Modern biomedical research is being transformed by what is increasingly becoming a flood of information. Electronic medical records have opened unprecedented opportunities for studies investigating a wide range of problems ranging from epidemiological aspects of human disease to the comparative effectiveness of various treatment protocols. Similarly, genomic technologies such as DNA microarrays and next-generation DNA sequencing have opened a floodgate of data and information that promises to shed light on the complex nature of human disease. In this information-rich age, one of the greatest challenges is therefore no longer generating data, but effectively integrating it so that it can be used to advantage. Because clinical practice has long been separate from the research enterprise at most academic health centers, a situation has evolved in which clinical and research data exist in separate, often incompatible domains with a variety of technical, institutional, and organizational barriers between them. Further limiting our ability to extract the maximal value of the data we have available is the fact that data within institutions is rarely integrated with the vast body of data available in the public domain. Two large sources of clinical data will be used in this program project. The data for both of these large IFM/DFCI collaborative clinical trials (1000 MM patients treated with RVD with or without high dose therapy and transplant;1210 patients with either monoclonal gammopathy of undetermined significance or smoldering MM (SMM) followed for progression to active MM) are collected in an established clinical trials database at the IFM.
The aims of Core B are to create, maintain and quality control the clinical database (Specific Aim 1);to collect, transport, process and store tissue samples from all sites utilizing standardized procedures in centralized tissue banks (Specific Aim 2);and to develop a data warehouse to integrate the sample tracking and oncogenomic information with the relevant clinical data from the clinical trials database with web based query capabilities (Specific Aim 3).
|GullÃ , Annamaria; Di Martino, Maria Teresa; Gallo Cantafio, Maria Eugenia et al. (2016) A 13 mer LNA-i-miR-221 Inhibitor Restores Drug Sensitivity in Melphalan-Refractory Multiple Myeloma Cells. Clin Cancer Res 22:1222-33|
|Prabhala, R H; Fulciniti, M; Pelluru, D et al. (2016) Targeting IL-17A in multiple myeloma: a potential novel therapeutic approach in myeloma. Leukemia 30:379-89|
|Magrangeas, Florence; Kuiper, Rowan; Avet-Loiseau, HervÃ© et al. (2016) A Genome-Wide Association Study Identifies a Novel Locus for Bortezomib-Induced Peripheral Neuropathy in European Patients with Multiple Myeloma. Clin Cancer Res 22:4350-5|
|Ohguchi, Hiroto; Hideshima, Teru; Bhasin, Manoj K et al. (2016) The KDM3A-KLF2-IRF4 axis maintains myeloma cell survival. Nat Commun 7:10258|
|Dimopoulos, Meletios A; Orlowski, Robert Z; Facon, Thierry et al. (2015) Retrospective matched-pairs analysis of bortezomib plus dexamethasone versus bortezomib monotherapy in relapsed multiple myeloma. Haematologica 100:100-6|
|Bianchi, Giada; Munshi, Nikhil C (2015) Pathogenesis beyond the cancer clone(s) in multiple myeloma. Blood 125:3049-58|
|Jagannathan, S; Vad, N; Vallabhapurapu, S et al. (2015) MiR-29b replacement inhibits proteasomes and disrupts aggresome+autophagosome formation to enhance the antimyeloma benefit of bortezomib. Leukemia 29:727-38|
|Hu, Y; Song, W; Cirstea, D et al. (2015) CSNK1Î±1 mediates malignant plasma cell survival. Leukemia 29:474-82|
|Hebraud, Benjamin; Magrangeas, Florence; Cleynen, Alice et al. (2015) Role of additional chromosomal changes in the prognostic value of t(4;14) and del(17p) in multiple myeloma: the IFM experience. Blood 125:2095-100|
|Mitsiades, Constantine S (2015) Therapeutic landscape of carfilzomib and other modulators of the ubiquitin-proteasome pathway. J Clin Oncol 33:782-5|
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