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).

Abstract:

The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled "An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics". The paper highlights four types of carefully curated survival endpoints, and recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type. The dataset also includes phenotypic information about GBM. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset.

Inspiration:

This dataset was uploaded to UBRITE for GTKB project.

Instruction:

The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed.

Acknowledgments:

Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8

Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. https://doi.org/10.1016/j.cell.2018.02.052

The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764

U-BRITE last update: 07/13/2023

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.

The following datasets were created for Project Cognoma:expression-matrix.tsv.bz2 is a sample × gene matrix indicating a gene's expression level for a given sample. This dataset will be the feature/x/predictor for Project Cognoma.mutation-matrix.tsv.bz2 is a sample × gene matrix indicating whether a gene is mutated for a given sample. Select columns (or unions of several columns) in this dataset will be the status/y/outcome for Project Cognoma.These are preliminary datasets for development use and machine learning. The data was retrieved from the UCSC Xena Browser. All original work in the data is released under CC0. However, the license of TCGA and Xena data is currently unclear.These two datasets are from this GitHub directory linked to below, although they were not tracked due to large file size.

Abstract:

The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. These datasets contain gene expression profiles of bladder urothelial carcinoma (BLCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), glioblastoma multiforme (GBM), head & neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), and lower grade glioma (LGG).

The gene expression profiles for BLCA, CESC, HNSC, KIRC, and LGG were measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The gene expression profile of the GBM dataset was measured experimentally using the Affymetrix HT Human Genome U133a microarray platform by the Broad Institute of MIT and Harvard University cancer genomic characterization center.

Inspiration:

This dataset was uploaded to UBRITE for GTKB project.

Instruction:

The log2(x+1) normalization was removed, and z-normalization was performed on the BLCA, CESC, HNSC, KIRC, and LGG datasets.

The log2(x) normalization was removed, and z-normalization was performed on the GBM dataset.

Acknowledgments:

Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8.

The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764.

U-BRITE last update: 07/13/2023

This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute Imaging Data Commons (IDC) [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: TCGA-THCA. You can use the manifests included in this Zenodo record to download the content of the collection following the Download instructions below.

Collection description

The Cancer Genome Atlas-Thyroid Cancer (TCGA-THCA) data collection is part of a larger effort to enhance the TCGA http://cancergenome.nih.gov/ data set with characterized radiological images. The Cancer Imaging Program (CIP) with the cooperation of several of the TCGA tissue-contributing institutions are working to archive a large portion of the radiological images of the genetically-analyzed THCA cases.

Please see the TCGA-THCA page to learn more about the images and to obtain any supporting metadata for this collection.

Files included

A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, collection_id-idc_v8-aws.s5cmd corresponds to the contents of the collection_id collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.

1. tcga_thca-idc_v8-aws.s5cmd: manifest of files available for download from public IDC Amazon Web Services buckets
2. tcga_thca-idc_v8-gcs.s5cmd: manifest of files available for download from public IDC Google Cloud Storage buckets
3. tcga_thca-idc_v8-dcf.dcf: Gen3 manifest (for details see https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids)

Note that manifest files that end in -aws.s5cmd reference files stored in Amazon Web Services (AWS) buckets, while -gcs.s5cmd reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.

Download instructions

Each of the manifests include instructions in the header on how to download the included files.

To download the files using .s5cmd manifests:

1. install idc-index package: pip install --upgrade idc-index
2. download the files referenced by manifests included in this dataset by passing the .s5cmd manifest file: idc download manifest.s5cmd.

To download the files using .dcf manifest, see manifest header.

Acknowledgments

Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.

References

[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. RadioGraphics (2023). https://doi.org/10.1148/rg.230180

TCGA Cancer Variant and Clinical Data

  Dataset Description

This dataset combines genetic variant information at the protein level with clinical data from The Cancer Genome Atlas (TCGA) project, curated by the International Cancer Genome Consortium (ICGC). It provides a comprehensive view of protein-altering mutations and clinical characteristics across various cancer types.

  Dataset Summary

The dataset includes:

Protein sequence data for both mutated and… See the full description on the dataset page: https://huggingface.co/datasets/seq-to-pheno/TCGA-Cancer-Variant-and-Clinical-Data.

This compound data set comprises the following information from the The Cancer Genome Atlas:

• RNA-Seq counts for 60483 genes across 11093 samples
• HuEx 1.0 ST gene expression data for 18632 genes across 1211 samples
• clinical indicators for 11160 patients

All gene expression data is annotated across ENSEMBL, ENTREZ and symbols. Samples are annotated by TCGA barcodes.

To read the data set into R (requires 6 GB of RAM) use:

tcga <- readRDS("tcga.rds")

Following the same steps that we used in the previous course we downloaded the TCGA-BRCA using R and Bioconductor and in particular the TCGABiolinks package. We downloaded transcriptome profiling of gene expression quantification where the experimental strategy is (RNAseq) and the workflow type is HTSeq-FPKM-UQ and only primary solid tumor data of the affymetrix GPL86 profile and clinical data.

Abstract:

The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled "An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics". The paper highlights four types of carefully curated survival endpoints, and recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type. The dataset also includes phenotypic information about LGG. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset.

Inspiration:

This dataset was uploaded to UBRITE for GTKB project.

Instruction:

The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed.

Acknowledgments:

Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8

Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. https://doi.org/10.1016/j.cell.2018.02.052

The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764

U-BRITE last update: 07/13/2023

Dataset Card for The Cancer Genome Atlas (TCGA) Multimodal Dataset

The Cancer Genome Atlas (TCGA) Multimodal Dataset is a comprehensive collection of clinical data, pathology reports, slide images, molecular data, and radiology images for cancer patients. This dataset aims to facilitate research in multimodal machine learning for oncology by providing embeddings generated using state-of-the-art models including GatorTron, MedGemma, Qwen, Llama, UNI, SeNMo, REMEDIS, and… See the full description on the dataset page: https://huggingface.co/datasets/Lab-Rasool/TCGA.

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.

Abstract:

The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled "An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics". The paper highlights four types of carefully curated survival endpoints, and recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type. The dataset also includes phenotypic information about KIRC. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset.

Inspiration:

This dataset was uploaded to UBRITE for GTKB project.

Instruction:

The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed.

Acknowledgments:

Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8

Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. https://doi.org/10.1016/j.cell.2018.02.052

The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764

U-BRITE last update: 07/13/2023

This dataset contains summary data visualizations and clinical data from a broad sampling of over 200 acute myeloid leukemias from 200 patients. The data was gathered as part of the PanCancer Atlas initiative, which aims to answer big, overarching questions about cancer by examining the full set of tumors characterized in the robust TCGA dataset. The clinical data includes mutation count, information about mutated genes, patient demographics, disease status, tumor typing, and chromosomal gain or loss. The data set also includes copy-number segment data downloadable as .seg files and viewable via the Integrative Genomics Viewer.

Dataset on chemotherapeutic drug responses in TCGA cancer patients, cross-referenced for a hit in TCIA.at database, consisting of clinical (TCGA), cancer tissue gene-expression (TCGA) and tumor-immunome (TCIA) features. The dataset consists of 5 common chemotherapy agents, 3 CRC agents (FOLFOX, 5FU, Oxaliplatin) and 2 Lung agents (Carboplatin, Cisplatin). FOLFOX as a combinational therapy or regimen, was compiled from timings of monotherapies given to patients and as such is a novel dataset derived from TCGA data. FOLFOX dataset is primarily firstline treatment, while other drugs are not to be interpreted as firstline treatments. Drug datasets are individually available in own CSV files.

Citation
Dalibor Hrg, Balthasar Huber, Lukas A. Huber. (2020). TCGA Chemotherapy Response Dataset. Zenodo. http://doi.org/10.5281/zenodo.3719291

The results here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

License
CC BY-SA 4.0 International https://creativecommons.org/licenses/by-sa/4.0. Authors take no liability for any use of this data.

Contributions
D. Hrg and B. Huber acknowledge major and equal work effort: data understanding, data science and dataset preparation (monotherapies and FOLFOX); L. A. Huber: help with dictionary of drug names and curration/cleaning of FOLFOX entries, clinical validation.

Contact & Maintenance
dalibor.hrg@gmail.com
dalibor.hrg@i-med.ac.at

HDF files containing data from The Cancer Genome Atlas (TCGA).

https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga

Please see the Broad Institute's TCGA data usage policy: https://broadinstitute.atlassian.net/wiki/spaces/GDAC/pages/844333156/Data+Usage+Policy

The HDF files were generated by the code in this repository: https://github.com/dpmerrell/tcga-pipeline

* tcga_omic.tar.gz contains multi-omic data for 11,000+ patients, divided into 38 cancer types. This includes copy number variation, methylation, gene expression, and RPPA data.

* tcga_clinical.tar.gz contains clinical annotations for those same patients. E.g., age, sex, survival, smoking.

See https://github.com/dpmerrell/tcga-pipeline/blob/main/README.md for more information.

The RDF representation of TCGA was achieved by representing data elements from the TCGA dataset as statements from the S3DB Core Model (see S3DB Core Model for more information on the S3DB Core Model). As such, the RDF graph that fuels this endpoint is structured according the S3DB Core Model entities: Collections, Items, Rules and Statements. The advantage of this annotation is that entities that belong to the description of the domain are annotated as "Collections" (for example, "Sample" is a Collection) or Rules (for example, "GenomicCharacterization-obtainedFrom-Sample" is a Rule) and their instances are annotated as Items (for example, "TCGA-01-0001" is an Item of the collection "Samples) or Statements (for example, "TCGA-01-0001"-"provided"-"GC1234" is as Statement that uses the Rule "GenomicCharacterization-obtainedFrom-Sample"). For more information see Deus HF, DF Veiga, PR Freire, JN Weinstein, GB Mills, JS Almeida (2010) Exposing The Cancer Genome Atlas as a SPARQL endpoint. Journal Biomedical Informatics [PMID 20851208].

Abstract:

The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled "An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics". The paper highlights four types of carefully curated survival endpoints, and recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type. The dataset also includes phenotypic information about HNSC. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset.

Inspiration:

This dataset was uploaded to UBRITE for GTKB project.

Instruction:

The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed.

Acknowledgments:

Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8

Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. https://doi.org/10.1016/j.cell.2018.02.052

The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764

U-BRITE last update: 07/13/2023

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 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.