Update on immune‐based therapy strategies targeting cancer stem cells
Bibliographic record
Abstract
Accumulating data reveals that tumors possess a specialized subset of cancer cells named cancer stem cells (CSCs), responsible for metastasis and recurrence of malignancies, with various properties such as self-renewal, heterogenicity, and capacity for drug resistance. Some signaling pathways or processes like Notch, epithelial to mesenchymal transition (EMT), Hedgehog (Hh), and Wnt, as well as CSCs' surface markers such as CD44, CD123, CD133, and epithelial cell adhesion molecule (EpCAM) have pivotal roles in acquiring CSCs properties. Therefore, targeting CSC-related signaling pathways and surface markers might effectively eradicate tumors and pave the way for cancer survival. Since current treatments such as chemotherapy and radiation therapy cannot eradicate all of the CSCs and tumor relapse may happen following temporary recovery, improving novel and more efficient therapeutic options to combine with current treatments is required. Immunotherapy strategies are the new therapeutic modalities with promising results in targeting CSCs. Here, we review the targeting of CSCs by immunotherapy strategies such as dendritic cell (DC) vaccines, chimeric antigen receptors (CAR)-engineered immune cells, natural killer-cell (NK-cell) therapy, monoclonal antibodies (mAbs), checkpoint inhibitors, and the use of oncolytic viruses (OVs) in pre-clinical and clinical studies. This review will mainly focus on blood malignancies but also describe solid cancers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".