Adam Walker, Camila S Fang, Chanel Schroff, Jonathan Serrano, Varshini Vasudevaraja, Yiying Yang, Sarra Belakhoua, Arline Faustin, Christopher M William, David Zagzag, Sarah Chiang, Andres Martin Acosta, Misha Movahed-Ezazi, Kyung Park, Andre L Moreira, Farbod Darvishian, Kristyn Galbraith, Matija Snuderl
{"title":"基于甲基化数量性状位点的未知原发癌深度学习分类器。","authors":"Adam Walker, Camila S Fang, Chanel Schroff, Jonathan Serrano, Varshini Vasudevaraja, Yiying Yang, Sarra Belakhoua, Arline Faustin, Christopher M William, David Zagzag, Sarah Chiang, Andres Martin Acosta, Misha Movahed-Ezazi, Kyung Park, Andre L Moreira, Farbod Darvishian, Kristyn Galbraith, Matija Snuderl","doi":"10.1093/jnen/nlae123","DOIUrl":null,"url":null,"abstract":"<p><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.</p>","PeriodicalId":16682,"journal":{"name":"Journal of Neuropathology and Experimental Neurology","volume":" ","pages":"147-154"},"PeriodicalIF":3.0000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11747144/pdf/","citationCount":"0","resultStr":"{\"title\":\"Deep learning-based classifier for carcinoma of unknown primary using methylation quantitative trait loci.\",\"authors\":\"Adam Walker, Camila S Fang, Chanel Schroff, Jonathan Serrano, Varshini Vasudevaraja, Yiying Yang, Sarra Belakhoua, Arline Faustin, Christopher M William, David Zagzag, Sarah Chiang, Andres Martin Acosta, Misha Movahed-Ezazi, Kyung Park, Andre L Moreira, Farbod Darvishian, Kristyn Galbraith, Matija Snuderl\",\"doi\":\"10.1093/jnen/nlae123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><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.</p>\",\"PeriodicalId\":16682,\"journal\":{\"name\":\"Journal of Neuropathology and Experimental Neurology\",\"volume\":\" \",\"pages\":\"147-154\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11747144/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Neuropathology and Experimental Neurology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/jnen/nlae123\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neuropathology and Experimental Neurology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/jnen/nlae123","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
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 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.
期刊介绍:
Journal of Neuropathology & Experimental Neurology is the official journal of the American Association of Neuropathologists, Inc. (AANP). The journal publishes peer-reviewed studies on neuropathology and experimental neuroscience, book reviews, letters, and Association news, covering a broad spectrum of fields in basic neuroscience with an emphasis on human neurological diseases. It is written by and for neuropathologists, neurologists, neurosurgeons, pathologists, psychiatrists, and basic neuroscientists from around the world. Publication has been continuous since 1942.