Patients with the cancers acute myelogenous leukemia (AML) and non-Hodgkins lymphoma (NHL) fall into several subtypes. Mechanistic models of how therapies, environment and genotype affect clinical outcome in AML and NHL patients are needed. Development of these models requires measuring data on transcription, signaling and protein interactions from patients in various stages of disease. This is a daunting task due to the complexity of mammalian cells. In collaborative research project 1 (RP1, Dynamic State Space Modeling of Cancer Cell Response to Therapy), we will carry out synchronized experiments to measure protein abundance, protein-interactions and protein modification in genetically defined animal models of AML and NHL in years 1-4 and in human samples in year 5. These measurements will be integrated using new computational methods to analyze the topology of regulatory and signaling networks and dynamic parameters of these networks. To facilitate this analysis, our experiments will be collected as time series and different measurement technologies (i.e., measurements of protein modification, protein levels and protein interactions) will be carried out on cells drawn from synchronized samples. This strict coordination of samples across genomic measurement technologies will enable the integration of our combined dataset into predictive molecular networks that can be used by RP2 and RPS (projects that model the evolution and spatial growth of the tumor explicitly) to predict cell state given tumor microenvironment, genotype and treatment. Our team includes primary developers of genomics technologies and our novel capabilities include scalable methods for making biochemically detailed measurements of real-time protein interaction, technologies for measuring the abundance and types of protein modifications, novel methods for shotgun proteomics and new methods for reconstructing regulatory networks. The project will be defined by our strict focus on proteins/genes critical for tumor development and proteins active in AML and NH which will insure the clinical relevance of our models.
Patients with the cancers acute myelogenous leukemia (AML) and non-Hodgkins lymphoma (NHL) have dramatic differences in response to therapy and in progression and prognosis. By describing a dynamic model of the molecular and regulatory responses of these cancers, we will provide a means for predicting response to therapy based on an individual's genotype and molecular phenotype.
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