Associations between Selected Biomarkers and Prognosis in a Population-Based Pancreatic Cancer Tissue Microarray
Bibliographic record
Abstract
Pancreatic cancer is the fourth leading cause of cancer death in the United States. Prognostic biomarkers are lacking, and treatment has limited effect on survival. Tissues from Surveillance, Epidemiology, and End Results registries (Iowa, Hawaii, and Los Angeles) were used to build a tissue microarray of 161 pancreatic tumors (113 resections and 48 biopsies). Proportional hazard models adjusted for age, race, sex, stage, time-period of diagnosis, and treatment. Associations were examined between markers (MUC1, MUC2, MUC5AC, synaptophysin, chromogranin, neuron specific enolase, epidermal growth factor receptor, HER2, CD5, CD138, CK5/6, CK19, CK20, and p53) and survival time from diagnosis. After adjusting for covariates, borderline statistically significant associations were seen between expression of each of the three mucins (MUC1, MUC2, and MUC5AC) and shorter survival time. The associations strengthened for 154 (96%) adenocarcinomas, particularly the 120 (75%) well-differentiated to moderately differentiated ductal adenocarcinomas, a tumor type that occurred more often in the cohort among White cases than cases of other racial origin (P<0.01). For differentiated ductal adenocarcinomas, associations with shorter survival time were seen for expression of all three mucins combined versus other mucin expression patterns (adjusted hazard ratio, 1.8; 95% confidence interval, 1.2-2.6) and for MUC2(+) versus MUC2(-) expression (adjusted hazard ratio, 1.6; 95% confidence interval, 1.1-2.4). Mucin gene expression, particularly MUC2 expression, may have prognostic value for differentiated adenocarcinomas. Tumor histologies differed in this and Japanese cohorts. The tissue microarray is available to evaluate other biomarkers. Tissue-based surveillance can be used to monitor tumor histology in populations and facilitate applied research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".