Abstract A44: Coordination of stress signals by the lysine methyltransferase SMYD2 promotes pancreatic cancer
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) is a lethal form of cancer with few therapeutic options. We find that levels of the lysine methyltransferase SMYD2 are elevated in precancerous lesions and PDAC and that SMYD2 normally promotes Ras-driven development of PDAC. Notably, loss of SMYD2 correlates with diminished inflammation in PDAC and we identify the stress-response kinase MAPKAPK3 as a new and physiologically relevant SMYD2 substrate. Inhibition of MAPKAPK3 impedes PDAC growth, identifying a potential new kinase target in PDAC. Finally, we show that inhibition of SMYD2 cooperates with standard chemotherapy to treat PDAC cells and tumors. Together, our findings suggest new roles for SMYD2 in inflammation and stress responses, and identify SMYD2 and MAPKAPK3 as potential therapeutic targets to treat pancreatic cancer. Citation Format: Pawel K. Mazur, Nicolas Reynoird, Timo Stellfeld, Natasha M. Flores, Shane M. Lofgren, Scott M. Carlson, Elisabeth Brambilla, Pierre Hainaut, Ewa B. Kaznowska, Cheryl H. Arrowsmith, Purvesh Khatri, Carlo Stresemann, Or Gozani, Julien Sage.{Authors}. Coordination of stress signals by the lysine methyltransferase SMYD2 promotes pancreatic cancer. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2016 May 12-15; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(24 Suppl):Abstract nr A44.
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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) | 0.006 | 0.002 |
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".