NGS Data Research Articles

EP107: Accurate genotyping of UGT1A1 dinucleotide repeat polymorphism from targeted NGS data for the assessment of irinotecan chemotherapy adverse events

Colorectal cancer (CRC) is a leading cause of cancer-related death across the world. Irinotecan (IRI) is commonly used to treat metastatic CRC. The gene UGT1A1 encodes the enzyme responsible for the glucuronidation of SN-38, the active metabolite of IRI. Wild-type UGT1A1 contains six TA repeats [A(TA)6TAA] in its promoter region (also known as the *1 allele). Polymorphic UGT1A1 alleles with a higher number of TA repeats, such as UGT1A1 *28/(TA)7 and *37/(TA)8 alleles, cause decreased enzyme activity and are associated with severe toxicity in patients receiving IRI-based chemotherapy, for which dose reductions are recommended.

Actionable genomic landscapes from a real-world cohort of localized urothelial carcinoma patients.

525 Background: Recent targeted therapies for advanced and metastatic urothelial cancer have generated enthusiasm, but the actionable genomic landscape of early-stage disease remains largely unknown. Here, we used real-world evidence to investigate differences between somatic and germline mutations in localized, early-stage urothelial cancers and advanced urothelial cancers. Methods: We retrospectively analyzed de-identified NGS data from 1,146 bladder cancer patients (stages I-IV) with formalin-fixed, paraffin-embedded tumor biopsies sequenced using the Tempus|xT solid tumor assay (DNA-seq of 595-648 genes at 500x coverage; whole-exome capture RNA-seq). For the subset of patients with tumor-normal match sequencing (n=758), additional incidental germline alterations in 46 different genes were assessed. Results: A total of 1,146 bladder cancer tumors were investigated: stage I-II (n=124), stage III (n=159), and stage IV (n=863)—summarized in Table. Tumor mutational burden (TMB) was calculated for 1,126 tumors, and TMB-high (TMB-H; ≥10 mutations per megabase) was similar across tumor stages. PD-L1 immunohistochemical staining was performed on 698 tumors, and no significant differences were observed. Microsatellite instability high (MSI-H) status was detected in only 2 (1.6%) stage I-II tumors and 8 (0.9%) stage IV tumors. Alterations—single nucleotide variants, insertions/deletions, and copy number variants—in FGFR2/3, homologous recombination repair genes (18 genes including BRCA1/2 and ATM), additional DNA repair gene mutations ( ERCC2, RB1, FANCC) and NTRK fusions were detected at similar frequencies across disease stages. In 758 patients with tumor/normal matched sequencing, we identified a low rate of incidental germline mutations in MUTYH (stage III, 1%; stage IV, 1.9%), BRCA2 (stages I-II, 1.2%; stage III, 1%; stage IV, 0.5%), BRIP1 (stages I-II, 1.2%), ATM (stage III, 1%; stage IV, 0.7%), MSH6 (stage III, 1%; stage IV, 0.2%), and TP53 (stage III, 1%; stage IV, 0.2%). Conclusions: Patients with bladder cancer have similar rates of potentially actionable mutations and genomic landscapes regardless of clinical disease stage. These findings provide a rationale for further investigating targeted therapies among early-stage bladder cancer patients.[Table: see text]

Genomic predictors of benefit from checkpoint inhibition in neuroendocrine carcinoma.

512 Background: The role of immune checkpoint inhibitors (ICIs) in the treatment of extrapulmonary neuroendocrine carcinoma (EP-NEC) has yet to be established. While objective responses have been observed, it is still unknown which patients are likely to derive benefit. We investigated the genomic profiles of patients who did and did not benefit from ICIs. Methods: Previously we reported the objective responses to ICI in a retrospective series of patients with EP-NEC. RECIST 1.1 criteria were used to categorize patients as achieving disease control (DC = CR, PR, or SD) vs progressive disease (PD). The EMR was reviewed to identify patients who had genomic panels performed, and results were extracted for analysis. Results: Of 31 patients eligible for RECIST assessment, 19 had genomic panels available (9 with DC: 4 SD, 5 PR vs 10 with PD). Of those with NEC histology specified, 9 were small cell, 1 combined large and small cell, 3 were large cell. All tumors were microsatellite-stable. All but one (with TMB = 25) of 16 tumors with TMB status available were < 10 m/MB. Of those with disease control, 67% had both TP53 and RB1 alterations, compared with only 10% in those with progressive disease; this was statistically significant ( p = 0.0198, Fisher exact). Of 7 tumors with TP53 + RB1 alterations, 4 were specified as small cell carcinoma. Half of those with PD showed alterations in β-catenin pathway genes CTNNB1 or APC, compared to only one of the DC group, but this did not reach significance ( p = 0.1409, Fisher exact). In an analysis of all Mayo patients (not just those treated with ICI) with NGS data available (Tempus and FoundationOne), concurrent TP53 + RB1 alteration was significantly more common in SCLC than in EP-NEC (Table). Conclusions: In this small series of patients with EP-NEC treated with ICIs, the SCLC-like genomic signature of concurrent TP53 + RB1 alterations was significantly more common in those with disease control than in those with progressive disease. An analysis of all patients with NGS data (not just on ICI) showed that the dual alteration was more common in SCLC, and SCLC also had more TMB-high tumors. This may explain why ICIs are more effective in SCLC than in EP-NEC. Further study is warranted to determine whether TP53 + RB1 mutations predict response to ICI in NEC.[Table: see text]

Case report of recurrent fibromatosis with laryngeal involvement: Treatment based on network analyses of NGS data.

Aggressive fibromatosis (AF) is a rare, benign neoplasm originating from musculoaponeurotic stromal structures characterized by aggressive growth and infiltration of local tissues. To date, only six previous cases of AF involving the larynx have been reported. The present case was that of a 70-year-old female patient with a 5-year history of hoarseness and an enlarging neck mass consistent with aggressive recurrent fibromatosis. MRI displayed a large, solid mass arising from the left anterior cervical space displacing the trachea and upper airway to the right. At one year after the initial radical resection, the patient presented with recurrence. A second radical excision was performed not including a laryngectomy. Radiation therapy was considered for possible local treatment to prevent subsequent tumor recurrence. According to the literature, AF has recurrence rates of up to 40-70% within 18 months. There is only sparse literature to guide treatment. Using the mutations detected in the patient's AF tissue, an Ingenuity Pathway Analysis (IPA) was used to guide treatment of the recurrence. In the present case, the IPA analysis indicated the use of pazopanib to treat the patient's cancer. In general, surgery appears to be the treatment of choice for head and neck AF, but the management of recurrence is controversial.

GenomeTornadoPlot: a novel R package for CNV visualization and focality analysis.

Analysis of focal copy number variations (CNVs) is highly relevant for cancer research, as they pinpoint driver genes. More specifically, due to selective pressure oncogenes and tumor suppressor genes are more often affected by these events than neighboring passengers. In cases where multiple candidates co-reside in a genomic locus, careful comparison is required to either identify multigenic minimally deleted regions of synergistic co-mutations, or the true single driver gene. The study of focal CNVs in large cancer genome cohorts requires specialized visualization and statistical analysis. We developed the GenomeTornadoPlot R-package which generates gene-centric visualizations of CNV types, locations and lengths from cohortwise NGS data. Furthermore, the software enables the pairwise comparison of proximate genes to identify co-mutation patterns or driver-passenger hierarchies. The visual examination provided by GenomeTornadoPlot is further supported by adaptable local and global focality scoring. Integrated into the GenomeTornadoPlot R-Package is the comprehensive PCAWG database of CNVs, comprising 2976 cancer genome entities from 46 cohorts of the Pan-cancer Analysis of Whole Genomes project. The GenomeTornadoPlot R-package can be used to perform exploratory or hypothesis-driven analyses on the basis of the PCAWG data or in combination with data provided by the user. GenomeTornadoPlot is written in R script and released via github: <https://github.com/chenhong-dkfz/GenomeTornadoPlot/>. The package is under the license of GPL-3.0.

NetSeekR: a network analysis pipeline for RNA-Seq time series data

BackgroundRecent development of bioinformatics tools for Next Generation Sequencing data has facilitated complex analyses and prompted large scale experimental designs for comparative genomics. When combined with the advances in network inference tools, this can lead to powerful methodologies for mining genomics data, allowing development of pipelines that stretch from sequence reads mapping to network inference. However, integrating various methods and tools available over different platforms requires a programmatic framework to fully exploit their analytic capabilities. Integrating multiple genomic analysis tools faces challenges from standardization of input and output formats, normalization of results for performing comparative analyses, to developing intuitive and easy to control scripts and interfaces for the genomic analysis pipeline.ResultsWe describe here NetSeekR, a network analysis R package that includes the capacity to analyze time series of RNA-Seq data, to perform correlation and regulatory network inferences and to use network analysis methods to summarize the results of a comparative genomics study. The software pipeline includes alignment of reads, differential gene expression analysis, correlation network analysis, regulatory network analysis, gene ontology enrichment analysis and network visualization of differentially expressed genes. The implementation provides support for multiple RNA-Seq read mapping methods and allows comparative analysis of the results obtained by different bioinformatics methods.ConclusionOur methodology increases the level of integration of genomics data analysis tools to network inference, facilitating hypothesis building, functional analysis and genomics discovery from large scale NGS data. When combined with network analysis and simulation tools, the pipeline allows for developing systems biology methods using large scale genomics data.

Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View.

The diagnostic and prognostic value of miRNAs in cutaneous melanoma (CM) has been broadly studied and supported by advanced bioinformatics tools. From early studies using miRNA arrays with several limitations, to the recent NGS-derived miRNA expression profiles, an accurate diagnostic panel of a comprehensive pre-specified set of miRNAs that could aid timely identification of specific cancer stages is still elusive, mainly because of the heterogeneity of the approaches and the samples. Herein, we summarize the existing studies that report several miRNAs as important diagnostic and prognostic biomarkers in CM. Using publicly available NGS data, we analyzed the correlation of specific miRNA expression profiles with the expression signatures of known gene targets. Combining network analytics with machine learning, we developed specific non-linear classification models that could successfully predict CM recurrence and metastasis, based on two newly identified miRNA signatures. Subsequent unbiased analyses and independent test sets (i.e., a dataset not used for training, as a validation cohort) using our prediction models resulted in 73.85% and 82.09% accuracy in predicting CM recurrence and metastasis, respectively. Overall, our approach combines detailed analysis of miRNA profiles with heuristic optimization and machine learning, which facilitates dimensionality reduction and optimization of the prediction models. Our approach provides an improved prediction strategy that could serve as an auxiliary tool towards precision treatment.

New mutation of the TP53 gene associated with the hereditary breast cancer in a young Tuvinian woman

Background. The identification of the ethnospecific mutations associated with hereditary breast cancer remains challenging. Next generation sequencing (Ngs) technology fully enables the compilation of germline variants associated with the risk for inherited diseases. Despite the success of the Ngs, up to 20 % of molecular tests report genetic variant of unknown significance (Vus) or novel variants that have never been previously described and their clinical significances are unknown. To obtain extended information about the variants of the unknown significance, it is necessary to use an alternative approach for the analysis of the Ngs data. To obtain extended characteristic about the unknown significance variants, it is necessary to search for additional tools for the analysis of the Ngs data. Material and methods. We reclassified the mutation of the unknown significance using the activedrivedb database that assessed the effect of mutations on sites of post-translational modifications, and the proteinpaint tool that complemented the existing cancer genome portals and provided a comprehensive and intuitive view of cancer genomic data. Results. In this study, we report a 44-year-old tuvinian woman with a family history of breast cancer. Based on the Ngs data, mutational analysis revealed the presence of the lrg_321t1: c.80c&gt;t heterozygous variant in exon 2, which led to the proline to leucine change at codon 27 of the protein. In the dbpubmed database, this mutation was determined as unknown significance due to data limitation. According to the data of the activedriverdb tool, this mutation is located distally at the site of post-translational protein modification, which is responsible for binding to kinases that regulate genes of the cell cycle, etc. (atm, chek2, cdk, mapk). In accordance with proteinpaint tool, the lrg_321t1: c.80c&gt;t mutation is located in functionally specialized transactivation domains and codon of the tp53 gene, where the pathogenic mutation associated with li-Fraumeni syndrome has been earlier described. Conclusion. This report is the first to describe a new variant in the tp53 gene (rs1555526933), which is likely to be associated with hereditary cancer-predisposing syndrome, including li-Fraumeni syndrome, in a tuvinian Bc patient with young-onset and familial Bc.

QuasiFlow: a Nextflow pipeline for analysis of NGS-based HIV-1 drug resistance data.

Next-generation sequencing (NGS) enables reliable detection of resistance mutations in minority variants of human immunodeficiency virus type 1 (HIV-1). There is paucity of evidence for the association of minority resistance to treatment failure, and this requires evaluation. However, the tools for analyzing HIV-1 drug resistance (HIVDR) testing data are mostly web-based which requires uploading data to webservers. This is a challenge for laboratories with internet connectivity issues and instances with restricted data transfer across networks. We present QuasiFlow, a pipeline for reproducible analysis of NGS-based HIVDR testing data across different computing environments. Since QuasiFlow entirely depends on command-line tools and a local copy of the reference database, it eliminates challenges associated with uploading HIV-1 NGS data onto webservers. The pipeline takes raw sequence reads in FASTQ format as input and generates a user-friendly report in PDF/HTML format. The drug resistance scores obtained using QuasiFlow were 100% and 99.12% identical to those obtained using web-based HIVdb program and HyDRA web respectively at a mutation detection threshold of 20%. QuasiFlow and corresponding documentation are publicly available at https://github.com/AlfredUg/QuasiFlow. The pipeline is implemented in Nextflow and requires regular updating of the Stanford HIV drug resistance interpretation algorithm. Supplementary data are available at Bioinformatics Advances online.

Covariation of viral recombination with single nucleotide variants during virus evolution revealed by CoVaMa.

Adaptation of viruses to their environments occurs through the acquisition of both novel single-nucleotide variants (SNV) and recombination events including insertions, deletions, and duplications. The co-occurrence of SNVs in individual viral genomes during their evolution has been well-described. However, unlike covariation of SNVs, studying the correlation between recombination events with each other or with SNVs has been hampered by their inherent genetic complexity and a lack of bioinformatic tools. Here, we expanded our previously reported CoVaMa pipeline (v0.1) to measure linkage disequilibrium between recombination events and SNVs within both short-read and long-read sequencing datasets. We demonstrate this approach using long-read nanopore sequencing data acquired from Flock House virus (FHV) serially passaged in vitro. We found SNVs that were either correlated or anti-correlated with large genomic deletions generated by nonhomologous recombination that give rise to Defective-RNAs. We also analyzed NGS data from longitudinal HIV samples derived from a patient undergoing antiretroviral therapy who proceeded to virological failure. We found correlations between insertions in the p6Gag and mutations in Gag cleavage sites. This report confirms previous findings and provides insights on novel associations between SNVs and specific recombination events within the viral genome and their role in viral evolution.

Identification of missed viruses by metagenomic sequencing of clinical respiratory samples from Kenya

Pneumonia remains a major cause of mortality and morbidity. Most molecular diagnoses of viruses rely on polymerase chain reaction (PCR) assays that however can fail due to primer mismatch. We investigated the performance of routine virus diagnostics in Kilifi, Kenya, using random-primed viral next generation sequencing (viral NGS) on respiratory samples which tested negative for the common viral respiratory pathogens by a local standard diagnostic panel. Among 95 hospitalised pneumonia patients and 95 household-cohort individuals, analysis of viral NGS identified at least one respiratory-associated virus in 35 (37%) and 23 (24%) samples, respectively. The majority (66%; 42/64) belonged to the Picornaviridae family. The NGS data analysis identified a number of viruses that were missed by the diagnostic panel (rhinovirus, human metapneumovirus, respiratory syncytial virus and parainfluenza virus), and these failures could be attributed to PCR primer/probe binding site mismatches. Unexpected viruses identified included parvovirus B19, enterovirus D68, coxsackievirus A16 and A24 and rubella virus. The regular application of such viral NGS could help evaluate assay performance, identify molecular causes of missed diagnoses and reveal gaps in the respiratory virus set used for local screening assays. The results can provide actionable information to improve the local pneumonia diagnostics and reveal locally important viral pathogens.

ImmunoDataAnalyzer: a bioinformatics pipeline for processing barcoded and UMI tagged immunological NGS data

BackgroundNext-generation sequencing (NGS) is nowadays the most used high-throughput technology for DNA sequencing. Among others NGS enables the in-depth analysis of immune repertoires. Research in the field of T cell receptor (TCR) and immunoglobulin (IG) repertoires aids in understanding immunological diseases. A main objective is the analysis of the V(D)J recombination defining the structure and specificity of the immune repertoire. Accurate processing, evaluation and visualization of immune repertoire NGS data is important for better understanding immune responses and immunological behavior.ResultsImmunoDataAnalyzer (IMDA) is a pipeline we have developed for automatizing the analysis of immunological NGS data. IMDA unites the functionality from carefully selected immune repertoire analysis software tools and covers the whole spectrum from initial quality control up to the comparison of multiple immune repertoires. It provides methods for automated pre-processing of barcoded and UMI tagged immune repertoire NGS data, facilitates the assembly of clonotypes and calculates key figures for describing the immune repertoire. These include commonly used clonality and diversity measures, as well as indicators for V(D)J gene segment usage and between sample similarity. IMDA reports all relevant information in a compact summary containing visualizations, calculations, and sample details, all of which serve for a more detailed overview. IMDA further generates an output file including key figures for all samples, designed to serve as input for machine learning frameworks to find models for differentiating between specific traits of samples.ConclusionsIMDA constructs TCR and IG repertoire data from raw NGS reads and facilitates descriptive data analysis and comparison of immune repertoires. The IMDA workflow focus on quality control and ease of use for non-computer scientists. The provided output directly facilitates the interpretation of input data and includes information about clonality, diversity, clonotype overlap as well as similarity, and V(D)J gene segment usage. IMDA further supports the detection of sample swaps and cross-sample contamination that potentially occurred during sample preparation. In summary, IMDA reduces the effort usually required for immune repertoire data analysis by providing an automated workflow for processing raw NGS data into immune repertoires and subsequent analysis. The implementation is open-source and available on https://bioinformatics.fh-hagenberg.at/immunoanalyzer/.

Deciphering the mutation spectrum in south Indian children with congenital anomalies of the kidney and urinary tract

BackgroundCongenital anomalies of the kidney and urinary tract (CAKUT) cover a spectrum of structural malformations that result from aberrant morphogenesis of kidney and urinary tract. It is the most prevalent cause of kidney failure in children. Hence, it is important from a clinical perspective to unravel the molecular etiology of kidney and urinary tract malformations. Causal variants in genes that direct various stages of development of kidney and urinary tract in fetal life have been identified in 5–20% of CAKUT patients from Western countries. Recent advances in next generation sequencing technology and decreasing cost offer the opportunity to characterize the genetic profile of CAKUT in Indian population and facilitate integration of genetic diagnostics in care of children with CAKUT.MethodsCustomized targeted panel sequencing was performed to identify pathogenic variants in 31 genes known to cause human CAKUT in 69 south Indian children with CAKUT. The NGS data was filtered using standardized pipeline and the variants were classified using ACMG criteria. Genotype and phenotype correlations were performed.ResultsThe cohort consisted of children mostly with posterior urethral valve (PUV) (39.1%), vesico-ureteric reflux (VUR) (33.3%) and multi-cystic dysplastic kidney (MCDK) (7.2%). No pathogenic or likely pathogenic variants were identified in the study. Most of our variants (n = 39, 60%) were variants of unknown significance with 25.6% (10/39) of them were identified as potentially damaging but were novel variants.ConclusionsThe present study did not identify any disease-causing monogenic variants in the cohort. The absence of genetic cause may be due to limitations of panel-based testing and also due to higher proportion of children with abnormalities in lower urinary tract than hypodysplasia of kidneys. Clinical, larger targeted panel or whole exome sequencing may be a better method to characterize the genetic profile of Indians patients with CAKUT.

Two Novel Mutations in LAMC2 Gene in Iranian Families Affected by Junctional Epidermolysis Bullosa.

Junctional epidermolysis bullosa (JEB) is an autosomal recessive skin disorder with defective adhesion of dermal- epidermal within the lamina lucida region of the basement membrane zone. The main characterization of JEB is blistering and fragile skin and mucous membrane. Laminins are noncollagenous part of basement membrane and classified as a family of extracellular matrix glycoprotein. Laminins contain three chains: Laminin α, Laminin β and Laminin γ. LAMC2 (laminin subunit gamma 2) gene encodes γ subunit of laminin and its mutation contributes to JEB. Here, we report a disease-causing nonsense mutation and a large deletion mutation in LAMC2 gene in two families affected by JEB. Whole exome sequencing (WES) was carried out on the mother of patient in family I and the patient himself in family II to detect the underlying mutations. Then, sanger sequencing was performed to confirm the identified mutations. Next generation sequencing (NGS) data analysis of the first family showed a novel, nonsense mutation in LAMC2 gene (LAMC2: NM_005562: exon14:c.C2143T: p.R715X). The heterozygous state of the mutation was confirmed by sanger sequencing in the parents and unaffected brother. In Family II, NGS data had no coverage in the large area of LAMC2 gene. Thus, to confirm the possible deletion sanger sequencing was done and blasting of sequence showed the deleted region of 9.4 kb (exon10-17) in LAMC2 gene. In summary, current study reported a novel disease-causing premature termination codon (PTC) mutation in LAMC2 gene and a large deletion mutation in patients affected by JEB.

Review on Applications of R programming in Biological Data Analysis

The key programming language used in the field of data science is R programming, because of the freeavailability and ability to handle huge data. Packages developed in the R are used widely by scientists toobtain solutions for various real-world problems in several disciplines like healthcare, agriculture, andinformation technology. Next-generation sequencing data requires various bioinformatics analyses likequality checking, differential gene expression studies, and annotation which is facilitated by R packages andsoftware. Bioconductorprojectremains a hub of R packages that are open access and move the researchers tothe comfort zone for various kinds of analysis. In this review, we briefly discussed various packages usefulfor NGS data analysis and also explained how to use the packages for basic level analysis that benefitsresearchers having less exposure to R programming.

Whole Exome Sequencing Data Analysis for Detection of Breast Cancer Gene Variants and Pathway Study

Introduction: Whole Exome Sequencing (WES) involves sequencing, analysis of protein coding regions in genome. In present investigation, the potential gene variants were identified in human breast cancer genome using WES data analysis. Materials and Method: The NGS data samples with accession numbers (SRR1274896_1, SRR1274896_2) and (SRR1275000_1, SRR1275000_2) were collected from ENA database. The quality of the samples was assessed by using FastQC tool and followed by aligning samples with reference genome sequence hg38 using Bowtie2 tool. The results were retrieved in SAM format and converted to BAM format and then to sorted bam file using SAM tools, then duplicates were removed using Picard tool. Finally, Variant Calling format file was generated using BCF tools which projected the possible gene variants in the samples. Results: The results showed variant types out of them MUC3A1 showed an average of 53 mutations, highlighting its importance as a potential gene variant observed in breast cancer. Out of nonsynonymous mutations of samples, common gene variants in samples that possess 5 and more mutations were selected. The study was carried out on pathway analysis, domain analysis, gene involvement in biological processes and gene function. Conclusion: Majority of gene variants were involved in DNA Biosynthesis and Protein Biosynthesis and also resulted in tissue specific location. The location of these genes showed mutated genes in cytoplasm and in nucleus indicating the impact of gene variation on intracellular process.

MutaXome: A Novel Database for Identified Somatic Variations of In silico Analyzed Cancer Exome Datasets.

Advancements in the field of cancer research have enabled researchers and clinicians to access a massive amount of data to aid cancer patients and to add to the existing knowledge of research. However, despite the existence of reliable sources for extricating this data, it remains a challenge to accurately comprehend and draw conclusions based on the entirety of available information. Therefore, the current study aimed to design and develop a database for the identified variants of 5 different cancer types using 20 different cancer exomes. The exome data were retrieved from NCBI SRA and an NGS data clean-up protocol was implemented to obtain the best quality reads. The reads which passed the quality checks were then used for calling the variants which were then processed and filtered. This data was used to normalize and the normalized data generated was used for developing the database. MutaXome, which stands for mutations in cancer exome was designed in SQL, with the front end in bootstrap and HTML, and backend in PHP. The normalized data containing the variants inclusive of Single Nucleotide Polymorphisms (SNPs), were added into MutaXome, which contains detailed information regarding each type of identified variant. This database, available online via http://www.vidyalab.rf.gd/, serves as a knowledge base for cancer exome variations and holds much potential for enriching it by linking it to a decision support system as prospective studies.

Preanalytical Variables and Sample Quality Control for Clinical Variant Analysis.

Broad molecular profiling by next-generation sequencing of solid tumors has become a critical tool for clinical decision-making in the era of precision oncology. In addition to many already approved targeted therapies, more than half of ongoing oncology-related clinical trials are biomarker-driven. Therefore, accurate and reliable assays are needed to assess the genetic make-up of tumor cells and guide clinicians in the therapy decision process. In order to obtain high-quality NGS data for variant detection, certain preanalytical steps and quality metrics should be followed. These include assessment of sample types, choice of extraction method, library preparation technology, sequencing platform, and finally sequencing quality control. Each of these steps has certain challenges and pitfalls that need to be addressed and overcome, respectively. In this chapter, we address the preanalytical quality control and how each of the involved steps may influence the final result. Following these guidelines and QC metrics may help in obtaining optimalresults that will allow the precise and robust assessment of genetic variants in a clinical setting.

Molecular Characteristics of the Uncommon EGFR Exon 21 T854A Mutation and Response to Osimertinib in Patients With Non-Small Cell Lung Cancer

BackgroundEpidermal growth factor receptor (EGFR) T854A is an uncommon exon 21 mutation in patients with non-small cell lung cancer (NSCLC). It was first reported in samples collected after first generation EGFR tyrosine kinase inhibitor (TKI) treatment as an acquired resistant mutation to first generation EGFR-TKI. The efficacy of osimertinib, a third generation EGFR-TKI, in these patients was not clear. MethodsIn this study, a total of 8932 NSCLC patients with NGS data were retrospectively analyzed to investigate the molecular characteristics and clinical outcomes of patients with EGFR T854A mutation. ResultsEight of 8932 patients (0.09%) had EGFR T854A mutation, and 5 of them (62.5%) were treatment-naïve. Interestingly, all EGFR T854A mutations were co-occurred with EGFR L858R mutation in cis. TP53 was the most common concomitant mutation and no other driver mutation was found. Five of the 8 patients received treatment of osimertinib. Four patients achieved partial response, and one had stable disease, resulting in an overall objective response rate of 80% and disease control rate of 100%. The median progression-free survival of patients who received osimertinib was 10 months. Moreover, EGFR C797S mutation was detected in 1 patient after resistant to osimertinib treatment. ConclusionPresence of EGFR T854A mutation was rare in NSCLC patients and our retrospective study provides clinical evidence that osimertinib may be an effective treatment to improve survival outcomes in patients with EGFR T854A.

NanoString evaluation of murine Cytomegalovirus transcription in vivo and in vitro

BackgroundNext Generation Sequencing allows for deep analysis of transcriptional activity in cells and tissues, however it is still a cost intensive method that demands well versed data handling. Reverse transcription quantitative PCR (RT-qPCR) is the most commonly used method to measure gene expression levels, however the information gathered is quite small in comparison to NGS. A newer method called nanoString allows for highly multiplexed gene expression analysis by detecting mRNAs without the use of enzymes for reverse transcription or amplification even for single cells or low input material. The method can be done in 1.5 days and data are quickly analyzed by the accompanied user friendly software.Our aim was to investigate this new method and compare it to the existing alternatives, while investigating murine Cytomegalovirus (mCMV) infection and latency. MethodsmCMV infected murine embryonic fibroblasts (MEF), lung and salivary glands from BALB/c mice were evaluated at different stages of infection. A set of 30 custom designed nanoString probes were tested, 20 probes specific for mCMV genes, 6 probes for host genes known to be influenced by viral infection and 4 reference gene specific probes. nanoString counts were compared to published RNA-Seq RPKM. ResultsWe found that nanoString can be used for analysis of cytomegalovirus gene expression during acute infection in vitro and in vivo, both for virus specific and host genes. Although some transcripts show different expression rates in comparison to NGS data, the most abundant transcripts are comparable. When tissues are infected, there are significantly fewer transcripts than in MEFs, and consistent with previous work there are significant differences in relevant abundance between MEF and tissues. We were unable to detect our viral transcripts of interest in latently infected tissue. ConclusionsFor viruses with annotated transcriptomes, nanoString allows simultaneous quantitation of multiple virus and host genes. One huge advantage of the platform is rapid turnaround and simplicity of analysis. It should prove to be very useful to explore host virus interactions during acute infection, but it is unclear if it has adequate sensitivity for analysis during latency in immunocompetent mice.