dia-PASEF Proteomics of Tumor and Stroma LMD Enriched from Archived HNSCC Samples
Bibliographic record
Abstract
We employed laser microdissection to selectively harvest histology-resolved tumors and stroma from formalin-fixed, paraffin-embedded head and neck squamous cell carcinoma (HNSCC) tissues. Peptide digests from the LMD-enriched HNSCC tissue were analyzed by quantitative mass-spectrometry-based proteomics using a data independent analysis approach. In paired samples, excellent proteome coverage was achieved, having quantified 6668 proteins with a median quantitative coefficient of variation under 10%. Significant differences in relevant functional pathways between the tumor and the stroma regions were observed. Extracellular matrix (ECM) was identified as a major component enriched in the stroma, including many cancer-associated fibroblast signature proteins in this compartment. We demonstrate the potential for comparative deep proteome analysis from a very low starting input in a scalable format. Correlating such results with clinical features or disease progression will likely enable the identification of novel targets for disease classification and interventions.
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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 | 0.000 |
| 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 | 0.001 |
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".