Kit and application thereof

Abstract

The invention relates to a kit and application thereof. The invention provides a kit. The kit comprises a reagent for detecting the expression level of Ras related binding (RAB) protein 5 (RAB5). The invention also provides a kit. The kit comprises a reagent for detecting the expression level of Ras related binding (RAB) protein 4 (RAB4). The invention also provides a kit. The kit comprises a reagent for detecting the expression level of Ras related binding (RAB) protein 11 (RAB11). The invention also provides a kit. The kit comprises a reagent for detecting the expression level of heat shock protein (HSP) 90 (HSP90). The invention also provides the use of an agent for detecting the expression level of Ras-related binding (RAB) protein 5 (RAB5) for the preparation of a diagnostic kit for an in vitro method.

Description

[0001] This application is a divisional application of a Chinese patent application with an application date of June 22, 2018, an application number of "2018800478289", and an invention title of "Diagnosis and Treatment of Cancer".

[0002] Cross-reference to related applications

[0003] This application claims the priority and benefit of U.S. Provisional Application No. 62 / 524,116, filed on June 23, 2017, which is incorporated herein by reference in its entirety. Technical field

[0004] The present invention relates to compositions and methods for cancer diagnosis, research, and therapy, including but not limited to cancer markers. In particular, the present invention relates to compositions and methods for predicting the response of a subject to cancer therapy. Background art

[0005] The increasing focus on personalized medicine, combined with our growing understanding of cancer biology, has revealed the great potential of biomarkers in cancer treatment. A variety of different biomarkers have been introduced into clinical practice to predict patient survival, evaluate treatment efficacy, or monitor disease progression (Bailey et al, Discovery medicine, 2014, 17:101-114). The high cost of targeted cancer therapies is a powerful incentive for the development of biomarkers to select those patients who are most likely to benefit from treatment. To this end, regulatory agencies are increasingly requiring the inclusion of predictive biomarkers for new therapies in clinical evaluations (Marton & Weiner, Biomed Res Int. 2013; 2013:891391.

[0006] Breast cancer is the second most common form of cancer among women in the United States and the second leading cause of cancer death in women. In the 1980s, the number of new breast cancer cases increased sharply, but this number now appears to have stabilized. The decline in breast cancer mortality may be due to the increasing number of women undergoing mammography. If detected early, the chances of successful treatment of breast cancer are greatly increased.

[0007] When detected early, breast cancer treated by surgery, radiotherapy, chemotherapy, and hormone therapy is most often curable. Mammography is the most important screening tool for early detection of breast cancer. Breast cancer is divided into multiple subtypes, but currently only a few of them are known to affect prognosis or treatment selection. The management of patients suspected of having breast cancer initially usually includes confirmation of the diagnosis, assessment of the disease stage, and selection of therapy. The diagnosis can be confirmed by fine needle aspiration cytology, core needle biopsy using stereotactic or ultrasound techniques for non-palpable lesions, or incisional or excisional biopsy.

[0008] Prognosis is influenced by the patient's age, disease stage, pathological characteristics of the primary tumor, including the presence of tumor necrosis, levels of estrogen receptor (ER) and progesterone receptor (PR) in the tumor tissue, HER2 overexpression status, and measures of proliferative capacity, as well as by menopausal status and general health. Overweight patients may have a poorer prognosis (Bastarrachea et al., Annals of Internal Medicine, 120:18

[1994] ). Prognosis also varies by race, with blacks and to a lesser extent Hispanics having a poorer prognosis compared to whites (Elledge et al., Journal of the National Cancer Institute 86:705

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

[1998] ).

[0009] The three main treatments for breast cancer are surgery, radiation, and drug therapy. There is no one treatment that is suitable for every patient, and often two or more treatments are required. The choice depends on many factors, including the patient's age and menopausal status, type of cancer (e.g., ductal or lobular), cancer stage, whether the tumor is hormone receptive, and its degree of invasion.

[0010] Treatment of breast cancer is classified as local or systemic. Surgery and radiation are considered local therapies because they directly treat the tumor, breast, lymph nodes, or other specific areas. Drug therapy is called a systemic therapy because its effects are widespread. Drug therapies include classical chemotherapy drugs, hormone-blocking treatments (e.g., aromatase inhibitors, selective estrogen receptor modulators, and estrogen receptor downregulators), and monoclonal antibody therapies (e.g., against HER2). They can be used alone or most commonly in different combinations.

[0011] HER2 (ERBB2) is a validated biomarker in breast cancer, and HER2 gene amplification or protein overexpression has been found in ~20% of newly diagnosed breast cancer patients (Slamon, et al., Science, 1989, 244, 707-712; Hernandez-Blanquisett, et al, Breast (Edinburgh, Scotland), 2016, 29, 170-177). HER2 is used as a therapeutic biomarker for the treatment using monoclonal antibodies (mAbs) targeting HER2 (trastuzumab and pertuzumab) and tyrosine kinase inhibitors (TKIs) (lapatinib and afatinib) (Hernandez-Blanquisett, et al, Breast (Edinburgh, Scotland), 2016, 29, 170-177). The pharmacological effects of HER2-targeting mAbs and TKIs are a direct result of drug-target interactions and include antibody-dependent cell cytotoxicity (ADCC) (mAbs), HER2 downregulation, and inhibition of growth-promoting signal transduction (Rimawi, et al, Annual review of medicine, 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 delivering cytotoxic agents to cancer cells. The antibody-drug conjugate (ADC) trastuzumab emtansine (T-DM1) has indeed exemplified this (LewisPhillips, 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), which received FDA approval in 2013 for the treatment of metastatic breast cancer.

[0012] T-DM1 consists of trastuzumab conjugated with the highly cytotoxic drug DM-1 via a thioether (N-maleimidomethyl cyclohexane-1-carboxylate (MCC)) derived from maytansine (Baron, et al, Journal of oncology pharmacy practice, 2015, 21, 132-142). After administration, T-DM1 binds to HER2 and is internalized into cells via HER2-mediated endocytosis. Proteolytic degradation of the trastuzumab moiety within the endosome / lysosome pathway is postulated to be the mechanism for cytosolic release of DM1, which subsequently 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 generated by its trastuzumab moiety, T-DM1 also induces a cytotoxic mechanism within cells.

[0013] Another experimental approach that utilizes HER2 as a drug transporter is through the use of HER2-targeted fusion toxins such as MH3-B1 / rGel, which consists of a 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 and subsequently released into the cytosol, where it binds to ribosomes and inhibits translation (Stirpe, et al, J Biol. Chem., 1980, 255, 6947-6953). Thus, in addition to its binding to HER2, MH3-B1 / rGel induces a cytotoxic effect within cells.

[0014] Compared with HER2-targeted mAbs and TKIs, the mechanism of drugs that bind HER2 with an intracellular site of action is apparently more complex, and this should be reflected in 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 not only on HER3 but also on HER2 and its downstream signaling (Baselga, et al, Clinical cancer research, 2016, 22, 3755-3763; Kim, et al, Int J Cancer, 2016, 139, 2336-2342), while little is known about the effects on proteins involved in endocytosis, endocytic vesicle trafficking, and exocytosis. Additional biomarkers are needed to improve the predictive value of antibody-drug conjugates and immunotoxins targeting HER2 and other target receptors. This study aimed to evaluate proteins in the Rab GTPase family as possible biomarkers for the therapeutic efficacy of HER2-targeted ADCs and immunotoxins (Stenmark, Naturereviews.Molecular cell biology, 2009, 10, 513-525), as well as proteins specifically involved in HER2 endocytosis. SUMMARY OF THE INVENTION

[0015] The present invention relates to compositions and methods for cancer therapy, including but not limited to therapies utilizing cancer biomarkers. In particular, the present invention relates to compositions and methods for predicting a subject's response to cancer therapy.

[0016] Targeted therapies strongly rely on validated biomarkers to select patients who are most likely to benefit from treatment. HER2 has been used as a therapeutic biomarker for multiple tyrosine kinase inhibitors (TKIs) and monoclonal antibodies (mAbs) against HER2. However, HER2 can also serve as a transport gate for delivering cytotoxic agents into the cytosol, such as through HER2-targeted antibody-drug conjugates (ADCs) and antibody-toxin conjugates (immunotoxins). Compared with the biomarkers for TKIs and mAbs, the therapeutic biomarkers for such drugs may be more complex because they not only act as targets but may also reflect the mechanisms of drug uptake and intracellular action.

[0017] In the present study, a panel of HER2-positive breast 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 was associated with the expression levels of HER2 binding to HER3 and EGFR, which may affect the pharmacological action of the targeting moiety of the drug, and with Rab4, Rab5, Rab11, and HSP90 involved in endocytic trafficking, which may affect the pharmacological action of the toxic moiety. The early endosomal marker Rab5 and the early recycling marker Rab4 were indicated as possible therapeutic biomarkers for both T-DM1 and MH3-B1 / rGel. Additionally, it was shown that the toxicity of MH3-B1 / rGel depends on HSP90 and Rab11 (inversely). These results for the first time outline proteins involved in endocytic trafficking that are potential biomarkers for ADCs and antibody-toxin conjugates generally targeting HER2, as well as for ADCs and targeted antibody-toxin conjugates. Additionally, a mathematical method is provided to validate combinations of biomarkers with different contributing factors.

[0018] Accordingly, in some embodiments, the present invention provides a method of treating a patient diagnosed with cancer with an antibody-drug conjugate or an antibody-toxin conjugate, comprising: a) determining the normalized protein expression levels of the receptor for the antibody component of the antibody-drug or 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 composite expression metric of the protein expression levels of the receptor and the biomarker; and c) treating the patient with the antibody-drug or toxin conjugate based on the composite expression metric. In some embodiments, the receptor is HER2, HER3, or EGFR.

[0019] In some embodiments, the method comprises administering the antibody-drug conjugate or the antibody-toxin conjugate when an expression metric is determined in which the protein expression level of HER2 is also elevated in addition to RAB5. In some embodiments, the method comprises administering the antibody-drug conjugate or the antibody-toxin conjugate when an expression metric is determined in which the protein expression level of HER2 is also elevated in addition to Rab5 and Rab4. In some embodiments, the method comprises administering the antibody-drug conjugate or the antibody-toxin conjugate when an expression metric is determined in which the protein expression level of HER2 is also elevated in addition to Rab5 and Rab4. In some embodiments, the method comprises administering the antibody-drug conjugate or the antibody-toxin conjugate when an expression metric is determined in which the protein expression level of HER2 is also elevated in addition to RAB5, RAB4, and HSP90.

[0020] 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 cancer, ovarian cancer, bladder cancer, gastrointestinal cancer, mesothelioma, multiple myeloma, acute myeloid leukemia, acute lymphoblastic leukemia, and non-Hodgkin lymphoma.

[0021] 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), Lifastuzumab 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), Milatuzumab doxorubicin (IMMU-110), Indusatumab vedotin (MLN-0264), Vadastuximab talirine (SGN-CD33A), Denintuzumab mafodotin (SGN-CD19A), Enfortumab vedotin (ASG-22ME), Rovalpituzumab tesirine (SC16LD6.5), Vandortuzumab vedotin (DSTP3086S, RG7450), Mirvetuximab soravtansine (IMGN853), ABT-414, IMGN289, or AMG595.

[0022] In some embodiments, the antibody-drug conjugate is MH3-B1 / rGel, denileukindiftitox (DAB389IL2), moxetumomab pasudotox (CAT-8015), oportuzumab monotox (VB4-845), Resimmune, LMB-2, DT2219ARL, HuM195 / rGel, RG7787, MOC31PE or D2C7-IT. In some embodiments, the determination includes an immunoassay.

[0023] Additional embodiments provide a method for determining a course of treatment, the method comprising: a) determining the normalized protein expression levels of the receptor of the antibody component of an antibody-drug conjugate or an antibody-toxin conjugate and at least one additional marker selected from RAB5, RAB4, RAB11, and HSP90 in a biological sample from the patient; b) generating a composite expression metric of the protein expression levels of the receptor and the marker; and c) recommending a course of treatment based on the composite expression metric.

[0024] Additional embodiments provide a method for determining a composite expression metric in a biological sample from a patient diagnosed with cancer, the method comprising: a) determining the normalized protein expression levels of the ligand of the antibody component of an antibody-drug conjugate or an antibody-toxin conjugate and at least one additional marker selected from, for example, RAB5, RAB4, RAB11, and HSP90 in a biological sample from the patient; and b) generating a composite expression metric of the protein expression levels of the ligand and the marker.

[0025] Yet additional embodiments provide a kit comprising: at least one first reagent for detecting the protein 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. In some embodiments, the reagent is an antibody.

[0026] Still additional embodiments provide a system comprising: 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; b) a computer processor and computer software for calculating a composite expression metric based on the expression levels.

[0027] In still other embodiments, the present invention provides a method for treating cancer in a patient, comprising: obtaining a sample containing cancer cells from the patient; measuring the expression level of RAB5 in the cancer cells by an in vitro assay; and administering an effective amount of an immunoconjugate targeting a cancer cell surface antigen if the expression level of RAB5 in the cancer cell sample is increased compared to a predetermined reference level; or administering an antigen-binding protein not containing a drug or toxin if the expression level of RAB5 in the cancer cell sample is decreased compared to a predetermined reference level.

[0028] In some preferred embodiments, measuring the expression level of RAB5 in the cancer cells comprises measuring the level of RAB5 mRNA. In some preferred embodiments, measuring the expression level of RAB5 in the cancer cells comprises 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.

[0029] In some preferred embodiments, the method further comprises the step of determining the expression level of one or more of RAB4, RAB11 or HSP90. In some preferred embodiments, the method further comprises the following steps: incorporating the expression level of one or more of RAB4, RAB11 or HSP90 into an expression index having the expression level of RAB5, and administering an effective amount of an immunoconjugate targeting a cancer cell surface antigen if the expression index is increased compared to a predetermined reference level.

[0030] In some preferred embodiments, the cancer cells are obtained from a surgical tumor sample, a biopsy sample or a blood sample.

[0031] In some preferred embodiments, the immunoconjugate 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 immunoconjugate is an antibody-drug conjugate selected from the group consisting of: trastuzumab-emtansine, vedotin-brentuximab, inotuzumab ozogamicin, pinatuzumab vedotin, polatuzumab vedotin, lifastuzumab vedotin, glembatuzumab vedotin, coltuximab ravtansine, lorvotuzumab mertansine, indatuximab ravtansine, sacitizumab govitecan, labetuzumab govitecan, milatuzumab doxorubicin, indusatumab vedotin, vadastuximab talirine, denintuzumab mafodotin, enfortumab vedotin, rovalpituzumab tesirine, vandortuzumab vedotin, mirvetuximab soravtansine, ABT-414, IMGN289, and AMG595. In some preferred embodiments, the immunoconjugate 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.

[0032] 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.

[0033] In some preferred embodiments, the method further comprises the step of determining the expression of surface antigens on cancer cells.

[0034] 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 immunoconjugate targets 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 immunoconjugate is trastuzumab-emtansine (T-DM1).

[0035] In some preferred embodiments, the present invention provides an immunoconjugate targeting a surface antigen for use in a method of treating cancer in a patient, wherein cancer cells from the patient express the antigen and exhibit an increased level of RAB5 expression compared to a predetermined reference level, as determined by an in vitro expression assay. In other preferred embodiments, the present invention provides an antigen-binding protein not conjugated to a drug or toxin for use in a method of treating cancer in a patient, wherein cancer cells from the patient express the antigen and exhibit a decreased level of RAB5 expression compared to a predetermined reference level, as determined by an in vitro expression assay.

[0036] In some preferred embodiments, the in vitro expression assay is an RAB5 mRNA assay. In some preferred embodiments, the in vitro expression assay is an RAB5 protein assay. In some preferred embodiments, RAB5 is RAB5A. In some preferred embodiments, RAB5 is RAB5B. In some preferred embodiments, RAB5 is RAB5C.

[0037] In some preferred embodiments, the immunoconjugate 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 immunoconjugate is an antibody-drug conjugate selected from the group consisting of: trastuzumab-emtansine, vedotin-brentuximab, inotuzumab ozogamicin, pinatuzumab vedotin, polatuzumab vedotin, lifastuzumab vedotin, glembatuzumab vedotin, coltuximab ravtansine, lorvotuzumab mertansine, indatuximab ravtansine, sacitizumab govitecan, labetuzumab govitecan, milatuzumab doxorubicin, indusatumab vedotin, vadastuximab talirine, denintuzumab mafodotin, enfortumab vedotin, rovalpituzumab tesirine, vandortuzumab vedotin, mirvetuximab soravtansine, ABT-414, IMGN289, and AMG595. In some preferred embodiments, the immunoconjugate 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.

[0038] 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.

[0039] 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 targets 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 immunoconjugate is trastuzumab emtansine (T-DM1).

[0040] In yet additional preferred embodiments, the present invention provides an in vitro method for determining whether human cancer cells are responsive to an immunoconjugate that targets a surface antigen on the cancer cells, comprising: obtaining a sample comprising cancer cells from a patient; and measuring the expression level of RAB5 in the cancer cells by an in vitro assay, wherein an increase in the expression level of RAB5 compared to a predetermined reference level indicates responsiveness to the immunoconjugate.

[0041] In some preferred embodiments, the in vitro expression assay is an RAB5 mRNA assay. In some preferred embodiments, the in vitro expression assay is an RAB5 protein assay. 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 comprises the step of measuring the expression level of one or more of RAB4, RAB11, or HSP90. In some preferred embodiments, the method further comprises the step of incorporating the expression level of one or more of RAB4, RAB11, or HSP90 into an expression index having the RAB5 expression level.

[0042] 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 lymphocytic leukemia cells, and non-Hodgkin lymphoma.

[0043] In some preferred embodiments, the cancer cells are breast cancer cells. In some preferred embodiments, the method further comprises the step of determining the expression of surface antigens on 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.

[0044] In some preferred embodiments, the method further comprises the step of administering an immunoconjugate to a subject when the expression level of RAB5 is increased compared to a reference RAB5 expression level.

[0045] In some preferred embodiments, the method further comprises the step of administering an antibody that does not contain a drug or toxin to a subject when the expression level of RAB5 is decreased compared to a reference RAB5 expression level.

[0046] Additional embodiments are described herein. Description of the Drawings

[0047] Figures 1A - 1B : Figure 1A : Western blot of HER2 and γ-tubulin expression in SK-BR-3, SKOV-3, HCC1954, AU-565, MDA-MB-435, and MDA-MB-231 cells. Figure 1B : Relative viability (MTT) of SK-BR-3, SKOV-3, AU-565, HCC1954, and MDA-MB-435 after treatment with the indicated drugs for 72 hours. For T-DM1, an S-shaped curve fitting model a / (1 + exp(-(x - x0) / b)) was used. Data points represent the mean of three independent experiments (trastuzumab, error bars: SE) or one representative value of at least three independent experiments (T-DM1 and MH3-B1 / rGel, error bars: SD).

[0048] Figures 2A - 2E : Figure 2A : Schematic diagram of the cellular sensitivity to trastuzumab and intracellular acting therapeutics targeting HER2. Figure 2B : Cellular 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 : Drug concentration that inhibits 50% of cell viability. TI: IC 50 (rGel) / IC 50(MH3-B1 / rGel). Representative ( Figure 2C ) and quantitative ( Figure 2D ) Western blots (n = 2) 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. Figure 2E : Curve of linear regression analysis between HER2 and T-DM1 sensitivity (1 / IC 50 (T-DM1)) (E1) or MH3-B1 / rGel sensitivity (TI) (E2).

[0049] Figure 3 : Effects of HER3 and EGFR expression together with HER2 on the sensitivity to T-DM1 (A1-A3 and B1-B3) and MH3-B1 / rGel (C1-C3, D1-D3, and E1-E2). A1-A3 and B1-B3: The left panel shows the curve of linear regression analysis between HER3 (A1) or EGFR (B1) expression and T-DM1 sensitivity in five cell lines. The middle panel shows the R 2 value, which is a function of the contribution factor of HER3 (A2) or EGFR (B2) expression to T-DM1 sensitivity except for the contribution of HER2 expression in five cell lines. A3 represents the optimized linear regression analysis curve, in which T-DM1 sensitivity is linearly correlated with both HER2 and HER3 expression (contribution factor: 0.2). B3 shows the R 2 value, which is a function of the factor of the effect of EGFR expression on T-DM1 sensitivity except for the effects of HER2 expression and HER3 expression (contribution factor: 0.2) in five cell lines. C1-C3 and D1-D3: The left panel shows the curve of linear regression analysis between HER3 (C1) or EGFR (D1) expression and MH3-B1 / rGel sensitivity in five cell lines. The middle panel shows the R 2 value, which is a function of the contribution of HER3 (A2) or EGFR (B2) expression to MH3-B1 / rGel sensitivity except for the contribution of HER2 expression in five cell lines. The right panel represents the optimized linear regression analysis curve, in which MH3-B1 / rGel sensitivity is linearly correlated with both HER2 and HER3 expression (contribution factor: 0.4) (C3) or HER2 and EGFR expression (contribution factor: 0.3) (D3). E1 shows the R 2A value that is a function of the factor of the effect of HER3 expression on MH3-B1 / rGel sensitivity, excluding the effects of HER2 and EGFR expression (contributing factor: 0.3), in five cell lines. E2 represents the optimized linear regression analysis curve, in which MH3-B1 / rGel sensitivity is linearly correlated with HER2, EGFR (contributing factor: 0.3), and HER3 expression (contributing factor: 0.4).

[0050] Figures 4A - 4E : Representative ( Figure 4A ) and quantitative ( Figures 4B - 4E ) Western blots (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.

[0051] Figure 5 : The effect of Rab5, Rab4, HSP90, and Rab11 expression together with HER2 on T-DM1 sensitivity. The left panel 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 panel shows the R 2 value that is a function of the factor of the effect of Rab5 (A2), Rab4 (B2), HSP90 (C2), or 1 / Rab11 (D2) expression on T-DM1 sensitivity, excluding the effect of HER2 expression, in five cell lines. The right panel represents the optimized linear regression analysis curve, in which T-DM1 sensitivity is linearly correlated with both HER2 and Rab5 (contributing factor: 0.3) (A3), Rab4 (contributing factor: 0.4) (B3), or 1 / Rab11 (contributing factor: 0.2) (D3) expression.

[0052] Figure 6 : The effect of Rab5, Rab4, HSP90, and Rab11 expression together with HER2 on MH3-B1 / rGel sensitivity. The left panel 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 panel shows the R 2A value that is a function of factors influencing the sensitivity of MH3 - B1 / rGel by the expression of Rab5(A2), Rab4(B2), HSP90(C2), or 1 / Rab11(D2) in five cell lines, excluding the influence of HER2 expression. The right inset represents the optimized linear regression analysis curve, where the sensitivity of MH3 - B1 / rGel is linearly correlated with both HER2 and the expression of Rab5 (contribution factor: 0.3)(A3), Rab4 (contribution factor: 0.6)(B3), HSP90 (contribution factor: 1)(C3), or 1 / Rab11 (contribution factor: 1)(D3).

[0053] Figure 7 : The combined influence of the expression of Rab5, Rab4, and HSP90 or 1 / Rab11 together with HER2 on the sensitivity of MH3 - B1 / rGel. A1 shows the R 2 A value that is a function of factors influencing the sensitivity of MH3 - B1 / rGel by the expression of Rab4 in five cell lines, excluding the influence of HER2 and Rab5 expression (contribution factor 0.3). A2 represents the optimized linear regression analysis curve, where the sensitivity of MH3 - B1 / rGel is linearly correlated with HER2, Rab5 (contribution factor: 0.3), and Rab4 expression (contribution factor: 0.6). B1 shows the R 2 A value that is a function of factors influencing the sensitivity of MH3 - B1 / rGel by the expression of HSP90 in five cell lines, excluding the influence of HER2, Rab5 (contribution factor 0.3), and Rab4 (contribution factor 0.6). B2 represents the optimized linear regression analysis curve, where the sensitivity of MH3 - B1 / rGel is linearly correlated with HER2, Rab5 (contribution factor 0.3), Rab4 expression (contribution factor 0.6), and HSP90 expression (contribution factor 0.8). C1 shows the R 2 A value that is a function of factors influencing the sensitivity of MH3 - B1 / rGel by the expression of 1 / Rab11 in five cell lines, excluding the influence of HER2, Rab5 (contribution factor: 0.3), Rab4 (contribution factor: 0.6), and HSP90 (contribution factor: 0.8). C2 represents the optimized linear regression analysis curve, where the sensitivity of MH3 - B1 / rGel is linearly correlated with HER2, Rab5 (contribution factor 0.3), Rab4 expression (contribution factor 0.6), HSP90 expression (contribution factor 0.8), and 1 / Rab11 expression (contribution factor 0.4).

[0054] Figure 8: The effects of the combinations of HER3 and EGFR (A1, B1) or Rab5, Rab4, HSP90, and 1 / Rab11 expression (A2, B2) together with HER2 on the sensitivity to T-DM1 (A1 - A2) or MH3 - B1 / rGel (B1 - B2). The results presented are the same as those reported in Figure 5 , Figure 6 and Figure 7 and are merged here in the same figure. Schematic diagram of biomarkers indicating sensitivity to T-DM1 and MH3 - B1 / rGel in this report (C). Arrows indicate drugs for which the current biomarkers are recommended, and the width of the arrow illustrates the effect of the protein on drug sensitivity. Circles represent two libraries of biomarkers for the recommended combinations.

[0055] Figure 9 : Results of the Monte-Carlo 2-fold cross-validation procedure to determine the threshold that minimizes the p-value for biomarker x treatment interaction.

[0056] Figure 10 : Bayesian pCR probability curve related to RAB5A expression. Detailed implementation mode

[0057] Definition

[0058] To facilitate understanding of the present invention, a number of terms and phrases are defined below:

[0059] 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 allows the antigen-binding portion to adopt a conformation that promotes binding of the antigen-binding protein to the antigen. Examples of antigen-binding proteins include antibodies, antibody fragments (e.g., antigen-binding portions of antibodies), antibody derivatives, and antibody analogs. Antigen-binding proteins can include, for example, alternative protein scaffolds or artificial scaffolds with grafted CDRs or CDR derivatives. Such scaffolds include, but are not limited to, antibody-derived scaffolds containing mutations introduced, for example, to stabilize the three-dimensional structure of the antigen-binding protein, and fully synthetic scaffolds including, 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, etc.

[0060] As used herein, the term "immunoconjugate" refers to a molecule comprising an antigen-binding protein that is linked or conjugated, such as by a chemical bond or a peptide linker, to another agent, such as a drug or a toxin. The term "immunoconjugate" encompasses antibody-drug conjugates, immunotoxins, and affinity toxins. The antigen-binding protein portion of the molecule can be an immunoglobulin or an antigen-binding fragment or an antigen-binding derivative thereof, such as 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, etc. The antigen-binding protein portion can also be a protein ligand capable of binding to a cell surface antigen. For example, EGF can target the EGF receptor expressed on the cell surface.

[0061] As used herein, the term "antibody-drug conjugate (ADC)" refers to a molecule comprising an antigen-binding protein that is typically linked or conjugated to a drug molecule via a chemical bond or otherwise.

[0062] As used herein, the term "immunotoxin" refers to a molecule comprising an antigen-binding protein that is typically linked or conjugated to a toxin molecule via a peptide linker or otherwise.

[0063] As used herein, the term "affinity toxin" refers to a molecule comprising a protein ligand capable of binding to a cell surface antigen, wherein the protein ligand is typically linked or conjugated to a toxin molecule via a peptide linker or otherwise.

[0064] As used herein, a cancer cell "responds" to an immunoconjugate when a measurable toxic response can be detected when the cell is contacted with the immunoconjugate.

[0065] As used herein, the terms "detect," "detecting," or "detection" can describe the general act of finding or discerning or a particular observation of a composition.

[0066] As used herein, the term "nucleic acid molecule" refers to any molecule that contains nucleic acid, including but not limited to DNA or RNA. Sequences covered by this term include any 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, β-D-mannosyl queosine, 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, methyl ester of N-uracil-5-oxyacetic acid, uracil-5-oxyacetic acid, pseudouracil, queosine, 2-thiocytosine, and 2,6-diaminopurine.

[0067] As used herein, the term "amplification oligonucleotide" refers to an oligonucleotide that hybridizes to a target nucleic acid or its complementary sequence and participates in a nucleic acid amplification reaction. An example of an amplification oligonucleotide is a "primer" that hybridizes to a template nucleic acid and contains a 3'OH terminus that is extended by a polymerase during amplification. Another example of an amplification oligonucleotide is an oligonucleotide that is not extended by a polymerase (e.g., because it has a 3'-blocked terminus), but participates in or facilitates amplification. Amplification oligonucleotides can optionally include modified nucleotides or analogs, or other nucleotides that participate in the amplification reaction but are not complementary to the target nucleic acid or not included in the target nucleic acid. Amplification oligonucleotides can contain sequences that are not complementary to the target or template sequence. For example, the 5' region of a primer can include a promoter sequence that is not complementary to the target nucleic acid (referred to as a "promoter-primer"). Those skilled in the art will understand that an amplification oligonucleotide that functions as a primer can be modified to include a 5' promoter sequence and thus function as a promoter-primer. Similarly, a promoter-primer can be modified by removing the promoter sequence or synthesizing it without a promoter sequence and still function as a primer. A 3'-blocked amplification oligonucleotide can provide a promoter sequence and serve as a template for polymerization (referred to as a "promoter-provider").

[0068] As used herein, the term "primer" refers to an oligonucleotide, whether occurring naturally as a purified restriction digest or produced synthetically, which is capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product complementary to a nucleic acid strand is induced (e.g., in the presence of nucleotides and an agent such as DNA polymerase and at a suitable temperature and pH). For maximum amplification efficiency, the primer is preferably single-stranded but may alternatively be double-stranded. If double-stranded, the primer is first treated to separate its strands before being used to prepare the extension product. Preferably, the primer is an oligodeoxyribonucleotide. The primer should be long enough to prime the synthesis of the extension product in the presence of the agent. The exact length of the primer will depend on many factors including temperature, primer source, and the method of use.

[0069] As used herein, the term "probe" refers to an oligonucleotide (i.e., a nucleotide sequence), whether occurring naturally as a purified restriction digest or produced synthetically, recombinantly, or by PCR amplification, which is capable of hybridizing to at least a portion of another oligonucleotide of interest. The probe may be single-stranded or double-stranded. Probes can be used to detect, identify, and isolate specific gene sequences. It is contemplated that any probe used in the present invention will be labeled with any "reporter molecule" so as to be detectable in any detection system, including but not limited to enzymatic (e.g., ELISA, and enzyme-based histochemical assays), fluorescent, radioactive, and luminescent systems. The invention is not intended to be limited to any particular detection system or label. In some embodiments, the reporter molecule is an "exogenous reporter molecule".

[0070] The term "exogenous reporter molecule" refers to a reporter molecule or label that is not found in nature ligated to a detection reagent (e.g., a probe, nucleic acid, or antibody). Examples include but are not limited to enzymatic, fluorescent, radioactive, or luminescent reporter molecules.

[0071] As used in reference to nucleic acids, the term "isolated", as in "isolated oligonucleotide" or "isolated polynucleotide", refers to a nucleic acid sequence that has been identified and separated from at least one component or contaminant with which it is ordinarily associated in its natural source. Isolated nucleic acids exist in a form or setting that is different from that in which they are found in nature. In contrast, non-isolated nucleic acids, such as nucleic acids of DNA and RNA, are found in their natural state. For example, a given DNA sequence (e.g., a gene) is found on a host cell chromosome in proximity to adjacent genes; an RNA sequence (such as a specific mRNA sequence encoding a particular protein) is found in a cell as a mixture with many other mRNAs encoding multiple proteins. However, for example, an isolated nucleic acid encoding a given protein includes such nucleic acid in a cell that ordinarily expresses the given protein, where the nucleic acid is in a chromosomal location different from that in the natural cell, or is flanked by nucleic acid sequences different from those in the natural situation. Isolated nucleic acids, oligonucleotides or polynucleotides can exist in single-stranded or double-stranded form. When an isolated nucleic acid, oligonucleotide or polynucleotide is used to express a protein, the oligonucleotide or polynucleotide will contain at least the sense strand or coding strand (i.e., the oligonucleotide or polynucleotide can be single-stranded), but can also contain both the sense and antisense strands (i.e., the oligonucleotide or polynucleotide can be double-stranded).

[0072] As used herein, the term "purified" or "purification" refers to the removal of components (e.g., contaminants) from a sample. For example, an antibody is purified by removing contaminating non-immunoglobulins; they are also purified by removing immunoglobulins that do not bind to the target molecule. The removal of non-immunoglobulins and / or immunoglobulins that do not bind to the target molecule results in an increase in the percentage of target-reactive immunoglobulins in the sample. In other instances, a recombinant polypeptide is expressed in a bacterial host cell and the polypeptide is purified by removing host cell proteins; thus, the percentage of the recombinant polypeptide in the sample is increased.

[0073] As used herein, the term "sample" is used in its broadest sense. In a sense, this means including samples or cultures obtained from any source, as well as biological and environmental samples. Biological samples can be obtained from animals (including humans) and include fluids, solids, tissues and gases. Biological samples include blood products such as plasma, serum, etc. Environmental samples include environmental materials such as surface matter, soil, water, crystals and industrial samples. However, these examples should not be construed as limiting the types of samples applicable to the present invention.

[0074] The present invention relates to compositions and methods for cancer treatment, including but not limited to therapies that utilize cancer biomarkers. In particular, the present invention relates to compositions and methods for predicting a subject's response to cancer therapy.

[0075] The increasing focus on personalized medicine, coupled with our growing understanding of cancer biology, has revealed the great potential of using biomarkers in cancer treatment. A range of different biomarkers have been incorporated into clinical practice to predict patient survival, assess treatment efficacy, or monitor disease progression. 1 Predictive biomarkers enable the careful selection of patients most likely to benefit from a particular treatment, and thus, such knowledge is crucial for the rational use of current and future high-cost targeted cancer therapies.

[0076] Accordingly, the present disclosure provides 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 biomarkers. The invention is not limited to specific biomarkers. In some embodiments, the antigen of an antibody (e.g., HER2, HER3, EGFR) and a combination of one or more other biomarkers (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 the combination or biomarkers are combined to produce a composite expression metric.

[0077] In some preferred embodiments, the invention provides a method of treating a patient's cancer, comprising: obtaining a sample containing cancer cells from the patient; measuring the expression level of RAB5 in the cancer cells by an in vitro assay; and administering an effective amount of an immunoconjugate targeting a cancer cell surface antigen if the expression level of RAB5 in the cancer cell sample is increased 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 compared to a predetermined reference level. The expression of any one or a combination of RAB5A, RAB5B, or RAB5C can be assayed. In some particularly preferred embodiments, the expression of RAB5A is assayed.

[0078] As described, in some preferred embodiments, the decision to administer an immunoconjugate or an antigen-binding protein not conjugated to a toxin or drug is based on comparing the measured expression of RAB5 (preferably RAB5A) in a patient sample to a predetermined reference level or threshold level. Those skilled in the art will recognize that the reference level or threshold level can be determined by a statistical procedure applied to expression data obtained from a suitable patient population. Suitable statistical methods are provided in the Examples, but those skilled in the art will recognize that other statistical procedures can also be utilized. It will also be recognized that different statistical procedures or the same procedure run on different or expanded data sets may result in different reference levels or threshold levels. Accordingly, the 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 methods of the invention further comprise determining the expression level of one or more of RAB4, RAB11, or HSP90. In some preferred embodiments, the expression level of one or more of RAB4, RAB11, or HSP90 in a sample is incorporated into an expression metric having the RAB5 expression level (preferably the RAB5A expression level), and if the expression metric is increased compared to a predetermined reference level, an effective amount of an immunoconjugate targeting a cancer cell surface antigen is administered.

[0079] In some preferred embodiments, the patient sample for use in the methods of the invention comprises cancer cells. Suitable cell-containing samples can be obtained by a variety of methods, including but not limited to biopsy, surgical samples, and blood draw samples. In some preferred embodiments, the presence of one or more cell surface antigens in the sample has been previously determined. In some embodiments, the method further comprises determining the expression of one or more cell surface antigens in the sample if the sample has not been previously characterized. The invention is not limited to the determination of any particular cell surface antigen, but cell surface antigens that are readily internalized (e.g., by endocytosis) are preferred. Exemplary cell surface antigens that are readily internalized 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.

[0080] In some particularly preferred embodiments, the sample comprises breast cancer cells. In these embodiments, it is preferred to characterize the expression of epidermal growth factor receptor (HER1), HER2, and / or HER3 in the breast cancer cells. In even more preferred embodiments, the sample is determined to have, or has previously been determined and identified as having, the HER2 receptor.

[0081] As described above, in some embodiments, a composite expression metric is used in the methods of the present invention. The present invention is not limited to the use of any particular composite expression metric. Examples of suitable composite expression metrics are as follows.

[0082] In some embodiments, the composite expression metric 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).

[0083] In some embodiments, the composite expression metric 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).

[0084] In some embodiments, the composite expression metric 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).

[0085] In some embodiments, the composite expression metric is: relative HER2 expression × (1 - (1 - relative RAB4 expression) × 0.4) × (1 - (1 - relative RAB5 expression) × 0.2).

[0086] In some embodiments, the composite expression metric is: relative HER2 expression × (1 - (1 - relative RAB5 expression) × 0.3) × (1 - (1 - relative RAB4 expression) × 0.6).

[0087] In some embodiments, the composite expression metric is: relative HER2 expression × (1 - (1 - relative RAB5 expression) × 0.3).

[0088] In some embodiments, the composite expression metric is: relative HER2 expression × (1 - (1 - relative RAB4 expression) × 0.4).

[0089] In some embodiments, the composite expression metric is: relative HER2 expression × (1 - (1 - relative RAB4 expression) × 0.6).

[0090] In some embodiments, the composite expression index is: relative HER2 expression × (1 - (1 - relative RAB11 expression) × 0.2).

[0091] In some embodiments, the composite expression index is: relative HER2 expression × (1 - (1 - relative HSP90 expression)).

[0092] 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 with specific primers for the biomarker. In some embodiments, the detection technique is RT-PCR.

[0093] The expression assays used in the present invention can utilize a single biomarker, such as RAB5A, or a group of biomarkers (e.g., RAB5A and one or more of RAB4, RAB11, or HSP90; RAB5A and HER2; or RAB5A, HER2, and one or more of RAB4, RAB11, or HSP90). In some embodiments, the group or assay of the present invention includes fewer than 100, 75, 50, 25, 20, 15, 10, or five biomarkers, or in other preferred embodiments, up to a total of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 20 biomarkers.

[0094] In some embodiments, the protein expression level in a sample from a subject is detected. In some embodiments, the subject has been diagnosed with cancer (e.g., breast cancer). In some embodiments, the sample is tissue (e.g., biopsy tissue), blood, serum, urine, etc.

[0095] Exemplary methods for detecting protein markers are provided below. However, any suitable method for detecting a tumor marker protein can be used.

[0096] Illustrative non-limiting examples of immunoassays include, but are not limited to: immunoprecipitation; Western blotting; ELISA; immunohistochemistry; immunocytochemistry; flow cytometry; and immunological PCR. Polyclonal or monoclonal antibodies detectably labeled using various techniques known to those of ordinary skill in the art (e.g., colorimetric, fluorescent, chemiluminescent, or radioactive) are suitable for use in immunoassays.

[0097] Immunoprecipitation is a technique that uses antibodies specific for the antigen to precipitate the antigen out of solution. By targeting proteins that are thought to exist as complexes, this method can be used to identify protein complexes present in cell extracts. The complexes are brought out of solution by insoluble antibody-binding proteins (such as Protein A and Protein G) originally isolated from bacteria. These antibodies can also be linked to agarose beads that can be easily separated from the solution. After washing, the precipitate can be analyzed using mass spectrometry, Western blotting, or any of a variety of other methods to identify the components of the complex.

[0098] Western blotting or immunoblotting is a method for detecting proteins in a given sample of tissue homogenate or extract. It uses gel electrophoresis to separate denatured proteins by mass. The proteins are then transferred out of the gel and onto a membrane, usually a polyvinylidene difluoride or nitrocellulose membrane, where they are probed with antibodies specific for the protein of interest. As a result, researchers can examine the amount of protein in a given sample and compare the levels between several groups.

[0099] ELISA is the abbreviation for enzyme-linked immunosorbent assay, a biochemical technique used to detect the presence of antibodies or antigens in a sample. It utilizes at least two antibodies, one of which is specific for the antigen and the other is linked to an enzyme. The second antibody will cause a substrate that produces color or fluorescence to generate a signal. Variations of ELISA include sandwich ELISA, competitive ELISA, and ELISPOT. Since ELISA can be performed to assess the presence of antigens or antibodies in a sample, it is a useful tool for determining serum antibody concentrations as well as for detecting the presence of antigens.

[0100] Immunohistochemistry and immunocytochemistry refer to methods of localizing proteins in tissue sections or cells, respectively, based on the principle of antigens in the tissue or cells binding to their respective antibodies. Visualization can be achieved by labeling the antibodies with tags that produce color or fluorescence. Typical examples of colored tags include, but are not limited to, horseradish peroxidase and alkaline phosphatase. Typical examples of fluorophore tags include, but are not limited to, fluorescein isothiocyanate (FITC) or phycoerythrin (PE).

[0101] Flow cytometry is a technique used to count, examine, and sort microscopic particles suspended in a fluid stream. It allows for simultaneous multi-parameter analysis of the physical and / or chemical properties of individual cells flowing through an optical / electronic detection device. A beam of light of a single frequency or color (e.g., a laser) is directed onto a hydrodynamically focused fluid stream. Multiple detectors are aimed at the point where the stream passes through the beam; one in line with the beam (forward scatter or FSC), and several perpendicular to the beam (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 may be excited to emit light at a lower frequency than the light source. The combination of scattered light and fluorescence is picked up by the detectors, and by analyzing the brightness fluctuations of each detector (one for each fluorescent emission peak), various facts about the physical and chemical structure of each individual particle can be inferred. FSC is related to cell volume, while SSC is related to the density or internal complexity of the particle (e.g., the shape of the nucleus, the number and type of cytoplasmic granules, or the roughness of the membrane).

[0102] Immunopolymerase chain reaction (IPCR) utilizes nucleic acid amplification techniques to increase signal generation in antibody-based immunoassays. Since there is no equivalent of PCR for proteins, that is, proteins cannot be replicated in the same way as nucleic acids are replicated during PCR, the only way to increase detection sensitivity is through signal amplification. The target protein binds to an antibody that is directly or indirectly conjugated to an oligonucleotide. Unbound antibody is washed away, and the oligonucleotides of the remaining bound antibody are amplified. Protein detection is performed by detecting the amplified oligonucleotides using standard nucleic acid detection methods, including real-time methods.

[0103] 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).

[0104] In some embodiments, a computer-based analysis program is used to transform the raw data generated by a detection assay (e.g., the presence, absence, or quantity of biomarker expression) into data that is predictive for a clinician (e.g., the choice of cancer therapy or a composite expression metric). The clinician can use the predictive data in any suitable manner. Thus, in some preferred embodiments, the present invention provides the additional benefit that clinicians who are less likely to be trained in genetics or molecular biology do not need to 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 care of the subject.

[0105] The present invention contemplates any method capable of receiving, processing, and transmitting information among the laboratory where the assay is performed, the information provider, the medical staff, and the subject. For example, in some embodiments of the present invention, a sample (e.g., a biopsy or blood or serum sample) is obtained from the subject and submitted to an analytical service provider (e.g., a clinical laboratory of a medical institution, a genomic analysis enterprise, etc.) located anywhere in the world (e.g., a country different from the country where the subject resides or the country where the information will be ultimately used) to generate raw data. If the sample contains tissue or other biological samples, the subject may go to a medical center for the sample to be collected and sent to the analysis center, or the subject may collect the sample himself / herself (e.g., a urine sample) and send it directly to the analysis center. If the sample contains previously determined biological information, this information may be sent directly by the subject to the analytical service provider (e.g., an information card containing the information may be scanned by a computer, and the data may be transmitted to the computer at the analysis center using an electronic communication system). Once received by the analytical service provider, the sample is processed and an analytical profile (i.e., expression data) specific to the diagnostic or prognostic information required by the subject is generated.

[0106] Then the analytical profile data is prepared in a format suitable for interpretation by the attending clinician. For example, instead of providing the raw data, a format is prepared that can represent the subject's diagnosis or risk assessment (e.g., the likelihood of success of cancer treatment or a composite expression metric), as well as recommendations for specific treatment options. The data can be presented to the clinician by any suitable method. For example, in some embodiments, the analytical service provider generates a report that can be printed for the clinician (e.g., at the time of care) or displayed to the clinician on a computer monitor.

[0107] In some embodiments, the information is first analyzed at the point-of-care or by a regional device. Then the raw data is sent to a central processing device for further analysis and / or the raw data is converted into information useful to the clinician or patient. The central processing device offers the advantages of privacy (all data is stored in the central device using a unified security protocol), speed, and consistency in data analysis. Then, the central processing device can control the fate of the data after the subject has been treated. For example, using an electronic communication system, the central device can provide the data to the clinician, the subject, or a researcher.

[0108] In some embodiments, the subject is able to directly access the data using an electronic communication system. The subject can choose further intervention or consultation based on the results. In some embodiments, the data is used for research purposes. For example, the data can be used to further optimize the inclusion or exclusion of markers as useful indicators of a disease-specific condition or stage.

[0109] Compositions for 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 a marker expression level in a sample.

[0110] Any of these compositions can be provided alone or in combination with other compositions of the present invention in the form of a kit. For example, a single labeled probe and paired amplification oligonucleotides or antibodies, as well as immunoassay components, can be provided in a kit for amplifying and detecting a marker. The kit can also contain appropriate controls and / or detection reagents.

[0111] The probe and antibody compositions of the present invention can also be provided in the form of an array assay or a panel assay.

[0112] In some embodiments, the present invention provides systems, kits, and methods for determining and administering a therapeutic course.

[0113] The methods of the present invention can be used to treat a variety of cancers. Cancers that can be treated according to 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 described above, in a preferred embodiment, when the expression level of a biomarker or a combination of biomarkers in a patient sample is increased compared to a reference or threshold expression level or a composite expression metric, administration of an immunoconjugate to the patient is required. Similarly, in other preferred embodiments, if the expression level of a biomarker or a combination of biomarkers in a patient sample is decreased compared to a reference or threshold expression level or a composite expression metric, administration of an antigen-binding protein not conjugated to a drug or toxin to the patient is required. In an embodiment, if administration of an immunoconjugate is required, an immunoconjugate that binds to a surface antigen expressed by the patient's tumor or cancer cells is selected. Suitable surface antigens that the immunoconjugate can target 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.

[0114] In some embodiments, the immunoconjugate 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), lifastuzumab vedotin (DNIB0600A, RG-7599), glembatuzumab vedotin (CDX-011), coltuximab ravtansine (SAR3419), lorvotuzumab mertansine (IMGN-901), indatuximab ravtansine (BT-062), sacitizumab govitecan (IMMU-132), labetuzumab govitecan (IMMU-130), milatuzumab doxorubicin (IMMU-110), indusatumab vedotin (MLN-0264), vadastuximab talirine (SGN-CD33A), denintuzumab mafodotin (SGN-CD19A), enfortumab vedotin (ASG-22ME), rovalpituzumab tesirine (SC16LD6.5), vandortuzumab vedotin (DSTP3086S, RG7450), mirvetuximab 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 monotox (VB4-845), Resimmune, LMB-2, DT2219ARL, HuM195 / rGel, RG7787, MOC31PE, or D2C7-IT. As described above, which antibody-drug conjugate is selected for use in the methods of the present invention will depend on which specific surface antigen is expressed by the cancer cells in the subject.

[0115] In some embodiments, the immunoconjugate is an immunotoxin. Suitable immunotoxins include but are not limited to MH3-B1 / rGel, denileukin (DAB389IL2), moxetumomab pasudotox (CAT-8015), oportuzumab monotox (VB4-845), Resimmune, LMB-2, DT2219ARL, HuM195 / rGel, RG7787, MOC31PE, or D2C7-IT. Moreover, as described above, which immunotoxin to use in the methods of the present invention will depend on which specific surface antigen is expressed by the cancer cells in the subject.

[0116] 1. A method of treating cancer, comprising:

[0117] obtaining a sample containing cancer cells from a subject;

[0118] measuring the expression level of RAB5 in the cancer cells by an in vitro assay; and

[0119] administering an effective amount of an immunoconjugate targeting the surface antigen of the cancer cells if the expression level of RAB5 in the cancer cell sample is increased compared to a predetermined reference level; or administering an antigen-binding protein targeting the surface antigen of the cancer cells that does not include a drug or toxin if the expression level of RAB5 in the cancer cell sample is decreased compared to a predetermined reference level.

[0120] 2. The method according to embodiment 1, wherein measuring the expression level of RAB5 in the cancer cells comprises measuring the level of RAB5 mRNA.

[0121] 3. The method according to embodiment 1, wherein measuring the expression level of RAB5 in the cancer cells comprises measuring the level of RAB5 protein.

[0122] 4. The method according to any one of embodiments 1 to 3, wherein the RAB5 is RAB5A.

[0123] 5. The method according to any one of embodiments 1 to 3, wherein the RAB5 is RAB5B.

[0124] 6. The method according to any one of embodiments 1 to 3, wherein the RAB5 is RAB5C.

[0125] 7. The method according to any one of embodiments 1 to 6, further comprising the step of determining the expression level of one or more of RAB4, RAB11, or HSP90.

[0126] 8. The method according to embodiment 7 further comprises the following steps: incorporating the expression level of one or more of RAB4, RAB11, or HSP90 into an expression index having the RAB5 expression level, and if the expression index increases compared to a predetermined reference level, administering an effective amount of an immunoconjugate targeting the surface antigen of the cancer cell.

[0127] 9. The method according to any one of embodiments 1 to 8, wherein the cancer cell is obtained from a surgical tumor sample, a biopsy sample, or a blood sample.

[0128] 10. The method according to any one of embodiments 1 to 9, wherein the immunoconjugate 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.

[0129] 11. The method according to any one of embodiments 1 to 9, wherein the immunoconjugate is an antibody-drug conjugate selected from the group consisting of: trastuzumab emtansine, vedotin-brentuximab, inotuzumab ozogamicin, pinatuzumab vedotin, polatuzumab vedotin, lifastuzumab vedotin, glembatuzumab vedotin, coltuximab ravtansine, lorvotuzumab mertansine, indatuximab ravtansine, sacitizumab govitecan, labetuzumab govitecan, milatuzumab doxorubicin, indusatumab vedotin, vadastuximab talirine, denintuzumab mafodotin, enfortumab vedotin, rovalpituzumab tesirine, vandortuzumab vedotin, mirvetuximab soravtansine, ABT-414, IMGN289, and AMG595.

[0130] 12. The method according to any one of Embodiments 1 to 9, wherein the immunoconjugate is an immunotoxin selected from the group consisting of MH3-B1 / rGel, denileukin, moxetumomab pasudotox, oportuzumab monotox, resimmune, LMB-2, DT2219ARL, HuM195 / rGel, RG7787, MOC31PE, and D2C7-IT.

[0131] 13. The method according to any one of Embodiments 1 to 12, wherein 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.

[0132] 14. The method according to any one of Embodiments 1 to 13, further comprising the step of determining the expression of a surface antigen on the cancer cells.

[0133] 15. The method according to Embodiments 1 to 14, wherein the cancer cells are breast cancer cells.

[0134] 16. The method according to Embodiment 15, wherein 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 the surface antigen.

[0135] 17. The method according to Embodiment 15, wherein the antibody-drug conjugate or immunotoxin is selected from the group consisting of trastuzumab emtansine (T-DM1), ABT-414, IMGN289, AMG595, and AMG595.

[0136] 18. The method according to Embodiment 16, wherein the surface antigen is HER2.

[0137] 19. The method according to Embodiment 18, wherein the immunoconjugate is trastuzumab emtansine (T-DM1).

[0138] 20. An immunoconjugate that targets a surface antigen for use in a method of treating cancer in a patient, wherein cancer cells from the patient express the antigen and exhibit an increased level of RAB5 expression compared to a predetermined reference level, as determined by an in vitro expression assay.

[0139] 21. The immunoconjugate for use according to embodiment 20, wherein the in vitro expression assay is a RAB5 mRNA assay.

[0140] 22. The immunoconjugate for use according to embodiment 20, wherein the in vitro expression assay is a RAB5 protein assay.

[0141] 23. The immunoconjugate for use according to embodiments 20 to 22, wherein the RAB5 is RAB5A.

[0142] 24. The immunoconjugate for use according to embodiments 20 to 22, wherein the RAB5 is RAB5B.

[0143] 25. The immunoconjugate for use according to embodiments 20 to 22, wherein the RAB5 is RAB5C.

[0144] 26. The immunoconjugate for use according to any one of embodiments 17 to 25, wherein the immunoconjugate 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.

[0145] 27. An immunoconjugate for use according to any one of embodiments 17 to 25, wherein the immunoconjugate is an antibody-drug conjugate selected from the group consisting of: trastuzumab-emtansine, vedotin-brentuximab, inotuzumab ozogamicin, pinatuzumab vedotin, polatuzumab vedotin, lifastuzumab vedotin, glembatuzumab vedotin, coltuximab ravtansine, lorvotuzumab mertansine, indatuximab ravtansine, sacitizumab govitecan, labetuzumab govitecan, milatuzumab doxorubicin, indusatumab vedotin, vadastuximab talirine, denintuzumab mafodotin, enfortumab vedotin, rovalpituzumab tesirine, vandortuzumab vedotin, mirvetuximab soravtansine, ABT-414, IMGN289, and AMG595.

[0146] 28. An immunoconjugate for use according to any one of embodiments 17 to 25, wherein the immunoconjugate 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.

[0147] 29. An immunoconjugate for use according to any one of embodiments 17 to 28, wherein 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.

[0148] 30. An immunoconjugate for use according to embodiment 28, wherein the cancer cells are breast cancer cells.

[0149] 31. An immunoconjugate for the use according to Embodiment 30, wherein 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 the surface antigen.

[0150] 32. An immunoconjugate for the use according to Embodiment 31, wherein the antibody-drug conjugate or immunotoxin is selected from the group consisting of trastuzumab-emtansine (T-DM1), ABT-414, IMGN289, AMG595, and AMG595.

[0151] 33. An immunoconjugate for the use according to Embodiment 30, wherein the surface antigen is HER2.

[0152] 34. An immunoconjugate for the use according to Embodiment 33, wherein the immunoconjugate is trastuzumab-emtansine (T-DM1).

[0153] 35. An in vitro method for determining whether human cancer cells are responsive to an immunoconjugate that targets a surface antigen on the cancer cells, comprising:

[0154] obtaining a sample containing cancer cells from a patient; and

[0155] measuring the expression level of RAB5 in the cancer cells by an in vitro assay, wherein an increase in the expression level of RAB5 compared to a predetermined reference level indicates responsiveness to the immunoconjugate.

[0156] 36. The method according to Embodiment 35, wherein measuring the expression level of RAB5 in the cancer cells comprises measuring the level of RAB5 mRNA.

[0157] 37. The method according to Embodiment 35, wherein measuring the expression level of RAB5 in the cancer cells comprises measuring the level of RAB5 protein.

[0158] 38. The method according to any one of Embodiments 35 to 37, wherein the RAB5 is RAB5A.

[0159] 39. The method according to any one of Embodiments 35 to 37, wherein the RAB5 is RAB5B.

[0160] 40. The method according to any one of Embodiments 35 to 37, wherein the RAB5 is RAB5C.

[0161] 41. The method according to any one of embodiments 35 to 40 further comprises the step of determining the expression level of one or more of RAB4, RAB11, or HSP90.

[0162] 42. The method according to embodiment 41 further comprises the step of incorporating the expression level of one or more of RAB4, RAB11, or HSP90 into an expression index having the RAB5 expression level.

[0163] 43. The method according to any one of embodiments 35 to 42, wherein the cancer cells are obtained from a surgical tumor sample, a biopsy sample, or a blood sample.

[0164] 44. The method according to any one of embodiments 35 to 43, wherein 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.

[0165] 45. The method according to embodiment 44, wherein the cancer cells are breast cancer cells.

[0166] 46. The method according to any one of embodiments 35 to 45 further comprises the step of determining the expression of a surface antigen on the cancer cells.

[0167] 47. The method according to embodiment 46, wherein 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.

[0168] 48. The method according to any one of embodiments 35 to 47, wherein the surface antigen is HER2.

[0169] 49. The method according to any one of embodiments 35 to 48 further comprises the step of administering an immunoconjugate to the subject when the expression level of RAB5 is increased compared to a reference RAB5 expression level.

[0170] 50. The method according to any one of embodiments 35 to 48 further comprises the step of administering an antibody that does not contain a drug or toxin to the subject when the expression level of RAB5 is decreased compared to a reference RAB5 expression level.

[0171] Experiment

[0172] The following examples are provided to illustrate and further exemplify certain embodiments of the present invention and should not be construed as limiting its scope.

[0173] Example 1

[0174] Materials and Methods

[0175] Cells and Cultures. Five human cell lines expressing HER2 were used in this study; 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). The HER2-negative human breast cancer cell line MDA-MB-231 was used as a negative control for HER2 expression. All cell lines except SK-BR-3 were obtained from the American Type Culture Collection (Manassas, VA, USA); SK-BR-3 was kindly provided by the Department of Biochemistry, Institute for Cancer Research, Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway. All cell lines used were between passages 3 and 25 to avoid changes in cell line characteristics over time and were routinely tested for mycoplasma infection. SK-BR-3 and SKOV-3 cells were cultured in McCoy's 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 as previously described 19 。

[0176] Cytotoxicity Assays. Cells were seeded at 8x10 3 (SK-BR-3), 1.8x10 3 (SKOV-3), 6x10 3 (AU-565), 4x10 3 (HCC1954) or 1x10 4Cells / well (MDA-MB-453) were seeded in 96-well plates (Nunc, Roskilde, Denmark) and allowed to adhere overnight. The cells were then incubated with trastuzumab ( Roche, Basel, Switzerland), T-DM1 (ado-trastuzumab emtansine, Genentech, San Francisco, CA, USA), MH3-B1 / rGel or rGel (expressed and purified as previously described 13,20 ) at increasing concentrations for 72 h, and then cell viability was evaluated by MTT assay as previously described 21 . IC 50 values were calculated from the sigmoidal curve (fitting model: a / (1+exp(-(x-x0) / b)).

[0177] Western blot analysis. Total cell extracts were obtained and analyzed by Western blot as previously described 19 . Protein blotting transfer was performed using a Trans- Turbo TM transfer system (Bio-Rad Laboratories, CA, USA). Cell protein expression was detected using antibodies against EGFR (#4267), HER2 (#2165), HER3 (#12708), HSP90 (#4877) from Cell Signaling Technology (Danvers, MA, USA), Rab5 (610281) and Rab11 (610656) antibodies from BD Biosciences (San Jose, CA, USA), and Rab4 (R5780) antibody from Sigma-Aldrich. Protein expression was associated with γ-tubulin as detected by an antibody from Sigma-Aldrich (#T6557). Protein bands on the membrane were detected using Supersignal West Dura Extended duration Substrate (Thermo Scientific, Rockford, IL, USA) and a ChemiDoc TM densitometer (Bio-Rad). Protein expression was quantified using ImageLab 4.1 (Bio-Rad) (software). The expression of each protein was calculated relative to the cell line with the highest expression.

[0178] Correlation analysis. The relative expression of HER2, HER3, EGFR, Rab4, Rab5, Rab11, and HSP90 in the cell lines was plotted against 1 / IC 50Cell lines were plotted for sensitivity to two HER2-targeted therapies as measured by (T-DM1) or a targeting index (TI) (MH3-B1 / rGel), and linear regression was evaluated. Several proteins can affect the toxicity of T-DM1 or MH3-B1 / rGel together with HER2. However, the degree of influence may vary compared to HER2. Here, it was calculated whether the relative expression of other proteins (HER3, EGFR, Rab4, Rab5, Rab11, and HSP90) could be incorporated into the regression to improve the 1 / IC linearly related to HER2 50 (T-DM1) or the R obtained from the TI (MH3-B1 / rGel) 2 value. The contribution factor of each protein together with HER2 was gradually decreased from 1 to 0, and the following formula was used to establish these regression curves:

[0179] For curves with a positive slope, HER2 x (1 - (1 - protein) x F), and

[0180] For curves with a negative slope, HER2 / (1 - (1 - protein) x F)

[0181] where HER2 is the relative expression of HER2, protein is the relative expression of the protein of interest, and F is a contribution factor ranging from 1 to 0.

[0182] The R 2 values were plotted as a function of the contribution factor of each protein. Proteins that resulted in an increase in R 2 at a certain contribution factor compared to that obtained with HER2 alone were incorporated into the final regression curves for T-DM1 and MH3-B1 / rGel sensitivities in order to set the combination of expression parameters with the highest correlation (measured by R 2 ) with T-DM1 and MH3-B1 / rGel sensitivities. 2

[0183] Results

[0184] The efficacy of T-DM1 and MH3-B1 / rGel was not related to trastuzumab sensitivity. Strong expression of HER2 was recorded in five HER2-expressing cell lines used in this study compared to the low expression in MDA-MB-231 as reported for HER2-negative( Figure 1A ) 22 . The anti-proliferative effects of the HER2-targeted mAb trastuzumab and the HER2-targeted therapeutic agents T-DM1 and MH3-B1 / rGel with intracellular action were established in five HER2-positive cell lines( Figure 1B ​) After treating the cells with trastuzumab, T-DM1 or MH3-B1 / rGel for 72 hours, it was found that SK-BR-3 and AU-565 cells were highly sensitive to all three therapeutic agents. However, in contrast, SKOV-3 cells were found to be unresponsive to trastuzumab and showed low sensitivity to T-DM1 and MH3-B1 / rGel, which were demonstrated by a relatively high IC 50 of 1.2 μg / ml and a low TI 50 of 2.4 ([[]] Figures 1A - 1B and Figure 2B ). HCC1954 and MDA-MB-453 cells were found to have low to moderate sensitivity to trastuzumab treatment, but had different responses to the HER2-targeted therapeutic agents acting intracellularly. HCC1954 cells showed high sensitivity to both T-DM1 and MH3-B1 / rGel, while MDA-MB-453 cells showed low sensitivity to these two drugs ([[]] Figures 1A - 1B and Figure 2B ). Therefore, there was no clear link between trastuzumab sensitivity and sensitivity to T-DM1 and MH3-B1 / rGel among the five cell lines ([[]] Figures 1A - 1B and Figure 2B ). Even though T-DM1 and MH3-B1 / rGel have significantly different sites of action intracellularly, there was still a consistency in 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.

[0185] 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 ).

[0186] The correlation between HER2 expression and T-DM1 toxicity was stronger than that with MH3-B1 / rGel toxicity. HER2 expression was crucial for both T-DM1 and MH3-B1 / rGel toxicity. However, due to differences in drug processing between cells (such as uptake, intracellular trafficking, and interaction with intracellular drug targets), the expression level may not necessarily be directly related to drug sensitivity. Quantification of HER2 expression in the five cell lines showed that AU-565 had the highest HER2 expression level, followed by HCC1954 (0.9) and SK-BR-3 (0.8)(Figure 2C and Figure 2D D1). Compared with AU-565, the HER2 expression in SKOV-3 cells was reduced by 50%, and MDA-MB-453 was considered the cell line with the lowest HER2 expression (0.4) in this group( Figure 2C and Figure 2D D1). The HER2 expression levels reported here are consistent with recent reports 22,23 . In addition, a linear relationship was found between HER2 expression and the sensitivity to T-DM1 and MH3-B1 / rGel among the cell lines, resulting in an R 2 value of 0.926 for T-DM1, and an R 2 value of 0.800( Figure 2E E1 and E2).

[0187] For T-DM1 sensitivity, HER3 can serve as an additional biomarker for HER2. It is known that HER2 complexes with other members of the EGFR family. Therefore, HER3 and EGFR were quantified to examine the relationship between the expression of these two members of the EGFR family and the toxicity of T-DM1 and MH3-B1 / rGel. The selected group of cell lines was found to vary greatly in their HER3 and EGFR expression levels( Figure 2C , Figure 2D D2 and D3). However, no significant correlation was found between the levels of HER3 or EGFR and the sensitivity to T-DM1( Figure 3 A1 and B1) or MH3-B1 / rGel( Figure 3 C1 and D1). Further evaluation was conducted to determine whether EGFR and HER3, together with HER2, could be correlated with the sensitivity to T-DM1 and MH3-B1 / rGel. The R 50 (T-DM1) and HER2, as well as the R 2 values of the linear regression with the increasing contribution factor of HER3 were established, showing that when compared with using HER2 alone (R 2 =0.926, Figure 2E E1), the R 2 was higher, at 0.953( Figure 3 A2 and A3) when HER3 expression was included in the correlation analysis with a contribution factor of 0.2. By incorporating EGFR with an increasing influence factor into the regression analysis between T-DM1 sensitivity and HER2 expression, no increase in linear correlation was found( Figure 3 B2). The influence of EGFR expression in the regression between T-DM1 and HER2 x HER3 (contribution factor 0.2) was also evaluated. Again here, the R 2The values are all less than R observed when only HER2 and HER3 (contribution factor 0.2) are used 2 value ( Figure 3 of B3).

[0188] For MH3-B1 / rGel sensitivity, HER3 and EGFR can serve as additional biomarkers for HER2. Compared with the results obtained only through HER2 expression, incorporating the contributions of EGFR and HER3 into the regression analysis between MH3-B1 / rGel sensitivity and HER2 expression results in an increase in the R 2 value (R of HER3 with a contribution factor of 0.4 for HER2 2 = 0.970, and R of EGFR with a contribution factor of 0.3 for HER2 2 = 0.826) ( Figure 2E of E2, Figure 3 of C2, Figure 3 of C3, Figure 3 of D2 and Figure 3 of D3). Incorporating the effect of HER3 expression with increasing contribution factors into the regression between HER2 and EGFR (contribution factor 0.3) and MH3-B1 / rGel sensitivity shows an increase in correlation, as measured by the increasing R 2 value, with a maximum value observed at a HER3 contribution factor of 0.4 (R 2 = 0.997, Figure 3 of E1 and E2).

[0189] Cell lines expressing HER2 differ in the expression levels of the proteins involved in their endocytic transport. Since T-DM1 and MH3B1 / rGel rely on internalization and intracellular trafficking to exert their intracellular mechanisms of action, the proteins involved in the endocytic mechanism in this group of cell lines were quantified. These proteins include Rab5 ( Figure 4A and Figure 4B ); Rab4, which is involved in delivering cargo from the plasma membrane to early endosomes and endosome fusion; involved in recycling from early endosomes ( Figure 4A and Figure 4C ), HSP90 ( Figure 4A and Figure 4D ), which is reported to regulate HER2 recycling, and Rab11; involved in recycling through perinuclear recycling endosomes and plasma membrane-Golgi trafficking ( Figure 4A and Figure 4E ) 24,25 . Among the cell lines, the expression levels of these proteins showed great differences, and no simple relationship was found among the expression levels.

[0190] Rab5 and Rab4 can be used as additional biomarkers of HER2 to predict T-DM1 sensitivity. The proteins involved in the endocytic mechanism under study ( Figures 4A - 4E ) were further evaluated for their effect on T-DM1 sensitivity in these cell lines. The expression level of Rab5 was found to be 2.5-3 times higher in the highly T-DM1-sensitive SK-BR-3 and AU-565 cells than in the other cell lines in this group ( Figure 4A and Figure 4B ). A relatively weak correlation was found between T-DM1 toxicity and Rab5 expression (R 2 = 0.643) ( Figure 5 A1). A linear regression curve of T-DM1 toxicity with HER2 expression and increasing contribution factor of Rab5 expression was established, indicating that Rab5 together with HER2 affects T-DM1 toxicity ( Figure 5 A2). When the contribution factor of added Rab5 was 0.3, the maximum R 2 value was found ( Figure 5 A2), and by including 30% contribution from Rab5, R 2 increased from 0.926 to 0.986 ( Figure 5 A3). No linear correlation was found between Rab4 expression and T-DM1 sensitivity (R2 = 0.088, Figure 5 B1). When T-DM1 sensitivity was correlated with HER2 with a contribution factor of Rab4 of 0.4, a slight increase in the R 2 value was found ( Figure 5 B2 and B3). It was also investigated whether Rab4 together with HER2 was negatively correlated with T-DM1 sensitivity. However, no increase in the R 2 value was found when 1 / Rab4 expression was incorporated into the regression formula compared to the R 2 value obtained with HER2 alone (data not shown). The linear regression of HSP90 expression and T-DM1 sensitivity also showed a poor correlation with an R 2 value of 0.266 ( Figure 5 C1), and no increase in the R 2 value was observed by incorporating the effect of HSP90 into the regression formula of HER2 compared to the R 2 value obtained with HER2 alone ( Figure 5 C2). A negative linear correlation was found between Rab11 expression and T-DM1 sensitivity (R 2 = 0.459) ( Figure 5 D1). Compared to the R 2Compared with the 2 value, the linear regression between T-DM1 sensitivity and HER2 and the increasing 1 / Rab11 expression of the contributing factor showed that the Figure 5 R value increased little (0.929 and 0.926,

[0191] for D2 and D3). Figure 5 As 2 shown, the expression levels of Rab5 and Rab4 were indicated as possible biomarkers for T-DM1 sensitivity together with HER2 one by one. Then it was evaluated whether combining Rab5 and Rab4 together with HER2 further increased the correlation with T-DM1 sensitivity. However, compared with the results observed with HER2 x0.3Rab5, no 2 increase in the Figure 5 R value was found when the influence of Rab4 was incorporated into the regression formula (data not shown). In summary, when HER2 was combined only with Rab5 with an influencing factor of 0.3, the best linear correlation with T-DM1 sensitivity was found (

[0192] R = 0.986, Figures 4A - 4E for A3). Figure 6 For MH3-B1 / rGel sensitivity, Rab5, Rab4, HSP90, and Rab11 can all be used as additional biomarkers for HER2. The cellular expression levels of Rab5, Rab4, HSP90, and Rab11 ( 2 R = 0.486, Figure 6 for A1). Incorporating Rab5 with an increasing contributing factor into the established linear regression curve of HER2 and MH3-B1 / rGel toxicity indicated that Rab5 together with HER2 had an impact on MH3-B1 / rGel toxicity ( Figure 6 for A2), as shown by the increase in the 2 R value compared with the regression using only HER2 ( Figure 2E R = 0.800, 2 for E2). By including a 30% contribution of Rab5, the highest 2 R value was found to be 0.856 ( Figure 6 for A2 and A3), as also observed for the T-DM1 correlation ( Figure 5 for A3). No correlation was found between Rab4 expression and MH3B1 / rGel sensitivity ( 2 R = 0.024, Figure 6However, using the increasing Rab4 expression as an additional biomarker for HER2 enhanced the linear correlation with TI compared to HER2 alone. By adding Rab4 with an influence factor of 0.6 to the regression analysis of HER2 and TI, the maximum R 2 value, 0.930, was obtained ([[]] Figure 6 B1 and B2). HSP90 and 1 / Rab11 were also shown to be associated with MH3B1 / rGel sensitivity, although not strongly (R2 = 0.552 for HSP90 and R2 = 0.406 for 1 / Rab11, Figure 6 C1 and D1). Further, HSP90 and 1 / Rab11 were shown to enhance the correlation between HER2 expression and MH3B1 / rGel sensitivity one by one, with both proteins achieving the maximum R 2 value ([[]] Figure 6 C2, C3, D2, and D3).

[0193] As Figure 6 shown, Rab5, Rab4, HSP90, and 1 / Rab11 were all indicated to be possible biomarkers for MH3B1 / rGel sensitivity together with HER2 one by one. It was further investigated whether the combination of these 4 biomarkers together with HER2 would further increase the correlation with MH3B1 / rGel sensitivity. The order of combining different proteins into the regression formula was based on the pathways of endocytosis and trafficking, starting from Rab5 (endocytosis), and then adding Rab4 (early recycling), HSP90 (early recycling), and 1 / Rab11 (late recycling). As Figure 6 shown in A3, Rab5 (influence factor 0.3) was well correlated with MH3B1 / rGel sensitivity together with HER2 (R 2 = 0.856). However, adding the increasing Rab4 expression in the regression analysis increased the R 2 value. When Rab4 was added to HER2 and 0.3x Rab5 with a contribution factor of 0.6, the maximum R 2 value, 0.938, was reached ([[]] Figure 7 A1 and A2). The HSP90 expression was further incorporated into the regression analysis of HER2, Rab5, and Rab4. By incorporating 80% contribution of HSP90 into the regression analysis between MH3B1 / rGel sensitivity and HER2, Rab5 (contribution factor 0.3), and Rab4 (contribution factor 0.6), the maximum R 2 value, 0.974, was obtained ([[]] Figure 7 B1 - B2). As Figure 6As shown in D1 - D3, it was shown that 1 / Rab11 and HER2 together were well - correlated with MH3B1 / rGel sensitivity (R 2 = 0.894). Adding the effect of 1 / Rab11 expression in the regression analysis between MH3B1 / rGel sensitivity and HER2, Rab5, Rab4, and HSP90 expression also increased the R 2 value, as shown in Figure 7 C1. When MH3B1 / rGel sensitivity was correlated with HER2, Rab5 (contribution factor 0.3), Rab4 (contribution factor 0.6), and HSP90 (contribution factor 0.8), the best correlation between 1 / Rab11 expression and other proteins together with MH3B1 / rGel sensitivity was found at an influencing factor of 0.4 ( Figure 7 C2), yielding the maximum R 2 value, 0.993 ( Figure 7 C2). Regression analysis was also performed by adding the contributions of different proteins in a non - biological order, which had no major effect on the R 2 values obtained when correlated with MH3B1 / rGel sensitivity (data not shown). Overall, when plotting the TI against the following, the best correlation between MH3 - B1 / rGel toxicity and protein expression levels was found when adding the contributions of different proteins in a biological logical order, (R 2 = 0.993) ( Figure 7 C2):

[0194] Relative HER2 expression x

[0195] (1 - (1 - relative Rab5 expression) x 0.3) x

[0196] (1 - (1 - relative Rab4 expression) x 0.6) x

[0197] (1 - (1 - relative HSP90 expression) x 0.8) /

[0198] (1 - (1 - (1 / relative Rab11 expression)) x 0.4)

[0199] The field of biomarker - driven personalized cancer therapy is rapidly evolving together with an increasing number of clinically approved targeted anti - cancer therapies. Overall, the efficacy of drug - based personalized cancer therapies depends on the reliability of the selected biomarkers to predict prognosis, response, and / or resistance. The development of the HER2 - targeted mAb trastuzumab represents a success story in the field of biomarker - driven personalized cancer therapy, as breast cancer patients classified as HER2 - positive (∼20% of all breast cancers) typically receive trastuzumab as part of their treatment26 Another example is the evaluation of the BCR-ABL fusion in chronic myeloid leukemia (CML) for the use of imatinib, or the evaluation of EGFR and RAS wild-type expression for the use of cetuximab in the treatment of colorectal cancer 27 .

[0200] Most of the targeted drugs currently approved for the treatment of cancer are mAbs or small molecule inhibitors, and their drug targets also represent the targets of the mechanism of action. The target itself represents a clear biomarker for the treatment with these drugs, even though additional biomarkers may be required to de-select patients who may experience drug resistance or low tolerance. However, drug development in cancer therapy is currently shifting towards more complex targeted therapeutics consisting of both a targeting moiety and a cytotoxic component (such as drugs that inhibit cell growth (ADCs) or toxins (targeted toxins)). For these multifunctional therapeutics, additional biomarkers related to the mechanism of intracellular trafficking and / or cytotoxic action may affect the treatment efficacy 15 . This is demonstrated herein by the lack of concordance between trastuzumab and T-DM1 / MH3-B1 / rGel sensitivity in a selected group of HER2-positive cell lines

[0201] This article reports a strong linear correlation between cellular HER2 expression and response to T-DM1 ( Figure 2E E1). This is consistent with several clinical studies, which have shown that the response rate of T-DM1 is higher in patients with HER2 mRNA levels above the median compared to those in the sub-group below the median 16,17,28 . This article points out that compared with MH3-B1 / rGel, for T-DM1, the correlation between drug activity and HER2 expression measured by the R 2 value is stronger ( Figure 2E E2), and this may be caused by the differences in the cytotoxic components of these drugs. The cytotoxic component of T-DM1 (DM1) is a relatively small lipophilic drug, which, once released from the trastuzumab component in the endocytic vesicle, can diffuse through the endocytic membrane and enter the cytosol, where it exerts its effect on microtubules. This is in sharp contrast to the cytotoxic part of MH3-B1 / rGel, which is a 28 kDa hydrophilic type I ribosome-inactivating protein toxin (gelonin), which lacks an efficient transport mechanism to enter the cytosol but enters the cytosol to some extent by an unknown mechanism. Compared with MH3-B1 / rGel and HER2 expression, the higher R obtained by the correlation analysis of T-DM1 and HER2 expression 2The values may reflect the differences in the endocytic escape mechanisms between these two drugs, with more obstacles indicating the gelonin pathway. Compared to DM1, the intracellular release pathway of gelonin is more complex, which is also reflected in the number of proteins (Rab5, Rab4, HSP90, and Rab11) that affect the sensitivity to MH3-B1 / rGel studied, compared to only Rab5 and Rab4 for T-DM1.

[0202] This article shows that the cellular sensitivity to both T-DM1 and MH3-B1 / rGel is related to HER3 in addition to being related to HER2. The therapeutic effect of T-DM1 in clinical phase III trials has been previously reported to be similar in the HER3 expression subgroup. 17 . This is consistent with the data shown here, where no correlation was found between only HER3 expression and T-DM1 sensitivity ( Figure 3 A1). However, this report focuses on mathematical methods to combine biomarkers with different contributing factors. These calculations show that the combination of HER3 and HER2 can be a better biomarker for T-DM1 response compared to HER2 alone. HER3 is considered a preferred dimerization partner of HER2, and it has been reported that the heterodimer can induce highly active tyrosine kinase signal transduction. 29 . Therefore, the correlation between T-DM1 and MH3-B1 / rGel sensitivity and HER3 expression together with HER2 may reflect the indirect inhibition of these heterodimers after the 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, it cannot be excluded that MH3-B1 binds to HER2 as part of the heterodimer (both HER2 / HER3 and HER2 / EGFR). This is also indicated by the following results: compared to T-DM1 (R 2 increased from 0.926 to 0.953, Figure 8 A1), the correlation between MH-3B1 / rGel sensitivity and HER3 expression in addition to HER2 ( Figure 8 B1) is significantly stronger (R 2 increased from 0.800 to 0.970), and when correlating MH3-B1 / rGel sensitivity with EGFR in addition to HER2 and HER3, the R 2 value increased slightly, while there was no such correlation for T-DM1 ( Figure 8 A1 and B1).

[0203] Rab5 is localized to early endosomes and regulates the endocytosis and endosome fusion of clathrin-coated vesicles. 18。This article shows that the sensitivities to both T-DM1 and MH3-B1 / rGel depend on Rab5 with a contribution factor of 0.3 for contributing factors other than HER2 Figure 5 of A3 and Figure 6 of A3). The similar effects of Rab5 together with HER2 on the two HER2-targeted therapeutic agents( Figure 8 of A2 and B2) may be related to the early function of Rab5 in endocytosis and subsequent endocytic trafficking, in which the two drugs are likely to follow the same HER2-mediated endocytic pathway. Rab4 also plays a role in the early stage of the endocytic pathway by controlling recycling from early endosomes 30 and is shown in this article to be associated with the sensitivities to T-DM1 and MH3-B1 / rGel together with HER2( Figure 5 of B3 and Figure 6 of B3). The rapid recycling of HER2 can increase the drug uptake by cells but also passively localizes the drug in early endosomes within the cell.

[0204] When incorporated together with Rab4 and HER2, R 2 increases from 0.830 to 0.930( Figure 8 of B2), indicating that the cytosolic translocation from early endosomes is the mechanism for the cytosolic release of MH3-B1 / rGel. The contribution of Rab4 other than HER2 to the sensitivity to T-DM1 is small (R 2 increases from 0.926 to 0.944, Figure 8 of A2), indicating that a certain amount of mertansine is also released from early endosomes, suggesting that the cytosolic translocation of mertansine is not completely dependent on the lysosomal sequestration of the trastuzumab moiety of T-DM1 as previously reported 7,10 . It has been previously demonstrated that Rab5 expression can predict poor outcomes in breast cancer patients, and Rab5 / Rab4 recycling can promote extracellular matrix invasion and metastasis 31 . Therefore, these results may indicate that Rab5 / Rab4 and HER2-positive breast cancer patients are promising candidates for T-DM1 and MH3-B1 / rGel therapies.

[0205] HSP90 is a HER2 chaperone and is thought to inhibit HER2 degradation through multiple mechanisms including rapid recycling 32,33 . This article shows that HSP90 together with HER2 is associated with the toxicity of MH3-B1 / rGel( Figure 8 of B2), in contrast, no such correlation was found for T-DM1( Figure 8A2). As commented on the differences in Rab4 dependence, the differences in the cytotoxic effects of HSP90 on these drugs may reflect differences in the cytosolic translocation mechanism. The differences may also be due to the different HER2-targeting moieties between these drugs. On the other hand, Rab11 localizes to the endocytic recycling compartment (ERC) and then functions in endocytosis by recycling cargo back to the plasma membrane 34 . A negative correlation was found here between MH3-B1 / rGel efficacy and Rab11 expression ( Figure 6 B1-B3), indicating that recycling and subsequent exocytosis inhibit MH3-B1 / rGel efficacy. The lack of an effect of Rab11 and HER2 together on T-DM1 toxicity ( Figure 5 D1-D3) may indicate that T-DM1 and MH3-B1 / rGel follow different intracellular pathways, which is consistent with the chemical nature of their cytotoxic moieties. For example, DM1 may have reasonably escaped the endocytic vesicles before accumulating in Rab11-positive recycling endosomes.

[0206] A total of six proteins were tested here together with HER2 as potential biomarkers for the response to MH3-B1 / rGel and T-DM1. The biomarkers tested here were divided into two groups, which respectively reflected the targeting moieties (EGFR and HER3) of the HER2-targeting drugs or the intracellular trafficking components of the drugs (Rab5, Rab4, HSP90, and Rab11) ( Figure 8 ). The best correlations with drug sensitivity were obtained by combining the biomarkers within these groups, and no increase in the correlation with drug sensitivity was shown by combining the two groups of biomarkers (data not shown). These results indicate that the proteins in the two groups are not completely independent, which is not surprising since the endocytic transport of the drugs clearly depends on cell binding and endocytosis. Therefore, HER2 as a biomarker should be combined with HER3 and EGFR, or Rab4, Rab5, HSP90, and 1 / Rab11 to predict cellular sensitivity to T-DM1 and MH3-B1 / rGel ( Figure 8 ). Figure 3 、 5 Figures 6 and 7 illustrate the effect of adding these six proteins as biomarkers with increasing contribution factors relative to HER2. However, the importance of different proteins can be compared, as shown in Figure 8 A1-A2 and B1-B2, where the R2 correlation data for the two groups of biomarkers are included in the same figure. By adding the expression of the extracellular protein HER3 or the intracellular proteins Rab5 and Rab4 to the expression of HER2 as a biomarker, an improved correlation with drug sensitivity was found for both HER2-targeting drugs, but with different effects on the R 2 value, asFigure 8 as shown for A1 - A2, B1 - B2, and C. It is also shown that to some extent, MH3 - B1 / rGel sensitivity depends on EGFR expression in addition to HER2 and HER3, and on HSP90 and 1 / Rab11 expression in addition to HER2, Rab5, and Rab4. Compared to T - DM1, the repertoire of biomarkers associated with MH3 - B1 / rGel sensitivity is more complex, likely due to greater barriers in the cytosolic translocation pathway of MH3 - B1 / rGel compared to T - DM1, and may also reflect differences in the HER2 - targeting moieties between these drugs.

[0207] In summary, this report shows for the first time that in addition to HER2, proteins involved in endocytic trafficking can be used to predict response to HER2 - targeting therapeutic agents with an intracellular site of action. However, the effects of different proteins seem to be related to both the HER2 - targeting moiety and the intracellular active component of the drug, as well as the subsequent endocytic trafficking and cytosolic release mechanisms. Future development of ADCs and other targeted drugs with intracellular mechanisms of action should include a repertoire of drug - dependent biomarkers, and in addition to the currently most commonly used targeting and resistance biomarkers, should also include markers of uptake and cellular trafficking. As used herein, mathematical methods can be used to establish a repertoire of biomarkers with different contributing factors for prognosis and treatment.

[0208] 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 listed in Table 1 (ADCs) and Table 2 (immunotoxins).

[0209] Table 1 - Leading Clinical - Stage ADCs

[0210]

[0211] Table 2 - Leading Clinical - Stage Immunotoxins

[0212]

[0213] Example 2

[0214] 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 31 patients in the trastuzumab control (TH) group were analyzable. For this analysis, patients who progressed, withdrew consent, left the treatment facility, or received off - protocol treatment prior to surgery were considered non - pCR. The following table shows the pCR rates for the HR subtypes in each cohort:

[0215] HR - HER2+ HR+HER2+ TDM1+P 12 / 17 18 / 35 TH 5 / 12 3 / 19

[0216] Expression data. All I-SPY 2 samples were analyzed on one of two Agilent custom arrays (designs 15746 and 32627). All samples in the TDM1+P cohort were analyzed on the 32627 array, while the TH cohort was split between platforms, with 22 samples on the older 15746 platform and 9 samples on the 32627 array. To combine data from the two designs, we updated the probe annotations for the 15746 platform (September 2016); and for each platform, the expression data was folded and normalized, and genes represented by multiple probes were calculated as the average of the probes. The ComBat algorithm was then applied to adjust for platform bias and combine data from the two platforms. This procedure was performed on the pre-treatment data for the first 880 I-SPY 2 patients regardless of experimental cohort. The combined platform-adjusted data for the TH and TDM1+P cohorts, as well as the annotation files for the 32627 and updated 15746 array designs, are included in this delivery.

[0217] Qualifying biomarker analysis. First, the pre-treatment expression levels of normalized platform-corrected RAB5A, RAB4A, RAB11A, and HSP90AA1 were tested separately according to the Qualifying Biomarker Evaluation (QBE) protocol as specific biomarkers for response to TDM1+P.

[0218] Step 1: Evaluate biomarkers as specific predictors of response to TDM1+P

[0219] Model 1A: pCR in the TDM1+P cohort Biomarker

[0220] Model 1B: pCR in the TH cohort Biomarker

[0221] Model 1C: pCR Treatment + Biomarker + Treatment x Biomarker

[0222] Model 1D: pCR Treatment + Biomarker + Treatment x Biomarker + HR status

[0223] Among the biomarkers evaluated, only RAB5A was associated with response in the TDM1+P cohort (likelihood ratio (LR) test p = 0.012), while not associated with the control cohort (LR test p = 0.242). The p-value for the interaction between RAB5A expression and treatment was 0.024, which remained < 0.05 after adjusting for HR status. The following table summarizes the results for the four biomarkers evaluated.

[0224]

[0225] RAB5A has been successfully identified as a sequential eligibility biomarker and will be evaluated in QBE Step 2.

[0226] Step 2: Determine the dichotomization threshold

[0227] We used a Monte Carlo 2-fold cross-validation procedure to determine the threshold that minimizes the p-value of the biomarker x treatment interaction. Specifically, for 100 iterations, we randomly selected half of the cases with balanced treatment groups and pCR status as the training set. We considered each value between the 10th and 90th percentiles as a potential threshold for dichotomizing the training set into "high" and "low" RAB5A expression groups; and a series of logistic regression models were fitted to evaluate the biomarker x treatment interaction (Model 1C). We selected the threshold that minimized the LR test p-value of 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. Then, we used the logit method to combine the LR p-values in the 100 test sets; then the threshold that produced the smallest combined LR test p-value was selected.

[0228] Using this procedure, a threshold of 9.76 was selected. See Figure 9 . As a dichotomous variable, having a high RAB5A level was also associated with response in TDM1+P (OR = 5.99 (95% CI: 1.23 - 40.27), Fisher's exact test p = 0.01), but not in the control group (OR: 0 (95% CI: 0 - 1.74), Fisher's exact test p = 0.06); and there was a biomarker x treatment interaction term with LR p = 0.001.

[0229] Although 9.76 was the optimal threshold determined by this procedure, only 2 patients in the TH group had RAB5A levels < 9.76. This may be partly attributed to the difference in RAB5A expression between the TH and TDM1+P groups, where the RAB5A level in the TH group was significantly higher than that in the TDM1+P group. The array design may have caused this difference. We may consider evaluating the performance of the biomarker only within the TDM1+P group (instead of using the model for evaluating biomarker x treatment interaction pre-specified in the analysis plan). Using a Monte Carlo procedure similar to the above method (but fitting Model 1A only in TDM1+P), the optimal threshold selected was still 9.76.

[0230] Step 3: Bayesian estimated pCR rate within the RAB5A group in the case of HER2+ graduation characteristics

[0231] Model 2: pCR HR + RAB5A + Treatment + HR x Treatment + RAB5A x Treatment

[0232] When we dichotomized patients into RAB5A-high (>=9.76) and RAB5A-low (<9.76) groups using the determined optimal threshold (9.76), the Bayesian estimated pCR probability in the TCM1+P group was 68%, while in the control group of RAB5A-high patients it was 24%. In contrast, in the RAB5A-low subset of the TDM1+P group, the estimated pCR probability was 28%, and in the TH group it was 42%. 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 in.

[0233] Example 3

[0234] This example shows the evaluation of the correlation between the HSP90, Rab11a, Rab4A, and Rab5a expression profiles and treatment outcomes in the TH- and T-DM1+P groups in the 1-SPY2 study. The RNA expression profiles from the 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 receiving T-trastuzumab + chemotherapy (TH) or trastuzumab-emtansine + pertuzumab + chemotherapy (TDM1+P). There was a significant difference in Rab5A expression levels between the pCR 0 and pCR 1 groups in the T-DM1+P group. There were no significant differences in any other protein in either of the two groups.

[0235] Trastuzumab + Chemotherapy (TH)

[0236]

[0237] Trastuzumab-emtansine + Pertuzumab + Chemotherapy

[0238]

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[0274] All publications, patents, patent applications, and accession numbers mentioned in the above specification are hereby incorporated by reference in their entirety. Although the invention has been described in connection with specific embodiments, it should be understood that the claimed invention should not be unduly limited to these specific embodiments. Indeed, various modifications and variations of the described compositions and methods of the invention will be apparent to those of ordinary skill in the art and are intended to fall within the scope of the appended claims.

Claims

1. A kit, the kit comprising a reagent for detecting the expression level of Ras-related binding (RAB) protein 5 (RAB5), optionally, wherein the RAB5 is selected from the group consisting of RAB5A, RAB5B, and RAB5C. Optionally, wherein the kit further comprises one or more reagents for detecting any one or more markers selected from RAB4, RAB11, and / or HSP90.

2. A kit, the kit comprising a reagent for detecting the expression level of Ras-related binding (RAB) protein 4 (RAB4). Optionally, wherein the kit further comprises one or more reagents for detecting any one or more markers selected from RAB5, RAB11, and / or HSP90.

3. A kit, the kit comprising a reagent for detecting the expression level of Ras-related binding (RAB) protein 11 (RAB11). Optionally, wherein the kit further comprises one or more reagents for detecting any one or more markers selected from RAB5, RAB4, and / or HSP90.

4. A kit, the kit comprising a reagent for detecting the expression level of heat shock protein (HSP) 90 (HSP90). Optionally, wherein the kit further comprises one or more reagents for detecting any one or more markers selected from RAB5, RAB4, and / or RAB11.

5. The kit according to any one of claims 1 to 4, wherein the reagent or each reagent for detecting the expression level of any one or more of RAB5, RAB4, RAB11, and / or HSP90 is selected from the group consisting of a probe, an amplification oligonucleotide, and an antibody, and the antibody comprises a monoclonal antibody.

6. The kit according to any one of claims 1 to 5, wherein the kit further comprises a reagent for detecting the protein expression level of at least one cell surface marker.

7. The kit according to claim 6, wherein the cell surface marker is selected from the group consisting of: HER2, HER3, EGFR, CD3ε, CD19, CD22, CD25, CD30, CD33, CD56, CEA, CD74, CD79a, CD138, NaPi2b, gpNMB, TROP-2, GUCY2C, Nectin-4, SC-16, STEAP1, FRα, IL-2R, EpCAM, and MSLN.

8. Use of a reagent for detecting the expression level of Ras-related binding (RAB) protein 5 (RAB5) in the preparation of a diagnostic kit for an in vitro method for selecting an antibody-drug conjugate or an antibody-toxin conjugate for treating cancer in a subject. Optionally, the antibody-drug conjugate is selected from the group consisting of: Trastuzumab emtansine (T-DM1, Kadcyla), Brentuximab vedotin (SGN-35), Inotuzumab ozogamicin (CMC-544), Pinatuzumab vedotin (RG-7593), Polatuzumab vedotin (RG-7596), Lifastuzumab 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), Milatuzumab doxorubicin (IMMU-110), Indusatumab vedotin (MLN-0264), Vadastuximab talirine (SGN-CD33A), Denintuzumab mafodotin (SGN-CD19A), Enfortumab vedotin (ASG-22ME), Rovalpituzumab tesirine (SC16LD6.5), Vandortuzumab vedotin (DSTP3086S, RG7450), Mirvetuximab soravtansine (IMGN853), ABT-414, IMGN289, and AMG595, and / or 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.

9. The use according to claim 8, wherein the RAB5 is selected from the group consisting of RAB5A, RAB5B, and RAB5C.

10. The use according to claim 8 or 9, wherein the diagnostic kit further comprises a reagent for detecting the expression level of RAB4, a reagent for detecting the expression level of RAB11, and / or a reagent for detecting the expression level of HSP90.

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