A systems biological approach to identify key transcription factors and their genomic neighborhoods in human sarcomas

Somatic loss of function mutations in neurofibromin 1 and MYC associated factor X genes identified by exome-wide sequencing in a wild-type GIST case

Background

Approximately 10–15 % of gastrointestinal stromal tumors (GISTs) lack gain of function mutations in the KIT and platelet-derived growth factor receptor alpha (PDGFRA) genes. An alternate mechanism of oncogenesis through loss of function of the succinate-dehydrogenase (SDH) enzyme complex has been identified for a subset of these “wild type” GISTs.

Methods

Paired tumor and normal DNA from an SDH-intact wild-type GIST case was subjected to whole exome sequencing to identify the pathogenic mechanism(s) in this tumor. Selected findings were further investigated in panels of GIST tumors through Sanger DNA sequencing, quantitative real-time PCR, and immunohistochemical approaches.

Results

A hemizygous frameshift mutation (p.His2261Leufs*4), in the neurofibromin 1 (NF1) gene was identified in the patient’s GIST; however, no germline NF1 mutation was found. A somatic frameshift mutation (p.Lys54Argfs*31) in the MYC associated factor X (MAX) gene was also identified. Immunohistochemical analysis for MAX on a large panel of GISTs identified loss of MAX expression in the MAX-mutated GIST and in a subset of mainly KIT-mutated tumors.

Conclusion

This study suggests that inactivating NF1 mutations outside the context of neurofibromatosis may be the oncogenic mechanism for a subset of sporadic GIST. In addition, loss of function mutation of the MAX gene was identified for the first time in GIST, and a broader role for MAX in GIST progression was suggested.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1872-y) contains supplementary material, which is available to authorized users.

A pan-cancer modular regulatory network analysis to identify common and cancer-specific network components

Many human diseases including cancer are the result of perturbations to transcriptional regulatory networks that control context-specific expression of genes. A comparative approach across multiple cancer types is a powerful approach to illuminate the common and specific network features of this family of diseases. Recent efforts from The Cancer Genome Atlas (TCGA) have generated large collections of functional genomic data sets for multiple types of cancers. An emerging challenge is to devise computational approaches that systematically compare these genomic data sets across different cancer types that identify common and cancer-specific network components. We present a module- and network-based characterization of transcriptional patterns in six different cancers being studied in TCGA: breast, colon, rectal, kidney, ovarian, and endometrial. Our approach uses a recently developed regulatory network reconstruction algorithm, modular regulatory network learning with per gene information (MERLIN), within a stability selection framework to predict regulators for individual genes and gene modules. Our module-based analysis identifies a common theme of immune system processes in each cancer study, with modules statistically enriched for immune response processes as well as targets of key immune response regulators from the interferon regulatory factor (IRF) and signal transducer and activator of transcription (STAT) families. Comparison of the inferred regulatory networks from each cancer type identified a core regulatory network that included genes involved in chromatin remodeling, cell cycle, and immune response. Regulatory network hubs included genes with known roles in specific cancer types as well as genes with potentially novel roles in different cancer types. Overall, our integrated module and network analysis recapitulated known themes in cancer biology and additionally revealed novel regulatory hubs that suggest a complex interplay of immune response, cell cycle, and chromatin remodeling across multiple cancers.

Integrative genomic analyses of secreted protein acidic and rich in cysteine and its role in cancer prediction

Secreted protein acidic and rich in cysteine (SPARC), also termed osteonectin or basement‑membrane‑40 (BM‑40), is a matrix‑associated protein that elicits changes in cell shape, inhibits cell‑cycle progression and affects the synthesis of extracellular matrix (ECM). The final mature SPARC protein has 286 amino acids with three distinct domains, including an NH2‑terminal acidic domain (NT), follistatin‑like domain (FS) and C terminus domain (EC). The present study identified SPARC genes from 14 vertebrate genomes and revealed that SPARC existed in all types of vertebrates, including fish, amphibians, birds and mammals. In total, 21 single nucleotide polymorphisms (SNPs) causing missense mutations were identified, which may affect the formation of the truncated form of the SPARC protein. The human SPARC gene was found to be expressed in numerous tissues or organs, including in the bone marrow, whole blood, lymph node, thymus, brain, cerebellum, retina, heart, smooth muscle, skeletal muscle, spinal cord, intestine, colon, adipocyte, kidney, liver, pancreas, thyroid, salivary gland, skin, ovary, uterus, placenta, cervix and prostate. When searched in the PrognoScan database, the human SPARC gene was also found to be expressed in bladder, blood, breast, glioma, esophagus, colorectal, head and neck, ovarian, lung and skin cancer tissues. It was revealed that the association between the expression of SPARC and prognosis varied in different types of cancer, and even in the same cancer from different databases. It implied that the function of SPARC in these tumors may be multidimensional, functioning not just as a tumor suppressor or oncogene.

Identification of SNP-containing regulatory motifs in the myelodysplastic syndromes model using SNP arrays and gene expression arrays

Myelodysplastic syndromes have increased in frequency and incidence in the American population, but patient prognosis has not significantly improved over the last decade. Such improvements could be realized if biomarkers for accurate diagnosis and prognostic stratification were successfully identified. In this study, we propose a method that associates two state-of-the-art array technologies—single nucleotide polymorphism (SNP) array and gene expression array—with gene motifs considered transcription factor-binding sites (TFBS). We are particularly interested in SNP-containing motifs introduced by genetic variation and mutation as TFBS. The potential regulation of SNP-containing motifs affects only when certain mutations occur. These motifs can be identified from a group of co-expressed genes with copy number variation. Then, we used a sliding window to identify motif candidates near SNPs on gene sequences. The candidates were filtered by coarse thresholding and fine statistical testing. Using the regression-based LARS-EN algorithm and a level-wise sequence combination procedure, we identified 28 SNP-containing motifs as candidate TFBS. We confirmed 21 of the 28 motifs with ChIP-chip fragments in the TRANSFAC database. Another six motifs were validated by TRANSFAC via searching binding fragments on co-regulated genes. The identified motifs and their location genes can be considered potential biomarkers for myelodysplastic syndromes. Thus, our proposed method, a novel strategy for associating two data categories, is capable of integrating information from different sources to identify reliable candidate regulatory SNP-containing motifs introduced by genetic variation and mutation.

Cancer systems biology: signal processing for cancer research

In this editorial we introduce the research paradigms of signal processing in the era of systems biology. Signal processing is a field of science traditionally focused on modeling electronic and communications systems, but recently it has turned to biological applications with astounding results. The essence of signal processing is to describe the natural world by mathematical models and then, based on these models, develop efficient computational tools for solving engineering problems. Here, we underline, with examples, the endless possibilities which arise when the battle-hardened tools of engineering are applied to solve the problems that have tormented cancer researchers. Based on this approach, a new field has emerged, called cancer systems biology. Despite its short history, cancer systems biology has already produced several success stories tackling previously impracticable problems. Perhaps most importantly, it has been accepted as an integral part of the major endeavors of cancer research, such as analyzing the genomic and epigenomic data produced by The Cancer Genome Atlas (TCGA) project. Finally, we show that signal processing and cancer research, two fields that are seemingly distant from each other, have merged into a field that is indeed more than the sum of its parts.

Analysis of beta-cell gene expression reveals inflammatory signaling and evidence of dedifferentiation following human islet isolation and culture

The stresses encountered during islet isolation and culture may have deleterious effects on beta-cell physiology. However, the biological response of human islet cells to isolation remains poorly characterized. A better understanding of the network of signaling pathways induced by islet isolation and culturing may lead to strategies aimed at improving islet graft survival and function. Laser capture microdissection (LCM) was used to extract beta-cell RNA from 1) intact pancreatic islets, 2) freshly isolated islets, 3) islets cultured for 3 days, and changes in gene expression were examined by microarray analysis. We identified a strong inflammatory response induced by islet isolation that continues during in-vitro culture manifested by upregulation of several cytokines and cytokine-receptors. The most highly upregulated gene, interleukin-8 (IL-8), was induced by 3.6-fold following islet isolation and 56-fold after 3 days in culture. Immunofluorescence studies showed that the majority of IL-8 was produced by beta-cells themselves. We also observed that several pancreas-specific transcription factors were down-regulated in cultured islets. Concordantly, several pancreatic progenitor cell-specific transcription factors like SOX4, SOX9, and ID2 were upregulated in cultured islets, suggesting progressive transformation of mature beta-cell phenotype toward an immature endocrine cell phenotype. Our findings suggest islet isolation and culture induces an inflammatory response and loss of the mature endocrine cell phenotype. A better understanding of the signals required to maintain a mature beta-cell phenotype may help improve the efficacy of islet transplantation.