Integrating dynamic omics responses for universal personalized medicine

Abstract Text:

The advent of readily available omics technologies, and the recent Precision Medicine Initiative announced by the White House and National Institutes of Health are guiding our efforts to make advances in the implementation of personalized medicine. High quality genomes are now complemented with other dynamic omics data (e.g. transcriptomes, proteomes, metabolomes), that may be used to profile temporal patterns of thousands of molecular components in individuals. We are pursuing the profiling of multiple such omics in parallel n=1 studies that extend the pilot integrative Personal Omics Profiling (iPOP) approach to diseases affecting the immune system. In particular, we will describe our investigations that follow longitudinally healthy and asthmatic individuals, and the integration of multiple omics obtained from peripheral blood cells, that we believe may provide novel medical insights. Concurrently, we are developing the necessary statistical and computational methodology for integrating the different omics platforms towards a medical interpretation, including our MathIOmica framework. Our approach enables us to query RNA sequencing, mass spectrometry (proteomics/metabolomics) and any longitudinal omics data, starting from lab samples to raw data, and including downstream quantitation methods for each analysis. We will present a clinically relevant classification scheme of longitudinal patterns, integration that accounts for missing data and uneven time sampling, and ultimately a biological interpretation and dynamic visualization of an integrated profile. Additionally, we are developing the necessary experiments and data sets for future iPOP investigations, with dense profiling of cell-drug treatment responses utilizing Rituximab and other interventions. Our combined transcriptome-proteome profiles enable us to reconstruct dynamic pathways of Rituximab’s action on B-cells on a global scale.  In summary, our clinical, laboratory and computational investigations are providing the next steps in the development of omics data generation and integration, towards a universal personalized medicine implementation.

G.I.M. and research reported in this presentation are supported by grants from MSU and the National Human Genome Research Institute of the National Institutes of Health under Award Number 4R00HG007065. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Keywords: disease, personal omics profiling, transcriptome-proteome profiles