Hub genes in a pan-cancer co-expression network show potential for predicting drug responses

Abstract

Background: The topological analysis of networks extracted from different types of “omics” data is a useful strategy for characterizing biologically meaningful properties of the complex systems underlying these networks. In particular, the biological significance of highly connected genes in diverse molecular networks has been previously determined using data from several model organisms and phenotypes. Despite such insights, the predictive potential of candidate hubs in gene co-expression networks in the specific context of cancer-related drug experiments remains to be deeply investigated. The examination of such associations may offer opportunities for the accurate prediction of anticancer drug responses. Methods: Here, we address this problem by: a) analyzing a co-expression network obtained from thousands of cancer cell lines, b) detecting significant network hubs, and c) assessing their capacity to predict drug sensitivity using data from thousands of drug experiments. We investigated the prediction capability of those genes using a multiple linear regression model, independent datasets, comparisons with other models and our own in vitro experiments. Results: These analyses led to the identification of 47 hub genes, which are implicated in a diverse range of cancer-relevant processes and pathways. Overall, encouraging agreements between predicted and observed drug sensitivities were observed in public datasets, as well as in our in vitro validations for four glioblastoma cell lines and four drugs. To facilitate further research, we share our hub-based drug sensitivity prediction model as an online tool. Conclusions: Our research shows that co-expression network hubs are biologically interesting and exhibit potential for predicting drug responses in vitro. These findings motivate further investigations about the relevance and application of our unbiased discovery approach in pre-clinical, translationally-oriented research.

Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models

“…Aside from the original COXEN method and randomly selected genes, we also compared the enhanced COXEN method to a set of hub genes derived from co-expression network analysis that are predictive of drug response [ 24 ]. Azuaje et al performed a pan-cancer co-expression network analysis based on more than 1000 CCLs, and identified 47 genes as representing “hubs” in the network [ 24 ]. A total of 45 out of the 47 genes were included in the gene expression data used in our analysis.…”

“… Enhanced COXEN and Hub Genes refer to the enhanced COXEN method and the hub genes identified from co-expression network analysis [ 24 ], respectively. In the R 2 columns, the number before the parenthesis is the average R 2 across cross-validation trails and the number in the parenthesis is the standard deviation.…”

Predicting Tumor Response to Drugs based on Gene-Expression Biomarkers of Sensitivity Learned from Cancer Cell Lines

“…For example, cancer cell lines with mutations in the BRAF genes are significantly more sensitive to PLX4730, a BRAF-inhibitor, than those with wild-type BRAF (Yang et al, 2013). Additional computational methods for predicting sensitivity to drugs using gene expression data from cancer cell lines have been developed, for example (Azuaje et al, 2018;Nguyen et al, 2016;Suphavilai et al, 2018;Wei et al, 2019). Several publications have recent efforts in this area (Azuaje, 2017;Guan et al, 2019;Guvenc Paltun et al, 2019;Reinhold et al, 2015).…”

Predicting Tumor Response to Drugs Based on Gene-expression Biomarkers of Sensitivity Learned From Cancer Cell Lines

“…For example, cancer cell lines with mutations in the BRAF genes are significantly more sensitive to PLX4730, a BRAF-inhibitor, than those with wild-type BRAF [3]. Additional computational methods for predicting sensitivity to drugs using gene expression data from cancer cell lines have been developed, for example [11][12][13][14]. Several publications have recent efforts in this area [15][16][17][18].…”