P02.08 The role of the inflammasome in the spatiotemporal evolution of the immune cell landscape in post-resection glioblastoma
Bibliographic record
Abstract
<h3>Background</h3> Glioblastomas (GB) are the most severe and deadliest brain tumors in adults. Survival is estimated < 15 months after diagnosis and with a relapse rate > 95%. The current standard-of-care involves surgery, when possible, and radiotherapy coupled with chemotherapy. Two characteristics might underlie the high relapse rate in GB: 1) the infiltrative capacity of tumor cells that spread out of the hypoxic and acidic tumor core, and 2) the unique composition of the tumor immune microenvironment (TME) that is sparce in T lymphocytes and natural killer (NK) cells but dominated by glioma-associated macrophages (GAMs). Although surgery is a standard treatment in GB, it fails to remove infiltrative tumor cells and causes an inflammatory and immunosuppressive trauma that might promote GB recurrence by altering the TME. However, the post-resection diversity of immune cells in GB and the pathways that determine their functions in primary growth versus post-resection recurrence remain largely unknown. In this project, we characterize the immune landscape of GB before and after surgical resection and explore the role of the inflammasome in its dynamics and regulation. <h3>Materials and Methods</h3> GL261-GFP-GLuc mesenchymal-type GB cells were orthotopically injected in WT or inflammasome-deficient(<i>Ice<sup>-/-</sup></i>) mice. On day 18 post-implantation, tumors and adjacent parenchyma tissue were collected from the unresected group (group 1). In parallel, tumor resection was performed on a second group of mice (group 2). 10 days later, tumors and adjacent parenchyma tissue were collected from group 2. Following tissue dissociation, immune cells were FACS-sorted from GB tumor-bearing mouse brains. Sorted immune cells were multiplexed using barcoaded lipid indices into 6 different pools and ScRNAseq (10x Genomics) was performed. For the scRNAseq, 30,000 cells/pool, corresponding to 7,500 viable cells/sample were loaded on the 10x chip. <h3>Results</h3> Following putative doublet removal and exclusion of stressed or dead cells, we analysed the transcriptomes of ~61,000 single immune cells. Following data integration with Seurat, community detection, non-linear dimension reduction and graph clustering, 23 Louvain clusters were identified, including 15 from the myeloid lineage and 8 from the lymphoid lineage. we observed a significant depletion of microglia (MG)/MG-TAM from the tumor compared to the adjacent non-tumoral parenchyma, which was accompanied by a significant influx of bone-marrow-derived (BM)-TAM and monocytes as already known. Little differences were observed between WT or <i>Ice<sup>-/-</sup></i>mice before resection. However, post-resection remodelling of the GB TME was regulated by the inflammasome. Notably, monocytes, dendritic cells and regulatory T cells (Treg) subsets increased post resection in the adjacent non-tumoral tissue in WT but not <i>Ice<sup>-/-</sup></i>mice. Similarly, the intra-tumoral influx of Treg and the compositional changes of BM-TAMs observed in WT mice were blunted in inflammasome-deficient conditions. These TME differences correlated with faster tumor regrowth and decreased survival rates in WT mice compared to inflammasome-deficient mice. <h3>Conclusion</h3> Our data reveal a significant impact of GB resection on TME remodeling and implicate the inflammasome in post-resection recurrence. <h3>Disclosure Information</h3> <b>S. Lillo:</b> None. <b>D. Chalopin:</b> None. <b>M. Derieppe:</b> None. <b>J. Martineau:</b> None. <b>J. Giraud:</b> None. <b>A. Le Dantec:</b> None. <b>O. Mollier:</b> None. <b>M. Nikolski:</b> None. <b>T. Daubon:</b> None. <b>M. Saleh:</b> None.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".