Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction
Authors: Salcedo Adriana , Tarabichi Maxime , Buchanan Alex , Espiritu Shadrielle M. G. , Zhang Hongjiu , Zhu Kaiyi , Ou Yang Tai-Hsien , Leshchiner Ignaty , Anastassiou Dimitris , Guan Yuanfang , Jang Gun Ho , Mootor Mohammed F. E. , Haase Kerstin , Deshwar Amit G. , Zou William , Umar Imaad , Dentro Stefan , Wintersinger Jeff A. , Chiotti Kami , Demeulemeester Jonas , Jolly Clemency , Sycza Lesia , Ko Minjeong ,   PCAWG Evolution and Heterogeneity Working Group, Dentro Stefan C. , Leshchiner Ignaty , Gerstung Moritz , Tarabichi Maxime , Wintersinger Jeff , Deshwar Amit G. , Yu Kaixian , Gonzalez Santiago , Rubanova Yulia , Macintyre Geoff , Adams David J. , Anur Pavana , Beroukhim Rameen , Boutros Paul C. , Bowtell David D. , Campbell Peter J. , Cao Shaolong , Christie Elizabeth L. , Cmero Marek , Cun Yupeng , Dawson Kevin J. , Donmez Nilgun , Drews Ruben M. , Eils Roland , Fan Yu , Fittall Matthew , Garsed Dale W. , Getz Gad , Ha Gavin , Imielinski Marcin , Jerman Lara , Ji Yuan , Kleinheinz Kortine , Lee Juhee , Lee-Six Henry , Livitz Dimitri G. , Malikic Salem , Markowetz Florian , Martincorena Inigo , Mitchell Thomas J. , Mustonen Ville , Oesper Layla , Peifer Martin , Peto Myron , Raphael Benjamin J. , Rosebrock Daniel , Sahinalp S. Cenk , Salcedo Adriana , Schlesner Matthias , Schumacher Steven , Sengupta Subhajit , Shi Ruian , Shin Seung Jun , Stein Lincoln D. , Spiro Oliver , Vázquez-García Ignacio , Vembu Shankar , Wheeler David A. , Yang Tsun-Po , Yao Xiaotong , Yuan Ke , Zhu Hongtu , Wang Wenyi , Morris Quaid D. , Spellman Paul T. , Wedge David C. , Van Loo Peter ,   SMC-Het Participants, Jha Alokkumar , Huang Tanxiao , Yang Tsun-Po , Peifer Martin , Sahinalp S. Cenk , Malikic Salem , Vázquez-García Ignacio , Mustonen Ville , Yang Hsih-Te , Lee Ken-Ray , Ji Yuan , Sengupta Subhajit , Justine Rudewicz , Macha Nikolski , Quentin Schaeverbeke , Yuan Ke , Markowetz Florian , Macintyre Geoff , Cmero Marek , Chaudhary Belal , Leshchiner Ignaty , Livitz Dimitri , Getz Gad , Loher Phillipe , Yu Kaixian , Wang Wenyi , Zhu Hongtu , Wedge David C. , Morris Quaid D. , Ellrott Kyle , Van Loo Peter , Boutros Paul C. 
Published: June 2024

Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumor evolution, allowing an assessment of how cancers initiate, progress and respond to selective pressures. We launched the ICGC–TCGA (International Cancer Genome Consortium–The Cancer Genome Atlas) DREAM Somatic Mutation Calling Tumor Heterogeneity and Evolution Challenge to benchmark existing subclonal reconstruction algorithms. This 7-year community effort used cloud computing to benchmark 31 subclonal reconstruction algorithms on 51 simulated tumors. Algorithms were scored on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance substantially more than tumor features but purity-adjusted read depth, copy-number state and read mappability were associated with the performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumor evolution.