ATM and MET kinases are synthetic lethal with non-genotoxic activation of p53

Howard Hughes Medical Institute, University of Colorado at Boulder, Boulder, Colorado, USA.
Nature Chemical Biology (Impact Factor: 13). 06/2012; 8(7):646-54. DOI: 10.1038/nchembio.965
Source: PubMed


The p53 tumor suppressor orchestrates alternative stress responses including cell cycle arrest and apoptosis, but the mechanisms defining cell fate upon p53 activation are poorly understood. Several small-molecule activators of p53 have been developed, including Nutlin-3, but their therapeutic potential is limited by the fact that they induce reversible cell cycle arrest in most cancer cell types. We report here the results of a genome-wide short hairpin RNA screen for genes that are lethal in combination with p53 activation by Nutlin-3, which showed that the ATM and MET kinases govern cell fate choice upon p53 activation. Genetic or pharmacological interference with ATM or MET activity converts the cellular response from cell cycle arrest into apoptosis in diverse cancer cell types without affecting expression of key p53 target genes. ATM and MET inhibitors also enable Nutlin-3 to kill tumor spheroids. These results identify new pathways controlling the cellular response to p53 activation and aid in the design of p53-based therapies.

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Available from: James Degregori
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    • "(B) The main steps and functions used in an analysis of shRNA-seq screen data in edgeR are shown. (C) Example of a multidimensional scaling (MDS) plot showing the relationships between replicate dimethyl sulfoxide (DMSO) and Nutlin treated samples (data from Sullivan et al. (2012) 3 "
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    ABSTRACT: Pooled short hairpin RNA sequencing (shRNA-seq) screens are becoming increasingly popular in functional genomics research, and there is a need to establish optimal analysis tools to handle such data. Our open-source shRNA processing pipeline in edgeR provides a complete analysis solution for shRNA-seq screen data, that begins with the raw sequence reads and ends with a ranked lists of candidate shRNAs for downstream biological validation. We first summarize the raw data contained in a fastq file into a matrix of counts (samples in the columns, hairpins in the rows) with options for allowing mismatches and small shifts in hairpin position. Diagnostic plots, normalization and differential representation analysis can then be performed using established methods to prioritize results in a statistically rigorous way, with the choice of either the classic exact testing methodology or a generalized linear modelling that can handle complex experimental designs. A detailed users’ guide that demonstrates how to analyze screen data in edgeR along with a point-and-click implementation of this workflow in Galaxy are also provided. The edgeR package is freely available from
    Full-text · Article · Apr 2014 · F1000 Research
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    • "We have recently performed a genome-wide functional genetic screen to identify synthetic lethality genes for Nutlin-3 (p53 inhibitor) in p53 wild-type cancer cell lines [13]. From this screening, we identified MET as a synthetic lethal gene with Nutlin-3 in killing cancer cell. "
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    ABSTRACT: Unlabelled: Protein kinases play important roles in regulating signal transduction in eukaryotic cells. Due to evolutionary conserved binding sites in the catalytic domain of the kinases, most inhibitors that target these sites promiscuously inhibit multiple kinases. Quantitative analysis can reveal complex and unexpected interactions between protein kinases and kinase inhibitors, providing opportunities for identifying multi-targeted inhibitors of specific diverse kinases for drug repurposing and development. We have developed K-Map-a novel and user-friendly web-based program that systematically connects a set of query kinases to kinase inhibitors based on quantitative profiles of the kinase inhibitor activities. Users can use K-Map to find kinase inhibitors for a set of query kinases (obtained from high-throughput 'omics' experiments) or to reveal new interactions between kinases and kinase inhibitors for rational drug combination studies. Availability and implementation: K-Map has been implemented in python scripting language and the website is freely available at:
    Full-text · Article · Sep 2013 · Human genomics
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    • "An important cluster of genes identified was involved in regulation of the cell cycle, in accordance with the pivotal role of p53 in cell cycle checkpoints [46,72,73]. Because p53 is a key regulator of G1/S checkpoints, and can promote cell cycle arrest or apoptosis in response to DNA damage, cancer cells with p53 mutations often have defects in the G1/S checkpoint while keep normal function in the G2/M checkpoint. "
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    ABSTRACT: Identification of genes that are synthetic lethal to p53 is an important strategy for anticancer therapy as p53 mutations have been reported to occur in more than half of all human cancer cases. Although genome-wide RNAi screening is an effective approach to finding synthetic lethal genes, it is costly and labor-intensive. To illustrate this approach, we identified potentially druggable genes synthetically lethal for p53 using three microarray datasets for gene expression profiles of the NCI-60 cancer cell lines, one next-generation sequencing (RNA-Seq) dataset from the Cancer Genome Atlas (TCGA) project, and one gene expression data from the Cancer Cell Line Encyclopedia (CCLE) project. We selected the genes which encoded kinases and had significantly higher expression in the tumors with functional p53 mutations (somatic mutations) than in the tumors without functional p53 mutations as the candidates of druggable synthetic lethal genes for p53. We identified important regulatory networks and functional categories pertinent to these genes, and performed an extensive survey of literature to find experimental evidence that support the synthetic lethality relationships between the genes identified and p53. We also examined the drug sensitivity difference between NCI-60 cell lines with functional p53 mutations and NCI-60 cell lines without functional p53 mutations for the compounds that target the kinases encoded by the genes identified. Our results indicated that some of the candidate genes we identified had been experimentally verified to be synthetic lethal for p53 and promising targets for anticancer therapy while some other genes were putative targets for development of cancer therapeutic agents. Our study indicated that pre-screening of potential synthetic lethal genes using gene expression profiles is a promising approach for improving the efficiency of synthetic lethal RNAi screening.
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