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Modeling oncogene addiction using RNA interference.

ABSTRACT: The clinical efficacy of selective kinase inhibitors suggests that some cancer cells may become dependent on a single oncogene for survival. RNAi has been increasingly used to understand such "oncogene addiction" and validate new therapeutic targets. However, RNAi approaches suffer from significant off-target effects that limit their utility. Here, we combine carefully titrated lentiviral-mediated short hairpin RNA knockdown of the epidermal growth factor receptor (EGFR) with heterologous reconstitution by EGFR mutants to rigorously analyze the structural features and signaling activities that determine addiction to the mutationally activated EGFR in human lung cancer cells. EGFR dependence is differentially rescued by distinct EGFR variants and oncogenic mutants, is critically dependent on its heterodimerization partner ErbB-3, and surprisingly, does not require autophosphorylation sites in the cytoplasmic domain. Quantitative "oncogene rescue" analysis allows mechanistic dissection of oncogene addiction, and, when broadly applied, may provide functional validation for potential therapeutic targets identified through large-scale RNAi screens.

SUBMITTER: Rothenberg SM

PROVIDER: S-EPMC2517605 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature

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Modeling oncogene addiction using RNA interference.

Rothenberg S Michael SM Engelman Jeffrey A JA Le Sheila S Riese David J DJ Haber Daniel A DA Settleman Jeffrey J

Proceedings of the National Academy of Sciences of the United States of America 20080818 34

The clinical efficacy of selective kinase inhibitors suggests that some cancer cells may become dependent on a single oncogene for survival. RNAi has been increasingly used to understand such "oncogene addiction" and validate new therapeutic targets. However, RNAi approaches suffer from significant off-target effects that limit their utility. Here, we combine carefully titrated lentiviral-mediated short hairpin RNA knockdown of the epidermal growth factor receptor (EGFR) with heterologous recons ...[more]

PMID: 18711136

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Project description:RNA interference (RNAi) is a regulatory cellular process that controls post-transcriptional gene silencing. During RNAi double-stranded RNA (dsRNA) induces sequence-specific degradation of homologous mRNA via the generation of smaller dsRNA oligomers of length between 21-23nt (siRNAs). siRNAs are then loaded onto the RNA-Induced Silencing multiprotein Complex (RISC), which uses the siRNA antisense strand to specifically recognize mRNA species which exhibit a complementary sequence. Once the siRNA loaded-RISC binds the target mRNA, the mRNA is cleaved and degraded, and the siRNA loaded-RISC can degrade additional mRNA molecules. Despite the widespread use of siRNAs for gene silencing, and the importance of dosage for its efficiency and to avoid off target effects, none of the numerous mathematical models proposed in literature was validated to quantitatively capture the effects of RNAi on the target mRNA degradation for different concentrations of siRNAs. Here, we address this pressing open problem performing in vitro experiments of RNAi in mammalian cells and testing and comparing different mathematical models fitting experimental data to in-silico generated data. We performed in vitro experiments in human and hamster cell lines constitutively expressing respectively EGFP protein or tTA protein, measuring both mRNA levels, by quantitative Real-Time PCR, and protein levels, by FACS analysis, for a large range of concentrations of siRNA oligomers.We tested and validated four different mathematical models of RNA interference by quantitatively fitting models' parameters to best capture the in vitro experimental data. We show that a simple Hill kinetic model is the most efficient way to model RNA interference. Our experimental and modeling findings clearly show that the RNAi-mediated degradation of mRNA is subject to saturation effects.Our model has a simple mathematical form, amenable to analytical investigations and a small set of parameters with an intuitive physical meaning, that makes it a unique and reliable mathematical tool. The findings here presented will be a useful instrument for better understanding RNAi biology and as modelling tool in Systems and Synthetic Biology.

Dataset Information

Modeling oncogene addiction using RNA interference.

Publications

Modeling oncogene addiction using RNA interference.

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