Abstract A35: Discovery of driver-type therapeutic targets to reverse glucocorticoid resistance in T-ALL

Abstract

Abstract Glucocorticoid (GC) resistance is a major driver of therapeutic failure in T-cell acute lymphoblastic leukemia (T-ALL). Here we developed a systems biology framework, Network-based Bayesian Inference of Disease Drivers (NetBID2), using transcriptomic data, to predict signaling or regulatory factors driving GC-resistance in T-ALL. With this algorithm, we identified the AKT1 kinase as a signaling modulator of GC-resistance. Mechanistically, AKT1 directly phosphorylates the glucocorticoid receptor (GCR) NR3C1 protein at position S134 and blocks glucocorticoid-induced NR3C1 translocation to the nucleus. Consistently, pharmacologic inhibition of AKT1 increases the response of T-ALL cells to glucocorticoid therapy and effectively reverses glucocorticoid resistance in vitro and in vivo. These results warrant the clinical testing of ATK1 inhibitors and glucocorticoids in combination for the treatment of T-ALL. Genome-wide RNA interference screening emerges as a powerful tool for loss-of-function studies and searching for new therapeutic targets of diseases. We integrated this technology with our computational framework and identified 16 master regulators of GC-resistance in T-ALL, when repressed, sensitizing resistant cells. Out of 16 candidates, 13 were validated in vitro, and 10 outperformed positive controls. Moreover, 75% of only predicted drivers demonstrated significant effects on changing GC-sensitivity. Network analysis of validated drivers indicated that they might work cooperatively to induce resistance, suggesting potential combinatorial therapies to reverse GC-resistance in T-ALL. Citation Format: Jiyang Yu, Ericah Piovan, Maria Sol Flaherty, Jose Silva, Adolfo Ferrando, Andrea Califano. Discovery of driver-type therapeutic targets to reverse glucocorticoid resistance in T-ALL [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr A35.