Molecular and clinical characterization of 1,577 primary prostate cancer tumors to reveal novel clinical and biological insights into its subtypes.

Authors

Felix Feng

Felix Yi-Chung Feng

Department of Radiation Oncology, University of Michigan Health System, Ann Arbor, MI

Felix Yi-Chung Feng , Scott Tomlins , Mohammed Alshalalfa , Nicholas Erho , Kasra Yousefi , Shuang Zhao , Robert Benjamin Den , Adam Dicker , Edward M. Schaeffer , Eric A. Klein , Cristina Magi-Galluzzi , R. Jeffrey Karnes , Robert B. Jenkins , Bruce J. Trock , Angelo Demarzo , Elai Davicioni

Organizations

Department of Radiation Oncology, University of Michigan Health System, Ann Arbor, MI, University of Michigan, Ann Arbor, MI, GenomeDx Biosciences, Inc., Vancouver, BC, Canada, Univerisity of Michigan, Baltimore, MI, Canada, Department of Radiation Oncology, The Sidney Kimmel Cancer Center at Thomas Jefferson University, Philadelphia, PA, Johns Hopkins Health Center, Baltimore, MD, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, Department of Urology, Mayo Clinic, Rochester, MN, Rochester, MN, Mayo Clinic, Rochester, MN, Rochester, MN, The Johns Hopkins University, Baltimore, MD, The James Buchanan Brady Urological Institute, Baltimore, MD

Research Funding

No funding sources reported

Background: Prostate cancer molecular subtypes based on ETS gene fusions and SPINK1 were originally identified through outlier gene expression profiling analysis. Such molecular subtypes may have utility in disease stratification and clonality assessment, complementing available purely prognostic tests. Hence, we determined the analytical validity of molecular subtyping in a large sample of PCa treated with radical prostatectomy. Methods: We analyzed Affymetrix Human Exon 1.0ST GeneChip expression profiles for 1,577 patients from 8 radical prostatectomy (RP) cohorts. Multi-feature random forest classifiers and outlier analysis were used to define microarray-based molecular subtypes. Results: A random forest (RF) classifier was trained and validated to predict ERG fusion status using a subset with known ERG rearrangement status defined by FISH, achieving >95% sensitivity and specificity in the validation subset. Less frequent rearrangements involving other ETS genes or SPINK1 over-expression were predicted based on gene expression outlier analysis. Across cohorts, 45%, 9% 8% and 38% of PCa were classified as ERG+, ETS+, SPINK+, and Triple Negative, respectively. Global gene expression analysis shows that the four subtypes could be collapsed into three entities (ERG+, ETS+ and SPINK+/Triple Negative) based on expression patterns and clinical characteristics similarity. A series of multivariable analyses further revealed, ERG+ to be associated with lower pre PSA and Gleason scores but more likely to have EPE and occur in patients with European American ancestry compared to the ETS+, SPINK+/Triple Negative tumors (p<0.001). In contrast, patients with ETS+ were more likely to have SVI compared to both ERG+ and SPINK/Triple Negative (p=0.01), while SPINK+/Triple Negative had higher Gleason scores and were more likely to occur in African Americans (p<0.001). Conclusions: The Decipher platform can accurately determine ERG rearrangement status and PCa molecular subtypes. Inclusion of molecular subtyping, such as m-ERG status, may enable additional precision medicine opportunities in prognostic tests

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Abstract Details

Meeting

2015 Genitourinary Cancers Symposium

Session Type

Poster Session

Session Title

General Poster Session A: Prostate Cancer

Track

Prostate Cancer

Sub Track

Prostate Cancer - Localized Disease

Citation

J Clin Oncol 33, 2015 (suppl 7; abstr 9)

DOI

10.1200/jco.2015.33.7_suppl.9

Abstract #

9

Poster Bd #

B1

Abstract Disclosures