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

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 transcriptomics 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 subgroups were able to consistently predict biochemical relapse (p = 0.0017 and p = 0.016 respectively) and were further 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 gained from 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 published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue. A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p = 0.0001). We further show how our molecular profiles can be used for the early detection of aggressive cases in a clinical setting, and inform treatment decisions. INTERPRETATION: For the first time in prostate cancer this study demonstrates the importance of integrated genomic analyses incorporating both benign and tumour tissue data in identifying molecular alterations leading to the generation of robust gene sets that are predictive of clinical outcome in independent patient cohorts.

N-acetyl-L-aspartyl-L-glutamate peptidase-like 2 is overexpressed in cancer and promotes a pro-migratory and pro-metastatic phenotype.

N-acetyl-L-aspartyl-L-glutamate peptidase-like 2 (NAALADL2) is a member of the glutamate carboxypeptidase II family, best characterized by prostate-specific membrane antigen (PSMA/NAALAD1). Using immunohistochemistry (IHC), we have shown overexpression of NAALADL2 in colon and prostate tumours when compared with benign tissue. In prostate cancer, NAALADL2 expression was associated with stage and Grade, as well as circulating mRNA levels of the NAALADL2 gene. Overexpression of NAALADL2 was shown to predict poor survival following radical prostatectomy. In contrast to PSMA/NAALAD1, NAALADL2 was localized at the basal cell surface where it promotes adhesion to extracellular matrix proteins. Using stable knockdown and overexpression cell lines, we have demonstrated NAALADL2-dependent changes in cell migration, invasion and colony-forming potential. Expression arrays of the knockdown and overexpression cell lines have identified nine genes that co-expressed with NAALADL2, which included membrane proteins and genes known to be androgen regulated, including the prostate cancer biomarkers AGR2 and SPON2. Androgen regulation was confirmed in a number of these genes, although NAALADL2 itself was not found to be androgen regulated. NAALADL2 was also found to regulate levels of Ser133 phosphorylated C-AMP-binding protein (CREB), a master regulator of a number of cellular processes involved in cancer development and progression. In combination, these data suggest that changes in expression of NAALADL2 can impact upon a number of pro-oncogenic pathways and processes, making it a useful biomarker for both diagnosis and prognosis.