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Biology subjects

Barthorpe, S.

Publications and source records attributed to Barthorpe, S..

2 recordsLinked to original sources

Base editing screens map mutations affecting IFNγ signalling in cancer

IFN{gamma}signalling underpins host responses to infection, inflammation and anti-tumour immunity. Mutations in the IFN{gamma}signalling pathway cause immunological disorders, haematological malignancies, and resistance to immune checkpoint blockade (ICB) in cancer, however the function of most clinically observed variants remain unknown. Here, we systematically investigate the genetic determinants of IFN{gamma}response in colorectal cancer cells using CRISPR-Cas9 screens and base editing mutagenesis. Deep mutagenesis of JAK1 with cytidine and adenine base editors, combined with pathway-wide screens, reveal loss-of-function and gain-of-function mutations with clinical precedence, including causal variants in haematological malignancies and mutations detected in patients refractory to ICB. We functionally validate variants of uncertain significance in primary tumour organoids, where engineering missense mutations in JAK1 enhanced or reduced sensitivity to autologous tumour-reactive T cells. By classifying > 300 missense variants altering IFN{gamma} pathway activity, we demonstrate the utility of base editing for mutagenesis at scale, and generate a resource to inform genetic diagnosis.

cancer biology↗

Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities

The proteome provides unique insights into biology and disease beyond the genome and transcriptome. Lack of large proteomic datasets has restricted identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant workflow to quantify 8,498 proteins, these data capture evidence of cell type and post-transcriptional modifications. Integrating multi-omics, drug response and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline revealed thousands of protein-specific biomarkers of cancer vulnerabilities. Proteomic data had greater power to predict drug response than the equivalent portion of the transcriptome. Further, random downsampling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger), available at https://cellmodelpassports.sanger.ac.uk, is a comprehensive resource revealing principles of protein regulation with important implications for future clinical studies.

systems biology↗