Peptide Blockers of PD-1-PD-L1 Interaction Reinvigorate PD-1-Suppressed T Cells and Curb Tumor Growth in Mice
Bibliographic record
Abstract
The programmed cell death protein 1 (PD-1) plays a critical role in cancer immune evasion. Blocking the PD-1-PD-L1 interaction by monoclonal antibodies has shown remarkable clinical efficacy in treating certain types of cancer. However, antibodies are costly to produce, and antibody-based therapies can cause immune-related adverse events. To address the limitations associated with current PD-1/PD-L1 blockade immunotherapy, we aimed to develop peptide-based inhibitors of the PD-1/PD-L1 interaction as an alternative means to PD-1/PD-L1 blockade antibodies for anti-cancer immunotherapy. Through the functional screening of peptide arrays encompassing the ectodomains of PD-1 and PD-L1, followed by the optimization of the hit peptides for solubility and stability, we have identified a 16-mer peptide, named mL7N, with a remarkable efficacy in blocking the PD-1/PD-L1 interaction both in vitro and in vivo. The mL7N peptide effectively rejuvenated PD-1-suppressed T cells in multiple cellular systems designed to recapitulate the PD-1/PD-L1 interaction in the context of T-cell receptor signaling. Furthermore, PA-mL7N, a chimera of the mL7N peptide coupled to albumin-binding palmitic acid (PA), significantly promoted breast cancer cell killing by peripheral blood mononuclear cells ex vivo and significantly curbed tumor growth in a syngeneic mouse model of breast cancer. Our work raises the prospect that mL7N may serve as a prototype for the development of a new line of peptide-based immunomodulators targeting the PD-1/PD-L1 immune checkpoint with potential applications in cancer treatment.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".