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The study of music provides many examples of the superposition of waves and the constructive and destructive interference that occurs. Very few examples of music being performed consist of a single source playing a single frequency for an extended period of time. A single frequency of sound for an extended period might be monotonous to the point of irritation, similar to the unwanted drone of an aircraft engine or a loud fan. Music is pleasant and exciting due to mixing the changing frequencies...
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Predicting response to BET inhibitors using computational modeling: A BEAT AML project study.

Leylah M Drusbosky1, Robinson Vidva2, Saji Gera2

  • 1Department of Medicine/Division of Hematology Oncology, University of Florida, Gainesville, FL, United States.

Leukemia Research
|January 16, 2019
PubMed
Summary

Computational modeling accurately predicts acute myeloid leukemia (AML) patient response to BET inhibitors. Genomics identified specific mutations and chromosomal aberrations associated with treatment sensitivity, aiding personalized therapy development.

Keywords:
AMLBET inhibitorComputational modelingDrug responseGeneticsJQ1

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Area of Science:

  • Oncology
  • Computational Biology
  • Genomics

Background:

  • Acute myeloid leukemia (AML) survival rates remain low despite advances in understanding its molecular pathogenesis.
  • Personalized medicine approaches, including computational modeling of cancer genomics, are crucial for predicting treatment response and developing novel therapeutics for AML.

Purpose of the Study:

  • To characterize AML sensitivity to a bromodomain (BRD) and extra-terminal (BET) inhibitor using a combination of genomics, computational biology modeling (CBM), ex vivo chemosensitivity assays, and clinical data.
  • To identify genomic predictors of response to BET inhibitors in AML patients.

Main Methods:

  • Genomics, computational biology modeling (CBM), ex vivo chemosensitivity assays, and clinical data from 100 AML patients were utilized.
  • CBM generated patient-specific protein network maps from genomic profiles to simulate digital drug effects of a BET inhibitor (JQ1).
  • Drug effect was quantified using an AML disease inhibition score, and predictions were compared with ex vivo IC50 values.

Main Results:

  • CBM accurately predicted ex vivo drug sensitivity, with 93% of predicted disease inhibition scores matching the associated ex vivo IC50 values.
  • The sensitivity and specificity of CBM predictions were 97.67% and 64.29%, respectively.
  • Genomic predictors of response, including specific chromosomal aberrations (del(7q) or -7, +8, or del(5q)) and ERK pathway mutations, were identified, correlating with JQ1 sensitivity in silico and ex vivo.

Conclusions:

  • The integration of genomics, computational modeling, and chemosensitivity testing effectively identifies network signatures associated with AML treatment response.
  • This approach can guide the selection of patient populations for future clinical trials investigating BET inhibitors in AML.
  • Computational modeling holds significant promise for personalizing AML treatment strategies and accelerating the development of targeted therapies.