Diagnosis and treatment of cancer

Abstract

To provide a composition and a method for cancer treatment (including, but not limited to, treatments utilizing cancer biomarkers), in particular, a composition and a method for predicting the response of a subject to cancer treatment.SOLUTION: Provided is a method for treating cancer in a patient. The method includes: obtaining cancer cells from the patient; measuring an expression level of RAB5 in cancer cells according to in vitro analysis; and, if an expression level of RAB5 in a cancer cell sample is higher than a prescribed reference level, administering an effective amount of an immune complex which targets a surface antigen of the cancer cell, or if the expression level of RAB5 in the cancer cell sample is lower than the prescribed reference level, administering an antigen binding protein not containing medicine or toxin.SELECTED DRAWING: None

Description

Detailed Description of the Invention

[0001] <Cross - Reference to Related Applications> This application claims the priority and benefit of U.S. Provisional Application No. 62 / 524,116, filed Jun. 23, 2017, which is hereby incorporated by reference in its entirety.

[0002] <Technical Field> The present invention relates to compositions and methods for cancer diagnosis, research, and treatment, including but not limited to cancer markers. In particular, the present invention relates to compositions and methods for predicting a subject's response to cancer treatment.

[0003] <Background Art> The increasing attention to personalized medicine, along with the growth of our knowledge of cancer biology, has revealed the high potential of biomarkers in cancer treatment. Spectra of different biomarkers are already incorporated into clinical practice to predict patient survival, evaluate treatment efficacy, or monitor disease progression (Bailey et al, Discovery medicine, 2014, 17:101 - 114). High - cost targeted cancer therapeutics are a powerful driver for the development of biomarkers to select patients most likely to benefit from treatment. For this purpose, regulatory authorities increasingly require the incorporation of predictive biomarkers for new therapies under clinical evaluation (Marton & Weiner, Biomed Res Int. 2013; 2013:891391.).

[0004] Breast cancer is the second most common form of cancer in women in the United States and the second most common cause of cancer death in women. The number of new breast cancer cases increased rapidly in the 1980s but now appears to be stable. The decline in breast cancer mortality is probably due to the fact that more women are having mammograms. When detected early, the chances of successful treatment of breast cancer are significantly improved.

[0005] Breast cancer is treated by surgery, radiation therapy, chemotherapy, and hormone therapy, and is most often curable when detected at an early stage. Mammography is the most important screening modality for early detection of breast cancer. Breast cancer is classified into various subtypes, but only a small number of these are currently known to affect prognosis or treatment choice. Management of patients initially suspected of having breast cancer generally involves establishing a diagnosis, assessing the stage of the disease, and selecting a treatment option. The diagnosis can be confirmed by aspiration cytology, core needle biopsy using stereotactic or ultrasound techniques for non-palpable lesions, or open or excisional biopsy.

[0006] Prognosis is influenced by the patient's age, stage of the disease, pathological characteristics of the primary tumor including the presence of tumor necrosis, estrogen receptor (ER) and progesterone receptor (PR) levels in the tumor tissue, measurement of HER2 overexpression status and proliferative capacity, as well as menopausal status and general health. Overweight patients may have a worse prognosis (Bastarrachea et al, Annals of Internal Medicine, 120: 18

[1994] ). Prognosis may also vary by race, being worse for blacks and, to a lesser extent, for Hispanic ethnic groups which have a worse prognosis than whites (Elledge et al, Journal of National Cancer Institute 86: 705

[1994] ; Edwards et al, Journal of Clinical Oncology 16: 2693

[1998] ).

[0007] The three main treatments for breast cancer are surgery, radiation, and drug therapy. There is no treatment that is suitable for all patients, and often two or more are required. This choice is determined by many factors, including the patient's age and menopausal status, type of cancer (e.g., ductal vs. lobular), its stage, whether the tumor is hormone receptive, and its level of invasiveness.

[0008] Breast cancer treatment is defined as either local or systemic. Surgery and radiation therapy are considered local therapies because they directly treat the tumor, breast, lymph nodes, or other specific areas. Drug therapy is called systemic therapy because its effects are widespread. Drug therapy includes classical chemotherapy drugs, hormonal blockade therapy (e.g., aromatase inhibitors, selective estrogen receptor modulators, and estrogen receptor downregulators), and monoclonal antibody therapy (e.g., against HER2). They may be used separately or, in most cases, in different combinations.

[0009] HER2 (ERBB2) is an effective biomarker in breast cancer, and HER2 gene amplification or protein overexpression is found in approximately 20% of newly diagnosed breast cancer patients (Slamon et al, Science, 1989, 244, 707 - 712; Hernandez - Blanquistt et al, Breast (Edinburgh, Scotland), 2016, 29, 170 - 177). HER2 is utilized as a therapeutic biomarker for treatment with HER2 - targeted monoclonal antibodies (mAbs) (trastuzumab and pertuzumab) and tyrosine kinase inhibitors (TKIs) (lapatinib and afatinib) (Hernandez - Blanquistt, et al., Breast (Edinburgh, Scotland), 2016, 29, 170 - 177). The pharmacological effects of HER2 - targeted mAbs and TKIs are a direct result of drug - target interactions and include antibody - mediated cell cytotoxicity (ADCC) (mAbs), HER2 downregulation, and inhibition of growth - promoting signals (Rimawi et al, Annual review of medicing, 2015, 66, 111 - 128; Clynes et al, Nature medicine, 2000, 6, 443 - 446; Harbeck et al, Breast care, 2013, 8, 49 - 55). The ability of HER2 to undergo receptor-mediated endocytosis also makes this transmembrane protein a candidate for the delivery of cytotoxic substances to cancer cells. This is actually exemplified by the antibody-drug conjugate (ADC) trastuzumab emtansine (T-DM1) which received FDA approval for the treatment of metastatic breast cancer in 2013 (Lewis Phillips et al, cancer research, 2008, 68, 9280 - 9290; Verma et al, The New England journal of medicine, 2012, 367, 1783 - 1791; Barok et al, Breast Cancer research, 2011, 13, R46).

[0010] T-DM1 consists of trastuzumab conjugated to the highly cytotoxic maytansine-derived drug DM-1 by a thioether (N-maleimidomethylcyclohexane-1-carboxylate (MCC)) (Baron et al, Journal of oncology pharmacy practice, 2015, 21, 132 - 142). Upon administration, T-DM1 binds to HER2 and is taken up into cells by HER2-mediated endocytosis. Proteolysis of the trastuzumab component within the endo / lysosomal pathway is subsequently hypothesized to be the mechanism for the cytosolic release of DM1 which induces microtubule destabilization and cell death (Erickson et al, Cancer research, 2006, 66, 4426 - 4433; Martinez et al, Critical reviews in oncology / hematology, 2016, 97, 96 - 106). Thus, in addition to the pharmacological effects caused by its trastuzumab component, T-DM1 induces a cytotoxic mechanism of action intracellularly.

[0011] Another more experimental approach that utilizes HER2 as a drug transporter is by the use of HER2-targeted fusion toxins such as MH3-B1 / rGel, which consists of the HER2-binding single-chain variable fragment MH3-B1 genetically fused to the type I ribosome-inactivating protein toxin gelonin (Cao et al, Cancer Res., 2009, 69, 8987-8995; Cao et al, Mol. Cancer Ther., 2012, 11, 143-153). MH3-B1 / rGel is taken up via HER2-mediated endocytosis, subsequently released into the cytosol where it binds to ribosomes and inhibits translation (Stirpe et al, J Biol. Chem., 1980, 255, 6947-6953). Thus, MH3-B1 / rGel also induces an intracellular cytotoxic effect in addition to its binding effect on HER2.

[0012] The mechanism of HER2-binding drugs with intracellular action sites is clearly more complex compared to HER2-targeted mAbs and TKIs, and this should be reflected in the biomarkers used to predict drug response (Ritchie et al, mAbs, 2013, 5, 13-21). However, the evaluation of biomarkers for T-DM1 efficacy has focused on HER2 and its downstream signaling in addition to HER3 (Baselga et al, Clinical cancer research, 2016, 22, 3755-3763; Kim et al, Int J Cancer, 2016, 139, 2336-2342), and little is known about the influence of proteins involved in endocytosis, endocytic vesicle trafficking, and exocytosis. Additional biomarkers are needed to improve the predictive value of antibody-drug conjugates and immunotoxins that target HER2 and other targeted receptors. This study investigated proteins of the Rab GTPase family (Stenmark, Nature The aim was to evaluate proteins specifically involved in HER2 endocytosis as potential biomarkers for the therapeutic efficacy of HER2-targeted ADCs and immunotoxins (Reviews. Molecular cell biology, 2009, 10, 513-525).

[0013] <Summary of the Invention> The present invention relates to compositions and methods for cancer treatment, including but not limited to treatments that utilize cancer biomarkers. In particular, the present invention relates to compositions and methods for predicting the response of a subject to cancer treatment.

[0014] Targeted therapies rely strongly on validated biomarkers to select the therapy for which the patient is most likely to benefit. HER2 already serves as a therapeutic biomarker for several tyrosine kinase inhibitors (TKIs) and monoclonal antibodies (mAbs) against HER2. However, HER2 can also be utilized as a transport gate for delivering cytotoxic substances, such as, for example, HER2-targeted antibody-drug conjugates (ADCs) and antibody-toxin conjugates (immunotoxins), to the cytosol. The therapeutic biomarkers for such drugs can be more complex than those for TKIs and mAbs, as they can reflect the mechanisms of drug uptake and intracellular action in addition to the biomarker that acts as the target.

[0015] In this study, a panel of HER2-positive breast cancer cell lines and ovarian cancer cell lines was evaluated for sensitivity to two HER2-targeted drugs, the ADC trastuzumab-emtansine (T-DM1) and the antibody-toxin conjugate MH3-B1 / rGel. Drug sensitivity correlated with the expression levels of HER2 in combination with HER3 and EGFR, which, similar to Rab4, Rab5, Rab11, and HSP90 involved in endocytic transport, could affect the pharmacology of the toxic moiety and the pharmacology of the drug's target moiety. The early endosome marker Rab5 and the early recycling marker Rab4 were shown to be potential therapeutic biomarkers for both T-DM1 and MH3-B1 / rGel. Furthermore, the toxicity of MH3-B1 / rGel was shown to be dependent on HSP90 and Rab11 (inverse). These results describe, for the first time, an overview of proteins involved in endocytic transport as potential biomarkers for HER2-targeted ADCs and antibody-toxin conjugates, as well as ADCs and target antibody-toxin conjugates in general. Furthermore, a mathematical approach is provided to validate combinations of biomarkers with diverse contribution factors.

[0016] Accordingly, in some embodiments, the present invention provides a method for treating a patient diagnosed with cancer using an antibody-drug conjugate or an antibody-toxin conjugate. The method comprises: a) determining the expression levels of a receptor of the antibody component of the antibody-drug conjugate or antibody-toxin conjugate and at least one additional protein biomarker selected from Rab5, Rab4, Rab11, and HSP90 in a biological sample from the patient; b) generating a compound expression index of the expression levels of the receptor and the biomarker proteins; and c) treating the patient with the antibody-drug conjugate or antibody-toxin conjugate based on the compound expression index. In some embodiments, the receptor is HER2, HER3, or EGFR.

[0017] In some embodiments, the method comprises administering an antibody-drug conjugate or an antibody-toxin conjugate when an expression index is determined in which the level of protein expression of HER2 is increased in addition to RAB5. In some embodiments, the method comprises administering an antibody-drug conjugate or an antibody-toxin conjugate when an expression index is determined in which the level of protein expression of HER2 is increased in addition to Rab5 and Rab4. In some embodiments, the method comprises administering an antibody-drug conjugate or an antibody-toxin conjugate when an expression index is determined in which the level of protein expression of HER2 is increased in addition to Rab5 and Rab4. In some embodiments, the method comprises the step of administering an antibody-drug conjugate or an antibody-toxin conjugate when an expression index is determined in which the level of protein expression of HER2 is increased in addition to RAB5, RAB4, and HSP90.

[0018] In some embodiments, the biological sample from the patient is a surgical tumor sample, a biopsy sample, or a blood sample. In some embodiments, the cancer is breast cancer, colorectal cancer, lung cancer, prostate cancer, melanoma, glioblastoma, pancreatic cancer, renal cell carcinoma, ovarian cancer, bladder cancer, gastrointestinal cancer, mesothelioma, multiple myeloma, acute myeloid leukemia, acute lymphoblastic leukemia, and non-Hodgkin lymphoma.

[0019] In some embodiments, the antibody-drug conjugate is trastuzumab emtansine (T-DM1, Kadcyla), brentuximab vedotin (SGN-35), inotuzumab ozogamicin (CMC-544), pinatuzumab vedotin (RG-7593), polatuzumab vedotin (RG-7596), rifatuzumab vedotin (DNIB0600A, RG-7599), glembatuzumab ravtansine (CDX-011), coltuximab ravtansine (SAR3419), lorvotuzumab mertansine (IMGN-901), indatuximab ravtansine (BT-062), sacitizumab govitican (IMMU-132), labetuzumab govitican (IMMU-130), miratuzumab doxorubicin (IMMU-110), indusatumab vedotin (MLN-0264), vadastuximab talirine (SGN-CD33A), denintuzumab mafodotin (SGN-CD19A), enfortumab vedotin (ASG-22ME), lobupetuzumab tesirine (SC16LD6.5), bintuzumab vedotin (DSTP3086S, RG7450), milabesuximab soravtansine (IMGN853), ABT-414, IMGN289, or AMG595.

[0020] In some embodiments, the antibody-toxin conjugate is MH3-B1 / rGel, denileukin diftitox (DAB389IL2), moxetumomab pasudotox (CAT-8015), oportuzumab monotox (VB4-845), resimmune, LMB-2, DT2219ARL, HuM195 / rGel, RG7787, MOC31PE or D2C7-IT. In some embodiments, determining includes immunoassays.

[0021] Further embodiments provide a method of determining a treatment plan. The method includes: a) determining the expression levels of a receptor of an antibody component of an antibody-drug conjugate or an antibody-toxin conjugate, and of at least one additional marker selected from RAB5, RAB4, RAB11, and HSP90, in a biological sample from the patient, as normalized proteins; b) generating a compound expression index of the expression levels of the receptor and the marker proteins; and c) recommending a treatment plan based on the compound expression index.

[0022] Further embodiments provide a method for determining a compound expression index in a biological sample from a patient diagnosed with cancer. The method includes: a) determining the expression levels of a ligand of an antibody component of an antibody-drug conjugate or an antibody-toxin conjugate, and of at least one additional marker selected, for example, from RAB5, RAB4, RAB11, and HSP90, in a biological sample from the patient, as normalized proteins, and b) generating a compound expression index of the expression levels of the ligand and the marker proteins.

[0023] Still other embodiments provide a kit. The kit includes at least one first reagent for detecting the expression level of a protein of at least one first marker selected, for example, from HER2, HER3, and EGFR, and at least one second reagent for detecting the expression level of at least one second marker selected, for example, from RAB5, RAB4, RAB11, or HSP90. In some embodiments, the reagent is an antibody.

[0024] A further embodiment provides a system. The system includes: a) at least one first reagent for detecting the expression level of at least one first marker selected from, for example, HER2, HER3, and EGFR, and at least one second reagent for detecting the expression level of at least one second marker selected from, for example, RAB5, RAB4, RAB11, or HSP90; and b) a computer processor and computer software for calculating a compound expression index based on the expression levels.

[0025] In yet another embodiment, the present invention provides a method for treating cancer in a patient. The method includes obtaining cancer cells from the patient, measuring the expression level of RAB5 in the cancer cells by in vitro analysis, administering an effective amount of an immune complex that targets the surface antigen of the cancer cells if the expression level of RAB5 in the cancer cell sample is increased as compared to a predetermined reference level, and administering an antigen-binding protein that does not contain a drug or toxin if the expression level of RAB5 in the cancer cell sample is decreased as compared to the predetermined reference level.

[0026] In some preferred embodiments, measuring the expression level of RAB5 in cancer cells includes measuring the level of RAB5 mRNA. In some preferred embodiments, measuring the expression level of RAB5 in cancer cells includes measuring the level of RAB5 protein. In some preferred embodiments, RAB5 is RAB5A. In some preferred embodiments, RAB5 is RAB5B. In some preferred embodiments, RAB5 is RAB5C.

[0027] In some preferred embodiments, the method further comprises analyzing the expression level of one or more of RAB4, RAB11 or HSP90. In some preferred embodiments, the method comprises incorporating the expression level of one or more of RAB4, RAB11 or HSP90 into an expression index together with the RAB5 expression level, and, when the expression index increases compared to a predetermined reference level, further comprising administering an effective amount of an immune complex that targets a surface antigen of cancer cells.

[0028] In some preferred embodiments, the cancer cells are obtained from a surgical tumor sample, a biopsy sample or a blood sample.

[0029] In some preferred embodiments, the immune complex binds to an antigen selected from the group consisting of HER2, HER3, EGFR, CD3ε, CD19, CD22, CD25, CD30, CD33, CD56, CEA (CD66e), CD74, CD79a, CD138, NaPi2b, gpNMB, TROP-2, GUCY2C, Nectin-4, SC-16, STEAP1, FRα, IL-2R, EpCAM, and MSLN. In some preferred embodiments, the immune complex is an antibody-drug conjugate selected from the group consisting of trastuzumab emtansine, brentuximab vedotin, inotuzumab ozogamicin, pinatuzumab vedotin, polatuzumab vedotin, refametinib vedotin, glembatuzumab vedotin, coltuximab ravtansine, lorvotuzumab mertansine, indatuximab ravtansine, sacitizumab govitican, labetuzumab govitican, mirvetuximab soravtansine, indusatumab vedotin, vadastuximab talirine, denintuzumab mafodotin, enfortumab vedotin, robatupizumab tesirine, vandortuzumab vedotin, milabesuximab soravtansine, ABT-414, IMGN289, and AMG595. In some preferred embodiments, the immune complex is an immunotoxin selected from the group consisting of MH3-B1 / rGel, denileukin diftitox, moxetumomab pasudotox, oportuzumab monotox, resimmune, LMB-2, DT2219ARL, HuM195 / rGel, RG7787, MOC31PE, and D2C7-IT.

[0030] In some preferred embodiments, the cancer cells are selected from the group consisting of breast cancer cells, colorectal cancer cells, lung cancer cells, prostate cancer cells, melanoma cells, glioblastoma cells, pancreatic cancer cells, renal cell carcinoma cells, ovarian cancer cells, bladder cancer cells, endometrial cancer cells, gastrointestinal cancer cells, mesothelioma cells, multiple myeloma cells, acute myeloid leukemia cells, acute lymphoblastic leukemia cells, and non-Hodgkin lymphoma.

[0031] In some preferred embodiments, the method further comprises analyzing the expression of surface antigens on cancer cells.

[0032] In some particularly preferred embodiments, the cancer cells are breast cancer cells. In some preferred embodiments, the surface antigen is selected from the group consisting of at least one of epidermal growth factor receptor (HER1), HER2, HER3, and combinations thereof, and the immune complex is targeted to the surface antigen. In some preferred embodiments, the antibody-drug conjugate or immunotoxin is selected from the group consisting of trastuzumab emtansine (T-DM1), ABT-414, IMGN289, AMG595, and AMG595. In some preferred embodiments, the surface antigen is HER2. In some preferred embodiments, the immune complex is trastuzumab emtansine (T-DM1).

[0033] In some preferred embodiments, the present invention provides an immune complex that targets a surface antigen for use in a method for treating cancer in a patient, wherein the cancer cells derived from the patient express the antigen and exhibit an increased expression level of RAB5 compared to a predetermined reference level as analyzed by in vitro expression analysis. In other preferred embodiments, the present invention provides an antigen-binding protein that is not conjugated to a drug or toxin for use in a method for treating cancer in a patient, wherein the cancer cells derived from the patient express the antigen and exhibit a decreased expression level of RAB5 compared to a predetermined reference level as analyzed by in vitro expression analysis.

[0034] In some preferred embodiments, the in vitro expression analysis is RAB5 mRNA analysis. In some preferred embodiments, the in vitro expression analysis is analysis of the RAB5 protein. In some preferred embodiments, RAB5 is RAB5A. In some preferred embodiments, RAB5 is RAB5B. In some preferred embodiments, RAB5 is RAB5C.

[0035] In some preferred embodiments, the immune complex binds to an antigen selected from the group consisting of HER2, HER3, EGFR, CD3ε, CD19, CD22, CD25, CD30, CD33, CD56, CEA (CD66e), CD74, CD79a, CD138, NaPi2b, gpNMB, TROP-2, GUCY2C, Nectin-4, SC-16, STEAP1, FRα, IL-2R, EpCAM, and MSLN. In some preferred embodiments, the immune complex is an antibody-drug conjugate selected from the group consisting of trastuzumab emtansine, brentuximab vedotin, inotuzumab ozogamicin, pinatuzumab vedotin, polatuzumab vedotin, refametinib vedotin, glembatuzumab vedotin, coltuximab ravtansine, lorvotuzumab mertansine, indatuximab ravtansine, sacitizumab govitican, labetuzumab govitican, mirvetuximab soravtansine, indusatumab vedotin, vadastuximab talirine, denileukin diftitox, oportuzumab monotox, resimmune, LMB-2, DT2219ARL, HuM195 / rGel, RG7787, MOC31PE, and D2C7-IT. In some preferred embodiments, the immune complex is selected from the group consisting of MH3-B1 / rGel, denileukin diftitox, moxetumomab pasudotox, oportuzumab monotox, resimmune, LMB-2, DT2219ARL, HuM195 / rGel, RG7787, MOC31PE, and D2C7-IT.

[0036] In some preferred embodiments, the cancer cells are selected from the group consisting of breast cancer cells, colorectal cancer cells, lung cancer cells, prostate cancer cells, melanoma cells, glioblastoma cells, pancreatic cancer cells, renal cell carcinoma cells, ovarian cancer cells, bladder cancer cells, endometrial cancer cells, gastrointestinal cancer cells, mesothelioma cells, multiple myeloma cells, acute myeloid leukemia cells, acute lymphoblastic leukemia cells, and non-Hodgkin lymphoma.

[0037] In some preferred embodiments, the cancer cells are breast cancer cells. In some preferred embodiments, the surface antigen is selected from the group consisting of at least one of epidermal growth factor receptor (HER1), HER2, HER3, and combinations thereof, and the antibody-drug conjugate or immunotoxin is targeted to the surface antigen. In some preferred embodiments, the antibody-drug conjugate or immunotoxin is selected from the group consisting of trastuzumab emtansine (T-DM1), ABT-414, IMGN289, AMG595, and AMG595. In some preferred embodiments, the surface antigen is HER2. In some preferred embodiments, the immunocomplex is trastuzumab emtansine (T-DM1).

[0038] In yet further preferred embodiments, the present invention provides an in vitro method for determining whether human cancer cells are responsive to an immunocomplex that targets a surface antigen on the cancer cells. The method includes obtaining a sample containing cancer cells from a patient; and measuring the expression level of RAB5 in the cancer cells by in vitro expression analysis, wherein an increased RAB5 expression level compared to a predetermined reference level indicates responsiveness to the immunocomplex.

[0039] In some preferred embodiments, the in vitro expression analysis is RAB5 mRNA analysis. In some preferred embodiments, the in vitro expression analysis is RAB5 protein analysis. In some preferred embodiments, RAB5 is RAB5A. In some preferred embodiments, RAB5 is RAB5B. In some preferred embodiments, RAB5 is RAB5C. In some preferred embodiments, the method further includes analyzing the expression level of one or more of RAB4, RAB11, or HSP90. In some preferred embodiments, the method further includes incorporating the expression level of one or more of RAB4, RAB11, or HSP90 into an expression index together with the RAB5 expression level.

[0040] In some preferred embodiments, the cancer cells are obtained from a surgical tumor sample, a biopsy sample or a blood sample. In some preferred embodiments, the cancer cells are selected from the group consisting of breast cancer cells, colorectal cancer cells, lung cancer cells, prostate cancer cells, melanoma cells, glioblastoma cells, pancreatic cancer cells, renal cell carcinoma cells, ovarian cancer cells, bladder cancer cells, endometrial cancer cells, gastrointestinal cancer cells, mesothelioma cells, multiple myeloma cells, acute myeloid leukemia cells, acute lymphoblastic leukemia cells, and non-Hodgkin lymphoma.

[0041] In some preferred embodiments, the cancer cells are breast cancer cells. In some preferred embodiments, the method further comprises analyzing the expression of surface antigens of the cancer cells. In some preferred embodiments, the surface antigen is selected from the group consisting of at least one of epidermal growth factor receptor (HER1), HER2, HER3, and combinations thereof, and the antibody-drug conjugate or immunotoxin targets one of epidermal growth factor receptor (HER1), HER2, and HER3. In some particularly preferred embodiments, the surface antigen is HER2.

[0042] In some preferred embodiments, the method further comprises administering an immune complex to the subject when the expression level increases compared to the reference expression level of RAB5.

[0043] In some preferred embodiments, the method further comprises administering an antibody without a drug or toxin to the subject when the expression level of RAB5 decreases compared to the reference expression level of RAB5.

[0044] Additional embodiments are described herein.

[0045] <Brief Description of the Drawings> A in Figure 1 shows the results of Western blot of the expression of HER2 and γ-tubulin in SK-BR-3, SKOV-3, HCC1954, AU-565, MDA-MB-435 and MDA-MB-231 cells. B in Figure 1 shows the relative survival rate (MTT) of SK-BR-3, SKOV-3, AU-565, HCC1954 and MDA-MB-435 after 72-hour treatment with each drug. For T-DM1, the sigmoidal curve fitting model a / (1+exp(-(x-x 0 )) / b) was used. The data values (%) represent the mean of three independent experiments (trastuzumab, error bar: SE), or the result of at least one of three separate experiments (T-DM1 and MH3-B1 / rGel, error bar: SD).

[0046] A in Figure 2 is a schematic diagram of the intracellularly acting targeted therapeutic drug trastuzumab and cell sensitivity to HER2. B in Figure 2 shows the cell sensitivity of SK-BR-3, SKOV-3, AU-565, HCC1954 and MDA-MB-435 cells to trastuzumab, T-DM1 and MH3-B1 / rGel. IC 50 represents the drug concentration that inhibits the survival rate of 50% of the cells. TI is IC 50 (rGel) / IC 50 (MH3-B1 / rGel). Representative results (C) and expression level results (D) of HER2 (D1), HER3 (D2), EGFR (D3) and γ-tubulin expression in SK-BR-3, SKOV-3, AU-565, HCC1954 and MDA-MB-435 cells by Western blot (n = 2) are shown. Also shown are the linear regression analysis curves of HER2 and T-DM1 sensitivity (1 / IC 50 (T-DM1)) (E1) or MH3-B1 / Gel sensitivity (TI) (E2).

[0047] Figure 3 shows the influence of HER3 and EGFR expression on T-DM1 sensitivity (A and B) and MH3-B1 / rGel sensitivity (C, D, and E) with HER2. For A and B in Figure 3, the left figure shows the linear regression analysis curves of HER3 (A1) or EGFR (B1) expression and T-DM1 sensitivity in five cell lines. The middle figure shows the contribution coefficient function from HER3 (A2) or EGFR (B2) expression in addition to HER2 expression for T-DM1 sensitivity in five cell lines, and the R 2 value by linear regression is shown. A3 represents the optimized linear regression analysis curve, where a linear correlation was observed between T-DM1 sensitivity and both HER2 and HER3 expression (contribution coefficient: 0.2). B3 shows the R 2 value by linear regression as a function of the influence coefficient from EGFR expression in addition to HER2 expression and HER3 expression (contribution coefficient 0.2) for T-DM1 sensitivity in five cell lines. For C and D in Figure 3, the left figure shows the linear regression analysis curves between HER3 (C1) or EGFR (D1) expression and MH3-B1 / rGel sensitivity in five cell lines. The middle figure shows the R 2 value by linear regression as a function of the influence of HER3 (C2) or EGFR (D2) expression in addition to HER2 expression for MH3-B1 / rGel sensitivity in five cell lines. The right figure represents the optimized linear regression analysis curve where MH3-B1 / rGel sensitivity is linearly correlated with both HER2 and HER3 expression (contribution coefficient: 0.4) (C3) or HER2 and EGFR expression (contribution coefficient: 0.3) (D3). E1 shows the R 2 value by linear regression as a function of the influence coefficient from HER3 expression in addition to HER2 and EGFR expression (contribution coefficient 0.3) for MH3-B1 / rGel sensitivity in five cell lines. E2 represents the optimized linear regression analysis curve where MH3-B1 / rGel sensitivity is linearly correlated with HER2, EGFR (contribution coefficient 0.3), and HER3 expression (contribution coefficient 0.4).

[0048] Figure 4 shows the representative results (A) by Western blot (n = 2) of Rab5, Rab4, HSP90, Rab11 and γ-tubulin expression in SK-BR-3, SKOV-3, AU-565, HCC1954 and MDA-MB-435 cells and the results (B - E) of the expression levels.

[0049] Figure 5 shows the influence of Rab5, Rab4, HSP90 and Rab11 expression on T-DM1 sensitivity with HER2. The left figure shows the linear regression analysis curves between Rab5 (A1), Rab4 (B1), HSP90 (C1) or Rab11 (D1) expression and T-DM1 sensitivity in five cell lines. The middle figure shows, in addition to that of HER2 expression on T-DM1 sensitivity in five cell lines, the R 2 value by linear regression as a function of the influence coefficient from Rab5 (A2), Rab4 (B2), HSP90 (C2) or 1 / Rab11 (D2) expression. The right figure represents the optimized linear regression analysis curve where T-DM1 sensitivity is linearly correlated with both HER2 and Rab5 (contribution coefficient: 0.3) (A3), Rab4 (contribution coefficient: 0.4) (B3) or 1 / Rab11 (contribution coefficient: 0.2) (D3) expression.

[0050] Figure 6 shows the influence of Rab5, Rab4, HSP90 and Rab11 expression on MH3-B1 / rGel sensitivity with HER2. The left figure shows the linear regression analysis curves between Rab5 (A1), Rab4 (B1), HSP90 (C1) or Rab11 (D1) expression and MH3-B1 / rGel sensitivity in five cell lines. The middle figure shows, in addition to that of HER2 expression on MH3-B1 / rGel sensitivity in five cell lines, the R 2 value by linear regression as a function of the influence coefficient from Rab5 (A2), Rab4 (B2), HSP90 (C2) or 1 / Rab11 (D2) expression. The right figure represents the optimized linear regression analysis curve where MH3-B1 / rGel sensitivity is linearly correlated with both HER2 and Rab5 (contribution coefficient: 0.3) (A3), Rab4 (contribution coefficient: 0.6) (B3), HSP90 (contribution coefficient: 1) (C3) or 1 / Rab11 (contribution coefficient: 1) (D3) expression.

[0051] Figure 7 shows the influence of Rab5, Rab4, and HSP90 or 1 / Rab11 expression on MH3-B1 / rGel sensitivity with HER2. A1 shows the R value by linear regression as a function of the influence coefficient from Rab4 expression in addition to HER2 and Rab5 expression (contribution coefficient 0.3) on MH3-B1 / rGel sensitivity in five cell lines. 2 A2 represents the optimized linear regression analysis curve in which MH3-B1 / rGel sensitivity is linearly correlated with HER2, Rab5 (contribution coefficient 0.3), and Rab4 expression (contribution coefficient 0.6). B1 shows the R value by linear regression as a function of the influence coefficient from HSP90 expression in addition to HER2, Rab5 (contribution coefficient 0.3), and Rab4 (contribution coefficient 0.6) on MH3-B1 / rGel sensitivity in five cell lines. 2 B2 represents the optimized linear regression analysis curve in which MH3-B1 / rGel sensitivity is linearly correlated with HER2, Rab5 (contribution coefficient 0.3), Rab4 expression (contribution coefficient 0.6), and HSP90 expression (contribution coefficient 0.8). C1 shows the R value by linear regression as a function of the influence coefficient from 1 / Rab11 expression in addition to HER2, Rab5 (contribution coefficient 0.3), Rab4 (contribution coefficient 0.6), and HSP90 (contribution coefficient 0.8) on MH3-B1 / rGel sensitivity in five cell lines. 2 C2 represents the optimized linear regression analysis curve in which MH3-B1 / rGel sensitivity is linearly correlated with HER2, Rab5 (contribution coefficient 0.3), Rab4 expression (contribution coefficient 0.6), HSP90 expression (contribution coefficient 0.8), and 1 / Rab11 expression (contribution coefficient 0.4).

[0052] Figure 8 shows the effect of the combination of HER3 and EGFR (A1, B1), or the combination of Rab5, Rab4, HSP90 and 1 / Rab11 (A2, B2) expression on T-DM1 (A) or MH3-B1 / rGel sensitivity (B), with HER2. The results presented are the same as those shown in Figures 5, 6 and 7, but are incorporated here in the same drawing. The biomarkers for T-DM1 and MH3-B1 / Ger sensitivity in (C) shown previously are schematically shown. Arrows indicate the drugs suggested by the biomarker, and the width of the arrow indicates the effect of the protein on drug sensitivity. Circles indicate the two pooled biomarkers shown for the combination.

[0053] Figure 9 shows the results of the Monte Carlo 2-fold cross-validation procedure to determine the threshold that minimizes the p-value of the biomarker x treatment interaction.

[0054] Figure 10 shows the Bayesian pCR probability curve related to RAB5A expression.

[0055] <Definition> To facilitate understanding of the present invention, some terms and expressions are defined as follows.

[0056] As used herein, the term "antigen-binding protein" refers to a protein that includes a portion that binds to an antigen and, optionally, a scaffold or framework portion that enables the antigen-binding portion to assume a conformation that promotes binding of the antigen-binding protein to the antigen. Examples of antigen-binding proteins include antibodies, antibody fragments (e.g., the antigen-binding portion of an antibody), antibody derivatives, and antibody analogs. The antigen-binding protein can include, for example, an alternative protein scaffold or an artificial scaffold having grafted CDRs or CDR derivatives. Such scaffolds include, but are not limited to, antibody-derived scaffolds that contain mutations introduced to stabilize the three-dimensional structure of the antigen-binding protein, as well as fully synthetic scaffolds that contain, for example, biocompatible polymers. Examples of antigen-binding proteins include, but are not limited to, polyclonal antibodies, monoclonal antibodies, chimeric antibodies, single-chain antibodies, humanized antibodies, minibodies, Fab fragments, F(ab’)2 fragments, Fv fragments, single-chain Fv fragments, and the like.

[0057] As used herein, the term "immunocomplex" refers to a molecule that includes an antigen-binding protein linked or conjugated, for example, by a chemical bond or a peptide linker, to another agent such as a drug or a toxin. The term "immunocomplex" encompasses antibody-drug conjugates, immunotoxins, and affinity toxins. The antigen-binding protein portion of this molecule is an immunoglobulin or an antigen-binding fragment or antigen-binding derivative thereof, and can be, for example, a polyclonal antibody, a monoclonal antibody, a chimeric antibody, a single-chain antibody, a humanized antibody, a minibody, a Fab fragment, an F(ab’)2 fragment, an Fv fragment, a single-chain Fv fragment, and the like. The antigen-binding protein portion can also be a protein ligand that can bind to a cell surface antigen. For example, EGF can target the EGF receptor expressed on the cell surface.

[0058] As used herein, the term "antibody-drug conjugate (ADC)" refers to a molecule that includes an antigen-binding protein that is linked to, or otherwise binds, a drug molecule, typically via a chemical bond.

[0059] As used herein, the term "immunotoxin" refers to a molecule that includes an antigen-binding protein that is linked to, or otherwise binds, a toxin molecule, typically via a peptide linker.

[0060] As used herein, the term "affinity toxin" refers to a molecule that includes a protein ligand capable of binding to a cell surface antigen, where the protein ligand is linked to, or otherwise binds, a toxin molecule, typically via a peptide linker.

[0061] As used herein, a cancer cell is "responsive" to an immune complex if a measurable toxin response can be detected upon contact of the cell with the immune complex.

[0062] As used herein, the terms "detect," "detected," or "detection" can refer to either the general act of discovering or identifying a composition, or a particular observation.

[0063] As used herein, the term "nucleic acid molecule" refers to any nucleic acid-containing molecule, including but not limited to DNA or RNA. This term includes any of the known base analogs of DNA and RNA, including but not limited to 4-acetylcytosine, 8-hydroxy-N6-methyladenosine, aziridinylcytosine, pseudoisocytosine, 5-(carboxyhydroxymethyl)uracil, 5-fluorouracil, 5-bromouracil, 5-carboxymethylaminomethyl-2-thiouracil, 5-carboxymethylaminomethyluracil, dihydrouracil, inosine, N6-isopentenyladenine, 1-methyladenine, 1-methylpseudouracil, 1-methylguanine, 1-methylinosine, 2,2-dimethylguanine, 2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, N6-methyladenine, 7-methylguanine, 5-methylaminomethyluracil, 5-methoxyaminomethyl-2-thiouracil, beta-D-mannosylqueosine, 5'-methoxycarbonylmethyluracil, 5-methoxyuracil, 2-methylthio-N6-isopentenyladenine, methyl ester of uracil-5-oxyacetic acid, uracil-5-oxyacetic acid, oxybutoxosine, pseudouracil, queosine, 2-thiocytosine, 5-methyl-2-thiouracil, 2-thiouracil, 4-thiouracil, 5-methyluracil, N-uracil-5-oxyacetic acid methyl ester, uracil-5-oxyacetic acid, pseudouracil, queosine, 2-thiocytosine, and 2,6-diaminopurine.

[0064] As used herein, the term "amplification oligonucleotide" relates to an oligonucleotide that hybridizes to a target nucleic acid or its complement and is involved in a nucleic acid amplification reaction. Examples of amplification oligonucleotides are "primers" that, in an amplification process, hybridize to a template nucleic acid and contain a 3' OH end that is extended by a polymerase. Another example of an amplification oligonucleotide is an oligonucleotide that is not extended by a polymerase (e.g., because it has a 3' blocked end), but is involved in or promotes amplification. Amplification oligonucleotides can optionally contain modified nucleotides or analogs, or additional nucleotides, that are involved in the amplification reaction but are not complementary to the target nucleic acid or not contained in the target nucleic acid. An amplification oligonucleotide can contain a sequence that is not complementary to the target or template sequence. For example, the 5' region of a primer can contain a promoter sequence (referred to as a "promoter - primer") that is non - complementary to the target nucleic acid. One of ordinary skill in the art will understand that an amplification oligonucleotide that functions as a primer can be modified to contain a 5' promoter sequence and thus can function as a promoter - primer. Similarly, a promoter - primer can be modified to remove the promoter sequence or can be synthesized to not have a promoter sequence and still function as a primer. A 3' blocked amplification oligonucleotide can provide a promoter sequence and serve as a template (referred to as a "promoter - provider") for polymerization.

[0065] As used herein, the term "primer" refers to an oligonucleotide that, regardless of whether it exists naturally as a purified restriction digest or is synthetically produced, can act as a starting point for synthesis under conditions that induce the synthesis of a primer extension product that is complementary to a nucleic acid strand (e.g., in the presence of agents such as nucleotides and DNA polymerase, and under an appropriate temperature and pH environment). The primer is preferably single-stranded in order to maximize efficiency in amplification, but may alternatively be double-stranded. In the case of double-stranded, the primer is first treated to separate the strands before being used to prepare an extension product. Preferably, the primer is an oligodeoxyribonucleotide. The primer should be of sufficient length to initiate the synthesis of an extension product in the presence of an agent. The exact length of the primer depends on many factors including temperature, source of the primer, and the method used.

[0066] As used herein, the term "probe" refers to an oligonucleotide (i.e., a nucleotide sequence) that, regardless of whether it exists naturally as a purified restriction digest or is produced synthetically, recombinantly, or by PCR amplification, can hybridize to at least a portion of another oligonucleotide of interest. The probe may be single-stranded or double-stranded. The probe is useful for the detection, identification, and isolation of specific gene sequences. Any probe used in the present invention is labeled with any "reporter molecule" such that it is intended to be detectable in any detection system including, but not limited to, enzyme (e.g., ELISA, as well as enzyme-based histochemical analysis), fluorescence-based, radioactivity-based, and luminescence-based detection systems. It is not intended to limit the present invention to any particular detection system or label. In some embodiments, the reporter molecule is an "exogenous reporter molecule".

[0067] The term "exogenous reporter molecule" relates to a reporter molecule or label not found in nature that is associated with a detection reagent (e.g., a probe, nucleic acid, or antibody). Examples include, but are not limited to, enzyme reporter molecules, fluorescent reporter molecules, radioactive reporter molecules, or luminescent reporter molecules.

[0068] The term "isolated," when used with respect to a nucleic acid, refers to a nucleic acid sequence that has been identified and separated from at least one component or contaminant normally associated with it in its natural source, such as an "isolated oligonucleotide" or "isolated polynucleotide." Isolated nucleic acids exist in a form or setting different from that found in nature. In contrast, non-isolated nucleic acids, such as DNA and RNA, are found in the state in which they naturally occur. For example, a given DNA sequence (e.g., a gene) is found in the vicinity of adjacent genes on a host cell chromosome; an RNA sequence, such as a specific mRNA sequence encoding a particular protein, is found intracellularly as a mixture with many other mRNAs encoding many other proteins. However, an isolated nucleic acid encoding a given protein includes, for example, such a nucleic acid in a cell that normally expresses the given protein, where the nucleic acid is at a chromosomal location different from its natural chromosomal location in a cell or is flanked by a nucleic acid sequence different from that found in nature. Isolated nucleic acids, oligonucleotides, or polynucleotides can exist in single-stranded or double-stranded form. When using an isolated nucleic acid, oligonucleotide, or polynucleotide to express a protein, the oligonucleotide or polynucleotide contains a minimal sense strand or coding strand (i.e., the oligonucleotide or polynucleotide may be single-stranded), but may also contain both a sense strand and an antisense strand (i.e., the oligonucleotide or polynucleotide may be double-stranded).

[0069] As used herein, the terms "purified" or "purifying" relate to the removal of components (e.g., contaminants) from a sample. For example, antibodies are purified by removal of contaminating non-immunoglobulin proteins, and they are also purified by removal of immunoglobulins that do not bind to the target molecule. Removal of non-immunoglobulin proteins and / or immunoglobulins that do not bind to the target molecule results in an increase in the target reactivity ratio of the immunoglobulins in the sample. In another example, a recombinant polypeptide is expressed in a bacterial host cell and the polypeptide is purified by removal of host cell proteins, thereby increasing the ratio of the recombinant polypeptide in the sample.

[0070] The term "sample" as used herein is used in its broadest sense. In one sense, it means including a specimen or culture obtained from any source, similar to biological samples and environmental samples. Biological samples are obtained from animals (including humans) and include liquids, solids, tissues, and gases. Biological samples include blood products such as plasma, serum, etc. Environmental samples include environmental materials such as surface substances, soil, water, crystals, and industrial samples. However, such examples should not be construed as limiting the types of samples applicable to the present invention.

[0071] <Detailed Description of the Invention> The present invention relates to compositions and methods for cancer treatment, including but not limited to treatments that utilize cancer biomarkers. In particular, the present invention relates to compositions and methods for predicting a subject's response to cancer treatment.

[0072] The increased focus on personalized medicines has revealed great potential for the use of biomarkers in cancer treatment, along with our growing knowledge of cancer biology. A spectrum of different biomarkers is already incorporated into clinical practice to predict patient survival, assess treatment efficacy, or monitor disease progression. Predictive biomarkers enable informed selection of patients most likely to benefit from a particular treatment, and thus such knowledge is extremely important for the rational use of current and future high-cost targeted cancer therapies.

[0073] Accordingly, provided herein are systems and methods for determining, recommending, and / or administering treatment to a subject having cancer (e.g., breast cancer) based on the expression of one or more protein markers. The present invention is not limited to specific markers. In some embodiments, a combination of an antigen to an antibody (e.g., HER2, HER3, EGFR) and one or more additional markers (e.g., RAB5 (preferably RAB5A), RAB4, RAB11, and HSP90 or HER3 and EGFR) are detected alone or in combination. In some embodiments, the expression levels of said combination or plurality of markers are combined to generate a compound expression index.

[0074] In some preferred embodiments, the present invention provides a method for treating cancer in a patient. The method includes obtaining cancer cells from the patient, measuring the expression level of RAB5 in the cancer cells by in vitro expression analysis, and administering an effective amount of an immune complex that targets the surface antigen of the cancer cells if the expression level of RAB5 in the cancer cell sample is increased as compared to a predetermined reference level, or administering an antigen-binding protein that does not contain a drug or toxin if the expression level of RAB5 in the cancer cell sample is decreased as compared to a predetermined reference level. The expression of any one of RAB5A, RAB5B, or RAB5C, or combinations thereof, may be analyzed. In some particularly preferred embodiments, the expression of RAB5A is analyzed.

[0075] As described above, in some preferred embodiments, the determination of whether to administer an antigen-binding protein not conjugated to an immune complex or a toxin or a drug is based on a comparison of the measured expression of RAB5, preferably RAB5A, in a patient sample, compared to a predetermined reference level or threshold level. One of ordinary skill in the art will recognize that the reference level or threshold level can be determined by statistical procedures applied to expression data obtained from an appropriate patient population. Appropriate statistical methodologies are provided in the examples, but one of ordinary skill in the art will recognize that other statistical procedures can also be utilized. Further, it is recognized that different statistical procedures or the same procedure performed on different or expanded data sets can generate different reference levels or threshold levels. Accordingly, the present invention is not limited to the use of any particular reference level or threshold level for the expression of any particular marker (e.g., RAB5A) or combination of markers. In this regard, in some embodiments, the method of the present invention further comprises analyzing the expression level of one or more of RAB4, RAB11, or HSP90. In some preferred embodiments, the expression level in a sample of one or more of RAB4, RAB11, or HSP90 is incorporated into an expression index having the expression level of RAB5, preferably RAB5A, and when the expression index increases compared to a predetermined reference level, an effective amount of an immune complex targeting a surface antigen of cancer cells is administered.

[0076] In some preferred embodiments, the patient sample used in the method of the present invention contains cancer cells. Suitable samples containing cells can be obtained by various methods including, but not limited to, biopsies, samples from surgery, and samples from blood draws. In some preferred embodiments, the sample has been pre-analyzed for the presence of one or more cell surface antigens. In some embodiments, the method further includes analyzing the sample for the expression of one or more cell surface antigens if the sample has not been previously characterized. The present invention is not limited to the analysis of any particular cell surface antigen, but cell surface antigens that are readily internalizable (e.g., by endocytosis) are preferred. Examples of cell surface antigens that are readily internalizable include, but are not limited to, HER2, HER3, EGFR, CD3ε, CD19, CD22, CD25, CD30, CD33, CD56, CEA (CD66e), CD74, CD79a, CD138, NaPi2b, gpNMB, TROP-2, GUCY2C, Nectin-4, SC-16, STEAP1, FRα, IL-2R, EpCAM, and MSLN.

[0077] In some particularly preferred embodiments, the sample contains breast cancer cells. In these embodiments, the breast cancer cells are preferably characterized for the expression of epidermal growth factor receptor (HER1), HER2, and / or HER3. In an even more preferred embodiment, the sample is analyzed for the HER2 receptor or the sample has been previously analyzed and identified as having the HER2 receptor.

[0078] As described above, in some embodiments, a compound expression index is utilized in the method of the present invention. The present invention is not limited to the use of any particular compound expression index. Examples of suitable compound expression indexes are shown below.

[0079] In some embodiments, the compound expression index is relative HER2 expression × (1 - (1 - relative RAB5 expression) × 0.3) × (1 - (1 - relative RAB4 expression) × 0.6) × (1 - (1 - relative HSP90 expression) × 0.8) / (1 - (1 - (1 / relative RAB11 expression)) × 0.4).

[0080] In some embodiments, the compound expression index is relative HER2 expression × (1 - (1 - relative RAB4 expression) × 0.6) × (1 - (1 - relative RAB5 expression) × 0.3) × (1 - (1 - relative HSP90 expression) × 0.8).

[0081] In some embodiments, the compound expression index is relative HER2 expression × (1 - (1 - relative RAB4 expression) × 0.6) × (1 - (1 - relative RAB5 expression) × 0.2) × (1 - (1 - relative HSP90 expression) × 0.6).

[0082] In some embodiments, the compound expression index is relative HER2 expression × (1 - (1 - relative RAB4 expression) × 0.4) × (1 - (1 - relative RAB5 expression) × 0.2).

[0083] In some embodiments, the compound expression index is relative HER2 expression × (1 - (1 - relative RAB5 expression) × 0.3) × (1 - (1 - relative RAB4 expression) × 0.6).

[0084] In some embodiments, the compound expression index is relative HER2 expression × (1 - (1 - relative RAB5 expression) × 0.3).

[0085] In some embodiments, the compound expression index is relative HER2 expression × (1 - (1 - relative RAB4 expression) × 0.4).

[0086] In some embodiments, the compound expression index is relative HER2 expression × (1 - (1 - relative RAB4 expression) × 0.6).

[0087] In some embodiments, the compound expression index is relative HER2 expression × (1 - (1 - relative RAB11 expression) × 0.2).

[0088] In some embodiments, the compound expression index is relative HER2 expression × (1 - (1 - relative HSP90 expression)).

[0089] In some embodiments, the expression is protein expression. In some embodiments, the determining step includes an immunoassay. In some embodiments, the expression is mRNA expression. In some embodiments, the determining step includes reverse transcription of mRNA to provide cDNA and amplification of the cDNA using primers specific for the biomarker. In some embodiments, the detection technique is RT-PCR.

[0090] The expression analysis utilized in the present invention can utilize a single biomarker such as RAB5A, or a panel of biomarkers (e.g., one or more of RAB5A and RAB4, RAB11 or HSP90; RAB5A and HER2; RAB5A, HER2 and one or more of RAB4, RAB11 or HSP90). In some embodiments, the panel or analysis of the present invention includes 100, 75, 50, 25, 20, 15, 10, or less than 5 biomarkers, or in other preferred embodiments, a total of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or up to 20 biomarkers.

[0091] In some embodiments, the level of protein expression is detected in a sample from a subject. In some embodiments, the subject is diagnosed with cancer (e.g., breast cancer). In some embodiments, the sample is tissue (e.g., a biopsy tissue), blood, serum, urine, etc.

[0092] Exemplary methods for detecting protein markers are provided below. However, any suitable method for detecting tumor marker proteins can be utilized.

[0093] Non-limiting examples of immunoassays include, but are not limited to: immunoprecipitation, Western blot, ELISA, immunohistochemistry, immunocytometry, flow cytometry, and immun-PCR. Polyclonal or monoclonal antibodies detectably labeled using various techniques known to those of skill in the art (e.g., colorimetric, fluorescent, chemiluminescent or radioactive) are suitable for use in immunoassays.

[0094] Immunoprecipitation is a technique in which an antigen is eluted and precipitated using an antibody specific for that antigen. This process can be used to identify protein complexes present in cell extracts by targeting proteins that are thought to be present in the complex. The complex is eluted by an insoluble antibody-binding protein first isolated from bacteria, such as Protein A and Protein G. Antibodies can also be bound to Sepharose beads, which can be easily isolated from the eluate. After washing, the precipitate may be analyzed using mass spectrometry, Western blot, or any number of other methods for identifying components in the complex.

[0095] A Western blot or immunoblot is a method for detecting proteins in a given sample of tissue homogenate or extract. In this method, denatured proteins are separated by mass using gel electrophoresis. The proteins are then transferred from the gel onto a membrane (typically polyvinylidene fluoride or nitrocellulose), where they are labeled using an antibody specific for the protein of interest. As a result, researchers can test the amount of protein in a given sample and compare the levels between several groups.

[0096] ELISA, which is short for enzyme-linked immunosorbent assay, is a biochemical technique for detecting the presence of antibodies or antigens in a sample. This technique utilizes at least two antibodies, one of which is specific to the antigen and the other is conjugated to an enzyme. The second antibody generates a signal in a chromogenic or fluorogenic substrate. Variations of ELISA include sandwich ELISA, competitive ELISA, and ELISPOT. Since ELISA can be performed to evaluate either the presence of an antigen or an antibody in a sample, it is a useful tool both for determining serum antibody concentrations and for detecting the presence of an antigen.

[0097] Immunohistochemistry and immunocytochemistry relate to the process of localizing proteins in tissue sections or cells, respectively, through the principle of antigens in the tissue or cells that bind to their respective antibodies. Visualization is made possible by tagging the antibodies with chromogenic or fluorescent tags. Typical examples of chromogenic tags include, but are not limited to, horseradish peroxidase and alkaline phosphatase. Typical examples of fluorescent tags include, but are not limited to, fluorescein isothiocyanate (FITC) or phycoerythrin (PE).

[0098] Flow cytometry is a technique for counting, examining, and sorting minute particles suspended in a fluid stream. This technique enables the simultaneous multi-parametric analysis of the physical and / or chemical properties of single cells flowing through an optical / electronic detection device. A beam of light of a single frequency or color (e.g., a laser) is directed at a hydrodynamically focused fluid stream. Several detectors are aimed at the point where the stream passes through the light beam, one is aligned with the light beam (forward scatter or FSC), and several are perpendicular to it (side scatter (SSC) and one or more fluorescence detectors). Each suspended particle passing through the beam scatters light in some way, and fluorescent chemicals in the particle can be excited to emit light at a lower frequency than the light source. The combination of scattered light and fluorescence is detected by the detectors, and by analyzing the fluctuations in brightness at each detector, one by one for each fluorescence emission peak, it is possible to infer various facts about the respective physical and chemical structures of the individual particles. FSC correlates with cell volume, and SSC correlates with the density or internal complexity of the particle (e.g., nuclear shape, amount and type of cytoplasmic granules, or membrane roughness).

[0099] Immuno-polymerase chain reaction (IPCR) utilizes nucleic acid amplification technology to increase signal generation in antibody-based immunoassays. Since there is no protein equivalent of PCR, i.e., proteins cannot be replicated in the same manner as nucleic acids are replicated during PCR, the only way to increase detection sensitivity is by signal amplification. The target protein binds to an antibody that binds directly or indirectly to an oligonucleotide. Unbound antibodies are washed away, and the remaining bound antibodies have amplified oligonucleotides. Protein detection is performed by detecting the amplified oligonucleotides using standard nucleic acid detection methods, including real-time methods.

[0100] In some embodiments, immunomagnetic detection is utilized. In some embodiments, the detection is automated. Exemplary immunomagnetic detection methods include, but are not limited to, those commercially available from Veridex (Raritan, NJ).

[0101] In some embodiments, a computer-based analysis program is used to translate raw data (e.g., the presence, absence, or amount of marker expression) generated by a detection assay into data of a clinician's predictive value (e.g., selection of cancer treatment or compound expression index). The clinician can access the predictive data using any suitable means. Thus, in some preferred embodiments, the present invention provides the further benefit that a clinician, who is likely to be untrained in gene or molecular biology, need not understand the raw data. The data is presented directly to the clinician in its most useful form. The clinician can then immediately utilize the information to optimize the treatment of the subject.

[0102] The present invention contemplates a research facility that conducts an analysis, an information provider, a healthcare professional, and any method capable of receiving, processing, and transmitting information from or to a subject. For example, in some embodiments of the present invention, a sample (e.g., a biopsy or a blood or serum sample) is obtained from a subject and submitted to a profiling service (e.g., a clinical laboratory in a medical facility, a genomic profiling business, etc.) located anywhere in the world (e.g., a country different from the country where the subject is located or the country where the information will ultimately be used) to generate raw data. If the sample includes tissue or other biological samples, the subject can visit a medical center to obtain the sample and send it to a profiling center, or the subject can collect the sample itself (e.g., a urine sample) and send it directly to the profiling center. If the sample contains pre-determined biological information, the information may be sent directly by the subject to the profiling service (e.g., an information card containing the information may be scanned by a computer, and the data may be transmitted to the computer of the profiling center using an electronic communication system). Once received by the profiling service, the sample is processed to generate a profile specific to the diagnosis or prognostic information desired by the subject (i.e., expression data).

[0103] The profile data is then prepared in a format suitable for interpretation by the treating clinician. For example, instead of providing the raw data, the prepared format may represent a diagnosis or risk assessment for the subject (e.g., the likelihood of success of cancer treatment or a compound expression index) along with a faucet for specific treatment options. The data may be presented to the clinician by any suitable method. For example, in some embodiments, the profiling service can generate a report that can be printed for the clinician (e.g., at the time of treatment) or displayed to the clinician on a computer monitor.

[0104] In some embodiments, the information is analyzed at the point of first care or at a regional facility. The raw data is then sent to a central processing facility for further analysis and / or to convert the raw data into information useful to a clinician or patient. The central processing facility provides the advantages of privacy (all data is stored at the central facility with a uniform security protocol), speed, and uniformity of data analysis. The central processing facility can then control the fate of the data after treatment of the subject. For example, using an electronic communication system, the central facility can provide data to a clinician, subject, or researcher.

[0105] In some embodiments, the subject can directly access the data using an electronic communication system. The subject can select further intervention or counseling based on the results. In some embodiments, the data is used for research purposes. For example, this data can be used to further optimize the inclusion or exclusion of markers as useful indicators of a particular condition or stage of disease.

[0106] Compositions for use in the diagnostic, prognostic, and therapeutic methods of the present invention include, but are not limited to, probes, amplification oligonucleotides, and antibodies. Particularly preferred compositions detect the presence of the expression level of a marker in a sample.

[0107] Any of these compositions can be provided in the formation of a kit, either alone or in combination with other compositions of the present invention. For example, a single-labeled probe and a pair of amplification oligonucleotides or antibodies, as well as components of an immunoassay, can be provided in a kit for amplification and detection of a marker. The kit can further include appropriate control reagents and / or detection reagents.

[0108] The probe and antibody compositions of the present invention can also be provided in the form of an array or a panel assay.

[0109] In some embodiments, the present invention provides systems, kits, and methods for determining and implementing a treatment plan.

[0110] The methods of the present invention find use in the treatment of various cancers. Cancers that can be treated in accordance with the present invention include, but are not limited to, breast cancer, colorectal cancer, lung cancer, prostate cancer, melanoma, glioblastoma, pancreatic cancer, renal cell carcinoma, ovarian cancer, bladder cancer, endometrial cancer, gastrointestinal cancer, mesothelioma, multiple myeloma, acute myeloid leukemia, acute lymphoblastic leukemia, and non-Hodgkin lymphoma. As noted above, in preferred embodiments where the expression level of a biomarker or combination of biomarkers increases in a patient sample as compared to a reference or threshold level or compound expression index, a call is then made to administer an immune complex to the patient. Similarly, in other preferred embodiments where the expression level of a biomarker or combination of biomarkers decreases in a patient sample as compared to a reference or threshold expression level or compound expression index, a call is then made to administer to the patient an antigen-binding protein not conjugated to a drug or toxin. In embodiments where administration of an immune complex is required, an immune complex that binds to a surface antigen expressed on the patient's tumor or cancer cells is then selected. Suitable surface antigens to which the immune complex can be directed include, but are not limited to, HER2, HER3, EGFR, CD3ε, CD19, CD22, CD25, CD30, CD33, CD56, CEA (CD66e), CD74, CD79a, CD138, NaPi2b, gpNMB, TROP-2, GUCY2C, Nectin-4, SC-16, STEAP1, FRα, IL-2R, EpCAM, and MSLN.

[0111] In some embodiments, the immune complex is an antibody-drug conjugate. Suitable antibody-drug conjugates include, but are not limited to, trastuzumab emtansine (T-DM1, Kadcyla), brentuximab vedotin (SGN-35), inotuzumab ozogamicin (CMC-544), pinatuzumab vedotin (RG-7593), polatuzumab vedotin (RG-7596), rifatuzumab vedotin (DNIB0600A, RG-7599), glembatuzumab vedotin (CDX-011), coltuximab ravtansine (SAR3419), lorvotuzumab mertansine (IMGN-901), indatuximab ravtansine (BT-062), sacitizumab govitican (IMMU-132), labetuzumab govitican (IMMU-130), mirvetuximab doxorubicin (IMMU-110), indusatumab vedotin (MLN-0264), vadastuximab talirine (SGN-CD33A), denintuzumab mafodotin (SGN-CD19A), enfortumab vedotin (ASG-22ME), robatupizumab tesirine (SC16LD6.5), vandortuzumab vedotin (DSTP3086S, RG7450), milabesuximab soravtansine (IMGN853), ABT-414, IMGN289, or AMG595. In some embodiments, the antibody-toxin conjugate is MH3-B1 / rGel, denileukin diftitox (DAB389IL2), moxetumomab pasudotox (CAT-8015), oportuzumab monatox (VB4-845), resimmune, LMB-2, DT2219ARL, HuM195 / rGel, RG7787, MOC31PE or D2C7-IT. As described above, the selection of the antibody-drug conjugate to be used in the methods of the present invention depends on the specific surface antigen expressed on the cancer cells in the subject.

[0112] In some embodiments, the immune complex is an immunotoxin. Suitable immunotoxins include, but are not limited to, MH3-B1 / rGel, denileukin diftitox, (DAB389IL2), moxetumomab pasudotox (CAT-8015), oportuzumab monatox (VB4-845), resimmune, LMB-2, DT2219ARL, HuM195 / rGel, RG7787, MOC31PE, or D2C7-IT. Further, as described above, the selection of the immunotoxin used in the methods of the invention depends on the specific surface antigen expressed by the cancer cells in the subject.

[0113] <Example> The following examples are provided to demonstrate and further illustrate certain embodiments of the invention and should not be construed as limiting its scope.

[0114] [Example 1] (Materials and Methods) Cells and Culture: In this study, five HER2-expressing human cell lines, the breast cancer cell lines SK-BR-3, AU-565 (CRL-2351), HCC1954 (CRL-2338), and MDA-MB-453 (HTB-131) and the ovarian cancer cell line SKOV-3 (HTB-77) were used. The HER2-negative human breast cancer cell line MDA-MB-231 was used as a negative control for HER2 expression. All cell lines were obtained from the American Type Culture Collection (Manassas, VA, USA), except for SK-BR-3, which was provided by the Department of Biochemistry, Cancer Institute, Oslo University Hospital, Oslo, Norway. To avoid changes in the characteristics of the cell lines over time, all cell lines were used between passage numbers 3 and 25, and the cells were constantly checked for mycoplasma infection. SK-BR-3 and SKOV-3 cells were cultured in McCoy 5A medium, AU-565, HCC1954, and MDA-MB-231 cells were cultured in RPMI-1640 medium (both obtained from Sigma-Aldrich, St. Louis, MO, USA), while MDA-MB-453 was cultured in Leibovitz’s L-15 medium (Lonza, Verviers, Belgium). All media were supplemented according to reference 19.

[0115] Cytotoxicity assay: 8×10 3 (SK-BR-3), 1.8×10 3 (SKOV-3), 6×10 3 (AU-565), 4×10 3 (HCC1954) or 1×10 4Cells / well (MDA-MB-453) were seeded and allowed to adhere overnight. The cells were then incubated with trastuzumab (Herceptin®, Roche, Basel, Switzerland), T-DM1 (ado-trastuzumab emtansine, Kadcyla®, Genentech, South San Francisco, CA, USA), MH3-B1 / rGel or rGel (expressed and purified according to references 13, 20) for 72 hours with increasing concentrations, and then the cell viability was evaluated by MTT assay according to reference 21. The IC 50 values were calculated from the sigmoidal curve (fitting model: a / (1+exp(-(x-x 0 )) / b).

[0116] Western blot analysis: Total cell extracts were obtained and analyzed by Western blot as described in reference 19. Protein blot transfer was performed using the Trans-Blot® Turbo™ Transfer System (Bio-Rad Laboratories, CA, USA). Cell protein expression was detected using antibodies against EGFR (#4267), HER2 (#2165), HER3 (#12708), HSP90 (#4877) obtained from Cell Signaling Technology (Danvers, MA, USA), antibodies against Rab5 (610281) and Rab11 (610656) obtained from BD Biosciences (San Jose, CA, USA), and the Rab4 (R5780) antibody (Sigma-Aldrich). Protein expression was correlated with γ-tubulin detected by the antigen (#T6557) (Sigma-Aldrich). Supersignal West Dura Extended duration Substrate (Thermo Scientific, Rockford, IL, In the USA, the ChemiDoc (trademark) densitometer (Bio-Rad) was used for detecting protein bands on the membrane. ImageLab 4.1 (Bio-Rad software) was used for quantifying protein expression. The expression of each protein was calculated relative to the highest expressing cell line.

[0117] Correlation analysis: The relative expression of HER2, HER3, EGFR, Rab4, Rab5, Rab11, and HSP90 in cell lines was plotted against the cell line sensitivity to two HER2-targeted therapeutic agents measured by 1 / IC 50 (T-DM1) or the targeting index (TI) (MH3-B1 / rGel), and linear regression was evaluated. Some proteins may affect the toxicity of T-DM1 or MH3-B1 / rGel together with HER2. However, the level of the effect may vary when compared to HER2. Here, it was calculated whether the R 50 (T-DM1) or TI (MH3-B1 / rGel) obtained by 1 / IC 2 values could be increased by incorporating the relative expression of other proteins (HER3, EGFR, Rab4, Rab5, Rab11, and HSP90) into the regression. These regression curves were established by decreasing the contribution coefficient in the range of 1 to 0 for each protein together with HER2 using the following equations; For curves with a positive gradient, HER2×(1 - (1 - protein)×F), and for curves with a negative gradient, HER2 / (1 - (1 - protein)×F), where HER2 is the relative expression of HER2, protein is the relative expression of the protein of interest, and F is the contribution coefficient from 1 to 0.

[0118] R 2 values were plotted as a function of the contribution coefficient of each protein. Proteins belonging to the contribution coefficients that resulted in an increase in R 2 compared to that obtained with HER2 alone were incorporated into the final regression curves for T-DM1 and MH3-B1 / rGel sensitivity, and R 2The combination of expression parameters having the highest correlation with T-DM1- and MH3-B1 / rGel sensitivity measured thereby was set.

[0119] (Results) The efficacy of T-DM1 and MH3-B1 / rGel was not correlated with trastuzumab sensitivity. Strong HER2 expression was demonstrated in the five HER2-expressing cell lines used in this study compared with the low expression in MDA-MB-231 reported as HER2 negative (A in Figure 1) (Reference 22). The antiproliferative effects of the HER2-targeted mAb trastuzumab and the therapeutic agents T-DM1 and MH3-B1 / rGel targeting intracellular active HER2 were established in the five HER2-positive cell lines (B in Figure 1). When the cells were treated with trastuzumab, T-DM1 or MH3-B1 / rGel for 72 hours, it was revealed that SK-BR-3 and AU-565 cells were highly sensitive to all three therapeutic agents. Conversely, SKOV-3 cells were found to be non-responsive to trastuzumab and were low sensitive to T-DM1 and MH3-B1 / rGel, as demonstrated by the relatively high IC 50 of 1.2 μg / mL and the low TI of 2.4 50 (Figure 1 and B in Figure 2). Both HCC1954 and MDA-MB-453 cells were found to be low to medium sensitive to trastuzumab treatment but showed different responses to the intracellular active HER2-targeted therapeutic agents. HCC1954 cells were highly sensitive to both T-DM1 and MH3-B1 / rGel, while MDA-MB-453 cells were low sensitive to these two drugs (Figure 1 and B in Figure 2). Therefore, no clear correlation was observed between trastuzumab sensitivity and sensitivity to T-DM1 and MH3-B1 / rGel among the five cell lines (Figure 1 and B in Figure 2). T-DM1 and MH3-B1 / rGel have clearly different sites of action intracellularly, but a consistency was revealed in the sensitivity to these two therapeutic agents among the cell lines, where a high / low response to one of the two therapeutic agents seemed to predict a similar high / low response to the other.

[0120] Based on these findings, the cell lines were classified into three categories: (i) high sensitivity to trastuzumab, T-DM1, and MH3-B1 / rGel (SK-BR-3 and AU-565), (ii) low / moderate sensitivity to trastuzumab, T-DM1, and MH3-B1 / rGel (SKOV-3 and MDA-MB-453), (iii) low / moderate sensitivity to trastuzumab but high sensitivity to T-DM1 and MH3-B1 / rGel (HCC1954) (Figure 2A).

[0121] The correlation between HER2 expression and T-DM1 toxicity is stronger than that observed for MH3-B1 / rGel toxicity. HER2 expression is essential for both T-DM1 and MH3-B1 / rGel toxicity. However, the expression level may not necessarily directly correlate with drug sensitivity due to differences in drug processing (e.g., uptake, intracellular transport, and interaction with intracellular drug targets) between cells. Quantification of HER2 expression in the five cell lines showed that AU-565 had the highest expression level of HER2, followed closely by HCC1954 (0.9) and SK-BR-3 (0.8) (Figures 2C and D1). SKOV-3 cells showed 50% lower HER2 expression compared to AU-565, and MDA-MB-453 was shown as the cell line with the lowest HER2 expression (0.4) in the panel (Figures 2C and D1). The HER2 expression levels reported in this manuscript are consistent with recent reports (References 22, 23). Furthermore, a linear relationship was observed between HER2 expression and sensitivity to T-DM1 and MH3-B1 / rGel, resulting in R 2 values of 0.926 for T-DM1 and 0.800 for MH3-B1 / rGel (Figures 2E1 and E2).

[0122] HER3 may serve as an additional biomarker for HER2 with respect to T-DM1 sensitivity. HER2 is known to form a complex with other members of the EGFR family. Therefore, HER3 and EGFR were quantified to examine the relationship between T-DM1 and MH3-B1 / rGel toxicity and the expression of these two members of the EGFR family. A selected panel of cell lines was found to vary substantially in both levels of HER3 and EGFR expression (Figures 2C, 2D2, and 2D3). However, no clear correlation was seen between the levels of HER3 or EGFR and sensitivity to T-DM1 (Figures 3A1 and B1) or MH3-B1 / rGel (Figures 3C1 and D1). Whether EGFR and HER3 correlate with T-DM1- and MH3-B1 / rGel sensitivity together with HER2 was further evaluated. The R 50 (T-DM1) and the R of the linear regression of HER2 and HER3 2 value, establishing with an increasing contribution coefficient, when HER2 was used alone (R 2 = 0.926, Figure 2E1), compared to when HER3 expression was included in the correlation analysis with a contribution coefficient of 0.2 (Figures 3A2 and 3A3), demonstrated a larger R of 0.953 2 . By incorporating EGFR with an increasing influence coefficient into the regression analysis between T-DM1 sensitivity and HER2 expression, no increase in linear correlation was detected (Figure 3B2). The influence of EGFR expression (contribution coefficient 0.2) on the regression between T-DM1 and HER2×HER3 was also evaluated. Also here, it was found that all R 2 values were less than those observed with HER2 and HER3 (contribution coefficient 0.2) alone (Figure 3B3).

[0123] HER3 and EGFR may serve as additional biomarkers for HER2 with respect to MH3-B1 / rGel sensitivity. Incorporating the contributions of EGFR and HER3 into the regression analysis between MH3-B1 / rGel sensitivity and HER2 expression, compared to that obtained by HER2 expression alone, R 2The value increased (for HER2×HER3 with a contribution coefficient of 0.4, R 2 = 0.970; for HER2×EGFR with a contribution coefficient of 0.3, R 2 = 0.826) (Figure 2E2, 3C2, 3C3, 3D2, and 3D3). When the effect of HER3 expression associated with the increase in the contribution coefficient was added to the regression between HER2 and EGFR (contribution coefficient of 0.3) and MH3-B1 / rGel sensitivity, an increase in the R 2 value was measured, indicating an increase in the correlation (R 2 = 0.997, Figure 3E1 and 3E2).

[0124] HER2-expressing cell lines have different expression levels of proteins involved in endocytic transport. Since T-DM1 and MH3B1 / rGel depend on intracellular translocation and intracellular transport to exert their intracellular mechanisms of action, proteins involved in the endocytic mechanism were quantified in the panel of cell lines. These proteins include Rab5 involved in the transport of cargo from the cell membrane to early endosomes and in endosome fusion (Figure 4A and B), Rab4 involved in recycling from early endosomes (Figure 4A and C), HSP90 reported to regulate HER2 recycling (Figure 4A and D), and Rab11 involved in recycling via perinuclear recycling endosomes and cell membrane-Golgi transport (Figure 4A and E) (References 24, 25). The expression levels of these proteins among the cell lines showed large differences, and no simple relationship was found between the expression levels.

[0125] Rab5 and Rab4 may function as additional biomarkers for HER2 to predict T-DM1 sensitivity. It was further evaluated whether the investigated proteins involved in the endocytic mechanism (Figure 4) affect T-DM1 sensitivity among the cell lines. Expression levels of Rab5 that were 2.5 - 3-fold higher were found in highly T-DM1-sensitive SK-BR-3 and AU-565 cells compared to the other cell lines in the panel (Figure 4A and B). A relatively weak correlation was observed between T-DM1 toxicity and Rab5 expression (R2 = 0.643) (Figure 5A1). The establishment of linear regression curves for T-DM1 toxicity and HER2 expression with an increasing contribution coefficient of Rab5 expression indicated that Rab5 affects T-DM1 toxicity together with HER2 (Figure 5A2). The maximum R of HER2 and Rab5 2 was found when Rab5 was added with a contribution coefficient of 0.3 (Figure 5A2) and increased from 0.926 to 0.986 by including a 30% contribution from Rab5 (Figure 5A3). No linear correlation was seen between Rab4 expression and T-DM1 sensitivity (R2 = 0.088, Figure 5B1). When T-DM1 sensitivity was correlated with HER2 and the contribution coefficient from Rab4 was 0.4, a 2 slight increase in the R value was seen (Figures 5B2 and 5B3). It was also investigated whether Rab4 is inversely correlated with HER2 and T-DM1 sensitivity. However, when 1 / Rab4 expression was incorporated into the regression equation, no increase in R 2 was seen compared to that obtained with HER2 alone (data not shown). The linear regression of HSP90 expression and T-DM1 sensitivity showed a poor correlation with an R 2 value of 0.266 (Figure 5C1), and no increase in the R value was observed when the effect of HSP90 was incorporated into the regression equation with HER2 compared to that obtained with HER2 alone (Figure 5C2). An inverse linear correlation (R 2 = 0.459) was seen between Rab11 expression and T-DM1 sensitivity (Figure 5D1). The linear regression between T-DM1 sensitivity and HER2 with an increasing contribution coefficient of 1 / Rab11 expression showed almost no increase in the R 2 value compared to that obtained with HER2 alone (0.929 vs. 0.926, Figures 5D2 and 5D3). 2 As shown in Figure 5, the expression levels of Rab5 and Rab4 were each shown as potential biomarkers of T-DM1 sensitivity together with HER2. Next, it was evaluated whether combining Rab5 and Rab4 with HER2 would further increase the correlation with T-DM1 sensitivity. However, when the effect of Rab4 was incorporated into the regression equation, no increase in R was seen compared to that observed with HER2 × 0.3 Rab5

[0126] Figure 5 as shown, the expression levels of Rab5 and Rab4 were each shown as potential biomarkers of T-DM1 sensitivity together with HER2. Next, it was evaluated whether combining Rab5 and Rab4 with HER2 would further increase the correlation with T-DM1 sensitivity. However, when the effect of Rab4 was incorporated into the regression equation, no increase in R was seen compared to that observed with HER2 × 0.3 Rab52 No increase was observed (data not shown). Overall, the best linear correlation with T-DM1 sensitivity was found only with a combination of HER2 and Rab5 with an influence coefficient of 0.3 (R 2 = 0.986, Figure 5A3).

[0127] Rab5, Rab4, HSP90, and Rab11 may all function as additional biomarkers for HER2 with respect to MH3-B1 / rGel sensitivity. The cellular expression levels of Rab5, Rab4, HSP90, and Rab11 (Figure 4) were also plotted against MH3-B1 / rGel sensitivity measured by TI (Figure 6). A linear correlation between Rab5 and TI was detected among cell lines (R 2 = 0.486, Figure 6A1). Incorporating Rab5, which increased the contribution coefficient to the established linear regression curve of HER2 and MH3-B1 / rGel toxicity, was visualized by an increase in the R 2 value (R 2 = 0.800, Figure 2E2), and Rab5, which affects MH3-B1 / rGel toxicity, was shown together with HER2 (Figure 6A2). The highest R 2 of 0.856 was seen by including a 30% contribution from Rab5, as was also observed for the T-DM1 correlation (Figure 5A3) (Figure 6A2 and 6A3). No correlation was found between Rab4 expression and MH3B1 / rGel sensitivity (R 2 = 0.024, Figure 6B1). However, using Rab4 expression with an increasing influence coefficient as an additional biomarker for HER2 enhanced the linear correlation with TI compared to HER2 alone. The maximum R 2was obtained by adding Rab4, which has an influence coefficient of 0.6 on HER2 and TI regression analysis (Figs. 6B1 and 6B2). HSP90 and 1 / Rab11 were also shown to correlate, albeit not as strongly, with MH3B1 / rGel sensitivity (R2 = 0.552 for HSP90 and 0.406 for 1 / Rab11, Figs. 6C1 and D1). HSP90 and 1 / Rab11 were further shown, one by one, to increase the association between HER2 expression and MH3B1 / rGel sensitivity, with a maximum R 2 obtained with a contribution coefficient of 1 for both proteins (Figs. 6C2, C3, D2, and D3).

[0128] As shown in Fig. 6, Rab5, Rab4, HSP90, and 1 / Rab11 were all shown, one by one, as potential biomarkers together with HER2 for MH3B1 / rGel sensitivity. Whether the combination of these four biomarkers and HER2 further increases the correlation with MH3B1 / rGel sensitivity was further investigated. The order of combining different proteins in the regression equation was based on the endocytosis and transport pathways starting with Rab5 (endocytosis), followed by the addition of Rab4 (early recycling), HSP90 (early recycling), and 1 / Rab11 (late recycling). As shown in Fig. 6A3, Rab5 (influence coefficient 0.3) correlated well with MH3B1 / rGel sensitivity together with HER2 (R 2 = 0.856). However, when Rab4 expression with an increased influence coefficient was added to the regression analysis, the R 2 value increased. When Rab4 was added with a contribution coefficient of 0.6 to HER2 and the contribution coefficient of 0.3×Rab5, a maximum R 2 of 0.938 was reached (Figs. 7A1 and A2). HSP90 expression was further incorporated into the regression analysis using HER2, Rab5, and Rab4. By including 80% contribution from HSP90 in the regression analysis between MH3B1 / rGel sensitivity and HER2, Rab5 (contribution coefficient 0.3), and Rab4 (contribution coefficient 0.6), a maximum R 2was obtained (Figure 7B). As shown in Figure 6D, 1 / Rab11 was shown to be well correlated with MH3B1 / rGel sensitivity together with HER2 (R 2 = 0.894). When the influence of 1 / Rab11 expression was added to the regression analysis between MH3B1 / rGel sensitivity and HER2, Rab5, Rab4, and HSP90 expression, R 2 also increased as shown in Figure 7C1. When MH3B1 / rGel sensitivity was correlated with HER2, Rab5 (contribution coefficient 0.3), Rab4 (contribution coefficient 0.6), and HSP90 (contribution coefficient 0.8), the maximum correlation between 1 / Rab11 expression and other proteins and MH3B1 / rGel sensitivity was 0.4 (Figure 7C2), and the maximum R 2 was 0.993 (Figure 7C2). The regression analysis was also performed by adding the contributions of various proteins in a non-biological order without significantly affecting the R 2 values obtained when correlated with MH3B1 / rGel sensitivity (data not shown). Generally, the best correlation between MH3-B1 / rGel toxicity established when adding the contributions of various proteins and the protein expression levels was found when TI was plotted against the biological theoretical order (R 2 = 0.993) (Figure 7C2); relative HER2 expression × (1 - (1 - Rab5 expression) × 0.3) × (1 - (1 - Rab4 expression) × 0.6) × (1 - (1 - relative HSP90 expression) × 0.8) / (1 - (1 - (1 / relative Rab11 expression)) × 0.4) The field of biomarker-guided personalized cancer therapy is growing rapidly as the number of clinically approved targeted anticancer agents increases. Overall, the efficacy of drug-based individualized cancer therapy depends on the reliability of selected biomarkers to predict prognosis, response, and / or resistance. The development of the HER2-targeted mAb trastuzumab is a story of success in the field of biomarker-guided individualized cancer therapy as a breast cancer patient classified as HER2-positive (about 20% of all breast cancers), with routine administration of trastuzumab as part of the treatment (Reference 26). Other examples include the evaluation of BCR-ABL fusion in chronic myeloid leukemia (CML) for the use of imatinib, or EGFR and RAS wild-type expression for the use of cetuximab in the treatment of colorectal cancer (Reference 27).

[0129] Most of the currently approved targeted drugs for cancer treatment are mAbs or small molecule inhibitors, and the drug targets for them also indicate the targets of the mechanism of action. Although the targets themselves may require additional biomarkers to exclude patients who are likely to experience resistance or low tolerance, they indicate clear biomarkers for the treatment with these drugs. However, drug development in cancer treatment is currently shifting to more complex targeted therapies consisting of both a targeted moiety and a cytotoxic component (e.g., cell growth inhibitors (ADCs) or toxins (targeted toxins)). For these multifunctional therapeutic agents, other biomarkers related to intracellular signaling and / or cytotoxic mechanisms of action are likely to affect treatment outcomes (Reference 15). This is demonstrated here by the lack of consistency between trastuzumab and T-DM1 / MH3-B1 / rGel sensitivity in a selected panel of HER2-positive cell lines.

[0130] Here, a strong linear correlation has been reported between cellular HER2 expression and response to T-DM1 (Figure 2E1). This is consistent with several clinical studies demonstrating higher response rates to T-DM1 in patients with HER2 mRNA levels above the median compared to a subgroup of patients with levels below the median (References 16, 17, 28). The correlation between drug activity and HER2 expression is R here2 As measured by value, it is shown more strongly for T-DM1 compared to MH3-B1 / rGel (Figure 2E2), which could be caused by differences in the cytotoxic components of these drugs. The cytotoxic component of T-DM1 (DM1) is a relatively small and lipophilic drug that, when released from the trastuzumab component in endocytic vesicles, can diffuse across the endocytic membrane into the cytosol, where it exerts its effect on microtubules. This is in sharp contrast to the cytotoxic moiety of MH3-B1 / rGel, which is a 28 kDa hydrophilic type I ribosome-inactivating protein toxin (gelonin), which lacks an effective transport mechanism to enter the cytosol but is still somewhat enabled to enter the cytosol by an unknown mechanism. Compared to MH3-B1 / rGel and HER2 expression, the higher R obtained from the correlation analysis of T-DM1 and HER2 expression 2 probably reflects the differences in the mechanisms of endocytosis escape between these two drugs, where more obstacles are shown for the gelonin pathway. The more complex intracellular release pathway of gelonin compared to DM1 is also reflected in the importance of the proteins investigated (Rab5, Rab4, HSP90 and Rab11) that affect MH3-B1 / rGel sensitivity compared to Rab5 and Rab4 for T-DM1.

[0131] Cell sensitivity to both T-DM1 and MH3-B1 / rGel was shown here to correlate with HER3 in addition to HER2. It has previously been reported that the therapeutic effect of T-DM1 in phase III clinical trials is similar in HER3 expression subgroups (Ref. 17). This is consistent with the data presented herein, where no correlation is found between HER3 expression alone and T-DM1 sensitivity (Fig. 3A1). However, the present invention focuses on a mathematical approach to combine biomarkers with different contribution factors. These calculations show that HER3 in combination with HER2 may serve as a better biomarker for T-DM1 response compared to HER2 alone. HER3 has been recognized as a preferred dimerization partner of HER2, and heterodimers have been reported to induce highly active tyrosine kinase signaling (Ref. 29). Thus, the correlation between T-DM1, MH3-B1 / rGel sensitivity, and HER3 expression together with HER2 may reflect the indirect inhibition of these heterodimers upon binding of trastuzumab and MH3-B1 to HER2. Compared to the full-length antibody trastuzumab in T-DM1, MH3-B1 / rGel consists of a single-chain fv fragment. Therefore, binding of MH3-B1 to HER2 as part of a heterodimer cannot be excluded (both HER2 / HER3 and HER2 / EGFR). This further explains the MH-3B1 / rGel sensitivity (R 2 increased from 0.800 to 0.970) and a clearly stronger correlation (R 2 increased from 0.926 to 0.953, Fig. 8A1), and when MH3-B1 / rGel sensitivity was correlated with EGFR in addition to HER2 and HER3, R 2 No such correlation was observed for T-DM1, as indicated by a slight increase in

[0132] Rab5 localizes to early endosomes and controls both endocytosis and endosomal fusion of clathrin-coated vesicles (Reference 18). Both T-DM1 and MH3-B1 / rGel sensitivity were shown here to depend on Rab5 with a contribution factor of 0.3 in addition to HER2 (Figures 5A3 and 6A3). The similar effect of Rab5 on both HER2-targeted therapeutic agents is probably related to the initial function of Rab5 in endocytosis and subsequent endocytic trafficking, where the two drugs are likely to follow the same HER2-mediated pathway for endocytosis. Also, Rab4 acts early in the endocytic pathway by controlling recycling from early endosomes and was shown to correlate with T-DM1 and MH3-B1 / rGel sensitivity together with HER2 (Figures 5B3 and 6B3) (Reference 30). The rapid recycling of HER2 can increase drug uptake by cells, but also passively localizes the drug in the early endosomes within the cell.

[0133] The increase in R from 0.830 to 0.930 when Rab4 was incorporated with HER2 (Figure 8B2) indicates cytosolic translocation from early endosomes as a mechanism for cytosolic release of MH3-B1 / rGel. The minor contribution of Rab4 in addition to HER2 to T-DM1 sensitivity (R 2 increased from 0.926 to 0.944, Figure 8A2) indicates that emtansine is also released from early endosomes to some extent, showing that the cytosolic translocation of emtansine does not solely depend on lysosomal sequestration of the trastuzumab portion of T-DM1, as suggested by previous reports (References 7, 10). So far, Rab5 expression has been shown to predict poor prognosis in breast cancer patients, and Rab5 / Rab4 recycling promotes extracellular matrix invasion and metastasis (Reference 31). Therefore, these results indicate that Rab5 / Rab4 and HER2-positive breast cancer patients are the most promising candidates for T-DM1- and MH3-B1 / rGel-based therapies. 2

[0134] HSP90 is a HER2 chaperone and has been claimed to inhibit HER2 degradation by several mechanisms including rapid recycling (references 32, 33). HSP90 has been shown here to correlate with MH3-B1 / rGel toxicity together with HER2 (Figure 8B2), but in contrast to T-DM1, such a correlation was not seen (Figure 8A2). As described for the difference in Rab4 dependency, the difference in the effect of HSP90 on the cytotoxicity of these drugs may reflect a difference in the mechanism of cytosolic translocation. This difference may also be the result of different HER2 targeting moieties between these drugs. On the other hand, Rab11 is localized in the endocytic recycling compartment (ERC) and acts in the endocytosis process by recycling the conjugate back to the cell membrane later (reference 34). A negative correlation was seen between MH3-B1 / rGel efficacy and Rab11 expression (Figure 6B), indicating recycling and subsequent exocytosis to inhibit MH3-B1 / rGel efficacy. The lack of an effect of Rab11 together with HER2 on T-DM1 toxicity (Figure 5D) probably indicates that T-DM1 and MH3-B1 / rGel follow different intracellular pathways along the chemical properties of their cytotoxic moieties. DM1, for example, is likely to be able to escape from endocytic vesicles before accumulating in the Rab11 positive recycling endosome.

[0135] In total, six proteins were tested in this study as potential biomarkers together with HER2 for MH3-B1 / rGel and T-DM1 responses. The tested biomarkers were here divided into two groups reflecting either the targeting moiety of the HER2-targeting drugs (EGFR and HER3) or the intracellular transport components of the drugs (Rab5, Rab4, HSP90 and Rab11) (Figure 8). The best correlations with drug sensitivity were obtained by pooling the biomarkers within these groups, and an increased correlation with drug sensitivity was not shown by combining these two groups of biomarkers (data not shown). These results indicate that the two groups of proteins are not completely independent, which is not surprising as the endocytic transport of the drugs clearly depends on cell binding and endocytosis. Thus, HER2 as a biomarker should be combined with either HER3 and EGFR or Rab4, Rab5, HSP90 and 1 / Rab11 to predict cell sensitivity to T-DM1 and MH3-B1 / rGel (Figure 8). Figures 3, 5, 6 and 7 show the effect of the addition of the six proteins as biomarkers with an increasing contribution coefficient to the contribution coefficient of HER2. However, the importance of the different proteins can be compared as illustrated in Figures 8A and 8B, and the R2 correlation data for both groups of biomarkers are incorporated in the same figure. An increased correlation with drug sensitivity was found for both HER2-targeting drugs by adding the expression of the extracellular protein HER3 or the intracellular proteins Rab5 and Rab4 to the expression of HER2 as a biomarker, but as visualized in Figures 8A, B and C, R 2With various effects on values, MH3-B1 / rGel sensitivity was also shown to be dependent on EGFR expression in addition to HER2 and HER3, and to some extent on HER2, Rab5, and Rab4 for HSP90 and 1 / Rab11 expression. A more complex pool of biomarkers correlating with MH3-B1 / rGel sensitivity compared to T-DM1 may be caused by more impaired cytosolic translocation pathways of MH3-B1 / rGel compared to T-DM1, and may also reflect differences in the HER2-targeted moieties between these drugs.

[0136] In conclusion, this report shows for the first time that proteins involved in endocytic transport can be used to predict response to HER2-targeted therapeutic agents that have intracellular sites of action in addition to HER2. However, the effects of different proteins appear to be related to both the HER2-targeted moiety and the intracellular acting component of the drug, as well as the subsequent mechanisms of endocytic transport and cytosolic release. Future development of ADCs as well as other targeted drugs with intracellular acting mechanisms should incorporate drug-dependent pools of biomarkers and should include markers for uptake and cellular transport in addition to the most commonly used markers for targeting and resistance today. Mathematical approaches such as those used herein can be used to establish pools of biomarkers with different contribution coefficients for prognostic as well as therapeutic use.

[0137] The general inventive concept described herein is applicable to all antibody-drug conjugates (ADCs) and antibody-toxin conjugates (immunotoxins). Some preferred drugs that can be administered according to this method are presented in Table 1 (ADCs) and Table 2 (immunotoxins).

[0138]

Table 1

[0139]

Table 2

[0140] 〔Example 2〕 Patient population: Pre-treatment expression and pathologic complete response (pCR) data were available for 52 patients in the T-DM1 + pertuzumab (TDM1+P) group and were available for analysis for 31 patients in the trastuzumab control (TH) group. Patients who progressed, withdrew consent, left the treatment facility, or received non-protocol therapy prior to surgery were considered non-pCR for this analysis. The following table shows the pCR rates by HR subtype within each arm;

[0141]

Table 3

[0142] TH expression data: All I-SPY2 samples are analyzed on either one of two Agilent custom arrays (designs 15746 and 32627). All samples in the TDM1+P arm were assayed on the 32627 array, while the TH arm was split between platforms, using 22 samples on the old 15746 platform and 9 samples on the 32627 array. To combine data across the two designs, the probe annotation for the 15746 platform was updated (September 2016), and expression data normalized by averaging such that genes represented by multiple probes were calculated as the average across probes was collapsed for each platform. Next, the ComBat algorithm was applied to adjust for platform bias and combine data from the two platforms. This procedure was done for pre-treatment data of the first 880 I-SPY 2 patients regardless of experimental group. The combined platform-adjusted data from the TH and TDM1+P arms are included in this delivery along with annotation files for the 32627 and updated 15746 array designs.

[0143] Verification of Eligibility for Biomarker Analysis: The normalized, platform-corrected pre-treatment expression levels of RAB5A, RAB4A, RAB11A, and HSP90AA1 were first individually tested as specific biomarkers for response to TDM1+P according to the Qualified Biomarker Evaluation (QBE) protocol.

[0144] = Step 1: Evaluate Biomarkers as Specific Predictors of Response to TDM1+P = · Model 1A: pCR ~ Biomarker in the TDM1+P Arm · Model 1B: pCR ~ Biomarker in the TH Arm · Model 1C: pCR ~ Treatment + Biomarker + Treatment x Biomarker · Model 1D: pCR ~ Treatment + Biomarker + Treatment x Biomarker + HR Status Of the biomarkers evaluated, only RAB5A was associated with response in the TDM1+p arm (likelihood ratio (LR) test p = 0.012), but not in the control arm (LR test p = 0.242).

[0145]

Table 4

[0146] The p-value for the interaction between RAB5A expression and treatment was 0.024, which remained < 0.05 after adjustment for HR status. The following table summarizes the results for the four biomarkers evaluated.

[0147] RAB5A was successful as a continuous qualified biomarker and was evaluated in QBE Step 2.

[0148] = Step 2: Identify Dichotomous Cutoffs = The inventors used a Monte Carlo 2-fold cross-validation procedure to identify a threshold that minimizes the p-value of the biomarker x treatment interaction. Specifically, for 100 iterations, the inventors randomly selected half of the cases, balanced between the treatment group and the pCR status, as their training set. We considered every value between the 10th and 90th percentiles as a potential threshold for dichotomizing the training set into "high" vs. "low" RAB5A expression groups and fit a series of logistic regression models to evaluate the biomarker x treatment interaction (Model 1C). The inventors selected the threshold that minimized the LR test p-value for the interaction term in the training set, used it to dichotomize the test set, and evaluated the significance of the biomarker x treatment interaction in the test set. Next, the LR p-values were combined across 100 test sets using the logit method, and the threshold that yielded the minimum combined LR test p-value was selected.

[0149] Using this procedure, a threshold of 9.76 was selected. See Figure 9. Having a high RAB5A level as the dichotomous variable was also associated with response in TDM1 + p (OR = 5.99 (95% CI: 1.23 - 40.27), exact test p = 0.01 by Thermo Fisher Scientific), but not with the control group (OR: 0 (95% CI: 0 - 1.74), exact test p = 0.06 by Thermo Fisher Scientific), and had a biomarker x treatment interaction term with LRp = 0.001.

[0150] 9.76 was the optimal threshold identified by this procedure, but only 2 patients with RAB5A values less than 9.76 were in the TH arm. This could be due in part to differences in RAB5A expression between the TH and TDM1+P arms, where RAB5A levels were significantly higher in the TH arm than in the TDM1+P arm. The array design could contribute to this difference. We may wish to consider evaluating biomarker performance in the TDM1+P arm only (rather than using a model to evaluate biomarker×treatment interactions as specified a priori in the analysis plan). Using a Monte Carlo method similar to that described above (but fitting model 1A to TDM1+P only), the optimal threshold selected remained 9.76.

[0151] = Bayesian estimated pCR rates within the RAB5A group in the context of the HER2+ stepwise signature · Model 2: pCR ~ HR + RAB5A + treatment + HRx treatment + RAB5Ax treatment When applying the optimal threshold (9.76) identified to dichotomize patients into RAB5A-high (>=9.76) and RAB5A-low (<9.76) groups, the Bayesian estimated pCR probability was 68% in the TDM1+P group versus 24% in the control group of RAB5A-high patients. In contrast, the estimated pCR probability was 28% in the RAB5A-low subset in the TDM1+P arm and 42% in the TH arm. For comparison, using the same model, the estimated pCR probability for the entire HER2+ group was 61% in the TDM1+P group and 27% in the TH group. The Bayesian pCR probability curves are shown in Figure 10.

[0152] 〔Example 3〕 This example shows the evaluation of the correlation between the expression profiles of HSP90, Rab11a, Rab4A, and Rab5a and the treatment outcomes in the TH- and T-DM1+P groups in the 1-SPY2 trial. RNA expression profiles from 1-SPY-2 data were evaluated for the correlation between pCR and the expression levels of HSP90, Rab11A, Rab4A, and Rab5a in two treatment groups that received trastuzumab + chemotherapy (TH) or trastuzumab-emtansine + pertuzumab + chemotherapy (TDM1+P). In the T-DM1+P arm, there was a significant difference in the expression level of Rab5A between the pCR0 group and the pCR1 group. No significant difference was found for any of the other proteins in either arm.

[0153]

Table 5

[0154]

Table 6

[0155] <List of references> 1 A. de Gramont, S. Watson, L. M. Ellis, J. Rodon, J. Tabernero, A. de Gramont and S. R. Hamilton, Nature reviews. Clinical oncology, 2015, 12, 197-212. 2 D. J. Slamon, W. Godolphin, L. A. Jones, J. A. Holt, S. G. Wong, D. E. Keith, W. J. Levin, S. G. Stuart, J. Udove, A. Ullrich and et al., Science, 1989, 244, 707-712. 3 A. Hernandez-Blanquisett, D. Touya, K. Strasser-Weippl, R. Ruiz, J. St Louis and P. Goss, Breast (Edinburgh, Scotland), 2016, 29, 170-177. 4 M. F. Rimawi, R. Schiff and C. K. Osborne, Annual review of medicine, 2015, 66, 111-128. 5 R. A. Clynes, T. L. Towers, L. G. Presta and J. V. Ravetch, Nature medicine, 2000, 6, 443-446. 6 N. Harbeck, M. W. Beckmann, A. Rody, A. Schneeweiss, V. Muller, T. Fehm, N. Marschner, O. Gluz, I. Schrader, G. Heinrich, M. Untch and C. Jackisch, Breast care (Basel, Switzerland), 2013, 8, 49-55. 7 G. D. Lewis Phillips, G. Li, D. L. Dugger, L. M. Crocker, K. L. Parsons, E. Mai, W. A. Blattler, J. M. Lambert, R. V. Chari, R. J. Lutz, W. L. Wong, F. S. Jacobson, H. Koeppen, R. H. Schwall, S. R. Kenkare-Mitra, S. D. Spencer and M. X. Sliwkowski, Cancer research, 2008, 68, 9280-9290. 8 S. Verma, D. Miles, L. Gianni, I. E. Krop, M. Welslau, J. Baselga, M. Pegram, D. Y. Oh, V. Dieras, E. Guardino, L. Fang, M. W. Lu, S. Olsen and K. Blackwell, The New England journal of medicine, 2012, 367, 1783-1791. 9 J. M. Baron, B. L. Boster and C. M. Barnett, Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners, 2015, 21, 132-142. 10 H. K. Erickson, P. U. Park, W. C. Widdison, Y. V. Kovtun, L. M. Garrett, K. Hoffman, R. J. Lutz, V. S. Goldmacher and W. A. Blattler, Cancer research, 2006, 66, 4426-4433. 11 M. T. Martinez, J. A. Perez-Fidalgo, P. Martin-Martorell, J. M. Cejalvo, V. Pons, B. Bermejo, M. Martin, J. Albanell and A. Lluch, Critical reviews in oncology / hematology, 2016, 97, 96-106. 12 Y. Cao, J. D. Marks, J. W. Marks, L. H. 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Pienkowski and R. Bell, The oncologist, 2006, 11 Suppl 1, 4-12. 27 J. Baselga, Eur.J.Cancer, 2001, 37 Suppl 4, S16-S22. 28 E. A. Perez, S. A. Hurvitz, L. C. Amler, K. E. Mundt, V. Ng, E. Guardino and L. Gianni, Breast cancer research : BCR, 2014, 16, R50. 29 A. Citri, K. B. Skaria and Y. Yarden, Experimental cell research, 2003, 284, 54-65. 30 B. D. Grant and J. G. Donaldson, Nature reviews. Molecular cell biology, 2009, 10, 597-608. 31 E. Frittoli, A. Palamidessi, P. Marighetti, S. Confalonieri, F. Bianchi, C. Malinverno, G. Mazzarol, G. Viale, I. Martin-Padura, M. Garre, D. Parazzoli, V. Mattei, S. Cortellino, G. Bertalot, P. P. Di Fiore and G. Scita, The Journal of cell biology, 2014, 206, 307-328. 32 V. Bertelsen and E. Stang, Membranes, 2014, 4, 424-446. 33 K. Cortese, M. T. Howes, R. Lundmark, E. Tagliatti, P. Bagnato, A. Petrelli, M. Bono, H. T. McMahon, R. G. Parton and C. Tacchetti, Molecular biology of the cell, 2013, 24, 129-144. 34 S. Takahashi, K. Kubo, S. Waguri, A. Yabashi, H. W. Shin, Y. Katoh and K. Nakayama, Journal of cell science, 2012, 125, 4049-4057. All publications, patents, patent applications, and accession numbers described in the above specification are hereby incorporated by reference in their entirety. It should be understood that the present invention has been disclosed with respect to particular embodiments, but it is not to be construed that the invention as claimed is unduly limited to such particular embodiments. Indeed, various modifications and variations of the compositions and methods described herein will be apparent to those skilled in the art and are intended to be included within the scope as set forth in the following claims.

Brief Description of the Drawings

[0156]

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Claims

1. 1. Use of a kit comprising a reagent for detecting the expression level of Ras-associated binding (RAB) protein 5 (RAB5), The use includes: (a) an immunoconjugate that targets a surface antigen on a cancer cell, which is an antigen-binding protein that is linked or conjugated to a drug or toxin; or (b) Drug- or toxin-free antigen-binding proteins targeting surface antigens on cancer cells: Use of the kit in an in vitro method for determining whether a patient is responsive to The method includes using the reagent for detecting the expression level of RAB5 to measure the expression level of RAB5 in cancer cells obtained from a subject by in vitro analysis; an increased level of RAB5 expression compared to a dichotomous threshold indicates responsiveness to said immune complex; Use of a kit, wherein reduced RAB5 expression levels compared to a dichotomous threshold indicate responsiveness to said antigen binding protein without drug or toxin.

2. 1. Use of a kit for selecting an antibody drug conjugate or an antibody toxin conjugate for treating cancer in a subject, comprising: The kit comprises a reagent for detecting the expression level of Ras-associated binding (RAB) protein 5 (RAB5); The antibody drug conjugates include trastuzumab emtansine (T-DM1, Kadcyla), brentuximab vedotin (SGN-35), inotuzumab ozogamicin (CMC-544), pinatuzumab vedotin (RG-7593), polatuzumab vedotin (RG-7596), rifastuzumab vedotin (DNIB0600A, RG-7599), glembatuzumab ravtansine (CDX-011), cortuzumab ravtansine (SAR3419), lorvotuzumab mertansine (IMGN-901), indatuxima ravtansine (BT-062), sacitizumab govitican (sacitizumab govitican (IMMU-132), labetuzumab govitican (IMMU-130), milatuzumab doxorubicin (IMMU-110), indusatumab vedotin (MLN-0264), vadastuximab butariline (SGN-CD33A), denintuzumab mafodotin (SGN-CD19A), enfortumab vedotin (ASG-22ME), rovalpituzumab tesirine (SC16LD6.5), bundletuzumab vedotin (DSTP3086S, RG7450), mirvetuximab soravtansine (IMGN853), ABT-414, IMGN289, and AMG595; 20. The use of a kit, wherein the antibody-toxin conjugate is selected from the group consisting of MH3-B1 / rGel, denileukin diftitox (DAB389IL2), moxetumomab pasudotox (CAT-8015), oportuzumab monotox (VB4-845), resimmune, LMB-2, DT2219ARL, HuM195 / rGel, RG7787, MOC31PE, or D2C7-IT.

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