Christopher K. Rushton

LCR-modules provides a flexible, automated suite of workflows for cancer genome analysis, supporting diverse sequencing types and enabling reproducible, scalable analysis of over 10,800 samples.

The paper reanalyzes Reddy et al.'s 2017 DLBCL study, revealing discrepancies in mutation detection and outcome associations that render their findings non-replicable.

This paper introduces a self-training method for gene expression-based cancer classification that works across different data types and normalization methods.

This study identifies genetic drivers of mantle cell lymphoma through exome and genome sequencing, revealing novel mutations in genes involved in RNA splicing and regulation.

This study identifies somatic coding and regulatory variants in DLBCL, revealing novel mechanisms of oncogene deregulation and potential therapeutic targets.