Background
Germline variants explain more than a third of prostate cancer (PrCa) risk, but very few associations have been identified between heritable factors and clinical progression.
Objective
To find rare germline variants that predict time to biochemical recurrence (BCR) after radical treatment in men with PrCa and understand the genetic factors associated with such progression.
Design, setting, and participants
Whole-genome sequencing data from blood DNA were analysed for 850 PrCa patients with radical treatment from the Pan Prostate Cancer Group (PPCG) consortium from the UK, Canada, Germany, Australia, and France. Findings were validated using 383 patients from The Cancer Genome Atlas (TCGA) dataset.
Outcome measurements and statistical analysis
A total of 15, 822 rare (MAF <1%) predicted-deleterious coding germline mutations were identified. Optimal multifactor and univariate Cox regression models were built to predict time to BCR after radical treatment, using germline variants grouped by functionally annotated gene sets. Models were tested for robustness using bootstrap resampling.
Results and limitations
Optimal Cox regression multifactor models showed that rare predicted-deleterious germline variants in “Hallmark” gene sets were consistently associated with altered time to BCR. Three gene sets had a statistically significant association with risk-elevated outcome when modelling all samples: PI3K/AKT/mTOR, Inflammatory response, and KRAS signalling (up). PI3K/AKT/mTOR and KRAS signalling (up) were also associated among patients with higher-grade cancer, as were Pancreas-beta cells, TNFA signalling via NKFB, and Hypoxia, the latter of which was validated in the independent TCGA dataset.
Conclusions
We demonstrate for the first time that rare deleterious coding germline variants robustly associate with time to BCR after radical treatment, including cohort-independent validation. Our findings suggest that germline testing at diagnosis could aid clinical decisions by stratifying patients for differential clinical management.
