Our long-term goal is to understand how the dynamic behavior of biological signals is controlled and how these dynamics affect cellular responses. This proposal focuses on fundamental cellular mechanisms of the p53 signaling network. The p53 system orchestrates cellular responses to environmental insult and spontaneous damage, particularly those that damage DNA. Loss or mutation of the p53 system strongly predisposes human cells to cancer, and is observed in a large fraction of cancers. While the molecular function and regulation of the p53 pathway have been extensively investigated, exactly how wild-type or mutant p53 determines the fate of individual cells, and why cells exposed to the same insult end up having different fates, is poorly understood. Answering these questions requires a quantitative understanding of the order of events in single cells and the causal relationships between cellular background (cancer and non-cancerous cells), p53 status (wild-type or mutated), p53 dynamics and cellular outcomes (growth, arrest or death). In the previous funding period/s we found that the temporal dynamics of p53 (i.e. changes in the levels of p53 over time) play a role in cell fate decisions. Different stresses lead to different p53 dynamics and modulation of p53 dynamics alters cellular outcomes. In addition, cell-to-cell variability in the rate of p53 induction explained factional killing in response to a drug. We now propose to combine quantitative dynamic measurements of p53 and its target genes in single cells, together with single cell RNA-Seq and mathematical modeling to determine the origin of heterogeneity in the p53 response and the transcriptional cascades that link p53 dynamics with specific phenotypic outcomes. We will also investigate the dynamics of p53 across different cancer models, species and tissues in vivo and will search for new tools to perturb p53 dynamics. Our ultimate goal is to understand the regulation and function of p53 under different genetic backgrounds and to identify treatments that will increase the efficacy of irradiation and chemotherapy for cancer cells with specific mutations. Our results will provide new insights into the control and manipulation of the p53 pathway, perhaps the most important pathway protecting human cells against the development of cancer. We anticipate that a detailed quantitative understanding of the p53 circuit, the key circuit controlling the decision to grow or die in single cells, will help us understand why some cells die in response to chemotherapeutic drugs while others survive, and may suggest novel strategies to selectively push cancer cells toward permanent arrest or death. It addition, our study will be help predict the effects of specific drugs on tumors with specific genotypes and will provide a prototype for the analysis, description, and understanding of the dynamics of other signaling pathways in human cells.

Public Health Relevance

Our study will provide new insights into the control and manipulation of the p53 pathway, perhaps the most important pathway protecting human cells against the development of cancer. Specifically it will give a quantitative understanding of how p53 controls cell fate decisions in individual cells and why a subpopulation of cells escape drug treatment. The knowledge we gain should help us develop novel strategies to selectively push cancer cells toward terminate fates.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
2R01GM083303-10
Application #
9381247
Study Section
Cellular Signaling and Regulatory Systems Study Section (CSRS)
Program Officer
Dunsmore, Sarah
Project Start
2008-03-01
Project End
2021-05-31
Budget Start
2017-08-01
Budget End
2018-05-31
Support Year
10
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Harvard Medical School
Department
Biology
Type
Schools of Medicine
DUNS #
047006379
City
Boston
State
MA
Country
United States
Zip Code
02115
Arbelle, Assaf; Reyes, Jose; Chen, Jia-Yun et al. (2018) A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos. Med Image Anal 47:140-152
Reyes, José; Lahav, Galit (2018) Leveraging and coping with uncertainty in the response of individual cells to therapy. Curr Opin Biotechnol 51:109-115
Stewart-Ornstein, Jacob; Cheng, Ho Wa Jacky; Lahav, Galit (2017) Conservation and Divergence of p53 Oscillation Dynamics across Species. Cell Syst 5:410-417.e4
Stewart-Ornstein, Jacob; Lahav, Galit (2017) Integrating genomic information and signaling dynamics for efficient cancer therapy. Curr Opin Syst Biol 1:38-43
Oppenheim, Ariella; Lahav, Galit (2017) The puzzling interplay between p53 and Sp1. Aging (Albany NY) 9:1355-1356
Hafner, Antonina; Stewart-Ornstein, Jacob; Purvis, Jeremy E et al. (2017) p53 pulses lead to distinct patterns of gene expression albeit similar DNA-binding dynamics. Nat Struct Mol Biol 24:840-847
Stewart-Ornstein, Jacob; Lahav, Galit (2017) p53 dynamics in response to DNA damage vary across cell lines and are shaped by efficiency of DNA repair and activity of the kinase ATM. Sci Signal 10:
Chen, Sheng-Hong; Lahav, Galit (2016) Two is better than one; toward a rational design of combinatorial therapy. Curr Opin Struct Biol 41:145-150
Stewart-Ornstein, Jacob; Lahav, Galit (2016) Dynamics of CDKN1A in Single Cells Defined by an Endogenous Fluorescent Tagging Toolkit. Cell Rep 14:1800-1811
Chen, Sheng-Hong; Forrester, William; Lahav, Galit (2016) Schedule-dependent interaction between anticancer treatments. Science 351:1204-8

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