Next-Generation Sequencing Informatics: Challenges and Strategies for Implementation in a Clinical Environment

Molecular pathology as basis for timely cancer diagnosis and therapy

Precision and personalized therapeutics have witnessed significant advancements in technology, revolutionizing the capabilities of laboratories to generate vast amounts of genetic data. Coupled with computational resources for analysis and interpretation, and integrated with various other types of data, including genomic data, electronic medical health (EMH) data, and clinical knowledge, these advancements support optimized health decisions. Among these technologies, next-generation sequencing (NGS) stands out as a transformative tool in the field of cancer treatment, playing a crucial role in precision oncology. NGS-based workflows are employed across a range of applications, including gene panels, exome sequencing, and whole-genome sequencing, supporting comprehensive analysis of the entire cancer genome, including mutations, copy number variations, gene expression profiles, and epigenetic modifications. By utilizing the power of NGS, these workflows contribute to enhancing our understanding of disease mechanisms, diagnosis confirmation, identifying therapeutic targets, and guiding personalized treatment decisions. This manuscript explores the diverse applications of NGS in cancer treatment, highlighting its significance in guiding diagnosis and treatment decisions, identifying therapeutic targets, monitoring disease progression, and improving patient outcomes.

Molecular profiling in cholangiocarcinoma: A practical guide to next-generation sequencing

Cholangiocarcinomas (CCA) are a heterogeneous group of tumors that are classified as intrahepatic, perihilar, or distal according to the anatomic location within the biliary tract. Each CCA subtype is associated with distinct genomic alterations, including single nucleotide variants, copy number variants, and chromosomal rearrangements or gene fusions, each of which can influence disease prognosis and/or treatment outcomes. Molecular profiling using next-generation sequencing (NGS) is a powerful technique for identifying unique gene variants carried by an individual tumor, which can facilitate their accurate diagnosis as well as promote the optimal selection of gene variant-matched targeted treatments. NGS is particularly useful in patients with CCA because between one-third and one-half of these patients have genomic alterations that can be targeted by drugs that are either approved or in clinical development. NGS can also provide information about disease evolution and secondary resistance alterations that can develop during targeted therapy, and thus facilitate assessment of prognosis and choice of alternative targeted treatments. Pathologists play a critical role in assessing the viability of biopsy samples for NGS, and advising treating clinicians whether NGS can be performed and which of the available platforms should be used to optimize testing outcomes. This review aims to provide clinical pathologists and other healthcare professionals with practical step-by-step guidance on the use of NGS for molecular profiling of patients with CCA, with respect to tumor biopsy techniques, pre-analytic sample preparation, selecting the appropriate NGS panel, and understanding and interpreting results of the NGS test.

Ocular Surface Microbiota in Naïve Keratoconus: A Multicenter Validation Study

In the field of Ophthalmology, the mNGS 16S rRNA sequencing method of studying the microbiota and ocular microbiome is gaining more and more weight in the scientific community. This study aims to characterize the ocular microbiota of patients diagnosed with keratoconus who have not undergone any prior surgical treatment using the mNGS 16S rRNA sequencing method. Samples of naïve keratoconus patients were collected with an eNAT with 1 mL of Liquid Amies Medium (Copan Brescia, Italy), and DNA was extracted and analyzed with 16S NGS. The microbiota analysis showed a relative abundance of microorganisms at the phylum level in each sample collected from 38 patients with KC and 167 healthy controls. A comparison between healthy control and keratoconus samples identified two genera unique to keratoconus, Pelomonas and Ralstonia. Our findings suggest that alterations in the microbiota may play a role in the complex scenario of KC development.

Advanced variant classification framework reduces the false positive rate of predicted loss-of-function variants in population sequencing data

Predicted loss of function (pLoF) variants are often highly deleterious and play an important role in disease biology, but many pLoF variants may not result in loss of function (LoF). Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines' PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 genes associated with autosomal-recessive disease from the Genome Aggregation Database (gnomAD v.2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in a low proportion expressed across transcripts (pext) scored region, or the presence of cryptic in-frame splice rescues. Variants predicted to evade LoF or to be potential artifacts were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of pLoF variants predicted as likely not LoF/not LoF, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.

Advanced variant classification framework reduces the false positive rate of predicted loss of function (pLoF) variants in population sequencing data

Predicted loss of function (pLoF) variants are highly deleterious and play an important role in disease biology, but many of these variants may not actually result in loss-of-function. Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines's PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 autosomal recessive disease-genes from the Genome Aggregation Database (gnomAD, v2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in low per-base expression (pext) score regions, or the presence of cryptic splice rescues. Variants predicted to be potential artifacts or to evade LoF were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of LoF evading variants assessed, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines, and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.

Diagnosis of Mosaic Tuberous Sclerosis Complex Using Next-Generation Sequencing of Subtle or Unusual Cutaneous Findings

Skin findings can be critical to determining whether a patient with lymphangioleiomyomatosis (LAM), a progressive pulmonary disease that predominantly affects adult women, has sporadic LAM or LAM in association with tuberous sclerosis complex (TSC). Three individuals with LAM underwent evaluation for TSC-associated mucocutaneous and internal findings. We used our previously published algorithm to confirm the clinical suspicion for mosaicism and guide the selection of tissue specimens and genetic workup. Next-generation sequencing of cutaneous findings was used to confirm clinical suspicion for mosaic TSC in individuals with LAM. Two individuals previously thought to have sporadic LAM were diagnosed with mosaic TSC-associated LAM upon next-generation sequencing of unilateral angiofibromas in one and an unusual cutaneous hamartoma in the other. A third individual, diagnosed with TSC in childhood, was found to have a mosaic pathogenic variant in TSC2 in cutaneous tissue from a digit with macrodactyly. Accurate diagnosis of mosaic TSC-associated LAM may require enhanced genetic testing and is important because of the implications regarding surveillance, prognosis, and risk of transmission to offspring.

Development and Optimization of an Unbiased, Metagenomics-Based Pathogen Detection Workflow for Infectious Disease and Biosurveillance Applications

Rapid, specific, and sensitive identification of microbial pathogens is critical to infectious disease diagnosis and surveillance. Classical culture-based methods can be applied to a broad range of pathogens but have long turnaround times. Molecular methods, such as PCR, are time-effective but are not comprehensive and may not detect novel strains. Metagenomic shotgun next-generation sequencing (NGS) promises specific identification and characterization of any pathogen (viruses, bacteria, fungi, and protozoa) in a less biased way. Despite its great potential, NGS has yet to be widely adopted by clinical microbiology laboratories due in part to the absence of standardized workflows. Here, we describe a sample-to-answer workflow called PanGIA (Pan-Genomics for Infectious Agents) that includes simplified, standardized wet-lab procedures and data analysis with an easy-to-use bioinformatics tool. PanGIA is an end-to-end, multi-use workflow that can be used for pathogen detection and related applications, such as biosurveillance and biothreat detection. We performed a comprehensive survey and assessment of current, commercially available wet-lab technologies and open-source bioinformatics tools for each workflow component. The workflow includes total nucleic acid extraction from clinical human whole blood and environmental microbial forensic swabs as sample inputs, host nucleic acid depletion, dual DNA and RNA library preparation, shotgun sequencing on an Illumina MiSeq, and sequencing data analysis. The PanGIA workflow can be completed within 24 h and is currently compatible with bacteria and viruses. Here, we present data from the development and application of the clinical and environmental workflows, enabling the specific detection of pathogens associated with bloodstream infections and environmental biosurveillance, without the need for targeted assay development.

Organizational and Technical Security Considerations for Laboratory Cloud Computing

BACKGROUND

Clinical and anatomical pathology services are increasingly utilizing cloud information technology (IT) solutions to meet growing requirements for storage, computation, and other IT services. Cloud IT solutions are often considered on the promise of low cost of entry, durability and reliability, scalability, and features that are typically out of reach for small- or mid-sized IT organizations. However, use of cloud-based IT infrastructure also brings additional security and privacy risks to organizations, as unfamiliarity, public networks, and complex feature sets contribute to an increased surface area for attacks.

CONTENT

In this best-practices guide, we aim to help both managers and IT professionals in healthcare environments understand the requirements and risks when using cloud-based IT infrastructure within the laboratory environment. We will describe how technical, operational, and organizational best practices that can help mitigate security, privacy, and other risks associated with the use of could infrastructure; furthermore, we identify how these best practices fit into healthcare regulatory frameworks.Among organizational best practices, we identify the need for specific hiring requirements, relationships with parent IT groups, mechanisms for reviewing and auditing security practices, and sound practices for onboarding and offboarding employees. Then, we highlight selected specific operational security, account security, and auditing/logging best practices. Finally, we describe how individual cloud technologies have specific resource-level security features.

SUMMARY

We emphasize that laboratory directors, managers, and IT professionals must ensure that the fundamental organizational and process-based requirements are addressed first, to establish the groundwork for technical security solutions and successful implementation of cloud infrastructure.

Optimizing Insertion and Deletion Detection Using Next-Generation Sequencing in the Clinical Laboratory

Detection of insertions and deletions (InDels) by short-read next-generation sequencing (NGS) technology can be challenging because of frequent misaligned reads. A systematic analysis of short InDels (1 to 30 bases) and fms-related receptor tyrosine kinase 3 (FLT3) internal tandem duplications (ITDs; 6 to 183 bases) from 46 clinical cases of solid or hematologic malignancy processed with a clinical NGS assay identified misaligned reads in every case, ranging from 3% to 100% of reads with the InDel showing mismapped bases. Mismaps also increased with InDel size. As a consequence, the clinical NGS bioinformatics pipeline undercalled the variant allele frequency by 1% to 84%, incorrectly called simultaneous single-base substitutions along with InDels, or did not report an FLT3 ITD that had been detected by capillary electrophoresis. To improve the ability of the pipeline to better detect and quantify InDels, we utilized a software program called Assembly-Based ReAligner (ABRA2) to more accurately remap reads. ABRA2 was able to correct 41% to 100% of the reads with mismapped bases and led to absolute increases in the variant allele frequency from 1% to 61% along with correction of all of the single-base substitutions except for two cases. ABRA2 could also detect multiple FLT3 ITD clones except for one 183-base ITD. Our analysis has found that ABRA2 performs well on short InDels as well as FLT3 ITDs that are <100 bases.

Exploring the Healthy Eye Microbiota Niche in a Multicenter Study

Purpose: This study aims to explore and characterize healthy eye microbiota. Methods: Healthy subjects older than 18 years were selected for this descriptive cross-sectional study. Samples were collected with an eSwab with 1 mL of Liquid Amies Medium (Copan Brescia, Italy). Following DNA extraction, libraries preparation, and amplification, PCR products were purified and end-repaired for barcode ligation. Libraries were pooled to a final concentration of 26 pM. Template preparation was performed with Ion Chef according to Ion 510, Ion 520, and Ion 530 Kit-Chef protocol. Sequencing of the amplicon libraries was carried out on a 520 or 530 chip using the Ion Torrent S5 system (Thermo Fisher; Waltham, MA, USA). Raw reads were analyzed with GAIA (v 2.02). Results: Healthy eye microbiota is a low-diversity microbiome. The vast majority of the 137 analyzed samples were highly enriched with Staphylococcus, whereas only in a few of them, other genera such as Bacillus, Pseudomonas, and Corynebacterium predominate. We found an average of 88 genera with an average Shannon index of 0.65. Conclusion: We identified nine different ECSTs. A better understanding of healthy eye microbiota has the potential to improve disease diagnosis and personalized regimens to promote health.

A scalable high-throughput targeted next-generation sequencing assay for comprehensive genomic profiling of solid tumors

Timely and accurate identification of molecular alterations in solid tumors is essential for proper management of patients with advanced cancers. This has created a need for rapid, scalable comprehensive genomic profiling (CGP) systems that detect an increasing number of therapeutically-relevant variant types and molecular signatures. In this study, we assessed the analytical performance of the TruSight Oncology 500 High-Throughput assay for detection of somatic alterations from formalin-fixed paraffin-embedded tissue specimens. In parallel, we developed supporting software and automated sample preparation systems designed to process up to 70 clinical samples in a single NovaSeq 6000TM sequencing run with a turnaround time of <7 days from specimen receipt to report. The results demonstrate that the scalable assay accurately and reproducibly detects small variants, copy number alterations, microsatellite instability (MSI) and tumor mutational burden (TMB) from 40ng DNA, and multiple gene fusions, including known and unknown partners and splice variants from 20ng RNA. 717 tumor samples and reference materials with previously known alterations in 96 cancer-related genes were sequenced to evaluate assay performance. All variant classes were reliably detected at consistent and reportable variant allele percentages with >99% overall accuracy and precision. Our results demonstrate that the high-throughput CGP assay is a reliable method for accurate detection of molecular alterations in support of precision therapeutics in oncology. The supporting systems and scalable workflow allow for efficient interpretation and prompt reporting of hundreds of patient cancer genomes per week with excellent analytical performance.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

Enabling Precision Medicine in Cancer Care Through a Molecular Data Warehouse: The Moffitt Experience

PURPOSE

The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research.

METHODS

Moffitt Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse (Health and Research Informatics). Seven different sequencing experiment types were included in the warehouse, with data from institutional research studies and clinical sequencing.

RESULTS

The implementation of the molecular warehouse involved the close collaboration of many teams with different expertise and a use case-focused approach. Cornerstones of project success included project planning, open communication, institutional buy-in, piloting the implementation, implementing custom solutions to address specific problems, data quality improvement, and data governance, unique aspects of which are featured here. We describe our experience in selecting, configuring, and loading molecular data into the molecular data warehouse. Specifically, we developed solutions for heterogeneous genomic sequencing cohorts (many different platforms) and integration with our existing clinical data warehouse.

CONCLUSION

The implementation was ultimately successful despite challenges encountered, many of which can be generalized to other research cancer centers.

Design, implementation and clinical utility of next generation sequencing in myeloid malignancies: acute myeloid leukaemia and myelodysplastic syndrome

Next generation sequencing (NGS) based technology has contributed enormously to our understanding of the biology of myeloid malignancies including acute myeloid leukaemia (AML) and myelodysplastic syndrome (MDS). Assessment of clinically important mutations by NGS is a powerful tool to define diagnosis, determine prognostic risk, monitor measurable residual disease and uncover predictive mutational markers/therapeutic targets, and is now a routine component in the workup and monitoring of haematological disorders. There are many technical challenges in the design, implementation, analysis and reporting of NGS based results, and expert interpretation is essential. It is vital to distinguish relevant somatic disease associated mutations from those that are known polymorphisms, rare germline variants and clonal haematopoiesis of indeterminate potential (CHIP) associated variants. This review highlights and addresses the technical and biological challenges that should be considered before the implementation of NGS based testing in diagnostic laboratories and seeks to outline the essential and expanding role NGS plays in myeloid malignancies. Broad aspects of NGS panel design and reporting including inherent technological, biological and economic considerations are covered, following which the utility of NGS based testing in AML and MDS are discussed. In current practice, patient care is now strongly shaped by the results of NGS assessment and is considered a vital piece of the puzzle for clinicians as they manage these complex haematological disorders.

Development of a program for in silico optimized selection of oligonucleotide-based molecular barcodes

Short DNA oligonucleotides (~4 mer) have been used to index samples from different sources, such as in multiplex sequencing. Presently, longer oligonucleotides (8–12 mer) are being used as molecular barcodes with which to distinguish among raw DNA molecules in many high-tech sequence analyses, including low-frequent mutation detection, quantitative transcriptome analysis, and single-cell sequencing. Despite some advantages of using molecular barcodes with random sequences, such an approach, however, makes it impossible to know the exact sequences used in an experiment and can lead to inaccurate interpretation due to misclustering of barcodes arising from the occurrence of unexpected mutations in the barcodes. The present study introduces a tool developed for selecting an optimal barcode subset during molecular barcoding. The program considers five barcode factors: GC content, homopolymers, simple sequence repeats with repeated units of dinucleotides, Hamming distance, and complementarity between barcodes. To evaluate a selected barcode set, penalty scores for the factors are defined based on their distributions observed in random barcodes. The algorithm employed in the program comprises two steps: i) random generation of an initial set and ii) optimal barcode selection via iterative replacement. Users can execute the program by inputting barcode length and the number of barcodes to be generated. Furthermore, the program accepts a user’s own values for other parameters, including penalty scores, for advanced use, allowing it to be applied in various conditions. In many test runs to obtain 100000 barcodes with lengths of 12 nucleotides, the program showed fast performance, efficient enough to generate optimal barcode sequences with merely the use of a desktop PC. We also showed that VFOS has comparable performance, flexibility in program running, consideration of simple sequence repeats, and fast computation time in comparison with other two tools (DNABarcodes and FreeBarcodes). Owing to the versatility and fast performance of the program, we expect that many researchers will opt to apply it for selecting optimal barcode sets during their experiments, including next-generation sequencing.

Genetic Information Insecurity as State of the Art

Genetic information is being generated at an increasingly rapid pace, offering advances in science and medicine that are paralleled only by the threats and risk present within the responsible systems. Human genetic information is identifiable and contains sensitive information, but genetic information security is only recently gaining attention. Genetic data is generated in an evolving and distributed cyber-physical system, with multiple subsystems that handle information and multiple partners that rely and influence the whole ecosystem. This paper characterizes a general genetic information system from the point of biological material collection through long-term data sharing, storage and application in the security context. While all biotechnology stakeholders and ecosystems are valuable assets to the bioeconomy, genetic information systems are particularly vulnerable with great potential for harm and misuse. The security of post-analysis phases of data dissemination and storage have been focused on by others, but the security of wet and dry laboratories is also challenging due to distributed devices and systems that are not designed nor implemented with security in mind. Consequently, industry standards and best operational practices threaten the security of genetic information systems. Extensive development of laboratory security will be required to realize the potential of this emerging field while protecting the bioeconomy and all of its stakeholders.

Next Generation Sequencing and Bioinformatics Analysis of Family Genetic Inheritance

Mendelian and complex genetic trait diseases continue to burden and affect society both socially and economically. The lack of effective tests has hampered diagnosis thus, the affected lack proper prognosis. Mendelian diseases are caused by genetic mutations in a singular gene while complex trait diseases are caused by the accumulation of mutations in either linked or unlinked genomic regions. Significant advances have been made in identifying novel diseases associated mutations especially with the introduction of next generation and third generation sequencing. Regardless, some diseases are still without diagnosis as most tests rely on SNP genotyping panels developed from population based genetic analyses. Analysis of family genetic inheritance using whole genomes, whole exomes or a panel of genes has been shown to be effective in identifying disease-causing mutations. In this review, we discuss next generation and third generation sequencing platforms, bioinformatic tools and genetic resources commonly used to analyze family based genomic data with a focus on identifying inherited or novel disease-causing mutations. Additionally, we also highlight the analytical, ethical and regulatory challenges associated with analyzing personal genomes which constitute the data used for family genetic inheritance.

Bibliometric Analysis of Research on Telomere Length in Children: A Review of Scientific Literature

Telomere length in early life has been recently associated with biological aging and development of negative consequences in later adult life. A relevant area of research has emerged to understand the factors that impact telomere length in children. We conducted a bibliometric analysis to track research output and identify global trends and gaps in the knowledge of telomere length in children. Bibliographic data were retrieved from the Web of Science database and then analyzed by using Bibliometrix R package. A total of 840 publications were yielded from 1991 to 2019. The references were prominently published in journals, with 20 high ranked journals contributing to 30% of literature on telomere length in children. The USA was the most productive country (35.7%), followed by Europe (12.1%), and Asia (11.9%). A knowledge map of telomere length in children through keyword analyses revealed that there were two potential main lines of research based on two different approaches: genomic research and epidemiological research. This study shows that telomere length in children is a topic of research that has gained significant relevance in the last decade. This bibliometric study may be helpful in identifying research trends and finding research hot spots and gaps in this research field.

Development and validation of Houston Methodist Variant Viewer version 3: updates to our application for interpretation of next-generation sequencing data

Objectives

Informatics tools that support next-generation sequencing workflows are essential to deliver timely interpretation of somatic variants in cancer. Here, we describe significant updates to our laboratory developed bioinformatics pipelines and data management application termed Houston Methodist Variant Viewer (HMVV).

Materials and Methods

We collected feature requests and workflow improvement suggestions from the end-users of HMVV version 1. Over 1.5 years, we iteratively implemented these features in five sequential updates to HMVV version 3.

Results

We improved the performance and data throughput of the application while reducing the opportunity for manual data entry errors. We enabled end-user workflows for pipeline monitoring, variant interpretation and annotation, and integration with our laboratory information system. System maintenance was improved through enhanced defect reporting, heightened data security, and improved modularity in the code and system environments.

Discussion and Conclusion

Validation of each HMVV update was performed according to expert guidelines. We enabled an 8× reduction in the bioinformatics pipeline computation time for our longest running assay. Our molecular pathologists can interpret the assay results at least 2 days sooner than was previously possible. The application and pipeline code are publicly available at https://github.com/hmvv.

From Somatic Variants Toward Precision Oncology: An Investigation of Reporting Practice for Next-Generation Sequencing-Based Circulating Tumor DNA Analysis

BACKGROUND

With the accelerated development of next-generation sequencing (NGS), identified variants, and targeted therapies, clinicians who confront the complicated and multifarious genetic information may not effectively incorporate NGS-based circulating tumor DNA (ctDNA) analysis into routine patient care. Consequently, standardized ctDNA testing reports are of vital importance. In an effort to guarantee high-quality reporting performance, we conducted an investigation of the current detection and reporting practices for NGS-based ctDNA analysis.

MATERIALS AND METHODS

A set of simulated ctDNA samples with known variants at known allelic frequencies and a corresponding case scenario were distributed to 66 genetic testing laboratories for ctDNA analysis. Written reports were collected to evaluate the detection accuracy, reporting integrity, and information sufficiency using 21 predefined criteria.

RESULTS

Current reporting practices for NGS-based ctDNA analysis were found to be far from satisfactory, especially regarding testing interpretation and methodological details. Only 42.4% of laboratories reported the results in complete concordance with the expected results. Moreover, 74.2% of reports only listed aberrations with direct and well-known treatment consequences for the tumor type in question. Genetic aberrations for which experimental agents and/or drug access programs are available may thus be overlooked. Furthermore, methodological details for the interpretation of results were missing from the majority of reports (87.9%).

CONCLUSION

This proof-of-principle study suggests that the capacity for accurate identification of variants, rational interpretation of genotypes, comprehensive recommendation of potential medications, and detailed description of methodologies need to be further improved before ctDNA analysis can be formally implemented in the clinic.

IMPLICATIONS FOR PRACTICE

Accurate, comprehensive, and standardized clinical sequencing reports can help to translate complex genetic information into patient-centered clinical decisions, thereby shepherding precision oncology into daily practice. However, standards, guidelines, and quality requirements for clinical reports of next-generation sequencing (NGS)-based circulating tumor DNA (ctDNA) analysis are currently absent. By using a set of simulated clinical ctDNA samples and a corresponding case scenario, current practices were evaluated to identify deficiencies in clinical sequencing reports of ctDNA analysis. The recommendations provided here may serve as a roadmap for the improved implementation of NGS-based ctDNA analysis in the clinic.

Evaluation of Commercial Next-Generation Sequencing Bioinformatics Software Solutions

Next-generation sequencing (NGS) diagnostics continue to expand rapidly in clinical medicine. An ever-expanding menu of molecular biomarkers is deemed important for diagnostic, prognostic, and therapeutic assessment in patients. The increasing role of NGS in the clinic is driven mainly by the falling costs of sequencing. However, the data-intensive nature of NGS makes bioinformatic analysis a major challenge to many clinical laboratories. Critically needed NGS bioinformatics personnel are hard to recruit and retain in small- to mid-size clinical laboratories. Also, NGS software often lacks the scalability necessary for expanded clinical laboratory testing volumes. Commercial software solutions aim to bridge the bioinformatics barrier via turnkey informatics solutions tailored specifically for the clinical workplace. Yet, there has been no systematic assessment of these software solutions thus far. This article presents an end-to-end vendor evaluation experience of commercial NGS bioinformatics solutions. Six different commercial vendor solutions were assessed systematically. Key metrics of NGS software evaluation to aid in the robust assessment of software solutions are described. Comprehensive feedback, provided by the TriCore Reference Laboratories molecular pathology team, enabled the final vendor selection. Many key lessons were learned during the software evaluation process, which are described herein. This article aims to provide a detailed road map for small- to mid-size clinical laboratories interested in evaluating commercial bioinformatics solutions available in the marketplace.

External Quality Assurance of Current Technology for the Testing of Cancer-Associated Circulating Free DNA Variants

Liquid biopsy testing is rapidly emerging as a diagnostic means of identifying circulating free DNA (cfDNA) disease-associated variants. However, the reporting of cfDNA variants remains inconsistent due in part to the application of multiple testing pipelines which raise uncertainty about current cfDNA detection efficiency. External quality assurance (EQA) programs are required to monitor, evaluate and help improve laboratory performance for cfDNA variant detection and in clinical interpretation. This study therefore evaluated the performance of diagnostic laboratories currently performing cfDNA testing in China, Australia and New Zealand. A total of 89 laboratories participated in this EQA program. Reference testing material comprised of cfDNA manufactured to contain six different genotypes in four different genes (EGFR, KRAS, BRAF, NRAS). The predicted genotypic variant allelic frequencies ranged between 0.5% - 2.5%. Proficiency testing used a z-score on the laboratory consensus allelic frequency data to compare laboratory performance for the detection of the different genotypes. Allelic frequency genotyping data were received from 88 of the 89 laboratories. Next generation sequencing and digital PCR testing platforms were primarily used by participants in this pilot EQA. The average consensus data for each cfDNA genotype identified allelic frequencies ranging between 0.39% - 4.4%. Z-score proficiency testing found that >92% of clinical laboratories were concordant for detecting the cfDNA variants. The data from this pilot study suggest that current cfDNA testing platforms can detect cfDNA allelic frequency variants from 0.39% and above with high levels of confidence. In addition, these data highlight the importance of laboratories enrolling on EQA programs so that proficiency in cfDNA diagnostic testing can be determined and potential sources of error identified and addressed.

Interactive Browser-Based Genomics Data Visualization Tools for Translational and Clinical Laboratory Applications

Visualization-driven data exploration is a highly effective modality for interpreting and discovering insights from high-throughput genomics data sets; however, it is vastly underutilized in routine workflows in clinical and translation settings. We have developed three open-source, browser-based, interactive genomics data visualization widgets that can be used as intuitive stand-alone applications or integrated with existing web-based laboratory information solutions. The widgets were developed in JavaScript using the D3.js library. These widgets run in any modern web browser across desktop and mobile devices for easy accessibility but are designed for client-side data processing to address data privacy concerns. jsProteinMapper plots the location of a variant of interest relative to the protein domains and multiple variant databases, assisting with clinical interpretation of sequence variants. jsComut generates a highly interactive and customizable comutation plot for visual exploration of genomic data sets with clinicopathologic annotations to reveal unique molecular profiles and clinical correlates. jsCodonWheel is an interactive version of the ubiquitous circular codon-to-amino acid translation table, which lets users quickly map nucleotide changes onto resulting amino acid differences. These open-source visualization tools may improve some of the key laboratory workflows that involve the review of large-scale genomics data sets in a high-volume setting. The intuitive and responsive user interface, highly customizable visualizations, and easy integration with existing web-based laboratory software are significant highlights of these tools.

A novel knowledge-derived data potentizing method revealed unique liver cancer-associated genetic variants

Background

Next-generation sequencing (NGS) has been advancing the progress of detection of disease-associated genetic variants and genome-wide profiling of expressed sequences over the past decade. NGS enables the analyses of multiple regions of a genome in a single reaction format and has been shown to be a cost-effective and efficient tool for root-cause analysis of disease and optimization of treatment. NGS has been leading global efforts to device personalized and precision medicine (PM) in clinical practice. The effectiveness of NGS for the aforementioned applications has been proven unequivocal for multifactorial diseases like cancer. However, definitive prediction of cancer markers for all types of diseases and for global populations still remains highly rewarding because of the diversity of cancer types and genetic variants in human.

Results

We performed exome sequencing of four samples in quest of critical genetic factor/s associated with liver cancer. By imposing knowledge-based filter chains, we have revealed a panel of genetic variants, which are unrecognized by current major genomics data repositories. Total 20 MNV-induced, 5 INDEL-induced, and 31 SNV-induced neoplasm-exclusive genes were revealed through NGS data acquisition followed by data curing with the application of quality filter chains. Liver-specific expression profile of the identified gene pool is directed to the selection of 17 genes which could be the as likely causative genetic factors for liver cancer. Further study on expression level and relevant functional significance enables us to identify and conclude the following four novel variants, viz., c.416T>C (p.Phe139Ser) in SORD, c.1048_1049delGCinsCG (p.Ala350Arg) in KRT6A, c.1159G>T (p.Gly387Cys) in SVEP1, and c.430G>C (p.Gly144Arg) in MRPL38 as a critical genetic factor for liver cancer.

Conclusion

By applying a novel data prioritizing rationale, we explored a panel of previously unaddressed liver cancer-associated variants. These findings may have an opportunity for early prediction of neoplasm/cancer in liver and designing of relevant personalized/precision liver cancer therapeutics in clinical practice. Since NGS protocol is associated with tons of non-specific mutations due to the variation in background genetic makeup of subjects, therefore, our method of data curing could be applicable for more effective screening of global genetic variants related to disease onset, progression, and remission.

Electronic supplementary material

The online version of this article (10.1186/s40246-019-0213-7) contains supplementary material, which is available to authorized users.

Metagenomics in ophthalmology: Hypothesis or real prospective?

Metagenomic analysis was originally associated with the studies of genetic material from environmental samples. But, with the advent of the Human Microbiome Project, it has now been applied in clinical practices. The ocular surface (OS) is the most exposed part of the eye, colonized by several microbial communities (both, OS and environmental) that contribute to the maintenance of the physiological state. Limited knowledge has been acquired on these microbes due to the limitations of conventional diagnostic methods. Emerging fields of research are focusing on Next Generation Sequencing (NGS) technologies to obtain reliable information on the OS microbiome. Currently only pre-specified pathogens can be detected by conventional culture-based techniques or Polymerase Chain Reaction (PCR), but there are conditions to state whether metagenomics could revolutionize the diagnosis of ocular diseases. The aim of this review is to provide an updated overview of the studies involving NGS technology for OS microbiome.

Pulsed-field gel electrophoresis (PFGE): A review of the "gold standard" for bacteria typing and current alternatives

Pulsed-field gel electrophoresis (PFGE) is considered the "gold standard" for bacteria typing. The method involves enzyme restriction of bacteria DNA, separation of the restricted DNA bands using a pulsed-field electrophoresis chamber, followed by clonal assignment of bacteria based on PFGE banding patterns. Various PFGE protocols have been developed for typing different bacteria, leading it to be one of the most widely used methods for phylogenetic studies, food safety surveillance, infection control and outbreak investigations. On the other hand, as PFGE is lengthy and labourious, several PCR-based typing methods can be used as alternatives for research purposes. Recently, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and whole genome sequencing (WGS) have also been proposed for bacteria typing. In fact, as WGS provides more information, such as antimicrobial resistance and virulence of the tested bacteria in comparison to PFGE, more and more laboratories are currently transitioning from PFGE to WGS for bacteria typing. Nevertheless, PFGE will remain an affordable and relevant technique for small laboratories and hospitals in years to come.

Logical Observation Identifiers Names and Codes for Laboratorians

CONTEXT.—

The Logical Observation Identifiers Names and Codes (LOINC) system is supposed to facilitate interoperability, and it is the federally required code for exchanging laboratory data.

OBJECTIVE.—

To provide an overview of LOINC, emerging issues related to its use, and areas relevant to the pathology laboratory, including the subtleties of test code selection and importance of mapping the correct codes to local test menus.

DATA SOURCES.—

This review is based on peer-reviewed literature, federal regulations, working group reports, the LOINC database (version 2.65), experience using LOINC in the laboratory at several large health care systems, and insight from laboratory information system vendors.

CONCLUSIONS.—

The current LOINC database contains more than 55 000 numeric codes specific for laboratory tests. Each record in the LOINC database includes 6 major axes/parts for the unique specification of each individual observation or measurement. Assigning LOINC codes to a laboratory's test menu should be a defined process. In some cases, LOINC can aid in distinguishing laboratory data among different information systems, whereby such benefits are not achievable by relying on the laboratory test name alone. Criticisms of LOINC include the complexity and resource-intensive process of selecting the most correct code for each laboratory test, the real-world experience that these codes are not uniformly assigned across laboratories, and that 2 tests that may have the same appropriately assigned LOINC code may not necessarily have equivalency to permit interoperability of their result data. The coding system's limitations, which subsequently reduce the potential utility of LOINC, are poorly understood outside of the laboratory.

Application of deep sequencing methods for inferring viral population diversity

The first deep sequencing method was announced in 2005. Due to an increasing number of sequencing data and a reduction in the costs of each sequencing dataset, this innovative technique was soon applied to genetic investigations of viral genome diversity in various viruses, particularly RNA viruses. These deep sequencing findings documented viral epidemiology and evolution and provided high-resolution data on the genetic changes in viral populations. Here, we review deep sequencing platforms that have been applied in viral quasispecies studies. Further, we discuss recent deep sequencing studies on viral inter- and intrahost evolution, drug resistance, and humoral immune selection, especially in emerging and re-emerging viruses. Deep sequencing methods are becoming the standard for providing comprehensive results of viral population diversity, and their applications are discussed.

EasyQC: Tool with Interactive User Interface for Efficient Next-Generation Sequencing Data Quality Control

The advent of next-generation sequencing (NGS) technologies has revolutionized the world of genomic research. Millions of sequences are generated in a short period of time and they provide intriguing insights to the researcher. Many NGS platforms have evolved over a period of time and their efficiency has been ever increasing. Still, primarily because of the chemistry, glitch in the sequencing machine and human handling errors, some artifacts tend to exist in the final sequence data set. These sequence errors have a profound impact on the downstream analyses and may provide misleading information. Hence, filtering of these erroneous reads has become inevitable and myriad of tools are available for this purpose. However, many of them are accessible as a command line interface that requires the user to enter each command manually. Here, we report EasyQC, a tool for NGS data quality control (QC) with a graphical user interface providing options to carry out trimming of NGS reads based on quality, length, homopolymer, and ambiguous bases. EasyQC also possesses features such as format converter, paired end merger, adapter trimmer, and a graph generator that generates quality distribution, length distribution, GC content, and base composition graphs. Comparison of raw and processed sequence data sets using EasyQC suggested significant increase in overall quality of the sequences. Testing of EasyQC using NGS data sets on a standalone desktop proved to be relatively faster. EasyQC is developed using PERL modules and can be executed in Windows and Linux platforms. With the various QC features, easy interface for end users, and cross-platform compatibility, EasyQC would be a valuable addition to the already existing tools facilitating better downstream analyses.

Academic Medical Centers as Innovation Ecosystems: Evolution of Industry Partnership Models Beyond the Bayh-Dole Act

Innovation ecosystems tied to academic medical centers (AMCs) are inextricably linked to policy, practices, and infrastructure resulting from the Bayh-Dole Act in 1980. Bayh-Dole smoothed the way to patenting and licensing new drugs and, to some degree, medical devices and diagnostic reagents. Property rights under Bayh-Dole provided significant incentive for industry investments in clinical trials, clinical validation, and industrial scale-up of products that advanced health care. Bayh-Dole amplified private investment in biotechnology drug development and, from the authors' perspective, did not significantly interfere with the ability of AMCs to produce excellent peer-reviewed science. In today's policy environment, it is increasingly difficult to patent and license products based on the laws of nature-as the scope of patentability has been narrowed by case law and development of a suitable clinical and business case for the technology is increasingly a gating consideration for licensees. Consequently, fewer academic patents are commercially valuable. The role of technology transfer organizations in engaging industry partners has thus become increasingly complex. The partnering toolbox and organizational mandate for commercialization must evolve toward novel collaborative models that exploit opportunities for future patent creation (early drug discovery), data exchange (precision medicine using big data), cohort assembly (clinical trials), and decision rule validation (clinical trials). These inputs contribute to intellectual property rights, and their clinical exploitation manifests the commercialization of translational science. New collaboration models between AMCs and industry must be established to leverage the assets within AMCs that industry partners deem valuable.

Society and personal genome data

Genomic data offer a goldmine of information for understanding the contribution of genetic variation makes to health and disease. The potential of genomic medicine, to predict, diagnose, manage and treat genetic disease, is underpinned by accurate variant interpretation. This in itself hinges on the ability to access large and varied genomic databases. There is now recognition that international collaboration between research and healthcare systems are paramount to delivering the scale of genomic data required. No single research group, institute or country will liberate our understanding, it is only through global cooperation, together with super computing power, will we truly make sense of how genotype and phenotype correlate. Whilst it is logistically possible to create computing systems that talk to each other and aggregate datasets ready to reveal novel correlations, the bottom line is that this will only happen if people (whether they be scientists, clinicians, patients, research participants, policy makers, politicians, law makers) support the principle that we should be donating, accessing and sharing our DNA data in this way. And in order to make the most sense of genomics, given the geographical and ancestral variation between us, such people are likely to be the majority of society. Within this review, a perspective is proffered on the human story that underpins genomic 'big data' access and how we are at a tipping point as a society-we need to decide collectively, are we in? and if so, what needs to be in place to protect us? or are we out?

Artificial Intelligence Approach for Variant Reporting

Purpose

Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex pipeline output. As a result, the decision to include a variant in the final report during routine clinical sign-out remains challenging.

Methods

We used an artificial intelligence approach to capture the collective clinical sign-out experience of six board-certified molecular pathologists to build and validate a decision support tool for variant reporting. We extracted all reviewed and reported variants from our clinical database and tested several machine learning models. We used 10-fold cross-validation for our variant call prediction model, which derives a contiguous prediction score from 0 to 1 (no to yes) for clinical reporting.

Results

For each of the 19,594 initial training variants, our pipeline generates approximately 500 features, which results in a matrix of > 9 million data points. From a comparison of naive Bayes, decision trees, random forests, and logistic regression models, we selected models that allow human interpretability of the prediction score. The logistic regression model demonstrated 1% false negativity and 2% false positivity. The final models' Youden indices were 0.87 and 0.77 for screening and confirmatory cutoffs, respectively. Retraining on a new assay and performance assessment in 16,123 independent variants validated our approach (Youden index, 0.93). We also derived individual pathologist-centric models (virtual consensus conference function), and a visual drill-down functionality allows assessment of how underlying features contributed to a particular score or decision branch for clinical implementation.

Conclusion

Our decision support tool for variant reporting is a practically relevant artificial intelligence approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.

Successful preimplantation genetic diagnosis by targeted next-generation sequencing on an ion torrent personal genome machine platform

Hearing loss may place a heavy burden on the patient and patient's family. Given the high incidence of hearing loss among newborns and the huge cost of treatment and care (including cochlear implantation), prenatal diagnosis is strongly recommended. Termination of the fetus may be considered as an extreme outcome to the discovery of a potential deaf fetus, and therefore preimplantation genetic diagnosis has become an important option for avoiding the birth of affected children without facing the risk of abortion following prenatal diagnosis. In one case, a couple had a 7-year-old daughter affected by non-syndromic sensorineural hearing loss. The affected fetus carried a causative compound heterozygous mutation c.919-2 A>G (IVS7-2 A>G) and c.1707+5 G>A (IVS15+5 G>A) of the solute carrier family 26 member 4 gene inherited from maternal and paternal sides, respectively. The present study applied multiple displacement amplification for whole genome amplification of biopsied trophectoderm cells and next-generation sequencing (NGS)-based single nucleotide polymorphism haplotyping on an Ion Torrent Personal Genome Machine. One unaffected embryo was transferred in a frozen-thawed embryo transfer cycle and the patient was impregnated. To conclude, to the best of our knowledge, this may be the first report of NGS-based preimplantation genetic diagnosis (PGD) for non-syndromic hearing loss caused by a compound heterozygous mutation using an Ion Torrent Personal Genome Machine. NGS provides unprecedented high-throughput, highly parallel and base-pair resolution data for genetic analysis. The method meets the requirements of medium-sized diagnostics laboratories. With decreased costs compared with previous techniques (such as Sanger sequencing), this technique may have potential widespread clinical application in PGD of other types of monogenic disease.

Academic medical centers as innovation ecosystems to address population -omics challenges in precision medicine

While the promise of the Human Genome Project provided significant insights into the structure of the human genome, the complexities of disease at the individual level have made it difficult to utilize -omic information in clinical decision making. Some of the existing constraints have been minimized by technological advancements that have reduced the cost of sequencing to a rate far in excess of Moore's Law (a halving in cost per unit output every 18 months). The reduction in sequencing costs has made it economically feasible to create large data commons capturing the diversity of disease across populations. Until recently, these data have primarily been consumed in clinical research, but now increasingly being considered in clinical decision- making. Such advances are disrupting common diagnostic business models around which academic medical centers (AMCs) and molecular diagnostic companies have collaborated over the last decade. Proprietary biomarkers and patents on proprietary diagnostic content are no longer driving biomarker collaborations between industry and AMCs. Increasingly the scope of the data commons and biorepositories that AMCs can assemble through a nexus of academic and pharma collaborations is driving a virtuous cycle of precision medicine capabilities that make an AMC relevant and highly competitive. A rebalancing of proprietary strategies and open innovation strategies is warranted to enable institutional precision medicine asset portfolios. The scope of the AMC's clinical trial and research collaboration portfolios with industry are increasingly dependent on the currency of data, and less on patents. Intrapeneurial support of internal service offerings, clinical trials and clinical laboratory services for example, will be important new points of emphasis at the academic-industry interface. Streamlining these new models of industry collaboration for AMCs are a new area for technology transfer offices to offer partnerships and to add value beyond the traditional intellectual property offering.

Does next-generation sequencing of cyst fluid improve management of pancreatic cystic neoplasms?

Pancreatic cystic lesions represent a heterogeneous group of diseases ranging from benign to malignant lesions. They are increasingly being detected due to the widespread use of cross-sectional imaging. Their management is a challenge because it is often not possible to reliably discriminate between malignant and nonmalignant lesions using current imaging technology. Next-generation sequencing (NGS) has the ability of both whole genome and targeted sequencing at a low cost and from a limited amount of DNA. NGS of cyst fluid aspired by endoscopic ultrasonography-guided fine-needle aspiration provides a valuable tool in biomarker research and may in the future help improve diagnosis and management of pancreatic cystic lesions.

The role of informatics in patient-centered care and personalized medicine

The practice of cytopathology has dramatically changed due to advances in genomics and information technology. Cytology laboratories have accordingly become increasingly dependent on pathology informatics support to meet the emerging demands of precision medicine. Pathology informatics deals with information technology in the laboratory, and the impact of this technology on workflow processes and staff who interact with these tools. This article covers the critical role that laboratory information systems, electronic medical records, and digital imaging plays in patient-centered personalized medicine. The value of integrated diagnostic reports, clinical decision support, and the use of whole-slide imaging to better evaluate cytology samples destined for molecular testing is discussed. Image analysis that offers more precise and quantitative measurements in cytology is addressed, as well as the role of bioinformatics tools to cope with Big Data from next-generation sequencing. This article also highlights the barriers to the widespread adoption of these disruptive technologies due to regulatory obstacles, limited commercial solutions, poor interoperability, and lack of standardization. Cancer Cytopathol 2017;125(6 suppl):494-501. © 2017 American Cancer Society.

Versatile ion S5XL sequencer for targeted next generation sequencing of solid tumors in a clinical laboratory

BACKGROUND

Next generation sequencing based tumor tissue genotyping involves complex workflow and a relatively longer turnaround time. Semiconductor based next generation platforms varied from low throughput Ion PGM to high throughput Ion Proton and Ion S5XL sequencer. In this study, we compared Ion PGM and Ion Proton, with a new Ion S5XL NGS system for workflow scalability, analytical sensitivity and specificity, turnaround time and sequencing performance in a clinical laboratory.

METHODS

Eighteen solid tumor samples positive for various mutations as detected previously by Ion PGM and Ion Proton were selected for study. Libraries were prepared using DNA (range10-40ng) from micro-dissected formalin-fixed, paraffin-embedded (FFPE) specimens using the Ion Ampliseq Library Kit 2.0 for comprehensive cancer (CCP), oncomine comprehensive cancer (OCP) and cancer hotspot panel v2 (CHPv2) panel as per manufacturer's instructions. The CHPv2 were sequenced using Ion PGM whereas CCP and OCP were sequenced using Ion Proton respectively. All the three libraries were further sequenced individually (S540) or multiplexed (S530) using Ion S5XL. For S5XL, Ion chef was used to automate template preparation, enrichment of ion spheres and chip loading. Data analysis was performed using Torrent Suite 4.6 software on board S5XL and Ion Reporter. A limit of detection and reproducibility studies was performed using serially diluted DLD1 cell line.

RESULTS

A total of 241 variant calls (235 single nucleotide variants and 6 indels) expected in the studied cohort were successfully detected by S5XL with 100% and 97% concordance with Ion PGM and Proton, respectively. Sequencing run time was reduced from 4.5 to 2.5 hours with output range of 3-5 GB (S530) and 8-9.3Gb (S540). Data analysis time for the Ion S5XL is faster 1 h (S520), 2.5 h (S530) and 5 h (S540) chip, respectively as compared to the Ion PGM (3.5-5 h) and Ion Proton (8h). A limit detection of 5% allelic frequency was established along with high inter-run reproducibility.

CONCLUSION

Ion S5XL system simplified workflow in a clinical laboratory, was feasible for running smaller and larger panels on the same instrument, had a shorter turnaround time, and showed good concordance for variant calls with similar sensitivity and reproducibility as the Ion PGM and Proton.

Advances in renal genetic diagnosis

Most genetic disorders are clinically and genetically heterogeneous. Next-generation sequencing (NGS) has revolutionized the field and is providing rapidly growing insights into the pathomechanism of hereditary nephropathies. Current best-practice guidelines for most hereditary nephropathies include genetic diagnostics. The increasing number of genes that have to be considered in patients with hereditary nephropathies is often challenging when addressed by conventional techniques and largely benefits from NGS-based approaches that allow the parallel analysis of all disease genes in a single test at relatively low cost, e.g., by the use of multi-gene panels. Knowledge of the underlying genotype is of advantage in discussions with regard to transplantation and therapeutic options. Further, genetics may aid the early detection and treatment of renal and extrarenal complications and the reduction of invasive procedures. An accurate genetic diagnosis is crucial for genetic counselling, provides information about the recurrence risk and may help to improve the clinical management of patients and their families. The bottleneck in genetics is no longer the primary wet lab process but the interpretation of the obtained genetic data, which is by far the most challenging and work-intensive part of the analysis. This can only be managed in a multidisciplinary setting that brings together expert knowledge in genetics and the respective medical field. In the future, bench and bedside benefits can be expected from this kind of digitized medicine.

Clinical Genomic Profiling of a Diverse Array of Oncology Specimens at a Large Academic Cancer Center: Identification of Targetable Variants and Experience with Reimbursement

Large cancer panels are being increasingly used in the practice of precision medicine to generate genomic profiles of tumors with the goal of identifying targetable variants and guiding eligibility for clinical trials. To facilitate identification of mutations in a broad range of solid and hematological malignancies, a 467-gene oncology panel (Columbia Combined Cancer Panel) was developed in collaboration with pathologists and oncologists and is currently available and in use for clinical diagnostics. Herein, we share our experience with this testing in an academic medical center. Of 255 submitted specimens, which encompassed a diverse range of tumor types, we were able to successfully sequence 92%. The Columbia Combined Cancer Panel assay led to the detection of a targetable variant in 48.7% of cases. However, although we show good clinical performance and diagnostic yield, third-party reimbursement has been poor. Reimbursement from government and third-party payers using the 81455 Current Procedural Terminology code was at 19.4% of billed costs, and 55% of cases were rejected on first submission. Likely contributing factors to this low level of reimbursement are the delays in valuation of the 81455 Current Procedural Terminology code and in establishing national or local coverage determinations. In the absence of additional demonstrations of clinical utility and improved patient outcomes, we expect the reimbursement environment will continue to limit the availability of this testing more broadly.

Big data from small samples: Informatics of next-generation sequencing in cytopathology

The rapid adoption of next-generation sequencing (NGS) in clinical molecular laboratories has redefined the practice of cytopathology. Instead of simply being used as a diagnostic tool, cytopathology has evolved into a practice providing important genomic information that guides clinical management. The recent emphasis on maximizing limited-volume cytology samples for ancillary molecular studies, including NGS, requires cytopathologists not only to be more involved in specimen collection and processing techniques but also to be aware of downstream testing and informatics issues. For the integration of molecular informatics into the clinical workflow, it is important to understand the computational components of the NGS workflow by which raw sequence data are transformed into clinically actionable genomic information and to address the challenges of having a robust and sustainable informatics infrastructure for NGS-based testing in a clinical environment. Adapting to needs ranging from specimen procurement to report delivery is crucial for the optimal utilization of cytology specimens to accommodate requests from clinicians to improve patient care. This review presents a broad overview of the various aspects of informatics in the context of NGS-based testing of cytology specimens. Cancer Cytopathol 2017;125:236-244. © 2016 American Cancer Society.

Molecular digital pathology: progress and potential of exchanging molecular data

Many of the demands to perform next generation sequencing (NGS) in the clinical laboratory can be resolved using the principles of telepathology. Molecular telepathology can allow facilities to outsource all or a portion of their NGS operation such as cloud computing, bioinformatics pipelines, variant data management, and knowledge curation. Clinical pathology laboratories can electronically share diverse types of molecular data with reference laboratories, technology service providers, and/or regulatory agencies. Exchange of electronic molecular data allows laboratories to perform validation of rare diseases using foreign data, check the accuracy of their test results against benchmarks, and leverage in silico proficiency testing. This review covers the emerging subject of molecular telepathology, describes clinical use cases for the appropriate exchange of molecular data, and highlights key issues such as data integrity, interoperable formats for massive genomic datasets, security, malpractice and emerging regulations involved with this novel practice.