Abstract PR-07: Immunosuppressive γδ T cells limit anti-tumor immunity in ICI-resistant tumors from autoimmune-prone mice
Bibliographic record
Abstract
Abstract Despite the clinical success of immune checkpoint inhibitors (ICIs), therapeutic resistance and the unpredictable development of immune-related adverse events (irAEs) remain major challenges. While the use of ICIs in combination can improve treatment efficacy, this also leads to an increase in the frequency and severity of irAEs. This has created an urgent need to enhance anti-tumor immunity without triggering immunotoxicity. However, current preclinical models are limited in their ability to model both ICI efficacy and irAE development. This is due to mouse strains commonly used for studying anti-tumor immunity, such as C57BL/6 and BALB/c mice, being resistant to ICI-induced irAEs and failing to recapitulate the full spectrum of clinical responses seen in patients with cancer. To overcome these limitations, we generated transplantable, syngeneic tumor models with variable ICI responses using the autoimmune-prone non-obese diabetic (NOD) mouse. The NOD model, which spontaneously develops multiple autoimmune diseases, exhibits a broad range of irAEs following ICI treatment with a shared etiology to clinical conditions. This approach offers insight into how genetic predisposition to autoimmunity influences both ICI response and irAE susceptibility, enabling us to explore how autoimmune-associated host-intrinsic factors uniquely alter the tumor microenvironment and contribute to ICI resistance. In ICI-resistant NOD tumors, CD8+ T cells exhibited a naïve-like phenotype, marked by elevated TCF-1 and reduced PD-1 and CTLA-4 expression, compared to ICI-sensitive tumors. Despite comparable CD8+ T cell frequency and number, these phenotypic differences led us to investigate other immune cell populations that regulate ICI response and CD8+ T cell activation. Single-cell RNA sequencing and spectral flow cytometry revealed enrichment of immunosuppressive γδ T cells in ICI-resistant tumors. These γδ T cells highly expressed the transcription factors RORγt+ and TCF1+ and lacked expression of T-bet and the mouse Vγ1 TCR chain, suggesting that distinct γδ T cell subsets may play different roles in tumor control. Functionally, antibody-mediated blockade of the γδ T cell receptor (γδTCR) significantly improved tumor control in ICI-resistant, but not ICI-sensitive, models, implicating γδ T cells in promoting tumor growth. Tumors enriched with immunosuppressive RORγt+ and TCF1+ γδ T cells also exhibited increased B cell infiltration. In B cell-deficient NOD mice, γδ T cells skewed toward T-bet and Vγ1 expression, which correlated with enhanced CD8+ T cell activation. These findings reveal a coordinated immunosuppressive network between γδ T cells and B cells that limits anti-tumor immunity. Given their roles in promoting autoimmunity, these cell types may represent therapeutic targets to boost anti-tumor immunity and overcome ICI resistance without exacerbating irAEs. Citation Format: Camille Hansen, Arabella Young. Immunosuppressive γδ T cells limit anti-tumor immunity in ICI-resistant tumors from autoimmune-prone mice [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr PR-07.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".