MedicalResearch.com Interview with: Saud H AlDubayan, M.D. Instructor in Medicine, Harvard Medical School Attending Physician, Division of Genetics, Brigham and Women's Hospital Computational Biologist, Department of Medical Oncology, Dana-Farber Cancer Institute Associate Scientist, The Broad Institute of MIT and Harvard  MedicalResearch.com: What is the background for this study? What are the main findings? Response: The overall goal of this study was to assess the performance of the standard method currently used to detect germline (inhered) genetic variants in cancer patients and whether we could use recent advances in machine learning techniques to further improve the detection rate of clinically relevant genetic alterations. To investigate this possibility, we performed a head to head comparison between the current gold-standard method for germline analysis that has been universally used in clinical and research laboratories and a new deep learning analysis approach using germline genetic data of thousands of patients with prostate cancer or melanoma. This analysis showed that across all different gene sets that were tested, the deep learning-based framework was able to identify additional cancer patients with clinically relevant germline variants that went undetected by the standard method. For example, several patients in our study also had germline variants that are associated with an increased risk of ovarian cancer, for which the surgical removal of the ovaries (at a certain age) is highly recommended. However, these genetic alterations were only identified by the proposed deep learning framework.     (more…)