Table_1_Multimodal analysis of genome-wide methylation, copy number aberrations, and end motif signatures enhances detection of early-stage breast cancer.docx

IntroductionBreast cancer causes the most cancer-related death in women and is the costliest cancer in the US regarding medical service and prescription drug expenses. Breast cancer screening is recommended by health authorities in the US, but current screening efforts are often compromised by high false positive rates. Liquid biopsy based on circulating tumor DNA (ctDNA) has emerged as a potential approach to screen for cancer. However, the detection of breast cancer, particularly in early stages, is challenging due to the low amount of ctDNA and heterogeneity of molecular subtypes.
MethodsHere, we employed a multimodal approach, namely Screen for the Presence of Tumor by DNA Methylation and Size (SPOT-MAS), to simultaneously analyze multiple signatures of cell free DNA (cfDNA) in plasma samples of 239 nonmetastatic breast cancer patients and 278 healthy subjects.
ResultsWe identified distinct profiles of genome-wide methylation changes (GWM), copy number alterations (CNA), and 4-nucleotide oligomer (4-mer) end motifs (EM) in cfDNA of breast cancer patients. We further used all three signatures to construct a multi-featured machine learning model and showed that the combination model outperformed base models built from individual features, achieving an AUC of 0.91 (95% CI: 0.87-0.95), a sensitivity of 65% at 96% specificity.
DiscussionOur findings showed that a multimodal liquid biopsy assay based on analysis of cfDNA methylation, CNA and EM could enhance the accuracy for the detection of early- stage breast cancer.

引言
乳腺癌是导致女性癌症相关死亡人数最多的癌种，同时也是美国医疗服务与处方药支出最高的癌症。美国卫生主管部门推荐开展乳腺癌筛查，但当前筛查工作常因较高的假阳性率而大打折扣。基于循环肿瘤DNA（circulating tumor DNA, ctDNA）的液体活检已成为癌症筛查的潜在方案。然而，由于ctDNA含量极低且分子亚型存在异质性，乳腺癌的检测（尤其是早期阶段）仍颇具挑战。

方法
本研究采用一种多模态检测方法——即通过DNA甲基化与片段长度筛查肿瘤（Screen for the Presence of Tumor by DNA Methylation and Size, SPOT-MAS），同时分析239例非转移性乳腺癌患者与278例健康受试者血浆样本中游离DNA（cell free DNA, cfDNA）的多组学生物标志物特征。

结果
本研究在乳腺癌患者的cfDNA中鉴定出全基因组甲基化变化（genome-wide methylation changes, GWM）、拷贝数变异（copy number alterations, CNA）以及4核苷酸寡聚体（4-nucleotide oligomer, 4-mer）末端基序（end motifs, EM）的独特特征谱。随后，我们利用这三类特征构建多特征机器学习模型，结果显示该联合模型优于单特征构建的基础模型，其受试者工作特征曲线下面积（AUC）达0.91（95%置信区间：0.87~0.95），在特异性为96%时的灵敏度为65%。

讨论
本研究结果表明，基于cfDNA甲基化、拷贝数变异与末端基序分析的多模态液体活检检测方法，可提升早期乳腺癌的检测准确率。