Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study

article i nfo Background: Understanding the heterogeneous genotypes and phenotypes of prostate cancer is fundamental to improving the way we treat this disease. As yet, there are no validated descriptions of prostate cancer subgroups derived from integrated genomics linked with clinical outcome. Methods: In a study of 482 tumour, benign and germline samples from 259 men with primary prostate cancer, we used integrative analysis of copy number alterations (CNA) and array transcriptomics to identify genomic loci that affect expression levels of mRNA in an expression quantitative trait loci(eQTL) approach, to stratify patients into subgroups that we then associated with future clinical behaviour, and compared with either CNA or tran- scriptomics alone. Findings: We identified five separate patient subgroups with distinct genomic alterations and expression profiles based on 100 discriminating genes in our separate discovery and validation sets of 125 and 103 men. These sub- groups were able to consistently predict biochemical relapse(p = 0.0017 and p = 0.016 respectively)and were fur- ther validated in a third cohort with long-term follow-up (p = 0.027). We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gainedfrom integrative analyses. We confirm alterations in six genes previously associated with prostate cancer ( MAP3K7, MELK, RCBTB2, ELAC2, TPD52, ZBTB4), and also identify 94 genes not previously linked to prostate cancer progression that would not have been detected using either transcript or copy number data alone. We confirm a number of previously pub- lished molecular changes associated with high risk disease, including MYC amplification, and NKX3-1,RB1 and PTEN deletions, as well as over-expression of PCA3and AMACR ,a nd loss ofMSMB in tumour tissue. A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously pub- lished gene signatures(p = 0.0001). We further show how our molecular profiles can be used for the early detec- tion of aggressive cases in a clinical setting, and inform treatment decisions.