Detecting Cancer Gene Networks Characterized by Recurrent Genomic Alterations in a Population

High resolution, system-wide characterizations have demonstrated the capacity to identify genomic regions that undergo genomic aberrations. Such research efforts often aim at associating these regions with disease etiology and outcome. Identifying the corresponding biologic processes that are responsible for disease and its outcome remains challenging. Using novel analytic methods that utilize the structure of biologic networks, we are able to identify the specific networks that are highly significantly, nonrandomly altered by regions of copy number amplification observed in a systems-wide analysis. We demonstrate this method in breast cancer, where the state of a subset of the pathways identified through these regions is shown to be highly associated with disease survival and recurrence

Cancer Molecular Analysis Project: Weaving a rich cancer research tapestry

AbstractThe Cancer Molecular Analysis Project (CMAP) of the NCI is integrating diverse cancer research data to elucidate fundamental etiologic processes, enable development of novel therapeutic approaches, and facilitate the bridging of basic and clinical science

Allele-Specific Chromatin Immunoprecipitation Studies Show Genetic Influence on Chromatin State in Human Genome

Several recent studies have shown a genetic influence on gene expression variation, including variation between the two chromosomes within an individual and variation between individuals at the population level. We hypothesized that genetic inheritance may also affect variation in chromatin states. To test this hypothesis, we analyzed chromatin states in 12 lymphoblastoid cells derived from two Centre d'Etude du Polymorphisme Humain families using an allele-specific chromatin immunoprecipitation (ChIP-on-chip) assay with Affymetrix 10K SNP chip. We performed the allele-specific ChIP-on-chip assays for the 12 lymphoblastoid cells using antibodies targeting at RNA polymerase II and five post-translation modified forms of the histone H3 protein. The use of multiple cell lines from the Centre d'Etude du Polymorphisme Humain families allowed us to evaluate variation of chromatin states across pedigrees. These studies demonstrated that chromatin state clustered by family. Our results support the idea that genetic inheritance can determine the epigenetic state of the chromatin as shown previously in model organisms. To our knowledge, this is the first demonstration in humans that genetics may be an important factor that influences global chromatin state mediated by histone modification, the hallmark of the epigenetic phenomena

Genome-wide loss of heterozygosity and copy number alteration in esophageal squamous cell carcinoma using the Affymetrix GeneChip Mapping 10 K array

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a common malignancy worldwide. Comprehensive genomic characterization of ESCC will further our understanding of the carcinogenesis process in this disease. RESULTS: Genome-wide detection of chromosomal changes was performed using the Affymetrix GeneChip 10 K single nucleotide polymorphism (SNP) array, including loss of heterozygosity (LOH) and copy number alterations (CNA), for 26 pairs of matched germ-line and micro-dissected tumor DNA samples. LOH regions were identified by two methods – using Affymetrix's genotype call software and using Affymetrix's copy number alteration tool (CNAT) software – and both approaches yielded similar results. Non-random LOH regions were found on 10 chromosomal arms (in decreasing order of frequency: 17p, 9p, 9q, 13q, 17q, 4q, 4p, 3p, 15q, and 5q), including 20 novel LOH regions (10 kb to 4.26 Mb). Fifteen CNA-loss regions (200 kb to 4.3 Mb) and 36 CNA-gain regions (200 kb to 9.3 Mb) were also identified. CONCLUSION: These studies demonstrate that the Affymetrix 10 K SNP chip is a valid platform to integrate analyses of LOH and CNA. The comprehensive knowledge gained from this analysis will enable improved strategies to prevent, diagnose, and treat ESCC

SNPdetector: A Software Tool for Sensitive and Accurate SNP Detection

Identification of single nucleotide polymorphisms (SNPs) and mutations is important for the discovery of genetic predisposition to complex diseases. PCR resequencing is the method of choice for de novo SNP discovery. However, manual curation of putative SNPs has been a major bottleneck in the application of this method to high-throughput screening. Therefore it is critical to develop a more sensitive and accurate computational method for automated SNP detection. We developed a software tool, SNPdetector, for automated identification of SNPs and mutations in fluorescence-based resequencing reads. SNPdetector was designed to model the process of human visual inspection and has a very low false positive and false negative rate. We demonstrate the superior performance of SNPdetector in SNP and mutation analysis by comparing its results with those derived by human inspection, PolyPhred (a popular SNP detection tool), and independent genotype assays in three large-scale investigations. The first study identified and validated inter- and intra-subspecies variations in 4,650 traces of 25 inbred mouse strains that belong to either the Mus musculus species or the M. spretus species. Unexpected heterozgyosity in CAST/Ei strain was observed in two out of 1,167 mouse SNPs. The second study identified 11,241 candidate SNPs in five ENCODE regions of the human genome covering 2.5 Mb of genomic sequence. Approximately 50% of the candidate SNPs were selected for experimental genotyping; the validation rate exceeded 95%. The third study detected ENU-induced mutations (at 0.04% allele frequency) in 64,896 traces of 1,236 zebra fish. Our analysis of three large and diverse test datasets demonstrated that SNPdetector is an effective tool for genome-scale research and for large-sample clinical studies. SNPdetector runs on Unix/Linux platform and is available publicly (http://lpg.nci.nih.gov)

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

International audienceIncreasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system

Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation

A novel method for quantifying the similarity between phenotypes by the use of ontologies can be used to search for candidate genes, pathway members, and human disease models on the basis of phenotypes alone

Synergistic combination of cytotoxic chemotherapy and cyclin-dependent kinase 4/6 inhibitors in biliary tract cancers

Background and aims: Biliary tract cancers (BTCs) are uncommon, but highly lethal, gastrointestinal malignancies. Gemcitabine/cisplatin is a standard-of-care systemic therapy, but has a modest impact on survival and harbors toxicities, including myelosuppression, nephropathy, neuropathy, and ototoxicity. Whereas BTCs are characterized by aberrations activating the cyclinD1/cyclin-dependent kinase (CDK)4/6/CDK inhibitor 2a/retinoblastoma pathway, clinical use of CDK4/6 inhibitors as monotherapy is limited by lack of validated biomarkers, diffident preclinical efficacy, and development of acquired drug resistance. Emerging studies have explored therapeutic strategies to enhance the antitumor efficacy of CDK4/6 inhibitors by the combination with chemotherapy regimens, but their mechanism of action remains elusive.Approach and results: Here, we report in vitro and in vivo synergy in BTC models, showing enhanced efficacy, reduced toxicity, and better survival with a combination comprising gemcitabine/cisplatin and CDK4/6 inhibitors. Furthermore, we demonstrated that abemaciclib monotherapy had only modest efficacy attributable to autophagy-induced resistance. Notably, triplet therapy was able to potentiate efficacy through elimination of the autophagic flux. Correspondingly, abemaciclib potentiated ribonucleotide reductase catalytic subunit M1 reduction, resulting in sensitization to gemcitabine.Conclusions: As such, these data provide robust preclinical mechanistic evidence of synergy between gemcitabine/cisplatin and CDK4/6 inhibitors and delineate a path forward for translation of these findings to preliminary clinical studies in advanced BTC patients.</p