Highlights d Clock-like mutation process attributed to APOBEC3 mediates earliest mutations in PC d Identification of four molecular subgroups that stratifies intermediate-risk disease d Rearrangements at the ESRP1 locus associated with aggressive and proliferative cancer d Development of method to predict clinical trajectories of PC from DNA sequencing data

Biglycan (BGN), a proteoglycan of the extracellular matrix, is included in mRNA signatures for prostate cancer aggressiveness. To understand the impact of BGN on prognosis and its relationship to molecularly defined subsets, we analyzed BGN expression by immunohistochemistry on a tissue microarray containing 12,427 prostate cancers. Seventy-eight percent of 11,050 interpretable cancers showed BGN expression, which was considered as low intensity in 47.7% and as high intensity in 31.1% of cancers. BGN protein expression rose with increasing pathological tumor stage, Gleason grade, lymph node metastasis and early PSA recurrence (P < .0001 each). Comparison with our molecular database attached to the TMA revealed that BGN expression was linked to presence of TMPRRS2:ERG fusion and PTEN deletion (P < .0001 each). In addition, BGN was strongly linked to androgen-receptor (AR) levels (P < .0001), suggesting a hormone-depending regulation of BGN. BGN up-regulation is a frequent feature of prostate cancer that parallels tumor progression and may be useful to estimate tumor aggressiveness particularly if combined with other molecular markers.

DNA mismatch repair (MMR) is integral to the maintenance of genetic stability. We aimed to evaluate the clinical impact of MMR gene expression in prostate cancer. The MMR genes MSH6, MLH1 and PMS2 were analyzed by immunohistochemistry on a tissue microarray containing 11152 prostate cancer specimens. Results were compared with ETS-related gene status and deletions of PTEN, 3p13, 5q21 and 6q15. MSH6, MLH1 and PMS2 expression was detectable in 89.5%, 85.4% and 85.0% of cancers and was particularly strong in cancers with advanced pathological tumor stage (P < 0.0001 each), high Gleason grade (P < 0.0001 each), nodal metastasis (P ≤ 0.0083) and early biochemical recurrence (P < 0.0001). High levels of MMR gene expression paralleled features of genetic instability, such as the number of genomic deletions per cancer; strong expression of all three MMR genes was found in 24%, 29%, 30%, 33% and 42% of cancers with no, one, two, three or four to five deletions (P < 0.0001). The prognostic value of the analyzed MMR genes was largely driven by the subset of cancers lacking ERG fusion (P < 0.0001), while the prognostic impact of MMR gene overexpression was only marginal in ERG-positive cancers. Multivariate analyses suggested an independent prognostic relevance of MMR genes in ERG-negative prostate cancers when compared with prognostic parameters available at the time of initial biopsy. In conclusion, MMR overexpression is common in prostate cancer and is linked to poor outcome as well as features indicating genetic instability. ERG fusion should be analyzed along with MMR gene expression in potential clinical tests.

BackgroundThe clinical course of prostate cancer (PCa) is highly variable, demanding an individualized approach to therapy. Overtreatment of indolent PCa cases, which likely do not progress to aggressive stages, may be associated with severe side effects and considerable costs. These could be avoided by utilizing robust prognostic markers to guide treatment decisions.ResultsWe present a random forest-based classification model to predict aggressive behaviour of prostate cancer. DNA methylation changes between PCa cases with good or poor prognosis (discovery cohort with n = 70) were used as input. DNA was extracted from formalin-fixed tumour tissue, and genome-wide DNA methylation differences between both groups were assessed using Illumina HumanMethylation450 arrays. For the random forest-based modelling, the discovery cohort was randomly split into a training (80%) and a test set (20%). Our methylation-based classifier demonstrated excellent performance in discriminating prognosis subgroups in the test set (Kaplan-Meier survival analyses with log-rank p value < 0.0001). The area under the receiver operating characteristic curve (AUC) for the sensitivity analysis was 95%. Using the ICGC cohort of early- and late-onset prostate cancer (n = 222) and the TCGA PRAD cohort (n = 477) for external validation, AUCs for sensitivity analyses were 77.1% and 68.7%, respectively. Cancer progression-related DNA hypomethylation was frequently located in ‘partially methylated domains’ (PMDs)—large-scale genomic areas with progressive loss of DNA methylation linked to mitotic cell division. We selected several candidate genes with differential methylation in gene promoter regions for additional validation at the protein expression level by immunohistochemistry in > 12,000 tissue micro-arrayed PCa cases. Loss of ZIC2 protein expression was associated with poor prognosis and correlated with significantly shorter time to biochemical recurrence. The prognostic value of ZIC2 proved to be independent from established clinicopathological variables including Gleason grade, tumour stage, nodal stage and prostate-specific-antigen.ConclusionsOur results highlight the prognostic relevance of methylation loss in PMD regions, as well as of several candidate genes not previously associated with PCa progression. Our robust and externally validated PCa classification model either directly or via protein expression analyses of the identified top-ranked candidate genes will support the clinical management of prostate cancer.