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Published on: August 29, 2015
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.
Abstract:
Kinase inhibitors as targeted therapies have played an important role in improving cancer outcomes. However, there are still considerable challenges, such as resistance, non-response, patient stratification, polypharmacology, and identifying combination therapy where understanding a tumor kinase activity profile could be transformative. Here, we develop a graph- and statistics-based algorithm, called KSTAR, to convert phosphoproteomic measurements of cells and tissues into a kinase activity score that is generalizable and useful for clinical pipelines, requiring no quantification of the phosphorylation sites. In this work, we demonstrate that KSTAR reliably captures expected kinase activity differences across different tissues and stimulation contexts, allows for the direct comparison of samples from independent experiments, and is robust across a wide range of dataset sizes. Finally, we apply KSTAR to clinical breast cancer phosphoproteomic data and find that there is potential for kinase activity inference from KSTAR to complement the current clinical diagnosis of HER2 status in breast cancer patients.
Insights
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.
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.
