Xianyu Zhang, Yanling Yin, Zhujia Ye, Xingda Zhang, Wei Wei, Yi Hao, Liuhong Zeng, Ting Yang, Dalin Li, Jun Wang, Dezhi Zhao, Yanbo Chen, Shan Lei, Yongdong Jiang, Youxue Zhang, Shouping Xu, Abiyasi Nanding, Yajie Gong, Siwei Li, Yuanyuan Yu, Shilu Zhao, Siyu Liu, Yashuang Zhao, Zhiwei Chen, Shihui Yu, Jian-Bing Fan, Da Pang
{"title":"ctDNA甲基化测序在乳腺肿瘤鉴别诊断中的应用","authors":"Xianyu Zhang, Yanling Yin, Zhujia Ye, Xingda Zhang, Wei Wei, Yi Hao, Liuhong Zeng, Ting Yang, Dalin Li, Jun Wang, Dezhi Zhao, Yanbo Chen, Shan Lei, Yongdong Jiang, Youxue Zhang, Shouping Xu, Abiyasi Nanding, Yajie Gong, Siwei Li, Yuanyuan Yu, Shilu Zhao, Siyu Liu, Yashuang Zhao, Zhiwei Chen, Shihui Yu, Jian-Bing Fan, Da Pang","doi":"10.1002/cam4.71004","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Breast ultrasonography and mammography remain predominant in breast tumor evaluations, yet they often result in false positives, particularly for tumors classified as BI-RADS 4a or those no more than 10 mm, which are not ideal for core needle biopsy (CNB). Early-stage breast cancer detection via circulating tumor DNA (ctDNA) methylation holds potential to bridge these diagnostic gaps.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We curated a breast cancer-specific panel by harnessing methylation profiles from in-house and public databases. Leveraging breast tissue-plasma-leukocyte samples, we identified breast cancer-specific markers, culminating in a 103-marker methylation model which underwent rigorous validation in two independent cohorts. To assess its performance, we compared it against the accuracy of ultrasonography, mammography, and CNB.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The 103-marker model exhibited remarkable proficiency in discerning benign from malignant breast tumors in plasma, with AUCs of 0.838, 0.838 and 0.823 in the validation set and two independent test sets, respectively. In BI-RADS 4a breast cancer, when compared to ultrasonography or mammography, the model augmented breast cancer diagnostic accuracy by 40.58% and 25.49%, separately. Retrospective analyses suggested that our model achieved a sensitivity of 66.67% (4/6) and a specificity of 80.36% (45/56) for surgical patients in the BI-RADS 4a category with tumors ≤ 10 mm, who did not undergo CNB, potentially sparing 45 benign patients from overtreatment. Notably, significant differences emerged in cancer scores between DCIS and invasive ductal carcinoma (<i>p</i> < 0.05). Higher cancer scores correlated with a more unfavorable prognosis (<i>p</i> < 0.05).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>The 103-marker methylation model demonstrates impressive performance in distinguishing between malignant and benign tumors, facilitating precise early diagnosis of BC, and holds promise as a prognostic tool.</p>\n </section>\n </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"14 12","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cam4.71004","citationCount":"0","resultStr":"{\"title\":\"An Approach for Differential Diagnosis of Breast Tumors by ctDNA Methylation Sequencing\",\"authors\":\"Xianyu Zhang, Yanling Yin, Zhujia Ye, Xingda Zhang, Wei Wei, Yi Hao, Liuhong Zeng, Ting Yang, Dalin Li, Jun Wang, Dezhi Zhao, Yanbo Chen, Shan Lei, Yongdong Jiang, Youxue Zhang, Shouping Xu, Abiyasi Nanding, Yajie Gong, Siwei Li, Yuanyuan Yu, Shilu Zhao, Siyu Liu, Yashuang Zhao, Zhiwei Chen, Shihui Yu, Jian-Bing Fan, Da Pang\",\"doi\":\"10.1002/cam4.71004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Breast ultrasonography and mammography remain predominant in breast tumor evaluations, yet they often result in false positives, particularly for tumors classified as BI-RADS 4a or those no more than 10 mm, which are not ideal for core needle biopsy (CNB). Early-stage breast cancer detection via circulating tumor DNA (ctDNA) methylation holds potential to bridge these diagnostic gaps.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We curated a breast cancer-specific panel by harnessing methylation profiles from in-house and public databases. Leveraging breast tissue-plasma-leukocyte samples, we identified breast cancer-specific markers, culminating in a 103-marker methylation model which underwent rigorous validation in two independent cohorts. To assess its performance, we compared it against the accuracy of ultrasonography, mammography, and CNB.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>The 103-marker model exhibited remarkable proficiency in discerning benign from malignant breast tumors in plasma, with AUCs of 0.838, 0.838 and 0.823 in the validation set and two independent test sets, respectively. In BI-RADS 4a breast cancer, when compared to ultrasonography or mammography, the model augmented breast cancer diagnostic accuracy by 40.58% and 25.49%, separately. Retrospective analyses suggested that our model achieved a sensitivity of 66.67% (4/6) and a specificity of 80.36% (45/56) for surgical patients in the BI-RADS 4a category with tumors ≤ 10 mm, who did not undergo CNB, potentially sparing 45 benign patients from overtreatment. Notably, significant differences emerged in cancer scores between DCIS and invasive ductal carcinoma (<i>p</i> < 0.05). Higher cancer scores correlated with a more unfavorable prognosis (<i>p</i> < 0.05).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>The 103-marker methylation model demonstrates impressive performance in distinguishing between malignant and benign tumors, facilitating precise early diagnosis of BC, and holds promise as a prognostic tool.</p>\\n </section>\\n </div>\",\"PeriodicalId\":139,\"journal\":{\"name\":\"Cancer Medicine\",\"volume\":\"14 12\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cam4.71004\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cam4.71004\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Medicine","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cam4.71004","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
An Approach for Differential Diagnosis of Breast Tumors by ctDNA Methylation Sequencing
Background
Breast ultrasonography and mammography remain predominant in breast tumor evaluations, yet they often result in false positives, particularly for tumors classified as BI-RADS 4a or those no more than 10 mm, which are not ideal for core needle biopsy (CNB). Early-stage breast cancer detection via circulating tumor DNA (ctDNA) methylation holds potential to bridge these diagnostic gaps.
Methods
We curated a breast cancer-specific panel by harnessing methylation profiles from in-house and public databases. Leveraging breast tissue-plasma-leukocyte samples, we identified breast cancer-specific markers, culminating in a 103-marker methylation model which underwent rigorous validation in two independent cohorts. To assess its performance, we compared it against the accuracy of ultrasonography, mammography, and CNB.
Results
The 103-marker model exhibited remarkable proficiency in discerning benign from malignant breast tumors in plasma, with AUCs of 0.838, 0.838 and 0.823 in the validation set and two independent test sets, respectively. In BI-RADS 4a breast cancer, when compared to ultrasonography or mammography, the model augmented breast cancer diagnostic accuracy by 40.58% and 25.49%, separately. Retrospective analyses suggested that our model achieved a sensitivity of 66.67% (4/6) and a specificity of 80.36% (45/56) for surgical patients in the BI-RADS 4a category with tumors ≤ 10 mm, who did not undergo CNB, potentially sparing 45 benign patients from overtreatment. Notably, significant differences emerged in cancer scores between DCIS and invasive ductal carcinoma (p < 0.05). Higher cancer scores correlated with a more unfavorable prognosis (p < 0.05).
Conclusions
The 103-marker methylation model demonstrates impressive performance in distinguishing between malignant and benign tumors, facilitating precise early diagnosis of BC, and holds promise as a prognostic tool.
期刊介绍:
Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas:
Clinical Cancer Research
Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations
Cancer Biology:
Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery.
Cancer Prevention:
Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach.
Bioinformatics:
Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers.
Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.