This proposal builds upon the on-going multi-center family study entitled International Study of FamilialGlioma (Gliogene - R01CA119215), and the funded Brain SPORE at University of California, San Francisco(UCSF) to explore genetic predictors of neurocognitive (NC) and clinical outcome. We will leverage thesefunded resources by using the existing infrastructures for recruiting glioma patients at The University of Texas. D. Anderson Cancer Center (UTMDACC) and UCSF. We will recruit from both sites a total of 800 patientsbetween the ages of 18 and 70 years with newly diagnosed high-grade glioma (GBM). NC outcomes areshown to vary across patients and such variation may be partly due to genetic differences among patients thatcould potentially modulate treatment response, disease progression, and NC sequelae. We thereforepropose a multidisciplinary integrative approach using a panel of biomarkers in patients receivingstandardized treatment protocols to determine factors related to NC function, symptom burden and clinicaloutcomes (such as treatment toxicity and survival) all of which affect overall QOL. Hypothesis: Geneticpolymorphisms in NC, metabolizing genes, DMA repair pathway, and inflammation genes alter the NCand clinical outcome of brain tumor patients and, in turn, impact their QOL.
Specific Aim 1 : To recruit approximately 100 newly diagnosed GBM patients per year from the neurooncologyclinics at UTMDACC and UCSF. From these patients' DNA, we will genotype a panel of NC,metabolizing, DNA repair and inflammation genes.
Specific Aim 2 : To conduct NC evaluations on all patients treated and followed at each institution(200/year N=800 over 4 years) following the standardized testing established for a number of collaborativegroup and pharmaceutical company sponsored studies.
Specific Aim 3 : To integrate data derived from Aims 1 and 2 to determine if variants in the NC,metabolizing, DNA repair, and inflammation pathway genes alter overall QOL. Using the wealth of clinicalinformation abstracted from medical records and data generated from this proposal, we will develop a RiskPrediction Model to determine which patients are more likely to develop NC dysfunction during their diseasecourse and could therefore be targeted for personalized treatments that would potentially impact their QOL.Brain tumors cause progressive NC deficits similar to other neurodegenerative diseases, and thesedeficits frequently worsen because of treatment effects. Some patients even develop treatment relateddementia that leads to death without evidence of tumor progression. Thus, the ability to determine markersthat can identify those vulnerable patients would be of tremendous benefit and allow for alterations ofprimary treatment or neuroprotective therapies to reduce the risk of brain tumor therapy.
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