TY - JOUR
T1 - Genome methylation accurately predicts neuroendocrine tumor origin - an online tool
AU - Hackeng, Wenzel M
AU - Dreijerink, Koen M A
AU - de Leng, Wendy W J
AU - Morsink, Folkert H
AU - Valk, Gerlof D
AU - Vriens, Menno R
AU - Offerhaus, G Johan A
AU - Geisenberger, Christoph
AU - Brosens, Lodewijk A A
N1 - Funding Information:
We thank Aaron Isaacs for his introduction into methylation data analysis. We thank Paul van Diest for his generous support and Erwin van der Biezen for supervising the processing of samples. We would like to thank all research teams for sharing their data, including the lungNENomics project and the Rare Cancers Genomics initiative. The results here are, in part, based upon data generated by TCGA Research Network: https://www.cancer.gov/tcga. This work was supported by the Dutch Digestive Foundation/Maag Lever Darm Stichting (grant no., CDG 14-020 to L.A.A. Brosens and W.M. Hackeng).
Publisher Copyright:
© 2020 American Association for Cancer Research.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - 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 = 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.
AB - 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 = 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.
UR - http://www.scopus.com/inward/record.url?scp=85102530461&partnerID=8YFLogxK
U2 - 10.1158/1078-0432.CCR-20-3281
DO - 10.1158/1078-0432.CCR-20-3281
M3 - Article
C2 - 33355250
SN - 1078-0432
VL - 27
SP - 1341
EP - 1350
JO - Clinical cancer research : an official journal of the American Association for Cancer Research
JF - Clinical cancer research : an official journal of the American Association for Cancer Research
IS - 5
ER -