Clinical diagnostics form an important part of disease prognosis and diagnosis. It affects about 70% of medical decisions (1). Any incorrect result could negatively affect the patient through an erroneous medical decision or treatment. Therefore, quality control (QC), meaning accuracy, reliability and timeliness of clinical diagnostics, is of utmost importance for patient safety (2). Sequencing and particularly next generation sequencing (NGS) has become cheaper, and an increasingly popular technology in clinical diagnostics. Comprehensive QC for NGS in clinical diagnostics is still a challenge, both for laboratories as well as auditing authorities themselves. This article will address the challenges of QC implementation for NGS data and examine the steps required to go from stand alone QC tools to a comprehensive NGS data quality management system.

Clinical NGS

NGS technology started penetrating clinical diagnostics about 10 years ago, but standard guidelines from authoritative bodies like CLIA, CAP, and EuroGentest have not been available for more than the past 5 years. This delay reflects the complexity of the use of NGS in clinical diagnostics. Indeed, applications vary widely from cancer diagnostics to heritable or rare disease testing, and so do the protocols used for these various diagnostic applications of NGS. Moreover, the volume of generated data and its stochastic nature require a very active approach to QC. Labs need more than stand alone tools for monitoring the quality data. They also need capabilities to manage protocol requirements and how the tests abide to them.

Limitations of existing QC tools

Currently available QC open source tools have several limitations and constraints. They require manual work, are time consuming, and can be error prone. Most of the small or medium scale labs manage their QC using available open source software tools like FastQC, Samtools, Picard, etc. and then transfer the QC data into spreadsheets for further data management. Such a process is in conflict with recommended standard guidelines.

Larger labs encounter similar limitations using laboratory information management systems (LIMS) to manage their QC. A conventional LIMS handles general lab workflows such as sample identification, lot number tracking, etc. and does not specifically calculate or manage quality control, much less NGS QC data. A LIMS designed to handle the complexities of NGS workflows is likely to be expensive and might require a significant amount of customisation. Therefore, any NGS data QC management system should overcome these limitations.

Cost effectiveness of a QC system

There is a wide array of commercial and lab developed genetic tests available on the market as well as a wide variety of extraction, library preparation and capture kits for different sequence and software platforms. This diversity puts labs in varying position with respect to the tests. Existing QC tools are not able to address the challenges of such variations but rather exacerbate the problem by adding an additional layer of diversity. Running a control template helps labs to identify lab-specific variations. In a CAP-TODAY – webinar, Dr. Deignan of UCLA acknowledged that the decision whether to run a control template is one of the challenges of NGS QC, since running controls for each analysis is not cost effective (3). This challenge can be mitigated with a comprehensive and on-going yet less expensive quality control process where the generated data from each sample tested can be used to improve current and future tests.

Comprehensive QC includes validation

Validation is about concordance with a base truth from a reference material or alternative method. It is about characterising the analysis pipeline in order to define the required QC thresholds. According to international recommendations, QC as well as validation with reference material (or alternate sequencing method) are basic requirements for the establishment of genetic tests in a clinical setting (e.g. CDC recommendations, 4). Labs perform tests which are either commercially available and regulated, or developed and customised according to the lab’s own requirements (laboratory developed tests, LDT). To perform LDTs, validation is imperative for ensuring accuracy and precision. Regular proficiency testing (quality assessment) is likewise recommended. A comprehensive QC system should therefore encompass test validation.

Features of a comprehensive QC system:

Platform Agnostic – Due to the various options available in the NGS industry the system needs to be platform agnostic. It should be easily adaptable to the different sequencing platforms, capture kits and bioinformatics software.

Efficient and scalable – Considering the increasing usage of NGS and the amount of data generated, the system should support the increasing number of tests without consuming human times.

Reliable – The system should allow the user to follow best practice guidelines and comply with evolving quality standards from various bodies like ACMG, CAP, EuroGentest, NATA, ISO15189 etc.

Database for QC data –The structure provided by a database makes it possible to query for historic data and identify trends over time, by instrument, chemistry, or protocol. A database also makes more complex analysis of the stored QC data, such as charting, outlier identification, and reporting possible.

Interoperability – Interoperability refers to the capacity of different information systems (e.g. LIMS or patient electronic health records) and software applications to communicate with other systems and exchange data (5).The QC system should be cognisant of interoperability needs.

External Benchmarking – A QC system that enables performance comparison and benchmarking among peers ensures commensurable results and prepares the lab for external quality assessment for accreditations.

Data Security – Since measures have to be taken to secure the patient data, the QC system needs to handle the QC data separately from the sensitive patient data.

To find out how omnomicsQ can help your lab in these tasks, please contact us.

  1. Dighe MS, Markar RS, Lewandrowski KB. Medicolegal liability in laboratory medicine. Semin Diagn Pathol 2007;24(2):98–107.
  2. Laboratory quality management system: handbook by WHO, the CDC and the CLS
  3. Paths validating next gen sequencing assays –
  4. Gargis AS, Kalman L, Berry MW, et al. Assuring the quality of next-generation sequencing in clinical laboratory practice. Nat Biotechnol 2012;30:1033–1036.3
  5. Roy et al. Next-Generation Sequencing Informatics: Challenges and Strategies for Implementation in a Clinical Environment. Archives of Pathology & Laboratory Medicine: September 2016, Vol. 140, No. 9, pp. 958-975.22

Back to news listing