Utilization of high-throughput genomic biotechnologies to a great extent accelerated the elucidation of the molecular heterogeneity of multiple myeloma (MM). In view of the success of gene expression profiling (GEP) of more than 350 patients on the Total Therapy 2 protocol and more than 450 patients on the Total Therapy 3 protocols who have been studied with this techniqueand the need to better understand the cellular and molecular biology of MM, including its microenvironment (ME), in order to develop treatments that effectively target tumor cells and the MEwe propose to continue, and expand, this work on all newly diagnosed patients entering clinical trials in Project 1, as well as patients on Total Therapies 3,4, and 5 experiencing relapse who enter salvage trials with novel allogeneic natural killer cell therapies in Project 2. With the recent advances in proteomic technologies, we will also perform proteomic profiling (PP) on tumor cells from the majority of these patients. GEP has been and will be an indispensable tool in our investigation of MM tumor cell manipulation of the bone marrow ME in Project 3 and Project 4. The primary objective of this Genomics and Proteomics Core is to provide a highly specialized, molecular shared resource that will serve established research projects. This resource combines the facilities and expertise of the Donna D. and Donald M. Lambert Laboratory of Myeloma Genetics and the Nancy and Stephen Grand Laboratory for Myeloma Proteomics at the Myeloma Institute for Research and Therapy of the Winthrop P. Rockefeller Cancer Institute. The objective of this Core will be achieved through the following specific aims: Specific Aim 1: Assist in the conduct of research of each P01 project by providing genomic and proteomic profiling of clinical material and cells derived from in vitro and in vivo models. Specifically, GEP and PP will be performed on patient samples and on samples from in vivo animal model studies and in vitro cell culture studies. Specific Aim 2: Maintain and correlate data from the genomic and proteomic studies. Data mining and statistical analyses of GEP and PP data in collaboration with the biostatistical components of Core A and Project 3 will provide the basis for the development of predictive models for treatment and risk stratification of MM. In Project 4, GEP and PP will help identify genetic pathways altered in the interaction between myeloma cells and osteoclasts and osteoblasts in in vitro model systems.