In this webinar, we’ll introduce algorithms required to perform data analysis for resequencing next-generation sequencing data. Together, we’ll explore:
• Read mapper
• Variant callers
• Annotations and filters
• Genome Browser
Bring any questions you may have, and we will answer them during the webinar.
Scale your NGS analysis to match your sequencing throughput using our QIAGEN CLC Genomics cloud solution. In this webinar, you’ll learn about the flexible and powerful setup for running your NGS sequence analyses on Amazon Web Services (AWS). You’ll learn how to use QIAGEN CLC Genomics Workbench to customize workflows and send analyses to AWS Batch for execution. Together we’ll also explore analysis automation solutions, third-party dockers, audit trails and user management, all of which are available with QIAGEN CLC Genomics Server software.
Together, we’ll explore how to:
• Customize template workflows to meet your NGS analysis requirements
• Set up a QIAGEN CLC Genomics cloud environment on AWS
• Submit workflows to run on AWS and to retrieve results from these analyses.
• Use third-party dockers and automate workflow execution
The composition of the immune repertoire, consisting of the T cell and B cell receptors (TCR and BCR), is important for an organism's adaptive immune system and plays a pivotal role in an individual's overall health. Understanding the complex array of TCR and BCR allows for developing precision medicine and immunotherapy. Analyzing next-generation sequencing (NGS) data from RNA-seq experiments to characterize and understand TCR and BCR clonotypes may aid in identifying cases that could benefit from personalized immunotherapy or potentially predict therapeutic outcomes.
In this training, we'll use QIAGEN CLC Genomics Workbench to analyze immune repertoire NGS data generated from RNA-seq or single-cell RNA-seq data. Together we'll explore how to import, analyze and interpret your NGS data. Specifically, you'll learn how to:
-Import NGS data into QIAGEN CLC Genomics Workbench
-Analyze the data using template workflows
-Interpret the results using the interactive graphics produced by the workflows
The Fall 2021 Release of the Human Gene Mutation Database (HGMD) Professional is now available, expanding the world’s largest collection of human inherited disease mutations to 344,012 entries–that’s 20,351 more than the previous release.
For over 30 years, HGMD Professional has been used worldwide by researchers, clinicians, diagnostic laboratories and genetic counselors as an essential tool for the annotation of next-generation sequencing (NGS) data in routine clinical and translational research. Founded and maintained by the Institute of Medical Genetics at Cardiff University, HGMD Professional provides users with a unique resource containing expert-curated mutations all backed by peer-reviewed publications where there is evidence of clinical impact.
Whether searching for an overview of known mutations associated with a particular disease, interpreting clinical test results, looking for the likely causal mutation in a list of variants, or seeking to integrate mutation content into your custom NGS pipeline or data repository—HGMD is the defacto-standard repository for heritable mutations that can be adapted to a broad range of applications.
detailed mutation reports
new mutation entries in 2020 alone
summary reports listing all known
inherited disease mutations
HGMD is powered by a team of expert curators at Cardiff University. Data are collected weekly by a combination of manual and computerized search procedures. In excess of 250 journals are scanned for articles describing germline mutations causing human genetic disease. The required data are extracted from the original articles and augmented with the necessary supporting data.
The number of disease-associated germline mutations published per year has more than doubled in the past decade (Figure 1). As rare and novel genetic mutations continue to be uncovered, having access to the latest scientific evidence is critical for timely interpretations of next-generation sequencing (NGS) data.
View the complete HGMD Professional statistics here.
Join us for a webinar on October, 21, 2021, as our experts will show you how HGMD Professional simplifies and streamlines variant classification in hereditary workflows.
Read more about the importance of having access to the most up-to-date and comprehensive database for human disease mutations in our white paper.
HGMD Professional helps clinical testing labs analyze and annotate next-generation sequencing (NGS) data with current and trusted information. Unlike other mutation databases, HGMD mutations are all backed by peer-reviewed publications where there is evidence of clinical impact.
To get the most out of your HGMD Professional subscription, visit our Resources webpage for case studies, technical notes, and video tutorials.
You’re invited to QIAGEN Digital Insights’ first annual Every Lab Summit, a free-to-attend virtual event exploring how any lab, anywhere, of any size can offer precision oncology NGS testing to advance community cancer care.
It’s a new world of NGS. The challenge is no longer how to rapidly sequence DNA, but how to understand and use this genomic data at an equally accelerated pace to improve patient outcomes.
At the Every Lab Summit, our experts will demonstrate how small to mid-sized clinical diagnostic labs can compete to offer comprehensive genomic profiling services for precision oncology with the same speed, expertise, and confidence of large hospital networks and university centers.
Attendees will receive virtual demonstrations of QIAGEN Clinical Insights software, as well as qualify for free trials and/or complimentary consultations of QIAGEN's clinical decision support software and solutions.
View event agenda here.
Today genetic innovation is advancing at breakneck pace. Since the first whole genome was sequenced in 2003, a feat that took 15 years, 20 different labs, and more than $3 billion to complete, a patient can now have his or her whole genome sequenced by one lab, in one day, for under $200.
Yet, only a fraction of patients receives genetic testing. Despite incredible advances in sequencing technology, most notable being next-generation sequencing (NGS), genetic has only recently become a clinical application. The challenge is no longer how to sequence DNA, but how to interpret a patient’s genetic variation in a meaningful and actionable way.
We are on the cusp of a healthcare revolution where genetic testing will be able to provide answers and insight into critical health questions. For a couple planning their family, genetic testing can identify carrier status. When a child has a rare condition with unexplainable symptoms, genetic testing can pinpoint a diagnosis. If a patient has a family history of cancer, genetic testing can predict the risk of developing disease.
A new reality is emerging in which genetic testing will transform our understanding and management of hereditary diseases. But before genetic testing becomes a routine part of clinical care for every patient, we must first address the complexity, cost, and consistency of NGS test interpretation.
On April 22, Nature Medicine published the first results from the UK's TARGET (Tumor chARacterisation to Guide Experimental Targeted therapy) study. The letter, written by researchers funded by Cancer Research UK, The Christie Charity, AstraZeneca, and the NIHR Manchester Biomedical Research Centre (BRC), adds new evidence for the feasibility and potential utility of liquid biopsy to identify clinically actionable mutations and guide clinical trial enrollment for patients with advanced cancer.
Currently, enrollment to trials depends on a patient's type of cancer or genetic data obtained from an invasive tumor biopsy, which is often months or years old and may not represent a patient's current disease due to the tumor's evolutionary changes.
TARGET is a molecular profiling program with the primary aim to match patients with different types of advanced cancers to early phase clinical trials on the basis of analysis of both somatic mutations and copy number alterations across a 641 cancer-associated-gene panel in a single ctDNA assay. In the first of the two-part trial, the investigators were able to collect, process and analyze blood samples from 100 patients in the Manchester area.
The results show that a small volume of blood can contain up-to-date genetic information about a patient's cancer to inform treatment choices. In this feasibility study of the first 100 patients, 11 were molecularly matched and enrolled into an available therapy.
Investigators used QIAGEN Clinical Insight (QCI) to identify the clinically actionable variants and geographically available clinical trials, stating:
Functional annotation of somatic variants was performed using ANNOVAR, the resultant VCF file was analyzed through the QIAGEN Clinical Insight for Somatic Cancer platform and reports were generated for discussion in the TARGET Molecular Tumor Board. ‘Actionable’ was defined as a target of known pathogenic significance for which either a licensed or experimental agent or relevant clinical trial was available at the time of discussion.
The investigators go on to describe the importance of using clinical interpretation and reporting tools that are connected to comprehensive knowledge resources needed to minimize the number of variants of unknown significance (VUS) with evidence. As they explain, it is nearly impossible for tumor boards to have knowledge of every actionable variant:
A potential reason why large molecular screening programs have traditionally allocated only 10–15% of patients to studies may be in the interpretation of variants of unknown significance7,8,9. It is challenging for any MTB to have knowledge of all possible variants, and databases are in development for pooling relevance of variants of unknown significance23,24. We addressed this issue by accessing software packages to aid interpretation of the relevance of specific variants and to identify appropriate trials in different regions of the United Kingdom or Europe. The QIAGEN Clinical Interface software package was considered valuable in differentiating actionable mutations (and recommended matched therapies) from those of unlikely clinical relevance, and provided tiering following ACMG/AMP/CAP guidelines.
The researchers now hope the second part of TARGET, which is already underway, will show how often the blood test is successful at matching patients to early phase clinical trials and the impact this has on their overall survival. There is also an option of referring patients to other clinical trial sites, if suitable matched trials are available in other parts of the country--another great feature of QCI.
Reference: *Rothwell, et al. Utility of ctDNA to support patient selection for early phase clinical trials: The TARGET Study. Nature Medicine. (2019) DOI: https://doi.org/10.1038/s41591-019-0380-z
Genetic disease is the leading cause of infant death in the United States, accounting for approximately 20% of annual infant mortality.1 Screening for genetic disease has been a long-established part of preconception and prenatal care, with a community wide screening program for Tay-Sachs disease (TSD) dating back to the 1970s; however, traditional methods of carrier screening have been offered gene-by-gene, disorder-by-disorder.
Recent developments in laboratory technologies have led to the commercial availability of expanded carrier screening (ECS) panels capable of assessing hundreds of mutations associated with genetic diseases. ECS panels have the ability to identify mutations that would otherwise not be detected. While many of the disorders on these panels are individually rare, the overall risk of having an affected offspring is 1 in 280, which is higher than the risk of having a child with a neural tube defect, for which screening is universal.2
In 2012, one of the first DNA testing and genetic counselling companies to offer ECS in the United States launched a flagship ECS panel that used next-generation sequencing (NGS) technology to assess thousands of mutations associated with more than 175 of the most relevant recessive diseases. For cancer-focused screens, the lab developed a 36 gene panel for hereditary cancer risk assessment.2
In the first three years of offering ECS, the lab screened over 400,000 individuals.3 By 2016, the lab served a network of more than 10,000 health professionals, and demand for preconception screening was soaring, owing to the increasing public awareness of the ill effects related to the transfer of genetic disease.4 Unique to the lab's ECS offering was the company’s “real-time manual curation” to support the classification of each genetic variant they encountered. Extremely thorough and highly accurate, the lab's manual literature curation enabled the company to elevate the actionable information provided to the ordering physicians and the patients they served. However, this process was labor-intensive and costly, which was ironic given the dwindling cost of DNA sequencing and the supporting technology. The question became how to scale-up without cutting corners.
Clinical decision support solutions have long been touted as the way of the future for clinical genetic testing laboratories. Combining big data analytics with advanced tools and knowledge bases, clinical decision support solutions are designed to organize, filter, and present useful information at the appropriate point in time to the person who can use it to make a decision. In 2017, the lab evaluated the use of a clinical decision support solution to help scale their genomic interpretation processes: QIAGEN Clinical Insight (QCI).*
QCI is QIAGEN’s clinical decision support solution for genetic testing laboratories. Software that reproducibly converts highly complex NGS data into clinician-ready reports, QCI is the tool through which actionable information is extracted from the sequencing results. Unlike any other clinical decision support solution on the market, QCI is largely powered by manual curation.
The knowledge base inside QCI is maintained by hundreds of Ph.D. scientists certified in clinical case curation who are committed to reading and recording all publications for a given mutation. This information is then mapped to over 2.8 million ontology classes contained within the QIAGEN Knowledge Base, providing further context by establishing relationships between variants, genes, tissue types, and pathways. When a genetic testing lab runs NGS data through QCI, the software computes the ACMG classification based on evidence curated from full-text articles, public, and private data sources. The knowledge extracted from full-text articles include observed genes, variants, function, phenotype, drug, dose, clinical cases, etc. With all this information stored in a structured knowledge base, the QIAGEN KB can quickly retrieve the relevant evidence that triggers all 28 ACMG criteria to more accurately compute an ACMG classification. Further this evidence is presented at the clinician’s fingertips for quick reference. Additionally, using natural language processing, the QIAGEN KB can auto-generate a one-sentence “finding” that is representative of the relevant evidence found in the published article.
This critical feature—automated curation of manually sourced content—saves genetic testing labs considerable time and effort when searching for variant-specific articles to satisfy the levels of evidence needed to definitively determine a classification. Especially for ECS, which is a testing practice that frequently encounters novel rare variants, the value of automation is fast becoming a necessity. To accurately and robustly appraise a novel rare variant’s pathogenicity, lab personnel must manually curate multiple lines of evidence to assess clinical significance. Therefore, if the majority of this information was autogenerated, the genomic interpretation process could be economically shortened.
The lab recognized the opportunity of integrating QCI into their curation workflow and designed a study to evaluate the concordance between the clinical evidence that QCI automatically retrieves for each observed variant classification and the clinical evidence that the lab’s curation team locates and ultimately uses in the physician reports. If the results were comparable, QCI could introduce significant time and cost savings.
The lab's manual curation workflow is outlined in Figure 1. A semi-automated process, the workflow utilizes proprietary software to initially classify variants into three categories: those with high population frequency; those that have never been reported; and those needing more information before pathogenicity can be assessed. For those remaining variants, the curation team manually searches online databases, in-house article libraries, and other available resources to find variant-specific references.
Figure 1. The lab's curation workflow
The curation workflow used to determine clinical significance of variants is summarized graphically. (a) The curation process is shown in the context of the overall laboratory workflow, in which inbound samples are eventually transformed into patient reports. (b) The curation workflow contributes lines of primary evidence that are reviewed manually, which are then combined with multiple lines of autogenerated supporting evidence to assess clinical significance.
Once evidence is collected for a variant —if any is to be found—the information is then used to assess the variant’s potential pathogenicity. As recommended by the American College of Medical Genetics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for the assessment of variants in genes associated with Mendelian diseases, the lab classifies variants following a two-step process:
First, the collected evidence is categorized into one of 28 defined criteria set forth by the ACMG-AMP guidelines and assigned a code that addresses the strength of evidence, such as population data, case-control analyses, functional data, computational predictions, allelic data, segregation studies, and de novo observations. Each code is assigned a weight (stand-alone, very strong, strong, moderate, or supporting) and direction (benign or pathogenic).
Next, the lab combines these evidence codes to arrive at one of five classifications: pathogenic (P), likely pathogenic (LP), variant of uncertain significance (VUS), likely benign (LB), or benign (B). Important in this step is the lab's ability to modify the strength of individual criteria based on expert discretion—a safeguard that goes away with computerized systems.
To determine whether QCI could provide value to the lab’s curation team, the software was tasked with pulling a bibliography for 2,324 variants that had been recently detected by the lab’s ECS and hereditary cancer risk assessment panels. For each of these variants, the curation team had been able to match at least one published article with a specific disease-gene reference. QCI’s variant bibliography was expected to present the same quantity and quality of clinical evidence.
The study found that QCI’s variant bibliography was highly concordant with lab’s manual curation efforts. Of the 2,324 unique article-variant pairs identified by the lab, QCI pulled 2,075 of the references (89.3%) and an additional 13,938 article-variant pairs not captured by the lab's curation team.
Figure 2. Overlap of bibliographic content
Figure 2 shows the overlap in content quantity between the two sources. As depicted, QCI (QIAGEN) presents significantly more data for the evaluated variants. This outcome reflects the comprehensive nature of QIAGEN’s article-centric approach, which aims to collect all publications for a given variant. While exhaustive and not always necessary, QCI’s ability to glean information from numerous sources affords the software greater accuracy in predicting variant classifications, which is seen in the second phase of the lab's evaluation.
More important than the number of bibliographic sources, accuracy of cited content ultimately dictates clinical significance. Counsyl measured the quality of QCI’s variant bibliography by looking at how the software would classify variants based on the information it pulled. What they found was a concordance of 98.8% of the pathogenic cases (Figure 2).
During the study period, a total of 682 variants were classified as pathogenic by lab’s genetic scientists. Of these, only eight would be downgraded to VUS utilizing only QCI bibliographies. Therefore, the false negative rate for using QCI’s bibliographies was ~1.2% and is expected to decrease to <1%. Further, for a sample of 50 VUS variants examined, none would change classification with additional unique references in QCI, primarily because QCI includes secondary reports and studies for other disease contexts that may be listed as 'reviewed but not curated' in their curations.
As a result of these positive findings, QCI bibliographies have been integrated into the lab’s manual curation workflow, eliminating the need for manual searches in the majority of cases. (Left: variant-specific page in QCI). After several months, a comparison of the time taken for reference searches before and after the adoption of QCI was performed (Figure 3).
Figure 3. Before and after adopting QCI
The goal of this evaluation was to assess whether utilization of QIAGEN’s variant-specific bibliographies could match the level of accuracy and quality of the lab’s more time-intensive manual article selection approach. Investigators concluded that there are clear benefits for adopting QCI for reference identification: an exceptionally high variant-specific article coverage, and significant time savings in a search process that can take up to ~45 minutes.
The results also serve to validate the efficacy of the lab’s previous article search and selection method, with the vast majority of variant classifications being unaltered by use of QIAGEN’s bibliographies. The lab now employs QCI bibliographies for every curated variant. Consequently, manual search methods are still employed at the lab, but can now be reserved for variants nearer VUS/pathogenic evidence thresholds.
QCI has already proven a valuable resource for increasing the efficiency of the lab’s in-house curation. Work is underway to additionally incorporate QIAGEN’s continually-updated bibliographies into the automated components of our variant classification workflows: the initial software-based auto-curation step for newly-identified variants, and the identification of those requiring re-curation in response to new publications becoming available. Accordingly, we expect QCI to further contribute to the lab’s continuing efforts to improve turnaround time by increasing curation efficiency while maintaining classification accuracy in patient reports.
*Data taken from a joint study conducted by Counsyl and QIAGEN: Cox et al. ClinGen 2017. Counsyl has since been acquired.
Learn more about QIAGEN Clinical Insight for here.
References
At the beginning of 2019, QIAGEN announced the acquisition of N-of-One, Inc., a molecular oncology decision support company that provides case-specific, expert-powered clinical NGS interpretation services and solutions.
We sat down with Sean Scott, QIAGEN’s Chief Business Officer and Vice President of Business Development for Clinical Genomics and Bioinformatics, to discuss QIAGEN’s plans for post-acquisition incorporation and what new value QIAGEN customers can expect.
How does the acquisition of N-of-One fit into QIAGEN’s clinical bioinformatics strategy?
Sean Scott: This acquisition represents a unique opportunity for QIAGEN and N-of-One to combine respective strengths to deliver the industry’s most robust portfolio of molecular oncology decision support solutions from one provider. N-of-One’s technology-enabled, yet human-driven, services and the proprietary MarkerMine™ database are planned to be integrated into QIAGEN Clinical Insight (QCI), our platform for NGS analysis and interpretation. We are opening the door to real-world evidence (RWE) and creating new opportunities for supporting healthcare providers and payers.
What does the acquisition mean from a pharmaceutical company’s perspective?
Sean Scott: The addition of N-of-One’s MarkerMine database and commercial data rights creates an attractive and expandable link into RWE insights. N-of-One’s Genomic Insights and analytics services can be commercialized to pharmaceutical industry partners—in particular to more than 25 companies with which QIAGEN has deep companion diagnostic co-development relationships—to support patient cohort analytics, patient stratification, trial protocol design, assay design and interpretation, trial accrual and market forecasting, patient-to-trial matching and other features.
How does N-of-One differ from other molecular decision support providers?
Sean Scott: N-of-One is one of the best-known brands in molecular oncology decision support. It is well-established with labs, pharma companies, and payers, and N-of-One has been the solution-of-choice for leading diagnostic companies, such as Foundation Medicine. Unlike other providers, N-of-One employs a team of over 30 PhD scientists and oncologists to research and analyze each patient case, and in the process, N-of-One has amassed one of the most comprehensive resources of oncology clinical and scientific evidence in the industry with more than 125,000 anonymized patient samples.
How could real-world evidence and patient data impact clinical development program design?
Sean Scott: Today, all stakeholders in the healthcare spectrum—pharmaceutical developers, payers, regulators, physicians and patients—are putting their money on the collection and analysis of many different types of RWE as a key enabling strategy, to close critical gaps in knowledge, give physicians and patients broader access to therapies, and help payers realize the actual value of those therapies in improving health and reducing costs. While still at an early stage, RWE is becoming increasingly used to complement traditional RCT data to inform important healthcare decisions. This suggests that RWE will have a significant impact on the healthcare industry in the years to come.
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Day One kicked-off with numerous informational sessions, including talks on the role of AI in clinical decision-making, the importance of standardization for reimbursement, and the tremendous potential of genomic profiling in disease prevention, diagnosis, and treatment.
Dan Richards, Vice President of Biomedical Informatics at QIAGEN, spoke about the clinician's current challenge of curating all the evidence he or she needs to confidently sign-off on variant reports before they go to the prescribing physician. QIAGEN Clinical Insight (QCI) and N-of-One were featured as solutions providing options for either in-house curation with tailored workflows or on-demand curation services.
On Tuesday morning, the conversation continued with a panel hosted by Sean Scott, Chief Business Officer of Clinical Genomics and Bioinformatics at QIAGEN, that discussed the emergence and application of real-world evidence in the clinical setting, especially in precision diagnostics and clinical trial protocol design.
The panel consisted of Raju K Pillai, MD, Hematopathologist and Molecular Pathologist at City of Hope National Medical Center, James Hadfield, Director and Principal Diagnostic Scientist at AstraZeneca, and Sheryl Krevsky Elkin, Chief Scientific Officer of N-of-One.
Also on Tuesday morning, Mary Napier, Associate Director of NGS Strategy at QIAGEN, gave a timely talk on how diagnostic labs and pharma companies can gain a comprehensive understanding of tumor mutational burden signature by implementing our new QIAseq Tumor Mutational Burden panel.
What does she mean by "comprehensive"?
Find out here!
Thank you to everyone who visited the QIAGEN booth, we truly enjoyed talking to all of you about the industry challenges, and changes you see happening now and in the future.
See you at our next event:
Advances in Genome Biology and Technology (AGBT) 2019 in Marco Island, Florida!
February 27th - March 2nd
Want to know more about our clinical solutions and real-world evidence?
