Presentation Title

Characterizing the Transcriptome of Glioblastoma Multiforme from Publicly Available Databases

Presenter's Name(s)

Piotr SowulewskiFollow

Abstract

Glioblastoma Multiform (GBM) is a brain cancer with a mean life expectancy of 15 months. Currently, the standard of care is resection followed by temozolomide (TMZ) treatment and radiation. Most patients gain resistance to TMZ through activation of the protein O6-methyl guanine methyltransferase (MGMT) and no therapeutic alternatives are currently available. Poly-(ADP-Ribose) Polymerase 1 (PARP1) and Poly(ADP-Ribose) Glycohydrolase (PARG) play a key role in the maintenance of genome stability, and inhibitors targeting these proteins have shown potential in cancer treatment, including GBM. However, it is unknown which patients will benefit from PARP and/or PARG inhibitors (PARPi, PARGi). Characterizing molecular markers that predict patient response to these drugs is imperative for patients to receive the most effective care within the limited prognostic window.

Using transcriptomic profiles from publicly available databases such as the cancer genome atlas (TCGA), and the gene-tissue expression project (GTex), we performed differential gene expression analysis with the TCGAbiolinkGUI software package. We characterized the transcriptional environment of GBM and identified dysregulated transcripts and molecular pathways. We confirmed previously-described GBM markers, including IDH and PTEN. We also revealed a unique upregulation of inflammation (IL-8 signaling pathway) and metabolism in our PTEN mutant cohort. Interestingly, PARP1 and PARG are both involved in the regulation of these processes and could provide a model for therapeutic treatment using PARGi and PARPi.

Our study identifies transcriptomic patient profiles that have the potential to correlate with responsiveness to PARPi and/or PARGi. This provides a starting point for studies that could provide definitive data to validate our model.

Primary Faculty Mentor Name

Dr. Delphine Quenet

Graduate Student Mentors

Trevor Wolf

Status

Undergraduate

Student College

College of Arts and Sciences

Program/Major

Biochemistry

Primary Research Category

Health Sciences

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Characterizing the Transcriptome of Glioblastoma Multiforme from Publicly Available Databases

Glioblastoma Multiform (GBM) is a brain cancer with a mean life expectancy of 15 months. Currently, the standard of care is resection followed by temozolomide (TMZ) treatment and radiation. Most patients gain resistance to TMZ through activation of the protein O6-methyl guanine methyltransferase (MGMT) and no therapeutic alternatives are currently available. Poly-(ADP-Ribose) Polymerase 1 (PARP1) and Poly(ADP-Ribose) Glycohydrolase (PARG) play a key role in the maintenance of genome stability, and inhibitors targeting these proteins have shown potential in cancer treatment, including GBM. However, it is unknown which patients will benefit from PARP and/or PARG inhibitors (PARPi, PARGi). Characterizing molecular markers that predict patient response to these drugs is imperative for patients to receive the most effective care within the limited prognostic window.

Using transcriptomic profiles from publicly available databases such as the cancer genome atlas (TCGA), and the gene-tissue expression project (GTex), we performed differential gene expression analysis with the TCGAbiolinkGUI software package. We characterized the transcriptional environment of GBM and identified dysregulated transcripts and molecular pathways. We confirmed previously-described GBM markers, including IDH and PTEN. We also revealed a unique upregulation of inflammation (IL-8 signaling pathway) and metabolism in our PTEN mutant cohort. Interestingly, PARP1 and PARG are both involved in the regulation of these processes and could provide a model for therapeutic treatment using PARGi and PARPi.

Our study identifies transcriptomic patient profiles that have the potential to correlate with responsiveness to PARPi and/or PARGi. This provides a starting point for studies that could provide definitive data to validate our model.