Immunological Tolerance to Luciferase and Fluorescent Proteins Using Tol Mice Enables Development of Improved Tumor Models for Investigating Immunity and Metastasis
Bibliographic record
Abstract
There is a continuing need for improved preclinical mouse models of cancer that more accurately predict therapy outcomes for future clinical translation. Luciferase and bioluminescence have long been utilized to generate models conducive to noninvasive imaging to monitor tumor growth, disease progression, and response to therapy. However, luciferase, as well as fluorescent reporter proteins, are highly immunogenic, limiting their use in some syngeneic tumor models in immunocompetent mice. In this study, we described the utility of transgenic mice engineered to have tolerance to luciferase and several other reporter proteins, known as Tol mice, in cancer immunology research. Some tumor cell lines expressing both luciferase and GFP were completely rejected in wild-type mice but maintained robust growth in Tol mice. Additionally, Tol mice allowed the development of an experimental brain metastasis model and a postsurgical resection spontaneous metastasis model. Importantly, even when certain cell lines carrying reporter proteins successfully formed tumors in immunocompetent wild-type mice, underlying immunity existed that could be reinvigorated by immune checkpoint inhibitors. Therefore, caution is needed when using such models in wild-type mice, as exaggerated effects may be induced by immunotherapy. Tol mice circumvent this problem and will likely widen the number of orthotopic and metastatic tumor models that can be used in immunotherapy studies in both C57Bl/6 and BALB/c mice. SIGNIFICANCE: Tol transgenic mice, tolerant to reporter proteins like luciferase and GFP, can be used to develop improved tumor models for studying metastasis and immunotherapy, avoiding immune rejection issues in immunocompetent mice. See related commentary by Grzelak and Ghajar, p. 2143.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".