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Microenvironmental Determinants of Pancreatic Cancer
Abstract
Pancreatic ductal adenocarcinoma (PDAC) belongs to the most lethal solid tumors in humans. A histological hallmark feature of PDAC is the pronounced tumor microenvironment (TME) that dynamically evolves during tumor progression. The TME consists of different non-neoplastic cells such as cancer-associated fibroblasts, immune cells, endothelial cells, and neurons. Furthermore, abundant extracellular matrix components such as collagen and hyaluronic acid as well as matricellular proteins create a highly dynamic and hypovascular TME with multiple biochemical and physical interactions among the various cellular and acellular components that promote tumor progression and therapeutic resistance. In recent years, intensive research efforts have resulted in a significantly improved understanding of the biology and pathophysiology of the TME in PDAC, and novel stroma-targeted approaches are emerging that may help to improve the devastating prognosis of PDAC patients. However, none of anti-stromal therapies has been approved in patients so far, and there is still a large discrepancy between multiple successful preclinical results and subsequent failure in clinical trials. Furthermore, recent findings suggest that parts of the TME may also possess tumor-restraining properties rendering tailored therapies even more challenging.
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Metadata
- Authors
- Hessmann, E., Buchholz, S. M., Demir, I. E., Singh, S. K., Gress, T. M., Ellenrieder, V., & Neesse, A.
- Venue
- Physiological Reviews
- Publisher
- American Physiological Society
- Published
- 2020-10-01
- Volume
- 100
- Issue
- 4
- Pages
- 1707-1751
- ISSN
- 0031-9333 (ISSN-L)
- 1522-1210
- OpenAlex
- W3016561671
- Cited by
- 331
- References
- 389
- FWCI
- 5.46
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.