This project will use novel quantitative imaging methods to guide biopsies to biologically distinct regions of primary and post-treatment recurrent GBM for targeted exome, epigenome and transcriptome analysis. Our goal is to identify naturally evolving and treatment-induced mutations and epimutations that promote the selective outgrowth of malignant subclones over lime. Genomic analysis of cancer is typically conducted at a single time point and on a single piece of the bulk resection without knowledge of its original context within the heterogeneous tumor. In contrast to these traditional genomic studies, an image guided approach to newly diagnosed and recurrent tumors could enrich for the detection of drivers of tumor growth by linking mutations and epimutations to regions of aggressive tumor growth in vivo. We will use innovative metabolic and physiologic imaging to identify regions with different levels of proliferation and hypoxia within the same patient. To our knowledge, this would be the first time that advanced imaging will be used to guide genomic or epigenomic analysis of any human tumor.
In Aim 1, we will identify functional mutations and epimutations that exhibit intratumoral heterogeneity within newly diagnosed GBM.
In Aim 2, we will identify functional mutations and epimutations commonly acquired during tumor progression using image guided tissue samples from treated, recurrent GBM, including paired samples from individual patients over time. Our preliminary data show that chemotherapy can have a profound effect on selective outgrowth of malignant subclones. The integration of data from Aims 1 and 2 will identify subclones in newly diagnosed tumor that exhibit selective outgrowth to become the dominant clone(s) at recurrence, and the sequential biallelic events involving intersecting genetic and epigenetic mechanisms that contribute to their enhanced growth potential. Candidate driver alterations will be evaluated using a mature computational pipeline, and will experimentally be tested for predicted functional effect. These studies could therefore impact patient care by the identification of common drivers specific to recurrence, defining the influence of therapy on tumor evolution, and incorporating profiles of primary and recurrent tumors into personalized treatment plans.

Public Health Relevance

State-of-the-art genomics and epignomics techniques will be guided by advanced imaging and tissue analyses to comprehensively characterize regional heterogeneity and evolution of glioblastoma. We hope to identify driver mutations and mutations that are uniquely associated with tumor progression, including therapy induced mutations. This will better inform the design of clinical trials and assist clinicians in tailoring treatment in individual patients.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA118816-07
Application #
8738071
Study Section
Special Emphasis Panel (ZCA1-RPRB-2)
Project Start
Project End
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
7
Fiscal Year
2014
Total Cost
$160,909
Indirect Cost
$58,921
Name
University of California San Francisco
Department
Type
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
Ohba, Shigeo; Mukherjee, Joydeep; See, Wendy L et al. (2014) Mutant IDH1-driven cellular transformation increases RAD51-mediated homologous recombination and temozolomide resistance. Cancer Res 74:4836-44
Park, Ilwoo; Larson, Peder E Z; Tropp, James L et al. (2014) Dynamic hyperpolarized carbon-13 MR metabolic imaging of nonhuman primate brain. Magn Reson Med 71:19-25
Elkhaled, Adam; Jalbert, Llewellyn; Constantin, Alexandra et al. (2014) Characterization of metabolites in infiltrating gliomas using ex vivo ¹H high-resolution magic angle spinning spectroscopy. NMR Biomed 27:578-93
Ahluwalia, Manmeet S; Chang, Susan M (2014) Medical therapy of gliomas. J Neurooncol 119:503-12
Lupo, Janine M; Nelson, Sarah J (2014) Advanced magnetic resonance imaging methods for planning and monitoring radiation therapy in patients with high-grade glioma. Semin Radiat Oncol 24:248-58
Park, Ilwoo; Mukherjee, Joydeep; Ito, Motokazu et al. (2014) Changes in pyruvate metabolism detected by magnetic resonance imaging are linked to DNA damage and serve as a sensor of temozolomide response in glioblastoma cells. Cancer Res 74:7115-24
Rosenbluth, Kathryn Hammond; Gimenez, Francisco; Kells, Adrian P et al. (2013) Automated segmentation tool for brain infusions. PLoS One 8:e64452
Rosenbluth, Kathryn H; Martin, Alastair J; Mittermeyer, Stephan et al. (2013) Rapid inverse planning for pressure-driven drug infusions in the brain. PLoS One 8:e56397
Ozhinsky, Eugene; Vigneron, Daniel B; Chang, Susan M et al. (2013) Automated prescription of oblique brain 3D magnetic resonance spectroscopic imaging. Magn Reson Med 69:920-30
Constantin, Alexandra; Elkhaled, Adam; Jalbert, Llewellyn et al. (2012) Identifying malignant transformations in recurrent low grade gliomas using high resolution magic angle spinning spectroscopy. Artif Intell Med 55:61-70

Showing the most recent 10 out of 23 publications