Ciara Lespron-Hammett
Ciara Lespron-Hammett
Ciara Lespron-Hammett
Ivy Neurological Sciences Internship Program
School:Perry High School
Hometown: Chandler, Arizona
Mentor: Michael Berens, Ph.D.
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Genomic Characterization of Patient-Derived Xenograft Avatars of Glioblastoma

Glioblastoma (GBM) is an aggressive, malignant brain tumor with a median survival rate of 14.6 months. The current standard of care for glioblastoma is: surgery, radiation, and adjuvant chemotherapy with temozolomide (TMZ). However, some patients do not respond well to TMZ, resulting in a decreased survival rate from the already poor prognosis. GBM tumors were studied in vivo through the use of a xenograft model, implanting patient GBM tumors into immunodeficient mice. Genomic characterization of patient-derived tumor xenograft avatars (PDX) enables researchers to identify potential biomarkers to predict whether a patient will be sensitive or resistant to TMZ. As a result, treatment could be tailored according to a patient’s unique genome. Additionally, the data obtained from genomic sequencing could be utilized to compile a GBM database for future research. Exome sequencing was performed for 50 patient xenografts (35 sensitive, 15 resistant). Mouse nucleotide reads were removed through the pipeline to ensure accurate downstream analysis. Copy number alterations (CNA) and single nucleotide variants (SNV) were compared between sensitive and resistant PDX groups. While the correlation of one specific biomarker to TMZ response was not discovered, several factors might serve as indicators of potential response. TMZ resistant GBM samples exhibited a high frequency of mutations in the ZNF gene family, which have been reported to cause chemotherapy resistance in gastrointestinal tumors. In contrast, TMZ sensitive GBM samples illustrated a high frequency of mutations in TP53, a gene that encodes a tumor suppressor protein.  Further research, possibly with high-throughput RNA sequencing and proteomics, could be conducted to expand the variant database and discover better predictive biomarkers. In the future, thanks to the newly compiled database, not only can researchers design experiments— with the guide of existing knowledge, therefore saving resources for other uses— but the underlying factor(s) of TMZ resistance could be elucidated leading to the personalized treatment of Glioblastoma patients.