A gene signature associated with PTEN activation defines good prognosis intermediate risk prostate cancer cases.

J Pathol Clin Res

Movember FASTMAN Centre of Excellence, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Northern Ireland, UK.

Published: April 2018

Accurate identification of intermediate risk (Gleason 3 + 4 = 7) prostate cancer patients with low risk of disease progression is an unmet challenge in treatment decision making. Here we describe a gene signature that could guide clinicians in the selection of patients with intermediate stage clinically localized prostate cancer for active surveillance. We examined six major drivers of aggressive disease - PTEN, MYC, RB1, TP53, AURKA, AR - by immunohistochemistry in a focused (N = 69) cohort predominantly consisting of intermediate risk prostate cancer. Fuzzy clustering and unsupervised hierarchical clustering were utilized to determine the correlation of gene expression and methylation values with immunohistochemical expression. From the immunohistochemistry observation, we found that intermediate risk prostate cancer cases could be classified as 'complex' (differential expression of more than one driver) or 'simple' (differential expression of only one). Focussing on the 'simple' cases, expression and methylation profiling generated signatures which correlated tightly only with differential PTEN expression and not with any of the other drivers assessed by immunohistochemistry. From this, we derived a geneset of 35 genes linked to high PTEN expression. Subsequently we determined its prognostic significance in intermediate-risk cases extracted from three publicly available clinical datasets (Total N = 215). Hence, this study shows that, by using immunohistochemistry as an upfront stratifier of intermediate risk prostate cancers, it is possible to identify through differential gene expression profiling a geneset with prognostic power across multiple cohorts. This strategy has not been used previously and the signature has the potential to impact on treatment decisions in patients for whom decision making is currently empirical at best.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5903700PMC
http://dx.doi.org/10.1002/cjp2.94DOI Listing

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