Spatial tumor immune heterogeneity facilitates subtype co-existence and therapy response in pancreatic cancer
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) displays a high degree of spatial subtype heterogeneity and co-existence, linked to a diverse microenvironment and worse clinical outcome. However, the underlying mechanisms remain unclear. Here, by combining preclinical models, multi-center clinical, transcriptomic, proteomic, and patient bioimaging data, we identify an interplay between neoplastic intrinsic AP1 transcription factor dichotomy and extrinsic macrophages driving subtype co-existence and an immunosuppressive microenvironment. ATAC-, ChIP-, and RNA-seq analyses reveal that JUNB/AP1- and HDAC-mediated epigenetic programs repress pro-inflammatory signatures in tumor cells, antagonizing cJUN/AP1 signaling, favoring a therapy-responsive classical neoplastic state. This dichotomous regulation is amplified via regional TNF-α + macrophages, which associates with a reactive phenotype and reduced CD8 + T cell infiltration in patients. Consequently, combined preclinical anti-TNF-α immunotherapy and chemotherapy reduces macrophages and promotes CD3 + /CD8 + T cell infiltration in basal-like PDAC, improving survival. Hence, tumor cell-intrinsic epigenetic programs, together with extrinsic microenvironmental cues, facilitate intratumoral subtype heterogeneity and disease progression.
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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 | 0.001 | 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".