Towards the Discovery of Prognostic Biomarkers for Glioblastoma Using Resting-State Functional Connectivity
Date of Award
Doctor of Philosophy (PhD)
Gliomas are highly diffusive, primary brain tumors. The most malignant form, glioblastoma, has a dismal survival rate: 14-17 months following the current standard of care, which consists of surgery, radiation, and chemotherapy. Insights into the molecular, cellular, and microenvironmental components of glioblastoma have revealed a vast array of factors utilized to support its proliferation, infiltration, and resistance to treatment. Recent advancements have also identified diagnostic and prognostic biomarkers that are now being used to guide treatment planning. However, survival has improved only marginally, thus emphasizing the continued need for novel biomarkers and treatment strategies. Given its delicate location in the brain, extreme care is taken to limit damage while maximizing surgical resections. As such, brain mapping is routinely performed to identify areas responsible for functions that if impaired, would lead to life-altering deficits. Resting-state functional magnetic resonance imaging (rs-fMRI) is one technique recently employed for preoperative mapping of brain function noninvasively. Using blood-level-dependent-oxygen (BOLD) signals, rs-fMRI identifies brain regions that are temporally synchronous (i.e., functionally connected,) and are consistent with known brain networks. In research settings, rs-fMRI is used to understand brain function and how it is altered by disease. Here, we considered rs-fMRI as a potential tool to identify glioma characteristics and glioma-induced changes in brain function that may have prognostic value for preoperative planning. As gliomas are heterogenous and infiltrative, preserved function can be found within tumor boundaries. This observation, however, has been limited to the somatomotor and language systems due to routine intraoperative stimulation mapping of these cortical regions, which ignores crucial functionality elsewhere. Moreover, function within tumor boundaries may vary across patients and its prognostic significance remains ambiguous. Using rs-fMRI, we examined the extent to which functional connectivity is preserved in glioblastoma. Our results demonstrate that functional connectivity can be identified within most glioblastoma tumors and its strength may serve as a survival biomarker before treatment interventions. Thus, intratumor functional connectivity provides an innovative method to assess tumor malignancy. Tumor malignancy has been shown to modify the extent of connectivity disturbances across the ipsilesional and contralesional hemispheres. Since high functional connectivity between homologous brain regions (i.e., homotopic connectivity) is a common feature of normal brain functioning, the neuronal pathways facilitating these connections may be disturbed in glioma patients. Further, the degree of this disruption may depend on tumor aggressiveness. Thus, taken together, the variability of this homotopic connectivity may have prognostic implications. Using rs-fMRI, we assessed homotopic connectivity differences in low- and high-grade glioma patients and found consistent associations between tumor severity and altered connections. Furthermore, homotopic connectivity was positively correlated with overall survival, with higher connectivity in the somatomotor network demonstrating statistically significant improvements in median overall survival. Therefore, understanding homotopic connectivity changes in glioma patients provides novel insight into brain dysfunction and may impact treatment strategies. In summary, we showed that rs-fMRI can provide deeper insights into tumor-induced changes in brain function beyond what is traditionally assessed for preoperative planning. Future studies should investigate whether the identified findings are consistent in larger populations and across various demographics. Overall, our findings suggest a potential role for rs-fMRI as a driver of biomarker discovery for glioma outcomes.
Eric C. Leuthardt
ShiNung Ching, Abhinav Jha, Joshua S. Shimony, Abraham Z. Snyder,
Biomedical Engineering and Bioengineering Commons, Neuroscience and Neurobiology Commons