Marc J. Williams

The paper analyzes the evolutionary trajectories of high-level copy-number amplifications in cancer cell genomes to distinguish between extrachromosomal and intrachromosomal amplification mechanisms.

The paper identifies genomic deletions as the rate-limiting mutational processes in breast and ovarian cancer initiation, requiring 1-3 deletions for tumor development.

This study tracks clonal evolution of drug resistance in ovarian cancer using structural variants in cfDNA, finding polyclonal resistance with dominant high-fitness clones and genomic features linked to drug resistance.

This study shows that ongoing whole-genome doubling in ovarian cancer promotes cell diversity and immune dysregulation through chromosomal instability and quiescent states.

The paper analyzes copy number alterations in breast epithelial cells from BRCA mutation carriers and wildtype individuals, finding lineage-specific CNAs that mirror cancer genomes.

The paper identifies genomic and non-genomic contributions to drug resistance in triple negative breast cancer using single cell transcriptomics and copy number data.

The paper introduces a scalable Bayesian method for phylogenetic tree inference from single-cell whole genome sequencing data, demonstrating its application on three datasets.

This paper introduces CNAqc, a toolset for validating clonal and subclonal copy number alterations in bulk tumor sequencing by addressing tumor purity, ploidy, and heterogeneity using simulations and single-cell data.

This paper introduces a model-based subclonal reconstruction method that integrates evolutionary theory with machine learning to improve accuracy in tumor subclonal analysis.

The paper quantifies tumor immunogenicity using dN/dS ratios in the immunopeptidome and shows it predicts immunotherapy response by distinguishing adaptation from escape mechanisms.

This study links dN/dS ratios to selective coefficients in somatic evolution, revealing the distribution of fitness effects for mutations in human tissues.

This paper shows that spatial tumour structure affects mutation patterns in sequencing data and introduces a framework to account for these effects in cancer genomic analysis.

The paper responds to criticism of a method used to infer neutral cancer evolution from variant allele frequency distributions, arguing that neutral evolution explains intra-tumour heterogeneity in some cancers.