Nonlinear mixed-effects models for modeling in vitro drug response data to determine problematic cancer cell lines

Brooke L. Fridley,Farnoosh Abbas-Aghababazadeh,Pengcheng Lu

doi:10.1038/s41598-019-50936-0

Brooke L. Fridley, Farnoosh Abbas-Aghababazadeh + Show 1 more

Open Access

https://doi.org/10.1038/s41598-019-50936-0

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Journal: Scientific Reports	Publication Date: Oct 8, 2019
Citations: 8	License type: open-access

Abstract

Cancer cell lines (CCLs) have been widely used to study of cancer. Recent studies have called into question the reliability of data collected on CCLs. Hence, we set out to determine CCLs that tend to be overly sensitive or resistant to a majority of drugs utilizing a nonlinear mixed-effects (NLME) modeling framework. Using drug response data collected in the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC), we determined the optimal functional form for each drug. Then, a NLME model was fit to the drug response data, with the estimated random effects used to determine sensitive or resistant CCLs. Out of the roughly 500 CCLs studies from the CCLE, we found 17 cell lines to be overly sensitive or resistant to the studied drugs. In the GDSC, we found 15 out of the 990 CCLs to be excessively sensitive or resistant. These results can inform researchers in the selection of CCLs to include in drug studies. Additionally, this study illustrates the need for assessing the dose-response functional form and the use of NLME models to achieve more stable estimates of drug response parameters.

Highlights

Over the past decades, the cancer cell lines (CCLs) have been widely used to study the biological processes in cancer, as well as in vitro drug screening for discovering and assessing the effectiveness of anticancer therapeutics[1]
Two independent large-scale studies, the Cancer Cell Line Encyclopedia (CCLE)[4] and the Genomics of Drug Sensitivity in Cancer (GDSC)[1,5], were completed in which drug response information was collected on a number of therapeutic agents in addition to extensive molecular information
Nonlinear models with functional forms of 4P and 3P logistic and a linear model (LM) were fit to the 497 Cancer cell lines (CCLs) in the CCLE, with best functional form based on Akaike’s information criterion (AIC) for each CCL and drug combination presented in Supplemental Tables 1 and 2

Summary

Introduction

The cancer cell lines (CCLs) have been widely used to study the biological processes in cancer, as well as in vitro drug screening for discovering and assessing the effectiveness of anticancer therapeutics[1]. The nonlinear mixed-effects (NLME) model has become an important approach to improve the accuracy of EC50 estimates (or similar parameters), through the borrowing of information across all CCLs15,16 Such models allow one to account for the repeated measures aspect in the data (i.e., correlation between measurements taken on the same cell lines) through the inclusion of random effects in model, along with fixed effects (i.e., dose of the drug) as included in traditional nonlinear regression models. Using the CCL specific estimated random effects from the NLME model, a set of cell lines were determined to be consistently sensitive or resistant to a large number of drugs. This study illustrated the need for assessing model functional form for drug-response data and the ability to model all cell lines simultaneously using a NLME model to provide more stable estimates of drug response parameters

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