Quantifying Antigen-Specific T Cell Responses When Using Antigen-Agnostic Immunotherapies
Why this work is in the frame
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Bibliographic record
Abstract
Immunotherapies are at the forefront of the fight against cancers, and researchers continue to develop and test novel immunotherapeutic modalities. Ideal cancer immunotherapies induce a patient's immune system to kill their own cancer and develop long-lasting immunity. Research has demonstrated a critical requirement for CD8 + and CD4 + T cells in achieving durable responses. In the path to the clinic, researchers require robust tools to effectively evaluate the capacity for immunotherapies to generate adaptive anti-tumor responses. To study functional tumor-specific T cells, researchers have relied on targeting tumor-associated antigens (TAAs) or the inclusion of surrogate transgenes in pre-clinical models, which facilitate detection of T cells by using the targeted antigen(s) in peptide re-stimulation or tetramer-staining assays. Unfortunately, many pre-clinical models lack a defined TAA, and epitope mapping of TAAs is costly. Surrogate transgenes can alter tumor engraftment and influence the immunogenicity of tumors, making them less relevant to clinical tumors. Further, some researchers prefer to develop therapies that do not rely on pre-defined TAAs. Here, we describe a method to exploit major histocompatibility complex expression on murine cancer cell lines in a co-culture assay to detect T cells responding to bulk, undefined, tumor antigens. This is a tool to support the preclinical evaluation of novel, antigen-agnostic immunotherapies.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it