Uncovering the role of Tregs in immune surveillance against cancer with a mouse model that reveals premalignancy at single cell resolution 3379
Bibliographic record
Abstract
Abstract Description The immune surveillance hypothesis posits that the immune system controls pre-cancerous cells prior to tumor formation. However, direct visualization of the process has been nearly impossible, because pre-cancerous cells are difficult to identify in conventional cancer models. To address this technical gap, our lab established a unique mouse model called MADM (Mosaic Analysis with Double Markers) that generates rare mutant cells with unequivocal GFP labeling. In a MADM model for triple negative breast cancer, we have discovered the specific and prominent presence of T/B cell-dominated immune aggregates adjacent to premalignant mutant ducts. Characterization via flow cytometry and spatial staining has revealed these aggregates are heterogenous in size, composition, and organization, and tend to dissipate as malignant tumors form. Interestingly, a significant portion of T cells in these aggregates are CD4+ Foxp3+ Tregs, which typically play an important role in preventing excess inflammation. We hypothesize that Tregs regulate immune surveillance against premalignancy to prevent excess inflammation/autoimmunity, but in doing so also prevent eradication of premalignant cells. We are testing this hypothesis using Treg ablation during premalignancy and expect that Treg ablated mice will show increased T/B cell presence and activation in the mammary gland and decreased premalignant cell expansion. Funding Sources Supported by the Basser Center for BRCA; University of Virginia (UVA) Pinn Scholarship; NIH/NCI R01-CA256199; UVA Cancer Center Training Grant Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 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".