Gain-of-function p53R172H mutation drives accumulation of neutrophils in pancreatic tumors, promoting resistance to immunotherapy
Bibliographic record
Abstract
Tumor genotype can influence the immune microenvironment, which plays a critical role in cancer development and therapy resistance. However, the immune effects of gain-of-function Trp53 mutations have not been defined in pancreatic cancer. We compare the immune profiles generated by Kras G12D -mutated mouse pancreatic ductal epithelial cells (PDECs) engineered genetically to express the Trp53 R172H mutation with their p53 wild-type control. Kras G12D/+ ; Trp53 R172H/+ tumors have a distinct immune profile characterized by an influx of CD11b + Ly6G + neutrophils and concomitant decreases in CD3 + T cells, CD8 + T cells, and CD4 + T helper 1 cells. Knockdown of CXCL2, a neutrophil chemokine, in the tumor epithelial compartment of CRISPR Kras G12D/+; Trp53 R172H/+ PDEC tumors reverses the neutrophil phenotype. Neutrophil depletion of mice bearing CRISPR Kras G12D/+ ; Trp53 R172H/+ tumors augments sensitivity to combined CD40 immunotherapy and chemotherapy. These data link Trp53 R172H to the presence of intratumoral neutrophils in pancreatic cancer and suggest that tumor genotypes could inform selection of affected individuals 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".