2014
DOI: 10.1016/j.eururo.2014.02.035
|View full text |Cite
|
Sign up to set email alerts
|

ClearCode34: A Prognostic Risk Predictor for Localized Clear Cell Renal Cell Carcinoma

Abstract: Background Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting. Objective To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clin… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

8
251
3
3

Year Published

2015
2015
2023
2023

Publication Types

Select...
9

Relationship

1
8

Authors

Journals

citations
Cited by 245 publications
(265 citation statements)
references
References 24 publications
8
251
3
3
Order By: Relevance
“…We are pleased that de Velasco et al [1] have compared the performance of a 34‐gene model predictor (ClearCode34) developed by several authors [2] with our 8‐gene predictor [3] in an independent RNA‐sequencing dataset derived from frozen metastatic renal cell carcinoma (mRCC) samples [4]. We are also glad that that the authors have successfully reproduced our analyses as reported in reference [3].…”
mentioning
confidence: 75%
See 1 more Smart Citation
“…We are pleased that de Velasco et al [1] have compared the performance of a 34‐gene model predictor (ClearCode34) developed by several authors [2] with our 8‐gene predictor [3] in an independent RNA‐sequencing dataset derived from frozen metastatic renal cell carcinoma (mRCC) samples [4]. We are also glad that that the authors have successfully reproduced our analyses as reported in reference [3].…”
mentioning
confidence: 75%
“…We believe that validation for clinical application would ideally be in a prospective real‐world setting, using an independent cohort of formalin‐fixed paraffin‐embedded (FFPE) tissue materials. In our view, it would have been relevant for readers to consider additional background that our 8‐gene predictor was developed and optimized specifically on FFPE RCC tissue, and that the 34‐gene model predictor reported by the authors was developed on frozen‐tissue microarray data, with additional validation in FFPE RCC tissue [2]. Thus, we hope de Velasco et al would agree that outcomes in testing from a 54‐sample frozen‐tissue RNA‐sequencing dataset requiring additional data preprocessing, although an interesting adjunct for consideration, may not be a definitive comparison of these two scores, especially for a real‐world clinical setting.…”
mentioning
confidence: 99%
“…Yeni alt-tipler rekürensiz, kansere özgü ve genel sağkalım bakımından değerlendirildiğinde, ccA alt-tipinin avantajlı olduğu belirlendi. Bu metod, standard klinik ve patolojik değişkenlerin üzerine moleküler fenotiplendirme de ekleyerek tedavi planlamasının öngörüsünde kullanılmaya başlanmıştır (16). Papiller RHK, böbrek hücreli karsinomların ikinci en yaygın alt-tipini temsil eder ve altta yatan moleküler profil farklıdır.…”
Section: Renal Hücreli Karsinomun Moleküler Patogeneziunclassified
“…Despite high predictive capacity of these models (C-statistics of 0.809 [9] and 0.823 [10], respectively), patients with similar clinicopathological features or risk scores can still have divergent outcomes [11]. Therefore, addition of molecular markers to the current prognostic models could improve their prognostic value, which was demonstrated for ClearCode34 [12].…”
Section: Introductionmentioning
confidence: 99%