Abstract 5666: Exploring spatially resolved intra-tumoral heterogeneity of glioblastoma and neuronal mechanisms facilitating brain invasion
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is a highly lethal brain cancer, comprising about half of all central nervous system malignancies. Its recurrence and resistance to aggressive treatments, such as surgery, radiation, and chemotherapy, underscore its biological complexity. A key feature of GBM is intra-tumoral heterogeneity (ITH), stemming from a diversity of transcriptional cell states within the tumor. While genetic contributions to ITH are well-researched, the role of non-genetic factors, especially in promoting transitions between the various transcriptional cell states (referred to as plasticity), is less understood. This plasticity enables GBM cells to survive treatment pressures, necessitating a deeper dive into the epigenetic underpinnings that govern it and the role of the tumor microenvironment (TME) in influencing it. We proposed that the TME significantly impacts the epigenetic landscape of both malignant and surrounding non-malignant cells, thereby promoting ITH and cell state plasticity. To assess this, we analyzed samples from different anatomical areas of GBM tumors collected using MRI-guided selection. Utilizing advanced multimodal single-cell sequencing technologies, we profiled the transcriptomes and epigenomes of individual cells, revealing their spatially dependent phenotypic and epigenetic diversity. Our results indicated a higher enrichment of progenitor-like malignant cells, resembling neural or oligodendrocyte progenitors, around the tumor margins. In contrast, the core mainly contained differentiated cells with mesenchymal-like characteristics. Intriguingly, progenitor-like cells in the peri-tumoral zone expressed a unique neuronal activity program, including elevated levels of PRC2 complex targets, which are critical for maintaining the pluripotency and self-renewal capabilities of neural stem cells. Additionally, our analysis of cell-cell interactions between neurons and malignant cells identified ligand-receptor pairs crucial for axon guidance, neurogenesis, and tumor invasion. Proneural factors, critical for neural differentiation, were more accessible in peri-tumoral progenitor-like cells, suggesting that malignant cells might exploit neurodevelopmental routes for growth and invasion. This work underscores the significant spatial cellular heterogeneity of GBM and the variation in epigenetic states across the tumor, demonstrating how GBM cells can hijack neurodevelopmental pathways to foster invasion. Although surgical resection can target the tumor core, our insights into the invasive mechanisms of malignant cells provide a foundation for developing targeted therapies to combat the highly invasive cells persisting in regions adjacent to the tumor. Citation Format: Federico Gaiti, Yiyan Wu, Sheila Mansouri, Benson Wu, Yosef Ellenbogen, Xuyao Li, Parnian Habibi, Joan Kant, Pathum Kossinna, Sandra Ruth Lau Rodriguez, Gelareh Zadeh. Exploring spatially resolved intra-tumoral heterogeneity of glioblastoma and neuronal mechanisms facilitating brain invasion [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5666.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".