Acute myeloid leukemia (AML) is rapidly fatal, poorly controlled disease that can be rapidly brought into complete remission but where relapse kills the vast majority of patients. The genetic evolution of the disease has been defined retrospectively, but the basis for resistance to therapy and effective strategies to overcome it are lacking. This project seeks to take advantage of well-defined mouse models where a human leukemogenic allele induces highly penetrant, lethal AML that can be temporarily brought into remission by chemotherapy agents used in patients. Combining these basic biologic features with novel strategies for quantitatively assessing clonal behavior, clonal molecular features, physical localization and the in vivo parameters of growth pathway, cell cycle and apoptosis over time will provide multidimensional datasets for mathematical modeling of the parameters correlating with: 1. Clonal dominance in vivo (Specific Aim 1) and, 2. Sensitivity/resistance to chemotherapy in vivo (Specific Aim 2). The models will guide experimental testing of the role of the parameters in the in vivo behavior of the disease that will then be used to develop and test methods for enhancing durable control of AML (Specific Aim 3).

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA193461-04
Application #
9481819
Study Section
Special Emphasis Panel (ZCA1)
Project Start
Project End
Budget Start
2018-05-01
Budget End
2019-04-30
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Dana-Farber Cancer Institute
Department
Type
DUNS #
076580745
City
Boston
State
MA
Country
United States
Zip Code
Ozawa, Tatsuya; Arora, Sonali; Szulzewsky, Frank et al. (2018) A De Novo Mouse Model of C11orf95-RELA Fusion-Driven Ependymoma Identifies Driver Functions in Addition to NF-?B. Cell Rep 23:3787-3797
Cimino, Patrick J; Kim, Youngmi; Wu, Hua-Jun et al. (2018) Increased HOXA5 expression provides a selective advantage for gain of whole chromosome 7 in IDH wild-type glioblastoma. Genes Dev 32:512-523
Stein, Shayna; Zhao, Rui; Haeno, Hiroshi et al. (2018) Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients. PLoS Comput Biol 14:e1005924
Hinohara, Kunihiko; Wu, Hua-Jun; Vigneau, Sébastien et al. (2018) KDM5 Histone Demethylase Activity Links Cellular Transcriptomic Heterogeneity to Therapeutic Resistance. Cancer Cell 34:939-953.e9
Smith, Zachary D; Shi, Jiantao; Gu, Hongcang et al. (2017) Epigenetic restriction of extraembryonic lineages mirrors the somatic transition to cancer. Nature 549:543-547
Campbell, Peter T; Rebbeck, Timothy R; Nishihara, Reiko et al. (2017) Proceedings of the third international molecular pathological epidemiology (MPE) meeting. Cancer Causes Control 28:167-176
Temko, Daniel; Cheng, Yu-Kang; Polyak, Kornelia et al. (2017) Mathematical Modeling Links Pregnancy-Associated Changes and Breast Cancer Risk. Cancer Res 77:2800-2809
Malone, Clare F; Emerson, Chloe; Ingraham, Rachel et al. (2017) mTOR and HDAC Inhibitors Converge on the TXNIP/Thioredoxin Pathway to Cause Catastrophic Oxidative Stress and Regression of RAS-Driven Tumors. Cancer Discov 7:1450-1463
Chakrabarti, Shaon; Michor, Franziska (2017) Pharmacokinetics and Drug Interactions Determine Optimum Combination Strategies in Computational Models of Cancer Evolution. Cancer Res 77:3908-3921
Yu, Vionnie W C; Yusuf, Rushdia Z; Oki, Toshihiko et al. (2017) Epigenetic Memory Underlies Cell-Autonomous Heterogeneous Behavior of Hematopoietic Stem Cells. Cell 168:944-945

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