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Updated: Jun 15, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Maximal entropy inference of oncogenicity from phosphorylation signaling

T G Graeber1, J R Heath, B J Skaggs

  • 1Department of Molecular and Medical Pharmacology, Crump Institute for Molecular Imaging, University of California, Los Angeles, CA 90095, USA.

Proceedings of the National Academy of Sciences of the United States of America
|March 13, 2010
PubMed

Insights

Point mutations in Bcr-Abl oncogenes cause drug resistance in chronic myelogenous leukemia. Information theory predicts leukemic transformation potency by analyzing phosphorylation events and identifying key signaling constraints.

Area of Science:

  • Biochemistry
  • Systems Biology
  • Oncology

Background:

  • Point mutations in the Bcr-Abl fusion oncogene's phosphorylation domain are linked to drug resistance in chronic myelogenous leukemia (CML).
  • These mutations impact kinase-mediated signaling pathways and cellular transformation.
  • Understanding these signaling alterations is crucial for developing effective CML therapies.

Purpose of the Study:

  • To apply information theory to correlate phosphoproteomic profiles with the transformation potency of Bcr-Abl mutants.
  • To develop a predictive model for leukemic transformation based on observed signaling patterns.
  • To identify critical phosphorylation events and signaling constraints that drive oncogenic phenotypes.

Main Methods:

  • Utilized an information theoretic approach to analyze phosphoproteomic data.
  • Constructed a maximal entropy distribution of site-specific phosphorylation events.
  • Quantified the relevance of signaling constraints by measuring entropy reduction.
  • Applied the developed theory to experimental phospho-proteomics data from kinase inhibitor-resistant Bcr-Abl mutants.

Main Results:

  • Identified a single dominant constraint significantly correlating phosphorylation events with oncogenic potency.
  • Demonstrated that this constraint effectively predicts trends in phenotypic output.
  • Found that other identified constraints were less relevant to the overall oncogenic potency.
  • Observed an additional constraint that may explain biological fine structure in signaling.

Conclusions:

  • A dominant signaling constraint in Bcr-Abl mutants largely predicts their leukemic transformation potency.
  • This information theoretic framework provides a powerful tool for systems biology analysis of high-throughput omics data.
  • The findings offer insights into drug resistance mechanisms and potential therapeutic targets in CML.

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