Abstract LB-290: Tranilast inhibits breast cancer stem cells
Bibliographic record
Abstract
Abstract Cancer stem cells (CSCs) have increased resistance to anti-cancer drugs, and may be responsible for chemotherapeutic failure. CSCs can be enriched from cancer cell lines by growth with anti-cancer drugs (e.g., doxorubicin, mitoxantrone), and form mammospheres in low-adherence, serum-free cultures. We are studying tranilast, a non-toxic orally active drug in clinical use for allergic diseases in Japan, but that we found also targets cancer cells. Previously, we reported that tranilast exerts multiple anti-cancer effects and protects against breast cancer metastasis. Our recent studies show that tranilast strongly inhibits breast CSCs in several assays, at pharmacologically relevant concentrations. We enriched CSCs from breast cancer cell lines by cell sorting for ALDH-1 positive cells (ALDEFLUOR+), or by growth in mitoxantrone-containing medium to select drug-surviving CSCs. In both cases, CSCs readily formed mammospheres in vitro. Tranilast inhibited mammosphere formation,’ and dissociated formed mammospheres. It was more effective than paclitaxel or etoposide. We recently identified molecular targets of this drug. We found it has aryl hydrocarbon receptor (AHR) agonist activity and this might explain its anti-cancer effects. The AHR is a receptor for toxins such as dioxin and polycyclic aromatic hydrocarbons. It is a transcription factor that exerts a number of ligand-dependent effects, such as cell-cycle arrest, reduced RB phosphorylation, and decreased cytokine and growth factor production. Unlike many AHR ligands, tranilast has very low toxicity (LD50 > 1 gm/kg in rats), and is well tolerated by patients. In accord with a role for the AHR, breast CSCs expressed higher levels of this receptor than the main population of cancer cells, and the effects of tranilast were markedly diminished by an AHR antagonist (alpha-naphthoflavone). Moreover, we found tranilast inhibits some ATP-binding cassette (ABC) multiple drug resistance transporters, and it enhances the effects of other anti-cancer drugs. Our studies show that tranilast is a drug of low toxicity that has potential as an anti-CSC drug. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr LB-290.
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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) | 0.000 | 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) | 0.007 | 0.001 |
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".