Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression

Molecular Cancer

Macha Nikolski¹  Domitille Chalopin-Fillot¹  Jean-Marc Ferrero²  Alain Ravaud³  Jean-Christophe Bernhard⁴  Damien Ambrosetti⁵  Gilles Pagès⁶  Maeva Dufies⁶  Sebastien Benzekry⁷  Arturo Alvarez-Arenas⁷  Andrea Emanuelli⁸  Lindsay S. Cooley⁸  Andreas Bikfalvi⁸  Wilfried Souleyreau⁸  Justine Rudewicz⁸  Raphael Pineau⁹  Francesco Falciani^1,10  Kim Clarke^1,10  Sylvie Négrier^1,11  Elodie Modave^1,12  Diether Lambrechts^1,12 

[1] Bordeaux Bioinformatics Center, CBiB, University of Bordeaux;Centre Antoine Lacassagne, Clinical Research Department;Centre Hospitalier Universitaire (CHU) de Bordeaux, service d’oncologie médicale;Centre Hospitalier Universitaire (CHU) de Bordeaux, service d’urologie;Centre Hospitalier Universitaire (CHU) de Nice, Hôpital Pasteur, Central laboratory of Pathology;Centre Scientifique de Monaco, Biomedical Department;Mathematical Modeling for Oncology Team, Inria Bordeaux Sud-Ouest;University of Bordeaux, LAMC;University of Bordeaux, “Service Commun des Animaleries”;University of Liverpool, Institute of Systems, Molecular and Integrative Biology;Université de Lyon, Centre Léon Bérard;VIB-KU Leuven Center for Cancer Biology;

关键词: Metastasis;    Prognostic markers renal cell carcinoma;    Systems biology approach;    Tumor model;    SAA2;    CFB;   

DOI  :  10.1186/s12943-021-01416-5

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown