Anti-estrogen therapy is the standard of adjuvant care for luminal breast cancer. However, more than 30% of the cases either do not respond to the therapy or develop resistance and progress to metastatic disease. In this work we use a network-based approach to identify the regulatory motifs driving resistance to estrogen inhibitors and to predict small molecule compounds that can abrogate the activity of such regulatory modules, thus reverting the resistance phenotype. This is similar to what we have accomplished in glucocorticoid resistance in T-ALL (Piovan et.al (2013) Cancer Cell 24(6):766). To accomplish this goal, we established genome-wide expression signatures characteristic of the resistant phenotype (ER-GES) both from patients' data and cell lines. Then, we used the VIPER algorithm to interrogate a breast carcinoma-specific transcriptional network (BRCAnet) assembled from 844 TCGA tumor samples with these signatures, to infer master regulators of resistance. Finally we leveraged the connectivity map (CMAP) dataset, containing expression profiles from MCF7 cells exposed to 1,300 small molecule compounds, to identify the effectors of each profiled compounds. By comparing drug effectors with master regulators of ER-inhibitors chemoresistance, we identified small molecule compounds predicted to revert resistant cells to a sensitive state.

We reasoned that a compound able to reverse such resistance should induce synergistic activity when combined with anti-estrogen agents, like tamoxifen. To validate our prediction in an unbiased way, we assessed the synergistic nature of the interaction between tamoxifen and 100 compounds (sensitizer set) selected to have the most orthogonal mechanism of action, using 4 x 4 dilutions assays, with cell viability as readout. Synergistic interactors were identified by combination index (CI < 0.9) and Excess over BLISS (EOB > 10%). Interestingly, compounds predicted to rescue sensitivity to ER inhibitors in cell lines were highly enriched in those that were experimentally validated to be synergistic with tamoxifen. Taken together, these results suggest that network-based analysis is effective in capturing drivers of chemoresistance to estrogen deprivation, as well as the specific compounds that can rescue sensitivity by inhibiting them.

Citation Format: Mariano Javier Alvarez, Yao Shen, Brygida Bisikirska, Ronald Realubit, Sergey Pampou, Charles Karan, Andrea Califano. Network-based inference of resistance mechanisms to estrogen inhibitors in ER+ breast cancer. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Drug Sensitivity and Resistance: Improving Cancer Therapy; Jun 18-21, 2014; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(4 Suppl): Abstract nr A24.

©2015 American Association for Cancer Research.
2015