Glioblastoma states are defined by cohabitating cellular populations with progression-, imaging- and sex-distinct patterns

Abstract

Glioblastomas (GBMs) are biologically heterogeneous within and between patients. Many previous attempts to characterize this heterogeneity have classified tumors according to their omics similarities. These discrete classifications have predominantly focused on characterizing malignant cells, neglecting the immune and other cell populations that are known to be present. We leverage a manifold learning algorithm to define a low-dimensional transcriptional continuum along which heterogeneous GBM samples organize. This reveals three polarized states: invasive, immune/inflammatory, and proliferative. The location of each sample along this continuum correlates with the abundance of eighteen malignant, immune, and other cell populations. We connect these cell abundances with magnetic resonance imaging and find that the relationship between contrast enhancement and tumor composition varies with patient sex and treatment status. These findings suggest that GBM transcriptional biology is a predictably constrained continuum that contains a limited spectrum of viable cell cohabitation ecologies. Since the relationships between this ecological continuum and imaging vary with patient sex and tumor treatment status, studies that integrate imaging features with tumor biology should incorporate these variables in their design.

Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma

“…Future research should therefore aim to expand patient enrollment across multiple centers, facilitating a more substantial collective sample base. Additionally, considering recent findings on sex differences in GBM (45), the development of demographic-specific models, such as sex-specific variants, may further refine prediction accuracy. The second bottleneck of this work is the model's explainability.…”

Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol

“…Unfortunately, biopsies from this contrast-enhancing (CE) tumor region alone fails to address the diverse molecularly-distinct subpopulations that extend into the surrounding non-enhancing (NE) parenchyma, which is visible on T2-weighted/Fluid-Attenuated Inversion Recovery (T2W/FLAIR) MRI [ 16 ]. These generally unresected NE tumor regions contribute to tumor recurrence and can have different cellular compositions and genetic signatures to that of enhancing regions [ 25 , 50 , 51 ]. Furthermore, T1+C MRI fails to localize cancer in the surrounding NE tumor region during radiation treatment (RT) planning, as non-tumoral edema typically appears visually indistinguishable from NE tumor.…”

Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol

“…Unfortunately, biopsies from the contrast-enhancing (CE) tumor regions fail to address the diverse molecularly-distinct subpopulations that extend beyond the enhancement into the surrounding non-enhancing (NE) parenchyma, which is visible on T2-weighted/Fluid-Attenuated Inversion Recovery (T2W/FLAIR) MRI [12]. These unresected NE tumor regions contribute to tumor recurrence and can have different cellular compositions and genetic signatures to that of enhancing regions [21,30,31]. Furthermore, T1+C MRI fails to localize cancer in the surrounding NE tumor region during radiation treatment (RT) planning, as non-tumoral edema typically appears visually indistinguishable from NE tumor.…”