Biology subjects

Caravagna, G.

Publications and source records attributed to Caravagna, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Tumour evolution as ground truth for cancer whole-genome sequencing

Cancer genomes are shaped by evolutionary processes that couple mutagenesis, clonal selection, chromosomal instability, spatial growth and treatment response into structured genomic patterns, yet current benchmarking strategies largely ignore this evolutionary dependency. Here, we present SCOUT, a large-scale synthetic whole-genome sequencing resource of over 200 samples, designed for systematic benchmarking of tumour genomic analysis and evolutionary inference under controlled evolutionary ground truth. Unlike conventional task-specific simulations, SCOUT models tumour evolution as a latent generative process that simultaneously shapes mutations, copy-number alterations, variant allele frequencies, mutational signatures and clonal architectures. SCOUT recapitulates key features of solid and haematological malignancies, including driver mutations, chromosomal instability, intratumour heterogeneity, spatial sampling and treatment-associated evolutionary dynamics in tumour and matched-normal longitudinal and multi-region sequencing designs. Using SCOUT, we benchmarked widely used methods for somatic variant detection, copy-number analysis, mutational signature inference and tumour evolutionary reconstruction. Across analytical tasks, performance deteriorated in low-purity, highly subclonal and structurally complex tumours, while spatial sampling bias and hypermutation generated spurious evolutionary signals that confounded tumour interpretation across multiple inference layers. Evolutionary simulations further distinguished lineage-restricted genetic bottlenecks from multi-lineage resistance dynamics associated with tumour plasticity. Tumour purity consistently exerted a stronger effect on inference accuracy than sequencing depth. Together, our results establish evolutionary ground truth as a prerequisite for reproducible benchmarking and biologically interpretable analysis of cancer whole-genome sequencing data.

bioinformatics↗

bioRxiv2025

Scalable, fast and accurate differential gene expression testing from millions of cells of multiple patients

Since the development of DNA microarrays and later RNA bulk sequencing, testing with statistically independent samples has been the standard method for detecting genes with different transcription patterns. Single-cell assays challenge these assumptions because individual cells are statistically dependent, and all proposed methodologies present mathematical limitations or computational bottlenecks that prevent a seamless integration of data from many cells and patients simultaneously. In this work, we solve this crucial limitation by introducing a Bayesian framework that retrieves the independence structure at the level of individual patients, separating differences across individuals from actual transcriptional differences. Leveraging multi-GPU and variational inference, our approach excels across different experimental designs and scales to analyse over 10 million cells. This framework enables single-cell differential expression analysis that can finally integrate datasets from large clinical cohorts, atlas projects, or drug-response screens with thousands of samples and millions of cells.

bioinformatics↗

bioRxiv2025

A reference-free strategy for circulating tumor DNA detection 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 reference-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 ctDNAmers 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.

bioinformatics↗

bioRxiv2025

Extending differential gene expression testing to handle genome aneuploidy in cancer

Genome aneuploidy, characterized by copy number variations (CNVs), profoundly alters gene expression in cancer through direct gene dosage effects and indirect compensatory regulatory mechanisms. However, existing differential gene expression (DGE) testing methods do not differentiate between these mechanisms, conflating all expression changes, limiting biological interpretability and obscuring key genes involved in tumor progression. To address this, we developed DeConveil, a computational framework that extends traditional DGE analysis by integrating CNV data. Using a generalized linear model with a negative binomial distribution, DeConveil models RNA-seq expression counts while accounting for copy number gene dosage effects. We proposed a more fine-grained gene decomposition into dosage-sensitive (DSGs), dosage-insensitive (DIGs), and dosage-compensated (DCGs), which explicitly de-couples changes due to CNVs and bona fide changes in transcriptional regulation. Analysis of TCGA datasets from aneuploid solid cancers resulted in notable reclassification of genes, refining and expanding upon the results from conventional methods. Functional enrichment analysis identified distinct biological roles for DSGs, DIGs, and DCGs in tumor progression, immune regulation, and cell adhesion. In a breast cancer case study, DeConveils CN-aware analysis facilitated the identification of both known and novel prognostic biomarkers, including long non-coding RNAs, linking gene expression signatures to survival outcomes. Utilizing these biomarkers for each gene group significantly improved patient risk stratification, yielding more accurate predictions compared to conventional methods. These results highlight DeConveils ability to disentangle CNV-driven from regulatory transcriptional changes, enhancing gene classification and biomarker discovery. By improving transcriptomic analysis, DeConveil provides a powerful tool for cancer research, precision oncology, with potential applications in therapeutic target identification. Author SummaryIdentifying genes whose expression changes in cancer is fundamental to understand disease aetiology and to propose therapeutic targets. However, alterations to the copy number of genes, due to amplification or deletion events, can represent a significant confounder to differential expression quantification. Here we propose a simple model to correct for this confounder, identifying a finer characterization of coordinated changes in gene expression and copy number. We show on several data sets that this new characterization has prognostic value and sheds light on gene regulation in cancer.

bioinformatics↗

bioRxiv2025

Timing and clustering co-occurring genome amplifications incancers

Clonal evolution in cancer is driven by genomic alterations that accumulate over time, shaping tumour progression, therapy resistance, and metastasis. Among these somatic events, genomic amplifications are a broad class of copy number alterations (CNAs) that can be mathematically timed (i.e., mapped to an abstract timeline). Existing methods successfully order amplifications in time but fail to understand their co-occurrence patterns. This limitation makes it harder to understand abrupt shifts of clonal and selection dynamics possibly linked to clones that acquire profound mutant genotypes and hold the potential to establish a novel evolutionary lineage. Here, we introduce TickTack, a hierarchical Bayesian mixture model for reconstructing the temporal order of copy number amplifications across the genome while simultaneously detecting co-occurrent events, offering a more comprehensive view of tumour evolutionary dynamics. This new model allows us to determine whether copy number amplifications accumulate gradually over multiple generations or occur in rapid succession within short time frames, providing deeper insights into genomic instability and tumor progression beyond traditional linear models. We validated our approach with synthetic data under various uncertainty settings and against competing approaches. Applying TickTack to 2,777 samples from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project, a comprehensive resource spanning 38 tumor types, we inferred the temporal order of copy number amplifications, identifying cancer-specific co-occurring events. Our analysis revealed associations between early chromosomal instability and key driver mutations (TP53, BRCA1/2) in Esophageal Adenocarcinoma and uncovered recurrent evolutionary trajectories shaped by focal and arm-level copy gains. These findings highlight the role of saltational evolution in tumorigenesis and provide insights into genomic instability with possible implications for prognosis and targeted therapies. AvailabilitytickTack is available as an R package at https://caravagnalab.github.io/tickTack/ and the code to replicate the analysis is available from https://zenodo.org/records/14870458.

cancer biology↗

bioRxiv2025

Model-based tumour subclonal deconvolution accounting for spatio-temporal sampling biases

Bulk DNA sequencing has entered the clinic, and understanding tumour evolution in space and time has become critical for advancing precision oncology. A recent work by us has brought a population genetics perspective into the classical tumour subclonal deconvolution problem, showing how multiple spatiotemporal sampling biases arise when we collect more than one sample of the same tumour. Sampling biases complicate the mapping of the mutations into the evolutionary process, a complex issue undermined by existing multi-sample analysis methods. This work presents MOBSTERm, a novel mixture-within-mixture Bayesian framework that extends our earlier approach to a multi-dimensional formulation. Our model incorporates distinct mathematical distributions that capture sampling bias patterns in tumour data, allowing the deconvolution to resist the effect of some confounders. This works presents the simulation of one source of sampling bias analysed with this new approach, together with a large scale test based on simulations. Moreover, we apply our model to understand subclonal deconvolution from a multi-region whole-genome colorectal cancer sample and from longitudinal whole-genome glioblastoma samples of 15 patients collected before and after treatment. This new deconvolution approach offers a better account of the effect of spatio-temporal tumour biases, allowing us to better elucidate complex clonal dynamics from multi-sample cancer sequencing data. Code availabilityMOBSTERm is available at the GitHub page https://github.com/caravagnalab/MOBSTERm. The code to reproduce our analyses is available at Zenodo https://doi.org/10.5281/zenodo.14867256.

cancer biology↗

bioRxiv2024

Model-based Bayesian inference of cancer dynamics from heterogenous longitudinal data

The kinetic parameters of cancer population dynamics are critical for developing reliable predictors of tumour growth patterns, extracting metrics for patient stratification and creating algorithms that can forecast clinically significant events. Here, we introduce a model-based Bayesian framework that leverages longitudinal phenotypic (e.g., tumour volume, cell counts) or genotypic (e.g., mutation frequency) data to infer critical parameters of tumour progression within a single patient. Our models uses population genetics to estimate probability distributions for tumour growth rates, initiation and extinction times, pinpointing abrupt shifts in tumour dynamics due to treatment response and revealing associations between drug resistance and pre-existing cancer cell populations. We apply our framework to address pivotal clinical questions across three major cancer types. In colorectal cancer, we use tumour markers data to identify extensive pre-existing RAS-linked resistance to cetuximab. In lung cancer, we use somatic mutation frequencies in citculating tumour DNA to determine prognostic growth rates and develop a test for monitoring minimal residual disease. In chronic lymphocytic leukaemia, we use white blood cell counts to stratify patients by growth patterns and predict time to treatment, advancing adaptive monitoring strategies.

cancer biology↗

bioRxiv2024

A Bayesian framework to infer and cluster mutational signatures leveraging prior biological knowledge.

Mutational signatures provide key insights into cancer mutational processes, but the availability of signature catalogues generated by different groups using distinct methodologies underscores a need for standardisation. We introduce a Bayesian framework that offers a systematic approach to expanding existing signature catalogues for any type of mutational signature, while grouping patients based on shared signature patterns. We demonstrate that this approach can identify both known and novel molecular subtypes across nearly 8,000 samples spanning six cancer types, and show that stratifications derived from signature yield prognostic groups, further enhancing the translational potential of mutational signatures.

cancer biology↗

bioRxiv2024

Interpretable Multi-Omics Data Integration with Deep Archetypal Analysis

High-throughput multi-omic molecular profiling allows probing biological systems at unprecedented resolution. However, the integration and interpretation of high-dimensional, sparse, and noisy multimodal datasets remains challenging. Deriving new biology using current methods is particularly difficult because they are not based on biological principles, but instead focus exclusively on a dimensionality reduction task. Here we introduce MIDAA (Multiomic Integration with Deep Archetypal Analysis), a framework that combines archetypal analysis, an approach grounded in biological principles, with deep learning. Using the concept of archetypes that are based on evolutionary trade-offs and Pareto optimality - MIDAA finds extreme data points that define the geometry of the latent space, preserving the complexity of biological interactions while retaining an interpretable output. We demonstrate that indeed these extreme points represent cellular programmes reflecting the underlying biology. We show on real and simulated multi-omics data how MIDAA outperforms state-of-the-art methods in identifying parsimonious, interpretable, and biologically relevant patterns.

bioinformatics↗

bioRxiv2023

Epigenetic heritability of cell plasticity drives cancer drug resistance through one-to-many genotype to phenotype mapping

Cancer drug resistance is multi-factorial, driven by heritable (epi)genetic changes but also phenotypic plasticity. Here we dissect it by perturbing colorectal cancer patient-derived organoids longitudinally with drugs in sequence. Combining longitudinal tracking, single cell omics, evolutionary modelling, and machine leaning, we found that different targeted drugs select for distinct subclones, supporting rationally designed drug sequences. The cellular memory was encoded as a heritable epigenetic configuration, from which multiple transcriptional programmes could run, supporting a one-to-many (epi)genotype-to- phenotype map that explains how clonal expansions and plasticity manifest together. This may ensure drug resistance subclones can exhibit distinct phenotypes in changing environments while still preserving the cellular memory encoding for their selective advantage. Chemotherapies resistance was instead entirely driven by plasticity. Inducing further chromosomal instability before drug application changed clonal evolution but not convergent transcriptional programmes. Collectively, our data show how genetic and epigenetic alterations are selected to "permissive epigenome" enabling phenotypic plasticity.

cancer biology↗

bioRxiv2023

Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients

By leveraging the ever-increasing availability of cancer omics data and the continuous advances in cancer data science and machine learning, we have discovered the existence of cancer type-specific evolutionary signatures associated with different disease outcomes. These signatures represent "favored trajectories" of acquisition of driver mutations that are repeatedly detected in patients with similar prognosis. In this work, we present a novel framework named ASCETIC (Agony-baSed Cancer EvoluTion InferenCe) that extracts such signatures from NGS experiments generated by different technologies such as bulk and single-cell sequencing data. In our study, we applied ASCETIC to (i) single-cell sequencing data from 146 patients with distinct myeloid malignancies and bulk whole-exome sequencing data from 366 acute myeloid leukemia patients, (ii) multi-region sequencing data from 100 early-stage lung cancer patients from the TRACERx project, (iii) whole-exome/genome sequencing data from more than 10,000 Pan-Cancer Atlas samples, and (iv) targeted bulk sequencing data from more than 25,000 MSK-MET metastatic patients (both datasets including multiple cancer types). As a result, we extracted different cancer (sub)type-specific single-nucleotide variants evolutionary signatures associated with clusters of patients with statistically significant different prognoses. In addition, we conducted several validations using diverse and previously unexplored datasets to evaluate the reliability and applicability of the evolutionary signatures extracted by ASCETIC. Such analyses provided evidence of the robustness and generalizability of the identified evolutionary patterns.

genetics↗

bioRxiv2023

A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing

Single-cell RNA and ATAC sequencing technologies allow one to probe expression and chromatin accessibility states as a proxy for cellular phenotypes at the resolution of individual cells. A key challenge of cancer research is to consistently map such states on genetic clones, within an evolutionary framework. To this end we introduce CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles generated from independent or multimodal assays on the latent space of copy numbers clones. CONGAS+ can detect tumour subclones associated with aneuploidy by clustering cells with the same ploidy profile. The framework is implemented in a probabilistic language that can scale to analyse thousands of cells thanks to GPU deployment. Our tool exhibits robust performance on simulations and real data, highlighting the advantage of detecting aneuploidy from two distinct molecules as opposed to other single-molecule models, and also leveraging real multi-omic data. In the application to prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ did retrieve complex subclonal architectures while providing a coherent mapping among ATAC and RNA, facilitating the study of genotype-phenotype mapping, and their relation to tumour aneuploidy. Author summaryAneuploidy is a condition caused by copy number alterations (CNAs), which brings cells to acquire or lose chromosomes. In the context of cancer progression and treatment response, aneuploidy is a key factor driving cancer clonal dynamics, and measuring CNAs from modern sequencing assays is therefore important. In this framing, we approach this problem from new single-cell assays that measure both chromatin accessibility and RNA transcripts. We model the relation between single-cell data and CNAs and, thanks to a sophisticated Bayesian model, we are capable of determining tumour clones from clusters of cells with the same copy numbers. Our model works when input cells are sequenced independently for both assays, or even when modern multi-omics protocols are used. By linking aneuploidy to gene expression and chromatin conformation, our new approach provides a novel way to map complex genotypes with phenotype-level information, one of the missing factors to understand the molecular basis of cancer heterogeneity.

bioinformatics↗

bioRxiv2022

Whole genome sequencing of 2,023 colorectal cancers reveals mutational landscapes, new driver genes and immune interactions

To characterise the somatic alterations in colorectal cancer (CRC), we conducted whole-genome sequencing analysis of 2,023 tumours. We provide the most detailed high-resolution map to date of somatic mutations in CRC, and demonstrate associations with clinicopathological features, in particular location in the large bowel. We refined the mutational processes and signatures acting in colorectal tumorigenesis. In analyses across the sample set or restricted to molecular subtypes, we identified 185 CRC driver genes, of which 117 were previously unreported. New drivers acted in various molecular pathways, including Wnt (CTNND1, AXIN1, TCF3), TGF-{beta}/BMP (TGFBR1) and MAP kinase (RASGRF1, RASA1, RAF1, and several MAP2K and MAP3K loci). Non-coding drivers included intronic neo-splice site alterations in APC and SMAD4. Whilst there was evidence of an excess of mutations in functionally active regions of the non-coding genome, no specific drivers were called with high confidence. Novel recurrent copy number changes included deletions of PIK3R1 and PWRN1, as well as amplification of CCND3 and NEDD9. Putative driver structural variants included BRD4 and SOX9 regulatory elements, and ACVR2A and ANKRD11 hotspot deletions. The frequencies of many driver mutations, including somatic Wnt and Ras pathway variants, showed a gradient along the colorectum. The Pks-pathogenic E. coli signature and TP53 mutations were primarily associated with rectal cancer. A set of unreported immune escape driver genes was found, primarily in hypermutated CRCs, most of which showed evidence of genetic evasion of the anti-cancer immune response. About 25% of cancers had a potentially actionable mutation for a known therapy. Thirty-three of the new driver genes were predicted to be essential, 17 possessed a druggable structure, and nine had a bioactive compound available. Our findings provide further insight into the genetics and biology of CRC, especially tumour subtypes defined by genomic instability or clinicopathological features.

genomics↗

bioRxiv2022

Clinical application of tumour in normal contamination assessment from whole genome sequencing

The unexpected contamination of normal samples with tumour cells reduces variant detection sensitivity, compromising downstream analyses in canonical tumour-normal analyses. Leveraging whole-genome sequencing data available at Genomics England, we develop a tool for normal sample contamination assessment, which we validate in silico and against minimal residual disease testing. From a systematic review of 771 patients with haematological malignancies and sarcomas, we find contamination across a range of cancer clinical indications and DNA sources, with highest prevalence in saliva samples from acute myeloid leukaemia patients, and sorted CD3+ T-cells from myeloproliferative neoplasms. Further exploration reveals 108 hotspot mutations in genes associated with haematological cancers at risk of being subtracted by standard variant calling pipelines. Our work highlights the importance of contamination assessment for accurate somatic variants detection in research and clinical settings, especially with large-scale sequencing projects being utilised to deliver accurate data from which to make clinical decisions for patient care.

bioinformatics↗

bioRxiv2022

Mutation Rate Evolution Drives Immune Escape In Mismatch Repair-Deficient Cancer

Mutation rate optimisation drives evolution and immune evasion of bacteria and lentiviral strains, including HIV. Whether evolving cancer lineages similarly adapt mutation rates to increase tumour cell fitness is unknown. Here, by mapping the clonal topography of mismatch repair-deficient (MMRd) colorectal cancer, we show that genomic MMRd mutability co-evolves with neoantigen selection to drive intratumour diversification and immune escape. Mechanistically, we find that microsatellite instability modulates subclonal DNA repair by toggling two hypermutable mononucleotide homopolymer runs in the mismatch repair genes MSH6 and MSH3 (C8 and A8, respectively) through stochastic frameshift switching. Spontaneous mutation and reversion at these evolvability switches modulates subclonal mutation rate, mutation bias, and clonal HLA diversity during MMRd cancer evolution. Combined experimental and simulation studies demonstrate that subclonal immune selection favours incremental MMR mutations. MMRd cancers thus fuel intratumour heterogeneity by adapting subclonal mutation rate and mutation bias to immune selection, revealing a conserved co-evolutionary arms race between neoantigen selection and adaptive genomic mutability. Our work reveals layers of mutational complexity and microsatellite biology in MMRd cancer evolution previously hidden in bulk analyses.

cancer biology↗

bioRxiv2021

Phenotypic plasticity and genetic control in colorectal cancer evolution

Cancer evolution is driven by natural selection acting upon phenotypic trait variation. However, the extent to which phenotypic variation within a tumour is a consequence of intra-tumour genetic heterogeneity remains undetermined. Here we show that colorectal cancer cells frequently have highly plastic phenotypic traits in vivo in patient tumours. We measured the degree to which trait variation reflects genetic ancestry by quantifying the phylogenetic signal of gene expression across 297 samples with multi-region paired whole genome and transcriptome sequencing collected from 27 primary colorectal cancers. Within-tumour phylogenetic signal for genes and pathways was detected only infrequently, suggesting that the majority of intra-tumour variation in gene expression programmes was not strongly heritable. Expression quantitative trait loci analyses (eQTL) identified a small number of putative mechanisms of genetic control of gene expression due to the cis-acting coding, non-coding and structural genetic alteration, but most gene expression variation was not explained by our genetic analysis. Leveraging matched chromatin-accessibility sequencing data, enhancer mutations with cis regulatory effects on gene expression were associated with a change in chromatin accessibility, indicating that non-coding variation can have phenotypic consequence through modulation of the 3D architecture of the genome. This study maps the evolution of transcriptional variation during cancer evolution, highlighting that intra-tumour phenotypic plasticity is pervasive in colorectal malignancies, and may play key roles in further tumour evolution, from metastasis to therapy resistance.

cancer biology↗

bioRxiv2021

Assessment of the evolutionary consequence of putative driver mutations in colorectal cancer with spatial multiomic data

Cancer genomic medicine relies on targeting driver genes. However, current catalogues of cancer drivers are mostly based on indirect measurements of mutation frequencies, positions or types, rather than their effect on clonal expansions in vivo. Moreover, non-genetic drivers are largely unknown, as are the epigenetic and transcriptomic effects of genetic drivers. Here we perform spatial computational inference on multiomic data with matched whole-genome sequencing, ATAC-seq and RNA-seq. Using 436 samples, we directly quantify the contribution, or lack thereof, of putative driver genes to subclonal expansions in vivo in 30 colorectal carcinomas (4-33 samples per patient, median=15). Although subclonal neutral evolution was widespread (13/26 cases with sufficient data), there were cases with clear evidence of subclonal selection (6/26) in which we measured epigenetic and transcriptomic differences between subclones in vivo. In 7/26 cases we could not distinguish between neutral or selective evolution with the available data. We identified expanding subclones that were not driven by known genetic alterations, and propose candidate epigenetic drivers. We identified the distinguishing patterns of genomic heterogeneity produced in fast, exponentially growing tumours (7/26) versus neoplasms growing only at the periphery (19/26), as well as identifying clonally intermixed (16/28 cases with sufficient data) versus segregated malignancies (10/28). Our model-based approach measures genetic and non-genetic subclonal selection, or lack thereof, in space and time and allows in vivo comparisons of the emergent phenotypic properties of subclones within human tumours.

cancer biology↗

bioRxiv2021

The co-evolution of the genome and epigenome in colorectal cancer

Colorectal malignancies are a leading cause of cancer death. Despite large-scale genomic efforts, DNA mutations do not fully explain malignant evolution. Here we study the co-evolution of the genome and epigenome of colorectal tumours at single-clone resolution using spatial multi-omic profiling of individual glands. We collected 1,373 samples from 30 primary cancers and 9 concomitant adenomas and generated 1,212 chromatin accessibility profiles, 527 whole-genomes and 297 whole-transcriptomes. We found positive selection for DNA mutations in chromatin modifier genes and recurrent chromatin changes in regulatory regions of cancer drivers with otherwise no mutation. Genome-wide alterations in transcription factor binding accessibility involved CTCF, downregulation of interferon, and increased accessibility for SOX and HOX, indicating developmental genes reactivation. Epigenetic aberrations were heritable, distinguishing adenomas from cancers. Mutational signature analysis showed the epigenome influencing DNA mutation accumulation. This study provides a map of (epi)genetic tumour heterogeneity, with fundamental implications for understanding colorectal cancer biology.

cancer biology↗