Biology subjects

Antonello, A.

Publications and source records attributed to Antonello, A..

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

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

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↗