Houtan Noushmehr

Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes.

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGAs research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.

Clonal expansion and epigenetic reprogramming following deletion or amplification of mutant IDH1

Significance Identifying the drivers of tumorigenesis provides insight into mechanisms of transformation and can suggest novel therapeutic targets. IDH1 mutations in gliomas are one such promising target. Drivers of tumor initiation may be distinct from those at tumor recurrence, however. Here, we demonstrate that in a subset of initially IDH1 mutant gliomas IDH1 is deleted or amplified at recurrence, yielding a higher grade tumor with a reprogrammed epigenome. We also report systematic selection for cells with IDH1 CNA in vitro and in vivo. Thus, while IDH1 mutation likely initiates gliomagenesis, neither mutant IDH1 nor the oncometabolite 2HG that it produces are required at recurrence. These findings have important implications for emerging therapeutic strategies targeting mutant IDH1. IDH1 mutation is the earliest genetic alteration in low-grade gliomas (LGGs), but its role in tumor recurrence is unclear. Mutant IDH1 drives overproduction of the oncometabolite d-2-hydroxyglutarate (2HG) and a CpG island (CGI) hypermethylation phenotype (G-CIMP). To investigate the role of mutant IDH1 at recurrence, we performed a longitudinal analysis of 50 IDH1 mutant LGGs. We discovered six cases with copy number alterations (CNAs) at the IDH1 locus at recurrence. Deletion or amplification of IDH1 was followed by clonal expansion and recurrence at a higher grade. Successful cultures derived from IDH1 mutant, but not IDH1 wild type, gliomas systematically deleted IDH1 in vitro and in vivo, further suggestive of selection against the heterozygous mutant state as tumors progress. Tumors and cultures with IDH1 CNA had decreased 2HG, maintenance of G-CIMP, and DNA methylation reprogramming outside CGI. Thus, while IDH1 mutation initiates gliomagenesis, in some patients mutant IDH1 and 2HG are not required for later clonal expansions.

TCGA Workflow : Analyze cancer genomics and epigenomics data using Bioconductor packages

Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAToolbox, TCGAbiolinks.Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAToolbox,xa0 TCGAbiolinks.

SpidermiR: An R/Bioconductor Package for Integrative Analysis with miRNA Data.

Gene Regulatory Networks (GRNs) control many biological systems, but how such network coordination is shaped is still unknown. GRNs can be subdivided into basic connections that describe how the network members interact e.g., co-expression, physical interaction, co-localization, genetic influence, pathways, and shared protein domains. The important regulatory mechanisms of these networks involve miRNAs. We developed an R/Bioconductor package, namely SpidermiR, which offers an easy access to both GRNs and miRNAs to the end user, and integrates this information with differentially expressed genes obtained from The Cancer Genome Atlas. Specifically, SpidermiR allows the users to: (i) query and download GRNs and miRNAs from validated and predicted repositories; (ii) integrate miRNAs with GRNs in order to obtain miRNA–gene–gene and miRNA–protein–protein interactions, and to analyze miRNA GRNs in order to identify miRNA–gene communities; and (iii) graphically visualize the results of the analyses. These analyses can be performed through a single interface and without the need for any downloads. The full data sets are then rapidly integrated and processed locally.

Post-Sepsis State Induces Tumor-Associated Macrophage Accumulation through CXCR4/CXCL12 and Favors Tumor Progression in Mice.

Patients who recover from sepsis are immunosuppressed and at higher risk for cancer. Studying melanoma in post–septic-shock mice revealed that tumor composition, progression rate, and microenvironment were biased toward attracting tumor-associated macrophages that support tumor growth. Survivors from sepsis are in an immunosuppressed state that is associated with higher long-term mortality and risk of opportunistic infections. Whether these factors contribute to neoplastic proliferation, however, remains unclear. Tumor-associated macrophages (TAM) can support malignant cell proliferation, survival, and angiogenesis. We addressed the relationship between the post-sepsis state, tumor progression and TAM accumulation, and phenotypic and genetic profile, using a mouse model of sepsis resolution and then B16 melanoma in mice. In addition, we measured the serum concentrations of TNFα, TGFβ, CCL2, and CXCL12 and determined the effect of in vivo CXCR4/CXCL12 inhibition in this context. Mice that survived sepsis showed increased tumor progression both in the short and long term, and survival times were shorter. TAM accumulation, TAM local proliferation, and serum concentrations of TGFβ, CXCL12, and TNFα were increased. Naïve mice inoculated with B16 together with macrophages from post-sepsis mice also had faster tumor progression and shorter survival. Post-sepsis TAMs had less expression of MHC-II and leukocyte activation-related genes. Inhibition of CXCR4/CXCL12 prevented the post-sepsis–induced tumor progression, TAM accumulation, and TAM in situ proliferation. Collectively, our data show that the post-sepsis state was associated with TAM accumulation through CXCR4/CXCL12, which contributed to B16 melanoma progression. Cancer Immunol Res; 4(4); 312–22. ©2016 AACR.

Glioma CpG island methylator phenotype (G-CIMP): biological and clinical implications

Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties. Despite advances in surgical techniques and clinical regimens, treatment of high-grade glioma remains challenging and carries dismal rates of therapeutic success and overall survival. Challenges include the molecular complexity of gliomas, as well as inconsistencies in histopathological grading, resulting in an inaccurate prediction of disease progression and failure in the use of standard therapy. The updated 2016 World Health Organization (WHO) classification of tumors of the central nervous system reflects a refinement of tumor diagnostics by integrating the genotypic and phenotypic features, thereby narrowing the defined subgroups. The new classification recommends molecular diagnosis of isocitrate dehydrogenase (IDH) mutational status in gliomas. IDH-mutant gliomas manifest the cytosine-phosphate-guanine (CpG) island methylator phenotype (G-CIMP). Notably, the recent identification of clinically relevant subsets of G-CIMP tumors (G-CIMP-high and G-CIMP-low) provides a further refinement in glioma classification that is independent of grade and histology. This scheme may be useful for predicting patient outcome and may be translated into effective therapeutic strategies tailored to each patient. In this review, we highlight the evolution of our understanding of the G-CIMP subsets and how recent advances in characterizing the genome and epigenome of gliomas may influence future basic and translational research.

RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes

Abstract We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis. We evaluate our regularization framework in conjunction with two non-linear ML techniques, namely gradient boosting machines (GBM) and random-forests (GENIE), resulting in a regularized feature selection based method specifically called RGBM and RGENIE respectively. RGBM has been used to identify the main transcription factors that are causally involved as master regulators of the gene expression signature activated in the FGFR3-TACC3-positive glioblastoma. Here, we illustrate that RGBM identifies the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators characterizing the difference between G-CIMP-high and G-CIMP-low subtypes and between PA-like and LGm6-GBM, thus providing a clue to the yet undetermined nature of the transcriptional events among these subtypes.

Src as a novel therapeutic target for endometriosis.

BACKGROUNDnEndometriosis is a common condition that is associated with an increased risk of developing ovarian carcinoma. Improved in vitro models of this disease are needed to better understand how endometriosis, a benign disease, can undergo neoplastic transformation, and for the development of novel treatment strategies to prevent this progression.nnnMETHODSnWe describe the generation and in vitro characterization of novel TERT immortalized ovarian endometriosis epithelial cell lines (EEC16-TERT).nnnRESULTSnExpression of TERT alone was sufficient to immortalize endometriosis epithelial cells. TERT immortalization induces an epithelial-to-mesenchymal transition and perturbation in the expression of genes involved in the development of ovarian cancer. EEC16-TERT was non-tumorigenic when xenografted into immunocompromised mice but grew in anchorage-independent growth assays in an epidermal growth factor and hydrocortisone dependent manner. Colony formation in agar was abolished by inhibition of Src, and the Src pathway was found to be activated in human endometriosis lesions.nnnCONCLUSIONSnThis new in vitro model system mimics endometriosis and the early stages of neoplastic transformation in the development of endometriosis associated ovarian cancer. We demonstrate the potential clinical relevance of this model by identifying Src activation as a novel pathway in endometriosis that could be targeted therapeutically, perhaps as a novel strategy to manage endometriosis clinically, or to prevent the development of endometriosis-associated ovarian cancer.

Gene expression profiling of bone marrow mesenchymal stem cells from Osteogenesis Imperfecta patients during osteoblast differentiation

Mesenchymal stem cells (MSCs) are precursors present in adult bone marrow that are able to differentiate into osteoblasts, adipocytes and chondroblasts that have gained great importance as a source for cell therapy. Recently, a number of studies involving the analysis of gene expression of undifferentiated MSCs and of MSCs in the differentiation into multiple lineage processes were observed but there is no information concerning the gene expression of MSCs from Osteogenesis Imperfecta (OI) patients. Osteogenesis Imperfecta is characterized as a genetic disorder in which a generalized osteopenia leads to excessive bone fragility and severe bone deformities. The aim of this study was to analyze gene expression profile during osteogenic differentiation from BMMSCs (Bone Marrow Mesenchymal Stem Cells) obtained from patients with Osteogenesis Imperfecta and from control subjects. Bone marrow samples were collected from three normal subjects and five patients with OI. Mononuclear cells were isolated for obtaining mesenchymal cells that had been expanded until osteogenic differentiation was induced. RNA was harvested at seven time points during the osteogenic differentiation period (D0, D+1, D+2, D+7, D+12, D+17 and D+21). Gene expression analysis was performed by the microarray technique and identified several differentially expressed genes. Some important genes for osteoblast differentiation had lower expression in OI patients, suggesting a smaller commitment of these patients MSCs with the osteogenic lineage. Other genes also had their differential expression confirmed by RT-qPCR. An increase in the expression of genes related to adipocytes was observed, suggesting an increase of adipogenic differentiation at the expense osteogenic differentiation.