Islet amyloid polypeptide does not suppress pancreatic cancer
Bibliographic record
Abstract
Pancreatic cancer risk is elevated approximately two-fold in type 1 and type 2 diabetes. Islet amyloid polypeptide (IAPP) is an abundant beta-cell peptide hormone that declines with diabetes progression. IAPP has been reported to act as a tumour-suppressor in p53-deficient cancers capable of regressing tumour volumes. Given the decline of IAPP during diabetes development, we investigated the actions of IAPP in pancreatic ductal adenocarcinoma (PDAC; the most common form of pancreatic cancer) to determine if IAPP loss in diabetes may increase the risk of pancreatic cancer. PANC-1, MIA PaCa-2, and H1299 cells were treated with rodent IAPP, and the IAPP analogs pramlintide and davalintide, and assayed for changes in proliferation, death, and glycolysis. An IAPP-deficient mouse model of PDAC (Iapp−/−; Kras+/LSL-G12D; Trp53flox/flox; Ptf1a+/CreER) was generated for survival analysis. IAPP did not impact glycolysis in MIA PaCa-2 cells, and did not impact cell death, proliferation, or glycolysis in PANC-1 cells or in H1299 cells, which were previously reported as IAPP-sensitive. Iapp deletion in Kras+/LSL-G12D; Trp53flox/flox; Ptf1a+/CreER mice had no effect on survival time to lethal tumour burden. In contrast to previous reports, we find that IAPP does not function as a tumour suppressor. This suggests that loss of IAPP signalling likely does not increase the risk of pancreatic cancer in individuals with diabetes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".