The chromatin accessibility landscape of primary human cancers

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Publications → The chromatin accessibility landscape of primary human cancers
TCGA
TCGA

Science. Volume 362, Issue 6413 p.eaav1898, 25 October 2018 10.1126/science.aav1898

We present the genome-wide chromatin accessibility profiles of 410 tumor samples spanning 23 cancer types from The Cancer Genome Atlas. We identify 562,709 transposase-accessible DNA elements that substantially extend the compendium of known cis-regulatory elements. Integration of ATAC-seq with TCGA multi-omic data identifies a large number of putative distal enhancers that distinguish molecular subtypes of cancers, uncovers specific driving transcription factors via protein-DNA footprints, and nominates long-range gene-regulatory interactions in cancer. These data reveal genetic risk loci of cancer predisposition as active DNA regulatory elements in cancer, identify gene-regulatory interactions underlying cancer immune evasion, and pinpoint noncoding mutations that drive enhancer activation and may impact patient survival. These results suggest a systematic approach to understand the noncoding genome in cancer to advance diagnosis and therapy.

Data in the GDC

Views of the Data

The ATAC-seq peak accessibility and computed peak-to-gene linkage predictions are publicly available for interactive visualization and exploration at the UCSC Xena Browser (https://atacseq.xenahubs.net).

Supplemental Data Files

    • Data S1. Cancer types studied, donor characteristics, and sequencing statistics. [xlsx]
    • Data S2. Pan-cancer and breast cancer peak calls. [xlsx]
    • Data S3. Overlap of peaks with Roadmap DNase-seq, peak saturation analysis, and t-SNE positions of all samples. [xlsx]
    • Data S4. Distal binarization analysis and enrichment of motifs in cluster-specific peak sets. [xlsx]
    • Data S5. GWAS and eQTL analyses and overlap with peak-to-gene links. [xlsx]
    • Data S6. TF footprinting analyses and correlation to gene expression. [xlsx]
    • Data S7. Pan-cancer and breast cancer-specific peak-to-gene links and enhancer-to-gene links. [xlsx]
    • Data S8. ELMER and Regulon analyses. [xlsx]
    • Data S9. Peak-to-gene links related to immune response in cancer. [xlsx]
    • Data S10. Open Access. Integration of ATAC-seq and WGS to identify noncoding mutations. Only contains mutation positions, no base changes provided. [xlsx]
    • Data S10. Controlled Access. Integration of ATAC-seq and WGS to identify noncoding mutations. Contains mutation positions and base changes.[xlsx]
    • Protocol S1. Processing of frozen tissue fragments for ATAC-seq. [pdf]

Other Supplemental Data Files

  • ATAC-seq Counts Matrices
    • README to facilitate usage of count matrices. [TXT]
    • Normalized ATAC-seq insertion counts within the pan-cancer peak set. Recommended format. [RDS]
    • Normalized ATAC-seq insertion counts within the pan-cancer peak set. Not recommended due to size. [TXT]
    • Raw ATAC-seq insertion counts within the pan-cancer peak set. [RDS]
    • Raw ATAC-seq insertion counts within the pan-cancer peak set. [TXT]
    • All cancer type-specific count matrices in normalized counts. [ZIP]
    • All cancer type-specific count matrices in raw counts. [ZIP]
  • ATAC-seq Peak Calls
    • README to facilitate usage of peak call bed files. [TXT]
    • All cancer type-specific peak sets. [ZIP]
    • Pan-cancer peak set. [TXT]
    • Lookup table for various TCGA sample identifiers. [TXT]
  • BigWig Files for All Samples
    • README to facilitate usage of bigWig files [TXT]
    • Normalized bigWig files for the ACC cohort. [TAR-GZ]
    • Normalized bigWig files for the BLCA cohort. [TAR-GZ]
    • Normalized bigWig files for the BRCA cohort. [TAR-GZ]
    • Normalized bigWig files for the CESC cohort. [TAR-GZ]
    • Normalized bigWig files for the CHOL cohort. [TAR-GZ]
    • Normalized bigWig files for the COAD cohort. [TAR-GZ]
    • Normalized bigWig files for the ESCA cohort. [TAR-GZ]
    • Normalized bigWig files for the GBM cohort. [TAR-GZ]
    • Normalized bigWig files for the HNSC cohort. [TAR-GZ]
    • Normalized bigWig files for the KIRC cohort. [TAR-GZ]
    • Normalized bigWig files for the KIRP cohort. [TAR-GZ]
    • Normalized bigWig files for the LGG cohort. [TAR-GZ]
    • Normalized bigWig files for the LIHC cohort. [TAR-GZ]
    • Normalized bigWig files for the LUAD cohort. [TAR-GZ]
    • Normalized bigWig files for the LUSC cohort. [TAR-GZ]
    • Normalized bigWig files for the MESO cohort. [TAR-GZ]
    • Normalized bigWig files for the PCPG cohort. [TAR-GZ]
    • Normalized bigWig files for the PRAD cohort. [TAR-GZ]
    • Normalized bigWig files for the SKCM cohort. [TAR-GZ]
    • Normalized bigWig files for the STAD cohort. [TAR-GZ]
    • Normalized bigWig files for the TGCT cohort. [TAR-GZ]
    • Normalized bigWig files for the THCA cohort. [TAR-GZ]
    • Normalized bigWig files for the UCEC cohort. [TAR-GZ]

Analysis Code

    • Code for ELMER probe-to-gene analysis. [HTML]

Additional Resources

Instructions for Data Download

Open Access Data

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  2. Use the manifest file to download data using the GDC Data Transfer Tool (DTT) or the GDC API

Controlled Access Data

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  2. Download a token from the GDC Data Portal
  3. Use the manifest file and token to download data using the GDC DTT or the GDC API

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