CSIG-15. PROTEOMIC AND PHOSPHOPROTEOMIC ANALYSIS OF HUMAN MEDULLOBLASTOMA REVEALS DISTINCT ACTIVATED PATHWAYS BETWEEN SUBGROUPS
Bibliographic record
Abstract
Deregulations in fundamental signaling pathways are key events in pathogenesis of cancer. One intriguing illustration that still holds blind spots is the pediatric brain tumor arising from the developing cerebellum: medulloblastoma (MB). Extensive high-throughput sequencing led to the characterization of four MB subgroups (WNT, SHH, Group 3 and Group 4) delineated with distinct molecular signatures and clinical outcomes. However, up-to-date these analyses have not attained the global comprehension of their dynamic network complexity. Wishing to get a comprehensive view of all MB subgroups we employed a multi-scale analysis to integrate genomic copy number (CN), DNA methylation, mRNA, protein and phosphorylation levels across 38 flash frozen primary human MBs (5 WNT, 10 SHH, 10 Group 3 and 13 Group 4). Our proteomic analysis allows to distinguish the four MB subgroups. Interestingly, Group 4 MB is particularly distinguishable from the other subgroups based on phosphorylation profile. Moreover, integration of all omic layers by similarity network fusion allowed precise definition of the four subgroups. Correlation studies between CN, RNA and protein levels showed convergent effects of CN alterations toward cell cycle, RNA and nucleotide metabolism. Importantly, protein levels were poorly predicted by levels of corresponding mRNA with consequence on active pathway prediction. This observation was of particular importance in Group 4 MB in which protein and phosphorylation levels allowed the identification of pathways that were unable to be predicted by RNA data. Ultimately, this led us to the characterization of potential biomarkers of Group 4 MB and promising targetable pathways across subgroups. Altogether, combined multi-scale analyses of MB have allowed us to identify and prioritize novel molecular drivers involved in human MB genesis and progression.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".