Genome Methylation Accurately Predicts Neuroendocrine Tumor Origin: An Online Tool.
Algorithms
Biomarkers, Tumor
/ genetics
DNA Methylation
Gene Expression Regulation, Neoplastic
Genome, Human
Humans
Intestinal Neoplasms
/ diagnosis
Intestine, Small
/ pathology
Lung Neoplasms
/ diagnosis
Neuroendocrine Tumors
/ diagnosis
Online Systems
Pancreatic Neoplasms
/ diagnosis
Prognosis
Software
Journal
Clinical cancer research : an official journal of the American Association for Cancer Research
ISSN: 1557-3265
Titre abrégé: Clin Cancer Res
Pays: United States
ID NLM: 9502500
Informations de publication
Date de publication:
01 03 2021
01 03 2021
Historique:
received:
19
08
2020
revised:
01
12
2020
accepted:
17
12
2020
pubmed:
24
12
2020
medline:
11
2
2022
entrez:
23
12
2020
Statut:
ppublish
Résumé
The primary origin of neuroendocrine tumor metastases can be difficult to determine by histopathology alone, but is critical for therapeutic decision making. DNA methylation-based profiling is now routinely used in the diagnostic workup of brain tumors. This has been enabled by the availability of cost-efficient array-based platforms. We have extended these efforts to augment histopathologic diagnosis in neuroendocrine tumors. Methylation data was compiled for 69 small intestinal, pulmonary, and pancreatic neuroendocrine tumors. These data were used to build a ridge regression calibrated random forest classification algorithm (neuroendocrine neoplasm identifier, NEN-ID). The model was validated during 3 × 3 nested cross-validation and tested in a local and an external cohort ( NEN-ID predicted the origin of tumor samples with high accuracy (>95%). In addition, the diagnostic approach was determined to be robust across a range of possible confounding experimental parameters, such as tumor purity and array quality. A software infrastructure and online user interface were built to make the model available to the scientific community. This DNA methylation-based prediction model can be used in the workup for patients with neuroendocrine tumors of unknown primary. To facilitate validation and clinical implementation, we provide a user-friendly, publicly available web-based version of NEN-ID.
Identifiants
pubmed: 33355250
pii: 1078-0432.CCR-20-3281
doi: 10.1158/1078-0432.CCR-20-3281
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1341-1350Informations de copyright
©2020 American Association for Cancer Research.
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