Cotargeting of MEK and PDGFR/STAT3 Pathways to Treat Pancreatic Ductal Adenocarcinoma
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) is among the most lethal human diseases and remains largely refractory to available drug treatments. Insufficient targeting of the known oncogenic drivers and activation of compensatory feedback loops and inability to prevent metastatic spread contribute to poor prognosis for this disease. The KRAS-driven MEK pathway is mutationally activated in most pancreatic cancers and is an important target for therapeutics. Using a two-dimensional monolayer culture system as well as three-dimensional spheroid culture system, we conducted a screen of a large panel of anticancer agents and found that MAP2K (MEK) inhibitors were most effective in targeting PDAC spheroids in comparison with monolayer cultures. Combination treatment with an MEK inhibitor and the multikinase inhibitor ponatinib was effective in targeting pancreatic cancer cells both in monolayer and spheroids by effectively blocking signaling via the PDGFRα and MEK kinases, while also preventing the activation of STAT3- and S6-mediated compensatory feedback loops in cancer cells. Furthermore, using xenograft models, we demonstrate that cotreatment with a MEK inhibitor and ponatinib causes significant tumor regression. PDAC patient samples also provided evidence of increased STAT3 activation in PDAC tumors and MAPK1 (ERK) activation in liver metastases, implicating STAT3 and ERK as key drivers in primary tumors and metastases, respectively. These results reveal a combination drug treatment strategy that may be effective in pancreatic cancer. Mol Cancer Ther; 16(9); 1729–38. ©2017 AACR.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".