ADAM protease inhibition overcomes resistance of breast cancer stem-like cells to γδ T cell immunotherapy
Bibliographic record
Abstract
Abstract Breast cancer stem cells (BCSC) are highly resistant to current therapies, and are responsible for metastatic burden and relapse. Gamma delta T cells (γδTc) are immunosurveillance cells with tremendous anti-tumoral activity, and a growing number of clinical trials have confirmed the safety of γδTc immunotherapy for various malignancies. Herein, we demonstrate that γδTc can kill BCSC, but to a lesser extent than non-cancer stem cells (NSC). Immune evasion was orchestrated by several mechanisms. The BCSC secretome rendered γδTc hypo-responsive by reducing proliferation, cytotoxicity and IFN-γ production, while increasing expression of co-inhibitory receptors on γδTc. BCSC and target cells surviving γδTc cytotoxicity had higher PD-L1 co-inhibitory ligand expression, and blocking PD-1 on γδTc significantly overcame BCSC resistance to γδTc killing. Fas/FasL signaling was dysfunctional in BCSC due to upregulation of the anti-apoptotic protein MCL-1, which could be partially overcome using dMCL1-2, an MCL-1 degrader. Moreover, the BCSC fraction shed higher levels of the NKG2D ligand MICA compared to NSC. Inhibiting MICA shedding using the ADAM inhibitor GW280264X overcame BCSC resistance to γδTc killing, rendering BCSC as sensitive to γδTc cytotoxicity as NSC. Collectively, our data unravel multiple mechanisms exploited by BCSC to evade γδTc killing, which may also come into play in BCSC resistance to other cytotoxic lymphocytes. Developing strategies to overcome this resistance will increase the efficacy of cancer immunotherapy and lead to improved outcomes for cancer patients. One Sentence Summary Breast cancer stem-like cells are resistant to γδ T cell targeting, which can be overcome by inhibiting ADAM proteases that facilitate MICA/B shedding.
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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 | 0.001 |
| 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".