ArticleNature communications2023
Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients.
Article in Nature communications, 2023. 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, 15 citations in OpenAlex.
- A guide to understanding tumour evolution through the lens of population genetics.Nature reviews. Cancer · 2026Review
- Quantitative coupling of clonal CNV evolution and spatially restricted malignant states identifies MFGE8 as a candidate late-state-associated target in breast cancer.Journal of translational medicine · 2026Article
- Competing subclones and fitness diversity shape tumor evolution across cancer types.Bioinformatics (Oxford, England) · 2026Article
- Article
- Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition.bioRxiv : the preprint server for biology · 2025Article
- Recurrent somatic mutations of FAT family cadherins induce an aggressive phenotype and poor prognosis in anaplastic large cell lymphoma.British journal of cancer · 2024Article
- Joint analysis of mutational and transcriptional landscapes in human cancer reveals key perturbations during cancer evolution.Genome biology · 2024Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recurring sequences of genomic alterations occurring across patients can highlight repeated evolutionary processes with significant implications for predicting cancer progression. Leveraging the ever-increasing availability of cancer omics data, here we unveil cancer's evolutionary signatures tied to distinct disease outcomes, representing "favored trajectories" of acquisition of driver mutations detected in patients with similar prognosis. We present a framework named ASCETIC (Agony-baSed Cancer EvoluTion InferenCe) to extract such signatures from sequencing experiments generated by different technologies such as bulk and single-cell sequencing data. We apply ASCETIC to (i) single-cell data from 146 myeloid malignancy patients and bulk sequencing from 366 acute myeloid leukemia patients, (ii) multi-region sequencing from 100 early-stage lung cancer patients, (iii) exome/genome data from 10,000+ Pan-Cancer Atlas samples, and (iv) targeted sequencing from 25,000+ MSK-MET metastatic patients, revealing subtype-specific single-nucleotide variant signatures associated with distinct prognostic clusters. Validations on several datasets underscore the robustness and generalizability of the extracted signatures.
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