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Abstract:
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Cancer is a heterogeneous disease, as a typical tumor contains multiple evolutionarily related subpopulations of cells with a different complement of somatically acquired mutations, microenvironment, and functional characteristics. When chemotherapeutic agents are administered to the patient, some of these subpopulations may gain a selective advantage and develop resistance to the treatment. We use multiple '-omic' profiling data types to provide a multi-dimensional, longitudinal 'window' into a patient's tumor biology and selective response to treatment. We present novel approaches for standardizing and integrating heterogeneous data produced by different labs, protocols, or profiling platforms. In addition, we present robust Bayesian factor analysis and structural equations models that for the simultaneous profiling functional oncogenic pathways and for the adaptation of pathway signatures into specific disease contexts. We will discuss appropriate future extensions to our models that include integrated longitudinal profiling, accommodations for multiple cancer subpopulations, and adaptations for single cell profiling.
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