Ciara S. McNevin

Since 2010, significant progress has been made in the treatment of metastatic castrate resistant prostate cancer (mCRPC). While these advancements have improved survival, mCRPC remains a lethal disease, with a precision medicine framework that is lagging behind compared to other cancers. Poly (ADP-ribose) polymerase (PARP) inhibitor (PARPi) studies in prostate cancer (PCa) have focused primarily on the homologous recombination repair (HRR) genes, specifically BRCA1 and BRCA2. While homologous recombination deficiency (HRD) can be prompted by germline or somatic BRCA1/2 genetic mutations, it can also exist in tumors with intact BRCA1/BRCA2 genes. While the sensitivity of PARPi in tumors with non-BRCA DNA damage signatures is not as well established, it has been suggested that genomic alterations in DNA damage repair (DDR) genes other than BRCA may confer synthetic lethality with PARPI in mCRPC. The aim of this review is to summarize the literature on PARPi and their activity treating BRCA and non BRCA tumors with DNA damage signatures.

Studies have demonstrated that men with Prostate Cancer (PCa) harboring BRCA2/BRCA1 genetic aberrations, are more likely to have worse disease and a poorer prognosis. A mutation in BRCA2 is known to confer the highest risk of PCa for men (8.6 fold in men ≤65 years) making BRCA genes a conceivable genomic biomarker for risk in PCa. These genes have attracted a lot of research attention however their role in the clinical assessment and treatment of PCa remains complex. Multiple studies have been published examining the relationship between prostate cancer and BRCA mutations. Here BRCA mutations are explored specifically as a biomarker for risk in PCa. It is in this context, we examined the prognostic, clinical and therapeutic role of BRCA2/BRCA1 mutations across the evolution of PCa. The impact of the inclusion of BRCA genes on genetic screening will also be outlined.

Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individual through developments in immunotherapies and targeted therapies. However, long turnaround times and high costs of molecular testing hinder the widespread implementation of targeted cancer treatments. Meanwhile, gold-standard histopathological assessment carried out by a trained pathologist is widely regarded as routine and mandatory in most cancers. Recently, methods have been developed to mine hidden information from histopathological slides using deep learning applied to scanned and digitized slides; deep learning comprises a collection of computational methods which learn patterns in data in order to make predictions. Such methods have been reported to be successful in a variety of cancers for predicting the presence of biomarkers such as driver mutations, tumour mutational burden, and microsatellite instability. This information could prove valuable to pathologists and oncologists in clinical decision making for cancer treatment and triage for in-depth sequencing. In addition to identifying molecular features, deep learning has been applied to predict prognosis and treatment response in certain cancers. Despite reported successes, many challenges remain before the clinical implementation of such diagnostic strategies in the clinical setting is possible. This review aims to outline recent developments in the field of deep learning for predicting molecular genetics from histopathological slides, as well as to highlight limitations and pitfalls of working with histopathology slides in deep learning.

Prostate Cancer (PCa) is a leading cause of morbidity and mortality among men worldwide. For most men with PCa, their disease will follow an indolent course. However, advanced PCa is associated with poor outcomes. There has been an advent of new therapeutic options with proven efficacy for advanced PCa in the last decade which has improved survival outcomes for men with this disease. Despite this, advanced PCa continues to be associated with a high rate of death. There is a lack of strong evidence guiding the timing and sequence of these novel treatment strategies. This paper focuses on a review of the strategies for diagnostic and the current evidence available for treatment selection in advanced PCa.