In Vivo Bioluminescence Tomography for Monitoring Breast Tumor Growth and Metastatic Spreading: Comparative Study and Mathematical Modeling

Séverine Mollard 1, 2, 3 Raphaelle Fanciullino 1 Sarah Giacometti 1 Cindy Serdjebi 1 Sebastien Benzekry 4, 5 Joseph Ciccolini 1
CRO2 - Centre de Recherches en Oncologie biologique et Oncopharmacologie
5 MONC - Modélisation Mathématique pour l'Oncologie
IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest, Institut Bergonié - CRLCC Bordeaux
Abstract : This study aimed at evaluating the reliability and precision of Diffuse Luminescent Imaging Tomography (DLIT) for monitoring primary tumor and metastatic spreading in breast cancer mice, and to develop a biomathematical model to describe the collected data. Using orthotopic mammary fat pad model of breast cancer (MDAMB231-Luc) in mice, we monitored tumor and metastatic spreading by three-dimensional (3D) bioluminescence and cross-validated it with standard bioluminescence imaging, caliper measurement and necropsy examination. DLIT imaging proved to be reproducible and reliable throughout time. It was possible to discriminate secondary lesions from the main breast cancer, without removing the primary tumor. Preferential metastatic sites were lungs, peritoneum and lymph nodes. Necropsy examinations confirmed DLIT measurements. Marked differences in growth profiles were observed, with an overestimation of the exponential phase when using a caliper as compared with bioluminescence. Our mathematical model taking into account the balance between living and necrotic cells proved to be able to reproduce the experimental data obtained with a caliper or DLIT imaging, because it could discriminate proliferative living cells from a more composite mass consisting of tumor cells, necrotic cell, or inflammatory tissues. DLIT imaging combined with mathematical modeling could be a powerful and informative tool in experimental oncology. Breast cancer is the most frequent spontaneous malignancy diagnosed in women and metastatic disease still bears a high mortality rate despite continuous efforts to improve clinical outcome 1,2. It is estimated that there will be more than 400 000 new cases diagnosed every years in Europe, claiming approximately 90 000 deaths 3. Despite current medical and technological advances, metastasis development remains a critical issue since advanced disease is associated with poor prognosis with most solid tumors. Metastasis has always been portrayed as the ultimate step of the progressing breast cancers; in fact 90% of mortality from cancer is attributable to metastases 4,5. Recent evidence indicates that about a third of women diagnosed with small asymptomatic breast tumors (i.e., ~4 mm) already bear disseminated breast cancer cells in different organs 6,7. Moreover, these occult micro-metastases can remain dormant for years before reemerging as potentially incurable secondary tumors as they can be surprisingly insensitive to adjuvant therapies that were originally effective against the primary tumor 4,6. In this context, the study of metastatic spreading and the development of new strategies to address this issue remains an important challenge in experimental and translational oncology. The use of orthotopic murine models has been a very useful tool for studying breast cancer in translational research 8,9. For decades, regular killing of the tumor-bearing animals to monitor the secondary lesions throughout time was the only way to evaluate and to understand such metastasis spreading. This strategy required an extremely high number of animals, thus infringing the " 3 Rs rule " when conducting animal research. To address this issue, different non-invasive
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Article dans une revue
Scientific Reports, Nature Publishing Group, 2016, 6, pp.10. 〈10.1038/srep36173〉
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Séverine Mollard, Raphaelle Fanciullino, Sarah Giacometti, Cindy Serdjebi, Sebastien Benzekry, et al.. In Vivo Bioluminescence Tomography for Monitoring Breast Tumor Growth and Metastatic Spreading: Comparative Study and Mathematical Modeling. Scientific Reports, Nature Publishing Group, 2016, 6, pp.10. 〈10.1038/srep36173〉. 〈hal-01392861〉



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