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Prolaris: A Novel Genetic Test for Prostate Cancer Prognosis
Jack Cuzick, PhD1, Matthew Cooperberg, MD, MPH2, Greg Swanson, MD3, Stephen Freedland, MD4, Julia Reid, MStat5, Gabrielle Fisher, MD1, Jerry Lanchbury, PhD5, Alexander Gutin, PhD5, Steve Stone, PhD5, Peter Carroll, MD, MPH2, Michael Brawer, MD6.
1Wolfson Institute of Preventive Medicine, London, United Kingdom, 2UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA, 3University of Texas Health Science Center, San Antonio, TX, USA, 4Durham VA Medical Center and Duke University School of Medicine, Durham, NC, USA, 5Myriad Genetics, Inc., Salt Lake City, UT, USA, 6Myriad Genetic Laboratories, Inc., Salt Lake City, UT, USA.
BACKGROUND:
The natural history of prostate cancer is highly variable and difficult to predict accurately. Improved tools are needed to match treatment more appropriately to a patient’s risk of progression. Therefore, we developed an expression signature composed of genes involved in cell cycle progression (Prolaris®) and tested its utility in prostate cancer.
METHODS:
We’ve developed an expression signature composed of 31 cell cycle progression and 15 housekeeper genes. An expression score (Prolaris score) was derived as the mean of all cell cycle progression genes. The expression signature was tested at disease diagnosis in two conservatively managed cohorts (N=337 and 349), after radical prostatectomy in two cohorts (N=366 and 413), and after external beam radiation therapy (N=141) in a final cohort. All studies were retrospective.
RESULTS:
The cell cycle progression signature was a highly significant predictor of outcome in all five studies. In the conservatively managed patients the Prolaris score was the dominant variable for predicting death from prostate cancer in univariate analysis (p=6.1 x 10-22 after diagnosis by TURP, and p=8.6 x 10-10 after diagnosis by needle biopsy). In both studies, Prolaris remained highly significant in multivariate analysis, and in fact, was a stronger predictor of disease-specific mortality than other prognostic variables. After radical prostatectomy, the Prolaris score predicted biochemical recurrence (BCR) in univariate analysis (p=5.6 x 10-9 and p=2.23 x 10-6) and provided additional prognostic information in multivariate analysis (p=3.3 x10-6 and p=9.5 x10-5). After radiation therapy, Prolaris predicted BCR (Phoenix criteria) in univariate (p=0.0017) and multivariate analysis (p=0.034). In all five studies the HR per unit change in the Prolaris score was remarkably similar, ranging from 1.89 to 2.92, indicating that the effect size for the Prolaris score is robust to clinical setting and patient composition.
CONCLUSIONS:
The Prolaris test predicts prostate cancer outcome in multiple patient cohorts and diverse clinical settings. In all cases, it provides information beyond clinicopathologic variables to help differentiate aggressive from indolent disease.
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