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Record W2995010222 · doi:10.18632/oncotarget.26725

AXL knockdown gene signature reveals a drug repurposing opportunity for a class of antipsychotics to reduce growth and metastasis of triple-negative breast cancer

2019· article· en· W2995010222 on OpenAlexafffundabout
Marie-Anne Goyette, Rebecca Cusseddu, Islam E. Elkholi, Afnan Abu-Thuraia, Nehmé El-Hachem, Benjamin Haibe‐Kains, Jean‐Philippe Gratton, Jean‐François Côté

Bibliographic record

VenueOncotarget · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsVector InstitutePrincess Margaret Cancer CentreUniversity Health NetworkUniversity of TorontoOntario Institute for Cancer ResearchUniversité de MontréalMontreal Clinical Research Institute
FundersFonds de Recherche du Québec - SantéTerry Fox Research InstituteCanadian Institutes of Health ResearchCancer Research Society
KeywordsBreast cancerTriple-negative breast cancerMedicineCancerMetastasisGerontologyLibrary scienceOncologyInternal medicineComputer science

Abstract

fetched live from OpenAlex

// Marie-Anne Goyette 1 , 2 , Rebecca Cusseddu 1 , 2 , Islam Elkholi 1 , 2 , Afnan Abu-Thuraia 1 , 2 , Nehme El-Hachem 1 , Benjamin Haibe-Kains 3 , 4 , 5 , 6 , 7 , Jean-Philippe Gratton 8 and Jean-François Côté 1 , 2 , 9 , 10 1 Montreal Clinical Research Institute (IRCM), Montréal, QC, H2W 1R7, Canada 2 Molecular Biology Programs, Université de Montréal, Montréal, QC, H3T 1J4, Canada 3 Princess Margaret Cancer Centre, Toronto, University Health Network, ON M5G 1L7, Canada 4 Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada 5 Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada 6 Ontario Institute for Cancer Research, Toronto, ON M5G 1L7, Canada 7 Vector Institute, Toronto, ON M5G 1L7, Canada 8 Department of Pharmacology and Physiology, Université de Montréal, Montréal, QC, H3C 3J7, Canada 9 Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, QC, H3C 3J7, Canada 10 Department of Anatomy and Cell Biology, McGill University, Montréal, QC, H3A 0C7, Canada Correspondence to: Jean-François Côté, email: jean-francois.cote@ircm.qc.ca Keywords: triple-negative breast cancer; drug repurposing; AXL; phenothiazines; metastasis Received: October 03, 2018     Accepted: February 15, 2019     Published: March 12, 2019 ABSTRACT Triple-Negative Breast Cancer (TNBC) is an aggressive cancer subtype that is associated with a poor prognosis due to its propensity to form metastases. The receptor tyrosine kinase AXL plays a role in tumor cell dissemination and its expression in breast cancers correlates with poor patient survival. Here, we explored whether already used drugs might elicit a gene signature similar to that seen with AXL knockdown in TNBC cells and which could, therefore, offer an opportunity for drug repurposing. To this end, we queried the Connectivity Map with an AXL gene signature which revealed a class of dopamine receptors antagonists named phenothiazines (Thioridazine, Fluphenazine and Trifluoperazine) typically used as anti-psychotics. We next tested if these drugs, similarly to AXL depletion, were able to limit growth and metastatic progression of TNBC cells and found that phenothiazines are able to reduce cell invasion, proliferation, viability and increase apoptosis of TNBC cells in vitro . Mechanistically, these drugs did not affect AXL activity but instead reduced PI3K/AKT/mTOR and ERK signaling. When administered to mice bearing TNBC xenografts, phenothiazines were able to reduce tumor growth and metastatic burden. Collectively, these results suggest that these antipsychotics display anti-tumor and anti-metastatic activity and that they could potentially be repurposed, in combination with standard chemotherapy, for the treatment of TNBC.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.067
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.018
GPT teacher head0.284
Teacher spread0.266 · 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 teacher head, 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

Citations44
Published2019
Admission routes3
Has abstractyes

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