Keratinocyte differentiation antigen-specific T cells in immune checkpoint inhibitor-treated NSCLC patients are associated with improved survival
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) have improved the survival of patients with non-small cell lung cancer (NSCLC) by reinvigorating tumor-specific T cell responses. However, the specificity of such T cells and the human leukocyte antigen (HLA)-associated epitopes recognized, remain elusive. In this study, we identified NSCLC T cell epitopes of recently described NSCLC-associated antigens, termed keratinocyte differentiation antigens. Epitopes of these antigens were presented by HLA-A 03:01 and HLA-C 04:01 and were associated with responses to ICI therapy. Patients with CD8+ T cell responses to these epitopes had improved overall and progression-free survival. T cells specific for such epitopes could eliminate HLA class I-matched NSCLC cells ex vivo and were enriched in patient lung tumors. The identification of novel lung cancer HLA-associated epitopes that correlate with improved ICI-dependent treatment outcomes suggests that keratinocyte-specific proteins are important tumor-associated antigens in NSCLC. These findings improve our understanding of the mechanisms of ICI therapy and may help support the development of vaccination strategies to improve ICI-based treatment of these tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 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".