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Biomarkers and Strategy to Detect Preinvasive and Early Pancreatic Cancer: State of the Field and the Impact of the EDRN
Open accessAbstract
Patients afflicted with pancreatic ductal adenocarcinoma (PDAC) face a dismal prognosis, but headway could be made if physicians could identify the disease earlier. A compelling strategy to broaden the use of surveillance for PDAC is to incorporate molecular biomarkers in combination with clinical analysis and imaging tools. This article summarizes the components involved in accomplishing biomarker validation and an analysis of the requirements of molecular biomarkers for disease surveillance. We highlight the significance of consortia for this research and highlight resources and infrastructure of the Early Detection Research Network (EDRN). The EDRN brings together the multifaceted expertise and resources needed for biomarker validation, such as study design, clinical care, biospecimen collection and handling, molecular technologies, and biostatistical analysis, and studies coming out of the EDRN have yielded biomarkers that are moving forward in validation. We close the article with an overview of the current investigational biomarkers, an analysis of their performance relative to the established benchmarks, and an outlook on the current needs in the field. The outlook for improving the early detection of PDAC looks promising, and the pace of further research should be quickened through the resources and expertise of the EDRN and other consortia. See all articles in this CEBP Focus section, “NCI Early Detection Research Network: Making Cancer Detection Possible.”
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“Such biomarker “panels” often build upon CA 19-9 and demonstrate improved performance in early-stage disease compared with CA 19-9 alone.40–43”
From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [40]
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“A tiered approach to screening has been posited by groups such as the Early Detection Research Network (127, 128).”
Metadata
- Authors
- Liu, Y., Kaur, S., Huang, Y., Fahrmann, J. F., Rinaudo, J. A., Hanash, S. M., Batra, S. K., Singhi, A. D., Brand, R. E., Maitra, A., & Haab, B. B.
- Affiliations
- Van Andel Institute (US)
- Nebraska Medical Center (US)
- Fred Hutch Cancer Center (US)
- The University of Texas MD Anderson Cancer Center (US)
- National Cancer Institute (US)
- University of Pittsburgh Medical Center (US)
- ORCID
- Venue
- Cancer Epidemiology, Biomarkers & Prevention
- Published
- 2020-12-01
- Volume
- 29
- Issue
- 12
- Pages
- 2513-2523
- ISSN
- 1055-9965 (ISSN-L)
- 1538-7755
- PMC
- PMC7710622
- OpenAlex
- W3035524190
- Cited by
- 17
- References
- 83
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 4 · 2023: 3 · 2022: 2 · 2021: 3 · 2020: 1
- FWCI
- 0.81
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Cancer Epidemiology Biomarkers & Prevention (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NCI
- Early Detection Research Network (U01 CA152653)
- Alliance of Glycobiologists for Cancer Detection (U01 CA226158, U01 CA200468, U01 CA196403, R01 CA218004, P50 CA221707)
- Topics
- Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
- Health Sciences › Medicine › Surgery › Pancreatitis Pathology and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- pancreatic ductal adenocarcinoma, early cancer detection, molecular biomarkers, biomarker validation, Early Detection Research Network, cancer surveillance, research consortium, biospecimen collection
- MeSH terms
- Aged, Female, Humans, Male, Pancreatic Neoplasms, Biomarkers, Tumor, Early Detection of Cancer
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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.
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