Multimodal analysis of cell-free DNA whole-methylome sequencing for cancer detection and localization.
Fenglong BieZhijie WangYulong LiWei GuoYuanyuan HongTiancheng HanFang LvShunli YangSuxing LiXi LiPeiyao NieShun XuRuochuan ZangMoyan ZhangPeng SongFeiyue FengJianchun DuanGuangyu BaiYuan LiQilin HuaiBolun ZhouYu S HuangWeizhi ChenFeng-Wei TanShugeng GaoPublished in: Nature communications (2023)
Multimodal epigenetic characterization of cell-free DNA (cfDNA) could improve the performance of blood-based early cancer detection. However, integrative profiling of cfDNA methylome and fragmentome has been technologically challenging. Here, we adapt an enzyme-mediated methylation sequencing method for comprehensive analysis of genome-wide cfDNA methylation, fragmentation, and copy number alteration (CNA) characteristics for enhanced cancer detection. We apply this method to plasma samples of 497 healthy controls and 780 patients of seven cancer types and develop an ensemble classifier by incorporating methylation, fragmentation, and CNA features. In the test cohort, our approach achieves an area under the curve value of 0.966 for overall cancer detection. Detection sensitivity for early-stage patients achieves 73% at 99% specificity. Finally, we demonstrate the feasibility to accurately localize the origin of cancer signals with combined methylation and fragmentation profiling of tissue-specific accessible chromatin regions. Overall, this proof-of-concept study provides a technical platform to utilize multimodal cfDNA features for improved cancer detection.
Keyphrases
- papillary thyroid
- genome wide
- dna methylation
- squamous cell
- copy number
- early stage
- end stage renal disease
- loop mediated isothermal amplification
- newly diagnosed
- ejection fraction
- squamous cell carcinoma
- lymph node metastasis
- pain management
- single cell
- real time pcr
- label free
- dna damage
- chronic kidney disease
- young adults
- sensitive detection
- deep learning
- mitochondrial dna
- peritoneal dialysis
- patient reported
- convolutional neural network