Faculty Opinions recommendation of Notch-1-PTEN-ERK1/2 signaling axis promotes HER2+ breast cancer cell proliferation and stem cell survival.
Bibliographic record
Abstract
Trastuzumab targets the HER2 receptor on breast cancer cells to attenuate HER2-driven tumor growth. However, resistance to trastuzumab-based therapy remains a major clinical problem for women with HER2+ breast cancer. Breast cancer stem cells (BCSCs) are suggested to be responsible for drug resistance and tumor recurrence. Notch signaling has been shown to promote BCSC survival and self-renewal. Trastuzumab-resistant cells have increased Notch-1 expression. Notch signaling drives cell proliferation in vitro and is required for tumor recurrence in vivo. We demonstrate herein a mechanism by which Notch-1 is required for trastuzumab resistance by repressing PTEN expression to contribute to activation of ERK1/2 signaling. Furthermore, Notch-1-mediated inhibition of PTEN is necessary for BCSC survival in vitro and in vivo. Inhibition of MEK1/2-ERK1/2 signaling in trastuzumab-resistant breast cancer cells mimics effects of Notch-1 knockdown on bulk cell proliferation and BCSC survival. These findings suggest that Notch-1 contributes to trastuzumab resistance by repressing PTEN and this may lead to hyperactivation of ERK1/2 signaling. Furthermore, high Notch-1 and low PTEN mRNA expression may predict poorer overall survival in women with breast cancer. Notch-1 protein expression predicts poorer survival in women with HER2+ breast cancer. These results support a potential future clinical trial combining anti-Notch-1 and anti-MEK/ERK therapy for trastuzumab-resistant breast cancer. PMID: 29743588
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.625 | 0.416 |
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".