“Happy families are all alike; every unhappy family is unhappy in its own way.” I have always thought the opening of Anna Karenina applies for many things beyond familial harmony (or lack thereof). Certainly, in the world of molecular genetic diagnostics, conclusive results are usually obtained for most patients; however, there are times when a final result is more elusive than conclusive. When this occurs, it may seem as though no two challenges are ever the same. The following are real examples – presented in general terms for patient and institutional confidentiality – of difficult, unanticipated, and even bizarre cases I encountered during my time in clinical testing for predisposition to hereditary disease. Each of these situations required extraordinary effort, dedicated time, and additional resources for resolution. At the end of each day, satisfaction came from knowing that another problem solved was another patient helped in making life-altering medical management decisions.

During my time spent in academia, I studied in a laboratory focused on molecular switches. Although cellular signaling cascades appear as nothing more than incomprehensible spiderwebs when viewed graphically, the concept is very straightforward: certain molecules can act to regulate cellular processes by functioning like electrical switches. Just as light switches in your home complete or break a circuit to control the flow of electrical current, molecular switches such as Ras GTPases (PDF) control whether certain signals can elicit a cellular response.

Previously, we wrote about some of the Quality Control challenges that clinical laboratories performing Next Generation Sequencing (NGS) face towards ensuring their assays are safe and effective for guiding medical management decisions. Reliable access to high quality reference materials is necessary to help overcome these challenges; however, it is not sufficient. Insights that reference materials provide into the health of an NGS assay are only as good as laboratories’ ability to use their QC data effectively. With limited time and resources to collect, organize, access, and analyze QC metrics, laboratories may frequently rely on reference materials as binary indicators of Pass/Fail: As long as the expected endpoint results are obtained, an assay is considered to be performing well. The drawback of this approach is that it is reactive, rather than proactive: A sufficient number of failures must occur within a given timeframe before a troubleshooting investigation is performed. By the time a problem is recognized, resources have been wasted and turnaround times (TAT) delayed; in some cases, fidelity of patient results may even have been put at risk. Additional time and costs are then incurred as the investigation proceeds. Specimen analysis by NGS yields a wealth of information in addition to endpoint variant calls that is indicative of assay performance. Data such as nucleic acid quantity and quality at different steps throughout the workflow (PDF) and sequencing library characteristics are generated every time a reference material is tested. However, these data must be carefully tracked and trended to allow use as highly informative QC parameters. For clinical laboratories whose primary focus is on patient testing and reporting, granular QC metrics may not be captured and reviewed as part of routine test monitoring.

Since the introduction of the GS20 in 2005 by 454 Life Sciences, Next Generation Sequencing (NGS) has found many applications in clinical diagnostics. As a result of this transition from the long-held gold standard, Sanger sequencing, the primary challenge for clinical laboratories has shifted from data acquisition to ensuring these tests are safe and effective for guiding medical management decisions. Many laboratories struggle to gain a thorough understanding of the analytic performance characteristics of their NGS tests. The difficulty arises from the fact that these assays are comprised of highly complex, fragmented workflows, and have many different intended uses. However, across the various practices currently used for NGS assay development, validation, and performance monitoring, there is a common goal: results must be as accurate, precise, and consistent as possible.

If you took a university introductory statistics course, you may have learned the distinction between accuracy and precision. It may likely have been presented with an archery analogy, where ‘Accurate’ was represented by arrows loosely clustered around the target’s bull’s-eye, ‘Precise’ was shown as a tight grouping displaced from the center, and ‘Accurate and Precise’ was depicted as what every archer aims for, a tight grouping directly at the bull’s-eye. Suddenly, words that are used interchangeably in everyday conversation took on dramatically different meanings.