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.
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.
|Alagpulinsa, David A; Ayyadevara, Srinivas; Yaccoby, Shmuel et al. (2016) A Cyclin-Dependent Kinase Inhibitor, Dinaciclib, Impairs Homologous Recombination and Sensitizes Multiple Myeloma Cells to PARP Inhibition. Mol Cancer Ther 15:241-50|
|McDonald, James E; Kessler, Marcus M; Gardner, Michael W et al. (2016) Assessment of Total Lesion Glycolysis by 18F FDG PET/CT Significantly Improves Prognostic Value of GEP and ISS in Myeloma. Clin Cancer Res :|
|VÃ¥tsveen, Thea Kristin; Sponaas, Anne-Marit; Tian, Erming et al. (2016) Erythropoietin (EPO)-receptor signaling induces cell death of primary myeloma cells in vitro. J Hematol Oncol 9:75|
|Pawlyn, Charlotte; Kaiser, Martin F; Heuck, Christoph et al. (2016) The Spectrum and Clinical Impact of Epigenetic Modifier Mutations in Myeloma. Clin Cancer Res 22:5783-5794|
|Weinhold, Niels; Ashby, Cody; Rasche, Leo et al. (2016) Clonal selection and double-hit events involving tumor suppressor genes underlie relapse in myeloma. Blood 128:1735-44|
|Jethava, Yogesh; Mitchell, Alan; Epstein, Joshua et al. (2016) Adverse metaphase cytogenetics can be overcome by adding bortezomib and thalidomide to fractionated melphalan transplants. Clin Cancer Res :|
|Weinhold, N; Heuck, C J; Rosenthal, A et al. (2016) Clinical value of molecular subtyping multiple myeloma using gene expression profiling. Leukemia 30:423-30|
|Pawlyn, C; Fowkes, L; Otero, S et al. (2016) Whole-body diffusion-weighted MRI: a new gold standard for assessing disease burden in patients with multiple myeloma? Leukemia 30:1446-8|
|Heuck, C J; Jethava, Y; Khan, R et al. (2016) Inhibiting MEK in MAPK pathway-activated myeloma. Leukemia 30:976-80|
|Mitchell, Jonathan S; Li, Ni; Weinhold, Niels et al. (2016) Genome-wide association study identifies multiple susceptibility loci for multiple myeloma. Nat Commun 7:12050|
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