Molecular genetics and the impact on information management
Continuing breakthroughs in genetics and molecular diagnostics are resulting in an increase in the volume of ordered tests and the amount of data being introduced into genetics laboratories. The volume of data, the need for workflow automation and the complexity of the testing, reporting and interpretations all require specialised management by the LIS.
Described as a “game-changer”, the rate of gene discovery is increasing, and is approaching one new Mendelian disease gene per day! Molecular and genomic testing is becoming the “new bread and butter of diagnosis”. It is being adopted in the mainstream lab at an escalating rate and is used as
a tool for diagnostics, screening, prediction, theranostics (how the patient metabolises drugs) and personalised medicine.
The advent of whole exome and next generation sequencing technologies has brought about significant decreases in costs and increases in throughput capacity and automation.
Exome analysis is much less complex than genome analysis. The benefit of analysing exomes is the ability to get the coding function of the genome. It is faster and more specific than genome analysis and has often been described as the ‘sweet spot’ in genomics where a lot can be achieved for moderate cost. As testing with whole-exome sequencing evolves to characterise more patients with atypical presentations of known genetic diseases, the spectrum of phenotypes associated with genetic disorders will expand, revealing more gene variants. With this escalation of exome sequencing the lab is presented with a number of challenges:
• Informatics of analyses where a huge amount of genetic information is produced requiring the storage of extensive data sets
• Reporting is complex – what to report? Correlation of data based on patient and medicines.
• Managing the growing number of testing protocols and ensuring quality
• Advising clinicians on appropriate test ordering
• Managing incidental or unexpected findings
Lab information management will need to collate and report the volume of data in a way that is easy for the clinician to interpret. Ideally, genetic reports will combine the right information and present it to the healthcare provider so that it complements their clinical assessment of the patient and assists them to make a decision personalised to that patient on the right medicines. Pharmacogenomics creates a scientific pathway between genetics and treatments. Information will be crucial to the successful practice of personalised medicine, providing the most current test interpretations and recommendations.
With increased knowledge and confidence in genetic testing for common conditions, there is a potential for greatly increased public demand for these tests. The range of available tests is expanding constantly and a further challenge for information management is to support clinicians with knowledge on new test protocols and the appropriateness of the tests.
The development of next gen sequencing technologies and the associated decrease in costs has seen a surge in direct to consumer market. There are concerns by mainstream labs and clinicians on the quality of these testing protocols and the impact on the patient without appropriate interpretation assistance or access to genetic counselling.
Incidental findings present another issue for the lab. When a genetic test for a certain type of cancer provides additional information that could affect the patient’s health, what is the ethical course of action for pathologists and clinical laboratory scientists? Should this information be disclosed to the physician who ordered that cancer test? In turn, should that clinician inform his or her patient about these “incidental findings?”
It is a fascinating and exciting area – our genetic code holds potential to reveal the medical future as well as history, and with the increase in targeted therapies, makes personalised medicine a reality. Next generation sequencing methods have caused the costs of genetic testing to plummet. With this affordability, more patients will be considering requesting genome assessments and at more regular intervals as gene variants can be uncovered.
There are significant challenges for the lab. Of these the ability to manage the analysis of complex data sets will be essential. Utilising clinical histories, diagnoses and targeted therapeutics, there is an opportunity for medical laboratories to provide diagnostic information that has significant clinical value thereby providing the lead in personalised medicine. Establishing the right approach to information management will be a key enabler of this.