METHODS FOR TREATING HEMATOLOGICAL CANCER AND THE USE OF BIOMARKERS AS A PREDICTOR FOR RESPONSIVENESS TO TREATMENT COMPOUNDS

1. FIELD

The present application relates generally to methods for predicting whether a patient having a hematological cancer will be responsive to a treatment compound such as lenalidomide or 3-(5-amino-2-methyl-4-oxo-4H-quinazolin-3-yl)-piperidine-2,6-dione (Compound A), or a stereoisomer, pharmaceutically acceptable salt, solvate, hydrate, co-crystal, clathrate, or polymorph thereof. More specifically, the present application relates to utilizing biomarkers or classifiers that correlate with responsiveness to lenalidomide or Compound A.

Also provided herein are said treatment compounds for use in methods of treating a patient having a hematological cancer wherein the treatment comprises predicting whether the patient will be responsive to said treatment compound by utilizing biomarkers or classifiers that correlate with responsiveness to lenalidomide or Compound A.

2. BACKGROUND

2.1 Hematological Cancer

Hematological cancer generally includes three main types: lymphoma, leukemia, and myeloma. Lymphoma refers to cancers that originate in the lymphatic system. Lymphoma includes, but is not limited to, Hodgkin's lymphoma, non-Hodgkin's lymphoma (NHL), diffuse large B-cell lymphoma (DLBCL), and peripheral T-cell lymphomas (PTCL), etc. Leukemia refers to malignant neoplasms of the blood-forming tissues. Acute leukemia involves predominantly undifferentiated cell populations, whereas chronic leukemia involves more mature cell forms. Acute leukemia is divided into acute lymphoblastic leukemia (ALL) and acute myeloblastic leukemia (AML) types. The Merck Manual, 946-949 (17th ed. 1999). Chronic leukemia is divided into chronic lymphocytic leukemia (CLL) or chronic myelocytic leukemia (CML). The Merck Manual, 949-952 (17th ed. 1999). Myeloma is a cancer of plasma cells in the bone marrow. Because myeloma frequently occurs at many sites in the bone marrow, it is often referred to as multiple myeloma (MM).

2.2 Pathobiology of DLBCL

The non-Hodgkin lymphomas (NHLs) are a diverse group of blood cancers that include any kind of lymphoma except Hodgkin's lymphomas. Types of NHL vary significantly in their severity, from indolent to very aggressive. Less aggressive non-Hodgkin lymphomas are compatible with a long survival while more aggressive non-Hodgkin lymphomas can be rapidly fatal without treatment. They can be formed from either B-cells or T-cells. B-cell non-Hodgkin lymphomas include Burkitt lymphoma, chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL), diffuse large B-cell lymphoma, follicular lymphoma, immunoblastic large cell lymphoma, precursor B-lymphoblastic lymphoma, and mantle cell lymphoma. T-cell non-Hodgkin lymphomas include mycosis fungoides, anaplastic large cell lymphoma, and precursor T-lymphoblastic lymphoma. Prognosis and treatment depend on the stage and type of disease.

DLBCL accounts for approximately one-third of non-Hodgkin's lymphomas. While some DLBCL patients are cured with traditional chemotherapy, the remainder dies from the disease. Anticancer drugs cause rapid and persistent depletion of lymphocytes, possibly by direct apoptosis induction in mature T and B cells. See K. Stahnke. et al., Blood 2001, 98:3066-3073. Absolute lymphocyte count (ALC) has been shown to be a prognostic factor in follicular non-Hodgkin's lymphoma and recent results have suggested that ALC at diagnosis is an important prognostic factor in diffuse large B-cell lymphoma.

DLBCL can be divided into distinct molecular subtypes according to their gene profiling patterns: germinal-center B-cell-like DLBCL (GCB-DLBCL), activated B-cell-like DLBCL (ABC-DLBCL), primary mediastinal B-cell lymphoma (PMBL), or not otherwise specified DLBCL (NOS-DLBCL). These subtypes are characterized by distinct differences in survival, chemo-responsiveness, and signaling pathway dependence, particularly the NF-□B pathway. See D. Kim et al., Journal of Clinical Oncology, 2007 ASCO Annual Meeting Proceedings Part I. Vol 25, No. 18S (June 20 Supplement), 2007: 8082. See Bea S, et al., Blood 2005; 106: 3183-90; Ngo V. N. et al., Nature 2011; 470: 115-9. Such differences have prompted the search for more effective and subtype-specific treatment strategies in DLBCL.

The incidence of cancer continues to climb as the general population ages, as new cancers develop, and as susceptible populations grow. A tremendous demand therefore exists for new methods and compositions that can be used to treat patients with NHL including DLBCL.

2.3 Methods of Treatment

Current cancer therapy, in general, can involve surgery, chemotherapy, hormonal therapy and/or radiation treatment to eradicate neoplastic cells in a patient (see, for example, Stockdale, 1998, Medicine, vol. 3, Rubenstein and Federman, eds., Chapter 12, Section IV). Recently, cancer therapy could also involve biological therapy or immunotherapy. All of these approaches pose significant drawbacks for the patient. Surgery, for example, may be contraindicated due to the health of a patient or may be unacceptable to the patient. Additionally, surgery may not completely remove neoplastic tissue. Radiation therapy is only effective when the neoplastic tissue exhibits a higher sensitivity to radiation than normal tissue. Radiation therapy can also often elicit serious side effects. Hormonal therapy is rarely given as a single agent. Although hormonal therapy can be effective, it is often used to prevent or delay recurrence of cancer after other treatments have removed the majority of cancer cells. Biological therapies and immunotherapies are limited in number and may produce side effects such as rashes or swellings, flu-like symptoms, including fever, chills and fatigue, digestive tract problems or allergic reactions.

With respect to chemotherapy, there are a variety of chemotherapeutic agents available for treatment of cancer. A majority of cancer chemotherapeutics act by inhibiting DNA synthesis, either directly or indirectly by inhibiting the biosynthesis of deoxyribonucleotide triphosphate precursors, to prevent DNA replication and concomitant cell division. Gilman et al., Goodman and Gilman's: The Pharmacological Basis of Therapeutics, Tenth Ed. (McGraw Hill, New York).

Despite availability of a variety of chemotherapeutic agents, chemotherapy has many drawbacks. Stockdale, Medicine, vol. 3, Rubenstein and Federman, eds., ch. 12, sect. 10, 1998. Almost all chemotherapeutic agents are toxic, and chemotherapy causes significant and often dangerous side effects including severe nausea, bone marrow depression, and immunosuppression. Additionally, even with administration of combinations of chemotherapeutic agents, many tumor cells are resistant or develop resistance to the chemotherapeutic agents. In fact, those cells resistant to the particular chemotherapeutic agents used in the treatment protocol often prove to be resistant to other drugs, even if those agents act by different mechanism from those of the drugs used in the specific treatment. This phenomenon is referred to as pleiotropic drug or multidrug resistance. Because of the drug resistance, many cancers prove refractory to standard chemotherapeutic treatment protocols.

In the context of DLBCL, treatment usually includes administration of a combination of chemotherapy and antibody therapy. The most widely used treatment of DLBCL is a mixture of the antibody rituximab and several chemotherapy drugs (cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), and in some cases etoposide is added (R-EPOCH)). DLBCL also typically requires immediate treatment upon diagnosis due to how quickly the disease can advance. For some patients, DLBCL returns or becomes refractory following treatment. Several alternative treatments, some of which can include use of lenalidomide, are currently being tested in clinical trials for patients with newly diagnosed, relapsed or refractory DLBCL.

Thus, there is a significant need for safe and effective methods of treating, preventing and managing cancers including DLBCL, particularly for cancers that are refractory to standard treatments, such as surgery, radiation therapy, chemotherapy and hormonal therapy, while reducing or avoiding the toxicities and/or side effects associated with the conventional therapies. Moreover, there remains a need for the ability to predict and monitor response to therapy in order to increase the quality of care for patients, avoid unnecessary treatment and to increase the success rate in treating cancer, including DLBCL, in clinical practice.

3. SUMMARY

The present disclosure is based, in part, on the discovery that pre-treatment transcriptional profiles derived from tumor biopsies obtained from a patient having a hematological cancer (e.g., DLBCL) can be used to predict responsiveness of the patient having the hematological cancer (e.g., DLBCL) to treatment with lenalidomide and/or Compound A.

In one aspect, provided herein are methods for predicting the responsiveness of a patient having a hematological cancer to a treatment compound.

In some embodiments, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a biological sample from the patient having the hematological cancer;

(b) determining the expression level of one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4; and

(c) comparing the expression level of the one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4 in the biological sample with a reference expression level of the same genes;

wherein the differential expression level of the one, two, three, four, five, or more of the genes in the biological sample relative to the reference expression level of the one, two, three, four, five, or more of the genes indicates that the first patient will be responsive to the treatment compound; and

In certain embodiments, the reference expression level of the one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4 is the expression level of the same genes in a reference sample. In one embodiment, the reference sample is a second biological sample. In another embodiment, the reference sample is synthetically derived.

In certain embodiments, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression level of one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4; and

(c) comparing the expression level of the one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4 in the first biological sample with the expression level of the same genes in a second biological sample obtained from a second patient having the hematological cancer, wherein the second patient is not responsive to the treatment compound,

wherein the differential expression level of the one, two, three, four, five, or more of the genes in the first biological sample relative to the expression level of the one, two, three, four, five, or more of the genes in the second biological sample indicates that the first patient will be responsive to the treatment compound, and

In some embodiments, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression level of one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4; and

In other embodiments, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression level of one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4; and

- (i) the gene expression profile of the one, two, three, four, five, or more of the genes in a second biological sample from a second patient having the hematological cancer, wherein the second patient is responsive to the treatment compound, and
- (ii) the gene expression profile of the one, two, three, four, five, or more of the genes in a third biological sample from a third patient having the hematological cancer, wherein the third patient is not responsive to the treatment compound,

wherein the similar expression level of the one, two, three, four, five, or more of the genes in the first biological sample relative to the expression level of the one, two, three, four, five, or more of the genes in the second biological sample indicates that the first patient will be responsive to the treatment compound, and the similar expression level of the one, two, three, four, five, or more of the genes in the first biological sample relative to the expression level of the one, two, three, four, five, or more of the genes in the third biological sample indicates that the first patient will not be responsive to the treatment compound, and

In some embodiments, the one, two, three, four, five, or more of genes are a subset of the genes in Table 1, a subset of the genes in Table 2, a subset of the genes in Table 3, and/or a subset of the genes in Table 4. In other embodiments, the one, two, three, four, five, or more of genes are a combination of a subset of the genes in two tables selected from Tables 1-4. In yet other embodiments, the one, two, three, four, five, or more of genes are a combination of a subset of the genes in three tables selected from Tables 1-4. In still other embodiments, the one, two, three, four, five, or more of genes are a combination of a subset of the genes in Table 1, a subset of the genes in Table 2, a subset of the genes in Table 3, and a subset of the genes in Table 4.

In another aspect, provided herein are methods for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising: (a) obtaining a biological sample from a patient having the hematological cancer; (b) determining the expression of the genes or a certain subset of genes set forth in Tables 1, 2, 3 and/or 4, or any combination thereof, in the biological sample and generating a score based on the expression of the genes or subset of genes, (c) comparing the score to a reference score, wherein a score similar to a reference score indicates that the patient will be responsive to the treatment compound. In specific embodiments, the reference score is based on the expression of the same genes or subset of genes in a population of patients having the hematological cancer who are responsive to the treatment compound. In a specific embodiment, the treatment compound is a compound disclosed herein. In one embodiment, the treatment compound is lenalidomide. In another embodiment, the treatment compound is Compound A.

In another aspect, provided herein are methods for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising: (a) obtaining a biological sample from a patient having the hematological cancer; (b) determining the expression of the genes or a certain subset of genes set forth in Tables 1, 2, 3, and/or 4, or any combination thereof, in the biological sample and generating a score based on the expression of the genes or subset of genes, (c) comparing the score to a first reference score and a second reference score, wherein (i) the score similar to the first reference score indicates that the patient will be responsive to the treatment compound, and/or (ii) the score similar to the second reference score indicates that the patient will be responsive to the treatment compound. In specific embodiments, the first reference score is based on the expression of the same genes or subset of genes in a population of patients having the hematological cancer who are responsive to the treatment compound, and the second reference score is based on the expression of the same genes or subset of genes in a population of patients having the hematological cancer who are not responsive to the treatment compound. In a specific embodiment, the treatment compound is a compound disclosed herein. In one embodiment, the treatment compound is lenalidomide. In another embodiment, the treatment compound is Compound A.

In another aspect, provided herein are methods for treating a patient having a hematological cancer with a treatment compound. Accordingly, provided herein is a treatment compound for use in said methods for treating a patient having a hematological cancer.

In some embodiments, provided herein is a method for treating a patient having a hematological cancer with a treatment compound, comprising:

(a) obtaining a biological sample from the patient having the hematological cancer;

(b) determining the expression level of one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4;

(d) administering the treatment compound to the patient if the one, two, three, four, five, or more of the genes in the biological sample are differentially expressed relative to the reference expression level of the same genes;

wherein the treatment compound is 3-(4-amino-1-oxo-1,3-dihydro-isoindol-2-yl)-piperidine-2,6-dione (lenalidomide), 3-(5-amino-2-methyl-4-oxo-4H-quinazolin-3-yl)-piperidine-2,6-dione (Compound A), or a stereoisomer, pharmaceutically acceptable salt, solvate, hydrate, co-crystal, clathrate, or a polymorph thereof. Accordingly, provided herein is said treatment compound for use in said method for treating a patient having a hematological cancer.

In certain embodiments, provided herein is a method for treating a patient having a hematological cancer with a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression level of one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4;

(d) administering the treatment compound to the first patient if the one, two, three, four, five, or more of the genes in the first biological sample are differentially expressed relative to the expression level of the one, two, three, four, five, or more of the genes in the second biological sample,

In some embodiments, provided herein is a method for treating a patient having a hematological cancer with a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression level of one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4;

(d) administering the treatment compound to the first patient if the one, two, three, four, five, or more of the genes in the first biological sample are similarly expressed relative to the expression level of the one, two, three, four, five, or more of the genes in the second biological sample,

In other embodiments, provided herein is a method for treating a patient having a hematological cancer with a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression level of one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4;

- (i) the gene expression profile of the one, two, three, four, five, or more of the genes in a second biological sample from a second patient having the hematological cancer, wherein the second patient is responsive to the treatment compound, and
- (ii) the gene expression profile of the one, two, three, four, five, or more of the genes in a third biological sample from a third patient having the hematological cancer, wherein the third patient is not responsive to the treatment compound; and

(d) administering the treatment compound to the first patient if: (i) the one, two, three, four, five, or more of the genes in the first biological sample are similarly expressed relative to the expression level of the one, two, three, four, five, or more of the genes in the second biological sample, and/or (ii) the one, two, three, four, five, or more of the genes in the first biological sample are differentially expressed relative to the expression level of the one, two, three, four, five, or more of the genes in the third biological sample,

In some embodiments of the various methods provided herein, expression levels of multiple genes (biomarkers) provided herein are used to predict a patient's response to the compound provided herein. When expression levels of multiple genes (biomarkers) are used to predict a patient's response to a treatment with the compound provided herein, e.g., lenalidomide or Compound A, any classifier that classifies based on two or more features can be used in the present disclosure.

In some embodiments, the methods provided herein further comprise (a) generating a score of the sample based on the expression levels of the genes or a subset thereof provided herein in the sample; and (b) determining the probability of the subject being responsive to the compound provided herein by comparing the score of the sample to a reference score.

In some more specific embodiments, the methods provided herein further comprise: (a) determining the expression levels of the genes or a subset thereof provided herein in a biological sample from a population of subjects that previously have been administered with the compound provided herein, (b) generating a score for the expression levels of the genes or a subset thereof provided herein for each subject of the population; (c) differentiating the subjects that are responsive to the compound provided herein from those subjects that are not responsive to the compound provided herein; and (d) determining responsiveness to the compound provided herein based on the scores for the subjects that are responsive to the compound and those subjects that are not responsive to the compound.

In other more specific embodiments, the methods provided herein further comprise: (a) determining the expression levels of the genes or a subset thereof provided herein in a biological sample from a population of subjects that previously have been administered with the compound, (b) generating a score for the expression levels of the genes or a subset thereof provided herein for each subject of the population; (c) differentiating the subjects that are responsive to the compound provided herein from those subjects that are not responsive to compound provided herein; and (d) generating a reference score that is predictive of the responsiveness of a subject to the compound provided herein using a model based on the scores for the subjects that are responsive to the compound and those subjects that are not responsive to the compound.

In another aspect, provided herein are methods for treating a patient having a hematological cancer with a treatment compound, comprising: (a) obtaining a biological sample from a patient having the hematological cancer; (b) determining the expression of the genes or a certain subset of genes set forth in Tables 1, 2, 3, and/or 4, or any combination thereof, in the biological sample and generating a score based on the expression of the genes or subset of genes, (c) comparing the score to a reference score; and (d) administering the treatment compound to the patient if the score is similar to the reference score. Accordingly, provided herein is said treatment compound for use in said method for treating a patient having a hematological cancer. In specific embodiments, the reference score is based on the expression of the same genes or subset of genes in a population of patients having the hematological cancer who are responsive to the treatment compound. In a specific embodiment, the treatment compound is a compound disclosed herein. In one embodiment, the treatment compound is lenalidomide. In another embodiment, the treatment compound is Compound A.

In specific embodiments, the first reference score is based on the expression of the same genes or subset of genes in a population of patients having the hematological cancer who are responsive to the treatment compound, and the second reference score is based on the expression of the same genes or subset of genes in a population of patients having the hematological cancer who are responsive to the treatment compound. In a specific embodiment, the treatment compound is a compound disclosed herein. In one embodiment, the treatment compound is lenalidomide. In another embodiment, the treatment compound is Compound A.

In certain embodiments, the hematological cancer is selected from the group consisting of leukemia, lymphoma, and multiple myeloma. In one embodiment, the hematological cancer is leukemia. In another embodiment, the hematological cancer is lymphoma. In still another embodiment, the hematological cancer is multiple myeloma.

In some embodiments, the lymphoma is DLBCL. In one embodiment, the DLBCL is refractory in the first patient. In another embodiment, the DLBCL is relapsed in the first patient. In yet another embodiment, the DLBCL is a germinal center B-cell-like subtype (GCB-DLBCL) in the first patient. In still another embodiment, the DLBCL is activated B-cell like subtype (ABC-DLBCL) in the first patient. In yet another embodiment, the DLBCL is not otherwise specified (NOS-DLBCL) in the first patient.

In some embodiments of the various methods provided herein, the expression of all of the genes in Table 1 is determined and compared. In some embodiments of the various methods provided herein, the expression of all of the genes in Table 2 is determined and compared. In some embodiments of the various methods provided herein, the expression of all of the genes in Table 3 is determined and compared. In some embodiments of the various methods provided herein, the expression of all of the genes in Table 4 is determined and compared.

In another aspect, provided herein are methods for predicting the clinical sensitivity of a hematological cancer to treatment with an immunomodulatory therapy comprising: (a) obtaining a first tumor sample from a first patient having the hematological cancer, (b) measuring the proportion of B cells in the first tumor sample, and (c) comparing the proportion of B cells in the first tumor sample with the proportion of B cells in a second tumor sample from a second patient having the same type of hematological cancer, wherein the second patient's hematological cancer is clinically insensitive to treatment with an immunomodulatory therapy, and wherein a lower proportion of B cells in the first tumor sample is measured relative the proportion of B cells in the second tumor sample indicates that the hematological cancer in the first patient will be clinically sensitive to treatment with the immunomodulatory therapy. In certain embodiments, the B cells are CD20+ B cells. In a specific embodiment, the hematological cancer is diffuse large B-cell lymphoma (DLBCL). In certain embodiments, the DLBCL is refractory to certain therapies, such as chemotherapy. In some embodiments, the DLBCL is relapsed in a patient. In a specific embodiment, the DLBCL is an activated B-cell-like subtype. In another specific embodiment, the DLBCL is a germinal center B-cell-like subtype. In another specific embodiment, the immunomodulatory therapy is lenalidomide. In another specific embodiment, the immunomodulatory therapy is Compound A.

In another aspect, provided herein are methods for predicting the clinical sensitivity of a hematological cancer to treatment with an immunomodulatory therapy comprising: (a) obtaining a first tumor sample from a first patient having the hematological cancer, (b) measuring the proportion of dendritic cells in the first tumor sample, and (c) comparing the proportion of dendritic cells in the first tumor sample with the proportion of dendritic cells in a second tumor sample from a second patient having the same type of hematological cancer, wherein the second patient's hematological cancer is clinically insensitive to treatment with an immunomodulatory therapy, and wherein a higher proportion of dendritic cells in the first tumor sample is measured relative the proportion of dendritic cells in the second tumor sample indicates that the hematological cancer in the first patient will be clinically sensitive to treatment with the immunomodulatory therapy. In certain embodiments, the dendritic cells are CD11c+ dendritic cells. In a specific embodiment, the hematological cancer is diffuse large B-cell lymphoma (DLBCL). In certain embodiments, the DLBCL is refractory to certain therapies, such as chemotherapy. In some embodiments, the DLBCL is relapsed in a patient. In a specific embodiment, the DLBCL is an activated B-cell-like subtype. In another specific embodiment, the DLBCL is a germinal center B-cell-like subtype. In another specific embodiment, the immunomodulatory therapy is lenalidomide. In another specific embodiment, the immunomodulatory therapy is Compound A.

In another aspect, provided herein are methods for predicting the clinical sensitivity of a hematological cancer to treatment with an immunomodulatory therapy comprising: (a) obtaining a first tumor sample from a first patient having the hematological cancer, (b) measuring the proportion of T cells in the first tumor sample, and (c) comparing the proportion of T cells in the first tumor sample with the proportion of T cells in a second tumor sample from a second patient having the same type of hematological cancer, wherein the second patient's hematological cancer is clinically insensitive to treatment with an immunomodulatory therapy, and wherein a higher proportion of T cells in the first tumor sample is measured relative the proportion of T cells in the second tumor sample indicates that the hematological cancer in the first patient will be clinically sensitive to treatment with the immunomodulatory therapy. In certain embodiments, the T cells are CD3+ T cells. In certain embodiments, the T cells are CD8+ T cells. In a specific embodiment, the hematological cancer is diffuse large B-cell lymphoma (DLBCL). In certain embodiments, the DLBCL is refractory to certain therapies, such as chemotherapy. In some embodiments, the DLBCL is relapsed in a patient. In a specific embodiment, the DLBCL is an activated B-cell-like subtype. In another specific embodiment, the DLBCL is a germinal center B-cell-like subtype. In another specific embodiment, the immunomodulatory therapy is lenalidomide. In another specific embodiment, the immunomodulatory therapy is Compound A.

In another aspect, provided herein are methods for predicting the clinical sensitivity of a hematological cancer to treatment with an immunomodulatory therapy comprising: (a) obtaining a first tumor sample from a first patient having the hematological cancer, (b) measuring the proportion of macrophage cells in the first tumor sample, and (c) comparing the proportion of macrophage cells in the first tumor sample with the proportion of macrophage cells in a second tumor sample from a second patient having the same type of hematological cancer, wherein the second patient's hematological cancer is clinically insensitive to treatment with an immunomodulatory therapy, and wherein a higher proportion of macrophage cells in the first tumor sample is measured relative the proportion of macrophage cells in the second tumor sample indicates that the hematological cancer in the first patient will be clinically sensitive to treatment with the immunomodulatory therapy. In certain embodiments, the macrophage cells are CD163+ macrophage cells. In a specific embodiment, the hematological cancer is diffuse large B-cell lymphoma (DLBCL). In certain embodiments, the DLBCL is refractory to certain therapies, such as chemotherapy. In some embodiments, the DLBCL is relapsed in a patient. In a specific embodiment, the DLBCL is an activated B-cell-like subtype. In another specific embodiment, the DLBCL is a germinal center B-cell-like subtype. In another specific embodiment, the immunomodulatory therapy is lenalidomide. In another specific embodiment, the immunomodulatory therapy is Compound A.

In another aspect, provided herein are methods for predicting the clinical sensitivity of a hematological cancer to treatment with an immunomodulatory therapy comprising: (a) obtaining a first tumor sample from a first patient having the hematological cancer, (b) measuring the proportion of PD-L1+ cells in the first tumor sample, and (c) comparing the proportion of PD-L1+ cells in the first tumor sample with the proportion of PD-L1+ cells in a second tumor sample from a second patient having the same type of hematological cancer, wherein the second patient's hematological cancer is clinically insensitive to treatment with an immunomodulatory therapy, and wherein a lower proportion of PD-L1+ cells in the first tumor sample is measured relative the proportion of PD-L1+ cells in the second tumor sample indicates that the hematological cancer in the first patient will be clinically sensitive to treatment with the immunomodulatory therapy. In a specific embodiment, the hematological cancer is diffuse large B-cell lymphoma (DLBCL). In certain embodiments, the DLBCL is refractory to certain therapies, such as chemotherapy. In some embodiments, the DLBCL is relapsed in a patient. In a specific embodiment, the DLBCL is an activated B-cell-like subtype. In another specific embodiment, the DLBCL is a germinal center B-cell-like subtype. In another specific embodiment, the immunomodulatory therapy is lenalidomide. In another specific embodiment, the immunomodulatory therapy is Compound A.

In another aspect, provided herein are for predicting the clinical sensitivity of a hematological cancer to treatment with an immunomodulatory therapy comprising: (a) obtaining a first tumor sample from a first patient having a hematological cancer, (b) measuring the proportion of immune cells in the first tumor sample, and (c) comparing the proportion of the immune cells in the first tumor sample to (i) the proportion of the same immune cells in tumor samples from patients having the same type of hematological cancer which are clinically sensitive to an immunomodulatory therapy and (ii) the proportion of the same immune cells in tumor samples from patients having the same type of hematological cancer which are clinically insensitive to the immunomodulatory therapy, and wherein that the proportion of the immune cells in the first tumor sample is (i) similar to the proportion of the same immune cells in tumor samples from patients having the same type of hematological cancer which are clinically sensitive to the immunomodulatory therapy, and (ii) not similar to the proportion of the same immune cells in tumor samples from patients having the same type of hematological cancer which are clinically insensitive to the immunomodulatory therapy indicates that the hematological cancer in the first page will be clinically sensitive to treatment with the immunomodulatory therapy. In some embodiments, the immune cells are subset of immune cells, such as subset of B cells. In certain embodiments, the immune cells are dendritic cells. In some embodiments, the immune cells are plasma cells. In certain embodiments, the immune cells are monocytes. In some embodiments, the immune cells are tumor infiltrating immune cells. In certain embodiments, the immune cells are T cells. In some embodiments, the immune cells are B cells. In certain embodiments, the immune cells are NK cells. In some embodiments, the immune cells are two, three or more subsets of immune cells, such as two more types of T cells (e.g., CD4+ and CD8+ T cells). In some embodiments, the proportion of different populations of immune cells in the first tumor sample are compared to (i) the proportion of the same populations of immune cells in the tumor samples from patients having the same type of hematological cancer which are clinically sensitive to the immunomodulatory therapy and (ii) the proportion of the same populations of immune cells in the tumor samples from patients having the same type of hematological cancer which are clinically insensitive to the immunomodulatory therapy. In a specific embodiment, the hematological cancer is diffuse large B-cell lymphoma (DLBCL). In certain embodiments, the DLBCL is refractory to certain therapies, such as chemotherapy. In some embodiments, the DLBCL is relapsed in a patient. In a specific embodiment, the DLBCL is an activated B-cell-like subtype. In another specific embodiment, the DLBCL is a germinal center B-cell-like subtype. In another specific embodiment, the immunomodulatory therapy is lenalidomide. In another specific embodiment, the immunomodulatory therapy is Compound A.

In another aspect, provided herein are methods for managing or treating a hematological cancer comprising: (a) obtaining a first tumor sample from a first patient having the hematological cancer, (b) measuring the proportion of B cells in the first tumor sample, (c) comparing the proportion of B cells in the first tumor sample with the proportion of B cells in a second tumor sample from a second patient having the same type of hematological cancer, wherein the second patient's hematological cancer is clinically insensitive to treatment with an immunomodulatory therapy, and (d) administering the immunomodulatory therapy to the first patient if a lower proportion of B cells in the first tumor sample is measured relative the proportion of B cells in the second tumor sample. In certain embodiments, the B cells are CD20+ B cells. In a specific embodiment, the hematological cancer is diffuse large B-cell lymphoma (DLBCL). In certain embodiments, the DLBCL is refractory to certain therapies, such as chemotherapy. In some embodiments, the DLBCL is relapsed in a patient. In a specific embodiment, the DLBCL is an activated B-cell-like subtype. In another specific embodiment, the DLBCL is a germinal center B-cell-like subtype. In another specific embodiment, the immunomodulatory therapy is lenalidomide. In another specific embodiment, the immunomodulatory therapy is Compound A.

In another aspect, provided herein are methods for managing or treating a hematological cancer comprising: (a) obtaining a first tumor sample from a first patient having the hematological cancer, (b) measuring the proportion of dendritic cells in the first tumor sample, (c) comparing the proportion of dendritic cells in the first tumor sample with the proportion of dendritic cells in a second tumor sample from a second patient having the same type of hematological cancer, wherein the second patient's hematological cancer is clinically insensitive to treatment with an immunomodulatory therapy, and (d) administering the immunomodulatory therapy to the first patient if a higher proportion of dendritic cells in the first tumor sample is measured relative the proportion of dendritic cells in the second tumor sample. In certain embodiments, the dendritic cells are CD11c+ dendritic cells. In a specific embodiment, the hematological cancer is diffuse large B-cell lymphoma (DLBCL). In certain embodiments, the DLBCL is refractory to certain therapies, such as chemotherapy. In some embodiments, the DLBCL is relapsed in a patient. In a specific embodiment, the DLBCL is an activated B-cell-like subtype. In another specific embodiment, the DLBCL is a germinal center B-cell-like subtype. In another specific embodiment, the immunomodulatory therapy is lenalidomide. In another specific embodiment, the immunomodulatory therapy is Compound A.

In another aspect, provided herein are methods for managing or treating a hematological cancer comprising: (a) obtaining a first tumor sample from a first patient having the hematological cancer, (b) measuring the proportion of T cells in the first tumor sample, (c) comparing the proportion of T cells in the first tumor sample with the proportion of T cells in a second tumor sample from a second patient having the same type of hematological cancer, wherein the second patient's hematological cancer is clinically insensitive to treatment with an immunomodulatory therapy, and (d) administering the immunomodulatory therapy to the first patient if a higher proportion of T cells in the first tumor sample is measured relative the proportion of T cells in the second tumor sample. In certain embodiments, the T cells are CD3+ T cells. In certain embodiments, the T cells are CD8+ T cells. In a specific embodiment, the hematological cancer is diffuse large B-cell lymphoma (DLBCL). In certain embodiments, the DLBCL is refractory to certain therapies, such as chemotherapy. In some embodiments, the DLBCL is relapsed in a patient. In a specific embodiment, the DLBCL is an activated B-cell-like subtype. In another specific embodiment, the DLBCL is a germinal center B-cell-like subtype. In another specific embodiment, the immunomodulatory therapy is lenalidomide. In another specific embodiment, the immunomodulatory therapy is Compound A.

In another aspect, provided herein are methods for managing or treating a hematological cancer comprising: (a) obtaining a first tumor sample from a first patient having the hematological cancer, (b) measuring the proportion of macrophage cells in the first tumor sample, (c) comparing the proportion of macrophage cells in the first tumor sample with the proportion of macrophage cells in a second tumor sample from a second patient having the same type of hematological cancer, wherein the second patient's hematological cancer is clinically insensitive to treatment with an immunomodulatory therapy, and (d) administering the immunomodulatory therapy to the first patient if a higher proportion of macrophage cells in the first tumor sample is measured relative the proportion of macrophage cells in the second tumor sample. In certain embodiments, the macrophage cells are CD163+ macrophage cells. In a specific embodiment, the hematological cancer is diffuse large B-cell lymphoma (DLBCL). In certain embodiments, the DLBCL is refractory to certain therapies, such as chemotherapy. In some embodiments, the DLBCL is relapsed in a patient. In a specific embodiment, the DLBCL is an activated B-cell-like subtype. In another specific embodiment, the DLBCL is a germinal center B-cell-like subtype. In another specific embodiment, the immunomodulatory therapy is lenalidomide. In another specific embodiment, the immunomodulatory therapy is Compound A.

In another aspect, provided herein are methods for managing or treating a hematological cancer comprising: (a) obtaining a first tumor sample from a first patient having the hematological cancer, (b) measuring the proportion of PD-L1+ cells in the first tumor sample, (c) comparing the proportion of PD-L1+ cells in the first tumor sample with the proportion of PD-L1+ cells in a second tumor sample from a second patient having the same type of hematological cancer, wherein the second patient's hematological cancer is clinically insensitive to treatment with an immunomodulatory therapy, and (d) administering the immunomodulatory therapy to the first patient if a higher proportion of PD-L1+ cells in the first tumor sample is measured relative the proportion of PD-L1+ cells in the second tumor sample. In a specific embodiment, the hematological cancer is diffuse large B-cell lymphoma (DLBCL). In certain embodiments, the DLBCL is refractory to certain therapies, such as chemotherapy. In some embodiments, the DLBCL is relapsed in a patient. In a specific embodiment, the DLBCL is an activated B-cell-like subtype. In another specific embodiment, the DLBCL is a germinal center B-cell-like subtype. In another specific embodiment, the immunomodulatory therapy is lenalidomide. In another specific embodiment, the immunomodulatory therapy is Compound A.

In another aspect, provided herein are for managing or treating a hematological cancer comprising: (a) obtaining a first tumor sample from a first patient having a hematological cancer, (b) measuring the proportion of immune cells in the first tumor sample, and (c) comparing the proportion of the immune cells in the first tumor sample to (i) the proportion of the same immune cells in tumor samples from patients having the same type of hematological cancer which are clinically sensitive to an immunomodulatory therapy and (ii) the proportion of the same immune cells in tumor samples from patients having the same type of hematological cancer which are clinically insensitive to the immunomodulatory therapy, and (d) administering the immunomodulatory therapy to the first patient if the proportion of the immune cells in the first tumor sample is (i) similar to the proportion of the same immune cells in tumor samples from patients having the same type of hematological cancer which are clinically sensitive to the immunomodulatory therapy, and (ii) not similar to the proportion of the same immune cells in tumor samples from patients having the same type of hematological cancer which are clinically insensitive to the immunomodulatory therapy. In some embodiments, the immune cells are subset of immune cells, such as subset of B cells. In certain embodiments, the immune cells are dendritic cells. In some embodiments, the immune cells are plasma cells. In certain embodiments, the immune cells are monocytes. In some embodiments, the immune cells are tumor infiltrating immune cells. In certain embodiments, the immune cells are T cells. In some embodiments, the immune cells are B cells. In certain embodiments, the immune cells are NK cells. In some embodiments, the immune cells are two, three or more subsets of immune cells, such as two more types of T cells (e.g., CD4+ and CD8+ T cells). In some embodiments, the proportion of different populations of immune cells in the first tumor sample are compared to (i) the proportion of the same populations of immune cells in the tumor samples from patients having the same type of hematological cancer which are clinically sensitive to the immunomodulatory therapy and (ii) the proportion of the same populations of immune cells in the tumor samples from patients having the same type of hematological cancer which are clinically insensitive to the immunomodulatory therapy. In a specific embodiment, the hematological cancer is diffuse large B-cell lymphoma (DLBCL). In certain embodiments, the DLBCL is refractory to certain therapies, such as chemotherapy. In some embodiments, the DLBCL is relapsed in a patient. In a specific embodiment, the DLBCL is an activated B-cell-like subtype. In another specific embodiment, the DLBCL is a germinal center B-cell-like subtype. In another specific embodiment, the immunomodulatory therapy is lenalidomide. In another specific embodiment, the immunomodulatory therapy is Compound A.

In another aspect, provided herein is a kit for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising an agent for determining the expression levels of the genes identified in Tables 1, 2, 3, and/or 4, or a subset thereof in a biological sample obtained from the patient prior to the treatment with the treatment compound, wherein the treatment compound is 3-(4-amino-1-oxo-1,3-dihydro-isoindol-2-yl)-piperidine-2,6-dione (lenalidomide), 3-(5-amino-2-methyl-4-oxo-4H-quinazolin-3-yl)-piperidine-2,6-dione (Compound A), or a stereoisomer, pharmaceutically acceptable salt, solvate, hydrate, co-crystal, clathrate, or a polymorph thereof.

In some embodiments, the agent is for determining the expression levels of the genes identified in Table 1. In some embodiments, the agent is for determining the expression levels of the genes identified in Table 2. In other embodiments, the agent is for determining the expression levels of the genes identified in Table 3. In some embodiments, the agent is for determining the expression levels of the genes identified in Table 4. In other embodiments, the agent is for determining the expression levels of the genes identified in Tables 1, 2, 3, and 4.

In some embodiments, the agent is for determining nucleic acid levels. In other embodiments, the agent is for determining protein levels.

4. BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows Monte-Carlo cross-validation results of possible models, which demonstrate a plateau correlation of 0.95 with the outcomes using parameters that assigned non-zero weights to approximately 50 genes.

FIG. 2 shows a correlation of 0.972 using the current linear model to evaluate the validation set.

FIG. 3 shows the rescale of the current linear model.

FIGS. 4A-4D show Kaplan-Meier plots of clinical evaluation of the current linear model. FIGS. 4A and 4B demonstrate the performance in the Lenz cohort using overall survival as the outcome. FIGS. 4C and 4D demonstrate the performance in the Compound A cohort using progression free survival as the outcome. FIGS. 4A and 4C show the original 26 gene classifier. FIGS. 4B and 4D show the approximately 50 gene classifier generated from the current linear model.

FIG. 5 shows Kaplan-Meier plot for the prior 26 gene classifier in NOS-DLBCL population treated with Compound A.

FIG. 6 shows Kaplan-Meier plot for the M1 classifier (Table 1) in NOS-DLBCL population treated with Compound A.

FIG. 7 shows Kaplan-Meier plot for the M2 classifier (Table 2) in NOS-DLBCL population treated with Compound A.

FIG. 8 shows Kaplan-Meier plot for the M3 classifier (Table 3) in NOS-DLBCL population treated with Compound A.

FIGS. 9A-9D show estimated cellular proportions on Lenz cohort dataset (FIG. 9A: GSVA method; FIG. 9B: CIBERSORT method; FIG. 9C: xCell method; FIG. 9D: unmix method).

FIGS. 10A-10H show cellular proportions in tumor and immune content analysis in 26g+ and 26− DLBCL subgroups on Compound A cohort dataset (FIG. 10A: CD20+ B cells; FIG. 10B: CD3+ T cells; FIG. 10C: CD11c+ Dendritic cells; FIG. 10D: PD-L1+ cells; FIG. 10E: CD8+ T cells; FIG. 10F: CD163+ Macrophages; FIG. 10G: PD-1 total cells; FIG. 10H: CD56+NK cells).

5. DETAILED DESCRIPTION OF THE INVENTION
5.1 Terminology

The use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and/or the specification can mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.”

The term “about” or “approximately” means an acceptable error for a particular value as determined by one of ordinary skill in the art, which depends in part on how the value is measured or determined. In certain embodiments, the term “about” or “approximately” means within 1, 2, 3, or 4 standard deviations. In certain embodiments, the term “about” or “approximately” means within 20%, 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, or 0.05% of a given value or range.

As used herein, the terms “comprising” and “including” can be used interchangeably. The terms “comprising” and “including” are to be interpreted as specifying the presence of the stated features or components as referred to, but does not preclude the presence or addition of one or more features, or components, or groups thereof. Additionally, the terms “comprising” and “including” are intended to include examples encompassed by the term “consisting of”. Consequently, the term “consisting of” can be used in place of the terms “comprising” and “including” to provide for more specific embodiments of the invention.

The term “consisting of” means that a subject-matter has at least 90%, 95%, 97%, 98% or 99% of the stated features or components of which it consists. In another embodiment the term “consisting of” excludes from the scope of any succeeding recitation any other features or components, excepting those that are not essential to the technical effect to be achieved.

As used herein, “hematological cancer” includes myeloma, lymphoma, and leukemia. In one embodiment, the myeloma is multiple myeloma. In some embodiments, the leukemia is, for example, acute myelogenous leukemia (AML), acute lymphocytic leukemia (ALL), adult T-cell leukemia, chronic lymphocytic leukemia (CLL), hairy cell leukemia, myelodysplasia, myeloproliferative disorders, chronic myelogenous leukemia (CML), myelodysplastic syndrome (MDS), human lymphotropic virus-type 1 (HTLV-1) leukemia, mastocytosis, or B-cell acute lymphoblastic leukemia. In some embodiments, the lymphoma is, for example, diffuse large B-cell lymphoma (DLBCL), B-cell immunoblastic lymphoma, small non-cleaved cell lymphoma, human lymphotropic virus-type 1 (HTLV-1) leukemia/lymphoma, adult T-cell lymphoma, peripheral T-cell lymphoma (PTCL), cutaneous T-cell lymphoma (CTCL), mantle cell lymphoma (MCL), Hodgkin's lymphoma (HL), non-Hodgkin's lymphoma (NHL), AIDS-related lymphoma, follicular lymphoma, small lymphocytic lymphoma, T-cell/histiocyte rich large B-cell lymphoma, transformed lymphoma, primary mediastinal (thymic) large B-cell lymphoma, splenic marginal zone lymphoma, Richter's transformation, nodal marginal zone lymphoma, or ALK-positive large B-cell lymphoma. In one embodiment, the hematological cancer is indolent lymphoma including, for example, DLBCL, follicular lymphoma, or marginal zone lymphoma.

As used herein, unless otherwise specified or indicated from context, the term “pre-treatment” as used in accordance with the methods described herein refers to prior to administration of a drug.

As used herein, unless otherwise specified or indicated from context, the terms “treatment compound,” “compound,” or “drug” refer to a compound described in Section 5.4, infra.

As used herein, the terms “patient” and “subject” refer to an animal, such as a mammal. In a specific embodiment, the patient is a human. In other embodiments, the patient is a non-human animal, such as a dog, cat, farm animal (e.g., horse, pig, or donkey), chimpanzee, or monkey. In specific embodiments, the patient is a human with a hematological cancer (e.g., DLBCL) in need of treatment.

As used herein, and unless otherwise specified, the terms “treat,” “treating” and “treatment” refer to an action that occurs while a patient is suffering from a hematological cancer (e.g., DLBCL). Treatment with a drug may results in, e.g., one, two or more of the following: reduction in the severity of the cancer, reduction in tumor size, or retardation or slowing the progression of the hematological cancer (e.g., DLBCL), increase in progression free survival, increase in time to tumor progression, and/or improvement in one or more symptoms or treatment outcomes such as complete response or partial response.

The term “complex,” when used in reference to detection of gene expression, refers to a molecule in which one or more groups are linked by bonds, which includes a non-naturally occurring molecule that is dependent upon the presence of the gene product (RNA and/or protein) in a sample. For example, a complex can include, but is not limited to, a hybridization complex, an enzyme-substrate complex, or an antibody-antigen complex. A “hybridization complex” refers to the interaction of two or more nucleic acid molecules by intermolecular forces including, but not limited to, covalent or non-covalent bonds, Van der Waals forces, dipole-dipole interactions, hydrogen bonding, and London dispersion forces.

The phrase “reaction product,” when used in reference to detection of gene expression, refers to a substance that is formed as a result of a chemical or biological reaction that is dependent upon the presence of the target substrate in a sample. In some embodiments, the reaction product is a non-naturally occurring molecule. Such a non-naturally occurring molecule can be the result of methods well-known in the art for detecting gene expression. Such methods include, but are not limited to, polymerase chain reaction (PCR) based methods including quantitative reverse transcription PCR (qRT-PCR) and reverse transcription PCR (RT-PCR), physical property assays (e.g., gel electrophoresis, Northern blot, Western blot), fluorescent in situ hybridization, serial analysis of gene expression (SAGE), Whole Transcriptome Shotgun Sequencing (WTSS), deep sequencing, microarrays/gene chips, digital gene expression analysis (e.g., NanoString assay), flow cytometry, immunofluorscence, and enzyme-linked immunosorbent assay-based methodologies (ELISA).

By “solid support,” “solid substrate” or other grammatical equivalents herein refers to any material that contains and/or can be modified to contain one or more sites (e.g., discrete individual sites, pre-defined sites, random sites, etc.) appropriate for the attachment or association of compositions disclosed herein and is amenable to at least one detection method. As will be appreciated by those in the art, there are many possible substrates. Possible substrates include, but are not limited to, glass and modified or functionalized glass, plastics (including acrylics, polystyrene and copolymers of styrene and other materials, polypropylene, polyethylene, polybutylene, polyurethanes, Teflon®, etc.), polysaccharides, nylon or nitrocellulose, resins, silica or silica-based materials (including silicon and modified silicon), carbon, metals, inorganic glasses, plastics, optical fiber bundles, and a variety of other polymers. In general, the substrates can allow optical detection and do not themselves appreciably fluoresce.

A solid support can be flat (planar), although as will be appreciated by those in the art, other configurations of substrates may be used as well; for example, three dimensional configurations can be used, for example by embedding beads in a porous block of plastic that allows sample access to the beads and using a confocal microscope for detection. Similarly, the beads may be placed on the inside surface of a tube, for flow-through sample analysis to minimize sample volume. In some aspects substrates include optical fiber bundles and flat planar substrates such as glass, polystyrene and other plastics and acrylics. A bead includes a small discrete particle, the composition of which will depend on the class of probe used and the method of synthesis. Suitable bead compositions include those used in peptide, nucleic acid and organic moiety synthesis, including, but not limited to, plastics, ceramics, glass, polystyrene, methylstyrene, acrylic polymers, paramagnetic materials, thoria sol, carbon graphite, titanium dioxide, latex or cross-linked dextrans such as Sepharose, cellulose, nylon, cross-linked micelles and Teflon® may all be used. “Microsphere Detection Guide” from Bangs Laboratories, Fishers Ind. is a helpful guide.

A “label” or the phrase “detectably labeled” when used in reference to a complex or reaction product, refers to a composition that, when linked with a complex or reaction product, renders the complex or reaction product detectable, for example, by spectroscopic, photochemical, biochemical, immunochemical, or chemical means. Exemplary labels include, but are not limited to, radioactive isotopes, magnetic beads, metallic beads, colloidal particles, fluorescent dyes, enzymes, biotin, digoxigenin, haptens, and the like. A “labeled complex” or “labeled reaction product” is generally one that is bound, either covalently, through a linker or a chemical bond, or noncovalently, through ionic bonds, van der Waals forces, electrostatic attractions, hydrophobic interactions, or hydrogen bonds, to a label such that the presence of the complex or reaction product can be detected by detecting the presence of the label bound to the complex or reaction product.

As used herein, and unless otherwise specified, the term “effective amount” of a compound is an amount sufficient to provide a therapeutic benefit in the treatment of a hematological cancer (e.g., DLBCL), or to delay or minimize one or more symptoms associated with the presence of the hematological cancer (e.g., DLBCL). The term “effective amount” can encompass an amount that improves overall therapy, reduces or avoids symptoms or causes of the hematological cancer (e.g., DLBCL), or enhances the therapeutic efficacy of another therapeutic agent. In certain embodiments, an effective amount of a drug improves overall survival, disease free survival, objective response rate, time to tumor progression, progression free survival or time-to-treatment failure. In some embodiments, an effective amount of a drug results in a partial response or complete response.

The term “responsiveness” or “responsive” when used in reference to a treatment refers to the degree of effectiveness of treatment with the drug for a patient having a hematological cancer (e.g., DLBCL). For example, the term “increased responsiveness” when used in reference to treatment of a DLBCL patient can refer to an increase in the effectiveness of the drug when measured using any methods known in the art. As another example, response of a DLBCL patient to the drug can be characterized as a complete or partial response. As another example, increased responsiveness of a DLBCL patient to the drug can be characterized as overall survival, disease free survival, objective response rate, time to tumor progression, progression free survival or time-to-treatment failure. “Responsiveness” or “response” to a treatment as used herein can also refer to displaying phenotypic physical or molecular characteristics associated with a compound treatment. “Responsiveness” or “response” to a DLBCL treatment as used herein can be determined based on reduced symptoms associated with DLBCL, such as fever, weight loss, or night sweats. In some embodiments, whether a DLBCL patient is responsive to a treatment is determined by staging tests, which are tests helpful for determining which areas of the body have been affected by follicular lymphoma. Such tests include, but not limited to, blood tests, bone marrow biopsy, CT scan, and PET/CT scan. Staging involves dividing patients into groups (stages) based upon how much of the lymphatic system is involved. Stages of lymphoma can be defined as follows: stage I—only one lymph node region is involved, only one lymph structure is involved, or only one extranodal site (IE) is involved; stage II—two or more lymph node regions or lymph node structures on the same side of the diaphragm are involved; stage III—lymph node regions or structures on both sides of the diaphragm are involved; stage IV—there is widespread involvement of a number of organs or tissues other than lymph node regions or structures, such as the liver, lung, or bone marrow. A lymph node region refers to an area of lymph nodes and the surrounding tissue, e.g., the cervical nodes in the neck, the axillary nodes in the armpit, the inguinal nodes in the groin, and the mediastinal nodes in the chest. A lymph structure refers to an organ or a structure that is part of the lymphatic system, such as the lymph nodes, spleen, and thymus gland.

“Complete response” refers to an absence of clinically detectable disease with normalization of any previously abnormal radiographic studies, bone marrow, and cerebrospinal fluid (CSF) or abnormal monoclonal protein measurements. “Partial response” refers to at least about a 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% decrease in any measurable tumor burden (i.e., the number of malignant cells present in the subject, or the measured bulk of tumor masses or the quantity of abnormal monoclonal protein) in the absence of new lesions.

“Overall survival” is defined as the time from randomization until death from any cause, and is measured in the intent-to-treat population. Overall survival should be evaluated in randomized controlled studies. Demonstration of a statistically significant improvement in overall survival can be considered to be clinically significant if the toxicity profile is acceptable, and has often supported new drug approval.

Several endpoints are based on tumor assessments. These endpoints include disease free survival (DFS), objective response rate (ORR), time to progression (TTP), progression-free survival (PFS), and time-to-treatment failure (TTF). The collection and analysis of data on these time-dependent endpoints are based on indirect assessments, calculations, and estimates (e.g., tumor measurements).

Generally, “disease free survival” (DFS) is defined as the time from randomization until recurrence of tumor or death from any cause. Although overall survival is a conventional endpoint for most adjuvant settings, DFS can be an important endpoint in situations where survival may be prolonged, making a survival endpoint impractical. DFS can be a surrogate for clinical benefit or it can provide direct evidence of clinical benefit. This determination is based on the magnitude of the effect, its risk-benefit relationship, and the disease setting. The definition of DFS can be complicated, particularly when deaths are noted without prior tumor progression documentation. These events can be scored either as disease recurrences or as censored events. Although all methods for statistical analysis of deaths have some limitations, considering all deaths (deaths from all causes) as recurrences can minimize bias. DFS can be overestimated using this definition, especially in patients who die after a long period without observation. Bias can be introduced if the frequency of long-term follow-up visits is dissimilar between the study arms or if dropouts are not random because of toxicity.

“Objective response rate” (ORR) is defined as the proportion of patients with tumor size reduction of a predefined amount and for a minimum time period. Response duration usually is measured from the time of initial response until documented tumor progression. Generally, the FDA has defined ORR as the sum of partial responses plus complete responses. When defined in this manner, ORR is a direct measure of drug antitumor activity, which can be evaluated in a single-arm study. If available, standardized criteria should be used to ascertain response. A variety of response criteria have been considered appropriate (e.g., RECIST criteria) (Therasse et al., (2000) J. Natl. Cancer Inst, 92: 205-16). The significance of ORR is assessed by its magnitude and duration, and the percentage of complete responses (no detectable evidence of tumor).

“Time to progression” (TTP) and “progression-free survival” (PFS) have served as primary endpoints for drug approval. TTP is defined as the time from randomization until objective tumor progression; TTP does not include deaths. PFS is defined as the time from randomization until objective tumor progression or death. Compared with TTP, PFS is the preferred regulatory endpoint. PFS includes deaths and thus can be a better correlate to overall survival. PFS assumes patient deaths are randomly related to tumor progression. However, in situations where the majority of deaths are unrelated to cancer, TTP can be an acceptable endpoint.

As an endpoint to support drug approval, PFS can reflect tumor growth and be assessed before the determination of a survival benefit. Its determination is not confounded by subsequent therapy. For a given sample size, the magnitude of effect on PFS can be larger than the effect on overall survival. However, the formal validation of PFS as a surrogate for survival for the many different malignancies that exist can be difficult. Data are sometimes insufficient to allow a robust evaluation of the correlation between effects on survival and PFS. Cancer trials are often small, and proven survival benefits of existing drugs are generally modest. The role of PFS as an endpoint to support licensing approval varies in different cancer settings. Whether an improvement in PFS represents a direct clinical benefit or a surrogate for clinical benefit depends on the magnitude of the effect and the risk-benefit of the new treatment compared to available therapies.

“Time-to-treatment failure” (TTF) is defined as a composite endpoint measuring time from randomization to discontinuation of treatment for any reason, including disease progression, treatment toxicity, and death. TTF is not recommended as a regulatory endpoint for drug approval. TTF does not adequately distinguish efficacy from these additional variables. A regulatory endpoint should clearly distinguish the efficacy of the drug from toxicity, patient or physician withdrawal, or patient intolerance.

The term “similar” or an equivalent thereof as used herein refers to resembling the reference or comparative term. In some embodiments, a numerical value is the value similar to a reference value if the difference between the numerical value and the reference value is within 5%, 10%, 15%, 20%, or 25% of the reference value. In some embodiments, a numerical value is similar to a reference value if the difference between the numerical value and the reference value is within 0.5% to 5%, 5% to 10%, 10% to 20%, or 15% to 25% of the reference value. In other embodiments, the term “similar” or an equivalent thereof is determined based on multiple features (or values) of a sample, for example, the expression levels of multiple genes of a patient. In yet other embodiments, the term “similar” or an equivalent thereof refers to belonging to the same group as determined by a classifier. In some embodiments, the term “similar” or an equivalent thereof is a relative term. For example, the similarity of a sample to a first reference sample is determined by comparing the sample with the first reference sample and a second reference sample, i.e., the sample is similar to the first reference sample as compared with the second reference sample.

The term “not similar,” “differential,” or an equivalent thereof as used herein refers to not resembling the reference or comparative term. In certain embodiments, a numerical value is the value not similar or differential to a reference value if the difference between the numerical value and the reference value is greater than 25% of the reference value. In other embodiments, the term “not similar,” “differential,” or an equivalent thereof is determined based on multiple features (or values) of a sample, for example, the expression levels of multiple genes of a patient. In yet other embodiments, the term “not similar,” “differential,” or an equivalent thereof refers to not belonging to the same group as determined by a classifier. In some embodiments, the term “not similar,” “differential,” or an equivalent thereof is a relative term. For example, a sample is not similar to a first reference sample is determined by comparing the sample with the first reference sample and a second reference sample, i.e., the sample is not similar to the first reference sample as compared with the second reference sample.

The term “predict” generally means to determine or tell in advance. When used to “predict” the effectiveness of a drug to treat a patient having a hematological cancer (e.g., DLBCL), the term can mean, for example, the likelihood that the patient having the hematological cancer (e.g., DLBCL) will be responsive to the drug, as assessed prior to treatment with the drug or within a short period (e.g., within in hours, 1, 2, 3, 4 or more days, 1 week, or 2 weeks) of the treatment with the drug has begun.

As used herein the terms “polypeptide” and “protein” as used interchangeably herein, refer to a polymer of amino acids of three or more amino acids in a serial array, linked through peptide bonds. The term “polypeptide” includes proteins, protein fragments, protein analogues, oligopeptides and the like. The term polypeptide as used herein can also refer to a peptide. The amino acids making up the polypeptide may be naturally derived, or may be synthetic. The polypeptide can be purified from a biological sample.

An mRNA that is “upregulated” is generally increased upon a given treatment or condition. An mRNA that is “downregulated” generally refers to a decrease in the level of expression of the mRNA in response to a given treatment or condition. In some situations, the mRNA level can remain unchanged upon a given treatment or condition.

An mRNA from a patient sample can be “upregulated” when treated with a drug, as compared to a control. This upregulation can be, for example, an increase of about 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 90%, 100%, 200%, 300%, 500%, 1,000%, 5,000% or more of the comparative control mRNA level.

Alternatively, an mRNA can be “downregulated”, or expressed at a lower level, in response to administration of certain drug or other therapies. A downregulated mRNA can be, for example, present at a level of about 99%, 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, 1% or less of the comparative control mRNA level.

Similarly, a protein that is “upregulated” is generally increased upon a given treatment or condition. A protein that is “downregulated” generally refers to a decrease in the level of the protein in response to a given treatment or condition. In some situations, the protein level can remain unchanged upon a given treatment or condition.

The level of a polypeptide or protein biomarker from a patient sample can be increased when treated with a drug, as compared to a non-treated control. This increase can be about 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 90%, 100%, 200%, 300%, 500%, 1,000%, 5,000% or more of the comparative control protein level.

Alternatively, the level of a protein biomarker can be decreased in response to administration of certain drugs/compounds or other agents. This decrease can be, for example, present at a level of about 99%, 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, 1% or less of the comparative control protein level.

The terms “determining”, “measuring”, “evaluating”, “assessing” and “assaying” as used herein generally refer to any form of measurement, and include determining if an element is present or not. These terms include both quantitative and/or qualitative determinations. Assessing may be relative or absolute. “Assessing the presence of” can include determining the amount of something present, as well as determining whether it is present or absent.

The terms “nucleic acid” and “polynucleotide” are used interchangeably herein to describe a polymer of any length composed of nucleotides, e.g., deoxyribonucleotides or ribonucleotides, or compounds produced synthetically, which can hybridize with naturally occurring nucleic acids in a sequence specific manner analogous to that of two naturally occurring nucleic acids, e.g., can participate in Watson-Crick base pairing interactions. As used herein in the context of a polynucleotide sequence, the term “bases” (or “base”) is synonymous with “nucleotides” (or “nucleotide”), i.e., the monomer subunit of a polynucleotide. A nucleic acid also includes, in some aspects, a complementary DNA (cDNA). The terms “nucleoside” and “nucleotide” are intended to include those moieties that contain not only the known purine and pyrimidine bases, but also other heterocyclic bases that have been modified. Such modifications include methylated purines or pyrimidines, acylated purines or pyrimidines, alkylated riboses or other heterocycles. In addition, the terms “nucleoside” and “nucleotide” include those moieties that contain not only conventional ribose and deoxyribose sugars, but other sugars as well. Modified nucleosides or nucleotides also include modifications on the sugar moiety, e.g., wherein one or more of the hydroxyl groups are replaced with halogen atoms or aliphatic groups, or are functionalized as ethers, amines, or the like. “Analogues” refer to molecules having structural features that are recognized in the literature as being mimetics, derivatives, having analogous structures, or other like terms, and include, for example, polynucleotides incorporating non-natural nucleotides, nucleotide mimetics such as 2′-modified nucleosides, peptide nucleic acids, oligomeric nucleoside phosphonates, and any polynucleotide that has added substituent groups, such as protecting groups or linking moieties.

The terms “isolated” and “purified” refer to isolation of a substance (such as mRNA or protein) such that the substance comprises a substantial portion of the sample in which it resides, i.e. greater than the substance is typically found in its natural or un-isolated state. Typically, a substantial portion of the sample comprises, e.g., greater than 1%, greater than 2%, greater than 5%, greater than 10%, greater than 20%, greater than 50%, or more, usually up to about 90%-100% of the sample. For example, a sample of isolated mRNA can typically comprise at least about 1% total mRNA. Techniques for purifying polynucleotides are well known in the art and include, for example, gel electrophoresis, ion-exchange chromatography, affinity chromatography, flow sorting, and sedimentation according to density.

The term “sample” as used herein relates to a material or mixture of materials, typically, although not necessarily, in fluid form, containing one or more elements of interest.

The term “sample,” as used herein, can be a biological sample or a synthetically derived sample.

“Biological sample” as used herein refers to a sample obtained from a biological subject, including sample of biological tissue or fluid origin, obtained, reached, or collected in vivo or in situ. A biological sample also includes samples from a region of a patient containing precancerous or cancer cells or tissues. Such samples can be, but are not limited to, organs, tissues, fractions and cells isolated from a patient. Exemplary biological samples include but are not limited to cell lysate, a cell culture, a cell line, a tissue, oral tissue, gastrointestinal tissue, an organ, an organelle, a biological fluid, a blood sample, a urine sample, a skin sample, and the like. Other exemplary biological samples include whole blood, partially purified blood, PBMCs, tissue biopsies, and the like. In a specific embodiment, the biological sample is a tumor biopsy.

A “classifier” refers to a mathematical function that separates two or more groups, bodies or distributions of data according to the values with which each example of data is described, or an output of a function applied to those values in a manner that may be used to assign new data examples to one or more of the groups, bodies or distributions separated. The term “classifier” as used herein also includes regression or a function on data values that maps to a continuous target variable associated with the data examples, such as a function that relates gene expression values to PFS.

A biological marker or “biomarker” is a substance whose detection indicates a particular biological state, such as, for example, the responsiveness of a patient having a hematological cancer (e.g., DLBCL) to treatment with a drug. In some embodiments, biomarkers can either be measured individually, or several biomarkers can be measured simultaneously.

In some embodiments, a “biomarker” indicates a change in the level of mRNA expression that may correlate with responsiveness of a patient having a hematological cancer (e.g., DLBCL) to treatment with a drug. In some embodiments, the biomarker is a nucleic acid, such as an mRNA or cDNA.

In additional embodiments, a “biomarker” indicates a change in the level of polypeptide or protein expression that may correlate with the responsiveness of a patient having a hematological cancer (e.g., DLBCL) to treatment with a drug. In some embodiments, the biomarker can be a polypeptide or protein, or a fragment thereof. The relative level of specific proteins can be determined by methods known in the art. For example, antibody based methods, such as an immunoblot, enzyme-linked immunosorbent assay (ELISA), or other methods can be used.

As used herein and unless otherwise indicated, the term “pharmaceutically acceptable salt” encompasses non-toxic acid and base addition salts of the compound to which the term refers. Acceptable non-toxic acid addition salts include those derived from organic and inorganic acids or bases know in the art, which include, for example, hydrochloric acid, hydrobromic acid, phosphoric acid, sulfuric acid, methanesulphonic acid, acetic acid, tartaric acid, lactic acid, succinic acid, citric acid, malic acid, maleic acid, sorbic acid, aconitic acid, salicylic acid, phthalic acid, embolic acid, enanthic acid, and the like.

Compounds that are acidic in nature are capable of forming salts with various pharmaceutically acceptable bases. The bases that can be used to prepare pharmaceutically acceptable base addition salts of such acidic compounds are those that form non-toxic base addition salts, i.e., salts containing pharmacologically acceptable cations such as, but not limited to, alkali metal or alkaline earth metal salts and the calcium, magnesium, sodium or potassium salts in particular. Suitable organic bases include, but are not limited to, N,N-dibenzylethylenediamine, chloroprocaine, choline, diethanolamine, ethylenediamine, meglumaine (N-methylglucamine), lysine, and procaine.

As used herein and unless otherwise indicated, the term “solvate” means a compound provided herein or a salt thereof, which further includes a stoichiometric or non-stoichiometric amount of solvent bound by non-covalent intermolecular forces. Where the solvent is water, the solvate is a hydrate.

As used herein the term “enantiomer,” “isomer” or “stereoisomer” encompasses all enantiomerically/stereomerically pure and enantiomerically/stereomerically enriched compounds provided herein.

As used herein and unless otherwise indicated, the term “stereomerically pure” means a composition that comprises one stereoisomer of a compound and is substantially free of other stereoisomers of that compound. For example, a stereomerically pure composition of a compound having one chiral center will be substantially free of the opposite enantiomer of the compound. A stereomerically pure composition of a compound having two chiral centers will be substantially free of other diastereomers of the compound. A typical stereomerically pure compound comprises greater than about 80% by weight of one stereoisomer of the compound and less than about 20% by weight of other stereoisomers of the compound, more preferably greater than about 90% by weight of one stereoisomer of the compound and less than about 10% by weight of the other stereoisomers of the compound, even more preferably greater than about 95% by weight of one stereoisomer of the compound and less than about 5% by weight of the other stereoisomers of the compound, and most preferably greater than about 97% by weight of one stereoisomer of the compound and less than about 3% by weight of the other stereoisomers of the compound. As used herein and unless otherwise indicated, the term “stereomerically enriched” means a composition that comprises greater than about 60% by weight of one stereoisomer of a compound, preferably greater than about 70% by weight, more preferably greater than about 80% by weight of one stereoisomer of a compound. As used herein and unless otherwise indicated, the term “enantiomerically pure” means a stereomerically pure composition of a compound having one chiral center. Similarly, the term “stereomerically enriched” means a stereomerically enriched composition of a compound having one chiral center.

As used herein the term “polymorph” means solid crystalline forms of a compound provided herein or complex thereof. Different polymorphs of the same compound can exhibit different physical, chemical and/or spectroscopic properties.

It should be noted that if there is a discrepancy between a depicted structure and a name given that structure, the depicted structure is to be accorded more weight. In addition, if the stereochemistry of a structure or a portion of a structure is not indicated with, for example, bold or dashed lines, the structure or portion of the structure is to be interpreted as encompassing all stereoisomers of it.

The practice of the embodiments provided herein will employ, unless otherwise indicated, conventional techniques of molecular biology, microbiology, and immunology, which are within the skill of those working in the art. Such techniques are explained fully in the literature. Examples of particularly suitable texts for consultation include the following: Sambrook et al. (1989) Molecular Cloning; A Laboratory Manual (2d ed.); D. N Glover, ed. (1985) DNA Cloning, Volumes I and II; M. J. Gait, ed. (1984) Oligonucleotide Synthesis; B. D. Hames & SJ. Higgins, eds. (1984) Nucleic Acid Hybridization; B. D. Hames & S. J. Higgins, eds. (1984) Transcription and Translation; R. I. Freshney, ed. (1986) Animal Cell Culture; Immobilized Cells and Enzymes (IRL Press, 1986); Immunochemical Methods in Cell and Molecular Biology (Academic Press, London); Scopes (1987) Protein Purification: Principles and Practice (2d ed.; Springer Verlag, N.Y.); and D. M. Weir and C. C. Blackwell, eds. (1986) Handbook of Experimental Immunology, Volumes I-IV.

5.2 Methods for Predicting Responsiveness of Patients Having a Hematological Cancer (e.g., DLBCL) to Treatment Compounds

The present application is based, in part, on the finding that the expression levels of several groups of genes (e.g., those listed in Tables 1, 2, 3, and 4) correlate with responsiveness of a patient having a hematological cancer (e.g., DLBCL) to certain treatment compounds, such as lenalidomide or Compound A. Thus, in one aspect, provided herein are methods that can use the biomarkers and/or classifiers identified in Tables 1, 2, 3, and 4 below to predict responsiveness of a patient having a hematological cancer (e.g., DLBCL) to a drug, such as lenalidomide or Compound A.

TABLE 1

Gene

BICC1

C10orf54

C1RL

C20orf112

C8orf4

CCDC88C

CD97

CEBPB

CFB

CHRDL1

CIRH1A

CPSF3

CSF1R

DDR2

DLGAP5

DUSP1

EMR2

EPB41L3

FBXO32

GAS2L1

GINS4

IFITM2

KCNMB1

LGALS3BP

LRP11

MEGF6

MEIS1

MYO1F

PDGFC

PRNP

PTPRM

RAB31

RBBP8

RUVBL1

SEMA3C

SERPING1

SMOC2

SNRPD1

TBC1D2B

THEMIS2

TMEM173

TPSAB1

TRIP13

TXNIP

ULK1

WDR12

WWTR1

XAF1

ZNF215

TABLE 2

Gene

ALKBH2

AOAH

C10orf54

C1RL

C8orf4

CD84

CD97

CDC20

CEBPB

CEBPD

CHRDL1

CIRH1A

CPSF3

CYP1B1

FBXO32

FOSL2

GAS2L1

GBP3

GINS4

GNAQ

GPR155

GPRIN3

HJURP

KCNMB1

LRP11

MAFB

MEGF6

MEIS1

MYO1F

MYOF

PCSK5

PDGFC

PHACTR2

PTPRM

RAB31

RALGDS

RUVBL1

SASH1

SERPING1

SMOC2

SNRPD1

SSH1

TBC1D2B

TMEM173

TP53INP2

TRIP13

ULK1

WWTR1

XAF1

ZNF215

TABLE 3

Gene

C10orf54

C1RL

C20orf112

C8orf4

CCDC88C

CILP

CIRH1A

CLU

CPVL

CSF1R

CTSB

EPB41L3

FBXO32

IFI44

LRP11

MEGF6

MEIS1

PLAT

SERPING1

SPC25

THEMIS2

TPSAB1

ULK1

XAF1

ZNF215

TABLE 4

Gene

C10orf54

C1RL

C20orf112

C8orf4

CCNB1

CDC6

CILP

CLU

CPSF3

CPVL

CSF1R

CTSB

EPB41L3

FBXO32

IFI44

MEIS1

PHACTR2

PLAT

RUVBL1

SERPING1

SNRPD1

TRIP13

XAF1

ZNF215

In some embodiments, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a biological sample from the patient having the hematological cancer;

(b) determining the expression level of one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4; and

In some embodiments, the expression levels of all genes identified in Tables 1, 2, 3, or 4 is determined. In some embodiments, the expression levels of all genes identified in Table 1 is determined. In some embodiments, the expression levels of all genes identified in Table 2 is determined. In some embodiments, the expression levels of all genes identified in Table 3 is determined. In some embodiments, the expression levels of all genes identified in Table 4 is determined.

In a specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a biological sample from the patient having the hematological cancer;

(b) determining the expression levels of all the genes identified in Table 1; and

(c) comparing the expression levels of the genes identified in Table 1 in the biological sample with reference expression levels of the same genes;

wherein the differential expression levels of the genes identified in Table 1 in the biological sample relative to the reference expression levels of the genes identified in Table 1 indicates that the first patient will be responsive to the treatment compound; and

In another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a biological sample from the patient having the hematological cancer;

(b) determining the expression levels of all the genes identified in Table 2; and

(c) comparing the expression levels of the genes identified in Table 2 in the biological sample with reference expression levels of the same genes;

wherein the differential expression levels of the genes identified in Table 2 in the biological sample relative to the reference expression levels of the genes identified in Table 2 indicates that the first patient will be responsive to the treatment compound; and

In yet another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a biological sample from the patient having the hematological cancer;

(b) determining the expression levels of all the genes identified in Table 3; and

(c) comparing the expression levels of the genes identified in Table 3 in the biological sample with reference expression levels of the same genes;

wherein the differential expression levels of the genes identified in Table 3 in the biological sample relative to the reference expression levels of the genes identified in Table 3 indicates that the first patient will be responsive to the treatment compound; and

In yet another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a biological sample from the patient having the hematological cancer;

(b) determining the expression levels of all the genes identified in Table 4; and

(c) comparing the expression levels of the genes identified in Table 4 in the biological sample with reference expression levels of the same genes;

wherein the differential expression levels of the genes identified in Table 4 in the biological sample relative to the reference expression levels of the genes identified in Table 4 indicates that the first patient will be responsive to the treatment compound; and

wherein the treatment compound is 3-(4-amino-1-oxo-1,3-dihydro-isoindol-2-yl)-piperidine-2,6-dione (lenalidomide), 3-(5-amino-2-methyl-4-oxo-4H-quinazolin-3-yl)-piperidine-2,6-dione (Compound A), or a stereoisomer, pharmaceutically acceptable salt, solvate, hydrate, co-crystal, clathrate, or a polymorph thereof. In certain embodiments, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression level of one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4; and

In some embodiments, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression level of one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4; and

In a specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression levels of all genes identified in Table 1; and

(c) comparing the expression levels of the genes identified in Table 1 in the first biological sample with the expression levels of the same genes in a second biological sample obtained from a second patient having the hematological cancer, wherein the second patient is not responsive to the treatment compound,

wherein the differential expression levels of the genes identified in Table 1 in the first biological sample relative to the expression levels of the genes identified in Table 1 in the second biological sample indicates that the first patient will be responsive to the treatment compound, and

In another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression levels of all genes identified in Table 1; and

wherein the similar expression levels of the genes identified in Table 1 in the first biological sample relative to the expression levels of the genes identified in Table 1 in the second biological sample indicates that the first patient will be responsive to the treatment compound, and

In yet another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression levels of all genes identified in Table 2; and

(c) comparing the expression levels of the genes identified in Table 2 in the first biological sample with the expression levels of the same genes in a second biological sample obtained from a second patient having the hematological cancer, wherein the second patient is not responsive to the treatment compound,

wherein the differential expression levels of the genes identified in Table 2 in the first biological sample relative to the expression levels of the genes identified in Table 2 in the second biological sample indicates that the first patient will be responsive to the treatment compound, and

In yet another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression levels of all genes identified in Table 2; and

wherein the similar expression levels of the genes identified in Table 2 in the first biological sample relative to the expression levels of the genes identified in Table 2 in the second biological sample indicates that the first patient will be responsive to the treatment compound, and

In yet another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression levels of all genes identified in Table 3; and

(c) comparing the expression levels of the genes identified in Table 3 in the first biological sample with the expression levels of the same genes in a second biological sample obtained from a second patient having the hematological cancer, wherein the second patient is not responsive to the treatment compound,

wherein the differential expression levels of the genes identified in Table 3 in the first biological sample relative to the expression levels of the genes identified in Table 3 in the second biological sample indicates that the first patient will be responsive to the treatment compound, and

In yet another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression levels of all genes identified in Table 3; and

wherein the similar expression levels of the genes identified in Table 3 in the first biological sample relative to the expression levels of the genes identified in Table 3 in the second biological sample indicates that the first patient will be responsive to the treatment compound, and

In yet another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression levels of all genes identified in Table 4; and

(c) comparing the expression levels of the genes identified in Table 4 in the first biological sample with the expression levels of the same genes in a second biological sample obtained from a second patient having the hematological cancer, wherein the second patient is not responsive to the treatment compound,

wherein the differential expression levels of the genes identified in Table 4 in the first biological sample relative to the expression levels of the genes identified in Table 4 in the second biological sample indicates that the first patient will be responsive to the treatment compound, and

In yet another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression levels of all genes identified in Table 4; and

wherein the similar expression levels of the genes identified in Table 4 in the first biological sample relative to the expression levels of the genes identified in Table 4 in the second biological sample indicates that the first patient will be responsive to the treatment compound, and

In other embodiments, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression level of one, two, three, four, five, or more of the genes identified in Tables 1, 2, 3, and/or 4; and

- (i) the gene expression profile of the one, two, three, four, five, or more of the genes in a second biological sample from a second patient having the hematological cancer, wherein the second patient is responsive to the treatment compound, and
- (ii) the gene expression profile of the one, two, three, four, five, or more of the genes in a third biological sample from a third patient having the hematological cancer, wherein the third patient is not responsive to the treatment compound,

In a specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression levels of genes identified in Table 1; and

- (i) the gene expression profile of the genes identified in Table 1 in a second biological sample from a second patient having the hematological cancer, wherein the second patient is responsive to the treatment compound, and
- (ii) the gene expression profile of the genes identified in Table 1 in a third biological sample from a third patient having the hematological cancer, wherein the third patient is not responsive to the treatment compound,
  
  wherein the similar expression levels of the genes in the first biological sample relative to the expression levels of the genes in the second biological sample indicates that the first patient will be responsive to the treatment compound, and the similar expression levels of the genes in the first biological sample relative to the expression levels of the genes in the third biological sample indicates that the first patient will not be responsive to the treatment compound, and wherein the treatment compound is 3-(4-amino-1-oxo-1,3-dihydro-isoindol-2-yl)-piperidine-2,6-dione (lenalidomide), 3-(5-amino-2-methyl-4-oxo-4H-quinazolin-3-yl)-piperidine-2,6-dione (Compound A), or a stereoisomer, pharmaceutically acceptable salt, solvate, hydrate, co-crystal, clathrate, or a polymorph thereof.

In another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression levels of genes identified in Table 2; and

- (i) the gene expression profile of the genes identified in Table 2 in a second biological sample from a second patient having the hematological cancer, wherein the second patient is responsive to the treatment compound, and
- (ii) the gene expression profile of the genes identified in Table 2 in a third biological sample from a third patient having the hematological cancer, wherein the third patient is not responsive to the treatment compound,

wherein the similar expression levels of the genes in the first biological sample relative to the expression levels of the genes in the second biological sample indicates that the first patient will be responsive to the treatment compound, and the similar expression levels of the genes in the first biological sample relative to the expression levels of the genes in the third biological sample indicates that the first patient will not be responsive to the treatment compound, and

wherein the treatment compound is 3-(4-amino-1-oxo-1,3-dihydro-isoindol-2-yl)-piperidine-2,6-dione (lenalidomide), 3-(5-amino-2-methyl-4-oxo-4H-quinazolin-3-yl)-piperidine-2,6-dione (Compound A), or a stereoisomer, pharmaceutically acceptable salt, solvate, hydrate, co-crystal, clathrate, or a polymorph thereof. In yet another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression levels of genes identified in Table 3; and

- (i) the gene expression profile of the genes identified in Table 3 in a second biological sample from a second patient having the hematological cancer, wherein the second patient is responsive to the treatment compound, and
- (ii) the gene expression profile of the genes identified in Table 3 in a third biological sample from a third patient having the hematological cancer, wherein the third patient is not responsive to the treatment compound,

In yet another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising:

(a) obtaining a first biological sample from a first patient having the hematological cancer;

(b) determining the expression levels of genes identified in Table 4; and

- (i) the gene expression profile of the genes identified in Table 4 in a second biological sample from a second patient having the hematological cancer, wherein the second patient is responsive to the treatment compound, and
- (ii) the gene expression profile of the genes identified in Table 4 in a third biological sample from a third patient having the hematological cancer, wherein the third patient is not responsive to the treatment compound,

In some embodiments, expression levels of multiple genes (biomarkers) provided herein are used to predict a patient's response to the compound provided herein. When expression levels of multiple genes (biomarkers) are used to predict a patient's response to a treatment with the compound provided herein, e.g., lenalidomide or Compound A, any classifier that classifies based on two or more features can be used in the present disclosure. For example, in some embodiments, a population of DLBCL patients that have received a treatment with the compound provide herein (e.g., lenalidomide or Compound A) are divided into two groups based on their responsiveness to the compound treatment, i.e., responsive patient group and non-responsive patient group. The expression levels of two or more genes provided herein (e.g., those listed in Tables 1-4) for each patient in the population are analyzed using a classifier and a score can be generated. A reference score or a threshold score can be generated based on the scores of the responsive patients and the scores of non-responsive patients. Such reference score can be used to predict a patient's responsiveness to the compound based on the expression levels of the genes of this patient.

In some embodiments, the methods provided herein further comprise: (a) determining the expression levels of the genes or a subset thereof provided herein in a biological sample from a population of subjects that previously have been administered with the compound provided herein, (b) generating a score for the expression levels of the genes or a subset thereof provided herein for each subject of the population; (c) differentiating the subjects that are responsive to the compound provided herein from those subjects that are not responsive to the compound provided herein; and (d) determining responsiveness to the compound provided herein based on the scores for the subjects that are responsive to the compound and those subjects that are not responsive to the compound.

In other embodiments, the methods provided herein further comprise: (a) determining the expression levels of the genes or a subset thereof provided herein in a biological sample from a population of subjects that previously have been administered with the compound, (b) generating a score for the expression levels of the genes or a subset thereof provided herein for each subject of the population; (c) differentiating the subjects that are responsive to the compound provided herein from those subjects that are not responsive to compound provided herein; and (d) generating a reference score that is predictive of the responsiveness of a subject to the compound provided herein using a model based on the scores for the subjects that are responsive to the compound and those subjects that are not responsive to the compound.

In a specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising: (a) obtaining a biological sample from a patient having the hematological cancer; (b) determining the expression of the genes identified in Table 1 in the biological sample and generating a score based on the expression of the genes, (c) comparing the score to a reference score, wherein a score similar to a reference score indicates that the patient will be responsive to the treatment compound. In specific embodiments, the reference score is based on the expression of the same genes or subset of genes in a population of patients having the hematological cancer who are responsive to the treatment compound. In a specific embodiment, the treatment compound is a compound disclosed herein. In one embodiment, the treatment compound is lenalidomide. In another embodiment, the treatment compound is Compound A.

In another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising: (a) obtaining a biological sample from a patient having the hematological cancer; (b) determining the expression of the genes identified in Table 2 in the biological sample and generating a score based on the expression of the genes, (c) comparing the score to a reference score, wherein a score similar to a reference score indicates that the patient will be responsive to the treatment compound. In specific embodiments, the reference score is based on the expression of the same genes or subset of genes in a population of patients having the hematological cancer who are responsive to the treatment compound. In a specific embodiment, the treatment compound is a compound disclosed herein. In one embodiment, the treatment compound is lenalidomide. In another embodiment, the treatment compound is Compound A.

In a specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising: (a) obtaining a biological sample from a patient having the hematological cancer; (b) determining the expression of the genes identified in Table 3 in the biological sample and generating a score based on the expression of the genes, (c) comparing the score to a reference score, wherein a score similar to a reference score indicates that the patient will be responsive to the treatment compound. In specific embodiments, the reference score is based on the expression of the same genes or subset of genes in a population of patients having the hematological cancer who are responsive to the treatment compound. In a specific embodiment, the treatment compound is a compound disclosed herein. In one embodiment, the treatment compound is lenalidomide. In another embodiment, the treatment compound is Compound A.

In a specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising: (a) obtaining a biological sample from a patient having the hematological cancer; (b) determining the expression of the genes identified in Table 4 in the biological sample and generating a score based on the expression of the genes, (c) comparing the score to a reference score, wherein a score similar to a reference score indicates that the patient will be responsive to the treatment compound. In specific embodiments, the reference score is based on the expression of the same genes or subset of genes in a population of patients having the hematological cancer who are responsive to the treatment compound. In a specific embodiment, the treatment compound is a compound disclosed herein. In one embodiment, the treatment compound is lenalidomide. In another embodiment, the treatment compound is Compound A.

In another aspect, provided herein are methods for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising: (a) obtaining a biological sample from a patient having the hematological cancer; (b) determining the expression of the genes or a certain subset of genes set forth in Tables 1, 2, 3, and/or 4, or any combination thereof, in the biological sample and generating a score based on the expression of the genes or subset of genes, (c) comparing the score to a first reference score and a second reference score, wherein (i) the score similar to the first reference score indicates that the patient will be responsive to the treatment compound, and/or (ii) the score similar to the second reference score indicates that the patient will be responsive to the treatment compound. In specific embodiments, the first reference score is based on the expression of the same genes or subset of genes in a population of patients having the hematological cancer who are responsive to the treatment compound, and the second reference score is based on the expression of the same genes or subset of genes in a population of patients having the hematological cancer who are not responsive to the treatment compound. In a specific embodiment, the treatment compound is a compound disclosed herein. In one embodiment, the treatment compound is lenalidomide. In another embodiment, the treatment compound is Compound A.

In a specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising: (a) obtaining a biological sample from a patient having the hematological cancer; (b) determining the expression of genes identified in Table 1 in the biological sample and generating a score based on the expression of the genes, (c) comparing the score to a first reference score and a second reference score, wherein (i) the score similar to the first reference score indicates that the patient will be responsive to the treatment compound, and/or (ii) the score similar to the second reference score indicates that the patient will be responsive to the treatment compound, wherein the first reference score is based on the expression of the same genes in a population of patients having the hematological cancer who are responsive to the treatment compound, and the second reference score is based on the expression of the same genes in a population of patients having the hematological cancer who are not responsive to the treatment compound, wherein the treatment compound the treatment compound is lenalidomide or Compound A.

In another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising: (a) obtaining a biological sample from a patient having the hematological cancer; (b) determining the expression of genes identified in Table 2 in the biological sample and generating a score based on the expression of the genes, (c) comparing the score to a first reference score and a second reference score, wherein (i) the score similar to the first reference score indicates that the patient will be responsive to the treatment compound, and/or (ii) the score similar to the second reference score indicates that the patient will be responsive to the treatment compound, wherein the first reference score is based on the expression of the same genes in a population of patients having the hematological cancer who are responsive to the treatment compound, and the second reference score is based on the expression of the same genes in a population of patients having the hematological cancer who are not responsive to the treatment compound, wherein the treatment compound the treatment compound is lenalidomide or Compound A.

In yet another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising: (a) obtaining a biological sample from a patient having the hematological cancer; (b) determining the expression of genes identified in Table 3 in the biological sample and generating a score based on the expression of the genes, (c) comparing the score to a first reference score and a second reference score, wherein (i) the score similar to the first reference score indicates that the patient will be responsive to the treatment compound, and/or (ii) the score similar to the second reference score indicates that the patient will be responsive to the treatment compound, wherein the first reference score is based on the expression of the same genes in a population of patients having the hematological cancer who are responsive to the treatment compound, and the second reference score is based on the expression of the same genes in a population of patients having the hematological cancer who are not responsive to the treatment compound, wherein the treatment compound the treatment compound is lenalidomide or Compound A.

In yet another specific embodiment, provided herein is a method for predicting the responsiveness of a patient having a hematological cancer to a treatment compound, comprising: (a) obtaining a biological sample from a patient having the hematological cancer; (b) determining the expression of genes identified in Table 4 in the biological sample and generating a score based on the expression of the genes, (c) comparing the score to a first reference score and a second reference score, wherein (i) the score similar to the first reference score indicates that the patient will be responsive to the treatment compound, and/or (ii) the score similar to the second reference score indicates that the patient will be responsive to the treatment compound, wherein the first reference score is based on the expression of the same genes in a population of patients having the hematological cancer who are responsive to the treatment compound, and the second reference score is based on the expression of the same genes in a population of patients having the hematological cancer who are not responsive to the treatment compound, wherein the treatment compound the treatment compound is lenalidomide or Compound A.

In some embodiments, the determining step of the methods described herein comprising determining the expression of the genes or a certain subset of genes set forth in Table 1 in the first biological sample. For example, in certain embodiments, the subset of genes can include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 1. In another embodiment, the subset of genes can include between 2 to 40 genes, alternatively between 3 to 45 genes, alternatively between 5 to 15 genes, or alternatively between 10 to 20 genes listed in Table 1. In some embodiments, the determining step of the methods described herein comprises determining the expression of one or more genes selected from the group consisting of BICC1, C10orf54, C1RL, C20orf112, C8orf4, CCDC88C, CD97, CEBPB, CFB, CHRDL1, CIRH1A, CPSF3, CSF1R, DDR2, DLGAP5, DUSP1, EMR2, EPB41L3, FBXO32, GAS2L1, GINS4, IFITM2, KCNMB1, LGALS3BP, LRP11, MEGF6, MEIS1, MYO1F, PDGFC, PRNP, PTPRM, RAB31, RBBP8, RUVBL1, SEMA3C, SERPING1, SMOC2, SNRPD1, TBC1D2B, THEMIS2, TMEM173, TPSAB1, TRIP13, TXNIP, ULK1, WDR12, WWTR1, XAF1, and ZNF215.

In some embodiments of the various methods provided herein, the determining step of the methods described herein comprises determining the expression of all the genes listed in Table 1—BICC1, C10orf54, C1RL, C20orf112, C8orf4, CCDC88C, CD97, CEBPB, CFB, CHRDL1, CIRH1A, CPSF3, CSF1R, DDR2, DLGAP5, DUSP1, EMR2, EPB41L3, FBXO32, GAS2L1, GINS4, IFITM2, KCNMB1, LGALS3BP, LRP11, MEGF6, MEIS1, MYO1F, PDGFC, PRNP, PTPRM, RAB31, RBBP8, RUVBL1, SEMA3C, SERPING1, SMOC2, SNRPD1, TBC1D2B, THEMIS2, TMEM173, TPSAB1, TRIP13, TXNIP, ULK1, WDR12, WWTR1, XAF1, and ZNF215.

In some embodiments, the determining step of the methods described herein comprising determining the expression of the genes or a certain subset of genes set forth in Table 2 in the first biological sample. For example, in certain embodiments, the subset of genes can include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 2. In another embodiment, the subset of genes can include between 2 to 40 genes, or alternatively between 3 to 45 genes, alternatively between 5 to 15 genes, or alternatively 10 to 20 genes listed in Table 2. In some embodiments, the determining step of the methods described herein comprises determining the expression of one or more genes selected from the group consisting of ALKBH2, AOAH, C10orf54, C1RL, C8orf4, CD84, CD97, CDC20, CEBPB, CEBPD, CHRDL1, CIRH1A, CPSF3, CYP1B1, FBXO32, FOSL2, GAS2L1, GBP3, GINS4, GNAQ, GPR155, GPRIN3, HJURP, KCNMB1, LRP11, MAFB, MEGF6, MEIS1, MYO1F, MYOF, PCSK5, PDGFC, PHACTR2, PTPRM, RAB31, RALGDS, RUVBL1, SASH1, SERPING1, SMOC2, SNRPD1, SSH1, TBC1D2B, TMEM173, TP53INP2, TRIP13, ULK1, WWTR1, XAF1, and ZNF215.

In some embodiments of the various methods provided herein, the determining step of the methods described herein comprises determining the expression of all the genes listed in Table 2—ALKBH2, AOAH, C10orf54, C1RL, C8orf4, CD84, CD97, CDC20, CEBPB, CEBPD, CHRDL1, CIRH1A, CPSF3, CYP1B1, FBXO32, FOSL2, GAS2L1, GBP3, GINS4, GNAQ, GPR155, GPRIN3, HJURP, KCNMB1, LRP11, MAFB, MEGF6, MEIS1, MYO1F, MYOF, PCSK5, PDGFC, PHACTR2, PTPRM, RAB31, RALGDS, RUVBL1, SASH1, SERPING1, SMOC2, SNRPD1, SSH1, TBC1D2B, TMEM173, TP53INP2, TRIP13, ULK1, WWTR1, XAF1, and ZNF215.

In some embodiments, the determining step of the methods described herein comprising determining the expression of the genes or a certain subset of genes set forth in Table 3 in the first biological sample. For example, in certain embodiments, the subset of genes can include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, or more genes listed in Table 3. In another embodiment, the subset of genes can include between 2 to 20 genes, or alternatively between 3 to 24 genes, alternatively between 5 to 15 genes, or alternatively 10 to 24 genes listed in Table 3. In some embodiments, the determining step of the methods described herein comprises determining the expression of one or more genes selected from the group consisting of C10orf54, C1RL, C20orf112, C8orf4, CCDC88C, CILP, CIRH1A, CLU, CPVL, CSF1R, CTSB, EPB41L3, FBXO32, IFI44, LRP11, MEGF6, MEIS1, PLAT, SERPING1, SPC25, THEMIS2, TPSAB1, ULK1, XAF1, and ZNF215.

In some embodiments of the various methods provided herein, the determining step of the methods described herein comprises determining the expression of all the genes listed in Table 3—C10orf54, C1RL, C20orf112, C8orf4, CCDC88C, CILP, CIRH1A, CLU, CPVL, CSF1R, CTSB, EPB41L3, FBXO32, IFI44, LRP11, MEGF6, MEIS1, PLAT, SERPING1, SPC25, THEMIS2, TPSAB1, ULK1, XAF1, and ZNF215.

In some embodiments, the determining step of the methods described herein comprising determining the expression of the genes or a certain subset of genes set forth in Table 4 in the first biological sample. For example, in certain embodiments, the subset of genes can include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, or more genes listed in Table 4. In another embodiment, the subset of genes can include between 2 to 20 genes, or alternatively between 3 to 24 genes, alternatively between 5 to 15 genes, or alternatively 10 to 24 genes listed in Table 4. In some embodiments, the determining step of the methods described herein comprises determining the expression of one or more genes selected from the group consisting of C10orf54, C1RL, C20orf112, C8orf4, CCNB1, CDC6, CILP, CLU, CPSF3, CPVL, CSF1R, CTSB, EPB41L3, FBXO32, IFI44, MEIS1, PHACTR2, PLAT, RUVBL1, SERPING1, SNRPD1, TRIP13, XAF1, and ZNF215.

In some embodiments of the various methods provided herein, the determining step of the methods described herein comprises determining the expression of all the genes listed in Table 4—C10orf54, C1RL, C20orf112, C8orf4, CCNB1, CDC6, CILP, CLU, CPSF3, CPVL, CSF1R, CTSB, EPB41L3, FBXO32, IFI44, MEIS1, PHACTR2, PLAT, RUVBL1, SERPING1, SNRPD1, TRIP13, XAF1, and ZNF215.

In some embodiments, the determining step of the methods described herein comprising determining the expression of the genes or a certain subset of genes set forth in a combination of Tables 1, 2, 3, and 4 in the first biological sample. For example, in certain embodiments, the subset of genes can include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 50, 60, 70, or more genes listed in Tables 1, 2, 3, and 4. In another embodiment, the subset of genes can include between 2 to 71 genes, alternatively between 2 to 20 genes, alternatively between 3 to 20 genes, alternatively between 5 to 15 genes, or alternatively 10 to 20 genes listed in Tables 1, 2, 3, and 4. In some embodiments, the determining step of the methods described herein comprises determining the expression of one or more genes (biomarkers) selected from the group consisting of ALKBH2, AOAH, BICC1, C10orf54, C1RL, C20orf112, C8orf4, CCDC88C, CCNB1, CD84, CD97, CDC20, CDC6, CEBPB, CEBPD, CFB, CHRDL1, CILP, CIRH1A, CLU, CPSF3, CPVL, CSF1R, CTSB, CYP1B1, DDR2, DLGAP5, DUSP1, EMR2, EPB41L3, FBXO32, FOSL2, GAS2L1, GBP3, GINS4, GNAQ, GPR155, GPRIN3, HJURP, IFI44, IFITM2, KCNMB1, LGALS3BP, LRP11, MAFB, MEGF6, MEIS1, MYO1F, MYOF, PCSK5, PDGFC, PHACTR2, PLAT, PRNP, PTPRM, RAB31, RALGDS, RBBP8, RUVBL1, SASH1, SEMA3C, SERPING1, SMOC2, SNRPD1, SPC25, SSH1, TBC1D2B, THEMIS2, TMEM173, TP53INP2, TPSAB1, TRIP13, TXNIP, ULK1, WDR12, WWTR1, XAF1, and ZNF215. In one embodiment, the biomarker is ALKBH2. In another embodiment, the biomarker is AOAH. In yet another embodiment, the biomarker is BICC1. In still another embodiment, the biomarker is C10orf54. In one embodiment, the biomarker is C1RL. In another embodiment, the biomarker is C20orf112. In yet another embodiment, the biomarker is C8orf4. In still another embodiment, the biomarker is CCDC88C. In one embodiment, the biomarker is CCNB1. In another embodiment, the biomarker is CD84. In yet another embodiment, the biomarker is CD97. In still another embodiment, the biomarker is CDC20. In one embodiment, the biomarker is CDC6. In another embodiment, the biomarker is CEBPB. In yet another embodiment, the biomarker is CEBPD. In still another embodiment, the biomarker is CFB. In one embodiment, the biomarker is CHRDL1. In another embodiment, the biomarker is CILP. In yet another embodiment, the biomarker is CIRH1A. In still another embodiment, the biomarker is CLU. In one embodiment, the biomarker is CPSF3. In another embodiment, the biomarker is CPVL. In yet another embodiment, the biomarker is CSF1R. In still another embodiment, the biomarker is CTSB. In one embodiment, the biomarker is CYP1B1. In another embodiment, the biomarker is DDR2. In yet another embodiment, the biomarker is DLGAP5. In still another embodiment, the biomarker is DUSP1. In one embodiment, the biomarker is EMR2. In another embodiment, the biomarker is EPB41L3. In yet another embodiment, the biomarker is FBXO32. In still another embodiment, the biomarker is FOSL2. In one embodiment, the biomarker is GAS2L1. In another embodiment, the biomarker is GBP3. In yet another embodiment, the biomarker is GINS4. In still another embodiment, the biomarker is GNAQ. In one embodiment, the biomarker is GPR155. In another embodiment, the biomarker is GPRIN3. In yet another embodiment, the biomarker is HJURP. In still another embodiment, the biomarker is IFI44. In one embodiment, the biomarker is IFITM2. In another embodiment, the biomarker is KCNMB1. In yet another embodiment, the biomarker is LGALS3BP. In still another embodiment, the biomarker is LRP11. In one embodiment, the biomarker is MAFB. In another embodiment, the biomarker is MEGF6. In yet another embodiment, the biomarker is MEIS1. In still another embodiment, the biomarker is MYO1F. In one embodiment, the biomarker is MYOF. In another embodiment, the biomarker is PCSK5. In yet another embodiment, the biomarker is PDGFC. In still another embodiment, the biomarker is PHACTR2. In one embodiment, the biomarker is PLAT. In another embodiment, the biomarker is PRNP. In yet another embodiment, the biomarker is PTPRM. In still another embodiment, the biomarker is RAB31. In one embodiment, the biomarker is RALGDS. In another embodiment, the biomarker is RBBP8. In yet another embodiment, the biomarker is RUVBL1. In still another embodiment, the biomarker is SASH1. In one embodiment, the biomarker is SEMA3C. In another embodiment, the biomarker is SERPING1. In yet another embodiment, the biomarker is SMOC2. In still another embodiment, the biomarker is SNRPD1. In one embodiment, the biomarker is SPC25. In another embodiment, the biomarker is SSH1. In yet another embodiment, the biomarker is TBC1D2B. In still another embodiment, the biomarker is THEMIS2. In one embodiment, the biomarker is TMEM173. In another embodiment, the biomarker is TP53INP2. In yet another embodiment, the biomarker is TPSAB1. In still another embodiment, the biomarker is TRIP13. In one embodiment, the biomarker is TXNIP. In another embodiment, the biomarker is ULK1. In yet another embodiment, the biomarker is WDR12. In still another embodiment, the biomarker is WWTR1. In one embodiment, the biomarker is XAF1. In another embodiment, the biomarker is ZNF215.

Tables 1, 2, 3, and 4 provide lists of genes that can be used as biomarkers to predict the responsiveness of a patient having a hematological cancer (e.g., DLBCL) to a drug. In certain embodiments, a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40, 45, or more genes listed in Table 1, Table 2, Table 3, or Table 4, or any combination thereof, can be used as biomarkers to predict the responsiveness of a DLBCL patient to a drug. In some embodiments, 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, or 40 to 45 genes in Table 1, Table 2, Table 3, or Table 4 can be sued as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

In certain embodiments, a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 1; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 2; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, or more genes listed in Table 3; and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, or more genes listed in Table 4 can be used as biomarkers to predict the responsiveness of a DLBCL patient to a drug. In some embodiments, a combination of 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 1; 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 2; 1 to 5, 5 to 10, 10 to 15, 15 to 20 genes in Table 3; and 1 to 5, 5 to 10, 10 to 15, 15 to 20 genes in Table 4 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

In some embodiments, a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 1 and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 2 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug. In some embodiments, a combination of 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 1 and 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 2 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

In other embodiments, a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 1 and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, or more genes listed in Table 3 can be used as biomarkers to predict the responsiveness of a DLBCL patient to a drug. In other embodiments, a combination of 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 1 and 1 to 5, 5 to 10, 10 to 15, 15 to 20 genes in Table 3 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

In other embodiments, a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 1 and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, or more genes listed in Table 4 can be used as biomarkers to predict the responsiveness of a DLBCL patient to a drug. In other embodiments, a combination of 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 1 and 1 to 5, 5 to 10, 10 to 15, 15 to 20 genes in Table 4 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

In other embodiments, a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 2 and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, or more genes listed in Table 3 can be used as biomarkers to predict the responsiveness of a DLBCL patient to a drug. In other embodiments, a combination of 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 2 and 1 to 5, 5 to 10, 10 to 15, 15 to 20 genes in Table 3 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

In other embodiments, a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 2 and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, or more genes listed in Table 4 can be used as biomarkers to predict the responsiveness of a DLBCL patient to a drug. In other embodiments, a combination of 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 2 and 1 to 5, 5 to 10, 10 to 15, 15 to 20 genes in Table 4 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

In other embodiments, a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, or more genes listed in Table 3 and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, or more genes listed in Table 4 can be used as biomarkers to predict the responsiveness of a DLBCL patient to a drug. In other embodiments, a combination of 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25 genes in Table 3 and 1 to 5, 5 to 10, 10 to 15, 15 to 20 genes in Table 4 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

In some embodiments, a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 1, and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 2, and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, or more genes listed in Table 3 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug. In some embodiments, a combination of 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 1, and 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 2, and 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25 genes in Table 3 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

In some embodiments, a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 1, and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 2, and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, or more genes listed in Table 4 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug. In some embodiments, a combination of 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 1, and 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 2, and 1 to 5, 5 to 10, 10 to 15, 15 to 20 genes in Table 4 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

In some embodiments, a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 2, and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, or more genes listed in Table 3, and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, or more genes listed in Table 4 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug. In some embodiments, a combination of 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 2, and 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25 genes in Table 3, and 1 to 5, 5 to 10, 10 to 15, 15 to 20 genes in Table 4 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

In some embodiments, a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 1, and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, or more genes listed in Table 3, and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, or more genes listed in Table 4 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug. In some embodiments, a combination of 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 1, and 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25 genes in Table 3, and 1 to 5, 5 to 10, 10 to 15, 15 to 20 genes in Table 4 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

In some embodiments, a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 1, and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 25, 30, 35, 40, 45, or more genes listed in Table 2, and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, or more genes listed in Table 3, and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, or more genes listed in Table 4 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug. In some embodiments, a combination of 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 1, and 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45 genes in Table 2, and 1 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25 genes in Table 3, and 1 to 5, 5 to 10, 10 to 15, 15 to 20 genes in Table 4 are used as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

Additionally, in some embodiments, all of the genes listed in Table 1, Table 2, Table 3, and Table 4 can be used as biomarkers to predict the responsiveness of a DLBCL patient to a drug.

The biomarkers in Table 1, Table 2, Table 3, and/or Table 4 can be used by a health professional in combination with other factors to determine whether or not to treat the DLBCL patient with the drug, e.g., lenalidomide or Compound A.

In certain embodiments, in accordance with the methods described herein, the gene expression profile or data is derived from the same type of biological sample. In other words, the biological sample used to generate each gene expression profile or data referenced in the methods is the same type of biological sample. In some embodiments, the biological samples are tumor biopsy samples.

In some embodiments, the determining step of the methods described herein comprises detecting the presence and/or amount of a complex in the biological sample, wherein the presence and/or amount of the complex indicates the expression level of the genes. The complex detected in the methods described herein can be a hybridization complex and in some embodiments, the hybridization complex is attached to a solid support. In further embodiments, the complex is detectably labeled.

In some embodiments, the determining step of the methods described herein comprises detecting the presence and/or amount of a reaction product in the biological sample, wherein the presence and/or amount of the reaction product indicates the expression level of the genes in each subset of genes. In further embodiments, the reaction product is detectably labeled.

In some embodiments, the expression of the genes or biomarkers provided herein is determined by determining the protein levels of the genes or biomarkers. In other embodiments, the expression of the genes or biomarkers provided herein is determined by determining the mRNA levels of the genes or biomarkers. In yet other embodiments, the expression of the genes provided herein is determined by determining the levels of cDNA generated using the mRNA of a gene or biomarker. Accordingly, the upregulation or downregulation of the nucleic acids (e.g., mRNA or cDNA) or proteins of the genes provided herein (e.g., those listed in Tables 1-4) can be used to predict responsiveness of a patient having a hematological cancer (e.g., DLBCL) to a compound treatment.

In some embodiments, a statistical analysis or other analysis is performed on data from the assay utilized to measure an RNA transcript or protein. In certain specific embodiments, the p value of those RNA transcripts or proteins differentially expressed is 0.1, 0.5, 0.4, 0.3, 0.2, 0.01, 0.05, 0.001, or 0.0001. In some embodiments, the p-value provided herein is the output of a statistical test of difference between two or more groups of values or data examples. In specific embodiments, a false discovery rate (FDR) of 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2% or less is selected. FDR correction and associated thresholds are commonly applied to correct p-values for multiple hypothesis testing, i.e. applying the same test or comparison to many groups of values before seeking to assign statistical significance to a subset of the differences observed. In some embodiments, hypothesis testing for difference may also be applied to output of a functional transformation of assay output. In some embodiments, additional processes other than tests of difference can be used to classify or predict drug response from assay output.

In some embodiments, the lymphoma is DLBCL. In one embodiment, the DLBCL is refractory in the first patient. In another embodiment, the DLBCL is relapsed in the first patient. In yet another embodiment, the DLBCL is a GCB-DLBCL in the first patient. In still another embodiment, the DLBCL is ABC-DLBCL in the first patient. In yet another embodiment, the DLBCL is NOS-DLBCL in the first patient.

In some embodiments, the methods described herein include determining the gene expression profile of a subset of genes in DLBCL patients that have taken the drug prior to treatment with the drug, wherein each subset of genes relates to a tumor biopsy composition.

In certain embodiments, the B cells are CD20+ B cells.

In certain embodiments, the dendritic cells are CD11c+ dendritic cells.

In certain embodiments, the T cells are CD3+ T cells. In certain embodiments, the T cells are CD8+ T cells.

In certain embodiments, the macrophage cells are CD163+ macrophage cells.

In certain embodiments, the B cells are CD20+ B cells.

In certain embodiments, the dendritic cells are CD11c+ dendritic cells.

In certain embodiments, the T cells are CD3+ T cells. In certain embodiments, the T cells are CD8+ T cells.

In certain embodiments, the macrophage cells are CD163+ macrophage cells.

In certain embodiments of the foregoing paragraphs in this section, the second patient is a single patient. In other embodiments of the foregoing paragraphs in this section, the second patient is a population of patients. In specific embodiments, the population comprises 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 200, 225, 250, 300 or more patients.

Techniques known to one skilled in the art may be used to measure the proportion of cells in a tumor sample. In certain embodiments, the proportion of cells is measured by flow cytometry, immunofluorscence, enzyme-linked immunosorbent assay-based methodologies (ELISA) and similar assays known in the art. See Section 5.8, infra, regarding techniques for measuring and distinguishing cell types. In other embodiments, the proportion of cells is measured by inference from gene expression profiles.

In accordance with the methods described herein, the hematological cancer can be any hematological cancer. In a specific embodiment, the hematological cancer is a lymphoma. In another specific embodiment, the hematological cancer is a non-Hodgkin's lymphoma. In yet another embodiment, the hematological cancer is a diffuse target B-cell lymphoma (DLBCL). In certain embodiments, the DLBCL is a germinal center B-cell-like DLBCL. In other embodiments, the DLCBL is an activated B-cell-like DLBCL.

In accordance with the methods described herein, the immunomodulatory therapy can be any therapy that modulates the immune system or immune response. In a specific embodiment, the immunomodulatory therapy is lenolidomide. In a specific embodiment, the immunomodulatory therapy is Compound A.

5.3 Treatment Compound for Use in Methods of Treating DLBCL

The treatment compounds provided herein can be used in all methods of treatment of a hematological cancer in a patient as provided herein.

In one aspect, provided herein are methods for treating a DLBCL patient with lenalidomide or Compound A, wherein the method uses the biomarkers and/or classifiers identified in Tables 1, 2, 3 and/or 4. In another aspect, provided herein are methods for treating a patient having a hematological cancer with a treatment compound.