Dataset Information

National Institutes of Health The Cancer Genome Atlas (TCGA)

ABSTRACT:

The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing. TCGA is a joint effort of the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI), which are both part of the National Institutes of Health, U.S. Department of Health and Human Services.

TCGA projects are organized by cancer type or subtype. Click here for a current list of cancer types selected for study in TCGA.

Data from TCGA (e.g., gene expression, copy number variation and clinical information), are available via the TCGA Data Portal, EXCEPT for the genomic sequence data (.bam files), which are hosted at the Cancer Genomics Hub (CGHub).

Data from TCGA projects are organized into two tiers: Open Access and Controlled Access.

Open Access data tier contains data that cannot be attributed to an individual research participant. The Open Access data tier does not require user certification. Data in Open Access tier are available in the TCGA Data Portal.
Controlled Access data tier contains individual-level genotype data that are unique to an individual. Access to data in the Controlled Access data tier requires user certification through dbGaP Authorized Access.
Controlled Access data types consist of the following:
- Individual germline variant data (SNP .cel files)
- Primary sequence data (.bam files), which are available at CGHub
- Clinical free text fields
- Exon Array files (for Glioblastoma and Ovarian projects only)

NOTE: TCGA strives to release most data in the open access tier. Individual genotype or sequence files are prominent exceptions. Commonly requested files such as descriptions of somatic mutations or clinical data are open access.

The TCGA study is utilized in the following dbGaP substudies. To view genotypes and other molecular data collected in these substudies, please click on the following substudies below or in the "Substudies" box located on the right hand side of this top-level study page phs000178 TCGA study.

phs000441 Integrated Genomic Analyses of Ovarian Carcinoma (OV)
phs000489 Comprehensive Genomic Characterization Defines Human Glioblastoma Genes and Core Pathways

OTHER RELATED OMICS DATASETS IN: MSV000084252PRJNA225826PAe005191PAe005196PAe005192PAe005122PAe005304PAe005186PAe005188

PROVIDER: phs000178.v6.p6 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Publications

Comprehensive genomic characterization defines human glioblastoma genes and core pathways.

Nature 20080904 7216

Human cancer cells typically harbour multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas (TCGA) pilot project aims to assess the value of large-scale multi-dimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations ...[more]

PMID: 18772890

Publication: 1/3

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Project description:The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing. TCGA is a joint effort of the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI), which are both part of the National Institutes of Health, U.S. Department of Health and Human Services. TCGA projects are organized by cancer type or subtype. Click <a href="http://cancergenome.nih.gov/cancersselected" target="_blank">here</a> for a current list of cancer types selected for study in TCGA. Data from TCGA (e.g., gene expression, copy number variation and clinical information), are available via the <a href="https://gdc.cancer.gov/" target="_blank">Genomic Data Commons (GDC)</a>. Data from TCGA projects are organized into two tiers: Open Access and Controlled Access. <ul> <li>Open Access data tier contains data that cannot be attributed to an individual research participant. The Open Access data tier does not require user certification. Data in Open Access tier are available in the TCGA Data Portal.</li> <li>Controlled Access data tier contains individual-level genotype data that are unique to an individual. Access to data in the Controlled Access data tier requires user certification through <a href="https://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?login=&page=login" target="_blank">dbGaP Authorized Access</a>.</li> <li>Controlled Access data types consist of the following: <ul> <li>Individual germline variant data (SNP .cel files)</li> <li>Primary sequence data (.bam files), which are available at GDC</li> <li>Clinical free text fields</li> <li>Exon Array files (for Glioblastoma and Ovarian projects only)</li> </ul> </li> </ul> NOTE: TCGA strives to release most data in the open access tier. Individual genotype or sequence files are prominent exceptions. Commonly requested files such as descriptions of somatic mutations or clinical data are open access. Please go to this page: <a href="https://tcga-data.nci.nih.gov/docs/publications/" target="_blank">https://tcga-data.nci.nih.gov/docs/publications/</a> to access all data associated with TCGA tumor specific publications. The TCGA study is utilized in the following dbGaP substudies. To view genotypes and other molecular data collected in these substudies, please click on the following substudies below or in the "Substudies" section of this top-level study page phs000178 TCGA study. <ul> <li><a href="./study.cgi?study_id=phs000854">phs000854</a> Genome-wide Analysis of Noncoding Regulatory Mutations in Cancer</li> </ul>

Project description:<a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Breast_Cancer_Proteome|TCGA_Breast_Cancer_Phosphoproteome" target="_blank">Explore This Study at the NCI Proteomic Data Commons</a>Global proteome and phosphoproteome data have been acquired for 105 TCGA breast cancer samples using iTRAQ (isobaric Tags for Relative and Absolute Quantification) protein quantification methods. Samples were selected from each of the 4 breast tumor subtypes (Luminal A, Luminal B, Basal-like, HER2-enriched) described in the publication, <a href="http://www.ncbi.nlm.nih.gov/pubmed/23000897" target="_blank">Comprehensive molecular portraits of human breast tumors (Cancer Genome Atlas Network, Nature 2012)</a>.Three TCGA samples and 1 common internal reference control sample are included in each iTRAQ experiment, consisting of 25 proteome and 13 phosphoproteome files. The internal reference is comprised of a mixture of 40 TCGA samples (of the 105 breast cancer samples) with equal representation of the 4 breast subtypes. Three of the TCGA samples have been assayed in duplicate for quality control purposes.05TCGA_AO-A12D-01A_AN-A04A-01A_BH-A0AV-01A_Proteome_BI iTRAQ proteome data were acquired twice, before (20130310) and after (20130416) maintenance of the Q Exactive MS system. The "20130416" dataset was used in the final publication (see <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S029" target="_blank">here</a>).Global proteome and phosphoproteome data were acquired for three normal breast samples in Experiment 37. Samples "263d3f-I", "blcdb9-I", and "c4155b-C" are normal breast tissue samples that were measured in the iTRAQ-114, -115, and -116 channels, respectively. All normal samples were compared to the internal reference sample in the iTRAQ-117 channel (see CPTAC, TCGA Breast Cancer iTRAQ Sample Mapping file below).Information on the complete TCGA Breast invasive carcinoma cohort (BRCA) can be found <a href="https://portal.gdc.cancer.gov/projects/TCGA-BRCA" target="_blank">here</a>. Genomic data for the 105 TCGA samples used in the CPTAC Proteome study can be downloaded from <a href="https://portal.gdc.cancer.gov/legacy-archive/search/f?filters=%7B%22op%22:%22and%22,%22content%22:%5B%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.program.name%22,%22value%22:%5B%22TCGA%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.project_id%22,%22value%22:%5B%22TCGA-BRCA%22%5D%7D%7D%5D%7D" target="_blank">here</a>.<ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S015">https://cptac-data-portal.georgetown.edu/cptac/s/S015</a> on 10/15/18</li><li>Dataset source: <a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Breast_Cancer_Proteome|TCGA_Breast_Cancer_Phosphoproteome" target="_blank">https://pdc.esacinc.com/pdc/browse/filters/study_name:...</a></li></ul>

Project description:<a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Colon_Cancer_Proteome" target="_blank">Explore This Study at the NCI Proteomic Data Commons</a>The goal of the CPTAC, TCGA Cancer Proteome Study of Colorectal Tissue is to analyze the proteomes of TCGA tumor samples that have been comprehensively characterized by molecular methods (<a href="http://www.ncbi.nlm.nih.gov/pubmed/22810696" target="_blank">Cancer Genome Atlas Network, Nature 2012</a>).Ninety-five TCGA tumor samples were used in this study from 90 patients, with 5 samples representing different portions from the same tumor. The samples originated from two TCGA cohorts: 64 are from <a href="https://portal.gdc.cancer.gov/projects/TCGA-COAD" target="_blank">Colon Adenocarcinoma (COAD)</a> samples and 31 are from the <a href="https://portal.gdc.cancer.gov/projects/TCGA-READ" target="_blank">Rectum Adenocarcinoma (READ)</a> collection. The primary data from the liquid chromatography-tandem mass spectrometry (LC-MS/MS) global proteomic profiling of each tumor sample is associated with a data set in the table below. This work was accomplished by the Proteome Characterization Center (PCC) at Vanderbilt University led by Dr. Daniel C. Liebler. Clinical data files contain both the human readable TCGA bar codes and the UUIDs for each sample.Complete Data Analysis from Vanderbilt University as published in Nature (Zhang, B., et al., Nature (2014) doi:10.1038/nature13438 Published online) is available <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S022">here</a>.Data available on this study page (peptide spectrum matches and protein reports) are from the CPTAC Common Data Analysis Pipeline.COAD tumor sample genomic data can be downloaded from <a href="https://portal.gdc.cancer.gov/legacy-archive/search/f?filters=%7B%22op%22:%22and%22,%22content%22:%5B%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.program.name%22,%22value%22:%5B%22TCGA%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.project_id%22,%22value%22:%5B%22TCGA-COAD%22%5D%7D%7D%5D%7D" target="_blank">here</a>. READ tumor sample genomic data can be downloaded from <a href="https://portal.gdc.cancer.gov/legacy-archive/search/f?filters=%7B%22op%22:%22and%22,%22content%22:%5B%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.program.name%22,%22value%22:%5B%22TCGA%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.project_id%22,%22value%22:%5B%22TCGA-READ%22%5D%7D%7D%5D%7D" target="_blank">here</a>. Colon tissue samples (ascending and descending) were obtained from 30 patients. Each sample was analyzed with label free global proteomic profiling. These colon samples, while derived from colon cancer subjects, did not contain tumor. Samples were obtained from the <a href="http://www.vicc.org/jimayersinstitute" target="_blank">Jim Ayers Institute for Precancer Detection and Diagnosis.</a> Data sets below are labeled with the patient identification number and contain data for both ascending and descending tissue samples. Data from colon tumor samples are available in the <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S016" target="_blank">TCGA Colorectal Cancer study</a>.<ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S016">https://cptac-data-portal.georgetown.edu/cptac/s/S016</a> and <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S019">https://cptac-data-portal.georgetown.edu/cptac/s/S019</a> on 06/23/16</li><li>Dataset source: <a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Colon_Cancer_Proteome">https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Colon_Cancer_Proteome</a></li></ul>

Project description:<a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Ovarian_JHU_Glycoproteome|TCGA_Ovarian_JHU_Proteome|TCGA_Ovarian_PNNL_Proteome|TCGA_Ovarian_PNNL_Phosphoproteome_Velos_Qexatvive" target="_blank">Explore This Study at the NCI Proteomic Data Commons</a><a href="http://www.ncbi.nlm.nih.gov/pubmed/27372738" target="_blank">Zhang H, Liu T, Zhang Z, Payne SH, Zhang B, McDermott JE et al., Cell (2016), DOI: 10.1016/j.cell.2016.05.069, Published online June 29, 2016.</a>To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC.Information on the complete TCGA Ovarian Serous Cystadenocarcinoma (OV) cohort can be found <a href="https://portal.gdc.cancer.gov/projects/TCGA-OV" target="_blank">here</a>. The Cancer Genome Atlas Research Network published on the Genomic characterization of the OV cohort in <a href="http://www.ncbi.nlm.nih.gov/pubmed/21720365" target="_blank">Nature</a> on June 30, 2011Genomic data for the 174 TCGA samples used in the CPTAC Proteome study can be downloaded from <a href="https://portal.gdc.cancer.gov/legacy-archive/search/f?filters=%7B%22op%22:%22and%22,%22content%22:%5B%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.program.name%22,%22value%22:%5B%22TCGA%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.project_id%22,%22value%22:%5B%22TCGA-OV%22%5D%7D%7D%5D%7D" target="_blank">here</a>.Peptide-Spectrum-Matches and Protein Reports from the CPTAC Common Data Analysis Pipeline (CDAP) can be downloaded from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S020" target="_blank">here</a>.<ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S026">https://cptac-data-portal.georgetown.edu/cptac/s/S026</a> on 08/28/19</li><li>Dataset source: <a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Ovarian_JHU_Glycoproteome|TCGA_Ovarian_JHU_Proteome|TCGA_Ovarian_PNNL_Proteome|TCGA_Ovarian_PNNL_Phosphoproteome_Velos_Qexatvive" target="_blank">https://pdc.esacinc.com/pdc/browse/filters/study_name:...</a></li></ul>

Dataset Information

National Institutes of Health The Cancer Genome Atlas (TCGA)

Publications

Comprehensive genomic characterization defines human glioblastoma genes and core pathways.

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