Tailoring In Vivo Cytotoxicity Assays to Study Immunodominance in Tumor-specific CD8<sup>+</sup> T Cell Responses
Bibliographic record
Abstract
cytolytic T lymphocyte (CTL) responses elicited against tumor- and pathogen-derived peptides. They offer several advantages over traditional killing assays. First, they permit the monitoring of CTL-mediated cytotoxicity within architecturally intact secondary lymphoid organs, typically in the spleen. Second, they allow for mechanistic studies during the priming, effector and recall phases of CTL responses. Third, they provide useful platforms for vaccine/drug efficacy testing in a truly in vivo setting. Here, we provide an optimized protocol for the examination of concomitant CTL responses against more than one peptide epitope of a model tumor antigen (Ag), namely, simian virus 40 (SV40)-encoded large T Ag (T Ag). Like most other clinically relevant tumor proteins, T Ag harbors many potentially immunogenic peptides. However, only four such peptides induce detectable CTL responses in C57BL/6 mice. These responses are consistently arranged in a hierarchical order based on their magnitude, which forms the basis for TCD8 "immunodominance" in this powerful system. Accordingly, the bulk of the T Ag-specific TCD8 response is focused against a single immunodominant epitope while the other three epitopes are recognized and responded to only weakly. Immunodominance compromises the breadth of antitumor TCD8 responses and is, as such, considered by many as an impediment to successful vaccination against cancer. Therefore, it is important to understand the cellular and molecular factors and mechanisms that dictate or shape TCD8 immunodominance. The protocol we describe here is tailored to the investigation of this phenomenon in the T Ag immunization model, but can be readily modified and extended to similar studies in other tumor models. We provide examples of how the impact of experimental immunotherapeutic interventions can be measured using in vivo cytotoxicity assays.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".