The applicant is an Assistant Professor at Beth Israel Deaconess Medical Center and Harvard Medical School. The applicant's research program focuses on developing methods in translational bioinformatics to integrate heterogeneous biomedical data types (genomic, transcriptomic, microscopic, clinical) enabling the development of improved diagnostics and therapeutics for patients. Recent projects completed by the applicant include: the development of the Computational Pathology (C-Path) platform for building prognostic models from microscopic image data (Science Translational Medicine, 2011); and the development of the Significance Analysis of Prognostic Signatures method for identifying robust prognostic gene expression signatures form clinically annotated genomic data (PLoS Computational Biology, 2013). This K22 award will provide the applicant with the support necessary to accomplish the following goals: (1) to become an expert at designing and using ontologies to model Computational Pathology data and to support a Computational Pathology Knowledgebase; (2) to become an expert at integrative predictive modeling of Computational Pathology data, gene expression data, and clinical outcomes data; and (3) to develop and manage an independent research career in translational bioinformatics. To achieve these goals, the applicant has assembled a team of mentors and collaborators with expertise in each of these areas of translational biomedical informatics. The team includes: Dr. Isaac Kohane, who is a Professor of Pediatrics, directs the Children's Hospital Informatics Program and leads an NLM-supported national center for biomedical computing; Dr. Ron Kikinis who is a Professor at Harvard Medical School, and the Robert Greenes Distinguished Director of Biomedical Informatics in the Department of Radiology at Brigham and Women's Hospital; and Dr. John Quackenbush, who is a Professor of Biostatistics and Computational Biology and Professor of Cancer Biology at the Dana-Farber Cancer Institute. During the K22 award program, the applicant and his study team will develop informatics models and methods for Computational Pathology data.
In Aim 1, they will develop a Computational Pathology Ontology to support a Computational Pathology Knowledgebase. The knowledgebase will be populated with microscopic phenotype data, gene expression data, and clinical outcomes data from over 2,500 cancer patients that underwent molecular profiling as part of The Cancer Genome Atlas project.
In Specific Aim 2, they will develop and apply methods in machine learning to identify associations between gene expression and microscopic phenotypes. This information will be incorporated into the C-Path Knowledgebase.
In Aim 3, they will use the C-Path Knowledgebase to build integrative prognostic models that jointly model quantitative morphological data and quantitative gene expression data to predict patient survival. The integrative prognostic models generated in breast, brain, kidney, and lung cancer will lead to more effective diagnostics for these malignancies. The central hypothesis for this application is that morphological and molecular data are inherently complementary, and the most biologically informative and clinically useful predictive models will incorporate information from both of these heterogeneous data types. This research will form the basis for an R01 application to further develop and validate the informatics methods and models developed in this project.
This project will develop new computational methods for modeling quantitative microscopic phenotype data and for integrating microscopic disease phenotypes with gene expression data and clinical outcomes data. These advances will lead to the development of improved diagnostics for patients, allowing clinicians to more accurately predict the outcome of a patient's disease, leading to more effective individualized therapies.
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