Nature, Published online: 10 December 2025; doi:10.1038/s41586-025-09792-4
High-depth sequencing of non-cancerous tissue from patients with metastatic cancer reveals single-base mutational signatures of alcohol, smoking and cancer treatments, and reveals how exogenous factors, including cancer therapies, affect somatic cell evolution.
Nature, Published online: 13 August 2025; doi:10.1038/s41586-025-09398-w
A study presents ALPACA, a computational method for inferring clone- and allele-specific copy numbers of individual clones from multi-sample bulk DNA-sequencing data, and demonstrates its use to study metastasis trajectories.
Genome Biol. 2025 Jun 13;26(1):165. doi: 10.1186/s13059-025-03557-y.
ABSTRACT
Existing approaches to identifying cancer genes rely overwhelmingly on DNA sequencing data. Here, we introduce RVdriver, a computational tool that leverages paired bulk genomic and transcriptomic data to classify RNA variant allele frequencies (VAFs) of non-synonymous mutations relative to a synonymous mutation background. We analyze 7882 paired exomes and transcriptomes from 31 cancer types and identify novel, as well as known, cancer genes, complementing other DNA-based approaches. Furthermore, RNA VAFs of individual mutations are able to distinguish "driver" from "passenger" mutations within established cancer genes. This approach highlights the value of multi-omic approaches for cancer gene discovery.
PMID:40514689 | PMC:PMC12164115 | DOI:10.1186/s13059-025-03557-y
Nature, Published online: 12 April 2023; doi:10.1038/s41586-023-05706-4
Computational and machine-learning approaches that integrate genomic and transcriptomic variation from paired primary and metastatic non-small cell lung cancer samples from the TRACERx cohort reveal the role of transcriptional events in tumour evolution.
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