Molecular Pathways Enhance Drug Response Prediction Using Transfer Learning From Cell Lines To Tumors and Patient-Derived Xenografts

Authors

Language

English

Publication Date

9-27-2022

Journal

Scientific Reports

DOI

10.1038/s41598-022-20646-1

PMID

36168036

PMCID

PMC9515168

PubMedCentral® Posted Date

September 2022

PubMedCentral® Full Text Version

Post-print

Abstract

Computational models have been successful in predicting drug sensitivity in cancer cell line data, creating an opportunity to guide precision medicine. However, translating these models to tumors remains challenging. We propose a new transfer learning workflow that transfers drug sensitivity predicting models from large-scale cancer cell lines to both tumors and patient derived xenografts based on molecular pathways derived from genomic features. We further compute feature importance to identify pathways most important to drug response prediction. We obtained good performance on tumors (AUROC = 0.77) and patient derived xenografts from triple negative breast cancers (RMSE = 0.11). Using feature importance, we highlight the association between ER-Golgi trafficking pathway in everolimus sensitivity within breast cancer patients and the role of class II histone deacetylases and interlukine-12 in response to drugs for triple-negative breast cancer. Pathway information support transfer of drug response prediction models from cell lines to tumors and can provide biological interpretation underlying the predictions, serving as a steppingstone towards usage in clinical setting.

Keywords

Cell Line, Cell Line, Tumor, Everolimus, Heterografts, Histone Deacetylases, Humans, Machine Learning, Triple Negative Breast Neoplasms

Published Open-Access

yes

Recommended Citation

, "Molecular Pathways Enhance Drug Response Prediction Using Transfer Learning From Cell Lines To Tumors and Patient-Derived Xenografts" (2022). Faculty, Staff and Student Publications. 195.
https://digitalcommons.library.tmc.edu/uthshis_docs/195