Targeting resistance to radiation-immunotherapy in cold HNSCCs by modulating the Treg-dendritic cell axis
Bibliographic record
Abstract
Background Numerous trials combining radiation therapy (RT) and immunotherapy in head and neck squamous cell carcinoma (HNSCC) are failing. Using preclinical immune cold models of HNSCC resistant to RT-immune checkpoint inhibitors, we investigate therapeutic approaches of overcoming such resistance by examining the differential microenvironmental response to RT. Methods We subjected two HPV-negative orthotopic mouse models of HNSCC to combination RT, regulatory T cells (Treg) depletion, and/or CD137 agonism. Tumor growth was measured and intratumorous and lymph node immune populations were compared among treatment groups. Human gene sets, genetically engineered mouse models DEREG and BATF3–/– , flow and time-of-flight cytometry, RNA-Seq, Treg adoptive transfer studies, and in vitro experiments were used to further evaluate the role of dendritic cells (DCs) and Tregs in these treatments. Results In MOC2 orthotopic tumors, we find no therapeutic benefit to targeting classically defined immunosuppressive myeloids, which increase with RT. In these radioresistant tumors, supplementing combination RT and Treg depletion with anti-CD137 agonism stimulates CD103 + DC activation in tumor-draining lymph nodes as characterized by increases in CD80 + and CCR7 + DCs, resulting in a CD8 T cell-dependent response. Simultaneously, Tregs are reprogrammed to an effector phenotype demonstrated by increases in interferonγ + , tumor necrosis factorα + , PI3K + , pAKT + and Eomes + populations as well as decreases in CTLA4 + and NRP-1 + populations. Tumor eradication is observed when RT is increased to an 8 Gy x 5 hypofractionated regimen and combined with anti-CD25+ anti-CD137 treatment. In a human gene set from oral squamous cell carcinoma tumors, high Treg number is associated with earlier recurrence. Conclusions Regulating Treg functionality and DC activation status within the lymph node is critical for generating a T cell effector response in these highly radioresistant tumors. These findings underscore the plasticity of Tregs and represent a new therapeutic opportunity for reprogramming the tumor microenvironment in HNSCCs resistant to conventional radioimmunotherapy approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".