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 noninvasive 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. ARTICLE HISTORY

DNA methylation changes in biomarker loci occur early in cancer progression

“…Together, these findings indicate that the biomarkers are capable, from the qualitative point of view, to detect cancer at its earliest stages. However, the detection of cancer-specific DNA methylation in blood or other body fluids is quantitative in nature and depends on the tumor size and its propensity to shed DNA into bloodstream; e.g., our previous report 14 shows that the DNA methylation signal from this biomarker set in cfDNA samples depends on the NSCLC tumor size. Later disease stages are thus relatively easy to detect since larger tumors of later cancer stages shed a large amount of DNA into bloodstream resulting in high DNA methylation signal.…”

“…Recently, we developed qPCR amplicons specific for a subset of these biomarker loci designed to detect common carcinoma types and tested them on clinical cfDNA samples from healthy individuals and non-small cell lung cancer (NSCLC) patients. We demonstrated that these biomarkers can distinguish between healthy subjects and NSCLC patients with high sensitivity and specificity 14 . Moreover, in blood samples from lung cancer patients the biomarker DNA methylation signal positively correlates with tumor size 14 .…”

“…We demonstrated that these biomarkers can distinguish between healthy subjects and NSCLC patients with high sensitivity and specificity 14 . Moreover, in blood samples from lung cancer patients the biomarker DNA methylation signal positively correlates with tumor size 14 . The purpose of the current study was to find how early during carcinogenesis the biomarker loci gain DNA methylation in order to assess their potential as detectors of early stage cancer.…”

“…All data were analyzed in the R programming environment, version 3.6.1 15 as follows: The beta values were normalized as described 13 . The normalized beta values for 10 biomarker CpGs ( Table 1, 14 ) were used in further analysis. Boxplots were created using the R function boxplot and the R library beeswarm,version 0.2.3.…”

Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis

“…The redundant biomarkers tended to cause over-fitting of the classifier and resulted in poor generalization performance, while fewer biomarkers including incomplete or damaged information often resulted in under-fitting. Although DNAm was an important biological phenomenon in the development of many different types of cancer, there was a lack of greater insight into the physiological mechanisms of disease based solely on DNAm (Vrba et al, 2020;Zhou et al, 2018). Therefore, our multi-omics-based analysis provided a unified view for understanding the interrelationship between different molecular mechanisms and the combined effects on disease processes as well as for screening biomarkers with high sensitivity and specificity (Gao et al, 2019).…”