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 AND RESULTS: We compiled data of 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) that predicts the origin of tumor samples with high accuracy (>95%). The model was validated during 3 × 3 nested cross-validation and tested in a local and an external cohort (n = 198 cases). In addition, we show that our diagnostic approach is 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.
|Journal||Clinical cancer research : an official journal of the American Association for Cancer Research|
|Publication status||E-pub ahead of print - 22 Dec 2020|