Description: This dataset contains summary data visualizations and clinical data from a broad sampling of 283 kidney renal papillary cell carcinomas. 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, chromosomal gain or loss, Subtype, American Joint Committee on Cancer Metastasis Stage Code, American Joint Committee on Cancer Publication Version Type, American Joint Committee on Cancer Tumor Stage Code, Aneuploidy Score, and Birth from Initial Pathologic Diagnosis Date. The data set also includes copy-number segment data downloadable as .seg files and viewable via the Integrative Genomics Viewer.

Subject of Study

Homo sapiens

Subject(s)

Carcinoma, Renal Cell

DNA Mutational Analysis

OncoTree Cancer Type(s)

Papillary Renal Cell Carcinoma

Access via cBioPortal
Access Restrictions: Free to All
Access Instructions: This cBioPortal for Cancer Genomics dataset is publicly available for viewing in tables or, for copy-number segments, via integrated IGV application. The data is also available for download in its entirety as a tarball or as individual TSV (tab separated value) or SEG files. Some features are only available by logging in with a Google or Microsoft account. These features include storing your virtual studies and groups which allows you to access your studies and groups from any computer, and to allow cBioPortal to remember your study view charts preferences for each study (i.e. order of the charts, type of charts and visibility). Login is optional and not required to access any of the other features of cBioPortal. Downloaded SEG files (tab-delimited segmented data text files that list loci and associated numeric values) can be viewed by downloading and installing the Integrative Genomics Viewer desktop application.
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.
Data Type: Genomic
Software Used: Integrative Genomics Viewer (IGV)
High-performance visualization tool for interactive exploration of large, integrated genomic datasets.

OncoTree
Open-source ontology that was developed at Memorial Sloan Kettering Cancer Center (MSK) for standardizing cancer type diagnosis from a clinical perspective by assigning each diagnosis a unique OncoTree code.
Dataset Format(s): TSV, TAR, SEG
Dataset Size: 46.8 MB

Data Catalog Record Updated: 2021-11-09

Kidney Renal Papillary Cell Carcinoma (TCGA, PanCancer Atlas)