Detection Of Somatic Variants Research Articles

TSomVar: a tumor-only somatic and germline variant identification method with random forest.

Somatic variants act as critical players during cancer occurrence and development. Thus, an accurate and robust method to identify them is the foundation of cutting-edge cancer genome research. However, due to low accessibility and high individual-/sample-specificity of the somatic variants in tumor samples, the detection is, to date, still crammed with challenges, particularly when lacking paired normal samples as control. To solve this burning issue, we developed a tumor-only somatic and germline variant identification method (TSomVar) using the random forest algorithm established on sample-specific variant datasets derived from genotype imputation, reads-mapping level annotation and functional annotation. We trained TSomVar by using genomic variant datasets of three major cancer types: colorectal cancer, hepatocellular carcinoma and skin cutaneous melanoma. Compared with existing tumor-only somatic variant identification tools, TSomVar shows excellent performances in somatic variant detection with higher accuracy and better capability of recalling for test datasets from colorectal cancer and skin cutaneous melanoma. In addition, TSomVar is equipped with the competence of accurately identifying germline variants in tumor samples. Taken together, TSomVar will undoubtedly facilitate and revolutionize somatic variant explorations in cancer research.

109P Improved accuracy of somatic variant detection from high throughput sequencing (HTS) data via site-specific noise estimation

109P Improved accuracy of somatic variant detection from high throughput sequencing (HTS) data via site-specific noise estimation

Accurate somatic variant detection using weakly supervised deep learning

Identification of somatic mutations in tumor samples is commonly based on statistical methods in combination with heuristic filters. Here we develop VarNet, an end-to-end deep learning approach for identification of somatic variants from aligned tumor and matched normal DNA reads. VarNet is trained using image representations of 4.6 million high-confidence somatic variants annotated in 356 tumor whole genomes. We benchmark VarNet across a range of publicly available datasets, demonstrating performance often exceeding current state-of-the-art methods. Overall, our results demonstrate how a scalable deep learning approach could augment and potentially supplant human engineered features and heuristic filters in somatic variant calling.

Ultra-rapid somatic variant detection via real-time targeted amplicon sequencing

Molecular markers are essential for cancer diagnosis, clinical trial enrollment, and some surgical decision making, motivating ultra-rapid, intraoperative variant detection. Sequencing-based detection is considered the gold standard approach, but typically takes hours to perform due to time-consuming DNA extraction, targeted amplification, and library preparation times. In this work, we present a proof-of-principle approach for sub-1 hour targeted variant detection using real-time DNA sequencers. By modifying existing protocols, optimizing for diagnostic time-to-result, we demonstrate confirmation of a hot-spot mutation from tumor tissue in ~52 minutes. To further reduce time, we explore rapid, targeted Loop-mediated Isothermal Amplification (LAMP) and design a bioinformatics tool—LAMPrey—to process sequenced LAMP product. LAMPrey’s concatemer aware alignment algorithm is designed to maximize recovery of diagnostically relevant information leading to a more rapid detection versus standard read alignment approaches. Using LAMPrey, we demonstrate confirmation of a hot-spot mutation (250x support) from tumor tissue in less than 30 minutes.

Abstract 5323: Analytic and clinical validation of a new pan-cancer NGS liquid biopsy test for the detection of copy number variations, fusions/exon skipping, somatic variants and indels

Abstract Liquid biopsy tests are an integral part of the molecular diagnostic workup of patients with multiple cancer types. We have extended validation studies of a new liquid biopsy test panel that uses an amplicon-based targeted next-generation sequencing (NGS) panel with a semiconductor-based system, the Ion GeneStudio S5 Prime. The workflow requires very low input (20 ng) of cell-free nucleic acids extracted from human plasma and has been demonstrated to yield results in less than 72 hours from sample receipt at the laboratory. This report details the analytic performance of the test panel for detecting copy number variations (CNVs), fusions and exon skipping variants in plasma. These data add to previously reported validation data for the detection of somatic nucleotide variants (SNV) and indels [97.7% - 100% concordant with orthogonal droplet digital PCR (ddPCR) tests]. The updated study sample sets included consented clinical and normal healthy, donor specimens, and reference cell lines to detect fusions and exon skipping [HCC78 (ROS1), CRL-5935 (EML4-ALK), KM12 (NTRK1) and HTB-178 (MET exon 14 skipping)], and CNVs [CRL-5928 (HER2 gain), SK-BR-3 (HER2, CDK6, EGFR and MYC gains), HCC-827 (EGFR gain) and CRL-5909 (MET gain)]. The limit of detection (LOD) for fusion/skipping was initially established at a molecular coverage of 42 copies and was then verified by detection of three additional fusion/skipping variants. The LOD for CNVs was initially established at 1.40 fold-change and was then verified with the detection of three additional CNVs. Verification was conducted with specimens near the initially established LOD. In the precision studies, all expected fusion/skipping variants were detected in all runs and all days of testing (n=18/18) generating a hit rate of 100%. An average CV of 20.5% (range 8.7% to 34.8%) for repeatability and 20.8% (range 15.7% to 30.5%) for reproducibility was established. Twenty eight of 29 CNVs were detected, generating a hit rate of 96.6%. An average CV of 1.85% (range 0% to 5.49%) for repeatability and 6.59% (range 1.65% to 9.22%) for reproducibility was established for detected CNVs. We conclude that the validated test panel meets the criteria for being rapid, highly sensitive and specific, and has utility for the reporting of clinically relevant variants of interest in multiple cancer types. Citation Format: Audrey Audetat, Cherie Tschida, Sarah Kreston, Leisa Jackson, Hestia Mellert, Adam Stephen, Gary A. Pestano. Analytic and clinical validation of a new pan-cancer NGS liquid biopsy test for the detection of copy number variations, fusions/exon skipping, somatic variants and indels [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5323.

Abstract 5784: Overcoming FFPE hurdles to enable high quality hybrid capture libraries and somatic mutation detection in matched tumor-normal patient samples

Abstract In cancer genomics, a common source of DNA is formalin-fixed, paraffin-embedded (FFPE) tissue from patient surgical samples, where in most cases high quality fresh or frozen tissue samples are not available. FFPE DNA poses many notable challenges for preparing NGS libraries, including low input amounts and highly variable damage from fixation, storage, and extraction methods. Due to the high cost of sequencing and variability of coverage, regions of interest are often specifically enriched using hybrid capture-based approaches, but these methods require a high input of diverse, uniform DNA library to achieve the coverage required for somatic mutation identification in tumor samples.We applied a multi-faceted approach to improving the yield, quality, and coverage from libraries generated from cancer patient sample sets. Using tumor-normal pairs, we evaluated the impact of our method on the sensitivity and accuracy of somatic variant detection. Matched frozen tissue provided a truth set of somatic variants for a particular tumor as well as a gold standard by which we could assess the quality of our FFPE hybrid capture libraries. Combining DNA damage repair and a novel enzymatic fragmentation mix upstream of library preparation not only reduced the false positive rate in somatic variant detection by repairing damage-derived mutations but also improved the library yield, complexity, coverage uniformity, and hybrid capture library quality metrics. The use of unique molecular identifier (UMI)-containing adaptors ensured accurate duplicate marking. Finally, a new PCR master mix boosts the library yield without compromising library quality in FFPE samples ranging from very poor quality to high quality. This combinatorial approach allows even the poorest quality FFPE samples to achieve high quality libraries with sufficient input for hybrid capture in our study. Citation Format: Margaret R. Heider, Jian Sun, Brittany S. Sexton, Bradley W. Langhorst, Andrew Gray, Lauren Higgins, Lixin Chen, Lynne Apone, Thomas C. Evans, Nicole M. Nichols, Eileen T. Dimalanta, Theodore B. Davis, Pingfang Liu. Overcoming FFPE hurdles to enable high quality hybrid capture libraries and somatic mutation detection in matched tumor-normal patient samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5784.

Abstract 4104: Low-cost, simple and rapid assay for single-molecule detection of gene fusions from RNA with ASPYRE technology

Abstract Introduction: The FDA has approved 11 therapies for non-small cell lung cancer (NSCLC) patients diagnosed with gene fusions. Importantly, these life-saving targeted therapies remain underutilized due to a lack of cost-effective, comprehensive, and simple-to-use technologies for gene fusion detection. Our previous work using ASPYRE technology (allele-specific pyrophosphorolysis reaction; Silva et al, 2020) demonstrated detection of somatic variants at single-molecule level from DNA extracted from tumor tissue. However, DNA-based fusion detection requires tiling across large introns, which limits sensitivity particularly if breakpoints are located in repetitive regions of the genome. Here, we show that ASPYRE technology can also be utilized for highly sensitive detection of RNA fusions, scaling to over 30 fusion targets covering the main 3’ fusion partners (ALK, ROS1, RET, NTRK1, NTRK3) and from a single reverse transcription PCR (RT-PCR) amplification reaction. Methods: The ASPYRE assay consists of four sequential enzymatic stages: RT-PCR; enzymatic clean-up; combined exonuclease digestion, hybridization, pyrophosphorolysis and ligation; rolling circle isothermal amplification and detection. All targets are detected across two reaction wells using four channels on a standard real-time PCR instrument. A panel of synthetic RNA oligonucleotides comprising gene fusion boundaries was spiked into a background pool of lung-RNA, quantified using digital PCR (dPCR) and compared against a commercial reference standard to validate its suitability for assessing assay performance. RNA was extracted from formalin-fixed paraffin-embedded (FFPE) lung tissue from healthy donors to determine assay specificity, and from FFPE lung tissue from NSCLC patients with known fusions to exemplify end-to-end sample analysis. Results: We demonstrate equivalence of our in-house reference samples to commercial reference standards. Using serial dilutions of input RNA standards, we show that detection by ASPYRE is consistent with detection of single molecules as input for common ROS1, ALK, RET and NTRK3 fusions. In addition, ASPYRE was able to detect previously confirmed ROS1 and ALK fusions and MET exon skipping variants in FFPE samples. ASPYRE had high specificity, correctly confirming the absence of RNA fusions and exon skipping variants in healthy control FFPE samples. Conclusions: ASPYRE approached single-molecule sensitivity for over 30 RNA fusion targets in a single reaction. The assay required only 1 ng RNA input, with a turn-around time of <4 hours from extracted RNA to result, opening the door to routine detection of RNA fusions from limited specimens. The simplicity and ease of use of ASPYRE, combined with its rapid turnaround time and analytical performance characteristics, provides a new tool for rapid and comprehensive treatment selection in NSCLC. Citation Format: Eleanor Gray, Justyna Mordaka, Efthimia Christoforou, Kristine von Bargen, Nicola Potts, Rebecca Palmer, Barnaby Balmforth. Low-cost, simple and rapid assay for single-molecule detection of gene fusions from RNA with ASPYRE technology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4104.

Abstract 5161: Exome-scale longitudinal tracking of emerging therapeutic resistance in GIST via analysis of circulating tumor DNA

Abstract Gastrointestinal stromal tumors (GIST) are lethal tumors characterized by constitutively activating mutations to KIT or PDGFRA. Transient disease control in the first-line setting is achieved via inhibition of tyrosine kinase signaling using the KIT inhibitor imatinib. As patients progress through subsequent lines of therapy a molecularly heterogeneous disease evolves, characterized by distinct subtypes and shifting repertoires of exon-specific KIT variants which directly impact treatment outcomes. Here, we use tumor-informed exome-scale liquid biopsy to identify and track the evolution of multiple resistance mechanisms in patients receiving tyrosine kinase inhibitors (TKIs) to address the unmet need of comprehensive understanding of GIST evolution in response to therapy. Matched tumor, normal and serial plasma samples were obtained from 15 heavily pretreated metastatic GIST patients. Following baseline sample collection, all patients received systemic TKI therapy, and were monitored until disease progression. Exome-scale detection of somatic variants in cfDNA from longitudinal matched plasma samples was achieved using the NeXT Liquid BiopsyTM platform. The ImmunoID NeXT PlatformⓇ, an augmented exome/transcriptome platform and analysis pipeline which generates comprehensive tumor and immune data was used to profile paired tumor and normal samples. Longitudinal whole exome sequencing of plasma identified dynamic shifts in existing clones harboring exon-specific KIT mutations, and evolution of new KIT mutations arising prior to identification of tumor progression using standard imaging techniques. We detected a correlation between the number of damaging mutations detected in baseline ctDNA and tumor exon 11 KIT mutation status, suggesting that plasma mutation profiles may be KIT-variant dependent. ctDNA from patients with shorter overall survival (OS) was enriched for variants in the PI3K-AKT and MAPK pathway, potentially contributing to immune evasion observed in those patients. Additional associations were observed between gene copy-number changes and OS (P = .0097). Previous studies have demonstrated that immune infiltration and activity may be KIT variant specific, here we broaden those findings, identifying a significant correlation between TCRɑ clonality and variants detected only in plasma (P = .04), as well as a significant association between TCRβ diversity and OS (HR = 2.55, log rank P = .04). Comprehensive profiling of paired tumor tissue (WES and RNA-Seq) and WES of serially collected ctDNA sensitively and repeatedly identified evolving KIT mutations and other molecular alterations prior to radiologically confirmed disease progression. These findings suggest plasma-based monitoring of late-stage GIST malignancies may be useful for non-invasive disease tracking, providing treatment guidance prior to traditional approaches. Citation Format: Charles W. Abbott, Niamh Coleman, Jing Wang, Josette Northcott, Jason Pugh, Dan Norton, Fábio C. Navarro, Lee D. McDaniel, Eric Levy, Rachel Marty Pyke, John Lyle, Jason Harris, Gabor Bartha, Filip Janku, John West, Richard O. Chen, Sean Boyle. Exome-scale longitudinal tracking of emerging therapeutic resistance in GIST via analysis of circulating tumor DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5161.

Abstract 1900: Rapid variant detection and annotations from next generation sequencing data using a GPU accelerated framework

Abstract Introduction Next Generation Sequencing (NGS) played a crucial role in revolutionizing the field of human genetics to become an integral part of research and clinical management. Some examples are diagnosis and treatment of cancer patients, identifying syndromes in the NICU (Neonatal Intensive Care Units), insights into neuro-degenerative disease to name a few. NGS integrated with multi-omics data provides greater insight to underlying molecular mechanisms and helps to identify alterations specific to the disease condition. Accurate and robust detection of somatic and germline genomic alterations from whole genome sequencing (WGS) data is complex where the datasets tend to be very large, and they require highly compute intensive operations that often take days to weeks to complete. By leveraging the Graphical Processing Unit (GPU) accelerated framework, it is possible to analyze the WGS data and identify clinically actionable variants with fast turnaround times without compromising on the accuracy. In this study, we implemented a GPU accelerated framework for most used variant callers ie. Mutect2, MuSE, Somatic Sniper and LoFreq with significantly faster run times in comparison to their Central Processing Unit (CPU) counterpart. Methods Commonly used CPU based somatic variant calling algorithms Mutect2, MuSE, Somatic Sniper and LoFreq, were selected and subsequently accelerated to run on GPUs. The goal of this work is to generate results that are equivalent to the CPU version of these algorithms, but at much faster run times. As a control data set, we utilize the SEQC2 consortium WGS dataset consisting of ~50x tumor and normal samples to assess the performance of individual CPU variant callers to their GPU equivalents. Results The GPU accelerated framework overall provides 10-66x faster run times compared to their CPU-based counterparts. Individually, CPU vs GPU comparisons are, Mutect2 achieved a maximum acceleration of 66x, followed by MuSE, LoFreq both 11x and Somatic Sniper 10x acceleration respectively. The results from accelerated MuSE, LoFreq, Somatic sniper are identical in comparison to the CPU-based somatic variant callers, For Mutect2 we achieve 99.9% sensitivity and 99.85% precision. Discussion Recently, machine-learning based variant detection algorithms exhibited better performance compared to the statistics based heuristic approaches. Here, we have presented the results for an ensemble of four variant callers to be used as the starting point for an ongoing effort to develop a deep learning-based approach for somatic variant detection and smart filtering algorithms. The goal is to enable improved variant calling and provide a GPU computing framework for faster, better, and reproducible workflows to analyze large scale NGS datasets. Citation Format: Pankaj Vats, Ankit Sethia, Mehrzad Samadi, Timothy T. Harkins. Rapid variant detection and annotations from next generation sequencing data using a GPU accelerated framework [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1900.

Abstract 5015: Precision profile simulation study for a next generation sequencing bTMB assay

Abstract Background: Precision profile simulations (PPS) can be used to assess variability of biomarker profiles and provide valuable insight into assay performance, especially when reliable precision estimates can not be obtained empirically due to scarcity of representative samples or insufficient materials per sample. A PPS was conducted for the GuardantOMNI assay to characterize the expected variability in blood tumor mutational burden (bTMB) score across a representative range of expected bTMB scores in clinical samples. The simulations were aligned to, but not completely prescribed by, the PPS guidance provided in Guidance for Industry and and Food and Drug Administration Staff Class II Special Controls Guidance Document: Ovarian Adnexal Mass Assessment Score Test System. A sample’s bTMB score is a real valued quantity (e.g., bTMB = 21.04 mut/Mb) that is derived by multiplying the number of qualified mutations observed within a targeted panel by a scaling factor. Variability in observed bTMB scores for a given blood sample is governed primarily by sample coverage, tumor shedding level, and the assay somatic variant detection probabilities (a function of underlying variant allele frequencies, VAFs). Methods: The relationship between site-specific total molecule counts and coverage was modeled utilizing a composite dataset consisting of both clinical and contrived samples. Sample coverage was modeled using variance component estimates from Precision Study data (18 cancer samples each with 6 to 18 replicates). The reference, single-strand mutant, and double-strand mutant molecule counts for a somatic variant site detected in at least one sample replicate were modeled utilizing a bias corrected Dirichlet Multinomial model. The variants with the simulated VAF and coverage levels were processed with the GuardantOMNI germline/somatic classifier to account for the uncertainty in germline/somatic classification at lower coverage values. Results: Precision profiles consisting of simulation derived %CV estimates for 18 clinical samples with a representative set of mean bTMB scores were generated. The PPS bTMB score distributions were consistent with the bTMB scores observed in the Precision Study, supported by visualization and confidence intervals at level 0.05 margins of equivalence for the empirical mean bTMB and standard deviation estimates. The sample specific %CV estimates were observed, in most instances, to decrease with increasing input levels for matched targeted LoD (Limit of Detection) simulation results. Precision profile %CV estimates were observed to be inversely related to mean bTMB scores. Conclusions: The results provide proof of principle that estimation of GuardantOMNI bTMB score precision via an intuitive and interpretable simulation model is viable. The simulation results were consistent with empirical data and general expectations regarding the precision of the bTMB scores. Citation Format: Kevin Doubleday, Daniel Gaile, Ravi Vijaya-Satya, Xianxian Liu, Kevin D'Auria, Soni Shukla, Han-Yu Chuang, Katie Quinn, Darya Chudova. Precision profile simulation study for a next generation sequencing bTMB assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5015.

Evaluation of differential contribution of a circulating epithelial cell signal component in a multimodal colorectal neoplasia assay.

e15527 Background: Multimodal diagnostic classifiers survey signals from multiple biological compartments and provide iterative, independent and interactive information. Detection of both CRC and AA is critical for noninvasive colorectal screening to improve overall survival and prevent the 2.5-5% annual transition of AA to CRC. FirstSight integrates clinically validated somatic variants from cfDNA and circulating epithelial cells (CECs) adjusting for age and sex. CECs provide information from both intrinsic factors of the adenoma and extrinsic factors such as adenoma microenvironment which facilitates early systemic entry. We sought to assess differential information from CEC signals for the detection of colorectal cancer and/or advanced adenoma (AA). Methods: Blood samples and colonoscopy pathology results were obtained from 438 asymptomatic screening subjects enriched for CRC/AA obtained from 15 US medical centers. The cohort included 18 CRCs and 64 AAs. Somatic variants from cfDNA were identified using NGS and qPCR. CECs were captured by the CellMax biomimetic platform (CMx) using high-avidity EpCAM antibody embedded in the CMx biochip and confirmed with immunostaining (DAPI: nucleus, CK20: epithelial cell and CD45: WBC). CMx platform’s AI/ML analyses of CEC images quantify stain intensities and cellular features. CEC derived signal GM1was evaluated for its predictive capability beyond somatic variants from cfDNA. Results: Genetic or epigenetic variants were not detected in 52% (33/64) of AA cases, limiting the sensitivity of these markers for early disease. However, distinct CEC signals have been identified that aid in the detection of subjects with CRC or AA, or conversely subjects with negative colonoscopies or non-advanced adenomas (nAA). Among them, a novel feature (referred to as GM1) derived from CEC signals was able to differentiate with statistical significance AA from negative/non-neoplastic findings or nAAs in study subjects with negative results in corresponding somatic variants from cfDNA (p < 0.0001). While targeted somatic variants from cfDNA performed well on CRC (17/18), they provided no predictive information for detection of AA for the 353 subjects which were negative for variants, GM1 provided 100.0% (33/33) sensitivity at 35.1% (112/319) specificity, showing its ability to rule out AA with high negative predictive value. Conclusions: Somatic variant detection modes of the First Sight multi-modal assay have high sensitivity for CRC and modest sensitivity for AA. We demonstrate that CEC signal GM1 of FirstSight provides significant independent information for the detection of CRC and AA beyond somatic variants from cfDNA. Additional CEC signals may further improve the sensitivity and specificity for detection of early-stage colorectal neoplasia. The GM1 CEC signal marker will need to be validated further in future studies.

Benchmarking Low-Frequency Variant Calling With Long-Read Data on Mitochondrial DNA.

Background: Sequencing quality has improved over the last decade for long-reads, allowing for more accurate detection of somatic low-frequency variants. In this study, we used mixtures of mitochondrial samples with different haplogroups (i.e., a specific set of mitochondrial variants) to investigate the applicability of nanopore sequencing for low-frequency single nucleotide variant detection. Methods: We investigated the impact of base-calling, alignment/mapping, quality control steps, and variant calling by comparing the results to a previously derived short-read gold standard generated on the Illumina NextSeq. For nanopore sequencing, six mixtures of four different haplotypes were prepared, allowing us to reliably check for expected variants at the predefined 5%, 2%, and 1% mixture levels. We used two different versions of Guppy for base-calling, two aligners (i.e., Minimap2 and Ngmlr), and three variant callers (i.e., Mutserve2, Freebayes, and Nanopanel2) to compare low-frequency variants. We used F1 score measurements to assess the performance of variant calling. Results: We observed a mean read length of 11 kb and a mean overall read quality of 15. Ngmlr showed not only higher F1 scores but also higher allele frequencies (AF) of false-positive calls across the mixtures (mean F1 score = 0.83; false-positive allele frequencies < 0.17) compared to Minimap2 (mean F1 score = 0.82; false-positive AF < 0.06). Mutserve2 had the highest F1 scores (5% level: F1 score >0.99, 2% level: F1 score >0.54, and 1% level: F1 score >0.70) across all callers and mixture levels. Conclusion: We here present the benchmarking for low-frequency variant calling with nanopore sequencing by identifying current limitations.

CfDNA Measurement as a Diagnostic Tool for the Detection of Brain Somatic Mutations in Refractory Epilepsy.

Epilepsy is a neurological disorder that affects more than 50 million people. Its etiology is unknown in approximately 60% of cases, although the existence of a genetic factor is estimated in about 75% of these individuals. Hundreds of genes involved in epilepsy are known, and their number is increasing progressively, especially with next-generation sequencing techniques. However, there are still many cases in which the results of these molecular studies do not fully explain the phenotype of the patients. Somatic mutations specific to brain tissue could contribute to the phenotypic spectrum of epilepsy. Undetectable in the genomic DNA of blood cells, these alterations can be identified in cell-free DNA (cfDNA). We aim to review the current literature regarding the detection of somatic variants in cfDNA to diagnose refractory epilepsy, highlighting novel research directions and suggesting further studies.

Concordance Study of a 520-Gene Next-Generation Sequencing-Based Genomic Profiling Assay of Tissue and Plasma Samples.

Next-generation sequencing (NGS) enables simultaneous detection of actionable somatic variants and estimation of genomic signatures such as tumor mutational burden (TMB) or microsatellite instability (MSI) status, which empowers therapeutic decisions in clinical oncology. Our retrospective study investigated the clinical performance of somatic variant detection in paired tissue and blood samples using a large targeted gene panel, the OncoScreen Plus, which interrogates 520 cancer-related genes. We analyzed sequencing data derived from paired tissue and blood samples of 3005 patients spanning 20 solid tumor types, including lung (n = 1971), gastrointestinal (n = 625), breast (n = 120) and gynecological (n = 110), genitourinary (n = 38), and other cancers (n = 141). Across tumor types, the OncoScreen Plus panel achieved a high tissue detection rate, with an average of 97.9%. The average plasma detection rate was 72.2%, with an average tissue concordance rate of 36.6%. Considering all variant types, the plasma assay yielded an average sensitivity/true positive rate of 45.7%, with a positive predictive value of 64.7% relative to tissue assay. Pearson correlation analysis revealed a strong correlation in TMB estimated from blood and tissue samples (correlation coefficient 0.845, R2 = 0.756). MSI-high status was identified in five tumor types, including endometrial cancer (28.6%), colorectal cancer (2.5%), ovarian cancer (2.0%), gastric cancer (1.5%), and lung adenocarcinoma (0.2%). Paired tumor and blood samples from a large cohort of patients spanning 20 tumor types demonstrated that the OncoScreen Plus is a reliable pan-cancer panel for the accurate detection of somatic variants and genomic signatures that could guide individualized treatment strategies to improve the care of patients with advanced cancer.

EP352: Systematic exploration of mosaic variants detected in blood samples from a primary immunodeficiency cohort referred for research exome sequencing

Exome and genome sequencing (ES and GS) allow detection of somatic mosaic variants that are present at a lower variant allele fraction (VAF) than is observed for a typical heterozygous germline variant, which is around 50%. Recent guidelines from the American College of Medical Genetics and Genomics highlight 21 genes in which mosaic variants are commonly identified as a part of routine ES/GS for germline testing in patients with personal and/or family history inconsistent with disease associated with pathogenic germline variants these genes.

Personalized HLA typing leads to the discovery of novel HLA alleles and tumor-specific HLA variants.

Accurate and full‐length typing of the HLA region is important in many clinical and research settings. With the advent of next generation sequencing (NGS), several HLA typing algorithms have been developed, including many that are applicable to whole exome sequencing (WES). However, most of these solutions operate by providing the closest‐matched HLA allele among the known alleles in IPD‐IMGT/HLA Database. These database‐matching approaches have demonstrated very high performance when typing well characterized HLA alleles. However, as they rely on the completeness of the HLA database, they are not optimal for detecting novel or less well characterized alleles. Furthermore, the database‐matching approaches are also not adequate in the context of cancer, where a comprehensive characterization of somatic HLA variation and expression patterns of a tumor's HLA locus may guide therapy and clinical outcome, because of the pivotal role HLA alleles play in tumor antigen recognition and immune escape. Here, we describe a personalized HLA typing approach applied to WES data that leverages the strengths of database‐matching approaches while simultaneously allowing for the discovery of novel HLA alleles and tumor‐specific HLA variants, through the systematic integration of germline and somatic variant calling. We applied this approach on WES from 10 metastatic melanoma patients and validated the HLA typing results using HLA targeted NGS sequencing from patients where at least one HLA germline candidate was detected on Class I HLA. Targeted NGS sequencing confirmed 100% performance for the 1st and 2nd fields. In total, five out of the six detected HLA germline variants were because of Class I ambiguities at the third or fourth fields, and their detection recovered the correct HLA allele genotype. The sixth germline variant let to the formal discovery of a novel Class I allele. Finally, we demonstrated a substantially improved somatic variant detection accuracy in HLA alleles with a 91% of success rate in simulated experiments. The approach described here may allow the field to genotype more accurately using WES data, leading to the discovery of novel HLA alleles and help characterize the relationship between somatic variation in the HLA region and immunosurveillance.

Halvade somatic: Somatic variant calling with Apache Spark.

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.

PipeIT2: Somatic Variant Calling Workflow for Ion Torrent Sequencing Data.

The detection of somatic genetic variants forms the cornerstone of precision oncology. Molecular diagnostics laboratories require economical and rapid protocols to generate accurate variant calls for clinical purposes. The Ion Torrent sequencing platform is frequently used but lacks robust analysis workflows tailored to the needs of custom sequencing panels. We present PipeIT2, a somatic variant calling workflow for both matched tumor-germline and tumor-only analyses. PipeIT2 is enclosed in a Singularity container to ensure reproducibility and ease of deployment.

Somatic and Germline Variant Calling from Next-Generation Sequencing Data.

Re-sequencing of the human genome by next-generation sequencing (NGS) has been widely applied to discover pathogenic genetic variants and/or causative genes accounting for various types of diseases including cancers. The advances in NGS have allowed the sequencing of the entire genome of patients and identification of disease-associated variants in a reasonable timeframe and cost. The core of the variant identification relies on accurate variant calling and annotation. Numerous algorithms have been developed to elucidate the repertoire of somatic and germline variants. Each algorithm has its own distinct strengths, weaknesses, and limitations due to the difference in the statistical modeling approach adopted and read information utilized. Accurate variant calling remains challenging due to the presence of sequencing artifacts and read misalignments. All of these can lead to the discordance of the variant calling results and even misinterpretation of the discovery. For somatic variant detection, multiple factors including chromosomal abnormalities, tumor heterogeneity, tumor-normal cross contaminations, unbalanced tumor/normal sample coverage, and variants with low allele frequencies add even more layers of complexity to accurate variant identification. Given the discordances and difficulties, ensemble approaches have emerged by harmonizing information from different algorithms to improve variant calling performance. In this chapter, we first introduce the general scheme of variant calling algorithms and potential challenges at distinct stages. We next review the existing workflows of variant calling and annotation, and finally explore the strategies deployed by different callers as well as their strengths and caveats. Overall, NGS-based variant identification with careful consideration allows reliable detection of pathogenic variant and candidate variant selection for precision medicine.

Detection of PKD1 and PKD2 Somatic Variants in Autosomal Dominant Polycystic Kidney Cyst Epithelial Cells by Whole-Genome Sequencing.

Autosomal dominant polycystic kidney disease (ADPKD) is a genetic disorder characterized by the development of multiple cysts in the kidneys. It is often caused by pathogenic mutations in PKD1 and PKD2 genes that encode polycystin proteins. Although the molecular mechanisms for cystogenesis are not established, concurrent inactivating germline and somatic mutations in PKD1 and PKD2 have been previously observed in renal tubular epithelium (RTE). To further investigate the cellular recessive mechanism of cystogenesis in RTE, we conducted whole-genome DNA sequencing analysis to identify germline variants and somatic alterations in RTE of 90 unique kidney cysts obtained during nephrectomy from 24 unrelated participants. Kidney cysts were overall genomically stable, with low burdens of somatic short mutations or large-scale structural alterations. Pathogenic somatic "second hit" alterations disrupting PKD1 or PKD2 were identified in 93% of the cysts. Of these, 77% of cysts acquired short mutations in PKD1 or PKD2 ; specifically, 60% resulted in protein truncations (nonsense, frameshift, or splice site) and 17% caused non-truncating mutations (missense, in-frame insertions, or deletions). Another 18% of cysts acquired somatic chromosomal loss of heterozygosity (LOH) events encompassing PKD1 or PKD2 ranging from 2.6 to 81.3 Mb. 14% of these cysts harbored copy number neutral LOH events, while the other 3% had hemizygous chromosomal deletions. LOH events frequently occurred at chromosomal fragile sites, or in regions comprising chromosome microdeletion diseases/syndromes. Almost all somatic "second hit" alterations occurred at the same germline mutated PKD1/2 gene. These findings further support a cellular recessive mechanism for cystogenesis in ADPKD primarily caused by inactivating germline and somatic variants of PKD1 or PKD2 genes in kidney cyst epithelium.