Predicting chemosensitivity using drug perturbed gene dynamics

Joshua D Mannheimer; Ashok Prasad; Daniel L Gustafson

doi:10.1186/s12859-020-03947-y

Predicting chemosensitivity using drug perturbed gene dynamics

BMC Bioinformatics. 2021 Jan 7;22(1):15. doi: 10.1186/s12859-020-03947-y.

Authors

Joshua D Mannheimer^{1

2}, Ashok Prasad^{1

3}, Daniel L Gustafson^{4

5

6

7}

Affiliations

¹ School of Biomedical Engineering, Colorado State University, Fort Collins, CO, USA.
² Flint Animal Cancer Center, Colorado State University, Fort Collins, CO, USA.
³ Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, USA.
⁴ School of Biomedical Engineering, Colorado State University, Fort Collins, CO, USA. daniel.gustafson@colostate.edu.
⁵ Flint Animal Cancer Center, Colorado State University, Fort Collins, CO, USA. daniel.gustafson@colostate.edu.
⁶ Department of Clinical Sciences, Colorado State University, Fort Collins, CO, USA. daniel.gustafson@colostate.edu.
⁷ University of Colorado, Cancer Center Developmental Therapeutics Program, University of Colorado, Aurora, CO, USA. daniel.gustafson@colostate.edu.

Abstract

Background: One of the current directions of precision medicine is the use of computational methods to aid in the diagnosis, prognosis, and treatment of disease based on data driven approaches. For instance, in oncology, there has been a particular focus on development of algorithms and biomarkers that can be used for pre-clinical and clinical applications. In particular large-scale omics-based models to predict drug sensitivity in in vitro cancer cell line panels have been used to explore the utility and aid in the development of these models as clinical tools. Additionally, a number of web-based interfaces have been constructed for researchers to explore the potential of drug perturbed gene expression as biomarkers including the NCI Transcriptional Pharmacodynamic Workbench. In this paper we explore the influence of drug perturbed gene dynamics of the NCI Transcriptional Pharmacodynamics Workbench in computational models to predict in vitro drug sensitivity for 15 drugs on the NCI60 cell line panel.

Results: This work presents three main findings. First, our models show that gene expression profiles that capture changes in gene expression after 24 h of exposure to a high concentration of drug generates the most accurate predictive models compared to the expression profiles under different dosing conditions. Second, signatures of 100 genes are developed for different gene expression profiles; furthermore, when the gene signatures are applied across gene expression profiles model performance is substantially decreased when gene signatures developed using changes in gene expression are applied to non-drugged gene expression. Lastly, we show that the gene interaction networks developed on these signatures show different network topologies and can be used to inform selection of cancer relevant genes.

Conclusion: Our models suggest that perturbed gene signatures are predictive of drug response, but cannot be applied to predict drug response using unperturbed gene expression. Furthermore, additional drug perturbed gene expression measurements in in vitro cell lines could generate more predictive models; but, more importantly be used in conjunction with computational methods to discover important drug disease relationships.

Keywords: Cancer; Chemotherapy; Drug response; Genomics models; Machine learning; NCI60.

MeSH terms

Animals
Antineoplastic Agents* / pharmacology
Antineoplastic Agents* / therapeutic use
Cell Line, Tumor
Computational Biology / methods*
Drug Resistance, Neoplasm* / drug effects
Drug Resistance, Neoplasm* / physiology
Gene Regulatory Networks / drug effects
Humans
Neoplasms / drug therapy*

Substances

Antineoplastic Agents