Predicting cancer cell line dependencies fro the protein expression data of reverse-Phase protein arrays

Mei Ju May Chen, Jun Li, Gordon B. Mills, Han Liang

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations

    Abstract

    PURPOSE Predicting cancer dependencies from molecular data can help stratify patients and identify novel therapeutic targets. Recently available data on large-scale cancer cell line dependency allow a systematic assessment of the predictive power of diverse molecular features; however, the protein expression data have not been rigorously evaluated. By using the protein expression data generated by reverse-phase protein arrays, we aimed to assess their predictive power in identifying cancer dependencies and to develop a related analytic tool for community use. MATERIALS AND METHODS By using a machine learning schema, we conducted an analysis of feature importance based on cancer dependency and multiomic data from the DepMap and Cancer Cell Line Encyclopedia projects. We assessed the consistency of cancer dependency data between CRISPR/Cas9 and short hairpin RNA–mediated perturbation platforms. For a fair comparison, we focused on a set of genes with robust dependency data and four available expression-related features (copy number alteration, DNA methylation, messenger RNA expression, and protein expression) and performed the same-gene predictions of the cancer dependency using different molecular features. RESULTS For the genes surveyed, we observed that the protein expression data contained substantial predictive power for cancer dependencies, and they were the best predictive feature for the CRISPR/Cas9-based dependency data. We also developed a user-friendly protein-dependency analytic module and integrated it with The Cancer Proteome Atlas; this module allows researchers to explore and analyze our results intuitively. CONCLUSION This study provides a systematic assessment for predicting cancer dependencies of cell lines from different expression-related features of a gene. Our results suggest that protein expression data are a highly valuable information resource for understanding tumor vulnerabilities and identifying therapeutic opportunities.

    Original languageEnglish (US)
    Pages (from-to)357-366
    Number of pages10
    JournalJCO Clinical Cancer Informatics
    Volume4
    DOIs
    StatePublished - 2020

    ASJC Scopus subject areas

    • Cancer Research
    • Oncology
    • Health Informatics

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