Gene expression in normal-appearing tissue adjacent to prostate cancers are predictive of clinical outcome: evidence for a biologically meaningful field effect
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Cristina Magi-Galluzzi1,2, Tara Maddala3, Sara Moscovita Falzarano1, Diana B. Cherbavaz3, Nan Zhang3, Dejan Knezevic3, Phillip G. Febbo3, Mark Lee3, Hugh Jeffrey Lawrence3 and Eric A. Klein2
1 Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio, USA
2 Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, Ohio, USA
3 Genomic Health, Inc., Redwood City, California, USA
Hugh Jeffrey Lawrence, email:
Keywords: prostate cancer, gene expression profiling, molecular diagnostics, prognosis, risk assessment
Received: April 07, 2016 Accepted: April 13, 2016 Published: April 22, 2016
Purpose: We evaluated gene expression in histologically normal-appearing tissue (NT) adjacent to prostate tumor in radical prostatectomy specimens, assessing for biological significance based on prediction of clinical recurrence (cR - metastatic disease or local recurrence).
Results: A total of 410 evaluable patients had paired tumor and NT. Forty-six genes, representing diverse biological pathways (androgen signaling, stromal response, stress response, cellular organization, proliferation, cell adhesion, and chromatin remodeling) were associated with cR in NT (FDR < 20%), of which 39 concordantly predicted cR in tumor (FDR < 20%). Overall GPS and its stromal response and androgen-signaling gene group components also significantly predicted time to cR in NT (RM-corrected HR/20 units = 1.25; 95% CI: 1.01−1.56; P = 0.024).
Experimental Design: Expression of 732 genes was measured by quantitative reverse transcriptase polymerase chain reaction (RT-PCR) separately in tumor and adjacent NT specimens from 127 patients with and 374 without cR following radical prostatectomy for T1/T2 prostate cancer. A 17-gene expression signature (Genomic Prostate Score [GPS]), previously validated to predict aggressive prostate cancer when measured in tumor tissue, was also assessed using pre-specified genes and algorithms. Analysis used Cox proportional hazards models, Storey’s false discovery rate (FDR) control, and regression to the mean (RM) correction.
Conclusions: Gene expression profiles, including GPS, from NT adjacent to tumor can predict prostate cancer outcome. These findings suggest that there is a biologically significant field effect in primary prostate cancer that is a marker for aggressive disease.
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