Error Characterization and Statistical Modeling Improves Circulating Tumor DNA Detection by Droplet Digital PCR.
BACKGROUND: Droplet digital PCR (ddPCR) is a widely used and sensitive application for circulating tumor DNA (ctDNA) detection. As ctDNA is often found in low abundance, methods to separate low-signal readouts from noise are necessary. We aimed to characterize the ddPCR-generated noise and, informed...
| Publicado en: | Clinical Chemistry Vol. 68; no. 5; pp. 657 - 668 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
May2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157165614&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157165614 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00099147 10CS jtl: Clinical Chemistry issn: 00099147 maglogo: N pubinfo: dt: May2022 vid: 68 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 157165614 10.1093/clinchem/hvab274 157165614 ppf: 657 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Error Characterization and Statistical Modeling Improves Circulating Tumor DNA Detection by Droplet Digital PCR. aug: au: Henriksen, Tenna V. Drue, Simon O. Frydendahl, Amanda Demuth, Christina Rasmussen, Mads H. Reinert, Thomas Pedersen, Jakob S. Andersen, Claus L. affil: Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark sug: ab: BACKGROUND: Droplet digital PCR (ddPCR) is a widely used and sensitive application for circulating tumor DNA (ctDNA) detection. As ctDNA is often found in low abundance, methods to separate low-signal readouts from noise are necessary. We aimed to characterize the ddPCR-generated noise and, informed by this, create a sensitive and specific ctDNA caller. METHODS: We built 2 novel complimentary ctDNA calling methods: dynamic limit of blank and concentration and assay-specific tumor load estimator (CASTLE). Both methods are informed by empirically established assay-specific noise profiles. Here, we characterized noise for 70 mutation-detecting ddPCR assays by applying each assay to 95 nonmutated samples. Using these profiles, the performance of the 2 new methods was assessed in a total of 9447 negative/positive reference samples and in 1311 real-life plasma samples from colorectal cancer patients. Lastly, performances were compared to 7 literature-established calling methods. RESULTS: For many assays, noise increased proportionally with the DNA input amount. Assays targeting transition base changes were more error-prone than transversion-targeting assays. Both our calling methods successfully accounted for the additional noise in transition assays and showed consistently high performance regardless of DNA input amount. Calling methods that were not noise-informed performed less well than noiseinformed methods. CASTLE was the only calling method providing a statistical estimate of the noisecorrected mutation level and call certainty. CONCLUSIONS: Accurate error modeling is necessary for sensitive and specific ctDNA detection by ddPCR. Accounting for DNA input amounts ensures specific detection regardless of the sample-specific DNA concentration. Our results demonstrate CASTLE as a powerful tool for ctDNA calling using ddPCR. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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