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基因突变可能引起多种癌症，因此许多科学家都通过基因测序寻找癌症发生原因，探索哪些基因异常可能导致癌症。不过，基因只是细胞合成蛋白质的编码模板，真正对人体内部生理和病理过程发挥作用的还是这些蛋白质。不同的蛋白质在人体内部有时可相互结合形成复合体，调节各种功能。基因突变引起疾病的原因，很大程度由于影响了这些蛋白质的功能。深入了解蛋白质与蛋白质的相互作用，能够让我们充分理解癌症如何发生。例如，两个蛋白质可能形成关键的复合体，修复受损的基因，而一旦编码这两个蛋白质的基因出现变异，这两个蛋白质的结构就可能出现变化。如果这些变化让蛋白质无法相互作用，就无法形成蛋白复合体修复基因。久而久之，细胞内受损基因就越来越多，最终从量变到质变，引起癌症。因此，根据已知致癌突变，将目光聚焦于蛋白质与蛋白质的相互作用，将有助于我们重新理解癌症的发生、精准选择现有治疗的获益人群、开发新型靶向治疗手段。 2021年10月1日，全球自然科学三大期刊之一美国科学促进会《科学》正刊发表旧金山加利福尼亚大学、大卫·格莱斯顿研究所、癌细胞图谱倡议、圣迭戈加利福尼亚大学、罗格斯大学新泽西癌症研究所、俄勒冈医科大学的研究报告，首次超越基因水平，系统分析并绘制了乳腺癌的蛋白质相互作用图谱，为乳腺癌带来了全新认知。 该研究首先从人类三阴性乳腺癌细胞MDA-MB-231和人类激素受体阳性乳腺癌细胞MCF7筛选出近60种最常出现变化的基因，分析其蛋白质产物形成的复合体。随后，与人类正常乳腺细胞MCF10A的蛋白质复合体进行对照，从而确定癌细胞的蛋白复合体究竟发生了什么变化。 结果，该研究发现乳腺癌有40种蛋白质发生显著变化，其中大约79%的蛋白质相互作用为首次发现。 例如，常见的PIK3CA突变，首次在乳腺癌细胞内发现两种蛋白质BPIFA1和 SCGB2A1相互作用充当了PI3K → AKT信号传导通路的有效抑制因子，为该关键信号传导通路的调节提供了新的机制和治疗靶点，这也表明即使同一种癌症相关蛋白质，在不同细胞里也有不同的功能，可激活不同的信号通路。 此外，该研究还发现乳腺癌易感基因BRCA1编码的蛋白质可与泛素结合酶UBE2N结合，蛋白质磷酸酶PP1调节亚基亲棘蛋白可与BRCA1蛋白质以及其他DNA修复蛋白质相互作用并调节去磷酸化，可促进DNA双链断裂修复，这些该蛋白质的表达水平可以作为多腺苷二磷酸核糖聚合酶PARP抑制剂以及其他DNA修复靶向治疗药物的潜在生物标志物，预测这类药物对某一乳腺癌患者有效还是耐药。 最后，该研究将这些蛋白质相互作用数据与公开数据库的数据进行整合，开发了为整个癌症研究领域工作者提供服务的全新工具，以便其他研究者进行分析和验证，该工具有望让我们更好地认识致癌基因及其蛋白质的相互作用，并找到潜在的治疗靶点。 对此，斯坦福大学医学院发表评论：蛋白质相互作用分析可以发现以前未知的致癌驱动因素。 相关链接 Science. 2021 Oct 1;374(6563):eabf3066. A protein interaction landscape of breast cancer. Kim M, Park J, Bouhaddou M, Kim K, Rojc A, Modak M, Soucheray M, McGregor MJ, O'Leary P, Wolf D, Stevenson E, Foo TK, Mitchell D, Herrington KA, Munoz DP, Tutuncuoglu B, Chen KH, Zheng F, Kreisberg JF, Diolaiti ME, Gordan JD, Coppé JP, Swaney DL, Xia B, van 't Veer L, Ashworth A, Ideker T, Krogan NJ. University of California, San Francisco, CA, USA; The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA; University of California, San Diego, CA, USA; Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA. 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. 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. PMID: 34591612 DOI: 10.1126/science.abf3066 Science. 2021 Oct 1;374(6563):eabf3067. Interpretation of cancer mutations using a multiscale map of protein systems. Zheng F, Kelly MR, Ramms DJ, Heintschel ML, Tao K, Tutuncuoglu B, Lee JJ, Ono K, Foussard H, Chen M, Herrington KA, Silva E, Liu SN, Chen J, Churas C, Wilson N, Kratz A, Pillich RT, Patel DN, Park J, Kuenzi B, Yu MK, Licon K, Pratt D, Kreisberg JF, Kim M, Swaney DL, Nan X, Fraley SI, Gutkind JS, Krogan NJ, Ideker T. University of California San Diego, La Jolla, CA, USA; Cancer Cell Map Initiative, La Jolla, CA, USA; Oregon Health and Science University, Portland, OR, USA; University of California, San Francisco, CA, USA; J. David Gladstone Institutes, San Francisco, CA, USA. 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. INTRODUCTION: Tumor genome sequencing has revealed that, beyond a few commonly mutated genes, most mutations that affect cancer genomes are rare. To interpret these rare events, a powerful approach has been to organize mutations by their effects on commonly dysregulated cellular systems. Understanding the cancer genome in this way requires surmounting two challenges: (i) How do we comprehensively map cancer cell systems? (ii) How do we identify which systems are under mutational selection? RATIONALE: To address these questions, we used proteomic mass spectrometry and data integration to build a structured map of protein assemblies found in human cancer cells. We then developed a statistical model of mutation, pinpointing which assemblies are under strong mutational selection and in which cancer types. The goal was to interpret the many rare gene mutations that affect tumor genomes by their convergence on higher-order entities. RESULTS: We amassed a large compendium of cancer protein interactions, combining the screens in breast cancer (Kim et al., this issue) and head-and-neck cancer (Swaney et al., this issue) with multi-omic evidence from 127 previous studies. Lines of evidence were integrated quantitatively to yield a continuous metric of association for each protein pair (integrated association stringency, or IAS). This network of protein associations exhibited clear multiscale and modular structure, revealing 2338 robust assemblies of interacting proteins (hereafter “protein systems”) across different stringencies. Systems were organized hierarchically, with small high-stringency systems (e.g., specific complexes) combining in larger ones (e.g., processes and organelles) as stringency was relaxed. We next developed a statistical model, HiSig, to identify a parsimonious set of systems that best explains the gene mutation frequencies observed in tumors. HiSig analysis of 13 tumor types yielded a map of 395 mutated protein systems we call NeST (Nested Systems in Tumors, http:///nest/). NeST comprised numerous small complexes, most mutated within specific tumor types, organized within larger systems relevant to most cancers. Although NeST recapitulated cancer hallmarks, the majority of systems had not been previously described or had not been associated with cancer mutation. Nonetheless, many were recurrently mutated in independent cohorts, supporting their significance. Notable systems included a PIK3CA-actomyosin complex that points to a new mode of phosphatidylinositol 3-kinase regulation, as well as recurrent mutations in collagen complexes that we found to disrupt the extracellular matrix, thereby promoting proliferation. Finally, we identified NeST systems that serve as biomarkers of cancer outcomes, leading to 548 genes for potential use in clinical sequencing panels. CONCLUSION: In their classic description of the “Hallmarks of Cancer,” Hanahan and Weinberg predicted that the “complexities of cancer ... will become understandable in terms of a small number of underlying principles.” Around the same time, Alberts provided his seminal perspective of the cell as a collection of “protein assemblies [interacting] in an elaborate network.” By organizing disparate tumor mutations into underlying principles captured by a multiscale map of protein assemblies, this work represents a synthesis of these visions. The strategies developed here may generalize to other diseases that are affected by rare genetic alterations. Mapping cancer protein systems: Protein interaction datasets were integrated to identify protein communities (“systems”) at multiple scales of analysis (left). Each system was tested for cancer mutational selection as a system versus its substituent proteins, revealing a hierarchy of protein systems under selection in cancer (center). Discoveries from this hierarchy (right) were validated with clinical data and functional experiments. A major goal of cancer research is to understand how mutations distributed across diverse genes affect common cellular systems, including multiprotein complexes and assemblies. Two challenges—how to comprehensively map such systems and how to identify which are under mutational selection—have hindered this understanding. Accordingly, we created a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. We then developed a unified statistical model that pinpoints 395 specific systems under mutational selection across 13 cancer types. This map, called NeST (Nested Systems in Tumors), incorporates canonical processes and notable discoveries, including a PIK3CA-actomyosin complex that inhibits phosphatidylinositol 3-kinase signaling and recurrent mutations in collagen complexes that promote tumor proliferation. These systems can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. This work shows how disparate tumor mutations converge on protein assemblies at different scales. PMID: 34591613 DOI: 10.1126/science.abf3067 Science. 2021 Oct 1;374(6563):38-39. Identifying cancer drivers: Analysis of protein interaction networks can identify previously unknown oncogenic drivers. Cheng R, Jackson PK. Stanford University School of Medicine, Stanford, CA, USA. A comprehensive protein-protein interaction (PPI) network is a critical tool for understanding how pathways are organized in normal cells and altered in cancer. For many cancers, there is an extensive catalog of genetic mutations, but a consolidated map that organizes these mutations into pathways that drive tumor growth is missing. On pages 49 and 50 of this issue, Swaney et al. (1) and Kim et al. (2), respectively, use affinity purification-mass spectrometry (AP-MS) to examine PPI networks in head and neck squamous cell carcinoma (HNSCC) and breast cancer (BC) and find many previously unknown interactions in cancer cells. On page 51 of this issue, Zheng et al. (3) combine the new PPI data with existing public data to generate a structured map of protein pathways to help validate these PPIs, suggesting a framework for future analyses that could drive our understanding of oncogenic transformation and identify therapeutic targets. PMID: 34591644 DOI: 10.1126/science.abl9080
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