Heterogeneity of breast cancer stem cells as evidenced with <scp>N</scp>otch‐dependent and <scp>N</scp>otch‐independent populations
Bibliographic record
Abstract
Studies have suggested the potential importance of Notch signaling to the cancer stem cell population in some tumors, but it is not known whether all cells in the cancer stem cell fraction require Notch activity. To address this issue, we blocked Notch activity in MCF-7 cells by expressing a dominant-negative MAML-GFP (dnMAML) construct, which inhibits signaling through all Notch receptors, and quantified the effect on tumor-initiating activity. Inhibition of Notch signaling reduced primary tumor sphere formation and side population. Functional quantification of tumor-initiating cell numbers in vivo showed a significant decrease, but not a complete abrogation, of these cells in dnMAML-expressing cells. Interestingly, when assessed in secondary assays in vitro or in vivo, there was no difference in tumor-initiating activity between the dnMAML-expressing cells and control cells. The fact that a subpopulation of dnMAML-expressing cells was capable of forming primary and secondary tumors indicates that there are Notch-independent tumor-initiating cells in the breast cancer cell line MCF-7. Our findings thus provide direct evidence for a heterogeneous cancer stem cell pool, which will require combination therapies against multiple oncogenic pathways to eliminate the tumor-initiating cell population.
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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 | 0.000 |
| 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 | 0.000 |
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".