Next-generation Sequencing Experiments Research Articles

Abstract 6282: Novel androgen related gene network in prostate cancer cell model

Abstract Prostate cancer stands as the foremost cause of cancer-related deaths among men in the United States, with the primary treatment being androgen receptor (AR) antagonists relying on current androgen signaling knowledge. Most of this knowledge is derived from next-generation sequencing experiments, but the oversight of splice variants and highly similar sequences in multiple loci can hinder a comprehensive understanding. In an effort to unveil the complete androgen receptor transcriptional program, we employed a novel sequencing analysis protocol. Using a R1881-LNCaP model, we identified 212 upregulated and 92 downregulated genes, of which a notable one-third (68 upregulated and 39 downregulated) had not been previously associated with androgen. Among these novel transcripts, 5.6% lacked splice variants, while 16.3% exhibited one or more splice variants, including 3.8% from seven unique gene families. Recognized androgen-regulated genes, such as ORM1, STEAP4, NPPC, ORM2, and KLK3, were validated as upregulated, while GRB10, FN1, PPFIA2, and PLA2G2A were confirmed as downregulated in this in-vitro model. Utilizing chromatin immunoprecipitation-sequencing (ChIP-seq) data, we found that 13% of the genes contained at least one androgen response element within 5 kb upstream or downstream. Further validation through real-time PCR confirmed the expression levels of 90% of the selected genes (18). Ingenuity Pathway Analysis revealed two major regulators (ESRRA and ARFGEF2) in causal networks likely interacting with the novel genes. Intriguingly, 57.1% of ESRRA-involved genes contained at least one ESRRA binding site in a ChIP-seq dataset. Although ESRRA is known to activate androgen biosynthesis in castration-resistant prostate cancer, its interaction with AR and co-regulation of androgen signaling remain unknown. Ongoing studies involve the characterization of these networks in prostate cancer cell lines, examining anchorage-independent capacity, invasion, migration, proliferation, and progression. Data collection and comparison will ascertain whether the signaling is driven by mutated AR, and the role of ARv7 will be elucidated through gene lockdown experiments. Citation Format: Alex L. McDonald, Spencer Snow, Shuk-mei Ho, Yuet-kin Leung. Novel androgen related gene network in prostate cancer cell model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6282.

Low-grade parental gonosomal mosaicism in CHD2 siblings with Smith-Magenis-like syndrome.

Loss-of-function CHD2 (chromodomain helicase DNA-binding protein 2) mutations are associated with a spectrum of neurodevelopmental disorders often including early-onset generalized seizures, photosensitivity, and epileptic encephalopathies. Patients show psychomotor delay/intellectual disability (ID), autistic features, and behavior disorders, such as aggression and impulsivity. Most reported cases are sporadic with description of germline mosaicism only in two families. We detect the first case of parental gonosomal CHD2 mosaicism disclosed by two brothers showing mild ID, born to healthy parents. The eldest brother has a history of drug-controlled generalized tonic-clonic seizures and displays sleep disorder and aggressive behavior suggestive of Smith-Magenis syndrome (SMS). Analysis of brothers' DNAs by next-generation sequencing (NGS) custom gene panel for pediatric epilepsy and/or ID disclosed in both the same pathogenic CHD2 variant. Additional NGS experiment on genomic DNA from parents' peripheral blood and from buccal swab raised the suspicion of low-grade gonosomal mosaicism in the unaffected mother subsequently confirmed by digital polymerase chain reaction (dPCR). This report underlines as worthwhile CHD2 screening in individuals presenting ID/developmental delay, with/without epilepsy, and behavior and sleep disorders suggestive of SMS. Detecting a CHD2 variant should prime testing probands' parents by NGS coupled to dPCR on different tissues to exclude/confirm gonosomal mosaicism and define the recurrence risk.

Machine Learning Methods for Gene Selection in Uveal Melanoma.

Uveal melanoma (UM) is the most common primary intraocular malignancy with a limited five-year survival for metastatic patients. Limited therapeutic treatments are currently available for metastatic disease, even if the genomics of this tumor has been deeply studied using next-generation sequencing (NGS) and functional experiments. The profound knowledge of the molecular features that characterize this tumor has not led to the development of efficacious therapies, and the survival of metastatic patients has not changed for decades. Several bioinformatics methods have been applied to mine NGS tumor data in order to unveil tumor biology and detect possible molecular targets for new therapies. Each application can be single domain based while others are more focused on data integration from multiple genomics domains (as gene expression and methylation data). Examples of single domain approaches include differentially expressed gene (DEG) analysis on gene expression data with statistical methods such as SAM (significance analysis of microarray) or gene prioritization with complex algorithms such as deep learning. Data fusion or integration methods merge multiple domains of information to define new clusters of patients or to detect relevant genes, according to multiple NGS data. In this work, we compare different strategies to detect relevant genes for metastatic disease prediction in the TCGA uveal melanoma (UVM) dataset. Detected targets are validated with multi-gene score analysis on a larger UM microarray dataset.

Flow Sorting, Whole Genome Amplification and Next-Generation Sequencing as Combined Tools to Study Heterogeneous Acute Lymphoblastic Leukemia.

Next-generation sequencing (NGS) methods have been introduced for immunoglobulin (IG)/T-cell receptor (TR) gene rearrangement analysis in acute lymphoblastic leukemia (ALL) and lymphoma (LBL). These methods likely constitute faster and more sensitive approaches to analyze heterogenous cases of ALL/LBL, yet it is not known whether gene rearrangements constituting low percentages of the total sequence reads represent minor subpopulations of malignant cells or background IG/TR gene rearrangements in normal B-and T-cells. In a comparison of eight cases of B-cell precursor ALL (BCP-ALL) using both the EuroClonality NGS method and the IdentiClone multiplex-PCR/gene-scanning method, the NGS method identified between 29% and 139% more markers than the gene-scanning method, depending on whether the NGS data analysis used a threshold of 5% or 1%, respectively. As an alternative to using low thresholds, we show that IG/TR gene rearrangements in subpopulations of cancer cells can be discriminated from background IG/TR gene rearrangements in normal B-and T-cells through a combination of flow cytometry cell sorting and multiple displacement amplification (MDA)-based whole genome amplification (WGA) prior to the NGS. Using this approach to investigate the clonal evolution in a BCP-ALL patient with double relapse, clonal TR rearrangements were found in sorted leukemic cells at the time of second relapse that could be identified at the time of diagnosis, below 1% of the total sequence reads. These data emphasize that caution should be exerted when interpreting rare sequences in NGS experiments and show the advantage of employing the flow sorting of malignant cell populations in NGS clonality assessments.

RNA folding studies inside peptide-rich droplets reveal roles of modified nucleosides at the origin of life.

Compartmentalization of RNA in biopolymer-rich membraneless organelles is now understood to be pervasive and critical for the function of extant biology and has been proposed as a prebiotically plausible way to accumulate RNA. However, compartment-RNA interactions that drive encapsulation have the potential to influence RNA structure and function in compartment- and RNA sequence-dependent ways. Here, we detail next-generation sequencing (NGS) experiments performed in membraneless compartments called complex coacervates to characterize the fold of many different transfer RNAs (tRNAs) simultaneously under the potentially denaturing conditions of these compartments. Notably, we find that natural modifications favor the native fold of tRNAs in these compartments. This suggests that covalent RNA modifications could have played a critical role in metabolic processes at the origin of life.

RNAlysis: analyze your RNA sequencing data without writing a single line of code

BackgroundAmong the major challenges in next-generation sequencing experiments are exploratory data analysis, interpreting trends, identifying potential targets/candidates, and visualizing the results clearly and intuitively. These hurdles are further heightened for researchers who are not experienced in writing computer code since most available analysis tools require programming skills. Even for proficient computational biologists, an efficient and replicable system is warranted to generate standardized results.ResultsWe have developed RNAlysis, a modular Python-based analysis software for RNA sequencing data. RNAlysis allows users to build customized analysis pipelines suiting their specific research questions, going all the way from raw FASTQ files (adapter trimming, alignment, and feature counting), through exploratory data analysis and data visualization, clustering analysis, and gene set enrichment analysis. RNAlysis provides a friendly graphical user interface, allowing researchers to analyze data without writing code. We demonstrate the use of RNAlysis by analyzing RNA sequencing data from different studies using C.elegans nematodes. We note that the software applies equally to data obtained from any organism with an existing reference genome.ConclusionsRNAlysis is suitable for investigating various biological questions, allowing researchers to more accurately and reproducibly run comprehensive bioinformatic analyses. It functions as a gateway into RNA sequencing analysis for less computer-savvy researchers, but can also help experienced bioinformaticians make their analyses more robust and efficient, as it offers diverse tools, scalability, automation, and standardization between analyses.

MiR-95-3p/EPM2A/MMP2 contributes to the pathogenesis of severe preeclampsia through the regulation of trophoblast biological behaviour

ObjectivePreeclampsia (PE) is a maternal multisystem disease with an unclear mechanism. Data showed that MiR-95-3p promoted cell migration, invasion and proliferation, leading to the occurrence and development of many cancers, and placental trophoblasts and tumor cells had similar migration, invasion and proliferation abilities. Meanwhile we found that MiR-95-3p was differentially expressed in PE and normal placenta. Therefore, this article aimed to explore the biological function and mechanism of miR-95-3p in PE. MethodsThe expression of miR-95-3p in PE and normal placental tissue was explored by high-throughput sequencing and qRT–PCR. The effects of miR-95-3p on trophoblast migration, invasion, proliferation, angiogenesis and apoptosis were investigated by Transwell migration and invasion assays, cell viability assay, tube formation assay and flow cytometry in two trophoblast cell lines (HTR-8/SVneo and JAR). The miR-95-3p target gene EPM2A was identified and verified by unique identifier mRNA next-generation sequencing and dual-luciferase reporter gene experiments. Rescue experiments were conducted to investigate whether miR-95-3p regulated EPM2A to participate in trophoblast migration and invasion. Finally, the effects of miR-95-3p and EPM2A on the expression of angiogenic factors and inflammation-related factors were investigated by ELISA. ResultsWe found that miR-95-3p was expressed at low levels in the placental tissue of patients with PE and was negatively correlated with EPM2A expression. In vitro upregulation of miR-95-3p and downregulation of EPM2A promote trophoblast migration, invasion and proliferation. Furthermore, EPM2A was confirmed as a target mRNA of miR-95-3p. Upregulation of EPM2A mitigated miR-95-3p-mediated promotion of trophoblast migration and invasion and vice versa. Finally, both miR-95-3p and EPM2A regulate the expression of trophoblast angiogenesis-related factors and inflammation-related factors. ConclusionOur findings demonstrated that miR-95-3p promoted the migration and invasion of trophoblast cells by targeting EPM2A to inhibit the occurrence and development of PE.

Copy Number Variations (CNVs) Account for 10.8% of Pathogenic Variants in Patients Referred for Hereditary Cancer Testing.

Germline copy number variation (CNV) is a type of genetic variant that predisposes significantly to inherited cancers. Today, next-generation sequencing (NGS) technologies have contributed to multi gene panel analysis in clinical practice. A total of 2,163 patients were screened for cancer susceptibility, using a solution-based capture method. A panel of 52 genes was used for targeted NGS. The capture-based approach enables computational analysis of CNVs from NGS data. We studied the performance of the CNV module of the commercial software suite SeqPilot (JSI Medical Systems) and of the non-commercial tool panelcn.MOPS. Additionally, we tested the performance of digital multiplex ligation-dependent probe amplification (digitalMLPA). Pathogenic/likely pathogenic variants (P/LP) were identified in 464 samples (21.5%). CNV accounts for 10.8% (50/464) of pathogenic variants, referring to deletion/duplication of one or more exons of a gene. In patients with breast and ovarian cancer, CNVs accounted for 10.2% and 6.8% of pathogenic variants, respectively. In colorectal cancer patients, CNV accounted for 28.6% of pathogenic/likely pathogenic variants. In silico CNV detection tools provide a viable and cost-effective method to identify CNVs from NGS experiments. CNVs constitute a substantial percentage of P/LP variants, since they represent up to one of every ten P/LP findings identified by NGS multigene analysis; therefore, their evaluation is highly recommended to improve the diagnostic yield of hereditary cancer analysis.

XAtlas: scalable small variant calling across heterogeneous next-generation sequencing experiments

The growing volume and heterogeneity of next-generation sequencing (NGS) data complicate the further optimization of identifying DNA variation, especially considering that curated high-confidence variant call sets frequently used to validate these methods are generally developed from the analysis of comparatively small and homogeneous sample sets. We have developed xAtlas, a single-sample variant caller for single-nucleotide variants (SNVs) and small insertions and deletions (indels) in NGS data. xAtlas features rapid runtimes, support for CRAM and gVCF file formats, and retraining capabilities. xAtlas reports SNVs with 99.11% recall and 98.43% precision across a reference HG002 sample at 60× whole-genome coverage in less than 2 CPU hours. Applying xAtlas to 3,202 samples at 30× whole-genome coverage from the 1000 Genomes Project achieves an average runtime of 1.7 hours per sample and a clear separation of the individual populations in principal component analysis across called SNVs. xAtlas is a fast, lightweight, and accurate SNV and small indel calling method. Source code for xAtlas is available under a BSD 3-clause license at https://github.com/jfarek/xatlas.

Treasures from trash in cancer research.

Cancer research has significantly improved in recent years, primarily due to next-generation sequencing (NGS) technology. Consequently, an enormous amount of genomic and transcriptomic data has been generated. In most cases, the data needed for research goals are used, and unwanted reads are discarded. However, these eliminated data contain relevant information. Aiming to test this hypothesis, genomic and transcriptomic data were acquired from public datasets. Metagenomic tools were used to explore genomic cancer data; additional annotations were used to explore differentially expressed ncRNAs from miRNA experiments, and variants in adjacent to tumor samples from RNA-seq experiments were also investigated. In all analyses, new data were obtained: from DNA-seq data, microbiome taxonomies were characterized with a similar performance of dedicated metagenomic research; from miRNA-seq data, additional differentially expressed sncRNAs were found; and in tumor and adjacent to tumor tissue data, somatic variants were found. These findings indicate that unexplored data from NGS experiments could help elucidate carcinogenesis and discover putative biomarkers with clinical applications. Further investigations should be considered for experimental design, providing opportunities to optimize data, saving time and resources while granting access to multiple genomic perspectives from the same sample and experimental run.

Unique Molecular Identifiers and Multiplexing Amplicons Maximize the Utility of Deep Sequencing To Critically Assess Population Diversity in RNA Viruses.

Next generation sequencing (NGS)/deep sequencing has become an important tool in the study of viruses. The use of unique molecular identifiers (UMI) can overcome the limitations of PCR errors and PCR-mediated recombination and reveal the true sampling depth of a viral population being sequenced in an NGS experiment. This approach of enhanced sequence data represents an ideal tool to study both high and low abundance drug resistance mutations and more generally to explore the genetic structure of viral populations. Central to the use of the UMI/Primer ID approach is the creation of a template consensus sequence (TCS) for each genome sequenced. Here we describe a series of experiments to validate several aspects of the Multiplexed Primer ID (MPID) sequencing approach using the MiSeq platform. We have evaluated how multiplexing of cDNA synthesis and amplicons affects the sampling depth of the viral population for each individual cDNA and amplicon to understand the relationship between broader genome coverage versus maximal sequencing depth. We have validated reproducibility of the MPID assay in the detection of minority mutations in viral genomes. We have also examined the determinants that allow sequencing reads of PCR recombinants to contaminate the final TCS data set and show how such contamination can be limited. Finally, we provide several examples where we have applied MPID to analyze features of minority variants and describe limits on their detection in viral populations of HIV-1 and SARS-CoV-2 to demonstrate the generalizable utility of this approach with any RNA virus.

Abstract 6348: A cloud-scalable software suite for large-cohort proteogenomics data analysis and visualization

Abstract Comprehensive assessment of the flow of genetic information through multi-omic data integration can reveal the molecular consequences of genetic variation underlying human disease. Next generation sequencing (NGS) is used to identify genetic variants and characterize gene function (e.g. transcriptome and epigenome), while mass spectrometry is used to assess the proteome through characterization of protein abundances, modifications, and interactions. A new plasma profiling platform, the Proteograph࣪ Product Suite, leverages multiple nanoparticles with distinct physiochemical properties to enable deep plasma proteome analyses at scale. Here, we present a cloud-based, data analysis software platform called Proteograph Analysis Suite (PAS) for proteogenomic data analyses through the integration of proteomics data derived from the Proteograph with genomic variant information derived from NGS experiments. PAS features include an experiment data management system, analysis protocols, an analysis setup wizard, and tools for reviewing and visualizing results. PAS can support both Data Independent Analysis (DIA) and Data Dependent Analysis (DDA) proteomics workflows and is compatible with variant call format (vcf) files from NGS workflows to enable personalized database searches. To assess quality of the resulting data PAS includes various metrics like peptide/protein group intensity, protein sequence coverage, relative protein abundance distribution, peptide and protein group counts. Visualizations including principal component analysis, hierarchical clustering, and heatmaps allow intuitive identification of experimental trends. To enable biological insights, differential expression analyses results are reported with interactive visualizations such as volcano plots, protein interaction maps, and protein-set enrichment. From data to insight, PAS provides an easy-to-use and efficient suite of functionality to enable proteogenomic data analysis. Integration of proteomics and genomics data require a variety of tools, many of which require command-line interfaces and operating system-specific requirements that can act as a barrier for researchers to adapt new data analysis tools. Here, we demonstrate the utility of PAS by analyzing samples from the Proteograph NSCLC plasma dataset1. PAS can analyze VCF files generated from NGS pipelines in combination with mass spec data to identify peptide variants using personalized libraries. Using the cloud-based architecture computational tasks are distributed for rapid analysis. The integrated proteogenomics viewers allow variant IDs to be interpreted in the context of genomic coordinates, protein sequence, functional domains and features. Together, these results show the utility of PAS for seamless and fast proteomic data analysis. Reference: 1 Blume, J. E. et al. Nature Communications, 2020 Citation Format: Aaron S. Gajadhar, Margaret K. Donovan, Harsharn Auluck, Yan Berk, Yuandan Lou, Theo Platt, Serafim Batzoglou. A cloud-scalable software suite for large-cohort proteogenomics data analysis and visualization [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 6348.

Loop-Mediated Isothermal Amplification as Point-of-Care Testing for EGFR-Mutated Lung Adenocarcinoma.

Liquid biopsy has been adapted as a diagnostic test for EGFR mutations in patients with advanced or metastatic non-small cell lung cancer (NSCLC). Loop-mediated isothermal amplification (LAMP) has been widely used for the rapid detection of pathogens through DNA amplification. This study investigated the efficacy of an EGFR-LAMP assay using plasma samples of patients with resected NSCLC tumors. The EGFR status was investigated using both LAMP and next-generation sequencing (NGS) assays in cases that met the following criteria: (1) pulmonary adenocarcinoma with EGFR mutation detected by the Therascreen EGFR PCR Kit and (2) preoperative plasma samples contained enough DNA for the LAMP and NGS experiments. Among 51 specimens from patients with EGFR-mutated tumors or metastatic lymph nodes, the LAMP assay detected 1 EGFR mutation that was also detected in the NGS assay. However, a plasma sample that demonstrated EGFR wild type in the LAMP assay showed an EGFR mutant status in NGS. The detection rates (1.9% in LAMP and 3.9% in NGS) were very low in both assays, demonstrating a similar performance in detecting EGFR mutations in NSCLC tumors; therefore, it could be a more suitable test for the advanced stage, not the early stage. Notably, the LAMP assay was more time-saving, cost-effective, and straightforward. However, further investigation is required to develop a more sensitive assay.

Next-Generation Sequencing in Lung Cancer Patients: A Comparative Approach in NSCLC and SCLC Mutational Landscapes.

Background: Lung cancer remains one of the most diagnosed malignancies, being the second most diagnosed cancer, while still being the leading cause of cancer-related deaths. Late diagnosis remains a problem, alongside the high mutational burden encountered in lung cancer. Methods: We assessed the genetic profile of cancer genes in lung cancer using The Cancer Genome Atlas (TCGA) datasets for mutations and validated the results in a separate cohort of 32 lung cancer patients using tumor tissue and whole blood samples for next-generation sequencing (NGS) experiments. Another separate cohort of 32 patients was analyzed to validate some of the molecular alterations depicted in the NGS experiment. Results: In the TCGA analysis, we identified the most commonly mutated genes in each lung cancer dataset, with differences among the three histotypes analyzed. NGS analysis revealed TP53, CSF1R, PIK3CA, FLT3, ERBB4, and KDR as being the genes most frequently mutated. We validated the c.1621A>C mutation in KIT. The correlation analysis indicated negative correlation between adenocarcinoma and altered PIK3CA (r = −0.50918; p = 0.0029). TCGA survival analysis indicated that NRAS and IDH2 (LUAD), STK11 and TP53 (LUSC), and T53 (SCLC) alterations are correlated with the survival of patients. Conclusions: The study revealed differences in the mutational landscape of lung cancer histotypes.

Tailer: a pipeline for sequencing-based analysis of nonpolyadenylated RNA 3' end processing.

Post-transcriptional trimming and tailing of RNA 3′ ends play key roles in the processing and quality control of noncoding RNAs (ncRNAs). However, bioinformatic tools to examine changes in the RNA 3′ “tailome” are sparse and not standardized. Here we present Tailer, a bioinformatic pipeline in two parts that allows for robust quantification and analysis of tail information from next-generation sequencing experiments that preserve RNA 3′ end information. The first part of Tailer, Tailer-processing, uses genome annotation or reference FASTA gene sequences to quantify RNA 3′ ends from SAM-formatted alignment files or FASTQ sequence read files produced from sequencing experiments. The second part, Tailer-analysis, uses the output of Tailer-processing to identify statistically significant RNA targets of trimming and tailing and create graphs for data exploration. We apply Tailer to RNA 3′ end sequencing experiments from three published studies and find that it accurately and reproducibly recapitulates key findings. Thus, Tailer should be a useful and easily accessible tool to globally investigate tailing dynamics of nonpolyadenylated RNAs and conditions that perturb them.

Abstract P2-09-10: Different CNVs account for 10.4% of pathogenic variants in 1418 patients referred for hereditary breast cancer testing

Abstract Background: Breast cancer is the most frequently diagnosed cancer in women and about 10% of breast cancer cases are hereditary. BRCA1 and BRCA2 are the genes most frequently associated with Hereditary Breast Cancer, although there are numerous other genes, such as PALB2, CHEK2 and ATM, that require to be considered as well. Germline Copy Number Variation (CNV) is one mutation type that is an important contributor to hereditary breast cancer. Nowadays, next-generation sequencing (NGS) technologies has contributed to multi-gene panel analysis used in clinical practice. Methods: In total, 1418 individuals were tested for breast cancer predisposition, using a solution-based capture approach. Targeted NGS was performed with a panel of 36 genes. The capture-based approach allowed for computational analysis of CNVs from NGS data. Results: We investigate the performance of the CNV module of the commercial software suite SeqPilot (JSI Medical Systems) and the non-commercial tool panelcn.MOPS. Both algorithms are specifically developed for CNV analysis of sequencing data reporting 99-100% sensitivity and up to 100% specificity for the prediction of CNVs up to the level of a single gene exon. All CNVs detected with these algorithms were then verified experimentally using the MLPA technique as an orthogonal assay. At least one pathogenic/likely pathogenic variant was identified in 289 samples (20.4%). CNVs accounted for 10.4% (30/289), referring to the deletion of one or more exons of a gene. Interestingly, 50% of deletions were single exon and approximately 36% of CNVs were detected in genes other than BRCA1/2. In specific, of the 30 CNVs detected, 60% occurred in BRCA1, 3.3% in BRCA2, 20% in CHEK2, 6.7% in FANCA, 6.7% in PMS2, and 3.3% in ATM. The majority of CNVs in BRCA1 were deletions of exons 19, 22, and 22-23 whereas deletions of exons 9-10 were the most common deletions in CHEK2. Detailed information of all CNVs detected is provided in Table 1. Conclusions: Our results suggest that CNV analysis should not be restricted to BRCA1/2 due to the significant proportion of CNVs (36%) in additional breast cancer predisposition genes. Furthermore, in silico CNV detection tools provide a cost-effective and feasible methodology for the identification of CNVs from NGS experiments. This outlines the clinical utility of comprehensive genetic testing that includes full sequencing and CNV analysis in hereditary breast cancer facilitating personalized management decisions for patients. Table 1.Pathogenic Copy Number Variations (CNVs) identified in this studyGeneHGVS nomenclatureOther nomenclature# detectedATMNM_000051:c.(-30+1_-29-1)_(331+1_332-1)deldeletion of exons 2-41BRCA1NM_007294:c.(5467+1_5468-1)-(*1_?)deldeletion of exon 237BRCA1NM_007294:c.(5406+1_5407-1)_(*1_?)deldeletion of exons 23-245BRCA1NM_007294:c.(5193+1_5194-1)-(5277+1_5278-1)deldeletion of exon 196BRCA2NM_000059:c.(6841+1_6842-1)_(7007+1_7008-1)deldeletion of exons 12-131CHEK2NM_007194:c.(908+1_909-1)_(1095+1_1096-1)deldeletion of exons 9-104CHEK2NM_007194:c.(792+1_793-1)_(846+1_847-1)deletion of exon 72FANCANM_000135:c.(1626+1_1627-1)_ (2852+1_2853-1)deldeletion of exons 18-291FANCANM_000135:c.(893+1_894-1)_(1359+1_1360-1)deldeletion of exons 11-141PMS2NM_000535: c.(903+1_904-1)_(988+1_989-1)deldeletion of exon 91PMS2NM_000535:c.(705+1_706-1)_(2006+1_2007-1)deldeletion of exons 7-111 Citation Format: Nikolaos Tsoulos, Konstantinos Agiannitopoulos, Georgia Pepe, Eirini Papadopoulou, Georgios N Tsaousis, Despina Apostolopoulou, Angeliki Meintani, Vassileios Venizelos, Christos Markopoulos, Rodoniki Iosifidou, Sofia Karageorgopoulou, Christos Christodoulou, Ioannis Natsiopoulos, Konstantinos Papazisis, Maria Vasilaki-Antonatou, Eleftherios Kabletsas, Amanta Psyrri, Stylianos Giassas, Dimitrios Ziogas, Efthalia Lalla, Anna Koumarianou, Christos Papadimitriou, Vahit Ozmen, Sualp Tansan, Kerim Kaban, Tahsin Ozatlı, Dan Tudor Eniu, Angelica Chiorean, Alexandru Blidaru, George Nasioulas. Different CNVs account for 10.4% of pathogenic variants in 1418 patients referred for hereditary breast cancer testing [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-09-10.

A MicroRNA Next-Generation-Sequencing Discovery Assay (miND) for Genome-Scale Analysis and Absolute Quantitation of Circulating MicroRNA Biomarkers.

The plasma levels of tissue-specific microRNAs can be used as diagnostic, disease severity and prognostic biomarkers for chronic and acute diseases and drug-induced injury. Thereby, the combination of diverse microRNAs into biomarker signatures using multivariate statistics seems especially powerful from the perspective of tissue and condition specific microRNA shedding into the plasma. Although next-generation sequencing (NGS) technology enables one to analyse circulating microRNAs on a genome-scale level, it suffers from potential biases (e.g., adapter ligation bias) and lacks absolute transcript quantitation as well as tailor-made quality controls. In order to develop a robust NGS discovery assay for genome-scale quantitation of circulating microRNAs, we first evaluated the sensitivity, repeatability and ligation bias of four commercially available small RNA library preparation protocols. The protocol from RealSeq Biosciences was selected based on its performance and usability and coupled with a novel panel of exogenous small RNA spike-in controls to enable quality control and absolute quantitation, thus ensuring comparability of data across independent NGS experiments. The established microRNA Next-Generation-Sequencing Discovery Assay (miND) was validated for its relative accuracy, precision, analytical measurement range and sequencing bias and was considered fit-for-purpose for microRNA biomarker discovery. Summarized, all these criteria were met, and thus, our analytical platform is considered fit-for-purpose for microRNA biomarker discovery from biofluids in the setting of any diagnostic, prognostic or patient stratification need. The established miND assay was tested on serum, cerebrospinal fluid (CSF), synovial fluid (SF) and extracellular vesicles (EV) extracted from cell culture medium of primary cells and proved its potential to be used across different sample types.

FeatureCorr: An R package to study feature correlations aided with data transformation for sequencing and microarray data

<h2>Abstract</h2> FeatureCorr is an R package that aids in association and network analysis of data obtained from preliminary bioinformatic analysis of next-generation sequencing(NGS) or microarray experiments. These experiments are widely used for various applications like mutation and expression profiles, detection of epigenetic changes in the genomic DNA, etc. FeatureCorr enables users in cleaning and preprocessing data to minimize batch effects and for background noise removal. FeatureCorr can help in the analysis of feature correlation in different ways: Correlation of one Feature vs multiple Features, pairwise correlation of multiple features against multiple features, and in-depth correlation and distributions of two features.

How to Distinguish Solitary Neurofibroma From Neurofibromatosis Type 1.

Neurofibroma is a benign tumor originating from Schwann cells. It is diagnosed as a symptom of neurofibromatosis type 1 (NF1) or solitary neurofibroma. Neurofibromatosis type 1 belongs to a class of hereditary diseases, whereas solitary neurofibroma is not. Presence of germline NF1 gene mutations can be used to distinguish the 2 conditions. However, due to false negative results in gene tests, NF1 may be misdiagnosed as solitary neurofibroma. This calls for development of more accurate diagnostic methods. The authors report 2 patients with neurofibroma who required surgery and fertility consulting. using primary cell culture and next-generation sequencing experiments, the authors found NF1 mutation in neurofibroma Schwann cells. But this mutation was not exit in peripheral blood, hence demonstrate this NF1 mutation was somatic rather than germline. These results confirmed the diagnosis of solitary neurofibroma rather than NF1. The presented method is, therefore, suitable for fertility consultation and diagnosis of solitary neurofibroma patient.

MicroRNA Isoforms Contribution to Melanoma Pathogenesis.

Cutaneous melanoma (CM) is the most lethal tumor among skin cancers, and its incidence is constantly increasing. A deeper understanding of the molecular processes guiding melanoma pathogenesis could improve diagnosis, treatment and prognosis. MicroRNAs play a key role in melanoma biology. Recently, next generation sequencing (NGS) experiments, designed to assess small-RNA expression, revealed the existence of microRNA variants with different length and sequence. These microRNA isoforms are known as isomiRs and provide an additional layer to the complex non-coding RNA world. Here, we collected data from NGS experiments to provide a comprehensive characterization of miRNA and isomiR dysregulation in benign nevi (BN) and early-stage melanomas. We observed that melanoma and BN express different and specific isomiRs and have a different isomiR abundance distribution. Moreover, isomiRs from the same microRNA can have opposite expression trends between groups. Using The Cancer Genome Atlas (TCGA) dataset of skin cancers, we analyzed isomiR expression in primary melanoma and melanoma metastasis and tested their association with NF1, BRAF and NRAS mutations. IsomiRs differentially expressed were identified and catalogued with reference to the canonical form. The reported non-random dysregulation of specific isomiRs contributes to the understanding of the complex melanoma pathogenesis and serves as the basis for further functional studies.