Predicting Cancer Cell Line Dependencies From the Protein Expression Data of Reverse-Phase Protein Arrays.

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 asses

SUBMITTER: Chen MM 

PROVIDER: S-EPMC7259880 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature