Description: The Pan-Cancer Atlas (PanCanAtlas) initiative aims to answer big, overarching questions about cancer by examining the full set of tumors characterized in the robust TCGA dataset. The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer Atlas initiative compares the 33 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile. The datasets gathered as part of the PanCan Atlas are included in the larger Genomic Data Commons (GDC) repository.

Subject(s)

Gene Expression Profiling

Neoplasms/genetics

Oncogenes

Signal Transduction

Access via PanCanAtlas
Manifest files for open access and controlled access data, as well as supplemental data.
Access Restrictions: Free to All

Free to all with registration
Access Instructions: Datasets in the PanCanAtlas are organized as Open Access Data and Controlled Access Data. Datasets may be downloaded, accessed via API, or viewed online via the Data Portal. API and download instructions are included at the bottom of the PanCanAtlas site. The method for downloading open access and controlled data is essentially the same: download the appropriate manifest file from the publication page and use the manifest file to download data using the GDC Data Transfer Tool (DTT) or the GDC API. For Controlled Access Data, you must first obtain a token. Tokens may be obtained online for free from the GDC Data Portal once a user is logged in.
Associated Publications: Hoadley, K.A., Yau, C., Hinoue, T., et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell, 173(2), 291-304.e6.

Gao Q, Liang WW, Foltz SM, et al. Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell Rep. 2018;23(1):227–238.e3.

Sanchez-Vega F, Mina M, Armenia J, et al. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell. 2018;173(2):321–337.e10.

Liu J, Lichtenberg T, Hoadley KA, et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018;173(2):400–416.e11.

Taylor AM, Shih J, Ha G, et al. Genomic and Functional Approaches to Understanding Cancer Aneuploidy. Cancer Cell. 2018;33(4):676–689.e3.

Ellrott K, Bailey MH, Saksena G, et al. Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines. Cell Syst. 2018;6(3):271–281.e7.

Chang, K., Creighton, C., Davis, C. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013).
Data Type: Genomic
Dataset Format(s): Microsoft Excel, CSV, Plain Text, TSV, TAR, SEG

Other Resources: Pan-Cancer Atlas Publications
Index of flagship and companion publications about the Pan-Cancer Atlas, maintained by Cell Press

Genomic Data Commons Data Dictionay
The GDC Data Dictionary identifies the properties and accepted types or values for required, preferred, and optional data elements as well as their relationships. This data dictionary also applies to the PanCan Atlas.
Data Catalog Record Updated: 2019-11-19

Pan-Cancer Atlas (PanCanAtlas)