Genomic Health, Inc.

Nsabp Foundation, Inc.

Bristol-myers Squibb Compnay

Instituto Nazionale Tumori

Fondazione Irccs Istituto Nazionale Dei Tumori

Steven Shak

Joffre B Baker

Tara Maddala

Soonmyung Paik

John L Bryant

Audrey Goddard

Dejan Knezevic

Michael C Kiefer

Frederick L Baehner

Maureen T Cronin

Wayne Cowens

Michael R Crager

Melody A Cobleigh

Drew Watson

Luca Gianni

Mark Lee

Diana Cherbavaz

Robert J Pelham

Margarita Lopatin

George Andrew Watson

Christer Svedman

William F Novotny

Xitong Li

Lawrence Lee

Carl L Millward

Shirin K Ford

Jose Baselga

Kim Clark-Langone

David J Mauro

Robert Pelham

The present disclosure provides gene and gene sets, the expression of which is important in the classification and/or prognosis of cancer, in particular of renal cell carcinoma.

Method to use gene expression to determine likelihood of clinical outcome of renal cancer

The present invention provides algorithm-based molecular assays that involve measurement of expression levels of genes from a biological sample obtained from a kidney cancer patient. The present invention also provides methods of obtaining a quantitative score for a patient with kidney cancer based on measurement of expression levels of genes from a biological sample obtained from a kidney cancer patient. The genes may be grouped into functional gene subsets for calculating the quantitative score and the gene subsets may be weighted according to their contribution to cancer recurrence.

Gene expression profile algorithm for calculating a recurrence score for a patient with kidney cancer

The present invention provides gene sets the expression of which is important in the diagnosis and/or prognosis of breast cancer.

Gene expression markers for breast cancer prognosis

The present invention provides algorithm-based molecular assays that involve measurement of expression levels of genes, or their co-expressed genes, from a biological sample obtained from a prostate cancer patient. The genes may be grouped into functional gene subsets for calculating a quantitative score useful to predict a likelihood of a clinical outcome for a prostate cancer patient.

Gene expression profile algorithm and test for determining prognosis of prostate cancer

The present invention provides gene sets the expression of which is important in the diagnosis and/or prognosis of cancer, in particular of breast cancer.

Prediction of likelihood of cancer recurrence

The present disclosure includes assays that involve measurement of expression levels of prognostic biomarkers, or co-expressed biomarkers, from a biological sample obtained from a prostate cancer patient, and analysis of the measured expression levels to provide information concerning the likely prognosis for the patient, and likelihood that the patient will have a recurrence of prostate cancer, or to classify the tumor by likelihood of clinical outcome or TMPRSS2 fusion status.

Method for using gene expression to determine prognosis of prostate cancer

Algorithm-based molecular assays that involve measurement of expression levels of prognostic and/or predictive genes, or co-expressed genes thereof, from a biological sample obtained from a cancer patient, and analysis of the measured expression levels to provide information concerning the likelihood of recurrence of colorectal cancer and/or the likelihood of a beneficial response to chemotherapy for the patient are provided herein. Methods of analysis of gene expression values of prognostic and/or predictive genes, as well as methods of identifying gene expression-tumor region ratios, tumor-associated stromal surface area, and gene cliques, i.e. genes that co-express with a validated biomarker and thus may be substituted for that biomarker in an assay, are also provided.

Gene expression profile algorithm and test for likelihood of recurrence of colorectal cancer and response to chemotherapy

The present invention provides sets of genes the expression of which is important in the prognosis of cancer. In particular, the invention provides gene expression information useful for predicting whether cancer patients are likely to have a beneficial treatment response to chemotherapy FHIT; MTA1; ErbB4; FUS; BBC3; IGF1R; CD9; TP53BP1; MUC1; IGFBP5; rhoC; RALBP1; STAT3; ERK1; SGCB; DHPS; MGMT; CRIP2; ErbB3; RAP1GDS1; CCND1; PRKCD; Hepsin; AK055699; ZNF38; SEMA3F; COL1A1; BAG1; AKT1; COL1A2; Wnt.5a; PTPD1; RAB6C; GSTM1, BCL2, ESR1; or the corresponding expression product, is determined, said report includes a prediction that said subject has a decreased likelihood of response to chemotherapy.

Gene expression markers for predicting response to chemotherapy

The present invention provides gene expression information useful for predicting whether a cancer patient is likely to have a beneficial response to treatment with chemotherapy, comprising measuring, in a biological sample comprising a breast tumor sample obtained from the patient, the expression levels of gene subsets to obtain a risk score associated with a likelihood of a beneficial response to chemotherapy, wherein the score comprises at least one of the following variables: (i) Recurrence Score, (ii) ESRI Group Score; (iii) Invasion Group Score; (iv) Proliferation Group Score; and (v) the expression level of the RNA transcript of at least one of MYBL2 and SCUBE2, or the corresponding expression product. The invention further comprises a molecular assay-based algorithm to calculate the likelihood that the patient will have a beneficial response to chemotherapy based on the risk score.

Predicting response to chemotherapy using gene expression markers

The present invention provides methods and compositions to facilitate determining whether an EGFR-expressing cancer in an individual is an EGFR inhibitor-responsive cancer, as well as methods for determining the likelihood that a patient having an EGFR-expressing cancer will exhibit a beneficial response to an EGFR inhibitor therapy. The methods generally involve determining a normalized expression level of a gene product that correlates with EGFR inhibitor responsiveness.

Predictors of patient response to treatment with EGF receptor inhibitors

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Hillsborough, CA, United States of America

Steven Shak