Integration of Network Biology and Imaging to Study Cancer Phenotypes and Responses.

Ever growing "omics" data and continuously accumulated biological knowledge provide an unprecedented opportunity to identify molecular biomarkers and their interactions that are responsible for cancer phenotypes that can be accurately defined by clinical measurements such as in vivo imaging. Since signaling or regulatory networks are dynamic and context-specific, systematic efforts to characterize such structural alterations must effectively distinguish significant network rewiring from random background fluctuations. Here we introduced a novel integration of network biology and imaging to study cancer phenotypes and responses to treatments at the molecular systems level. Specifically, Differential Dependence Network (DDN) analysis was used to detect statistically significant topological rewiring in molecular networks between two phenotypic conditions, and in vivo Magnetic Resonance Imaging (MRI) was used to more accurately define phenotypic sample groups for such differential analysis. We applied DDN to analyze two distinct phenotypic groups of breast cancer and study how genomic instability affects the molecular network topologies in high-grade ovarian cancer. Further, FDA-approved arsenic trioxide (ATO) and the ND2-SmoA1 mouse model of Medulloblastoma (MB) were used to extend our analyses of combined MRI and Reverse Phase Protein Microarray (RPMA) data to assess tumor responses to ATO and to uncover the complexity of therapeutic molecular biology.

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

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.

Quantifying the CDK inhibitor VMY-1-103's activity and tissue levels in an in vivo tumor model by LC-MS/MS and by MRI.

The development of new small molecule-based therapeutic drugs requires accurate quantification of drug bioavailability, biological activity and treatment efficacy. Rapidly measuring these endpoints is often hampered by the lack of efficient assay platforms with high sensitivity and specificity. Using an in vivo model system, we report a simple and sensitive liquid chromatography-tandem mass spectrometry assay to quantify the bioavailability of a recently developed novel cyclin-dependent kinase inhibitor VMY-1-103, a purvalanol B-based analog whose biological activity is enhanced via dansylation. We developed a rapid organic phase extraction technique and validated wide and functional VMY-1-103 distribution in various mouse tissues, consistent with its enhanced potency previously observed in a variety of human cancer cell lines. More importantly, in vivo MRI and single voxel proton MR-Spectroscopy further established that VMY-1-103 inhibited disease progression and affected key metabolites in a mouse model of hedgehog-driven medulloblastoma.

CAM-CM: a signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues.

SUMMARY: In vivo dynamic contrast-enhanced imaging tools provide non-invasive methods for analyzing various functional changes associated with disease initiation, progression and responses to therapy. The quantitative application of these tools has been hindered by its inability to accurately resolve and characterize targeted tissues due to spatially mixed tissue heterogeneity. Convex Analysis of Mixtures - Compartment Modeling (CAM-CM) signal deconvolution tool has been developed to automatically identify pure-volume pixels located at the corners of the clustered pixel time series scatter simplex and subsequently estimate tissue-specific pharmacokinetic parameters. CAM-CM can dissect complex tissues into regions with differential tracer kinetics at pixel-wise resolution and provide a systems biology tool for defining imaging signatures predictive of phenotypes. AVAILABILITY: The MATLAB source code can be downloaded at the authors' website www.cbil.ece.vt.edu/software.htm CONTACT: yuewang@vt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.