Open AccessAlgorithm Optimization in Methylation Detection with Multiple RT-qPCR
Author(s) -
Lele Song,
Yuemin Li,
Jia Jia,
Zhou Guangpeng,
Jianming Wang,
Qian Kang,
Peng Jin,
Jian-qiu Sheng,
Guoxiang Cai,
Sanjun Cai,
Xiaochen Han
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0163333
Subject(s) - replicate , dna methylation , methylation , epigenetics , polymerase chain reaction , real time polymerase chain reaction , cutoff , biology , algorithm , computational biology , gene , genetics , computer science , mathematics , gene expression , statistics , physics , quantum mechanics
Epigenetic markers based on differential methylation of DNA sequences are used in cancer screening and diagnostics. Detection of abnormal methylation at specific loci by real-time quantitative polymerase chain reaction (RT-qPCR) has been developed to enable high-throughput cancer screening. For tests that combine the results of multiple PCR replicates into a single reportable result, both individual PCR cutoff and weighting of the individual PCR result are essential to test outcome. In this opportunistic screening study, we tested samples from 1133 patients using the triplicate Epi proColon assay with various algorithms and compared it with the newly developed single replicate SensiColon assay that measures methylation status of the same SEPT9 gene sequence. The Epi proColon test approved by the US FDA (1/3 algorithm) showed the highest sensitivity (82.4%) at a lower specificity (82.0%) compared with the Epi proColon 2.0 CE version with 2/3 algorithm (75.1% sensitivity, 97.1% specificity) or 1/1 algorithm (71.3% sensitivity, 92.7% specificity). No significant difference in performance was found between the Epi proColon 2.0 CE and the SensiColon assays. The choice of algorithm must depend on specific test usage, including screening and early detection. These considerations allow one to choose the optimal algorithm to maximize the test performance. We hope this study can help to optimize the methylation detection in cancer screening and early detection.