Predicting response to BET inhibitors using computational modeling: A BEAT AML project study. (February 2019) - Drusbosky, Leylah M.; Vidva, Robinson; Gera, Saji; Lakshminarayana, Anjanasree V.; Shyamasundar, Vijayashree P.; Agrawal, Ashish Kumar; Talawdekar, Anay; Abbasi, Taher; Vali, Shireen; Tognon, Cristina E.; Kurtz, Stephen E.; Tyner, Jeffrey W.; et al.
Highlights: Predict AML treatment response based on tumor genomics using computational modeling. Predict response to bromodomain (BRD) and extra-terminal (BET) inhibitor JQ1. Patient cohort was 100 patients randomly selected from the BEAT AML project. Predicted disease inhibition scores matched ex vivo IC50 with 86% accuracy. Genomic predictors of response to BET inhibitor JQ1 were identified. Abstract: Despite advances in understanding the molecular pathogenesis of acute myeloid leukaemia (AML), overall survival rates remain low. The ability to predict treatment response based on individual cancer genomics using computational modeling will aid in the development of novel therapeutics and personalize care. Here, we used a combination of genomics, computational biology modeling (CBM), ex vivo chemosensitivity assay, and clinical data from 100 randomly selected patients in the Beat AML project to characterize AML sensitivity to a bromodomain (BRD) and extra-terminal (BET) inhibitor. Computational biology modeling was used to generate patient-specific protein network maps of activated and inactivated protein pathways translated from each genomic profile. Digital drug simulations of a BET inhibitor (JQ1) were conducted by quantitatively measuring drug effect using a composite AML disease inhibition score. 93% of predicted disease inhibition scores matched the associated ex vivo IC50 value. Sensitivity and specificity of CBM predictions were 97.67%, and 64.29%, respectively. Genomic predictors of response were identified. Patient samples harbouring chromosomal aberrations del(7q) or -7, +8, or del(5q) and somatic mutations causing ERK pathway dysregulation, responded to JQ1 in both in silico and ex vivo assays. This study shows how a combination of genomics, computational modeling and chemosensitivity testing can identify network signatures associating with treatment response and can inform priority populations for future clinical trials of BET inhibitors.