The goal of the research projects in this program is to understand the oncogenomic changes in myeloma central to disease pathogenesis which impact clinical outcome using data from two large clinical trials To support this effort, the Bioinformatics and Biostatistics Core E will provide 1) support and direction on the formatting, quality control and annotation procedures of clinical and research data as well as the process of transferring the research data a data warehouse for integrative analysis, 2) bioinformatics support for primary and integrative analysis of the high-throughput data on genetic lesions, gene expression and transcriptome modification by differential miRNA expression and alternative splicing and 3) biostatistics support in terms of design and analysis for all projects. Core members will work closely with project members as well as Cores A and B with regard to quality control, specimen tracking and data warehouse procedures as well as Cores C and D which will perform microarray based gene and miRNA expression profiling, and SNP genotyping and next generation sequencing for identifying genomic and transcriptomic changes. Bioinformatics and biostatistics support and analysis is not only required for individual projects, but it is crucial for the integrative analysis from data across the projects and thus overall success of the program.
This core will help analyze both clinical data to identify role of transplantation in myeloma in the era of novel agents and identify correlates of clinical out come. The bioinformatic support will unravel genomic changes to better understand disease biology, features predictive of progression to myeloma and define those changes that will predict outcome and will form the basis for development of novel therapeutics.
|Bae, J; Prabhala, R; Voskertchian, A et al. (2015) A multiepitope of XBP1, CD138 and CS1 peptides induces myeloma-specific cytotoxic T lymphocytes in T cells of smoldering myeloma patients. Leukemia 29:218-29|
|Hu, Y; Song, W; Cirstea, D et al. (2015) CSNK1?1 mediates malignant plasma cell survival. Leukemia 29:474-82|
|Moreau, Philippe; Cavo, Michele; Sonneveld, Pieter et al. (2014) Combination of international scoring system 3, high lactate dehydrogenase, and t(4;14) and/or del(17p) identifies patients with multiple myeloma (MM) treated with front-line autologous stem-cell transplantation at high risk of early MM progression-related J Clin Oncol 32:2173-80|
|Lu, R; Pal, J; Buon, L et al. (2014) Targeting homologous recombination and telomerase in Barrett's adenocarcinoma: impact on telomere maintenance, genomic instability and tumor growth. Oncogene 33:1495-505|
|Anderson, K K; Flora, N; Archie, S et al. (2014) A meta-analysis of ethnic differences in pathways to care at the first episode of psychosis. Acta Psychiatr Scand 130:257-68|
|Chretien, Marie-Lorraine; Hebraud, Benjamin; Cances-Lauwers, Valérie et al. (2014) Age is a prognostic factor even among patients with multiple myeloma younger than 66 years treated with high-dose melphalan: the IFM experience on 2316 patients. Haematologica 99:1236-8|
|Samur, Mehmet Kemal (2014) RTCGAToolbox: a new tool for exporting TCGA Firehose data. PLoS One 9:e106397|
|Cottini, Francesca; Hideshima, Teru; Xu, Chunxiao et al. (2014) Rescue of Hippo coactivator YAP1 triggers DNA damage-induced apoptosis in hematological cancers. Nat Med 20:599-606|
|Hebraud, B; Leleu, X; Lauwers-Cances, V et al. (2014) Deletion of the 1p32 region is a major independent prognostic factor in young patients with myeloma: the IFM experience on 1195 patients. Leukemia 28:675-9|
|Cooke, Susanna L; Shlien, Adam; Marshall, John et al. (2014) Processed pseudogenes acquired somatically during cancer development. Nat Commun 5:3644|
Showing the most recent 10 out of 35 publications