Concordance Between Genomic Alterations Detected by Tumor and Germline Sequencing: Results from a Tertiary Care Academic Center Molecular Tumor Board.



The majority of tumor sequencing currently performed on cancer patients does not include a matched normal control, and in cases where germline testing is performed, it is usually run independently of tumor testing. The rates of concordance between variants identified via germline and tumor testing in this context are poorly understood. We compared tumor and germline sequencing results in patients with breast, ovarian, pancreatic, and prostate cancer who were found to harbor alterations in genes associated with homologous recombination deficiency (HRD) and increased hereditary cancer risk. We then evaluated the potential for a computational somatic-germline-zygosity (SGZ) modeling algorithm to predict germline status based on tumor-only comprehensive genomic profiling (CGP) results.


A retrospective chart review was performed using an academic cancer center's databases of somatic and germline sequencing tests, and concordance between tumor and germline results was assessed. SGZ modeling from tumor-only CGP was compared to germline results to assess this method's accuracy in determining germline mutation status.


A total of 115 patients with 146 total alterations were identified. Concordance rates between somatic and germline alterations ranged from 0% to 85.7% depending on the gene and variant classification. After correcting for differences in variant classification and filtering practices, SGZ modeling was found to have 97.2% sensitivity and 90.3% specificity for the prediction of somatic versus germline origin.


Mutations in HRD genes identified by tumor-only sequencing are frequently germline. Providers should be aware that technical differences related to assay design, variant filtering, and variant classification can contribute to discordance between tumor-only and germline sequencing test results. In addition, SGZ modeling had high predictive power to distinguish between mutations of somatic and germline origin without the need for a matched normal control, and could potentially be considered to inform clinical decision-making.





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Publication Info

Green, Michelle F, Catherine H Watson, Sarah Tait, Jie He, Dean C Pavlick, Garrett Frampton, Jinny Riedel, Jennifer K Plichta, et al. (2023). Concordance Between Genomic Alterations Detected by Tumor and Germline Sequencing: Results from a Tertiary Care Academic Center Molecular Tumor Board. The oncologist, 28(1). pp. 33–39. 10.1093/oncolo/oyac164 Retrieved from

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Jennifer K Plichta

Associate Professor of Surgery

Dr. Jennifer Plichta is an Associate Professor of Surgery & Population Health Sciences at Duke University. She serves as the Director of the Breast Risk Assessment Clinic in the Duke Cancer Institute, where she cares for patients with breast cancer, benign breast problems, and those with an increased risk of breast cancer. Her clinical interests include establishing routine breast cancer risk assessment for women and creating personalized management strategies for those found to be “high risk”.


Dr. Plichta’s research focuses of identifying and managing women with risk factors for breast cancer, including those with genetic mutations, such as BRCA, those with abnormal breast biopsies, and those with a family history of breast cancer. She is also studying metastatic breast cancer and how breast cancer staging can be used to improve patient care and education. 


However, her dedication to breast cancer extends beyond her clinical and research interests. She also enjoys educating the community about breast cancer and helping to raise money for breast cancer research and education. She is the creator and primary coordinator of Duke’s free, annual breast education day for the community, “What’s best for breasts?”.


John Strickler

Professor of Medicine

Carolyn Sue Menendez

Assistant Professor of Surgery

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