Data from Notch1 Inhibition Alters the CD44<sup>hi</sup>/CD24<sup>lo</sup> Population and Reduces the Formation of Brain Metastases from Breast Cancer
Bibliographic record
Abstract
Abstract Brain metastasis from breast cancer is an increasingly important clinical problem. Here we assessed the role of CD44hi/CD24lo cells and pathways that regulate them, in an experimental model of brain metastasis. Notch signaling (mediated by γ-secretase) has been shown to contribute to maintenance of the cancer stem cell (CSC) phenotype. Cells sorted for a reduced stem-like phenotype had a reduced ability to form brain metastases compared with unsorted or CD44hi/CD24lo cells (P < 0.05; Kruskal–Wallis). To assess the effect of γ-secretase inhibition, cells were cultured with DAPT and the CD44/CD24 phenotypes quantified. 231-BR cells with a CD44hi/CD24lo phenotype was reduced by about 15% in cells treated with DAPT compared with DMSO-treated or untreated cells (P = 0.001, ANOVA). In vivo, mice treated with DAPT developed significantly fewer micro- and macrometastases compared with vehicle treated or untreated mice (P = 0.011, Kruskal–Wallis). Notch1 knockdown reduced the expression of CD44hi/CD24lo phenotype by about 20%. In vitro, Notch1 shRNA resulted in a reduction in cellular growth at 24, 48, and 72 hours time points (P = 0.033, P = 0.002, and P = 0.009, ANOVA) and about 60% reduction in Matrigel invasion was observed (P < 0.001, ANOVA). Cells transfected with shNotch1 formed significantly fewer macrometastases and micrometastases compared with scrambled shRNA or untransfected cells (P < 0.001; Kruskal–Wallis). These data suggest that the CSC phenotype contributes to the development of brain metastases from breast cancer, and this may arise in part from increased Notch activity. Mol Cancer Res; 9(7); 834–44. ©2011 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".