Abstract 755: Identifying the molecular signature of infiltrating edge cells in glioblastoma as drivers of tumor invasion and recurrence
Abstract Background: Glioblastoma (GBM) is the most common malignant brain tumor in adults. Despite extensive research, there haven’t been remarkable gains in resolving the seeds of glioblastoma recurrence, and the outcomes for many patients suffering from this devastating disease remain poor. Complete tumor resection in GBM patients is not possible. Residual therapy-resistant cells drive tumor recurrence and infiltrative expansion. Our knowledge on GBM heterogeneity is mostly restricted to the surgically resectable tumor core, while the functional characterization of tumor cells at the infiltrating edge remains largely elusive due to the presence of normal functional brain tissue in the peritumoural lesion. Edge-derived cells exhibit larger capacity for infiltrative expansion and are the main drivers of treatment failure and tumor recurrence, making them action targets for novel treatment approaches. Methods: To resolve the transcriptional heterogeneity of GBM within the spatial context, we profiled gene expression of tumor regions selected based on histological features (“edge”, “core”, and “infiltrating zone”) obtained from 4 primary and 2 matched pairs of primary/recurrent IDH-WT GBM patients at single-cell resolution with Visium HD. Results: We show that infiltrative cells are spatially segregated and are characterized by regionally shared distinct transcriptomic signatures which define their cell state and identity. We complement non-spatial leiden clustering approach with BANKSY spatial clustering to augment the features of each cell with both an average of the features of its spatial neighbors along with neighborhood feature gradients. Using pathologically annotated H&E images integrated with spatial gene expression, we identify patterns related to tissue structure and identify transcriptional programs that promote invasiveness and underly disease recurrence in GBM. To further characterize invasive cells at the normal brain-tumor boundary, we identify top spatially variable genes in this cell population using unsupervised cell phenotyping. Upregulated DEGs of tumor edge cells are significantly associated with chemical synaptic transmission, and nervous system development. These modules represent tumor cell hijacking of neuronal programs as described in the context of glioma-neuron synaptic communication and formation of neurite-like tumor microtubes. Upregulation of ion regulation transport at the tumor periphery indicates enhanced neuronal activity and excitability driving infiltrating growth. Significance: Identifying biomarkers that are specific to malignant edge-derived cells may serve as new diagnostic feature that would help assess treatment response before or within early phases of therapy and allow for individual tailoring of the treatment plan to slow disease progression. Citation Format: Alyona Ivanova, Shamini Ayyadhury, Megan Wu, David G. Munoz, Trevor J. Pugh, Sunit Das. Identifying the molecular signature of infiltrating edge cells in glioblastoma as drivers of tumor invasion and recurrence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 755.
Authors
- Shamini Ayyadhury (ORCID: https://orcid.org/0000-0002-8717-637X)
- Sunit Das (ORCID: https://orcid.org/0000-0002-2146-4168)
- David G. Muñoz (ORCID: https://orcid.org/0000-0003-0957-6244)
- Trevor J. Pugh (ORCID: https://orcid.org/0000-0002-8073-5888)
- Megan Wu
- Alyona Ivanova (ORCID: https://orcid.org/0009-0003-7182-5528)
Institutions
- St. Michael's Hospital (CA)
- University Health Network (CA)
- University of Toronto (CA)
- Hospital for Sick Children (CA)
- Princess Margaret Cancer Centre (CA)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-755
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00