Targeting Met and Notch in the<i>Lfng</i>-deficient,<i>Met</i>-amplified triple-negative breast cancer
Bibliographic record
Abstract
Triple negative breast cancer (TNBC) accounts for 15-20% of breast carcinomas and represents one of the most aggressive forms of this disease. Basal and claudin-low are the two main molecular subtypes among TNBCs. We previously reported that deletion of Lfng in mouse mammary gland caused deregulated Notch activation and induced basal-like and claudin-low tumors with co-selection for Met amplification. In human breast cancers, the vast majority of basal tumors and a subset of claudin-low tumors show reduced Lfng expression. Elevated Met expression and activation is associated with basal as well as claudin-low subtypes. To examine roles of Met and Notch in TNBC cells, we established two cell lines that harbor Met amplification as well as Lfng deletion, and possess features of basal and claudin-low breast cancer subtypes. Pharmacological inhibition of Met not only suppressed cell growth, tumorsphere and colony formation, but also reversed epithelial-to-mesenchymal transition and inhibited cell migration in both cell lines. In contrast, inhibition of Notch signaling using a γ-secretase inhibitor (GSI) only suppressed colony formation. Interestingly, GSI had no effect as single agent, but exerted a synergistic effect with Met inhibitor, on cell growth in 2D culture. We found that inhibition of Met resulted in downregulation of Dll ligands and upregulation of Jagged ligands, leading to differential modulation of Notch signaling. Our results suggest that combination targeting of Met and Notch may prove beneficial for TNBC patients with Met overexpression and Notch hyperactivation.
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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) | 0.001 | 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".