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WO2016197131A1 - Méthode de prédiction d'une réponse clinique à une immunothérapie anticancéreuse - Google Patents

Méthode de prédiction d'une réponse clinique à une immunothérapie anticancéreuse Download PDF

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WO2016197131A1
WO2016197131A1 PCT/US2016/036065 US2016036065W WO2016197131A1 WO 2016197131 A1 WO2016197131 A1 WO 2016197131A1 US 2016036065 W US2016036065 W US 2016036065W WO 2016197131 A1 WO2016197131 A1 WO 2016197131A1
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idiotype
patient
tumor
cells
selection pressure
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Arash Ash Alizadeh
Aaron M. NEWMAN
Ronald Levy
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Leland Stanford Junior University
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Leland Stanford Junior University
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    • G—PHYSICS
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    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56966—Animal cells
    • G01N33/56977—HLA or MHC typing
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
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    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
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    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6806—Determination of free amino acids
    • G01N33/6812—Assays for specific amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/30—Unsupervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • C—CHEMISTRY; METALLURGY
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers
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    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • 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.
  • NHLs Non-Hodgkin Lymphomas
  • 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.
  • Id a clonotypic surface immunoglobulin expressed by malignant B-cells
  • Id idiotype 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.
  • KLH keyhole limpet hemocyanin
  • 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.
  • 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.
  • 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.
  • 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 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.
  • the threshold number of tyrosine residues is 2 or less.
  • 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.
  • 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.
  • 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.
  • 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.
  • the threshold selection pressure is in the range of 0.3 to -0.3.
  • 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.
  • the determining step may include using one or more of flow cytometry, sequencing, and microarray analysis, on the tumor sample obtained from the patient.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • MALT mantle cell lymphoma
  • Burkitt lymphoma mediastinal large B cell lymph
  • the anti-tumor vaccine therapy may be an idiotype vaccine therapy.
  • 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.
  • the anti-tumor vaccine is an idiotype vaccine.
  • 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.
  • 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
  • the sequence-specific data include a gene expression profile of the tumor sample and nucleotide sequences containing sequences encoding idiotype CDR amino acid sequences.
  • the anti-tumor vaccine is an idiotype vaccine.
  • 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.
  • 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.
  • 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.
  • Id idiotype vaccine
  • FIG. 2 is a collection of images schematically depicting a workflow for using
  • FIG. 3A-3B are a collection of graphs showing benchmarking results of CIBERSORT against flow cytometry.
  • 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.
  • 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.
  • FIG. 6 is a graphs showing progression free survival (PFS) in two stratified cohorts of patients receiving MyVax®.
  • 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.
  • FIG. 8 is an image showing CIBERSORT benchmarking experiments.
  • 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.
  • 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.
  • FIG. 1 1 shows a table showing clinical features of randomized Genitope patients, according to embodiments of the present disclosure.
  • polynucleotide refers 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.
  • 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.
  • loci defined from linkage analysis, exons, introns, messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA, ribozymes, small interfering RNA, (siRNA), microRNA (mi
  • LNA often referred to as inaccessible RNA
  • LNA nucleotide 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.
  • an oligonucleotide having a nucleotide sequence encoding a polypeptide 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.
  • 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.
  • 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.
  • 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).
  • 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.).
  • 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.)
  • the term "purified” or “to purify” refers to the removal of contaminants from a sample.
  • 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.
  • 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.
  • 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 10 6 , at least 10 7 , at least 10 8 or at least 10 9 or more members.
  • 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.
  • 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 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.
  • 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.
  • 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.
  • antibody 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).
  • CDRs complementarity determining regions
  • FR framework regions
  • Each variable region contains 3 CDRs, designated CDR1 , CDR2 and CDR3.
  • Each variable region also contains 4 framework sub-regions, designated FRI, FR2, FR3 and FR4.
  • antibody fragments refers to a portion of an intact antibody.
  • 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.
  • CDR complementarity determining region
  • CDRL1 light chain variable region
  • CDRL2 CDRL3
  • CDRH1 heavy chain variable region
  • IMGT IMGT designations are used (see Brochet et al. (2008) Nucleic Acids Res.
  • 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.
  • framework refers to the residues of the variable region other than the CDR residues as defined herein.
  • L 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).
  • IMGT IMGT.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • active immunotherapy 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.
  • the immunogenic composition used in active immunotherapy includes one or more antigens derived from a subject's malignant cells.
  • 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.
  • selection pressure 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.
  • 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.
  • 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
  • a reduced frequency of non-synonymous mutations observed relative to expectation may indicate negative selection (i.e., preservation of the B cell receptor).
  • Predicting 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the lymphocyte subtypes are classified based on the types of cell surface receptors expressed by a lymphocyte, e.g., using flow cytometry.
  • 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.
  • GEP gene expression profile
  • 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.
  • the lymphocyte subtypes of interest may be combined into broader subgroups that include two or more of the subtypes.
  • 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.
  • the lymphocyte subgroup may include B cells, CD8T cells, regulatory T cells, and CD4 T cells.
  • a measured T cell type is activated CD4 memory T cells, e.g. CD4 memory T cells, memory T cells, or CD4 T cells.
  • 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.
  • 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%.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • the analysis may be repeated on multiple nucleotide sequences encoding idiotype CDR amino acid sequences from a tumor sample.
  • a higher confidence and statistical power for the calculated selection pressure may be achieved.
  • a more positive sigma value denotes positive selection and a higher observed frequency of non-synonymous mutations relative to expectation
  • a more negative sigma value denotes negative selection and a lower observed frequency of non-synonymous mutations relative to expectation.
  • 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.
  • 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.
  • 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.
  • 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.
  • deep sequencing of the mRNA may provide the nucleotide sequences of nucleic acids encoding an idiotype. Any other suitable method may be used.
  • the patient-specific parameters 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.
  • 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.
  • the tumor samples are obtained before the patients of the reference population are treated with the anti-tumor vaccine therapy.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 .
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the B cell malignancy is Non-Hodgkin's lymphoma (NHL).
  • the B cell malignancy is follicular lymphoma (FL).
  • 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.
  • CLL chronic lymphocytic leukemia
  • MALT mucosa-Associated lymphatic tissue lymphoma
  • 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.
  • 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.
  • 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 5x10 6 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).
  • the sample is a biopsy sample.
  • the sample includes less than about 50% malignant cells.
  • the sample includes less than about 10% malignant cells.
  • the sample e.g., tumor biopsy sample
  • the sample 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • sequencing methods 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.
  • 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
  • CDR1 idiotype first complementarity determining region
  • 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.
  • a specific modification to the tyrosines within CDR1 eg, sulfonation, nitrosylation, phosphorylation, glycosylation
  • 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.
  • 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.
  • the immunotherapy to which a patient's response is predicted may be an active immunotherapy, e.g., anti-tumor vaccine therapy.
  • 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.
  • the clinical response to an immunotherapy may include any suitable measure of clinical outcome/endpoint in response to immunotherapy.
  • 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.
  • PFS progression-free survival
  • TTP time to progression
  • response rate complete or partial response
  • overall survival disease free survival
  • immunological response to the immunotherapy 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.
  • 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.
  • 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.
  • 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.
  • the clinically beneficial response of a patient who receives an immunotherapy may be a clinical outcome that is better than the clinical outcome of a patient who does not receive the anti-tumor vaccine therapy.
  • the clinically beneficial response of a patient who receives the immunotherapy e.g., 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.
  • the clinically beneficial response of a patient who receives the immunotherapy 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.
  • the clinically beneficial response of a patient who receives the immunotherapy 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.
  • the clinically beneficial response of a patient who receives the immunotherapy 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.
  • the clinical response to immunotherapy e.g., anti-tumor vaccine therapy
  • a first patient who is predicted to exhibit a clinically beneficial response to the anti-tumor vaccine therapy 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.
  • the clinical response to immunotherapy e.g., anti-tumor vaccine therapy
  • the clinical response to immunotherapy 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.
  • the prognosis of the patient whose disease, e.g., B cell lymphoma, has been diagnosed may be known according to a prognostic index.
  • 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.
  • I PI international progrnostic index
  • FLIPI Follicular Lymphoma International Prognostic Index
  • 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.
  • an immunotherapy e.g., anti-tumor vaccine therapy
  • a prognostic index e.g., the FLIPI
  • other factors such as sex.
  • 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.
  • an anti-tumor vaccine e.g., an idiotype vaccine
  • 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.
  • 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).
  • a patient is treated with immunogenic compositions to induce the patient's immune system to produce a specific immune response to a malignancy.
  • the immunogenic composition used in active immunotherapy includes one or more antigens derived from a patient's malignant cells.
  • the immunogenic composition includes at least an idiotypic portion of an immunoglobulin derived from a subject's own malignant cell(s).
  • 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.
  • the idiotypic proteins are produced recombinantly.
  • 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.
  • 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 ( ⁇ ) or a lambda ( ⁇ ) light chain, obtained from each patient.
  • the immunogenic composition includes the same heavy and light chain V region sequences expressed by the patient's tumor.
  • the idiotypic protein is conjugated to a carrier, e.g., a protein using any suitable techniques.
  • 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.
  • KLH manufactured under Current Good Manufacturing Practice (cGMP) conditions is obtained from, e.g., Biosyn Arzneiffen 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.
  • GM-CSF granulocyte-macrophage colony stimulating factor
  • a cytokine is linked to the idiotypic protein.
  • 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).
  • sequences encoding the desired cytokine are added to the 3' end of sequences encoding the idiotypic protein.
  • 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, 125 l, 123 l, 99 Tc, 67 Ga, 111 ln, 188 Re, 186 Re, and 90 Y.
  • 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.
  • Active immunotherapy e.g., anti-tumor vaccine
  • a chemotherapeutic program e.g. CVP (cyclophosphamide, vincristine, prednisone) or CHOP (CVP plus doxorubicin)
  • 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).
  • 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.
  • antibodies are suitably administered by pulse infusion, particularly with declining doses.
  • the dosing is given by injections, such as intravenous or subcutaneous injections, depending in part on whether the administration is brief or chronic.
  • 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.
  • 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.
  • a dosage of active ingredient can be about 0.01 to 10 milligrams per kilogram of body weight every four weeks.
  • the immunogen can be incorporated into pharmaceutical compositions suitable for administration to a subject.
  • the pharmaceutical composition may include an immunogen and a pharmaceutically acceptable carrier.
  • pharmaceutically acceptable carrier includes solvents, dispersion media, coatings, antibacterial and antifungal agents, isotonic and absorption delaying agents, and the like that are physiologically compatible.
  • 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.
  • 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.
  • 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.
  • liquid solutions e.g., injectable and infusible solutions
  • dispersions or suspensions e.g., tablets, pills, powders, liposomes and suppositories.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • a non-vaccine therapy e.g., chemotherapy, radiotherapy, passive immunotherapy and/or stem cell 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.
  • 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.
  • a prognostic index e.g., an IPI as discussed above
  • 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.
  • the sequence-specific data may include any sequence-specific data that is suitable for determining the patient-specific parameters, as described above.
  • 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.
  • 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.
  • 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
  • the threshold number of tyrosine residues is 2 or less, e.g., 1 or less.
  • 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.
  • the tumor samples are obtained before the patients are treated with the anti-tumor vaccine therapy.
  • 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.
  • 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.
  • the result of the integrative evaluation and/or classification may be provided as an output to a user in any convenient form.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • FL Follicular Lymphoma
  • NDL Non- Hodgkin's Lymphoma
  • 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.
  • KLH non-specific immunotherapy
  • MyVax® comprised of 23 responders and 34 non-responders, as determined by Id-specific humoral immune response.
  • AutoSOME an unsupervised clustering method that requires no user assumptions about cluster number or structure.
  • 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).
  • 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).
  • 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).
  • 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).
  • 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.
  • FIG. 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.
  • FCM flow cytometry
  • CIBERSORT GEP deconvolution
  • 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 ⁇ 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.
  • PFS progression free survival
  • FIG. 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.
  • 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.
  • 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.
  • FR1/2/3/4 structurally defined sub-regions
  • CDR1/2/3 Complementarity Determining Regions 1 through 3
  • a given amino acid was required to achieve statistical significance (i.e., P ⁇ 0.05) among vaccinated patients in both training and validation cohorts.
  • 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.
  • 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.
  • FR or FWR Framework Regions
  • 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).
  • FIG. 1 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.
  • CD4-Y-SP Activated memory CD4 T cell high + Y low + Selection Pressure low
  • 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.
  • 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.
  • 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).
  • a series of additional analyses were performed, detailed below.
  • CD4-Y-SP 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.
  • 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.
  • 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.
  • CIBERSORT 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • KLH-KLH non-specific immunotherapy
  • MyVax® 71 patients receiving MyVax®, comprised of 36 responders and 35 non-responders.
  • the Mem T cell-Tyr integrative model was significantly associated with PFS in the MyVax®, but not control arm (Fig. 10, panel a).
  • FIG. 10 An integrative model for Id vaccination candidate biomarkers.
  • 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
  • MyVax R dashed gray line; this curve was not used for significance testing. All p-values were calculated using the log-rank test.

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Abstract

La présente invention concerne une méthode de classification d'un patient. Un mode de réalisation de la méthode de la présente invention peut comprendre les étapes consistant i) à déterminer, pour un échantillon de tumeur prélevé sur un patient qui a été diagnostiqué comme présentant une affection maligne à lymphocytes B, a) la proportion des lymphocytes T CD4+ mémoire activés parmi une pluralité de lymphocytes, b) le nombre de résidus tyrosine dans les séquences d'acides aminés des premières régions idiotypiques déterminant la complémentarité (CDR1), et c) la pression de sélection sur les séquences nucléotidiques codant pour lesdites CDR idiotypiques, et ii) à prédire si le patient va présenter une réponse bénéfique sur le plan clinique à une thérapie de vaccin anti-tumeur sur la base d'une évaluation par intégration de la proportion de lymphocytes T, du nombre de résidus tyrosine, et de la pression de sélection déterminée pour l'échantillon de tumeur.
PCT/US2016/036065 2015-06-04 2016-06-06 Méthode de prédiction d'une réponse clinique à une immunothérapie anticancéreuse Ceased WO2016197131A1 (fr)

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US10636512B2 (en) 2017-07-14 2020-04-28 Cofactor Genomics, Inc. Immuno-oncology applications using next generation sequencing
CN111164700A (zh) * 2017-07-14 2020-05-15 余因子基因组学公司 使用下一代测序的免疫-肿瘤学应用
US11008609B2 (en) 2017-09-01 2021-05-18 Life Technologies Corporation Compositions and methods for immune repertoire sequencing
US12473663B2 (en) 2018-07-18 2025-11-18 Life Technologies Corporation Compositions and methods for immune repertoiresequencing

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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10636512B2 (en) 2017-07-14 2020-04-28 Cofactor Genomics, Inc. Immuno-oncology applications using next generation sequencing
CN111164700A (zh) * 2017-07-14 2020-05-15 余因子基因组学公司 使用下一代测序的免疫-肿瘤学应用
EP3652663A4 (fr) * 2017-07-14 2021-04-21 Cofactor Genomics, Inc. Applications immuno-oncologiques mettant en oeuvre un séquençage de nouvelle génération
US11008609B2 (en) 2017-09-01 2021-05-18 Life Technologies Corporation Compositions and methods for immune repertoire sequencing
US12227878B2 (en) 2017-09-01 2025-02-18 Life Technologies Corporation Compositions and methods for immune repertoire sequencing
WO2019183582A1 (fr) * 2018-03-23 2019-09-26 Life Technologies Corporation Surveillance du répertoire immun
EP4286531A3 (fr) * 2018-03-23 2024-01-17 Life Technologies Corporation Surveillance du répertoire immun
US12065705B2 (en) 2018-03-23 2024-08-20 Life Technologies Corporation Immune repertoire monitoring
US12473663B2 (en) 2018-07-18 2025-11-18 Life Technologies Corporation Compositions and methods for immune repertoiresequencing

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