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Master regulators of infiltrate recruitment in autoimmune disease identified through network-based molecular deconvolution.

ABSTRACT: Network-based molecular modeling of physiological behaviors has proven invaluable in the study of complex diseases such as cancer, but these approaches remain largely untested in contexts involving interacting tissues such as autoimmunity. Here, using Alopecia Areata (AA) as a model, we have adapted regulatory network analysis to specifically isolate physiological behaviors in the skin that contribute to the recruitment of immune cells in autoimmune disease. We use context-specific regulatory networks to deconvolve and identify skin-specific regulatory modules with IKZF1 and DLX4 as master regulators (MRs). These MRs are sufficient to induce AA-like molecular states in vitro in three cultured cell lines, resulting in induced NKG2D-dependent cytotoxicity. This work demonstrates the feasibility of a network-based approach for compartmentalizing and targeting molecular behaviors contributing to interactions between tissues in autoimmune disease.

SUBMITTER: Chen JC

PROVIDER: S-EPMC4670983 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Master regulators of infiltrate recruitment in autoimmune disease identified through network-based molecular deconvolution.

Chen James C JC Cerise Jane E JE Jabbari Ali A Clynes Raphael R Christiano Angela M AM

Cell systems 20151101 5

Network-based molecular modeling of physiological behaviors has proven invaluable in the study of complex diseases such as cancer, but these approaches remain largely untested in contexts involving interacting tissues such as autoimmunity. Here, using Alopecia Areata (AA) as a model, we have adapted regulatory network analysis to specifically isolate physiological behaviors in the skin that contribute to the recruitment of immune cells in autoimmune disease. We use context-specific regulatory ne ...[more]

PMID: 26665180

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Project description:BackgroundTumor invasiveness reflects numerous biological changes, including tumorigenesis, progression, and metastasis. To decipher the role of transcriptional regulators (TR) involved in tumor invasiveness, we performed a systematic network-based pan-cancer assessment of master regulators of cancer invasiveness.Materials and methodsWe stratified patients in The Cancer Genome Atlas (TCGA) into invasiveness high (INV-H) and low (INV-L) groups using consensus clustering based on an established robust 24-gene signature to determine the prognostic association of invasiveness with overall survival (OS) across 32 different cancers. We devise a network-based protocol to identify TRs as master regulators (MRs) unique to INV-H and INV-L phenotypes. We validated the activity of MRs coherently associated with INV-H phenotype and worse OS across cancers in TCGA on a series of additional datasets in the Prediction of Clinical Outcomes from the Genomic Profiles (PRECOG) repository.ResultsBased on the 24-gene signature, we defined the invasiveness score for each patient sample and stratified patients into INV-H and INV-L clusters. We observed that invasiveness was associated with worse survival outcomes in almost all cancers and had a significant association with OS in ten out of 32 cancers. Our network-based framework identified common invasiveness-associated MRs specific to INV-H and INV-L groups across the ten prognostic cancers, including COL1A1, which is also part of the 24-gene signature, thus acting as a positive control. Downstream pathway analysis of MRs specific to INV-H phenotype resulted in the identification of several enriched pathways, including Epithelial into Mesenchymal Transition, TGF-β signaling pathway, regulation of Toll-like receptors, cytokines, and inflammatory response, and selective expression of chemokine receptors during T-cell polarization. Most of these pathways have connotations of inflammatory immune response and feasibility for metastasis.ConclusionOur pan-cancer study provides a comprehensive master regulator analysis of tumor invasiveness and can suggest more precise therapeutic strategies by targeting the identified MRs and downstream enriched pathways for patients across multiple cancers.

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Project description:BackgroundKRAS is the most frequently mutated gene in pancreatic ductal adenocarcinoma (PDAC), but the mechanisms underlying the transcriptional response to oncogenic KRAS are still not fully understood. We aimed to uncover transcription factors that regulate the transcriptional response of oncogenic KRAS in pancreatic cancer and to understand their clinical relevance.Methods and findingsWe applied a well-established network biology approach (master regulator analysis) to combine a transcriptional signature for oncogenic KRAS derived from a murine isogenic cell line with a coexpression network derived by integrating 560 human pancreatic cancer cases across seven studies. The datasets included the ICGC cohort (n = 242), the TCGA cohort (n = 178), and five smaller studies (n = 17, 25, 26, 36, and 36). 55 transcription factors were coexpressed with a significant number of genes in the transcriptional signature (gene set enrichment analysis [GSEA] p < 0.01). Community detection in the coexpression network identified 27 of the 55 transcription factors contributing to three major biological processes: Notch pathway, down-regulated Hedgehog/Wnt pathway, and cell cycle. The activities of these processes define three distinct subtypes of PDAC, which demonstrate differences in survival and mutational load as well as stromal and immune cell composition. The Hedgehog subgroup showed worst survival (hazard ratio 1.73, 95% CI 1.1 to 2.72, coxPH test p = 0.018) and the Notch subgroup the best (hazard ratio 0.62, 95% CI 0.42 to 0.93, coxPH test p = 0.019). The cell cycle subtype showed highest mutational burden (ANOVA p < 0.01) and the smallest amount of stromal admixture (ANOVA p < 2.2e-16). This study is limited by the information provided in published datasets, not all of which provide mutational profiles, survival data, or the specifics of treatment history.ConclusionsOur results characterize the regulatory mechanisms underlying the transcriptional response to oncogenic KRAS and provide a framework to develop strategies for specific subtypes of this disease using current therapeutics and by identifying targets for new groups.

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Master regulators of infiltrate recruitment in autoimmune disease identified through network-based molecular deconvolution.

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Master regulators of infiltrate recruitment in autoimmune disease identified through network-based molecular deconvolution.

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