Frontiers in Oncology (May 2023)

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

  • Thi Mong Quynh Pham,
  • Thi Mong Quynh Pham,
  • Thanh Hai Phan,
  • Thanh Xuan Jasmine,
  • Thuy Thi Thu Tran,
  • Thuy Thi Thu Tran,
  • Le Anh Khoa Huynh,
  • Le Anh Khoa Huynh,
  • Thi Loan Vo,
  • Thi Huong Thoang Nai,
  • Thuy Trang Tran,
  • My Hoang Truong,
  • Ngan Chau Tran,
  • Van Thien Chi Nguyen,
  • Van Thien Chi Nguyen,
  • Trong Hieu Nguyen,
  • Trong Hieu Nguyen,
  • Thi Hue Hanh Nguyen,
  • Thi Hue Hanh Nguyen,
  • Nguyen Duy Khang Le,
  • Nguyen Duy Khang Le,
  • Thanh Dat Nguyen,
  • Thanh Dat Nguyen,
  • Duy Sinh Nguyen,
  • Duy Sinh Nguyen,
  • Dinh Kiet Truong,
  • Thi Thanh Thuy Do,
  • Minh-Duy Phan,
  • Minh-Duy Phan,
  • Hoa Giang,
  • Hoa Giang,
  • Hoai-Nghia Nguyen,
  • Hoai-Nghia Nguyen,
  • Le Son Tran,
  • Le Son Tran

DOI
https://doi.org/10.3389/fonc.2023.1127086
Journal volume & issue
Vol. 13

Abstract

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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.

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