Glioblastoma (GBM) is the most common and aggressive form of primary brain tumor in adults and is diagnosed more often in males and in females. The current standard of care includes surgical resection, chemotherapy, and radiation therapy, though the tumor often recurs and overall survival for this disease remains low, necessitating the investigation of potential new nodes of treatment. In the search for individualized therapies for GBM, receptor tyrosine kinases (RTKs) have been discovered to mediate different responses and outcomes between male and female patients. This thesis aims to investigate the differential role two RTKs, EGFR and PDGFR, in mediating proliferation, migration, and invasion of GBM cells between males and females. Cell proliferation, migration, and invasion assays were performed using isogenic matched male and female murine GBM cell lines with specific RTK expression mimicking in vivo alterations to their respective oncogenes. Statistical analysis to compare the means of these markers was used to determine discrete trends in male and female cell lines, as well as between males and females with the same mutations. In vitro proliferation, migration, and invasion assays revealed distinct patterns of sex mediated molecular RTK function between males and females. EGFR and PDGFR expression were shown to play different roles in progression between these three metrics. Additionally, the used of an isogenic murine model with sex as a controlled variable allowed male-to-female comparisons, yielding data suggesting some RTKs may attenuate progression in one or more of these benchmarks in one sex more than the other. This study highlights the need for further investigation into the role RTKs play in male versus female GBM progression, which could potentially lead to the creation of new targeted treatments and personalized medicine approaches.

Glioblastoma (GBM) is the most lethal primary brain tumor in adults with a less than 5% chance of survival beyond 5 years. With few effective therapies beyond the standard of care, there are often treatment resistant recurrences seen in most patients. STAT5 is a protein that has shown to be upregulated in highly invasive and treatment resistant GBM. Elucidating the role of STAT5 in GBM could reveal a node of therapeutic vulnerability in primary and recurrent GBM.

Background: Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM.

Methods: We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set.

Results: We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients).

Conclusion: Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.