We are pleased to announce that the latest QIAGEN Clinical Insight (QCI) Interpret software release is now available. Expanding on the software’s current capabilities, the update adds new features and guidelines to enhance the interpretation and reporting of genomic variants.
Immediately determine and filter to genes implicated in hereditary diseases that are most relevant to report with Strong or Definitive Clinical Validity. With this new features, users can quickly review clinical validity statements that summarize the evidence supporting the strength of a gene-disease relationship. Gene-disease relationships include those determined by the ClinGen Gene Curation Working Group and are extended to cover all gene-diseases via computing clinical validity based on the ClinGen classification guidelines using the expert-curated and integrated evidence in the QIAGEN Knowledge Base.
Interactively add and remove symptoms (and diseases) in hereditary workflows at anytime. With this new feature, users can rapidly adjust symptoms if more case information becomes available. The ranking of candidate diseases for variants in the Phenotype Driven Ranking (PDR) view is dynamically updated based on the updated symptoms.
Review curated evidence supporting each candidate disease for a case in the hereditary workflow with the Phenotype Network—a new feature that provides a summary of the gene-disease clinical validity and a visual diagram of the paths via QIAGEN Knowledge Base relationships from symptoms provided for a case to a candidate disease. This enables users to quickly and interactively review relationship-specific supporting evidence, including source citations.
The NICE Guidelines for Oncology are now available for clinical reporting in QCI Interpret and QCI Interpret One. QIAGEN’s expert guideline curation provides the most up-to-date evidence-based guidance from NICE to support treatment selection for patients. NICE guideline recommendations are also used to support the computed AMP/ASCO/CAP variant classification to ensure relevant variants are indicated for review.
For information about the latest release, including the full release notes, please contact your QIAGEN Digital Insights account manager or customer support at ts-bioinformatics@qiagen.com.
We also invite you to watch our on-demand webinar, "Overcoming Challenges of Copy Number Variant (CNV) Interpretation," where our experts provide a virtual demonstration of QCI Interpret, showing how users can quickly evaluate CNVs and compute their pathogenicity using the new ACMG/ClinGen guidelines.
Visit our QCI Interpret webpage to request a complimentary demonstration.
We are pleased to announce a minor update to QIAGEN Clinical Insight (QCI), a clinical informatics platform for streamlined NGS variant analysis, interpretation, and reporting of oncology and inherited disease tests. The update adds new features, functionalities, and improvements, further enhancing QCI's ease-of-use, performance, and responsiveness.
We are excited to announce the general availability of the Fall 2020 Release of QIAGEN Clinical Insight (QCI) Interpret. This new release brings the following new features:
To learn more about the new features and functionalities of QCI Interpret, please attend our free webinar on November 10 at 11 AM EST. Register here.
If you are a current QCI customer and have questions about the new release, please contact support at ts-bioinformatics@qiagen.com.
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.
Today, at the 60th Annual Meeting of the American Society of Hematology (ASH) in San Diego, California, QIAGEN announced the launch of two novel products to deliver actionable insights on a wide range of blood cancers: a new workflow for the QCI Interpret bioinformatics solution for hematological malignancies, and the new QIAact Myeloid DNA UMI Panel for use in myeloid neoplasm research as a Sample to Insight workflow on QIAGEN's GeneReader NGS System.
Meet and talk with our experts at ASH 18, booth #1557!
Featured Products and Solutions
Stop by booth #1557 at ASH to explore our complete Sample to Insight solution for interrogating 25 genes for variants with known significance to clonal myeloid malignancies.
Coupled with QIAGEN Clinical Insight (QCI) Analyze and Interpret, QIAGEN’s secondary and tertiary NGS analysis platform that provides seamless variant detection, interpretation and reporting based on actionability tiers from the 2017 AMP/ASCO/CAP guidelines, the streamlined solution enables sub-classification and prognostic assessment of hematological malignancies, including leukemia, Non-Hodgkin lymphoma, Hodgkin lymphoma and multiple myeloma.
QCI Interpret for myeloid malignancies offers a specialized workflow that guides prognostication and treatment decisions. With features that incorporate cytogenetic information, World Health Organization (WHO) somatic frequencies, and variant-level prognostic evidence from the QIAGEN Knowledge Base, QCI Interpret helps you assess actionability through multiple levels of information.
Meet and talk with our experts at ASH 18, booth #1557!
Featured Products and Solutions
Check out our Sample to Insight oncohematology solutions here.
See you in San Diego!
Your QIAGEN team
Though not technically summer, on May 25th, the EU passed the General Data Protection Regulation (GDPR) into law, creating a global ripple effect. The law impacts the world of clinical decision support software because it stipulates the “right to explanation,” around automated decision-making (i.e., algorithms) and the expected consequences of applying those decisions. This requirement for transparency does not bode well for the walled-off “black box” approach to clinical decision support. For another perspective, read this contributed piece in The Pathologist, written by our own Ramon Felciano, in which he positions QCI as an enabling tool to transition to precision medicine in a cost-effective, scalable, and transparent way.
Artificial intelligence (AI) was frequently in the news over the past few months. In particular, we saw quite a few stories about IBM’s Watson and its limitations in beating cancer. Though Watson has not yet lived up to its promise of generating insights and identifying new approaches to cancer treatment, there remains hope in the industry that AI will eventually revolutionize medicine—whether through data pattern recognition, its impact on pharmaceutical development, or—even someday—cancer. In the meantime, we at QIAGEN continue to focus on our clinical decision support tools (big data, informatics and augmented intelligence) to improve test interpretation and accuracy of results.
QIAGEN was in the news as well.
Our second consecutive win during AMP Europe’s Battle of the Bioinformatics Pipeline event was covered in GenomeWeb; we published our own recap of the results to provide additional detail and background around standardizing variant interpretation and reporting. Finally, we recently hosted three international OmicSoft User Group Meetings:
We hope you had a wonderful summer, and we look forward to the busier pace and renewed activity that fall brings.
Check out this recent article by American Health Leader (AHL) on how QIAGEN is helping clinical diagnostic and pathology labs adopt genomics-guided precision medicine workflows.
Sean P. Scott, Chief Business Officer and Vice President of Clinical Market Development at QIAGEN, explains QIAGEN’s holistic approach to developing and expanding NGS-based test services. “No matter the size of the lab, we’re focused on helping them understand how to develop a more insightful and actionable report for the ordering physician . . .”
Read the full article here!
QCI Interpret makes precision medicine possible by offering one, cloud-based platform to handle a range of genomic testing, from somatic to germline, from small panels to exome and whole genome.
Get in touch with one of our QCI Interpret experts today!
If you answered yes, we invite you to watch a free recording of our webinar that addresses one of the key bottlenecks of today’s clinical testing laboratory: producing standardized interpretation that is consistent among personnel, reproducible within the testing community, and in accordance with professional guidelines. We show how our clinical NGS reporting and interpretation software, QCI Interpret, not only makes precision medicine possible, but simplifies workflows and increases productivity.
The recent AMP Europe 2018 conference was a wonderful chance to catch up with old friends and establish new relationships—our team provided demos at the booth and we had a wonderful symposium. We also participated in a fun challenge, known as “Innovation Lab: Battle of the Bioinformatics Pipeline.” According to this story by Julia Karow in GenomeWeb, the aim of the exercise was “to provide commercial vendors of NGS analysis and interpretation pipelines with sequencing data from real patient samples, generated by a routine molecular diagnostics laboratory, and to see how similar or different their results would be.”
QIAGEN was one of three vendors who participated, using Biomedical Genomics Workbench data analysis platform and Qiagen Clinical Insight (QCI) Interpret software to identify mutations in tumor sequence data, down to a level of 5 percent. Participants were instructed to name and annotate the variants, state their allele frequencies and interpret them according to a five-tier classification system ranging from “benign” to “clinically significant.”
The session was organized and led by Winand Dinjens, head of molecular diagnostics in the Department of Pathology at Erasmus University Medical Center (Erasmus MC) Rotterdam, whose lab also analyzed the data, to establish a benchmark against which the other outcomes were compared. During the session, all three vendors presented their results and compared them to those of Erasmus MC. Though there was plenty of overlap amongst the three vendors’ results, none were identical. The session concluded with all participants agreeing that context (of a patient’s disease) is important in variant interpretation, and that laboratories must define their own thoughtful criteria to effectively frame a clinical report.
We are honored to have been included in the #AMPEurope2018 challenge, and that Biomedical Genomics Workbench and QCI were part of the process. We are also very proud of our team’s positive results—this is our third such challenge, 1) ECP 2017 and 2) AG MolPath, and we welcome the chance to compete again!
