ArticleStatistical science : a review journal of the Institute of Mathematical Statistics2020
Statistical Inference for the Evolutionary History of Cancer Genomes.
Article in Statistical science : a review journal of the Institute of Mathematical Statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Accurate detection of tumor clonality and ongoing expansion mode from genomic data.bioRxiv : the preprint server for biology · 2026Article
- Dysregulated mitochondrial energy metabolism drives the progression of mucosal field effects to invasive bladder cancer.The Journal of pathology · 2025Article
- Statistical and Evolutionary Analysis of Sequenced DNA from Breast Cancer FFPE Specimens.bioRxiv : the preprint server for biology · 2025Article
- Single-cell mutational burden distributions in birth-death processes.PLoS computational biology · 2025Article
- Bayesian inference of fitness landscapes via tree-structured branching processes.Bioinformatics (Oxford, England) · 2025Article
- CINner: Modeling and simulation of chromosomal instability in cancer at single-cell resolution.PLoS computational biology · 2025Article
- Limit theorems for the site frequency spectrum of neutral mutations in an exponentially growing population.Stochastic processes and their applications · 2025Article
- Inferring Bladder Cancer Evolution from Mucosal field Effects by Whole-Organ Spatial Mutational, Proteomic, and Metabolomic Mapping.Research square · 2024Article
- CINner: modeling and simulation of chromosomal instability in cancer at single-cell resolution.bioRxiv : the preprint server for biology · 2024Article
- cloneRate: fast estimation of single-cell clonal dynamics using coalescent theory.Bioinformatics (Oxford, England) · 2023Article
- Inferring parameters of cancer evolution in chronic lymphocytic leukemia.PLoS computational biology · 2022Article
- Article
- Modes of Selection in Tumors as Reflected by Two Mathematical Models and Site Frequency Spectra.Frontiers in ecology and evolution · 2022Article
- Elements and evolutionary determinants of genomic divergence between paired primary and metastatic tumors.PLoS computational biology · 2021Article
- Statistical Inference for the Evolutionary History of Cancer Genomes.Statistical science : a review journal of the Institute of Mathematical Statistics · 2020Article
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5 authors.
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Abstract
Recent years have seen considerable work on inference about cancer evolution from mutations identified in cancer samples. Much of the modeling work has been based on classical models of population genetics, generalized to accommodate time-varying cell population size. Reverse-time, genealogical views of such models, commonly known as coalescents, have been used to infer aspects of the past of growing populations. Another approach is to use branching processes, the simplest scenario being the classical linear birth-death process. Inference from evolutionary models of DNA often exploits summary statistics of the sequence data, a common one being the so-called Site Frequency Spectrum (SFS). In a bulk tumor sequencing experiment, we can estimate for each site at which a novel somatic point mutation has arisen, the proportion of cells that carry that mutation. These numbers are then grouped into collections of sites which have similar mutant fractions. We examine how the SFS based on birth-death processes differs from those based on the coalescent model. This may stem from the different sampling mechanisms in the two approaches. However, we also show that despite this, they are quantitatively comparable for the range of parameters typical for tumor cell populations. We also present a model of tumor evolution with selective sweeps, and demonstrate how it may help in understanding the history of a tumor as well as the influence of data pre-processing. We illustrate the theory with applications to several examples from The Cancer Genome Atlas tumors.
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