TGFβ engages MEK/ERK to differentially regulate benign and malignant pancreas cell function
Bibliographic record
Abstract
While TGFβ signals are anti-proliferative in benign and well-differentiated pancreatic cells, TGFβ appears to promote the progression of advanced cancers. To better understand dysregulation of the TGFβ pathway, we first generated mouse models of neoplastic disease with TGFβ receptor deficiencies. These models displayed reduced levels of pERK irrespective of KRAS mutation. Furthermore, exogenous TGFβ led to rapid and sustained TGFBR1-dependent ERK phosphorylation in benign pancreatic duct cells. Similar to results that our group has published in colon cancer cells, inhibition of ERK phosphorylation in duct cells mitigated TGFβ-induced upregulation of growth suppressive pSMAD2 and p21, prevented downregulation of the pro-growth signal CDK2 and ablated TGFβ-induced EMT. These observations suggest that ERK is a key factor in growth suppressive TGFβ signals, yet may also contribute to detrimental TGFβ signaling such as EMT. In neoplastic PanIN cells, pERK was not necessary for either TGFβ-induced pSMAD2 phosphorylation or CDK2 repression, but was required for upregulation of p21 and EMT indicating a partial divergence between TGFβ and MEK/ERK in early carcinogenesis. In cancer cells, pERK had no effect on TGFβ-induced upregulation of pSMAD2 and p21, suggesting the two pathways have completely diverged with respect to the cell cycle. Furthermore, inhibition of pERK both reduced levels of CDK2 and prevented EMT independent of exogenous TGFβ, consistent with most observations identifying pERK as a tumor promoter. Combined, these data suggest that during carcinogenesis pERK initially facilitates and later antagonizes TGFβ-mediated cell cycle arrest, yet remains critical for the pathological, EMT-inducing arm of TGFβ signaling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".