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Open Access 01-12-2021 | Breast Cancer | Research article

Predicting breast cancer drug response using a multiple-layer cell line drug response network model

Authors: Shujun Huang, Pingzhao Hu, Ted M. Lakowski

Published in: BMC Cancer | Issue 1/2021

Abstract

Background

Predicting patient drug response based on a patient’s molecular profile is one of the key goals of precision medicine in breast cancer (BC). Multiple drug response prediction models have been developed to address this problem. However, most of them were developed to make sensitivity predictions for multiple single drugs within cell lines from various cancer types instead of a single cancer type, do not take into account drug properties, and have not been validated in cancer patient-derived data. Among the multi-omics data, gene expression profiles have been shown to be the most informative data for drug response prediction. However, these models were often developed with individual genes. Therefore, this study aimed to develop a drug response prediction model for BC using multiple data types from both cell lines and drugs.

Methods

We first collected the baseline gene expression profiles of 49 BC cell lines along with IC₅₀ values for 220 drugs tested in these cell lines from Genomics of Drug Sensitivity in Cancer (GDSC). Using these data, we developed a multiple-layer cell line-drug response network (ML-CDN2) by integrating a one-layer cell line similarity network based on the pathway activity profiles and a three-layer drug similarity network based on the drug structures, targets, and pan-cancer IC₅₀ profiles. We further used ML-CDN2 to predict the drug response for new BC cell lines or patient-derived samples.

Results

ML-CDN2 demonstrated a good predictive performance, with the Pearson correlation coefficient between the observed and predicted IC₅₀ values for all GDSC cell line-drug pairs of 0.873. Also, ML-CDN2 showed a good performance when used to predict drug response in new BC cell lines from the Cancer Cell Line Encyclopedia (CCLE), with a Pearson correlation coefficient of 0.718. Moreover, we found that the cell line-derived ML-CDN2 model could be applied to predict drug response in the BC patient-derived samples from The Cancer Genome Atlas (TCGA).

Conclusions

The ML-CDN2 model was built to predict BC drug response using comprehensive information from both cell lines and drugs. Compared with existing methods, it has the potential to predict the drug response for BC patient-derived samples.

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Title: Predicting breast cancer drug response using a multiple-layer cell line drug response network model
Authors: Shujun Huang
Pingzhao Hu
Ted M. Lakowski
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Breast Cancer
Breast Cancer
Published in: BMC Cancer / Issue 1/2021
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-021-08359-6

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Abstract

Background

Methods

Results

Conclusions

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