Trevor Clancy

Regulators of cyclin-dependent kinases are crucial for maintaining genome integrity in S phase

WEE1 and CHK1 jointly regulate Cdk activity to prevent DNA damage during replication.

The Genomic HyperBrowser: inferential genomics at the sequence level

The immense increase in the generation of genomic scale data poses an unmet analytical challenge, due to a lack of established methodology with the required flexibility and power. We propose a first principled approach to statistical analysis of sequence-level genomic information. We provide a growing collection of generic biological investigations that query pairwise relations between tracks, represented as mathematical objects, along the genome. The Genomic HyperBrowser implements the approach and is available at http://hyperbrowser.uio.no.

Identification of eight candidate target genes of the recurrent 3p12–p14 loss in cervical cancer by integrative genomic profiling

The pathogenetic role, including its target genes, of the recurrent 3p12–p14 loss in cervical cancer has remained unclear. To determine the onset of the event during carcinogenesis, we used microarray techniques and found that the loss was the most frequent 3p event, occurring in 61% of 92 invasive carcinomas, in only 2% of 43 high‐grade intraepithelial lesions (CIN2/3), and in 33% of 6 CIN3 lesions adjacent to invasive carcinomas, suggesting a role in acquisition of invasiveness or early during the invasive phase. We performed an integrative DNA copy number and expression analysis of 77 invasive carcinomas, where all genes within the recurrent region were included. We selected eight genes, THOC7, PSMD6, SLC25A26, TMF1, RYBP, SHQ1, EBLN2, and GBE1, which were highly down‐regulated in cases with loss, as confirmed at the protein level for RYBP and TMF1 by immunohistochemistry. The eight genes were subjected to network analysis based on the expression profiles, revealing interaction partners of proteins encoded by the genes that were coordinately regulated in tumours with loss. Several partners were shared among the eight genes, indicating crosstalk in their signalling. Gene ontology analysis showed enrichment of biological processes such as apoptosis, proliferation, and stress response in the network and suggested a relationship between down‐regulation of the eight genes and activation of tumourigenic pathways. Survival analysis showed prognostic impact of the eight‐gene signature that was confirmed in a validation cohort of 74 patients and was independent of clinical parameters. These results support the role of the eight candidate genes as targets of the 3p12–p14 loss in cervical cancer and suggest that the strong selection advantage of the loss during carcinogenesis might be caused by a synergetic effect of several tumourigenic processes controlled by these targets. Copyright

The Genomic HyperBrowser: an analysis web server for genome-scale data

The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.

Metastasis-associated protein S100A4 induces a network of inflammatory cytokines that activate stromal cells to acquire pro-tumorigenic properties

Tumor cells have the ability to exploit stromal cells to facilitate metastasis. By using malignant melanoma as a model, we show that the stroma adjacent to metastatic lesions is enriched in the known metastasis-promoting protein S100A4. S100A4 stimulates cancer cells to secrete paracrine factors, such as inflammatory cytokines IL8, CCL2 and SAA, which activate stromal cells (endothelial cells and monocytes) so that they acquire tumor-supportive properties. Our data establishes S100A4 as an inducer of a cytokine network enabling tumor cells to engage angiogenic and inflammatory stromal cells, which might contribute to pro-metastatic activity of S100A4.

Combining network modeling and gene expression microarray analysis to explore the dynamics of Th1 and Th2 cell regulation

Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks. By combining these algorithms with in silico knockouts and gene expression microarray data from human T cells, we examined if the results were compatible with a counter-regulatory role of Th1 and Th2 cells. We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation. We identified four attractors in the network, three of which included genes that corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease.

CLC and IFNAR1 are differentially expressed and a global immunity score is distinct between early- and late-onset colorectal cancer

Colorectal cancer (CRC) incidence increases with age, and early onset of the disease is an indication of genetic predisposition, estimated to cause up to 30% of all cases. To identify genes associated with early-onset CRC, we investigated gene expression levels within a series of young patients with CRCs who are not known to carry any hereditary syndromes (n=24; mean 43 years at diagnosis), and compared this with a series of CRCs from patients diagnosed at an older age (n=17; mean 79 years). Two individual genes were found to be differentially expressed between the two groups, with statistical significance; CLC was higher and IFNAR1 was less expressed in early-onset CRCs. Furthermore, genes located at chromosome band 19q13 were found to be enriched significantly among the genes with higher expression in the early-onset samples, including CLC. An elevated immune content within the early-onset group was observed from the differentially expressed genes. By application of outlier statistics, H3F3A was identified as a top candidate gene for a subset of the early-onset CRCs. In conclusion, CLC and IFNAR1 were identified to be overall differentially expressed between early- and late-onset CRC, and are important in the development of early-onset CRC.

Immunological network signatures of cancer progression and survival

BackgroundThe immune contribution to cancer progression is complex and difficult to characterize. For example in tumors, immune gene expression is detected from the combination of normal, tumor and immune cells in the tumor microenvironment. Profiling the immune component of tumors may facilitate the characterization of the poorly understood roles immunity plays in cancer progression. However, the current approaches to analyze the immune component of a tumor rely on incomplete identification of immune factors.MethodsTo facilitate a more comprehensive approach, we created a ranked immunological relevance score for all human genes, developed using a novel strategy that combines text mining and information theory. We used this score to assign an immunological grade to gene expression profiles, and thereby quantify the immunological component of tumors. This immunological relevance score was benchmarked against existing manually curated immune resources as well as high-throughput studies. To further characterize immunological relevance for genes, the relevance score was charted against both the human interactome and cancer information, forming an expanded interactome landscape of tumor immunity. We applied this approach to expression profiles in melanomas, thus identifying and grading their immunological components, followed by identification of their associated protein interactions.ResultsThe power of this strategy was demonstrated by the observation of early activation of the adaptive immune response and the diversity of the immune component during melanoma progression. Furthermore, the genome-wide immunological relevance score classified melanoma patient groups, whose immunological grade correlated with clinical features, such as immune phenotypes and survival.ConclusionsThe assignment of a ranked immunological relevance score to all human genes extends the content of existing immune gene resources and enriches our understanding of immune involvement in complex biological networks. The application of this approach to tumor immunity represents an automated systems strategy that quantifies the immunological component in complex disease. In so doing, it stratifies patients according to their immune profiles, which may lead to effective computational prognostic and clinical guides.

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex–complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery‐potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.

Membranous Expression of Ectodomain Isoforms of the Epidermal Growth Factor Receptor Predicts Outcome after Chemoradiotherapy of Lymph Node–Negative Cervical Cancer

Purpose: We compared the prognostic significance of ectodomain isoforms of the epidermal growth factor receptor (EGFR), which lack the tyrosine kinase (TK) domain, with that of the full-length receptor and its autophosphorylation status in cervical cancers treated with conventional chemoradiotherapy. Experimental Design: Expression of EGFR isoforms was assessed by immunohistochemistry in a prospectively collected cohort of 178 patients with squamous cell cervical carcinoma, and their detection was confirmed with Western blotting and reverse transcriptase PCR. A proximity ligation immunohistochemistry assay was used to assess EGFR-specific autophosphorylation. Pathways associated with the expression of ectodomain isoforms were studied by gene expression analysis with Illumina beadarrays in 110 patients and validated in an independent cohort of 41 patients. Results: Membranous expression of ectodomain isoforms alone, without the coexpression of the full-length receptor, showed correlations to poor clinical outcome that were highly significant for lymph node–negative patients (locoregional control, P = 0.0002; progression-free survival, P < 0.0001; disease-specific survival, P = 0.005 in the log-rank test) and independent of clinical variables. The ectodomain isoforms were primarily 60-kD products of alternative EGFR transcripts. Their membranous expression correlated with transcriptional regulation of oncogenic pathways including activation of MYC and MAX, which was significantly associated with poor outcome. This aggressive phenotype of ectodomain EGFR expressing tumors was confirmed in the independent cohort. Neither total nor full-length EGFR protein level, or autophosphorylation status, showed prognostic significance. Conclusion: Membranous expression of ectodomain EGFR isoforms, and not TK activation, predicts poor outcome after chemoradiotherapy for patients with lymph node–negative cervical cancer. Clin Cancer Res; 17(16); 5501–12. ©2011 AACR.