PS1 - 170 Bmi1 is a Therapeutic Target in Recurrent Childhood Medulloblastoma
Bibliographic record
Abstract
Medulloblastoma (MB) is the most common malignant pediatric brain tumour, and is categorized into four molecular subgroups, with Group 3 MB having the worst prognosis due to the highest rate of metastatic dissemination and relapse. In this work, we describe the epigenetic regulator Bmi1 as a novel therapeutic target for treatment of recurrent Group 3 MB. Through comparative profiling of primary and recurrent MB, we show that Bmi1 defines a treatment-refractory cell population that is uniquely targetable by a novel class of small molecule inhibitors. We have optimized an in vivo mouse-adapted therapy model that has the advantage of generating recurrent, human, treatment-refractory MBs. Our preliminary studies showed that although chemoradiotherapy administered to mice engrafted with human MB showed reduction in tumour size, Bmi1 expression was enriched in the post-treatment residual tumour. Furthermore, we found that knockdown of Bmi1 in human recurrent MB cells decreases proliferation and self-renewing capacities of MB cells in vitro as well as both tumour size and extent of spinal leptomeningeal metastases in vivo. Oral administration of a potent Bmi1 inhibitor, PTC 028, resulted in a marked reduction in tumour burden and an increased survival in treatment cohort. Bmi1 inhibitors showed high specificity for MB cells and spared normal human neural stem cells, when treated with doses relevant for MB cells. As Group 3 medulloblastoma is often metastatic and uniformly fatal at recurrence, with no current or planned trials of targeted therapy, an efficacious agent such as Bmi1 inhibitor could be rapidly transitioned to clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".