Identification and Validation of Novel Subtype-Specific Protein Biomarkers in Pancreatic Ductal Adenocarcinoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Pancreatic ductal adenocarcinoma (PDAC) has been subclassified into 3 molecular subtypes: classical, quasi-mesenchymal, and exocrine-like. These subtypes exhibit differences in patient survival and drug resistance to conventional therapies. The aim of the current study is to identify novel subtype-specific protein biomarkers facilitating subtype stratification of patients with PDAC and novel therapy development. METHODS: A set of 12 human patient-derived primary cell lines was used as a starting material for an advanced label-free proteomics approach leading to the identification of novel cell surface and secreted biomarkers. Cell surface protein identification was achieved by in vitro biotinylation, followed by mass spectrometric analysis of purified biotin-tagged proteins. Proteins secreted into a chemically defined serum-free cell culture medium were analyzed by shotgun proteomics. RESULTS: Of 3288 identified proteins, 2 pan-PDAC (protocadherin-1 and lipocalin-2) and 2 exocrine-like-specific (cadherin-17 and galectin-4) biomarker candidates have been validated. Proximity ligation assay analysis of the 2 exocrine-like biomarkers revealed their co-localization on the surface of exocrine-like cells. CONCLUSIONS: The study reports the identification and validation of novel PDAC biomarkers relevant for the development of patient stratification tools. In addition, cadherin-17 and galectin-4 may serve as targets for bispecific antibodies as novel therapeutics in PDAC.
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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 it