31P - Gene isoforms as expression-based biomarkers predictive of drug response in vitro

Date 04 May 2017
Event IMPAKT 2017
Session Welcome reception and Poster Walk
Topics Breast Cancer
Translational Research
Basic Principles in the Management and Treatment (of cancer)
Presenter Zhaleh Safikhani
Authors K.L. Thu, Z. Safikhani, P. Smironov, B. Haibe-Kains
  • Princess Margaret Cancer Centre, University Health Network, M5G 1L7 - Toronto/CA



Introduction: Advances in genome-­wide molecular profiling and high­-throughput drug screening technologies offer an unique opportunity to identify novel biomarkers predictive of response to anticancer therapies. The vast majority of predictive biomarkers for targeted therapies are based on genetic aberrations or protein expressions, as opposed to transcriptomic biomarkers. However, the recent adoption of next­-generation sequencing technologies enables accurate profiling of not only gene expression but also alternative­ and trans-­spliced transcripts in pharmacogenomic studies.

Methods: We applied multiple machine learning modeling techniques towards identification of transcriptomic biomarkers for drug response in cancer. To address the lack of reproducibility of drug sensitivity measurements across studies, we developed a framework to combine the pharmacological data from large studies, the Cancer Cell Line Encyclopedia (CCLE), the Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Therapeutic Response Portal version two (CTRPv2) Genentech Compound Screening Initiative (gCSI). Our framework consists of fitting predictive models using the cell lines RNA­-seq profiles as predictor variables, controlled for tissue type and batch indicators, and combined drug sensitivity calls from mentioned studies as dependent variables. The accuracy and significance of the fitted models have been assessed using cross-­validation.

Results: Independent pharmacogenomic datasets developed by the Gray and Neel laboratories have been exploited to validate the biomarkers that predict the response of breast cancer cell lines. We validated in vitro our most promising in silico predictions, such as NM_004207(SLC16a3­002) as a significant predictive biomarker for the MEK inhibitor AZD6244.

Conclusion: Despite initial promises, biomarker discovery from large pharmacogenomic datasets did not fully realize their potential, with only few robust biomarkers being reproduced across studies. Our study is the first to implement a meta-analysis pipeline of such valuable data, opening new avenues of research for the identification of isoform-based biomarkers predictive of response to targeted therapies in breast cancer.

Clinical trial identification