Interpretation of cancer mutations using a multiscale map of protein systems

Abstract

Mapping protein interactions driving cancer Cancer is a genetic disease, and much cancer research is focused on identifying carcinogenic mutations and determining how they relate to disease progression. Three papers demonstrate how mutations are processed through networks of protein interactions to promote cancer (see the Perspective by Cheng and Jackson). Swaney et al . focus on head and neck cancer and identify cancer-enriched interactions, demonstrating how point mutant–dependent interactions of PIK3CA, a kinase frequently mutated in human cancers, are predictive of drug response. Kim et al . focus on breast cancer and identify two proteins functionally connected to the tumor-suppressor gene BRCA1 and two proteins that regulate PIK3CA. Zheng et al . developed a statistical model that identifies protein networks that are under mutation pressure across different cancer types, including a complex bringing together PIK3CA with actomyosin proteins. These papers provide a resource that will be helpful in interpreting cancer genomic data. —VV

A protein interaction landscape of breast cancer

“…Finally, although the approach we described in this study was applied to BC, it is equally powerful against other cancers, including head and neck squamous cell carcinoma (HNSCC) ( 36 ). Efforts such as this will ultimately lead to hierarchical maps of protein complexes and systems in both healthy and diseased cells ( 123 ), which, based on the mutational landscape, can be predictive for use of known treatments and can also be used to uncover previously unidentified therapeutic strategies across a multitude of disease areas.…”

“…In this study, we displayed stringent protein interactions with IAS > 0.3 when the IAS network was used in figures. More details are described in the companion paper ( 123 ).…”

LLPSWise - fast and accurate prediction of LLPS constituents

“…Possible explanations are that some LLPS systems are seeded before induction or LLPS interactions might be formed during the AP-MS process. In future studies, it would be interesting to combine computational methods, such as the method presented in this study, and PPIs that are measured from specific conditions, e.g., specific cancer cell lines instead of from workhorse cell lines [29][30][31] , to see whether PPI network changes could be related to LLPS system changes.…”

“…LLPS systems and PPI networks have a long history of coevolution, i.e., if a particular protein concomitantly requires both its PPI functionality and LLPS participation to execute its biological function, the two modes of actions need to be compatible with each other and are spontaneously evolved. When an experimental approach is applied for measuring the PPI network from different conditions, the PPI landscape drifts dramatically [29][30][31] of which transcriptional activity and expression level of proteins are critical contributing factors. The LLPS mechanism is also powerful in regulating proteins' spatiotemporal distributions, therefore exerting constraints on the PPI landscape.…”

From systems to structure — using genetic data to model protein structures

“…Such networks have proved highly informative, enabling functional annotations of proteins and conveying information on the architectures of entire biological systems 1 , 2 . Protein–protein interaction (PPI) networks describe which proteins interact 3 – 5 (Fig. 1a ).…”