Clustering-based method for developing a genomic copy number alteration signature for predicting the metastatic potential of prostate cancer

Alexander Pearlman, Christopher Campbell, Eric Brooks, Alex Genshaft, Shahin Shajahan, Michael Ittman, G. Steven Bova, Jonathan Melamed, Ilona Holcomb, Robert J. Schneider, Harry Ostrer

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3 Scopus citations


The transition of cancer from a localized tumor to a distant metastasis is not well understood for prostate and many other cancers, partly, because of the scarcity of tumor samples, especially metastases, from cancer patients with long-term clinical follow-up. To overcome this limitation, we developed a semi-supervised clustering method using the tumor genomic DNA copy number alterations to classify each patient into inferred clinical outcome groups of metastatic potential. Our data set was comprised of 294 primary tumors and 49 metastases from 5 independent cohorts of prostate cancer patients. The alterations were modeled based on Darwins evolutionary selection theory and the genes overlapping these altered genomic regions were used to develop a metastatic potential score for a prostate cancer primary tumor. The function of the proteins encoded by some of the predictor genes promote escape from anoikis, a pathway of apoptosis, deregulated in metastases. We evaluated the metastatic potential score with other clinical predictors available at diagnosis using a Cox proportional hazards model and show our proposed score was the only significant predictor of metastasis free survival. The metastasis gene signature and associated score could be applied directly to copy number alteration profiles from patient biopsies positive for prostate cancer.

Original languageEnglish (US)
Article number873570
JournalJournal of Probability and Statistics
Publication statusPublished - Aug 17 2012


ASJC Scopus subject areas

  • Statistics and Probability

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