IMMU-11. PRECLINICAL HIGH-RISK PEDIATRIC BRAIN TUMOR MODELS FOR IMMUNOTHERAPY: HURDLES AND THE WAY FORWARD
Bibliographic record
Abstract
Abstract Despite recent therapeutic advancements in the treatment of pediatric brain tumors, high-risk brain tumors (pHRBT) remain the leading cause of cancer-related deaths in children. In recent years, immunotherapy has become a viable treatment option for several cancers including brain tumors. However, several factors have limited the use of immunotherapy in the treatment of pHRBT. For example, an "immunologically cold" tumor environment has been predominantly implicated in the failure of checkpoint inhibitors (ICIs) as monotherapy in pHRBT. Nevertheless, priming the effects of ICIs with epigenetic modulators through a process called “viral mimicry” that induces the expression of human endogenous retroviruses has the potential to enhance immunotherapy by sparking a T-cell mediated immune response. However, the inadequate understanding of the limitations of the preclinical models has prevented the development of efficient immunotherapy strategies for pHRBT. Although several studies and reviews have compiled some limitations of the available models, a hands-on experience using preclinical models for ICIs testing has not been fully communicated. Here, we share our bedside to bench experience with syngeneic and humanized mouse models of DIPG, ATRT, and medulloblastoma using ICIs therapy in conjunction with an epigenetic alteration. We will present and discuss the limitations of mouse models for the development of immunotherapies for pHRBT. For instance, our results indicated that the baseline levels of MHC-I expression in patients’ DIPG tumors were comparable to matched normal tissue. However, these levels were significantly lower in mouse DIPG tumors. To compensate for this difference, we treated mice with IFN-γ in combination with an epigenetic modulator. Surprisingly, the addition of INF-γ had a negative effect on animal overall survival compared to the treatment with the epigenetic modulator alone. Together, our experience demonstrated the importance of suitability assessment of current models for developing and translating successful immunotherapy strategies to treat pHRBT.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".