Distinct Mechanisms of Innate and Adaptive Immune Regulation Underlie Poor Oncologic Outcomes Associated with <i>KRAS-TP53</i> Co-Alteration in Pancreatic Cancer
Bibliographic record
Abstract
ABSTRACT Co-occurrent KRAS and TP53 mutations define a majority of patients with pancreatic ductal adenocarcinoma (PDAC) and define its pro-metastatic proclivity. Here, we demonstrate that KRAS - TP53 co-alteration is associated with worse survival compared with either KRAS -alone or TP53 -alone altered PDAC in 245 patients with metastatic disease treated at a tertiary referral cancer center, and validate this observation in two independent molecularly annotated datasets. Compared with non- TP53 mutated KRAS -altered tumors, KRAS - TP53 co-alteration engenders disproportionately innate immune-enriched and CD8 + T-cell-excluded immune signatures. Leveraging in silico, in vitro , and in vivo models of human and murine PDAC, we discover a novel intersection between KRAS - TP53 co-altered transcriptomes, TP63 -defined squamous trans-differentiation, and myeloid-cell migration into the tumor microenvironment. Comparison of single-cell transcriptomes between KRAS - TP53 co-altered and KRAS -altered/ TP53 WT tumors revealed cancer cell-autonomous transcriptional programs that orchestrate innate immune trafficking and function. Moreover, we uncover granulocyte-derived inflammasome activation and TNF signaling as putative paracrine mediators of innate immunoregulatory transcriptional programs in KRAS - TP53 co-altered PDAC. Immune subtyping of KRAS - TP53 co-altered PDAC reveals conflation of intratumor heterogeneity with progenitor-like stemness properties. Coalescing these distinct molecular characteristics into a KRAS - TP53 co-altered “immunoregulatory program” predicts chemoresistance in metastatic PDAC patients enrolled in the COMPASS trial, as well as worse overall survival.
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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 | 0.000 |
| 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 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".