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 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 | 0.000 | 0.000 |
| 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".