Abstract 4029: The impact of multiple drug resistance mechanisms on microarray predictive gene signature performance.
Authors: Charlotte K Y Ng, Britta Weigelt, Roger A'Hern, Jorge S. Reis-Filho
Journal: Cancer Research (2013)

Abstract Background: Resistance to a given therapeutic agent or combinatorial treatment can be caused by multiple distinct mechanisms. Microarray gene expression signatures have been derived to predict sensitivity to therapeutic agents, however these have not resulted in clinically useful tests. We hypothesise that the existence of multiple mechanisms of resistance to a given therapeutic agent or combinatorial treatment in a given patient cohort may contribute to the limited discriminatory power of predictive gene signatures. Design: A breast cancer microarray dataset comprising 1,550 samples was obtained through the merging of publicly available gene expression datasets generated on a single microarray platform. Cases were randomly allocated to display either a sensitive or resistant phenotype to a hypothetical agent/ combinatorial therapy. In the resistant group, up to five different mechanisms of drug resistance resulting in either distinct or overlapping gene expression changes were generated. We then investigated the impact of the existence of &amp;gt;1 mechanism of resistance resulting in distinct or overlapping gene expression changes on the performance of the predictive signatures generated. Results: Increasing the number of mechanisms of resistance to a given therapeutic agent or combinatorial therapy that result in different gene expression changes has a negative impact on the performance of the predictor. Even in an ideal scenario, where changes for each gene are sufficiently strong and informative, a significant decrease in the sensitivity, specificity, and predictive values of the classifier was observed when resistance is driven by more than one mechanism with distinct gene expression patterns. When the gene expression changes are weaker and closer to those seen in the clinical setting, the ability to identify useful predictive signatures is severely compromised. The proportion of cases where resistance is driven by different mechanisms and differences in the prevalence of each resistance mechanism differs between training and test cohorts were shown to have a significant impact on the classifier performance. Overlaps in the gene expression changes driven by different mechanisms at least partially mitigate the impact of the existence of multiple resistance mechanisms. Conclusions: The existence of different resistance mechanisms for a given therapeutic agent or combinatorial therapy in a given dataset, even in an otherwise ‘best case scenario’, limits the ability to derive clinically useful microarray-based signatures predictive of therapy response. Citation Format: Charlotte K Y Ng, Britta Weigelt, Roger A'Hern, Jorge S. Reis-Filho. The impact of multiple drug resistance mechanisms on microarray predictive gene signature performance. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4029. doi:10.1158/1538-7445.AM2013-4029