Stromal p53 Regulates Breast Cancer Development, the Immune Landscape, and Survival in an Oncogene-Specific Manner
Bibliographic record
Abstract
Coevolution of tumor cells and adjacent stromal elements is a key feature during tumor progression; however, the precise regulatory mechanisms during this process remain unknown. Here, we show stromal p53 loss enhances oncogenic KrasG12D, but not ErbB2, driven tumorigenesis in murine mammary epithelia. Stroma-specific p53 deletion increases both epithelial and fibroblast proliferation in mammary glands bearing the KrasG12D oncogene in epithelia, while concurrently increasing DNA damage and/or DNA replication stress and decreasing apoptosis in the tumor cells proper. Normal epithelia was not affected by stromal p53 deletion. Tumors with p53-null stroma had a significant decrease in total, cytotoxic, and regulatory T cells; however, there was a significant increase in myeloid-derived suppressor cells, total macrophages, and M2-polarized tumor-associated macrophages, with no impact on angiogenesis or connective tissue deposition. Stroma-specific p53 deletion reprogrammed gene expression in both fibroblasts and adjacent epithelium, with p53 targets and chemokine receptors/chemokine signaling pathways in fibroblasts and DNA replication, DNA damage repair, and apoptosis in epithelia being the most significantly impacted biological processes. A gene cluster in p53-deficient mouse fibroblasts was negatively associated with patient survival when compared with two independent datasets. In summary, stroma-specific p53 loss promotes mammary tumorigenesis in an oncogene-specific manner, influences the tumor immune landscape, and ultimately impacts patient survival. IMPLICATIONS: Expression of the p53 tumor suppressor in breast cancer tumor stroma regulates tumorigenesis in an oncogene-specific manner, influences the tumor immune landscape, and ultimately impacts patient survival.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".