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Original Investigation
November 17, 2020

Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma

Author Affiliations
  • 1Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Harvard University, Boston, Massachusetts
  • 2Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge
  • 3Division of Genetics, Brigham and Women’s Hospital, Boston, Massachusetts
  • 4College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
  • 5Division of Medical Sciences, Department of Biomedical Informatics, Harvard University, Boston, Massachusetts
  • 6Genetics Training Program, Harvard Medical School, Harvard University, Boston, Massachusetts
  • 7Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard University, Boston, Massachusetts
  • 8Department of System and Computer Engineering, Al-Azhar University, Cairo, Egypt
  • 9Department of Urology, Massachusetts General Hospital, Boston
  • 10Department of Biochemistry, Cancer Metabolism and Epigenetic Unit, Faculty of Science, Cancer and Mutagenesis Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
  • 11College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
JAMA. 2020;324(19):1957-1969. doi:10.1001/jama.2020.20457
Key Points

Question  In patients with cancer, is the detection of pathogenic germline genetic variation improved by incorporation of automated deep learning technology compared with standard methods?

Findings  In this cross-sectional analysis of 2 retrospectively collected convenience cohorts of patients with prostate cancer and melanoma, more pathogenic variants in 118 cancer-predisposition genes were found using deep learning technology compared with a standard genetic analysis method (198 vs 182 variants identified in 1072 patients with prostate cancer; 93 vs 74 variants identified in 1295 patients with melanoma).

Meaning  The number of cancer-predisposing pathogenic variants identified in patients with prostate cancer and melanoma depends partially on the automated approach used to analyze sequence data, but further research is needed to understand possible implications for clinical management and patient outcomes.


Importance  Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic variant detection.

Objective  To evaluate if deep learning approaches identify more germline pathogenic variants in patients with cancer.

Design, Setting, and Participants  A cross-sectional study of a standard germline detection method and a deep learning method in 2 convenience cohorts with prostate cancer and melanoma enrolled in the US and Europe between 2010 and 2017. The final date of clinical data collection was December 2017.

Exposures  Germline variant detection using standard or deep learning methods.

Main Outcomes and Measures  The primary outcomes included pathogenic variant detection performance in 118 cancer-predisposition genes estimated as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The secondary outcomes were pathogenic variant detection performance in 59 genes deemed actionable by the American College of Medical Genetics and Genomics (ACMG) and 5197 clinically relevant mendelian genes. True sensitivity and true specificity could not be calculated due to lack of a criterion reference standard, but were estimated as the proportion of true-positive variants and true-negative variants, respectively, identified by each method in a reference variant set that consisted of all variants judged to be valid from either approach.

Results  The prostate cancer cohort included 1072 men (mean [SD] age at diagnosis, 63.7 [7.9] years; 857 [79.9%] with European ancestry) and the melanoma cohort included 1295 patients (mean [SD] age at diagnosis, 59.8 [15.6] years; 488 [37.7%] women; 1060 [81.9%] with European ancestry). The deep learning method identified more patients with pathogenic variants in cancer-predisposition genes than the standard method (prostate cancer: 198 vs 182; melanoma: 93 vs 74); sensitivity (prostate cancer: 94.7% vs 87.1% [difference, 7.6%; 95% CI, 2.2% to 13.1%]; melanoma: 74.4% vs 59.2% [difference, 15.2%; 95% CI, 3.7% to 26.7%]), specificity (prostate cancer: 64.0% vs 36.0% [difference, 28.0%; 95% CI, 1.4% to 54.6%]; melanoma: 63.4% vs 36.6% [difference, 26.8%; 95% CI, 17.6% to 35.9%]), PPV (prostate cancer: 95.7% vs 91.9% [difference, 3.8%; 95% CI, –1.0% to 8.4%]; melanoma: 54.4% vs 35.4% [difference, 19.0%; 95% CI, 9.1% to 28.9%]), and NPV (prostate cancer: 59.3% vs 25.0% [difference, 34.3%; 95% CI, 10.9% to 57.6%]; melanoma: 80.8% vs 60.5% [difference, 20.3%; 95% CI, 10.0% to 30.7%]). For the ACMG genes, the sensitivity of the 2 methods was not significantly different in the prostate cancer cohort (94.9% vs 90.6% [difference, 4.3%; 95% CI, –2.3% to 10.9%]), but the deep learning method had a higher sensitivity in the melanoma cohort (71.6% vs 53.7% [difference, 17.9%; 95% CI, 1.82% to 34.0%]). The deep learning method had higher sensitivity in the mendelian genes (prostate cancer: 99.7% vs 95.1% [difference, 4.6%; 95% CI, 3.0% to 6.3%]; melanoma: 91.7% vs 86.2% [difference, 5.5%; 95% CI, 2.2% to 8.8%]).

Conclusions and Relevance  Among a convenience sample of 2 independent cohorts of patients with prostate cancer and melanoma, germline genetic testing using deep learning, compared with the current standard genetic testing method, was associated with higher sensitivity and specificity for detection of pathogenic variants. Further research is needed to understand the relevance of these findings with regard to clinical outcomes.