Abstract PO-107: Fibroblast differentiation trajectories elicit regional tissue states in pancreatic cancer
Bibliographic record
Abstract
Abstract Intratumoral heterogeneity is a critical frontier in understanding how the tumor microenvironment (TME) propels malignant progression. We recently deconvoluted regional heterogeneity in the human PDAC stroma to assess its role in disease progression and discovered two types of ‘sub-tumor microenvironments’ (subTMEs), called ‘reactive’ and ‘deserted’. These histologically definable tissue states exhibit strong regional relationships with tumor immunity, subtypes, differentiation, and treatment response. Here, we set out to define their cell biological underpinnings through a combination of subTME-specific cancer-associated fibroblast (CAF) models, integrative histopathology, quantitative image analysis, multiOMICs, scRNAseq, and controlled functional assays. Remarkably, the growth patterns of CAF cultures closely recapitulated the characteristic histomorphology of their originating subTMEs, and these distinct phenotypes were accompanied by behavioral differences. Unsupervised graph-based clustering of scRNAseq profiles showed that CAFs largely grouped by their originating subTME yet comprised up to 10 individual clusters. The subTME-specific multi-subpopulation CAF communities self-organized into distinct ‘coordinated states’, represented by cluster-overarching functional profiles and distinct morpho-histological and behavioral phenotypes. These differences originated in cellular differentiation trajectories, with an ‘intermediate’ transitory state evident both in single cell transcriptomics and in situ. Noticeably, this CAF differentiation potential was associated with distinct tumor-related functions and, similar to stem cells, was marked by RNA diversity and pluripotency markers. Therefore, regional TME programs in PDAC appear to result largely from transitions between subpopulation-overarching fibroblast differentiation states that guide multifaceted CAF and immune cell communities into recurrent tissue self-organizational units. Citation Format: Barbara T. Grünwald, Curtis McCloskey, Antoine Devisme, Foram Vyas, Geoffroy Andrieux, Kazeera Aliar, Faiyaz Notta, Grainne O’Kane, Julie Wilson, Jennifer Knox, Sandra Fischer, Thomas Kislinger, Melanie Boerries, Steven Gallinger, Rama Khokha. Fibroblast differentiation trajectories elicit regional tissue states in pancreatic cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-107.
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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) | 0.001 | 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".