DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal.
Ontology highlight
ABSTRACT: Motivation
Somatic mutations are usually called by analyzing the DNA sequence of a tumor sample in conjunction with a matched normal. However, a matched normal is not always available, for instance, in retrospective analysis or diagnostic settings. For such cases, tumor-only somatic variant calling tools need to be designed. Previously proposed approaches demonstrate inferior performance on whole-genome sequencing (WGS) samples.Results
We present the convolutional neural network-based approach called DeepSom for detecting somatic single nucleotide polymorphism and short insertion and deletion variants in tumor WGS samples without a matched normal. We validate DeepSom by reporting its performance on five different cancer datasets. We also demonstrate that on WGS samples DeepSom outperforms previously proposed methods for tumor-only somatic variant calling.Availability and implementation
DeepSom is available as a GitHub repository at https://github.com/heiniglab/DeepSom.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Vilov S
PROVIDER: S-EPMC9843587 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Publications
DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal.
Vilov Sergey S Heinig Matthias M
Bioinformatics (Oxford, England) 20230101 1
<h4>Motivation</h4>Somatic mutations are usually called by analyzing the DNA sequence of a tumor sample in conjunction with a matched normal. However, a matched normal is not always available, for instance, in retrospective analysis or diagnostic settings. For such cases, tumor-only somatic variant calling tools need to be designed. Previously proposed approaches demonstrate inferior performance on whole-genome sequencing (WGS) samples.<h4>Results</h4>We present the convolutional neural network- ...[more]
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Project description:BackgroundResearch grade Fresh Frozen (FF) DNA material is not yet routinely collected in clinical practice. Many hospitals, however, collect and store Formalin Fixed Paraffin Embedded (FFPE) tumor samples. Consequently, the sample size of whole genome cancer cohort studies could be increased tremendously by including FFPE samples, although the presence of artefacts might obfuscate the variant calling. To assess whether FFPE material can be used for cohort studies, we performed an in-depth comparison of somatic SNVs called on matching FF and FFPE Whole Genome Sequence (WGS) samples extracted from the same tumor.MethodsFour variant callers (i.e. Strelka2, Mutect2, VarScan2 and Shimmer) were used to call somatic variants on matching FF and FFPE WGS samples from a metastatic prostate tumor. Using the variants identified by these callers, we developed a heuristic to maximize the overlap between the FF and its FFPE counterpart in terms of sensitivity and precision. The proposed variant calling approach was then validated on nine matched primary samples. Finally, we assessed what fraction of the discrepancy could be attributed to intra-tumor heterogeneity (ITH), by comparing the overlap in clonal and subclonal somatic variants.ResultsWe first compared variants between an FF and an FFPE sample from a metastatic prostate tumor, showing that on average 50% of the calls in the FF are recovered in the FFPE sample, with notable differences between callers. Combining the variants of the different callers using a simple heuristic, increases both the precision and the sensitivity of the variant calling. Validating the heuristic on nine additional matched FF-FFPE samples, resulted in an average F1-score of 0.58 and an outperformance of any of the individual callers. In addition, we could show that part of the discrepancy between the FF and the FFPE samples can be attributed to ITH.ConclusionThis study illustrates that when using the correct variant calling strategy, the majority of clonal SNVs can be recovered in an FFPE sample with high precision and sensitivity. These results suggest that somatic variants derived from WGS of FFPE material can be used in cohort studies.
| S-EPMC7336445 | biostudies-literature
Project description:MotivationAccurate classification of somatic variants in a tumor sample is often accomplished by utilizing a paired normal tissue sample from the same patient to enable the separation of private germline mutations from somatic variants. However, a paired normal sample is not always available, making a reliable somatic variant calling more challenging. In silico screening of variants against public or private databases and other filtering approaches are often used in absence of a paired normal sample. Nevertheless, difficulties in performing a tumor-only calling with sufficient accuracy and lack of open-source software have limited their applications in clinical research.ResultsTo address these limitations, we developed TOSCA, the first automated tumor-only somatic calling workflow in whole-exome sequencing and targeted panel sequencing data which performs an end-to-end analysis from raw read files, via quality checks, alignment and variant calling to functional annotation, databases filtering, tumor purity and ploidy estimation and variant classification. Application of our workflow to tumor-only data provides estimates of somatic and germline variants that are consistent with results from paired analyses.Availability and implementationTOSCA is a Snakemake-based workflow and freely available at https://github.com/mdelcorvo/TOSCA.Supplementary informationSupplementary data are available at Bioinformatics Advances online.
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Project description:BackgroundObservations of recurrent somatic mutations in tumors have led to identification and definition of signaling and other pathways that are important for cancer progression and therapeutic targeting. As tumor cells contain both an individual's inherited genetic variants and somatic mutations, challenges arise in distinguishing these events in massively parallel sequencing datasets. Typically, both a tumor sample and a "normal" sample from the same individual are sequenced and compared; variants observed only in the tumor are considered to be somatic mutations. However, this approach requires two samples for each individual.ResultsWe evaluate a method of detecting somatic mutations in tumor samples for which only a subset of normal samples are available. We describe tuning of the method for detection of mutations in tumors, filtering to remove inherited variants, and comparison of detected mutations to several matched tumor/normal analysis methods. Filtering steps include the use of population variation datasets to remove inherited variants as well a subset of normal samples to remove technical artifacts. We then directly compare mutation detection with tumor-only and tumor-normal approaches using the same sets of samples. Comparisons are performed using an internal targeted gene sequencing dataset (n = 3380) as well as whole exome sequencing data from The Cancer Genome Atlas project (n = 250). Tumor-only mutation detection shows similar recall (43-60%) but lesser precision (20-21%) to current matched tumor/normal approaches (recall 43-73%, precision 30-82%) when compared to a "gold-standard" tumor/normal approach. The inclusion of a small pool of normal samples improves precision, although many variants are still uniquely detected in the tumor-only analysis.ConclusionsA detailed method for somatic mutation detection without matched normal samples enables study of larger numbers of tumor samples, as well as tumor samples for which a matched normal is not available. As sensitivity/recall is similar to tumor/normal mutation detection but precision is lower, tumor-only detection is more appropriate for classification of samples based on known mutations. Although matched tumor-normal analysis is preferred due to higher precision, we demonstrate that mutation detection without matched normal samples is possible for certain applications.
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Project description:ObjectiveRNA-seq delivers valuable insights both to transcriptional patterns and mutational landscapes for transcribed genes. However, as tumour cell lines frequently lack their matched-normal counterpart, variant calling without the paired normal sample is still challenging. In order to exclude variants of common genetic variation without a matched-normal control, filtering strategies need to be developed to identify tumour relevant variants in cell lines.ResultsHere, variants of 29 breast cancer cell lines were called on RNA-seq data via HaplotypeCaller. Low read depth sites, RNA-edit sites, and low complexity regions in coding regions were excluded. Common variants were filtered using 1000 genomes, gnomAD, and dbSNP data. Starting from hundred thousands of single nucleotide variants and small insertions and deletions, about thousand variants remained after filtering for each sample. Extracted variants were validated against the Catalogue of Somatic Mutations in Cancer (COSMIC) for 10 cell lines included in both data sets. Approximately half of the COSMIC variants were successfully called. Importantly, missing variants could mainly be attributed to sites with low read depth. Moreover, filtered variants also included all 10 cancer gene census COSMIC variants, a condensed hallmark variant set.
| S-EPMC11829467 | biostudies-literature
Project description:DNA sequencing of tumours to identify somatic mutations has become a critical tool to guide the type of treatment given to cancer patients. The gold standard for mutation calling is comparing sequencing data from the tumour to a matched normal sample to avoid mis-classifying inherited SNPs as mutations. This procedure works extremely well, but in certain situations only a tumour sample is available. While approaches have been developed to find mutations without a matched normal, they have limited accuracy or require specific types of input data (e.g. ultra-deep sequencing). Here we explore the application of single molecule long read sequencing to calling somatic mutations without matched normal samples. We develop a simple theoretical framework to show how haplotype phasing is an important source of information for determining whether a variant is a somatic mutation. We then use simulations to assess the range of experimental parameters (tumour purity, sequencing depth) where this approach is effective. These ideas are developed into a prototype somatic mutation caller, smrest, and its use is demonstrated on two highly mutated cancer cell lines. Finally, we argue that this approach has potential to measure clinically important biomarkers that are based on the genome-wide distribution of mutations: tumour mutation burden and mutation signatures.
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Project description:BackgroundThe widespread use of next-generation sequencing has identified an important role for somatic mosaicism in many diseases. However, detecting low-level mosaic variants from next-generation sequencing data remains challenging.ResultsHere, we present a method for Position-Based Variant Identification (PBVI) that uses empirically-derived distributions of alternate nucleotides from a control dataset. We modeled this approach on 11 segmental overgrowth genes. We show that this method improves detection of single nucleotide mosaic variants of 0.01-0.05 variant allele fraction compared to other low-level variant callers. At depths of 600 × and 1200 ×, we observed > 85% and > 95% sensitivity, respectively. In a cohort of 26 individuals with somatic overgrowth disorders PBVI showed improved signal to noise, identifying pathogenic variants in 17 individuals.ConclusionPBVI can facilitate identification of low-level mosaic variants thus increasing the utility of next-generation sequencing data for research and diagnostic purposes.
| S-EPMC8028235 | biostudies-literature
Project description:Somatic mosaicism refers to the existence of somatic mutations in a fraction of somatic cells in a single biological sample. Its importance has mainly been discussed in theory although experimental work has started to emerge linking somatic mosaicism to disease diagnosis. Through novel statistical modeling of paired-end DNA-sequencing data using blood-derived DNA from healthy donors as well as DNA from tumor samples, we present an ultra-fast computational pipeline, LocHap that searches for multiple single nucleotide variants (SNVs) that are scaffolded by the same reads. We refer to scaffolded SNVs as local haplotypes (LH). When an LH exhibits more than two genotypes, we call it a local haplotype variant (LHV). The presence of LHVs is considered evidence of somatic mosaicism because a genetically homogeneous cell population will not harbor LHVs. Applying LocHap to whole-genome and whole-exome sequence data in DNA from normal blood and tumor samples, we find wide-spread LHVs across the genome. Importantly, we find more LHVs in tumor samples than in normal samples, and more in older adults than in younger ones. We confirm the existence of LHVs and somatic mosaicism by validation studies in normal blood samples. LocHap is publicly available at http://www.compgenome.org/lochap.
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Project description:BackgroundThe accurate detection of somatic variants from sequencing data is of key importance for cancer treatment and research. Somatic variant calling requires a high sequencing depth of the tumor sample, especially when the detection of low-frequency variants is also desired. In turn, this leads to large volumes of raw sequencing data to process and hence, large computational requirements. For example, calling the somatic variants according to the GATK best practices guidelines requires days of computing time for a typical whole-genome sequencing sample.FindingsWe introduce Halvade Somatic, a framework for somatic variant calling from DNA sequencing data that takes advantage of multi-node and/or multi-core compute platforms to reduce runtime. It relies on Apache Spark to provide scalable I/O and to create and manage data streams that are processed on different CPU cores in parallel. Halvade Somatic contains all required steps to process the tumor and matched normal sample according to the GATK best practices recommendations: read alignment (BWA), sorting of reads, preprocessing steps such as marking duplicate reads and base quality score recalibration (GATK), and, finally, calling the somatic variants (Mutect2). Our approach reduces the runtime on a single 36-core node to 19.5 h compared to a runtime of 84.5 h for the original pipeline, a speedup of 4.3 times. Runtime can be further decreased by scaling to multiple nodes, e.g., we observe a runtime of 1.36 h using 16 nodes, an additional speedup of 14.4 times. Halvade Somatic supports variant calling from both whole-genome sequencing and whole-exome sequencing data and also supports Strelka2 as an alternative or complementary variant calling tool. We provide a Docker image to facilitate single-node deployment. Halvade Somatic can be executed on a variety of compute platforms, including Amazon EC2 and Google Cloud.ConclusionsTo our knowledge, Halvade Somatic is the first somatic variant calling pipeline that leverages Big Data processing platforms and provides reliable, scalable performance. Source code is freely available.
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Project description:Somatic variants play a crucial role in the occurrence and progression of cancer. However, in the absence of matched normal controls, distinguishing between germline and somatic variants becomes challenging in tumor samples. The existing tumor-only genomic analysis methods either suffer from limited performance or insufficient interpretability due to an excess of features. Therefore, there is an urgent need for an alternative approach that can address these issues and have practical implications. Here, we presented OncoTOP, a computational method for genomic analysis without matched normal samples, which can accurately distinguish somatic mutations from germline variants. Reference sample analysis revealed a 0% false positive rate and 99.7% reproducibility for variant calling. Assessing 2864 tumor samples across 18 cancer types yielded a 99.8% overall positive percent agreement and a 99.9% positive predictive value. OncoTOP can also accurately detect clinically actionable variants and subclonal mutations associated with drug resistance. For the prediction of mutation origins, the positive percent agreement stood at 97.4% for predicting somatic mutations and 95.7% for germline mutations. High consistency of tumor mutational burden (TMB) was observed between the results generated by OncoTOP and tumor-normal paired analysis. In a cohort of 97 lung cancer patients treated with immunotherapy, TMB-high patients had prolonged PFS (P = .02), proving the reliability of our approach in estimating TMB to predict therapy response. Furthermore, microsatellite instability status showed a strong concordance (97%) with polymerase chain reaction results, and leukocyte antigens class I subtypes and homozygosity achieved an impressive concordance rate of 99.3% and 99.9% respectively, compared to its tumor-normal paired analysis. Thus, OncoTOP exhibited high reliability in variant calling, mutation origin prediction, and biomarker estimation. Its application will promise substantial advantages for clinical genomic testing.
| S-EPMC11684894 | biostudies-literature
Project description:Archival tumor samples represent a rich resource of annotated specimens for translational genomics research. However, standard variant calling approaches require a matched normal sample from the same individual, which is often not available in the retrospective setting, making it difficult to distinguish between true somatic variants and individual-specific germline variants. Archival sections often contain adjacent normal tissue, but this tissue can include infiltrating tumor cells. As existing comparative somatic variant callers are designed to exclude variants present in the normal sample, a novel approach is required to leverage adjacent normal tissue with infiltrating tumor cells for somatic variant calling. Here we present lumosVar 2.0, a software package designed to jointly analyze multiple samples from the same patient, built upon our previous single sample tumor only variant caller lumosVar 1.0. The approach assumes that the allelic fraction of somatic variants and germline variants follow different patterns as tumor content and copy number state change. lumosVar 2.0 estimates allele specific copy number and tumor sample fractions from the data, and uses a to model to determine expected allelic fractions for somatic and germline variants and to classify variants accordingly. To evaluate the utility of lumosVar 2.0 to jointly call somatic variants with tumor and adjacent normal samples, we used a glioblastoma dataset with matched high and low tumor content and germline whole exome sequencing data (for true somatic variants) available for each patient. Both sensitivity and positive predictive value were improved when analyzing the high tumor and low tumor samples jointly compared to analyzing the samples individually or in-silico pooling of the two samples. Finally, we applied this approach to a set of breast and prostate archival tumor samples for which tumor blocks containing adjacent normal tissue were available for sequencing. Joint analysis using lumosVar 2.0 detected several variants, including known cancer hotspot mutations that were not detected by standard somatic variant calling tools using the adjacent tissue as presumed normal reference. Together, these results demonstrate the utility of leveraging paired tissue samples to improve somatic variant calling when a constitutional sample is not available.
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