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KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data.

Sam Crowl1, Ben T Jordan1, Hamza Ahmed1

  • 1University of Virginia, Department of Biomedical Engineering and the Center for Public Health Genomics, Charlottesville, VA, 22903, USA.

Nature Communications
|July 25, 2022
PubMed
Summary

A new algorithm, KSTAR, analyzes phosphoproteomics to reveal kinase activity, aiding cancer treatment. This kinase activity profiling can improve patient stratification and combination therapy selection for better cancer outcomes.

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Area of Science:

  • Oncology
  • Biochemistry
  • Bioinformatics

Background:

  • Targeted cancer therapies, including kinase inhibitors, have improved patient outcomes.
  • Challenges remain, including drug resistance, patient stratification, and identifying optimal combination therapies.
  • Understanding tumor kinase activity profiles is crucial for advancing precision oncology.

Purpose of the Study:

  • To develop a novel algorithm, KSTAR, for inferring kinase activity from phosphoproteomic data.
  • To create a generalizable kinase activity score applicable to clinical settings.
  • To assess the utility of KSTAR in analyzing clinical cancer phosphoproteomic data.

Main Methods:

  • Development of a graph- and statistics-based algorithm named KSTAR.
  • Conversion of phosphoproteomic measurements into kinase activity scores.
  • Validation of KSTAR using diverse tissue and stimulation contexts, and across various dataset sizes.

Main Results:

  • KSTAR reliably captures kinase activity differences across tissues and stimulations.
  • The algorithm enables direct comparison of samples from independent experiments.
  • KSTAR demonstrates robustness across different dataset sizes.

Conclusions:

  • KSTAR provides a valuable tool for kinase activity inference from phosphoproteomic data.
  • The algorithm shows potential to complement existing diagnostic methods, such as HER2 status determination in breast cancer.
  • KSTAR may facilitate improved patient stratification and personalized combination therapy selection in oncology.