Toward Informed Selection and Interpretation of Clinical Genomic Tests in Prostate Cancer

Abstract

Clinical genomic testing of patient germline, tumor tissue, or plasma cell-free DNA can enable a personalized approach to cancer management and treatment. In prostate cancer (PCa), broad genotyping tests are now widely used to identify germline and/or somatic alterations in BRCA2 and other DNA damage repair genes. Alterations in these genes can confer cancer sensitivity to poly (ADP-ribose) polymerase inhibitors, are linked with poor prognosis, and can have potential hereditary cancer implications for family members. However, there is huge variability in genomic tests and reporting standards, meaning that for successful implementation of testing in clinical practice, end users must carefully select the most appropriate test for a given patient and critically interpret the results. In this white paper, we outline key pre- and post-test considerations for choosing a genomic test and evaluating reported variants, specifically for patients with advanced PCa. Test choice must be based on clinical context and disease state, availability and suitability of tumor tissue, and the genes and regions that are covered by the test. We describe strategies to recognize false positives or negatives in test results, including frameworks to assess low tumor fraction, subclonal alterations, clonal hematopoiesis, and pathogenic versus nonpathogenic variants. We assume that improved understanding among health care professionals and researchers of the nuances associated with genomic testing will ultimately lead to optimal patient care and clinical decision making.

Using early on-treatment circulating tumor DNA measurements as response assessment in metastatic castration resistant prostate cancer

“…The custom assay used by Tolmeijer et al has been shown to have strong prognostic utility for baseline ctDNA assessments [ 14 – 16 ]. The limit of detection of this assay is approximately 1%, which is in line with most other commercially available assays [ 21 ]. Use of personalized mutation panels could boost the limit of detection to below 1% and potentially refine outcome prediction.…”