Primary GBM, accounting for over 90% of human GBMs, develops rapidly or de novo with no prior clinical disease. Large-scale genomic analyses have contributed greatly to the definition of the overall glioma landscape and datasets (TCGA) have enabled the division of GBMs into subclasses based on their genomic, transcriptomic, and signal transduction patterns. Sadly, despite these insights into the genetics of the disease and advances in neurosurgery, radiation and chemotherapy, its dismal prognosis has not changed significantly. The current paradigm for glioblastoma (GBM) therapy is based on the concept that each patient is treated with the standard of care consisting of temozolomide and ionizing radiation (TMZ/IR) with no upfront knowledge if they will benefit from first-line therapy. Following completion of treatment, a month delay (or more) is needed to assess outcome. Patients found nonresponsive are assigned to a second-line treatment and so on. Care of GBM patients is sub-optimal in the current era wherein the technology for whole genome sequencing is a reality and thus enabling personalized medicine using targeted agents based on genetic alterations of each patient is possible. This Project will establish the accuracy of voxel-based quantitative MRI-derived metrics for detection of early treatment response and tumor recurrence during TMZ/IR treatment and during administration of molecularly targeted therapies.
Specific Aim 1 will conduct a multi-center validation of the parametric response map (PRM) as a voxel-based imaging biomarker of early response in standard of care treatment of newly diagnosed glioblastoma. Tumors harvested at the time of initial surgical excision will be genetically profiled by Project 1 for stratification with responsiveness.
Specific Aim 2 will evaluate PRM early response imaging metrics in Phase II (MCC-15412/ABTC-0703) and Phase 1 (RTOG-0825/ACR1N-6686) clinical trials of molecularly targeted agents in patients with newly diagnosed malignant glioma.
Specific Aim 3 will develop and evaluate a novel PRM approach for automated and objective detection and spatial depiction of early GBM progression prior to detection by currently available conventional MRI-based criteria. Overall, this Project is designed to advance the clinical application and utility of novel MRI-based biomarker metrics as quantitative and predictive surrogates of patient tumor status. These approaches will provide for significant improvements in how imaging can be integrated into clinical trials as well as in improving clinical management and care of GBM patients. Overall, this effort is anticipated to lead to improved outcome for this patient population.

Public Health Relevance

This research effort will yield new and validated imaging biomarkers which will be used for significantly improving the care of GBM patients. Imaging biomarkers and patient-derived primary xenografts developed as part of this effort will allow determination of an individual GBM subtype will respond (or fail) therapy much earlier than currently possible. These capabilities will provide the foundation for changing clinical care to an individualized approach which will yield improved outcomes and lower cost to the health care system.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
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Special Emphasis Panel (ZCA1-RPRB-W)
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University of Michigan Ann Arbor
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Boes, Jennifer L; Hoff, Benjamin A; Bule, Maria et al. (2015) Parametric response mapping monitors temporal changes on lung CT scans in the subpopulations and intermediate outcome measures in COPD Study (SPIROMICS). Acad Radiol 22:186-94
Leder, Kevin; Pitter, Ken; Laplant, Quincey et al. (2014) Mathematical modeling of PDGF-driven glioblastoma reveals optimized radiation dosing schedules. Cell 156:603-16
Tsien, Christina; Cao, Yue; Chenevert, Thomas (2014) Clinical applications for diffusion magnetic resonance imaging in radiotherapy. Semin Radiat Oncol 24:218-26
Nazem-Zadeh, Mohammad-Reza; Chapman, Christopher H; Chenevert, Thomas et al. (2014) Response-driven imaging biomarkers for predicting radiation necrosis of the brain. Phys Med Biol 59:2535-47
Berezovsky, Artem D; Poisson, Laila M; Cherba, David et al. (2014) Sox2 promotes malignancy in glioblastoma by regulating plasticity and astrocytic differentiation. Neoplasia 16:193-206, 206.e19-25
Chenevert, Thomas L; Malyarenko, Dariya I; Newitt, David et al. (2014) Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling. Transl Oncol 7:65-71
Weber, Thomas G; Osl, Franz; Renner, Anja et al. (2014) Apoptosis imaging for monitoring DR5 antibody accumulation and pharmacodynamics in brain tumors noninvasively. Cancer Res 74:1913-23
Boes, Jennifer L; Hoff, Benjamin A; Hylton, Nola et al. (2014) Image registration for quantitative parametric response mapping of cancer treatment response. Transl Oncol 7:101-10
Galbán, Craig J; Boes, Jennifer L; Bule, Maria et al. (2014) Parametric response mapping as an indicator of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation. Biol Blood Marrow Transplant 20:1592-8
Galban, Stefanie; Jeon, Yong Hyun; Bowman, Brittany M et al. (2013) Imaging proteolytic activity in live cells and animal models. PLoS One 8:e66248

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