Related Experiment Video
Updated: Dec 13, 2025
A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors
Published on: April 29, 2022
Co-phosphorylation networks reveal subtype-specific signaling modules in breast cancer
Marzieh Ayati1, Mark R Chance2,3,4, Mehmet Koyutürk3,4,5
1Department of Computer Science, University of Texas Rio Grande Valley, Edinburg, TX 78539, USA.
Motivation:
Protein phosphorylation is a ubiquitous mechanism of post-translational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as downregulation of tumor suppressors and upregulation of oncogenes by the dysregulation of associated kinase and phosphatase networks are shown to have key roles in tumor growth and progression. Despite recent advances that enable large-scale monitoring of protein phosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers.
Results:
We develop a network-based algorithm, CoPPNet, to enable unsupervised subtyping of cancers using phosphorylation data. For this purpose, we integrate prior knowledge on evolutionary, structural and functional association of phosphosites, kinase-substrate associations and protein-protein interactions with the correlation of phosphorylation of phosphosites across different tumor samples (a.k.a co-phosphorylation) to construct a context-specific-weighted network of phosphosites. We then mine these networks to identify subnetworks with correlated phosphorylation patterns. We apply the proposed framework to two mass-spectrometry-based phosphorylation datasets for breast cancer (BC), and observe that (i) the phosphorylation pattern of the identified subnetworks are highly correlated with clinically identified subtypes, and (ii) the identified subnetworks are highly reproducible across datasets that are derived from different studies. Our results show that integration of quantitative phosphorylation data with network frameworks can provide mechanistic insights into the differences between the signaling mechanisms that drive BC subtypes. Furthermore, the reproducibility of the identified subnetworks suggests that phosphorylation can provide robust classification of disease response and markers.
Availability And Implementation:
CoPPNet is available at http://compbio.case.edu/coppnet/.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
We developed CoPPNet, a network-based algorithm for unsupervised cancer subtyping using protein phosphorylation data. This approach integrates co-phosphorylation patterns with network analysis to identify robust cancer subtypes and potential biomarkers.
Area of Science:
- Biochemistry
- Computational Biology
- Oncology
Background:
- Protein phosphorylation is a critical post-translational modification regulating cellular signaling pathways.
- Dysregulation of kinase and phosphatase networks in cancer contributes to tumor growth and progression.
- Existing computational methods have not fully leveraged large-scale phosphorylation data for cancer subtyping.
Purpose of the Study:
- To develop a novel network-based algorithm, CoPPNet, for unsupervised cancer subtyping using protein phosphorylation data.
- To integrate diverse biological network information with co-phosphorylation patterns for enhanced subtyping accuracy.
- To identify robust and reproducible cancer subtypes based on phosphorylation signaling mechanisms.
Main Methods:
- Developed CoPPNet, a network-based algorithm integrating prior biological knowledge (evolutionary, structural, functional associations, kinase-substrate, protein-protein interactions) with co-phosphorylation data.
- Constructed context-specific-weighted phosphosite networks.
- Applied network mining to identify subnetworks with correlated phosphorylation patterns.
- Validated the framework on mass-spectrometry-based breast cancer phosphorylation datasets.
Main Results:
- Identified subnetworks whose phosphorylation patterns strongly correlate with established clinical cancer subtypes.
- Demonstrated high reproducibility of identified subnetworks across independent breast cancer datasets.
- Showcased the utility of integrating quantitative phosphorylation data with network analysis for cancer research.
Conclusions:
- CoPPNet provides a robust framework for unsupervised cancer subtyping using phosphorylation data.
- Phosphorylation patterns offer mechanistic insights into distinct cancer subtypes.
- The identified subnetworks suggest potential biomarkers for disease classification and response prediction.
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