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Leuk Res. 2019 Feb;77:42-50. doi: 10.1016/j.leukres.2018.11.010. Epub 2019 Jan 7.

Predicting response to BET inhibitors using computational modeling: A BEAT AML project study.

Author information

1
Department of Medicine/Division of Hematology Oncology, University of Florida, Gainesville, FL, United States.
2
Cellworks Research India Pvt. Ltd, Bangalore, India.
3
Cellworks Group Inc., San Jose, CA, United States.
4
Knight Cancer Institute, Division of Hematology and Medical Oncology, Oregon Health & Science University, Portland, OR, United States.
5
Knight Cancer Institute, Department of Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, OR, United States.
6
Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, United States.
7
Department of Medicine/Division of Hematology Oncology, University of Florida, Gainesville, FL, United States. Electronic address: Christopher.cogle@medicine.ufl.edu.

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.

KEYWORDS:

AML; BET inhibitor; Computational modeling; Drug response; Genetics; JQ1

PMID:
30642575
DOI:
10.1016/j.leukres.2018.11.010
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