ArticleNature communications2020
Estimating growth patterns and driver effects in tumor evolution from individual samples.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- LeafRank: A phylodynamic framework for inferring relative fitness from single-cell phylogenies in chromosomally unstable tumors.bioRxiv : the preprint server for biology · 2026Article
- Competing subclones and fitness diversity shape tumor evolution across cancer types.Bioinformatics (Oxford, England) · 2026Article
- Genetic, Epidemiological, Clinical, and Therapeutic Trajectories in Colon and Rectal Cancers.Cancers · 2025Review
- Bayesian inference of fitness landscapes via tree-structured branching processes.Bioinformatics (Oxford, England) · 2025Article
- Strength of selection in lung tumors correlates with clinical features better than tumor mutation burden.Scientific reports · 2024Article
- Latent evolutionary signatures: a general framework for analysing music and cultural evolution.Journal of the Royal Society, Interface · 2024Article
- Quantification of rare somatic single nucleotide variants by droplet digital PCR using SuperSelective primers.Scientific reports · 2023Article
- Netie: inferring the evolution of neoantigen-T cell interactions in tumors.Nature methods · 2022Article
- Inferring parameters of cancer evolution in chronic lymphocytic leukemia.PLoS computational biology · 2022Article
- Integrative analysis of genomic and transcriptomic data of normal, tumour, and co-occurring leukoplakia tissue triads drawn from patients with gingivobuccal oral cancer identifies signatures of tumour initiation and progression.The Journal of pathology · 2022Article
- Review
- Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning.PLoS computational biology · 2022Article
- Topographic correlates of driver mutations and endogenous gene expression in pediatric diffuse midline gliomas and hemispheric high-grade gliomas.Scientific reports · 2021Article
- Article
- Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples.Frontiers in physiology · 2021Article
- New Insights into Therapy-Induced Progression of Cancer.International journal of molecular sciences · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Tumors accumulate thousands of mutations, and sequencing them has given rise to methods for finding cancer drivers via mutational recurrence. However, these methods require large cohorts and underperform for low recurrence. Recently, ultra-deep sequencing has enabled accurate measurement of VAFs (variant-allele frequencies) for mutations, allowing the determination of evolutionary trajectories. Here, based solely on the VAF spectrum for an individual sample, we report on a method that identifies drivers and quantifies tumor growth. Drivers introduce perturbations into the spectrum, and our method uses the frequency of hitchhiking mutations preceding a driver to measure this. As validation, we use simulation models and 993 tumors from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium with previously identified drivers. Then we apply our method to an ultra-deep sequenced acute myeloid leukemia (AML) tumor and identify known cancer genes and additional driver candidates. In summary, our framework presents opportunities for personalized driver diagnosis using sequencing data from a single individual.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.