LGG-09. REBOUND GROWTH AFTER MAPKI WITHDRAWAL INVOLVES RAPID MAPK REACTIVATION AND CYTOKINE-MEDIATED MICROGLIA RECRUITMENT IN PEDIATRIC LOW-GRADE GLIOMA MODEL
Bibliographic record
Abstract
Abstract Patients with pediatric low-grade gliomas (pLGG), the most common primary brain tumor in children, can often benefit from MAPK inhibitor (MAPKi) treatment. However, rapid tumor regrowth, also referred to as rebound growth, may occur once treatment is stopped, constituting a significant clinical challenge. Four patient-derived pLGG models were investigated to model rebound growth in vitro based on viable cell counts in response to MAPKi treatment and withdrawal. A multi-omics dataset of the rebound model encompassing different MAPKi withdrawal timepoints was generated using RNA sequencing and LC-MS/MS based phospho-/proteomics to investigate possible driving mechanisms of rebound growth. Following in vitro validation, putative rebound driving mechanisms were validated in vivo using the BT-40 orthotopic xenograft model. Of the tested models, BT-40 (BRAFV600E, CDKN2A/Bdel) showed rebound growth upon MAPKi withdrawal, characterized by faster cell regrowth after MAPKi withdrawal compared to standard-of-care chemotherapy. Using this model, we observed MAPK pathway reactivation within hours after withdrawal, associated with a transient overactivation of key MAPK molecules at transcriptional (e.g. FOS) and phosphorylation (e.g. pMEK) levels. Additionally, we observed increased expression and secretion of cytokines (in particular CCL2, CX3CL1, CXCL10 and CCL7) upon MAPKi treatment, maintained during early withdrawal (at least until 24 h). While increased cytokine expression did not affect response to MAPKi or rebound growth upon withdrawal in an autocrine manner, increased attraction of microglia cells mediated by these cytokines was observed. MAPK pathway reactivation during rebound growth and increased expression of CX3CL1 and CXCL10 induced by MAPKi treatment could further be confirmed in vivo. Taken together, these data indicate a rapid MAPK reactivation upon MAPKi withdrawal as a tumor cell intrinsic rebound driving mechanism. Furthermore, increased microglia recruitment during MAPKi treatment and withdrawal, mediated by cytokines, may play a role in response to MAPKi treatment and rebound growth upon withdrawal, warranting further evaluation.
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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) | 0.001 | 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 | 0.001 |
| 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".