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Record W1976205021 · doi:10.1158/1538-7445.am10-lb-290

Abstract LB-290: Tranilast inhibits breast cancer stem cells

2010· article· en· W1976205021 on OpenAlexaff
Gérald J. Prud’homme, Yelena Glinka, Anna Toulina, Venkateswaran Subramaniam, Rabindranath Chakrabarti, Serge Jothy

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsTranilastCancer stem cellCancer researchCancerCancer cellPharmacologyCD44Breast cancerChemistryMedicineInternal medicineCellBiochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.051
GPT teacher head0.377
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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