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Publication

Loss of BCAA Catabolism during Carcinogenesis Enhances mTORC1 Activity and Promotes Tumor Development and Progression.

Cell metabolism | 2019

Tumors display profound changes in cellular metabolism, yet how these changes aid the development and growth of tumors is not fully understood. Here we use a multi-omic approach to examine liver carcinogenesis and regeneration, and find that progressive loss of branched-chain amino acid (BCAA) catabolism promotes tumor development and growth. In human hepatocellular carcinomas and animal models of liver cancer, suppression of BCAA catabolic enzyme expression led to BCAA accumulation in tumors, though this was not observed in regenerating liver tissues. The degree of enzyme suppression strongly correlated with tumor aggressiveness, and was an independent predictor of clinical outcome. Moreover, modulating BCAA accumulation regulated cancer cell proliferation in vitro, and tumor burden and overall survival in vivo. Dietary BCAA intake in humans also correlated with cancer mortality risk. In summary, loss of BCAA catabolism in tumors confers functional advantages, which could be exploited by therapeutic interventions in certain cancers.

Pubmed ID: 30661928 RIS Download

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Associated grants

Agency: NCI NIH HHS, United States
Id: P30 CA014236
Agency: NIA NIH HHS, United States
Id: R01 AG045351
Agency: NIDDK NIH HHS, United States
Id: R01 DK115568
Agency: NCI NIH HHS, United States
Id: T32 CA059365

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The Cancer Genome Atlas (tool)

RRID:SCR_003193

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).

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HPA (tool)

RRID:SCR_006710

Public database with millions of high-resolution images showing the spatial distribution of proteins in different normal human tissues and cancer types, as well as different human cell lines. The data is released together with application-specific validation performed for each antibody, including immunohistochemisty, Western blot analysis and, for a large fraction, a protein array assay and immunofluorescent based confocal microscopy. The database has been developed in a gene-centric manner with the inclusion of all human genes predicted from genome efforts. Search functionalities allow for complex queries regarding protein expression profiles, protein classes and chromosome location. Antibodies included have been analyzed using a standardized protocol in a single attempt without further efforts to optimize the procedure and therefore it cannot be excluded that certain observed binding properties are due to technical rather than biological reasons and that further optimization could result in a different outcome. Submission of antibodies: The Swedish Human Proteome Atlas (HPA) program, invites submission of antibodies from both academic and commercial sources to be included in the human protein atlas. All antibodies will be validated by the HPA-program by a standard procedure and antibodies that are accepted will be use in the tissue- profiling program to generate high-resolution immunohistochemistry images representing a wide spectrum of normal tissues and cancer types.

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