לוח שנה משולב

p53 and Li-Fraumeni syndrome: Translating Basic Discovery into Clinical Practice
Li-Fraumeni Syndrome (LFS) is the most common syndrome known to predispose both children and adults to a spectrum of histopathologically diverse malignancies. Germline p53 mutations were first reported in LFS 20 years ago. Since then, the clinical relevance of p53 genetics and biology has become increasingly important. The clinical definition of the syndrome has been redefined to account for the association of germline p53 alterations with tumor phenotypes that do not reflect the classical LFS definition. In addition, we and others have identified genetic modifiers of the phenotype including MDM2 SNP309, PIN3, codon 72 R>P single nucleotide polymorphisms, telomere attrition and DNA copy number variation. The role that these modifiers play in determining cancer susceptibility will be discussed. Recent work from our group suggests that constitutional deletions across 17p13.1 confer distinct cancer or developmental delay/congenital anomaly phenotypes depending on the precise molecular breakpoint at or near the p53 locus. Furthermore, the molecular sequence characteristics of these breakpoints suggest mechanisms of recombination that may be peculiar to this site. Germline p53 mutation status and evolution of CNVs to copy number alterations (CAN) in tumors may also be relevant to clinical outcome Thus, the availability of germline p53 testing in at-risk individuals offers an important and unique opportunity for clinical intervention. We have developed a comprehensive surveillance protocol for p53 mutation carriers and have demonstrated it to have a beneficial effect on survival. This talk will present findings from these and emerging work that highlights the clinical relevance of germline p53 mutation analysis.

Neuronal cultures grown from hippocampal neurons exhibit a distinct all-or-none burst firing pattern. We introduce quantitative tools to investigate the properties of the network which lead to this kind of behavior, and identify the distribution of input connections as the dominant factor governing the behavior of the network. We show that one-dimensional networks display a significantly simpler behavior, and use this observation to design some computational neuronal circuits.