Justin R. Pritchard

This study identifies genetic dependencies of cell death following EGFR inhibition by analyzing functional genomics data.

The paper characterizes Variants of Uncertain Drug Resistance (VUDRs) using quantitative measurements from clinical exposures to imatinib, identifying resistance levels and ancestry-specific variants.

This paper develops tumor-targeting gene drive vectors that eliminate drug resistance in solid tumors through systemic delivery, showing durable tumor burden reduction with minimal toxicity.

The paper validates that synonymous mutations in the human genome rarely affect fitness, occurring 165-fold less frequently than non-synonymous mutations.

This study compares deep mutational scanning and CRISPR base editing for variant function annotation, finding strong correlation between base editor data and gold standard DMS results.

The paper identifies molecular determinants for phosphotyrosine recognition in tyrosine kinases and shows their roles in tumor promotion and suppression.

The paper derives scaling laws to predict mutant emergence in spatially expanding populations and shows gene amplifications promote mutant evolution in spatial settings but not in well-mixed populations.

The paper introduces MutSigCVsyn, an algorithm to identify synonymous cancer drivers by accounting for mutational heterogeneity, and identifies 30 novel synonymous drivers in cancer whole-genome sequences.

The paper develops a data integrator to uncover synthetic lethality, sex disparity, and immunotherapy targets in cancer by combining public and custom datasets.

The paper identifies a common genetic architecture that explains context-specific gene essentiality across cell lines using CRISPR screens and machine learning.

The paper examines how NMD pathway evolution affects immune cytolytic activity and patient survival in tumors using TCGA datasets and machine learning.