A protein interaction landscape of breast cancer

Description 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 Large-scale protein interaction maps provide a framework for interpreting cancer genomic data. INTRODUCTION Advances in DNA sequencing technology have enabled the widespread analysis of breast tumor genomes, creating a catalog of genetic mutations that may initiate or drive tumor progression. In addition to common mutations in well-known cancer genes, such as TP53 and PIK3CA, breast cancers harbor a variety of rare mutations with low prevalence across the patient population. Despite this heterogeneity, the majority of breast cancer patients are treated using broad chemotherapy or hormone therapies, which vary widely in effectiveness across patients. Therefore, there is an urgent need to develop targeted therapies matched to the specific molecular alterations in each patient’s tumor, with the goal of improving efficacy, reducing toxicity, and avoiding unnecessary treatment. RATIONALE A key question is how these rare alterations elicit pathologic consequences, control patient outcomes, and, ultimately, translate into personalized therapies. An answer lies in understanding how individual gene mutations converge on multigene functional modules, including the signaling pathways that orchestrate cell proliferation, apoptosis, and DNA repair. To broadly enable a pathway-based understanding of cancer, we must first generate comprehensive maps of cancer molecular networks in relevant malignant and premalignant cellular contexts. RESULTS To this end, we used affinity purification combined with mass spectrometry (AP-MS) to catalog protein-protein interactions (PPIs) for 40 proteins significantly altered in breast cancer, including multidimensional measurements across mutant and normal protein isoforms and across cancerous and noncancerous cellular contexts. Approximately 79% of the PPIs that we identified have not been previously reported, and 81% are not shared across cell lines, which illustrates a substantial rewiring of PPIs driven by different cellular contexts. Notably, interacting proteins specific to two breast cancer cell lines (MCF7 and MDA-MB-231) are more frequently mutated in breast tumors than interacting proteins recovered in nontumorigenic MCF10A cells, which implies that proteins interacting with known cancer drivers may also contribute to the onset of cancer. AP-MS analysis of PIK3CA identified previously unidentified interacting proteins (BPIFA1 and SCGB2A1) that act as potent negative regulators of the PI3K-AKT pathway in multiple breast cancer cell contexts, providing new mechanistic and therapeutic insights into the regulation of this key signaling pathway. Furthermore, UBE2N emerged as a functionally relevant interactor of BRCA1, and we show that its expression could serve as a potential biomarker of response to poly(ADP-ribose) polymerase (PARP) inhibitors and other DNA repair targeted therapies. We also found that the protein phosphatase 1 (PP1) regulatory subunit spinophilin interacts with and regulates dephosphorylation of BRCA1 and other DNA repair proteins to promote DNA double-strand break repair. CONCLUSION Our study demonstrates that systematic PPI maps provide a useful resource in contextualizing uncharacterized mutations within signaling pathways and protein complexes. Such maps effectively identify previously unidentified cancer susceptibility genes and druggable vulnerabilities in not only breast cancer but head and neck cancer as well (Swaney et al., this issue). These efforts are informing hierarchical maps of protein complexes and systems in both healthy and diseased cells (Zheng et al., this issue), which can be used to stratify patients for known anticancer therapies and drive the discovery of therapeutic targets for cancer as well as a variety of other diseases. Breast cancer interactome study. Large-scale protein interaction maps using breast cancer genes provide a framework for mechanistically interpreting cancer genomic data and can identify valuable previously unidentified therapeutic targets. OR, odds ratio; pCR, pathologic complete response. Cancers have been associated with a diverse array of genomic alterations. To help mechanistically understand such alterations in breast-invasive carcinoma, we applied affinity purification–mass spectrometry to delineate comprehensive biophysical interaction networks for 40 frequently altered breast cancer (BC) proteins, with and without relevant mutations, across three human breast cell lines. These networks identify cancer-specific protein-protein interactions (PPIs), interconnected and enriched for common and rare cancer mutations, that are substantially rewired by the introduction of key BC mutations. Our analysis identified BPIFA1 and SCGB2A1 as PIK3CA-interacting proteins, which repress PI3K-AKT signaling, and uncovered USP28 and UBE2N as functionally relevant interactors of BRCA1. We also show that the protein phosphatase 1 regulatory subunit spinophilin interacts with and regulates dephosphorylation of BRCA1 to promote DNA double-strand break repair. Thus, PPI landscapes provide a powerful framework for mechanistically interpreting disease genomic data and can identify valuable therapeutic targets.