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During the past decade, the age-adjusted mortality rate for endometrial cancer (EC) increased 1.9% annually with TP53 mutant (TP53-mu) EC disproportionally represented in advanced disease and deaths. Therefore, we aimed to assess pivotal molecular parameters that differentiate clinical outcomes in high- and low-risk EC. Using the Cancer Genome Atlas, we analyzed EC specimens with available DNA sequences and quantitative gene-specific RNA expression data. After polymerase ɛ (POLE)-mutant specimens were excluded, differential gene-specific mutations and mRNA expressions were annotated and integrated. Consequent to TP53-mu failure to induce p21, derepression of multiple oncogenes harboring promoter p21 repressive sites was observed, including CCNA2 and FOXM1 (P < .001 compared with TP53 wild type [TP53-wt]). TP53-wt EC with high CCNA2 expression (CCNA2-H) had a targeted transcriptomic profile similar to that of TP53-mu EC, suggesting CCNA2 is a seminal determinant for both TP53-wt and TP53-mu EC. CCNA2 enhances E2F1 function, upregulating FOXM1 and CIP2A, as observed in TP53-mu and CCNA2-H TP53-wt EC (P < .001). CIP2A inhibits protein phosphatase 2A, leading to AKT inactivation of GSK3β and restricted oncoprotein degradation; PPP2R1A and FBXW7 mutations yield similar results. Upregulation of FOXM1 and failed degradation of FOXM1 is evidenced by marked upregulation of multiple homologous recombination genes (P < .001). Integrating these molecular aberrations generated a molecular biomarker panel with significant prognostic discrimination (P = 5.8×10-7); adjusting for age, histology, grade, myometrial invasion, TP53 status, and stage, only CCNA2-H/E2F1-H (P = .0003), FBXW7-mu/PPP2R1A-mu (P = .0002), and stage (P = .017) were significant. The generated prognostic molecular classification system identifies dissimilar signaling aberrations potentially amenable to targetable therapeutic options.
Pubmed ID: 33503056 RIS Download
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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 mentionsPrivately held company that develops and produces antibodies, ELISA kits, ChIP kits, proteomic kits, and other related reagents used to study cell signaling pathways that impact human health.
View all literature mentionsSoftware repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.
View all literature mentionsAmerican chemical, life science and biotechnology company owned by Merck KGaA. Merger of Sigma Chemical Company and Aldrich Chemical Company. Provides organic and inorganic chemicals, building blocks, reagents, advanced materials and stable isotopes for chemical synthesis, medicinal chemistry and materials science, antibiotics, buffers, carbohydrates, enzymes, forensic tools, hematology and histology, nucleotides, proteins, peptides, amino acids and their derivatives.
View all literature mentionsCell line HEC-1-B is a Cancer cell line with a species of origin Homo sapiens (Human)
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