Giulio Caravagna | Search PubMed

PubMed2026

A guide to understanding tumour evolution through the lens of population genetics.

Every cancer carries the history of its own evolution, hidden in its genome. Modern DNA sequencing can catalogue millions of mutations and profile tumours across space and time, but sequencing alone struggles to answer the questions that matter most: when did key adaptations emerge, how strongly were they selected, why do some tumours relapse whereas others do not, and how will the cancer evolve next? The reason is fundamental: sequencing is a snapshot, whereas evolution is a dynamic process. Bridging this gap requires moving beyond descriptive cancer genomics towards quantitative evolutionary inference. In this Review, we argue that population genetics provides the mathematical framework needed to extract evolutionary dynamics from cancer genomes. We show how models of mutation, selection and drift transform allele frequencies from descriptive measurements into quantitative estimates of clonal fitness and evolutionary timings. We discuss how these principles extend to epigenetic inheritance, plasticity and ecological interactions within the tumour ecosystem, and examine the assumptions and limitations for their application to modern sequencing data. By reframing cancer genomes as quantitative records of evolutionary processes rather than catalogues of mutations, researchers have used population genetics to provide a foundation for understanding - and ultimately predicting - the trajectories of cancer evolution.

Journal Article↗

PubMed2026

An alignment-free strategy for circulating tumor DNA detection and tumor fraction estimation from whole-genome sequencing data.

Circulating tumor DNA (ctDNA) is emerging as a promising biomarker for postoperative monitoring of cancer patients. Precise estimation of circulating tumor fraction is crucial for evaluating treatment effects and timely detection of disease recurrence. All current ctDNA detection methods that utilize whole-genome sequencing (WGS) data rely on the reference genome alignment of sequencing reads and often apply separate tools for detecting different variant types. However, various bioinformatic analysis confounders and the application of external variant calling tools could be avoided by analyzing k-mers from unaligned sequencing reads. While k-mer-based methods have successfully been applied for somatic variant validation and detection, the potential of k-mer-based ctDNA detection is unexplored. We have developed a tumor-informed alignment-free ctDNA detection tool called ctDNAmer that detects tumor-specific somatic variation directly from unaligned sequencing data by identifying k-mers unique to the tumor DNA. ctDNAmer detects variant information across the genome by comparing the primary tumor and germline WGS data and accounts for sample-specific germline variability and technical noise in the same framework. We tested the utility of ctDNAmer for tumor fraction estimation on postoperative plasma cfDNA WGS data (mean sequencing depth ~ 28x) from 90 stage III colorectal cancer patients with three years of follow-up. The tumor fraction (TF) estimates agreed with the available clinical information and ctDNA was detected in 77% (17/22) of recurring patients with a median lead time of 8 months compared to radiological imaging. We further validated ctDNAmer's tumor fraction estimates based on a comparison with the mean cfDNA allele frequencies of somatic clonal SNVs identified from aligned primary tumor sequencing data. The TF estimates showed a strong Pearson correlation of 0.897 with the mean allele frequencies and improved ctDNA detection results across samples with an AUC of 0.79 compared to 0.75 if the mean allele frequency of clonal mutations is used.

Circulating Tumor DNA↗