Abstract 5139: An atlas of perturbed functional proteomics profiles of cancer cell lines

Abstract

Abstract In recent years, tremendous efforts have been made to systematically characterize the molecular profiles of tumor tissues from individuals with cancer, laying a critical foundation for elucidating the molecular basis of tumorigenesis and developing biomarker-based diagnostic, prognostic and therapeutic approaches. In particular, cancer genomic data at the DNA or RNA level are being accumulated at an unprecedented speed. However, it remains to be a big challenge in cancer research to systematically understand causality and mechanisms underlying the behaviors of cancer cells. To address it, perturbation experiments are a very powerful approach in which the cells are first modulated by perturbagens and the downstream consequences are then monitored. Recently, large-scale compendia of the phenotypic and cellular effects of perturbed cancer cell lines have been established. However, similar resources for the proteomic responses of perturbed cancer cell lines have yet to be established. Reverse-phase protein arrays (RPPAs) is a powerful targeted functional proteomics approach to studying cancer mechanisms, biomarkers and therapies. This quantitative antibody-based assay is able to assess a large number of protein markers in many samples in a cost-effective, sensitive manner. More recently, we have applied this technology to quantify the protein expression levels of large patient cohorts and cancer cell lines (>8,000 patient samples of 32 cancer types from The Cancer Genome Atlas, >650 cell lines across 19 lineages). Here, using RPPAs, we have generated and compiled the perturbed functional proteomic profiles of >12,000 cancer cell line samples in response to >150 drug compounds and other perturbagens using reverse-phase protein arrays. We show that integrating protein response signals substantially increases the predictive power for drug sensitivity and gains insights into the mechanisms of drug resistance. We build a comprehensive map of “protein-drug” connectivity and develop an open-access, user-friendly data portal for community use. Our study provides a valuable proteomic resource for a broad range of quantitative modeling and biomedical applications. Citation Format: Wei Zhao, Jun Li, Mei-Ju Chen, Rehan Akbani, Yiling Lu, Gordon Mills, Han Liang. An atlas of perturbed functional proteomics profiles of cancer cell lines [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5139.