Browsing by Author "Previs, Rebecca A"

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    ItemOpen Access
    Concordance Between Genomic Alterations Detected by Tumor and Germline Sequencing: Results from a Tertiary Care Academic Center Molecular Tumor Board.
    (The oncologist, 2023-01) Green, Michelle F; Watson, Catherine H; Tait, Sarah; He, Jie; Pavlick, Dean C; Frampton, Garrett; Riedel, Jinny; Plichta, Jennifer K; Armstrong, Andrew J; Previs, Rebecca A; Kauff, Noah; Strickler, John H; Datto, Michael B; Berchuck, Andrew; Menendez, Carolyn S

    Objective

    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.

    Methods

    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.

    Results

    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.

    Conclusions

    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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    Development and Characterization of a Luciferase Labeled, Syngeneic Murine Model of Ovarian Cancer.
    (Cancers, 2022-08) Russell, Shonagh; Lim, Felicia; Peters, Pamela N; Wardell, Suzanne E; Whitaker, Regina; Chang, Ching-Yi; Previs, Rebecca A; McDonnell, Donald P
    Despite advances in surgery and targeted therapies, the prognosis for women with high-grade serous ovarian cancer remains poor. Moreover, unlike other cancers, immunotherapy has minimally impacted outcomes in patients with ovarian cancer. Progress in this regard has been hindered by the lack of relevant syngeneic ovarian cancer models to study tumor immunity and evaluate immunotherapies. To address this problem, we developed a luciferase labeled murine model of high-grade serous ovarian cancer, STOSE.M1 luc. We defined its growth characteristics, immune cell repertoire, and response to anti PD-L1 immunotherapy. As with human ovarian cancer, we demonstrated that this model is poorly sensitive to immune checkpoint modulators. By developing the STOSE.M1 luc model, it will be possible to probe the mechanisms underlying resistance to immunotherapies and evaluate new therapeutic approaches to treat ovarian cancer.