Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring.
Asaf ZviranRafael C SchulmanMinita ShahSteven T K HillSunil DeochandCole C KhamneiDillon MaloneyKristofer PatelWill LiaoAdam J WidmanPhillip WongMargaret K CallahanGavin HaSarah C ReedDenisse RotemDennie FrederickTatyana SharovaBenchun MiaoTommy KimGreg GydushJustin RhoadesKevin Y HuangNathaniel D OmansPatrick O BolanAndrew H LipskyChelston AngMurtaza MalbariCatherine F SpinelliSelena KazanciogluAlexi M RunnelsSamantha FennesseyChristian StolteFederico GaitiGiorgio Ga InghiramiViktor AdalsteinssonBrian Houck-LoomisJennifer IshiiJedd D WolchokGenevieve Marie BolandNicolas RobineNasser K AltorkiDan A LandauPublished in: Nature medicine (2020)
In many areas of oncology, we lack sensitive tools to track low-burden disease. Although cell-free DNA (cfDNA) shows promise in detecting cancer mutations, we found that the combination of low tumor fraction (TF) and limited number of DNA fragments restricts low-disease-burden monitoring through the prevailing deep targeted sequencing paradigm. We reasoned that breadth may supplant depth of sequencing to overcome the barrier of cfDNA abundance. Whole-genome sequencing (WGS) of cfDNA allowed ultra-sensitive detection, capitalizing on the cumulative signal of thousands of somatic mutations observed in solid malignancies, with TF detection sensitivity as low as 10-5. The WGS approach enabled dynamic tumor burden tracking and postoperative residual disease detection, associated with adverse outcome. Thus, we present an orthogonal framework for cfDNA cancer monitoring via genome-wide mutational integration, enabling ultra-sensitive detection, overcoming the limitation of cfDNA abundance and empowering treatment optimization in low-disease-burden oncology care.
Keyphrases
- sensitive detection
- genome wide
- loop mediated isothermal amplification
- papillary thyroid
- palliative care
- quantum dots
- high resolution
- squamous cell
- dna methylation
- single cell
- patients undergoing
- squamous cell carcinoma
- machine learning
- gene expression
- optical coherence tomography
- single molecule
- microbial community
- lymph node metastasis
- young adults
- deep learning
- wastewater treatment
- cell free
- label free
- circulating tumor cells
- smoking cessation