Targeting Aberrant TGF-β Signaling in Pre-Clinical Models of Cancer
Bibliographic record
Abstract
The TGF-beta signaling pathway is central to the control of diverse biological processes including cellular proliferation, cell survival, apoptosis, extracellular matrix deposition/remodeling, migration, invasion and immune regulation/inflammation. Given the pleiotropic effects of this cytokine, it comes as no surprise that numerous pathological conditions are associated with alterations in the TGF-beta pathway, including chronic fibrosis, airway remodeling (asthma), cardiovascular disease and cancer. Thus, there are increasing efforts to develop reagents and therapeutic strategies to impair TGF-beta signaling. Here we review several classes of inhibitors, including knock-down strategies aimed at signaling components of the TGF-beta pathway, TGF-beta neutralizing antibodies, TGF-beta receptor extracellular domains that function as ligand traps and small molecule kinase inhibitors. Strategies with potential for application as anti-cancer therapeutics that have been evaluated in pre-clinical animal models will be discussed. TGF-beta action is complex, shifting from a tumor suppressor to a promoter of tumor cell invasion and metastasis in several types of cancer. This raises important issues regarding not only the status of the TGF-beta pathway in the individual patient but also the precise stage during disease progression that such inhibitors should be employed. Potential consequences of targeting the TGF-beta pathway will also be considered.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".