α-linolenic acid and docosahexaenoic acid, alone and combined with trastuzumab, reduce HER2-overexpressing breast cancer cell growth but differentially regulate HER2 signaling pathways
Bibliographic record
Abstract
BACKGROUND: Diets rich in the n-3 fatty acid alpha-linolenic acid (ALA) have been shown to reduce breast tumor growth, enhance the effectiveness of the HER2-targeted drug trastuzumab (TRAS) and reduce HER2 signaling in mouse models. It is unclear whether this is due to direct effects of ALA or due to its long-chain n-3 fatty acids metabolites including docosahexaenoic acid (DHA). METHODS: The ability of HER2-overexpressing BT-474 human breast cancer cells to convert ALA to long-chain n-3 fatty acids was determined by measurement of phospholipid fatty acids by gas chromatography following treatment with 100 μM ALA. The effects of 96 h treatment with ALA or DHA, at serum levels seen in mice (50-100 μM), alone and combined with TRAS (10 μg/ml), on BT-474 cell growth measured by trypan blue exclusion, apoptosis measured by flow cytometric analysis of Annexin-V/7-AAD stained cells (ALA and TRAS treatment only) and protein biomarkers HER2 signaling measured by western blot were determined. RESULTS: ALA-treated BT-474 cells had higher phospholipid ALA but no increase in downstream n-3 metabolites including DHA. Both ALA and DHA reduced cell growth with and without TRAS. ALA had no effect on apoptosis. ALA and DHA showed opposite effects on Akt and MAPK phosphorylation; ALA increased and DHA decreased phosphorylation. CONCLUSIONS: Together these data suggest that, while both ALA and its DHA metabolite can reduce HER2-overexpressing breast cancer growth with and without TRAS, they demonstrate for the first time that DHA is responsible for the effects of ALA-rich diets on HER2 signaling pathways.
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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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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".