ArticlePLoS computational biology2019
On measuring selection in cancer from subclonal mutation frequencies.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 29 citations in OpenAlex.
- A guide to understanding tumour evolution through the lens of population genetics.Nature reviews. Cancer · 2026Review
- The end of the genetic paradigm of cancer.PLoS biology · 2025Review
- Article
- Modeling the Effect of Spatial Structure on Solid Tumor Evolution and Circulating Tumor DNA Composition.Cancers · 2024Article
- Modeling the effect of spatial structure on solid tumor evolution and ctDNA composition.bioRxiv : the preprint server for biology · 2023Article
- Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues.Cell genomics · 2023Article
- Inferring parameters of cancer evolution in chronic lymphocytic leukemia.PLoS computational biology · 2022Article
- Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning.PLoS computational biology · 2022Article
- Cancer evolution: Darwin and beyond.The EMBO journal · 2021Review
- Tumour heterogeneity and evolutionary dynamics in colorectal cancer.Oncogenesis · 2021Review
- Signatures of neutral evolution in exponentially growing tumors: A theoretical perspective.PLoS computational biology · 2021Article
- When, why and how tumour clonal diversity predicts survival.Evolutionary applications · 2020Article
- Delineating the evolutionary dynamics of cancer from theory to reality.Nature cancer · 2020Review
- A community effort to create standards for evaluating tumor subclonal reconstruction.Nature biotechnology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently available cancer sequencing data have revealed a complex view of the cancer genome containing a multitude of mutations, including drivers responsible for cancer progression and neutral passengers. Measuring selection in cancer and distinguishing drivers from passengers have important implications for development of novel treatment strategies. It has recently been argued that a third of cancers are evolving neutrally, as their mutational frequency spectrum follows a 1/f power law expected from neutral evolution in a particular intermediate frequency range. We study a stochastic model of cancer evolution and derive a formula for the probability distribution of the cancer cell frequency of a subclonal driver, demonstrating that driver frequency is biased towards 0 and 1. We show that it is difficult to capture a driver mutation at an intermediate frequency, and thus the calling of neutrality due to a lack of such driver will significantly overestimate the number of neutrally evolving tumors. Our approach provides quantification of the validity of the 1/f statistic across the entire range of relevant parameter values. We also show that our conclusions remain valid for non-exponential models: spatial 3d model and sigmoidal growth, relevant for early- and late stages of cancer growth.
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Registered trials
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.