Benjamin Werner

This paper introduces ecEvol, a method to infer ecDNA emergence time and other parameters from bulk whole-genome sequencing data, finding that ecDNA typically emerges early and is associated with poorer survival.

The paper compares somatic mutation accumulation in two coral species, finding distinct patterns suggesting different cell lineage development mechanisms.

This study models clonal competition in ageing haematopoiesis, showing that fitness landscapes evolve with age and predict high-risk clones for patient stratification.

This study shows ecDNA copy number plasticity drives tumour evolution by generating functional heterogeneity and promoting drug resistance.

This study investigates the spatial-temporal evolution of extrachromosomal DNA (ecDNA) in glioblastoma, showing that EGFR ecDNA drives tumor evolution and identifies a potential therapeutic window for early intervention.

This study uses stochastic models to analyze how genetic intra-tumour heterogeneity changes during and after treatment, finding distinct patterns in mutation frequencies and resistant clone sizes.

This study shows that ecDNA mis-segregation leads to micronuclei formation, reduced oncogene transcription, and improved patient survival in neuroblastoma.

The paper quantifies somatic genetic variation in corals and estimates their age using mutation rates, finding no positive selection and a neutral framework for studying genetic diversity.

This study develops computational models to show that mixed ecDNA subpopulations in tumour cells require variant switching and a positive fitness of ecDNA+ cells, not direct co-selection.

The paper quantifies joint fixation events of neutral mutations in constant populations, introducing the concept of mutation waves and deriving the Wave Frequency Distribution.

This paper develops a somatic genetic clock to estimate age in clonal species by analyzing fixed somatic genetic variation, demonstrating its application in Zostera marina and revealing genet ages up to 1,403 years.

This study analyzes mutational landscapes in normal breast tissue across age and pregnancy to understand cancer risk differences.

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.

This study measures single-cell division rates and survival in human cancers using multi-region sequencing data, revealing per-cell mutation and survival rates across different cancer types and healthy tissues.

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.

The paper examines whether tissue structure suppresses or amplifies selection to minimize cancer risk, finding that the distribution of mutant fitness effects determines the optimal tissue architecture.

The paper develops a mathematical model to estimate cancer stem cell fractions in tumors using tumor size measurements and linear regression, without requiring detailed microscopic data.