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" Brain-Mimetic Hydrogel Platform for Investigation of Glioblastoma Drug Resistance "
Xiao, Weikun
Seidlits, Stephanie K
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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904791
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TL1t5142dd
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| Main Entry
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Xiao, Weikun
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| Title & Author
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Brain-Mimetic Hydrogel Platform for Investigation of Glioblastoma Drug Resistance\ Xiao, WeikunSeidlits, Stephanie K
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| College
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UCLA
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| Date
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2019
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| student score
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2019
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| Abstract
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Glioblastoma (GBM) is the most lethal and malignant cancer originating from the central nervous system. Even with intense treatment involving surgery and radio-chemotherapy, median survival after prognosis remains within 12 months, as GBM constantly develops resistance to common therapies. Many novel therapies developed for GBM have shown promising results in in-vitro studies, but unfortunately failed in actual clinical practices, partially because traditional model systems failed to recapitulate the microenvironment surrounding GBM tumors. Therefore, we posit that unique brain extracellular matrix (ECM) facilitates therapeutic resistance in GBM. To study this problem, we investigated ECM deposition in GBM patient samples and fabricated brain-mimetic, orthogonally tunable hydrogel system in which to culture patient-derived GBM cells in 3-dimensional manner. To validate our novel ex-vivo culture system, genomic sequencing and gene expression profiling were performed for comparison with traditional in-vitro culture and animal xenograft models. At the same time, cell viability, proliferation and markers for cancer stem cell were assessed. Our model system was used to study the therapeutic response of GBM cells to commonly used therapeutics and to investigate resistance mechanisms. We found GBM cells displayed drug response kinetics comparable to in-vivo xenograft models. We also found novel molecular mechanisms describing how unique brain matrix facilitates therapeutic resistance through corresponding receptors in our 3D culture models. By utilizing novel engineered platforms to study drug resistance, we are able to uncover mechanisms that could not be observed through traditional methods.
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Seidlits, Stephanie K
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| Added Entry
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UCLA
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