Plasma DNA End-Motif Profiling as a Fragmentomic Marker in Cancer, Pregnancy, and Transplantation.
Peiyong JiangKun SunWenlei PengSuk Hang ChengMeng NiPhilip C YeungMacy M S HeungTingting XieHuimin ShangZe ZhouJason Ying-Kuen ChanJohn WongVincent W S WongLiona C PoonTak Yeung LeungW K Jacky LamAllen K C ChanRossa W K ChiuYuk Ming Dennis LoPublished in: Cancer discovery (2020)
Plasma DNA fragmentomics is an emerging area of research covering plasma DNA sizes, end points, and nucleosome footprints. In the present study, we found a significant increase in the diversity of plasma DNA end motifs in patients with hepatocellular carcinoma (HCC). Compared with patients without HCC, patients with HCC showed a preferential pattern of 4-mer end motifs. In particular, the abundance of plasma DNA motif CCCA was much lower in patients with HCC than in subjects without HCC. The aberrant end motifs were also observed in patients with other cancer types, including colorectal cancer, lung cancer, nasopharyngeal carcinoma, and head and neck squamous cell carcinoma. We further observed that the profile of plasma DNA end motifs originating from the same organ, such as the liver, placenta, and hematopoietic cells, generally clustered together. The profile of end motifs may therefore serve as a class of biomarkers for liquid biopsy in oncology, noninvasive prenatal testing, and transplantation monitoring. SIGNIFICANCE: Plasma DNA molecules originating from the liver, HCC and other cancers, placenta, and hematopoietic cells each harbor a set of characteristic plasma DNA end motifs. Such markers carry tissue-of-origin information and represent a new class of biomarkers in the nascent field of fragmentomics.This article is highlighted in the In This Issue feature, p. 627.
Keyphrases
- circulating tumor
- cell free
- single molecule
- induced apoptosis
- end stage renal disease
- nucleic acid
- squamous cell carcinoma
- chronic kidney disease
- pregnant women
- bone marrow
- stem cells
- mesenchymal stem cells
- oxidative stress
- palliative care
- ejection fraction
- cell therapy
- circulating tumor cells
- machine learning
- young adults
- wastewater treatment
- preterm birth
- deep learning
- prognostic factors
- peritoneal dialysis
- microbial community
- pregnancy outcomes