Multiplexed glycan immunofluorescence identification of pancreatic cancer cell subpopulations in both tumor and blood samples
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) tumor heterogeneity impedes the development of biomarker assays for early disease detection. We hypothesized that PDAC cell subpopulations could be identified by aberrant glycan signatures in both tumor tissue and blood samples. We used multiplexed glycan immunofluorescence to distinguish between PDAC and noncancer cell subpopulations within tumor tissue, and we developed hybrid glycan sandwich assays to determine whether the aberrant glycan signatures could be detected in blood samples. We found that PDAC cells were identified by signatures of glycans detected by four glycan-binding proteins (VVL, CA19-9, sTRA, and GM2) and that there are three types of glycan-defined PDAC tumors: sTRA type, CA19-9 type, and intermixed. In patient-matched tumor and blood samples, the PDAC tumor type could be determined by the aberrant glycans in the blood. As a result, the combined assays of aberrant glycan signatures were more sensitive and specific than any individual assay. Our results demonstrate a methodology to detect and stratify PDAC.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".