The intermediate conductance Ca<sup>2+</sup>‐activated K<sup>+</sup> channel inhibitor TRAM‐34 stimulates proliferation of breast cancer cells via activation of oestrogen receptors
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: K(+) channels play a role in the proliferation of cancer cells. We have investigated the effects of specific K(+) channel inhibitors on basal and oestrogen-stimulated proliferation of breast cancer cells. EXPERIMENTAL APPROACH: Using the mammary adenocarcinoma cell line MCF-7 we assayed cell proliferation by radiolabelled thymidine incorporation in the absence or presence of various K(+) channel inhibitors with or without 17beta-oestradiol. KEY RESULTS: Inhibitors of K(v)10.1 and K(Ca)3.1 K(+) channels suppressed basal proliferation of MCF-7 cells, but not oestrogen-stimulated proliferation. TRAM-34, a specific inhibitor of K(Ca)3.1 channels increased or decreased cell proliferation depending on the concentration. At intermediate concentrations (3-10 microM) TRAM-34 increased cell proliferation, whereas at higher concentrations (20-100 microM) TRAM-34 decreased cell proliferation. The enhancement of cell proliferation caused by TRAM-34 was blocked by the oestrogen receptor antagonists ICI182,780 and tamoxifen. TRAM-34 also increased progesterone receptor mRNA expression, decreased oestrogen receptor-alpha mRNA expression and reduced the binding of radiolabelled oestrogen to MCF-7 oestrogen receptor, in each case mimicking the effects of 17beta-oestradiol. CONCLUSIONS AND IMPLICATIONS: Our results demonstrate that K(+) channels K(v)10.1 and K(Ca)3.1 play a role in basal, but not oestrogen-stimulated MCF-7 cell proliferation. TRAM-34, as well as inhibiting K(Ca)3.1, directly interacts with the oestrogen receptor and mimics the effects of 17beta-oestradiol on MCF-7 cell proliferation and gene modulation. Our finding that TRAM-34 is able to activate the oestrogen receptor suggests a novel action of this supposedly specific K(+) channel inhibitor and raises concerns of interpretation in its use.
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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) | 0.000 | 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) | 0.002 | 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".