The Key to Unlocking the Chemotherapeutic Potential of PPAR<i>γ</i>Ligands: Having the Right Combination
Bibliographic record
Abstract
Despite extensive preclinical evidence that peroxisome proliferator-activated receptor (PPAR)γ activation protects against tumourigenesis, results from a few clinical trials using PPARγ ligands as monotherapy show modest success. In spite of this, several groups reported exciting results with therapeutic regimens that combine PPARγ ligands with other compounds: chemotherapeutic agents, retinoid x receptor (RXR)α agonists, statins, or cell-to-cell signaling molecules in preclinical cancer models and human trials. Here we have compiled an extensive review, consolidating the existing literature, which overwhelmingly supports a beneficial effect of treating with PPARγ ligands in combination with existing chemotherapies versus their monotherapy in cancer. There are many examples in which combination therapy resulted in synergistic/additive effects on apoptosis, differentiation, and the ability to reduce cell growth and tumour burden. There are also studies that indicate that PPARγ ligand pretreatment overcomes resistance and reduces toxicities. Several mechanisms are explored to explain these protective effects. This paper highlights each of these studies that, collectively, make a very strong case for the use of PPARγ ligands in combination with other agents in the treatment and management of several cancers.
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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 | 0.003 | 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 | 0.001 | 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".