Abstract PR03: Hypoxia promotes inflammatory fibroblast formation in pancreatic cancer
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy characterized by poor response to all existing therapies. Although immunotherapy has shown great promise against multiple deadly cancers, it has been largely ineffective in PDAC. This lack of response is in part attributed to its extensive, fibroinflammatory stroma and hypoxic microenvironment. Cancer-associated fibroblasts (CAFs) are a predominant and heterogeneous stromal cell type in PDAC, and single-cell transcriptomics of human and mouse PDAC has recently revealed a distinct CAF subpopulation, inflammatory CAFs (iCAFs). These inflammatory fibroblasts produce high levels of cytokines and chemokines in PDAC and have the potential to contribute to its immunosuppressive microenvironment and tumorigenesis. By injecting a hypoxia probe into PDAC mouse models, we recently found that iCAFs predominantly reside in hypoxic tumor regions. We also observed that the hypoxia-related gene signature is positively enriched in iCAFs in human PDAC samples. Importantly, by exposing three-dimensional (3D) co-cultures of pancreatic cancer cells and fibroblasts to either hypoxia or normoxia, we showed that hypoxia induces IL1α from cancer cells and that IL1α is required for hypoxia-mediated iCAF formation. Our data implicate hypoxia as a critical regulator of tumor stroma. Efforts are ongoing to understand the role of hypoxia in the crosstalk between cancer cells, fibroblasts, and immune cells in driving an immunosuppressive tumor microenvironment. Citation Format: Ashley M. Mello, Tenzin Ngodup, Yusoo Lee, Katelyn L. Donahue, Jinju Li, Arvind Rao, Eileen S. Carpenter, Howard C. Crawford, Marina Pasca Di Magliano, Kyoung Eun Lee. Hypoxia promotes inflammatory fibroblast formation in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr PR03.
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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) | 0.006 | 0.001 |
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".