Improving the efficiency of genomic analysis is crucial to meet the challenge of analyzing and interpreting an ever-growing number of data sets and unlock clinically relevant information.
Ahead of this year's Enabling Genomic Medicine 2020: Mainstreaming Genomics, we spoke to a number of industry leaders to find out their thoughts.
Those experts included:
- Michael S. Phillips, Chief Scientific Officer, Sequence Bio
- Tessa Homfray, Consultant in Medical Genetics, NHS St George’s Hospital
- Nick Lench, Chief Scientific Officer and Andrea Haworth, Head Scientist - Rare Disease, Congenica
- Miriam León Otegui, Head of Clinical Analysis, Veritas Genetics
Analysis of genetic data to provide actionable information is a major bottleneck
According to Nick Lench, data analysis and interpretation is a major bottleneck for many laboratories and still dependent on manual review and check by registered clinical scientists or genetic counsellors. While Clinical Decision Support platforms can massively reduce the time needed to analyze they are not yet used as widely as they could be. Michel Phillips agrees and adds that data analysis and interpretation are improving with access to new and improved genomic technologies and analysis tools. However, there is still a lot of variability in access to the appropriate tests, and turnaround times are often too slow for effective use of this information.
A shortage of trained professionals and genetic counsellors with the required expertise for interpretation limits effective sample processing at scale. As a result, case analyses can have very long wait times.
Tessa Homfray stresses that improving the data interpretation process using Clinical Decision Support platforms does improve the situation, but in her experience as soon as efficiency gains are made, any extra capacity created is used up as the unmet clinical need for genomic analysis is enormous. Ironically, as the number of cases increases, delivering results also becomes a significant bottleneck, says Andrea Haworth.
Genomic analysis platforms come to the rescue
Nick Lench points out that interpretation platforms like Congenica significantly reduce the analytical time required to reach an accurate assessment. This becomes increasingly beneficial when moving from small gene panels to exomes and genomes.
Miriam León Otegui agrees and adds her experience that being able to filter out variants at the click of a button and having as much information as possible about the variant in the platform without having to explore all other databases one by one provides huge time savings.
Tessa Homfray also believes that Clinical Decision Support platforms are absolutely vital and that, as the amount of genomic data increases worldwide, further platform development is the only way forward.
Up to date and high-quality Clinical Decision Support systems can operationalize and standardize genomic analysis to underpin our ability to scale provision of genomics services, says Andrea Haworth, and adds her experience that they are essential to provide a high-quality service to their users: In clinical laboratories we must have standardized tools, processes and procedures to ensure we get the right result at the right time. Clinical Decision Support platforms enable this and also play a vital role in ensuring that genetic services are compliant with the requirements of laboratory accreditation bodies.
According to Andrea Haworth, research has indicated that multidisciplinary team reviews of genomic data, where key members of the clinical and scientific team review the data together, improve the quality of genomic interpretation. Decision support platforms that facilitate and streamline this cross-team interaction are invaluable.
Is AI the future of medicine?
All experts agree that proven, validated automated technology will be well received from industry. AI is just beginning to make an impact in the diagnostic pathology. The key is to have proven validation and performance metrics for the technology in question, explains Nick Lench.
AI is becoming more mainstream. One reason for this is, as Michael Phillips points out, that non-hypothesis driven research and pattern recognition is a main driver to help stratify cases to improve and research potential treatments for precision medicine. In addition the use of chatbots for the dissemination of complex information to healthcare professionals and physicians is becoming more accepted. Researchers and clinicians are becoming more comfortable and receptive to the use of these technologies.
Andrea Haworth envisions that systems which utilize AI will increasingly allow rapid identification of variants of interest through automation and the application of sophisticated machine learning tools. This will liberate experts from more routine tasks.
From her own experience she reports that a genomics first approach that leverages tools such as those incorporated within the Congenica platform has enabled her to identify causal variants that she may not have even considered initially. Whilst she believes AI and automation are very exciting, quality and accuracy must be maintained if they are to be truly transformative.
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