A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer
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RESEARCH ARTICLE
Open Access
A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer Clinton L. Cario1,2, Emmalyn Chen2, Lancelote Leong2, Nima C. Emami1,2, Karen Lopez3, Imelda Tenggara3, Jeffry P. Simko3,4, Terence W. Friedlander5, Patricia S. Li5, Pamela L. Paris3,5, Peter R. Carroll3 and John S. Witte2,3*
Abstract Background: Cell-free DNA’s (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy for improving the detection of tumor variants in cfDNA. Using this approach, we first generated a model to classify and score candidate variants for inclusion on a prostate cancer targeted sequencing panel. We then used this panel to screen tumor variants from prostate cancer patients with localized disease in both in silico and hybrid capture settings. Methods: Whole Genome Sequence (WGS) data from 550 prostate tumors was analyzed to build a targeted sequencing panel of single point and small (< 200 bp) indel mutations, which was subsequently screened in silico against prostate tumor sequences from 5 patients to assess performance against commonly used alternative panel designs. The panel’s ability to detect tumor-derived cfDNA variants was then assessed using prospectively collected cfDNA and tumor foci from a test set 18 prostate cancer patients with localized disease undergoing radical proctectomy. Results: The panel generated from this approach identified as top candidates mutations in known driver genes (e.g. HRAS) and prostate cancer related transcription factor binding sites (e.g. MYC, AR). It outperformed two commonly used designs in detecting somatic mutations found in the cfDNA of 5 prostate cancer patients when analyzed in an in silico setting. Additionally, hybrid capture and 2500X sequencing of cfDNA molecules using the panel resulted in detection of tumor variants in all 18 patients of a test set, where 15 of the 18 patients had detected variants found in multiple foci. Conclusion: Machine learning-prioritized targeted sequencing panels may prove useful for broad and sensitive variant detection in the cfDNA of heterogeneous diseases. This strategy has implications for disease detection and monitoring when applied to the cfDNA isolated from prostate cancer patients. Keywords: Cell-free DNA, Prostate cancer, Machine learning, Panel design, Tumor variant detection
* Correspondence: [email protected] 2 Department of Epidemiology and Biostatistics, University of California, San Francisco, California 94158, USA 3 Department of Urology, University of California, San Francisco, California 94158, USA Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as lon
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