Lung tumor–infiltrating T<sub>reg</sub>have divergent transcriptional profiles and function linked to checkpoint blockade response
Why this work is in the frame
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Bibliographic record
Abstract
Regulatory T cells (T reg ) are conventionally viewed as suppressors of endogenous and therapy-induced antitumor immunity; however, their role in modulating responses to immune checkpoint blockade (ICB) is unclear. In this study, we integrated single-cell RNA-seq/T cell receptor sequencing (TCRseq) of >73,000 tumor-infiltrating T reg (TIL-T reg ) from anti–PD-1–treated and treatment-naive non–small cell lung cancers (NSCLC) with single-cell analysis of tumor-associated antigen (TAA)–specific T reg derived from a murine tumor model. We identified 10 subsets of human TIL-T reg , most of which have high concordance with murine TIL-T reg subsets. Only one subset selectively expresses high levels of TNFRSF4 (OX40) and TNFRSF18 (GITR), whose engangement by cognate ligand mediated proliferative programs and NF-κB activation, as well as multiple genes involved in T reg suppression, including LAG3 . Functionally, the OX40 hi GITR hi subset is the most highly suppressive ex vivo, and its higher representation among total TIL-T reg correlated with resistance to PD-1 blockade. Unexpectedly, in the murine tumor model, we found that virtually all TIL-T reg –expressing T cell receptors that are specific for TAA fully develop a distinct T H 1-like signature over a 2-week period after entry into the tumor, down-regulating FoxP3 and up-regulating expression of TBX21 ( Tbet) , IFNG , and certain proinflammatory granzymes. Transfer learning of a gene score from the murine TAA-specific T H 1-like T reg subset to the human single-cell dataset revealed a highly analogous subcluster that was enriched in anti–PD-1–responding tumors. These findings demonstrate that TIL-T reg partition into multiple distinct transcriptionally defined subsets with potentially opposing effects on ICB-induced antitumor immunity and suggest that TAA-specific TIL-T reg may positively contribute to antitumor responses.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it