Every tumour is a living archive. Buried in its genome is a detailed record of the evolutionary forces that shaped it: the mutations that arose by chance, the clones that flourished under selection, the populations that were lost to drift, and the timing of pivotal events that ultimately determined whether a cancer responds to treatment or returns with lethal force. Modern DNA sequencing can now catalogue millions of somatic mutations and profile tumours across space and time with extraordinary resolution, yet sequencing alone cannot answer the questions that matter most to patients and clinicians. When did a key adaptation emerge? How strongly was it selected? Why do some tumours relapse while others never do? And how will the cancer evolve next? A major new review published in Nature Reviews Cancer argues that answering these questions requires a conceptual shift: moving beyond descriptive cancer genomics towards quantitative evolutionary inference, using the mathematical machinery of population genetics.
The review, authored by Giulio Caravagna of the University of Trieste and Area Science Park, Trevor A. Graham of the Centre for Evolution and Cancer at The Institute of Cancer Research in London, and Andrea Sottoriva of Human Technopole in Milan, makes a deceptively simple but profound point. Sequencing is a snapshot, whereas evolution is a dynamic process. A single tumour biopsy tells us which mutations are present and at what frequencies, but it does not, by itself, reveal the underlying dynamics that produced them. Bridging this gap requires models. Population genetics, the discipline developed over the past century to understand how allele frequencies change in natural populations under the influence of mutation, selection and drift, provides exactly the framework needed to transform static measurements of variant allele frequencies into quantitative estimates of clonal fitness and evolutionary timings.
At the heart of the framework lies the concept of the site frequency spectrum, the distribution of mutations across different variant allele frequencies within a tumour sample. In a neutrally evolving tumour, one in which no clone enjoys a fitness advantage over its neighbours, mathematical theory predicts a characteristic power-law tail: a predictable excess of mutations at progressively lower frequencies, each arising in expanding lineages as passengers hitchhiking along with the growing clone. This ‘neutral tail’ has become a signature of neutral tumour evolution, first identified across cancer types in work led by the same research groups. Deviations from this expected distribution are the fingerprints of selection. When subclones carrying driver mutations expand faster than neutral expectations, the frequency spectrum distorts in characteristic and quantifiable ways, allowing researchers to estimate the strength of selection rather than merely guess at it.
The authors show how modern computational tools exploit these principles. Subclonal deconvolution, the process of resolving a bulk sequencing sample into its constituent clonal and subclonal populations, has traditionally been treated as a clustering problem. But clustering alone cannot distinguish between a tumour shaped by strong selection and one dominated by spatially constrained growth, where genetic diversity accumulates by neutral drift in separate geographic compartments. By embedding population genetic models directly into inference algorithms, for example approaches combining machine learning with branching process theory, researchers can distinguish genuine selection from the illusions created by tumour architecture and sampling bias. Simulations of mutation, drift and selection can be tuned until the synthetic genomic data they produce match patient samples, converting descriptive allele frequencies into estimates of evolutionary parameters such as selection coefficients and the timing of clonal expansions.
Timing is one of the most clinically valuable outputs of this framework. Clock-like mutational processes, such as the spontaneous deamination of methylated cytosines, accumulate at approximately constant rates, providing a molecular clock against which key events can be dated. Studies of clear cell renal cell cancer have used these principles to time landmark events in tumour evolution, revealing that many chromosomal catastrophes occur astonishingly early, sometimes decades before diagnosis. More recent theoretical work has shown that patient age itself can help distinguish selection from causation in cancer genomes, since a mutation that arises early and confers a growth advantage leaves a different statistical imprint than one that simply accumulates with time. Dating driver events, genome doublings and the origins of metastatic seeds transforms the tumour genome from a parts list into a chronicle.
The review also confronts the limitations and confounders that complicate evolutionary inference from real data. Bulk sequencing averages across millions of cells, obscuring rare subclones and entangling spatial structure with temporal dynamics. Multi-region sequencing and single-cell approaches help, but each introduces its own biases: sampling depth, copy number alterations that distort allele frequencies, and the fundamental fact that a biopsy represents only a fragment of a spatially extended population. Copy number changes in particular must be carefully modelled, since amplifications and deletions shift variant allele frequencies in ways that can mimic or mask selection. The authors emphasise that assumptions embedded in population genetic models, such as well-mixed populations or constant growth rates, must be tested rather than taken for granted, because spatially constrained tumour growth can generate patterns that superficially resemble selection in the absence of any fitness advantage.
Beyond genetics, the framework extends to epigenetic inheritance and phenotypic plasticity, two dimensions of cancer evolution that the standard genetic models handle poorly. Epigenetic states such as DNA methylation patterns are heritable across cell divisions and can be under selection, yet they are reversible and can switch stochastically, creating a one-to-many relationship between genotype and phenotype. Quantitative models adapted from evolutionary theory, including those describing phenotypic plasticity and stochastic switching in fluctuating environments, offer a way to measure the heritability, transition rates and fitness consequences of non-genetic states. This matters enormously for therapy, because drug-tolerant persister cells frequently arise through epigenetic reprogramming rather than genetic mutation, and their dynamics determine whether resistance emerges in weeks or years.
The ecological dimension of tumour evolution receives similar treatment. Cancers are not just populations of competing clones; they are ecosystems in which cells cooperate, cheat and interact with stromal and immune cells. Game theory and eco-evolutionary models capture frequency-dependent selection, in which the fitness of a clone depends on the composition of its neighbourhood, something classical population genetics assumes away. These models have practical consequences. Adaptive therapy strategies, which aim to maintain sensitive clones that suppress resistant ones rather than eradicate the tumour wholesale, draw directly on ecological and game-theoretic reasoning. Similarly, immune selection on neoantigens shapes both tumour antigenicity and response to checkpoint inhibitors, and can be quantified using selection metrics adapted from population genetics.
Ultimately, the review’s central message is one of reframing. Cancer genomes should be read not as catalogues of mutations but as quantitative records of evolutionary processes, in which every allele frequency, every frequency-spectrum distortion and every signature of mutational timing encodes information about the dynamics that produced them. The authors argue that population genetics provides the foundation for understanding and, ultimately, predicting the trajectories of cancer evolution. If the framework fulfils its promise, the implications for precision oncology are substantial: forecasts of relapse timing grounded in measured evolutionary parameters, treatment strategies designed around the predictable dynamics of resistance, and clinical decisions informed not merely by which mutations a tumour carries today, but by the evolutionary forces that will shape what it becomes tomorrow.
The intellectual roots of this framework stretch well beyond oncology. Population genetics matured through the study of natural populations, and its migration into cancer research has been gradual, beginning with early attempts to reconstruct individual tumour histories from genetic data and gaining momentum as sequencing costs fell. A parallel body of work on clonal haematopoiesis and on pre-malignant lesions in normal tissues has reinforced the point that the evolutionary processes described in the review operate long before a tumour is diagnosed. Studies of normal breast tissue, for example, have revealed rare aneuploid epithelial populations and copy number alterations shared with frank cancers, suggesting that the same population genetic tools can illuminate the earliest steps of carcinogenesis across a continuous spectrum from healthy tissue to invasive disease.
Selection in tumours is not exclusively positive. Adapted metrics such as the ratio of synonymous to non-synonymous mutations, borrowed directly from classical genetics, have revealed universal patterns of selection in cancer and somatic tissues, while complementary analyses indicate that negative selection acts on essential cellular functions and on the immunopeptidome, pruning mutations that would compromise basic biology or expose cells to immune attack. Copy number amplifications of wild-type regions may further allow tumours to tolerate otherwise deleterious coding mutations, a reminder that the interplay between different classes of genomic alteration can obscure simple fitness calculations. At population scale, large studies of somatic mutation across many individuals now provide the statistical power to measure these forces with unprecedented precision.
Practical questions of study design also fall within the framework’s remit. How many biopsies are needed to confidently identify truly clonal mutations in a heterogeneous tumour, and how sampling schemes affect the inferred frequency spectrum, are problems that can themselves be solved with evolutionary models rather than ad hoc rules. The review’s figures trace this progression, from conceptual overviews of genomic readouts through the dynamics of variant allele frequencies, the confounding effects of spatial structure, and the distinct evolutionary routes by which tumours respond to therapy and relapse. Together they map a research programme in which the reliability of every evolutionary claim is tied explicitly to the sampling strategy and the model assumptions behind it.
Subject of Research: Application of population genetics models to infer tumour evolutionary dynamics from cancer genome sequencing data.
Article Title: A guide to understanding tumour evolution through the lens of population genetics
Article References: Caravagna, G., Graham, T. A., & Sottoriva, A. (2026). A guide to understanding tumour evolution through the lens of population genetics. Nature Reviews Cancer. https://doi.org/10.1038/s41568-026-00973-5
Image Credits: AI Generated
DOI: 10.1038/s41568-026-00973-5
Keywords: tumour evolution, population genetics, cancer genomics, clonal selection, variant allele frequency, subclonal deconvolution, genetic drift, mutational signatures, phenotypic plasticity, adaptive therapy, tumour heterogeneity, molecular clock
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Nathaniel Bowman. (September 12, 2026). Reading the Evolutionary History of Cancer Written in Tumour Genomes. Scienmag. https://scienmag.com/reading-the-evolutionary-history-of-cancer-written-in-tumour-genomes/
Nathaniel Bowman. “Reading the Evolutionary History of Cancer Written in Tumour Genomes.” Scienmag, 12 September 2026, https://scienmag.com/reading-the-evolutionary-history-of-cancer-written-in-tumour-genomes/. Accessed 12 September 2026.
Nathaniel Bowman. “Reading the Evolutionary History of Cancer Written in Tumour Genomes.” Scienmag. September 12, 2026. https://scienmag.com/reading-the-evolutionary-history-of-cancer-written-in-tumour-genomes/
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Tags: adaptive therapycancer evolutionary historycancer genomicscancer genomics and evolutionary inferencecancer phylogeneticscancer relapse and resistanceclonal selectiongenetic driftimplications for cancer treatment and prognosismathematical modeling of cancer progressionmolecular clockmutational signaturesphenotypic plasticitypopulation geneticspopulation genetics in cancersomatic mutation profilingspatial and temporal tumour sequencingsubclonal deconvolutiontracking cancer clonal dynamicstumor evolution and adaptationtumour evolutiontumour genome analysistumour heterogeneityvariant allele frequency
