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Updated: Oct 16, 2025
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression
Lindsay S Cooley1,2, Justine Rudewicz1,2,3, Wilfried Souleyreau1,2
1University of Bordeaux, LAMC, Pessac, France.
Background:
Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy.
Methods:
In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data.
Results:
Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance.
Conclusion:
A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.
Insights
Researchers identified SAA2 and CFB as key biomarkers for predicting Renal Cell Carcinoma (RCC) progression and therapeutic response. Mathematical modeling using these biomarkers helps predict tumor relapse, offering hope for improved RCC treatment strategies.
Area of Science:
- Oncology
- Genomics
- Translational Medicine
Background:
- Renal Cell Carcinoma (RCC) presents a significant therapeutic challenge, particularly in metastatic stages, with a low 5-year survival rate.
- Therapy failure in RCC is often attributed to the absence of validated biomarkers and limited understanding of its progression mechanisms.
- Investigating the molecular drivers of RCC progression is crucial for developing more effective treatments.
Purpose of the Study:
- To identify molecular markers and gene expression patterns associated with Renal Cell Carcinoma (RCC) progression.
- To generate and analyze increasingly aggressive cell lines to model different stages of RCC, including primary tumor growth, circulation survival, and metastatic spread.
- To correlate molecular data with clinical outcomes and develop predictive models for tumor relapse.
Main Methods:
- Serial in vivo passaging of mouse renal cancer RENCA cells to generate lines with enhanced aggressiveness.
- Large-scale transcriptome, genome, and methylome analyses of generated cell lines.
- Functional genomics, clinical data correlation, and computational analysis including machine learning for relapse prediction.
Main Results:
- In vivo passaging increased RENCA cell aggressiveness, reducing survival and enhancing tumor growth and lung metastasis.
- Transcriptome and methylome analyses revealed distinct molecular signatures of aggressiveness without genomic variation.
- SAA2 and CFB were identified as soluble prognostic and predictive biomarkers; their combination significantly impacted distant metastasis-free survival.
Conclusions:
- A combination of experimental and mathematical modeling provided valuable data for predicting RCC clinical evolution.
- SAA2 and CFB serve as promising biomarkers for assessing therapeutic response and predicting prognosis in RCC.
- A validated computational model aids in predicting tumor progression and relapse in Renal Cell Carcinoma.
