Open AccessA community effort to create standards for evaluating tumor subclonal reconstruction
Author(s) -
Adriana Salcedo,
Maxime Tarabichi,
Shadrielle M. G. Espiritu,
Amit G. Deshwar,
Matei David,
Nathan M. Wilson,
Stefan C. Dentro,
Jeff Wintersinger,
Lydia Liu,
Minjeong Ko,
Srinivasan Sivanandan,
Hongjiu Zhang,
Kaiyi Zhu,
Tai-Hsien Ou Yang,
John Chilton,
Alex Buchanan,
Christopher M. Lalansingh,
Christine P’ng,
Catalina Anghel,
Imaad Umar,
Bryan Lo,
William Y. Zou,
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
Publication year - 2020
Publication title -
nature biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 15.358
H-Index - 445
eISSN - 1546-1696
pISSN - 1087-0156
DOI - 10.1038/s41587-019-0364-z
Subject(s) - tumor heterogeneity , computational biology , genome , benchmark (surveying) , biology , tumor progression , inference , dna sequencing , computer science , cancer , genetics , dna , gene , artificial intelligence , geodesy , geography
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.