Contributed Commentary by Patricia Biscaye
February 15, 2018 | The rate of adoption of next generation sequencing (NGS) testing in clinical labs has quickened in recent years as more advanced genetic tests have been developed and implemented for routine testing by clinical labs. These tests offer tremendous potential benefit to physicians and the patients they care for, from enabling diagnosis of rare diseases to identifying precision medicine therapies for cancer.
However, implementing NGS testing in the clinical lab is not for the faint of heart. NGS remains in the high complexity testing category as defined by the Clinical Laboratory Improvement Amendments (CLIA). Clinical laboratories who wish to implement NGS testing must carefully establish their standard operating procedures, from the management of clinical sample intake to interpretation and reporting of test results.
In an effort to assist clinical laboratories and provide standardization, professional organizations have provided guidelines ranging from validation of gene panels to recommendations in interpretation and reporting of sequence variants.
In 2008, the American College of Medical Genetics and Genomics (ACMG) was among the first to publish recommendations for interpretation and reporting of sequence variants. With the advent of high throughput NGS, and an increase in not only the number of labs performing testing, but also in the types of genetic tests being performed, the ACMG, working with the Association for Molecular Pathology (AMP), and the College of American Pathologists (CAP), instituted new guidelines published in 2015, along with standard terminology for variant classification, to provide a common language to be used within and between clinical laboratories.
As the number of labs offering NGS has increased, variability of how results are reported and variability in test results between labs have become a greater concern. A 2015 AMP-conducted survey of labs interpreting sequence variants from somatic cancer testing of solid tumor samples showed astonishing variation in reporting, such as the number of categories for classifying variants (3, 5, or other), whether or not variants in genes or regions which had failed QC criteria were reported, and whether or not therapeutic implications of variants were reported to oncologists.
The AMP survey underscored the need to provide interpretation guidelines for somatic cancer testing. AMP teamed up with the American Society of Clinical Oncology (ASCO) and CAP to publish a framework for interpreting and reporting the somatic sequence variants for actionability. Their recommendations include a four-tiered system which considers the clinical evidence from drug labels, peer-reviewed publications, and other sources to establish the significance of this evidence for the identified variants, with the goal of helping physicians identify potential treatments for their patients.
Underlying the ACMG/AMP variant classification and the AMP/ASCO/CAP tiers is the clinical evidence to support a variant’s pathogenicity classification and/or clinical actionability. Vast amounts of genetic information from various labs have been collected in databases, with varying degrees of oversight to the quality of the data being submitted. Further, labs using these databases may find that conflicting information has been submitted for a particular variant, potentially confounding the interpretation.
The exponential growth of the number of genetic studies published over the last decade has significantly contributed to our understanding of common and rare diseases and has led to the development of precision medicine and the availability of thousands of clinical trials to potentially benefit patients. Yet, with this information explosion comes another concern for clinical testing labs: how does the lab stay current on variant classification and the underlying clinical evidence to support actionability?
Fortunately, there exists clinical decision support software which offers clinical labs ready access to carefully curated genetic information, along with automated pathogenicity and actionability classifications for sequence variants, according to the guidelines provided by professional associations such as ACMG and AMP, ASCO and CAP.
With this approach, compliance with guidelines becomes straightforward and automatic—but it is just as important to respect the role of human judgment and experience. Any tool that incorporates genomic guidelines should also emphasize transparency, allowing users to click through to relevant source material. The tool should also allow labs to report information based on their own expertise in cases where they may disagree with a variant assessment or the supporting clinical evidence.
With clinical labs gaining experience and the evolution of professional guidelines, coupled with strong clinical decision support software tools, we will be in the best possible position to routinely implement NGS data for optimal patient care.
Patricia Biscay is Senior Director of Global Product Management at QIAGEN, where she is engaged in advancing next-generation sequencing (NGS) bioinformatics to facilitate standardized clinical genomic testing with the goal of improving patient care. Prior to her commercial experience, she conducted clinical oncology research at UCSF and Institut Gustave Roussy. Patricia can be reached at patricia.biscay@qiagen.com.