conclusion, when considered together, tumor infiltrating activated CD4 memory T cells, Id CDR1 tyrosine content, and BCR selection pressure, represent a new biomarker will have utility for guiding future trial design in relation to testing active immunotherapy. Such an approach could be used to identify patients predicted to benefit therapeutically from vaccination, and may have implications for other targeted therapies in diverse cancers.

Example 2: Validation of CIBERSORT

[00147] To further validate these results, an extensive technical assessment of CIBERSORT was performed, evaluating its performance against other methods on simulated and real biological mixtures, including 14 disaggregated FL lymph node biopsies from the Genitope trial.

[00148] A significant next step for the field is to achieve the capability for deep deconvolution, defined as the ability to simultaneously estimate the relative proportion of numerous cell types in complex GEP mixtures, even those of rare frequency. To evaluate CIBERSORT's capability for deconvolution of the cell subsets in the leukocyte signature matrix, the integrated immune reference profiles was first validated by analyzing external datasets of purified leukocyte subsets profiled in the signature matrix. CIBERSORT correctly classified 93% of datasets into distinct cell phenotypes, confirming the cell type specificities of signature matrix genes (Fig. 8). Flow cytometry was then used to enumerate nearly half of the 22 subsets in the leukocyte signature matrix from PBMC samples (n=27 subjects), including closely related cell types and subsets of rare frequency (e.g., FOXP3+ Tregs and activated memory CD4 T cells). Applied to paired GEP samples, CIBERSORT results were compared with four other deconvolution methods— PERT, quadratic programming (QP), linear least squares regression (LLSR), and robust linear regression (RLR). Additionally, all 5 deconvolution methods were evaluated on CD4 and CD8 T cells, and B cells enumerated by flow cytometry in 14 Genitope tumor biopsies with matching GEPs.

[00149] Figure 8. Application of a leukocyte signature matrix to deconvolution of 208 arrays of distinct variably purified leukocyte subsets. Confirming the cell type specificities of genes in the signature matrix, CIBERSORT correctly classifying 93% of datasets into distinct cell phenotypes. [00150] Leukocyte levels inferred by CIBERSORT were significantly correlated with flow cytometry for 92% of cell subsets tested (12 of 13), including 100% of cell subsets with median frequencies over 5% (e.g., all 3 FL subsets) and 4 of 5 subsets with median frequencies below 5% (e.g., Tregs and activated memory CD4 T cells) (Fig. 9, panels a-c). By contrast, the next best-performing method yielded significant correlations for no more than 46% of tested phenotypes (6 of 13), including only 1 of 3 cell types in FL (Fig. 9, panel d). These results demonstrate that CIBERSORT represents a significant improvement over previous methods for deconvolution of complex RNA mixtures from primary human samples. By evaluating CIBERSORT's accuracy for individual phenotypes, these new findings also constitute an important step toward validating the association between activated memory CD4 T cell levels and response to idiotypic vaccination in FL.

[00151] Figure 9. Deep deconvolution and enumeration of individual cell subsets in 41 human subjects, (a-c) Direct comparison between CIBERSORT and flow cytometry with respect to: (a) eight immune subsets in PBMCs from 20 subjects, (b) FOXP3+ Tregs in PBMCs from another set of 7 subjects, and (c) three immune subsets, including malignant B cells, in tumor biopsies from 14 subjects with FL. (d) Comparison of five expression-based deconvolution methods on the datasets analyzed in a-c. The shaded gray area denotes deconvolved cell types that significantly correlated with flow cytometry (P<0.05). In three instances, correlation coefficients could not be determined; these were assigned a value of zero for inclusion in this panel. Data are presented as means ± standard deviations.

Example 3: Validation of Mem T cell-Tyr integrative model

[00152] To internally validate the Mem T cell-Tyr integrative model, microarray GEPs for most of the remaining randomized patients were generated, using methodology described for the GEP training cohort (Example 1 , above). The GEP validation cohort consisted of 36 control patients receiving non-specific immunotherapy (KLH-KLH) and 71 patients receiving MyVax®, comprised of 36 responders and 35 non-responders. As observed for the training set, the Mem T cell-Tyr integrative model was significantly associated with PFS in the MyVax®, but not control arm (Fig. 10, panel a). Notably, this model remained significant in multivariate analyses incorporating gender and the FL International Prognostic Index (FLIPI), both in training (P=0.02) and validation (P=0.03) sets. Moreover, the relationship between the "Mem T cell-Tyr" model and PFS was even more significant when assessed across all evaluable patients (Table 1 , provided in Fig. 1 1), with patients in the "Mem T cell-Tyr" high subset showing superior PFS compared to controls (Fig. 10, panel b). Finally, when all patient subsets were stratified into "Mem T cell-Tyr" high and low subsets, those patients mounting an Id response within the former subset showed a considerably better PFS than remaining patients, including all Id responders considered as a group (Fig. 10, panel c). [00153] Figure 10. An integrative model for Id vaccination candidate biomarkers. (a) Kaplan Meier plot depicting patients in the vaccination arm of the GEP validation cohort stratified by a model combining inferred activated CD4 memory T cell frequency and Id CDR1 tyrosine content (denoted "Mem T cell-Tyr"). (b) Same as panel a, showing patients from panel b and Figure 6 combined. For reference, a survival curve for patients receiving non-specific immunotherapy ("Control"; dashed gray line) is also plotted, but was not used to calculate statistical significance of curve separation, (c) Kaplan Meier plot showing all patient treatment and response groups further divided into High and Low subgroups according to the "Mem T cell-Tyr" model. For reference, all patients with a measurable response to MyVax® are also shown ("MyVax R"; dashed gray line; this curve was not used for significance testing). All p-values were calculated using the log-rank test.

[00154] While the present invention has been described with reference to the specific embodiments thereof, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the true spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation, material, composition of matter, process, process step or steps, to the objective, spirit and scope of the present invention. All such modifications are intended to be within the scope of the claims appended hereto.

Claims

Claims What is claimed is:

1 . A method of classifying a patient, comprising:

i) determining, for a tumor sample obtained from a patient diagnosed with a B-cell malignancy:

a) the proportion of activated memory CD4⁺ T-cells among a plurality of lymphocytes; b) the number of tyrosine residues in idiotype first complementarity determining region (CDR1 ) amino acid sequences; and

c) the selection pressure on nucleotide sequences encoding idiotype CDRs; and ii) predicting whether the patient will exhibit a clinically beneficial response to an antitumor vaccine therapy based on an integrative evaluation of the proportion of T-cells, the number of tyrosine residues, and the selection pressure determined for the tumor sample.

2. The method according to claim 1 , wherein the selection pressure comprises a quantitative measure of a deviation of the frequency of non-synonymous mutations observed in the nucleotide sequences encoding idiotype CDR amino acid sequences relative to an expected frequency.

3. The method according to claim 1 or 2, wherein the selection pressure is quantified based on an analysis comprising:

a Bayesian model of the posterior probability of the non-synonymous-to-synonymous mutation frequency that provides an estimate of the non-synonymous-to-synonymous mutation frequency for a nucleotide sequence encoding an idiotype CDR amino acid sequence; and

a log odds ratio of the non-synonymous-to-synonymous mutation frequency in the nucleotide sequence encoding the idiotype CDR amino acid sequence normalized by its expected non-synonymous-to-synonymous mutation frequency in a paired germline nucleotide sequence encoding the immunoglobulin corresponding to the idiotype,

wherein the log odds ratio is used to quantify the selection pressure.

4. The method according to any of claims 1 -3, wherein the predicting step comprises predicting that the patient will exhibit the clinically beneficial response to the antitumor vaccine therapy based on an integrative evaluation that the proportion of T-cells is equal to or higher than a threshold proportion, the number of tyrosine residues is equal to or less than a threshold number, and the selection pressure is equal to or less than a threshold selection pressure.

5. The method according to claim 4, wherein the threshold number of tyrosine residues is 2 or less.

6. The method according to claim 4 or 5, wherein the threshold proportion of T- cells, the threshold number of tyrosine residues, and the threshold selection pressure are based on an analysis of a plurality of tumor samples from a plurality of patients diagnosed with the B-cell malignancy, wherein the plurality of patients is treated with the anti-tumor vaccine therapy.

7. The method according to claim 6, wherein the threshold proportion of T-cells is the median proportion of a plurality of proportions of activated memory CD4⁺ T-cells among a plurality of lymphocytes in at least a subset of the plurality of tumor samples, or higher.

8. The method according to claim 6 or 7, wherein the threshold number is the median number of a plurality of numbers of tyrosine residues in idiotype CDR1 amino acid sequences in at least a subset of the plurality of tumor samples, or less.

9. The method according to any of claims 6-8, wherein the threshold selection pressure, when used to dichotomize the plurality of tumor samples according to the plurality of selection pressures on nucleotide sequences encoding idiotype CDRs of the plurality of tumor samples, reduces a skew in the ratio between:

the number of tumor samples whose proportion of T-cells is equal to or higher than the threshold proportion and number of tyrosine residues is equal to or less than the threshold number; and

the number of tumor samples whose proportion of T-cells is lower than the threshold proportion or the number of tyrosine residues is more than the threshold number.

10. The method according to any of claims 1-9, wherein the threshold selection pressure has a sigma value in the range of 0.3 to -0.3.

1 1. The method according to any of claims 1-10, wherein the tumor sample is a sample obtained prior to beginning the anti-tumor vaccine therapy.

12. The method according to any of claims 1-10, wherein the tumor sample is obtained from a patient who has previously not been treated for the B-cell malignancy with an immunotherapy.

13. The method according to any of claims 1 -10, wherein the determining step comprises using one or more of flow cytometry, sequencing, and microarray analysis, on the tumor sample obtained from the patient.

14. The method according to claim 13, wherein the determining step i-a) comprises using flow cytometry or analyzing a gene expression profile of the tumor sample to obtain the proportion of activated memory CD4⁺ T-cells among the plurality of lymphocytes.

15. The method according to claim 14, wherein the analyzing comprises:

obtaining the gene expression profile from the tumor sample; and

computing the proportion of activated memory CD4⁺ T-cells based on the tumor sample gene expression profile and a plurality of cell type-specific gene expression profiles.

16. The method according to claim 13, wherein the determining step i-c) comprises quantitatively measuring a deviation of the frequency of non-synonymous mutations observed in the nucleotide sequences encoding idiotype CDR amino acid sequences relative to an expected frequency.

17. The method according to claim 16, wherein the determining step i-c) comprises:

sequencing a plurality of nucleotide sequences encoding a plurality of idiotype amino acid sequences;

estimating the non-synonymous-to-synonymous mutation frequency for a nucleotide sequence encoding the idiotype CDR amino acid sequence using a Bayesian model of the posterior probability of the non-synonymous mutation-to-synonymous mutation frequency in the nucleotide sequence encoding an idiotype CDR amino acid sequence; and

calculating a log odds ratio of the non-synonymous-to-synonymous mutation frequency in the nucleotide sequence encoding the idiotype CDR amino acid sequence normalized by its expected non-synonymous-to-synonymous mutation frequency in a paired germline nucleotide sequence encoding the immunoglobulin corresponding to the idiotype, thereby quantitatively measuring the selection pressure on a nucleotide sequence encoding the idiotype CDR amino acid sequence.

18. The method according to any of claims 1 -17, wherein the clinically beneficial response is a stronger humoral anti-idiotype immunological response, a longer progression- free survival, a longer overall survival and/or a longer time to subsequent therapy compared to a control subject who has not received the anti-tumor vaccine therapy.

19. The method according to any of claims 1 -18, wherein the B-cell malignancy is follicular lymphoma, chronic lymphocytic leukemia, lymphoblastic leukemia, small lymphocytic lymphoma, hairy cell leukemia, diffuse large B cell lymphoma , mucosa- Associated lymphatic tissue lymphoma (MALT); mantle cell lymphoma (MCL); Burkitt lymphoma; mediastinal large B cell lymphoma; Waldenstrom macroglobulinemia; nodal marginal zone B cell lymphoma (NMZL); splenic marginal zone lymphoma (SMZL) ; intravascular large B-cell lymphoma; primary effusion lymphoma; lymphomatoid granulomatosis; histologically transformed aggressive lymphomas; multiple myeloma; plasmacytoma; cutaneous B-cell lymphoma; or plasmablastic lymphoma.

20. The method according to any of claims 1 -19, wherein the anti-tumor vaccine therapy is idiotype vaccine therapy.

21 . A method for treating a patient diagnosed with a B-cell malignancy, comprising:

i) classifying a patient diagnosed with a B-cell malignancy according to the method of any of claims 1 -20; and

ii) administering the anti-tumor vaccine to the patient, wherein the patient is classified as a patient predicted to exhibit a clinically beneficial response to the anti-tumor vaccine therapy.

22. The method according to claim 21 , wherein the anti-tumor vaccine is an idiotype vaccine.

23. The method according to claim 21 , wherein the method further comprises administering a non-vaccine therapy to the patient, wherein the patient is classified as a patient predicted not to exhibit a clinically beneficial response to the anti-tumor vaccine therapy.

24. A computer-implemented method of classifying a patient, comprising:

i) accessing one or more files on a computer system, wherein the one or more files comprise sequence-specific data of a plurality of genes expressed in a tumor sample obtained from a patient diagnosed with a B-cell malignancy;

ii) analyzing the sequence-specific data to determine, for the sample: a) the proportion of activated memory CD4⁺ T-cells among a plurality of lymphocytes; b) the number of tyrosine residues in idiotype CDR1 amino acid sequences; and c) the selection pressure on nucleotide sequences encoding idiotype CDRs;

iii) classifying whether the patient will exhibit a clinically beneficial response to an anti-tumor vaccine therapy based on an integrative evaluation of the proportion of T-cells, the number of tyrosine residues, and the selection pressure determined for the tumor sample; and

iv) outputting the integrative evaluation and/or the classification.

25. The method according to claim 24, wherein the sequence-specific data comprise a gene expression profile of the tumor sample and nucleotide sequences comprising sequences encoding idiotype CDR amino acid sequences.

26. The method according to claim 24 or 25, wherein the selection pressure comprises a quantitative measure of a deviation of the frequency of non-synonymous mutations observed in the nucleotide sequences encoding idiotype CDR amino acid sequences relative to an expected frequency.

27. The method according to any of claims 24-26, wherein the selection pressure on nucleotide sequences encoding idiotype CDRs is quantified based on an analysis comprising:

a Bayesian model of the posterior probability of the non-synonymous mutation-to- synonymous mutation frequency that provides an estimate of the non-synonymous-to- synonymous mutation frequency for a nucleotide sequence encoding a idiotype CDR amino acid sequence; and

wherein the log odds ratio is used to quantify the selection pressure.

28. The method according to any of claims 24-27, wherein the classifying step comprises predicting that the patient will exhibit the clinically beneficial response to an antitumor vaccine therapy based on the integrative evaluation that the proportion of T-cells is equal to or higher than a threshold proportion, the number of tyrosine residues is equal to or lower than a threshold number, and the selection pressure is equal to or less than a threshold selection pressure.

29. The method according to claim 28, wherein the method comprises accessing a reference file that comprises reference data specifying one or more of the threshold proportion of T-cells, the threshold number of tyrosine residues, and the threshold selection pressure for classifying the patient, wherein the reference data is obtained from a plurality of patients diagnosed with the B-cell malignancy, wherein the plurality of patients diagnosed with the B-cell malignancy has been treated with the anti-tumor vaccine therapy.

30. The method according to any of claims 24-29, wherein the anti-tumor vaccine is an idiotype vaccine.

31. A non-transitory computer-readable storage medium comprising: instructions, executable by at least one processing device, that when executed cause the processing device to perform the computer-implemented method according to claim 24.