{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T11:44:48Z","timestamp":1781783088552,"version":"3.54.5"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004312","name":"Lilly","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004312","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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.<\/jats:p>","DOI":"10.1093\/jnen\/nlae123","type":"journal-article","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T21:14:36Z","timestamp":1732828476000},"page":"147-154","source":"Crossref","is-referenced-by-count":4,"title":["Deep learning-based classifier for carcinoma of unknown primary using methylation quantitative trait loci"],"prefix":"10.1093","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3754-838X","authenticated-orcid":false,"given":"Adam","family":"Walker","sequence":"first","affiliation":[{"name":"Department of Pathology, NYU Langone Health and NYU Grossman School of Medicine , New York, NY,","place":["United 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