α‐linolenic acid reduces growth in four breast cancer cell lines with varying receptor expression in high and low estrogen environments
Bibliographic record
Abstract
Breast cancer differs in cell receptors which then determines effective treatments. Alpha‐linolenic acid (ALA)‐rich flaxseed oil reduced estrogen receptor (ER)+ cell growth at low and high estrogen (E2) but its effect is unclear in cells with varying ER, progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2). Our objective was to determine in vitro the ALA and E2 effect on growth and membrane lipids of four breast cancer cell lines with varying ER, PR and HER2 status. Cells were treated with 0 – 200uM ALA ± 1nM E2 and tested for growth by trypan blue exclusion method and fatty acids by gas chromatography. Receptor status confirmed by Western Blot: MCF7‐ER+PR+low HER2; BT474‐ER+PR+HER2+, MDA MB 231‐ER‐PR‐low HER2; MDA MB 468‐ER‐PR‐HER2‐. Compared to –E2 control, MCF7 had a significant E2, ALA and interaction effect on growth; other cell lines only had an ALA effect. 50uM and 75uM ALA significantly decreased growth by 14, 48, 31 and 63% and 38, 77, 80 and 78% in MCF7, BT 474, MDA MB231 and MDA MB468, respectively. E2 significantly increased (91.8%) MCF7 growth. Cells showed significant increases (9–25 fold) in % ALA which related to growth. Thus ALA reduces breast cancer cell growth ± E2 at doses as low as 50uM, likely through alteration of lipid composition. It is effective regardless of ER, PR and HER2 status which may be of importance in difficult to treat cancers such as triple negative. Funded by NSERC. Grant Funding Source : NSERC
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".