Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction Journal Article
| Authors: | Salcedo, A.; Tarabichi, M.; Buchanan, A.; Espiritu, S. M. G.; Zhang, H.; Zhu, K.; Ou Yang, T. H.; Leshchiner, I.; Anastassiou, D.; Guan, Y.; Jang, G. H.; Mootor, M. F. E.; Haase, K.; Deshwar, A. G.; Zou, W.; Umar, I.; Dentro, S.; Wintersinger, J. A.; Chiotti, K.; Demeulemeester, J.; Jolly, C.; Sycza, L.; Ko, M.; PCAWG Evolution and Heterogeneity Working Group; SMC-Het Participants; Wedge, D. C.; Morris, Q. D.; Ellrott, K.; Van Loo, P.; Boutros, P. C. |
| Contributor: | Vázquez-García, I. |
| Article Title: | Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction |
| Abstract: | 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. © The Author(s) 2024. |
| Keywords: | somatic mutation; genetics; mutation; neoplasm; neoplasms; lung cancer; algorithms; algorithm; benchmarking; genome; performance; gene encoding; sequence analysis, dna; cancer genome; tumor heterogeneity; procedures; dna sequencing; humans; human; selective pressure; cloud computing; reconstruction algorithms; cloud-computing; independent tasks; single sample |
| Journal Title: | Nature Biotechnology |
| Volume: | 43 |
| Issue: | 4 |
| ISSN: | 1087-0156 |
| Publisher: | Nature Publishing Group |
| Date Published: | 2025-04-01 |
| Start Page: | 581 |
| End Page: | 592 |
| Language: | English |
| DOI: | 10.1038/s41587-024-02250-y |
| PUBMED: | 38862616 |
| PROVIDER: | scopus |
| PMCID: | PMC11994449 |
| DOI/URL: | |
| Notes: | Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PubMed and PDF -- Source: Scopus |
