ArticleNature biotechnology2025
Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction.
Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Accurate detection of tumor clonality and ongoing expansion mode from genomic data.bioRxiv : the preprint server for biology · 2026Article
- Article
- A complete human pancreatic cancer genome.bioRxiv : the preprint server for biology · 2026Article
- Single-cell omics data-driven decoding of tumor clonal evolution through reinforcement learning.Genome medicine · 2026Article
- Characterization of subclonal variants in HG002 Genome in a Bottle reference material as a resource for benchmarking variant callers.Cell genomics · 2026Article
- Metapipeline-DNA: A comprehensive germline and somatic genomics Nextflow pipeline.Cell reports methods · 2026Article
- A robust benchmark for detecting low-frequency variants in the HG002 Genome In A Bottle NIST reference material.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
30 authors.
Funding
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.
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