WO2011034919A2 - Sh2 domain profiling to characterize tyrosine phosphorylation signaling in cancer - Google Patents

Abstract

A phosphoproteomic method termed SH2 profiling to characterize phosphotyrosine (pTyr) signaling in lung cancer. This method provides quantitative values for the phosphorylated binding sites for Src Homology 2 (SH2) domains, which the cell uses to relay signals from tyrosine kinases. Lung cancer cell lines with known mutational status of the epidermal growth factor receptor (EGFR) and Ras were profiled. Changes in SH2 domain binding were characterized in response to the EGFR inhibitor erlotinib, and the SRC/multi-kinase inhibitor dasatinib. Cell lines grouped based on SH2 binding patterns. Clusters correlated with EGFR mutation status or MET activation. Binding of specific SH2 domains correlated with EGFR mutation and erlotinib sensitivity. SH2 domain profiling identifies subsets of lung cancer cells with distinct patterns of pTyr signaling and provides a powerful molecular diagnostic tool for classification and biomarker identification. This analysis has therapeutic importance for personalized use of tyrosine kinase inhibitors in cancer.

Description

SH2 DOMAIN PROFILING TO CHARACTERIZE TYROSINE PHOSPHORYLATION SIGNALING IN

CANCER

STATEMENT OF GOVERNMENT INTEREST

This invention was made with Government support under Grant No. W81 XWH-08-2-0101 , awarded by the US Army Medical Research and Materiel Command (ARMY/MRMC), Grant No. CA1 19997, awarded by the National Institutes of Health (NIH) and Grant No. CA107785, awarded by the National Institutes of Health (NIH). The Government has certain rights in the invention.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to currently pending U.S. Provisional Patent Application 61 /242,423, entitled, "Characterizing Tyrosine Phosphorylation Signaling in Lung Cancer Using SH2 Domain Profiling", filed September 15, 2009, the contents of which are herein incorporated by reference.

FIELD OF INVENTION

This invention relates to cancer therapy. More specifically, this invention relates to classification and biomarker identification in cancer cells leading to tailored cancer diagnosis and therapy.

BACKGROUND OF THE INVENTION

Lung cancer accounts for over 160,000 deaths per year in the U.S., more than breast, colon, prostate and pancreatic cancer combined [Jemal A, et al., Cancer statistics, 2008. CA Cancer J Clin (2008) 58: 71 -96]. There is therefore an important unmet need to better identify key drivers of lung cancer that can be therapeutically exploited. Receptor and non-receptor tyrosine kinases represent an important class of drug targets for the treatment of lung cancer. These key signaling proteins regulate many activities important for cancer, including cell proliferation, survival, invasion/metastasis, and angiogenesis [Blume-Jensen P, et al., Nature (2001 ) 41 1 : 355-65]. In lung cancer, the activity of the epidermal growth factor receptor (EGFR) is frequently elevated, and this can drive numerous downstream signaling pathways responsible for lung cancer growth and survival. Inhibition of EGFR through the small molecule inhibitor erlotinib can extend survival in patients with advanced lung cancer refractory to chemotherapy [Shepherd FA, et al. N Engl J Med (2005) 353: 123-32]. Eriotinib is representative of a large class of tyrosine kinase inhibitors (TKIs) now being developed. In addition to EGFR, a number of other important tyrosine kinases have been proposed as therapeutic targets in lung cancer. These include MET receptors, insulin-like growth factor receptors (IGFR), SRC kinases, fibroblast growth factor receptors (FGFR), platelet-derived growth factor receptors (PDGFR), anaplastic lymphoma kinase (ALK), ephrin (EPH) receptors, and others [Rikova K, et al. Cell (2007) 131 : 1 190-203; Davies H, et al. Cancer Res (2005) 65: 7591 -5; Johnson FM, et al, Clin Cancer Res (2005) 1 1 : 6924-32; Morgillo F, et al, Clin Cancer Res (2007) 13: 2795-803; Puri N, et al. Cancer Res (2007) 67: 3529-34; Song L, et al. Cancer Res (2006) 66: 5542-8; Soda M, et al. Nature (2007) 448: 561 -6; Brannan JM, et al, Clin Cancer Res (2009) 15: 4423-30; Fischer H, et al, Mol Cancer Ther (2008) 7: 3408-19].

Despite the promise shown by TKIs for the treatment of lung cancer, it has been difficult to predict which patients will benefit from these new drugs. This is an important parameter to resolve because a large group of patients receiving TKIs receive no benefit from treatment and costs of these agents are substantial. The present invention addresses this shortcoming, along with a number of other unresolved needs in the effective treatment of cancer, as will become apparent.

SUMMARY OF THE INVENTION

Receptor and non-receptor tyrosine kinases play a critical role in driving the proliferation and survival of lung cancer cells. A novel phosphoproteomic method, termed "SH2 profiling", has been applied to characterize phosphotyrosine (pTyr) signaling in lung cancer. This method provides quantitative values for the phosphorylated binding sites for Src Homology 2 (SH2) domains, which are used by the cell to relay signals from tyrosine kinases. To validate the method, lung cancer cell lines with known mutational status of the epidermal growth factor receptor (EGFR) and Ras were profiled. Changes in SH2 domain binding were characterized in response to the EGFR inhibitor eriotinib and the SRC/multi-kinase inhibitor dasatinib. The results show cell lines could be grouped based on SH2 binding patterns and that some clusters correlated with EGFR mutation status or MET activation. Binding of specific SH2 domains, most prominently the Ras pathway activators Grb2 and ShcA, correlated with EGFR mutation and eriotinib sensitivity. Groups of SH2 domains were responsive to eriotinib or dasatinib, suggesting both common core and unique pTyr signaling affected by these inhibitors. Finally, the results show that SH2 domain profiles can identify lung cancer cells driven by cooperative EGFR and MET signaling as well as by PDGFR signaling. Accordingly, SH2 domain profiling can identify subsets of lung cancer cells with distinct patterns of pTyr signaling and provides a powerful molecular diagnostic tool for classification and biomarker identification. This type of analysis has therapeutic importance for personalized use of tyrosine kinase inhibitors in cancer.

In a first aspect the present invention provides a method of performing targeted cancer therapy in a patient. The method includes the steps isolating a tumor sample from the patient, assaying SH2 binding patterns in the isolated tumor sample, comparing and/or correlating the observed patterns from the isolated sample with patterns of tumors with predetermined sensitivity to one or more tyrosine kinase (TK) inhibitors to determine a predicted sensitivity of the cancer to be treated to the one or more TK inhibitors and administering one or more TK inhibitors to the patient responsive to the predicted sensitivity of the tumor. The binding patterns can be assayed for a plurality of SH2 domain containing proteins, choosing selected subsets of those proteins, or the entire recognized set of SH2 domain containing proteins. In an advantageous embodiment the assay is employs rosette SH2 profiling or far-western SH2 profiling.

The method can be practiced on a patient that has a disease characterized by aberrant tyrosine kinase activity. Similarly, the method can be practiced on a patient with cancer including, but not limited to, non-small cell lung cancer, small-cell lung cancer, chronic myeloid leukemia, acute lymphoblastic leukemia, Philadelphia chromosome positive acute lymphoblastic leukemia (Ph+ ALL), squamous cell carcinoma, glioma, gastrointestinal cancer, renal cancer, ovarian cancer, liver cancer, colorectal cancer, endometrial cancer, kidney cancer, prostate cancer, thyroid cancer, neuroblastoma, pancreatic cancer, glioblastoma multiforme, cervical cancer, stomach cancer, bladder cancer, hepatoma, breast cancer, colon carcinoma, and head and neck cancer, gastric cancer, germ cell tumor, pediatric sarcoma, sinonasal natural killer, multiple myeloma, acute myelogenous leukemia (AML), and chronic lymphocytic leukemia (CML). In an advantageous embodiment the cancer is lung cancer.

The tyrosine kinase inhibitor used in the method can be any tyrosine kinase inhibitor including heretofore unrecognized tyrosine kinase inhibitors. In an advantageous embodiment the tyrosine kinase inhibitor is axitinib, bosutinib, cediranib, dasatanib, erlotinib, gefitinib, imatinib, lapatinib, lestaurtinib, nilotinib, semaxanib, sorafenib, sunitinib, toceranib, vandetanib, or vatalanib. In further advantageous embodiments a plurality of tyrosine kinase inhibitors can be administered to the patient responsive to the correlated sensitivity of the tumor. In particularly advantageous embodiments one of the plurality of tyrosine kinase inhibitors is dasatinib or erlotinib. Similarly, the tyrosine kinase inhibitor selected can bes an inhibitor of a molecule selected from the group consisting of epidermal growth factor receptor (EGFR), MET, insulin-like growth factor receptors (IGFR), SRC kinases, fibroblast growth factor receptors (FGFR), platelet-derived growth factor receptors (PDGFR), anaplastic lymphoma kinase (ALK), and EPH receptors. In a second aspect the present invention provides a method of characterizing or classifying tumor responsiveness to one or more TK inhibitors. The method includes the steps of providing a tumor sample having tumor cells and evaluating changes in SH2 binding patterns between untreated tumor cells and tumor cells treated with one or more TK inhibitors. In an advantageous embodiment the assay is employs rosette SH2 profiling or far-western SH2 profiling. The SH2 profile can be correlated with a prediction or prognosis to further clarify the relationship between the resulting observed binding patterns.

The tyrosine kinase inhibitor used in the method of the second aspect can be any tyrosine kinase inhibitor including heretofore unrecognized tyrosine kinase inhibitors. In an advantageous embodiment the tyrosine kinase inhibitor is axitinib, bosutinib, cediranib, dasatanib, eriotinib, gefitinib, imatinib, lapatinib, lestaurtinib, nilotinib, semaxanib, sorafenib, sunitinib, toceranib, vandetanib, or vatalanib. In further advantageous embodiments a plurality of tyrosine kinase inhibitors can be administered to the patient responsive to the correlated sensitivity of the tumor. In particularly advantageous embodiments one of the plurality of tyrosine kinase inhibitors is dasatinib or eriotinib. Similarly, the tyrosine kinase inhibitor selected can be an inhibitor of a molecule selected from the group consisting of epidermal growth factor receptor (EGFR), MET, insulin-like growth factor receptors (IGFR), SRC kinases, fibroblast growth factor receptors (FGFR), platelet-derived growth factor receptors (PDGFR), anaplastic lymphoma kinase (ALK), and EPH receptors.

The method can be practiced on a patient that has a disease characterized by aberrant tyrosine kinase activity. Similarly, the method can be practiced on a patient with cancer including, but not limited to, non-small cell lung cancer, small-cell lung cancer, chronic myeloid leukemia, acute lymphoblastic leukemia, Philadelphia chromosome positive acute lymphoblastic leukemia (Ph+ ALL), squamous cell carcinoma, glioma, gastrointestinal cancer, renal cancer, ovarian cancer, liver cancer, colorectal cancer, endometrial cancer, kidney cancer, prostate cancer, thyroid cancer, neuroblastoma, pancreatic cancer, glioblastoma multiforme, cervical cancer, stomach cancer, bladder cancer, hepatoma, breast cancer, colon carcinoma, and head and neck cancer, gastric cancer, germ cell tumor, pediatric sarcoma, sinonasal natural killer, multiple myeloma, acute myelogenous leukemia (AML), and chronic lymphocytic leukemia (CML). In an advantageous embodiment the cancer is lung cancer. In an advantageous embodiment of the method of the second aspect is a sample isolated from a human undergoing treatment or screening. The method of the second aspect can further include the steps of grouping cells according to SH2 binding patterns and correlating the cells with EGFR mutation status or MET activation status.

In a third aspect the invention provides a method of characterizing or classifying tumor responsiveness to one or more anti-proliferative agents. The method of the third aspect includes the steps of providing a tumor sample and evaluating changes in SH2 binding patterns between untreated tumor cells and tumor cells treated with one or more antiproliferative agents. In an advantageous embodiment the assay is employs rosette SH2 profiling or far-western SH2 profiling.

The anti-proliferative agent of the third aspect can be a TK inhibitor. The tumor of the third aspect can be a cancer selected from the group consisting of non-small cell lung cancer, small-cell lung cancer, chronic myeloid leukemia, acute lymphoblastic leukemia. Philadelphia chromosome positive acute lymphoblastic leukemia (Ph+ ALL), squamous cell carcinoma, glioma, gastrointestinal cancer, renal cancer, ovarian cancer, liver cancer, colorectal cancer, endometrial cancer, kidney cancer, prostate cancer, thyroid cancer, neuroblastoma, pancreatic cancer, glioblastoma multiforme, cervical cancer, stomach cancer, bladder cancer, hepatoma, breast cancer, colon carcinoma, and head and neck cancer, gastric cancer, germ cell tumor, pediatric sarcoma, sinonasal natural killer, multiple myeloma, acute myelogenous leukemia (AML), and chronic lymphocytic leukemia (CML).

In a fourth aspect the invention provides a method of performing targeted cancer therapy in a non-small cell lung cancer patient. The method of the fourth aspect includes the steps of isolating a non-small cell lung cancer tumor sample from the patient, assaying SH2 binding patterns in the isolated tumor sample, comparing and/or correlating the observed patterns from the isolated sample with patterns of tumors with predetermined sensitivity to one or more tyrosine kinase (TK) inhibitors to determine a predicted sensitivity of the cancer to be treated to the one or more TK inhibitors, and administering one or more TK inhibitors to the patient responsive to the predicted sensitivity of the tumor. In an advantageous embodiment the assay is employs rosette SH2 profiling or far-western SH2 profiling. The SH2 profile can be correlated with a prediction or prognosis to further clarify the relationship between the resulting observed binding patterns.

The tyrosine kinase inhibitor used in the method of the fourth aspect can be any tyrosine kinase inhibitor including heretofore unrecognized tyrosine kinase inhibitors. In an advantageous embodiment the tyrosine kinase inhibitor is axitinib, bosutinib, cediranib, dasatanib, eriotinib, gefitinib, imatinib, lapatinib, lestaurtinib, nilotinib, semaxanib, sorafenib, sunitinib, toceranib, vandetanib, or vatalanib. In further advantageous embodiments a plurality of tyrosine kinase inhibitors can be administered to the patient responsive to the correlated sensitivity of the tumor. In particularly advantageous embodiments one of the plurality of tyrosine kinase inhibitors is dasatinib or eriotinib. Similarly, the tyrosine kinase inhibitor selected can be an inhibitor of a molecule selected from the group consisting of epidermal growth factor receptor (EGFR), MET, insulin-like growth factor receptors (IGFR), SRC kinases, fibroblast growth factor receptors (FGFR), platelet-derived growth factor receptors (PDGFR), anaplastic lymphoma kinase (ALK), and EPH receptors.

BRIEF DESCRIPTION OF THE DRAWINGS

For a fuller understanding of the invention, reference should be made to the following detailed description, taken in connection with the accompanying drawings, in which:

FIG. 1 shows an overview of the approach for unsupervised clustering of SH2 domain patterns in lung cancer cell lines. Unsupervised clustering reveals large-scale SH2 domain patterns, several consistent clusters of lung cancer cell lines and similar groupings of EGFR- mutant cell lines.

FIG. 2 further shows unsupervised clustering of SH2 domain patterns in lung cancer cell lines. In particular, the figure shows dot-blot data clustered by SH2 domain and cell line. Each row represents a single SH2 domain, each column represents a single cell line. Biological characteristics, including EGFR mutation, KRAS mutation, and MET phosphorylation are indicated.

FIG. 3 further shows unsupervised clustering of SH2 domain patterns in lung cancer cell lines. In particular, the figure shows far western data is clustered by each individual SH2 domain- specific molecular weight band and cell line. The enlarged dendrogram (not shown) indicates a similar cluster structure overall and groups of EGFR mutants (e.g. HCC827, H4006, H820).

FIG. 4 is a histogram wherein the Mann-Whitney test identified 6 SH2 significant domains (p<0.01 , q<0.05). SH2 domains related to EGFR mutation status and MET phosphorylation. SH2 domains significantly different between mutant EGFR and wild-type EGFR lung cancer cell lines.

FIG. 5 illustrates EGFR mutation status for selected up-regulated and down-regulated EGFR mutants (q<0.1 ).

FIG. 6 is a bar plot of SH2 signal for mutant and wild-type EGFR cell lines. The median and median absolute deviations are shown.

FIG. 7 illustrates SH2 domains associated with MET phosphorylation. (D) Far western results showing correlation with MET phosphorylation.

FIG. 8 illustrates MET phosphorylation for selected up-regulated and down-regulated SH2 domains associated with MET phosphorylation (q<0.1 ).

FIG. 9 illustrates SH2 domains correlating with erlotinib sensitivity. Baseline SH2 domain signal correlated to IC₅₀ for cell exposure to EGFR TKI. (A) Domains significantly correlated to

IC₅₀ in untreated cells. The signal is increasing (Cis1 through Lnk) or decreasing (Tem6 and

Btk) with increasing sensitivity (lower IC₅₀values). FIG. 10 is a scatter plot of Grb2 domain (y axis) vs. the log(IC₅₀) (x axis) further illustrating SH2 domains correlating with erlotinib sensitivity.

FIG. 1 1 is a protein-protein interaction map showing reported direct interactions of indicated proteins for SH2 probes significantly correlated to erlotinib sensitivity.

FIG. 12 provides Far Western results showing domain-specific bands correlated to IC₅₀ sensitivity to EGFR TKI. Shaded boxes indicate increasing (dark shaded) or decreasing (light shaded) signal in far Western bands with increasing sensitivity.

FIG. 13 further provides Far Western results showing domain-specific bands correlated to IC₅₀ sensitivity to EGFR TKI.

FIG. 14 illustrates SH2 domains differentially bound to cells exposed to erlotinib. In particular, the figure provides Far Western results showing domain-specific bands different between base line cells and cells treated with erlotinib.

FIG. 15 further illustrates SH2 domains differentially bound to cells exposed to dasastinb. In particular, the figure provides Far Western results showing domain-specific bands different between base line cells and cells treated with dasastinb.

FIG. 16 is a Far western blot of 22 lung cancer cell lines using p85a SH2 domain. Cell lysates were run on SDS-PAGE and exposed to p85a SH2 domain probes. Cells HCC827 through H820 harbor activating EGFR mutations. The figure illustrates that SH2 domains identify cells with EGFR and MET cooperation and cells dependent on PDGFR.

FIG. 17 further illustrates that SH2 domains identify cells with EGFR and MET cooperation and cells dependent on PDGFR. H1648 cells were exposed to control (DMSO), erlotinib (E) 1000 nM, PHA665752 (P) 1000 nM, or combination for 3 hours after which total proteins were run on SDS-PAGE and exposed to indicated antibodies. Lysates from untreated H820 cells served as control for p-MET. β-actin was run to confirm equal loading.

FIG. 18 is a histogram showing the results of a cell viability assay for H1648 cells exposed to erlotinib 60 nM, PHA665752 300 nM, or combination.

FIG. 19 further illustrates that SH2 domains identify cells with EGFR and MET cooperation and cells dependent on PDGFR. H1703 cells were exposed to DMSO or imatinib 1000 nM for 3 hr after which cell lysates were run on SDS-PAGE and exposed to either anti-PDGFR antibodies (left panel) or p85A SH2 domain probes.

FIG. 20 is western blot showing a phosphorylated MET.

FIG. 21 is a histogram showing the results of the phosphorylated MET western blot.

FIG. 22 shows a network map of SH2 domains correlated with mutant EGFR. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Tyrosine kinases have been proposed as therapeutic targets in lung cancer through the application of a large class of drugs termed, appropriately, tyrosine kinase inhibitors (TKIs). Roughly 100 tyrosine kinases have been identified in the human genome. [Koomen, JM et al., Mol Cell Proteomics, (2008) 7(10) :1780-94]. Some of the tyrosine kinases are known to be involved in the pathogenesis of cancer, while others may have an undiscovered role. The tryrosine kinases targeted for therapy include epidermal growth factor receptor (EGFR), MET receptors, insulin-like growth factor receptors (IGFR), SRC kinases, fibroblast growth factor receptors (FGFR), platelet-derived growth factor receptors (PDGFR), anaplastic lymphoma kinase (ALK), ephrin (EPH) receptors, and others.

A key question in the field of tyrosine kinase therapy for lung cancer is which patients will benefit from these new drugs, an important consideration since a large group of patients receive no benefit from treatment and costs of these agents are substantial. An important breakthrough was the discovery of activating somatic mutations in EGFR that enhance receptor signaling and predict sensitivity to TKIs that target the EGFR, such as erlotinib and gefitinib [Lynch TJ, et al., N Engl J Med (2004) 350: 2129-39; Paez JG, et al., Science (2004) 304: 1497-500; Pao W, et al, Proc Natl Acad Sci U S A (2004) 101 :13306-1 1 ]. In patients who harbor these mutations, response rates to EGFR TKIs can be high and the survival of patients is better than that seen with cytotoxic agents [Sequist LV, et al, J Clin Oncol (2008) 26:2442-9]. Nonetheless, some patients who do not have EGFR mutation can still benefit from EGFR inhibitors, and markers such as EGFR gene amplification, autocrine TGFD production, or gene expression profiles could identify these subsets of lung cancer patients [Cappuzzo F, et al, J Natl Cancer Inst (2005) 97: 643-55; Balko JM, et al, BMC genomics

(2006) 7: 289; Yonesaka K, et al, Clin Cancer Res (2008) 14: 6963-73]. In addition, resistance mechanisms such as secondary mutations in EGFR or MET amplification can rapidly lead to drug resistance and tumor growth [Bean J, et al, Proc Natl Acad Sci U S A

(2007) 104:20932-7; Engelman JA, et al. Science (2007) 316:1039-43; Pao W, et al, PLoS Med (2005) 2:1 -1 1 ]. Finally, some tumor cells are likely to be driven by multiple tyrosine kinases, and methods to identify these cooperative drivers of signaling is critical [Stommel JM, et al. Science (2007) 318: 287-90].

Proteomic strategies, which examine global patterns of protein expression or phosphorylation, can be used to identify subsets of tumors [Kikuchi T, et al, Respirology (2007) 12:22-8]. Mass spectrometry coupled with anti-phosphotyrosine antibodies can be used to identify different patterns of tyrosine kinase signaling in lung cancer cells and tumors. This approach was used to identify cells driven by oncogenic EGFR, PDGFR, and ALK [Rikova K, et al. Cell (2007) 131 : 1 190-203]. Other studies using the same approach found patterns of tyrosine phosphorylation associated with mutant EGFR signaling [Guha U, et al., Proc Natl Acad Sci U S A {2008) 105:141 12-7; Guo A, et al., Proc Natl Acad Sci U S A (2008) 105:692-7]. It is shown herein that the characterization of global tyrosine phosphorylation patterns can provide key information for classifying lung cancers and demonstrates that multiple kinases can be active in a single cancer cell.

As a complement to cataloguing tyrosine phosphorylated proteins using MS-based approaches, a novel phosphoproteomic method, termed "SH2 profiling", can be used to profile phosphotyrosine (pTyr) signaling in cancer cells [Dierck K, et al., Nat Methods (2006) 3:737-44; Machida K, et al., Mol Ce// (2007) 26: 899-915]. SH2 profiling uses the cell's own phosphotyrosine signal response apparatus to interrogate the state of pTyr signaling. One of the most important consequences of protein tyrosine phosphorylation is regulation of protein- protein interactions [Pawson T., Cell (2004) 1 16:191 -203(29)] Many tyrosine-phosphorylated proteins serve as high-affinity binding sites for proteins containing modular pTyr-specific binding domain, which serve to couple tyrosine phosphorylation to the assembly of signaling complexes and the relocalization of signaling proteins. By far the most abundant such module in humans is the Src Homology 2 or SH2 domain [Machida K, and Mayer BJ., Biochim Biophys Acta Proteins and Proteomics (2005) 1747:1 -25]. There are 120 SH2 domains encoded by the human genome and each SH2 domain has binding specificity for a unique spectrum of tyrosine phosphorylated sites [Liu BA, et al., Mol Cell (2006) 22: 851 -68; Machida K, et al., Mol Cell (2007) 26: 899-915]. Because SH2 domains are what the cell actually uses to respond to or "read" changes in tyrosine phosphorylation during signaling, the extent of binding of different SH2 domains to a cell sample can provide a great deal of information about the signaling state and its underlying mechanisms. Differences in SH2 profiles (and therefore pTyr signaling) were discernable in cells transformed by distinct oncogenic tyrosine kinases, and allowed classification of different types of leukemia [Dierck K, et al., Nat Methods (2006) 3:737-44; Machida K, et al., Mol Cell (2007) 26: 899-915].

SH2 profiling provides a tool to characterize and classify complex tumor types, such as lung cancer, where multiple tyrosine kinases drive downstream signaling pathways and maintain tumor growth. SH2 profiling is applied herein to lung cancer cells to show that patterns of pTyr could be identified and related to known features of the cells including EGFR mutation status and sensitivity to EGFR TKI. Changes in SH2 domain binding (and therefore pTyr signaling) were examined in cells treated with either EGFR or SRC TKI. Finally, we show SH2 domain binding patterns can identify cells driven by RTK other than EGFR. Thus, SH2 profiling itself provides a means to classify tumors for prediction/prognosis, and allows for screening to identify specific biomarkers that might be more amenable to a clinical test.

Overview Receptor and non-receptor tyrosine kinases play a critical role in driving the proliferation and survival of lung cancer cells. SH2 profiling was applied to characterize phosphotyrosine (pTyr) signaling in lung cancer. This method provides quantitative values for the phosphorylated binding sites for Src Homology 2 (SH2) domains, which are used by the cell to relay signals from tyrosine kinases. Lung cancer cell lines with known mutational status of the epidermal growth factor receptor (EGFR) and Ras were profiled. Changes in SH2 domain binding were characterized in response to the EGFR inhibitor erlotinib, and the SRC/multi-kinase inhibitor dasatinib. The results show that cell lines could be grouped based on SH2 binding patterns, and that some clusters correlated with EGFR mutation status or MET activation. Binding of specific SH2 domains, most prominently the Ras pathway activators Grb2 and ShcA, correlated with EGFR mutation and erlotinib sensitivity. Groups of SH2 domains were responsive to erlotinib or dasatinib, suggesting both common core and unique pTyr signaling affected by these inhibitors. Finally, the results show that SH2 domain profiles can identify lung cancer cells driven by cooperative EGFR and MET signaling as well as by PDGFR signaling. The results indicate that SH2 domain profiling can identify subsets of lung cancer cells with distinct patterns of pTyr signaling and provide a powerful molecular diagnostic tool for classification and biomarker identification. This type of analysis has therapeutic importance for personalized use of tyrosine kinase inhibitors in cancer.

The results demonstrate that SH2 domains are useful as a biomarker for response to EGFR TKI, that they give additional biologic and mechanistic insights into mutant EGFR biology, that SH2 domains can serve as the basis for classification of tumors with potential prognostic and/or predictive value, and that SH2 domains can provide insights into driver tyrosine kinases and hierarchy of tyrosine kinases that provide growth and/or survival signals in lung cancer cells.

One important observation was that patterns of pTyr signaling (as measured by SH2 profiling) appear driven by EGFR mutation. Higher binding was found in EGFR mutant cells with Brk, Grap2, Grb2, ShcA, and Txk SH2 domains. These results, using cell lines that originated from different patients, show that binding sites for a group of SH2 domains are consistently increased in cells with activating EGFR mutations. Nonetheless, it was also observed that mutant EGFR does not drive the entire tyrosine phosphorylation pattern in lung cancer cells, as cells with mutant EGFR can form distinct clusters. The rich abundance of tyrosine kinases (MET, IGFR, SRC, ALK, and PDGFR, etc.) involved in the biology of lung cancer may explain these results. This global characterization is consistent with the distinct mutant allelles of EGFR leading to measurable differences in tyrosine phosphorylated peptides. SH2 profiling therefore reveals heterogeneity of signaling despite common genomic properties (EGFR mutation) and provides additional predictive or prognostic information in tumor classification. Classification based on SH2 profiles correlated closely with two other molecular markers, such as MET activation and K-RAS activation. In the case of MET, both rosette and far- western SH2 profiling clearly distinguished a cluster of cells with MET activation (assayed by activating phosphorylation and p85 SH2 binding). Therefore, in lung cancer, global changes in tyrosine phosphorylation are likely to be at least as dependent on MET signaling as they are on EGFR activation. This result is an indication of the value of nonbiased, global proteomic approaches for analysis of cancer. Second, far-western-based SH2 profiling showed a strong correlation between pTyr pattern and activating K-Ras mutations. This is somewhat surprising, as one might think that constitutive Ras activation would render cells independent of pTyr-based signals, e.g. from activated receptors. The fact that K-Ras mutant cells cluster together indicates that there are common patterns of tyrosine phosphorylation associated with cells in which Ras activity is decoupled from receptor activation, perhaps through feedback loops that normally down-regulate pTyr signaling after Ras activation.

Data obtained through SH2 profiling can generate additional insights regarding the biology of EGFR mutations. For example, the present analysis identified specific HSH2 SH2 domain binding to EGFR on far western (FIG. 2C) yet this interaction has not been reported in lung cancer cells. Tyrosine phosphorylated peptides on HSH2 have been identified in lung cancer cells with activating EGFR mutations. In addition, HSH2 tyrosine phosphorylation is a feature in cells stimulated with EGF. HSH2 modulates apoptotic response through mitochondrial signaling and thus could have a hitherto unknown role in EGFR mediated survival. Little evidence exists for a role of TXK in lung cancer, with prior data linking it to expression in T- cells. Without wishing to be bound by any particular theory, the reduction in CSK SH2 domain binding to mutant EGFR could explain SRC activation in these cells and hypersensitivity of EGFR mutant lung cancer cells to SRC TKI.

The present results also indicate that SH2 domains can be used as a biomarker for EGFR TKI sensitivity. Signaling from EGFR can be mediated by different mechanisms including activating mutations, overexpression, and autocrine stimulation. A key event is coupling of EGFR activation to downstream signaling events via protein tyrosine phosphorylation and protein-protein interactions mediated by SH2 domain containing proteins. Fourteen SH2 domains are shown whose binding correlated with erlotinib sensitivity and reflect engagement of downstream MAPK and PI3K/Akt signaling cascades. It is significant that binding sites for known Ras pathway activators, Grb2 and She, are associated both with activating EGFR mutations and with erlotinib sensitivity. Ras activation is associated with proliferative signals, and Ras itself is activated by mutation in a large number of human cancers. By examining pTyr signaling in an unbiased fashion on a global scale, these results evidence the central importance of Ras signaling in lung cancer, and indicate that particularly strong activation of Ras provides a hallmark of cancers driven by EGF receptor mutants, and of those that are most likely to respond to EGFR inhibitors. Thus, SH2 domains could be used to read upstream EGFR activity occurring through diverse mechanisms. Further work with SH2 domains will provide additional information beyond known predictors of EGFR TKI response, such as EGFR mutation status, gene amplification, gene expression profiles, autocrine signaling. Such work can be accompanied by evaluation in animal tumor models or human specimens.

Importantly, SH2 domain profiling can directly interrogate signal transduction by diverse tyrosine kinases. Similarly to the key roles for RTK, other than EGFR in lung cancer, for example MET, ALK, and PDGFRD , SH2 profiles are useful as a global assay to define kinase dependency. SH2 profiling interrogates the entire tyrosine kinome, as opposed to approaches where each component must be assayed individually. Furthermore, it provides a read-out of the actual in vivo activity of all tyrosine kinases in the cell, in their native milieu. In other words, rather than having to assay for expression/amplification/mutation of multiple tyrosine kinases, SH2 profiles reveal strong signaling emanating from multiple kinases. The analysis taught herein finds examples of cells driven both by EGFR and MET that would require dual EGFR and MET blockage for abrogation of downstream signaling. Treatment with either EGFR or MET TKI alone resulted in modest reductions in phosphorylated AKT, while combined treatment with both agents resulted in complete inhibition of phosphorylated Akt along with more substantial inhibition of cell growth compared to either agent alone. As with the H820 cell, SH2 domains can also interrogate potential resistance mechanisms to EGFR TKI by identifying activation of other RTK (MET). In addition to EGFR and MET, SH2 domain profiling also identified cells dependent on PDGFR. Thus, information provided by far WB using SH2 probes can be useful for guiding therapeutic decisions.

SH2 profiling can be applied to human tumor samples to evaluate the similarity of patterns observed in lung cancer cell lines. SH2 profiling adds additional information reflecting tyrosine kinase signaling that is prognostic or predictive of response to TKI. With the current approach using rosette assays, it is possible to profile the level of binding for virtually all SH2 domains in the genome using minimal amounts of protein sample (less than 100 micrograms total protein). A multiplexed SH2 profiling platform, based on tagging of SH2 domains with specific oligonucleotides, has also been developed and validated. After binding of probes to immobilized sample and washing, oligonucleotides are amplified by linear polymerase chain reaction (PCR) and quantified. The PCR amplification step makes the assay extremely sensitive, and this platform provides one basis for a standardized clinical SH2 profiling assay for molecular diagnostics.

SH2 profiling can be evaluated against other current methods for analyzing global tyrosine phosphorylation patterns. Currently, the use of phosphospecific antibodies and mass spectrometry are most prevalent. Phosphospecific antibodies (to specific tyrosine phosphorylated sites) can be sensitive and specific, but have the disadvantage that they require knowledge of the relevant phosphorylated sites, and they are available and validated for only a small fraction of known sites. Mass spectrometry (with or without initial enrichment by pull-down or immunoprecipitation) can be used to identify specific phosphorylated sites in a sample, but has the disadvantages that coverage and sensitivity are modest, relatively large amounts of sample are required, and absolute quantification of individual sites is difficult. Compared to these approaches, SH2 profiling combines the benefits of being comprehensive, highly sensitive, and quantitative. In addition, the limited number of SH2 domains in human genome (-100) reduces the computational complexity and reagent cost.

Thus, SH2 domains can recognize distinct patterns of EGFR signaling in lung cancer cells, but also provide additional ability to classify tumors beyond EGFR mutation status. A set of SH2 domains is correlated with EGFR TKI sensitivity and is useful in reading EGFR signaling in human tumors. In addition to EGFR, SH2 domains can also identify tumor cells dependent on MET and PDGFR for control of downstream signaling events. These results are important in therapeutic decisions regarding TKI in lung cancer and provide an additional approach to tumor classification.

Exemplary Indications, Conditions. Diseases, and Disorders:

The present invention contemplates the detection of aberrant tyrosine kinase activity through the assay of SH2 domain profiles. Furthermore, the invention contemplates the application of such detection to assess the responsiveness of a sample to the application of one or more antiproliferative agents, in particular, one or more tyrosine kinase inhibitors. Any methods available in the art for the assay of SH2 domain profiles, including the rosette SH2 profiling and far-western profiling outlined below, are encompassed herein.

A number of the tyrosine kinases are known to be involved in the pathogenesis of cancer, while others may have an undiscovered role. For example, tyrosine kinases are considered to be a factor contributing to non-small cell lung cancer. The tryrosine kinases targeted for therapy include epidermal growth factor receptor (EGFR), MET receptors, insulin-like growth factor receptors (IGFR), SRC kinases, fibroblast growth factor receptors (FGFR), platelet- derived growth factor receptors (PDGFR), anaplastic lymphoma kinase (ALK), ephrin (EPH) receptors, and others.

The terms "cancer", "cancerous", or "malignant" refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. Examples of cancer within the scope of the invention include, for example, lung cancer, leukemia, lymphoma, blastoma, carcinoma and sarcoma. More particular examples of such cancers include chronic myeloid leukemia, acute lymphoblastic leukemia, Philadelphia chromosome positive acute lymphoblastic leukemia (Ph+ ALL), squamous cell carcinoma, small-cell lung cancer, non-small cell lung cancer, glioma, gastrointestinal cancer, renal cancer, ovarian cancer, liver cancer, colorectal cancer, endometrial cancer, kidney cancer, prostate cancer, thyroid cancer, neuroblastoma, pancreatic cancer, glioblastoma multiforme, cervical cancer, stomach cancer, bladder cancer, hepatoma, breast cancer, colon carcinoma, and head and neck cancer, gastric cancer, germ cell tumor, pediatric sarcoma, sinonasal natural killer, multiple myeloma, acute myelogenous leukemia (AML), and chronic lymphocytic leukemia (CML).

Additional disorders included in the scope of the present invention include, for example, leukemias, including, for example, chronic myeloid leukemia, acute lymphoblastic leukemia, and Philadelphia chromosome positive acute lymphoblastic leukemia (Ph+ ALL), squamous cell carcinoma, small-cell lung cancer, non-small cell lung cancer, glioma, gastrointestinal cancer, renal cancer, ovarian cancer, liver cancer, colorectal cancer, endometrial cancer, kidney cancer, prostate cancer, thyroid cancer, neuroblastoma, pancreatic cancer, glioblastoma multiforme, cervical cancer, stomach cancer, bladder cancer, hepatoma, breast cancer, colon carcinoma, and head and neck cancer, gastric cancer, germ cell tumor, pediatric sarcoma, sinonasal natural killer, multiple myeloma, acute myelogenous leukemia, chronic lymphocytic leukemia, mastocytosis and any symptom associated with mastocytosis. In addition, disorders include urticaria pigmentosa, mastocytosises such as diffuse cutaneous mastocytosis, solitary mastocytoma in human, as well as dog mastocytoma and some rare subtypes like bullous, erythrodermic and teleangiectatic mastocytosis, mastocytosis with an associated hematological disorder, such as a myeloproliferative or myelodysplastic syndrome, or acute leukemia, myeloproliferative disorder associated with mastocytosis, and mast cell leukemia. Various additional cancers are also included within the scope of protein tyrosine kinase-associated disorders including, for example, the following: carcinoma, including that of the bladder, breast, colon, kidney, liver, lung, ovary, pancreas, stomach, cervix, thyroid, testis, particularly testicular seminomas, and skin; including squamous cell carcinoma; gastrointestinal stromal tumors ("GIST"); hematopoietic tumors of lymphoid lineage, including leukemia, acute lymphocytic leukemia, acute lymphoblastic leukemia, B-cell lymphoma, T-cell lymphoma, Hodgkins lymphoma, non-Hodgkins lymphoma, hairy cell lymphoma and Burketts lymphoma; hematopoietic tumors of myeloid lineage, including acute and chronic myelogenous leukemias and promyelocytic leukemia; tumors of mesenchymal origin, including fibrosarcoma and rhabdomyoscarcoma; other tumors, including melanoma, seminoma, tetratocarcinoma, neuroblastoma and glioma; tumors of the central and peripheral nervous system, including astrocytoma, neuroblastoma, glioma, and schwannomas; tumors of mesenchymal origin, including fibrosarcoma, rhabdomyoscaroma, and osteosarcoma; and other tumors, including melanoma, xenoderma pigmentosum, keratoactanthoma, seminoma, thyroid follicular cancer, teratocarcinoma, chemotherapy refractory non-seminomatous germ- cell tumors, and Kaposi's sarcoma. In certain preferred embodiments, the disorder is leukemia, breast cancer, prostate cancer, lung cancer, colon cancer, melanoma, or solid tumors. In certain preferred embodiments, the leukemia is T-ALL, chronic myeloid leukemia (CML), Ph+ ALL, AML, imatinib-resistant CML, imatinib-intolerant CML, accelerated CML, lymphoid blast phase CML.

A "solid tumor" includes, for example, sarcoma, melanoma, carcinoma, or other solid tumor cancer.

"Leukemia" refers to progressive, malignant diseases of the blood-forming organs and is generally characterized by a distorted proliferation and development of leukocytes and their precursors in the blood and bone marrow. Leukemia is generally clinically classified on the basis of (1 ) the duration and character of the disease-acute or chronic; (2) the type of cell involved; myeloid (myelogenous), lymphoid (lymphogenous), or monocytic; and (3) the increase or non-increase in the number of abnormal cells in the blood-leukemic or aleukemic (subleukemic). Leukemia includes, for example, acute nonlymphocytic leukemia, chronic lymphocytic leukemia, acute granulocytic leukemia, chronic granulocytic leukemia, acute promyelocytic leukemia, adult T-cell leukemia, aleukemic leukemia, a leukocythemic leukemia, basophylic leukemia, blast cell leukemia, bovine leukemia, chronic myelocytic leukemia, leukemia cutis, embryonal leukemia, eosinophilic leukemia, Gross' leukemia, hairy- cell leukemia, hemoblastic leukemia, hemocytoblastic leukemia, histiocytic leukemia, stem cell leukemia, acute monocytic leukemia, leukopenic leukemia, lymphatic leukemia, lymphoblastic leukemia, lymphocytic leukemia, lymphogenous leukemia, lymphoid leukemia, lymphosarcoma cell leukemia, mast cell leukemia, megakaryocytic leukemia, micromyeloblastic leukemia, monocytic leukemia, myeloblasts leukemia, myelocytic leukemia, myeloid granulocytic leukemia, myelomonocytic leukemia, Naegeli leukemia, plasma cell leukemia, plasmacytic leukemia, promyelocytic leukemia, Rieder cell leukemia, Schilling's leukemia, stem cell leukemia, subleukemic leukemia, and undifferentiated cell leukemia. In certain aspects, the present invention provides treatment for chronic myeloid leukemia, acute lymphoblastic leukemia, and/or Philadelphia chromosome positive acute lymphoblastic leukemia (Ph+ ALL).

In particular embodiments, the diagnostic and/or treatment methods of the invention comprise collecting a body sample from a patient, contacting the sample with at least one antibody specific for an epitope of interest, and detecting antibody binding. By "body sample" is intended any sampling of cells, tissues, or bodily fluids in which expression of a biomarker can be detected. Examples of such body samples include but are not limited to blood, lymph, urine, and biopsies. Body samples may be obtained from a patient by a variety of techniques including, for example, by scraping or swabbing an area or by using a needle to aspirate bodily fluids.

Kits for practicing the methods of the invention are further provided. By "kit" is intended any manufacture (e.g., a package or a container) comprising at least one reagent, e.g. , an antibody, a nucleic acid probe, etc. for specifically detecting the SH2 probe binding of the invention. The kit may be promoted, distributed, or sold as a unit for performing the methods of the present invention. Additionally, the kits may contain a package insert describing the kit and methods for its use. Any or all of the kit reagents may be provided within containers that protect them from the external environment, such as in sealed containers or pouches.

In a particular embodiment, the immunochemistry kits of the invention additionally comprise at least two reagents, e.g., antibodies, for specifically detecting the expression of at least two distinct biomarkers. Each antibody may be provided in the kit as an individual reagent or, alternatively, as an antibody cocktail comprising all of the antibodies directed to the different biomarkers of interest.

In an advantageous embodiment, kits for practicing the immunochemistry methods of the invention, particularly the rosette SH2 technique, are provided. Such kits are compatible with both manual and automated immunochemistry techniques. These kits comprise at least one antibody directed to an SH2 domain interest, and chemicals for the detection of the antibody binding to the SH2 domain. Any chemicals that detect antigen-antibody binding may be used in the practice of the invention. In some embodiments, a detection antibody is conjugated to an enzyme that catalyzes the calorimetric conversion of a substrate. Such enzymes and techniques for using them in the detection of antibody binding are well known in the art. In particular embodiments, chemicals for the detection of antibody binding comprise commercially available reagents and kits.

Positive and/or negative controls may be included in the kits to validate the activity and correct usage of reagents employed in accordance with the invention. Controls may include samples, such as tissue sections, cells fixed on glass slides, etc., known to be either positive or negative for the presence of the biomarker of interest. In a particular embodiment, the positive control is a solution comprising a biomarker protein of interest. The design and use of controls is standard and well within the routine capabilities of those of ordinary skill in the art. Additional Terminology:

As used throughout the entire application, the terms "a" and "an" are used in the sense that they mean "at least one", "at least a first", "one or more" or "a plurality" of the referenced components or steps, unless the context clearly dictates otherwise. For example, the term "a cell" includes a plurality of cells, including mixtures thereof. The term "and/or" wherever used herein includes the meaning of "and", "or" and "all or any other combination of the elements connected by said term".

The term "about" or "approximately" as used herein means within 20%, preferably within 10%, and more preferably within 5% of a given value or range.

Other than in the operating examples, or unless otherwise expressly specified, all of the numerical ranges, amounts, values and percentages such as those for amounts of materials, times and temperatures of reaction, ratios of amounts, values for molecular weight (whether number average molecular weight ("M„") or weight average molecular weight ("M_w"), and others in the following portion of the specification may be read as if prefaced by the word "about" even though the term "about" may not expressly appear with the value, amount or range. Accordingly, unless indicated to the contrary, the numerical parameters set forth in the following specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the present disclosure. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.

Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the disclosure are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. Any numerical value, however, inherently contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements. Furthermore, when numerical ranges of varying scope are set forth herein, it is contemplated that any combination of these values inclusive of the recited values may be used.

As used herein, the term "comprising" is intended to mean that the products, compositions and methods include the referenced components or steps, but not excluding others. "Consisting essentially of" when used to define products, compositions and methods, shall mean excluding other components or steps of any essential significance. Thus, a composition consisting essentially of the recited components would not exclude trace contaminants and pharmaceutically acceptable carriers. "Consisting of" shall mean excluding more than trace elements of other components or steps.

The term "about" or "approximately" means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system, i.e., the degree of precision required for a particular purpose, such as a pharmaceutical formulation. For example, "about" can mean within 1 or more than 1 standard deviations, per the practice in the art. Alternatively, "about" can mean a range of up to 20%, preferably up to 10%, more preferably up to 5%, and more preferably still up to 1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, preferably within 5-fold, and more preferably within 2-fold, of a value. Where particular values are described in the application and claims, unless otherwise stated the term "about" meaning within an acceptable error range for the particular value should be assumed.

The term "administration" and variants thereof (e.g., "administering" a compound) in reference to a compound of the invention means introducing the compound or a prodrug of the compound into the system of the animal in need of treatment. When a compound of the invention or prodrug thereof is provided in combination with one or more other active agents (e.g., a cytotoxic agent, etc.), "administration" and its variants are each understood to include concurrent and sequential introduction of the compound or prodrug thereof and other agents.

As used herein, the term "composition" is intended to encompass a product comprising the specified ingredients in the specified amounts, as well as any product which results, directly or indirectly, from combination of the specified ingredients in the specified amounts.

The term "therapeutically effective amount" as used herein means that amount of active compound or pharmaceutical agent that elicits the biological or medicinal response in a tissue, system, animal or human that is being sought by a researcher, veterinarian, medical doctor or other clinician. In reference to cancers or other unwanted cell proliferation, an effective amount comprises an amount sufficient to cause a tumor to shrink and/or to decrease the growth rate of the tumor (such as to suppress tumor growth) or to prevent or delay other unwanted cell proliferation. In some embodiments, an effective amount is an amount sufficient to delay development. In some embodiments, an effective amount is an amount sufficient to prevent or delay occurrence and/or recurrence. An effective amount can be administered in one or more doses. In the case of cancer, the effective amount of the drug or composition may: (i) reduce the number of cancer cells; (ii) reduce tumor size; (iii) inhibit, retard, slow to some extent and preferably stop cancer cell infiltration into peripheral organs; (iv) inhibit (i.e., slow to some extent and preferably stop) tumor metastasis; (v) inhibit tumor growth; (vi) prevent or delay occurrence and/or recurrence of tumor; and/or (vii) relieve to some extent one or more of the symptoms associated with the cancer.

The term "treating cancer" or "treatment of cancer" refers to administration to a mammal afflicted with a cancerous condition and refers to an effect that alleviates the cancerous condition by killing the cancerous cells, but also to an effect that results in the inhibition of growth and/or metastasis of the cancer.

As used herein, "treatment" refers to obtaining beneficial or desired clinical results. Beneficial or desired clinical results include, but are not limited to, any one or more of: alleviation of one or more symptoms (such as tumor growth or metastasis), diminishment of extent of cancer, stabilized (i.e., not worsening) state of cancer, preventing or delaying spread (e.g., metastasis) of the cancer, preventing or delaying occurrence or recurrence of cancer, delay or slowing of cancer progression, amelioration of the cancer state, and remission (whether partial or total). The methods of the invention contemplate any one or more of these aspects of treatment.

A "subject in need of treatment" is a mammal with cancer that is life-threatening or that impairs health or shortens the lifespan of the mammal.

A "pharmaceutically acceptable" component is one that is suitable for use with humans and/or animals without undue adverse side effects (such as toxicity, irritation, and allergic response) commensurate with a reasonable benefit/risk ratio.

A "safe and effective amount" refers to the quantity of a component that is sufficient to yield a desired therapeutic response without undue adverse side effects (such as toxicity, irritation, or allergic response) commensurate with a reasonable benefit/risk ratio when used in the manner of this invention.

"Therapeutically effective amount" refers to an amount of a compound of the present invention alone or an amount of the combination of compounds claimed or an amount of a compound of the present invention in combination with other active ingredients effective to treat the diseases described herein.

As used in relation to the invention, the term "treating" or "treatment" and the like should be taken broadly. They should not be taken to imply that a subject is treated to total recovery. Accordingly, these terms include amelioration of the symptoms or severity of a particular condition or preventing or otherwise reducing the risk of further development of a particular condition.

As used herein, the term "pretreating", or "pretreatment", is intended to mean that a first treatment is administered prior to, or in conjunction with, a second treatment. In other words, the pretreatment may be performed before another, later treatment, thus allowing the pretreatment time to take effect. Alternatively, the pretreatment may be performed or administered simultaneously with a second treatment without a temporal delay. Advantageously, a pretreatment is administered prior to a second treatment.

It should be appreciated that methods of the invention may be applicable to various species of subjects, preferably mammals, more preferably humans.

As used herein, the compounds of the present invention include the pharmaceutically acceptable derivatives thereof. A "pharmaceutically-acceptable derivative" denotes any salt, hydrate, solvate of ester of a compound of this invention, or any other compound which upon administration to a patient is capable of providing (directly or indirectly), such as a prodrug, a compound of this invention, or a metabolite or residue thereof.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art (e.g., in cell culture, molecular genetics, nucleic acid chemistry, hybridisation techniques and biochemistry). Standard techniques are used for molecular, genetic and biochemical methods. See, generally, Sambrook et al., Molecular Cloning: A Laboratory Manual, 2d ed. (1989) Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. and Ausubel et al., Short Protocols in Molecular Biology (1999) 4th Ed, John Wiley & Sons, Inc. ; as well as Guthrie et al.. Guide to Yeast Genetics and Molecular Biology, Methods in Enzymology, Vol. 194, Academic Press, Inc., (1991 ), PCR Protocols: A Guide to Methods and Applications (Innis, et al. 1990. Academic Press, San Diego, Calif.), McPherson et al., PCR Volume 1 , Oxford University Press, (1991 ), Culture of Animal Cells: A Manual of Basic Technique, 2nd Ed. (R. I. Freshney. 1987. Liss, Inc. New York, N.Y.), and Gene Transfer and Expression Protocols, pp. 109-128, ed. E. J. Murray, The Humana Press Inc., Clifton, N.J.).

The invention is described below in examples which are intended to further describe the invention without limitation to its scope.

Example 1 - Global SH2 domain profiles identifies subsets of lung cancer cell lines

A group of 22 non-small cell lung cancer cell lines with known EGFR mutation status, K-Ras mutation status, MET activation status, and sensitivity to EGFR TKI erlotinib was selected for study (Table 1 and Figures 20 and 21 ). The overall approach for the studies is shown in FIG. 1. Two approaches to generate SH2 profiles were examined. The first uses a reverse-phase (RP) protein array method in which multiple protein samples are spotted in arrays in register with the wells of a 96-well chamber apparatus. Each well is then filled with a solution containing a different GST-SH2 domain probe, and after incubation and washing, the bound probe is detected by chemiluminescence and quantified. The quantitative value for binding of the GST-SH2 probe depends both on the amount of tyrosine phosphorylated binding sites for that SH2 domain present in the sample, and on the affinity of those sites. With this rapid and quantitative approach, which we term the "rosette" assay, it is possible to profile the level of binding for virtually all SH2 domains in the genome using minimal amounts of protein sample. Table 1

IC50(nM)

RAS IC₅₀(n ) of of

Cell Histology EGFR Status Status Other Erlotinib Dasatinib

HCC827 Adeno del exon 19 WT 3.69^* 57.2

PC9 Adeno del exon 19 WT 7.5^* 244.5

H4006 Adeno del exon 19 WT 2.79* 137.7

PTEN

H1650 Adeno del exon 19 WT loss 12700 2000

H1975 Adeno L858R/T790M WT T790M 5600 329.7

HER3

H2279 Adeno del exon 19 WT loss 5300 62.7

T790M,

H820 Adeno del exon 19 WT MET >2000 61 .6

H292 Squamous WT WT 28.4

H358 BAC WT Mut exonl 199.1 27.4

H441 Adeno WT Mut exon2 6290 >2000

A549 Adeno WT Mut exonl 10970 413.3

H460 Large Cell WT Mut exon2 8880 >2000

H1299 Large Cell WT WT 8300 31 .6

H1648 Adeno WT WT 75.8^" 20.9

H2122 Adeno WT Mut exonl 747.8 485.5

H226 Squamous WT WT 45400 708.7

H157 Squamous WT Mut exonl 19600 48.9

H322 BAC WT WT ^::::::: 1 1 .6

H23 Adeno WT Mut exonl 7800 >2000

H596 Adeno/Squam WT WT 5000 77.9

H2172 ? WT WT 1 1900 >2000

UK29 Adeno WT Mut exon2 2200 2000

Quantitative SH2 profiling values for the lung cancer lines were subjected to hierarchical clustering. The mean values for four replicate spots (from at least two separate experiments) were used for clustering analysis. Results are shown in heat map format in FIG. 2. Data with low signal/background were discarded, and remaining data were normalized; darkly shaded boxes indicate higher than average binding, darkly shaded boxes lower than average. A web- based viewer for these data was also developed. Note that cell lines harboring mutant EGFR cluster together in distinct sub-clusters, while two large clusters consist entirely of cells with wildtype (wt) EGFR. In addition, a cluster of cells with MET activation that co-cluster amongst the cells with EGFR mutations was found. These results evidence that SH2 domain profiles can identify subsets of lung cancer cells, and that such clusters correlate to EGFR mutation and MET activation status. The second approach uses far-Western blotting to obtain more detailed information about the relative abundance and size of phosphoproteins that bind different SH2 domain probes. In this approach, protein samples are separated on the basis of size by gel electrophoresis and transferred to membranes, which are then probed with labeled SH2 domains. SH2 binding proteins are revealed as bands, and the apparent molecular weight of these bands may suggest their identity. We developed software tools that allow SH2 binding data from far- western blots to be quantified in "bins" by apparent molecular weight, e.g. 20 distinct bins per lane. The data from each bin (corresponding to phosphoproteins of a particular molecular weight range that bind to the SH2 probe) can then be used as the basis for classification of samples. Thus, instead of a single value for each sample and SH2 domain, as in the rosette assay, quantitative far-Western blotting provides at least 20 different data points, greatly increasing the potential discrimination between tumor samples.

When this type of analysis was applied to NSCLC cell lines (FIG. 3), the samples clustered into 3 distinct classes, plus two outliers. One of the clusters (cluster 3) consists entirely of cells with wt EGFR. In contrast, clusters 1 and 2 are enriched for cells that have EGFR mutations; within each of these clusters, cells with wt and mutant EGFR are clearly segregated. These data indicate that SH2 profiling data can functionally classify lung cancers, and predict EGFR mutation status.

We also compared clustering based on SH2 profiling with other known biological properties of these cell lines including sensitivity to erlotinib and dasatinib and K-Ras mutation status, as activating mutations in K-Ras occur frequently in lung cancer and could be important for targeted therapy efficacy. A number of interesting features are apparent. For example, in both the rosette and far-western assays, two core clusters of cell lines with wt EGFR are apparent (one consisting of H157, HH460, H1299, and UK29, the other of H292, H322, H596, and H2172). The first of these consists entirely of cell lines resistant to erlotinib, and may represent a wt, resistant pTyr phenotype. The second group contains cell lines both sensitive to and resistant to erlotinib, and thus represents a phosphorylation pattern associated with erlotinib sensitivity despite apparently wt EGFR status. Second, there is an interesting apparent correlation between clustering based on SH2 profiles and K-RAS mutation status. This is particularly clear in the case of far-western blotting, where two major clusters consist entirely of cells with wt K-Ras, whereas a third major cluster consists almost entirely of cells with wt EGFR but mutant K-Ras. All cell lines in this cluster are resistant to erlotinib. These data suggest tyrosine phosphorylation patterns can sub-classify cells with wt EGFR based on Ras mutation status.

Further analysis of SH2 binding patterns in far-western blots revealed a number of interesting relationships. For example, the H358 and H441 cells cluster tightly; the H358 cell is sensitive to EGFR TKI while the H441 generally is resistant. The similarity in SH2 profiles suggests that both have active EGFR signaling but some other component of signal transduction maintains H441 cell growth in the presence of EGFR inhibition. Second, the H820 and H1648 cells cluster tightly. The H820 cell has an activating EGFR mutation coupled with MET amplification [Bean J, et al, Proc Natl Acad Sci USA (2007) 104: 20932-7. The H1648 cell has wt EGFR and has also been recently shown to have MET amplification [Nakamura Y, et al., Cancer science (2008) 99: 14-22]. These results suggest that in these particular cell lines, downstream signaling may be driven by both EGFR and MET. Finally, a third cluster consists of H2279, H1650. and H1975 cells that harbor activating EGFR mutations but are insensitive to EGFR TKI. H1975 has a secondary gatekeeper mutation (T790M) that results in drug insensitivity while H1 650 was recently shown to have PTEN loss [Pao W, et al., PLoS Med (2005) 2: e73; Sos ML, et al., Cancer research (2009) 69: 3256-61 ]. Nonetheless, these results may also suggest other mechanisms of resistance in these cells.

Inspection of actual far-western blotting results provides clues to the identity of some major SH2 binding sites. Grb2, Grb7, HSHS2, and Vav2 SH2 domains appear relatively specific for the H ER family bands at approximately ~194 kD. GAP SH2 domains appear to bind ~145 kD bands likely representing activated MET. The P85 SH2 domains bind both H ER and MET bands consistent with known roles of these kinases in control of PI3K/Akt signaling [Stommel JM, et al. Science (2007) 318: 287-90].

Example 2 - A set of SH2 probes demonstrates enhanced binding in cells harboring activating EGFR mutations and MET activation

Studies were then conducted to investigate if any probes are highly associated with cells harboring activating EGFR mutations. Such probes could in the future serve as the basis for simpler molecular diagnostic tests. Eight SH2 domains corresponding to 7 proteins were identified that were statistically significant in relation to EGFR mutation status in rosette binding experiments. These include Brk, Grap2, Grb2, Grb2 (SH23) (which contains Grb2 SH3 domains in addition to the SH2), ShcA, Cbl, CbIB and Txk (FIGS. 4-6; Tables 2 and 3). To examine the biological significance of these results, these domains were input into PPI Spider, a tool for interpreting proteomics data in the context of protein-protein interaction networks (http://mips.helmholtz-muenchen.de/proj/ppispider/). This analysis finds 3 proteins (ShcA, Grb2, and Brk) with reported direct interactions to EGFR while Grap2 is potentially linked to EGFR signaling through ShcA (FIG. 22). The fact that binding sites for Grb2 and ShcA, in addition to the close Grb2 relative Grap2, are closely associated with EGFR inhibition is significant, as increased binding of these SH2 domains is predicted to lead to the direct activation of the Ras signaling pathway [Gale NW, et al. Nature (1993) 363: 88-92; Rozakis-Adcock M, et al., Nature (1992) 360: 689-92]. Thus, the results from the global SH2 profiling capture known interactions with EGFR signaling providing biological validation and relevance to the results.

Table 2

EGFR Mutant-related SH2 Domains

SH 2 Domai n D val ue c I val ue

Grb2 0.001 0.023

ShcA(ptb) 0.001 0.023

Grap2 0.006 0.085

Brk 0.009 0.085

Txk 0.009 0.085

CbI B 0.011 0.085

CbIA 0.011 0.085

A similar analysis was performed using data obtained from far-western blotting of specific SH2 domain probes. Specific bands on far-western blots were found to correlate significantly with mutant EGFR (FIG. 7). It is interesting to consider bands whose binding tends to increase in EGFR mutant cells (dark shaded) versus those whose binding tends to decrease in EGFR mutants (light shaded). For all probes predicted a priori to be associated with stimulation of the Ras pathway (Grb2 and She), bands with increased binding are seen for EGFR mutant cells, while probes predicted to be associated with downregulation of tyrosine kinase signaling, including CbIB (a ubiquitin E3 ligase that ubiquitinates tyrosine phosphorylated substrates and targets them for lysosomal degradation), Csk (the kinase that inhibits Src family kinases), RAS-GAP (which inactivates Ras) and Shp2 (a tyrosine phosphatase that inactivates Stat3 signaling), bands with decreased binding are associated with EGFR mutation.

A similar analysis was performed examining SH2 domains correlated with MET phosphorylation. Fourty-nine SH2 domains were found associated with MET phosphorylation (FIG. 8). Table 3

Met Activation-related SH2 Domains

SH2 Domain D value a value SH2 Domain D value a value

Sue 0.000006 0 000060 Hck 0.003310 0.006073

Lck 0.000006 0 000060 Chi men n1 0.003694 0.006326 p85b(NC) 0.000006 0 000060 Csk 0.003713 0.006326

SH P-2(NC) 0.000006 0 000060 Fes 0.007002 0.011518

CrkL 0.000006 0 000060 Fer 0.008521 0.013549

Shi 2 0.000013 0 000085 Fy nB 0.009478 0.014583

Arg 0.000013 0 000085 SH2-B 0.010251 0.015281

Abl 0.000025 0 000149 Sh3bp2 0.012680 0.018329

Lnk 0.000044 0 000209 Plcg2(NC) 0.015943 0.022367

\&v1 0.000044 0 000209 Cten 0.018504 0.025218

SH P-2(N) 0.000151 0 00051 5 Grb7 0.019652 0.026038

Yes 0.000151 0 00051 5 atk/Lsk 0.026498 0.033262

Fy nA 0.000151 0 00051 5 ShcA(ptb) 0.026498 0.033262

Plcgl(NC) 0.000151 0 00051 5 ShcD 0.037048 0.045313

Sck/ShcB 0.000173 0 000526 Mist/Gink 0.042149 0.050263

ShcA 0.000198 0 000526 GST 0.047278 0.054050

Gap(SH23) 0.000199 0 000526 HSh2 0.047592 0.054050 p55g(NC) 0.000199 0 000526 CbIC 0.051 620 0.057262 p85a(NC) 0.000260 0 00061 9 Grb2(SH23) 0.059374 0.064004

Gk 0.000260 0 00061 9 Fg r 0.060381 0.064004

Eat 2 0.000726 0 001575 SH E 0.065128 0.067535

\fev2 0.000726 0 001575 Grap2 0.070420 0.071469

Ptk70/Srms 0.001176 0 002439 Slp76 0.094116 0.092497

Nap4 0.001 500 0 002982 Grb2 0.095018 0.092497

Brk 0.002896 0 005525 Rin 1 0.108630 0. 103632

SH2 domains that correlate with Ras mutation status were also examined. In contrast to EGFR mutation and MET phosphorylation, no SH2 domains were found correlating with Ras mutation status. This could be secondary to predominant serine and threonine phosphorylation downstream of Ras signaling or lack of statistical power.

Example 3 - A set of SH2 probes is correlated with sensitivity of lung cancer cells to EGFR TKI

The correlation of a set of SH2 domain binding with sensitivity of lung cancer cell lines to eriotinib was investigated. For each cell line, data from rosette assays and far-western blotting were examined in relationship to the IC₅₀ for eriotinib. These assays were performed with the actual cell lines used for profiling, at the same time and under the same culture conditions. This was performed as an exploratory analysis given that the sample size was low (22 cell lines), confounding effects of EGFR mutation (which is an independent predictor of sensitivity), and cells were included that possess EGFR activation yet are nonetheless resistant because of other alterations (gatekeeper mutation (H1975), PTEN loss (H 1650), and MET amplification (H820)).

Fourteen probes were identified corresponding to 12 proteins correlated with eriotinib IC₅₀ (Tables 4 and 5). These include Grb2, Grb2 (SH23), ShcA, ShcA (ptb), p85B (NC), Cis1 , Arg, Eat2. Plcgi (NC), Ptk70/Srms, Fes, Lnk, Tem6, and Btk. Network analysis confirms reports of direct interactions between EGFR and Grb2, ShcA, p85B. Cis1 , Arg, Plcgi , Fes, and Btk (FIG. 1 1 ). The results are consistent with a model where eriotinib sensitivity is associated with particularly strong signaling from activated EGFR to two core downstream effector pathways: MAPK (Grb2 and ShcA) and PI3K/Akt (p85B). Enhanced Grb2 and ShcA SH2 binding was observed both in context of EGFR mutation and eriotinib sensitivity. Finally, use of far western with the SH2 probes also identifies patterns that correlated with eriotinib sensitivity (FIG. 12). While in principle the analysis could be confounded by cells with EGFR mutation, which independently predicts sensitivity, a number of cells that are resistant to eriotinib despite having activating EGFR mutations (H1975, H820, H2279, and H1650) were included in the analysis, as were cell lines with wild-type EGFR that are sensitive to eriotinib (H292, H358, H 1648, H2122, and H322). Thus, it does not appear that EGFR mutation is solely driving this signature.

Table 5

Tern 6 0, 50

Btk 0.53 Example 4 - SH2 domain binding characterizes global tyrosine phosphorylation state perturbed by tyrosine kinase inhibitors

SH2 domain profiling was examined to investigate changes in global tyrosine phosphorylation in cells exposed to tyrosine kinase inhibitors. Four lung cancer cell lines (H292, H441 , H358 and HCC827) were briefly exposed to erlotinib, an inhibitor of EGFR, and dasatinib, a SRC inhibitor that has broad effects on multiple tyrosine and serine/threonine kinases [Karaman MW, et al. Nature biotechnology (2008) 26: 127-32; Hantschel O, et al. Proc Natl Acad Sci USA (2007) 104: 13283-8; Rix U, et al. Blood (2007) 1 10: 4055-63]. In HCC827 cells with activating EGFR mutation, both inhibitors induce apoptosis; in H358 and H292 cells with wild- type EGFR, both agents induce cell cycle arrest; H441 cells are resistant to both agents with neither apoptosis nor growth arrest observed [Song L, et al., Cancer Res (2006) 66: 5542-8; and data not shown]. SH2 domain binding was again examined using the rosette assay and far-western blotting for select probes. Using rosette assays, changes of all domains were quantified and the data plotted as waterfall plots ranking changes in SH2 binding compared to control. In the HCC827 cells, erlotinib results in complete collapse of phosphotyrosine signaling as large decreases in SH2 binding amongst almost all probes is observed. The most significant reductions in binding occurred with Grb2, Grap2, Vav2, and Vav1 SH2 domains consistent with strong loss of binding to activated EGFR with erlotinib therapy. Similar changes were observed with dasatinib (R=0.70) suggesting a generalized overlap of mechanism, yet fold changes were less in Grb2, Grap2, and Vav probes consistent with less potent effects on EGFR phosphorylation [Song L, et al., Cancer Res (2006) 66: 5542-8]. H358 cells demonstrated a marked reduction in almost all SH2 domain binding probes, similar to that observed with HCC827 cells, and more significant overlap existed between erlotinib and dasatinib (R=0.94). These results indicate that phenotypic effects of both agents occur through near identical signaling networks. In H441 cells reductions in SH2 binding intensity were similar between erlotinib and dasatinib (R=0.86), however a group of SH2 domains that demonstrate increased binding in the presence of both inhibitors compared to control cells was also identified. This could suggest activation of a tyrosine kinase or shift of SH2 domain containing proteins to other phosphotyrosine-containing proteins that could account for resistance to these inhibitors. Finally, the H292 cell had the least dramatic fold changes in SH2 domain binding intensity and greater differences between erlotinib and dasatinib profiles were observed compared to the other cell lines tested (R=0.55). This could suggest that the targets of the drugs are less overlapping in H292 cells, and despite similar phenotypic effects on cell proliferation, the mechanism of action of the inhibitors is different. Thus, SH2 domains can detect a core phosphoproteome changing in response to both tyrosine kinase inhibitors, as well as distinct changes unique to each individual inhibitor. In addition to examining cells in response to EGFR TKI, studies were performed stimulating the EGFR with exogenous ligand (EGF). The data suggest differential binding patterns (data not shown). The experiments can be improved through an examination of kinetics would be instructive since EGFR activation and deactivation can occur rapidly and are dependent on cell context such as EGFR mutation status.

Example 5 - SH2 profiles can identify lung cancer cells dependent on both EGFR and MET for downstream signaling and growth

One particular strength of SH2 profiling is the identification of cells with hyperactivated tyrosine kinases that act in concert to drive downstream signaling. We noted that the H1648 cell line clustered tightly with the H820 cell line that has been previously characterized and found to have an activating EGFR mutation along with MET amplification [Bean J, et al., Proc Natl Acad Sci USA (2007) 104: 20932-7.] A closer examination of the far-western results of both H820 and H1648 cells probed with p85 SH2 domains demonstrates strong binding near ~190 kDa corresponding to HER family members and ~150 corresponding to activated MET (FIG 5A). These results suggest that H 1648 cells may be similar to H820 cells in having downstream signaling driven by dual EGFR and MET signaling. To test this, we exposed H1648 cells to inhibitors of EGFR (erlotinib), MET (PHA665752) or the combination and examined downstream Akt and ERK phosphorylation (FIG 5B). We observed modest reductions in phosphorylated Akt in response to either inhibitor alone but marked inhibition of phosphorylated Akt with dual EGFR and MET inhibition. The effects on cell growth mirrored the signaling responses as combination of both agents resulted in enhanced inhibition of cell growth (FIG 5C). Further examination of the p85 far-western blots identified a number of other cell lines with a strong band at -150 kDa corresponding to activated MET. These are HCC827, H4006, H358, and H441. Remarkably, in both rosette and far-western SH2 profiling, all these cell lines with presumptive MET activation cluster in a distinct, tight group (the lone exception is H358, which is an outlier in the rosette assay clustering). Thus global pTyr patterns, assayed by SH2 profiling, predict MET activation in these cell lines.

In addition, we examined p85A SH2 domain binding in H1703 lung cancer cells that were previously reported to be dependent on PDGFR for growth and survival [Rikova K, et al., Cell (2007) 131 : 1 190-203]. Imatinib, a PDGFR inhibitor, inhibited cell growth in concert with a reduction in phosphorylated Akt. This indicates that PDGFR is driving the Akt pathway, likely by regulating PI3K activity. H 1703 cells were exposed to imatinib for 1 hr and lysates used to perform far-western blotting with purified p85A SH2 domains. Blots were also probed with PDGFR antibody to align the bands. As shown in FIG. 5D, untreated H1703 cells have a single p85A SH2 binding band at the exact mobility of PDGFR and this binding is abolished by treatment with imatinib. These results highlight the ability of SH2 domain profiling to identify cooperation of receptor tyrosine kinases in control of distal signaling and cellular growth.

MATERIALS AND METHODS

Cell lines and Reagents

Human lung cancer cell lines H292, H358, H441 , A549, H460, H1703 and H1299 were obtained from ATCC (Manassas, VA). HCC827 cells were provided by Dr. Jon Kurie (MD Anderson Cancer Center, Houston, TX), H 1648, H2122, H226 and H157 cells were provided by Dr. John Minna (UT Southwestern Medical Center, Dallas, TX), H322 were provided by Dr. Paul Bunn (University of Colorado, Boulder, CO), H23 cells were provided by Dr. Gerald Bepler (Moffitt Cancer Center. Tampa, FL), and UKY cells were provided by Dr. Penni Black (University of Kentucky). All cell lines were maintained in RPMI-1640 medium supplemented with 10% newborn calf serum (NCS) from Sigma (St. Louis, MO) except H292 and H441 cells, which were grown in RPMI-1640 medium containing 1 .5 g/L sodium bicarbonate, 4.5 g/L glucose, 10 mM HEPES, 1 .0 mM sodium pyruvate and 10% NCS. Erlotinib was provided by OSI Pharmaceuticals (Melville, NY), dasatinib by Bristol Myers Oncology (Princeton, NJ), imatinib was provided by Novartis (Cambridge, MA), and PHA665752 by Pfizer (San Diego, CA) [Christensen JG, et al., Cancer research (2003) 63: 7345-55]. Stock solutions in 100% DMSO were diluted directly in the media to indicated concentrations.

SH2 Domain Profiling

The high throughput SH2/PTB domain binding assay was performed as described [Machida K, et al, Mol Cell (2007) 26: 899-915; Dierck K, et al. In: Methods in molecular biology (Clifton, NJ 2009) 527: 131 -55, ix]. Briefly, lung cancer cell lysates were spotted in duplicate on a nitrocellulose membrane in register with the wells of a 96-well chamber plate. The membrane was dried, blocked, and incubated with purified GST-SH2 or PTB domains at concentrations of -100 nM for 2 hours. Probe binding was detected by chemiluminescence (PerkinElmer) and digitally captured with Kodak Image Station (Kodak). The binding assay was performed four times, including at least two separate experiments, and the average signal intensity for each spot was manually quantified using densitometry (ImageJ v1.40). Far-Western analysis for lung cancer cell lysates was performed as described [Machida K, and Mayer BJ. In: Methods in molecular biology (Clifton, NJ 2009; 536: 313-29; Nollau P, and Mayer BJ. Proc Natl Acad Sci USA (2001 ) 98: 13531 -6]. Briefly, proteins were separated by SDS-PAGE and transferred to nitrocellulose membranes. The membrane replicas were incubated with various labeled GST-SH2 domains for 2 hours, and bands were detected by chemiluminescence and captured with Kodak Image Station. Blots were stripped and reprobed several times with additional SH2 domains and anti-phosphotyrosine antibody (PY100, Cell Signaling). To quantify bands on multiple blots derived from different gels, SH2 blot images were aligned in reference to corresponding anti-phosphotyrosine blots with Adobe Photoshop CS3 software (Adobe). The aligned blot images from two independent experiments were quantified using a custom-made plug-in written for ImageJ densitometry (H. Zhang, J. Maddox, and D.G. Shin, University of Connecticut).

Protein Expression Analysis

Cells were washed with ice-cold PBS and extracted with chilled lysis buffer (10 mM Tris, pH 8.0, 60 mM KCI, 1 mM EDTA, 1 mM DTT, 0.5% N P-40, 10 mM Na₃V0₄, 50 mM NaF, 1 mM PMSF, ^g/ml aprotinin, 1 μg /ml leupeptin, ^g /ml pepstatin). Total cellular proteins were separated on SDS-polyacrylamide gel electrophoresis (PAGE) and electroblotted onto nitrocellulose membranes. Primary antibodies used in these studies consisted of rabbit polyclonal antibody specific for pTyr 1344/45 MET, pThr202/Tyr204-p44/42 ERK , pSer473- AKT, and PDGFR from Cell Signaling (Beverly, MA) and β-actin antibody from Sigma (St. Louis, MO). Standard detection of proteins was accomplished using horseradish-peroxidase conjugated secondary antibodies and enhanced chemiluminescence (ECL) purchased through Amersham (Piscataway, NJ).

Cell Viability, Proliferation, and Apoptosis Assays

Cell viability assays (MTT) were performed according to the manufacturers recommendations of Cell Proliferation Kit from Roche (Indianapolis, IN). Cells were plated at 2-5 x10³ cells per well in 96-well plates, incubated overnight, exposed to a serial dilution of dasatinb or erlotinib) in 5% NCS complete media, and viability assessed following 5 days of incubation. The IC₅₀ was calculated by non-linear regression analyses using MATLAB scripts that pair data points with sigmoidal curves that predict a signal response based on a four-parameter fit. Data presented represents three separate experiments with 8 data points separating each dose per condition. Data are expressed as mean ± SD.

EGFR and K-Ras Genotyping

Genomic DNA extraction from each NSCLC cultured cell lines was performed using DNeasy Kit (Qiagen). Sequencing of exon 19, 20, and 21 of EGFR was performed as previously described. For K-RAS, the primers were K-Ras exon 1 (forward), 5' TTAACCTTATGTGTGACATGTTCTAA-3' and (reverse) 5'-AGAATGGTCCTGCACCAGTAA- 3', which generates a fragment of 225 bp, and K-Ras exon 2 (forward), 5'- TCAAGTCCTTTGCCCATTTT-3' and (reverse) 5'-TGCATGGCATTAGCAAAGAC-3', which generates a fragment of 374 bp. PCR amplification for K-Ras exon 1 consisted of 40 cycles (95°C for 45 s, 55°C for 45 s and 72°C for 45 s) and for K-Ras exon 2 consisted of 40 cycles (95°C for 45 s, 52°C for 45 s and 72°C for 45 s), and then followed by incubation at 72°C for 5 min. PCR products were separated on a 1 % agarose gel and purified by DNA Gel Extraction Kit (Millipore). DNA sequence was analyzed using the Applied Biosystems 3130X1 Genetic analyzer (HITACHI) and data analysis was done using Lasergene software V7.2.

Network Analysis

To examine the biological significance and provide improved visualization of these results, we input SH2 domains into PPI Spider, a tool for interpreting proteomics data in the context of protein-protein interaction networks (http://mips.helmholtz-muenchen.de/proj/ppispider/). Analysis were run with 100 random networks and only proteins that directly connect to each other through no more than one edge were allowed for visualization. Networks were subsequently input into Cytoscape for visualization.

Statistical Analysis

Rosette Assay: The quantified intensity for each assay was assessed by examining for batch effects using ANOVA and calculating coefficient of variation (CV) for each probe across replicates. Individual probes were characterized using a positive and negative control and probes with low signal and small differences between controls were excluded from clustering analysis.

Two-group Comparisons Domains were identified as statistically significant with respect to dichotomous characteristics (EGFR Mutation, RAS Mutation or Met Activation) using a Mann- Whitney test applied to each domain. The false discovery rates (q values) were calculated using the Q Value package in Bioconductor (Storey et al) and a 10% FDR was considered significant.

Correlation to Erlotinib sensitivity: Erlotinib IC50 values for the 22 cell lines were log transformed and the Pearson's correlation coefficient was computed for each domain. Domains were considered correlated with | R|≥0.5 (R²≥ 0.25).

Far Western Clustering. Data from individual grids was combined across the two replicates using the mean. Domain-specific bands were filtered with a standard deviation > 5, and at least 1 1 of 22 cell lines having intensity above 10.0 for a specific domain-specific band. There were 188 domain-specific bands remaining after the filtering step. The data was clustered using Cluster 3.0 and visualized using Java Treeview. For visualization purposes, the intensities were scaled so that light shaded represents below 10.0 (approximately the mean intensity of the data) and dark shaded above 10.0.

Far Western differences with tyrosine kinase inhibitors. Data from individual grids was quantified and domain-specific bands were filtered with a standard deviation > 5, and at least 3 of 8 cell lines/conditions having intensity above 10.0 for a specific domain-specific band. There were 197 domain-specific bands remaining after the filtering step. All references cited in the present application are incorporated in their entirety herein by reference to the extent not inconsistent herewith.

It will be seen that the advantages set forth above, and those made apparent from the foregoing description, are efficiently attained and since certain changes may be made in the above construction without departing from the scope of the invention, it is intended that all matters contained in the foregoing description or shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense.

It is also to be understood that the following claims are intended to cover all of the generic and specific features of the invention herein described, and all statements of the scope of the invention which, as a matter of language, might be said to fall therebetween. Now that the invention has been described,

Claims

What is claimed is:

1. A method of performing targeted cancer therapy in a patient comprising the steps of:

isolating a tumor sample from the patient;

assaying SH2 binding patterns in the isolated tumor sample;

comparing the observed patterns from the isolated sample with patterns of tumors with predetermined sensitivity to one or more tyrosine kinase (TK) inhibitors to determine a predicted sensitivity of the cancer to be treated to the one or more TK inhibitors; and administering one or more TK inhibitors to the patient responsive to the predicted sensitivity of the tumor.

2. The method according to claim 1 wherein the assay is selected from the group

consisting of rosette SH2 profiling and far-western SH2 profiling.

3. The method according to claim 1 wherein the binding patterns are assayed for a plurality of SH2 domain containing proteins.

4. The method according to claim 1 wherein the patient has a disease characterized by aberrant tyrosine kinase activity.

5. The method according to claim 1 wherein the cancer is a cancer selected from the group consisting of non-small cell lung cancer, small-cell lung cancer, chronic myeloid leukemia, acute lymphoblastic leukemia, Philadelphia chromosome positive acute lymphoblastic leukemia (Ph+ ALL), squamous cell carcinoma, glioma, gastrointestinal cancer, renal cancer, ovarian cancer, liver cancer, colorectal cancer, endometrial cancer, kidney cancer, prostate cancer, thyroid cancer, neuroblastoma, pancreatic cancer, glioblastoma multiforme, cervical cancer, stomach cancer, bladder cancer, hepatoma, breast cancer, colon carcinoma, and head and neck cancer, gastric cancer, germ cell tumor, pediatric sarcoma, sinonasal natural killer, multiple myeloma, acute myelogenous leukemia (AML), and chronic lymphocytic leukemia

(CML).

6. The method according to claim 1 wherein the cancer is lung cancer.

7. The method according to claim 1 where the tyrosine kinase inhibitor is selected from the group consisting of axitinib, bosutinib, cediranib, dasatanib, erlotinib, gefitinib. imatinib, lapatinib, lestaurtinib, nilotinib, semaxanib, sorafenib, sunitinib, toceranib, vandetanib, and vatalanib.

8. The method according to claim 1 wherein a plurality of tyrosine kinase inhibitors is administered to the patient responsive to the compared sensitivity of the tumor.

9. The method according to claim 8 wherein one of the plurality of tyrosine kinase

inhibitors is selected from the group consisting of dasatinib and erlotinib.

10. The method of claim 1 wherein the tyrosine kinase inhibitor is an inhibitor of a

molecule selected from the group consisting of epidermal growth factor receptor

(EGFR), MET, insulin-like growth factor receptors (IGFR), SRC kinases, fibroblast growth factor receptors (FG FR), platelet-derived growth factor receptors (PDGFR), anaplastic lymphoma kinase (ALK), and EPH receptors.

1 1. A method of characterizing or classifying tumor responsiveness to one or more TK inhibitors comprising the steps of:

providing a tumor sample having tumor cells;

evaluating changes in SH2 binding patterns between untreated tumor cells and tumor cells treated with one or more TK inhibitors.

12. The method according to claim 1 1 wherein the evaluation is performed using an assay selected from the group consisting of rosette SH2 profiling and far-western SH2 profiling.

13. The method according to claim 1 1 where the tyrosine kinase inhibitor is selected from the group consisting of axitinib, bosutinib, cediranib, dasatanib, erlotinib, gefitinib. imatinib, lapatinib, lestaurtinib, nilotinib, semaxanib, sorafenib, sunitinib, toceranib, vandetanib, and vatalanib.

14. The method according to claim 1 1 wherein the tumor is a cancer selected from the group consisting of non-small cell lung cancer, small-cell lung cancer, chronic myeloid leukemia, acute lymphoblastic leukemia, Philadelphia chromosome positive acute lymphoblastic leukemia (Ph+ ALL), squamous cell carcinoma, glioma, gastrointestinal cancer, renal cancer, ovarian cancer, liver cancer, colorectal cancer, endometrial cancer, kidney cancer, prostate cancer, thyroid cancer, neuroblastoma, pancreatic cancer, glioblastoma multiforme, cervical cancer, stomach cancer, bladder cancer, hepatoma, breast cancer, colon carcinoma, and head and neck cancer, gastric cancer, germ cell tumor, pediatric sarcoma, sinonasal natural killer, multiple myeloma, acute myelogenous leukemia (AML), and chronic lymphocytic leukemia (CML).

15. The method according to claim 1 1 wherein the tumor is non-small cell lung cancer.

16. The method according to claim 10 wherein the sample is a sample isolated from a human undergoing treatment or screening.

17. The method according to claim 1 1 wherein the SH2 profile is correlated with a

prediction or prognosis.

18. The method according to claim 1 1 further comprising the steps of grouping cells according to SH2 binding patterns and comparing the cells with EGFR mutation status or MET activation status.

19. A method of characterizing or classifying tumor responsiveness to one or more antiproliferative agents comprising the steps of:

providing a tumor sample having tumor cells;

evaluating changes in SH2 binding patterns between untreated tumor cells and tumor cells treated with one or more anti-proliferative agents.

20. The method according to claim 19 wherein the anti-proliferative agent is a TK

inhibitor.

21. The method according to claim 19 wherein the evaluation is performed using an assay selected from the group consisting of rosette SH2 profiling and far-western SH2 profiling.

22. The method according to claim 1 1 wherein the tumor is a cancer selected from the group consisting of non-small cell lung cancer, small-cell lung cancer, chronic myeloid leukemia, acute lymphoblastic leukemia, Philadelphia chromosome positive acute lymphoblastic leukemia (Ph+ ALL), squamous cell carcinoma, glioma, gastrointestinal cancer, renal cancer, ovarian cancer, liver cancer, colorectal cancer, endometrial cancer, kidney cancer, prostate cancer, thyroid cancer, neuroblastoma, pancreatic cancer, glioblastoma multiforme, cervical cancer, stomach cancer, bladder cancer, hepatoma, breast cancer, colon carcinoma, and head and neck cancer, gastric cancer, germ cell tumor, pediatric sarcoma, sinonasal natural killer, multiple myeloma, acute myelogenous leukemia (AML), and chronic lymphocytic leukemia (CML).

23. A method of performing targeted cancer therapy in a non-small cell lung cancer patient comprising the steps of:

isolating a non-small cell lung cancer tumor sample from the patient; assaying SH2 binding patterns in the isolated tumor sample; comparing the observed patterns from the isolated sample with patterns of tumors with predetermined sensitivity to one or more tyrosine kinase (TK) inhibitors to determine a predicted sensitivity of the cancer to be treated to the one or more TK inhibitors; and administering one or more TK inhibitors to the patient responsive to the predicted sensitivity of the tumor.

24. The method according to claim 23 where the tyrosine kinase inhibitor is selected from the group consisting of axitinib, bosutinib, cediranib, dasatanib, erlotinib, gefitinib. imatinib, lapatinib, lestaurtinib, nilotinib, semaxanib, sorafenib, sunitinib, toceranib, vandetanib, and vatalanib.

25. The method according to claim 23 wherein a plurality of tyrosine kinase inhibitors is administered to the patient responsive to the correlated sensitivity of the tumor.

26. The method according to claim 25 wherein one of the plurality of tyrosine kinase inhibitors is selected from the group consisting of dasatinib and erlotinib.