Quantifying Antigen-Specific T Cell Responses When Using Antigen-Agnostic Immunotherapies
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.
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 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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".