Abstract 2475: Bmi1 is a therapeutic target in recurrent medulloblastoma
Bibliographic record
Abstract
Abstract We describe the epigenetic regulator Bmi1 as a novel therapeutic target for the treatment of recurrent human Group 3 medulloblastoma, a childhood brain tumor for which there is virtually no treatment option beyond palliation. Through comparative profiling of primary and recurrent medulloblastoma, we show that Bmi1 defines a treatment-refractory cell population that is uniquely targetable by a novel class of small molecule inhibitors. When administered to mice xenografted with patient tumors, we observed significant reduction in tumor burden and increased mouse survival, without neurotoxicity. As Group 3 medulloblastoma is often metastatic and uniformly fatal at recurrence, with no current or planned trials of targeted therapy, an efficacious targeted agent would be rapidly transitioned to clinical trials. Current clinical trials for recurrent medulloblastoma patients who no longer respond to risk-adapted therapy are based on genomic profiles of primary, treatment-naïve tumors. These approaches will provide limited clinical benefit for patients since recurrent metastatic Group 3 medulloblastomas are highly genetically divergent from their primary tumor. Our experimental approach defines a tractable target, the epigenetic regulator Bmi1, which characterizes not only recurrent medulloblastoma, but many other metastatic and treatment-resistant cancers. As future clinical oncology trials will most likely begin with relapsed patients, therapeutic targets from comparative analyses in primary and matched-recurrent tumors offer the greatest clinical yield and may be readily translated to the patient bedside. Citation Format: Sheila K. Singh, Neha Garg, Branavan Manornajan, David Bakhshinyan, Chitra Venugopal, Robin Hallett, Kevin Xin Wang, Vijay Ramaswamy, Yoon-Jae Cho, Siddhartha Mitra, David Kaplan, Thomas Davis, Michael Taylor. Bmi1 is a therapeutic target in recurrent medulloblastoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2475.
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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) | 0.006 | 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".