Evaluating Stacked Methylation Markers for Blood-Based Multicancer Detection

SIMPLE SUMMARY: Tumors are known to shed DNA into the bloodstream, and since the tumor DNA is marked by aberrant methylation patterns, this can be exploited for their detection through a simple blood sample. However, specific methylation biomarkers that efficiently detect a broad range of tumors and are effective at early-stage disease are still lacking. In this study we identify two novel methylation biomarkers and combine these with an already existing biomarker to improve multi-cancer detection. We test their performances as individual and combined markers using large methylation array datasets covering multiple cancer types, mimic blood samples using data from healthy blood cell DNA, and finally test the biomarkers in cancer plasma samples. We find that the combination of markers greatly improves the ability of the test to distinguish between cancer and normal samples, and in addition we provide the research field with a complete workflow for evaluating novel methylation biomarkers based on pre-existing datasets. ABSTRACT: The ability to detect several types of cancer using a non-invasive, blood-based test holds the potential to revolutionize oncology screening. We mined tumor methylation array data from the Cancer Genome Atlas (TCGA) covering 14 cancer types and identified two novel, broadly-occurring methylation markers at TLX1 and GALR1. To evaluate their performance as a generalized blood-based screening approach, along with our previously reported methylation biomarker, ZNF154, we rigorously assessed each marker individually or combined. Utilizing TCGA methylation data and applying logistic regression models within each individual cancer type, we found that the three-marker combination significantly increased the average area under the ROC curve (AUC) across the 14 tumor types compared to single markers (p = 1.158 × 10(−10); Friedman test). Furthermore, we simulated dilutions of tumor DNA into healthy blood cell DNA and demonstrated increased AUC of combined markers across all dilution levels. Finally, we evaluated assay performance in bisulfite sequenced DNA from patient tumors and plasma, including early-stage samples. When combining all three markers, the assay correctly identified nine out of nine lung cancer plasma samples. In patient plasma from hepatocellular carcinoma, ZNF154 alone yielded the highest combined sensitivity and specificity values averaging 68% and 72%, whereas multiple markers could achieve higher sensitivity or specificity, but not both. Altogether, this study presents a comprehensive pipeline for the identification, testing, and validation of multi-cancer methylation biomarkers with a considerable potential for detecting a broad range of cancer types in patient blood samples.