The 10th anniversary of the drafted human genome, released by the Human Genome Project in 2000, is a milestone for personalized medicine. Our mantra – “get the right intervention to the right patient at the right time” – all but mandates the roll-out of genomic information in clinical practice. As we come closer to the goal of the $1,000 genome, I can now imagine a world in which an individual genomic profile allows us to tailor cancer treatment to a patient’s personal situation. In the next decade, genomics will provide us with the opportunity to refine treatment planning so that we use drugs when they are going to work, spare patients unnecessary side effects, and avoid wasting precious time, emotional resources, or funds on drugs that are unlikely to be effective. Even intimately personal decisions, such as how to preserve fertility, can be elucidated by and based on genomic data mixed with an understanding of effectiveness and toxic risk.
Coexisting with excitement at the possibilities of genomically-guided personalized medicine is a pervasive angst. The profusion of new information can be daunting. How will I know all of the relevant inputs into decision-making in the era of personalized medicine? How will I balance multiple important factors for each patient, without a roadmap or algorithm for this new type of clinical decision-making? In personalized medicine, when treatment choices rely on unique genomic data for each patient, the quantity of potential data points to be factored into any single clinical decision boggle the mind. How can I intelligently coordinate and consider all of this data?
Recent progress in health information technology (HIT) and in advancing our country’s data infrastructure provide hope that technology may come to the rescue, saving us from a morass of data and helping us make sense of the new plethora of information. The Human Genome Project yielded vast amounts of data; its completion required development of interoperable data, novel statistical methods, and new HIT systems. These same tools can also help us use genomic information, as well as rapidly increasing bodies of clinical and research evidence, to inform decision-making. Genomically-guided biomarkers and predictive tests will help generate personalized information, but tools will be needed to help clinicians understand and use the resulting data, integrated with myriad other personal data types like blood chemistries, clinical exam findings, pre-existing toxic exposures over a life-time, and patient reported concerns. The development of personalized clinical decision support tools and prediction models, tailored and designed for efficient use at the point of care, will assist us in connecting the dots between the promise of The Human Genome Project and the vision of personalized medicine. May this 10th anniversary energize us to move from theory to action, and to strive for ever more finely individualized care that optimizes outcomes for our patients.
The Age of Personalized Medicine Blog 
June 18, 2010 at 4:43 AM |
I certainly hope the It infrastructure is changing. Our little start up (http//:www.dynemobiosystems.com) not only provides information on predicting patient outcome and therapy response but also requires patient data for further improvements of prediction. As the IT infrastructure improves so too will our ability to manage that care better.