WO2011039734A2 - Use of genes involved in anchorage independence for the optimization of diagnosis and treatment of human cancer - Google Patents

Use of genes involved in anchorage independence for the optimization of diagnosis and treatment of human cancer Download PDF

Info

Publication number
WO2011039734A2
WO2011039734A2 PCT/IB2010/054470 IB2010054470W WO2011039734A2 WO 2011039734 A2 WO2011039734 A2 WO 2011039734A2 IB 2010054470 W IB2010054470 W IB 2010054470W WO 2011039734 A2 WO2011039734 A2 WO 2011039734A2
Authority
WO
WIPO (PCT)
Prior art keywords
gab2
cancer
signature
ilmn
expression
Prior art date
Application number
PCT/IB2010/054470
Other languages
French (fr)
Other versions
WO2011039734A3 (en
Inventor
Enzo Medico
Claudio Isella
Alessia Mira
Original Assignee
Enzo Medico
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Enzo Medico filed Critical Enzo Medico
Publication of WO2011039734A2 publication Critical patent/WO2011039734A2/en
Publication of WO2011039734A3 publication Critical patent/WO2011039734A3/en

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57415—Specifically defined cancers of breast
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/5743—Specifically defined cancers of skin, e.g. melanoma
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis

Definitions

  • the present invention is related to identification of GAB2-driven processes and anchorage independence associated with diagnosis, prognosis, metastasis, metastatic relapse, metastatic potential and prediction of response to treatment of cancers.
  • a GAB2-signature based on anchorage independence is identified which can serve to define processes relevant to progression and response to treatment of human cancers.
  • GFs growth factors
  • ECM extracellular matrix
  • MCF10A cells a spontaneously immortalized human breast line (Soule et al. 1990, Cancer Res, 50, 6075-6086) that relies on both GFs and anchorage to proliferate. When these cells are cultured in the absence of anchorage, for instance on polyhema-coated plates, they undergo growth arrest and detachment- induced apoptosis, also known as anoikis (Reginato et al. 2003).
  • MCF10A cells represent an ideal model to screen for genes conferring anchorage-independence.
  • Xenoarray analysis based on transduction of mammalian cells of a given species with an expression library from another species, followed by one-shot quantitative tracing with DNA microarrays of library-derived transcripts before and after a selective stress, to disclose genes conferring resistance to the selection (Martelli et al. 2008, BMC Genomics, 9, 254).
  • MCF10A cells were selected for growth in suspension and murine microarrays were used to compare signal intensities for the exogenous cDNAs before and after selection, to detect the enriched ones.
  • Independent infection-selection experiments highlighted significant and reproducible enrichment for murine Gab2-encoding transcripts, suggesting a role of this gene in anchorage-independent growth.
  • Gab2 promotes anchorage-independent growth of normal and neoplastic cells, and drives a transcriptional program linked to metastatic progression of breast cancer.
  • the invention provides identification of GAB2-driven processes and anchorage independence associated with diagnosis, prognosis, metastasis, metastatic relapse, metastatic potential and prediction of response to treatment of cancers. Furthermore, a GAB2-signature based on anchorage independence is identified which can serve to define processes relevant to progression and response to treatment of human cancers.
  • the invention provides a method for diagnosing or prognosing cancer in subjects comprising detecting expression of GAB2 and/or of its transcriptional target genes in the tumor tissue and/or in tumor cells isolated from the subject.
  • the method provides GAB 2- signature genes of the invention useful for diagnosis or prognosis of any human cancer, especially breast cancer and myeloma, comprising detecting in the tumor tissue and/or in tumor cells isolated from the subject expression of at least two of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5.
  • the two genes can be selected from a single independent list (single table) or across the tables (more than one table).
  • the invention provides a method for predicting metastasis or metastatic relapse or metastatic potential or response to treatment in cancer patients comprising detecting the expression of GAB2 and or its transcriptional target genes in the tumor tissue and/or in tumor cells isolated from the subject.
  • the method provides GAB2- signature genes of the invention useful for diagnosis or prognosis of any human cancer, especially breast cancer and myeloma, comprising detecting in the tumor tissue and/or in tumor cells isolated from the subject expression of at least two of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5.
  • the two genes can be selected from a single independent list (single table) or across the tables (more than one table).
  • the cancer treatment as provided herein encompasses all know cancer treatment including targeted drug therapy, chemotherapy, radiation therapy or a combination thereof.
  • the invention provides a method of treating a subject with cancer comprising the steps of:
  • the invention also provides a method of treating a subject suffering from cancer comprising the steps of:
  • the invention provides a method of using in vitro anchorage independence model for deriving gene signature, the said signature comprising a set of genes associated with diagnosis, prognosis, metastasis and predicting response to treatment in cancer.
  • the gene signature of the said method is GAB2-signature comprising at least two GAB2 and or its transcriptional target genes listed in Tables 1 , 2, 3, 4 or 5.
  • the two genes can be selected from a single independent list (single table) or across the tables (more than one table).
  • a method of predicting the grade of a tumor in a cancer patient comprising detecting the expression of GAB2 and/or its transcriptional target genes in the tumor tissue and/or in tumor cells isolated from the subject.
  • This method encompasses detecting the expression of at least two of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5.
  • the two genes can be selected from a single independent list (single table) or across the tables (more than one table).
  • the invention also provides a GAB2-signature for diagnosing or prognosing human cancer, especially breast cancer or myeloma, in subjects comprising GAB2 and/or its transcriptional target genes in the tumor tissue and/or in tumor cells isolated from the subject as diagnostic or prognostic markers.
  • the diagnosis or prognosis comprises detecting in the tumor tissue and/or in tumor cells isolated from the subject expression of at least two of GAB 2- signature genes listed in Tables 1, 2, 3, 4 or 5.
  • the two genes can be selected from a single independent list (single table) or across the tables (more than one table).
  • the invention provides a GAB 2- signature for predicting
  • cancer patients including breast cancer and myeloma patients, comprising GAB2 and or its transcriptional target genes.
  • the prediction of metastasis, metastatic relapse, metastatic potential or response to treatment is detected in tumor tissue and/or in tumor cells isolated from the patient expression of at least two of GAB2-signature genes listed in Tables 1, 2, 3, 4 or 5.
  • the two genes can be selected from a single independent list (single table) or across the tables (more than one table).
  • the cancer treatment as provided herein encompasses all know cancer treatment including targeted drug therapy, chemotherapy, radiation therapy or a combination thereof.
  • the invention provides an array comprising polynucleotides capable of specifically hybridizing to at least two genes listed in Table 1, 2, 3, 4 or 5.
  • the invention also encompasses kit comprising the array for diagnosing or prognosing cancer or predicting metastasis or metastatic relapse or metastatic potential of cancer cells in a subject by determining the expression of at least 2 genes listed in Table 1, 2, 3, 4 or 5. Furthermore, a kit for diagnosing or prognosing cancer cells or predicting metastasis or metastatic relapse or metastatic potential of cancer cells in a biological sample comprising a primer pair for amplifying a nucleic acid sequence selected from a group consisting of GAB 2- signature genes listed in Table 1, 2, 3, 4 and 5 and containers for the primers is also provided.
  • kits for diagnosing or prognosing cancer cells or predicting metastasis or metastatic relapse or metastatic potential of cancer cells in a biological sample comprising an oligonucleotide probe that binds under high stringency conditions to an isolated nucleic acid sequence selected from a group consisting of GAB2-signature genes listed in Table 1, 2, 3, 4 and 5 and a container for the probe is also provided by the invention.
  • the invention provides a kit for diagnosing or prognosing cancer cells or predicting metastasis or metastatic relapse or metastatic potential of cancer cells in a biological sample comprising an antibody which binds immunologically to a protein having an amino acid sequence encoded by a polynucleotide selected from a group consisting of GAB2-signature genes listed in Table 1, 2, 3, 4 and 5 and a container for the probe.
  • Figure 1 Xenoarray analysis on MCF10A cells and acquisition of anchorage independence by library-transduced selected cells.
  • A MTT growth assay on polyhema-selected populations after 48h and 72h in adhesion or suspension, as indicated. Cell growth is expressed as a ratio between library-transduced and GFP-transduced cells, after normalization to the amount of viable plated cells at day 0. The data represent the mean and standard error of triplicate values (Adhesion 48h p ⁇ 0.05, Suspension 48h p ⁇ 0.01, Suspension 72h p ⁇ 0.05).
  • B Soft agar assay on GFP- and library-transduced cells, unselected or selected on polyhema, as indicated. Phase- contrast images were captured by a BD Pathway microscopic station (BD biosciences) after 3 weeks in agar.
  • C Dot plot of single colony sizes as calculated by the Attovision software (BD Biosciences, version 1.5) for the GFP-SEL and LIB-SEL populations grown in soft agar.
  • A Real-time PCR validation of enriched transcripts in both selections.
  • the y-axis represents the relative increase in abundance of the transcripts in selected cells compared to unselected cells.
  • B Western blot analysis on GFP- and library-transduced cells before and after selection to detect Gab2 protein enrichment.
  • A Mock and GAB2-overexpressing (GAB2) MCF10A cells were incubated in adhesion (ADH) or suspension (SUSP) in the presence or absence of MEK inhibitor (PD98059, 40 ⁇ ), PBK inhibitor (LY294002, 50 ⁇ ), Src inhibitor (PP2, ⁇ ), or JNK inhibitor (SP600125, ⁇ ).
  • MEK inhibitor PD98059, 40 ⁇
  • PBK inhibitor LY294002, 50 ⁇
  • Src inhibitor PP2, ⁇
  • JNK inhibitor SP600125, ⁇
  • Cell vitality was assessed with the MTT assay after 24h from the treatment and the drug effect was expressed as percent growth inhibition (with respect to untreated cells). The data represent the mean and standard error of triplicate values from two independent experiments.
  • B Boxplots of detailed analysis of the effects of Src inhibition by PP2 on cell growth in various conditions.
  • C Western blot analysis on Mock and Gab2-expressing cells in adhesion or after 24h and 48h in suspension. Antibodies directed against the activated form of Src (phosphorylated at tyrosine 416) and Stat3 (phosphorylated at tyrosine 705), or total Src or Stat3 were used.
  • FIG. 5 Knock-down of endogenous Gab2 impairs MCF10A growth and anchorage-independent growth of human neoplastic cells.
  • C Soft agar growth of cells expressing Gab2 shRNA or scramble vector (CTRL).
  • Phase-contrast images were captured by a BD Pathway microscopic station (BD biosciences) after 3 weeks in agar.
  • D Western blot analysis of Src and Stat3 activation in control and GAB2 shRNA- transduced cells, as indicated.
  • A Heatmap showing the expression of the two main gene functional modules in the NKI311 breast cancer dataset.
  • the samples (columns) are ordered by decreasing GAB 2- signature metastasis score (GAB2 MTS Score), which is graphically reported in the second row.
  • GAB 2- signature metastasis score GAB 2- signature metastasis score
  • the white vertical line crossing the heatmap indicates the 0 threshold value of metastasis score discriminating good and poor prognosis samples.
  • White and black dots on the right highlight the genes annotated to the two functional modules, respectively downregulated and upregulated in poor prognosis samples.
  • the GAB2-signature is independent from existing clinical and genomic breast cancer classifiers, and from estrogen receptor status.
  • GP good prognosis
  • PP prognosis
  • G-H Kaplan-Meier analysis on the 198-samples dataset subdivided in ER-negative (G) and ER-positive (H) samples, then further subdivided by the GAB2-signature in good prognosis (GP) or poor prognosis (PP) samples.
  • the GAB2-signature predicts prognosis in Estrogen Receptor-negative breast cancer.
  • GP prognosis
  • PP prognosis
  • Figure 9 The GAB2-signature predicts response of breast cancer to antineoplastic treatment.
  • A Receptor-Operated Channel (ROC) analysis of the performance of the GAB2- signature Metastasis Score as a predictor of response to neoadjuvant treatment in the Hess dataset. AUC indicates the area under the Curve.
  • B Dot plot analysis of the the GAB2- signature Metastasis score (x-axis) for the samples from patients showing pathological complete response (pCR) or residual disease (RD), as indicated.
  • Microarrays and realtime PCR generate highly correlated diagnostic scores. Dot plot showing the correlation between Metastasis Score calculated for 32 breast cancer samples from microarray data (x-axis) and from realtime PCR data (y-axis), using 15 genes of the GAB2 signature.
  • FIG. 11 The GAB2-signature is correlated to melanoma progression.
  • Heatmap showing Log2Ratio expression values for 83 Affymetrix probesets (rows) across tissue samples of different stages of melanoma progression (columns). The first row indicates the type of sample (from Normal Skin, black, to Metastatic Melanoma, white, as indicated). Samples have been subdivided, based on expression of the GAB2 genes, in four clusters of progressively increasing aggressiveness, from normal skin and benign nevi to metastatic melanoma.
  • GAB2 is a key promoter of anchorage independence of human neoplastic cells a. Gain-of-function screening for anchorage independence in MCFIOA cells
  • MCFIOA cells were transduced with a commercial mouse testis retroviral expression library (Stratagene) or with GFP as a control.
  • infections were performed in duplicate (A and B), using an estimated multiplicity of infection of 1 , to avoid multiple integrations in the same cell.
  • To detect and quantify library-derived transcripts we performed Xenoarray analysis (Martelli et al. 2008, BMC Genomics, 9, 254), by extracting total RNA from the four cell populations and hybridizing the resulting cRNAs on murine expression arrays, to allow specific detection of library-derived transcripts of murine origin.
  • Gab2 is a scaffolding/docking protein involved in multiple signaling pathways downstream from membrane receptors (Nishida et al. 1999, Blood, 93, 1809-1816).
  • MCFIOA cells To directly assess whether Gab2 may promote anchorage-independent growth, we transduced MCFIOA cells with the human Gab2 coding sequence, cloned in a retroviral vector (Brummer et al. 2006).
  • adherent GAB2-overexpressing cells showed a significant increase in proliferation, which was further enhanced in the absence of anchorage.
  • Gab2-driven growth advantage was almost totally lost when cells were kept in starving medium (no EGF, and serum lowered to 2%), indicating that Gab2 promotes proliferation independently from cell anchorage to the ECM, but dependently from the presence of GFs and/or serum. Accordingly, Gab2 overexpressing cells formed larger and more abundant colonies in soft agar, compared to wild-type cells ( Figure 3B).
  • To evaluate whether Gab2 promotes survival of detached cells we estimated the fraction of dead cells after 48h of suspension culture. Surprisingly, after 48h of polyhema plating, we detected a comparable extent of cell death between wild-type and Gab2-expressing cells (Figure 3C).
  • Gab2-driven anchorage independence requires Src, which typically is activated by integrins when cells are adherent and becomes inactivated upon detachment (Playford and Schaller 2004, Oncogene, 23, 7928- 7946). Consistently, western blot analysis on cell lysates from control and Gab2- expressing cells cultured in adhesion or suspension confirmed Gab2-driven activation of Src and of one of its downstream targets, Stat3 ( Figure 4C). In adhesion, Gab2-expressing cells displayed a stronger basal phosphorylation of Src. Active Src levels were reduced in cells kept in suspension, but while in control cells Src activation was completely abolished at 48h, Gab2expressing cells maintained some phosphorylation.
  • Metastatic potential as meant in the current specification relates to the ability of a cancer cell to invade and to spread of cancer cells to other parts of the body.
  • the same method that was used to derive the signatures in MCFIOA cells transduced with GAB-2 or under different experimental conditions as described above can be easily replicated for a variety of cancers, in particular breast cancer, to determine their metastatic potential, the response to treatments and prognosis of the disease.
  • a micro-array seeded with probes that represent the signature that was derived in the above experiment and use it against a fluorescent, chemiluminescent or similar detection-capable tagged probes derived from tumor biopsies to determine their metastatic potential as well as the responsiveness to antineoplastic treatments, together with the general prognosis of the disease.
  • Any alternative method for measuring expression of the signature genes, at the RNA or protein level such as for example quantitative PCR in an array format or individually or immunohistochemistry using antibodies targeted to the proteins encoded by few or all of the signature genes, can be easily employed for the same task.
  • the GAB2-signature is enriched in genes correlated to response to treatment and to metastatic propensity
  • Each line of the NCI-60 panel is annotated for sensitivity to a wide number of drugs.
  • Dasatinib is an oral small molecule inhibitor of Src-family kinases (Lombardo et al., 2004), currently employed for the treatment of leukemias. Recently, multiple clinical trials are assessing its efficacy on various solid tumors, including breast cancer and melanoma. These results confirm that the Src-STAT3 signaling axis plays a key role in GAB2-driven biological and transcriptional responses independently of tissue and cell type, and propose GAB2 and its transcriptional targets as predictors of sensitivity to targeted drugs blocking SRC and/or STAT3 activation. These studies also demonstrate the value of the assay in defining GAB2-driven signatures as predictor of many anti-cancer drugs whether singly or in combination and also response to other cytotoxic agents like radiation.
  • Such signatures could be a powerful tool for determining prior to treatment for breast cancer or other cancers, which drug or combination of drugs (or radiation) would be most effective against the tumor of a particular patient.
  • Such signature would also to a significant extent be able to identify the dose of the drug or combination of the drug would be most effective for treating the tumor of a particular patient. Practically, this would help the physician in avoiding using drugs or therapies that would not be expected to have any meaningful effect on the tumor of a particular patient and also identify the best drug or combination of drug and its dose that would be expected to have the most effect at the least doses.
  • the GAB2-signature could be associated to human breast cancer aggressiveness.
  • the signature was mapped on a 311-sample breast cancer dataset generated at the Netherland Cancer Institute on 2-color oligonucleotide microarrays (NKI dataset) and published in two works (van, V et al. 2002b; van, V et al. 2002a). After filtering for expression, the GAB2-signature was mapped to 150 probes.
  • the signature resulted to be strongly enriched in genes discriminating breast cancer patients with or without metastatic recurrence within five years from the initial
  • Determining the aggressiveness of cancer is a critical component of any treatment plan for cancer at present. This is typically done using histochemical assays of a section of the tumor tissue derived from a biopsy and visually observing the difference in tissue architecture between normal tissue and the tumor tissue (level of differentiation) using a microscope. Based on the experience of the pathologist, a grade is assigned to the tumor - - higher the grade, the more aggressive the tumor. The physician makes important decisions on the treatment design from this score as to how to treat including whether to treat the disease aggressively or not. The main pitfall of determining the grade by this method is that the grade value derived by two pathologists could vary as much as 50% leading to the physician making the wrong treatment design.
  • GAB2-associated gene expression signatures can be used to determine the grade of a tumor even though additional clinical studies would be required to assign accurate grade of a patient's tumor with a particular GAB2- driven signature.
  • a breast cancer classifier based on the GAB2-signature predicts metastatic relapse
  • ILMN_90844 Hs.430502 0 79239592 5.492 5.975 7.555 7.244 1.666
  • IGFBP5 46094066 7.516 7.814 5.907 5.536 -1.944
  • ILMNJ 23833 Hs.571652 0 13579761 4.104 4.711 6.471 7.080 2.368
  • ILMN_12288 NMJ20879.1 IAA1505 55741666 5.482 6.055 7.407 7.915 1.892
  • ILMNJ 1202 NM_000481.2 AMT 44662837 6.966 6.825 8.480 8.461 1.575
  • ILMNJ 7B82 NM_000201.1 ICAM1 4557877 5.194 5.426 6.784 6.966 1.565
  • ILMNJ 5059 NM_013269.2
  • CLEC2D 52426785 10.826 10.913 12.253 12.563 1.538
  • ILMNJ1 39 NM_002198.1 IRF1 4504720 9.600 9.730 11.063 11.357 1.545
  • ILMN_71591 Hs.19339 0 34191392 7.770 7.846 9.050 9.362 1.398
  • ILMNJ 608 NM_001070.3 TUBG1 34222287 9.796 9.729 8.397 8.332 -1.398
  • ILMNJ 29103 Hs.576922 0 27552801 7.093 7.597 5.463 5.687 -1.770
  • ILMN 28750 NM 000067.1 CA2 4557394 11.209 11.329 9.358 9.048 -2.066
  • ILMNJ0005 NM_031966.2 CCNB1 34304372 10.136 10.300 6.759 6.498 -3.590

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Immunology (AREA)
  • Engineering & Computer Science (AREA)
  • Molecular Biology (AREA)
  • Hematology (AREA)
  • Urology & Nephrology (AREA)
  • Biomedical Technology (AREA)
  • Pathology (AREA)
  • Oncology (AREA)
  • Analytical Chemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Biotechnology (AREA)
  • Hospice & Palliative Care (AREA)
  • Cell Biology (AREA)
  • Microbiology (AREA)
  • Physics & Mathematics (AREA)
  • Biochemistry (AREA)
  • General Physics & Mathematics (AREA)
  • Medicinal Chemistry (AREA)
  • Food Science & Technology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Organic Chemistry (AREA)
  • Genetics & Genomics (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Biophysics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Engineering & Computer Science (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

The present invention discloses identification of GAB2-driven processes and anchorage independence associated with diagnosis, prognosis, metastasis, metastatic relapse, metastatic potential and prediction of response to treatment of cancers. In particular, a GAB2-signature based on anchorage independence is identified which can serve to define processes relevant to progression and response to treatment of human cancers.

Description

USE OF GENES INVOLVED IN ANCHORAGE INDEPENDENCE FOR THE
OPTIMIZATION OF DIAGNOSIS AND TREATMENT OF HUMAN CANCER Related Application
This application takes priority from US Provisional application USSN 61/247,967 filed 02-OCT-2009, entitled "Use of genes involved in anchorage independence for the optimization of diagnosis and treatment of human cancer", and is incorporated herein in its entirety.
Field of the Invention
The present invention is related to identification of GAB2-driven processes and anchorage independence associated with diagnosis, prognosis, metastasis, metastatic relapse, metastatic potential and prediction of response to treatment of cancers. In particular, a GAB2-signature based on anchorage independence is identified which can serve to define processes relevant to progression and response to treatment of human cancers.
Background of the Invention
Normal epithelial cells integrate signals from soluble ligands, like growth factors (GFs), cytokines and hormones, with signals derived from the binding of transmembrane integrins to the extracellular matrix (ECM), to ensure that they only proliferate in the 'correct' social context (Berrier and Yamada 2007, J Cell Physiol, 213, 565-573). Joint integrin/GF signaling is required for cell proliferation and for optimal cell survival: cell adhesion enhances GF-dependent responses, like cell proliferation, migration and/or protection from apoptosis (Miranti and Brugge 2002, Nat Cell Biol, 4, E83-E90). Conversely, cell detachment results in cellular desensitization to GF receptor signaling (Schwartz and Baron 1999, Curr Opin Cell Biol, 11, 197-202). Moreover, signals evoked by integrins and GFs are widely integrated at the cellular level, since both impinge on an overlapping set of cytoplasmic signaling pathways (Berrier and Yamada 2007, J Cell Physiol, 213, 565-573). Dependence on this reciprocal cross-talk is progressively lost by transformed cells during formation and spread of tumors (Guo and Giancotti 2004, Nat Rev Mol Cell Biol, 5, 816-826), and the acquisition of anchorage-independence is considered to be a crucial step during cancer progression towards invasion and metastasis (Tsatsanis and Spandidos 2004, Ann N Y Acad Sci, 1028, 168-175). Therefore, identification of genes or proteins promoting this step could provide novel targets or rationales for anti-cancer therapy. Among the possible ways for a genome-wide functional survey aimed at this scope are screenings based on the gain-of-function approach. Such screenings proved extremely valuable in the identification of genes involved in key cancer-related processes, like neoplastic transformation, resistance to apoptosis, or escape from senescence (Kitamura et al. 2003, Exp Hematol, 31, 1007- 1014). As a screening model, we chose MCF10A cells, a spontaneously immortalized human breast line (Soule et al. 1990, Cancer Res, 50, 6075-6086) that relies on both GFs and anchorage to proliferate. When these cells are cultured in the absence of anchorage, for instance on polyhema-coated plates, they undergo growth arrest and detachment- induced apoptosis, also known as anoikis (Reginato et al. 2003). It was previously shown that the low transforming potential of these cells renders them well- suited to monitor the effects of genes conferring oncogenic properties (Debnath and Brugge 2005, Nat Rev Cancer, 5, 675-688). Therefore, MCF10A cells represent an ideal model to screen for genes conferring anchorage-independence. For the screening we exploited a novel approach, named "Xenoarray analysis", based on transduction of mammalian cells of a given species with an expression library from another species, followed by one-shot quantitative tracing with DNA microarrays of library-derived transcripts before and after a selective stress, to disclose genes conferring resistance to the selection (Martelli et al. 2008, BMC Genomics, 9, 254). After transduction with a mouse testis retroviral expression library, MCF10A cells were selected for growth in suspension and murine microarrays were used to compare signal intensities for the exogenous cDNAs before and after selection, to detect the enriched ones. Independent infection-selection experiments highlighted significant and reproducible enrichment for murine Gab2-encoding transcripts, suggesting a role of this gene in anchorage-independent growth. Through biochemical studies, cell-based assays and genomic analysis we found that Gab2 promotes anchorage-independent growth of normal and neoplastic cells, and drives a transcriptional program linked to metastatic progression of breast cancer.
Summary of the invention:
The invention provides identification of GAB2-driven processes and anchorage independence associated with diagnosis, prognosis, metastasis, metastatic relapse, metastatic potential and prediction of response to treatment of cancers. Furthermore, a GAB2-signature based on anchorage independence is identified which can serve to define processes relevant to progression and response to treatment of human cancers.
In one aspect the invention provides a method for diagnosing or prognosing cancer in subjects comprising detecting expression of GAB2 and/or of its transcriptional target genes in the tumor tissue and/or in tumor cells isolated from the subject.
The method provides GAB 2- signature genes of the invention useful for diagnosis or prognosis of any human cancer, especially breast cancer and myeloma, comprising detecting in the tumor tissue and/or in tumor cells isolated from the subject expression of at least two of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5. The two genes can be selected from a single independent list (single table) or across the tables (more than one table).
In another aspect the invention provides a method for predicting metastasis or metastatic relapse or metastatic potential or response to treatment in cancer patients comprising detecting the expression of GAB2 and or its transcriptional target genes in the tumor tissue and/or in tumor cells isolated from the subject. The method provides GAB2- signature genes of the invention useful for diagnosis or prognosis of any human cancer, especially breast cancer and myeloma, comprising detecting in the tumor tissue and/or in tumor cells isolated from the subject expression of at least two of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5. The two genes can be selected from a single independent list (single table) or across the tables (more than one table). The cancer treatment as provided herein encompasses all know cancer treatment including targeted drug therapy, chemotherapy, radiation therapy or a combination thereof.
In yet another aspect, the invention provides a method of treating a subject with cancer comprising the steps of:
a) obtaining blood or tissue sample from the subject with cancer;
b) screening said sample for the expression of a polypeptide encoded by a polynucleotide selected from a group consisting of GAB2-signature genes listed in Table 1, 2, 3, 4 and 5; c) providing an antibody that reacts immunologically against said polypeptide; and d) administering an effective amount of said antibody to the subject with cancer. The invention also provides a method of treating a subject suffering from cancer comprising the steps of:
a) obtaining a sample of tissue from a subject suffering from cancer;
b) screening said sample for the expression of a polypeptide encoded by a polynucleotide selected from a group consisting of GAB2-signature genes listed in Table 1, 2, 3, 4 and 5; c) providing an antisense DNA molecule that encodes an RNA molecule that binds to said polynucleotide;
d) providing said antisense DNA molecule in the form of a human vector containing appropriate regulatory elements for the production of said RNA molecule; and e) administering an effective amount of said vector to the subject with cancer.
In another aspect, the invention provides a method of using in vitro anchorage independence model for deriving gene signature, the said signature comprising a set of genes associated with diagnosis, prognosis, metastasis and predicting response to treatment in cancer. The gene signature of the said method is GAB2-signature comprising at least two GAB2 and or its transcriptional target genes listed in Tables 1 , 2, 3, 4 or 5. The two genes can be selected from a single independent list (single table) or across the tables (more than one table).
In yet another aspect of the invention, a method of predicting the grade of a tumor in a cancer patient, comprising detecting the expression of GAB2 and/or its transcriptional target genes in the tumor tissue and/or in tumor cells isolated from the subject is provided. This method encompasses detecting the expression of at least two of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5. The two genes can be selected from a single independent list (single table) or across the tables (more than one table).
The invention also provides a GAB2-signature for diagnosing or prognosing human cancer, especially breast cancer or myeloma, in subjects comprising GAB2 and/or its transcriptional target genes in the tumor tissue and/or in tumor cells isolated from the subject as diagnostic or prognostic markers. The diagnosis or prognosis comprises detecting in the tumor tissue and/or in tumor cells isolated from the subject expression of at least two of GAB 2- signature genes listed in Tables 1, 2, 3, 4 or 5. The two genes can be selected from a single independent list (single table) or across the tables (more than one table).
In another aspect, the invention provides a GAB 2- signature for predicting
metastasis; or
metastatic relapse; or
metastatic potential; or
response to treatment
in cancer patients including breast cancer and myeloma patients, comprising GAB2 and or its transcriptional target genes. The prediction of metastasis, metastatic relapse, metastatic potential or response to treatment is detected in tumor tissue and/or in tumor cells isolated from the patient expression of at least two of GAB2-signature genes listed in Tables 1, 2, 3, 4 or 5. The two genes can be selected from a single independent list (single table) or across the tables (more than one table). The cancer treatment as provided herein encompasses all know cancer treatment including targeted drug therapy, chemotherapy, radiation therapy or a combination thereof.
In yet another aspect, the invention provides an array comprising polynucleotides capable of specifically hybridizing to at least two genes listed in Table 1, 2, 3, 4 or 5.
The invention also encompasses kit comprising the array for diagnosing or prognosing cancer or predicting metastasis or metastatic relapse or metastatic potential of cancer cells in a subject by determining the expression of at least 2 genes listed in Table 1, 2, 3, 4 or 5. Furthermore, a kit for diagnosing or prognosing cancer cells or predicting metastasis or metastatic relapse or metastatic potential of cancer cells in a biological sample comprising a primer pair for amplifying a nucleic acid sequence selected from a group consisting of GAB 2- signature genes listed in Table 1, 2, 3, 4 and 5 and containers for the primers is also provided.
In yet another aspect, a kit for diagnosing or prognosing cancer cells or predicting metastasis or metastatic relapse or metastatic potential of cancer cells in a biological sample comprising an oligonucleotide probe that binds under high stringency conditions to an isolated nucleic acid sequence selected from a group consisting of GAB2-signature genes listed in Table 1, 2, 3, 4 and 5 and a container for the probe is also provided by the invention.
Furthermore, the invention provides a kit for diagnosing or prognosing cancer cells or predicting metastasis or metastatic relapse or metastatic potential of cancer cells in a biological sample comprising an antibody which binds immunologically to a protein having an amino acid sequence encoded by a polynucleotide selected from a group consisting of GAB2-signature genes listed in Table 1, 2, 3, 4 and 5 and a container for the probe.
Brief Description of Drawings
Figure 1. Xenoarray analysis on MCF10A cells and acquisition of anchorage independence by library-transduced selected cells.
(A) MTT growth assay on polyhema-selected populations after 48h and 72h in adhesion or suspension, as indicated. Cell growth is expressed as a ratio between library-transduced and GFP-transduced cells, after normalization to the amount of viable plated cells at day 0. The data represent the mean and standard error of triplicate values (Adhesion 48h p<0.05, Suspension 48h p<0.01, Suspension 72h p<0.05). (B) Soft agar assay on GFP- and library-transduced cells, unselected or selected on polyhema, as indicated. Phase- contrast images were captured by a BD Pathway microscopic station (BD biosciences) after 3 weeks in agar. (C) Dot plot of single colony sizes as calculated by the Attovision software (BD Biosciences, version 1.5) for the GFP-SEL and LIB-SEL populations grown in soft agar.
Figure 2. Identification of enriched cDNAs in anchorage-independent, library- transduced MCF10A cells.
(A) Real-time PCR validation of enriched transcripts in both selections. The y-axis represents the relative increase in abundance of the transcripts in selected cells compared to unselected cells. (B) Western blot analysis on GFP- and library-transduced cells before and after selection to detect Gab2 protein enrichment.
Figure 3. Gab2 overexpression promotes anchorage-independent growth of MCF10A cells.
(A) Box -plot of an MTT growth assay on Mock- (M) or Gab2- (G) transduced MCF10A cells in adhesion or suspension, in the presence of complete medium or of starving medium (no EGF and 2% serum) for 48 hours. Cell vitality was normalized to the amount of cells in Mock-transduced, adherent cells in complete medium after 48 hours (CTRL) The data were obtained in triplicates, and t-test highlighted significant differences between G and M cells only in complete medium (* = p<0.01, ** = p<0.0001) (B) Dot plot of single colony sizes as calculated by the Attovision software (BD Biosciences, version 1.5) for the Mock and GAB2 cell populations grown in soft agar. (C) Flow cytometry analysis of apoptosis induction for Mock and Gab2-expressing MCF10A. Cell death was measured after 48h either in adhesion or suspension, by assessing the number of hypodiploid nuclei with the DNAcon3 kit. The percent of apoptotic cells is reported on the y-axis.
Figure 4. Evaluation of the contribution of different signaling pathways to Gab2- mediated enhancement of cell growth.
(A) Mock and GAB2-overexpressing (GAB2) MCF10A cells were incubated in adhesion (ADH) or suspension (SUSP) in the presence or absence of MEK inhibitor (PD98059, 40μΜ), PBK inhibitor (LY294002, 50μΜ), Src inhibitor (PP2, ΙΟμΜ), or JNK inhibitor (SP600125, ΙΟμΜ). Cell vitality was assessed with the MTT assay after 24h from the treatment and the drug effect was expressed as percent growth inhibition (with respect to untreated cells). The data represent the mean and standard error of triplicate values from two independent experiments. (B) Boxplots of detailed analysis of the effects of Src inhibition by PP2 on cell growth in various conditions. Cell growth, measured by the MTT assay, is expressed as percent of untreated Mock, adherent cells. The data were obtained in triplicates, and t-test highlighted significant responses to PP2 in all cases except for mock cells in suspension (* = p<0.05, ** = p<0.005). (C) Western blot analysis on Mock and Gab2-expressing cells in adhesion or after 24h and 48h in suspension. Antibodies directed against the activated form of Src (phosphorylated at tyrosine 416) and Stat3 (phosphorylated at tyrosine 705), or total Src or Stat3 were used.
Figure 5. Knock-down of endogenous Gab2 impairs MCF10A growth and anchorage-independent growth of human neoplastic cells. (A) MTT growth assay on wild-type MCF10A cells transduced with a scramble vector (CTRL) or a Gab2-shRNA in adhesion or suspension for 48h. Cell vitality was normalized to the amount of viable plated cells at time 0 and visualized independently for both cells by boxplots. The data were obtained in quintuplicate using two different GAB2-targeting shRNAs, and t-test highlighted significant differences between CTRL and GAB2-shRNA cells both in adhesion and suspension (* = p<5xl0 , ** = p<5xl0 ).
(B) MTT growth assay on MDA-MB-231 and MDA-MB-435 cells transduced with scramble vector (CTRL) or Gab2-shRNA in adhesion or suspension for 48h. Cell vitality was normalized to the amount of viable plated cells at time 0 and visualized independently for both cells by boxplots. The data were obtained in sextuplicate, and t- test highlighted modestly significant differences between CTRL and GAB2-shRNA cells in both MDA-MB-231 and MDA-MB-435 cells (* = p<0.05). (C) Soft agar growth of cells expressing Gab2 shRNA or scramble vector (CTRL). Phase-contrast images were captured by a BD Pathway microscopic station (BD biosciences) after 3 weeks in agar. (D) Western blot analysis of Src and Stat3 activation in control and GAB2 shRNA- transduced cells, as indicated.
Figure 6. The GAB2-signature predicts breast cancer metastatic relapse.
(A) Heatmap showing the expression of the two main gene functional modules in the NKI311 breast cancer dataset. The samples (columns) are ordered by decreasing GAB 2- signature metastasis score (GAB2 MTS Score), which is graphically reported in the second row. The first row shows the occurrence of metastatic relapse within five year (white space = no relapse). The white vertical line crossing the heatmap indicates the 0 threshold value of metastasis score discriminating good and poor prognosis samples. White and black dots on the right highlight the genes annotated to the two functional modules, respectively downregulated and upregulated in poor prognosis samples. (B) Kaplan-Meier analysis of metastasis-free survival on a dataset of 198 breast cancer samples classified as good prognosis (green line) or poor prognosis (red line) by the GAB2-signature. (C) Kaplan-Meier analysis of disease-specific survival on a dataset of 236 breast cancer samples classified as good prognosis (GP) or poor prognosis (PP) by the GAB2-signature.
Figure 7. The GAB2-signature is independent from existing clinical and genomic breast cancer classifiers, and from estrogen receptor status.
(A-F) Kaplan-Meier analysis on the 198-samples dataset subdivided in two prognostic subgroups (A,C,E = poor prognosis, B,D,F = good prognosis) by the Adjyvant! Online clinical score (A-B), the Veridex Index (C-D) and the Mammaprint classifier (E-F). Each subgroup is then further subdivided by the GAB2-signature in good prognosis (GP) or poor prognosis (PP) samples. (G-H) Kaplan-Meier analysis on the 198-samples dataset subdivided in ER-negative (G) and ER-positive (H) samples, then further subdivided by the GAB2-signature in good prognosis (GP) or poor prognosis (PP) samples.
Figure 8. The GAB2-signature predicts prognosis in Estrogen Receptor-negative breast cancer. Kaplan-Meier analysis of metastasis-free survival on a dataset of 175 Estrogen Receptor-negative breast cancer samples classified as good prognosis (GP) or poor prognosis (PP) by the GAB2-signature.
Figure 9: The GAB2-signature predicts response of breast cancer to antineoplastic treatment.
(A) Receptor-Operated Channel (ROC) analysis of the performance of the GAB2- signature Metastasis Score as a predictor of response to neoadjuvant treatment in the Hess dataset. AUC indicates the area under the Curve. (B) Dot plot analysis of the the GAB2- signature Metastasis score (x-axis) for the samples from patients showing pathological complete response (pCR) or residual disease (RD), as indicated.
Figure 10. Microarrays and realtime PCR generate highly correlated diagnostic scores. Dot plot showing the correlation between Metastasis Score calculated for 32 breast cancer samples from microarray data (x-axis) and from realtime PCR data (y-axis), using 15 genes of the GAB2 signature.
Figure 11. The GAB2-signature is correlated to melanoma progression. Heatmap showing Log2Ratio expression values for 83 Affymetrix probesets (rows) across tissue samples of different stages of melanoma progression (columns). The first row indicates the type of sample (from Normal Skin, black, to Metastatic Melanoma, white, as indicated). Samples have been subdivided, based on expression of the GAB2 genes, in four clusters of progressively increasing aggressiveness, from normal skin and benign nevi to metastatic melanoma. Detailed Description of the Invention
The invention will now be described in detail in connection with certain preferred and optional embodiments, so that various aspects thereof may be more fully understood and appreciated.
I. GAB2 is a key promoter of anchorage independence of human neoplastic cells a. Gain-of-function screening for anchorage independence in MCFIOA cells
For the functional screening, MCFIOA cells were transduced with a commercial mouse testis retroviral expression library (Stratagene) or with GFP as a control. To increase the screening robustness, infections were performed in duplicate (A and B), using an estimated multiplicity of infection of 1 , to avoid multiple integrations in the same cell. To detect and quantify library-derived transcripts we performed Xenoarray analysis (Martelli et al. 2008, BMC Genomics, 9, 254), by extracting total RNA from the four cell populations and hybridizing the resulting cRNAs on murine expression arrays, to allow specific detection of library-derived transcripts of murine origin. Expression measurements obtained, for infections A and B, in GFP-transduced cells (x-axis) versus library-transduced cells were then compared. Both library-transduced populations clearly showed a consistent number of detectable murine transcripts (945 and 1125 probes in infection A and B, respectively, with a detection p-value <0.01). Conversely, very few probes (around 100) cross-hybridized to endogenous transcripts and were detected also in GFP-transduced cells. Based on our previous observations on Xenoarray analysis sensitivity (Martelli et al. 2008, BMC Genomics, 9, 254), we estimated that a selection- driven 20-fold enrichment of even a rare transcript, bringing it from 8 to 160 parts per million, should be enough to render it clearly detectable by Xenoarray analysis. A selective, anchorage-independence screening was then carried out by culturing GFP- or library-transduced MCFIOA cells on polyhemacoated plates (polyhema does not allow the cells to attach to the substrate). The four transduced populations were each split in two sub-lines: one was grown in adherence, the other underwent six cycles of selection, each cycle consisting of 48h of culture on polyhema followed by 24h of recovery on regular plates. Cells recovered from GFP- and library-transduced cells after selection were named, respectively, "GFP-SEL" and "LIB-SEL", and assayed for their ability to grow in the presence or absence of anchorage. LIB-SEL, but not GFP-SEL cells displayed significantly higher growth rate than unselected cells, in both adherence and suspension (Figure 1A). Moreover, as shown in Figure 1B-C, only LIB-SEL cells could form large colonies in soft agar, an in vitro hallmark of cell transformation. These findings confirmed a "library effect" not explainable with insertional mutagenesis but likely deriving from the expression of advantageous exogenous transcripts. To identify library- derived transcripts promoting anchorage-independent growth, we conducted Xenoarray analysis on library-transduced cells, before and after selection. A significant number of probes displayed higher signal in selected cells, indicating that cells expressing the respective transcripts were enriched by the selection. To identify the genes that were reproducibly enriched in both selections we calculated, for each transcript, the Log(2) ratio of the signal before and after selection. Interestingly, the Gab2 transcript showed a strong enrichment in both selections (average enrichment = 14-fold). The enrichment was observed with 3 different probes, each designed in a different region of the Gab2 transcript. Other genes, including Ntrk3 and Cypl lal, displayed a stronger enrichment in selection A (respectively, 41- and 49-fold in selection A, and 2- and 1.3-fold in selection B). Quantitative Real-Time PCR analysis with mouse-specific primers confirmed that Gab2 was the most enriched transcript, followed by Cypl lal and Ntrk3 (Figure 2A). Therefore, we focused on this gene and validated its enrichment also at the protein level (Figure 2B), thereby showing that the exogenous cDNA enriched after the selection actually encodes the full-length Gab2 protein. Exogenous Gab2 was found to be essential for anchorage-independent growth of the LIB-SEL population, as its downregulation by RNAi strongly reduced the growth advantage of LIB-SEL cells, in adhesion, in suspension and in soft agar. b. Validation and characterization of Gab2-driven anchorage-independence
Gab2 is a scaffolding/docking protein involved in multiple signaling pathways downstream from membrane receptors (Nishida et al. 1999, Blood, 93, 1809-1816). To directly assess whether Gab2 may promote anchorage-independent growth, we transduced MCFIOA cells with the human Gab2 coding sequence, cloned in a retroviral vector (Brummer et al. 2006). As shown in Figure 3A, adherent GAB2-overexpressing cells showed a significant increase in proliferation, which was further enhanced in the absence of anchorage. Notably, Gab2-driven growth advantage was almost totally lost when cells were kept in starving medium (no EGF, and serum lowered to 2%), indicating that Gab2 promotes proliferation independently from cell anchorage to the ECM, but dependently from the presence of GFs and/or serum. Accordingly, Gab2 overexpressing cells formed larger and more abundant colonies in soft agar, compared to wild-type cells (Figure 3B). To evaluate whether Gab2 promotes survival of detached cells, we estimated the fraction of dead cells after 48h of suspension culture. Surprisingly, after 48h of polyhema plating, we detected a comparable extent of cell death between wild-type and Gab2-expressing cells (Figure 3C). These data indicate that Gab2 is not involved in the protection of MCF10A cells from anoikis, but rather allows their proliferation in the absence of adhesion to the ECM.
Previous analyses have identified signaling molecules that can bind to Gab2 upon receptor activation, including the tyrosine phosphatase Ptpnl 1/Shp2, leading to activation of Erk and Jnk (Yu et al. 2006, J Biol Chem, 281, 28615-28626), the p85 subunit of PI3K, leading to Akt activation (Bouscary et al. 2001, Oncogene, 20, 2197-2204), and Src family kinases (Kong et al. 2003, J Biol Chem, 278, 5837-5844). Therefore, to dissect the signaling pathways downstream Gab2 that could mediate anchorage-independent growth, we examined the effects on cell vitality of a panel of small molecule inhibitors targeting the above mentioned signaling kinases (Figure 4A). The PI3K inhibitor was the most effective, but with no differential between anchorage-dependent and independent growth, or between control and Gab2expressing cells, showing a general requirement of this pathway for survival of MCF10A cells. A similar but less pronounced effect was observed for Mek inhibition. The Jnk inhibitor displayed modest effects in all conditions. Interestingly, the Src inhibitor PP2 displayed the highest specificity towards Gab2- expressing cells in suspension. A more detailed analysis of the effects of Src inhibition is shown in Figure 4B. According to these data, Gab2-driven anchorage independence requires Src, which typically is activated by integrins when cells are adherent and becomes inactivated upon detachment (Playford and Schaller 2004, Oncogene, 23, 7928- 7946). Consistently, western blot analysis on cell lysates from control and Gab2- expressing cells cultured in adhesion or suspension confirmed Gab2-driven activation of Src and of one of its downstream targets, Stat3 (Figure 4C). In adhesion, Gab2-expressing cells displayed a stronger basal phosphorylation of Src. Active Src levels were reduced in cells kept in suspension, but while in control cells Src activation was completely abolished at 48h, Gab2expressing cells maintained some phosphorylation. Analysis of Stat3 activation highlighted an even more pronounced response to Gab2 expression, indicating the capacity of Gab2 to sustain the activation of Stat3 also in the absence of a substratum consensus. Since many studies provided evidence for Stat3 involvement in Src-mediated oncogenesis (Yu et al. 1995, Science, 269, 81-83) and anchorage- independent growth (Laird et al. 2003, Mol Cancer Ther, 2, 461-469), these data indicate that Gab2 signals through Src and Stat3 to accomplish anchorage-independent growth. c. Endogenous Gab2 is essential for anchorage-independent growth of normal and neoplastic cells
The data shown so far demonstrate that constitutive, exogenous expression of Gab2 promotes anchorage-independent growth. To verify if this effect is mirrored by physiologically controlled Gab2 expression, we silenced by RNAi the endogenous Gab2 in MCF10A and in human cancer cells. In MCF10A cells, Gab2 silencing markedly reduced their growth both in adhesion and in suspension (Figure 5A) while both MDA- MB-231 breast cancer cells and MDA-MB-435 melanoma cells responded to Gab2 silencing with a significant but modest reduction of proliferation (Figure 5B), indicating a minor role of Gab2 in the presence of attachment. Strikingly however, the ability of these cells to form colonies in soft agar was completely abrogated by Gab2 silencing (Figure 5C). In accordance with our previous western blot data, Gab2 loss determined a significant and concomitant decrease in Src and Stat3 activation (Figure 5D). Interestingly, the most evident reduction of Src-Stat3 phosphorylation was observed for MDA-MB 231 cells, which endogenously express the highest levels of Gab2 and most strongly reduce their soft agar growth upon Gab2 silencing. Altogether, these data confirmed the Gab2-Src-Stat3 axis as a key promoter of anchorage-independent growth of neoplastic cells, defining GAB2 as a potentially powerful diagnostic marker and therapeutic target for cancer treatment. While in the present invention we have used shRNA vectors as an example of a reagent that can be used to silence Gab2 and thus use it as a drug to treat cancer, a variety of other standard methods used to silence proteins or gene transcripts like monoclonal antibodies or diabodies or other protein neutralizing agents, antisense RNA and other nucleotide-based agents or small molecule inhibitors that can bind and to Gab2 or break its interaction with other proteins can also be easily employed as a therapeutic. The methods of preparation of these reagents are well known. A small molecule inhibitor can be identified through high-throughput screening methods or through rational design or others methods typically used to identify such compounds. Anyone skilled in the art can develop these reagents to specifically abrogate the function of Gab2 and thus act as a drug for the treatment of solid cancers. Moreover, in Tables 1 to 5 we describe a series of genes that are transcriptional targets of GAB2 and/or are involved in anchorage independence. Such target genes are additional therapeutical targets against which inhibitors can be developed to treat breast cancer and other human neoplastic diseases.
II. Definition of transcriptional signatures associated with GAB2, anchorage independence, cancer aggressiveness and response to treatment.
a. Definition of genomic signatures associated with GAB2 and with anchorage independence
To gain further insights on Gab2-driven anchorage-independence, we performed gene expression profiling on MCF10A cells transduced with Gab2 and selected by growth in the absence of anchorage. As control, we used RNA extracted from GFP-transduced and selected cells. The selection was performed as described for the library-transduced cells. As further controls, GFP- and GAB2-transduced cells were profiled also before the selection, together with wild-type MCF10A cells and with Library-transduced selected cells (LIBSEL). Statistical analysis was applied to retrieve genes differentially expressed between GAB2transduced and GFP-transduced cells after the selection, highlighting 221 probes, corresponding to 205 independent genes: the "GAB2-signature" (Table 1). Other signatures were also derived, reflecting different aspects of GAB2 activity and of acquisition of anchorage independence in MCF10A cells: (i) the GAB2SEL vs GAB2UNS signature, composed of genes differentially expressed in GAB2-transduced cells before and after polyhema selection (Table 2); (ii) the GAB2UNS vs GFPUNS signature, composed of genes differentially expressed between GAB2-transduced and GFP-transduced cells before polyhema selection (Table 3); (iii) the GFPSEL vs GFPUNS signature, composed of genes differentially expressed in GFP-transduced cells before and after polyhema selection (Table 4); (iv) the LIBSEL vs GFPSEL signature, composed of genes differentially expressed in the above mentioned LIBSEL population described in the functional screening (Figure 1) and GFP-transduced cells after polyhema selection (Table 5). All these signatures, individually or altogether, capture various aspects of GAB2-driven processes and anchorage independence. This clearly demonstrates the use of the signatures in determining the metastatic potential of cancer cells. Metastatic potential as meant in the current specification relates to the ability of a cancer cell to invade and to spread of cancer cells to other parts of the body. The same method that was used to derive the signatures in MCFIOA cells transduced with GAB-2 or under different experimental conditions as described above can be easily replicated for a variety of cancers, in particular breast cancer, to determine their metastatic potential, the response to treatments and prognosis of the disease. In a diagnostic kit, one would use a micro-array seeded with probes that represent the signature that was derived in the above experiment and use it against a fluorescent, chemiluminescent or similar detection-capable tagged probes derived from tumor biopsies to determine their metastatic potential as well as the responsiveness to antineoplastic treatments, together with the general prognosis of the disease. Any alternative method for measuring expression of the signature genes, at the RNA or protein level, such as for example quantitative PCR in an array format or individually or immunohistochemistry using antibodies targeted to the proteins encoded by few or all of the signature genes, can be easily employed for the same task. b. The GAB2-signature is enriched in genes correlated to response to treatment and to metastatic propensity
As an example of the clinical potential of the abovementioned signatures, we present here results obtained on the GAB2-signature. First of all, Probe identifiers contained in the annotation manifest provided by Illumina were loaded on the David Ease portal (Dennis G J et al, 2003, Genome Biology 4, P3) to generate a background list (all probes) and the GAB 2- signature list. Enrichment in biological functions for the GAB2-signature genes was evaluated using the "functional annotation chart" function on the portal, and highlighted a strong enrichment in genes controlling proliferation. Subsequently, the signature was mapped on a gene expression dataset obtained on the NCI-60 panel of cell lines (Shankavaram et al. 2007, Mol Cancer Ther. 6, 820-832). Each line of the NCI-60 panel is annotated for sensitivity to a wide number of drugs. We concentrated on drugs affecting the Src-STAT3 axis downstream of GAB2, and found that the GAB2signature is significantly enriched in genes whose expression correlates to sensitivity to Resveratrol
(p=lxl0 ), Piceatannol (p=lxl0 ) and SD-1029 (p=1.2xl0 ), small molecules that inhibit STAT3 activation by Src, Jak or other tyrosine kinases (Table 6). In another analysis conducted on a gene expression dataset obtained from human breast cancer cell lines (Huang et al., 2007), we found that the signature is also significantly enriched (p<0.0023) in genes whose expression distinguishes Dasatinib-sensitive and resistant breast cancer cells. The genes and their differential expression between dasatinib-sensitive and resistant cells are listed in Table 7. Dasatinib is an oral small molecule inhibitor of Src-family kinases (Lombardo et al., 2004), currently employed for the treatment of leukemias. Recently, multiple clinical trials are assessing its efficacy on various solid tumors, including breast cancer and melanoma. These results confirm that the Src-STAT3 signaling axis plays a key role in GAB2-driven biological and transcriptional responses independently of tissue and cell type, and propose GAB2 and its transcriptional targets as predictors of sensitivity to targeted drugs blocking SRC and/or STAT3 activation. These studies also demonstrate the value of the assay in defining GAB2-driven signatures as predictor of many anti-cancer drugs whether singly or in combination and also response to other cytotoxic agents like radiation. Such signatures could be a powerful tool for determining prior to treatment for breast cancer or other cancers, which drug or combination of drugs (or radiation) would be most effective against the tumor of a particular patient. Such signature would also to a significant extent be able to identify the dose of the drug or combination of the drug would be most effective for treating the tumor of a particular patient. Practically, this would help the physician in avoiding using drugs or therapies that would not be expected to have any meaningful effect on the tumor of a particular patient and also identify the best drug or combination of drug and its dose that would be expected to have the most effect at the least doses.
To extend the value of the in-vitro model of Gab2-driven anchorage independence, we verified if the GAB2-signature could be associated to human breast cancer aggressiveness. To this aim, the signature was mapped on a 311-sample breast cancer dataset generated at the Netherland Cancer Institute on 2-color oligonucleotide microarrays (NKI dataset) and published in two works (van, V et al. 2002b; van, V et al. 2002a). After filtering for expression, the GAB2-signature was mapped to 150 probes. Interestingly, the signature resulted to be strongly enriched in genes discriminating breast cancer patients with or without metastatic recurrence within five years from the initial
-8
diagnosis (p<10 ).
Determining the aggressiveness of cancer is a critical component of any treatment plan for cancer at present. This is typically done using histochemical assays of a section of the tumor tissue derived from a biopsy and visually observing the difference in tissue architecture between normal tissue and the tumor tissue (level of differentiation) using a microscope. Based on the experience of the pathologist, a grade is assigned to the tumor - - higher the grade, the more aggressive the tumor. The physician makes important decisions on the treatment design from this score as to how to treat including whether to treat the disease aggressively or not. The main pitfall of determining the grade by this method is that the grade value derived by two pathologists could vary as much as 50% leading to the physician making the wrong treatment design. Use of molecular markers to make more accurate prediction of the grade (or level of aggressiveness) of a patient's tumor would have considerable value in making treatment decisions more accurately. The data presented in this application shows that GAB2-associated gene expression signatures can be used to determine the grade of a tumor even though additional clinical studies would be required to assign accurate grade of a patient's tumor with a particular GAB2- driven signature. c. A breast cancer classifier based on the GAB2-signature predicts metastatic relapse
On the basis of the results mentioned above, using the nearest-mean classifier approach (Wessels et al. 2005, Bioinformatics, 21, 3755-3762), we built a classifier in the NKI dataset, based on the entire GAB2signature, which provides a "Metastasis Score", MS, discriminating patients with good and poor prognosis (Table 8). Data clustering and mining revealed two specific gene functional modules associated with prognosis: (i) a sizeable proliferation module positively correlated to metastatic progression, and (ii) an interesting module composed of negative regulators of cell-matrix interaction, migration and invasion, expressed at higher levels in good prognosis samples (Figure 6A and Table 9). To validate the classifier, we mapped it on two independent datasets of 198 and 236 samples, both obtained on Affymetrix microarrays and aimed at evaluating, respectively, metastasis-free survival and death of disease (Desmedt et al. 2007; Miller et al. 2005). In both cases the Gab2-signature classified patients with high accuracy, despite the change of microarray platform (Figure 6B-C). We then applied the Gab2- signature on the 198- sample dataset stratified by various clinical and genomic classifiers originally provided in the work. When samples were subdivided by their prognostic class according to the Adjuvant! Online score (Hess 2008, Am J Pathol, 174, 1524-1533), they could still be further subdivided in good- and poor-prognosis subgroups (GP and PP, respectively) by the GAB2-signature (Fig. 7A-B). Similarly, the GAB-2 signature correctly re-classified also samples previously subdivided in good- and poor-prognosis subgroups by genomic classifiers such as the 76-gene "Veridex Index" (Desmedt et al. 2007) (Fig. 7C-D) and the 70-gene "Mammaprint" classifier (Buyse et al. 2006, J Natl Cancer Inst, 98, 1183-1192) (Fig. 7EF). These results show that the GAB 2- signature predicts metastatic relapse of breast cancer independently of existing clinical and genomic classifiers. Such independence was further confirmed by univariate and multivariate statistics calculated, on the 198-sample dataset, for the Gab2 signature versus Adjuvant! online or three published genomic prognostic classifiers: Veridex index, MammaPrint and "Genomic Grade Index" (Sotiriou et al. 2006, J Natl Cancer Inst, 98, 262-272) (Table 10). Reciprocal significance impairment was only observed with the Mammaprint classifier, which, however, is obtained on a proprietary microarray platform, while the GAB2 signature can be directly tested on all publicly available Affymetrix microarray datasets. Finally, the GAB2-signature was found to maintain prognostic ability also in samples subdivided by estrogen receptor (ER) status (Fig. 7G-H). Intriguingly, a small fraction of the ER-negative samples was called "good-prognosis" with 100% precision, but with no statistical significance probably due to the limited number (64) of samples analyzed. We therefore added samples from two other datasets (Ivshina et al. 2006; Wang et al. 2005), reaching a total of 175 ER-negative samples, for which the GAB2-signature maintained 100% precision and reached statistical significance (Figure 8). Finally, we verified if the GAB 2- signature metastasis score described above. These results confirm that the GAB2- signature is a powerful predictor of metastatic propensity, in breast cancer but likely in other human cancers. A similar complementary diagnostic potential would therefore be possessed by the other signatures herein described at Tables 2 to 5, all capturing traits of GAB2-driven processes and of anchorage independence. Moreover, being differentially expressed in aggressive vs. non-aggressive cancers, genes of the signatures represent also therapeutic targets for cancer treatment. Being based on a basic cellular process, the GAB2-signatures would present similar diagnostic and therapeutic properties also for other types of solid cancers, for which they can be used employing the same methodology and reagents. d. A breast cancer classifier based on the GAB2-signature predicts response to treatment
To test if the classifier based on the GAB2-signature has the potential to also predict response of breast cancer to conventional chemotherapy, we mapped the classifier on a dataset of 133 samples, obtained on Affymetrix microarrays and aimed at evaluating response to preoperative treatment (Hess et al. 2006, J Clin Oncol, 24, 4236-4244). All samples were biopsies taken from the primary tumor before the start of neoadjuvant treatment with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide, and are annotated for the subsequent response of the patient. A pathological complete response (pCR) was observed in 34 patients, while the remaining had residual disease (RD) at surgery. We could therefore directly test the GAB2-signature metastasis score for its ability to discriminate responders from non-responders. Indeed, the GAB2-signature classifies patients with high accuracy, despite the fact that it is a prognostic classifier, as measured by ROC analysis (AUC>0.75; Figure 9A). The details of the classifier's performances are illustrated in Figure 9B. In this dot plot, it clearly emerges that the patients with low metastasis score are also those more unlikely to respond to the treatment. In clinical perspective, this result is very interesting because it would give two concomitant reasons to spare the patient from chemotherapy which has serious unwanted long-term and short-term side-effects: (i) unlikely to develop distant metastases; (ii) unlikely to respond to chemotherapy. Overall, these results confirm that the GAB2- signature is a powerful predictor of response to anticancer treatments, in breast cancer but likely in other human cancers. A similar and possibly complementary diagnostic potential is therefore likely to be possessed by the other signatures herein described at Tables 2 to 5, all capturing traits of GAB2-driven processes and of anchorage independence. Moreover, being differentially expressed in responsive vs. non-responsive cancers, genes of the signatures represents also therapeutic targets for cancer treatment. Being based on a basic cellular process, the GAB2-signatures are likely to present similar diagnostic and therapeutic properties also for other types of solid cancers, for which they can be used with the same methodology. Table 1 : The GAB2-Signature in MCF1 OA Cells (GAB2SEL vs GFPSEL)
GAB2
GAB2 GAB2
GFP SEL GFP SEL SEL 1
Gi SEL A SEL B lllumina ID Accession Sym bol A Log(2) B Log(2) GFP SEL
Accession Log(2) Log (2)
Signal Signal Log(2)
Signal Signal
Ratio
Table 1 , continued
IL N_28594 NM_000900.2 MGP 49574513 5.450 5.267 6.954 7.679 1.958
IL N_24333 NM_001025357.1 LOC441376 70778888 5.174 5.725 7.146 7.652 1.950
ILM N_25185 NM_002982.3 CCL2 56119169 5.963 5.585 7.480 7.965 1.948
ILM N_16225 NM_003155.2 STC1 61676083 5.728 5.609 7.370 7.828 1.931
ILM N_26854 NM_016095.1 Pfs2 7706366 6.370 6.217 8.257 8.188 1.929
ILM N_16900 NM_012485.1 HMMR 7108350 6.589 5.980 8.310 8.100 1.920
IL N_635 N _002281.2 KRTHB1 15431319 12.938 12.058 14.562 14.269 1.918
ILM N_138949 NM_005672.2 PSCA 29893565 5.661 6.375 8.084 7.735 1.891
ILMNJ 9849 NM_0010B7.2 TOP2A 19913405 6.874 6.580 8.599 8.634 1.890
ILM N 1375 NM 152594.1 SPRED1 22749220 7.360 7.465 9.390 9.211 1.888
ILM N_16399 NM_006086.2 TUBB3 50592995 7.648 6.982 9.337 9.061 1.884
ILM N_25781 NM_003504.3 CDC45L 34335230 4.755 3.818 6.090 6.225 1.871
ILM NJ39316 NM_054111.2 IHPK3 55769529 5.667 5.561 7.419 7.545 1.868
ILM N_21 1 NM_032814.1 TMEM118 14249505 5.482 5.049 7.071 7.178 1.859
ILM N_15264 NM_080757.1 C20orT127 50233782 9.726 8.978 11.181 11.235 1.856
ILM N_12145 NM_002742.1 PRKD1 4506074 5.916 4.991 7.138 7.480 1.855
ILMN_25474 NM J 78229.3 IQ.GAP3 39753960 7.216 7.113 9.110 8.925 1.853
ILMNJ 5159 NM_207409.1 UNQ3045 46409449 4.959 5.322 7.003 6.966 1.844
ILM N_3512 NM_015894.2 STMN3 14670374 6.338 5.720 7.981 7.763 1.843
ILM N_79597 Hs.159264 0 1710274 4.921 5.104 6.609 7.093 1.838
ILM N_23984 NM_001338.3 CXADR 45827793 8.317 8.588 10.272 10.307 1.837
ILM N_20465 NM_003981.2 PRC1 40807441 7.796 7.132 9.227 9.337 1.818
ILM N_6398 NM J 81803.1 UBE2C 32967290 7.550 6.533 8.799 8.917 1.816
IL N_408 NMJ18136.2 ASPM 24211028 7.031 6.674 8.360 8.965 1.810
ILMNJ 0261 NMJ12137.2 DDAH1 31881756 5.100 5.240 6.705 7.255 1.809
ILMNJ 38577 NM_031311.2 CPVL 22027517 8.522 7.918 10.007 10.046 1.807
ILM N 2929 NM 001958.2 EEF1A2 25453470 4.959 5.049 6.828 6.744 1.782
ILM N_8234 NM_013271.2 PCSK1N 20336240 5.739 5.252 7.167 7.386 1.781
ILM NJ 0126 NM_000527.2 LDLR 8051613 9.982 9.340 11.363 11.517 1.779
ILM N_2142 NM_002727.2 PRG1 45935370 5.750 5.661 7.432 7.534 1.778
ILM N_3335 NMJ06779.2 CDC42EP2 30089963 5.883 5.624 7.481 7.534 1.754
ILM NJ109 NMJ00755.2 CRAT 21618330 6.670 6.109 8.359 7.921 1.751
ILMNJ 32978 Hs.580797 0 12402397 9.327 9.174 10.721 11.267 1.743
ILM N_3628 NMJ24094.1 DCC1 13129095 5.766 5.830 7.446 7.634 1.742
ILM NJ 5254 NMJ04701.2 CCNB2 10938017 8.128 7.113 9.346 9.325 1.715
ILMN 18800 NM 001089.1 ABCA3 4501848 6.119 5.692 7.707 7.530 1.713
ILM N_8681 NM_207362.1 MGC42367 46409355 6.926 6.967 8.511 8.804 1.711
ILM NJ 5358 NM_001839.2 CNN3 47080096 8.562 8.707 10.383 10.308 1.711
ILMN J! 7289 NMJ02578.2 PAK3 46249379 7.126 6.821 8.676 8.667 1.697
ILM N_7092 NMJ03686.3 EX 01 39995068 6.300 6.235 7.934 7.984 1.692
ILMN_28552 NMJ32117.2 GAJ 38455411 4.838 5.579 6.910 6.884 1.689
ILMN_915 NMJ20675.3 SPBC25 23510353 3.700 4.217 5.807 5.479 1.684
ILM NJ 2434 NMJ32918.1 RERG 14249703 4.186 4.828 6.002 6.379 1.684
ILMN 12221 NM 000204.1 CFI 4504578 7.656 7.384 9.159 9.239 1.679
ILM N_7052 NMJ05727.2 TSPAN1 21264577 6.489 6.320 8.012 8.155 1.679
ILM N_8225 NM_016343.3 CENPF 55770833 5.805 5.615 7.342 7.431 1.677
ILM N_24855 NMJ21101.3 CLDN1 21536297 9.320 9.696 11.129 11.240 1.676
ILMN_90844 Hs.430502 0 79239592 5.492 5.975 7.555 7.244 1.666
ILM NJ 4069 NMJ00147.2 FUCA1 24475878 7.250 6.843 8.577 8.845 1.665
ILM N_20249 NM_001006933.1 TCEAL3 55749430 7.049 6.693 8.662 8.387 1.653
ILM N_23549 NM J 99327.1 SPRY1 40788000 5.202 5.594 6.891 7.209 1.653
ILM NJ 8797 NM J 98282.1 LOC340061 38093658 7.098 6.940 8.674 8.647 1.642
ILMN 36990 XM 930914.1 LOC653108 89058118 5.909 5.664 7.262 7.588 1.638
ILM N_6505 NMJ03282.2 TNNI2 50593000 7.676 7.965 9.410 9.498 1.634
ILMN_26085 NMJ03467.2 CXCR4 56790928 5.820 5.304 7.051 7.333 1.630
ILM N_24466 NMJ03641.2 IFITM1 40254449 6.272 6.184 7.895 7.816 1.628
ILM NJ3876 NMJ03632.1 CNTNAP1 4505462 6.127 5.825 7.673 7.527 1.624
ILM N_3836 NM_018367.3 PHCA 31543398 5.973 6.085 7.605 7.695 1.621 Table 1 , continued
ILM N_15608 NM_005766.2 FARP1 48928036 7.183 7.002 8.795 8.589 1.600
ILMN_23044 NMJ52972.2 LRG1 49574519 5.485 5.233 6.783 7.132 1.598
ILMNJ08776 Hs.545615 0 2901934 6.309 6.520 7.919 8.100 1.594
ILM N_25536 NMJ02386.2 MC1R 27477128 6.421 6.733 8.386 7.956 1.594
ILMNJ37805 NMJ04217.1 AURKB 4759177 6.036 6.106 7.727 7.571 1.578
IL N_5694 NM J 53322.1 PMP22 24430164 5.307 5.650 7.142 6.959 1.572
ILMN_25636 NM J 52666.1 PLD5 22749352 6.104 5.274 7.234 7.278 1.567
ILM N_30361 NMJ03896.2 ST3GAL5 28373079 4.986 5.079 6.681 6.503 1.559
ILM N_40146 XM_496129.2 LOC440349 89040448 6.227 6.075 7.466 7.941 1.553
I LMN 16130 NM 006997.2 TACC2 45827751 11.1 4 11.047 12.627 12.697 1.552
ILM N_27053 NM_003637.3 ITGA10 38569397 5.652 5.233 7.000 6.984 1.549
ILMNJ 0005 NMJ31966.2 CCNB1 34304372 6.759 6.498 8.045 8.309 1.549
ILM N_19730 NM_004091.2 E2F2 34485718 6.020 5.591 7.336 7.365 1.545
IL N_3597 NMJ04431.2 EPHA2 32967310 8.283 8.673 10.275 9.755 1.537
ILM N_18617 NM_031426.2 C9orf58 50428929 9.093 8.994 10.673 10.417 1.501
ILM N_13141 NM_005480.2 TROAP 33438581 5.591 5.326 6.990 6.916 1.495
ILMN_26277 NMJ24901.3 DENND2D 34147689 5.186 5.409 6.988 6.597 1.495
ILM N_13533 NM_016437.1 TUBG2 7706750 7.764 7.606 9.198 9.147 1.488
IL N_9405 NM_016445.1 PLEK2 7706642 6.318 6.498 7.900 7.887 1.485
ILM N_12351 NM_001002876.1 C22otf18 50845413 6.629 5.995 7.807 7.771 1.476
ILM N_23620 NMJ22346.3 HCAP-G 50658080 7.019 6.695 8.368 8.296 1.475
IL N_9074 NMJ01175.4 ARHGDIB 56676392 8.266 8.078 9.624 9.665 1.472
IL NJ 133 NM J 82776.1 MCM7 33469921 9.595 9.620 11.093 11.045 1.461
IL N_6272 NM_017823.3 DUSP23 56786143 11.433 11.594 12.951 12.955 1.439
ILM N_21136 NMJ14411.2 NAGS 77404393 7.232 6.991 8.316 8.783 1.438
ILM N_79861 Hs.162734 0 2276759 6.508 6.508 7.830 8.055 1.434
I LMN 11198 NM 005953.2 MT2A 31543214 13.935 13.691 15.221 15.263 1.429
ILM N_25046 NM_006479.2 RAD51AP1 19923778 5.463 5.888 7.066 7.141 1.428
ILM N_20154 NM_018728.1 MY05C 9055283 8.291 7.854 9.523 9.466 1.422
ILMNJ 38827 NMJ45810.1 CDCA7 22027513 7.436 7.393 8.885 8.779 1.418
ILM N_20107 NMJ06739.2 M CMS 23510447 5.890 5.928 7.176 7.477 1.417
ILMN_95224 Hs.473191 0 21750193 7.355 7.324 8.722 8.766 1.405
ILM N_22327 NM_033412.1 MCART1 15529971 12.615 12.944 14.243 14.121 1.403
ILM N_21027 NM_015651.1 PHF19 58331160 6.772 6.533 8.102 7.954 1.375
ILM NJ 2005 NM_013282.2 UHRF1 16507203 7.110 6.997 8.645 8.183 1.361
ILM N 1733 NM 198457.1 ZNF600 39930584 6.592 6.490 7.892 7.894 1.351
ILM N_25969 NMJ06845.2 KIF2C 13699832 5.763 5.456 7.094 6.810 1.342
ILM NJ 4281 NM_016359.2 NUSAP1 59710089 7.704 7.807 9.139 9.051 1.339
ILM NJ 2202 NMJ20127.1 TUFT1 9910595 7.420 7.354 8.613 8.816 1.327
IL N_5047 NM J 52308.1 MGC24665 24308244 6.079 6.096 7.319 7.500 1.322
ILM NJ 6427 NM_001237.2 CCNA2 16950653 6.477 6.651 7.736 8.036 1.322
ILMNJ 7477 NMJ32199.1 ARID5B 74136548 8.807 8.595 9.904 10.132 1.317
ILM NJ 8194 NM_021805.1 SIGIRR 11141876 6.154 6.064 7.453 7.341 1.288
ILM N_21987 NR_001568.1 BCYRN1 34850482 9.587 9.520 10.871 10.809 1.287
ILMNJ3355 NM_001010915.1 PTPLAD2 58219057 9.790 10.076 11.178 11.249 1.280
IL N_6890 NM_021034.1 IFITM3 11995467 12.872 12.732 14.039 14.112 1.274
ILMN_20682 NMJ04482.2 GALNT3 9945386 6.947 6.969 8.191 8.260 1.267
IL N_6588 NM_001613.1 ACTA 2 4501882 8.957 9.148 10.216 10.380 1.245
IL NJ 091 NMJ24581.3 C6orf60 40255142 5.797 6.049 7.165 7.143 1.231
ILM NJ 0210 NMJ30666.2 SERPINB1 20149554 8.976 8.964 10.174 10.226 1.229
ILM N_37765 XM_379668.3 LOC286208 89029959 7.301 7.257 8.543 8.462 1.223
ILM NJ7673 NMJ24298.2 LENG4 23308571 8.149 8.092 9.340 9.293 1.196
I LMN 12918 NM 022308.1 ICA1 12545396 9.005 9.006 10.204 10.200 1.196
ILMN_26916 NMJ04292.2 RIN1 68989255 8.743 8.745 7.574 7.626 -1.144
ILM NJ 9302 NMJ32823.3 C9orf3 24432057 8.423 8.535 7.311 7.313 -1.167
ILM N_22366 NMJ24617.2 ZCCHC6 58331271 11.512 11.524 10.403 10.279 -1.177
ILM NJ7864 NM_016525.3 UBAP1 22212941 10.120 9.984 8.809 8.839 -1.228
ILMN_9696 NM_014872.1 ZBTB5 7662073 7.985 8.136 6.645 6.955 -1.260 Table 1 , continued
IL N_5444 NMJD06285.2 TESK1 66932998 8.705 8.709 7.468 7.401 -1.273
ILMN_71954 Hs.25318 0 4406612 9.975 9.822 8.562 8.630 -1.302
IL _6140 NM J 74921.1 LOC201895 28372538 10.256 10.467 8.902 9.138 -1.342
ILMNJ 2831 NM_024302.3 MMP28 73808269 7.725 7.603 6.468 6.168 -1.346
IL NJ 3962 NM_001752.2 CAT 60302919 10.147 10.092 8.744 8.802 -1.347
ILMN_25965 NM_002202.1 ISL1 4504736 7.464 7.408 5.986 6.154 -1.366
IL N_28002 NM J 81353.1 ID1 31317296 11.104 11.119 9.866 9.606 -1.375
IL N_71591 Hs.19339 0 34191392 9.050 9.362 7.756 7.901 -1.377
ILMN_25748 NM_004432.1 ELAVL2 4758261 6.796 7.146 5.652 5.482 -1.403
I L N 87528 Hs.363156 0 18204365 7.867 8.105 6.289 6.682 -1.500
ILMN_7334 NM J 52574.1 C9orf52 22749190 8.734 8.928 7.296 7.329 -1.518
ILMNJ 0998 NM_014899.2 RHOBTB3 41281538 9.363 9.379 7.670 7.926 -1.573
ILMN_28967 NM_001540.2 HSPB1 4996892 15.340 15.213 13.665 13.742 -1.573
ILMN_29543 NMJD22165.2 LIN7B 56676320 7.506 7.491 5.670 6.075 -1.626
ILMNJ 9881 NMJ14214.1 IMPA2 7657235 11.886 11.636 10.041 10.189 -1.646
ILMNJ 5434 NM_012464.3 TLL1 22547220 7.141 7.258 5.629 5.453 -1.658
ILMNJ 3399 NM_001032278.1 MMP28 73808270 9.191 9.401 7.563 7.657 -1.686
ILMN_28493 NM_023944.1 CYP4F12 13184045 8.332 7.791 6.306 6.320 -1.749
ILM N_491 NM_021945.4 C6orf85 48526515 9.805 10.136 8.218 8.188 -1.768
ILMNJ 2568 NMJD13281.2 FLRT3 38202220 11.298 11.262 9.412 9.611 -1.769
ILMNJ 9665 NM J 98061.1 CES2 37622886 12.511 12.583 10.726 10.811 -1.778
ILMN_20221 NM_000896.1 CYP4F3 4503240 9.989 9.289 7.637 8.071 -1.785
ILMNJ 4614 NMJD00104.2 CYP1B1 13325059 9.757 9.954 7.779 8.316 -1.808
ILMNJ 6111 NM_023938.4 C1otfl16 56550121 10.183 10.415 8.503 8.460 -1.818
IL N_8954 NM_001072.2 UGT1A6 45827764 7.604 7.731 5.698 5.975 -1.831
ILMNJ 3999 NMJ)01018109.1 PIR 66363696 11.156 10.976 9.006 9.424 -1.851
I LMN 18558 NM 001512.2 GSTA4 23065568 11.286 11.093 9.1 1 9.476 -1.881
ILMN_26717 NM_006308.1 HSPB3 5453687 6.670 7.335 5.450 4.787 -1.884
ILMNJ 7286 NMJD02304.1 LFNG 58696421 7.679 7.992 6.198 5.600 -1.936
IL N_3066 NM_000599.2 IGFBP5 46094066 7.516 7.814 5.907 5.536 -1.944
ILMN_22744 NM_004925.3 AQP3 22165421 8.002 7.766 5.865 5.963 -1.969
ILMN_26240 NM_001257.3 CDH13 61676095 9.924 9.931 7.832 8.061 -1.981
ILM _9965 NM_033260.2 FOXQ1 32526906 9.762 10.181 7.942 7.937 -2.032
ILMNJ1871 NMJD03739.4 A R1C3 24497582 13.929 13.528 11.627 11.708 -2.061
ILMNJ 5343 NM J 53034.2 ZNF488 40255102 8.810 9.439 6.988 7.102 -2.080
I LMN 15188 NM 014689.1 DOC 10 58037090 7.811 7.347 5.178 5.807 -2.086
ILMN_22014 NM_001001548.1 CD36 48375179 7.526 7.709 5.133 5.860 -2.121
IL NJ3390 NMJD00691.3 ALDH3A1 22907048 12.978 12.009 10.311 10.379 -2.148
ILMN_26004 NM_213600.2 PLA2G4F 67972428 7.237 7.156 5.463 4.632 -2.149
IL NJ3829 NMJD02167.2 ID3 32171181 11.670 11.948 9.546 9.686 -2.193
IL _6827 NM_001885.1 CRYAB 4503056 8.985 9.125 6.820 6.758 -2.266
ILMNJ 39156 NM_001001669.1 FU41603 48717281 9.598 10.022 7.669 7.382 -2.285
ILMN_92725 Hs.444329 0 34365191 10.075 9.594 7.348 7.739 -2.291
ILM J 890 NM_000421.2 KRT10 40354191 12.224 13.005 10.177 10.301 -2.375
ILMNJ 5305 NMJ06456.1 ST6GALf*C2 5454091 9.604 10.207 7.223 7.524 -2.532
ILMN_20483 NM_001005340.1 GPNMB 52694751 9.470 11.604 7.834 8.150 -2.545
ILMN_23211 NMJD02612.2 PDK4 33589822 10.919 10.492 8.033 8.265 -2.556
ILMNJ 0249 NM_205862.1 UGT1A6 45827766 8.645 8.804 5.807 6.458 -2.592
IL N_796 NM_001218.3 CA12 45935381 11.675 11.715 9.061 9.047 -2.641
ILMNJ1770 NMJ05309.1 ΘΡΤ 4885350 6.582 6.343 3.848 3.263 -2.907
ILMNJ3314 XM_927593.1 ATP10B 88986422 8.347 9.330 6.178 5.591 -2.954
ILMN_28481 NM_002166.4 ID2 33946335 9.044 9.557 6.094 6.564 -2.972
I LMN 30616 XM 940680.1 LOC648517 89061897 12.827 12.382 9.410 9.443 -3.178
ILMNJ1333 NM_014907.1 FRMPD1 7662415 6.933 6.604 2.459 4.555 -3.262 Table 2: The GAB2-Selected vs GAB2-Unselected Signature in MCF10A Cells
GAB2 GAB2 GAB2 GAB2 GAB2 SEL/
Gi UNS A UNS B SELA SEL B GAB2 UNS
Mum ina ID Accession Symbol
Accession Log(2) Log(2) Log (2) Log(2) Log(2)
Signal Signal Signal Signal Ratio
ILMNJ 3685 NM_002638.2 PI3 31657130 5.545 5.843 11.031 11.487 5.565
ILMN_856 NM J 72080.1 CAMK2B 26051209 3.954 3.868 9.506 9.305 5.495
ILMN 22039 NM 000334.1 SERPINF2 11386142 4.113 4.597 8.634 8.707 4.316
IL N_9057 NM_006472.1 TXNIP 5454161 9.574 9.273 13.510 13.457 4.060
ILMN_5259 NM_032432.2 ABLIM2 40316949 3.644 4.573 7.966 8.196 3.973
ILMNJ 38170 NM_001001873.2 LOC283174 51036610 3.248 5.545 8.225 8.256 3.844
IL N_9653 NM J 39277.1 KLK7 21327704 7.232 6.409 10.471 10.674 3.752
ILMNJ 2238 NM_206963.1 RARRES1 46255042 5.274 5.912 8.953 9.110 3.438
IL N_8120 NM_020037.1 ABCC3 9955971 7.184 7.604 10.860 10.764 3.418
IL N_23106 NM_032501.2 ACSS1 28416952 5.182 5.469 8.861 8.612 3.411
IL N_28364 NM J 99161.1 SAA1 40316909 8.207 7.863 11.480 11.369 3.390
ILMNJ 9268 NM_001185.2 AZGP1 38372939 5.810 6.022 9.044 9.430 3.321
ILMNJ 38896 N _001692.2 ATP6V1B1 19913425 7.537 7.448 10.700 10.620 3.168
ILMNJ 6107 NM_001085.4 SERPINA3 73858562 11.730 11.833 14.832 14.956 3.113
ILMN _24486 NM_006105.3 RAPGEF3 45269150 6.131 5.561 8.971 8.712 2.996
ILMNJ 3615 NM_004433.3 ELF3 40255034 6.164 5.865 8.898 9.044 2.957
ILMNJ 2676 NM_004464.1 ETV5 4768315 5.202 5.603 8.589 8.113 2.949
ILMN 2104 NM 201553.1 FGL1 42544201 3.018 4.926 6.800 6.938 2.897
ILMNJ 9880 NM_000636.2 SOD2 67782304 5.142 5.443 7.916 8.399 2.866
ILMN_2486 NM_001570.3 IRAK2 58530885 5.117 5.615 8.111 8.299 2.839
IL N_3897 NM_001166.3 BIRC3 33946283 7.422 7.272 10.042 10.274 2.811
ILMNJ 0965 NM_002888.2 RARRES1 46255040 5.296 5.318 7.878 8.268 2.765
ILMN_21166 NM_005746.1 PBEF1 5031976 7.596 7.272 10.049 10.208 2.694
IL N_29078 NM_002083.1 CXCL2 4504154 3.609 4.472 6.244 7.176 2.669
ILM N J 38775 XM_936314.1 CAMK2B 89025915 4.766 5.100 7.590 7.565 2.645
IL N_8119 NM_000667.2 ADH1A 11496886 6.285 6.031 8.592 8.847 2.561
ILMNJ 7166 NM_001511.1 C CL1 4504152 7.293 7.284 9.685 9.970 2.539
IL N_29321 NM_001734.2 C1S 41393600 5.695 4.916 7.753 7.914 2.528
ILMN_22286 NM_005950.1 MT1G 10835229 6.615 6.083 8.729 8.959 2.495
ILMN_21545 NM_015864.2 C6orf32 14277689 5.396 5.552 7.830 8.055 2.469
IL N_6890 NM_021034.1 IFITM3 11995467 11.655 11.562 14.039 14.112 2.467
ILMNJ 4995 NM_004460.2 FAP 16933539 5.914 6.514 8.539 8.789 2.450
ILMN 12248 NM 001710.4 CFB 67782357 8.138 8.504 10.572 10.920 2.425
ILMN_32659 XM_940668.1 IGSF9B 83035217 4.760 5.146 7.433 7.256 2.391
IL N_23395 NM_000170.1 GLDC 4504012 7.184 7.106 9.649 9.407 2.383
ILMNJ 9577 NM J 78868.3 CMTM8 32130535 5.009 5.845 7.741 7.870 2.379
ILMNJ 23833 Hs.571652 0 13579761 4.104 4.711 6.471 7.080 2.368
ILMNJ 6372 NM_006291.2 TNFAIP2 26051239 4.873 4.912 7.307 7.180 2.351
ILMN_43594 XM_438369.2 LOC441013 88974818 9.170 9.081 11.350 11.544 2.322
ILM N J 35987 Hs.583806 0 21177747 8.749 8.605 10.857 11.113 2.308
ILMNJ 5973 NM_012116.2 CBLC 20149595 4.931 4.766 7.180 7.120 2.302
ILMNJ 0592 NM_000143.1 FUT3 4503808 5.739 6.309 8.447 8.189 2.294
ILMNJ 4229 NM_005630.1 SLC02A1 5032094 6.431 6.038 8.420 8.631 2.291
IL N_9112 NM_003378.2 VGF 17136077 4.912 4.162 6.672 6.983 2.291
ILMNJ 9619 NM_018530.1 GSDML 8924175 4.413 4.912 7.047 6.746 2.234
IL N_4882 NM_003246.2 ΤΗΒΞ1 40317625 6.830 6.807 9.139 8.960 2.231
ILMNJ 5792 NM_031415.1 MLZE 13899220 4.009 4.070 6.221 6.287 2.214
ILMN 2247 NM 000584.2 IL8 28610153 3.548 4.079 6.100 5.928 2.201
ILMN_24167 NM_002658.2 PLAU 53729348 7.204 7.239 9.471 9.368 2.198
IL NJ 025 NM_001080.3 ALDH5A1 25777719 5.315 5.236 7.541 7.401 2.195 Table 2, continued
ILMNJ 2302 NMJ32793.2 MFSD2 42713695 6.881 7.313 3.227 9.329 2.181
ILMN_25923 NMJ01005619.1 ITGB4 54607026 10.336 10.242 12.512 12.337 2.166
ILM N_11856 NM_207336.1 ZNF467 46409309 7.417 7.527 3.788 3.486 2.165
ILM N_19834 NM_001733.4 C1R 66347874 6.042 6.081 7.383 8.428 2.146
ILMNJ 6362 NM J) 01005474.1 NFKBIZ 53832023 6.156 6.644 8.328 8.730 2.129
IL _4328 NM_018948.2 ERRFI1 21314673 10.016 9.711 11.322 12.063 2.129
ILM _7980 NMJ 38712.2 PPA RG 62865852 5.514 5.687 7.840 7.615 2.127
ILMN_22529 NMJ06169.2 NNMT 62953139 10.465 10.377 12.663 12.386 2.103
ILMN_26715 NM_006509.2 RE LB 35493877 5.689 5.373 7.503 7.691 2.066
ILMN 11116 NM 016424.3 CROP 52426741 7.484 7.368 3.274 3.708 2.065
ILMNJ 0642 NMJ00991.3 RPL28 34486095 8.763 8.372 10.343 10.915 2.064
ILMNJ 6665 NM_018478.2 C20orf35 56676381 3.103 3.013 11.023 11.222 2.064
ILM N_28619 NM_000336.1 SCNN1FJ 4506816 6.490 6.446 8.334 8.721 2.059
ILM N_28190 NM_005449.3 FAIM3 34147517 5.482 5.711 7.548 7.724 2.039
ILM N_35015 XM_943094.1 LOC162073 89040701 9.048 8.907 10.869 11.153 2.034
ILMN_23984 NM_001338.3 CXADR 45827793 8.318 8.205 10.272 10.307 2.028
ILM N_22874 NMJ 82565.2 FAM100B 34222379 7.864 8.034 10.108 9.898 2.024
ILMN_25208 NMJ05534.2 IFNGR2 47419933 9.877 3.556 11.743 11.731 2.021
ILMN_25185 NMJ02982.3 CCL2 56119169 5.263 6.132 7.480 7.965 1.995
ILM N_23474 NMJ 44626.1 TMEM125 21389442 6.571 6.843 8.635 8.758 1.989
ILM N_24488 NM_024320.2 ATAD4 34147376 7.725 8.161 9.948 9.884 1.973
IL NJ376 NMJ 52594.1 SPRED1 22749220 7.296 7.370 9.390 9.211 1.968
ILMN_12497 NMJ02970.1 SAT 4506788 10.064 3.328 11.834 12.081 1.962
IL _6000 NM_000610.3 CD44 48255934 5.031 5.236 6.857 7.392 1.961
ILMN_17789 NM_000418.2 IL4R 56788403 3.476 3.440 11.531 11.230 1.953
ILM _2205 NM_020531.2 C20on*3 41327713 3.319 3.260 11.260 11.218 1.949
ILM N_775 NM_207311.1 CCDC64 46409267 9.599 3.663 11.584 11.571 1.946
IL N_6029 NM_203379.1 ACSL5 42794757 7.342 7.425 3.215 9.439 1.944
IL _9407 NMJ04843.2 IL27RA 18379338 6.190 6.227 7.341 8.354 1.939
IL _3488 NM_003357.3 SCGB1A1 39725696 5.508 4.345 6.862 7.430 1.919
ILM N_22135 NM_003900.3 SQSTM1 46251280 9.180 3.234 11.303 10.939 1.917
ILMN_20440 NMJ 39314.1 ANGPTL4 21536397 6.166 6.508 8.262 8.198 1.893
ILMN_12288 NMJ20879.1 IAA1505 55741666 5.482 6.055 7.407 7.915 1.892
ILM N_12372 NM_014428.1 TJP3 10092690 6.891 6.874 8.661 8.883 1.889
IL N_7322 NM_002291.1 LAH/I B1 4504950 9.341 3.374 11.117 11.365 1.884
ILMN_3001 NM_080591.1 PTGS1 18104368 4.536 4.733 6.461 6.552 1.869
ILM _6876 NM_003632.1 CNTNAP1 4505462 5.661 5.802 7.673 7.527 1.869
ILM N_18673 NM_002346.1 LY6E 4505048 10.200 10.252 12.074 12.103 1.862
ILMN_28594 NMJ00900.2 MGP 43574513 5.744 5.178 6.354 7.679 1.855
ILMN 14234 NM 016947.1 C6orT48 8333383 7.583 7.655 3.535 9.339 1.847
ILM N_24075 NM_000428.2 LTBP 46389563 4.959 5.225 6.303 6.953 1.836
ILMN_22221 NMJ 52286.2 C9otf111 38524588 6.601 6.501 8.470 8.296 1.832
IL _4207 NM_012329.2 MMD 52630444 6.589 6.803 8.371 8.672 1.823
ILM N_25134 NM_021809.4 TGIF2 39777595 6.327 6.527 8.361 8.127 1.817
ILMN_16420 NMJ21178.2 CCNB1IP1 33519433 7.985 8.322 3.333 9.918 1.802
ILMN_79597 Hs.159264 0 1710274 4.959 5.154 6.603 7.093 1.794
ILM N_21913 NM_017420.2 SIX4 61837500 6.358 6.781 8.361 8.365 1.793
ILM N_36990 XM_930914.1 LOC653108 89058118 5.591 5.684 7.262 7.588 1.788
ILM N_11739 NMJ02198.1 IRF1 4604720 9.662 9.475 11.300 11.400 1.782
ILMNJ 37046 NMJ00985.2 RPL17 14591906 9.443 3.381 11.012 11.363 1.775
ILM N_14011 NM_004666.1 VNN1 4759311 6.392 6.385 7.360 8.363 1.773
ILM N_19983 NM_001252.2 TNFSF7 24119161 8.057 8.037 3.832 9.803 1.770
ILMN_2142 NM_002727.2 PRG1 45335370 5.536 5.312 7.432 7.534 1.759
ILM N_26705 NM_006307.2 SRPX 21314633 7.438 7.288 8.370 9.271 1.757
ILMN 4070 NM 002354.1 TACSTD1 4505058 8.618 8.724 10.187 10.670 1.757
ILMNJ 5358 NMJ01839.2 CNN3 47080036 8.545 8.636 10.383 10.308 1.755
ILM N_14847 NM_032421.1 CYLN2 14702161 8.211 8.200 3.334 9.927 1.755 Table 2, continued
ILMNJ 1198 NM_005953.2 MT2A 31543214 13.561 13.416 15.221 15.263 1.754
ILMN_2038 NMJ06435.1 IFITM2 10835237 12.922 12.828 14.567 14.685 1.751
ILMN_27583 NM J 98053.1 CD3Z 37595564 5.329 5.300 7.018 7.103 1.746
ILMN_21293 NMJ05239.4 ETS2 56119171 8.371 8.263 10.157 9.967 1.745
ILMN_25961 NMJ01992.2 F2R 6031164 6.650 7.080 8.652 8.658 1.740
ILMN_9525 NMJ81782.2 NCOA7 42476174 9.509 9.252 10.975 11.261 1.737
ILMNJ 4782 NMJD13363.2 PCOLCE2 16904386 5.450 4.678 6.691 6.894 1.728
ILMN_26866 NMJ30938.2 TMEM49 20070348 7.198 7.029 8.738 8.912 1.712
ILMN_28725 NMJ03714.2 STC2 61676085 7.231 7.630 9.195 9.081 1.707
ILMN 17046 NM 080797.2 DID01 71044476 6.963 7.009 8.682 8.665 1.687
ILMNJ 6130 NM_006997.2 TACC2 45827751 11.101 10.880 12.627 12.697 1.671
ILMNJ 737 NM_005946.2 MT1A 71274112 13.304 13.101 14.904 14.836 1.668
ILMNJ 3004 NM_016535.3 ZNF581 50592985 10.139 10.070 11.820 11.723 1.667
ILMN_28415 NM_018234.2 STEAP3 59853424 9.125 9.277 11.044 10.680 1.661
ILMN_2929 NM_001958.2 EEF1A2 25453470 5.146 5.109 6.828 6.744 1.659
ILMNJ 7834 NM_006287.3 TFPI 40254845 9.324 9.425 10.988 11.077 1.658
ILMN_2113 NM_053000.1 TIGA1 16506300 11.207 11.193 12.721 12.983 1.652
ILMNJ 09653 Hs.547824 0 13572203 6.981 6.484 8.457 8.303 1.648
ILMNJ 0862 NM_001017915.1 INPP5D 64085166 8.602 8.658 10.321 10.195 1.628
ILMN_3809 NMJ 48177.1 FFJX032 22547143 8.644 8.478 10.171 10.204 1.627
ILMN_29B19 NM_022154.5 SLC39A8 59710105 8.170 8.316 9.638 10.095 1.624
ILMNJ 289 NMJ00930.2 PLAT 14702165 6.732 6.997 8.608 8.367 1.624
ILMN_2840 NMJ03986.1 FJFJOX1 4502368 8.822 8.749 10.343 10.470 1.621
ILMN_23858 NM_201525.1 GPR56 41584197 7.644 7.718 9.332 9.270 1.619
ILMN 12517 NM 022746.2 MOSC1 33285009 5.995 6.238 7.742 7.707 1.607
ILMN ! 4466 NM_003641.2 IFITM1 40254449 6.387 6.144 7.895 7.816 1.590
ILMNJ 37635 NMJ 33646.1 ZAK 19526766 9.555 9.659 11.013 11.376 1.588
ILMN_30361 NM_003896.2 ST3GAL5 28373079 4.700 5.311 6.681 6.503 1.586
ILMN_23476 NM_005980.2 S100P 45827727 8.725 8.574 10.232 10.216 1.575
ILMNJ 1202 NM_000481.2 AMT 44662837 6.966 6.825 8.480 8.461 1.575
ILMNJ 5082 NM_002012.1 FHIT 4503718 6.051 6.215 7.671 7.730 1.567
ILMNJ 7B82 NM_000201.1 ICAM1 4557877 5.194 5.426 6.784 6.966 1.565
ILMN_26B43 NM_080489.2 SDCBP2 38044113 6.265 6.317 7.897 7.789 1.552
ILMN_25878 NM_022817.1 PER2 12707561 7.787 7.788 9.164 9.513 1.551
ILMN_22093 NM_016323.1 HERC5 7705930 6.289 6.736 8.075 8.043 1.546
ILMNJ 5059 NM_013269.2 CLEC2D 52426785 10.826 10.913 12.253 12.563 1.538
ILMN_23549 NMJ 99327.1 SPRY1 40788000 5.413 5.621 6.891 7.209 1.534
ILMNJ 38827 NMJ 45810.1 CDCA7 22027513 7.118 7.481 8.885 8.779 1.533
ILMNJ 7170 NMJ 47161.2 ACOT11 25777709 8.295 8.384 9.984 9.735 1.520
ILMN 78206 Hs.145444 0 10432759 5.858 6.140 7.541 7.493 1.518
ILMNJ 0035 NM_002350.1 LYN 4505054 8.297 8.312 9.733 9.894 1.509
ILMNJ 7874 NM_005178.2 BCL3 20336471 6.208 5.986 7.711 7.491 1.504
ILMNJ 38392 NM_000997.3 RPL37 60218902 8.574 8.615 10.010 10.185 1.504
IL N_22527 NM_001031628.1 LOC57228 76496484 5.304 5.392 6.695 6.989 1.494
ILMN_32937 XM_939583.1 LOC650495 89056995 6.329 6.580 7.699 8.154 1.472
ILMN_6704 NM_001006623.1 DR33 55743158 5.615 5.771 7.025 7.304 1.471
ILMN_28050 NMJ 52705.1 MGC9850 22749406 8.516 8.519 9.956 10.016 1.468
ILMN_23535 NM_001196.2 BID 37574724 7.568 7.575 8.954 9.124 1.467
ILMN_21487 NM_003012.3 SFRP1 56117837 11.045 11.020 12.524 12.469 1.464
ILMN_23042 NM_001550.2 IFRD1 55953128 7.379 7.445 8.895 8.851 1.461
ILMN_5657 NM_006762.1 LAPTM5 5803055 7.131 7.249 8.616 8.679 1.457
ILMNJ 4880 NM_005384.2 NFIL3 52630428 8.886 8.659 10.066 10.339 1.430
ILMN_7434 NM_033306.2 CASP4 73622124 9.158 9.117 10.564 10.571 1.430
ILMNJ 9002 NM_000110.2 DPYD 4557874 6.961 6.920 8.188 8.553 1.430
ILMN 37027 XM 939368.1 LOC654103 89028579 9.465 9.345 10.726 10.944 1.430
ILMN_5773 NM_004776.2 B4GALT5 13929470 7.702 7.819 9.101 9.273 1.427
ILMNJ 7950 NM_024032.2 C17orf53 31543178 7.394 7.397 8.839 8.799 1.424 Table 2, continued
ILMNJ0324 NMJ14062.1 NOB1P 7661531 7.573 7.569 8.930 9.050 1.419
ILMN_2827 NM J 81077.2 GOLGA8A 66363690 6.350 6.453 7.604 8.032 1.417
IL N_38384 XM J43699.1 C6otf160 88999261 11.758 11.549 12.859 13.279 1.415
ILM N_414 NM J) 01007075.1 KLHL5 55770879 7.973 7.633 9.108 9.324 1.413
ILMNJ2329 XM_937528.1 C10on73 89031946 5.206 5.389 6.746 6.652 1.402
ILMNJ 4285 NMJ 74912.2 FU31204 30410024 6.894 6.981 8.375 8.300 1.400
ILMNJ 8682 NM_025079.1 ZC3H12A 13376631 7.039 6.760 8.230 8.362 1.396
ILMNJ 6445 NMJ02928.2 RGS16 34452689 7.065 7.112 8.373 8.586 1.392
ILMNJ 1237 NMJI21944.1 C14orf33 11345485 9.046 9.021 10.461 10.368 1.381
ILMN 14562 NM 020744.2 MTA3 50878291 8.186 8.154 9.611 9.471 1.371
ILMNJ? 0291 NMJ21212.1 ZF 10864024 7.147 7.252 8.444 8.667 1.356
ILMNJ! 8796 NMJ 72027.1 ABTB1 25777623 7.762 7.740 9.187 9.025 1.355
IL N_3780 NMJI13312.1 HOOK2 7019410 7.131 7.531 8.832 8.538 1.354
ILMNJ 7241 NMJI23009.4 MARC SL1 32401423 5.739 5.797 7.055 7.181 1.350
ILMNJ 8617 NM_03142B.2 CSotf58 50428929 9.185 9.207 10.673 10.417 1.349
ILMN_30002 NMJ12427.3 KL 5 22208993 6.685 6.800 8.063 8.111 1.345
IL NJ 429 NMJ) 01032281.1 TFPI 73760408 8.019 7.995 9.248 9.449 1.341
ILMNJ 0210 NMJI30666.2 SERPINB1 20149554 8.917 8.801 10.174 10.226 1.341
ILMN_30110 NMJ02342.1 LTBR 4505038 9.263 9.277 10.562 10.660 1.341
ILMNJ 6803 NMJ 72174.1 IL15 26787983 6.267 6.556 7.855 7.648 1.340
ILM N_3011 NMJI00386.2 BLMH 33591068 8.467 8.605 9.887 9.864 1.340
ILMNJ3563 NMJI05195.2 CEBPD 28872797 10.666 10.707 11.979 12.057 1.331
ILMNJ 5496 NM ) 01024668.1 LETMD1 67089166 8.272 8.120 9.498 9.518 1.312
IL NJ5835 NMJ 98129.1 LAM A3 38045909 9.868 9.903 11.261 11.118 1.304
ILMN 16629 NM 005952.2 MT1X 31543213 13.728 13.606 14.976 14.946 1.294
ILMNJ5772 NM ) 01010853.1 ACY1L2 58082084 7.136 7.165 8.350 8.538 1.294
ILMN_74159 Hs.91389 0 60552342 7.830 7.725 9.119 9.019 1.291
ILMNJ? 2701 NMJ 38288.2 C14orf147 46358345 9.450 9.287 10.713 10.578 1.277
ILMNJ 7925 NMJI31477.3 YPEL3 40255198 7.262 7.235 8.425 8.609 1.269
ILMNJ? 5787 NM J24310.2 PLEKHF1 31543411 5.469 5.347 6.764 6.573 1.260
ILMNJ603 NM J21242.3 MID1IP1 39725681 7.904 7.873 9.168 9.124 1.257
ILMNJ 5649 NMJ 52221.2 CSNK1E 40549400 8.261 8.477 9.636 9.609 1.254
ILMNJ? 6929 NM J0403B.2 ADCY3 10947058 9.792 9.764 11.007 10.991 1.221
ILMNJ 7550 NMJ06963.3 ZNF22 55775473 6.784 6.807 8.024 7.997 1.215
ILMNJ 8913 NMJI05803.2 FLOT1 6552331 7.912 7.890 9.155 9.071 1.212
IL NJ5272 NM_017823.3 DUSP23 56786143 11.807 11.774 12.951 12.955 1.163
ILMNJ 5537 NM ) 01017971.1 LOC92270 63003909 6.340 6.359 7.475 7.538 1.157
IL N_4354 NM J24336.1 IRX3 39930458 13.416 13.374 12.282 12.158 -1.175
ILMNJ? 8967 NMJI01540.2 HSPB1 4996892 15.104 14.844 13.665 13.742 -1.271
ILMN 28086 NM 001005741.1 GBA 54607044 8.073 7.958 6.713 6.728 -1.295
ILMNJ 37303 NM J0B888.2 CALM1 31377794 10.275 10.094 8.920 8.848 -1.300
IL NJ 475 NMJ18291.2 FU10986 21361729 8.858 8.768 7.323 7.610 -1.346
ILMNJ? 2825 NM_201650.1 LRRC23 42542395 8.422 8.428 7.049 7.106 -1.348
ILMNJ? 6237 NM J06082.2 K-ALPHA-1 57013275 14.730 1 .543 13.205 13.350 -1.359
ILMNJ 9382 NMJ02398.2 MEIS1 45006902 8.093 8.103 6.654 6.802 -1.370
ILMNJ 6042 NMJ 38463.2 TLCD1 34147548 8.576 8.624 7.270 7.148 -1.391
IL N_7061 NM J)13376.1 SERTAD1 7019524 9.561 9.686 8.196 8.239 -1.406
ILMNJ? 0854 NM_014187.1 HSPC171 7661829 9.525 9.526 8.138 8.067 -1.423
ILMNJ6679 XM J26231.1 LOC642829 89037242 7.820 7.952 6.370 6.553 -1.425
ILMNJ 8658 NMJI01456.1 FLNA 4503744 8.422 8.650 7.083 7.082 -1.453
IL NJ731 NM ) 01013672.2 LOC400566 62177143 7.199 7.354 5.802 5.838 -1.456
ILMN_21872 NMJ01956.2 EDN2 31642584 7.743 7.933 6.213 6.642 -1.460
IL N_2269 NMJI04924.3 ACTN4 34452697 10.974 10.971 9.572 9.429 -1.472
ILMNJ 8040 NM J00154.1 GALK1 4503894 10.374 10.277 8.828 8.876 -1.473
ILMN 2565 NM 001101.2 ACTB 5016088 12.978 12.768 11.369 11.418 -1.480
ILMNJ391 NM ) 01033030.1 FAI 74271910 9.644 9.745 7.945 8.421 -1.512
ILMNJ 0133 NMJ20349.2 ANKRD2 39812132 7.621 7.681 6.184 6.044 -1.537 Table 2, continued
ILMN_26165 NM_024684.2 PTD015 34328078 9.608 9.607 8.039 8.086 -1.545
IL N_8830 NM_001002857.1 AWXA2 50845385 10.850 10.697 3.243 9.193 -1.555
ILMNJ 0561 NM J 44653.3 BTBD14A 42716306 7.652 8.036 6.291 6.280 -1.559
ILMNJ 5343 NM J 53034.2 ZNF488 40255102 8.533 8.721 6.988 7.102 -1.582
ILMNJ 759 NMJ305175.2 ATP5G1 50659067 8.500 8.683 7.109 6.909 -1.582
IL N_6837 NM_007150.1 ZNF185 6005971 10.169 10.032 8.606 8.412 -1.592
ILMN_39336 XM_927609.1 LOC221710 88992438 8.253 8.077 6.571 6.567 -1.596
IL NJ 11840 Hs.554203 0 37547462 8.415 8.645 6.631 7.233 -1.598
ILMN_B827 NM_001885.1 CRYAB 4503056 8.372 8.412 6.820 6.758 -1.603
ILMN 26449 NM 152319.2 C12orf54 34303924 6.296 6.064 4.322 4.792 -1.623
ILMNJ 6111 NM_023938.4 C1orf116 56550121 10.105 10.143 8.503 8.460 -1.642
ILMN_6623 NM_005345.4 HSPA1A 26787973 11.687 11.878 9.956 10.315 -1.647
ILMN_37020 XM_938141.1 LOC647954 88988822 8.392 8.603 6.609 7.013 -1.686
ILMN_9690 NM J 98277.1 SLC37A2 38093648 8.272 8.334 6.555 6.641 -1.705
ILMNJ 9792 NM J 53714.1 C10orf67 24308459 5.977 5.426 3.536 4.372 -1.748
ILMN_27745 NM_005853.4 IRX5 47778932 9.486 9.535 7.592 7.929 -1.751
ILMN_25636 NM J 52666.1 PLD5 22749352 9.151 9.006 7.234 7.278 -1.823
ILMN_24250 NM_032319.1 C2on7 14150089 9.754 9.691 7.621 8.001 -1.912
ILMN >6204 NM_001159.3 AOX1 71773479 8.294 8.050 6.020 6.304 -2.010
ILMNJ 7578 NM_003944.2 SELENBP1 16306549 9.887 9.865 7.854 7.862 -2.018
ILMN_9547 NM_000702.2 ATP1A2 48762683 7.236 7.013 5.053 5.079 -2.059
ILMNJ 2568 NMJ313281.2 FLRT3 38202220 11.762 11.485 9.412 9.611 -2.112
ILMN_42462 XM_932354.1 LOC644760 88984918 8.964 8.955 7.022 6.556 -2.170
ILMNJ 8735 NM J 53046.1 TDRD9 42734387 8.453 8.369 6.018 6.350 -2.227
ILMN 29038 NM 052862.2 RCS D1 31377636 6.545 6.474 4.263 4.256 -2.250
IL N_6575 NM J 52780.2 FLJ14503 31343502 8.132 8.290 6.092 5.736 -2.297
IL NJ 37325 NM_031845.1 MAP2 14195617 8.651 8.540 6.269 6.302 -2.310
IL N_6829 NM_002167.2 ID3 32171181 12.029 12.000 9.546 9.686 -2.399
IL NJ 890 NM_000421.2 RT10 40354131 12.888 12.509 10.177 10.301 -2.460
ILMNJ 7286 NM_002304.1 LFNO 58696421 8.275 8.538 6.198 5.600 -2.508
ILMN_34738 XM_945010.1 LOC651913 83057548 10.900 10.601 8.143 8.277 -2.541
ILMNJ!8481 NM_002166.4 ID2 33946335 9.292 9.304 6.094 6.564 -2.969
Table 3: The GAB2UNS vs GFPUNS Signature in MCF10A Cells
GFP GFP G B2 GAB2 GAB2 UNS 1
Gi UNS A UNS B UNS A UNS B GFP UNS
Ilium ina ID Accession Symbol
Accession Log(2) Log(2) L g(2) Log(2) Log (2)
Signal Signal Signal Signal Ratio
ILMN_4712 NMJ300624.3 SERPINA5 34147643 6.242 5.993 9.707 9.622 3.547
ILM _7403 N J01425.1 EMP3 4503562 8.002 7.741 10.614 10.550 2.710
ILM N_26085 NMJ303467.2 O OR4 56790928 5.036 4.848 7.576 7.441 2.567
ILM N_26451 NM_001008540.1 CXCR4 56790926 5.579 5.472 8.053 7.857 2.429
ILM _9547 NMJ300702.2 ATP1A2 48762683 5.138 4.661 7.236 7.013 2.226
ILMNJ 38267 NM_000211.1 ITGB2 4557885 5.248 5.579 7.455 7.758 2.193
ILM N_34738 XM_945010.1 LOC651913 89057548 8.577 8.598 10.900 10.601 2.163
ILM NJ1551 N _201548.3 CERKL 65301113 5.022 5.396 7.201 7.229 2.006
ILM N_89562 Hs.400256 0 21 49338 5.961 6.404 8.172 8.163 1.985
ILM N_10408 NMJ320351.2 COL8A1 32895367 6.564 6.682 8.580 8.591 1.963
ILM N_21136 NMJ314411.2 NAGS 77404393 6.580 6.509 8.361 8.630 1.951
ILMNJ 38577 NMJ331311.2 OPVL 22027517 7.307 7.570 9.341 9.404 1.934
ILMN 17578 NM 003944.2 SELENBP1 16306549 8.163 7.772 9.887 9.865 1.909
ILM N_20249 NM_001006933.1 TCEAL3 55749430 7.912 7.977 9.802 9.801 1.857
ILMNJ 26586 Hs.574405 0 10437775 5.789 5.632 7.422 7.674 1.837
ILM N_22032 NMJ307168.2 ABCA8 41327761 5.672 5.952 7.623 7.649 1.824
ILM N_74694 Hs.101003 0 27838294 6.728 6.464 8.377 8.429 1.807
ILMNJ5657 NMJ306762.1 LAPTM5 5803055 5.396 5.413 7.131 7.249 1.786
ILM N_821B5 Hs.210390 0 10435476 7.049 6.759 8.61 8.727 1.767
ILMN_4794 NMJ53750.1 C21orf81 24371249 6.358 6.359 8.042 8.208 1.767
ILM N J? 7289 NM_002578.2 PA 3 46249379 6.940 6.721 8.581 8.595 1.757
ILM N_ 2462 XM_932354.1 LOC644760 88984918 7.238 7.167 8.964 8.955 1.757
ILM N_21872 N J01956.2 EDN2 31542584 6.142 6.025 7.743 7.933 1.755
ILM J5489 NMJ52527.3 SLC16A14 42415495 5.533 6.209 7.662 7.542 1.731
ILM NJ 7796 NMJ302274.2 KRT13 24234693 6.029 5.993 7.670 7.753 1.700
ILM NJ 9777 NMJ82796.1 MAT2B 33519454 7.719 7.720 9.500 9.265 1.663
ILMNJ 16404 Hs.563578 0 1364286 5.213 5.190 6.580 7.084 1.631
ILMN 16225 NM 003155.2 STC1 61676083 5.600 5.194 6.892 7.080 1.589
ILM N_25536 NMJ302386.2 MC1R 27477128 6.589 6.416 8.165 8.002 1.581
ILM N_29894 NMJ302975.2 CLEC11A 37595568 5.609 5.805 7.219 7.322 1.564
ILMNJ 896 NM_001006937.1 TGEAL4 55749458 11.234 11.339 12.873 12.782 1.541
ILMNJ 24625 Hs.572444 0 10437827 6.755 7.039 8.456 8.386 1.524
ILM N_29854 NMJ53233.1 FU36445 23397509 5.129 5.138 6.713 6.579 1.513
ILM N_36989 XM_936226.1 LOC653879 89057119 7.107 7.473 8.698 8.907 1.512
ILM NJ 9983 NMJ301252.2 TNFSF7 24119161 6.751 6.414 8.057 8.037 1.464
ILM NJ 8735 NMJ53046.1 TDRD9 42734387 6.864 7.030 8.453 8.369 1.464
ILM N_22373 NM_001033523.1 GUSBL1 75905496 6.450 6.794 8.264 7.895 1.457
ILM N_79861 Hs.162734 0 2276759 6.681 6.912 8.241 8.251 1.449
ILM N_975 NM_018945.3 PDE7B 57242789 5.527 5.695 6.965 7.154 1.448
ILMNJ11189 Hs.552999 0 7023310 5.588 5.576 7.104 6.939 1.440
ILMNJ 14516 Hs.560896 0 73475707 5.800 6.029 7.402 7.287 1.430
ILM N_27339 NM_015642.2 ZBTB20 52694664 6.343 6.318 7.779 7.705 1.411
ILMN 33220 XM_942936.1 LOG648394 89066728 7.205 7.141 8.601 8.560 1.408
ILMNJ 38785 NMJ378626.1 GDKN2C 17981698 7.113 7.094 8.341 8.638 1.386
ILM J 759 NMJ305175.2 ATP5G1 50659067 7.258 7.180 8.500 8.683 1.372
ILM _3755 NMJ303087.1 SNCG 4507112 6.751 6.958 8.194 8.237 1.361
ILMNJ 24068 Hs.571887 0 10437525 6.796 6.514 7.954 8.061 1.353
ILM N_90741 Hs.427242 0 27831153 6.423 6.650 7.840 7.922 1.345
ILM NJ 7250 NMJ304172.3 SLC1A3 34222301 6.568 6.564 7.946 7.875 1.345
ILM N_29462 NM_014343.1 CLDN15 7656980 7.016 7.062 8.395 8.335 1.326
ILMNJ 08776 Hs.545615 0 2901934 5.815 5.840 6.983 7.317 1.322 Table 3, continued
ILM N_114910 Hs.561493 0 27880608 5.970 5.838 7.150 7.295 1.318
ILMN_22825 NM_201650.1 LRRC23 42542395 7.167 7.052 8.422 8.428 1.316
ILMNJ 0248 NM_003195.4 TCEA2 38505154 7.343 7.228 8.656 8.541 1.313
ILMNJ 6651 NM_001976.2 EN03 16554590 6.695 6.610 7.895 8.025 1.307
ILMN_37765 XM_379668.3 LOC286208 89029959 6.607 6.598 7.874 7.946 1.307
ILMN_80530 Hs.176498 0 51474918 8.170 8.015 9.272 9.336 1.211
ILMN_23180 NM_006332.3 IFI30 29826337 9.149 9.134 10.308 10.366 1.195
ILMNJ 2021 NM_001407.1 CELSR3 13325065 7.182 7.307 8.483 8.334 1.164
IL N_4354 NM_024336.1 IRX3 39930458 12.254 12.227 13.416 13.374 1.154
I LMN 11901 NM 012302.2 LPHN2 57165356 9.209 9.252 8.127 7.995 -1.169
ILMNJ 9508 NM_014878.3 KIAA0020 33620772 9.937 10.048 8.847 8.795 -1.171
IL N_5224 NMJ31412.2 GABARAPL1 56676368 11.212 11.262 10.044 9.939 -1.245
ILMN_4452 NMJ303598.1 TEA D2 20070102 9.571 9.445 8.247 8.215 -1.277
ILMN_23486 NMJ73354.2 SNF1LK 48762713 9.364 9.689 8.229 8.241 -1.292
IL N_7334 NMJ52574.1 C9otf52 22749190 8.313 8.663 7.139 7.240 -1.299
ILMN_5444 NM_006285.2 TESK1 66932998 8.513 8.576 7.248 7.205 -1.318
ILMN_4328 NM_018948.2 ERRFI1 21314673 11.118 11.288 10.016 9.711 -1.340
ILMNJ 4281 NM_016359.2 NUSAP1 59710089 10.471 10.591 9.150 9.232 -1.340
ILMNJ 4524 NM_032899.4 FA 83A 46255015 10.557 10.633 9.364 9.132 -1.347
ILMNJ 4636 NM_207006.1 FA 83A 46255016 8.831 8.809 7.362 7.571 -1.354
ILMN_6798 NM_018474.2 C20oif19 32189414 7.879 7.714 6.349 6.501 -1.372
ILMN_6776 NM_016282.2 AK3 19923436 11.967 11.938 10.655 10.494 -1.378
ILMNJ 3696 NM_001011709.1 PNLIPRP3 58743370 9.563 9.535 8.195 8.146 -1.379
ILMN_29031 NMJ25182.2 KIAA1539 33636716 8.995 8.808 7.477 7.559 -1.383
ILM N 2937 NM 004839.2 HOMER2 46249348 8.573 8.453 7.075 7.184 -1.383
ILMN_26473 NM_018404.1 CENTA2 8923762 7.577 7.388 6.087 6.104 -1.387
IL N_3344 NMJ52362.1 TNFAIP8L1 22748780 8.151 8.260 6.823 6.796 -1.396
ILMN_23940 NM_003979.3 GPRC5A 63252917 7.984 8.089 6.456 6.805 -1.406
ILMN_28045 NMJ303064.2 SLPI 15834622 12.860 12.883 11.456 11.440 -1.423
ILM J 37089 NM_018304.1 PRR11 8922831 8.056 7.756 6.354 6.583 -1.437
ILMNJ 6867 NM_006328.2 RFJM14 50593004 10.543 10.643 9.231 9.078 -1.439
ILMN_6288 NMJ00196.2 HSD11B2 31542940 7.925 7.937 6.350 6.624 -1.444
ILMNJ 6427 NM_001237.2 CC A2 16950653 9.438 9.607 8.007 8.150 -1.444
ILMN_29273 NM_023016.2 C2otf26 54607076 10.224 10.547 8.988 8.867 -1.458
ILMN_6890 NM_021034.1 IFITM3 11995467 13.046 13.104 11.655 11.562 -1.466
ILMNJ 770 NMJ06342.1 TACC3 5454101 8.708 8.598 7.017 7.346 -1.472
ILMN_26449 NMJ52319.2 C12oif54 34303924 7.485 7.821 6.296 6.064 -1.473
ILMNJ 3119 NM_033397.2 IAA1 54 47271452 9.922 9.959 8.593 8.338 -1.475
ILMNJ 7074 NM_021220.2 OVOL2 40807462 9.947 10.108 8.451 8.651 -1.476
I LMN 33860 KM 926530.1 LOC643161 89031545 7.408 7.183 5.870 5.763 -1.479
ILMNJ 0501 NM_005213.3 CSTA 61743964 10.535 10.646 9.065 9.153 -1.482
ILMNJ 5718 NM_001001788.1 RAET1G 49169824 10.365 10.637 8.877 9.069 -1.528
ILMNJ 9849 NM_001067.2 TOP2A 19913405 10.080 10.069 8.384 8.705 -1.530
ILMN_8225 NM_016343.3 CENPF 55770833 8.532 8.931 7.042 7.358 -1.531
ILMN_26240 NM_001257.3 CDH13 61676095 9.255 9.276 7.951 7.402 -1.589
ILMNJ 2902 NMJ32793.2 FSD2 42713695 8.775 8.603 6.881 7.313 -1.591
ILMNJ 5185 NM_201631.1 TGM5 42518071 6.564 6.908 4.807 5.476 -1.594
ILMN_25878 NMJ22817.1 PER2 12707561 9.367 9.404 7.787 7.788 -1.598
ILMNJ 9035 NMJ07267.5 TM C6 34222212 7.385 7.522 5.982 5.700 -1.613
ILMN_25474 NMJ78229.3 IQGAP3 39753960 10.769 10.837 9.035 9.294 -1.639
ILMN_25582 NM_017779.3 DEPDC1 41282232 7.403 7.872 5.853 6.121 -1.651
ILM N_25445 NMJ74919.2 LOC201175 31341379 7.807 7.919 6.188 6.229 -1.654
ILMN_23211 NMJ02612.2 PD 4 33589822 10.123 10.171 8.558 8.418 -1.659
ILMNJ 0912 NM_000361.2 THBD 40288292 7.267 7.435 5.675 5.689 -1.668
ILM NJ 36986 NM_001024209.1 SPRR2E 66730572 6.509 6.729 4.954 4.931 -1.677
ILMNJ 1548 NM_000088.2 GOL1A1 14719826 7.438 7.403 5.890 5.591 -1.680
ILMN_7864 NM_016525.3 UBAP1 22212941 10.401 10.401 8.595 8.846 -1.680 Table 3, continued
ILM N_20465 NMJ03981.2 PRC1 40807441 11.103 11.093 9.228 9.595 -1.687
ILM N_23476 NM_005980.2 S100P 45827727 10.268 10.405 8.725 8.574 -1.687
ILM _9096 NM_005168.3 RND3 56676394 10.211 10.402 8.665 8.570 -1.689
ILM N_10853 NM_016006.3 ABHD5 33469972 9.680 9.992 8.290 8.000 -1.691
ILM N_13755 NM_001307.3 CLDN7 34222214 11.962 11.889 10.099 10.355 -1.699
ILM N_13141 NM_005480.2 TROAP 33438581 8.529 8.408 6.714 6.818 -1.703
ILM N_22039 NM_000934.1 SERPINF2 11386142 6.027 6.140 4.113 4.597 -1.728
ILMN_9525 NMJ81782.2 NCOA7 42476174 11.024 11.207 9.509 9.252 -1.734
ILM N_17166 NM_001511.1 CXCL1 4504152 8.896 9.170 7.293 7.284 -1.745
ILM N_26077 NM_020211.1 RGMA 24308188 7.472 7.648 5.621 5.977 -1.761
ILM N_26025 NM_024554.2 POBD5 25777747 7.445 7.490 5.629 5.779 -1.763
ILMN_25969 NM_006845.2 KIF2C 13699832 8.811 8.633 6.854 7.060 -1.765
ILM N_12592 NM_206833.1 CTXN1 45592953 9.429 9.500 7.654 7.730 -1.773
ILM N_17961 NM_001300.4 KLF6 56550115 12.280 12.315 10.397 10.619 -1.789
ILM N_15305 NM_006456.1 ST6GALNAC2 5454091 9.237 9.115 7.333 7.428 -1.796
ILM N_16107 NM_001085.4 SERPINA3 73858562 13.628 13.578 11.730 11.833 -1.822
ILM _2226 NM_032413.2 C15orf48 37694068 6.121 6.746 4.420 4.728 -1.860
ILM N_25992 NM_003485.3 GPR68 74316010 6.610 7.195 4.561 5.514 -1.865
ILM N_16399 NM_006086.2 TUBB3 50592995 10.431 10.369 8.433 8.632 -1.868
ILM N_29321 NM_001734.2 C1S 41393600 7.199 7.177 5.695 4.916 -1.882
ILMN_5176 NM_020428.2 SLC44A2 31377726 10.051 10.083 8.059 8.305 -1.885
ILMNJ 5028 NM_000359.1 TG 1 4507474 11.618 11.694 9.833 9.700 -1.890
ILM N_16900 NM_012485.1 HMMR 7108350 10.135 10.223 8.146 8.432 -1.890
ILM N_12381 NM_001008490.1 KLF6 56550082 9.155 9.264 7.155 7.470 -1.897
ILMN 112886 Hs.557559 0 5438942 7.825 7.382 5.597 5.797 -1.907
ILM N_22415 NM_020789.2 IGSF9 34147342 7.733 7.707 5.667 5.954 -1.910
ILMN_6398 NMJ81803.1 UBE2C 32967290 11.120 11.232 9.042 9.412 -1.949
ILMN_9624 NM_207381.2 TNFAIP8L3 59709435 8.503 8.689 6.394 6.849 -1.975
ILM N_28547 NM_002298.2 LCP1 7382490 7.474 7.761 5.282 5.995 -1.979
ILM N_15188 NM_014689.1 DOCK10 58037090 7.493 7.926 6.013 5.413 -1.996
ILMN_4880 NM_014750.3 DLG7 21361644 9.360 9.495 7.345 7.497 -2.006
ILM N_29078 NM_002089.1 CXCL2 4504154 5.870 6.227 3.609 4.472 -2.008
ILMNJ 35987 Hs.583806 0 21177747 10.660 10.715 8.749 8.605 -2.011
ILM N_24887 NMJ78448.2 C9orf140 31341967 8.212 8.114 6.049 6.211 -2.033
ILMNJ 38896 NM_001692.2 ATP6V1B1 19913425 9.556 9.496 7.537 7.448 -2.033
ILM N_24167 NM_002658.2 PLAU 53729348 9.262 9.340 7.204 7.239 -2.079
ILM NJ1466 NM_014911.2 AAK1 29570779 6.821 6.248 4.926 3.982 -2.081
ILMN_28750 NM_000067.1 CA2 4557394 11.209 11.329 9.256 9.075 -2.103
ILM NJ1693 NM_002317.3 LOX 21264603 9.131 9.340 7.319 6.928 -2.111
ILMN 22827 NM 003810.2 TNFSF10 23510439 8.501 8.604 6.588 6.235 -2.141
ILM NJ 4995 N J04460.2 FAP 16933539 8.298 8.426 5.914 6.514 -2.148
ILM NJ 4100 NM_005733.1 KIF20A 5032012 10.682 10.747 8.215 8.910 -2.152
ILM NJ 5254 NM_004701.2 CCNB2 10938017 10.973 11.049 8.723 8.972 -2.163
ILMNJ 5973 NM_012116.2 CBLC 20149595 7.087 6.939 4.931 4.766 -2.165
ILM N_26343 NM_004004.3 GJB2 42558282 8.512 8.685 6.333 6.500 -2.182
ILM N_73498 Hs.72010 0 13726849 7.012 7.188 5.213 4.536 -2.225
ILMN_9313 NM_005130.3 FGFBP1 49574208 10.762 10.873 8.505 8.661 -2.235
ILM NJ 9881 N _014214.1 IMPA2 7657235 11.938 11.901 9.742 9.601 -2.248
ILM NJ 5792 NM_031415.1 MLZE 13899220 6.231 6.385 4.009 4.070 -2.268
ILMN_3001 NM_080591.1 PTGS1 18104968 7.097 6.718 4.536 4.739 -2.270
ILMNJ 127 NM_005030.3 PLK1 34147632 7.831 7.643 5.863 5.044 -2.283
ILM NJ 6362 NM_001005474.1 NFKBIZ 53832023 8.688 8.724 6.156 6.644 -2.306
ILM N_20689 NM_000076.1 CD N1C 4557440 10.982 10.899 8.706 8.495 -2.340
ILMN_92725 Hs.444329 0 34365191 9.439 9.683 7.229 7.210 -2.342
ILM N_23858 NM_201525.1 GPR56 41584197 9.969 10.120 7.644 7.718 -2.363
ILM NJ 0005 NM_031966.2 CCNB1 34304372 10.136 10.300 7.764 7.930 -2.371
ILM NJ 2497 NM_002970.1 SAT 4506788 12.343 12.453 10.064 9.928 -2.402 Table 3, continued
ILMN_4602 NM_005988.2 SPRR2A 46094054 7.543 7.723 4.818 5.638 -2.405
ILMN_22377 NM_001333.2 CTSL2 23110959 9.660 9.604 7.098 7.343 -2.412
IL N_29470 NM_015150.1 RAFTLIN 41872576 7.091 6.944 4.256 4.878 -2.451
ILMN_6390 NM_000691.3 ALDH3A1 22907048 12.593 12.426 10.059 10.029 -2.465
IL N_11871 NM_003739.4 AKR1C3 24497582 13.157 13.113 10.627 10.690 -2.477
ILMN_10722 NM_004062.2 CDH16 16507958 7.670 7.401 4.505 5.603 -2.481
ILMN_22744 NM_004925.3 AQP3 22165421 8.699 8.321 5.907 6.117 -2.498
ILMN_26643 NM_080489.2 SDCBP2 38044113 8.787 8.804 6.265 6.317 -2.505
ILMN_20932 NM_001964.2 EOR1 31317226 9.528 9.425 7.062 6.810 -2.540
ILMN 6827 NM 001885.1 CRYAB 4503056 10.924 10.977 8.372 8.412 -2.558
ILM N_491 NM_021945.4 C6otf85 48526515 9.687 9.541 7.102 6.970 -2.578
ILMN_7567 NM_005558.3 LAD1 32455232 13.139 13.213 10.660 10.484 -2.604
ILMN_30616 XM_940680.1 LOC648517 89061897 11.345 11.392 8.767 8.652 -2.659
ILMN_30002 NM_012427.3 KLK5 22208993 9.377 9.444 6.685 6.800 -2.668
ILM N_796 NM_001218.3 CA12 45935381 11.142 10.952 8.360 8.393 -2.671
IL NJ768 NM_001001414.1 LOC342897 47825360 8.250 8.384 5.585 5.594 -2.728
ILMN_28364 NMJ99161.1 SAA1 40316909 10.853 10.721 8.207 7.863 -2.752
ILMN_2247 NM_000584.2 IL8 28610153 6.451 6.792 3.548 4.079 -2.808
ILMN_14915 NM_052815.1 IER3 16554596 10.474 10.515 7.700 7.589 -2.850
ILMN_2266 NM_002963.2 S100A7 9845518 8.926 9.086 6.013 6.285 -2.857
ILMN_3781 NM_001012632.1 IL32 61658631 6.497 6.493 3.278 3.907 -2.903
ILMN_9653 NMJ39277.1 KLK7 21327704 9.754 9.711 7.232 6.409 -2.912
ILMN_16252 NM_001878.2 CRAFJP2 6382069 9.793 10.069 6.739 7.280 -2.922
IL NJ 98 NMJ98538.1 SBSN 38348365 8.067 8.206 4.848 5.311 -3.057
ILMN 12614 NM 024702.1 FU13841 13375990 8.860 8.869 5.252 6.125 -3.177
ILMN_21964 NM_002648.2 PIM1 31543400 10.793 10.850 7.543 7.321 -3.390
ILMN_8892 NMJ81712.2 AN RD38 44917612 8.848 8.928 4.968 5.931 -3.439
ILMNJ 4466 NM_002575.1 SERPINB2 4505594 10.564 10.361 6.751 6.670 -3.752
ILMN_9057 NM_006472.1 TXNIP 5454161 13.282 13.240 9.574 9.273 -3.837
ILMN_7664 NM_005555.2 RT6B 17505187 12.319 12.560 7.660 7.568 -4.826
ILM N_138240 NM_003125.1 SPRR1B 4507186 10.904 10.825 5.760 5.888 -5.040
ILMN_13685 NM_002638.2 PI3 31657130 13.344 13.390 5.545 5.843 -7.673
Table 4: The GFPSEL vs GFPUNS Signature in MCF1 OA Cells
GFP GFP GFP GFP GFP SEL /
Gi UNS A UNS B SEL A SEL B GFP UNS lllumina ID Accession Sym bol
Accession Log (2) Log(2) Log (2) Log (2) Log(2)
Signal Signal Signal Signal Ratio
ILMN_20483 NM_001005340.1 GPNMB 52694751 7.548 7.000 9.470 11.604 3.263
ILMNJ 9880 NM_000636.2 SOD2 67782304 6.062 5.880 8.204 9.158 2.710
ILMNJ 2248 NM_001710.4 CFB 67782357 8.1 9 8.092 10.149 10.952 2.430
ILMN 36989 XM_936226.1 LOC653879 89057119 7.107 7.473 9.380 9.952 2.375
ILMN 3897 NM 001165.3 BIRC3 33946283 8.409 8.330 10.811 10.627 2.349
ILMN_3297 NM_000777.2 CYP3A5 15147331 5.838 5.372 7.764 8.019 2.287
ILMN_29078 NM_002089.1 CXCL2 4504154 5.870 6.227 8.215 8.407 2.262
ILMNJ 7250 NM_004172.3 SLC1A3 34222301 6.568 6.564 8.675 8.812 2.178
ILMNJ 6547 NM_013261.2 PPARGC1 A 29570796 5.850 5.446 7.307 7.737 1.873
ILMNJ 4847 NM_032421.1 CYLN2 14702161 7.659 7.796 9.390 9.746 1.840
ILMN_5682 NM_000064.1 C3 4557384 10.367 10.229 11.749 12.348 1.750
ILMN_3319 NM_015541.2 LRIG1 54607117 4.350 4.440 5.865 6.190 1.633
ILMN_28225 NM_207397.1 CD164L2 46409425 5.681 5.539 7.431 6.968 1.590
ILMN_23321 NMJ01734.2 C1S 41333600 7.139 7.177 8.774 8.768 1.583
ILMNJ1 39 NM_002198.1 IRF1 4504720 9.600 9.730 11.063 11.357 1.545
ILMN_35526 XM_935575.1 LOC641825 89027574 3.807 4.027 5.385 5.530 1.541
ILMN_2205 NM_020531.2 C20crf3 41327713 9.495 9.371 10.953 10.945 1.516
ILMN_7322 NM_002291.1 LAMB1 4504950 9.214 9.260 10.746 10.689 1.480
ILMN 13834 NM 001733.4 C1R 66347874 7.036 7.045 8.321 8.713 1.476
ILMN 20048 NMJ18370.1 FLJ11259 8922957 6.695 6.552 7.926 8.223 1.451
ILMN_5957 NM_015187.1 KIAA0746 39930348 9.031 8.995 10.452 10.434 1.430
ILMNJ 5496 NM_001024668.1 LETM D1 67089166 8.077 8.102 9.429 9.604 1.427
ILMN_71591 Hs.19339 0 34191392 7.770 7.846 9.050 9.362 1.398
ILMNJ3029 NM_203379.1 ACSL5 42794757 7.387 7.575 8.691 9.051 1.390
ILMN_29422 NMJ24101.4 MLPH 34222365 6.459 6.397 7.578 7.903 1.312
ILMNJ 5035 NM_000877.2 IL1R1 27894331 6.404 6.642 7.984 7.679 1.308
ILMNJ 38073 NM_021255.1 PEU2 10864062 5.443 5.638 6.853 6.805 1.288
ILMN_23910 NM J 78349.1 LCE1B 30387655 5.853 5.810 7.150 7.044 1.266
ILMN_9457 NM_013262.3 MYLIP 38788242 7.470 7.525 8.580 8.888 1.237
ILMN_7413 NM_003594.3 TTF2 40807470 9.111 9.085 7.837 8.007 -1.176
ILMNJ 03797 Hs.538259 0 23273338 7.881 7.906 6.794 6.579 -1.207
ILMN_2476 NM_006505.2 PVR 19923371 8.508 8.337 7.180 7.164 -1.251
ILMN_3629 NM J 82507.1 LOC144501 32638852 7.521 7.684 6.198 6.468 -1.270
ILMN 92 NM_003686.3 EX01 39995068 7.607 7.493 6.300 6.235 -1.283
ILMN 6133 NM 003173.1 SUV39H1 4507320 8.678 8.714 7.457 7.338 -1.298
ILMNJ 6808 NM_031423.2 CDCA1 22027505 6.994 7.223 5.855 5.750 -1.306
ILMNJ 8637 NM_001034.1 RRM2 4557844 7.050 7.272 5.807 5.890 -1.312
ILMN_4220 NMJ02106.3 H2AFZ 53753146 13.334 13.355 12.102 11.336 -1.356
ILMNJ 2816 NM_015703.3 CGI-96 62751922 9.245 9.027 7.840 7.673 -1.379
ILMNJ 2332 NM_001798.2 CDK2 16936527 8.254 8.311 6.890 6.901 -1.387
ILMN_3344 NM J 52362.1 TNFAIP8L1 22748780 8.151 8.260 6.736 6.896 -1.389
ILMNJ 608 NM_001070.3 TUBG1 34222287 9.796 9.729 8.397 8.332 -1.398
ILMNJ 5944 NM_001002799.1 SM C4L1 50658066 9.871 10.053 8.558 8.556 -1.405
ILMN_9096 NMJ05168.3 RND3 56676394 10.211 10.402 8.762 8.978 -1.436
ILMN_4289 NM_001018.3 RPS15 71284430 9.992 9.848 8.719 8.240 -1.440
ILMNJ 3450 NM_012145.2 DTYMK 42544173 9.372 9.580 8.188 7.867 -1.448
ILMNJ 6938 NM_017669.2 FLJ20105 58331267 7.421 7.271 5.860 5.860 -1.485
ILMNJ 6948 NM_030919.1 C20orf129 24308304 9.180 9.191 7.772 7.617 -1.491
ILMN_28902 NM_016240.2 SCARA3 33598923 6.819 6.601 5.027 5.326 -1.534 Table 4, continued
ILMNJ1313 NM_001826.1 CKS1B 4502856 11.652 11.600 9.961 10.219 -1.536
ILMNJ 9331 NMJ 52562.2 CDCA2 44681483 6.921 7.198 5.396 5.635 -1.544
ILMN_20484 NM_006397.2 RNASEH2A 38455390 8.356 8.222 6.768 6.714 -1.548
ILMNJ1654 NM_016639.1 TNFRSF12A 7706185 9.246 9.311 7.690 7.748 -1.559
ILMN_20327 NMJ301012271.1 BIRC5 59859881 7.388 7.115 5.692 5.689 -1.561
ILMN_20107 NM_006739.2 MCM5 23510447 7.480 7.477 5.890 5.928 -1.569
ILMN_21369 NM_003920.2 TIMELESS 52851463 8.974 8.911 7.452 7.290 -1.572
ILMN_1748 NMJ) 01005290.2 PSRC1 73858557 7.963 8.087 6.640 6.256 -1.578
ILMN_3628 NM_024094.1 DCC1 13129095 7.514 7.253 5.766 5.830 -1.585
ILMN 26621 NM 014865.2 CNAP1 41281520 8.550 8.228 6.825 6.744 -1.605
ILMN_22415 NM_020789.2 IGSF9 34147342 7.733 7.707 6.324 5.907 -1.605
ILM N_138879 NM_018193.1 IAA1794 42734338 8.317 8.354 6.677 6.785 -1.605
ILMNJ 2592 NM_206833.1 CTX N1 45592953 9.429 9.500 7.844 7.834 -1.626
ILMN_9870 NM_003878.1 GGH 4503986 9.670 9.730 8.361 7.773 -1.633
ILMN_2930 NM_052842.2 BCL2L12 20336331 9.441 9.341 7.779 7.712 -1.645
ILMN_25046 NM_006479.2 RAD51AP1 19923778 7.313 7.340 5.463 5.888 -1.651
IL NJ 8895 NM_030928.2 CDT1 19923847 8.468 8.317 6.759 6.711 -1.657
ILMNJ 5846 NM_014264.2 PLK4 21361432 8.244 8.233 6.541 6.621 -1.658
ILMN_23727 NM_052886.1 MAL2 16418396 11.709 11.826 10.159 10.052 -1.662
IL NJ 4464 NM_001012413.1 SGOL1 60302878 6.430 6.302 4.728 4.644 -1.680
ILMN_22897 NMJ 73608.1 C14orf80 27734692 8.196 8.090 6.250 6.651 -1.692
ILMN_21947 NMJ24053.3 C22orf18 50845412 7.299 7.319 5.675 5.552 -1.696
IL NJ 3755 NM_001307.3 CLDN7 34222214 11.962 11.889 10.453 10.007 -1.696
ILMN_5134 NM_018186.2 C1 orfl 12 40254930 8.266 8.137 6.568 6.428 -1.703
ILMN 11775 NM_005563.3 ΞΤΜΝ1 44889961 8.284 8.333 6.600 6.609 -1.704
ILMNJ 8980 NM_002130.4 HMGCS1 54020719 10.053 9.978 8.500 8.114 -1.708
ILMN_23585 NMJ02105.2 H2AFX 52630339 8.S56 8.935 7.128 7.241 -1.711
ILMN_9074 NM_001175.4 ARHGDIB 56676392 9.941 9.878 8.266 8.078 -1.737
ILMNJ 1802 NM_004856.4 KIF23 20143965 8.213 8.311 6.576 6.459 -1.745
ILMN_24712 NMJ32737.2 LM NB2 27436950 11.223 11.064 9.452 9.314 -1.761
ILMNJ 8676 NMJ19013.1 FAM64A 9506604 7.820 7.693 5.989 5.995 -1.764
ILMNJ 29103 Hs.576922 0 27552801 7.093 7.597 5.463 5.687 -1.770
IL NJ 38334 NM_015675.1 GADD45B 9945331 8.361 8.307 6.613 6.513 -1.771
ILMN_8503 NMJ04523.2 KIF11 13699823 8.519 8.570 6.709 6.824 -1.778
ILMNJ 37089 NM_018304.1 PRR11 8922831 8.056 7.756 6.229 6.027 -1.778
ILMN_29781 NM_014875.1 KIF14 7661877 7.725 8.220 6.406 5.933 -1.803
ILMNJ 0665 NM_018455.3 C16orf60 39725678 9.369 9.446 7.754 7.451 -1.805
ILMN_869 NMJ 45061.3 C13orf3 47419927 7.740 7.592 5.963 5.752 -1.808
ILMNJ 37325 NM_031845.1 MAP2 14195617 7.869 7.816 5.986 6.060 -1.819
ILMN 20794 NM 005564.2 LCN2 38455401 8.494 8.529 6.829 6.542 -1.826
ILMN 52 NMJ 83049.2 TMSL3 72255572 14.397 14.328 12.668 12.406 -1.826
ILMN_36910 XM_927270.1 LOC653400 88983843 7.504 7.947 6.174 5.624 -1.827
ILMNJ 5061 NMJ04237.2 TRIP13 20149561 8.564 8.703 6.863 6.746 -1.829
ILMN_26148 NMJ18154.2 ASF1B 67782340 7.167 7.191 5.350 5.347 -1.830
ILMNJ 37482 NM_017915.1 C12orf48 8923595 9.092 9.101 7.426 7.097 -1.835
ILMN_23940 NMJ03979.3 GPRC5A 63252917 7.984 8.089 6.188 6.208 -1.838
ILMNJ 1038 NMJ 45018.2 FU25416 25072198 7.400 7.659 5.498 5.875 -1.843
ILMN_2026 NM_014736.4 IAA0101 71773764 10.026 9.965 8.634 7.657 -1.850
ILMN_26854 NM_016095.1 Pfs2 7706366 8.260 8.036 6.370 6.217 -1.854
ILMNJ 4098 NM_006101.1 KNTC2 5174456 7.362 7.729 5.797 5.585 -1.855
ILMN_35948 XM_935208.1 LOC645625 89041729 8.978 9.107 6.890 7.443 -1.876
ILMNJ 768 N _001001414.1 LOC342897 47825360 8.250 8.384 6.691 6.180 -1.882
ILMNJ 6127 NM_001790.2 CDC25C 12408659 7.499 7.513 5.440 5.807 -1.883
ILMN_36283 XM_940001.1 MGC40489 89042906 8.118 8.588 6.094 6.840 -1.886
ILMN 6827 NM_001885.1 CRYAB 4503056 10.924 10.977 8.985 9.125 -1.896
ILMNJ 2202 NMJ) 20127.1 TUFT1 9910595 9.234 9.385 7.420 7.354 -1.922
ILMN_21027 NM_015651.1 PHF19 58331160 8.497 8.662 6.772 6.533 -1.927 Table 4, continued
IL NJ 2496 NM_005542.3 INSIG1 38327527 8.735 8.858 7.012 6.698 -1.942
ILMN_25582 NM_017779.3 DEPDC1 41282232 7.403 7.872 5.797 5.579 -1.950
ILMN_83 2 NM J) 05139.1 A NX A 3 4826642 9.626 9.674 7.700 7.697 -1.952
ILMN_30002 NMJ12427.3 KLK5 22208993 9.377 9.444 7.372 7.526 -1.962
ILMNJ 5028 NM_000359.1 TOM1 4507474 11.618 11.694 9.746 9.632 -1.967
ILMNJ 0840 NM_012310.2 IF4A 7305204 7.840 7.754 5.585 6.060 -1.974
IL N_2226 NM J) 32413.2 C15otT48 37694068 6.121 6.746 4.263 4.638 -1.983
ILM _9421 NM_003806.1 HR 4504492 7.617 7.404 5.472 5.520 -2.014
ILMN_28723 NM_013230.2 OD24 73623396 11.260 11.177 9.096 9.245 -2.048
ILMN 28750 NM 000067.1 CA2 4557394 11.209 11.329 9.358 9.048 -2.066
ILMN_23985 NM_002358.2 MAD2L1 6466452 9.236 9.505 7.571 7.032 -2.069
ILMNJ 2005 NMJ13282.2 UHRF1 16507203 9.082 9.190 7.110 6.997 -2.083
IL NJ 9730 NM J) 04091.2 E2F2 34485718 8.000 7.782 6.020 5.591 -2.085
ILMN_21714 NM J) 32814.1 TMEM118 14249505 7.421 7.303 5.482 5.049 -2.097
ILM _2839 NM_007174.1 CIT 32698687 7.395 7.563 5.508 5.206 -2.122
ILMNJ127 N J05030.3 PL 1 34147632 7.831 7.643 6.073 5.117 -2.142
ILMN_22926 NM_020770.1 CG 16262451 7.513 7.705 5.655 5.263 -2.150
ILMN_26449 NM J 52319.2 C120 If 54 34303924 7.485 7.821 5.495 5.508 -2.151
IL NJ5510 NMJ07085.3 FSTL1 34304366 9.312 9.258 7.847 6.416 -2.154
ILMNJ 4702 NM_001827.1 CKS2 4502858 10.523 10.589 8.280 8.504 -2.164
ILM _4549 NMJ18492.2 PFJK 18490990 9.511 9.741 7.352 7.546 -2.177
ILMN_29470 NMJ15150.1 RAFTUN 41872576 7.091 6.944 5.109 4.548 -2.189
IL NJ1844 NM_001018115.1 FANCD2 66528887 8.017 7.853 5.853 5.567 -2.225
ILMNJ 8200 NM_007280.1 OIP5 24307928 7.669 7.933 5.880 5.263 -2.229
ILMN 3162 NM 003254.2 TIMP1 73858576 14.517 14.557 12.579 11.988 -2.253
ILMN_20406 NM_004669.2 CUC3 40288289 8.619 8.630 6.736 5.916 -2.298
IL N_21364 NM_002648.2 PIM1 31543400 10.793 10.850 8.511 8.510 -2.311
ILMN_26237 NMJ06082.2 -ALPHA-1 57013275 14.750 14.928 12.733 12.304 -2.320
ILMNJ 2351 NM_001002876.1 C22otT18 50845413 8.632 8.648 6.629 5.995 -2.328
ILMNJ 3462 NM_018101.2 CDCA8 51593099 7.788 7.558 5.396 5.282 -2.334
IL N_915 NM_020675.3 SPBC25 23510353 6.380 6.231 3.700 4.217 -2.347
ILMN_24472 NM J) 03318.3 TT 34303964 7.951 8.336 5.755 5.760 -2.386
ILMNJ 37805 NM J) 04217.1 AURKFJ 4759177 8.514 8.402 6.036 6.106 -2.387
ILMNJ 2486 NM_005556.3 KRT7 67782364 11.280 11.079 9.058 8.459 -2.421
ILMNJ 4206 NMJ02266.2 PNA2 62388891 10.586 11.042 8.415 8.358 -2.428
ILMNJ 5300 NM J) 01211.4 BUB1B 59814246 7.757 7.927 5.658 5.142 -2.442
IL N_4602 NM_005988.2 SPRR2A 46094054 7.543 7.723 5.698 4.638 -2.465
ILMN_24887 NM J 78448.2 C9orf140 31341967 8.212 8.114 5.902 5.450 -2.487
ILMNJ 2352 NM J 98434.1 STK6 38327565 9.397 9.457 7.036 6.707 -2.555
ILMN 11503 NM 033379.2 CDC2 27886643 10.133 10.317 7.602 7.687 -2.580
ILMN_23620 NMJ22346.3 HCAP-G 50658080 9.458 9.434 7.019 6.695 -2.589
ILM _7509 NMJ01813.2 CENPE 71061467 8.1 8 8.361 6.044 5.256 -2.604
ILM N_S141 NMJ) 03784.1 SERPINB7 4505148 7.127 7.405 4.392 4.912 -2.614
ILMNJ 0590 NMJ) 04336.2 BUB1 56118215 8.700 8.853 6.049 6.154 -2.675
ILMNJ 0359 NM_006461.3 SPA05 73623034 9.231 9.148 6.748 6.276 -2.677
ILMNJ 4995 NM_004460.2 FAP 16933539 8.298 8.426 5.675 5.652 -2.698
ILMN_20208 N _0121 2.4 TPX2 40354199 8.862 9.005 6.306 6.117 -2.722
ILMNJ770 NMJ06342.1 TACC3 5454101 8.708 8.598 5.858 5.966 -2.741
ILM _9653 NM J 39277.1 KLK7 21327704 9.754 9.711 7.111 6.857 -2.749
ILMN_28483 NM J 52515.2 FU40629 32526889 8.399 8.503 5.980 5.385 -2.768
ILMN_30154 NMJ03258.1 TK1 4507518 8.703 8.212 5.916 5.459 -2. 69
ILMN_635 NMJ) 02281.2 KRTHB1 15431319 15.341 15.204 12.938 12.058 -2.775
ILMNJ 4281 NMJ16359.2 NUSAP1 59710089 10.471 10.591 7.704 7.807 -2.776
ILMN_24793 NM_001786.2 CDC2 16306490 8.042 8.277 5.409 5.307 -2.801
ILMN 2266 NM 002963.2 S100A7 9845518 8.926 9.086 6.349 5.919 -2.872
ILMNJ 6427 NM_001237.2 CCNA2 16950653 9.438 9.607 6.477 6.651 -2.959
ILMNJ 8763 NM_031299.3 CDCA3 34147595 8.583 8.588 5.825 5.389 -2.979 Table 4, continued
ILMNJ3141 NM_005480.2 TROAP 33438581 8.529 8.408 5.591 5.326 -3.010
ILMN_8225 NM_016343.3 CENPF 55770833 8.532 8.931 5.805 5.615 -3.022
ILMIN_16252 NM_001878.2 CRABP2 6382069 9.793 10.069 6.878 6.910 -3.037
ILMN_3057 NM_080B68.2 CDCA5 34147481 9.730 9.642 6.641 6.648 -3.042
ILMIN_16399 NM_006086.2 TUBB3 50592995 10.431 10.369 7.648 6.982 -3.085
ILMN_223T7 NM_001333.2 CTSL2 23110959 9.660 9.604 6.958 6.085 -3.110
ILMN_25969 NM_006845.2 IF2C 13699832 8.811 8.633 5.763 5.456 -3.112
ILMN_5449 NM_001809.2 CENPA 4585861 8.489 8.635 5.863 4.986 -3.137
ILMN_8892 NM_181712.2 ANKRD38 44917612 8.848 8.928 5.888 5.594 -3.147
ILMN 20921 NM 018685.2 ANLN 31657093 9.295 9.657 6.370 6.125 -3.228
ILMN_408 NM_018136.2 ASPM 24211028 10.003 10.263 7.031 6.674 -3.280
ILMNJ9849 NM_001067.2 TOP2A 19913405 10.080 10.069 6.874 6.580 -3.347
ILMN_25636 NM_152666.1 PLD5 22749352 9.014 9.064 6.104 5.274 -3.349
ILMNJ5254 NM_004701.2 CCNB2 10938017 10.973 11.049 8.128 7.113 -3.390
ILMNJ4466 NM_002575.1 SERPINB2 4505594 10.564 10.361 7.073 7.027 -3. 13
ILMNJ0005 NM_031966.2 CCNB1 34304372 10.136 10.300 6.759 6.498 -3.590
ILMN_20465 NM_003981.2 PRC1 40807441 11.103 11.093 7.796 7.132 -3.634
ILMN_25474 NM_178229.3 IQGAP3 39753960 10.769 10.837 7.216 7.113 -3.639
ILMN_9313 NM_005130.3 FGFBP1 49574208 10.762 10.873 7.822 6.469 -3.672
ILMN_4880 NM_014750.3 DLG7 21361644 9.360 9.495 5.984 5.489 -3.691
ILMN_7664 NM_005555.2 KRT6B 17505187 12.319 12.560 8.915 8.206 -3.879
ILMNJ6900 NM_012485.1 HMMR 7108350 10.135 10.223 6.589 5.980 -3.895
ILMN_6398 NM_181803.1 UBE2C 32967290 11.120 11.232 7.550 6.533 -4.134
ILMN_141 OG NM_005733.1 KIF20A 5032012 10.682 10.747 7.064 6.083 -4.141
ILMNJ3685 NM_002638.2 PI3 31657130 13.344 13.390 8.617 8.129 -4.994
Table 5: The LIBSEL vs GFPSEL Signature in MCF10A Cells
GFP GFP LIB LIB
LIB SEL /
Gi SEL A SEL B SEL A SEL B lllumina ID Accession Sym bol GFP SEL
Accession Log(2) Log(2) L g(2) L g(2)
Log (2) Ratio Signal Signal Signal Signal
.1
.1
.1
.1
.1
.1
1
.1 .1
.1 .1
.1 .1
.1
.1
.1 .1 .1 .1
Figure imgf000039_0001
Figure imgf000040_0001
Figure imgf000041_0001
Figure imgf000042_0001
Figure imgf000043_0001
Figure imgf000044_0001
Figure imgf000045_0001
Table 5, continued
ILM N J 8038 NM_001731.1 BT01 4502472 13.245 13.044 11.514 11.817 -1.479
ILMN 2416 NM 006877.2 GMPR 31542848 7.535 7.719 5.933 6.352 -1.484
ILMNJ 14974 Hs.561603 0 23066233 6.747 6.835 5.236 5.375 -1.485
IL N_6779 NM_020896.2 OSBPL5 22035607 10.481 10.609 9.105 9.006 -1.490
ILM N_20958 NM_016339.1 RAPGEFL1 7705938 10.109 10.047 8.436 8.733 -1.493
ILM N_74694 Hs.101003 0 27838294 6.845 6.773 5.057 5.570 -1.496
ILMNJ 02192 Hs.534279 0 21955385 9.192 9.422 7.693 7.899 -1.511
ILM N_27164 NM_005410.2 SEPP1 62530390 10.930 11.118 9.555 9.459 -1.517
IL NJ361 NM_007288.1 MME 6042201 9.557 9.855 7.965 8.407 -1.520
IL N_828 NM_080878.2 ITL 2 37622351 6.689 6.812 5.482 4.977 -1.521
ILM N J 3399 NM_001032278.1 MMP28 73808270 9.191 9.401 7.770 7.743 -1.539
ILMNJ 908 NM_018398.2 CACNA2D3 54112396 7.021 6.840 5.217 5.552 -1.546
ILM N J 7837 NM_014007.2 ZNF297B 45267833 10.229 9.900 8.356 8.671 -1.550
ILM N_27069 NM_006665.2 HPSE 19923365 6.574 6.918 5.221 5.170 -1.551
ILM N J 9750 NMJ01005404.3 YPEL2 56550087 6.681 7.232 5.399 5.406 -1.554
ILM N_26811 NMJ74902.2 LDLRAD3 31341355 8.797 8.990 7.408 7.268 -1.556
I LMN 26434 NM 030806.3 C1on*21 58761542 8.427 8.725 6.931 7.099 -1.561
ILM N_42462 XM_932354.1 LOC644760 88984918 6.891 6.874 5.446 5.190 -1.565
ILM N J 8735 NMJ53046.1 TDRD9 42734387 6.670 6.436 4.977 4.991 -1.569
ILM N_24736 NMJ52466.1 C17orf69 22748982 7.119 7.133 5.271 5.838 -1.572
IL N_3053 NMJ70744.2 UNC5B 32261317 6.361 6.421 4.878 4.750 -1.578
ILM N J 4320 NM_004428.2 EFNA1 33359681 10.630 10.646 8.629 9.489 -1.579
ILM N_37098 XM_942586.1 LOC651309 89036309 7.068 6.988 5.358 5.527 -1.586
ILM N_32329 XM_937528.1 Ο10ΟΠ73 89031946 6.776 6.910 5.350 5.146 -1.595
ILM N_28123 NM_001547.3 IFIT2 34222091 7.505 7.928 5.938 6.285 -1.605
ILM N_39623 XM_937048.1 LOC647993 89033514 7.122 7.127 5.113 5.912 -1.612
ILMNJ 15186 Hs.561940 0 16549932 7.509 7.604 5.687 6.190 -1.619
ILM N_92093 Hs.438937 0 34529519 7.149 7.578 5.684 5.805 -1.619
ILMN_2924 NM_005271.1 GLUD1 4885280 12.611 12.759 11.066 11.033 -1.635
ILM N J 6562 NM_004117.2 FKBP5 17149847 12.582 12.486 10.824 10.961 -1.641
IL N_8954 NM_001072.2 UGT1A6 45827764 7.604 7.731 6.018 6.020 -1.648
I LMN 11805 NM 003408.1 ZFP37 4507962 6.955 6.833 5.600 4.863 -1.663
ILMN_9078 NM_020152.2 C21orf7 31542267 8.254 8.549 6.594 6.842 -1.684
ILM N J 9900 NM_020747.1 ZNF608 55741877 7.184 7.238 5.456 5.594 -1.686
ILM N J 6923 NM_000396.2 CTSK 23110958 6.677 7.050 5.087 5.248 -1.696
ILM NJ0266 XM_928461.1 LOC653626 89035446 9.696 9.691 7.739 8.227 -1.710
ILM N_28493 NMJK3944.1 CYP4F12 13184045 8.332 7.791 5.989 6.700 -1.717
ILMNJ 491 NM_024650.2 FLJ22531 31542734 10.465 10.511 8.647 8.881 -1.724
ILM N_76686 Hs.128753 0 7021073 6.558 6.833 4.963 4.977 -1.725
IL N_8703 NMJ70600.1 SH2D3C 41281820 6.827 7.364 5.492 5.244 -1.727
ILM N J 6293 NMJ53377.3 LRIG3 40255156 9.228 9.482 7.264 7.988 -1.729
ILM N_33860 XM_926530.1 LOC643161 89031545 6.468 7.027 4.802 5.182 -1.755
ILM N J 3193 NM_020431.1 TMEM63C 55742804 6.918 6.902 5.517 4.787 -1.758
ILM N_27758 NM_022751.1 FAM59A 12232414 8.289 8.022 6.081 6.707 -1.761
ILM N_26240 NM_001257.3 CDH13 61676095 9.924 9.931 8.130 8.196 -1.764
ILM N J 5043 NM_017791.1 C14orf58 8923349 7.435 7.426 5.561 5.766 -1.768
I LMN 25095 NM 005971.2 FXYD3 11612675 10.448 10.394 8.568 8.728 -1.773
ILM N J 5496 NMJ01024668.1 LETMD1 67089166 9.429 9.604 7.696 7.789 -1.774
ILM N_71632 Hs.20255 0 9873865 7.258 7.533 5.206 6.036 -1.775
ILM NJ0428 XM_942885.1 LOC440928 88958963 8.025 7.859 5.928 6.382 -1.787
ILM N_20716 NMJ73653.1 SLC9A9 27734934 7.948 8.290 6.520 6.121 -1.798
IL N_4567 NMJ78008.1 STARD13 41281901 6.693 6.811 4.498 5.403 -1.802
ILM NJ1581 NM_017570.1 OPLAH 48314819 10.168 10.104 7.996 8.628 -1.824
ILM N_27652 NM_004071.2 GLK1 67551260 8.115 8.298 6.394 6.365 -1.827
ILM N_92725 Hs.444329 0 34365191 10.075 9.594 7.918 8.064 -1.843
IL N_4989 NM_017622.1 C17otT59 8923020 9.581 9.584 7.649 7.826 -1.845
ILM N_26717 NM_006308.1 HSPB3 5453687 6.670 7.335 4.921 5.347 -1.868
ILM N J 2567 NM_019005.2 FU20323 46358341 10.147 10.193 8.331 8.264 -1.873
ILM N J 8486 NM_032160.2 C18otf 59938787 7.028 7.385 5.453 5.213 -1.874 Table 5, continued
ILMN_7083 NM_814751.2 MTSS1 30023852 8.863 8.347 6.194 6.445 -1.886
IL N 20035 NM 858229.2 FBX032 22547142 8.836 9.274 7.405 6.909 -1.898
ILMNJ6107 NM_801085.4 SERPINA3 73858562 14.258 1 .644 12.751 12.278 -1.936
IL N_25965 NM_802202.1 ISL1 4584736 7.464 7.408 5.458 5.545 -1.938
IL N_29543 NM_822165.2 LIN7B 56676320 7.506 7.491 5.731 5.379 -1.944
ILMN_8744 NMJ53607.1 LOC153222 23957697 8.135 8.499 6.135 6.597 -1.951
IL NJ 0249 NM_205862.1 UST1A6 45827766 8.645 8.884 6.988 6.531 -1.965
ILMN_6288 NM_800196.2 HSD11B2 31542940 7.591 7.375 5.463 5.558 -1.973
IL NJ 4880 NM_805384.2 NFIL3 52630428 10.442 18.244 8.315 8.377 -1.997
ILMN_28225 NM_207397.1 CD164L2 46409425 7.431 6.968 5.194 5.190 -2.008
ILMNJ 5343 NMJ53034.2 ZNF488 40255102 8.810 9.439 7.324 6.870 -2.028
ILMN_3809 NMJ48177.1 FBX032 22547143 10.809 11.028 9.204 8.565 -2.034
IL N_27286 NM_081024646.1 CL 1 67551262 10.400 18.406 8.287 8.444 -2.038
IL N_22366 NM_824617.2 ZCCHC6 58331271 11.512 11.524 9.387 9.564 -2.043
ILMN_3567 NM_805012.1 ROR1 4826867 6.889 7.482 4.722 5.51 -2.066
IUV1N_137281 NM_805738.2 ARL4 47878225 9.756 9.742 7.708 7.633 -2.083
ILMN 19665 NM 198061.1 CES2 37622886 12.511 12.583 10.300 10.586 -2.184
ILMN_22398 NM_806113.3 VAV3 21614495 9.169 9.482 6.868 7.484 -2.150
ILMNJ1566 NM_800248.2 AOA 33469954 13.362 13.122 10.654 11.435 -2.198
ILMN_5404 NM_814454.1 SESN1 7657436 9.607 9.431 7.408 7.209 -2.214
IL N_540 NM_801706.2 BCL6 21840323 11.273 11.211 8.932 9.065 -2.244
ILMN_25981 NM_805823.4 MSLN 68303642 13.222 13.410 11.322 10.811 -2.249
ILMNJ 8558 NM_801512.2 GSTA4 23865568 11.286 11.093 8.691 9.070 -2.389
ILMN_8842 NM_802944.2 ROS1 19924164 8.290 8.296 5.959 5.998 -2.31
ILMNJ 0722 NM_804062.2 CDH16 16507958 7.513 6.944 4.792 5.036 -2.315
IL NJ1 39 NM_802198.1 IRF1 4584720 11.863 11.357 9.111 8.645 -2.332
ILMN_22942 NM_806393.1 NEBL 5453757 8.105 8.262 6.106 5.594 -2.333
ILMNJ 898 NM_800421.2 KRT10 40354191 12.224 13.085 9.907 10.583 -2.369
ILMN_30616 XM_940680.1 LOC648517 89061897 12.827 12.382 10.476 9.944 -2.395
ILM J3057 NM_806472.1 TXNIP 5454161 13.652 13.890 11.181 11.549 -2.486
ILMN_26458 NM_825165.2 ELL3 76781448 9.264 8.850 6.276 7.019 -2.489
ILMN_123332 Hs.571151 0 10722614 8.843 8.656 6.219 6.392 -2.444
ILMNJ 1778 NM_805309.1 GPT 4885350 6.582 6.343 3.078 4.883 -2.486
ILMN_3663 NM_805461.3 MAFB 31652256 11.926 11.898 8.726 9.990 -2.554
ILMNJ 6362 NMJ01005474.1 NFKBIZ 53832023 9.372 9.450 6.146 7.485 -2.596
ILMNJ 7486 NM_016335.2 PRODH 19924110 9.562 9.769 6.541 7.575 -2.688
ILMN_35558 XM_379623.2 FLJ41200 89829186 6.901 8.179 4.706 5.150 -2.612
ILMNJ 39156 NMJ01001669.1 FU41603 48717281 9.598 18.022 7.013 7.381 -2.613
ILMN_4181 XM_290629.6 C140H78 89837518 13.288 13.494 10.541 10.967 -2.637
ILMN_24488 NM_024328.2 ATAD4 34147376 9.889 8.451 5.858 6.331 -2.676
ILMN_4286 NMJ73462.2 PAPLN 50883294 8.530 9.280 5.895 6.497 -2.789
ILMN_34738 XM_945010.1 LOC651913 89857548 7.941 8.120 5.501 4.968 -2.796
ILMN_23998 NM_001039.2 SCNN1G 42476332 10.500 10.366 7.464 7.801 -2.881
ILMNJ 37748 XM_930828.1 LRR 2 89835472 6.231 6.760 4.053 3.186 -2.876
ILMN_82165 Hs.210390 0 10435476 7.970 8.391 5.083 5.520 -2.879
ILMN_20483 NMJ01005340.1 GPNMB 52694751 9.470 11.684 7.652 7.594 -2.91
ILMN 71591 Hs.19339 0 34191392 9.850 9.362 6.304 6.274 -2.917
ILMN_6827 NM_001885.1 CRYAB 4583056 8.985 9.125 5.888 6.345 -2.942
ILMNJ 3696 NM_001011709.1 PNLIPRP3 58743370 10.037 9.918 6.727 7.262 -2.983
ILMNJ 9114 NMJ39072.2 DNER 31542542 9.781 9.646 6.307 7.131 -2.994
ILMNJ 2568 NM_013281.2 FLRT3 38202220 11.298 11.262 8.007 8.384 -3.084
ILMN_4993 NM_018208.1 ETNK2 8922649 8.173 8.641 5.709 4.931 -3.087
ILMN_20221 NM_000896.1 CYP4F3 4583240 9.989 9.289 6.064 6.722 -3.246
ILMN_71322 Hs.13291 0 21751275 7.150 7.358 4.096 3.459 -3.476
ILMNJ3297 NM_000777.2 CYP3A5 15147331 7.764 8.019 3.926 4.744 -3.556
ILMN_28619 NM_000336.1 SCNN1B 4586816 8.376 8.553 4.505 4.760 -3.832
IL N_918S NM_002423.3 MP7 75709180 8.380 9.317 5.013 4.365 -4.159
ILMN_5569 NM_206857.1 RTN1 45827777 9.067 8.498 4.358 4.863 -4.172
ILMN_9893 NM_004089.3 TSC22D3 62865622 12.668 12.570 8.145 8.436 -4.328 Table 6: Affymetrix probe sets corresponding the GAB2 -signature and their Pearson correlation with sensitivity (GI-50) to
Resveratrol, Piceatannol and SD-1029 in the
NCI -60 cell line panel.
Pearson Pearson Pearson
Affymetrix Gene with GI50 ith GI50 with GEO
Probe Set Symbol to Resveto Piceato SD- ratrol tannol 1029
Figure imgf000048_0001
48
Figure imgf000049_0001
Figure imgf000049_0002
Figure imgf000049_0003
Figure imgf000050_0001
Table 7: Affym etrix probe sets corresponding to the GAB2 -signature and their differential expression between dasatinib-sensitive and resistant cells, expressed as
Signal-to-Noise Ratio (SNR).
Affymetrix Gene Affymetrix Gene Affymetrix Gene
SNR SNR SNR
Probe Set Symbol Probe Set Sym ol Probe Set Symbol
Table 7, continued Table 7, continued Table 7, continued
227717_at FLJ41603 -0.0237 209016_s_at l«T7 0.1193 210139_s_at PMP22 0.1274
241380 at FU41603 0.1083 214031 s at ΙΦΤ7 0.2079 218009 s at PRC1 0.2819
219250 s at FLRT3 0.3552 201720 s at U PTM5 0.0981 205880 at PRKD1 0.1444
222853 at FLRT3 0.4135 201721 s at U PTM5 0.5006 217705 at PRKD1 0.2954
227475_at F0XQ1 0.6437 212531_at LC 2 0.3741 205319_at PSCA -0.0597
206774_at FRMPD1 0.0773 202067_s_at LDLR -0.0216 236939_at PTPLAD2 -0.2552
202838 at FUCA1 0.2997 202068 s at LDLR 0.1907 244050 at PTPLAD2 0.1434
229137 at FUCA1 -0.2080 217005 at LDLR 0.0121 204146 at RAD51AP1 0.0724
214088 s at FLT3 0.2703 217103 at LDLR -0.0265 210051 at RAPGEF3 -0.2167
216010_x_at FLT3 0.2436 21 173_s_at LDLR -0.1375 206391_at RARRES1 0.3354
203397_s_at GALMT3 -0.0281 217183_at LDLR -0.2507 206392_s_at RARRES1 0.2153
203398 s at GALMT3 0.0226 209179 s at LENG4 0.7025 221872 at RARRES1 0.2271
204836 at GLDC -0.43B4 211037 s at LENG4 0.1078 227758 at RERG -0.4985
201141_at GPNMB 0.0807 215270_at LFNG -0.8127 244745_at RERG -0.2456
206709_x_at GPT -0.01 S3 228762_at LFNG -0.1513 202975_s_at RH0BTB3 0.5507
202967 at GSTA4 0.1563 219760 at LIN7B -0.1251 202976 s at RH0BTB3 0.5981
235405 at GSTA4 0.2410 241957 x at LIN7B -0.0530 216048 s at RH0BTB3 0.4641
207165 at HMMR 0.0572 239155 at LOC653108 0.0828 216049 at RH0BTB3 0.0948
209709_s_at HMMR 0.0853 228648_at LRG1 0.2809 225202_at RH0BTB3 0.7685
206864_s_at HRK 0.0784 202145_at LY6E -0.0463 240111_at RH0BTB3 0.3935
206865 at HR -0.0996 205458 at MC1R 0.1631 205211 s at RIN1 0.2119
201841 s at HSPB1 -0.1601 213476 x at MC1R 0.1376 209443 at SERPINA5 0.0393
206375 s at HSPB3 -0.0306 232092 at MCART1 0.4259 212268 at SERPINB1 0.7204
204002_s_at ICA1 -0.4092 238574_at MCART1 0.0635 213572_s_at SERPINB1 0.5011
207949_s_at ICA1 -0.9633 201755_at MCM5 0.1099 228726_at SERPINB1 0.3225
210547 x at ICA1 -0.8523 216237 s at M CM 5 0.0816 239213 at SERPINB1 0.2244
211740 at ICA1 -0.6783 208795 s at MCM7 -0.1668 205075 at SERPINF2 0.2460
214191_at ICA1 0.1783 210983_s_at MCM7 -0.1753 218921_at SIGIRR -0.3994
208937_s_at ID1 0.3430 202291_s_at MGP -0.1521 52940_at SIGIRR -0.3177
201565 s at ID2 -0.5049 238481 at MGP -0.0215 219795 at SLC6A14 0.0233
201566 x at ID2 -0.4726 219909 at MMP28 -0.0629 204368 at SLC02A1 0.1363
213931 at ID2 -0.1667 222937 s at MMP28 -0.2078 226837 at SPRED1 0.4914
207826_s_at ID3 0.2689 224207_x_at MMP28 0.1944 235074_at SPRED1 0.2638
201601_x_at IFITM1 0.6328 239272_at MMP28 0.0924 244439_at SPRED1 0.6733
214022 s at IFIT 1 0.5733 239273 s at MMP28 0.3107 212558 at SPRY1 0.2549
212203 x at IFITM3 0.6513 204745 x at MT1G 0.5408 203217 s at ST3GAL5 -0.1949
203424_s_at IGFBP5 -0.3914 210472_at MT1G -0.1444 239755_at ST3GAL5 0.0269
203425_s_at IGFBP5 -0.4747 212185_x_at T2A 0.3611 204542_at ST6GALI*C; -0.4321
203426 s at IGFBP5 -0.2139 218966 at MY05C -0.4062 204595 s at STC1 -0.1292
211958 at IGFBP5 -0.2989 218039 at NUSAP1 0.7152 204596 s at STC1 -0.1987
211953 at IGFBP5 -0.4112 219978 s at NUSAP1 0.5114 204597 x at STC1 -0.1196
231179_at IHP 3 -0.1870 214607_at PAK3 -0.3567 230746_s_at STC1 -0.0028
203126_at IMPA2 -0.3208 218952_at PCSK1N -0.2453 218207_s_at STMN3 -0.2173
229538 s at IQGAP3 0.1753 205960 at PDK4 0.0754 222557 at STMN3 -0.2538
241939 at IQGAP3 0.0851 225207 at PD 4 0.1803 202289 s at TACC2 -0.0011
231779 at IRAK2 0.3498 222687 s at PHCA -0.0311 211382 s at TACC2 0.0393
206104_at ISL1 0.1669 222688_at PHCA 0.0834 201839_s_at TACSTD1 -0.2499
206766_at ITGA10 0.1811 222689_at PHCA 0.0563 227279_at TCEAL3 -0.1189
202803 s at ITGB2 0.2992 231321 s at PHCA -0.1356 204106 at TESK1 -0.4211
236988 x at ITGB2 -0.1698 225533 at PHF19 0.6055 201107 s at THBS1 0.2892
209408_at 1F2C 0.4707 227211_at PHF19 0.3203 201108_s_at THBS1 0.3508
211519_s_at 1F2C 0.4824 22 212_s_at PHF19 0.1859 201109_s_at THBS1 0.2165
205778 at KLK7 0.3250 201397 at PHGDH 0.0473 201110 s at THBS1 0.2028
239381 at LK7 0.3196 207469 s at PIR -0.2290 215775 at ΤΗΒΞ1 -0.2847
207023 x at RT10 0.2529 207717 s at PKP2 0.2378 235086 at THBS1 0.1593
210633_x_at RT10 0.3828 214154_s_at P P2 -0.1900 239336_at THBS1 0.1047
213287_s_at RT10 0.5696 235958_at PLA2G4F -0.5375 206415_at TLL1 0.2816
207935 s at RT13 0.1074 218644 at PLE 2 0.1881 221908 at TMEM118 -0.4085 Ta le 7, continued
221 909_at T EM1 18 -0.6264
225822 at T EM1 25 -0.2078
20561 1 at TNFSF1 2 0.1025
206393_at TNNI2 0.1896
20 291 _s_at T0P2A 0.2640
201 292_at T0P2A 0.4519
237469 at T0P2A -0.0908
204649 at TROAP -0.4803
20911 4 at TSPAN1 0.0340
202154_x_at TUBB3 0.31 44
203S94_at TUBG2 0.0822
205S07 s at TUFT1 -0.2774
221 490 at UBAP1 0.2669
46270_at UBAP1 0.2421
202954_at UBE2C 0.2767
232654 s at UGT1A6 0.5998
232655 at UGT1A6 -0.1915
225655 at UHRF1 0.5735
203026_at ZBTB5 0.1349
220933_s_at ZCCHC6 0.1230
236155 at ZCCHC6 -0.3686
236243 at ZCCHC6 -0.21 79
238800 s at ZCCHC6 -0.0419
242776_at ZCCHC6 -0.5296
229901_at ZNF488 0.2833
242463ji_at ZNF600 0.01 76
Figure imgf000054_0001
Figure imgf000055_0001
Table 8, continued
PLEK2 NM_01 B445 NM_01 6445 218644_at 0.0353986486486487 -0.01 010672819081 79
PM P22 NM_000304 NM_000304 21 01 39 _at -0.061 3377092846271 -0.021 0373879940791
P RC1 NM_003981 NWI_003981 0.06666891 891 891 89 -0.1 36577856321 8390
P RKD1 NM_002742 NM_002742 -0.0631 21 621 621 621 6 -0.031 2609080459770
PS CA NM_005672 NM_005672 -0.239040540540541 0 -0.27534267241 37930
RARRES1 NM_002888 NM_002888 206391_at -0.1 31 1 283783783780 -0.1 24991 591 9540230
RERG A 294092 Contig50719_RC 227758_at -0.1 991 081081 081080 -0.063151 356321 8391
RHOBTB3 AI040030 Contig4539 202976_s_at -0.0708986486486486 -0.0674772420769384
RHOBTB3 NM_01 4899 N _01 4899 -0.073703481 7351598 -0.071 21 8479281 1 748
RIN1 NM_004292 NM_004292 205211 _s_at 0.0263581081 081081 0.01 70924885057471
SERPINA5 J02639 J 02639 -0.1 432297297297300 -0.06341 24085775032
SERPINA5 NM_000624 NM_000624 -0.250331 081 081 081 0 -0.1 5895704597701 1 0
SERPINB1 M 93056 M 93056 21 2268_at -0.0471 283783783784 -0.0057291 321839081
SERPINF 2 D001 74 D00174 205075_at -0.056851 351 351 351 3 -0.042332927081 2571
SIG I R AA 085764 Contig974_RC 0.0293040540540541 0.01 2120856321 8391
SLC6A1 4 NM_007231 NM_007231 219795_at -0.1 205675675675680 -0.2001074942528740
SLC02A1 NM_005630 NM_005630 204368_at -0.023331 081 081 081 1 -0.027457051 7241 379
SP RE D1 AI742347 Contig34355_RC -0.0298648648648649 -0.01 45794885057471
SPRY1 AF041 037 A F041 037 21 2558_at -0.042061 9292237443 -0.0065434269589658
ST3GAL5 NMJ03896 NM_003896 203217_s_at -0.0006824324324324 0.0038459425287356
ST6GALNAC2 NM_006456 NM_006456 204542_at -0.047891 891 891 891 9 -0.0592792528735632
STC1 NM_0031 55 NM_0031 55 204597_x_at -0.2473783783783780 -0.2237332236396250
TACC2 AF1 76646 A F1 76646 202289 _at -0.019891 891 891 891 9 -0.004148931 0344828
TA CSTD1 NM_002354 NM_002354 201 839_s_at -0.059331 081 081 081 1 -0.0975506896551 724
TCEAL3 AI340029 Contig52641 _RC -0.0344797297297297 0.00028856896551 72
TESK1 NM_006285 NM_006285 2041 06_at 0.01 50675675675676 -0.004006597701 1 494
ΤΗΒ Ξ1 A 1 39567 Contig42410_RC 201 1 10_s_at 0.0263243243243243 -0.01 424562251 67763
THB S1 NM_003246 NM_003246 -0.061 1 756756756757 -0.06975651 471 66301
TLL1 NM_01 2464 NM_01 2464 0.022371 621 621 621 6 0.0555686034482759
TME M 1 25 AI628756 Contig54290_RC 225822_at -0.0395270270270270 -0.031 35801 7241 3793
TROAP NM_005480 NM_005480 204649_at 0.0690270270270270 -0.01 55351 77031 4265
TS PAN1 NM_005727 NM_005727 -0.2038851 351 351350 -0.1 900224080459770
TUB B3 NM_006086 NM_006086 -0.09941 891891 891 90 -0.2448362377250680
TUBG2 NMJ1 6437 NM_01 6437 -0.0381 351 351 351351 0.0057381 264367816
TUFT1 NM_0201 27 NM_0201 27 205807_s_at -0.0080878378378378 -0.02391 02298850575
UBAP1 NM_01 6525 NM_01 6525 46270_at 0.0070675675675676 0.0024964597701 1 49
UB E2C NMJ0701 9 NM_007019 0.09461 48648648650 -0.07887831 1 540761 4
UGT1 AB NM_000463 NM_000463 0.0541 701221 76971 5 0.066438320221 3250
UHRF1 NM_01 3282 NM_01 3282 225655_at 0.05431 081 081 081 08 0.00089451 14942529
ZBTB5 NM_01 4872 N _01 4872 203026_at -0.00201 3701 201 201 2 -0.0031744330545327
ZCCHC6 AI800829 Contig41 991 _RC 220933 s at -0.0145472972972973 0.0054469827586207 Table 10: Univariate and multivariate Cox regression analyses, comparing the GAB2 signature with existing clinical and genomic predictors in the 198-sample dataset
Univariate analyses P HR lower .95 upper .95
Adjuvant!Online 0.03200 3.09008 1.10129 8.67038
GAB2-Signature 0.00170 23.81942 3.27349 173.32089
Veridex Index 0.00420 5.56595 1.71764 18.03627
MammaPrint 0.00170 23.84934 3.27744 173.54720
Genomic Grade Index 0.00005 5.99948 2.52165 14.27390
Pairwise Multivariate 1 P HR lower .95 upper .95
GAB2-Signature 0.003 20.947 2.843 154.350
Adjuvant!Online 0.330 1.676 0.593 4.733
Pairwise Multivariate 2 P HR lower .95 upper .95
GAB2-Signature 0.005 17.65819 2.38999 130.46544
Veridex Index 0.078 2.89930 0.88643 9.48289
Pairwise Multivariate 3 P HR lower .95 upper .95
GAB2-Signature 0.053 7.91200 0.97100 64.44500
MammaPrint 0.053 7.93600 0.97400 64.64900
Pairwise Multivariate 4 P HR lower .95 upper .95
GAB2-Signature 0.010 13.66311 1.61679 115.46353
Genomic Grade Index 0.220 1.99342 0.78514 5.06119
Tri le Multivariate 1 P HR lower .95 upper .95
GAB2-Signature 0.031 10.510 1.236 89.375
Veridex Index 0.086 2.825 0.864 9.245
Ggi 0.165 1.931 0.762 4.892
Tri le Multivariate 2 P HR lower .95 upper .95
GAB2-Signature 0.093 6.725 0.729 62.040
MammaPrint 0.079 7.035 0.800 61.859
Genomic Grade Index 0.626 1.269 0.487 3.310
Triple Multivariates P HR lower .95 upper .95
GAB2-Signature 0.087 6.267 0.766 51.258
MammaPrint 0.059 7.501 0.926 60.790
Veridex Index 0.094 2.750 0.842 8.981
Quadruple Multivariate P HR lower .95 upper .95
GAB2-Signature 0.140 5.28400 0.57000 49.00400
Veridex Index 0.093 2.75700 0.84400 9.00400
MammaPrint 0.086 6.65800 0.76600 57.88600
Genomic Grade Index 0.610 1.28000 0.49200 3.32900 Table 9: Two gene functional modules extracted from the GAB2 -signature are differentially expressed In breast cancer patients with good and poor prognosis
Gene
Module Functional Module Functional annotation
Symbol
FAP
SE PINB1
F UCA1
PMP22
ARHGDIB
SPRY1
CCNA2
CCNB1
CCNB2
CDC45L
DTL
EX01
CDT1
MCM5
MCM7
E2 F2
U HRF1
NUSAP1
ASPM
HMMR
TROAP Definitions
The present invention relates to identification of GAB2-signature genes and their association with diagnosis, prognosis, metastasis, metastatic relapse and prediction of treatment response.
The GAB2-signature genes of the present invention are listed in Tables 1, 2, 3, 4 and 5.
By claiming expression of at least two of GAB2-signature genes, it is meant that these two genes can be either from the same table or one each from different tables in a given scenario.
By 'metastatic potential' , it is meant that the ability of a cancer cell to invade and to spread of cancer cells to other parts of the body.
By 'metastatic relapse', it is meant that the relapse occurs when a person is affected again by a condition of metastasis that affected him in the past.
By "prognosing", it is meant the ability to predict the potential course and outcome of a particular patient's cancer including potential for metastasis, growth and response to treatment
"predicting response to treatment" shall mean the ability to determine ahead of a treatment the probability that a particular type of treatment could be fully/partially effective or be fully/partially be ineffective in a particular patient and such treatments shall include, but not limited to chemotherapy, targeted drug therapy, radiation therapy, other cytotoxic drug therapies, alternate therapies or any other therapies other cytotoxic drug therapies, alternate therapies or any other therapies used in the treatment of cancer or one or more combinations thereof.
'Cancer treatment' as meant in the current specification includes but not limited to targeted drug therapy, chemotherapy, radiation therapy other cytotoxic drug therapies, alternate therapies or any other therapies used in the treatment of cancer or one or more combinations thereof. 'Grading of tumor' is a system used to classify cancer cells in terms of how abnormal they look under a microscope and how quickly the tumor is likely to grow and spread and includes defining different stages of cancer as per histology or other parameters as defined by a pathologist and physician.
The 'cancer' as mentioned in the specification includes all types (solid, liquid, and lymphatic origin), and not limited to breast cancer, metastatic malignant melanoma, lymphomas (Hodgkins and non-Hodgkins), sarcomas (Ewing's sarcoma), carcinomas, brain tumors, central nervous system (CNS) metastases, gliomas, , prostate cancer, lung cancer (small cell and non-small cell), colon cancer, pancreatic cancer, Head and Neck cancers, oropharyngeal squamous cell carcinoma. The cancer cell may be originated from any part of the body, and not limited to any organ of human body such as brain, lung, adrenal glands, pituitary gland, breast, prostate, pancreas, ovaries, Gastro Intestinal Tract, kidneys, Liver, spleen, testicles, cervix, upper, lower, or middle esophagus either primary or secondary tumors of all types.
Innovative aspects of the invention
To ensure successful metastatic dissemination, malignant cells must acquire the ability to grow in the absence of their environment of origin. In fact, the capacity of cells to survive and proliferate in vitro in the absence of integrin-mediated adhesion strongly correlates with tumorigenesis in-vivo and may enable tumor cells to metastasize and grow at inappropriate sites in the body (Danen and Yamada 2001, J Cell Physiol, 189, 1-13). We hereby describe a key role in anchorage-independent growth for Gab2, a multiadaptor protein devoid of enzymatic activity. The role of Gab2 in anchorage-independent growth emerged within the context of a high-throughput selective functional screening, in which this protein competed with several thousand others. Apart from GAB2, the analysis revealed a reproducible enrichment also for a well-known transforming gene, NTRK3, previously found to play a key role in anoikis resistance (Geiger and Peeper 2007, Cancer Res, 67, 6221-6229) and useful as an internal control of the screening effectiveness. Gab2 is a member of the Grb2-associated binding protein (GAB) gene family (Gu et al. 1998, Mol Cell, 2, 729-740). They are so called "scaffolding" or "docking" proteins because of the presence of multiple functional motifs mediating interactions with many other signaling molecules (Nishida et al. 1999). GAB proteins are involved in signaling events triggered by a variety of stimuli, including GFs, cytokines, G-coupled receptors and T- and B-lymphocyte antigens, ultimately regulating cell growth, differentiation and transformation (Bouscary et al. 2001 ; Liu et al. 2001, Mol Cell Biol, 21, 3047-3056; Sattler et al. 2002, Cancer Cell, 1, 479-492). Among the Gab2 direct interactors are proteins with key roles in human cancer when mutated, such as PI3K and the tyrosine phosphatase Ptpnl l/Shp2. Recent work suggested that the oncogenic properties of Ptpnl l mutant proteins require signal enhancement by Gab2 (Zatkova et al. 2006, Cancer, 45, 798
807). Interestingly, Gab2 maps to a chromosomal region (l lql3) amplified in 10-15% of breast cancers, and its overexpression was confirmed in several breast cancer cell lines (Daly et al. 2002, Oncogene, 21, 5175-5181). The role of Gab2 in mammary tumor metastasis was also explored and confirmed in mouse models (Ke et al. 2007, Oncogene, 26, 4951-4960). More recently, a key role for GAB2 in motility/invasion of melanoma cells and metastatic progression of melanoma was also highlighted (Horst et al. 2009). We now show that Gab2 also promotes anchorage-independent growth of breast cancer and melanoma cells. This information extends the previously described growth-promoting activity of Gab2 in adherent cells (Brummer et al. 2006, J Biol Chem, 281, 626-637). We also found that Gab2-driven anchorage independence is not due to a protection from cell death upon detachment. This finding was unexpected, given the fact that Gab2 potentiates the PI3k/Akt and Ras/Erk pathways, but it is in line with previous reports indicating that Gab2 does not prevent apoptosis of luminal cells during morphogenesis of MCF10A cells (tires-Alj et al. 2006, Nat Med, 12, 114-121). Our experiments using small molecule inhibitors showed that, while the PI3k/Akt and Ras/Erk pathways are required independently of the adhesion status and of Gab2 expression, Src inhibition had no effect on wild-type cells in suspension, but strongly impaired their adherent growth, confirming that Src conveys the proliferative consensus provided by integrin engagement (Playford and Schaller 2004). Moreover, Gab2-expressing cells required Src activity also in suspension, providing a strong rationale for Src involvement in Gab2-driven anchorage- independence. The biochemical link between Gab2 and Src can be provided by Ptpnl l, previously described to directly bind Gab2 (Kong et al. 2003) and to activate Src (Zhang et al. 2004, Mol Cell, 13, 341-355). It is of particular interest that Gab2sustained growth in suspension was impaired when cells were cultured in the absence of EGF, indicating that Gab2 can only overcome the lack of adhesive consensus in the presence of an upstream signal from GFs. Recently, Gab2 was found to promote GF independence in cooperation with oncogenic Src (Bennett et al. 2007, Oncogene, 27, 2693-2704). Therefore, Gab2 can rescue cells from the need of two concomitant proliferative stimuli - activated GF receptors and activated Src when at least one of the two is present at sufficiently high levels. Therefore, Gab2 can be a key rheostat and integrator, allowing for a "spillover" of the signal across the two pathways, in the context of a network containing points of reciprocal influence and cross-talk (ffrench-Constant and Colognato 2004, Trends Cell Biol, 14, 678-686). In this view, proliferation of non-adherent cells could be promoted by a GF-driven direct activation of the Erk and Akt pathways and indirect, Gab2mediated activation of Src and of its downstream signaling molecules, in particular Stat3. Indeed, Stat3 has been already involved in Src and Jakl-driven proliferation of human breast carcinoma cells (Garcia et al. 2001, Oncogene, 20, 2499- 2502) and in anchorage-independent growth of cancer cells (Zhang et al. 2006, Mol Cell Biol, 26, 413-424). Moreover, Gab2 was found to contain a functional Stat3 binding motif promoting its recruitment and activation (Ni et al. 2007, Mol Cell Biol, 27, 3708- 3715). Our biochemical data confirmed the contribution of Src and Stat3 to Gab2- mediated anchorage-independent growth: Gab2 expression increased Src and Stat3 phosphorylation both basally and after prolonged suspension culture, and Gab2 downregulation by RNAi led to reduction of Src and Stat3 activation not only in MCF10A cells constitutively expressing exogenous Gab2, but also in neoplastic cells loosing anchorage independence as a consequence of endogenous Gab2 silencing. In line with this, our gene expression analysis showed that a significant fraction of the genes whose expression is regulated by Gab2 are also differentially expressed in cancer cells resistant or sensitive to small molecule inhibitors of the Src/Jakl-STAT3 signaling axis. Finally, it is of particular significance that the Gab2 transcriptional signature yields a robust classifier for metastatic relapse of human breast cancer. Of the two key functional modules found in the signature, the proliferation module, positively correlated with metastasis, adds further informative genes to the already described core of proliferation genes associated to breast cancer progression (Wirapati et al. 2008, Breast Cancer Res, 10, R65). More distinctive is the module of genes negatively correlated with metastasis, among which particularly interesting are: (i) two extracellular proteins that, respectively, inhibit matrix-degrading proteases (SERPINB1 (Cooley et al. 2001, Biochemistry, 40, 15762-15770)) and remove fucose from ECM glycans (FUCA1), thereby impairing ECM binding and invasion by cancer cells (Yuan et al. 2008, Pathol Oncol Res, 14, 145-156); (ii) the FAP gene, encoding an integral membrane protease whose expression is negatively correlated with metastatic progression (See, Ariga et al., 2001, Int J Cancer, 95, 67-72); (iii) PMP22, that encodes a 4-spanning integral membrane protein widely expressed at apical junctions of epithelial cells, increasing transepithelial electrical resistance and decreasing migration (Roux et al. 2005, Mol Biol Cell, 16, 1142-1151); (iv) the intracellular products of the ARHGDIB and SPRY1 genes, negative regulators of, respectively, Rho-family GTPases (DerMardirossian and Bokoch 2005, Trends Cell Biol, 15, 356-363) and tyrosine kinase receptors like EGFR and FGFR (Mason et al. 2006, Trends Cell Biol, 16, 45-54). These two functional modules, within the context of the GAB2-signature, generate a prognostic classifier predicting metastatic progression with high accuracy, independently from ER status and from other existing genomic signatures, and greatly outperforming the clinical-pathological prognostic parameters currently integrated into the Adjuvant! Online web tool (Hess 2008). Altogether, we demonstrated a key role for Gab2 in anchorage-independent growth of neoplastic cells and, as an example of the diagnostic potential of the signatures described herein, we validated the transcriptional GAB2-signature as a new, strong and independent prognostic and predictive classifier for breast cancer. This confirms that the signatures described in tables 1 to 5, or other signatures obtained in cellular models of GAB2-driven processes and anchorage independence, can be successfully used as prognostic and predictive classifiers of human cancers, and contain novel therapeutical targets for cancer treatment. Furthermore, we have also demonstrated that GAB2 driven signatures are also predictive of drug response as demonstrated by the unique signatures identified in presence of anticancer drugs like resveratrol, dasatinib and Piceatannol. Hence the method can be used to identify GAB2 associated signatures which could predict sensitivity or resistance to any anti-cancer drug or combination of drugs and its dose or even to radiotherapy by identifying the GAB2 associated signature in presence of these drugs or cytotoxic or biologic modifiers in cell lines or primary cells derived from the tumor biopsy of a patient thereby providing a power tool to pre-determine which treatment a particular patient will respond to or not thus improving the efficacy of treatment and reducing unwanted side effect and cost of treatment. Since GAB-2 signature also can be correlated with the aggressiveness of the tumor cells, it could also be used as a method for assigning grades to the tumor, which is currently done by less accurate histochemical grading methods. Methods
Cell Culture and Reagents
MCF10A cells were obtained from ATCC and cultured as described (Reginato et al. 2003, Nat Cell Biol, 5, 733-740). MDA-MB-231 and MDA-MB-435 cells were obtained from ATCC and cultured in DMEM (Gibco) supplemented with 10% fetal bovine serum (Sigma). The antibodies used were: antiGab2 (Upstate Biotechnology), anti- Tyr416- phosphorylated Src (Cell Signaling), anti-total Src (Cell Signaling), anti-Tyr705- phosphorylated Stat3 (Cell Signaling), anti-total Stat3 (Cell Signaling), goat anti-actin (Santa Cruz). Retroviral expression library and pFB-hrGFP retroviral supernatant, packaged in the VSV envelope, were purchased from Stratagene (ViraPort, Cat n.
972300) and used to infect 1.5x10 MCF10A cells in 60mm tissue culture plates using 10 μg/ml DEAE-dextran (Amersham Bioscence). GFP expression analysis was performed after 48 hours using a FACS Calibur flow cytometer (Becton Dickinson). Retroviral expression vector for GAB2 in pMIG (also known as pMSCV-IRES-GFP) was a gift of R. Daly. Virus production and transduction were performed as described (Brummer et al. 2006).
Anchorage-independent growth selection
Polyhema-coated 10cm Petri dishes were prepared by applying 4ml of a 12mg/ml solution of poly-hydroxy-ethyl-methacrylate (polyhema; Sigma) in ethanol, drying under tissue culture hood, repeating the application once and incubating the plates overnight at
37°C. For the selection, 3 x 10 cells were plated onto polyhema-coated plates in complete growth medium. Cells were cultured in suspension for 48h then were let to recover on regular plates for 24h before repeating the selection cycle.
Western Blot
Cell lysates from 2-5χ1θ' cells were prepared in RIPA buffer (150 mM NaCl, 1% NP40, 0.5% DOC, 50 mM TrisHCL at pH 8, 0.1% SDS, 10% glycerol, 5 Mm EDTA, 20 mM NaF and 1 mM Na3V04) supplemented with 1 μg /ml each of pepstatin, leupeptin, aprotinin, and 200 μg/ml phenyl methylsulphonyl fluoride (PMSF). Lysates were cleared by centrifugation at 12,000 rpm for 20 min at 4°C and normalized with the BCA Protein Assay Reagent Kit (Pierce). Extracts were run on SDS-polyacrylamide gels, transferred onto nitrocellulose membranes (Hybond; GE Healthcare) and incubated with different antibodies overnight at 4°C. Nitrocellulose-bound antibodies were detected by the ECL system (GE Healthcare).
Real-time PCR.
Two micrograms of total RNA were reverse transcribed with the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA). Quantitative Real-time PCR with Sybr Green was performed on the ABI Prism 7900HT Sequence Detection System (Applied Biosystems, Oak Brook, IL). Details on PCR primer design are available in Supplementary Methods.
Cell-based assays
For MTT cell growth assays, 10 cells were seeded in regular or polyhema-coated 96-well plates. Cells were cultured in growth medium containing all supplements or in starving medium (serum reduced to 2%, no EGF). At the indicated times, a tetrazolium salt-based reagent (CellTiter96 Aqueous One Solution, Promega) was added to each well according to the instructions provided by the manufacturer. After an incubation of 2 h, absorbance was read at 490 nm on a DTX 880 plate reader (Beckman Coulter). A control measurement after 4h from plating was used as a reference to adjust subsequent acquisitions of each cell line. For soft agar growth, 3x10 cells were resuspended in 2ml of 0.5% top agar (SeaPlaque Agarose from Cambrex) in growth medium and seeded in 6- well plates previously filled with 3ml of 1 % basal agar in growth medium. The assay was performed in duplicate. After 3 weeks, phase-contrast pictures were captured by a BD Pathway microscopic station (BD biosciences). Image analysis and quantification of single colonies number and size were performed by the Atto vision 1.5 software (BD biosciences). For detachment-induced cell death analysis, 3x10 cells were plated on regular or polyhemacoated 35mm plates for 48 hours. Cell death was then measured by assessing the number of hypodiploid nuclei with the DNAcon3 kit (ConsulTS, Rivalta, Italy), according to the manufacturer's protocol and with cytofluorimetric analysis using a FACSCalibur (Becton Dickinson, San Diego, CA). Hypodiploid, subGO/Gl nuclei were defined as those displaying a PI staining value lower than that of cells in the G0/G1 cell cycle phase (diploid DNA peak).
Xenoarray and gene expression analysis
RNA was extracted using the Trizol Plus purification Kit (Invitrogen, cat.no.12183555), according to the manufacturer's protocol. Quantification and quality analysis of RNA was performed on a Bioanalyzer 2100 (Agilent). Synthesis of cDNA and biotinylated cRNA were performed using the Illumina TotalPrep RNA Amplification Kit (Ambion Cat. n. IL1791), according to the manufacturer's protocol, with previously reported variations in the case of Xenoarray analysis (Martelli et al. 2008, BMC Genomics, 9, 254), for which hybridization was carried out on Illumina Mouse6_Vl arrays. For standard microarray analysis of the Gab-2 signature, 1500 nanograms of cRNAs were hybridized on Illumina Beadarrays (Human_6_V2). Array washing was performed using Illumina High-stringency wash buffer for 10 min at 55°C, and followed by staining and scanning according to standard Illumina protocols. Probe intensity data were obtained using the Illumina BeadStudio software, and further processed with R-Bioconductor (Gentleman et al. 2004, Genome Biol. 5, R80) and Excel software.
Definition of transcriptional signatures associated to GAB2 and anchorage independence in MCF10A cells
Preliminary data treatment
Data were rank-invariant normalized and filtered to remove probes for which the detection score was lower than 0.99 in the sample with higher signal. Filtered data were scaled by adding the arbitrary value of 40 to remove negative expression values from the analysis. As an additional filter, genes differentially regulated between the two controls, i.e. WT and GFP-transduced cells, were removed from the analysis. Such genes were defined according to Illumina Custom statistic, with a fold-change threshold of 2 and a differential score of 20 (corresponding to a p- value of 0.01).
Selection of differentially expressed genes
To identify genes differentially expressed between GAB2-SEL and GFP-SEL samples we employed the Dunnett's T-test (Dunnett et al. 1964, Biometrics 20, 482-491), an inferential parametric test designed to compare the mean of each of several experimental groups with the mean of a control group. A simple description of the properties of the Dunnett's T-test can be found at
https://davidmlane.eom/hyperstat/B 112114.html
The formula of the test is the following:
Figure imgf000067_0001
Where: E = average of the experimental group to test
C = average of the control condition
m = minimal difference threshold (optional)
MSEwg= mean square error within group, calculated from all experimental conditions like in ANOVA
t: the ratio of the test
Nh= harmonic mean of sample replicates for the two conditions tested
The test evaluates the hypothesis, in our case the change of log2 expression values, by means of an estimation of the mean square error within groups, corrected by the harmonic mean of the sample numbers. The test was performed on log2-scaled values, with m = 1. To increase accuracy of the MSEwg estimation, we calculated it from all experimental conditions, i.e. GAB2-selectet, GFP-selected, GAB2-unselected, GFP-unselected and Lib selected. In the standard Dunnett's test the m threshold is absent and the t threshold for significance can be derived from the Dunnett's t tables, available for example at https://davidmlane.com/hyperstat/table Dunnett.html. In our case, with m different from zero, we had to estimate the correct t value by running the test iteratively on permutations of experiments, thereby estimating the False Discovery Rate (FDR; Tucher et al.2001, Proc Natl Acad Sci U S A. 98, 5116-21). Indeed, our FDR analysis showed that the Dunnett's test with m different from zero is more powerful, and much more reliable than classical T-test. To prioritize differentially regulated genes, we choose t = 2, with an estimated alpha (FDR) of <0.05 according to the median distribution of 5000 randomly permutated datasets. The list of selected genes and their expression in control and Gab2- transduced cells is reported in Table 1. The same procedure was applied for the other signatures illustrated in Tables 2 to 5.
The following examples provide illustrative embodiments of the invention. A person skilled in the art will readily recognize the various modifications and variations that may be performed without altering the scope of the present invention. Such modifications and variations are encompassed within the scope of the invention and the examples do not in any way limit the scope of the invention.
Examples
Example 1 - Analysis of GAB2-signature enrichment in genes correlated to cancer aggressiveness and response to treatment
Enrichment in genes correlated with cell response to drugs
To verify if expression of GAB2-signature genes is correlated to responsiveness of cell lines to inhibitors of the Src/STAT3 signaling axis, we used two sets of data built on the NCI-60 panel of cell lines: (i) a gene expression dataset (Shankavaram et al. 2007) generated using Affymetrix HGU133 arrays and downloaded from the NCBI Gene Expression Omnibus (GEO, GSE5720); (ii) the database of Developmental Therapeutics Program NCI/NIH (https://dtp.nci.nih.gov/index.html), reporting for each cell line the GI50 (concentrations required to inhibit growth by 50%) for over 50.000 different compounds. In particular, we focused on 3 drugs (Resveratrol, Piceatannol, and SD-1029) targeting STAT3 activation by Src- or Jak-family kinases, and calculated the Pearson correlation, across all cell lines, between the GI50 and the expression of each gene of the Affy dataset. To assess the enrichment of the GAB2-signature in genes with high correlation with the GI50 of the above drugs, Gene Symbols corresponding to the signature were mapped on the dataset, resulting in 356 Affymetrix probe sets (Supplementary Table 2). Subsequently, the number of signature genes with correlation values falling in the top 5% of all the dataset was counted. Significance of the difference between expected and observed probe sets with high correlation was calculated by hypergeometric distribution analysis as illustrated in the following table:
Figure imgf000068_0001
assess enrichment of the GAB2-signatures for predictive markers of response to Dasatinib, we exploited a gene expression dataset obtained by Huang and colleagues using Affymetrix HGU133 arrays on a panel of 23 breast cancer cell lines, either resistant (16 lines) or sensitive (7 lines) to Dasatinib (Huang et al., 2007). The resulting dataset was downloaded from the NCBI Gene Expression Omnibus (GEO, GSE6569). For each probe in the array, the differential expression between Dasatinib-sensitive and resistant cells was calculated using the Signal to Noise ratio (Golub et al., 1999, Science 286, 531- 537). To assess the enrichment of the GAB2-signature in genes with high SNR, Gene Symbols corresponding to the signature were mapped on the dataset, resulting in 356 Affymetrix probe sets. Subsequently, the number of signature genes with absolute SNR values falling in the top 5% of all the dataset was counted. Significance of the difference between expected (18) and observed (30) probe sets with high SNR was calculated by hypergeometric distribution analysis.
Example 2
Enrichment in genes discriminating good and poor prognosis breast cancer
For meta-analysis on breast cancer microarray data, two public available data sets from the Netherlands Cancer Institute (van't., Veer et al 2002, Nature, 415, 530-536; van de Vijver et al. 2002, N Engl J Med, 347, 1999-2009) (NKI; https://www.rii.com/publications/2002/default.html) were used and merged into a unique 311sample dataset (NKI-311). The data were filtered to remove probes whose signal never reached the 50 percentile in any sample. Further filtering was applied on probes for which more than 99% of the expression values were missing. The probes were annotated with gene symbols obtained via Unigene (release Hs # 204), and for each of them the Signal- to-Noise Ratio (SNR; Golub et al.1999) between poor- and good -prognosis samples (presence or absence of metastatic relapse within 5 years) was calculated in the NKI-311 dataset, according to the following formula:
A VG^- A VG
STDEFpp÷STDEVGP
Where AVGPP and AVGGP are the average expression values in poor-prognosis and good- prognosis samples, respectively, and STDEVPP and STDEVGP are the standard deviations in poor-prognosis and good-prognosis samples, respectively. After mapping the GAB2- signature on this dataset via Gene Symbols, its enrichment in genes with high SNR was calculated as described above.
The results are displayed in the following table:
Figure imgf000070_0001
Example 3 - Construction and validation of a Breast cancer classifier based on the GAB2-signature
Classifier construction
To generate a classifier for breast cancer patients, we applied a modification of the nearest mean classifier approach (Wessels 2005). Briefly, we calculated for each gene of the GAB2Signature the median expression in the good and poor prognosis subgroups of the NKI-311 dataset. For a more accurate calculation of the median expression, the data were bootstrapped (1000 bootstraps each including a random selection of 80% of subgroup samples). The classifier is therefore composed of the list of the GAB2-signature genes mapped on the NKI dataset and, for each gene, the median expression values (means) for the good and poor prognosis groups (Supplementary Table 3). To classify samples, a "Metastasis Score" (MS) is then calculated, based on the GAB2-signature genes, according to the following formula:
MS = k + Pearson - PearsonGP
Where k is a scaling factor, PearsonPP is the correlation of the sample with the poor prognosis centroid, and PearsonGP is the correlation of the sample with the good prognosis centroid. The MS is therefore directly proportional to the risk of metastatic relapse within five years, and if it is greater than zero, patients are classified as poor prognosis, otherwise they are classified as good prognosis. We found that a k value of 0.16 minimizes the rate of false negatives (patients classified as "good prognosis" that instead developed metastasis within 5 years) in the NKI-311 dataset.
Classifier validation
To map the GAB2-signature on independent breast cancer datasets obtained on different microarray platforms, we used a univocal cross-mapping table generated by the Microarray Quality Control (MAQC) consortium (Maqc consortium, 2006, Nat Biotechnol. 24, 1151-1161) and applied it to five independent datasets of 198, 236, 286, 289 and 134 samples (Desmedt et al 2007, Clin Cancer Res, 13, 3207-3214; Miller et al. 2005, Proc Natl Acad Sci U S A, 102, 13550-13555; Wang et al. 2005, Lancet, 365, 671- 679; Ivshina et al. 2006, Cancer Res, 66, 10292-10; Hess et al. 2006, J Clin Oncol, 24, 4236-4244). To reach homogeneity in data structure and to properly apply the NMC obtained in the NKI-311 dataset, Affymetrix log2 expression signals were converted, for each dataset, into log2 ratios against median expression in that dataset. Univariate and multivariate analyses were conducted in the 198sample dataset using R-Bioconductor.
Example 4 - Neutralization of Gab-2 function using a short hairpin RNA vector as a method for treating breast cancer and other epithelial cancers
Lentiviral shRNA expression vectors against murine or human GAB2 can be purchased from Sigma (MISSION™ TRC shRNA Target Set), together with the pLKO. l-puro Control Vector. Viral supernatants are obtained according to the manufacturer's protocol. Infected cells are selected by puromycin treatment (2ug/ml for one week). Specific shRNA sequences which efficiently downregulate Gab2 protein, as assessed in Western blot, are the following:
CCGGCCGACACAATACAGAATTCAACTCGAGTTGAATTC-
(ϋ) Human: CCGGCAGCCAACTCTGTTCACGTTTCTC-
Figure imgf000071_0001
Neutralization of GAB2 by the shRNA leads to inhibition of proliferation in adherence, as measured by MTT cell growth assays or H-thymidine uptake and in suspension, as measured by soft agar growth assay. In a preferred embodiment, the shRNA vector would be replaced with an artificial molecule targeting the GAB2 mRNA, such as a peptide-nucleic acid (PNA) or other nuceleotide-based reagent. In another preferred embodiment, one would use a small molecule inhibitor against Gab2 selected using combinatorial chemistry, rational design or other methods. Such drugs would be properly formulated and administered parenterally, orally or as an injection. In fact, one could use any agent that has the ability to neutralize fully or partially the function of Gab2 or its interaction with other proteins by neutralizing either the gene, its mRNA or the protein itself. In a further embodiment of the invention, one would use the same strategies described above to inhibit the function of the transcriptional targets of GAB2 and/or of anchorage independence as described in Tables 1 to 5, or contained other signatures obtained in cellular models of GAB2-driven processes and anchorage independence.
Example 5 - A real-time PCR-based classifier for predicting metastasis, prognosis and sensitivity to chemotherapy in breast cancer patients
We analyzed RNAs extracted from 32 breast cancer samples, and measured gene expression using Illumina microarrays or realtime PCR as described in Methods, for 15 genes of the GAB2 signature (list of genes/TaqMan Inventoried Assay ID EEF1A2/Hs00157325_ml; PHGDH/Hs00198333_ml ; C9orf58/Hs00230107_ml IGFBP5/Hs00181213_ml ; CDCA7/Hs00230589_ml ; CCNB2/Hs00270424_ml EXOl/Hs00243513_ml ; TROAP/Hs00193896_ml ; CCNA2/Hs00153138_ml CDT1/Hs00368864_ml ; ASPM/Hs00250800_ml ; CYP1B 1/Hs00164383_ml NUSAP1/Hs00251213_ml ; CDC45L/Hs00185895_ml ; ARHGDIB/Hs00171288_ml). We then calculated for each sample the Metastasis-Score with the procedure described in Example 3, using the 15 genes measured either with microarrays or with realtime PCR. The dot plot in Figure 10 shows that the two platforms for gene expression analysis generate highly correlated scores (R2 = 0.70), confirming the possibility of using genes of the GAB2 signatures for cancer diagnosis independently of the measurement technique.
Example 6 - Application of the GAB2-signature to malignant melanoma
We have verified whether genes of the GAB2-signature show expression changes during the progression of human melanoma, from normal skin to nevus to primary malignant melanoma to metastatic melanoma. To this aim, we acquired publicly available DNA microarray gene expression data (Affymetrix) from two papers, including in total over 100 melanomas, primary and metastatic, 18 nevi and seven samples of healthy skin (Talantov et al. 2005, Clin Cancer Res, 11, 7234-7242; Xu et al. 2008, Mol Cancer Res, 6, 760-769). Genes of the GAB 2- signature were mapped onto this dataset via gene symbols, resulting in 83 Affymetrix probesets. For each gene, the log2ratio between each sample's signal and the median signal for that gene in all samples was then calculated. Expression data were then Fuzzy SOM-clustered using the GEDAS software (Fu and Medico, 2007, BMC Bioinformatics, 8, 3), with cosine correlation as the similarity metric. The result of this procedure is illustrated in Figure 11, clearly showing the the GAB 2 signautre discriminates benign lesions from melanomas, with some of the primary melanomas displaying a profile similar to nevi, and others more similar to metastatic melanoma, which show the highest divergence from benign tissues. Subdivision of primary melanomas in two subgroups, one similar to nevi and the other similar to metastases, highlights possible differences in the aggressiveness of the primary lesion which could guide therapeutic decisions. Is it of particular relevance to the present invention that the GAB2-signature, obtained in vitro on breast cells, without any further selection or training, displays such a strong discriminating capacity on such a different type of neoplastic lesion. It is likely that, similar properties will be displayed also on other cancer types, and also by genes of the other GAB-2 signatures presented here.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative examples and that the present invention may be embodied in other specific forms without departing from the essential attributes thereof, and it is therefore desired that the present embodiments and examples be considered in all respects as illustrative and not restrictive, reference being made to the appended claims, rather than to the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

Claims

We Claim:
1) A method of diagnosing or prognosing cancer in subjects comprising detecting expression of GAB2 and/or of its transcriptional target genes in the tumor tissue and/or in tumor cells isolated from the subject.
2) A method of diagnosing or prognosing cancer in subjects comprising detecting in the tumor tissue and/or in tumor cells isolated from the subject expression of at least two of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5, either from a single list or across the lists.
3) The method of diagnosing or prognosing cancer in subjects as in claim 2, wherein the cancer is breast cancer, comprising detecting in the subject expression of at least 2 of GAB2-signature genes listed in Table 1.
4) The method of diagnosing or prognosing cancer in subjects as in claim 2, wherein the cancer is melanoma, comprising detecting in the subject expression of at least 2 of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5, either from a single list or across the lists.
5) A method of predicting metastasis or metastatic relapse or metastatic potential or response to treatment in cancer patients comprising detecting the expression of GAB2 and or its transcriptional target genes in the tumor tissue and/or in tumor cells isolated from the subject.
6) The method of predicting metastasis in cancer patients as in claim 5, comprising detecting the expression of at least two of GAB2-signature genes listed in Table 1 , 2, 3, 4 or 5 either from a single list or across the lists.
7) The method of predicting metastasis in cancer patients as in claim 5, wherein the cancer is breast cancer, comprising detecting the expression of at least two of GAB2-signature genes listed in Table 1.
8) The method of predicting metastasis in cancer patients as in claim 5, wherein the cancer is melanoma, comprising detecting the expression of at least two of GAB2- signature genes listed in Table 1, 2, 3, 4 or 5 either from a single list or across the lists.
9) The method of predicting metastatic relapse or metastatic potential in cancer patients as in claim 5, comprising detecting the expression of at least two of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5 either from a single list or across the lists.
10) The method of predicting metastatic relapse or metastatic potential in cancer patients as in claim 5, wherein the cancer is breast cancer, comprising detecting the expression of at least two of GAB2-signature genes listed in Table 8 or 9.
11) The method of predicting metastatic relapse or metastatic potential in cancer patients as in claim 5, wherein the cancer is melanoma, comprising detecting the expression of at least two of GAB 2- signature genes listed in Table 1, 2, 3, 4 or 5 either from a single list or across the lists.
12) The method of predicting response to treatment in cancer patients as in claim 5, wherein the cancer treatment is targeted drug therapy, chemotherapy, radiation therapy or a combination thereof, comprising detecting the expression of at least two of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5 either from a single list or across the lists.
13) The method of predicting response to treatment in cancer patients as in claim 12, wherein the cancer is breast cancer, comprising detecting the expression of at least 2 of GAB2-signature genes listed in Table 1.
14) The method of predicting response to treatment in cancer patients as in claim 12, wherein the cancer is melanoma, comprising detecting the expression of at least 2 of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5 either from a single list or across the lists.
15) A method of treating a subject with cancer comprising the steps of:
a) obtaining blood or tissue sample from the subject with cancer;
b) screening said sample for the expression of a polypeptide encoded by a polynucleotide selected from a group consisting of GAB2-signature genes listed in Table 1, 2, 3, 4 and 5;
c) providing an antibody that reacts immunologically against said polypeptide; and
d) administering an effective amount of said antibody to the subject with cancer.
16) A method of treating a subject suffering from cancer comprising the steps of: a) obtaining a sample of tissue from a subject suffering from cancer; b) screening said sample for the expression of a polypeptide encoded by a polynucleotide selected from a group consisting of GAB2-signature genes listed in Table 1, 2, 3, 4 and 5;
c) providing an antisense DNA molecule that encodes an RNA molecule that binds to said polynucleotide;
d) providing said antisense DNA molecule in the form of a human vector containing appropriate regulatory elements for the production of said RNA molecule; and
e) administering an effective amount of said vector to the subject with cancer.
17) A method of using in vitro anchorage independence model for deriving gene signature, the said signature comprising a set of genes associated with diagnosis, prognosis, metastasis and predicting response to treatment in cancer.
18) The method of claim 17 wherein the said gene signature is GAB2-signature comprising at least two GAB2 and or its transcriptional target genes listed in Tables 1, 2, 3, 4 or 5 either from a single list or across the lists.
19) A method of predicting the grade of a tumor in a cancer patient, comprising detecting the expression of GAB2 and/or its transcriptional target genes in the tumor tissue and/or in tumor cells isolated from the subject.
20) The method of predicting the grade of a tumor in a cancer patient as in claim 19, comprising detecting the expression of at least two of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5 either from a single list or across the lists.
21) A GAB2-signature for diagnosing or prognosing cancer in subjects comprising GAB2 and/or its transcriptional target genes in the tumor tissue and/or in tumor cells isolated from the subject as diagnostic or prognostic markers.
22) The GAB2-signature comprising GAB2 and or its transcriptional target genes as diagnostic or prognostic markers for diagnosing or prognosing cancer in subjects as in claim 21, wherein the diagnosis or prognosis comprises detecting in the tumor tissue and/or in tumor cells isolated from the subject expression of at least two of GAB2-signature genes listed in Tables 1, 2, 3, 4 or 5 either from a single list or across the lists.
23) The GAB2-signature comprising GAB2 and or its transcriptional target genes as diagnostic or prognostic markers for diagnosing or prognosing breast cancer in subjects as in claim 21, wherein the diagnosis or prognosis comprises detecting in the tumor tissue and/or in tumor cells isolated from the subject expression of at least two of GAB2-signature genes listed in Table 1.
24) A GAB2-signature for predicting
metastasis; or
metastatic relapse; or
metastatic potential; or
response to treatment
in cancer patients comprising GAB2 and or its transcriptional target genes.
25) The GAB2-signature comprising GAB2 and or its transcriptional target genes as markers for predicting metastasis in cancer patients as in claim 24, wherein the prediction of metastasis comprises detecting in tumor tissue and/or in tumor cells isolated from the patient expression of at least two of GAB2-signature genes listed in Tables 1, 2, 3, 4 or 5 either from a single list or across the lists.
26) The GAB2-signature comprising GAB2 and or its transcriptional target genes as markers for predicting metastasis in cancer patients as in claim 24, wherein the cancer is breast cancer and wherein the prediction of metastasis comprises detecting in tumor tissue and/or in tumor cells isolated from the patient expression of at least two of GAB2-signature genes listed in Table 1.
27) The GAB2-signature comprising GAB2 and or its transcriptional target genes as markers for predicting metastasis in cancer patients as in claim 24, wherein the cancer is melanoma and wherein the prediction of metastasis comprises detecting in tumor tissue and/or in tumor cells isolated from the patient expression of at least two of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5 either from a single list or across the lists.
28) The GAB2-signature comprising GAB2 and or its transcriptional target genes as markers for predicting metastatic relapse in cancer patients as in claim 24, wherein the prediction of metastatic relapse comprises detecting in tumor tissue and/or in tumor cells isolated from the patient expression of at least two of GAB2- signature genes listed in Table 1, 2, 3, 4 or 5 either from a single list or across the lists. 29) A GAB2-signature comprising GAB2 and or its transcriptional target genes as markers for predicting metastatic relapse in cancer patients as in claim 24, wherein the cancer is breast cancer and wherein the prediction of metastatic relapse comprises detecting in tumor tissue and/or in tumor cells isolated from the patient expression of at least two of GAB2-signature genes listed in Table 1.
30) The GAB2-signature comprising GAB2 and or its transcriptional target genes as markers for predicting metastatic relapse in cancer patients as in claim 24, wherein the cancer is breast cancer and wherein the prediction of metastatic relapse comprises detecting in tumor tissue and/or in tumor cells isolated from the patient expression of at least two of GAB 2- signature genes listed in Table 8 or 9.
31) The GAB2-signature comprising GAB2 and or its transcriptional target genes as markers for predicting metastatic relapse in cancer patients as in claim 24, wherein the cancer is melanoma and wherein the prediction of metastatic relapse comprises detecting in tumor tissue and/or in tumor cells isolated from the patient expression of at least two of GAB 2- signature genes listed in Table 1, 2, 3, 4 or 5 either from a single list or across the lists.
32) The GAB2-signature comprising GAB2 and or its transcriptional target genes as markers for predicting response to treatment in cancer patients as in claim 24, wherein the treatment is targeted drug therapy, chemotherapy, radiation therapy or a combination thereof and wherein predicting response to the treatment comprises detecting in tumor tissue and/or in tumor cells isolated from the patient expression of at least two of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5 either from a single list or across the lists.
33) The GAB2-signature comprising GAB2 and or its transcriptional target genes as markers for predicting response to treatment in cancer patients as in claim 24, wherein the treatment is targeted drug therapy, chemotherapy, radiation therapy or a combination thereof and wherein the cancer is breast cancer and wherein predicting response to the treatment comprises detecting in tumor tissue and/or in tumor cells isolated from the patient expression of at least two of GAB2-signature genes listed in Table 1.
34) The GAB2-signature comprising GAB2 and or its transcriptional target genes as markers for predicting response to treatment in cancer patients as in claim 24, wherein the treatment is targeted drug therapy, chemotherapy, radiation therapy or a combination thereof and wherein the cancer is myeloma and wherein predicting response to the treatment comprises detecting in tumor tissue and/or in tumor cells isolated from the patient expression of at least two of GAB2-signature genes listed in Table 1, 2, 3, 4 or 5 either from a single list or across the lists.
35) An array comprising polynucleotides capable of specifically hybridizing to at least two genes listed in Table 1, 2, 3, 4 or 5 either from a single list or across the lists.
36) A kit comprising the array of claim 34 for diagnosing or prognosing cancer or predicting metastasis or metastatic relapse or metastatic potential of cancer cells in a subject by determining the expression of at least 2 genes listed in Table 1, 2, 3, 4 or 5 either from a single list or across the lists.
37) A kit for diagnosing or prognosing cancer cells or predicting metastasis or metastatic relapse or metastatic potential of cancer cells in a biological sample comprising a primer pair for amplifying a nucleic acid sequence selected from a group consisting of GAB2-signature genes listed in Table 1, 2, 3, 4 and 5 and containers for the primers.
38) A kit for diagnosing or prognosing cancer cells or predicting metastasis or metastatic relapse or metastatic potential of cancer cells in a biological sample comprising an oligonucleotide probe that binds under high stringency conditions to an isolated nucleic acid sequence selected from a group consisting of GAB 2- signature genes listed in Table 1, 2, 3, 4 and 5 and a container for the probe.
39) A kit for diagnosing or prognosing cancer cells or predicting metastasis or metastatic relapse or metastatic potential of cancer cells in a biological sample comprising an antibody which binds immunologically to a protein having an amino acid sequence encoded by a polynucleotide selected from a group consisting of GAB 2- signature genes listed in Table 1, 2, 3, 4 and 5 and a container for the probe.
40) A kit for diagnosing or prognosing cancer cells or predicting metastasis or metastatic relapse or metastatic potential of cancer cells in a biological sample comprising an array of probes selected from a group consisting of GAB2- signature genes listed in Table 1, 2, 3, 4 and 5 and a container for the probe and the expression of the genes is determined by Real-time PCR or other quantitative PCR assay.
PCT/IB2010/054470 2009-10-02 2010-10-04 Use of genes involved in anchorage independence for the optimization of diagnosis and treatment of human cancer WO2011039734A2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US24796709P 2009-10-02 2009-10-02
US61/247,967 2009-10-02

Publications (2)

Publication Number Publication Date
WO2011039734A2 true WO2011039734A2 (en) 2011-04-07
WO2011039734A3 WO2011039734A3 (en) 2012-02-23

Family

ID=43826728

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2010/054470 WO2011039734A2 (en) 2009-10-02 2010-10-04 Use of genes involved in anchorage independence for the optimization of diagnosis and treatment of human cancer

Country Status (1)

Country Link
WO (1) WO2011039734A2 (en)

Cited By (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102707058A (en) * 2012-05-30 2012-10-03 山东大学 Tumor necrosis factor-alpha induced protein 8 L3 (TIPE3) immunohistochemistry detection kit for diagnosing lung cancer
WO2013095793A1 (en) * 2011-12-22 2013-06-27 Aveo Pharmaceuticals, Inc. Identification of multigene biomarkers
WO2014158696A1 (en) * 2013-03-14 2014-10-02 Castle Biosciences, Inc. Methods for predicting risk of metastasis in cutaneous melanoma
US8906864B2 (en) 2005-09-30 2014-12-09 AbbVie Deutschland GmbH & Co. KG Binding domains of proteins of the repulsive guidance molecule (RGM) protein family and functional fragments thereof, and their use
US9057109B2 (en) 2008-05-14 2015-06-16 Dermtech International Diagnosis of melanoma and solar lentigo by nucleic acid analysis
WO2015100446A1 (en) * 2013-12-29 2015-07-02 Curelab Oncology, Inc. Methods and compositions relating to p62/sqstm1 for the treatment and prevention of inflammation-associated diseases
US9102722B2 (en) 2012-01-27 2015-08-11 AbbVie Deutschland GmbH & Co. KG Composition and method for the diagnosis and treatment of diseases associated with neurite degeneration
US9175075B2 (en) 2009-12-08 2015-11-03 AbbVie Deutschland GmbH & Co. KG Methods of treating retinal nerve fiber layer degeneration with monoclonal antibodies against a retinal guidance molecule (RGM) protein
EP2971156A4 (en) * 2013-03-15 2017-02-08 Myriad Genetics, Inc. Genes and gene signatures for diagnosis and treatment of melanoma
US9605069B2 (en) 2008-02-29 2017-03-28 AbbVie Deutschland GmbH & Co. KG Antibodies against the RGM a protein and uses thereof
EP3093343A4 (en) * 2014-01-10 2017-12-27 Juntendo Educational Foundation Method for assessing lymph node metastatic potential of endometrial cancer
EP3272880A3 (en) * 2013-03-15 2018-04-11 Fundació Institut de Recerca Biomèdica IRB (Barcelona) Method for the diagnosis, prognosis and treatment of metastatic cancer
US10260104B2 (en) 2010-07-27 2019-04-16 Genomic Health, Inc. Method for using gene expression to determine prognosis of prostate cancer
US10612099B2 (en) 2014-07-02 2020-04-07 Myriad Genetics, Inc Genes and gene signatures for diagnosis and treatment of melanoma
CN111402954A (en) * 2019-01-02 2020-07-10 中国人民解放军军事科学院军事医学研究院 Method for identifying and predicting human genes related to space radiation damage protection drug target
US10793642B2 (en) 2014-12-11 2020-10-06 Inbiomotion S.L. Binding members for human c-MAF
US11578373B2 (en) 2019-03-26 2023-02-14 Dermtech, Inc. Gene classifiers and uses thereof in skin cancers
US11976332B2 (en) 2018-02-14 2024-05-07 Dermtech, Inc. Gene classifiers and uses thereof in non-melanoma skin cancers

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1666595A1 (en) * 2000-10-26 2006-06-07 Beth Israel Deaconess Medical Center, Inc. GAB2 (P97) gene and methods of use thereof
US20050196767A1 (en) * 2001-05-18 2005-09-08 Sirna Therapeutics, Inc. RNA interference mediated inhibition of GRB2 associated binding protein (GAB2) gene expression using short interfering nucleic acis (siNA)
BRPI0518734A2 (en) * 2004-11-30 2008-12-02 Veridex Llc prognosis of lung cancer
FR2888252A1 (en) * 2005-07-07 2007-01-12 Biomerieux Sa METHOD FOR THE DIAGNOSIS OF BREAST CANCER
CA2674907C (en) * 2007-01-31 2017-04-04 Celera Corporation A molecular prognostic signature for predicting breast cancer distant metastasis, and uses thereof
JP2011509078A (en) * 2007-12-28 2011-03-24 イプソゲン Breast cancer expression profiling

Non-Patent Citations (60)

* Cited by examiner, † Cited by third party
Title
ARIGA ET AL., INT J CANCER, vol. 95, 2001, pages 67 - 72
BENNETT ET AL., ONCOGENE, vol. 27, 2007, pages 2693 - 2704
BERRIER, YAMADA, 2007, J CELL PHYSIOL, vol. 213, pages 565 - 573
BERRIER, YAMADA, J CELL PHYSIOL, vol. 213, 2007, pages 565 - 573
BOUSCARY ET AL., ONCOGENE, vol. 20, 2001, pages 2197 - 2204
BRUMMER ET AL., J BIOL CHEM, vol. 281, 2006, pages 626 - 637
BUYSE ET AL., J NATL CANCER INST, vol. 98, 2006, pages 1183 - 1192
COOLEY ET AL., BIOCHEMISTRY, vol. 40, 2001, pages 15762 - 15770
DALY ET AL., ONCOGENE, vol. 21, 2002, pages 5175 - 5181
DANEN, YAMADA, J CELL PHYSIOL, vol. 189, 2001, pages 1 - 13
DEBNATH, BRUGGE, NAT REV CANCER, vol. 5, 2005, pages 675 - 688
DERMARDIROSSIAN, BOKOCH, TRENDS CELL BIOL, vol. 15, 2005, pages 356 - 363
DESMEDT ET AL., CLIN CANCER RES, vol. 13, 2007, pages 3207 - 3214
DUNNETT ET AL., BIOMETRICS, vol. 20, 1964, pages 482 - 491
FFRENCH-CONSTANT, COLOGNATO, TRENDS CELL BIOL, vol. 14, 2004, pages 678 - 686
FU, MEDICO, BMC BIOINFORMATICS, vol. 8, 2007, pages 3
GARCIA ET AL., ONCOGENE, vol. 20, 2001, pages 2499 - 2502
GEIGER, PEEPER, CANCER RES, vol. 67, 2007, pages 6221 - 6229
GENTLEMAN ET AL., GENOME BIOL., vol. 5, 2004, pages R80
GOLUB ET AL., SCIENCE, vol. 286, 1999, pages 531 - 537
GU ET AL., MOL CELL, vol. 2, 1998, pages 729 - 740
GUO, GIANCOTTI, NAT REV MOL CELL BIOL, vol. 5, 2004, pages 816 - 826
HESS ET AL., J CLIN ONCOL, vol. 24, 2006, pages 4236 - 4244
HESS, AM J PATHOL, vol. 174, 2008, pages 1524 - 1533
IVSHINA ET AL., CANCER RES, vol. 66, 2006, pages 10292 - 10
KE ET AL., ONCOGENE, vol. 26, 2007, pages 4951 - 4960
KITAMURA ET AL., EXP HEMATOL, vol. 31, 2003, pages 1007 - 1014
KONG ET AL., J BIOL CHEM, vol. 278, 2003, pages 5837 - 5844
LAIRD ET AL., MOL CANCER THER, vol. 2, 2003, pages 461 - 469
LIU ET AL., MOL CELL BIOL, vol. 21, 2001, pages 3047 - 3056
MAQC CONSORTIUM, NAT BIOTECHNOL., vol. 24, 2006, pages 1151 - 1161
MARTELLI ET AL., BMC GENOMICS, vol. 9, 2008, pages 254
MASON ET AL., TRENDS CELL BIOL, vol. 16, 2006, pages 45 - 54
MILLER ET AL., PROC NATL ACAD SCI U S A, vol. 102, 2005, pages 13550 - 13555
MIRANTI, BRUGGE, NAT CELL BIOL, vol. 4, 2002, pages E83 - E90
NI ET AL., MOL CELL BIOL, vol. 27, 2007, pages 3708 - 3715
NISHIDA ET AL., BLOOD, vol. 93, 1999, pages 1809 - 1816
PLAYFORD, SCHALLER, ONCOGENE, vol. 23, 2004, pages 7928 - 7946
REGINATO ET AL., NAT CELL BIOL, vol. 5, 2003, pages 733 - 740
ROUX ET AL., MOL BIOL CELL, vol. 16, 2005, pages 1142 - 1151
SATTLER ET AL., CANCER CELL, vol. 1, 2002, pages 479 - 492
SCHWARTZ, BARON, CURR OPIN CELL BIOL, vol. 11, 1999, pages 197 - 202
SHANKAVARAM ET AL., MOL CANCER THER., vol. 6, 2007, pages 820 - 832
SOTIRIOU ET AL., J NATL CANCER INST, vol. 98, 2006, pages 262 - 272
SOULE ET AL., CANCER RES, vol. 50, 1990, pages 6075 - 6086
TALANTOV ET AL., CLIN CANCER RES, vol. 11, 2005, pages 7234 - 7242
TIRES-ALJ ET AL., NAT MED, vol. 12, 2006, pages 114 - 121
TSATSANIS, SPANDIDOS, ANN N Y ACAD SCI, vol. 1028, 2004, pages 168 - 175
TUCHER ET AL., PROC NATL ACAD SCI U S A., vol. 98, 2001, pages 5116 - 21
VAN DE VIJVER ET AL., N ENGL J MED, vol. 347, 2002, pages 1999 - 2009
VAN'T., VEER ET AL., NATURE, vol. 415, 2002, pages 530 - 536
WANG ET AL., LANCET, vol. 365, 2005, pages 671 - 679
WESSELS ET AL., BIOINFORMATICS, vol. 21, 2005, pages 3755 - 3762
WIRAPATI ET AL., BREAST CANCER RES, vol. 10, 2008, pages R65
XU ET AL., MOL CANCER RES, vol. 6, 2008, pages 760 - 769
YU ET AL., J BIOL CHEM, vol. 281, 2006, pages 28615 - 28626
YU ET AL., SCIENCE, vol. 269, 1995, pages 81 - 83
YUAN ET AL., PATHOL ONCOL RES, vol. 14, 2008, pages 145 - 156
ZHANG ET AL., MOL CELL BIOL, vol. 26, 2006, pages 413 - 424
ZHANG ET AL., MOL CELL, vol. 13, 2004, pages 341 - 355

Cited By (33)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8906864B2 (en) 2005-09-30 2014-12-09 AbbVie Deutschland GmbH & Co. KG Binding domains of proteins of the repulsive guidance molecule (RGM) protein family and functional fragments thereof, and their use
US9605069B2 (en) 2008-02-29 2017-03-28 AbbVie Deutschland GmbH & Co. KG Antibodies against the RGM a protein and uses thereof
US11753687B2 (en) 2008-05-14 2023-09-12 Dermtech, Inc. Diagnosis of melanoma and solar lentigo by nucleic acid analysis
US9057109B2 (en) 2008-05-14 2015-06-16 Dermtech International Diagnosis of melanoma and solar lentigo by nucleic acid analysis
US11332795B2 (en) 2008-05-14 2022-05-17 Dermtech, Inc. Diagnosis of melanoma and solar lentigo by nucleic acid analysis
US10407729B2 (en) 2008-05-14 2019-09-10 Dermtech, Inc. Diagnosis of melanoma by nucleic acid analysis
US9175075B2 (en) 2009-12-08 2015-11-03 AbbVie Deutschland GmbH & Co. KG Methods of treating retinal nerve fiber layer degeneration with monoclonal antibodies against a retinal guidance molecule (RGM) protein
US10260104B2 (en) 2010-07-27 2019-04-16 Genomic Health, Inc. Method for using gene expression to determine prognosis of prostate cancer
WO2013095793A1 (en) * 2011-12-22 2013-06-27 Aveo Pharmaceuticals, Inc. Identification of multigene biomarkers
US9365643B2 (en) 2012-01-27 2016-06-14 AbbVie Deutschland GmbH & Co. KG Antibodies that bind to repulsive guidance molecule A (RGMA)
US9102722B2 (en) 2012-01-27 2015-08-11 AbbVie Deutschland GmbH & Co. KG Composition and method for the diagnosis and treatment of diseases associated with neurite degeneration
US10106602B2 (en) 2012-01-27 2018-10-23 AbbVie Deutschland GmbH & Co. KG Isolated monoclonal anti-repulsive guidance molecule A antibodies and uses thereof
CN102707058A (en) * 2012-05-30 2012-10-03 山东大学 Tumor necrosis factor-alpha induced protein 8 L3 (TIPE3) immunohistochemistry detection kit for diagnosing lung cancer
EP3425061A3 (en) * 2013-03-14 2019-04-03 Castle Biosciences, Inc. Methods for predicting risk of metastasis in cutaneous melanoma
US11434536B2 (en) 2013-03-14 2022-09-06 Castle Biosciences, Inc. Diagnostic test for predicting metastasis and recurrence in cutaneous melanoma
US10577660B2 (en) 2013-03-14 2020-03-03 Castle Biosciences, Inc. Diagnostic test for predicting metastasis and recurrence in cutaneous melanoma
EP4265740A3 (en) * 2013-03-14 2024-04-24 Castle Biosciences, Inc. Methods for predicting risk of metastasis in cutaneous melanoma
WO2014158696A1 (en) * 2013-03-14 2014-10-02 Castle Biosciences, Inc. Methods for predicting risk of metastasis in cutaneous melanoma
EP3272880A3 (en) * 2013-03-15 2018-04-11 Fundació Institut de Recerca Biomèdica IRB (Barcelona) Method for the diagnosis, prognosis and treatment of metastatic cancer
US11591599B2 (en) 2013-03-15 2023-02-28 Fundació Institut De Recerca Biomèdica (Irb Barcelona) Method for the diagnosis, prognosis and treatment of cancer metastasis
US11834719B2 (en) 2013-03-15 2023-12-05 Myriad Mypath, Llc Genes and gene signatures for diagnosis and treatement of melanoma
EP2971156A4 (en) * 2013-03-15 2017-02-08 Myriad Genetics, Inc. Genes and gene signatures for diagnosis and treatment of melanoma
US11021752B2 (en) 2013-03-15 2021-06-01 Myriad Genetics, Inc. Genes and gene signatures for diagnosis and treatment of melanoma
US11098098B2 (en) 2013-12-29 2021-08-24 Curelab Oncology, Inc. Methods and compositions relating to p62/SQSTM1 for the treatment and prevention of inflammation-associated diseases
WO2015100446A1 (en) * 2013-12-29 2015-07-02 Curelab Oncology, Inc. Methods and compositions relating to p62/sqstm1 for the treatment and prevention of inflammation-associated diseases
EP3093343A4 (en) * 2014-01-10 2017-12-27 Juntendo Educational Foundation Method for assessing lymph node metastatic potential of endometrial cancer
US10612099B2 (en) 2014-07-02 2020-04-07 Myriad Genetics, Inc Genes and gene signatures for diagnosis and treatment of melanoma
US11584967B2 (en) 2014-07-02 2023-02-21 Myriad Mypath, Llc Genes and gene signatures for diagnosis and treatment of melanoma
US10793642B2 (en) 2014-12-11 2020-10-06 Inbiomotion S.L. Binding members for human c-MAF
US11976332B2 (en) 2018-02-14 2024-05-07 Dermtech, Inc. Gene classifiers and uses thereof in non-melanoma skin cancers
CN111402954B (en) * 2019-01-02 2023-07-21 中国人民解放军军事科学院军事医学研究院 Method for identifying and predicting human genes related to space radiation injury protection drug targets
CN111402954A (en) * 2019-01-02 2020-07-10 中国人民解放军军事科学院军事医学研究院 Method for identifying and predicting human genes related to space radiation damage protection drug target
US11578373B2 (en) 2019-03-26 2023-02-14 Dermtech, Inc. Gene classifiers and uses thereof in skin cancers

Also Published As

Publication number Publication date
WO2011039734A3 (en) 2012-02-23

Similar Documents

Publication Publication Date Title
WO2011039734A2 (en) Use of genes involved in anchorage independence for the optimization of diagnosis and treatment of human cancer
Gaedcke et al. Mutated KRAS results in overexpression of DUSP4, a MAP‐kinase phosphatase, and SMYD3, a histone methyltransferase, in rectal carcinomas
Hoek et al. Expression profiling reveals novel pathways in the transformation of melanocytes to melanomas
US20050266420A1 (en) Multigene predictors of response to chemotherapy
US20080234138A1 (en) TP53 gene expression and uses thereof
US20110251087A1 (en) Prognostic and diagnostic method for cancer therapy
CA2993142A1 (en) Gene signature for immune therapies in cancer
WO2015135035A2 (en) Determining cancer agressiveness, prognosis and responsiveness to treatment
WO2009158620A2 (en) Signatures and determinants associated with metastasis methods of use thereof
US10604809B2 (en) Methods and kits for the diagnosis and treatment of pancreatic cancer
CA2920062A1 (en) Signature of cycling hypoxia and use thereof for the prognosis of cancer
EP2307570B1 (en) Molecular signature of liver tumor grade and use to evaluate prognosis and therapeutic regimen
US11680298B2 (en) Method of identifying risk of cancer and therapeutic options
Tomei et al. The immune-related role of BRAF in melanoma
US20180230545A1 (en) Method for the prediction of progression of bladder cancer
Limame et al. Expression profiling of migrated and invaded breast cancer cells predicts early metastatic relapse and reveals Krüppel-like factor 9 as a potential suppressor of invasive growth in breast cancer
KR20220127182A (en) Composition for prediction prognosis of cancer and kit comprising the same
CN115612734A (en) Molecular marker group of human esophageal squamous cell carcinoma and application thereof
CN111269985B (en) Application of hsa _ circRNA6448-14 in diagnosis and prognosis prediction of esophageal squamous cell carcinoma
US20150322533A1 (en) Prognosis of breast cancer patients by monitoring the expression of two genes
JP2010537658A (en) Methods and tools for determining the prognosis of cancer in HER2 + patients
US20240102100A1 (en) Ribosomal rnas 2&#39;o-methylation as a novel source of biomarkers relevant for diagnosis, prognosis and therapy of cancers
US20220290243A1 (en) Identification of patients that will respond to chemotherapy
Šutić et al. Transcriptomic Profiling for Prognostic Biomarkers in Early-Stage Squamous Cell Lung Cancer (SqCLC)
Wang et al. Immune Related Signature Predicts the Prognosis and Immunotherapy Benefit in Bladder Cancer Through Immune Escape Mechanism

Legal Events

Date Code Title Description
NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 10819727

Country of ref document: EP

Kind code of ref document: A2