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 technique and 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 ME we 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.

Public Health Relevance

of this Core to the public health derives from its role in advancing the overall objective of this program project: understanding MM growth in the context of its interaction with the bone marrow ME in order to translate and exploit this knowledge into smarter MM growth control in patients.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1-RPRB-J (M1))
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Arkansas for Medical Sciences
Little Rock
United States
Zip Code
Mikulasova, Aneta; Wardell, Christopher P; Murison, Alexander et al. (2017) The spectrum of somatic mutations in monoclonal gammopathy of undetermined significance indicates a less complex genomic landscape than that in multiple myeloma. Haematologica 102:1617-1625
Rasche, Leo; Weinhold, Niels; Morgan, Gareth J et al. (2017) Immunologic approaches for the treatment of multiple myeloma. Cancer Treat Rev 55:190-199
Rasche, L; Chavan, S S; Stephens, O W et al. (2017) Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing. Nat Commun 8:268
Went, M; Sud, A; Law, P J et al. (2017) Assessing the effect of obesity-related traits on multiple myeloma using a Mendelian randomisation approach. Blood Cancer J 7:e573
Schinke, Carolina; Hoering, Antje; Wang, Hongwei et al. (2017) The prognostic value of the depth of response in multiple myeloma depends on the time of assessment, risk status and molecular subtype. Haematologica 102:e313-e316
Chavan, S S; He, J; Tytarenko, R et al. (2017) Bi-allelic inactivation is more prevalent at relapse in multiple myeloma, identifying RB1 as an independent prognostic marker. Blood Cancer J 7:e535
Sawyer, J R; Tian, E; Shaughnessy Jr, J D et al. (2017) Hyperhaploidy is a novel high-risk cytogenetic subgroup in multiple myeloma. Leukemia 31:637-644
Mohan, Meera; Samant, Rohan S; Yoon, Donghoon et al. (2017) Extensive Remineralization of Large Pelvic Lytic Lesions Following Total Therapy Treatment in Patients With Multiple Myeloma. J Bone Miner Res 32:1261-1266
Rasche, Leo; Angtuaco, Edgardo; McDonald, James E et al. (2017) Low expression of hexokinase-2 is associated with false-negative FDG-positron emission tomography in multiple myeloma. Blood 130:30-34
Stein, Caleb K; Pawlyn, Charlotte; Chavan, Shweta et al. (2017) The varied distribution and impact of RAS codon and other key DNA alterations across the translocation cyclin D subgroups in multiple myeloma. Oncotarget 8:27854-27867

Showing the most recent 10 out of 284 publications