CSCs in Breast Cancer—One Size Does Not Fit All: Therapeutic Advances in Targeting Heterogeneous Epithelial and Mesenchymal CSCs
Bibliographic record
Abstract
Unlike other breast cancer subtypes, triple-negative breast cancer (TNBC) has no specific targets and is characterized as one of the most aggressive subtypes of breast cancer that disproportionately accounts for the majority of breast cancer-related deaths. Current conventional chemotherapeutics target the bulk tumor population, but not the cancer stem cells (CSCs) that are capable of initiating new tumors to cause disease relapse. Recent studies have identified distinct epithelial-like (E) ALDH+ CSCs, mesenchymal-like (M) CD44+/CD24− CSCs, and hybrid E/M ALDH+/CD44+/CD24− CSCs. These subtypes of CSCs exhibit differential signal pathway regulations, possess plasticity, and respond differently to treatment. As such, co-inhibition of different subtypes of CSCs is key to viable therapy. This review serves to highlight different pathway regulations in E and M CSCs in TNBC, and to further describe their role in disease progression. Potential inhibitors targeting E and/or M CSCs based on clinical trials are summarized for further investigation. Since future research needs to adopt suitable tumor models and take into account the divergence of E and M CSCs for the development of effective treatments, TNBC models for clinically translatable studies are further discussed.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".