Exploitation of the vitamin A/retinoic acid axis depletes ALDH1-positive cancer stem cells and re-sensitises resistant non-small cell lung cancer cells to cisplatin
Bibliographic record
Abstract
Despite advances in personalised medicine and the emerging role of immune checkpoints in directing treatment decisions in subsets of lung cancer patients, non-small cell lung cancer (NSCLC) remains the most common cause of cancer-related deaths worldwide. The development of drug resistance plays a key role in the relapse of lung cancer patients in the clinical setting, mainly due to the unlimited renewal capacity of residual cancer stem cells (CSCs) within the tumour cell population during chemotherapy. In this study, we investigated the function of the CSC marker, aldehyde dehydrogenase (ALDH1) in retinoic acid cell signalling using an in vitro model of cisplatin resistant NSCLC. The addition of key components in retinoic acid cell signalling, all-trans retinoic acid (ATRA) and retinol to cisplatin chemotherapy, significantly reduced ALDH1-positive cell subsets in cisplatin resistant NSCLC cells relative to their sensitive counterparts resulting in the re-sensitisation of chemo-resistant cells to the cytotoxic effects of cisplatin. Furthermore, combination of ATRA or retinol with cisplatin significantly inhibited cell proliferation, colony formation and increased cisplatin-induced apoptosis. This increase in apoptosis may, at least in part, be due to differential gene expression of the retinoic acid (RARα/β) and retinoid X (RXRα) nuclear receptors in cisplatin-resistant lung cancer cells. These data support the concept of exploiting the retinoic acid signalling cascade as a novel strategy in targeting subsets of CSCs in cisplatin resistant lung tumours.
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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".