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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".