Neratinib and metformin: A novel therapeutic approach against HER2-Positive Breast Cancer
Bibliographic record
Abstract
BACKGROUND: HER2-positive breast cancer (BC) is highly aggressive with a poor prognosis. It is driven by HER2 oncoprotein activation/crosstalk with other receptors like EGFR/(HER1), HER3, and HER4, in addition to IGF-1R, making these receptors ideal therapeutic targets as they are expressed/overexpressed in this subtype. We postulated that targeting HER2 and IGF-1R together is a promising therapy for HER2-positive BC. Thus, we explored the outcome of a novel combination treatment using neratinib, a pan-HER inhibitor, and metformin, an IGF-1R inhibitor, on HER2-positive BC cells. METHODS: In this investigation, we used cellular and molecular biology techniques in addition to an angiogenesis model and tissue microarray analysis. RESULTS: Our data revealed that this combination therapy significantly reduced cell viability compared to individual treatments and exhibited a synergistic effect in HER2-positive BC cells. Moreover, the combination disrupted cell cycle progression and inhibited colony formation, and invasion of HER2-positive BC cells; this is accompanied by the deregulation of HER1-3 and IGF-1R expression patterns, in addition to Caspase-3, BCL2, Fascin, and Vimentin. Moreover, key regulator molecular pathways, including, ERK1/2, AKT, p38 MAPK, and mTOR, were significantly downregulated upon treatment with neratinib and metformin combination. Additionally, our data pointed out that neratinib and metformin combination inhibited angiogenesis, in-ovo, an important biological event in cancer progression. Finally, using a cohort of 55 HER2-positive BC samples, we revealed that HER2 and IGF-1R are co-expressed in most of the cases. CONCLUSIONS: These findings suggest that neratinib and metformin combination can present a promising strategy for targeting multiple pathways in HER2-positive BC.
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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".