Preclinical magnetic resonance imaging and systems biology in cancer research: current applications and challenges

• Published in: The American journal of pathology Vol. 182; no. 2; pp. 312 - 318
• Main Authors: Albanese, Chris, Rodriguez, Olga C, VanMeter, John, Fricke, Stanley T, Rood, Brian R, Lee, YiChien, Wang, Sean S, Madhavan, Subha, Gusev, Yuriy, Petricoin, 3rd, Emanuel F, Wang, Yue
• Format: Journal Article
• Language: English
• Published: United States American Society for Investigative Pathology 01-02-2013
• Subjects: Animals; Antineoplastic Agents - therapeutic use; Computational Biology; Humans; Magnetic Resonance Imaging - methods; Mini-Review; Neoplasms - diagnosis; Neoplasms - drug therapy; Systems Biology - methods
• ISSN: 1525-2191; 0002-9440; 1525-2191
• DOI: 10.1016/j.ajpath.2012.09.024

Biologically accurate mouse models of human cancer have become important tools for the study of human disease. The anatomical location of various target organs, such as brain, pancreas, and prostate, makes determination of disease status difficult. Imaging modalities, such as magnetic resonance imaging, can greatly enhance diagnosis, and longitudinal imaging of tumor progression is an important source of experimental data. Even in models where the tumors arise in areas that permit visual determination of tumorigenesis, longitudinal anatomical and functional imaging can enhance the scope of studies by facilitating the assessment of biological alterations, (such as changes in angiogenesis, metabolism, cellular invasion) as well as tissue perfusion and diffusion. One of the challenges in preclinical imaging is the development of infrastructural platforms required for integrating in vivo imaging and therapeutic response data with ex vivo pathological and molecular data using a more systems-based multiscale modeling approach. Further challenges exist in integrating these data for computational modeling to better understand the pathobiology of cancer and to better affect its cure. We review the current applications of preclinical imaging and discuss the implications of applying functional imaging to visualize cancer progression and treatment. Finally, we provide new data from an ongoing preclinical drug study demonstrating how multiscale modeling can lead to a more comprehensive understanding of cancer biology and therapy.