Develop novel therapeutics that augment the function of p53 in individual cancer cells

The Lahav lab's unique capability to perform quantitative live cell imaging at the single cell level may offer a variety of collaboration opportunities with corporate partners. The p53 tumor suppressor is thought to act as a surveillance molecule, detecting harmful insults to cells such as DNA damage, and initiating a cascade of events designed to stem the damage. Mutations in the p53 protein are found at a high frequency in a variety of tumors (e.g., 50% of breast cancers), and abnormal p53 activity has been linked to tumor growth. Dr. Lahav’s groundbreaking studies on p53 at the single cell level managed to shed new light on this protein and open new direction for investigating how to rescue p53 function in tumors. While many labs are exploring approaches such as gene therapy to restore p53 function, Dr. Lahav’s data have the potential to act as a springboard for the development of novel therapeutic efforts to stabilize and augment p53’s function without requiring ectopic supplementation of the normal p53 molecule. A simple example is that if p53 pulses are crucial for determining cell fate, the pharmacokinetic profile of a drug and the timing of drug delivery may have far more importance than one might otherwise think.

• Dr. Lahav’s lab is interested in understanding how cells process and transfer information about their internal and external environment and trigger the right cellular response. Their main approach is to choose key proteins in biological networks and study their dynamics in living human cells using fluorescence microscopy. By this approach they bypass the standard experimental method of averaging results obtained from large cell populations which could mask the true behavior of individual cells. 
• The approach of looking at protein dynamics provides new insights about the topology of the signaling network (rather than a simple static scheme of the circuit), and importantly, reveals the functional connective intricacies that produce different cellular outcomes such as growth, movements and death.
• Such discoveries are possible thanks to the integration of multiple disciplines including the development of new technologies for live cell imaging, new computational tools for image analysis, mathematical models for simulations of protein dynamics and designing new cellular systems for studying protein dynamics in single cells.
• The lab focuses on DNA damage responses and on the network regulating the tumor suppressor protein p53. This protein is perhaps the most studied protein in biology and medicine, due to its key role in cancer. Previous work on p53 focused on populations studies that average the dynamics of millions of cells together. Dr.  Lahav’s lab focuses on studying individual cells. Using a technically challenging live single-cell imaging system they were able to take movies of cancer cells following irradiation, and found that p53 levels show pulses that vary in number from cell to cell. These pulses were “digital”, in the sense that the number of pulses increased with increasing levels of irradiation, but the size of each pulse stayed the same. 

Dr. Lahav has been interested in the dynamic aspects of molecular signaling of complex networks and their effect on determining the path from specific inputs to different cellular outcomes. In a perspective article, Dr. Lahav laid out the theoretical argument for investigating oscillatory behavior in individual cells rather than averaging measurements of a population of cells, and argued that for proteins such as p53 and NF-kappaB, their downstream signaling can be correlated with the frequency of their oscillations. She hypothesized that these sequential pulses provide a sensitive and reversible rheostat to gauge response to upstream signals, allowing decision making for cell fate determinations to be based on an integration of pulses. This type of signal converter would reduce hasty decision-making.

In a high-profile article in Molecular Cell, Dr. Lahav and her colleagues extended their research on oscillatory behavior of signaling networks by investigating the effect of DNA damage on p53 activity. The accepted paradigm for p53 pulses was based solely on the dynamics of the p53/Mdm2 regulatory loop, but Dr. Lahav’s lab uncovered additional layers of complexity responsible for this phenomenon. Their research involved sensitive temporal modeling of the p53 response within individual cells to DNA damage induced by irradiation. In addition, these studies focused on the signaling intermediates that conveyed the presence of DNA damage to p53, namely the kinases ATM and its substrate Chk2. ATM is activated following binding to DNA double strand breaks, and stabilizes p53 by decreasing the affinity of the negative regulator Mdm2 for p53. The experiments revealed that ATM and Chk2 also displayed pulses of activity following DNA damage, suggesting that upstream oscillations were responsible for the p53 pulses, and allowed the lab to develop a computational model to describe the temporal signaling events. The lab also identified a negative feedback loop between p53 and the two kinases, mediated by the phosphatase Wip1, as important for regulating p53 dynamics. 

The tumor suppressor protein p53 (green) and its regulator Mdm2 (red) are shown in breast cancer cells using fluorescent tags. This figure demonstrates that individual cells have different levels of p53 and Mdm2. Time-lapse microscopy revealed that individual cells show multiple pulses of p53 which were masked in previous studies that averaged p53 levels in a population of cells.

Biological Mechanisms and Pathways

• Apoptosis •
• DNA damage •
• Dynamics •
• Feedback loop •
• Networks •
• Oncology •
• Oscillations •
• p53 •
• Protein kinase
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Cancer

• Apoptosis •
• Cancer •
• Oncology •
• p53 •
• Protein kinase
•  

Disease Mechanisms

• Cancer
•  

Research Tools and Instrumentation

• Cell cycle arrest •
• Computational modeling •
• Fluorescence microscopy •
• Human cells •
• Image analysis •
• Live cells imaging •
• Live cells imaging •
• Mathematical modeling •
• Single-cell measurement
•  

Therapeutic Discovery Tools and Assays

• Cancer •
• Computational modeling
•  

Therapeutics

• Protein kinase
•