A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer.

Journal: BMC cancer
Published Date: Aug 28, 2020

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.

Authors

  • Clinton L Cario
    Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA.
  • Emmalyn Chen
    Artera, Inc., Los Altos, CA.
  • Lancelote Leong
    Department of Epidemiology and Biostatistics, University of California, San Francisco, California, 94158, USA.
  • Nima C Emami
    Program in Biological and Medical Informatics, University of California, San Francisco, California, 94158, USA.
  • Karen Lopez
    Department of Urology, University of California, San Francisco, California, 94158, USA.
  • Imelda Tenggara
    Department of Urology, University of California, San Francisco, California, 94158, USA.
  • Jeffry P Simko
    Department of Radiation Oncology, University of California, San Francisco, San Francisco.
  • Terence W Friedlander
    Division of Hematology/Oncology, University of California, San Francisco, California, 94158, USA.
  • Patricia S Li
    Division of Hematology/Oncology, University of California, San Francisco, California, 94158, USA.
  • Pamela L Paris
    Department of Urology, University of California, San Francisco, California, 94158, USA.
  • Peter R Carroll
    University of California, San Francisco, San Francisco, CA.
  • John S Witte
    Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA.

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