Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

Kleftogiannis, Dimitrios; Punta, Marco; Jayaram, Anuradha; Sandhu, Shahneen; Wong, Stephen Q.; Tandefelt, Delila Gasi; Conteduca, Vincenza; Wetterskog, Daniel; Attard, Gerhardt; Lise, Stefano

doi:10.1101/475947

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…In this challenging scenario, tools designed to detect low allelic fraction variants (Carrot-Zhang and Majewski, 2017) or computational pipelines specifically tailored for cfDNA data are necessary. So far, cfDNA specific approaches were either tuned for amplicon based NGS targeted platforms (Kleftogiannis et al, 2019;Pécuchet et al, 2016) or yet partially benchmarked against standard SNVs methods across different scenarios of coverage depth and target size (Kockan et al, 2017), potentially limiting their widespread applicability.…”

Section: Discussionmentioning

confidence: 99%

ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA

et al. 2020

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Motivation The use of liquid biopsies for cancer patients enables the non-invasive tracking of treatment response and tumor dynamics through single or serial blood drawn tests. Next-generation sequencing assays allow for the simultaneous interrogation of extended sets of somatic single-nucleotide variants (SNVs) in circulating cell-free DNA (cfDNA), a mixture of DNA molecules originating both from normal and tumor tissue cells. However, low circulating tumor DNA (ctDNA) fractions together with sequencing background noise and potential tumor heterogeneity challenge the ability to confidently call SNVs. Results We present a computational methodology, called Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic SNVs in cfDNA. We tested the capability of our method to analyze data generated using different platforms with distinct sequencing error properties and we compared ABEMUS performances with other popular SNV callers on both synthetic and real cancer patients sequencing data. Results show that ABEMUS performs better in most of the tested conditions proving its reliability in calling low variant allele frequencies somatic SNVs in low ctDNA levels plasma samples. Availability and implementation ABEMUS is cross-platform and can be installed as R package. The source code is maintained on Github at http://github.com/cibiobcg/abemus, and it is also available at CRAN official R repository. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA

et al. 2020

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“…Overall, our results highlight the effectiveness of our pipeline, which echoes others in opening possibilities for longitudinal monitoring of cancer-genomic alterations directly from plasma, without the increased risk and cost of invasive needle biopsies. Importantly, our pipeline does not rely on statistical modelling for background noise estimation 37 , which require big cohorts of reference data (e.g. healthy) that are usually difficult to collect, and may not be easily extended or generalized (e.g.…”

Section: Discussionmentioning

confidence: 99%

Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing

Kleftogiannis

Liew

et al. 2020

Sci Rep

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Analysis of circulating cell-free DNA (cfDNA) has opened new opportunities for characterizing tumour mutational landscapes with many applications in genomic-driven oncology. We developed a customized targeted cfDNA sequencing approach for breast cancer (BC) using unique molecular identifiers (UMIs) for error correction. Our assay, spanning a 284.5 kb target region, is combined with a novel freely-licensed bioinformatics pipeline that provides detection of low-frequency variants, and reliable identification of copy number variations (CNVs) directly from plasma DNA. We first evaluated our pipeline on reference samples. Then in a cohort of 35 BC patients our approach detected actionable driver and clonal variants at low variant frequency levels in cfDNA that were concordant (77%) with sequencing of primary and/or metastatic solid tumour sites. We also detected ERRB2 gene CNVs used for HER2 subtype classification with 80% precision compared to immunohistochemistry. Further, we evaluated fragmentation profiles of cfDNA in BC and observed distinct differences compared to data from healthy individuals. Our results show that the developed assay addresses the majority of tumour associated aberrations directly from plasma DNA, and thus may be used to elucidate genomic alterations in liquid biopsy studies.

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“…These sites are usually not detectable in solid tumour sequencing due to lack of read depth. Importantly, our approach for SNV detection does not rely on statistical modelling for background noise estimation 12,30 .…”

Section: Discussionmentioning

confidence: 99%

Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing

Kleftogiannis

Liew

et al. 2019

Preprint

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Analysis of circulating cell-free DNA (cfDNA) data has opened new opportunities for characterizing tumour mutational landscapes with many applications in genomic-driven oncology. We developed a customized targeted cfDNA sequencing approach for breast cancer (BC) using unique molecular identifiers (UMIs) for error correction. Our assay, spanning a 284.5 kb target region, is combined with freely-available bioinformatics pipelines that provide ultra-sensitive detection of single nucleotide variants (SNVs), and reliable identification of copy number variations (CNVs) directly from plasma DNA. In a cohort of 35 BC patients, our approach detected actionable driver and clonal SNVs at low (~0.5%) frequency levels in cfDNA that were concordant (83.3%) with sequencing of primary and/or metastatic solid tumour sites. We also detected ERRB2 gene CNVs used for HER2 subtype classification with 80% precision compared to immunohistochemistry. Further, we evaluated fragmentation profiles of cfDNA in BC and observed distinct differences compared to data from healthy individuals. Our results show that the developed assay addresses the majority of tumour associated aberrations directly from plasma DNA, and thus may be used to elucidate genomic alterations in liquid biopsy studies.

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Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

Cited by 5 publications

References 24 publications

ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA

ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA

Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing

Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing

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