Genomic Health, Inc.

Michael R Crager

Tara Maddala

Dejan Knezevic

Steven Shak

Audrey Goddard

Mark Lee

Margarita Lopatin

Diana Cherbavaz

Robert J Pelham

Carl L Millward

Wayne Cowens

William F Novotny

Christer Svedman

George Andrew Watson

Joffre B Baker

Michael C Kiefer

Maureen T Cronin

Steve Shak

Frederick L Baehner

Kunbin Qu

John Morlan

Francois Collin

Athanasios Tsiatis

Mei-Lan Liu

Dominick V Sinicropi

Phillip Febbo

David P Miller

Christopher N Silk

William Novotny

Hugh Jeffrey Lawrence

Robert Pelham

James C Stephans

Ruixiao Lu

Nan Zhang

The present disclosure relates to methods of monitoring bladder cancer patients and analyzing patient samples for presence of methylated DNA and optionally particular gene mutations. In some embodiments, analysis results are correlated with clinical outcome measures such as risk of bladder cancer recurrence.

DNA methylation and mutational analysis methods for bladder cancer surveillance

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 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 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

The present disclosure provides methods for assessing a patient's cancer risk and/or recurrence risk, which methods comprise assaying, in a biological sample obtained from the gastrointestinal (GI) tract of the patient, an expression level of a risk gene. The present disclosure also provides methods involving a cancer risk/recurrence risk sequence, i.e. the V600E mutation of the BRAF gene, which is useful for assessing cancer risk and/or recurrence risk in a patient.

Methods of predicting cancer risk using gene expression in premalignant tissue

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Menlo Park, CA, United States of America

Michael R Crager