Simultaneous detection of eight cancer types using a multiplex droplet digital <scp>PCR</scp> assay

DNA methylation biomarkers have emerged as promising tools for cancer detection. Common methylation patterns across tumor types allow multi-cancer detection. Droplet digital PCR (ddPCR) has gained considerable attention for methylation detection. However, multi-cancer detection using multiple targets in ddPCR has never been performed before. Therefore, we developed a multiplex ddPCR assay for multi-cancer detection. Based on previous data analyses using The Cancer Genome Atlas (TCGA), we selected differentially methylated targets for eight frequent tumor types (lung, breast, colorectal, prostate, pancreatic, head and neck, liver, and esophageal cancer). Three targets were validated using ddPCR in 103 tumor and 109 normal adjacent fresh frozen samples. Two distinct ddPCR assays were successfully developed. Output data from both assays is combined to obtain a read-out from the three targets together. Our overall ddPCR assay has a cross-validated area under the curve (cvAUC) of 0.948. Performance between distinct cancer types varies, with sensitivities ranging from 53.8% to 100% and specificities ranging from 80% to 100%. Compared to previously published single-target parameters, we show that combining targets can drastically increase sensitivity and specificity, while lowering DNA input. In conclusion, we are the first to report a multi-cancer methylation ddPCR assay, which allows for highly accurate tumor predictions.

Authors

Institutions

Publication Details

Journal
Molecular Oncology
Published
2024-09-06
DOI
https://doi.org/10.1002/1878-0261.13708
Citations
11
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
3.14

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Simultaneous detection of eight cancer types using a multiplex droplet digital PCR assay

Guy Van Camp, Dieter Peeters, Marc Peeters, Ken Op de Beeck et al.
11 citations
Molecular Oncology
Cancer Genomics and Diagnostics
3.14
article

Simultaneous detection of eight cancer types using a multiplex droplet digital PCR assay

Guy Van Camp, Dieter Peeters, Marc Peeters, Ken Op de Beeck, Thomas Vanpoucke, Janah Vandenhoeck, Isabelle Neefs, Nele De Meulenaere
article en
11 citations

Abstract

DNA methylation biomarkers have emerged as promising tools for cancer detection. Common methylation patterns across tumor types allow multi-cancer detection. Droplet digital PCR (ddPCR) has gained considerable attention for methylation detection. However, multi-cancer detection using multiple targets in ddPCR has never been performed before. Therefore, we developed a multiplex ddPCR assay for multi-cancer detection. Based on previous data analyses using The Cancer Genome Atlas (TCGA), we selected differentially methylated targets for eight frequent tumor types (lung, breast, colorectal, prostate, pancreatic, head and neck, liver, and esophageal cancer). Three targets were validated using ddPCR in 103 tumor and 109 normal adjacent fresh frozen samples. Two distinct ddPCR assays were successfully developed. Output data from both assays is combined to obtain a read-out from the three targets together. Our overall ddPCR assay has a cross-validated area under the curve (cvAUC) of 0.948. Performance between distinct cancer types varies, with sensitivities ranging from 53.8% to 100% and specificities ranging from 80% to 100%. Compared to previously published single-target parameters, we show that combining targets can drastically increase sensitivity and specificity, while lowering DNA input. In conclusion, we are the first to report a multi-cancer methylation ddPCR assay, which allows for highly accurate tumor predictions.

Molecular OncologyVol. 19(1)
University of Antwerp (BE), Antwerp University Hospital (BE)
Fonds Wetenschappelijk Onderzoek, Universiteit Antwerpen, Vlaamse regering
Good health and well-being
Openalex Percentile: Top 7%
Cancer Genomics and Diagnostics
3.14
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.