Abstract C041: Highly sensitive protein quantification in cancer models using antibody-based proteomics

Abstract The Cancer Dependency Map (DepMap) aims to empower the research community to explore cancer vulnerabilities by sharing datasets of large-scale genetic perturbational screens and drug screens in cancer cell lines, alongside their deep genomic characterizations. In order to better characterize the models and discover potential novel therapeutic targets across all cancers, we provide a refined view of the cancer proteome. We evaluate an antibody-based proteomic assay, an increasingly popular method due to its throughput and power in detecting low abundance proteins. We profiled ∼5400 proteins using lysates from 162 cancer cell lines across 24 lineages. Overall, the protein quantification correlates well with existing RNA expression data, with a median correlation of 0.55. The protein panel covers over 1600 proteins not captured by previous mass spectrometry (MS) proteomic datasets, most of which are lowly expressed, highlighting the complementarity of the two assays. In addition, we interrogate this dataset in relation to genomic and perturbational screening datasets in DepMap, and observe agreement with existing data modalities as well as novel associations that shed light on potential discoveries of new targets. These datasets will help the research community make progress towards identifying the landscape of cancer vulnerabilities and create a roadmap for cancer therapeutics. Citation Format: Xiaomeng Zhang, Kirsty Wienand, Sowmya Iyer, Andrew Tuttle, Simon Kangoun, Konnor Yunghans, Francisca Vazquez, Katie Campbell. Highly sensitive protein quantification in cancer models using antibody-based proteomics [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr C041.