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Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells.

ABSTRACT: Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate nonintuitive behaviors. We applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell-cycle arrest caused by inhibition of MEK1/2. Through simulation of a reverse-engineered Bayesian network model, we generated predictions of G1-S transition. The model identified known components of the cell-cycle machinery, such as CCND1, CCNE2, and CDC25A, as well as revealed novel regulators of G1-S transition, IER2, TRIB1, TRIM27. Experimental validation of model predictions confirmed 10 of 12 predicted genes to have a role in G1-S progression. Further analysis showed that TRIB1 regulated the cyclin D1 promoter via NF?B and AP-1 sites and sensitized cells to TRAIL-induced apoptosis. In clinical specimens of breast cancer, TRIB1 levels correlated with expression of NF?B and its target genes (IL8, CSF2), and TRIB1 copy number and expression were predictive of clinical outcome. Together, our results establish a critical role of TRIB1 in cell cycle and survival that is mediated via the modulation of NF?B signaling. Cancer Res; 77(7); 1575-85. ©2017 AACR.

SUBMITTER: Gendelman R

PROVIDER: S-EPMC5410377 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells.

Gendelman Rina R Xing Heming H Mirzoeva Olga K OK Sarde Preeti P Curtis Christina C Feiler Heidi S HS McDonagh Paul P Gray Joe W JW Khalil Iya I Korn W Michael WM

Cancer research 20170113 7

Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate nonintuitive behaviors. We applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell-cycle arrest caused by inhibition of MEK1/2. Through simulation of a reverse-engineered Bayesian ...[more]

PMID: 28087598

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Project description:To elucidate mechanisms of cancer progression, we generated inducible human neoplasia in 3-dimensionally intact epithelial tissue. Gene expression profiling of both epithelia and stroma at specific time points during tumor progression revealed sequential enrichment of genes mediating discrete biologic functions in each tissue compartment. A core cancer progression signature was distilled using the increased signaling specificity of downstream oncogene effectors and subjected to network modeling. Network topology predicted that tumor development depends upon specific ECM-interacting network hubs. Blockade of one such hub, the b1 integrin subunit, disrupted network gene expression and attenuated tumorigenesis in vivo. Thus, integrating network modeling and temporal gene expression analysis of inducible human neoplasia provides an approach to prioritize and characterize genes functioning in cancer progression. There are 3 experiments in this study: (1a) a time course of human epidermal tissue transformed by oncogenic ER:H-Ras and IkBaM comprising days 0, 5, 20, and 35 post Ras activation using 4OHT; (1b) a matched time course of adjacent mouse stromal tissue during tumor progression; (2) Arrays comparing 4OHT-induced, Raf-1:ER/IkBaM transformed epidermal tissue and -4OHT controls; and (3) Arrays comparing Ras:ER/IkBaM grafts co-treated with 4OHT and either IgG control antibody or an anti-b1 integrin blocking antibody, P5D2 for 30 days. All arrays were done in biologic duplicate. Epidermal tissue co-expressing ER:H-RasG12V (ER:Ras) and IkBaM was regenerated on female scid/scid mouse recipients. Grafts were allowed to heal for at least 3 weeks before Ras activation via daily i.p. injections of 730ug of 4OHT (in 110ul of a corn oil/ethanol mixture). Duplicate grafts were harvested after 0, 5, 20, and 35 days of 4OHT treatment. Laser capture microdissection was utilized to independently isolate epithelial cells of the basal most layers and adajacent stromal tissue for each time point. RNA was subjected to one round of T7-based linear amplification (Ambion Message Amp II enhanced kit) and hybridized to either HG-U133A 2.0 or MG-430A affymetrix oligonucleotide arrays. Arrays from this experiment are labeled Ras and Stroma, respectively. The second experment was performed on Raf-1:ER/IkBaM expressing grafts with and without 4OHT treament for 30 days. The third experiment was done comparing ER:Ras/IkBaM grafts concomitantly treated with 4OHT and 1.5mg/week of either IgG control antibody or a mouse monoclonal blocking antibody against b1 integrin for 30 days. For experiments 2 and 3, HG-U133A 2.0 GeneChips were used; 4OHT treatment and RNA isolation/amplification was performed as above.

Dataset Information

Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells.

Publications

Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells.

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