Autoreactive napsin A–specific T cells are enriched in lung tumors and inflammatory lung lesions during immune checkpoint blockade
Bibliographic record
Abstract
Cancer treatment with immune checkpoint blockade (ICB) often induces immune-related adverse events (irAEs). We hypothesized that proteins coexpressed in tumors and normal cells could be antigenic targets in irAEs and herein described DITAS (discovery of tumor-associated self-antigens) for their identification. DITAS computed transcriptional similarity between lung tumors and healthy lung tissue based on single-sample gene set enrichment analysis. This identified 10 lung tissue–specific genes highly expressed in the lung tumors. Computational analysis was combined with functional T cell assays and single-cell RNA sequencing of the antigen-specific T cells to validate the lung tumor self-antigens. In patients with non–small cell lung cancer (NSCLC) treated with ICB, napsin A was a self-antigen that elicited strong CD8 + T cell responses, with ICB responders harboring higher frequencies of these CD8 + T cells compared with nonresponders. Human leukocyte antigen (HLA) class I ligands derived from napsin A were present in human lung tumors and in nontumor lung tissues, and napsin A tetramers confirmed the presence of napsin A–specific CD8 + T cells in blood and tumors of patients with NSCLC. Napsin A–specific T cell clonotypes were enriched in lung tumors and ICB-induced inflammatory lung lesions and could kill immortalized HLA-matched NSCLC cells ex vivo. Single-cell RNA sequencing revealed that these T cell clonotypes expressed proinflammatory cytokines and cytotoxic markers. Thus, DITAS successfully identified self-antigens, including napsin A, that likely mediate effective antitumor T cell responses in NSCLC and may simultaneously underpin lung irAEs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".