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Updated: Jan 22, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease
Published on: October 24, 2025
Mapping the protein-protein and genetic interactions of cancer to guide precision medicine
Mehdi Bouhaddou1, Manon Eckhardt2, Zun Zar Chi Naing1
1Cellular and Molecular Pharmacology, University of California, San Francisco, CA, United States; Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States.
Abstract:
Massive efforts to sequence cancer genomes have compiled an impressive catalogue of cancer mutations, revealing the recurrent exploitation of a handful of 'hallmark cancer pathways'. However, unraveling how sets of mutated proteins in these and other pathways hijack pro-proliferative signaling networks and dictate therapeutic responsiveness remains challenging. Here, we show that cancer driver protein-protein interactions are enriched for additional cancer drivers, highlighting the power of physical interaction maps to explain known, as well as uncover new, disease-promoting pathway interrelationships. We hypothesize that by systematically mapping the protein-protein and genetic interactions in cancer-thereby creating Cancer Cell Maps-we will create resources against which to contextualize a patient's mutations into perturbed pathways/complexes and thereby specify a matching targeted therapeutic cocktail.
Insights
Cancer mutations exploit hallmark cancer pathways. Mapping protein interactions reveals new disease pathways and can guide personalized cancer therapies by contextualizing patient mutations.
Area of Science:
- Genomics
- Cancer Biology
- Systems Biology
Background:
- Extensive cancer genome sequencing has identified numerous mutations and highlighted key cancer pathways.
- Understanding how these mutations disrupt signaling networks and affect treatment response remains a challenge.
Purpose of the Study:
- To investigate the role of protein-protein interactions in cancer.
- To develop a resource for contextualizing cancer mutations and guiding targeted therapies.
Main Methods:
- Analysis of cancer driver protein-protein interactions.
- Creation of comprehensive Cancer Cell Maps integrating protein-protein and genetic interactions.
Main Results:
- Cancer driver protein-protein interactions are enriched for additional cancer drivers.
- Physical interaction maps can elucidate known and uncover novel disease-promoting pathway relationships.
Conclusions:
- Protein interaction maps are powerful tools for understanding cancer biology.
- Cancer Cell Maps can personalize cancer treatment by matching patient mutations to targeted therapeutic strategies.
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