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Assay performance is assessed in tissue and plasma from healthy controls and patients with non-metastatic or metastatic disease, including sensitivity, specificity, and marker 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methylation-specific droplet digital PCR multiplex for lung cancer detection  \nCecilie Mondrup Jacobsen1,2, Peter Hjorth-Hansen2,3, Sara Witting Christensen Wen1,3, Janina Augustenas4, Mads MalikAagaard5, Claus Varnum3,6, Ole Hilberg3,7,  \nTorben Frøstrup Hansen2,3 & Rikke Fredslund Andersen1,2􀀍  \nBiomarkers are increasingly used in cancer management, including lung cancer. The use of circulating tumour DNA (ctDNA) detection has attracted significant interest as a non-invasive, highly specific, and sensitive strategy. In this study, we developed and validated a methylation-specific droplet digital PCR (ddPCR) multiplex assay with five tumour-specific methylation markers identified by insilico analysis for lung cancer detection across various clinical settings. The performance of the ddPCR multiplex was validated in tissue and plasma in cohorts of healthy controls and patients with both non-metastatic and metastatic disease by examining sensitivity, specificity, and marker dynamics. Furthermore, two different cut-off methods to determine ctDNA-status and their effects on sensitivity and specificity were examined. In non-metastatic disease, the ddPCR multiplex showed ctDNApositive rates of 38.7% and 46.8%, respectively, with the two cut-off methods. In metastatic cases, these rates increased to 70.2% and 83.0%. Higher sensitivities were observed for small cell lung cancer and squamous cell carcinoma. Analyses of longitudinal samples from patients with metastatic disease undergoing treatment indicated a potential use in prognostication and treatment guidance. We present a robust, cost-effective approach to lung cancer detection. Moving forward, its performance should be evaluated and validated in larger patient cohorts across a range of clinical scenarios, exploring its full potential.  \nKeywords ctDNA, ddPCR, Multiplex, Lung cancer  \nAbbreviations  \nCT Computed tomography  \nMRD Minimal residual disease  \nctDNA Circulating tumour DNA  \nCpG Cytosine-phosphate-Guanine dinucleotide ddPCR Droplet digital polymerase chain reaction TCGA The Cancer Genome Atlas  \nLAN Lung adjacent normal LT Lung tumour  \nGDC NCI Genomic Data Commons  \nDMC Differentially methylated CpGs  \nRFE Recursive feature elimination  \nFFPE Formalin-fixed paraffin-embedded NSCLC Non-small cell lung cancer  \n1Department of Biochemistry and Immunology, Vejle Hospital, University Hospital of Southern Denmark, Vejle, Denmark. 2Department of Oncology, Vejle Hospital, University Hospital of Southern Denmark, Vejle, Denmark.  \n3Department of Regional Health Research, University of Southern Denmark, Odense, Denmark. 4Department of Pathology, Vejle Hospital, University Hospital of Southern Denmark, Vejle, Denmark. 5Department of Clinical Genetics, Vejle Hospital, University Hospital of Southern Denmark, Vejle, Denmark. 6Department of Orthopedic Surgery, Vejle Hospital, University Hospital of Southern Denmark, Vejle, Denmark. 7Department of Medicine, Vejle Hospital, University Hospital of Southern Denmark, Vejle, Denmark. 􀀍 email: [rikke.fredslund.andersen@rsyd.dk](rikke.fredslund.andersen@rsyd.dk)  \n[www. nature.com/scientificreports/](www. nature.com/scientificreports/)  \ncfDNA Cell free DNA bp Base pairs  \nLoB Limit of blank  \nCI Confidence interval  \nSCLC Small cell lung cancer SCC Squamous cell carcinoma  \nAC Adenocarcinoma  \nPFS Progression-free survival  \nOS Overall survival  \nNR Not reached  \nGene symbol (gene name approved by the HGNC)  \nEMC7  \nALB HOXA9 MCIDASOTX1  \nSP9 TFAP2B  \nER membrane protein complex subunit 7  \nAlbumin  \nHomeobox A9  \nMulticiliate differentiation and dna synthesis associated cell cycle protein Orthodenticle homeobox 1  \nSp9 transcription factor  \nTranscription factor AP-2β  \nLung cancer was the most prevalent cancer globally in 2022 with ~ 2.5 million cases1. Despite advances in treatment and emphasis on early detection, most patien","cbCaik4N90fx0XI7","https://ap.wps.com/l/cbCaik4N90fx0XI7","pdf",3596772,14,"English","# Background\n## Need for non-invasive lung cancer biomarkers\n## Advantages of liquid biopsy and ctDNA\n# ctDNA and assay rationale\n## Differentiating ctDNA from genomic DNA\n# Study objective\n## Developing and validating the methylation-specific ddPCR multiplex\n# Validation strategy\n## Tissue and plasma cohorts\n## Sensitivity, specificity, and marker dynamics","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To develop and validate a methylation-specific ddPCR multiplex assay with tumor-specific methylation markers for detecting lung cancer using ctDNA.\"},{\"question\":\"Where was the ddPCR multiplex validated?\",\"answer\":\"The multiplex was validated in both tissue and plasma cohorts, including healthy controls and patients with non-metastatic and metastatic disease.\"},{\"question\":\"How were ctDNA-positive calls determined?\",\"answer\":\"Two different cut-off methods were evaluated, and their effects on sensitivity and specificity were analyzed.\"}]","Development and validation of a methylation-specific droplet digital PCR multiplex for lung cancer detection | PDF",1790202339,35]