First glimpse of immune surveillance during premalignant progression of triple negative breast cancer 3378
Bibliographic record
Abstract
Abstract Description Immune surveillance is believed to eradicate pre-cancerous cells to guard against cancer. However, its mechanisms remain unclear, as pre-cancerous cells are difficult to detect. To gain a glimpse into immune surveillance, our lab developed a genetically engineered mouse model recapitulating human triple negative breast cancer (TNBC), which generates rare, p53-Brca1 mutant cells with unequivocal GFP labeling. Guided by the visualization of premalignant cells, we observed tertiary lymphoid structure (TLS)-like immune aggregates near mutant ducts long before tumor formation. GeoMx-based spatial profiling showed not only activation signatures in T and B cells but also elevated interferon response genes in mutant ducts associated with immune aggregates when compared to those free of immune infiltration. FTY720 treatment at premalignancy, which blocks T and B cell egress from lymph nodes, led to increased mutant cells expansion and shortened tumor latency, demonstrating the restraint of mutant cell growth by the immune system. To clarify the roles of specific immune cells, we plan to deplete CD4 T, CD8 T, and B cells at premalignancy. We will also conduct longitudinal studies on T and B cells along the tumor evolution process from premalignancy to malignancy to understand how immune surveillance eventually falters. Taken together, our studies are poised to elucidate key mechanisms of immune surveillance in the premalignant stage of TNBC. Funding Sources Supported by NIH R01-CA256199; Basser Center for BRCA; the Pinn Scholarship (UVA); UVA Cancer Center Spatial Biology Funding; UVA Cancer Center Training Grant to X.Z. 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".