A community effort to create standards for evaluating tumor subclonal reconstruction.
journal contributionposted on 27.08.2020 by Adriana Salcedo, Maxime Tarabichi, Shadrielle Melijah G Espiritu, Amit G Deshwar, Matei David, Nathan M Wilson, Stefan Dentro, Jeff A Wintersinger, Lydia Y Liu, Minjeong Ko, Srinivasan Sivanandan, Hongjiu Zhang, Kaiyi Zhu, Tai-Hsien Ou Yang, John M Chilton, Alex Buchanan, Christopher M Lalansingh, Christine P'ng, Catalina V Anghel, Imaad Umar, Bryan Lo, William Zou, DREAM SMC-Het Participants, Jared T Simpson, Joshua M Stuart, Dimitris Anastassiou, Yuanfang Guan, Adam D Ewing, Kyle Ellrott, David C Wedge, Quaid Morris, Peter Van Loo, Paul C Boutros
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Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.