Targeting cancer stem cells with CAR-based immunotherapy: biology, evidence, and future directions
Bibliographic record
Abstract
Cancer stem cells (CSCs) are pivotal in tumor initiation, progression, and relapse, underscoring the need for targeted therapies to achieve lasting responses. This review delves into CSC biology, highlighting their tumor-initiating potential demonstrated through limiting dilution assays and their role in resistance to therapies. Although successful CAR therapies, such as anti-CD19 CAR T-cells, can induce complete responses without directly targeting CSCs, CAR strategies focusing on CSCs may offer promising avenues to prevent recurrence. We assess CAR therapies targeting CSC-specific antigens, including CD133 and GD2, in preclinical and clinical contexts, emphasizing their effectiveness against glioblastoma, breast cancer, and other malignancies. Nevertheless, challenges such as marker specificity and suppression by the tumor microenvironment (TME) persist. Future strategies, which may include dual-targeting and AI-driven marker discovery, aim to improve CSC elimination and advance personalized cancer immunotherapy.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".