Data from High-level Coexpression of JAG1 and NOTCH1 Is Observed in Human Breast Cancer and Is Associated with Poor Overall Survival
Bibliographic record
Abstract
Abstract Aberrant activation of Notch receptors has been shown to cause mammary tumors in mice. We therefore used in situ hybridization to analyze expression of Notch ligands and receptors in human breast cancer. High levels of JAG1 and NOTCH1 were noted in a subset of tumors with poor prognosis pathologic features (P < 0.05). We therefore used tissue microarrays to analyze the expression of these genes in a collection of breast cancers from patients representing a wide spectrum of clinical stages, and from whom associated follow-up survival data was available (n = 184). Patients with tumors expressing high levels of JAG1 or NOTCH1 had a significantly poorer overall survival compared with patients expressing low levels of these genes [5-year survival rate of 42% versus 65% and median survival of 50 versus 83 months, respectively, for JAG1Hi vs. Lo (P = 0.01); 49% versus 64% and 53 versus 91 months, respectively, for NOTCH1Hi vs. Lo (P = 0.02)]. Moreover, a synergistic effect of high-level JAG1 and high-level NOTCH1 coexpression on overall survival was observed (5-year survival rate of 32% and median survival of 40 months; P = 0.003). These data (a) identify novel prognostic markers for breast cancer, (b) suggest a mechanism whereby Notch is activated in aggressive breast tumors, and (c) may identify a signaling pathway activated in poor prognosis breast cancer which can be therapeutically targeted.
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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 | 0.001 | 0.001 |
| 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) | 0.004 | 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".