M. Malvezzi, European cancer mortality predictions for the year 2016 with focus on leukaemias, Annals of Oncology, vol.27, issue.4, pp.725-731, 2016.
DOI : 10.1093/annonc/mdw022

E. Senkus, F. Cardoso, and O. Pagani, Time for more optimism in metastatic breast cancer?, Cancer Treatment Reviews, vol.40, issue.2, pp.220-228, 2014.
DOI : 10.1016/j.ctrv.2013.09.015

D. Leo and A. , New approaches for improving outcomes in breast cancer in Europe, The Breast, vol.24, issue.4, pp.321-330, 2015.
DOI : 10.1016/j.breast.2015.03.001

G. P. Gupta and J. Massague, Cancer Metastasis: Building a Framework, Cell, vol.127, issue.4, pp.679-695, 2006.
DOI : 10.1016/j.cell.2006.11.001

S. Valastyan and R. A. Weinberg, Tumor Metastasis: Molecular Insights and Evolving Paradigms, Cell, vol.147, issue.2, pp.275-292, 2011.
DOI : 10.1016/j.cell.2011.09.024
URL : http://doi.org/10.1016/j.cell.2011.09.024

K. Augoff, B. Mccue, E. F. Plow, and K. Sossey-alaoui, miR-31 and its host gene lncRNA LOC554202 are regulated by promoter hypermethylation in triple-negative breast cancer, Mol Cancer, vol.11, issue.5, 2012.

J. Talmadge, Models of Metastasis in Drug Discovery, Methods Mol Biol, vol.602, pp.215-233, 2010.
DOI : 10.1007/978-1-60761-058-8_13

N. Hartung, Mathematical Modeling of Tumor Growth and Metastatic Spreading: Validation in Tumor-Bearing Mice, Cancer Research, vol.74, issue.22, pp.6397-6407, 2014.
DOI : 10.1158/0008-5472.CAN-14-0721
URL : https://hal.archives-ouvertes.fr/hal-01107681

I. S. Kim and S. H. Baek, Mouse models for breast cancer metastasis, Biochemical and Biophysical Research Communications, vol.394, issue.3, pp.443-447, 2010.
DOI : 10.1016/j.bbrc.2010.03.070

J. Condeelis and R. Weissleder, In Vivo Imaging in Cancer, Cold Spring Harbor Perspectives in Biology, vol.2, issue.12, p.3848, 2010.
DOI : 10.1101/cshperspect.a003848

T. Poeschinger, A. Renner, T. Weber, and W. Scheuer, Bioluminescence Imaging Correlates with Tumor Serum Marker, Organ Weights, Histology, and Human DNA Levels during Treatment of Orthotopic Tumor Xenografts with Antibodies, Molecular Imaging and Biology, vol.7, issue.1, pp.28-39, 2013.
DOI : 10.1007/s11307-012-0559-x

D. Razansky, N. C. Deliolanis, C. Vinegoni, and V. Ntziachristos, Deep Tissue Optical and Optoacoustic Molecular Imaging Technologies for Pre-Clinical Research and Drug Discovery, Current Pharmaceutical Biotechnology, vol.13, issue.4, pp.504-522, 2012.
DOI : 10.2174/138920112799436258

P. Ray, Multimodality molecular imaging of disease progression in living subjects, Journal of Biosciences, vol.12, issue.3, pp.499-504, 2011.
DOI : 10.1007/s12038-011-9079-0

R. A. Madero-visbal, Bioluminescence imaging correlates with tumor progression in an orthotopic mouse model of lung cancer, Surgical Oncology, vol.21, issue.1, pp.23-29, 2012.
DOI : 10.1016/j.suronc.2010.07.008

B. W. Rice, M. D. Cable, and M. B. Nelson, In vivo imaging of light-emitting probes, Journal of Biomedical Optics, vol.6, issue.4, pp.432-440, 2001.
DOI : 10.1117/1.1413210

C. Kuo, O. Coquoz, T. L. Troy, H. Xu, and B. W. Rice, Three-dimensional reconstruction of in vivo bioluminescent sources based on multispectral imaging, Journal of Biomedical Optics, vol.12, issue.2, p.24007, 2007.
DOI : 10.1117/1.2717898

L. Bu, X. Ma, Y. Tu, B. Shen, and Z. Cheng, Optical Image-Guided Cancer Therapy, Current Pharmaceutical Biotechnology, vol.14, issue.8, pp.723-732, 2013.
DOI : 10.2174/1389201014666131226112507

T. Xu, D. Close, W. Handagama, E. Marr, G. Sayler et al., The Expanding Toolbox of, In Vivo Bioluminescent Imaging. Front Oncol, vol.6, p.150, 2016.

C. Germain-genevois, O. Garandeau, and F. Couillaud, Detection of Brain Tumors and Systemic Metastases Using NanoLuc and Fluc for Dual Reporter Imaging, Molecular Imaging and Biology, vol.12, issue.1, pp.62-69, 2016.
DOI : 10.1007/s11307-015-0864-2

S. Pesnel, Quantitation in Bioluminescence Imaging by Correction of Tissue Absorption for Experimental Oncology, Molecular Imaging and Biology, vol.113, issue.4, pp.646-652, 2011.
DOI : 10.1007/s11307-010-0387-9

H. R. Basevi, Compressive sensing based reconstruction in bioluminescence tomography improves image resolution and robustness to noise, Biomedical Optics Express, vol.3, issue.9, pp.2131-2141, 2012.
DOI : 10.1364/BOE.3.002131

T. Mizuno, K. Mohri, S. Nasu, K. Danjo, and H. Okamoto, Dual imaging of pulmonary delivery and gene expression of dry powder inhalant by fluorescence and bioluminescence, Journal of Controlled Release, vol.134, issue.2, pp.149-154, 2009.
DOI : 10.1016/j.jconrel.2008.11.018

G. Carlsson, B. Gullberg, and L. Hafstrom, Estimation of liver tumor volume using different formulas?An experimental study in rats, Journal of Cancer Research and Clinical Oncology, vol.34, issue.1, pp.20-23, 1983.
DOI : 10.1007/BF00391826

M. Simeoni, Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents, Cancer Research, vol.64, issue.3, pp.1094-1101, 2004.
DOI : 10.1158/0008-5472.CAN-03-2524

S. Benzekry, Classical Mathematical Models for Description and Prediction of Experimental Tumor Growth, PLoS Computational Biology, vol.90, issue.8, p.1003800, 2014.
DOI : 10.1371/journal.pcbi.1003800.s010

S. Benzekry, A. Tracz, M. Mastri, R. Corbelli, D. Barbolosi et al., Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach, Cancer Research, vol.76, issue.3, pp.535-547, 2016.
DOI : 10.1158/0008-5472.CAN-15-1389
URL : https://hal.archives-ouvertes.fr/hal-01222046

P. Workman, UKCCCR Guidelines for the welfare of animals in experimental neoplasia, CANCER AND METASTASIS REVIEW, vol.8, issue.1, pp.195-201, 1988.
DOI : 10.1007/BF00047059

O. Saito, T. Aoe, and T. Yamamoto, Analgesic effects of nonsteroidal antiinflammatory drugs, acetaminophen, and morphine in a mouse model of bone cancer pain, Journal of Anesthesia, vol.123, issue.3, pp.218-224, 2005.
DOI : 10.1007/s00540-005-0323-3

M. Lavielle, Mixed effects models for the population approach, Boca RatonCRC, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01122873

E. Kuhn and M. Lavielle, Maximum likelihood estimation in nonlinear mixed effects models, Computational Statistics & Data Analysis, vol.49, issue.4, pp.1020-1038, 2005.
DOI : 10.1016/j.csda.2004.07.002