Abstract IA016: Mapping microglia – tumor crosstalk in pediatric brain cancer
Bibliographic record
Abstract
Abstract The management of diffuse midline gliomas (DMG) is one of the greatest challenges in the field of pediatric oncology. Available treatments do not improve the poor survival rates, and preclinical research is struggling to find new targets that could have the potential to revolutionize the therapeutic approach. In most of the current preclinical tools, microglia and macrophages are not taken into account, despite being the most abundant immune players in the brain tumor microenvironment. To address this issue, we established neural organoids and macrophages (iMacs) from induced pluripotent stem cells (iPSC) derived from pediatric patients treated for DMG ("autologous immuno-organoids"). We have reprogrammed fibroblasts into iPSC from 9 children with DMG and generated autologous immuno-organoids for 4 of them. These "avatars" provide a unique preclinical model to which tumor cells from the same patient can be added, enabling us to better understand the crosstalk between the tumor and its immunological microenvironment, and to evaluate new immunotherapies. In particular, we show here that iMacs decrease tumor proliferation and tumor cell-invasion capability in neural organoids, with such effect reduced upon iMac depletion using CSF1R inhibitor treatment. Genes/signaling pathways identified by multi-omics analysis and validated at proteomic level show a reciprocal reprogramming of tumor cells and iMacs over time. Altogther, our approach reveals an unique tumor macrophage crosstalk that will be targeted by tailored immunotherapies or gene modification to validate their impact on tumor progression. Citation Format: Florent Ginhoux, Claudia Pasqualini. Mapping microglia – tumor crosstalk in pediatric brain cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr IA016.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".