Evidence map›Paper›PMID 39227777›Full record

ArticleBMC genomics2024

Benchmarking UMI-aware and standard variant callers for low frequency ctDNA variant detection.

Rugare Maruzani, Liam Brierley, Andrea Jorgensen, Anna Fowler

Abstract read
In one paragraph

Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Rugare MaruzaniDepartment of Health Data Science, Institute of Population Health, University of Liverpool, Waterhouse Building, Block F, Brownlow Street, Liverpool, L69 3GF, UK. r.maruzani@liverpool.ac.uk.
Liam BrierleyDepartment of Health Data Science, Institute of Population Health, University of Liverpool, Waterhouse Building, Block F, Brownlow Street, Liverpool, L69 3GF, UK.
Andrea JorgensenDepartment of Health Data Science, Institute of Population Health, University of Liverpool, Waterhouse Building, Block F, Brownlow Street, Liverpool, L69 3GF, UK.
Anna FowlerDepartment of Health Data Science, Institute of Population Health, University of Liverpool, Waterhouse Building, Block F, Brownlow Street, Liverpool, L69 3GF, UK.

Funding

Medical Research Council DiMeN Doctoral Training Partnership iCASE studentship MR/R015902/1
6 · The paper itself

Abstract

backgroundCirculating tumour DNA (ctDNA) is a subset of cell free DNA (cfDNA) released by tumour cells into the bloodstream. Circulating tumour DNA has shown great potential as a biomarker to inform treatment in cancer patients. Collecting ctDNA is minimally invasive and reflects the entire genetic makeup of a patient's cancer. ctDNA variants in NGS data can be difficult to distinguish from sequencing and PCR artefacts due to low abundance, particularly in the early stages of cancer. Unique Molecular Identifiers (UMIs) are short sequences ligated to the sequencing library before amplification. These sequences are useful for filtering out low frequency artefacts. The utility of ctDNA as a cancer biomarker depends on accurate detection of cancer variants.

resultsIn this study, we benchmarked six variant calling tools, including two UMI-aware callers for their ability to call ctDNA variants. The standard variant callers tested included Mutect2, bcftools, LoFreq and FreeBayes. The UMI-aware variant callers benchmarked were UMI-VarCal and UMIErrorCorrect. We used both datasets with known variants spiked in at low frequencies, and datasets containing ctDNA, and generated synthetic UMI sequences for these datasets. Variant callers displayed different preferences for sensitivity and specificity. Mutect2 showed high sensitivity, while returning more privately called variants than any other caller in data without synthetic UMIs - an indicator of false positive variant discovery. In data encoded with synthetic UMIs, UMI-VarCal detected fewer putative false positive variants than all other callers in synthetic datasets. Mutect2 showed a balance between high sensitivity and specificity in data encoded with synthetic UMIs.

conclusionsOur results indicate UMI-aware variant callers have potential to improve sensitivity and specificity in calling low frequency ctDNA variants over standard variant calling tools. There is a growing need for further development of UMI-aware variant calling tools if effective early detection methods for cancer using ctDNA samples are to be realised.

Indexed as

BenchmarkingCirculating Tumor DNAHigh-Throughput Nucleotide SequencingBiomarkers, TumorGenetic VariationHumansNeoplasmsSensitivity and SpecificitySequence Analysis, DNASoftwareBiomarkers, TumorCirculating Tumor DNACancerctDNANext generation sequencingVariant calling

Identifiers

PMID39227777
PMCPMC11370058

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.