Early studies in personalized medicine have focused on genomic data and subsets of biomarkers and assays to predict disease risk. Personalized medicine holds a great deal of promise for the future of medicine, but reliability problems and disease complexities have marred these initial, small-scale studies. Recent advances in scientific technologies allow us to monitor over 10,000 different POP constituents through DNA sequencing, RNA sequencing and protein and metabolite profiling. Approaches that harness and integrate these large-scale omics profiles have the greatest potential to reduce reliability problems and make personalized medicine a reality.
Recognizing the potential of an iPOP approach, Dr. Snyder launched an ambitious longitudinal study profiling the blood samples of a generally healthy volunteer (Dr. Snyder himself) over a 14-month period. Dr. Snyder provided about 20 blood samples (once every two months while healthy, and more frequently during periods of illness) for analysis over the course of the study. Each was analyzed with a variety of assays for tens of thousands of biological variables, generating huge amounts of information. This study, published in the March 16 issue of Cell, provides the first comprehensive iPOP of a healthy individual, capturing genomic, transcriptomic, proteomic, metabolomic and autoantibody profiles. The wealth of data from this extensive study provided many interesting insights into the potential of an iPOP approach to personalized medicine.
At the genomic level, deep whole-genome sequencing identified disease-associated variations, allowing Dr. Snyder to estimate his disease risk. Dr. Snyder's genome revealed that he had an elevated risk for developing diabetes. With the knowledge of this genomic predisposition, the team monitored rising glucose levels through the course of the study, actually witnessing the onset of diabetes. This early diagnosis was a direct result of the identification of a genomic predisposition, since Dr. Snyder's health history would not have suggested a need for glucose monitoring.
Fortuitously for science, Dr. Snyder contracted two viruses during this study, providing a rare glimpse into the body's dynamic molecular response after a viral infection. Not surprisingly, the activation of the body's immune response was apparent. Unexpectedly though, his body's response to the virus coincided with down-regulation of insulin pathways and a concordant rise in blood glucose levels, which marked the onset of diabetes. Importantly, this is the first indication that a viral infection could trigger the onset of type 2 diabetes in a person with a genomic predisposition for the disease. Underscoring the effectiveness of early interventions, Dr. Snyder implemented life style changes that successfully reined in his rising glucose levels. Moving forward, these observations suggest that monitoring post-infection glucose levels in patients predisposed to diabetes may be the key for early detection and intervention.
From a general biology perspective, this study provides an unprecedented glimpse into the variability of the transcriptome through various states of disease. A transcriptome is the very small percentage of the genome that is transcribed into RNA molecules. Observed differential allele expression and RNA editing (in which cells will change the sequence of RNA after it is copied from the original DNA) suggests that cells have an underappreciated ability to respond to their environment. Furthermore, this initial analysis reveals the potential of using these expression biomarkers for monitoring disease progression.
Although one must be careful in drawing broad conclusions from a data set that applies to a single individual, the lessons learned in this landmark study should shape future iPOP cohort studies. Overall, this proof-of-principle study revealed the potential of applying an iPOP-based approach to personalized medicine, where disease risk can be identified from genomic sequence and disease state can be monitored through other molecular components. Dr. Snyder's group demonstrated that a wealth of information can be obtained from a single blood sample and analysis of samples over time can provide an intimate glimpse into disease progression. Furthermore, personalized medicine requires tackling enormous data sets and distilling that data into informative details. This pilot study provides a comprehensive approach to data analysis and highlights the potential of each type of omic data. Future personalized medicine studies can now extend beyond the framework provided by this study and be tailored to the particular diseases predicted by the genomic sequence of patients.
Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y. K., Chen, R., Miriami, E., et al. Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes. Cell, 148(6), 1293-1307. 2012. [PubMed]
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Posted: April 30, 2012