Dimitri G. Livitz

The paper develops DNA- and RNA-based models combining immune and tumor features to predict outcomes of checkpoint inhibition in melanoma.

The paper introduces a framework for assessing the quality of whole genome cancer sequences using a star rating system based on five QC measures, providing a tool for researchers to evaluate and exclude low-quality data.

This study analyzes RNA-seq data from 6,700 normal tissues to identify somatic mutations and their association with age and tissue proliferation, finding tissue-specific cancer mutation hotspots.

PhylogicNDT statistically reconstructs cancer driver timing, subclonal phylogenies, and population dynamics from single- or multi-timepoint sequencing, revealing subtype-specific trajectories and therapy resistance evolution across 498 lung adenocarcinomas.

Four levers for cheaper cloud genomics on FireCloud: size virtual disks dynamically, tune VM provisioning from monitoring data, buy preemptible machines, and parallelise tasks at the right width.

The paper describes the PCAWG project's large-scale analysis of cancer whole genomes across multiple computing environments, producing validated consensus variants for downstream research.