Literature Summaries

This page contains summaries of research papers reviewed and discussed during the course.

New insights into the genetics of glioblastoma multiforme by familial exome sequencing

Glioblastomas are some of the most difficult tumors to treat and detect, due to both their genetic heterogeneity and the lack of models to determine human specific mutations that lead to the rise of this tumor. In this specific case study of a family, both daughter and son, ages 12 and 10 respectively, developed glioblastomas while the parents were unaffected. Based on the family history, it could be deduced that the parents were carriers of a mutation or certain group of mutations while the children were homozygous as well as heterozygous for these mutations that led to the growth of GBM. Through exome sequencing of leukocyte DNA samples, and Genome Analysis Toolkit and ANNOVAR analyses to ensure the correct nucleotide sequences corresponded to correctly between parents and children, the paper was able to determine 85 single-nucleotide variations that were homozygous in the children and heterozygous in the parents. Determining which genes were the most mutated, the authors were able to narrow down the 73 genes that had SNV mutations to focus on only 10 (MYEOV, AKAP1, F5, OVCH1, CR1, LAMA1, RAI1, PTPRB, CROCC and PSG5). Through KEGG pathway analysis, it was determined that these genes impacted ECM to receptor interaction, focal adhesions, and complement and coagulation cascade sequences. Utilizing STRING, as another level of pathway analysis, the ERG/EGFR signaling pathway appeared to have close proximity to many of the mutated genes in the children. Essentially, by genome sequencing, the authors were better able to understand potential, specific sources of mutations that could lead to the growth of pediatric glioblastomas through this family case study.

Backes, Christina et al. “New insights into the genetics of glioblastoma multiforme by familial exome sequencing.” Oncotarget vol. 6,8 (2015): 5918-31. doi:10.18632/oncotarget.2950

Analyzing magnetic resonance imaging data from glioma patients using deep learning

Imaging has become a point of interest in predicting clinical variables, such as tumor characteristics, treatment response, and prognosis. Glioma imaging currently consists of a standardized measure established by the World Health Organization (WHO). However, further imaging techniques are suggested to be used by the authors of this paper, due to the increased knowledge that would be gathered. For example, perfusion weighted imaging, which detects fluid and blood flow, could be used to detect pseudo-progression (through detection of fluid buildup in the tumor and inflammation, due to immune responses, that appears as progression without this imaging technique. Due to the potential of imaging data in predicting important aspects of GBM tumors, the BraTS challenge, which compares different brain tumor segmentation algorithms, allows more development of effective deep-learning imaging algorithms to potentially use for clinical practice in detecting tumor boundaries and characteristics. Essentially, with the advancement of non-invasive methods of GBM analysis and detection, more opportunities become available for glioblastoma treatment and research exploration.

Menze, Bjoern et al. “Analyzing magnetic resonance imaging data from glioma patients using deep learning.” Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society vol. 88 (2021): 101828. doi:10.1016/j.compmedimag.2020.101828

Advances in the Knowledge of the Molecular Biology of Glioblastoma and Its Impact in Patient Diagnosis, Stratification, and Treatment

Glioblastomas are complex tumors with extreme heterogeneity between tumors and within the tumor itself. As a result, a classification system becomes important to understand general targets for treatment, rather than a focus on individual, extremely specific treatment methods. The overarching subgroups are primary and secondary GBMs, which are based on the populations they occur in (age groups) and rate of origination (“de novo” or derived from previous tumors). One such mutation in IDH, the most common and a biomarker for secondary glioblastomas. IDH is a metabolic gene that catalyzes isocitrate to alpha-ketoglutarate (α-KG) and carbon dioxide. IDH1 mutant type catalyzes D-2HG formation, which increases in concentration and, among other negative effects on transcription factors, inhibits EGLN from hydrolyzing HIF1a for ubiquitination. Treatment methods can also utilize immunotherapies and nanoparticles that can bypass the enhanced permeable vessels around glioblastomas to carry treatment drugs. The vast array of potential routes to target GBMs is derived from the continuous goal of classifying the tumor to increase the number of more specific and general categories for this tumor, and thus increasing the number of areas of interest to target.

Delgado-Martín, Belén, and Miguel Ángel Medina. “Advances in the Knowledge of the Molecular Biology of Glioblastoma and Its Impact in Patient Diagnosis, Stratification, and Treatment.” Advanced science (Weinheim, Baden-Wurttemberg, Germany) vol. 7,9 1902971. 12 Mar. 2020, doi:10.1002/advs.201902971

Advances in the management of glioblastoma

The standard for glioblastoma therapy involves gross total resection where possible, chemoradiotherapy with temozolomide (TMZ) and lomustine, as well as fractionated radiotherapy. Additionally, pre-treatment analysis utilizes various imaging tools such as DWI and MR spectroscopy to detect various features of glioblastomas that could provide increased evidence to guide treatment. Available surgical tools include using fluorescent markers to differentiate between GBM tissue and brain tissue, awake surgery to ensure resection does not inhibit functions, and Raman spectroscopy which can detect differences between IDH mutant and wildtype tumor tissue. Immunotherapy treatment methods utilize genetically engineered cells, vaccines with tumor specific antigens, that can help the immune system detect tumors through surface-cell markers.

Ma R, Taphoorn MJB, Plaha P, “Advances in the management of glioblastoma.” Journal of Neurology, Neurosurgery & Psychiatry, 2021; 92:1103-1111.

Immunotherapy for Glioblastoma: Current Progress and Challenges

The environment in the brain was originally thought to be immune-privileged, however lymphatic vessels (lining the dural sinuses) that drain to cervical lymph nodes suggest access for immune cells to reach the brain. Additionally, due to tumorigenesis and excessive mutations, GBM causes BBB integrity loss surrounding the tumor, which makes it easier to pass drugs through the blood brain barrier to the tumor site. However, this also means access for macrophages, which promote tumor growth, and this loss of selective permeability is variable throughout the GBM. However, this presents opportunities for immunotherapy responses to be applied to glioblastomas. CTL4 and PD-1 are immune system checkpoint molecules expressed by GBMs, thus preventing immune system responses against the tumor. Anti-CTLA-4, anti-PD-1 and an IL-12 expressing oncolytic virus counteract the effects of CTL4 and PD-1, and have a 89% cure rate in mouse models. Because tumor-associated macrophages (TAM) benefit GBMs, research has focused on TAM-targeted therapies as well to inhibit macrophage activity in the tumor environment. CAR-T therapies, which engineer patients’ T-cells to express cancer targeting CARS, are being explored to target EGFRvIII, IL13Ra2, and HER2 mutant glioblastomas. Essentially, current immunotherapy responses work to create an ideal immunogenic environment and produce drugs capable of helping the immune system target GBMs.

Yu, Miranda W, and Daniela F Quail. “Immunotherapy for Glioblastoma: Current Progress and Challenges.” Frontiers in immunology vol. 12 676301. 13 May. 2021, doi:10.3389/fimmu.2021.676301