William F. Hooper

This study characterizes the molecular differences in high-grade endometrial cancer across an ancestrally diverse cohort, identifying ancestry-associated mutations and immune microenvironment differences.

Lancet2 improves somatic variant calling with faster runtime and better performance, especially for InDels, using enhanced methods and machine learning.

This paper introduces an explainable boosting machine model to identify and filter variant artifacts caused by formalin-fixed paraffin embedding in cancer genomics.

This study identifies TMEJ as the main repair pathway for APOBEC3A-induced DSBs and shows Polθ inhibition causes synthetic lethality in cancer cells with APOBEC3A activity.

This paper introduces MRD-EDGE, a machine learning platform for ultrasensitive ctDNA detection in plasma WGS, showing improved sensitivity and broader applicability for MRD monitoring.

This study examines how mutagenesis and extrachromosomal DNA contribute to urothelial cancer evolution, revealing clonal APOBEC3 mutations and chemotherapy-induced mutational bursts within ecDNA.

This study applies low-cost whole genome sequencing to detect circulating tumor DNA at high sensitivity in plasma samples, achieving error rates as low as 10^-7 and enabling cancer monitoring without matched tumor sequencing.

The paper identifies a GRCh38 reference sequence change that prevents detection of U2AF1 mutations and provides a modified reference to address this issue.