scholarly journals Integration of intra-sample contextual error modeling for improved detection of somatic mutations from deep sequencing

2020 ◽  
Vol 6 (50) ◽  
pp. eabe3722
Author(s):  
Sagi Abelson ◽  
Andy G. X. Zeng ◽  
Ido Nofech-Mozes ◽  
Ting Ting Wang ◽  
Stanley W. K. Ng ◽  
...  

Sensitive mutation detection by next-generation sequencing is critical for early cancer detection, monitoring minimal/measurable residual disease (MRD), and guiding precision oncology. Nevertheless, because of artifacts introduced during library preparation and sequencing, the detection of low-frequency variants at high specificity is problematic. Here, we present Espresso, an error suppression method that considers local sequence features to accurately detect single-nucleotide variants (SNVs). Compared to other advanced error suppression techniques, Espresso consistently demonstrated lower numbers of false-positive mutation calls and greater sensitivity. We demonstrated Espresso’s superior performance in detecting MRD in the peripheral blood of patients with acute myeloid leukemia (AML) throughout their treatment course. Furthermore, we showed that accurate mutation calling in a small number of informative genomic loci might provide a cost-efficient strategy for pragmatic risk prediction of AML development in healthy individuals. More broadly, we aim for Espresso to aid with accurate mutation detection in many other research and clinical settings.

2017 ◽  
Author(s):  
Shibing Deng ◽  
Maruja Lira ◽  
Stephen Huang ◽  
Kai Wang ◽  
Crystal Valdez ◽  
...  

AbstractThe use of ultra-deep, next generation sequencing of circulating tumor DNA (ctDNA) holds great promise for early detection of cancer as well as a tool for monitoring disease progression and therapeutic responses. However, the low abundance of ctDNA in the bloodstream coupled with technical errors introduced during library construction and sequencing complicates mutation detection. To achieve high accuracy of variant calling via better distinguishing low frequency ctDNA mutations from background errors, we introduce TNER (Tri-Nucleotide Error Reducer), a novel background error suppression method that provides a robust estimation of background noise to reduce sequencing errors. It significantly enhances the specificity for downstream ctDNA mutation detection without sacrificing sensitivity. Results on both simulated and real healthy subjects’ data demonstrate that the proposed algorithm consistently outperforms a current, state of the art, position-specific error polishing model, particularly when the sample size of healthy subjects is small. TNER is publicly available at https://github.com/ctDNA/TNER.


2019 ◽  
Vol 47 (15) ◽  
pp. e87-e87 ◽  
Author(s):  
Ting Ting Wang ◽  
Sagi Abelson ◽  
Jinfeng Zou ◽  
Tiantian Li ◽  
Zhen Zhao ◽  
...  

Abstract Detection of cancer-associated somatic mutations has broad applications for oncology and precision medicine. However, this becomes challenging when cancer-derived DNA is in low abundance, such as in impure tissue specimens or in circulating cell-free DNA. Next-generation sequencing (NGS) is particularly prone to technical artefacts that can limit the accuracy for calling low-allele-frequency mutations. State-of-the-art methods to improve detection of low-frequency mutations often employ unique molecular identifiers (UMIs) for error suppression; however, these methods are highly inefficient as they depend on redundant sequencing to assemble consensus sequences. Here, we present a novel strategy to enhance the efficiency of UMI-based error suppression by retaining single reads (singletons) that can participate in consensus assembly. This ‘Singleton Correction’ methodology outperformed other UMI-based strategies in efficiency, leading to greater sensitivity with high specificity in a cell line dilution series. Significant benefits were seen with Singleton Correction at sequencing depths ≤16 000×. We validated the utility and generalizability of this approach in a cohort of >300 individuals whose peripheral blood DNA was subjected to hybrid capture sequencing at ∼5000× depth. Singleton Correction can be incorporated into existing UMI-based error suppression workflows to boost mutation detection accuracy, thus improving the cost-effectiveness and clinical impact of NGS.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shuo Li ◽  
Zorawar S. Noor ◽  
Weihua Zeng ◽  
Mary L. Stackpole ◽  
Xiaohui Ni ◽  
...  

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.


2021 ◽  
Author(s):  
Zhe Liu ◽  
Weijin Qiu ◽  
Shujin Fu ◽  
Xia Zhao ◽  
Jun Xia ◽  
...  

Sequencing depth has always played an important role in the accurate detection of low-frequency mutations. The increase of sequencing depth and the reasonable setting of threshold can maximize the probability of true positive mutation, or sensitivity. Here, we found that when the threshold was set as a fixed number of positive mutated reads, the probability of both true and false-positive mutations increased with depth. However, When the number of positive mutated reads increased in an equal proportion with depth (the threshold was transformed from a fixed number to a fixed percentage of mutated reads), the true positive probability still increased while false positive probability decreased. Through binomial distribution simulation and experimental test, it is found that the "fidelity" of detected-VAFs is the cause of this phenomenon. Firstly, we used the binomial distribution to construct a model that can easily calculate the relationship between sequencing depth and probability of true positive (or false positive), which can standardize the minimum sequencing depth for different low-frequency mutation detection. Then, the effect of sequencing depth on the fidelity of NA12878 with 3% mutation frequency and circulating tumor DNA (ctDNA of 1%, 3% and 5%) showed that the increase of sequencing depth reduced the fluctuation range of detected-VAFs around the expected VAFs, that is, the fidelity was improved. Finally, based on our experiment result, the consistency of single-nucleotide variants (SNVs) between paired FF and FFPE samples of mice increased with increasing depth, suggesting that increasing depth can improve the precision and sensitivity of low-frequency mutations.


Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 507
Author(s):  
Bernd Timo Hermann ◽  
Sebastian Pfeil ◽  
Nicole Groenke ◽  
Samuel Schaible ◽  
Robert Kunze ◽  
...  

Detection of genetic variants in clinically relevant genomic hot-spot regions has become a promising application of next-generation sequencing technology in precision oncology. Effective personalized diagnostics requires the detection of variants with often very low frequencies. This can be achieved by targeted, short-read sequencing that provides high sequencing depths. However, rare genetic variants can contain crucial information for early cancer detection and subsequent treatment success, an inevitable level of background noise usually limits the accuracy of low frequency variant calling assays. To address this challenge, we developed DEEPGENTM, a variant calling assay intended for the detection of low frequency variants within liquid biopsy samples. We processed reference samples with validated mutations of known frequencies (0%–0.5%) to determine DEEPGENTM’s performance and minimal input requirements. Our findings confirm DEEPGENTM’s effectiveness in discriminating between signal and noise down to 0.09% variant allele frequency and an LOD(90) at 0.18%. A superior sensitivity was also confirmed by orthogonal comparison to a commercially available liquid biopsy-based assay for cancer detection.


2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Ianthe A. E. M. van Belzen ◽  
Alexander Schönhuth ◽  
Patrick Kemmeren ◽  
Jayne Y. Hehir-Kwa

AbstractCancer is generally characterized by acquired genomic aberrations in a broad spectrum of types and sizes, ranging from single nucleotide variants to structural variants (SVs). At least 30% of cancers have a known pathogenic SV used in diagnosis or treatment stratification. However, research into the role of SVs in cancer has been limited due to difficulties in detection. Biological and computational challenges confound SV detection in cancer samples, including intratumor heterogeneity, polyploidy, and distinguishing tumor-specific SVs from germline and somatic variants present in healthy cells. Classification of tumor-specific SVs is challenging due to inconsistencies in detected breakpoints, derived variant types and biological complexity of some rearrangements. Full-spectrum SV detection with high recall and precision requires integration of multiple algorithms and sequencing technologies to rescue variants that are difficult to resolve through individual methods. Here, we explore current strategies for integrating SV callsets and to enable the use of tumor-specific SVs in precision oncology.


2020 ◽  
Author(s):  
Shuo Li ◽  
Zorawar Noor ◽  
Weihua Zeng ◽  
Xiaohui Ni ◽  
Zuyang Yuan ◽  
...  

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.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 31-32
Author(s):  
Jacob Higgins ◽  
Fang Yin Lo ◽  
Michael J. Hipp ◽  
Charles C. Valentine ◽  
Lindsey N. Williams ◽  
...  

Sensitive and specific detection of measurable residual disease (MRD) after treatment in pediatric acute myeloid leukemia (AML) is prognostic of relapse and is important for clinical decision making. Mutation-based methods are increasingly being used, but are hampered by the limited number of common driver gene mutations to target as clone markers. Additional targets would greatly increase MRD detection power. However, even in cases with many AML-defining mutations, it is the limited accuracy of current molecular methods which establishes the lower bounds of sensitivity. Here we describe an ultrasensitive approach for disease monitoring with personalized hybrid capture panels targeting hundreds of somatic mutations identified by whole genome sequencing (WGS), and using extremely accurate Duplex Sequencing (DS) in longitudinal samples. In a pilot cohort of 13 patients we demonstrate detection sensitivities several orders of magnitude beyond currently available single locus testing or less accurate sequencing. With multi-target panels, overall power for MRD detection is cumulative across sites. For example, if a patient has MRD at a true frequency of 1/30,000, sequencing a single mutant site to 10,000x molecular depth would be unlikely to detect MRD. However, sequencing 10 sites each to 10,000x would effectively total 100,000x informative site depth, increasing power to &gt;95%. However, standard sequencing assays are insufficiently accurate to achieve this theoretical limit of detection (LOD). DS enables accurate detection of individual variants to &lt;10-5 with an error rate &lt;10-7 and, thus, can achieve MRD sensitivities below one-in-one-million. Marrow aspirates were collected from 13 uniformly treated pediatric AML patients at time of diagnosis (TOD), during treatment (end of induction, EOI), in remission (end of therapy, EOT), and at relapse. 9/13 patients relapsed. DNA from TOD was analyzed by WGS. Germline variants were excluded and somatic single nucleotide variants (SNVs) were targeted by a custom probe panel designed for each patient. An average of 170 SNVs were targeted per patient (range 53-200). More than 90% of the SNVs were noncoding. Longitudinal samples were then analyzed with DS, which compares sequences from both strands of each DNA molecule to eliminate technical noise and reveal biological mutation signal with extreme accuracy and sensitivity. A median of 82% of WGS SNVs were validated by DS in the TOD DNA, and the vast majority of those were also present at relapse. Relapsers had more SNVs at diagnosis than non-relapsers. EOT samples were sequenced to an average Duplex molecular depth of 29,400x, with a maximum of 61,283x. The figure below shows time course plots tracking SNVs at diagnosis, EOT and relapse for 2 patients. Among mutations validated in TOD samples, a median of only 8 (5%) were detected per EOT sample (range 0-66 mutations). MRD was detected in 8/9 relapsers. Targeting 1 or even 10 SNVs would therefore have missed MRD in the majority of these patients. Among relapsers, median EOT SNV VAF was 0.069%. The lowest single VAF detected per EOT sample ranged from 0.036% to 0.002%. The presence of an SNV at diagnosis and relapse implies that it must truly be present at EOT, whether or not it is detected. Therefore, if a small minority of leukemic mutations are detected at EOT, the true overall MRD frequency is much lower than the LOD at any single site. In the only relapser where MRD was not detected, targeted SNVs were present at diagnosis and relapse, so additional sequencing depth at EOT would eventually reveal ultra-low frequency mutations. Among patients that did not relapse by the end of the study, median VAF at EOI (the latest time point DNA available) was 0.0258%. Therefore, non-relapsers have a lower median VAF at EOI than relapsers do even later at EOT, potentially indicating very early on that treatment is more successful. This study shows excellent performance of DS-based assays for detecting MRD with patient-specific panels. We have demonstrated that among large panels of validated somatic SNVs present at time of diagnosis, a median of 5% are identified at EOT in eventual relapsers. DS detected MRD in 8/9 patients, and at a median VAF well below the limit of detection of any other sequencing technology. Comprehensive personalized hybrid selection panels coupled with DS represents a powerful option for MRD monitoring in pediatric AML and potentially other cancers. Figure Disclosures Higgins: TwinStrand Biosciences: Current Employment. Lo:TwinStrand Biosciences: Current Employment. Hipp:TwinStrand Biosciences: Current Employment. Valentine:TwinStrand Biosciences: Current Employment. Williams:TwinStrand Biosciences: Current Employment. Radich:TwinStrand Biosciences: Research Funding. Salk:TwinStrand Biosciences: Current Employment.


2020 ◽  
Vol 10 (3) ◽  
pp. 165-171
Author(s):  
Ingrid E. Pereira ◽  
Kyssia P. Silva ◽  
Laura M. Menegati ◽  
Aimara C. Pinheiro ◽  
Elaine A. O. Assunção ◽  
...  

AbstractControl of canine visceral leishmaniasis (CVL), a major zoonotic disease in Brazil and many other tropical and subtropical countries, remains difficult as an accurate and reliable diagnosis is still missing. In endemic regions, infected dogs are the main parasitic reservoir host of human Visceral leishmaniasis (VL) infection. Vaccination of dogs against Leishmania infection constitutes an important strategy to prevent or to better control CVL, thus, a serological test that can discriminate between antibodies induced by immunization versus infection is highly desirable in order to improve and simplify diagnosis. Here, four recombinant proteins were evaluated for their ability to detect and differentiate between dogs that are infected with Leishmania or have been immunized with the anti-Leishmania vaccine Leish-Tec®. Receiver operating characteristic (ROC) curve analysis of the four Leishmania-specific IgG ELISA revealed superior performance of rK28, followed by rKLO8, rK39 and rLb6H. The rK28-based ELISA revealed not only the best accuracy against CVL, but also the lowest cross-reactivity with sera from Leish-Tec® immunized dogs. Our data show that the rK28-based ELISA is highly suitable for CVL screening as it shows high sensitivity with simultaneous low cross-reactivity. Further, the high specificity of the rKLO8 indicates its suitability for the confirmation of CVL diagnosis.


2015 ◽  
Vol 4 (2) ◽  
pp. 51-69 ◽  
Author(s):  
Sourav Paul ◽  
Provas Kumar Roy

PSSs are added to excitation systems to enhance the damping during low frequency oscillations. The non-linear model of a machine is linearized at different operating points. Chemical Reaction optimization (CRO), a new population based search algorithm is been proposed in this paper to damp the power system low-frequency oscillations and enhance power system stability. Computation results demonstrate that the proposed algorithm is effective in damping low frequency oscillations as well as improving system dynamic stability. The performance of the proposed algorithm is evaluated for different loading conditions. In addition, the proposed algorithm is more effective and provides superior performance when compared other population based optimization algorithms like differential evolution (DE) and particle swarm optimization (PSO).


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