An Automated Correction Algorithm (ALPACA) for ddPCR Data Using Adaptive Limit of Blank and Correction of False Positive Events Improves Specificity of Mutation Detection. (11th April 2021)
- Record Type:
- Journal Article
- Title:
- An Automated Correction Algorithm (ALPACA) for ddPCR Data Using Adaptive Limit of Blank and Correction of False Positive Events Improves Specificity of Mutation Detection. (11th April 2021)
- Main Title:
- An Automated Correction Algorithm (ALPACA) for ddPCR Data Using Adaptive Limit of Blank and Correction of False Positive Events Improves Specificity of Mutation Detection
- Authors:
- Vessies, Daan C L
Linders, Theodora C
Lanfermeijer, Mirthe
Ramkisoensing, Kalpana L
van der Noort, Vincent
Schouten, Robert D
Meijer, Gerrit A
van den Heuvel, Michel M
Monkhorst, Kim
van den Broek, Daan - Abstract:
- Abstract: Background: Bio-Rad droplet-digital PCR is a highly sensitive method that can be used to detect tumor mutations in circulating cell-free DNA (cfDNA) of patients with cancer. Correct interpretation of ddPCR results is important for optimal sensitivity and specificity. Despite its widespread use, no standardized method to interpret ddPCR data is available, nor have technical artifacts affecting ddPCR results been widely studied. Methods: False positive rates were determined for 6 ddPCR assays at variable amounts of input DNA, revealing polymerase induced false positive events (PIFs) and other false positives. An in silico correction algorithm, known as the adaptive LoB and PIFs: an automated correction algorithm (ALPACA), was developed to remove PIFs and apply an adaptive limit of blank (LoB) to individual samples. Performance of ALPACA was compared to a standard strategy (no PIF correction and static LoB = 3) using data from commercial reference DNA, healthy volunteer cfDNA, and cfDNA from a real-life cohort of 209 patients with stage IV nonsmall cell lung cancer (NSCLC) whose tumor and cfDNA had been molecularly profiled. Results: Applying ALPACA reduced false positive results in healthy cfDNA compared to the standard strategy (specificity 98 vs 88%, P = 10 −5 ) and stage IV NSCLC patient cfDNA (99 vs 93%, P = 10 −11 ), while not affecting sensitivity in commercial reference DNA (70 vs 68% P = 0.77) or patient cfDNA (82 vs 88%, P = 0.13). Overall accuracy inAbstract: Background: Bio-Rad droplet-digital PCR is a highly sensitive method that can be used to detect tumor mutations in circulating cell-free DNA (cfDNA) of patients with cancer. Correct interpretation of ddPCR results is important for optimal sensitivity and specificity. Despite its widespread use, no standardized method to interpret ddPCR data is available, nor have technical artifacts affecting ddPCR results been widely studied. Methods: False positive rates were determined for 6 ddPCR assays at variable amounts of input DNA, revealing polymerase induced false positive events (PIFs) and other false positives. An in silico correction algorithm, known as the adaptive LoB and PIFs: an automated correction algorithm (ALPACA), was developed to remove PIFs and apply an adaptive limit of blank (LoB) to individual samples. Performance of ALPACA was compared to a standard strategy (no PIF correction and static LoB = 3) using data from commercial reference DNA, healthy volunteer cfDNA, and cfDNA from a real-life cohort of 209 patients with stage IV nonsmall cell lung cancer (NSCLC) whose tumor and cfDNA had been molecularly profiled. Results: Applying ALPACA reduced false positive results in healthy cfDNA compared to the standard strategy (specificity 98 vs 88%, P = 10 −5 ) and stage IV NSCLC patient cfDNA (99 vs 93%, P = 10 −11 ), while not affecting sensitivity in commercial reference DNA (70 vs 68% P = 0.77) or patient cfDNA (82 vs 88%, P = 0.13). Overall accuracy in patient samples was improved (98 vs 92%, P = 10 −7 ). Conclusions: Correction of PIFs and application of an adaptive LoB increases specificity without a loss of sensitivity in ddPCR, leading to a higher accuracy in a real-life cohort of patients with stage IV NSCLC. … (more)
- Is Part Of:
- Clinical chemistry. Volume 67:Number 7(2021)
- Journal:
- Clinical chemistry
- Issue:
- Volume 67:Number 7(2021)
- Issue Display:
- Volume 67, Issue 7 (2021)
- Year:
- 2021
- Volume:
- 67
- Issue:
- 7
- Issue Sort Value:
- 2021-0067-0007-0000
- Page Start:
- 959
- Page End:
- 967
- Publication Date:
- 2021-04-11
- Subjects:
- Clinical chemistry -- Periodicals
Pharmaceutical chemistry -- Periodicals
Biochemistry -- Periodicals
Biochimie -- Périodiques
Diagnostics biologiques -- Périodiques
Biochemistry
Clinical chemistry
Pharmaceutical chemistry
Biochemistry
Laboratory Techniques and Procedures
Klinische chemie
Periodicals
616.075605 - Journal URLs:
- http://www.oxfordjournals.org/ ↗
https://academic.oup.com/clinchem ↗
http://catalog.hathitrust.org/api/volumes/oclc/1554929.html ↗
http://www.clinchem.org/ ↗ - DOI:
- 10.1093/clinchem/hvab040 ↗
- Languages:
- English
- ISSNs:
- 0009-9147
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
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- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23387.xml