? CORE C The three research projects outlined in this P50 Center proposal use multi-dimensional genomic and epigenomic assays, based on next-generation sequencing and microarray approaches (implemented in Core B), to gain understanding of the inherited variations and somatically acquired genetic and epigenetic alterations that drive treatment response of pediatric and adult acute lymphoblastic leukemia (ALL). The Bioinformatics/Biostatistics Core (Core C) provides biostatistical and bioinformatic support to ensure each project generates high quality data with analytical accuracy and reproducibility. Core C includes designated scientists to analyze genomic and epigenetic data for each project; to provide uniform and high quality approaches to variant calling, annotation, and functional assessments for germline and somatic tumor variations; and to perform statistical analyses. Working closely with Cores A and B, they develop and provide critical infrastructure necessary for data management and facilitate cross-project and public data sharing. Together with the project Leaders and Investigators, using statistically sound analyses, Core C will identify important biological processes involved in the classification and treatment response of ALL and determine the genetic and epigenetic markers that are associated with discrete phenotypes defined in the Projects: in vivo response (Project 1), drug sensitivitity (Project 2), and adverse effects (Project 3). Core C will integrate cross- platform genomic, transcriptomic, and epigenomic data. Core C also provides critical input on study design and power estimates for both clinical and preclinical aims of the Projects.
In Aim 3, Core C will coordinate addressing the Center's overarching goal of using genome variations identified in the Projects to integrate clinical and genomic data for designing precision medicine approaches that can be used to improve treatment outcomes for children and adults with ALL.
|Nishii, Rina; Moriyama, Takaya; Janke, Laura J et al. (2018) Preclinical evaluation of NUDT15-guided thiopurine therapy and its effects on toxicity and antileukemic efficacy. Blood 131:2466-2474|
|Pui, Ching-Hon; Liu, Yiwei; Relling, Mary V (2018) How to solve the problem of hypersensitivity to asparaginase? Pediatr Blood Cancer 65:|
|Zhang, Yingchi; Gao, Yufeng; Zhang, Hui et al. (2018) PDGFRB mutation and tyrosine kinase inhibitor resistance in Ph-like acute lymphoblastic leukemia. Blood 131:2256-2261|
|Gupta, Sumit; Devidas, Meenakshi; Loh, Mignon L et al. (2018) Flow-cytometric vs. -morphologic assessment of remission in childhood acute lymphoblastic leukemia: a report from the Children's Oncology Group (COG). Leukemia 32:1370-1379|
|Steeghs, Elisabeth M P; Bakker, Marjolein; Hoogkamer, Alex Q et al. (2018) High STAP1 expression in DUX4-rearranged cases is not suitable as therapeutic target in pediatric B-cell precursor acute lymphoblastic leukemia. Sci Rep 8:693|
|Diouf, Barthelemy; Lin, Wenwei; Goktug, Asli et al. (2018) Alteration of RNA Splicing by Small-Molecule Inhibitors of the Interaction between NHP2L1 and U4. SLAS Discov 23:164-173|
|Pui, Ching-Hon (2018) To delay or not to delay, that is the question for patients with acute lymphoblastic leukemia who do not receive prophylactic cranial irradiation. Cancer 124:4442-4446|
|Churchman, Michelle L; Qian, Maoxiang; Te Kronnie, Geertruy et al. (2018) Germline Genetic IKZF1 Variation and Predisposition to Childhood Acute Lymphoblastic Leukemia. Cancer Cell 33:937-948.e8|
|Browne, Emily K; Zhou, Yinmei; Chemaitilly, Wassim et al. (2018) Changes in body mass index, height, and weight in children during and after therapy for acute lymphoblastic leukemia. Cancer 124:4248-4259|
|Diouf, Barthelemy; Evans, William E (2018) Pharmacogenomics of Vincristine-Induced Peripheral Neuropathy: Progress Continues. Clin Pharmacol Ther :|
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