The Healthcare Theory Podcast: On the Future of Precision Medicine

“Fundamentally, therapeutics are targeting a protein. They’re not treating DNA. They’re largely not treating RNA. And so the action for therapeutics is around interacting with proteins. Through the Human Genome Project and the other genomics work that has been done, we know the number of genes – about 20,000 of them – and what they encode for in humans. Those will then encode for RNA, and those RNA will be translated into roughly 20,000 different types of proteins. From here, it gets extremely complicated, because the proteins themselves can exist in many different forms – proteo‑isoforms – and they can be modified in a lot of different ways after they’re translated. This is why multi-omics in precision medicine is critical, to move beyond the genome to assay additional molecular layers that are dynamic and that are the functional drivers of disease and response.

Any one gene in DNA can code for many different forms of the same type of protein, and the differences between them are really important when you get into nonfunction or the cause of disease. The proteins themselves are also interacting with a number of smaller molecules like metabolites and lipids and cytokines within the human system. Those can go up into the millions, into what we call the dark proteome or the dark metabolome – things that we fundamentally don’t understand yet, but that can be the cause of disease and the cause of response to therapy.

This is where Sapient focuses: measuring these complex, dynamic biomarkers and how they change over time. We take single snapshots of this biology at different time points and put them together into something that looks more like a video of a patient’s evolving state. We use mass spectrometry, which has been around for decades, but what’s different now is that is has throughput and speed. Mass spectrometry used to be very artisanal and manual. Now, through automation and AI-enabled data analysis, we can apply it at scale. This has enabled us to usher in the next era of multi-omics in precision medicine.

As a result, we can observe single patients for multiple years, and do so across large populations of patients. We watch how their disease state evolves from pre‑diagnosis to diagnosis, to the start of treatment, and then their response to therapy – all at the protein and metabolic level. Thousands of proteins, and thousands of metabolites, as that patient’s state changes over time. You can’t do this kind of network analysis if you only look at one patient in isolation or at one time point.”