PD-1 Blockade Promotes Epitope Spreading in Anticancer CD8+ T Cell Responses by Preventing Fratricidal Death of Subdominant Clones To Relieve Immunodomination
Bibliographic record
Abstract
Abstract The interactions between programmed death-1 (PD-1) and its ligands hamper tumor-specific CD8+ T cell (TCD8) responses, and PD-1-based “checkpoint inhibitors” have shown promise in certain cancers, thus revitalizing interest in immunotherapy. PD-1–targeted therapies reverse TCD8 exhaustion/anergy. However, whether they alter the epitope breadth of TCD8 responses remains unclear. This is an important question because subdominant TCD8 are more likely than immunodominant clones to escape tolerance mechanisms and may contribute to protective anticancer immunity. We have addressed this question in an in vivo model of TCD8 responses to well-defined epitopes of a clinically relevant oncoprotein, large T Ag. We found that unlike other coinhibitory molecules (CTLA-4, LAG-3, TIM-3), PD-1 was highly expressed by subdominant TCD8, which correlated with their propensity to favorably respond to PD-1/PD-1 ligand-1 (PD-L1)-blocking Abs. PD-1 blockade increased the size of subdominant TCD8 clones at the peak of their primary response, and it also sustained their presence, thus giving rise to an enlarged memory pool. The expanded population was fully functional as judged by IFN-γ production and MHC class I–restricted cytotoxicity. The selective increase in subdominant TCD8 clonal size was due to their enhanced survival, not proliferation. Further mechanistic studies utilizing peptide-pulsed dendritic cells, recombinant vaccinia viruses encoding full-length T Ag or epitope mingenes, and tumor cells expressing T Ag variants revealed that anti–PD-1 invigorates subdominant TCD8 responses by relieving their lysis-dependent suppression by immunodominant TCD8. To our knowledge, our work constitutes the first report that interfering with PD-1 signaling potentiates epitope spreading in tumor-specific responses, a finding with clear implications for cancer immunotherapy and vaccination.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".