Computational challenges in detection of cancer using cell-free DNA methylation

Abstract

Cell-free DNA(cfDNA) methylation profiling is considered promising and potentially reliable for liquid biopsy to study progress of diseases and develop reliable and consistent diagnostic and prognostic biomarkers. There are several different mechanisms responsible for the release of cfDNA in blood plasma, and henceforth it can provide information regarding dynamic changes in the human body. Due to the fragmented nature, low concentration of cfDNA, and high background noise, there are several challenges in its analysis for regular use in diagnosis of cancer. Such challenges in the analysis of the methylation profile of cfDNA are further aggravated due to heterogeneity, biomarker sensitivity, platform biases, and batch effects. This review delineates the origin of cfDNA methylation, its profiling, and associated computational problems in analysis for diagnosis. Here we also contemplate upon the multi-marker approach to handle the scenario of cancer heterogeneity and explore the utility of markers for 5hmC based cfDNA methylation pattern. Further, we provide a critical overview of deconvolution and machine learning methods for cfDNA methylation analysis. Our review of current methods reveals the potential for further improvement in analysis strategies for detecting early cancer using cfDNA methylation.

Downregulation of JAM3 occurs in cholangiocarcinoma by hypermethylation: A potential molecular marker for diagnosis and prognosis

“…17 Aberrant methylation patterns in ctDNA have been implicated as useful tools for non-invasive cancer detection. [31][32][33][34][35] We detected methylated JAM3 in cfDNA from patients with CCA and biliary diseases, with a sensitivity of 53.3% and specificity of 96.6%.…”

“…Junctional adhesion molecular 3 has been shown to participate in epithelial‐mesenchymal transition (EMT) in colonic epithelial cells and functions as a tumour suppressor in colorectal cancer 17 . Aberrant methylation patterns in ctDNA have been implicated as useful tools for non‐invasive cancer detection 31–35 . We detected methylated JAM3 in cfDNA from patients with CCA and biliary diseases, with a sensitivity of 53.3% and specificity of 96.6%.…”

Liquid biopsies for cancer: From bench to clinic

“…5hmC is the most abundant and stably oxidized product among them, 143 but when compared with the 8% methylated cytosine in the human genome, number of hydroxymethylated cytosines is really tiny with only 0.5 to 1% 144 . Meanwhile, it was reported that in contrast to 5mC, 5hmC‐based profiles are relatively more stable and robust, providing better specificities 145 . Since 5hmC are found to be enriched in enhancers, promoters, and other regulatory elements, it is understandable that changes in 5hmC are associated with changes in gene expression 146–148 .…”

Recommendations for the collection and annotation of biosamples for analysis of biomarkers in neurofibromatosis and schwannomatosis clinical trials

“…Batch effect from technical, non-biologic variations during sample collection, processing, and preparation can result in erroneous conclusions and hinder the reproducibility and generalizability of findings. [65][66][67][68] This challenge is amplified when combining data sets, 69,70 as we propose, to better power analyses of rare disease states and treatment effectiveness. To minimize batch effect and enhance the validity and reliability of NF1/SWN biomarker research, it is imperative that preprocessing conditions, detailed methodology, and complete analytic pipelines are described and publicly available.…”