John V. Pearson

This study identifies genomic mechanisms enabling BRCA1 methylation loss and promoter bypass in ovarian cancer, leading to PARP inhibitor resistance.

The paper develops a multi-omic random forest model to predict immunotherapy response in melanoma using SHAP for feature threshold identification.

This paper integrates tumor homologous recombination deficiency status with germline BRCA1 and BRCA2 variant classification to improve pathogenicity prediction.

This study performs a multi-ancestral GWAS of DSM-defined nicotine dependence, finding genetic correlations with other substance use disorders and health traits, and identifying a strong general addiction liability factor.

This paper benchmarks Bayesian deep learning models for predicting cancer of unknown primary, showing improved uncertainty generalisation and accuracy under distributional shifts.

The paper develops endometrial cancer PDX models that capture tumor heterogeneity and predict response to talazoparib.

The paper analyzes whole-genome sequencing data from cancer samples to identify genomic footprints of telomere maintenance mechanisms, finding specific telomere variant repeat patterns associated with ATRX/DAXX mutations.

The paper examines how somatic structural variations affect TAD boundaries and gene regulation in human cancers using whole genome sequencing data.

This study analyzes chromothripsis in 2,658 cancers using whole-genome sequencing, finding it prevalent across multiple cancer types with complex structural alterations.

The paper identifies five distinct subtypes of colorectal cancer using DNA methylation profiling and links them to specific mutations and gene body methylation patterns.

This paper introduces qCNV and sCNA-seq tools to profile somatic copy number alterations in cell-free DNA without a reference panel, showing effectiveness in detecting clinically relevant mutations from plasma samples.