Dataset Information

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 clinical samples to develop an integrated model for biologically defined risk stratification.

Design, setting, and participants

A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina.

Outcome measurements and statistical analysis

Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence.

Results and limitations

The subtypes were significantly associated with RFS (p<0.01), CSS (p<0.01), and OS (p<0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms.

Conclusions

The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients.

Patient summary

We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.

SUBMITTER: Brooks SA

PROVIDER: S-EPMC4058355 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Publications

ClearCode34: A prognostic risk predictor for localized clear cell renal cell carcinoma.

Brooks Samira A SA Brannon A Rose AR Parker Joel S JS Fisher Jennifer C JC Sen Oishee O Kattan Michael W MW Hakimi A Ari AA Hsieh James J JJ Choueiri Toni K TK Tamboli Pheroze P Maranchie Jodi K JK Hinds Peter P Miller C Ryan CR Nielsen Matthew E ME Rathmell W Kimryn WK

European urology 20140225 1

<h4>Background</h4>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.<h4>Objective</h4>To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that coul ...[more]

PMID: 24613583

Similar Datasets

Project description:BackgroundThe tumor immune microenvironment has become the focus of research in clear cell renal cell carcinoma (ccRCC) due to its important role in immune surveillance post nephrectomy. This study investigates the correlation of tumor infiltrating immune cell characteristics with rates of recurrence following surgery in localized ccRCC.MethodsWe morphologically identified and scored tumor infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) stained slides of patients with localized ccRCC (stage ≥T1b excluding stage IV). The University of Alabama at Birmingham (UAB) dataset (n = 159) was used to discover and the Fox Chase Cancer Center (FCCC) dataset (n = 198) was used to validate the results of morphologic immune cell analysis. We then performed gene expression analysis using the Immune Profile panel by NanoString in the UAB cohort and identified immune cells and pathways associated with recurrence, followed by validation in the Cancer Genome Atlas (TCGA) ccRCC dataset. Infiltrating immune cell types were identified by gene expression deconvolution.ResultsThe presence of TILs identified by morphology correlated with higher T cell, Th1, CD8+ T and Treg gene signatures. Recurrence was associated with lower T cells and higher neutrophils. Higher Teffector (Teff)/Treg ratio correlated with lower rate of recurrence and was validated in the TCGA dataset. Genes associated with adaptive immune response were downregulated in tumors that recurred. Unsupervised hierarchical clustering identified a subset of patients with over-expression of adaptive response genes including CD8, CD3, GZMA/B, PRF1, IDO1, CTLA4, PDL1, ICOS and TIGIT. These patients had higher morphologic lymphocyte infiltration and T cell gene expression. Higher levels of TILs identified by morphology correlated with higher rates of recurrence in our discovery dataset but not in our validation set.ConclusionsRecurrence of ccRCC following surgery was associated with lower T cell infiltrate, lower adaptive immune response and higher neutrophil gene expression. Presence of higher Teff/Treg ratio correlated with lower recurrence.

Dataset Information

ClearCode34: A prognostic risk predictor for localized clear cell renal cell carcinoma.

Background

Objective

Design, setting, and participants

Outcome measurements and statistical analysis

Results and limitations

Conclusions

Patient summary

Publications

ClearCode34: A prognostic risk predictor for localized clear cell renal cell carcinoma.

Similar Datasets

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