Monoamine oxidase-A activity is required for clonal tumorsphere formation by human breast tumor cells
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".