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.

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 Ovarian Cancer (TCGA-OV) 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 Ovarian Phenotype Research Group.

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

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.

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, and slide 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 such as GatorTron and UNI.

Curated by: Lab Rasool Language(s) (NLP): English

  Uses

from… See the full description on the dataset page: https://huggingface.co/datasets/Lab-Rasool/TCGA.

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

The clinical information and mRNA expression data from the TCGA database of 525 GBM cases.

All data was downloaded from UCSC Xena on 16 August 2016. The compressed folder includes the following files:

GBM_clinicalMatrix - tab separated file with 629 samples measured by 139 variables. Refer to the source for more details.

SOURCE: https://genome-cancer.soe.ucsc.edu/proj/site/xena/datapages/?dataset=TCGA.GBM.sampleMap/GBM_clinicalMatrix&host=https://tcga.xenahubs.net

HT_HG-U133A - tab separated gene expression (Affy U133A microarry) file with 539 samples measured by 12,043 genes. Refer to the source for more details.

SOURCE: https://genome-cancer.soe.ucsc.edu/proj/site/xena/datapages/?dataset=TCGA.GBM.sampleMap/HT_HG-U133A&host=https://tcga.xenahubs.net

This dataset includes the harmonised version of all The Cancer Genome Atlas (TCGA) RNA-Seq data (33 cancer types, ~ 11000 samples). Each file is a "SummarizedExperiment" object that contains:1) three assays (raw gene-level counts, FPKM and FPKM.UQ), 2) sample and batch information from different resources, 3) several details for individual genes. The data can also be explored by an Rshiny app published in our pre-print paper doi: https://doi.org/10.1101/2021.11.01.466731.

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.

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

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

Data consist of three omic blocks from The Cancer Genome Atlas (TCGA), containing whole-genome profiles of

-Gene expression (file GE.RData), -DNA methylation (file METH.RData), and -Copy number variants (file CNV.RData).

Omic profiles consist of information from 5,408 tumor samples across 33 cancer types (as matrix rows), and 60,112 features (expression of 20,319 genes, methylation of 28,241 CpG islands, and copy number variant intensity for 11,552 genes). GE profiles by sample corresponded with the logarithm of RNA-Seq counts by gene (Illumina HiSeq RNA V2 platform). METH profiles corresponded with CpG sites B-values from the Illumina HM450 platform, summarized at the CpG island level, using the maximum connectivity approach from the WGCNA R package (Langfelder and Horvath 2008) , and further transformed into M-values (M=beta/(1-beta); Du et al. 2010). Omic blocks were adjusted for batch and tissue specific effects (see Gonzalez-Reymundez and Vazquez (2020) and references therein for further details on quality controls and data edition).

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

Additional file 2: Table S2. Differentially expressed immune genes between endometrial cancer and normal tissues.

Objective: To explore the expression of the CXCR4 gene in the tumor microenvironment of gastric cancer tissues and its clinical significance.Methods: The transcriptome data and clinical data of gastric cancer from the The Cancer Genome Atlas (TCGA) database were downloaded, and the expression difference of the CXCR4 gene was analyzed using the ESTIMATES algorithm and DESeq2, and the clinicopathological parameters of the patients were also analyzed. Surgical tissue specimens of patients diagnosed with gastric cancer by surgical treatment and pathological examination at Hefei First People's Hospital from August 2022 to August 2023 were retrospectively collected for reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemistry to verify its expression. The CIBERSORT algorithm was used to evaluate the correlation between CXCR4 and immune cell infiltration.Results: The immune score might be more suitable for indicating the prognosis of STAD patients. TCGA data showed that the expression level of the CXCR4 gene in gastric cancer tissues was significantly higher than that in adjacent tissues (P < 0.001), and the expression of CXCR4 had significant differences in tumor invasion depth and distant metastasis (all P < 0.05). The experimental results showed that the expression of CXCR4 was positively correlated with tumor distant metastasis and differentiation degree (all P < 0.05). Kaplan - Meier survival analysis of both TCGA data and clinical data showed that the survival time of gastric cancer patients in the high - expression group was significantly shortened (P = 0.003, P < 0.001). Immune cell infiltration analysis: two types of TICs, such as B cell memory and CD8 + T cells, were positively correlated with the expression of CXCR4; six types of TICs, such as resting CD4 memory T cells, activated dendritic cells, plasma cells, activated NK killer cells, macrophages M0, and activated mast cells, were negatively correlated with the expression of CXCR4.Conclusion: The high expression of CXCR4 in gastric cancer indicates a poor prognosis, which is closely related to the progression and metastasis of the tumor. And it is related to immune cell infiltration and may become a potential target for immunotherapy.

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.

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 10,000+ patients. This includes copy number variation, somatic mutation, 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 about the data and its layout in the HDF5 files.

Version notes:

2022-08-09: Fixed some bugs in string formatting. (Pipeline updated on this date; data uploaded on 2022-09-26 due to Zenodo technical issues.)

2021-12-06: **Significant changes**. `tcga_omic.hdf` is organized very differently. It also includes more kinds of data (a) somatic mutation data and (b) full TCGA barcodes for each patient and omic type (useful for extracting batch information).

2021-03-17: improved the naming convention for RPPA data features: {GENE}_{ANTIBODY}_rppa

2021-02-28: improved HDF file format. We provide one big matrix of data, rather than one matrix per cancer type. Cancer type is indicated by a vector (key="cancer_types"). Updated the Omic and Clinical HDFs accordingly.

2021-02-01: added mutation annotation scores. removed GRSN from RPPA pipeline.

2021-01-24: removed redundant/combination datasets (COADREAD, STES, GBMLGG, KIPAN). Applied Global Rank-Invariant Set normalization (GRSN) to RPPA data.

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.