Zoe Steinsnyder

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

The paper describes a comprehensive genomic dataset from a tumor-normal pair with broad consent, generated using multiple sequencing technologies for benchmarking somatic variant detection.

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 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.