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Day-to-day and within-day biological variation of cell-free DNA.

ABSTRACT: BACKGROUND:Numerous studies have shown that cell-free DNA (cfDNA) levels may serve as a non-invasive biomarker of a broad spectrum of acute and chronic pathologies. However, in order to make clinical decisions based on cfDNA measurements, it is essential to understand the magnitude of biological variation so this variation is not confused with a variation that actually represent a clinically relevant change. The present study was designed to evaluate the biological variation of cfDNA in healthy subjects and lung cancer patients. METHODS:Plasma samples were collected from 33 healthy subjects and ten lung cancer patients over three days, as well as during the same day. CfDNA was quantified using droplet digital PCR. Biological variation data was estimated using mixed models. FINDINGS:The within-subject variation was 25% and the between-subject variation was 30%. The reference change value for the healthy subjects was 70%. There was no systematic difference in cfDNA levels from day-to-day (p?=?0?61), but there was a significant decline during the day (p<0?01). The within-subject variation in cancer patients was comparable to healthy subjects, whereas the between-subject variation was much larger (139%). No systematic differences from day-to-day were observed for the cancer patients (p>0?3). INTERPRETATION:Our findings show that cfDNA levels fluctuate significantly during the day and exhibit considerable within-subject variation. Thus, the data presented offer a substantial contribution to the interpretation of the clinical significance of cfDNA. FUNDING:Læge Sofus Carl Emil Friis og hustru Olga Doris Friis' Legat, Harboefonden, and Dagmar Marshalls Fond.

SUBMITTER: Madsen AT

PROVIDER: S-EPMC6945267 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Day-to-day and within-day biological variation of cell-free DNA.

Madsen Anne Tranberg AT Hojbjerg Johanne Andersen JA Sorensen Boe Sandahl BS Winther-Larsen Anne A

EBioMedicine 20191021

<h4>Background</h4>Numerous studies have shown that cell-free DNA (cfDNA) levels may serve as a non-invasive biomarker of a broad spectrum of acute and chronic pathologies. However, in order to make clinical decisions based on cfDNA measurements, it is essential to understand the magnitude of biological variation so this variation is not confused with a variation that actually represent a clinically relevant change. The present study was designed to evaluate the biological variation of cfDNA in ...[more]

PMID: 31648993

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Project description:MOTIVATION:Cancer is a complex disease that involves rapidly evolving cells, often forming multiple distinct clones. In order to effectively understand progression of a patient-specific tumor, one needs to comprehensively sample tumor DNA at multiple time points, ideally obtained through inexpensive and minimally invasive techniques. Current sequencing technologies make the 'liquid biopsy' possible, which involves sampling a patient's blood or urine and sequencing the circulating cell free DNA (cfDNA). A certain percentage of this DNA originates from the tumor, known as circulating tumor DNA (ctDNA). The ratio of ctDNA may be extremely low in the sample, and the ctDNA may originate from multiple tumors or clones. These factors present unique challenges for applying existing tools and workflows to the analysis of ctDNA, especially in the detection of structural variations which rely on sufficient read coverage to be detectable. RESULTS:Here we introduce SViCT?, a structural variation (SV) detection tool designed to handle the challenges associated with cfDNA analysis. SViCT?can detect breakpoints and sequences of various structural variations including deletions, insertions, inversions, duplications and translocations. SViCT?extracts discordant read pairs, one-end anchors and soft-clipped/split reads, assembles them into contigs, and re-maps contig intervals to a reference genome using an efficient k-mer indexing approach. The intervals are then joined using a combination of graph and greedy algorithms to identify specific structural variant signatures. We assessed the performance of SViCT?and compared it to state-of-the-art tools using simulated cfDNA datasets with properties matching those of real cfDNA samples. The positive predictive value and sensitivity of our tool was superior to all the tested tools and reasonable performance was maintained down to the lowest dilution of 0.01% tumor DNA in simulated datasets. Additionally, SViCT?was able to detect all known SVs in two real cfDNA reference datasets (at 0.6-5% ctDNA) and predict a novel structural variant in a prostate cancer cohort. AVAILABILITY:SViCT?is available at https://github.com/vpc-ccg/svict. Contact:faraz.hach@ubc.ca.

Dataset Information

Day-to-day and within-day biological variation of cell-free DNA.

Publications

Day-to-day and within-day biological variation of cell-free DNA.

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