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Multimodal analysis of cfDNA methylomes for early detecting esophageal squamous cell carcinoma and precancerous lesions.

Jiaqi LiuLijun DaiQiang WangChenghao LiZhi-Chao LiuTongyang GongHengyi XuZiqi JiaWanyuan SunXinyu WangMinyi LuTongxuan ShangNing ZhaoJiahui CaiZhi-Gang LiHongyan ChenJianzhong SuZhihua Liu
Published in: Nature communications (2024)
Detecting early-stage esophageal squamous cell carcinoma (ESCC) and precancerous lesions is critical for improving survival. Here, we conduct whole-genome bisulfite sequencing (WGBS) on 460 cfDNA samples from patients with non-metastatic ESCC or precancerous lesions and matched healthy controls. We develop an expanded multimodal analysis (EMMA) framework to simultaneously identify cfDNA methylation, copy number variants (CNVs), and fragmentation markers in cfDNA WGBS data. cfDNA methylation markers are the earliest and most sensitive, detectable in 70% of ESCCs and 50% of precancerous lesions, and associated with molecular subtypes and tumor microenvironments. CNVs and fragmentation features show high specificity but are linked to late-stage disease. EMMA significantly improves detection rates, increasing AUCs from 0.90 to 0.99, and detects 87% of ESCCs and 62% of precancerous lesions with >95% specificity in validation cohorts. Our findings demonstrate the potential of multimodal analysis of cfDNA methylome for early detection and monitoring of molecular characteristics in ESCC.
Keyphrases
  • copy number
  • early stage
  • genome wide
  • mitochondrial dna
  • dna methylation
  • squamous cell carcinoma
  • gene expression
  • single cell
  • artificial intelligence
  • lymph node
  • machine learning
  • deep learning
  • real time pcr