DNA methylation biomarkers discovered in silico detect cancer in liquid biopsies from non-small cell lung cancer patients

ABSTRACT

Identification of cancer-specific methylation of DNA released by tumours can be used for non-invasive diagnostics and monitoring. We previously reported in silico identification of DNA methylation loci specifically hypermethylated in common human cancers that could be used as epigenetic biomarkers. Using DNA methylation specific qPCR we now clinically tested a group of these cancer-specific loci on cell-free DNA (cfDNA) extracted from the plasma fraction of blood samples from healthy controls and non-small cell lung cancer (NSCLC) patients. These DNA methylation biomarkers distinguish lung cancer cases from controls with high sensitivity and specificity (AUC = 0.956), and furthermore, the signal from the markers correlates with tumour size and decreases after surgical resection of lung tumours. Presented observations suggest the clinical value of these DNA methylation biomarkers for NSCLC diagnostics and monitoring. Since we successfully validated the biomarkers using independent DNA methylation data from multiple additional common carcinoma cohorts (bladder, breast, colorectal, oesophageal, head and neck, pancreatic or prostate cancer) we predict that these DNA methylation biomarkers will detect additional carcinoma types from plasma samples as well.

KEYWORDS:

• Cancer marker
• DNA methylation
• biomarker
• cfDNA
• liquid biopsy
• NSCLC

Acknowledgments

The results shown here are in part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. We are grateful to Ms. Laura Duckett for assistance with consenting and blood collection. We thank all the anonymous blood sample donors, both lung cancer patients and healthy volunteers, who made this study possible.

Disclosure statement

M. Nelson and B. Futscher are Co-founders of DesertDx, LLC.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was supported by the Maynard Chair in Breast Cancer Epigenomics at the University of Arizona Cancer Center, the Cancer Center Support Grant (NCI of the NIH under award number P30 CA023074), the Tech Launch Arizona (UA17-061) and the Senner Endowment for Precision Health at the University of Arizona.