Researchers at the Cancer Cell Map Initiative have explored hundreds of previously unknown protein-protein interactions suspected to drive cancer. Their findings have been published in a trio of papers in Science.
The Cancer Cell Map Initiative (CCMI) was founded in 2015 at the University of California, San Diego and San Francisco campuses, with the aim to generate, assemble and analyse cancer networks. Their research is mainly focussed on head and neck squamous cell carcinoma, and breast cancer.
Recently, scientists at the CCMI have mapped hundreds of previously unknown protein-protein interactions driving cancer. Studying the molecular basis of cancer at the protein level, rather than at the more traditional gene level, has provided a more expansive view of tumorigenesis and allowed for far more detailed research to be conducted.
Nevan Krogan, Director of the Quantitative Biosciences Institute at the University of California, San Francisco, explained: “This is an entirely new way to do cancer research. We realised we needed another way to look at cancer that takes it a step beyond DNA.”
The trio of CCMI papers
The CCMI scientists used affinity purification-mass spectrometry (AP-MS) to catalogue protein-protein interactions across mutant and normal protein forms, and across cancerous and non-cancerous cell lines. AP-MS is a technique that examines specific interactions between proteins or protein complexes on a global scale. In this case, the protein complexes targeted were formed by around 60 genes commonly involved in either head and neck squamous cell carcinoma or breast cancer.
- The first paper: Network analysis revealed 771 protein-protein interactions from cancerous and non-cancerous states, 84% of which had not been previously reported in public databases. Moreover, the data revealed a previously unidentified association of the fibroblast growth factor receptor tyrosine kinase 3 with a guanine-nucleotide exchange factor called Daple. This interaction was found to result in the promotion of cancer cell migration. Additionally, analysis of PIK3CA mutations showed that interaction specificity could determine the in vivo response to HER3 inhibitors, suggesting that certain alterations can be used to predict drug response.
- The second paper: AP-MS was used to catalogue the interactions between 40 proteins significantly altered in breast cancer. Around 79% of the protein-protein interactions discovered had not been previously reported. For example, BPIFA1 and SCGB2A1 were found to act as potent negative regulators of the PI3K-AKT pathway, which is dysregulated in almost all human cancers. Additionally, UBE2N emerged as a functionally relevant interactor of BRCA1, a tumour-suppressor gene. Moreover, it was found that 81% of the protein interactions identified were not shared across cell lines, illustrating their variability based on different cellular contexts.
- The third paper: Proteomic mass spectrometry, integration of the protein interactions from the first and second CCMI papers, and data from 127 previous studies were used to build a structured map of protein assemblies found in cancer cells. This revealed 2,338 robust systems of interacting proteins. A statistical model, called HiSig, was then developed and used to pinpoint which protein-protein interactions were strongly selected for in which cancer types. HiSig analysis yielded a map of 395 mutated protein systems, called NeST. Although NeST showed many cancer hallmarks, most of the protein assemblies had not been previously described or associated with cancer. Additionally, 548 genes were identified as potential biomarkers for cancer therapies.
Protein-protein interactions in treating cancer
It is clear that the trio of CCMI papers have produced a hugely rich resource of new protein interactions with cancer relevance. The protein-protein interaction networks mapped by the CCMI team have revealed molecular mechanisms that drive tumour pathways and could be used to facilitate cancer diagnosis and prognosis in the future. Further research should now be carried out on the relationships between protein complexes to aid the identification of novel druggable targets and to reveal new classes of potential biomarkers.
Trey Ideker, Professor at the University of California, San Diego, explained: “The problem is that we’ve only found a few genes that we can work with in this way to help guide prescription of an FDA-approved drug. Our studies provide a new definition of biomarkers based not on single genes or proteins but on the large, multiprotein complexes.”
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