Effective treatment of human brain tumors poses a major challenge. One of the underlying problems is lack of reliable, noninvasive means of assessing tumor malignancy and of assessing treatment response. The current proposal aims to develop and evaluate a new, noninvasive method of assessing tumor activity based upon quantitative, statistical measures of vessel shape determined from magnetic resonance angiograms (MRA). Although we are simultaneously pursuing this approach in human subjects, there are many issues that cannot be readily addressed in human patients since whole-brain histological information is not available. This proposal employs two genetically engineered mouse models in which tumors reliably arise cte novo, in the appropriate anatomical location, with appropriate histology, and with a predictable time course. One model is a glioma that progresses from no tumor to low-grade glioma to grade 3 glioma to glioblastoma. The second model is a heavily vascular choroid plexus carcinoma that mimics the growth of metastatic tumors by producing microscopic foci of malignancy that subsequently grow to large size. This proposal aims to develop and test the proposed method of tumor activity assessment so as: a) to determine whether the new approach can both correctly classify changing tumor grade and predict tumor treatment response under both anti-angiogenic and more generic therapy, b) to compare the ability of the new method to that of more traditional imaging assessment techniques including measurement of tumor volume, perfusion imaging, and permeability imaging, and c) to determine if vessel shape abnormalities as perceived by MRA can be correlated with cancer-induced changes to the vessel wall as evaluated by histology. This proposal addresses a major clinical problem that has been highly refractory to solution by traditional methods. At the same time, the proposed work also addresses important biological questions that could lead to insights into mechanisms of tumor growth. The long term potential of the proposed work includes not only improved means of early clinical evaluation of tumor treatment, but also new methods of developing and testing therapeutic agents in both preclinical animal models and human subjects.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA124608-04
Application #
7793469
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Zhang, Huiming
Project Start
2007-08-06
Project End
2011-05-31
Budget Start
2010-06-01
Budget End
2011-05-31
Support Year
4
Fiscal Year
2010
Total Cost
$461,177
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Surgery
Type
Schools of Medicine
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Song, Yurong; Zhang, Qian; Kutlu, Burak et al. (2013) Evolutionary etiology of high-grade astrocytomas. Proc Natl Acad Sci U S A 110:17933-8
Freedman, Rachel A; Bullitt, Elizabeth; Sun, Lixian et al. (2011) A phase II study of sagopilone (ZK 219477; ZK-EPO) in patients with breast cancer and brain metastases. Clin Breast Cancer 11:376-83
Bullitt, Elizabeth; Ewend, Matthew; Vredenburgh, James et al. (2009) Computerized assessment of vessel morphological changes during treatment of glioblastoma multiforme: report of a case imaged serially by MRA over four years. Neuroimage 47 Suppl 2:T143-51
Piliere, Guillaume; Van Horn, Mark H; Dixon, Robert et al. (2009) Vessel target location estimation during the TIPS procedure. Med Image Anal 13:519-29
Bullitt, E; Aylward, S R; Van Dyke, T et al. (2007) Computer-assisted measurement of vessel shape from 3T magnetic resonance angiography of mouse brain. Methods 43:29-34
Bullitt, Elizabeth; Reardon, David A; Smith, J Keith (2007) A review of micro- and macrovascular analyses in the assessment of tumor-associated vasculature as visualized by MR. Neuroimage 37 Suppl 1:S116-9
Prastawa, Marcel; Bullitt, Elizabeth; Gerig, Guido (2005) Synthetic ground truth for validation of brain tumor MRI segmentation. Med Image Comput Comput Assist Interv 8:26-33