Gain-of-function p53R172H mutation drives accumulation of neutrophils in pancreatic tumors, promoting resistance to immunotherapy
Bibliographic record
Abstract
The immune microenvironment plays a critical role in cancer development, progression and resistance to therapy. Recent reports indicate different tumor genotypes can profoundly influence the composition of the immune microenvironment. After oncogenic KRAS, TP53 is the most frequently mutated gene in pancreatic ductal adenocarcinoma (PDAC). KRASG12D mutations in tumor cells can modify the immune landscape of PDAC, however, the immune effects of neomorphic TP53 mutations have not been defined. Focusing on the most common TP53 mutation in PDAC, we sought to evaluate the non-cell-autonomous role of mutant p53 in modulating the immune microenvironment of pancreatic cancer by comparing the immune profiles generated by KRASG12D-mutated mouse pancreatic ductal epithelial cells (PDEC) genetically engineered to express the Trp53 R172H gain-of-function mutation to their p53 wildtype control. We found orthotopically implanted tumors derived from KrasG12D/+;Trp53R172H/+ PDEC had a distinct immune profile in comparison to KrasG12D/+; Trp53 +/+ tumors, characterized by an influx of intratumoral CD11b+ Ly6G+ neutrophils and concomitant decreases in CD3+ T cells, CD8+ T cells, and CD4+ T helper 1 (Th1) cells. Analysis of publicly available human PDAC cohorts revealed enrichment of genes in neutrophil-related pathways in TP53 -mutated tumors. Knockdown of CXCL2, a neutrophil chemoattractant, in the tumor epithelial compartment of KrasG12D/+; Trp53R172H/+ PDEC tumors reversed the neutrophil phenotype. Depleting neutrophils in mice bearing G12D/+; Trp53R172H/+ PDEC tumors augmented sensitivity to combined CD40 immunotherapy and chemotherapy. Collectively, these data link mutant p53 to the presence of intratumoral neutrophils in pancreatic cancer and suggests that tumor genotypes could inform patient selection for immunotherapy.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".