ctDNA甲基化测序在乳腺肿瘤鉴别诊断中的应用

IF 3.1 2区 医学 Q2 ONCOLOGY
Cancer Medicine Pub Date : 2025-06-20 DOI:10.1002/cam4.71004
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
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引用次数: 0

摘要

乳腺超声检查和乳房x光检查在乳腺肿瘤评估中仍然占主导地位,但它们经常导致假阳性,特别是对于BI-RADS 4a或不超过10mm的肿瘤,这不是核心针活检(CNB)的理想选择。通过循环肿瘤DNA (ctDNA)甲基化检测早期乳腺癌具有弥补这些诊断空白的潜力。方法通过利用内部和公共数据库中的甲基化谱,我们策划了一个乳腺癌特异性小组。利用乳腺组织-血浆-白细胞样本,我们确定了乳腺癌特异性标志物,最终建立了103个标志物甲基化模型,该模型在两个独立的队列中进行了严格的验证。为了评估其性能,我们将其与超声、乳房x线摄影和CNB的准确性进行了比较。结果103标记模型对血浆乳腺肿瘤良恶性的识别能力显著,验证集和两个独立测试集的auc分别为0.838、0.838和0.823。在BI-RADS 4a乳腺癌中,与超声或乳房x光检查相比,该模型将乳腺癌的诊断准确率分别提高了40.58%和25.49%。回顾性分析表明,我们的模型对BI-RADS 4a类肿瘤≤10 mm且未行CNB的手术患者的敏感性为66.67%(4/6),特异性为80.36%(45/56),可能使45例良性患者免于过度治疗。值得注意的是,DCIS与浸润性导管癌的评分差异有统计学意义(p < 0.05)。癌症评分越高,预后越差(p < 0.05)。103标记物甲基化模型在区分恶性肿瘤和良性肿瘤方面表现出色,促进了BC的精确早期诊断,并有望作为预后工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An Approach for Differential Diagnosis of Breast Tumors by ctDNA Methylation Sequencing

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.

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来源期刊
Cancer Medicine
Cancer Medicine ONCOLOGY-
CiteScore
5.50
自引率
2.50%
发文量
907
审稿时长
19 weeks
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.
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