Genomic classifiers for treatment selection in newly diagnosed prostate cancer.
Publication/Presentation Date
10-1-2019
Abstract
OBJECTIVES: To review systematically the literature on genomic tests for prostate cancer (PCa) and to evaluate the current state of the evidence on their use in patients with newly diagnosed PCa.
METHODS: We conducted a systematic review by searching PubMed, Embase, Cochrane Central and conference abstracts from the American Urological Association, published between 2010 and 2018. Studies evaluating Prolaris, Oncotype Dx and Decipher assays were assessed for inclusion by two authors. Studies were excluded if the results were derived from surgical specimens rather than biopsy specimens. A meta-analysis was not performed owing to significant variations in methodologies, definitions and outcome measures.
RESULTS: A total of 729 articles were retrieved in our initial search. After removing duplicates (270) and excluding articles deemed not relevant (432), 21 full-text articles were deemed suitable for inclusion in the present analysis. The full-text articles comprised eight studies on Prolaris, eight studies on Oncotype Dx and five studies on Decipher. For each genomic test we extracted data regarding the risks of adverse pathology, biochemical recurrence, metastasis and PCa-specific mortality.
CONCLUSION: The results of genomic tests that use biomarkers derived from prostate biopsy can be used in conjunction with clinicopathological variables to improve our ability to risk-stratify patients with newly diagnosed PCa. Additional data are needed on the impact of using these tests on long-term patient outcomes and their cost-effectiveness.
Volume
124
Issue
4
First Page
578
Last Page
586
ISSN
1464-410X
Published In/Presented At
Fine, N. D., LaPolla, F., Epstein, M., Loeb, S., & Dani, H. (2019). Genomic classifiers for treatment selection in newly diagnosed prostate cancer. BJU international, 124(4), 578–586. https://doi.org/10.1111/bju.14799
Disciplines
Medicine and Health Sciences
PubMedID
31055874
Department(s)
Department of Surgery
Document Type
Article