{"title":"通过捕捉癌症相关分子特征的尿液 cfDNA 片段热点进行基于机器学习的膀胱癌检测","authors":"Xiang-Yu Meng, Xiong-Hui Zhou, Shuo Li, Ming-Jun Shi, Xuan-Hao Li, Bo-Yu Yang, Min Liu, Ke-Zhen Yi, Yun-Ze Wang, Hong-Yu Zhang, Jian Song, Fu-Bing Wang, Xing-Huan Wang","doi":"10.1093/clinchem/hvae156","DOIUrl":null,"url":null,"abstract":"Background cfDNA fragmentomics-based liquid biopsy is a potential option for noninvasive bladder cancer (BLCA) detection that remains an unmet clinical need. Methods We assessed the diagnostic performance of cfDNA hotspot-driven machine-learning models in a cohort of 55 BLCA patients, 51 subjects with benign conditions, and 11 healthy volunteers. We further performed functional bioinformatics analysis for biological understanding and interpretation of the tool’s diagnostic capability. Results Urinary cfDNA hotspots-based machine-learning model enabled effective BLCA detection, achieving high performance (area under curve 0.96) and an 87% sensitivity at 100% specificity. It outperformed models using other cfDNA-derived features. In stage-stratified analysis, the sensitivity at 100% specificity of the urine hotspots-based model was 71% and 92% for early (low-grade Ta and T1) and advanced (high-grade T1 and muscle-invasive) disease, respectively. Biologically, cfDNA hotspots effectively retrieved regulatory elements and were correlated with the cell of origin. Urine cfDNA hotspots specifically captured BLCA-related molecular features, including key functional pathways, chromosome loci associated with BLCA risk as identified in genome-wide association studies, or presenting frequent somatic alterations in BLCA tumors, and the transcription factor regulatory landscape. Conclusions Our findings support the applicability of urine cfDNA fragmentation hotspots for noninvasive BLCA diagnosis, as well as for future translational study regarding its molecular pathology and heterogeneity.","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning-Based Detection of Bladder Cancer by Urine cfDNA Fragmentation Hotspots that Capture Cancer-Associated Molecular Features\",\"authors\":\"Xiang-Yu Meng, Xiong-Hui Zhou, Shuo Li, Ming-Jun Shi, Xuan-Hao Li, Bo-Yu Yang, Min Liu, Ke-Zhen Yi, Yun-Ze Wang, Hong-Yu Zhang, Jian Song, Fu-Bing Wang, Xing-Huan Wang\",\"doi\":\"10.1093/clinchem/hvae156\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background cfDNA fragmentomics-based liquid biopsy is a potential option for noninvasive bladder cancer (BLCA) detection that remains an unmet clinical need. Methods We assessed the diagnostic performance of cfDNA hotspot-driven machine-learning models in a cohort of 55 BLCA patients, 51 subjects with benign conditions, and 11 healthy volunteers. We further performed functional bioinformatics analysis for biological understanding and interpretation of the tool’s diagnostic capability. Results Urinary cfDNA hotspots-based machine-learning model enabled effective BLCA detection, achieving high performance (area under curve 0.96) and an 87% sensitivity at 100% specificity. It outperformed models using other cfDNA-derived features. In stage-stratified analysis, the sensitivity at 100% specificity of the urine hotspots-based model was 71% and 92% for early (low-grade Ta and T1) and advanced (high-grade T1 and muscle-invasive) disease, respectively. Biologically, cfDNA hotspots effectively retrieved regulatory elements and were correlated with the cell of origin. Urine cfDNA hotspots specifically captured BLCA-related molecular features, including key functional pathways, chromosome loci associated with BLCA risk as identified in genome-wide association studies, or presenting frequent somatic alterations in BLCA tumors, and the transcription factor regulatory landscape. Conclusions Our findings support the applicability of urine cfDNA fragmentation hotspots for noninvasive BLCA diagnosis, as well as for future translational study regarding its molecular pathology and heterogeneity.\",\"PeriodicalId\":10690,\"journal\":{\"name\":\"Clinical chemistry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical chemistry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/clinchem/hvae156\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICAL LABORATORY TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical chemistry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/clinchem/hvae156","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
Machine Learning-Based Detection of Bladder Cancer by Urine cfDNA Fragmentation Hotspots that Capture Cancer-Associated Molecular Features
Background cfDNA fragmentomics-based liquid biopsy is a potential option for noninvasive bladder cancer (BLCA) detection that remains an unmet clinical need. Methods We assessed the diagnostic performance of cfDNA hotspot-driven machine-learning models in a cohort of 55 BLCA patients, 51 subjects with benign conditions, and 11 healthy volunteers. We further performed functional bioinformatics analysis for biological understanding and interpretation of the tool’s diagnostic capability. Results Urinary cfDNA hotspots-based machine-learning model enabled effective BLCA detection, achieving high performance (area under curve 0.96) and an 87% sensitivity at 100% specificity. It outperformed models using other cfDNA-derived features. In stage-stratified analysis, the sensitivity at 100% specificity of the urine hotspots-based model was 71% and 92% for early (low-grade Ta and T1) and advanced (high-grade T1 and muscle-invasive) disease, respectively. Biologically, cfDNA hotspots effectively retrieved regulatory elements and were correlated with the cell of origin. Urine cfDNA hotspots specifically captured BLCA-related molecular features, including key functional pathways, chromosome loci associated with BLCA risk as identified in genome-wide association studies, or presenting frequent somatic alterations in BLCA tumors, and the transcription factor regulatory landscape. Conclusions Our findings support the applicability of urine cfDNA fragmentation hotspots for noninvasive BLCA diagnosis, as well as for future translational study regarding its molecular pathology and heterogeneity.
期刊介绍:
Clinical Chemistry is a peer-reviewed scientific journal that is the premier publication for the science and practice of clinical laboratory medicine. It was established in 1955 and is associated with the Association for Diagnostics & Laboratory Medicine (ADLM).
The journal focuses on laboratory diagnosis and management of patients, and has expanded to include other clinical laboratory disciplines such as genomics, hematology, microbiology, and toxicology. It also publishes articles relevant to clinical specialties including cardiology, endocrinology, gastroenterology, genetics, immunology, infectious diseases, maternal-fetal medicine, neurology, nutrition, oncology, and pediatrics.
In addition to original research, editorials, and reviews, Clinical Chemistry features recurring sections such as clinical case studies, perspectives, podcasts, and Q&A articles. It has the highest impact factor among journals of clinical chemistry, laboratory medicine, pathology, analytical chemistry, transfusion medicine, and clinical microbiology.
The journal is indexed in databases such as MEDLINE and Web of Science.