Sensitive detection of tumor mutations from blood and its application to immunotherapy prognosis

AbstractLiquid biopsy using cell-free DNA (cfDNA) is attractive for a wide range of clinical applications, including cancer detection, locating, and monitoring. However, developing these applications requires precise and sensitive calling of somatic single nucleotide variations (SNVs) from cfDNA sequencing data. To date, no SNV caller addresses all the special challenges of cfDNA to provide reliable results. Here we present cfSNV, a revolutionary somatic SNV caller with five innovative techniques to overcome and exploit the unique properties of cfDNA. cfSNV provides hierarchical mutation profiling, thanks to cfDNA’s complete coverage of the clonal landscape, and multi-layer error suppression. In both simulated datasets and real patient data, we demonstrate that cfSNV is superior to existing tools, especially for low-frequency somatic SNVs. We also show how the five novel techniques contribute to its performance. Further, we demonstrate a clinical application using cfSNV to select non-small-cell lung cancer patients for immunotherapy treatment.

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Sensitive detection of tumor mutations from blood and its application to immunotherapy prognosis

Nature Communications ◽

10.1038/s41467-021-24457-2 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Shuo Li ◽

Zorawar S. Noor ◽

Weihua Zeng ◽

Mary L. Stackpole ◽

Xiaohui Ni ◽

...

Keyword(s):

Mutation Detection ◽

Low Frequency ◽

Sequencing Data ◽

Viable Option ◽

Real Patient ◽

Free Dna ◽

Whole Exome ◽

Mutation Profile ◽

Tissue Of Origin ◽

Error Suppression

AbstractCell-free DNA (cfDNA) is attractive for many applications, including detecting cancer, identifying the tissue of origin, and monitoring. A fundamental task underlying these applications is SNV calling from cfDNA, which is hindered by the very low tumor content. Thus sensitive and accurate detection of low-frequency mutations (<5%) remains challenging for existing SNV callers. Here we present cfSNV, a method incorporating multi-layer error suppression and hierarchical mutation calling, to address this challenge. Furthermore, by leveraging cfDNA’s comprehensive coverage of tumor clonal landscape, cfSNV can profile mutations in subclones. In both simulated and real patient data, cfSNV outperforms existing tools in sensitivity while maintaining high precision. cfSNV enhances the clinical utilities of cfDNA by improving mutation detection performance in medium-depth sequencing data, therefore making Whole-Exome Sequencing a viable option. As an example, we demonstrate that the tumor mutation profile from cfDNA WES data can provide an effective biomarker to predict immunotherapy outcomes.

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

10.1101/475947 ◽

2018 ◽

Author(s):

Dimitrios Kleftogiannis ◽

Marco Punta ◽

Anuradha Jayaram ◽

Shahneen Sandhu ◽

Stephen Q. Wong ◽

...

Keyword(s):

Deep Sequencing ◽

Low Frequency ◽

Poisson Model ◽

Real Data ◽

Analytical Sensitivity ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Deep Sequencing Data ◽

Targeted Deep Sequencing

AbstractBackgroundTargeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs).MethodsTo address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection.ResultsOur tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments.ConclusionsAmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve.

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Somatic Truth Data from Cell Lineage

10.1101/825042 ◽

2019 ◽

Author(s):

Megan Shand ◽

Jose Soto ◽

Lee Lichtenstein ◽

David Benjamin ◽

Yossi Farjoun ◽

...

Keyword(s):

Confidence Region ◽

Copy Number Variants ◽

Cell Lineage ◽

Synthetic Methods ◽

Sequencing Data ◽

Somatic Variation ◽

Single Nucleotide ◽

Mutation Discovery ◽

Benchmark Datasets ◽

Single Nucleotide Variations

Existing somatic benchmark datasets for human sequencing data use germline variants, synthetic methods, or expensive validations, none of which are satisfactory for providing a large collection of true somatic variation across a whole genome. Here we propose a dataset of short somatic mutations, that are validated using a known cell lineage. The dataset contains 56,974 (2,687 unique) Single Nucleotide Variations (SNV), 6,370 (316 unique) small Insertions and Deletions (Indels), and 144 (8 unique) Copy Number Variants (CNV) across 98 in silico mixed truth sets with a high confidence region covering 2.7 gigabases per mixture. The data is publicly available for use as a benchmarking dataset for somatic short mutation discovery pipelines.

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Stochastic Neighbor Embedding Algorithms and Its Application in Molecular Biological Data

Current Bioinformatics ◽

10.2174/1574893615999200414093636 ◽

2020 ◽

Vol 15 ◽

Author(s):

Pan Wang ◽

Guiyang Zhang ◽

You Li ◽

Ammar Oad ◽

Guohua Huang

Keyword(s):

Biological Data ◽

Nucleotide Polymorphisms ◽

Imaging Data ◽

Sequencing Data ◽

Single Nucleotide ◽

Potential Development ◽

Wide Range ◽

Hidden Patterns ◽

Visualization Of Data

: With the advent of the era of big data, the numbers and the dimensions of data are increasingly becoming larger. It is very critical to reduce dimensions or visualize data and then uncover the hidden patterns of characteristic or the mechanism underlying data. Stochastic Neighbor Embedding (SNE) has been developed for data visualization over the last ten years. Due to its efficiency in the visualization of data, SNE has been applied to a wide range of fields. We briefly reviewed the SNE algorithm and its variants, summarizing application of it in visualizing single-cell sequencing data, single nucleotide polymorphisms, and mass spectrometry imaging data. We also discussed the strength and the weakness of the SNE, with a special emphasis on how to set parameters to promote quality of visualization, and finally indicated potential development of SNE in the coming future.

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Moss enables high sensitivity single-nucleotide variant calling from multiple bulk DNA tumor samples

Nature Communications ◽

10.1038/s41467-021-22466-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Chuanyi Zhang ◽

Mohammed El-Kebir ◽

Idoia Ochoa

Keyword(s):

Cancer Genomics ◽

Low Frequency ◽

Variant Calling ◽

High Sensitivity ◽

Single Sample ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Additional Time ◽

Single Nucleotide ◽

Multiple Samples

AbstractIntra-tumor heterogeneity renders the identification of somatic single-nucleotide variants (SNVs) a challenging problem. In particular, low-frequency SNVs are hard to distinguish from sequencing artifacts. While the increasing availability of multi-sample tumor DNA sequencing data holds the potential for more accurate variant calling, there is a lack of high-sensitivity multi-sample SNV callers that utilize these data. Here we report Moss, a method to identify low-frequency SNVs that recur in multiple sequencing samples from the same tumor. Moss provides any existing single-sample SNV caller the ability to support multiple samples with little additional time overhead. We demonstrate that Moss improves recall while maintaining high precision in a simulated dataset. On multi-sample hepatocellular carcinoma, acute myeloid leukemia and colorectal cancer datasets, Moss identifies new low-frequency variants that meet manual review criteria and are consistent with the tumor’s mutational signature profile. In addition, Moss detects the presence of variants in more samples of the same tumor than reported by the single-sample caller. Moss’ improved sensitivity in SNV calling will enable more detailed downstream analyses in cancer genomics.

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Genetic Variants and Phenotypic Characteristics of Salmonella Typhimurium-Resistant Mutants after Exposure to Carvacrol

Microorganisms ◽

10.3390/microorganisms8060937 ◽

2020 ◽

Vol 8 (6) ◽

pp. 937 ◽

Cited By ~ 1

Author(s):

Daniel Berdejo ◽

Natalia Merino ◽

Elisa Pagán ◽

Diego García-Gonzalo ◽

Rafael Pagán

Keyword(s):

Salmonella Typhimurium ◽

Food Preservation ◽

Genotypic Resistance ◽

Food Preservatives ◽

Single Nucleotide ◽

Wide Range ◽

Single Nucleotide Variations ◽

Resistant Mutants ◽

Susceptibility Tests ◽

Cleaning And Disinfection

The emergence of antimicrobial resistance has raised questions about the safety of essential oils and their individual constituents as food preservatives and as disinfection agents. Further research is required to understand how and under what conditions stable genotypic resistance might occur in food pathogens. Evolution experiments on Salmonella Typhimurium cyclically exposed to sublethal and lethal doses of carvacrol permitted the isolation of SeSCar and SeLCar strains, respectively. Both evolved strains showed a significant increase in carvacrol resistance, assessed by minimum inhibitory and bactericidal concentrations, the study of growth kinetics in the presence of carvacrol, and the evaluation of survival under lethal conditions. Moreover, antibiotic susceptibility tests revealed a development of SeLCar resistance to a wide range of antibiotics. Whole genome sequencing allowed the identification of single nucleotide variations in transcriptional regulators of oxidative stress-response: yfhP in SeSCar and soxR in SeLCar, which could be responsible for the increased resistance by improving the response to carvacrol and preventing its accumulation inside the cell. This study demonstrates the emergence of S. Typhimurium-resistant mutants against carvacrol, which might pose a risk to food safety and should therefore be considered in the design of food preservation strategies, or of cleaning and disinfection treatments.

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PGG.SNV: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populations

Genome Biology ◽

10.1186/s13059-019-1838-5 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 4

Author(s):

Chao Zhang ◽

Yang Gao ◽

Zhilin Ning ◽

Yan Lu ◽

Xiaoxi Zhang ◽

...

Keyword(s):

Genetic Diversity ◽

Population Genetics ◽

Genetic Variants ◽

Population Genetic ◽

Unique Feature ◽

Indigenous Populations ◽

Diverse Populations ◽

Sequencing Data ◽

Single Nucleotide ◽

Single Nucleotide Variations

Abstract Despite the tremendous growth of the DNA sequencing data in the last decade, our understanding of the human genome is still in its infancy. To understand the implications of genetic variants in the light of population genetics and molecular evolution, we developed a database, PGG.SNV (https://www.pggsnv.org), which gives much higher weight to previously under-investigated indigenous populations in Asia. PGG.SNV archives 265 million SNVs across 220,147 present-day genomes and 1018 ancient genomes, including 1009 newly sequenced genomes, representing 977 global populations. Moreover, estimation of population genetic diversity and evolutionary parameters is available in PGG.SNV, a unique feature compared with other databases.

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Digital microfluidic isolation of single cells for -Omics

Nature Communications ◽

10.1038/s41467-020-19394-5 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Julian Lamanna ◽

Erica Y. Scott ◽

Harrison S. Edwards ◽

M. Dean Chamberlain ◽

Michael D. M. Dryden ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Digital Microfluidics ◽

Sequencing Data ◽

Initial Sample ◽

Single Nucleotide ◽

Single Nucleotide Variations ◽

Digital Microfluidic ◽

Laser Cell ◽

Generation Sequencing

Abstract We introduce Digital microfluidic Isolation of Single Cells for -Omics (DISCO), a platform that allows users to select particular cells of interest from a limited initial sample size and connects single-cell sequencing data to their immunofluorescence-based phenotypes. Specifically, DISCO combines digital microfluidics, laser cell lysis, and artificial intelligence-driven image processing to collect the contents of single cells from heterogeneous populations, followed by analysis of single-cell genomes and transcriptomes by next-generation sequencing, and proteomes by nanoflow liquid chromatography and tandem mass spectrometry. The results described herein confirm the utility of DISCO for sequencing at levels that are equivalent to or enhanced relative to the state of the art, capable of identifying features at the level of single nucleotide variations. The unique levels of selectivity, context, and accountability of DISCO suggest potential utility for deep analysis of any rare cell population with contextual dependencies.

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The Attenuation of Very Low Frequency Brain Oscillations in Transitions from a Rest State to Active Attention

Journal of Psychophysiology ◽

10.1027/0269-8803.23.4.191 ◽

2009 ◽

Vol 23 (4) ◽

pp. 191-198 ◽

Cited By ~ 26

Author(s):

Suzannah K. Helps ◽

Samantha J. Broyd ◽

Christopher J. James ◽

Anke Karl ◽

Edmund J. S. Sonuga-Barke

Keyword(s):

Brain Activity ◽

Sampling Rate ◽

Low Frequency ◽

Future Research ◽

Adhd Symptoms ◽

Default Mode ◽

Very Low Frequency ◽

Wide Range ◽

Interference Hypothesis ◽

Mode Interference

Background: The default mode interference hypothesis ( Sonuga-Barke & Castellanos, 2007 ) predicts (1) the attenuation of very low frequency oscillations (VLFO; e.g., .05 Hz) in brain activity within the default mode network during the transition from rest to task, and (2) that failures to attenuate in this way will lead to an increased likelihood of periodic attention lapses that are synchronized to the VLFO pattern. Here, we tested these predictions using DC-EEG recordings within and outside of a previously identified network of electrode locations hypothesized to reflect DMN activity (i.e., S3 network; Helps et al., 2008 ). Method: 24 young adults (mean age 22.3 years; 8 male), sampled to include a wide range of ADHD symptoms, took part in a study of rest to task transitions. Two conditions were compared: 5 min of rest (eyes open) and a 10-min simple 2-choice RT task with a relatively high sampling rate (ISI 1 s). DC-EEG was recorded during both conditions, and the low-frequency spectrum was decomposed and measures of the power within specific bands extracted. Results: Shift from rest to task led to an attenuation of VLFO activity within the S3 network which was inversely associated with ADHD symptoms. RT during task also showed a VLFO signature. During task there was a small but significant degree of synchronization between EEG and RT in the VLFO band. Attenuators showed a lower degree of synchrony than nonattenuators. Discussion: The results provide some initial EEG-based support for the default mode interference hypothesis and suggest that failure to attenuate VLFO in the S3 network is associated with higher synchrony between low-frequency brain activity and RT fluctuations during a simple RT task. Although significant, the effects were small and future research should employ tasks with a higher sampling rate to increase the possibility of extracting robust and stable signals.

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Selecting a PRO-CTCAE based subset for patient reported symptom monitoring during lung cancer treatment (Preprint)

10.2196/preprints.26574 ◽

2020 ◽

Author(s):

Evalien Veldhuijzen ◽

Iris Walraven ◽

Jose Belderbos

Keyword(s):

Lung Cancer ◽

Clinical Practice ◽

Cancer Patients ◽

Treatment Modalities ◽

European Organization ◽

Specific Patient ◽

Symptom Monitoring ◽

Lung Cancer Patients ◽

Wide Range ◽

Patient Reported

BACKGROUND The Patient Reported Outcomes Version of the Common Terminology Criteria of Adverse Events (PRO-CTCAE) item library covers a wide range of symptoms relevant for oncology care. To enable implementation of PRO-CTCAE-based symptom monitoring in clinical practice, there is a need to select a subset of items relevant for specific patient populations. OBJECTIVE The aim of this study was to develop a PRO-CTCAE subset relevant for patients with lung cancer. METHODS The PRO-CTCAE-based subset for lung cancer patients was generated using a mixed methods approach based on the European Organization for Research and Treatment of Cancer (EORTC) guidelines for developing questionnaires, consisting of a literature review and semi-structured interviews with both lung cancer patients and health care practitioners (HCPs). Both patients and HCPs were queried on the relevance and impact of all PRO-CTCAE items. Results were summarized and, after a final round of expert review, a selection of clinically relevant items for lung cancer patients was made. RESULTS A heterogeneous group of lung cancer patients (n=25) from different treatment modalities and HCPs (n=22) participated in the study. A final list of eight relevant PRO-CTCAE items was created: decreased appetite, cough, shortness of breath, fatigue, constipation, nausea, sadness, and pain (general). CONCLUSIONS Based on literature and both professional and patient input, a subset of PRO-CTCAE items has been identified for use in lung cancer patients in clinical practice. Future work is needed to confirm the validity and effectiveness of this PRO-CTCAE lung cancer subset internationally, and in the real-world clinical practice setting.

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