Constitutive NOTCH3 Signaling Promotes the Growth of Basal Breast Cancers
Bibliographic record
Abstract
Abstract Notch ligands signal through one of four receptors on neighboring cells to mediate cell–cell communication and control cell fate, proliferation, and survival. Although aberrant Notch activation has been implicated in numerous malignancies, including breast cancer, the importance of individual receptors in distinct breast cancer subtypes and the mechanisms of receptor activation remain unclear. Using a novel antibody to detect active NOTCH3, we report here that NOTCH3 signals constitutively in a panel of basal breast cancer cell lines and in more than one third of basal tumors. Selective inhibition of individual ligands revealed that this signal does not require canonical ligand induction. A NOTCH3 antagonist antibody inhibited growth of basal lines, whereas a NOTCH3 agonist antibody enhanced the transformed phenotype in vitro and in tumor xenografts. Transcriptomic analyses generated a Notch gene signature that included Notch pathway components, the oncogene c-Myc, and the mammary stem cell regulator Id4. This signature drove clustering of breast cancer cell lines and tumors into the common subtypes and correlated with the basal classification. Our results highlight an unexpected ligand-independent induction mechanism and suggest that constitutive NOTCH3 signaling can drive an oncogenic program in a subset of basal breast cancers. Cancer Res; 77(6); 1439–52. ©2017 AACR.
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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 | 0.001 |
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".