Biostatistics and Bioinformatics Core

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PROJECT SUMMARY Core C (Biostatistics and Bioinformatics Core) of the NYU Cancer Health Disparity (CHD) SPORE is designed to provide statistical and bioinformatics collaboration and consultation to all SPORE Research Projects, Developmental Research Projects, and Core B (Biospecimen Procurement and Utilization Core). Consultation is available from the study design and planning stages through implementation, data management, statistical and bioinformatics analysis, and interpretation of results. Core C will provide support for all proposed laboratory studies and translational studies, including biomarker development based on samples from existing and new research studies to support the overarching mission and central scientific strategy of the NYU CHD SPORE. Furthermore, strategies for the systematic selection of samples from all the projects and the coordination of informatics support in Core C will permit the overall integration of results across projects to develop comprehensive models to predict disease occurrence and treatment outcomes. Core C draws on and integrates an extensive fund of knowledge, resources, and expertise across the NYU Biostatistics Resource(BSR), Applied Bioinformatics Laboratories (ABL), and Perlmutter Cancer Center (PCC), to serve the NYU CHD SPORE. The Co-Directors, Dr. Huilin Li, the director of NYUBR, and Dr. Kelly Ruggles, the Assistant Director of Biomedical Informatics in the Institute for Innovation in Medical Education at NYU, will provide integrated biostatistical and bioinformatic support and ensure maximum utilization of all institutional resources and facilities. This will empower the provision of expertise in all aspects of: statistical design; power/sample size calculations; systematic sample selection strategies for efficient data integration and analyses; and integration of data from multiple sources including laboratory data, clinical data, and data from diverse sequencing platforms. Core C will develop innovative statistical and bioinformatics methods, including innovative methods to integrate metagenomic, somatic mutation and immune profiling data. The research uses a mediation analysis framework to investigate the role of the microbiome in health disparity research. We will make these newly developed algorithms publicly available to the larger cancer research community.