Genome methylation accurately predicts neuroendocrine tumor origin: An online tool

Wenzel M. Hackeng*, Koen M. A. Dreijerink, Wendy W. J. de Leng, Folkert H. M. Morsink, Gerlof D. Valk, Menno R. Vriens, G. A. Johan Offerhaus, Christoph Geisenberger, Lodewijk A. A. Brosens

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Purpose: 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. Experimental Design: 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 (n ¼ 198 cases). Results: 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. Conclusions: 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.
Original languageEnglish
Pages (from-to)1341-1350
Number of pages10
JournalClinical cancer research : an official journal of the American Association for Cancer Research
Issue number5
Early online date22 Dec 2020
Publication statusPublished - 1 Mar 2021

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