Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein–protein interactions (ChiPPI) that uses the domain–domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF β), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer.

This work is funded by the Project Retos BFU2015-71241-R of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC), co-funded by European Regional Development Fund (ERDF) and by the Project PT13/0001/0030, Instituto de Salud Carlos III (ISCIII), Strategic Action in Health, co-funded by European Regional Development Fund (ERDF). The work of MFM is supported by the Israel Cancer Association (ICA) fund, the work of ST is supported by the VaTaT Postdoctoral Fellowship for excellent students [22351, 20027, 26912]. AV is supported by the Joint BSC-CRG-IRB Programme in Computational Biology. Funding for open access charge: ICA [e-cancer-diagnosis].

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