Deep learning-based classifier for carcinoma of unknown primary using methylation quantitative trait loci

Cancer of unknown primary (CUP) constitutes between 2% and 5% of human malignancies and is among the most common causes of cancer death in the United States. Brain metastases are often the first clinical presentation of CUP; despite extensive pathological and imaging studies, 20%-45% of CUP are neve...

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Bibliographic Details
Published in:Journal of neuropathology and experimental neurology 2024-11
Main Authors: Walker, Adam, Fang, Camila S, Schroff, Chanel, Serrano, Jonathan, Vasudevaraja, Varshini, Yang, Yiying, Belakhoua, Sarra, Faustin, Arline, William, Christopher M, Zagzag, David, Chiang, Sarah, Acosta, Andres Martin, Movahed-Ezazi, Misha, Park, Kyung, Moreira, Andre L, Darvishian, Farbod, Galbraith, Kristyn, Snuderl, Matija
Format: Article
Language:English
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Summary:Cancer of unknown primary (CUP) constitutes between 2% and 5% of human malignancies and is among the most common causes of cancer death in the United States. Brain metastases are often the first clinical presentation of CUP; despite extensive pathological and imaging studies, 20%-45% of CUP are never assigned a primary site. DNA methylation array profiling is a reliable method for tumor classification but tumor-type-specific classifier development requires many reference samples. This is difficult to accomplish for CUP as many cases are never assigned a specific diagnosis. Recent studies identified subsets of methylation quantitative trait loci (mQTLs) unique to specific organs, which could help increase classifier accuracy while requiring fewer samples. We performed a retrospective genome-wide methylation analysis of 759 carcinoma samples from formalin-fixed paraffin-embedded tissue samples using Illumina EPIC array. Utilizing mQTL specific for breast, lung, ovarian/gynecologic, colon, kidney, or testis (BLOCKT) (185k total probes), we developed a deep learning-based methylation classifier that achieved 93.12% average accuracy and 93.04% average F1-score across a 10-fold validation for BLOCKT organs. Our findings indicate that our organ-based DNA methylation classifier can assist pathologists in identifying the site of origin, providing oncologists insight on a diagnosis to administer appropriate therapy, improving patient outcomes.
ISSN:1554-6578
1554-6578
DOI:10.1093/jnen/nlae123