An <i>In Vivo</i> Screen to Identify Short Peptide Mimotopes with Enhanced Antitumor Immunogenicity
Bibliographic record
Abstract
Tumor-associated self-antigens are potential cancer vaccine targets but suffer from limited immunogenicity. There are examples of mutated, short self-peptides inducing epitope-specific CD8+ T cells more efficiently than the wild-type epitope, but current approaches cannot yet reliably identify such epitopes, which are referred to as enhanced mimotopes ("e-mimotopes"). Here, we present a generalized strategy to develop e-mimotopes, using the tyrosinase-related protein 2 (Trp2) peptide Trp2180-188, which is a murine MHC class I (MHC-I) epitope, as a test case. Using a vaccine adjuvant that induces peptide particle formation and strong cellular responses with nanogram antigen doses, a two-step method systematically identified e-mimotope candidates with murine immunization. First, position-scanning peptide microlibraries were generated in which each position of the wild-type epitope sequence was randomized. Randomization of only one specific residue of the Trp2 epitope increased antitumor immunogenicity. Second, all 20 amino acids were individually substituted and tested at that position, enabling the identification of two e-mimotopes with single amino acid mutations. Despite similar MHC-I affinity compared with the wild-type epitope, e-mimotope immunization elicited improved Trp2-specific cytotoxic T-cell phenotypes and improved T-cell receptor affinity for both the e-mimotopes and the native epitope, resulting in better outcomes in multiple prophylactic and therapeutic tumor models. The screening method was also applied to other targets with other murine MHC-I restriction elements, including epitopes within glycoprotein 70 and Wilms' Tumor Gene 1, to identify additional e-mimotopes with enhanced potency.
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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) | 0.001 | 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) | 0.001 | 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 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".