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ID 114963
Author
Wei, Xiaona Institute of Bioengineering and Nanotechnology|MSD International GmbH
Choudhury, Yukti Institute of Bioengineering and Nanotechnology|Lucence Diagnostics
Lim, Weng Khong Duke-NUS Graduate Medical School|National University of Singapore
Anema, John Urologic Consultants
Kahnoski, Richard J. Spectrum Health Medical Group
Lane, Brian Spectrum Health Medical Group
Ludlow, John Western Michigan Urological Associates
Belldegrun, Arie University of California Los Angeles
Kim, Hyung L. Cedars-Sinai Medical Center
Rogers, Craig Henry Ford Hospital
Nicol, David The Royal Marsden NHS Foundation Trust|The Institute of Cancer Research
Teh, Bin Tean Duke-NUS Graduate Medical School|National Cancer Centre Singapore|National University of Singapore
Tan, Min-Han Institute of Bioengineering and Nanotechnology|Lucence Diagnostics|National University of Singapore|National Cancer Centre Singapore
Content Type
Journal Article
Description
Clear cell renal cell carcinoma (ccRCC) has been previously classified into putative discrete prognostic subtypes by gene expression profiling. To investigate the robustness of these proposed subtype classifications, we evaluated 12 public datasets, together with a new dataset of 265 ccRCC gene expression profiles. Consensus clustering showed unstable subtype and principal component analysis (PCA) showed a continuous spectrum both within and between datasets. Considering the lack of discrete delineation and continuous spectrum observed, we developed a continuous quantitative prognosis score (Continuous Linear Enhanced Assessment of RCC, or CLEAR score). Prognostic performance was evaluated in independent cohorts from The Cancer Genome Atlas (TCGA) (n = 414) and EMBL-EBI (n = 53), CLEAR score demonstrated both superior prognostic estimates and inverse correlation with anti-angiogenic tyrosine-kinase inhibition in comparison to previously proposed discrete subtyping classifications. Inverse correlation with high-dose interleukin-2 outcomes was also observed for the CLEAR score. Multiple somatic mutations (VHL, PBRM1, SETD2, KDM5C, TP53, BAP1, PTEN, MTOR) were associated with the CLEAR score. Application of the CLEAR score to independent expression profiling of intratumoral ccRCC regions demonstrated that average intertumoral heterogeneity exceeded intratumoral expression heterogeneity. Wider investigation of cancer biology using continuous approaches may yield insights into tumor heterogeneity; single cell analysis may provide a key foundation for this approach.
Journal Title
Scientific Reports
ISSN
20452322
Publisher
Springer Nature
Volume
7
Start Page
7342
Published Date
2017-08-04
Rights
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
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language
eng
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departments
Medical Sciences