CSIG-20. PROTEOGENOMICS PROFILING REVEALS ENRICHED PROTEIN TRANSLATION REGULATORS AS NEW THERAPEUTIC TARGETS IN DIPG
Bibliographic record
Abstract
Abstract Diffuse intrinsic pontine glioma (DIPG) is a devastating brain tumor arising in the brainstems of children. Current therapies are ineffective resulting in a median survival rate of less than one year and it is the leading cause of brain tumor-related death in children. A novel mutation in histone H3 protein (H3K27M) was recently identified as a genetic initiation event and affects global K27 trimethylation on histone H3 proteins and DNA methylation. The epigenetic changes caused by H3K27M mutation suggest the existence of an H3K27M-specific transcriptome and proteome. We investigated DIPG tissues at the multi-omics level including total proteome, phosphoproteome, methylproteome and metabolome by mass spectrometry. A total of 30 patient tumours were profiled to identify differentially expressed proteins, differentially phosphorylated proteins, and differentially methylated proteins in tumour tissues compared to normal brains. We identified 2995 proteins that are differentially regulated, suggesting changes in key oncogenic pathways including negative regulation of apoptosis, translation, and metabolic pathways such as methionine salvage and TCA cycle in DIPG. The deregulation of these metabolic pathways due to the differentially expressed proteins in DIPG cells was confirmed by metabolomics studies. Protein phosphorylation and protein methylation profiling of DIPG implicated that translation-related proteins were the most highly modified (post-translationally) proteins in DIPG tissues. Furthermore, protein translation measured by CyTOF showed higher translation rates in DIPG and immortalized astrocytes carrying H3K27M than their WT counterparts. We investigated the functional consequence of knockdown of the highest methylated translation regulator EEF1A1 and its methyltransferase METTL13. ShRNA knockdown of both, EEF1A and METTL13 in DIPG cells significantly reduced the cell growth. Multi-omics analysis of DIPG highlighted regulation of protein translation as a potential therapeutic target.
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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 | 0.001 |
| 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 | 0.000 |
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".