Wengler James

Background: Glioblastoma (GBM) is a malignant tumor of astrocytes that occurs in approximately 3.22 per 100,000 people¹. Unfortunately, the underlying pathophysiology is poorly understood and physicians lack targeted treatments to offer patients¹. In recent years, there has been a push to further classify GBM into discrete subtypes that harbor similar mutations or respond similarly to treatments¹. Currently, there are only two subtypes of GBM that are widely accepted to have clinical significance^3,6. These are MGMT methylation status as well as IDH1 methylation status^3,6. To further elucidate clinically distinct subtypes, researchers have begun to turn to statistical analyses to harness the power of big datasets^2-8. These analyses use a variety of statistical tests to help identify relationships between patients, mutations, and clinical outcomes^2-8. This literature review seeks to explore the specific statistical tests used, as well as clinical phenotypes that correspond to the identified subtype^2-8.

Objectives: To explore the statistical tests being utilized to further subclassify GBM. The identified subtypes will also be examined with an emphasis on clinical relevance.

Search Methods: Pubmed was used to query publications ranging from 2017 – 2023. Search terms included “Glioblastoma”, “Statistical”, “Pan-Cancer”, “Computational”, “Biomarkers”, “Classification”, and “Bioinformatics”.

Results: Both pan-cancer and cancer specific analyses can be used to identify important subtypes in GBM^2-8. The most common statistical tests used were T-tests, Mann-Whitney, Cox’s Regression, and Kaplan-Meir^2-8. These statistical tests were able to identify important subtypes in GBM. The first subtype is PARP1⁷. GBM tumors that include this mutation were found to be correlated with increased microsatellite instability⁷. This is clinically relevant because microsatellite has been associated with increased response to immunotherapies². The second subtype is PDC1 mutation status⁸. Contrary to other cancer types, GBM had a worse prognosis with a positive mutation status⁸. This result warrants further research, but could possibly be due to the status of the brain as an immune privileged site⁸. The next subtype is a six gene expression signature that is able to accurately classify GBM patients as high risk or low risk⁵. The underlying pathology of this signature is undergoing investigation, but many of the genes have been shown to have roles in DNA repair⁵. The last subtype is a novel MGMT fusion^3,6. The role of this fusion’s impact on prognosis is its ability to repair DNA damage^3,6. This means that any damage inflicted by chemotherapy would be rapidly repaired by tumors harboring this fusion^3,6.

Conclusions: Computational and statistical techniques are invaluable tools in discovering populations of GBM patients that may respond well to specific therapies^1-8. While many of these tumor populations require further research into their underlying pathology, the clinical relevance lies in a physician’s ability to know which patients require a more aggressive therapy^1-8. Future research lies in larger datasets as well as clinical trials proving increased survival when stratifying patients by these subtypes.

Works Cited:

1. Wen PY, Weller M, Lee EQ, et al. Glioblastoma in adults: a Society for Neuro-Oncology (SNO) and European Society of Neuro-Oncology (EANO) consensus review on current management and future directions. Neuro Oncol. 2020;22(8):1073-1113. doi:10.1093/neuonc/noaa106
2. Sareen H, Ma Y, Becker TM, Roberts TL, de Souza P, Powter B. Molecular Biomarkers in Glioblastoma: A Systematic Review and Meta-Analysis. Int J Mol Sci. 2022;23(16):8835. doi:10.3390/ijms23168835
3. Butler M, Pongor L, Su YT, et al. MGMT Status as a Clinical Biomarker in Glioblastoma. Trends Cancer. 2020;6(5):380-391. doi:10.1016/j.trecan.2020.02.010
4. Zhou L, Tang H, Wang F, et al. Bioinformatics analyses of significant genes, related pathways and candidate prognostic biomarkers in glioblastoma. Mol Med Rep. 2018;18(5):4185-4196. doi:10.3892/mmr.2018.9411
5. Zuo S, Zhang X, Wang L. A RNA sequencing-based six-gene signature for survival prediction in patients with glioblastoma. Sci Rep. 2019;9(1):2615. doi:10.1038/s41598-019-39273-4
6. Oldrini B, Vaquero-Siguero N, Mu Q, et al. MGMT genomic rearrangements contribute to chemotherapy resistance in gliomas. Nat Commun. 2020;11(1):3883. doi:10.1038/s41467-020-17717-0
7. Zhang X, Wang Y, A G, Qu C, Chen J. Pan-Cancer Analysis of PARP1 Alterations as Biomarkers in the Prediction of Immunotherapeutic Effects and the Association of Its Expression Levels and Immunotherapy Signatures. Front Immunol. 2021;12:721030. doi:10.3389/fimmu.2021.721030
8. Miao Y, Wang J, Li Q, et al. Prognostic value and immunological role of PDCD1 gene in pan-cancer. Int Immunopharmacol. 2020;89(Pt B):107080. doi:10.1016/j.intimp.2020.107080