无细胞DNA片段组学检测鉴别乳腺结节恶性肿瘤及评价治疗效果。

Jiaqi Liu, Yalun Li, Wanxiangfu Tang, Tianyi Qian, Lijun Dai, Ziqi Jia, Heng Cao, Chenghao Li, Yuchen Liu, Yansong Huang, Jiang Wu, Dongxu Ma, Guangdong Qiao, Hua Bao, Shuang Chang, Dongqin Zhu, Shanshan Yang, Xuxiaochen Wu, Xue Wu, Hengyi Xu, Hongyan Chen, Yang Shao, Xiang Wang, Zhihua Liu, Jianzhong Su
{"title":"无细胞DNA片段组学检测鉴别乳腺结节恶性肿瘤及评价治疗效果。","authors":"Jiaqi Liu, Yalun Li, Wanxiangfu Tang, Tianyi Qian, Lijun Dai, Ziqi Jia, Heng Cao, Chenghao Li, Yuchen Liu, Yansong Huang, Jiang Wu, Dongxu Ma, Guangdong Qiao, Hua Bao, Shuang Chang, Dongqin Zhu, Shanshan Yang, Xuxiaochen Wu, Xue Wu, Hengyi Xu, Hongyan Chen, Yang Shao, Xiang Wang, Zhihua Liu, Jianzhong Su","doi":"10.1093/gpbjnl/qzaf028","DOIUrl":null,"url":null,"abstract":"<p><p>The fragmentomics-based cell-free DNA (cfDNA) assays have recently illustrated prominent abilities to identify various cancers from non-conditional healthy controls, while their accuracy for identifying early-stage cancers from benign lesions with inconclusive imaging results remains uncertain. Especially for breast cancer, current imaging-based screening methods suffer from high false positive rates for women with breast nodules, leading to unnecessary biopsies, which add to discomfort and healthcare burden. Here, we enrolled 613 female participants in this multi-center study and demonstrated that cfDNA fragmentomics (cfFrag) is a robust non-invasive biomarker for breast cancer using whole-genome sequencing. Among the multimodal cfFrag profiles, the fragment size ratio (FSR), fragment size distribution (FSD), and copy number variation (CNV) show more distinguishing ability than Griffin, motif breakpoint (MBP), and neomer. The cfFrag model using the optimal three fragmentomics features discriminated early-stage breast cancers from benign nodules, even at a low sequencing depth (3×). Notably, it demonstrated a specificity of 94.1% in asymptomatic healthy women at a 90% sensitivity for breast cancers. Moreover, we comprehensively showcased the clinical utilities of the cfFrag model in predicting patient responses to neoadjuvant chemotherapy (NAC) and in combining with multimodal features, including radiological results and cfDNA methylation features [with area under the curve (AUC) values of 0.93-0.94 and 0.96, respectively].</p>","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cell-free DNA Fragmentomics Assay to Discriminate the Malignancy of Breast Nodules and Evaluate Treatment Response.\",\"authors\":\"Jiaqi Liu, Yalun Li, Wanxiangfu Tang, Tianyi Qian, Lijun Dai, Ziqi Jia, Heng Cao, Chenghao Li, Yuchen Liu, Yansong Huang, Jiang Wu, Dongxu Ma, Guangdong Qiao, Hua Bao, Shuang Chang, Dongqin Zhu, Shanshan Yang, Xuxiaochen Wu, Xue Wu, Hengyi Xu, Hongyan Chen, Yang Shao, Xiang Wang, Zhihua Liu, Jianzhong Su\",\"doi\":\"10.1093/gpbjnl/qzaf028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The fragmentomics-based cell-free DNA (cfDNA) assays have recently illustrated prominent abilities to identify various cancers from non-conditional healthy controls, while their accuracy for identifying early-stage cancers from benign lesions with inconclusive imaging results remains uncertain. Especially for breast cancer, current imaging-based screening methods suffer from high false positive rates for women with breast nodules, leading to unnecessary biopsies, which add to discomfort and healthcare burden. Here, we enrolled 613 female participants in this multi-center study and demonstrated that cfDNA fragmentomics (cfFrag) is a robust non-invasive biomarker for breast cancer using whole-genome sequencing. Among the multimodal cfFrag profiles, the fragment size ratio (FSR), fragment size distribution (FSD), and copy number variation (CNV) show more distinguishing ability than Griffin, motif breakpoint (MBP), and neomer. The cfFrag model using the optimal three fragmentomics features discriminated early-stage breast cancers from benign nodules, even at a low sequencing depth (3×). Notably, it demonstrated a specificity of 94.1% in asymptomatic healthy women at a 90% sensitivity for breast cancers. Moreover, we comprehensively showcased the clinical utilities of the cfFrag model in predicting patient responses to neoadjuvant chemotherapy (NAC) and in combining with multimodal features, including radiological results and cfDNA methylation features [with area under the curve (AUC) values of 0.93-0.94 and 0.96, respectively].</p>\",\"PeriodicalId\":94020,\"journal\":{\"name\":\"Genomics, proteomics & bioinformatics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genomics, proteomics & bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/gpbjnl/qzaf028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genomics, proteomics & bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/gpbjnl/qzaf028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

基于片段组学的无细胞DNA (cfDNA)检测最近显示出从非条件健康对照中识别各种癌症的突出能力,但其在从影像学结果不确定的良性病变中识别早期癌症的准确性仍不确定。特别是对于乳腺癌,目前基于成像的筛查方法对患有乳腺结节的妇女存在高假阳性率,导致不必要的活检,这增加了不适和医疗负担。在这项多中心研究中,我们招募了613名女性参与者,并通过全基因组测序证明了cfDNA片段组学(cfFrag)是一种强大的非侵入性乳腺癌生物标志物。在多模态cfFrag谱中,片段大小比(FSR)、片段大小分布(FSD)和拷贝数变异(CNV)比Griffin、motif breakpoint (MBP)和neomer具有更好的区分能力。使用最佳三个片段组学特征的cfFrag模型即使在低测序深度(3x)下也能区分早期乳腺癌和良性结节。值得注意的是,在无症状的健康女性中,它的特异性为94.1%,对乳腺癌的敏感性为90%。此外,我们全面展示了cfFrag模型在预测患者对新辅助化疗(NAC)反应方面的临床应用,并结合多模式特征,包括放射学结果和cfDNA甲基化特征[曲线下面积(AUC)分别为0.93-0.94和0.96]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cell-free DNA Fragmentomics Assay to Discriminate the Malignancy of Breast Nodules and Evaluate Treatment Response.

The fragmentomics-based cell-free DNA (cfDNA) assays have recently illustrated prominent abilities to identify various cancers from non-conditional healthy controls, while their accuracy for identifying early-stage cancers from benign lesions with inconclusive imaging results remains uncertain. Especially for breast cancer, current imaging-based screening methods suffer from high false positive rates for women with breast nodules, leading to unnecessary biopsies, which add to discomfort and healthcare burden. Here, we enrolled 613 female participants in this multi-center study and demonstrated that cfDNA fragmentomics (cfFrag) is a robust non-invasive biomarker for breast cancer using whole-genome sequencing. Among the multimodal cfFrag profiles, the fragment size ratio (FSR), fragment size distribution (FSD), and copy number variation (CNV) show more distinguishing ability than Griffin, motif breakpoint (MBP), and neomer. The cfFrag model using the optimal three fragmentomics features discriminated early-stage breast cancers from benign nodules, even at a low sequencing depth (3×). Notably, it demonstrated a specificity of 94.1% in asymptomatic healthy women at a 90% sensitivity for breast cancers. Moreover, we comprehensively showcased the clinical utilities of the cfFrag model in predicting patient responses to neoadjuvant chemotherapy (NAC) and in combining with multimodal features, including radiological results and cfDNA methylation features [with area under the curve (AUC) values of 0.93-0.94 and 0.96, respectively].

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信