Biology subjects

Buscaroli, E.

Publications and source records attributed to Buscaroli, E..

3 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

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

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↗