TRTH-13. BMI1 IS A THERAPEUTIC TARGET IN RECURRENT MEDULLOBLASTOMA
Bibliographic record
Abstract
Bmi1 is a master regulatory stem cell self-renewal gene and epigenetic regulator that presents a downstream target of developmental pathways active during the genesis of the childhood brain tumor medulloblastoma (MB). Here, we describe Bmi1 as a novel therapeutic target for the treatment of recurrent human Group 3 MB, a tumor that often presents with metastasis for which there is virtually no treatment option as children are limited to palliation. Current clinical trials for recurrent MB patients based on genomic profiles of primary, treatment-naïve tumors, will provide limited clinical benefit since recurrent metastatic MBs are highly genetically divergent from their primary tumor. By applying stem cell assays to primary and matched-recurrent Group 3 MB samples, we have identified Bmi1 as a key regulator of self-renewal that has the potential to drive treatment failure. We have shown Bmi1 to be enriched in treatment-refractory fractions in vivo and in vitro. Using a small molecule inhibitor against Bmi1, PTC-028, we were able to demonstrate complete ablation of self-renewal of MB stem cells in vitro and when administered to mice xenografted with patient tumors. Oral administration of Bmi1 inhibitor in our MB patient-derived xenograft (PDX) model resulted in a marked reduction in both tumor burden, extent of spinal metastases and Bmi1 protein level in tumors post- in vivo therapy. Recurrent MB carries a mortality rate approaching 100%, and currently there exists no targeted therapy to treat recurrence. For the first time, we show that Bmi1 inhibition may present a genuine therapeutic strategy for recurrent childhood MB, inhibiting not only self-renewal but metastatic spread and spinal tumor dissemination. Selective targeting of MB stem cells using small molecules inhibitors against Bmi1 may further provide avenues for deescalating therapies that have devastating neurological and psychosocial sequela in children with MB.
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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 | 0.000 | 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".