NGS-Based Clinical Testing (Part V)

Quality Management in Clinical Testing

Any validated bioinformatics pipeline must be continuously monitored. Quality management in clinical testing labs ensures that any divergence from predefined quality metrics during the analysis of clinical samples is investigated. For example:

  • There is an insufficient number of sequence reads that passed the predefined base quality score threshold
  • The number of variants identified in a data set may deviate substantially from an expected value

An appropriate quality management system is designed to provide the framework to deep dive into any divergences from the designed analytical process. It should allow the appointed investigator to determine possible root causes, as well as define corrective actions going forward. Now, laboratories are supposed to keep a record of any and all deviations from expected results.

Updates and Versions

Updates are a fact of life when it comes to software products. Commercial software vendors have to issue updates to fix known bugs and to release new features and capabilities. Similarly, they also occur with open source packages. Reference databases such as ClinVar are on a monthly update cycle. As much as labs would like to freeze a pipeline, the reality is that the individual software components are subject to constant change. Here are a few thoughts on how to handle this issue:

  • Implement a policy for monitoring updates, patch-releases and other upgrades to the bioinformatics pipeline. This policy should also outline when these updates will be implemented. For example, certain intervals such as quarterly updates, depending on the impact and urgency of the update, should be included. It goes with saying that once an update occurs, the entire pipeline has to be re-validated.
  • It is advisable for the laboratory to maintain records that document how the monitoring and implementation of updates will actually occur.
  • Create a lab specific versioning system that keeps track of the configuration of a pipeline. Documentation should record the version of the overall pipeline, as well as those of the individual software packages. Additionally, any scripts written by staff bioinformaticians should be versioned as well. The version number of the overall pipeline should be incorporated into any clinical report that leverages data generated by that pipeline.

Data Storage

A laboratory must be able to explain how it conducted its work. It needs to be able to show why and how the pipeline at a given point in time created results that were in line with best practices at that time. For that, it is necessary to store all data files generated by the bioinformatics pipeline. Of course, this can be a massive undertaking as the number of processed samples go in the thousands, tens of thousands or beyond in a given year.

At the very minimum, there needs to be a data retention policy in place that outlines which files are being retained. For example, it is possible to re-compute BAM files from the FASTQ or vice versa. So, it would be not necessary to archive both data sets for any given sample. Labs should also be aware of any local, state and national requirements for the storage of data.

Labs that operate on a higher standard are also putting systems into place that allow them to revisit clinical reports that have been generated in the past. We are operating in a very dynamic field. The scientific community constantly publishes new information about the association of a variant with a particular disease. New treatment options become available. Either could alter the diagnosis and treatment selection for a particular patient as time progresses. Warehousing sample data and versioning the pipeline including databases allow a lab to effectively revisit past analysis efficiently.

Exception Log

Similar to the wet lab process, deviations from the laboratory SOP during any step used in the bioinformatics pipeline have to be documented in an exception log file. This could include a number of issues that are being observed in practice such as:

  • Any changes in software packages or version numbers, new annotation sources and/or versioned updates.
  • Any alteration of scripts, command lines or system parameters.
  • Any failure of the bioinformatics process needs to be documented, including its investigation, resolution and corrective measures.
  • The exception log is also required to include references to the patient reports. The laboratory manager/director may also choose to communicate any clinically relevant deviations from the SOP to the treating physician.
  • Rerun of the analytic pipeline due to network, computer or storage failure or memory issues.
  • Rerun of a particular step with different parameters or cutoffs other than those used to validate the assay (e.g. a technician has to use different settings in a filter chain to analyze a specific variant in a given patient sample).
  • Bugs or failures in the bioinformatics pipelines. The bug, affected cases and proposed corrective action such as installing an updated release must be approved, signed and dated by the laboratory director or designee.

The exception log is a key factor in assessing how a lab is committed to remain in compliance with the College of American Pathologists (CAP). It allows any external auditor to understand the thought process that goes into maintaining a state of the art pipeline. It also enables a reviewer to understand the quality of results generated at any moment in time. The exception log is a key document as a lab implements an NGS-based clinical reporting pipeline. Done right, it will accurately reflect the ongoing changes that are inevitable in a highly dynamic field of medicine.

Transfer Confidentiality Policy

Next-generation sequencing labs generate large amounts of data. This includes all the artifacts of the NGS pipeline, along with other information such as patient name, date of birth, medical record numbers and other components of protected health information. Laboratories need robust policies regarding the transfer of genomic information to other health care entities that are in accordance with the Health Insurance Portability and Accountability Act (HIPAA). It’s an open question if third-party cloud-based companies provide a sufficiently secure platform in all instances. Many hospitals and testing labs are opting to install in-house servers to manage their data. At any rate, there must be procedures in place to ensure the required confidentiality. This includes:

  • Data encryption
  • Secure data transfer
  • User authentication with controlled access to protected health information
  • Audit trails that track the transmission of data.

 

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