The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma

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SUMMARY

Renal cell carcinoma (RCC) is not a single disease, but several histologically defined cancers with different genetic drivers, clinical courses, and therapeutic responses. The current study evaluated 843 RCC from the three major histologic subtypes, including 488 clear cell RCC, 274 papillary RCC, and 81 chromophobe RCC. Comprehensive genomic and phenotypic analysis of the RCC subtypes reveals distinctive features of each subtype that provide the foundation for the development of subtype-specific therapeutic and management strategies for patients affected with these cancers. Somatic alteration of BAP1, PBRM1, and PTEN and altered metabolic pathways correlated with subtype-specific decreased survival, while CDKN2A alteration, increased DNA hypermethylation, and increases in the immune-related Th2 gene expression signature correlated with decreased survival within all major histologic subtypes. CIMP-RCC demonstrated an increased immune signature, and a uniform and distinct metabolic expression pattern identified a subset of metabolically divergent (MD) ChRCC that associated with extremely poor survival.

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In Brief Ricketts et al. find distinctive features of each RCC subtype, providing the foundation for development of subtypespecific therapeutic and management strategies. Somatic alteration of BAP1, PBRM1, and metabolic pathways correlates with subtype-specific decreased survival, while CDKN2A alteration, DNA hypermethylation, and Th2 immune signature correlate with decreased survival within all subtypes.

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Most cited references 28

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Integrated detection and population-genetic analysis of SNPs and copy number variation.

Steven A McCarroll, Finny Kuruvilla, Joshua M. Korn … (2008)

Dissecting the genetic basis of disease risk requires measuring all forms of genetic variation, including SNPs and copy number variants (CNVs), and is enabled by accurate maps of their locations, frequencies and population-genetic properties. We designed a hybrid genotyping array (Affymetrix SNP 6.0) to simultaneously measure 906,600 SNPs and copy number at 1.8 million genomic locations. By characterizing 270 HapMap samples, we developed a map of human CNV (at 2-kb breakpoint resolution) informed by integer genotypes for 1,320 copy number polymorphisms (CNPs) that segregate at an allele frequency >1%. More than 80% of the sequence in previously reported CNV regions fell outside our estimated CNV boundaries, indicating that large (>100 kb) CNVs affect much less of the genome than initially reported. Approximately 80% of observed copy number differences between pairs of individuals were due to common CNPs with an allele frequency >5%, and more than 99% derived from inheritance rather than new mutation. Most common, diallelic CNPs were in strong linkage disequilibrium with SNPs, and most low-frequency CNVs segregated on specific SNP haplotypes.

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The somatic genomic landscape of chromophobe renal cell carcinoma.

Caleb F. Davis, Christopher Ricketts, Min Wang … (2014)

We describe the landscape of somatic genomic alterations of 66 chromophobe renal cell carcinomas (ChRCCs) on the basis of multidimensional and comprehensive characterization, including mtDNA and whole-genome sequencing. The result is consistent that ChRCC originates from the distal nephron compared with other kidney cancers with more proximal origins. Combined mtDNA and gene expression analysis implicates changes in mitochondrial function as a component of the disease biology, while suggesting alternative roles for mtDNA mutations in cancers relying on oxidative phosphorylation. Genomic rearrangements lead to recurrent structural breakpoints within TERT promoter region, which correlates with highly elevated TERT expression and manifestation of kataegis, representing a mechanism of TERT upregulation in cancer distinct from previously observed amplifications and point mutations.

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SomaticSniper: identification of somatic point mutations in whole genome sequencing data.

Elaine R. Mardis, Daniel C. Koboldt, Ken Chen … (2012)

The sequencing of tumors and their matched normals is frequently used to study the genetic composition of cancer. Despite this fact, there remains a dearth of available software tools designed to compare sequences in pairs of samples and identify sites that are likely to be unique to one sample. In this article, we describe the mathematical basis of our SomaticSniper software for comparing tumor and normal pairs. We estimate its sensitivity and precision, and present several common sources of error resulting in miscalls. Binaries are freely available for download at http://gmt.genome.wustl.edu/somatic-sniper/current/, implemented in C and supported on Linux and Mac OS X. delarson@wustl.edu; lding@wustl.edu Supplementary data are available at Bioinformatics online.

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Author and article information

Journal

Journal ID (nlm-journal-id): 101573691

Journal ID (pubmed-jr-id): 39703

Journal ID (nlm-ta): Cell Rep

Journal ID (iso-abbrev): Cell Rep

Title: Cell reports

ISSN (Electronic): 2211-1247

Publication date Nihms-submitted: 24 April 2018

Publication date (Print): 03 April 2018

Publication date PMC-release: 03 August 2018

Volume: 23

Issue: 1

Pages: 313-326.e5

Affiliations

[1 ]Urologic Oncology Branch, National Cancer Institute, Center for Cancer Research, Bethesda, MD 20892, USA

[2 ]Vanderbilt University School of Medicine, Nashville, TN 37232, USA

[3 ]Van Andel Research Institute, Grand Rapids, MI 49503, USA

[4 ]Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

[5 ]Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA

[6 ]Canada’s Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada

[7 ]The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

[8 ]The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA

[9 ]Dana-Farber Cancer Institute, Boston, MA 02215, USA

[10 ]Baylor College of Medicine, Houston, TX 77030, USA

[11 ]University of Kansas Medical Center, Kansas City, KS 66206, USA

[12 ]Brigham and Women’s Hospital, Boston, MA 02115, USA

[13 ]Washington University School of Medicine, St. Louis, MO 63110, USA

[14 ]Mayo Clinic Arizona, Phoenix, AZ 85054, USA

[15 ]Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA

[16 ]Centura Health, Centennial, CO 80112, USA

[17 ]Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA

[18 ]Basic Science Program, Leidos Biomedical Research, Inc. Frederick National Laboratory of Cancer Research, Frederick, MD 21702, USA

[19 ]Leukemia Therapeutics LLC., Hull, MA 02045, USA

[20 ]Yale University, New Haven, CT 06520, USA

[21 ]Oregon Health & Science University, Portland, OR 97239, USA

[22 ]Harvard Medical School, Boston, MA 02115, USA

[23 ]Lead Contact

Author notes

[* ]Correspondence: wml@ 123456nih.gov

Article

Manuscript ID: NIHMS958988

DOI: 10.1016/j.celrep.2018.03.075

PMC ID: 6075733

PubMed ID: 29617669

SO-VID: 6b1a6dbd-43a2-4cd1-89d9-9c493e3ac78a

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This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/).

The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma

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Evolutionary Cell Biology

Most cited references 28

Integrated detection and population-genetic analysis of SNPs and copy number variation.

The somatic genomic landscape of chromophobe renal cell carcinoma.

SomaticSniper: identification of somatic point mutations in whole genome sequencing data.

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