Synergistic targeting of breast cancer stem‐like cells by human γδ T cells and CD8<sup>+</sup> T cells
Bibliographic record
Abstract
The inherent resistance of cancer stem cells (CSCs) to existing therapies has largely hampered the development of effective treatments for advanced malignancy. To help develop novel immunotherapy approaches that efficiently target CSCs, an experimental model allowing reliable distinction of CSCs and non‐CSCs was set up to study their interaction with non‐MHC‐restricted γδ T cells and antigen‐specific CD8+ T cells. Stable lines with characteristics of breast CSC‐like cells were generated from ras‐transformed human mammary epithelial (HMLER) cells as confirmed by their CD44hi CD24lo GD2+ phenotype, their mesenchymal morphology in culture and their capacity to form mammospheres under non‐adherent conditions, as well as their potent tumorigenicity, self‐renewal and differentiation in xenografted mice. The resistance of CSC‐like cells to γδ T cells could be overcome by inhibition of farnesyl pyrophosphate synthase (FPPS) through pretreatment with zoledronate or with FPPS‐targeting short hairpin RNA. γδ T cells induced upregulation of MHC class I and CD54/ICAM‐1 on CSC‐like cells and thereby increased the susceptibility to antigen‐specific killing by CD8+ T cells. Alternatively, γδ T‐cell responses could be specifically directed against CSC‐like cells using the humanised anti‐GD2 monoclonal antibody hu14.18K322A. Our findings identify a powerful synergism between MHC‐restricted and non‐MHC‐restricted T cells in the eradication of cancer cells including breast CSCs. Our research suggests that novel immunotherapies may benefit from a two‐pronged approach combining γδ T‐cell and CD8+ T‐cell targeting strategies that triggers effective innate‐like and tumour‐specific adaptive responses.
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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 | 0.000 | 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".