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Record W2986394918 · doi:10.1186/s11658-019-0183-8

Monoamine oxidase-A activity is required for clonal tumorsphere formation by human breast tumor cells

2019· article· en· W2986394918 on OpenAlexafffund
William D. Gwynne, Mirza S. Shakeel, Jianhan Wu, Robin Hallett, Adele Girgis-Gabardo, Anna Dvorkin‐Gheva, John A. Hassell

Bibliographic record

VenueCellular & Molecular Biology Letters · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchCanadian Cancer Society
KeywordsCancer researchBiologyTranscriptomeBreast cancerPopulationMonoamine oxidaseCancerGene expressionGeneMedicineEnzymeGeneticsBiochemistry

Abstract

fetched live from OpenAlex

Abstract Background Breast tumor growth and recurrence are driven by an infrequent population of breast tumor-initiating cells (BTIC). We and others have reported that the frequency of BTIC is orders of magnitude higher when breast tumor cells are propagated in vitro as clonal spheres, termed tumorspheres, by comparison to adherent cells. We exploited the latter to screen > 35,000 small molecules to identify agents capable of targeting BTIC. We unexpectedly discovered that selective antagonists of serotonin signaling were among the hit compounds. To better understand the relationship between serotonin and BTIC we expanded our analysis to include monoamine oxidase-A (MAO-A), an enzyme that metabolizes serotonin. Methods We used the Nanostring technology and Western blotting to determine whether MAO-A is expressed in human breast tumor cell lines cultured as tumorspheres by comparison to those grown as adherent cells. We then determined whether MAO-A activity is required for tumorsphere formation, a surrogate in vitro assay for BTIC, by assessing whether selective MAO-A inhibitors affect the frequency of tumorsphere-forming cells. To learn whether MAO-A expression in breast tumor cells is associated with other reported properties of BTIC such as anticancer drug resistance or breast tumor recurrence, we performed differential gene expression analyses using publicly available transcriptomic datasets. Results Tumorspheres derived from human breast tumor cell lines representative of every breast cancer clinical subtype displayed increased expression of MAO-A transcripts and protein by comparison to adherent cells. Surprisingly, inhibition of MAO-A activity with selective inhibitors reduced the frequency of tumorsphere-forming cells. We also found that increased MAO-A expression is a common feature of human breast tumor cell lines that have acquired anticancer drug resistance and is associated with poor recurrence-free survival (RFS) in patients that experienced high-grade, ER-negative (ER − ) breast tumors. Conclusions Our data suggests that MAO-A activity is required for tumorsphere formation and that its expression in breast tumor cells is associated with BTIC-related properties. The discovery that a selective MAO-A inhibitor targets tumorsphere-forming cells with potencies in the nanomolar range provides the first evidence of this agent’s anticancer property. These data warrant further investigation of the link between MAO-A and BTIC.

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 categoriesMeta-epidemiology (narrow)
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 score0.020
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.247
Teacher spread0.240 · 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.

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

Citations19
Published2019
Admission routes2
Has abstractyes

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