Dynamic Evolution of Fibroblasts Revealed by Single-Cell RNA Sequencing of Human Pancreatic Cancer
Bibliographic record
Abstract
ABSTRACT: Cancer progression and response to therapy are inextricably reliant on the coevolution of a supportive tissue microenvironment. This is particularly evident in pancreatic ductal adenocarcinoma, a tumor type characterized by expansive and heterogeneous stroma. Herein, we employed single-cell RNA sequencing and spatial transcriptomics of normal, inflamed, and malignant pancreatic tissues to contextualize stromal dynamics associated with disease and treatment status, identifying temporal and spatial trajectories of fibroblast differentiation. Using analytical tools to infer cellular communication, together with a newly developed assay to annotate genomic alterations in cancer cells, we additionally explored the complex intercellular networks underlying tissue circuitry, highlighting a fibroblast-centric interactome that grows in strength and complexity in the context of malignant transformation. Our study yields new insights on the stromal remodeling events favoring the development of a tumor-supportive microenvironment and provides a powerful resource for the exploration of novel points of therapeutic intervention in pancreatic ductal adenocarcinoma. SIGNIFICANCE: Pancreatic cancer remains a high unmet medical need. Understanding the interactions between stroma and cancer cells in this disease may unveil new opportunities for therapeutic intervention.
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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) | 0.000 | 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".