1
|
Cooley LS, Rudewicz J, Souleyreau W, Emanuelli A, Alvarez-Arenas A, Clarke K, Falciani F, Dufies M, Lambrechts D, Modave E, Chalopin-Fillot D, Pineau R, Ambrosetti D, Bernhard JC, Ravaud A, Négrier S, Ferrero JM, Pagès G, Benzekry S, Nikolski M, Bikfalvi A. Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression. Mol Cancer 2021; 20:136. [PMID: 34670568 PMCID: PMC8527701 DOI: 10.1186/s12943-021-01416-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 08/30/2021] [Indexed: 01/22/2023] Open
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.
Collapse
Affiliation(s)
- Lindsay S Cooley
- University of Bordeaux, LAMC, Pessac, France
- INSERM U1029, Pessac, France
| | - Justine Rudewicz
- University of Bordeaux, LAMC, Pessac, France
- INSERM U1029, Pessac, France
- Bordeaux Bioinformatics Center, CBiB, University of Bordeaux, Bordeaux, France
| | | | - Andrea Emanuelli
- University of Bordeaux, LAMC, Pessac, France
- INSERM U1029, Pessac, France
| | - Arturo Alvarez-Arenas
- Mathematical Modeling for Oncology Team, Inria Bordeaux Sud-Ouest, Talence, France
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - Kim Clarke
- University of Liverpool, Institute of Systems, Molecular and Integrative Biology, Liverpool, UK
| | - Francesco Falciani
- University of Liverpool, Institute of Systems, Molecular and Integrative Biology, Liverpool, UK
| | - Maeva Dufies
- Centre Scientifique de Monaco, Biomedical Department, Principality of Monaco, Monaco
- University Côte d'Azur, Institute for Research on Cancer and Aging of Nice (IRCAN), CNRS UMR 7284; INSERM U1081, Centre Antoine Lacassagne, Nice, France
| | | | - Elodie Modave
- VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium
| | - Domitille Chalopin-Fillot
- Bordeaux Bioinformatics Center, CBiB, University of Bordeaux, Bordeaux, France
- University of Bordeaux, IBGC, Bordeaux, France
| | - Raphael Pineau
- University of Bordeaux, "Service Commun des Animaleries", Bordeaux, France
| | - Damien Ambrosetti
- Centre Hospitalier Universitaire (CHU) de Nice, Hôpital Pasteur, Central laboratory of Pathology, Nice, France
| | | | - Alain Ravaud
- Centre Hospitalier Universitaire (CHU) de Bordeaux, service d'oncologie médicale, Bordeaux, France
| | | | - Jean-Marc Ferrero
- Centre Antoine Lacassagne, Clinical Research Department, Nice, France
| | - Gilles Pagès
- Centre Scientifique de Monaco, Biomedical Department, Principality of Monaco, Monaco
- University Côte d'Azur, Institute for Research on Cancer and Aging of Nice (IRCAN), CNRS UMR 7284; INSERM U1081, Centre Antoine Lacassagne, Nice, France
| | - Sebastien Benzekry
- Mathematical Modeling for Oncology Team, Inria Bordeaux Sud-Ouest, Talence, France
- COMPO team-project, Inria Sophia Antipolis and CRCM, Inserm U1068, CNRS UMR7258, Aix-Marseille University UM105, Institut Paoli-Calmettes, Marseille, France
| | - Macha Nikolski
- Bordeaux Bioinformatics Center, CBiB, University of Bordeaux, Bordeaux, France
- University of Bordeaux, IBGC, Bordeaux, France
| | - Andreas Bikfalvi
- University of Bordeaux, LAMC, Pessac, France.
- INSERM U1029, Pessac, France.
| |
Collapse
|
2
|
Salaud C, Alvarez-Arenas A, Geraldo F, Belmonte-Beitia J, Calvo GF, Gratas C, Pecqueur C, Garnier D, Pérez-Garcià V, Vallette FM, Oliver L. Mitochondria transfer from tumor-activated stromal cells (TASC) to primary Glioblastoma cells. Biochem Biophys Res Commun 2020; 533:139-147. [PMID: 32943183 DOI: 10.1016/j.bbrc.2020.08.101] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 08/26/2020] [Indexed: 12/19/2022]
Abstract
The tumor microenvironment (TME) controls many aspects of cancer development but little is known about its effect in Glioblastoma (GBM), the main brain tumor in adults. Tumor-activated stromal cell (TASC) population, a component of TME in GBM, was induced in vitro by incubation of MSCs with culture media conditioned by primary cultures of GBM under 3D/organoid conditions. We observed mitochondrial transfer by Tunneling Nanotubes (TNT), extracellular vesicles (EV) and cannibalism from the TASC to GBM and analyzed its effect on both proliferation and survival. We created primary cultures of GBM or TASC in which we have eliminated mitochondrial DNA [Rho 0 (ρ0) cells]. We found that TASC, as described in other cancers, increased GBM proliferation and resistance to standard treatments (radiotherapy and chemotherapy). We analyzed the incorporation of purified mitochondria by ρ0 and ρ+ cells and a derived mathematical model taught us that ρ+ cells incorporate more rapidly pure mitochondria than ρ0 cells.
Collapse
Affiliation(s)
- Céline Salaud
- Université de Nantes, INSERM, CRCINA, Nantes, 44007, France; CHU de Nantes, Department of Neurosurgery, Nantes, 44007, France
| | - Arturo Alvarez-Arenas
- Department of Mathematics & MOLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Fanny Geraldo
- Université de Nantes, INSERM, CRCINA, Nantes, 44007, France
| | - Juan Belmonte-Beitia
- Department of Mathematics & MOLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Gabriel F Calvo
- Department of Mathematics & MOLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Catherine Gratas
- Université de Nantes, INSERM, CRCINA, Nantes, 44007, France; CHU de Nantes, Department of Neurosurgery, Nantes, 44007, France
| | | | - Delphine Garnier
- Université de Nantes, INSERM, CRCINA, Nantes, 44007, France; ICO, Laboratoire de Biologie Du Cancer et Théranostics, St Herblain, 44805, France
| | - Victor Pérez-Garcià
- Department of Mathematics & MOLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - François M Vallette
- Université de Nantes, INSERM, CRCINA, Nantes, 44007, France; ICO, Laboratoire de Biologie Du Cancer et Théranostics, St Herblain, 44805, France.
| | - Lisa Oliver
- Université de Nantes, INSERM, CRCINA, Nantes, 44007, France; CHU de Nantes, Department of Neurosurgery, Nantes, 44007, France.
| |
Collapse
|
3
|
Budia I, Alvarez-Arenas A, Woolley TE, Calvo GF, Belmonte-Beitia J. Radiation protraction schedules for low-grade gliomas: a comparison between different mathematical models. J R Soc Interface 2019; 16:20190665. [PMID: 31822220 DOI: 10.1098/rsif.2019.0665] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
We optimize radiotherapy (RT) administration strategies for treating low-grade gliomas. Specifically, we consider different tumour growth laws, both with and without spatial effects. In each scenario, we find the optimal treatment in the sense of maximizing the overall survival time of a virtual low-grade glioma patient, whose tumour progresses according to the examined growth laws. We discover that an extreme protraction therapeutic strategy, which amounts to substantially extending the time interval between RT sessions, may lead to better tumour control. The clinical implications of our results are also presented.
Collapse
Affiliation(s)
- I Budia
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
| | - A Alvarez-Arenas
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
| | - T E Woolley
- School of Mathematics, Cardiff University, Senghennydd Road, Cardiff CF24 4AG, UK
| | - G F Calvo
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
| | - J Belmonte-Beitia
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
| |
Collapse
|