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

Leylah M. Drusbosky, Robinson Vidva, Saji Gera, Anjanasree V. Lakshminarayana, Vijayashree P. Shyamasundar, Ashish Kumar Agrawal, Anay Talawdekar, Taher Abbasi, Shireen Vali, Cristina E. Tognon, Stephen E. Kurtz, Jeffrey Tyner, Shannon McWeeney, Brian Druker, Christopher R. Cogle

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)42-50
Number of pages9
JournalLeukemia Research
Volume77
DOIs
StatePublished - Feb 1 2019

Fingerprint

Acute Myeloid Leukemia
Genomics
Computational Biology
MAP Kinase Signaling System
Chromosome Aberrations
Pharmaceutical Preparations
Computer Simulation
Inhibitory Concentration 50
Proteins
Therapeutics
Survival Rate
Clinical Trials
Sensitivity and Specificity
Mutation
Population
Neoplasms

Keywords

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

ASJC Scopus subject areas

  • Hematology
  • Oncology
  • Cancer Research

Cite this

Drusbosky, L. M., Vidva, R., Gera, S., Lakshminarayana, A. V., Shyamasundar, V. P., Agrawal, A. K., ... Cogle, C. R. (2019). Predicting response to BET inhibitors using computational modeling: A BEAT AML project study. Leukemia Research, 77, 42-50. https://doi.org/10.1016/j.leukres.2018.11.010

Predicting response to BET inhibitors using computational modeling : A BEAT AML project study. / 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; McWeeney, Shannon; Druker, Brian; Cogle, Christopher R.

In: Leukemia Research, Vol. 77, 01.02.2019, p. 42-50.

Research output: Contribution to journalArticle

Drusbosky, LM, Vidva, R, Gera, S, Lakshminarayana, AV, Shyamasundar, VP, Agrawal, AK, Talawdekar, A, Abbasi, T, Vali, S, Tognon, CE, Kurtz, SE, Tyner, J, McWeeney, S, Druker, B & Cogle, CR 2019, 'Predicting response to BET inhibitors using computational modeling: A BEAT AML project study', Leukemia Research, vol. 77, pp. 42-50. https://doi.org/10.1016/j.leukres.2018.11.010
Drusbosky LM, Vidva R, Gera S, Lakshminarayana AV, Shyamasundar VP, Agrawal AK et al. Predicting response to BET inhibitors using computational modeling: A BEAT AML project study. Leukemia Research. 2019 Feb 1;77:42-50. https://doi.org/10.1016/j.leukres.2018.11.010
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 ; McWeeney, Shannon ; Druker, Brian ; Cogle, Christopher R. / Predicting response to BET inhibitors using computational modeling : A BEAT AML project study. In: Leukemia Research. 2019 ; Vol. 77. pp. 42-50.
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