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Although mutational inactivation of E-cadherin (CDH1) is the main driver of invasive lobular breast cancer (ILC), approximately 10-15% of all ILCs retain membrane-localized E-cadherin despite the presence of an apparent non-cohesive and invasive lobular growth pattern. Given that ILC is dependent on constitutive actomyosin contraction for tumor development and progression, we used a combination of cell systems and in vivo experiments to investigate the consequences of α-catenin (CTNNA1) loss in the regulation of anchorage independence of non-invasive breast carcinoma. We found that inactivating somatic CTNNA1 mutations in human breast cancer correlated with lobular and mixed ducto-lobular phenotypes. Further, inducible loss of α-catenin in mouse and human E-cadherin-expressing breast cancer cells led to atypical localization of E-cadherin, a rounded cell morphology, and anoikis resistance. Pharmacological inhibition experiments subsequently revealed that, similar to E-cadherin-mutant ILC, anoikis resistance induced by α-catenin loss was dependent on Rho/Rock-dependent actomyosin contractility. Finally, using a transplantation-based conditional mouse model, we demonstrate that inducible inactivation of α-catenin instigates acquisition of lobular features and invasive behavior. We therefore suggest that α-catenin represents a bona fide tumor suppressor for the development of lobular-type breast cancer and as such provides an alternative event to E-cadherin inactivation, adherens junction (AJ) dysfunction, and subsequent constitutive actomyosin contraction. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
Pubmed ID: 29774524
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Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).
View all literature mentionsOpen source software package for statistical programming language R to create plots based on grammar of graphics. Used for data visualization to break up graphs into semantic components such as scales and layers.
View all literature mentionsAn Antibody supplier; Dako was purchased by Agilent in 2012 and several years later the websites began to reflect the Dako products as part of the Agilent catalog.
View all literature mentionsCell line MCF-7 is a Cancer cell line with a species of origin Homo sapiens (Human)
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