Integrating multi-omics features enables non-invasive early diagnosis and treatment response prediction of diffuse large B-cell lymphoma

IF 7.9 1区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Clinical and Translational Medicine Pub Date : 2025-01-07 DOI:10.1002/ctm2.70174

Weilong Zhang, Bangquan Ye, Yang Song, Ping Yang, Wenzhe Si, Hairong Jing, Fan Yang, Dan Yuan, Zhihong Wu, Jiahao Lyu, Kang Peng, Xu Zhang, Lingli Wang, Yan Li, Yan Liu, Chaoling Wu, Xiaoyu Hao, Yuqi Zhang, Wenxin Qi, Jing Wang, Fei Dong, Zijian Zhao, Hongmei Jing, Yanzhao Li

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引用次数: 0

Abstract

Background

Multi-omics features of cell-free DNA (cfDNA) can effectively improve the performance of non-invasive early diagnosis and prognosis of cancer. However, multimodal characterization of cfDNA remains technically challenging.

Methods

We developed a comprehensive multi-omics solution (COMOS) to specifically obtain an extensive fragmentomics landscape, presented by breakpoint characteristics of nucleosomes, CpG islands, DNase clusters and enhancers, besides typical methylation, copy number alteration of cfDNA. The COMOS was tested on 214 plasma samples of diffuse large B-cell lymphoma (DLBCL) and matched healthy controls.

Results

For early diagnosis, COMOS improved the area under the curve (AUC) value to .993 compared with the individual omics model, with a sensitivity of 95% at 98% specificity. Detection sensitivity achieved 91% at 99% specificity in early-stage patients, while the AUC values of the individual omics model were 0.942, 0.968, 0.989, 0.935, 0.921, 0.781 and 0.917, respectively, with lower sensitivity and specificity. In the treatment response cohort, COMOS yielded a superior sensitivity of 88% at 86% specificity (AUC, 0.903). COMOS has achieved excellent performance in early diagnosis and treatment response prediction.

Conclusions

Our study provides an effectively improved approach with high accuracy for the diagnosis and prognosis of DLBCL, showing great potential for future clinical application.

Key points

A comprehensive multi-omics solution to specifically obtain an extensive fragmentomics landscape, presented by breakpoint characteristics of nucleosomes, CpG islands, DNase clusters and enhancers, besides typical methylation, copy number alteration of cfDNA.
Integrated model of cfDNA multi-omics could be used for non-invasive early diagnosis of DLBCL.
Integrated model of cfDNA multi-omics could effectively evaluate the efficacy of R-CHOP before DLBCL treatment.

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查看原文本刊更多论文

整合多组学特征可实现弥漫性大b细胞淋巴瘤的无创早期诊断和治疗反应预测。

背景：游离DNA （cell-free DNA, cfDNA）的多组学特征可以有效提高肿瘤无创早期诊断和预后的效能。然而，cfDNA的多模态表征在技术上仍然具有挑战性。方法：我们开发了一个综合的多组学解决方案（COMOS），专门获得广泛的片段组学景观，除了典型的甲基化，cfDNA拷贝数改变外，还包括核小体，CpG岛，dna酶簇和增强子的断点特征。对214例弥漫性大b细胞淋巴瘤（DLBCL）和匹配的健康对照者的血浆样本进行了COMOS测试。结果：与个体组学模型相比，COMOS将早期诊断的曲线下面积（AUC）值提高到0.993，敏感性为95%，特异性为98%。早期患者的检测灵敏度为91%，特异性为99%，而个体组学模型的AUC值分别为0.942、0.968、0.989、0.935、0.921、0.781和0.917，敏感性和特异性较低。在治疗反应队列中，COMOS的敏感性为88%，特异性为86% （AUC, 0.903）。COMOS在早期诊断和治疗反应预测方面取得了优异的成绩。结论：本研究为DLBCL的诊断和预后提供了一种有效的改进方法，准确率高，具有很大的临床应用潜力。重点：一个全面的多组学解决方案，专门获得广泛的片段组学景观，包括核小体，CpG岛，dna酶簇和增强子的断点特征，以及典型的甲基化，cfDNA拷贝数改变。cfDNA多组学集成模型可用于DLBCL的无创早期诊断。cfDNA多组学综合模型可有效评价大细胞淋巴瘤治疗前R-CHOP的疗效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical and Translational Medicine Multiple-

CiteScore

15.90

自引率

1.90%

发文量

450

审稿时长

4 weeks

期刊介绍： Clinical and Translational Medicine (CTM) is an international, peer-reviewed, open-access journal dedicated to accelerating the translation of preclinical research into clinical applications and fostering communication between basic and clinical scientists. It highlights the clinical potential and application of various fields including biotechnologies, biomaterials, bioengineering, biomarkers, molecular medicine, omics science, bioinformatics, immunology, molecular imaging, drug discovery, regulation, and health policy. With a focus on the bench-to-bedside approach, CTM prioritizes studies and clinical observations that generate hypotheses relevant to patients and diseases, guiding investigations in cellular and molecular medicine. The journal encourages submissions from clinicians, researchers, policymakers, and industry professionals.