Overcoming retinoic acid-resistance of mammary carcinomas by diverting retinoic acid from PPARβ/δ to RAR
Bibliographic record
Abstract
Retinoic acid (RA) displays potent anticarcinogenic activities that are mediated by the nuclear retinoic acid receptors (RARs). However, use of RA in oncology is limited by RA resistance acquired during carcinogenesis. Moreover, in some cancers, RA facilitates rather than inhibits growth. A clue to this paradoxical behavior was recently suggested by the findings that RA also activates PPARbeta/delta, a receptor involved in mitogenic and anti-apoptotic activities. The observations that partitioning of RA between its two receptors is regulated by two intracellular lipid-binding proteins-CRABP-II, which targets RA to RAR, and FABP5, which delivers it to PPARbeta/delta-further suggest that RA resistance may stem from the deregulation of the binding proteins, resulting in activation of PPARbeta/delta rather than RAR. Here, we show that, in the RA-resistant mouse model of breast cancer MMTV-neu, RA indeed activates the nonclassical RA receptor PPARbeta/delta. This behavior was traced to an aberrantly high intratumor FABP5/CRABP-II ratio. Decreasing this ratio in mammary tissue diverted RA from PPARbeta/delta to RAR and suppressed tumor growth. The data demonstrate the existence of a mechanism that underlies RA resistance in tumors, indicate that CRABP-II functions as a tumor suppressor, and suggest that the inhibition of FABP5 may comprise a therapeutic strategy for overcoming RA resistance in some tumors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".