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.
|Bolli, Niccolo; Biancon, Giulia; Moarii, Matahi et al. (2018) Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups. Leukemia 32:2604-2616|
|Botta, C; Cucè, M; Pitari, M R et al. (2018) MiR-29b antagonizes the pro-inflammatory tumor-promoting activity of multiple myeloma-educated dendritic cells. Leukemia 32:1003-1015|
|Zeid, Rhamy; Lawlor, Matthew A; Poon, Evon et al. (2018) Enhancer invasion shapes MYCN-dependent transcriptional amplification in neuroblastoma. Nat Genet 50:515-523|
|Maura, F; Petljak, M; Lionetti, M et al. (2018) Biological and prognostic impact of APOBEC-induced mutations in the spectrum of plasma cell dyscrasias and multiple myeloma cell lines. Leukemia 32:1044-1048|
|Bae, J; Hideshima, T; Zhang, G L et al. (2018) Identification and characterization of HLA-A24-specific XBP1, CD138 (Syndecan-1) and CS1 (SLAMF7) peptides inducing antigens-specific memory cytotoxic T lymphocytes targeting multiple myeloma. Leukemia 32:752-764|
|O'Donnell, Elizabeth K; Laubach, Jacob P; Yee, Andrew J et al. (2018) A phase 2 study of modified lenalidomide, bortezomib and dexamethasone in transplant-ineligible multiple myeloma. Br J Haematol 182:222-230|
|Guo, Guangwu; Raje, Noopur S; Seifer, Charles et al. (2018) Genomic discovery and clonal tracking in multiple myeloma by cell-free DNA sequencing. Leukemia 32:1838-1841|
|Szalat, R; Samur, M K; Fulciniti, M et al. (2018) Nucleotide excision repair is a potential therapeutic target in multiple myeloma. Leukemia 32:111-119|
|Nair, Shiny; Sng, Joel; Boddupalli, Chandra Sekhar et al. (2018) Antigen-mediated regulation in monoclonal gammopathies and myeloma. JCI Insight 3:|
|Gullà, A; Hideshima, T; Bianchi, G et al. (2018) Protein arginine methyltransferase 5 has prognostic relevance and is a druggable target in multiple myeloma. Leukemia 32:996-1002|
Showing the most recent 10 out of 218 publications