Abstract 6428: Imputing a more selective therapy: Identifying Wee1 and XPO1 inhibitors for TNBC

Clinical datasets often lack robust treatment information, and the information contained is necessarily limited to a single treatment paradigm. This hinders our ability to make the most use the information present in these datasets. However, our lab previously published on work that allows for the imputation of drug response from cell line screening datasets by building models between baseline gene expression of cell lines and that cell lines respective sensitivity to a particular drug. This, for example, allowed us to associate hundreds of drugs with both mutations and CNVs in TCGA that allowed us to recapitulate existing and identify new biomarkers of response. Our aim was to use adopt our computational framework for the purpose of drug repositioning by identifying trends between clinical subtypes of cancer and identify compounds imputed to be selective for that subtype. For this study, we chose to focus on triple-negative breast cancer (TNBC) since it is a clinically unmet need. We began by building unique drug-imputation models for all 545 drugs in the Cell Therapeutics Response Portal (Broad Institute) between cell line drug response and the cell line9s RNA-Seq profile. Then we applied the models to the transcriptomic data in The Cancer Genome Atlas9s (TCGA9s) breast cancer cohort. After simple stratification and statistical testing, we selected two targets of interest based on their selective imputed scores for TNBC over the receptor-positive breast cancers: the Wee1 inhibitor AZD1775 and an XPO1 inhibitor. For both drugs we were able to validate our predictions using standard cross-validation approaches as well as investigating Sanger9s Genomics of Drug Sensitivity in Cancer dataset. Additionally, we were able to capture biologically-relevant observations for Wee1 inhibition, such as enrichment for gene sets associated with the G2-M cell cycle checkpoint and an association between p53 mutations and increased efficacy of the Wee1 inhibitor. Additionally, using xenograft tumor growth assays as well as time-to-endpoint analysis, we were able to show that Wee1 inhibition with AZD1775 was able to inhibit tumor growth and increase survival of the mice, especially in combination with paclitaxel. For XPO1 inhibition, we were able to recapitulate strong inhibition of a panel of TNBC cell lines and work is currently ongoing to determine if similar analysis of gene sets and potential biomarkers of response is biologically meaningful. Overall, we believe these results indicate that computational methods can be employed to generate hypotheses for identifying selective or targeted agents for particular disease subtypes that could be explored in other cancer settings as well as supports the use for Wee1 and XPO1 inhibition as a potential therapeutic strategy for the treatment of triple-negative breast cancers. Citation Format: Robert F. Gruener, Montserrat Tijerina, Geoffrey L. Greene, R. Stephanie Huang. Imputing a more selective therapy: Identifying Wee1 and XPO1 inhibitors for TNBC [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6428.