Strategies for identification of somatic variants using the Ion Torrent deep targeted sequencing platform

The TiHoCL panel for canine lymphoma: a feasibility study integrating functional genomics and network biology approaches for comparative oncology targeted NGS panel design

Targeted next-generation sequencing (NGS) enables the identification of genomic variants in cancer patients with high sensitivity at relatively low costs, and has thus opened the era to personalized human oncology. Veterinary medicine tends to adopt new technologies at a slower pace compared to human medicine due to lower funding, nonetheless it embraces technological advancements over time. Hence, it is reasonable to assume that targeted NGS will be incorporated into routine veterinary practice in the foreseeable future. Many animal diseases have well-researched human counterparts and hence, insights gained from the latter might, in principle, be harnessed to elucidate the former. Here, we present the TiHoCL targeted NGS panel as a proof of concept, exemplifying how functional genomics and network approaches can be effectively used to leverage the wealth of information available for human diseases in the development of targeted sequencing panels for veterinary medicine. Specifically, the TiHoCL targeted NGS panel is a molecular tool for characterizing and stratifying canine lymphoma (CL) patients designed based on human non-Hodgkin lymphoma (NHL) research outputs. While various single nucleotide polymorphisms (SNPs) have been associated with high risk of developing NHL, poor prognosis and resistance to treatment in NHL patients, little is known about the genetics of CL. Thus, the ~100 SNPs featured in the TiHoCL targeted NGS panel were selected using functional genomics and network approaches following a literature and database search that shielded ~500 SNPs associated with, in nearly all cases, human hematologic malignancies. The TiHoCL targeted NGS panel underwent technical validation and preliminary functional assessment by sequencing DNA samples isolated from blood of 29 lymphoma dogs using an Ion Torrent™ PGM System achieving good sequencing run metrics. Our design framework holds new possibilities for the design of similar molecular tools applied to other diseases for which limited knowledge is available and will improve drug target discovery and patient care.

Prevalence of BRCA homopolymeric indels in an ION Torrent-based tumour-to-germline testing workflow in high-grade ovarian carcinoma

Tumour DNA sequencing is essential for precision medicine since it guides therapeutic decisions but also fosters the identification of patients who may benefit from germline testing. Notwithstanding, the tumour-to-germline testing workflow presents a few caveats. The low sensitivity for indels at loci with sequences of identical bases (homopolymers) of ion semiconductor-based sequencing techniques represents a well-known limitation, but the prevalence of indels overlooked by these techniques in high-risk populations has not been investigated. In our study, we addressed this issue at the homopolymeric regions of BRCA1/2 in a retrospectively selected cohort of 157 patients affected with high-grade ovarian cancer and negative at tumour testing by ION Torrent sequencing. Variant allele frequency (VAF) of indels at each of the 29 investigated homopolymers was systematically revised with the IGV software. Thresholds to discriminate putative germline variants were defined by scaling the VAF to a normal distribution and calculating the outliers that exceeded the mean + 3 median-adjusted deviations of a control population. Sanger sequencing of the outliers confirmed the occurrence of only one of the five putative indels in both tumour and blood from a patient with a family history of breast cancer. Our results indicated that the prevalence of homopolymeric indels overlooked by ion semiconductor techniques is seemingly low. A careful evaluation of clinical and family history data would further help minimise this technique-bound limitation, highlighting cases in which a deeper look at these regions would be recommended.

PipeIT2: A tumor-only somatic variant calling workflow for molecular diagnostic Ion Torrent sequencing data

Precision oncology relies on the accurate identification of somatic mutations in cancer patients. While the sequencing of the tumoral tissue is frequently part of routine clinical care, the healthy counterparts are rarely sequenced. We previously published PipeIT, a somatic variant calling workflow specific for Ion Torrent sequencing data enclosed in a Singularity container. PipeIT combines user-friendly execution, reproducibility and reliable mutation identification, but relies on matched germline sequencing data to exclude germline variants. Expanding on the original PipeIT, here we describe PipeIT2 to address the clinical need to define somatic mutations in the absence of germline control. We show that PipeIT2 achieves a > 95% recall for variants with variant allele fraction >10%, reliably detects driver and actionable mutations and filters out most of the germline mutations and sequencing artifacts. With its performance, reproducibility, and ease of execution, PipeIT2 is a valuable addition to molecular diagnostics laboratories.

Deep DNA sequencing of MGMT, TP53 and AGT in Mexican astrocytoma patients identifies an excess of genetic variants in women and a predictive biomarker

PURPOSE

Astrocytomas are a type of malignant brain tumor with an unfavorable clinical course. The impact of AGT and MGMT somatic variants in the prognosis of astrocytoma is unknown, and it is controversial for TP53. Moreover, there is a lack of knowledge regarding the molecular characteristics of astrocytomas in Mexican patients.

METHODS

We studied 48 Mexican patients, men and women, with astrocytoma (discovery cohort). We performed DNA deep sequencing in tumor samples, targeting AGT, MGMT and TP53, and we studied MGMT gene promoter methylation status. Then we compared our findings to a cohort which included data from patients with astrocytoma from The Cancer Genome Atlas (validation cohort).

RESULTS

In the discovery cohort, we found a higher number of somatic variants in AGT and MGMT than in the validation cohort (10.4% vs < 1%, p < 0.001), and, in both cohorts, we observed only women carried variants AGT variants. We also found that the presence of either MGMT variant or promoter methylation was associated to better survival and response to chemotherapy, and, in conjunction with TP53 variants, to progression-free survival.

CONCLUSIONS

The occurrence of AGT variants only in women expands our knowledge about the molecular differences in astrocytoma between men and women. The increased prevalence of AGT and MGMT variants in the discovery cohort also points towards possible distinctions in the molecular landscape of astrocytoma among populations. Our findings warrant further study.

DETECTION OF DISEASE‐CAUSING MUTATIONS IN PROSTATE CANCER BY NGS SEQUENCING

Gene mutations may affect the fate of many tumors including prostate cancer (PCa); therefore, the research of specific mutations associated with tumor outcomes might help the urologist to identify the best therapy for PCa patients such as surgical resection, adjuvant therapy or active surveillance. Genomic DNA (gDNA) was extracted from 48 paraffin‐embedded PCa samples and normal paired tissues. Next, gDNA was amplified and analyzed by next‐generation sequencing (NGS) using a specific gene panel for PCa. Raw data were refined to exclude false‐positive mutations; thus, variants with coverage and frequency lower than 100× and 5%, respectively were removed. Mutation significance was processed by Genomic Evolutionary Rate Profiling, ClinVar, and Varsome tools. Most of 3000 mutations (80%) were single nucleotide variants and the remaining 20% indels. After raw data elaboration, 312 variants were selected. Most mutated genes were KMT2D (26.45%), FOXA1 (16.13%), ATM (15.81%), ZFHX3 (9.35%), TP53 (8.06%), and APC (5.48%). Hot spot mutations in FOXA1, ATM, ZFHX3, SPOP, and MED12 were also found. Truncating mutations of ATM, lesions lying in hot spot regions of SPOP and FOXA1 as well as mutations of TP53 correlated with poor prognosis. Importantly, we have also found some germline mutations associated with hereditary cancer‐predisposing syndrome. gDNA sequencing of 48 cancer tissues by NGS allowed to detect new tumor variants as well as confirmed lesions in genes linked to prostate cancer. Overall, somatic and germline mutations linked to good/poor prognosis could represent new prognostic tools to improve the management of PCa patients.

Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences

With the advent of next-generation sequencing technology, it has become convenient and cost efficient to thoroughly characterize the microbial diversity and taxonomic composition in various environmental samples. Millions of sequencing data can be generated, and how to utilize this enormous sequence resource has become a critical concern for microbial ecologists. One particular challenge is the OTUs (operational taxonomic units) picking in 16S rRNA sequence analysis. Lucky, this challenge can be directly addressed by sequence clustering that attempts to group similar sequences. Therefore, numerous clustering methods have been proposed to help to cluster 16S rRNA sequences into OTUs. However, each method has its clustering mechanism, and different methods produce diverse outputs. Even a slight parameter change for the same method can also generate distinct results, and how to choose an appropriate method has become a challenge for inexperienced users. A lot of time and resources can be wasted in selecting clustering tools and analyzing the clustering results. In this study, we introduced the recent advance of clustering methods for OTUs picking, which mainly focus on three aspects: (i) the principles of existing clustering algorithms, (ii) benchmark dataset construction for OTU picking and evaluation metrics, and (iii) the performance of different methods with various distance thresholds on benchmark datasets. This paper aims to assist biological researchers to select the reasonable clustering methods for analyzing their collected sequences and help algorithm developers to design more efficient sequences clustering methods.

Genotyping data of routinely processed matched primary/metastatic tumor samples

Genotypic and phenotypic comparisons of tumors in multiple tissue samples from the same patient are important for understanding disease evolution and treatment possibilities. Panel NGS genotyping is currently widely used in this context, whereby NGS variant filtering and final evaluation constitute the basis for meaningful comparisons. Here, we present the genotype data used for genotype / phenotype comparisons between matched primary / metastatic colorectal tumors in the work by Chatzopoulos et al (doi: 10.1016/j.humpath.2020.10.009), as well as the process followed for obtaining these data. We describe key issues while processing routinely formalin-fixed paraffin-embedded (FFPE) tumors for genotyping, NGS application (Ion Torrent), a stringent variant filtering algorithm for genotype analyses in FFPE tissues and particularly in matched tumor samples, and provide the respective datasets. Apart from research, tumor NGS genotyping is currently applied for clinical diagnostic purposes in Oncology. The datasets and method description provided herein (a) are important for comprehending the peculiarities of FFPE tumor genotyping, which is still mostly based on principles of germline DNA genotyping; (b) can be used in pooled analyses, e.g., of primary / metastatic tumors for the investigation of tumor evolution.

Investigation of Somatic Mutations in Human Brains Targeting Genes Associated With Parkinson's Disease

Background: Somatic single nucleotide variant (SNV) mutations occur in neurons but their role in synucleinopathies is unknown. Aim: We aimed to identify disease-relevant low-level somatic SNVs in brains from sporadic patients with synucleinopathies and a monozygotic twin carrying LRRK2 G2019S, whose penetrance could be explained by somatic variation. Methods and Results: We included different brain regions from 26 Parkinson's disease (PD), one Incidental Lewy body, three multiple system atrophy cases, and 12 controls. The whole SNCA locus and exons of other genes associated with PD and neurodegeneration were deeply sequenced using molecular barcodes to improve accuracy. We selected 21 variants at 0.33-5% allele frequencies for validation using accurate methods for somatic variant detection. Conclusions: We could not detect disease-relevant somatic SNVs, however we cannot exclude their presence at earlier stages of degeneration. Our results support that coding somatic SNVs in neurodegeneration are rare, but other types of somatic variants may hold pathological consequences in synucleinopathies.

Comparison of somatic variant detection algorithms using Ion Torrent targeted deep sequencing data

Background

The application of next-generation sequencing in cancer has revealed the genomic landscape of many tumour types and is nowadays routinely used in research and clinical settings. Multiple algorithms have been developed to detect somatic variation from sequencing data using either paired tumour-blood or tumour-only samples. Most of these methods have been developed and evaluated for the identification of somatic variation using Illumina sequencing datasets of moderate coverage. However, a comprehensive evaluation of somatic variant detection algorithms on Ion Torrent targeted deep sequencing data has not been performed.

Methods

We have applied three somatic detection algorithms, Torrent Variant Caller, MuTect2 and VarScan2, on a large cohort of ovarian cancer patients comprising of 208 paired tumour-blood samples and 253 tumour-only samples sequenced deeply on Ion Torrent Proton platform across 330 amplicons. Subsequently, the concordance and performance of the three somatic variant callers were assessed.

Results

We have observed low concordance across the algorithms with only 0.5% of SNV and 0.02% of INDEL calls in common across all three methods. The intersection of all methods showed better performance when assessed using correlation with known mutational signatures, overlap with COSMIC variation and by examining the variant characteristics. The Torrent Variant Caller also performed well with the advantage of not eliminating a high number of variants that could lead to high type II error.

Conclusions

Our results suggest that caution should be taken when applying state-of-the-art somatic variant algorithms to Ion Torrent targeted deep sequencing data. Better quality control procedures and strategies that combine results from multiple methods should ensure that higher accuracy is achieved. This is essential to ensure that results from bioinformatics pipelines using Ion Torrent deep sequencing can be robustly applied in cancer research and in the clinic.

Driver Mutations in Normal Airway Epithelium Elucidate Spatiotemporal Resolution of Lung Cancer

Rationale: Uninvolved normal-appearing airway epithelium has been shown to exhibit specific mutations characteristic of nearby non-small cell lung cancers (NSCLCs). Yet, its somatic mutational landscape in patients with early-stage NSCLC is unknown.Objectives: To comprehensively survey the somatic mutational architecture of the normal airway epithelium in patients with early-stage NSCLC.Methods: Multiregion normal airways, comprising tumor-adjacent small airways, tumor-distant large airways, nasal epithelium and uninvolved normal lung (collectively airway field), matched NSCLCs, and blood cells (n = 498) from 48 patients were interrogated for somatic single-nucleotide variants by deep-targeted DNA sequencing and for chromosomal allelic imbalance events by genome-wide genotype array profiling. Spatiotemporal relationships between the airway field and NSCLCs were assessed by phylogenetic analysis.Measurements and Main Results: Genomic airway field carcinogenesis was observed in 25 cases (52%). The airway field epithelium exhibited a total of 269 somatic mutations in most patients (n = 36) including key drivers that were shared with the NSCLCs. Allele frequencies of these acquired variants were overall higher in NSCLCs. Integrative analysis of single-nucleotide variants and allelic imbalance events revealed driver genes with shared "two-hit" alterations in the airway field (e.g., TP53, KRAS, KEAP1, STK11, and CDKN2A) and those with single hits progressing to two in the NSCLCs (e.g., PIK3CA and NOTCH1).Conclusions: Tumor-adjacent and tumor-distant normal-appearing airway epithelia exhibit somatic driver alterations that undergo selection-driven clonal expansion in NSCLC. These events offer spatiotemporal insights into the development of NSCLC and, thus, potential targets for early treatment.

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

BACKGROUND

Targeted 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).

METHODS

To 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.

RESULTS

Our 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.

CONCLUSIONS

AmpliSolve 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 .

PipeIT: A Singularity Container for Molecular Diagnostic Somatic Variant Calling on the Ion Torrent Next-Generation Sequencing Platform

The accurate identification of somatic mutations has become a pivotal component of tumor profiling and precision medicine. In molecular diagnostics laboratories, somatic mutation analyses on the Ion Torrent sequencing platform are typically performed on the Ion Reporter platform, which requires extensive manual review of the results and lacks optimized analysis workflows for custom targeted sequencing panels. Alternative solutions that involve custom bioinformatics pipelines involve the sequential execution of software tools with numerous parameters, leading to poor reproducibility and portability. We describe PipeIT, a stand-alone Singularity container of a somatic mutation calling and filtering pipeline for matched tumor-normal Ion Torrent sequencing data. PipeIT is able to identify pathogenic variants in BRAF, KRAS, PIK3CA, CTNNB1, TP53, and other cancer genes that the clinical-grade Oncomine workflow identified. In addition, PipeIT analysis of tumor-normal paired data generated on a custom targeted sequencing panel achieved 100% positive predictive value and 99% sensitivity compared with the 68% to 80% positive predictive value and 92% to 96% sensitivity using the default tumor-normal paired Ion Reporter workflow, substantially reducing the need for manual curation of the results. PipeIT can be rapidly deployed to and ensures reproducible results in any laboratory and can be executed with a single command with minimal input files from the users.

System for Quality-Assured Data Analysis: Flexible, reproducible scientific workflows

The reproducibility of scientific processes is one of the paramount problems of bioinformatics, an engineering problem that must be addressed to perform good research. The System for Quality-Assured Data Analysis (SyQADA), described here, seeks to address reproducibility by managing many of the details of procedural bookkeeping in bioinformatics in as simple and transparent a manner as possible. SyQADA has been used by persons with backgrounds ranging from expert programmer to Unix novice, to perform and repeat dozens of diverse bioinformatics workflows on tens of thousands of samples, consuming over 80 CPU-months of computing on over 300,000 individual tasks of scores of projects on laptops, computer servers, and computing clusters. SyQADA is especially well-suited for paired-sample analyses found in cancer tumor-normal studies. SyQADA executable source code, documentation, tutorial examples, and workflows used in our lab is available from http://scheet.org/software.html.

Target amplicon exome-sequencing identifies promising diagnosis and prognostic markers involved in RTK-RAS and PI3K-AKT signaling as central oncopathways in primary central nervous system lymphoma

Exome-sequencing for somatic mutation detection and copy number variation analysis are effective and valid methods for evaluating human cancers in current molecular medicine. We conducted target amplicon exome-sequencing analyses using PCR target enrichment and next-generation sequencing on Ion Proton semiconductor sequencers. Twenty-seven primary central nervous system lymphoma (PCNSL) specimens and their corresponding noncancerous tissues were used for multiplex PCR amplification to obtain targeted coverages of the entire coding regions of 409 cancer-related genes. The average of the total numbers of somatic mutations including single-nucleotide variations and insertion/deletion mutations in each specimen was 13.3. Of these, the average of the ratios of nonsynonymous substitutions in each specimen was 74.8%. The most frequent mutations in 27 specimens were in PIM1, MYD88, CD79B, DST, IRF4, ERBB3, MYH11, DCC, and KMT2D. Furthermore, somatic mutations of MYH11 were related to poor prognoses in PCNSL patients. Copy number variations were also duplicated and/or deleted from deep-sequencing in segmental genomic islands. In addition to these prognostic marker candidates, analysis of RTK-RAS-MAPK signaling and the PTEN-PI3K-AKT proapoptotic pathway showed that somatic activations and aberrations, respectively, may be involved in a promising central oncopathway harboring mTOR, c-Myc, FOXO1, and p53. This study provides a foundation for molecular targeted therapies based on genome diagnostics and prognosis in PCNSL.