Abstract B032: Targeting the PD-1/PD-L1 immune evasion axis with DNA aptamers
Bibliographic record
Abstract
Abstract Blocking the immunoinhibitory PD-1:PD-L1 pathway using monoclonal antibodies has led to dramatic clinical responses by reversing tumor immune evasion and provoking robust and durable anti-tumor responses. Anti-PD-1 antibodies have now been approved for the treatment of melanoma, and are being clinically tested in a number of other tumor types as both a monotherapy and as part of combination regimens. Here, we report the development of DNA aptamers as synthetic, non-immunogenic antibody mimics, which bind specifically to the murine extracellular domain of PD-1 and block the PD-1:PD-L1 interaction. These aptamers did not bind to closely related proteins including human PD-1. One such aptamer, MP7, functionally inhibits the PD-L1-mediated suppression of IL-2 secretion in primary murine T-cells. A PEGylated form of MP7 retains the ability to block the PD-1:PD-L1 interaction, and significantly suppresses the growth of PD-L1+ colon carcinoma cells in-vivo with a potency equivalent to an antagonistic anti-PD-1 antibody. Importantly, the anti-PD-1 DNA aptamer treatment was not associated with off-target TLR-9-related immune responses. Due to the inherent advantages of aptamers including their lack of immunogenicity, low cost, long shelf life, and ease of synthesis, such PD-1 antagonistic aptamers may represent an attractive alternative over antibody-based anti PD-1 therapeutics. Citation Format: Aaron Prodeus, Jean Gariepy. Targeting the PD-1/PD-L1 immune evasion axis with DNA aptamers. [abstract]. In: Proceedings of the CRI-CIMT-EATI-AACR Inaugural International Cancer Immunotherapy Conference: Translating Science into Survival; September 16-19, 2015; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(1 Suppl):Abstract nr B032.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".