Leveraging Medulloblastoma Clonal Dynamics to Overcome Treatment Resistance
Bibliographic record
Abstract
PURPOSE: Medulloblastoma is a common pediatric brain tumor with distinct molecular subgroups. Among them, group 3 medulloblastoma is associated with increased recurrence, metastatic potential, and poor patient outcomes. Small-molecule inhibitors targeting B cell-specific Moloney murine leukemia virus insertion site 1 (BMI1) have demonstrated efficacy against several types of malignant tumors, including pediatric medulloblastoma. Although our previously published in vivo study provided a promising proof of concept for the therapeutic targeting of BMI1 in group 3 medulloblastoma with small-molecule inhibitors, it is not sufficient to eradicate the tumor. EXPERIMENTAL DESIGN: In this study, following preclinical validation of BMI1 inhibitor PTC596, DNA barcoding technology was leveraged to profile in vivo clonal dynamics of group 3 medulloblastoma in response to the established chemoradiotherapy regimen alone and in combination with PTC596. Following demonstration of a small number of treatment-refractory clones, we sought to identify potential druggable molecular vulnerabilities by utilizing phosphoproteomic profiling and genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) screening. RESULTS: By comparing the changes in the phosphorylation pattern of key signaling kinases after PTC596 treatment with the list of sensitizer genes from in vitro genome-wide CRISPR/CRISPR-associated protein 9 screen and with the essential genes in human neural stem cells, we identified several context-specific regulators of mTOR, AKT, and PLK1 pathways. Subsequently, targeting the PI3K pathway with enzastaurin was most amenable to synergistic targeting alongside BMI1 inhibition. CONCLUSIONS: This work provides the foundation for clinical validation of small-molecule inhibitors synergistic with PTC596 to improve the durability of remissions and extend the survival of patients with treatment-refractory group 3 medulloblastoma.
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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) | 0.001 | 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".