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

Abstract

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.

A comprehensive landscape of the zinc-regulated human proteome

“…Taken together, the ZnCPT this compendium provides the basis for the mechanistic characterization of targets underlying zinc regulation and can be used as a foundation for future work elucidating the critical role of zinc over cellular functions in health and disease. 59 ). Select lung cancer cell lines express high levels of glutathione reductase.…”

“…The Gene Expression dataset was retrieved from the DepMap portal ((https://depmap.org/portal) Public 13Q2 release: Expression Public 23Q2. The cancer cell line proteome dataset 59 was obtained from the Cell Model Passports website (https://cellmodelpassports.sanger.ac.uk ; release:2022.12.14). The cancer cell line metabolome dataset 60 was obtained from the DepMap portal (https://depmap.org/portal) Public 13Q2 release: Metabolomics dataset).…”

“…In particular, we found that among lung cancers, SKMES1 lung cancer cells display by far the highest selective GSR dependency (Figure S5C). To gain a deeper understanding of the potential role of GSR in lung cancer, we assessed the expression and protein abundance 59 of GSR across 685 cancer cells lines and determined a strong correlation (Figure S5D). Importantly, we discerned an elevation in GSR expression in lung compared to other cancer cell lines, which was driven by a population of high GSR-expressing lung cancer types.…”

Mass spectrometry‐based high‐throughput proteomics and its role in biomedical studies and systems biology

“…Large-scale studies are often run on multiple instruments in parallel and across a long period of time [18,91,111], with time-dependent signal drifts and cleaning cycles [112]. Further, non-standardized protocols, reagent lots or different lab personnel can introduce additional biases [112].…”

Computational estimation of quality and clinical relevance of cancer cell lines

“…In parallel, CCL characterizations have expanded in the direction of multi‐omic data assembly to reveal regulatory mechanisms associated with cancer vulnerabilities arising from cancer driver genomic (as well as, epigenetic and transcriptomic) alterations. For instance, CCLE moved beyond the initial genomic and transcriptomic space and characterised RNA splicing, DNA methylation, microRNA expression, global histone modifications, proteomic and metabolomic quantitative profiles in more than 1,000 CCLs from multiple lineages and ethnicities (Ghandi et al , 2019 ; Li et al , 2019 ; Nusinow et al , 2020 ; preprint: Goncalves et al , 2022 ). Among those, a subset of 198 CCLs in 22 cancer types have been recently profiled by single‐cell RNA‐seq to study intra‐tumour heterogeneity (Kinker et al , 2020 ).…”