Ig gene (IG) clonality analysis has an important role in the distinction of benign and malignant B-cell lymphoid proliferations and is mostly performed with the conventional EuroClonality/BIOMED-2 multiplex PCR protocol and GeneScan fragment size analysis. Recently, the EuroClonality-NGS Working Group developed a method for next-generation sequencing (NGS)-based IG clonality analysis. Herein, we report the results of an international multicenter biological validation of this novel method compared with the gold standard EuroClonality/BIOMED-2 protocol, based on 209 specimens of reactive and neoplastic lymphoproliferations. NGS-based IG clonality analysis showed a high interlaboratory concordance (99%) and high concordance with conventional clonality analysis (98%) for the molecular conclusion. Detailed analysis of the individual IG heavy chain and kappa light chain targets showed that NGS-based clonality analysis was more often able to detect a clonal rearrangement or yield an interpretable result. NGS-based and conventional clonality analysis detected a clone in 96% and 95% of B-cell neoplasms, respectively, and all but one of the reactive cases were scored polyclonal. We conclude that NGS-based IG clonality analysis performs comparable to conventional clonality analysis. We provide critical parameters for interpretation and discuss a first step toward a quantitative scoring approach for NGS clonality results. Considering the advantages of NGS-based clonality analysis, including its high sensitivity and possibilities for accurate clonal comparison, this supports implementation in diagnostic practice.

• MeSH
• Lymphoma, B-Cell genetics   MeSH
• B-Lymphocytes immunology   MeSH
• Clone Cells immunology   MeSH
• Phenotype MeSH
• Lymphoma, Follicular genetics   MeSH
• Gene Rearrangement * MeSH
• Genes, Immunoglobulin * MeSH
• Immunoglobulin kappa-Chains genetics   MeSH
• Humans MeSH
• Multiplex Polymerase Chain Reaction methods   MeSH
• Sensitivity and Specificity MeSH
• Data Accuracy MeSH
• Immunoglobulin Heavy Chains genetics   MeSH
• High-Throughput Nucleotide Sequencing methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH
• Research Support, Non-U.S. Gov't MeSH
• Validation Study MeSH

One of the hallmarks of B lymphoid malignancies is a B cell clone characterized by a unique footprint of clonal immunoglobulin (IG) gene rearrangements that serves as a diagnostic marker for clonality assessment. The EuroClonality/BIOMED-2 assay is currently the gold standard for analyzing IG heavy chain (IGH) and κ light chain (IGK) gene rearrangements of suspected B cell lymphomas. Here, the EuroClonality-NGS Working Group presents a multicentre technical feasibility study of a novel approach involving next-generation sequencing (NGS) of IGH and IGK loci rearrangements that is highly suitable for detecting IG gene rearrangements in frozen and formalin-fixed paraffin-embedded tissue specimens. By employing gene-specific primers for IGH and IGK amplifying smaller amplicon sizes in combination with deep sequencing technology, this NGS-based IG clonality analysis showed robust performance, even in DNA samples of suboptimal DNA integrity, and a high clinical sensitivity for the detection of clonal rearrangements. Bioinformatics analyses of the high-throughput sequencing data with ARResT/Interrogate, a platform developed within the EuroClonality-NGS Working Group, allowed accurate identification of clonotypes in both polyclonal cell populations and monoclonal lymphoproliferative disorders. This multicentre feasibility study is an important step towards implementation of NGS-based clonality assessment in clinical practice, which will eventually improve lymphoma diagnostics.

• MeSH
• Lymphoma, B-Cell genetics   MeSH
• Gene Rearrangement genetics   MeSH
• Genes, Immunoglobulin genetics   MeSH
• Immunoglobulin kappa-Chains genetics   MeSH
• Humans MeSH
• Lymphoproliferative Disorders genetics   MeSH
• Feasibility Studies MeSH
• Immunoglobulin Heavy Chains genetics   MeSH
• High-Throughput Nucleotide Sequencing methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Vysokokapacitní cílené sekvenování nové generace (NGS) umožňuje paralelní vyšetření mnoha nádorových predispozičních genů (panelů) u onkologicky nemocných s podezřením na výskyt dědičného nádorového syndromu. Identifikace nosičů patogenních mutací u pacientů a jejich příbuzných má zásadní prognostický ale i prediktivní význam. Plnou implementaci výsledků NGS do klinické péče o tyto vysoce rizikové osoby omezuje interpretace nacházených variant s nejasným klinickým významem (VUS). Pro zlepšení diagnostického přínosu NGS u nádorových syndromů provedeme komplexní bioinformatickou reanalýzu dat z nádorových NGS panelů (získaných při rutinních vyšetřeních indikovaných nemocných ve 4 centrech v ČR) a asociaci nalezených variant s klinickými a histopatologickými údaji pacientů. Analýza nádorového NGS panelu u 1000 nenádorových kontrol umožní identifikaci populačně specifického genetického pozadí. Vybrané VUS se zjištěným rekurentním výskytem budou charakterizovány pomocí in silico přístupů a funkčních in vitro testů identifikujících patogenetické mechanismy jejich působení.; High-throughput targeted next-gen sequencing (NGS) enable simultaneous analysis of many cancer-susceptibility genes (panels) in oncological patients with suspected hereditary cancer syndrome. Identification of pathogenic mutations in high-risk patients and their relatives has high prognostic and predictive importance. The utility of NGS data for clinical management of high-risk patients is hampered by complicated interpretation of variants of uncertain significance (VUS). In order to improve the diagnostic power of NGS, we will perform comprehensive reanalysis of NGS cancer panel data (obtained from analyses of high-risk individuals at 4 large Czech centers) and correlation of these data with patients’ clinical and histopathological characteristics. To uncover population specific genetic background, we will perform cancer panel NGS analysis of 1000 non-cancer controls. The VUS in cancer-susceptibility genes will be analyzed by in silico approaches. To describe mechanisms of their pathogenicity, selected recurrent variants will be enrolled into the in vitro functional analysis.

• MeSH
• Neoplastic Syndromes, Hereditary diagnosis   genetics   MeSH
• Genetic Association Studies MeSH
• Humans MeSH
• Mutation genetics   MeSH
• Prognosis MeSH
• Computational Biology methods   MeSH
• High-Throughput Nucleotide Sequencing methods   MeSH
• Check Tag
• Humans MeSH

• Conspectus
• Patologie. Klinická medicína
• NML Fields
• onkologie
• genetika, lékařská genetika
• NML Publication type
• závěrečné zprávy o řešení grantu AZV MZ ČR

Motivation: The study of immunoglobulins and T cell receptors using next-generation sequencing has finally allowed exploring immune repertoires and responses in their immense variability and complexity. Unsurprisingly, their analysis and interpretation is a highly convoluted task. Results: We thus implemented ARResT/Interrogate, a web-based, interactive application. It can organize and filter large amounts of immunogenetic data by numerous criteria, calculate several relevant statistics, and present results in the form of multiple interconnected visualizations. Availability and Implementation: ARResT/Interrogate is implemented primarily in R, and is freely available at http://bat.infspire.org/arrest/interrogate/ Contact: nikos.darzentas@gmail.com Supplementary Information: Supplementary data are available at Bioinformatics online.

• MeSH
• Genetic Variation MeSH
• Immunogenetics methods   MeSH
• Immunoglobulins genetics   MeSH
• Humans MeSH
• Receptors, Antigen, T-Cell genetics   metabolism   MeSH
• Software * MeSH
• High-Throughput Nucleotide Sequencing methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

BACKGROUND: Recent advances in genomics indicate functional significance of a majority of genome sequences and their long range interactions. As a detailed examination of genome organization and function requires very high quality genome sequence, the objective of this study was to improve reference genome assembly of banana (Musa acuminata). RESULTS: We have developed a modular bioinformatics pipeline to improve genome sequence assemblies, which can handle various types of data. The pipeline comprises several semi-automated tools. However, unlike classical automated tools that are based on global parameters, the semi-automated tools proposed an expert mode for a user who can decide on suggested improvements through local compromises. The pipeline was used to improve the draft genome sequence of Musa acuminata. Genotyping by sequencing (GBS) of a segregating population and paired-end sequencing were used to detect and correct scaffold misassemblies. Long insert size paired-end reads identified scaffold junctions and fusions missed by automated assembly methods. GBS markers were used to anchor scaffolds to pseudo-molecules with a new bioinformatics approach that avoids the tedious step of marker ordering during genetic map construction. Furthermore, a genome map was constructed and used to assemble scaffolds into super scaffolds. Finally, a consensus gene annotation was projected on the new assembly from two pre-existing annotations. This approach reduced the total Musa scaffold number from 7513 to 1532 (i.e. by 80%), with an N50 that increased from 1.3 Mb (65 scaffolds) to 3.0 Mb (26 scaffolds). 89.5% of the assembly was anchored to the 11 Musa chromosomes compared to the previous 70%. Unknown sites (N) were reduced from 17.3 to 10.0%. CONCLUSION: The release of the Musa acuminata reference genome version 2 provides a platform for detailed analysis of banana genome variation, function and evolution. Bioinformatics tools developed in this work can be used to improve genome sequence assemblies in other species.

• MeSH
• Molecular Sequence Annotation MeSH
• Musa genetics   MeSH
• Genetic Markers MeSH
• Genome, Plant * MeSH
• Contig Mapping MeSH
• Sequence Analysis, DNA MeSH
• Computational Biology methods   MeSH
• High-Throughput Nucleotide Sequencing MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Molecular profiling of tumor samples has acquired importance in cancer research, but currently also plays an important role in the clinical management of cancer patients. Rapid identification of genomic aberrations improves diagnosis, prognosis and effective therapy selection. This can be attributed mainly to the development of next-generation sequencing (NGS) methods, especially targeted DNA panels. Such panels enable a relatively inexpensive and rapid analysis of various aberrations with clinical impact specific to particular diagnoses. In this review, we discuss the experimental approaches and bioinformatic strategies available for the development of an NGS panel for a reliable analysis of selected biomarkers. Compliance with defined analytical steps is crucial to ensure accurate and reproducible results. In addition, a careful validation procedure has to be performed before the application of NGS targeted assays in routine clinical practice. With more focus on bioinformatics, we emphasize the need for thorough pipeline validation and management in relation to the particular experimental setting as an integral part of the NGS method establishment. A robust and reproducible bioinformatic analysis running on powerful machines is essential for proper detection of genomic variants in clinical settings since distinguishing between experimental noise and real biological variants is fundamental. This review summarizes state-of-the-art bioinformatic solutions for careful detection of the SNV/Indels and CNVs for targeted sequencing resulting in translation of sequencing data into clinically relevant information. Finally, we share our experience with the development of a custom targeted NGS panel for an integrated analysis of biomarkers in lymphoproliferative disorders.

• Publication type
• Journal Article MeSH

Metoda sekvenování nové generace (NGS) se stala velmi populární v biomedicínském výzkumu i v klinické praxi zejména proto, že umožňuje rychlý a detailní vhled do genomu pacienta. V kontextu nádorových onemocnění umožňují metody NGS přesnou detekci jak zárodečných záměn, tak zejména somatických mutací, které mohou pomoci rychle a precizně stanovit diagnózu a přizpůsobit léčbu podle individuálních potřeb pacienta. Vývojem nových výpočetních metod a jejich aplikací za účelem precizního zpracování NGS dat se zabývá vědní obor bioinformatika. Bioinformatická analýza je komplexní proces, jehož správné nastavení je klíčové pro získání relevantních výsledků. Je proto nutné, aby bioinformatik detailně porozuměl biologické podstatě sledovaného jevu, jako je například vznik genových mutací v průběhu onemocnění. Z hlediska bioanalytika i lékaře je naopak užitečné znát jak možnosti a limity NGS technologie, tak i základní bioinformatickou terminologii, na základě které jsou pak schopni s bioinformatiky efektivně komunikovat. V této souhrnné práci se proto autoři snaží obecně popsat bioinformatickou analýzu sekvenačních dat s důrazem na vysvětlení základních pojmů používaných v oblasti analýzy NGS dat.

Next generation sequencing (NGS) has become very popular both in research and clinical practice, in particular because it allows detailed and rapid insight into the patients genome, which can help to diagnose a disease quickly and precisely and thus enable treatment administration based on individual patient needs. The development of novel computing methods and their application for accurate processing of NGS data is the objective of the scientific field of bioinformatics. Bioinformatic analysis is a complex process and its precise set-up is absolutely crucial for obtaining relevant results. Thus, it is necessary for bioinformaticians to understand the biological principles of the given analysis, such as the development of somatic mutations during disease course. From the perspective of a bio-analyst or physician, it is essential to understand the challenges and limits of NGS technology; basic knowledge of bioinformatics and its terminology allows for effective communication with bioinformaticians. In this review, the authors attempt to describe bioinformatic analysis with emphasis on explaining the basic concepts used in the NGS data analysis.

• Keywords
• sekvenování nové generace (NGS),
• MeSH
• Humans MeSH
• Sequence Analysis, DNA * methods   trends   MeSH
• Computational Biology MeSH
• Check Tag
• Humans MeSH
• Publication type
• Review MeSH

The increasing interest in exploring the human genome and identifying genetic risk factors contributing to the susceptibility to and outcome of diseases has supported the rapid development of genome-wide techniques. However, the large amount of obtained data requires extensive bioinformatics analysis. In this work, we established an approach combining amplified fragment length polymorphism (AFLP), AFLP in silico and next generation sequencing (NGS) methods to map the malignant genome of patients with chronic myeloid leukemia. We compared the unique DNA fingerprints of patients generated by the AFLP technique approach with those of healthy donors to identify AFLP markers associated with the disease and/or the response to treatment with imatinib, a tyrosine kinase inhibitor. Among the statistically significant AFLP markers selected for NGS analysis and virtual fingerprinting, we identified the sequences of three fragments in the region of DNA repeat element OldhAT1, LINE L1M7, LTR MER90, and satellite ALR/Alpha among repetitive elements, which may indicate a role of these non-coding repetitive sequences in hematological malignancy. SNPs leading to the presence/absence of these fragments were confirmed by Sanger sequencing. When evaluating the results of AFLP analysis for some fragments, we faced the frequently discussed size homoplasy, resulting in co-migration of non-identical AFLP fragments that may originate from an insertion/deletion, SNP, somatic mutation anywhere in the genome, or combination thereof. The AFLP-AFLP in silico-NGS procedure represents a smart alternative to microarrays and relatively expensive and bioinformatically challenging whole-genome sequencing to detect the association of variable regions of the human genome with diseases.

• MeSH
• Amplified Fragment Length Polymorphism Analysis methods   MeSH
• Leukemia, Myelogenous, Chronic, BCR-ABL Positive drug therapy   genetics   MeSH
• DNA Fingerprinting methods   MeSH
• DNA, Neoplasm genetics   MeSH
• Adult MeSH
• Genome, Human MeSH
• Imatinib Mesylate therapeutic use   MeSH
• Protein Kinase Inhibitors therapeutic use   MeSH
• Cohort Studies MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Computer Simulation MeSH
• Antineoplastic Agents therapeutic use   MeSH
• Repetitive Sequences, Nucleic Acid * MeSH
• Base Sequence MeSH
• Sequence Analysis, DNA methods   MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Case-Control Studies MeSH
• Computational Biology methods   MeSH
• High-Throughput Nucleotide Sequencing methods   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Úvod: Pri dizajnovaní klinických štúdií môže pomôcť identifikácia nových prognostických faktorov prežívania. V prípade dia­gnózy pokročilého nemalobunkového karcinómu pľúc môžu byť vhodnými kandidátmi onkomarkery CYFRA 21-1, CEA alebo NSE [1–8]. Súvislosť ich expresie s prognózou umožňuje hodnotiť aj dataminingová metóda rekurzívneho delenia a zlučovania skupín. Metódy: Analyzovali sme údaje 162 pacientov Onkologickej kliniky FN Trnava. Všetci títo pacienti boli prijatí v rokoch 2008–2012 na podávanie prvej línie chemoterapie podľa platných odporúčaní. Hodnotili sme vplyv známych predliečebných prognostických markerov – výkonnostného stavu, úbytku hmotnosti, fajčenia, veku, pohlavia, štádia, histologického subtypu, komorbidity a onkomarkerov CYFRA 21-1, CEA alebo NSE, ako aj kombinácií týchto faktorov, na prežívanie. Výsledky: Výsledkom našej analýzy sú tri podskupiny pacientov s dobrou, strednou a nepriaznivou prognózou. Onkomarkery mali významnú úlohu pri utvorení podskupiny 49 pacientov s dobrou prognózou – sem patrili pacienti bez úbytku hmotnosti pred začatím liečby a nízkymi hladinami onkomarkerov CEA (≤ 4,1 ng/ml) alebo NSE (≤ 11,1 ng/ml). V tejto podskupine bol medián prežívania najmenej 16 mesiacov (nebol dosiahnutý) a rozdiel prežívania v porovnaní so zvyškom súboru bol vysoko štatisticky signifikantný (pomer rizík 5,21, 95% CI 1,41–19,28; p < 0,0001). Záver: V našom súbore sme preukázali prognostický význam nízkych hladín NSE a CEA v skupine pacientov bez úbytku hmotnosti v predchorobí. Rekurzívne delenie a spájanie skupín predstavuje užitočnú dataminingovú metódu; takto vygenerovanú hypotézu je však potrebné potvrdiť ďalšou klinickou štúdiou dizajnovanou na tento účel. Kľúčové slová: nemalobunkový karcinóm pľúc – onkomarkery – data mining – regresný strom – neurón-špecifická enoláza (NSE) – karcinoembryonálny antigén (CEA)

Introduction: Identification of new prognostic factors can help in designing future clinical studies. In the case of advanced non-small cell lung cancer, there might be good candidates – tumor markers CYFRA 21-1, CEA or NSE [1–8]. It is possible to evaluate the relationship between their expression and prognosis by data mining technique recursive partitioning and amalgamation. Patients and Methods: We analyzed retrospective data of 162 patients of Oncology clinics in Trnava. All of these patients were admitted between 2008 and 2012 for the administration of first-line chemotherapy according to current recommendations. We evaluated the impact of known pretreatment prognostic markers – performance status, weight loss, smoking, age, sex, stage, histologic subtype, comorbidity and oncomarkers CYFRA 21-1, CEA or NSE, as well as combinations of these factors on survival. Results: Our analyses showed that there are three subgroups of patients with good, intermediate and unfavorable prognosis. Oncomarkers played an important role in formation of a subgroup of 49 patients with good prognosis – including patients with no pretreatment weight loss and low levels of CEA (≤ 4.1 ng/ml) or NSE (≤ 11.1 ng/ml). In this subgroup, the median survival time was at least 16 months (not achieved) and the difference in survival compared to the rest of the group was highly statistically significant (risk ratio 5.21, 95% CI 1.41–19.28; p < 0.0001). Conclusion: We showed the prognostic significance of low levels of NSE and CEA oncomarkers in the group of patients with no pretreatment weight loss. Recursive partitioning and amalgamation is a useful data mining method, but the generated hypothesis needs to be confirmed by further clinical study designed for this purpose. Key words: non-small cell lung cancer – oncomarkers – data mining – regression tree – neuron-specific enolase (NSE) – carcinoembryonal antigen (CEA) The authors declare they have no potential conflicts of interest concerning drugs, products, or services used in the study. The Editorial Board declares that the manuscript met the ICMJE “uniform requirements” for biomedical papers. Submitted: 19. 3. 2014 Accepted: 25. 9. 2014

• Keywords
• regresní strom, strom přežití,
• MeSH
• Algorithms MeSH
• Antigens, Neoplasm blood   MeSH
• Phosphopyruvate Hydratase blood   MeSH
• Weight Loss MeSH
• Induction Chemotherapy MeSH
• Carcinoembryonic Antigen blood   MeSH
• Keratin-19 blood   MeSH
• Humans MeSH
• Survival Rate MeSH
• Multivariate Analysis MeSH
• Biomarkers, Tumor * blood   MeSH
• Carcinoma, Non-Small-Cell Lung * blood   pathology   therapy   MeSH
• Prognosis MeSH
• Antineoplastic Agents therapeutic use   MeSH
• Regression Analysis MeSH
• Retrospective Studies MeSH
• Cluster Analysis MeSH
• Neoplasm Staging MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH

Východisko. Včasná diagnostika karcinomu prostaty je stále ještě nejdůležitějším faktorem nádorově specifického přežití u nemocných postižených touto malignitou. Při neznalosti etiologie a markerů rychlosti nádorové progrese jen včasný záchyt onemocnění ohraničeného pouze na orgán, v tomto případě prostatu, může zachránit nemocnému život. Metody a výsledky. V průběhu 7 let bylo provedeno 1464 transrektálních biopsií prostaty u 1302 nemocných. U všech byl zjištěn věk, výsledek digitálního rektálního vyšetřeni (DRE – pozitivní nebo negativní), objem prostaty ultrazvukem (ccm), celkové PSA v periferní krvi (ng/ml) a u většiny i poměr volné frakce PSA a celkového PSA (%). Na rozdíl od dříve užívané lineární regrese jsme užili logistické regrese, neboť pouze věk byl lineárním regresorem a DRE bylo regresorem diskrétním, nabývajícím jen 2 možných hodnot (pozitivní nebo negativní). Výsledkem byla ROC křivka, jež ohraničuje plochu, která odráží vztah mezi senzitivitou a specificitou jakéhokoliv markeru. Multifaktorialni logistická regrese pak dosáhla nejlepších výsledků při hodnotě přes 0,8 ve všech testovaných věkových kategoriích s odchylkou maximálně 8 %, což se dříve nikdy nepodařilo. Závěry. Přes stovky sdělení publikovaných na toto téma se dosud nepodařilo ujednotit názory, kdy a za jakých podmínek přesně indikovat bioptickou punkci prostaty. Autoři na základě podrobného rozboru 1464 zdokumentovaných pozorování u relativně homogenní populace nabízejí počítačový model, vycházející z kombinace biologických a statistických metod, jenž je schopen určit až 9 z 10 pozorovani správně.

Background. Early diagnostics of prostate cancer is still the most important factor in tumor-specific survival of patients harbouring this malignant disease. Without better understanding of the etiology and without relevant markers of the disease progression, only the early diagnostics of organ-confined disease can save the patient's life. Methods and Results. Throughout 7 consecutive years, 1464 transrectal prostate biopsies in 1302 patients were performed. In all cases, the age, DRE (positive or negative), prostatic volume (ccm), total PSA in peripheral blood (ng/ml) were assigned, as well as free/total PSA ratio (%) in most of them. Apart from previously used linear regression, we applied logistic regression, since only age grows linear and DRE is determined only as positive or negative. The surrogate endpoint was ROC, which determines the area applying to the relations of sensitivity and specificity of any marker. Multifactorial logistic regression then reached best results at values over 0.8 in all tested age categories with maximal deviation of 8%, which had not been achieved before. Conclusions. Despite hundreds of papers published on this topic, the question of when and how the patient is indicated to the biopsy of the prostate has not been solved. A computer driven model based on 1464 documented examinations on the relatively homogeneous population is presented. On the basis of the combination of biological and statistical methods, the model can give correct predictions in 9 out of 10 cases.

• MeSH
• Survival Analysis MeSH
• Biopsy methods   statistics & numerical data   utilization   MeSH
• Early Diagnosis MeSH
• Adult MeSH
• Data Interpretation, Statistical MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Prostatic Neoplasms complications   MeSH
• Models, Theoretical MeSH
• Ultrasound, High-Intensity Focused, Transrectal methods   statistics & numerical data   utilization   MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Male MeSH