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Updated: Sep 4, 2025
Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
Published on: February 27, 2015
Pan-cancer proteomic map of 949 human cell lines
Emanuel Gonçalves1, Rebecca C Poulos2, Zhaoxiang Cai2
1Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK; Instituto Superior Técnico (IST), Universidade de Lisboa, 1049-001 Lisboa, Portugal; INESC-ID, 1000-029 Lisboa, Portugal.
Abstract:
The proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted the identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a 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 reveals thousands of protein biomarkers of cancer vulnerabilities that are not significant at the transcript level. The power of the proteome to predict drug response is very similar to that of the transcriptome. Further, random downsampling to only 1,500 proteins has limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger) is a comprehensive resource available at https://cellmodelpassports.sanger.ac.uk.
Insights
This study analyzed proteomes from 949 cancer cell lines, identifying thousands of protein biomarkers for cancer vulnerabilities. Proteomic data offers unique insights into disease biology and drug response prediction.
Area of Science:
- Proteomics
- Cancer Biology
- Biomarker Discovery
Background:
- Genomic and transcriptomic data have limitations in revealing disease biology.
- Large-scale proteomic datasets are scarce, hindering cancer biomarker identification.
Purpose of the Study:
- To analyze proteomes from a large cohort of cancer cell lines to identify novel cancer biomarkers.
- To explore the utility of proteomic data in predicting drug response and cancer vulnerabilities.
- To create a comprehensive proteomic resource for cancer research.
Main Methods:
- Mass spectrometry-based proteomic analysis of 949 cancer cell lines across 28 tissue types.
- Quantification of 8,498 proteins, capturing cell-type specificity and post-transcriptional modifications.
- Integration of multi-omics, drug response, and CRISPR-Cas9 screens with a deep learning pipeline.
Main Results:
- Identification of thousands of protein biomarkers associated with cancer vulnerabilities, many not significant at the transcript level.
- Proteome demonstrated similar predictive power for drug response as the transcriptome.
- Downsampling proteomic data to 1,500 proteins minimally impacted predictive power, indicating robust protein network co-regulation.
Conclusions:
- Proteomic analysis provides crucial insights into cancer biology and identifies novel biomarkers.
- The ProCan-DepMapSanger resource offers a valuable tool for cancer research and biomarker discovery.
- Protein networks are highly interconnected, allowing for robust predictions even with reduced data sets.
