Dynamic profiling of medulloblastoma surfaceome
Bibliographic record
Abstract
Medulloblastoma (MB) is the most common type of malignant pediatric brain cancer. The current standard of care (SOC) involves maximal safe resection and chemoradiotherapy in individuals older than 3 years, often leading to devastating neurocognitive and developmental deficits. Out of the four distinct molecular subgroups, Group 3 and 4 have the poorest patient outcomes due to the aggressive nature of the tumor and propensity to metastasize and recur post therapy. The toxicity of the SOC and lack of response in specific subtypes to the SOC underscores the urgent need for developing and translating novel treatment options including immunotherapies. To identify differentially enriched surface proteins that could be evaluated for potential future immunotherapeutic interventions, we leveraged N-glycocapture surfaceome profiling on Group 3 MB cells from primary tumor, through therapy, to recurrence using our established therapy-adapted patient derived xenograft model. Integrin 𝛼5 (ITGA5) was one of the most differentially enriched targets found at recurrence when compared to engraftment and untreated timepoints. In addition to being enriched at recurrence, shRNA-mediated knockdown and small molecule inhibition of ITGA5 have resulted in marked decrease in proliferation and self-renewal in vitro and demonstrated a survival advantage in vivo. Together, our data highlights the value of dynamic profiling of cells as they evolve through therapy and the identification of ITGA5 as a promising therapeutic target for recurrent Group 3 MB.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".