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In the 20 years since the Human Genome Project brought genomics to the mainstream, the challenge of analyzing its more than three-billion base pairs of data—and the high cost of reading an individual genome—has prevented whole genome sequencing from being a regular part of the healthcare conversation. With the cost of sequencing coming down—and the power of cloud-based artificial intelligence coming up—any provider with access to a patient’s genome sequence can now use that data to help diagnose rare diseases or guide patient care, reducing the time to a diagnosis from days to minutes. Inside Precision Medicine recently sat down with Jyoti Palaniappan, Chief Commercial Officer of Fabric Genomics, to find out how AI makes it all possible.
Your founder and CEO was involved in the original sequencing and bioinformatics of the Human Genome Project. How is genomics in clinical decision-making different today?
Key are the scale and speed by which we can both sequence and analyze an individual human genome. The first genome took 13 years to sequence, where now we can have an individual’s genome sequenced and analyzed in a few hours. Artificial intelligence makes it possible, as there is no way you can effectively bring together all the data points without the incredible technological advances we’ve seen in data science.
What is the core of Fabric’s AI technology platform?
For whole genome analysis, our Fabric GEM platform uses discovery AI to perform a multi-dimensional analysis of an entire genome and rank the most likely causative variants at the top with high confidence, allowing near instant identification of disease-causing variants. For gene panels, we developed an AI-based automated variant classification engine, ACE, which provides a scalable solution for panel interpretation. The AI does what teams of bioinformaticians and clinical geneticists cannot, in a timeframe and at a cost that makes it practical for routine care.
What role can genomics and AI play in population health?
As we see more WGS and WES in practice, we’ll see more impact in population health. We are proud to have been involved in a recent study from Genomics England and the 100,000 Genomes Project, which was just published in the New England Journal of Medicine, that demonstrated how whole genome sequencing was able to lead to new diagnoses for hundreds of patients. These foundational studies are leading to new treatment paths and showcasing methods to drive the widespread adoption of whole genome sequencing in population health.
You’ve talked about major national programs like the Human Genome Project in the US or the 100,000 Genomes Projects in the UK—what is AI doing to democratize genomics to help small, self-funded hospitals or labs bring sequencing services and analysis to their patients?
Since the Human Genome Project, we have been working to build the foundational knowledge bases and methods that enable us to consider whole genome sequencing as an everyday tool in clinical care. AI has really allowed us to bring all these pieces together and generate the high-confidence expert clinical insights more quickly and at a fraction of the cost that can help direct treatment decisions. The scalability of these technologies is democratizing access and allowing these capabilities to move beyond the big genome centers and into clinics and labs who are interested in brining high-quality genome-based diagnosis to their patients. In 2021 alone, we analyzed more than 30,000 people’s genes with our cloud-based AI tools and have learned a lot about how to deliver that capability to a broad range of clinics. We’ve also built out custom panels for clinics covering oncology and cardiovascular risk screening, cancer tumor profiling, and newborn screening or carrier testing for rare genetic disorders. It’s a very exciting time in clinical genomics where we’re really starting to see how genomics can guide patient care and is being reimbursed as such.
