Scientific ReportsCancer BiologyPeer-reviewed journal article
Recognizing the Continuous Nature of Expression Heterogeneity and Clinical Outcomes in Clear Cell Renal Cell Carcinoma
Xiaona Wei, Yukti Choudhury, Weng Khong LimiD, John Anema, Richard Kahnoski, Brian Lane, John Ludlow, Masayuki Takahashi, Hiro-omi Kanayama, Arie Belldegrun, Hyung Kim, Craig Rogers, David Nicol, Bin Tean TehiD, Min-Han Tan
Abstract
PURPOSEEvaluation of 12 ccRCC publicly-available ccRCC gene expression datasets showed that previously proposed discrete molecular subtypes are unstable. To reflect the continuous nature of gene expression observed, we developed a quantitative score (Continuous Linear Enhanced Assessment of Renal cell carcinoma, or CLEAR) using expression analysis founded on pathologic parameters.\n\nMATERIALS AND METHODS265 ccRCC gene expression profiles were used to develop the CLEAR score, representing a genetic correlate of the continuum of morphological tumor grade. A signature derivation method based on correlation of CLEAR score with gene expression ranking was used to derive an 18-transcript signature. External validation was conducted in multiple public expression datasets.\n\nResultsAs a measure of intertumoral gene expression heterogeneity, the CLEAR score demonstrated both superior prognostic estimates (94% vs 83% adequacy index in TCGA dataset) and inverse correlation with anti-angiogenic tyrosine-kinase inhibition (65% vs 55% adequacy index) in comparison to previously proposed discrete subtyping classifications. Inverse correlation with high-dose interleukin-2 outcomes was also observed for the CLEAR score (p=0.05). 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 its ability to reflect intratumoral expression heterogeneity and further analysis showed average intertumoral heterogeneity exceeded intratumoral heterogeneity.\n\nConclusionsThe CLEAR score, a gene expression signature developed on histopathology, outperformed discrete subtype-classification in prognostic estimates and correlated better with treatment outcomes. Recognizing cancer as a continuum has important implications for laboratory and clinical research.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: protein structure prediction
- approach: statistical model
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- COL7A1 expression improves prognosis prediction for patients with clear cell renal cell carcinoma atop of stage
D. Koca, I. Seraudie, … L. Guyon·bioRxiv·Jan 16, 2023
- Single-cell gene programs define subtype identity and metastatic trajectories in renal cell carcinoma
Ariel Madrigal, Minjun Kim, … Hamed Shateri Najafabadi·medRxiv·Jul 16, 2026
- Patient stratification of clear cell renal cell carcinoma using the global transcription factor activity landscape derived from RNA-seq data
Yanyan Zhu, Shundong Cang, … Xiaoyun Huang·Frontiers in OncologyPeer-reviewed·Dec 4, 2020
- Integrated analysis of single-cell and transcriptome based RNA-seq multilayer network and WGCNA for construction and validation of an immune cell-related prognostic model in clear cell renal cell carcinoma
- Classification of clear cell renal cell carcinoma based on PKM alternative splicing
Xiangyü Li, Beste Turanlı, … Adil Mardinoğlu·HeliyonPeer-reviewed·Feb 1, 2020
- Not All Predictors Of RCC Clusters Are The Same: An individual sample predictive model to classify patients with metastatic renal cell carcinoma
Anupama Reddy, W. Jared Brewer, … Brian I. Rini·bioRxiv·Jun 29, 2026