The Future of Bioinformatics in Personalized & Precision Medicine
Hi everyone. Welcome to this podcast from Cambridge Health Tech Institute for the 2017 Molecular Medicine Tri-Con, which runs from February 19th to the 24th in San Francisco. I'm Thomas Cahoon, Market Research Analyst at Cambridge Innovation Institute. We have with us today one of our chairmen for the Bioinformatics track, Dr. John Mattison, Assistant Medical Director and Chief Medical Information Officer at Kaiser Permanente. Dr. Mattison, thank you for joining us.
How does bioinformatics tie into your own work and where do you see it going into the future?
I've had a big interest in bringing bioinformatics to the bedside for decades. In fact, I have a team of a couple dozen folks who have been doing exactly that for that length of time. We have a very deep and broad array of bioinformatics skills within the company, looking at large data sets that we have in order to find better solutions that we can translate to the bedside, so called translational medicine.
We've always been an evidence-based medicine organization at Kaiser Permanente and we are seeing, really, a tectonic shift in that evidence basis from the conventionally observed phenotypic manifestations of wellness, resilience, and illness, disease. That shift is going more to a vast array of biomarkers. What I anticipate happening, along with many others in this field, is that we're going through a transition state today, where we have mountains of information coming from remote sensors, wearable devices, from fixed sensors, from the microbiome, the bacteria in our gut, from the genome, from the pharmacogenome, from the transcriptome, proteome, metabolome, the lipidome, and on and on and on. Clearly that's not a sustainable, steady state for the end game.
What is going to evolve over the next 5-10 years is an elucidation of what of those terabytes of data is meaningful, when we ought to be looking for it, who we need to be focusing on for which disorder, and so this whole notion of personalized medicine is something that is going to be supported immensely by the quantified self movement, by the tools that we have for a much deeper understanding of the pathways to disease. At the same time, we need to have the same kind of discipline and evidence based informatics applied to the social, psychological, and economic determinants of health, so that when we're treating a whole person, and this is why I prefer personalized medicine to precision medicine as a moniker, is that as others have pointed out in a New England Journal of Medicine article and in a Ted Talk, there is a much larger influence on our health from the micro decisions we make every day in how we eat, how we exercise, how we sleep, and how we maintain a healthy social support system.
The bioinformatics portion of this is essentially how the body responds to how we treat it. I like to think that the era we're going through right now is understanding what are the important markers of health and resilience? What are the important markers of disease? What are the genetic, microbiomic predispositions to disease so that we can focus more effort on those who may need more attention in a particular area and not so much on another population that's a very low risk because the false positive, the sensitivity, the specificity, and the positive and negative predictive values of a particular parameter is very much based upon their risk pre test. That's classic Bayesian math.
We're applying machine learning with neuronets, and Bayesian algorithms, and mining data, and coming up with some pretty interesting discoveries already. Given the advances in machine learning, processor speed, and the quantity and multiple sources of information, I expect the basic text book of physiology to be re-written from start to finish within four or five years. I think the microbiome is going to play a huge role in re-writing that book. Ultimately what is science and what is a more modern view of physiology needs to be reduced down to fewer parameters that we can easily measure and when appropriate, in a time series so that we can translate all of that massive knowledge of human health and disease into more actionable metrics and into more supportable outputs. Most importantly, we need to understand and have an evidence based discipline in informatics analysis of how people are motivated so we can take this information and motivate them towards their own goals in health.
What are you looking forward to most at this conference and what do you expect the audience will learn?
I think the most important thing that we should be looking for right now is kind of the outlier indicators and metrics from all this research: what seems to be emerging? Just as an example, in the cancer genomic space, we know that very few people have identical somatic variance that characterizes their cancer that are identical. We know that there's a huge array of somatic variance associated with cancers and associated with the polyclonality of the cancer, but we also know by the same token, that some of those are just random noise reflecting the mutations and the tumor. Others very much drive the biologic behavior, the drug responsiveness, and the drug resistance of those tumors.
What we really need to do is to transform this avalanche of data and tsunami of new knowledge into actionable insights that we can actually apply clinically. As we know, there are multiple somatic variance for every type of cancer. Some of them are essentially collateral noise, but some of them very much drive the behavior of a particular cancer in its drug responsiveness, its drug resistance, its metastatic behavior and so forth. To the extent that we can determine what are those specific markers across the waterfront from the genome, transcriptome, metabolome, proteome, lipidome, microbiome, exposome, sociolome.
To the extent that we can extract out those things that interact with each other and that are relevant and that are actionable, and distill out the signal in all this noise, I believe that we'll get particularly closer to the purse sized medicine, and especially if we include in that assessment an evidence based approach to the use of motivational tools that are personalized and evidence based because not everybody responds the same way. We really need to be able to apply the same discipline to personalize how we motivate people based upon the information that is specific to them. That opens up a whole new world of opportunity for bringing people to much healthier and resilient states and be more proactive and preventive rather than reactive to disease. I like to think of the term, "using modern technology to restore ancient wisdom".
To the extent that we can use all this information to help people lead healthier lives and more mindful and resilient lives with healthier social support structures, all of the underlying bioinformatics, and biochemistry, and evidence based medicine will support how best to do that for each individual. The final common pathway is to motivate them to behaviors that lead to healthier lifespans and healthier health spans.
Dr. Mattison, thank you for your time and insights today.
You're quite welcome. I very much look forward to a great conference. Thank you.
That was Dr. Mattison, Assistant Medical Director and Chief Medical Information Officer at Kaiser Permanente, speaking about the bioinformatics track at the upcoming 2017 Molecular Medicine Tri-Con taking place February 19-24 in San Francisco California. If you're interested in this event and would like to learn more, go to triconference.com for registration information. I'm Thomas Cahoon. Thank you for listening.