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 has 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). We hypothesize that the temporal dynamics of p53 (i.e. changes in the levels of p53 over time) plays a role in cell fate decisions: we have recently shown that different stresses lead to different p53 dynamics and that modulation of p53 dynamics alters cellular outcomes. In this grant we propose to combine quantitative dynamic measurements of p53 and cellular outcomes in single cells (using live cell imaging) with mathematical modeling and manipulation of the p53 circuit to determine the molecular mechanisms that link p53 dynamics to specific phenotypic outcomes. We will also investigate the effect of mutations in the p53 network on p53 dynamics and on cell fate decisions and how p53 dynamics and downstream decision-making respond to representative genotoxic chemotherapy drugs. 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 helpus 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.
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. The knowledge we gain should help us develop novel strategies to selectively push cancer cells toward terminate fates.
|Stewart-Ornstein, Jacob; Lahav, Galit (2017) Integrating genomic information and signaling dynamics for efficient cancer therapy. Curr Opin Syst Biol 1:38-43|
|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|
|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|
|Drayman, Nir; Ben-Nun-Shaul, Orly; Butin-Israeli, Veronika et al. (2016) p53 elevation in human cells halt SV40 infection by inhibiting T-ag expression. Oncotarget 7:52643-52660|
|Paek, Andrew L; Liu, Julia C; Loewer, Alexander et al. (2016) Cell-to-Cell Variation in p53 Dynamics Leads to Fractional Killing. Cell 165:631-42|
|Lande-Diner, Laura; Stewart-Ornstein, Jacob; Weitz, Charles J et al. (2015) Single-cell analysis of circadian dynamics in tissue explants. Mol Biol Cell 26:3940-5|
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