MB-44SUBGROUP-SPECIFIC QUANTITATIVE PROTEOMIC ANALYSIS OF MEDULLOBLASTOMA
Bibliographic record
Abstract
Despite extensive genomic characterization of medulloblastoma, very few actionable therapeutic targets have emerged. Given the discordance between gene and protein expression and because proteins are the functional components of the cell, understanding the proteome is integral to deciphering cancer cell biology. We used stable isotope labeling of amino acids in cell culture (SILAC) for accurate quantification of tumor tissue proteins. We created a SILAC reference atlas called the Labeled Atlas of Medulloblastoma Proteins (LAMP) from 8 primary and established medulloblastoma cell lines and spiked it equally into 38 medulloblastoma tumor tissue lysates from all subgroups. Mass spectrometry was used to quantitate proteins and statistical identification and quantification confidence was calculated at the level of the peptide. The correlation between gene methylation, gene expression and protein abundance was also analyzed. Accurate quantitation was achieved for an average of 1310 proteins per sample. Comparison of replicates yielded regression values of >0.95. Supervised hierarchical clustering and statistical testing were used to identify proteins enriched in each tumor subgroup. For example, we identified 359 proteins that were significantly differentially abundant in group 3 and group 4 tumors compared to cerebellum. Comparing group 3 and 4 tumors, 205 proteins were differentially abundant. Using these proteins, we performed Ingenuity Pathway Analysis and identified the eIF2 protein translation, regulation of eIF4F, mTOR signaling, axonal guidance, oxidative phosphorylation and mitochondrial dysfunction pathways to be most highly represented. Quantitative proteomics is a powerful platform for discovery biology, providing insight into tumor cellular function and yielding potential therapeutic targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".