A unified data repository of the National Cancer Institute (NCI)'s Genomic Data Commons (GDC) that enables data sharing across cancer genomic studies in support of precision medicine. The GDC supports several cancer genome programs at the NCI Center for Cancer Genomics (CCG), including The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the Cancer Genome Characterization Initiative (CGCI). The GDC Data Portal provides a platform for efficiently querying and downloading high quality and complete data. The GDC also provides a GDC Data Transfer Tool and a GDC API for programmatic access.

Historical NCI Genomic Data Commons data (v09-14-2017). Clinical ('phenotype') and gene expression (HTSeq FPKM-UQ).

TCGA-COAD.GDC_phenotype.tsv

dataset: phenotype - Phenotype

cohortGDC TCGA Colon Cancer (COAD) dataset IDTCGA-COAD/Xena_Matrices/TCGA-COAD.GDC_phenotype.tsv downloadhttps://gdc.xenahubs.net/download/TCGA-COAD/Xena_Matrices/TCGA-COAD.GDC_phenotype.tsv.gz; Full metadata samples570 version11-27-2017 hubhttps://gdc.xenahubs.net type of dataphenotype authorGenomic Data Commons raw datahttps://docs.gdc.cancer.gov/Data/Release_Notes/Data_Release_Notes/#data-release-90 raw datahttps://api.gdc.cancer.gov/data/ input data formatROWs (samples) x COLUMNs (identifiers) (i.e. clinicalMatrix) 570 samples X 151 identifiersAll IdentifiersAll Samples

TCGA-COAD.htseq_fpkm-uq.tsv

dataset: gene expression RNAseq - HTSeq - FPKM-UQ

cohortGDC TCGA Colon Cancer (COAD) dataset IDTCGA-COAD/Xena_Matrices/TCGA-COAD.htseq_fpkm-uq.tsv downloadhttps://gdc.xenahubs.net/download/TCGA-COAD/Xena_Matrices/TCGA-COAD.htseq_fpkm-uq.tsv.gz; Full metadata samples512 version09-14-2017 hubhttps://gdc.xenahubs.net type of datagene expression RNAseq unitlog2(fpkm-uq+1) platformIllumina ID/Gene Mappinghttps://gdc.xenahubs.net/download/probeMaps/gencode.v22.annotation.gene.probeMap.gz; Full metadata authorGenomic Data Commons raw datahttps://docs.gdc.cancer.gov/Data/Release_Notes/Data_Release_Notes/#data-release-80 raw datahttps://api.gdc.cancer.gov/data/ wranglingData from the same sample but from different vials/portions/analytes/aliquotes is averaged; data from different samples is combined into genomicMatrix; all data is then log2(x+1) transformed. input data formatROWs (identifiers) x COLUMNs (samples) (i.e. genomicMatrix) 60,484 identifiers X 512 samples

Here, the authors performed an in-silico analysis on a meta-dataset including gene-expression data from 5,342 clinically defined estrogen receptor-positive/ human epidermal growth factor receptor 2-negative (ER+/HER2-) breast cancers (BC), and DNA copy number/mutational and proteomic data, to determine whether the therapeutic response of ER+/HER2- breast cancers differs according to the molecular basal or luminal subtype.Data access: The dataset Breast_cancer_classifications.csv supporting figure 1, table 1, and supplementary tables 1-3 is publicly available in the figshare repository as part of this data record. This study used and analysed 36 publicly available datasets that are all listed in Supplementary table 8 and are cited from the data availability statement of the published article.Study aims and methodology: To evaluate the response and/or potential vulnerability to hormone treatment (HT) and other systemic therapies of BC, and to assess the degree of difference between basal and luminal breast cancer subtypes, the authors performed an in-silico analysis of a meta-dataset including gene expression data from 8,982 non-redundant BCs and DNA copy number/mutational and proteomic data from TCGA. The aim was to compare the Basal versus Luminal samples. Out of the 8,982 samples of the database, 6,563 were defined as ER+ (5,342 according to immunohistochemistry (IHC) and 1,221 according to inferred stratus).The authors analysed breast cancer gene expression data pooled from 36 public datasets (the publicly available datasets are listed in supplementary table 8), comprising 8,982 invasive primary BCs. The pre-analytic data processing was done as described previously in https://doi.org/10.1038/s41416-018-0309-1. Please refer to the published article for more details on the methodology and statistical analysis.Data supporting the figures, tables and supplementary tables in the published article: Data supporting figure 1, table 1, and supplementary tables 1-3: Dataset Breast_cancer_classifications.csv is in .csv file format. The dataset includes histo-clinical and molecular data of the tumors analysed in study, and is part of this data record.Data supporting supplementary table 4: Dataset genome.wustl.edu_BRCA.IlluminaGA_DNASeq.Level_2.3.2.0.tar.gz.1 is a tar archive gz compressed of maf format files. This dataset was accessed through the Genomic Data Commons (GDC) Data Portal and can be downloaded directly here: https://api.gdc.cancer.gov/data/afaf2790-04d4-453a-8c1b-75cf42ffd35f.Data supporting supplementary table 5: Dataset gdc_manifest.txt consists of gz archives of txt format files. The file was accessed through the GDC Data Portal here : https://portal.gdc.cancer.gov/repository?facetTab=files&filters={"op":"and","content":[{"op":"in","content":{"field":"cases.project.project_id","value":["TCGA-BRCA"]}},{"op":"in","content":{"field":"files.access","value":["open"]}},{"op":"in","content":{"field":"files.analysis.workflow_type","value":["HTSeq - Counts"]}},{"op":"in","content":{"field":"files.experimental_strategy","value":["RNA-Seq"]}}]}&searchTableTab=filesData supporting supplementary table 6: Dataset Table S5_Revised.xlsx is in .xlsx file format and is part of the supplementary information files of the published article.Data supporting supplementary table 7: Dataset BRCA.RPPA.Level_3.tar is a tar archive of txt format files. The file was accessed through the GDC Data Portal and can be downloaded directly here: https://api.gdc.cancer.gov/data/85988e1b-4f7d-493e-96ae-9eee61ac2833.

The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the TCGA Breast Phenotype Research Group.

The Cancer Genome Atlas (TCGA) provides comprehensive genomic data across various cancer types. However, complex file naming conventions and the necessity of linking disparate data types to individual case IDs can be challenging for first-time users. While other tools have been introduced to facilitate TCGA data handling, they lack a straightforward combination of all required steps. To address this, we developed a streamlined pipeline using the Genomic Data Commons (GDC) portal’s cart system for file selection and the GDC Data Transfer Tool for data downloads. We use the Sample Sheet provided by the GDC portal to replace the default 36-character opaque file IDs and filenames with human-readable case IDs. We developed a pipeline integrating customizable Python scripts in a Jupyter Notebook and a Snakemake pipeline for ID mapping along with automating data preprocessing tasks (https://github.com/alex-baumann-ur/TCGADownloadHelper). Our pipeline simplifies the data download process by modifying manifest files to focus on specific subsets, facilitating the handling of multimodal data sets related to single patients. The pipeline essentially reduced the effort required to preprocess data. Overall, this pipeline enables researchers to efficiently navigate the complexities of TCGA data extraction and preprocessing. By establishing a clear step-by-step approach, we provide a streamlined methodology that minimizes errors, enhances data usability, and supports the broader utilization of TCGA data in cancer research. It is particularly beneficial for researchers new to genomic data analysis, offering them a practical framework prior to conducting their TCGA studies.

The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the CIP TCGA Radiology Initiative.

TCGA RNA-seq V2 Level3 data were downloaded from TCGA Genomic Data Commons Data Portal (https://gdc-portal.nci.nih.gov), consisting of 11,303 samples in 34 cancer projects (33 cancer types). Nine cancer types that do not have corresponding non-tumour samples were filtered out, and the analysis was focused on tumour versus non-tumour comparison. 24 cancer types were used in this meta-analysis: BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, THCA, THYM, UCEC (https://gdc-portal.nci.nih.gov). The nine filtered cancer types were ACC, DLBC, LAML, LGG, MESO, OV, TGCT, UCS and UVM. To extract expression values from TCGA RNA-seq data, we used genomic coordinates to retrieve UCSC Transcript IDs that correspond to the identifiers in TCGA RNA-seq V2 Level3 data (isoform level). The GAF (General Annotation Format) file was used to map the coordinate to UCSC Transcript ID, and it was downloaded form https://tcga-data.nci.nih.gov/docs/GAF/GAF.hg19.June2011.bundle/outputs/TCGA.hg19.June2011.gaf. This file contains genomic annotations shared by all TCGA projects. More details of the GAF file format can be found at https://tcga-data.nci.nih.gov/docs/GAF/GAF3.0/GAF_v3_file_description.docx. We filtered out any coding exons overlapping UCSC Transcript IDs to eliminate expression value of coding genes and evaluate lncRNA expression.We could find the expression values of 443 pcRNAs and 203 tapRNAs in TCGA data, as many of non-coding regions are not yet fully annotated in the TCGA RNA-seq V2 Level3 data. The expression value of pcRNAs and tapRNAs were extracted and clustered by un-supervised Pearson correlation method (Supplementary Figure 18A). The expression values of tapRNA-associated coding genes were also extracted and used to generate the heat-map (Supplementary Figure 18B), which shows the similar pattern of expression with tapRNAs across the cancer types.To show that tapRNAs and associated coding genes have similar expression profiles in cancers we generated a Spearman's Rank-Order Correlation heatmap (Figure 6A) between tapRNAs and their associated coding genes based on the TCGA RNA-seq data. We used the MatLab function corr to calculate the Spearman's rho. This function takes two matrices X (197-by-8,850 expression profiling matrix of tapRNA) and Y (197-by-8,850 expression profiling matrix of tapRNA-assocated coding gene) and returns an 8,850-by-8,850 matrix containing the pairwise correlation coefficient between each pair of 8,850 columns (TCGA cancer samples in Supplementary Figure 18A and B). Thus, the rank-order correlation matrix that we computed from the matrices of expression profiling data (Supplementary Figure S18A and B) allowed us to compare the correlation between two column vectors i.e. cancer samples. This function also returns a matrix of p-values for testing the hypothesis of no correlation against the alternative that there is a nonzero correlation. Each element of a matrix of p-values is the p value for the corresponding element of Spearman's rho. The p-values for Spearman's rho are calculated using large-sample approximations. To check significance level of correlation between tapRNA and its associated coding gene, the diagonal of the p-value matrix was extracted and used. The median is 1.31x10-11 and the mean is 1.03x10-4 with standard deviation 0.0029.To identify cancer-specific tapRNAs, we considered not only the global expression pattern of a given tapRNA in each cancer type, but also expression pattern of specific sub-group that is significantly distinct, to take into account cancer sample heterogeneity. Thus, two conditions were applied: (1) average expression level of a tapRNA in a given cancer type is in top 10% or bottom 10% and (2) a tapRNA has at least 10% of samples in a given cancer type that are significantly up-regulated (Z-score > 2) or down-regulated (Z-score < -2).

The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

https://wiki.cancerimagingarchive.net/display/Public/TCGA-LUAD

The Cancer Genome Atlas Rectum Adenocarcinoma (TCGA-READ) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the CIP TCGA Radiology Initiative.

There are three sheets in this spreadsheet file, corresponding to each of the three samples (TCGA-AB-2821, TCGA-AB-2828, TCGA-AB-2839). Correlation and RMSD between the reprocessed counts and published counts are included in each sheet. (XLSX)

The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the CIP TCGA Radiology Initiative.

Information on molecular subtypes for TCGA cancer studies as provided by the TCGA_MolecularSubtype function.

This dataset is issued from the public repository TCGA (https://portal.gdc.cancer.gov/) and contain several files, each corresponding to a given omic on the same individuals with breast cancer. Raw data have been obtained from the mixOmics case study described in http://mixomics.org/mixdiablo/case-study-tcga/ [link accessed on August 18, 2021] and were made available by the package authors at http://mixomics.org/wp-content/uploads/2016/08/TCGA.normalised.mixDIABLO.RData_.zip (R data format). Data in the zip file had been normalised for technical biases by the package authors. Data from the train and test sets were exported as TXT/CSV files and completed with miRNA expression on the smae individuals and toy datasets to handle missing value cases and alike. They serve as a basis for the illustration of the web data analysis tool ASTERICS (Project 20008788 funded by Région Occitanie).

The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the CIP TCGA Radiology Initiative.

The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the TCGA Glioma Phenotype Research Group.

The data is too large, here is a downloadable synopsis of the data, detailed data is here https://portal.gdc.cancer.gov/projects/TCGA-STAD

The authors declare that the data analysis processes supporting the findings of this study are available within the article and its Supplementary Information files. The TCGA gene expression profile data, as recomputed based on gencode v23, were downloaded from UCSC Xena (http://xena.ucsc.edu/). The TCGA clinical data were downloaded from the GDC Data Portal (https://gdc-portal.nci.nih.gov/), with accession number phs000178.v9.p8 in dbGap. Supplementary Information: For analyzing the hCINAP expression in CRC, we downloaded the recomputed TCGA gene expression datasets for COAD and READ cancer types from the UCSC Xena (http://xena.ucsc.edu/). The gene model was based on gencode v23, and the expression unit is TPM (Transcript per million). The clinical data were downloaded from the GDC Data Portal (https://gdc-portal.nci.nih.gov/).

For differential expression analysis, we compiled a selected sample set, including 367 tumor- and 51 normal-samples, in which each sample has information available for clinical variables such as gender, age and race (Supplementary Table1). For expression analysis by pathological stages, we only used those tumor samples with stage information (Supplementary Table1). The dataset used for profiling gene expression by CRC subtypes was compiled based on the results of consensus molecular subtypes (CMSs) described previously [PMID: 26457759] , containing 265 tumor samples (Supplementary Table1).

The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the CIP TCGA Radiology Initiative.

RNA-sequencing expression (level 3) profiles and corresponding clinical information for several tumors were downloaded from the TCGA dataset (https://portal.gdc.com).

The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the CIP TCGA Radiology Initiative.