Introduction

The reduction in the cost of sequencing has increased the use of the Next Generation Sequencing (NGS) technology in various clinical applications. Within the past ten years the technology has spread from the research lab to the clinic. Today, more than 30% percent of the revenue around NGS technology is generated from the clinical and healthcare sector (1). However, comprehensive quality management and standardised procedures for clinical diagnostics have not co-evolved at the same speed. It is only in the past 5 years that the standards and guidelines have started to appear.

Best practice guidelines provide a framework for harmonising quality management by ensuring the consistency and accuracy of results. The NGS workflow is complex, involving multiple steps from the sample in the wet-lab all the way to the bioinformatics output files. The quality of many of these steps can be quantified and used to assess the workflow. The calculation of quality measures must rest on some kind of standard in order to be comparable. Such standards have not yet been agreed on although norms are emerging (11), and some of them are used as de facto standards. On a higher level, requirements concerning overall procedures in NGS-based clinical diagnostics have also been developed during the recent years. Such requirements, also known as standards, are now being implemented and used alongside other requirements for quality and competence such as the ISO15189 for medical laboratories. In this article we will only address this higher level of standards in NGS-based clinical diagnostics.

Analytic phases of the NGS technology

The complexity of NGS technology makes it difficult to define standard requirements for clinical testing. Indeed, NGS technology can be organised into fundamentally distinct steps:

  • Nucleic acid extraction and library preparation: This includes the wet-lab, which will be different among other things for amplification-based and hybrid capture-based assay designs.
  • Sequencing: Different vendors apply different detection methods and each platform requires different assay designs to optimise detection of different genomic variant types.
  • Bioinformatics pipelines: This includes “dry-lab” data processing starting from the detector signal all the way to variant identification.
  • Medical interpretation: Interpretation and reporting can be automated to some extent, depending on the complexity of the diagnostic procedure. It is based on the biomedical and genetic knowledge available in multiple dispersed electronic data repositories.

Technology Advancement

Considering the clinical implication of NGS for patient safety, there is a need for standardisation to ensure performance and reliability of the tests. The continuous advancement of platforms, methods, genomic regions of interest, and bioinformatics tools makes it difficult to standardise the process. In such a rapidly changing world, standardisation efforts need to be either very agile to follow the changes or very generic to withstand those changes. Both approaches pose difficulties of their own.

Regulatory agencies across the globe
Figure 1: Regulatory agencies across the globe

Current Guidelines, Recommendations and Regulations

As with any laboratory test, NGS is subjected to regulatory standards. Clinical laboratories must meet the regulations imposed by governmental regulatory agencies to become accredited. Different countries have different regulations for clinical NGS. These regulatory agencies collaborate with professional expert bodies which develop specific guidelines and recommendations and provide them to the respecitve regulatory agencies (Table 1). For instance, in Australia and New Zealand NATA is the agency and RCPA is the expert body, while in North America the College of American Pathology (CAP) is both the accreditation agency and the expert body for laboratories (Figure 1). With each region or country having its own regulatory agency, the result is a lack of uniformity in the implementation of these guidelines across the globe.

Table: Recommendation and guidelines
Table 1: Recommendation and guidelines

Numerous guidelines already exist. They run the gamut from test development and bioinformatics pipelines to clinical reporting and data storage. The guidelines likewise address external quality assessment and validation along with aspects of patient and laboratory director education. Although the recommendations and guidelines are fairly similar among all expert bodies, there are still distinct differences in the quality measures employed (Table 2). For instance, reads mapped is a recommended measure in the guideline of EuroGentest, but neither CAP nor RCPA recommend its use. Similarly, CAP does not require the monitoring of GC Bias, whereas it is considered to be important by EuroGentest. Thus, although the regulatory agencies have been set up for the same purpose of ensuring good lab practices, the differences in guidelines and recommendations make standardisation difficult.

Table: QC parameters covered by different organizations
Table2: QC parameters covered by different organizations

Summary

The rapid evolution and significant reduction in costs of Next Generation Sequencing technologies over the past few years have paved a path for the clinical use of this technology. The complexity of NGS makes it difficult to standardise the usage procedures. Various regulatory agencies and organisations have been set up to form recommendations and guidelines for clinical use of NGS. However, in most cases their scope is only at the national level, and the guidelines and recommendations vary between professional expert bodies, impeding universal harmonisation. The guidelines are already quite robust and cover steps for clinical NGS from the wet-lab and throughout the bioinformatics pipelines, and they even cover consequent consumer education. Nevertheless, international initiatives such as the Global Alliance for Genomics and Health (GA4GH) will help to accelerate and extend the reach of recommendation and standardisation efforts. Emerging guidelines should be feasible and easy to implement for individual laboratories. Established guidelines will in turn facilitate the adoption of NGS technology and widen the scope of its applications.

Reference

  1. Transparency Market Research: Next Generation Sequencing Market – Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2015-2023
  2. 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. Sian Ellard, Helen Lindsay, Nick Camm, et al. Practice guidelines for Targeted Next Generation Sequencing Analysis and Interpretation
  4. Rehm HL, Bale SJ, Bayrak-Toydemir P, et al. ACMG clinical laboratory standards for next-generation sequencing. Genetics Med. 2013;15:733-747. Professional standard
  5. Weiss MM, Van der Zwaag B, Jongbloed JD, et al. Best practice guidelines for the use of next-generation sequencing applications in genome diagnostics: a national collaborative study of Dutch genome diagnostic laboratories. Hum Mutat. 2013
  6. Matthijs G, Souche E, Alders M, et al. Guidelines for diagnostic nextgeneration sequencing. Eur J Hum Genet
  7. Massively Parallel Sequencing Implementation Guidelines – RCPA (https://www.rcpa.edu.au/getattachment/7d264a73-938f-45b5-912f-272872661aaa/Massively-Parallel-Sequencing-Implementation.aspx)
  8. THE NATA/RCPA ACCREDITATION OF NEXT GENERATION SEQUENCING – THE STORY SO FAR- Andrew Griffin, National Association of Testing Authorities (NATA), Australia (http://www.sciencedirect.com/science/article/pii/S0031302516307061)
  9. Aziz N, Zhao Q, Bry L, et al. College of American Pathologists’ laboratory standards for next-generation sequencing clinical tests. Arch Pathol Lab Med. 2015;139:481-493
  10. Next Generation Sequencing (NGS) Guidelines for Somatic Genetic Variant Detection. Albany, NY: New York State Department of Health: 2015
  11. Schröder CM, Hilke FJ, Löffler MW, Bitzer M, Lenz F, Sturm M. A comprehensive quality control workflow for paired tumor-normal NGS experiments. Bioinformatics. 2017 Jan 27 (epub ahead of print) doi: 10.1093/bioinformatics/btx032

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