PDF(Pubmed)
Abstract：
UNASSIGNED: Breast 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.
UNASSIGNED: Here, 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.
UNASSIGNED: We 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.
UNASSIGNED: Our 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.
UNASSIGNED: Here, 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.
UNASSIGNED: We 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.
UNASSIGNED: Our 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.
摘要：
乳腺癌是女性中与癌症有关的死亡，也是美国医疗服务和处方药费用最高的癌症。美国卫生当局建议进行乳腺癌筛查，但目前的筛查工作往往受到高假阳性率的影响.基于循环肿瘤DNA（ctDNA）的液体活检已成为筛查癌症的潜在方法。然而,乳腺癌的检测，特别是在早期阶段，由于ctDNA的含量低和分子亚型的异质性，因此具有挑战性。
■这里，我们采用了多模态方法，即通过DNA甲基化和大小（SPOT-MAS）筛选肿瘤的存在，同时分析239例非转移性乳腺癌患者和278例健康受试者血浆样本中无细胞DNA(cfDNA)的多重特征.
■我们确定了全基因组甲基化变化（GWM）的不同概况，拷贝数变更（CNA），和乳腺癌患者的cfDNA中的4-核苷酸寡聚物(4-mer)末端基序(EM)。我们进一步使用所有三个签名来构建多特征机器学习模型，并表明组合模型优于由单个特征构建的基础模型，达到0.91的AUC（95%CI：0.87-0.95），灵敏度为65%，特异性为96%。
■我们的研究结果表明，基于cfDNA甲基化分析的多模态液体活检检测，CNA和EM可以提高早期乳腺癌检测的准确性。
■这里，我们采用了多模态方法，即通过DNA甲基化和大小（SPOT-MAS）筛选肿瘤的存在，同时分析239例非转移性乳腺癌患者和278例健康受试者血浆样本中无细胞DNA(cfDNA)的多重特征.
■我们确定了全基因组甲基化变化（GWM）的不同概况，拷贝数变更（CNA），和乳腺癌患者的cfDNA中的4-核苷酸寡聚物(4-mer)末端基序(EM)。我们进一步使用所有三个签名来构建多特征机器学习模型，并表明组合模型优于由单个特征构建的基础模型，达到0.91的AUC（95%CI：0.87-0.95），灵敏度为65%，特异性为96%。
■我们的研究结果表明，基于cfDNA甲基化分析的多模态液体活检检测，CNA和EM可以提高早期乳腺癌检测的准确性。
