TY - JOUR T1 - Creating Standards for Evaluating Tumour Subclonal Reconstruction JF - bioRxiv DO - 10.1101/310425 SP - 310425 AU - Adriana Salcedo AU - Maxime Tarabichi AU - Shadrielle Melijah G. Espiritu AU - Amit G Deshwar AU - Matei David AU - Nathan M. Wilson AU - Stefan Dentro AU - Jeff A. Wintersinger AU - Lydia Y. Liu AU - Minjeong Ko AU - Srinivasan Sivanandan AU - Hongjiu Zhang AU - Kaiyi Zhu AU - Tai-Hsien Ou Yang AU - John M. Chilton AU - Alex Buchanan AU - Christopher M. Lalansingh AU - Christine P’ng AU - Catalina V. Anghel AU - Imaad Umar AU - Bryan Lo AU - DREAM SMC-Het Participants AU - Jared T. Simpson AU - Joshua M. Stuart AU - Dimitris Anastassiou AU - Yuanfang Guan AU - Adam D. Ewing AU - Kyle Ellrott AU - David C. Wedge AU - Quaid D. Morris AU - Peter Van Loo AU - Paul C. Boutros Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/04/30/310425.abstract N2 - Tumours evolve through time and space. To infer these evolutionary dynamics for DNA sequencing data, many subclonal reconstruction techniques have been developed and applied to large datasets. Surprisingly, though, there has been no systematic evaluation of these methods, in part due to the complexity of the mathematical and biological questions and the difficulties in creating gold-standards. To fill this gap, we systematically elucidated key algorithmic problems in subclonal reconstruction, and developed mathematically valid quantitative metrics for evaluating them. We then developed approaches to simulate realistic tumour genomes that harbour all known mutation types and processes. Finally, we benchmarked a set of 500 subclonal reconstructions, creating a key resource, and quantified the impact of sequencing read-depth and somatic variant detection strategies on the accuracy of specific subclonal reconstruction approaches. Inference of tumour phylogenies is rapidly becoming standard practice in cancer genome analysis, and this work sets standards for evaluating its accuracy. ER -