The goal of Core C is high-throughput production and analysis of AML genome sequence data. This includes data production, somatic and germline variant detection and validation, integration of various data types, and statistical inference of genetic alterations and their clinical implications. This will be achieved by employing cutting-edge technologies to generate high-quality DNA and RNA sequencing data and by using computational methods and tools to accurately analyze, identify, and categorize disease-causing and/or clinically relevant sequence variants and expression/methylation changes. The genomic data produced in Core C for cfe novo AML samples, AML relapse samples, MDS/AML families, and treatment-related AMLs will be comprehensive, including whole genome, exome, capture validation, and mRNA/sncRNA. Data from existing publically available catalogues of cancer-specific mutations, inherited variants related to genetic disease, and expression/methylation signatures will be used to further inform our analysis and clinical interpretation of identified genetic alterations in AML. Moreover, we will integrate genomic, epigenomic, and expression data from all projects at the pathway and network levels to understand how a diverse spectrum of genetic changes works together to drive AML initiation and progression. Finally, we will integrate genotypic and phenotypic data to facilitate the classification and discovery of genomic variants with clinical relevance and prognostic significance.
Core C will continue to produce and analyze high quality AML sequence data. This is critical for the success of this Program Project and, ultimately, for the better understanding of the genetic/genomic basis of AML.
|Engle, E K; Fisher, D A C; Miller, C A et al. (2015) Clonal evolution revealed by whole genome sequencing in a case of primary myelofibrosis transformed to secondary acute myeloid leukemia. Leukemia 29:869-76|
|Al-Hussaini, Muneera; DiPersio, John F (2014) Small molecule inhibitors in acute myeloid leukemia: from the bench to the clinic. Expert Rev Hematol 7:439-64|
|Jacoby, M A; De Jesus Pizarro, R E; Shao, J et al. (2014) The DNA double-strand break response is abnormal in myeloblasts from patients with therapy-related acute myeloid leukemia. Leukemia 28:1242-51|
|Sarkaria, S M; Christopher, M J; Klco, J M et al. (2014) Primary acute myeloid leukemia cells with IDH1 or IDH2 mutations respond to a DOT1L inhibitor in vitro. Leukemia 28:2403-6|
|Miller, Christopher A; White, Brian S; Dees, Nathan D et al. (2014) SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution. PLoS Comput Biol 10:e1003665|
|Klco, Jeffery M; Spencer, David H; Miller, Christopher A et al. (2014) Functional heterogeneity of genetically defined subclones in acute myeloid leukemia. Cancer Cell 25:379-92|
|Russler-Germain, David A; Spencer, David H; Young, Margaret A et al. (2014) The R882H DNMT3A mutation associated with AML dominantly inhibits wild-type DNMT3A by blocking its ability to form active tetramers. Cancer Cell 25:442-54|
|Hughes, Andrew E O; Magrini, Vincent; Demeter, Ryan et al. (2014) Clonal architecture of secondary acute myeloid leukemia defined by single-cell sequencing. PLoS Genet 10:e1004462|
|White, Brian S; DiPersio, John F (2014) Genomic tools in acute myeloid leukemia: From the bench to the bedside. Cancer 120:1134-44|
|Grieselhuber, N R; Klco, J M; Verdoni, A M et al. (2013) Notch signaling in acute promyelocytic leukemia. Leukemia 27:1548-57|
Showing the most recent 10 out of 65 publications