Abstract A2-37: Predictive modeling to identify novel associations between gene amplification status and cancer cell line chemosensitivity
Authors: Frost Sarah , Teber Erdahl , Byrne Jennifer A. 
Published: November 2015

Abstract
Background: Substantial research effort has been directed towards identifying gene amplification events during cancer development, with the view that these represent promising therapeutic targets or otherwise highlight pathways for therapeutic intervention. We considered whether datasets generated by recent large-scale pharmacogenomic studies (1, 2) could be mined to uncover novel associations between amplification status at particular genes of interest and cell line chemo-sensitivity or -resistance. As a case study, we examined the known amplification targets, ERBB2 and MYC, as well as a candidate amplification target, TPD52.
Experimental procedures: We accessed and curated datasets from the Cancer Cell Line Encyclopedia (1) and Cancer Therapeutics Response Portal (2). Multivariate linear regression modelling was performed to determine whether cell lines from diverse cancer types displayed differential responses to the same compound according to their gene copy number at ERBB2, MYC and TPD52. Associations between drug response and gene expression of ERBB2, MYC and TPD52 were also modelled to ascertain whether these recapitulated the gene-drug associations predicted by copy number. Both models were adjusted for cell lineage as a potential confounder by performing agglomerative hierarchical clustering of cell lines into 5 groups based on the 500 most highly varying genes. For each drug, chemo-sensitivity measures were discretised into three categories (resistant, intermediate and sensitive) using the waterfall method (1).
Results: Predictive modelling was possible for 24 drugs across 487 cell lines (1) and for an additional 323 drugs across 230 cell lines (2). Multivariate linear regression yielded 82 significant associations (p&lt;0.05) between drug response and copy number at ERBB2 (n=57), MYC (n=20) or TPD52 (n=5). This included the known sensitization of ERBB2-amplified cell lines to lapatinib, which was the compound most significantly associated with both ERBB2 copy number and gene expression. We identified 9 novel associations between chemo-sensitivity and copy number at ERBB2 (n=1), MYC (n=3) or TPD52 (n=5), however, increased copy number was more frequently associated with resistance (n=72, p&lt;0.05). Modelling also yielded 108 significant associations (p&lt;0.05) between drug response and gene expression of ERBB2 (n=28), MYC (n=42) or TPD52 (n=38). However, there was limited concordance between drugs associated with copy number and those identified through gene expression; approximately 25% concordance for ERBB2 and MYC, and zero concordance for TPD52. Grouping cell lines by global expression profiles yielded a significant contribution to drug sensitivity in ~25% drug-gene associations, confirming cell lineage as a potential predictor of drug response. Taken together, these analyses identified several lead compounds for experimental validation in a cohort of TPD52-amplified cancer cell lines, with and without co-amplification of ERBB2 and/or MYC.
Conclusions: We have applied predictive models to data from large-scale pharmacogenomic studies (1, 2) to detect both known and novel associations between gene amplification status and cell line chemo-sensitivity or -resistance. Such analyses have the potential to link existing anti-cancer drugs with specific gene amplifications, which may then be applied as new biomarkers to predict efficacy.
References:
(1) Barretina et al 2012 Nature 483, 603
(2) Basu et al 2013 Cell 154, 1151
Citation Format: Sarah Frost, Erdahl Teber, Jennifer A. Byrne. Predictive modeling to identify novel associations between gene amplification status and cancer cell line chemosensitivity. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-37.