Researchers recently have found that systematic protein-protein interaction mapping provides a useful resource for identifying the background of uncharacteristic mutations in signaling pathways and protein complexes.
Advances in DNA sequencing technology have made it possible to extensively analyze the breast tumor genome and construct a catalog of gene mutations that may initiate or drive tumor progression. In addition to the well-known common mutations in oncogenes (such as TP53 and PIK3CA), breast cancer also contains a variety of rare mutations that have a low incidence in the patient population. Despite this heterogeneity, most breast cancer patients are treated with extensive chemotherapy or hormone therapy, and the effects of these therapies vary greatly among patients. Therefore, there is an urgent need to develop targeted therapies that match the specific molecular changes of each patient's tumor to improve efficacy, reduce toxic side effects, and avoid unnecessary treatments.
A key question is how these rare changes can cause pathological consequences, control the patient's clinical results, and ultimately translate into personalized treatment methods. The answer lies in understanding how different gene mutations converge on multi-gene functional modules, including signaling pathways that coordinate cell proliferation, apoptosis, and DNA repair. In a new study, in order to broadly realize the understanding of cancer-based signaling pathways, researchers from the University of California, San Francisco and University of California, San Diego must first build in the context of related malignant tumor cells and precancerous cells a comprehensive cancer molecular network map. The relevant research results were published in Science, with the title "A protein interaction landscape of breast cancer".
To this end, these authors used affinity purification combined with mass spectrometry (AP-MS) to analyze the protein-protein interaction (PPI) among 40 proteins that are significantly altered in breast cancer. The cataloging includes multi-dimensional measurement of mutant proteins and normal protein isoforms in the context of cancer cells and non-cancerous cells. About 79% of the protein-protein interactions they found have not been reported before, and 81% of the protein-protein interactions are not shared in different cell lines, indicating that different cellular environments drive significant changes in protein-protein interactions. . It is worth noting that the mutations of the interaction proteins specific to the two breast cancer cell lines (MCF7 and MDA-MB-231) in breast tumors occur more frequently than the interaction proteins in non-tumorigenic MCF10A cells. This means that proteins that interact with known cancer drivers may also contribute to the occurrence of cancer.
AP-MS analysis of PIK3CA identified previously unidentified interacting proteins (BPIFA1 and SCGB2A1). These two proteins are powerful negative regulators of the PI3K-AKT pathway in a variety of breast cancer cell contexts. The regulation of a key signaling pathway provides new mechanisms and therapeutic insights. In addition, UBE2N is a functionally related interacting protein of BRCA1. These authors found that its expression can be used as a potential biomarker for response to PARP (poly (ADP-ribose) polymerase, poly (ADP-ribose) polymerase) inhibitors and other DNA repair-targeted therapies. They also found that protein phosphatase 1 (PP1) regulatory subunit spinophilin interacts with BRCA1 and other DNA repair proteins and regulates their dephosphorylation to promote double-strand break DNA repair.
Taken together, this new study suggests that systematic protein-protein interaction mapping provides a useful resource for identifying the background of uncharacteristic mutations in signaling pathways and protein complexes. Such maps effectively identify previously unrecognized cancer genes and weaknesses that can be targeted by drugs not only in breast cancer but also in head and neck cancer. These efforts are providing information for hierarchical maps of protein complexes and systems in healthy and diseased cells, which can be used to stratify patients for known anti-cancer therapies and drive the discovery of therapeutic targets for cancer as well as various other diseases.
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