Decoding the functional impact of the cancer genome through protein–protein interactions

Luck, K. et al. A reference map of the human binary protein interactome. Nature 580, 402–408 (2020).

Article  PubMed  PubMed Central  CAS  Google Scholar 

Li, Z. et al. The OncoPPi network of cancer-focused protein-protein interactions to inform biological insights and therapeutic strategies. Nat. Commun. 8, 14356 (2017). This cancer genomics-based study describes the generation of a cancer-associated PPI network, with the proposed oncoPPI concept, which utilizes supporting examples to illustrate the use of the oncoPPI data to inform potential PPI targets and network-implicated tumour vulnerabilities for therapeutic interrogation.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Li, Y. et al. Pan-cancer proteogenomics connects oncogenic drivers to functional states. Cell 186, 3921–3944.e25 (2023).

Article  PubMed  CAS  Google Scholar 

Clackson, T. & Wells, J. A. A hot spot of binding energy in a hormone–receptor interface. Science 267, 383–386 (1995). This foundational paper introduces the concept of hotspots in PPIs, by identifying specific residues that disproportionately contribute to binding energy at the PPI interface.

Article  PubMed  CAS  Google Scholar 

Wells, J. A. & McClendon, C. L. Reaching for high-hanging fruit in drug discovery at protein–protein interfaces. Nature 450, 1001–1009 (2007).

Article  PubMed  CAS  Google Scholar 

Ding, L. et al. Perspective on oncogenic processes at the end of the beginning of cancer genomics. Cell 173, 305–320.e10 (2018).

Article  PubMed  PubMed Central  CAS  Google Scholar 

Heath, A. P. et al. The NCI genomic data commons. Nat. Genet. 53, 257–262 (2021).

Article  PubMed  CAS  Google Scholar 

Hahn, W. C. et al. An expanded universe of cancer targets. Cell 184, 1142–1155 (2021).

Article  PubMed  PubMed Central  CAS  Google Scholar 

Martinez-Jimenez, F. et al. A compendium of mutational cancer driver genes. Nat. Rev. Cancer 20, 555–572 (2020).

Article  PubMed  CAS  Google Scholar 

Chang, M. T. et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat. Biotechnol. 34, 155–163 (2016). The authors develop a computational algorithm to identify driver mutations, rather than driver genes, and systematically evaluate individual recurrent mutations with lineage-specific variations and mutation-focused functions, revealing that different mutant amino acids within the same hotspot can be functionally different.

Article  PubMed  CAS  Google Scholar 

Ihle, N. T. et al. Effect of KRAS oncogene substitutions on protein behavior: implications for signaling and clinical outcome. J. Natl Cancer Inst. 104, 228–239 (2012).

Article  PubMed  PubMed Central  CAS  Google Scholar 

Blandino, G., Levine, A. J. & Oren, M. Mutant p53 gain of function: differential effects of different p53 mutants on resistance of cultured cells to chemotherapy. Oncogene 18, 477–485 (1999).

Article  PubMed  CAS  Google Scholar 

Ng, P. K. et al. Systematic functional annotation of somatic mutations in cancer. Cancer Cell 33, 450–462.e10 (2018).

Article  PubMed  PubMed Central  CAS  Google Scholar 

Dang, L. et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462, 739–744 (2009).

Article  PubMed  PubMed Central  CAS  Google Scholar 

Mo, X. et al. Systematic discovery of mutation-directed neo-protein–protein interactions in cancer. Cell 185, 1974–1985 e1912 (2022). This pan-cancer analysis unveils widespread driver-mutation-directed PPIs, termed neoPPIs and hypoPPIs, informing variant-mediated rewiring of oncogenic programmes and therapeutic approaches for precision medicine.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Swaney, D. L. et al. A protein network map of head and neck cancer reveals PIK3CA mutant drug sensitivity. Science 374, eabf2911 (2021).

Article  PubMed  PubMed Central  CAS  Google Scholar 

Kim, M. et al. A protein interaction landscape of breast cancer. Science 374, eabf3066 (2021).

Article  PubMed  PubMed Central  CAS  Google Scholar 

Fragoza, R. et al. Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations. Nat. Commun. 10, 4141 (2019).

Article  PubMed  PubMed Central  CAS  Google Scholar 

Woodsmith, J. et al. Protein interaction perturbation profiling at amino-acid resolution. Nat. Methods 14, 1213–1221 (2017).

Article  PubMed  CAS  Google Scholar 

Sahni, N. et al. Widespread macromolecular interaction perturbations in human genetic disorders. Cell 161, 647–660 (2015). This study characterizes disease mutations, revealing widespread mutation-induced perturbations of protein interactions across diverse diseases.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Zheng, F. et al. Interpretation of cancer mutations using a multiscale map of protein systems. Science 374, eabf3067 (2021).

Article  PubMed  PubMed Central  CAS  Google Scholar 

Kamburov, A. et al. Comprehensive assessment of cancer missense mutation clustering in protein structures. Proc. Natl Acad. Sci. USA 112, E5486–E5495 (2015). This study systematically examines how cancer missense mutations cluster within protein structures, providing insights into mutation hotspots that may disrupt protein stability or interactions.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Cheng, F. et al. Comprehensive characterization of protein–protein interactions perturbed by disease mutations. Nat. Genet. 53, 342–353 (2021). This comprehensive study characterizes how disease mutations affect PPIs across the interactome, providing insights into mutation-induced network perturbations and their implications for understanding disease mechanisms and potential therapeutic targets.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Sanchez-Vega, F. et al. Oncogenic signaling pathways in The Cancer Cenome Atlas. Cell 173, 321–337.e10 (2018).

Article  PubMed  PubMed Central  CAS  Google Scholar 

Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).

Article  PubMed  PubMed Central  CAS  Google Scholar 

Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas pan-cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

Article  PubMed Central  Google Scholar 

International Cancer Genome Consortium et al. International network of cancer genome projects. Nature 464, 993–998 (2010).

Article  Google Scholar 

Ostroverkhova, D., Przytycka, T. M. & Panchenko, A. R. Cancer driver mutations: predictions and reality. Trends Mol. Med. 29, 554–566 (2023).

Article  PubMed  CAS  Google Scholar 

Chang, M. T. et al. Accelerating discovery of functional mutant alleles in cancer. Cancer Discov. 8, 174–183 (2018).

Article  PubMed  CAS  Google Scholar 

Juul, R. I., Nielsen, M. M., Juul, M., Feuerbach, L. & Pedersen, J. S. The landscape and driver potential of site-specific hotspots across cancer genomes. NPJ Genom. Med. 6, 33 (2021).

Article  PubMed  PubMed Central  CAS 

Comments (0)

No login
gif