Targeting protein–protein interactions as an anticancer strategy
Open accessAbstract
The emergence and convergence of cancer genomics, targeted therapies, and network oncology have significantly expanded the landscape of protein-protein interaction (PPI) networks in cancer for therapeutic discovery. Extensive biological and clinical investigations have led to the identification of protein interaction hubs and nodes that are critical for the acquisition and maintenance of characteristics of cancer essential for cell transformation. Such cancer-enabling PPIs have become promising therapeutic targets. With technological advances in PPI modulator discovery and validation of PPI-targeting agents in clinical settings, targeting of PPI interfaces as an anticancer strategy has become a reality. Future research directed at genomics-based PPI target discovery, PPI interface characterization, PPI-focused chemical library design, and patient-genomic subpopulation-driven clinical studies is expected to accelerate the development of the next generation of PPI-based anticancer agents for personalized precision medicine. Here we briefly review prominent PPIs that mediate cancer-acquired properties, highlight recognized challenges and promising clinical results in targeting PPIs, and outline emerging opportunities.
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PPIs are involved in all phases of oncogenesis, from cell proliferation, cell survival, and inflammation to invasion and metastasis.
“PPIs are involved in all phases of oncogenesis, from cell proliferation, cell survival, and inflammation to invasion and metastasis (Fig. 1) [13, 14].”
From The design and development of covalent protein-protein interaction inhibitors for cancer treatment (2020) · cites this paper as [13]
Abnormal regulation of PPIs contributes to the majority of cancers.
“The abnormal regulation of PPIs contributes to the majority of cancers.”
From The design and development of covalent protein-protein interaction inhibitors for cancer treatment (2020) · cites this paper as [13]
With improvements in both chemical and biological technologies, the targeting of PPIs for cancer treatment is becoming an achievable reality.
“With improvements in both chemical and biological technologies, the targeting of PPIs for cancer treatment is becoming an achievable reality.”
From The design and development of covalent protein-protein interaction inhibitors for cancer treatment (2020) · cites this paper as [13]
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- Authors
- Ivanov, A. A., Khuri, F. R., & Fu, H.
- Affiliations
- Emory University (US)
- Corresponding author
- Fu, Haian
- Venue
- Trends in Pharmacological Sciences
- Publisher
- Elsevier BV
- Published
- 2013-07
- Volume
- 34
- Issue
- 7
- Pages
- 393-400
- ISSN
- 0165-6147 (ISSN-L)
- PMC
- PMC3773978
- OpenAlex
- W2155589321
- Cited by
- 401
- References
- 91
- Citations by year
- 2026: 28 · 2025: 34 · 2024: 25 · 2023: 40 · 2022: 25 · 2021: 39 · 2020: 33 · 2019: 29 · 2018: 22 · 2017: 37 · 2016: 24 · 2015: 36 · 2014: 25 · 2013: 3
- FWCI
- 2.77
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Trends in Pharmacological Sciences (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (U01CA217875, U01CA168449, P01CA116676)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- protein-protein interaction, anticancer drug discovery, chemical library design, precision medicine, cancer genomics, targeted cancer therapy
- MeSH terms
- Animals, Antineoplastic Agents, Humans, Models, Molecular, Neoplasm Proteins, Neoplasms, Structure-Activity Relationship, Drug Design, Molecular Structure, Signal Transduction, Protein Interaction Mapping, Protein Interaction Domains and Motifs, High-Throughput Screening Assays, Molecular Targeted Therapy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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