Screening for chemical modulators for LRRK2

After the discovery of leucine-rich repeat kinase 2 (LRRK2) as a risk factor for sporadic Parkinson's disease (PD) and mutations in LRRK2 as a cause of some forms of familial PD, there has been substantial interest in finding chemical modulators of LRRK2 function. Most of the pathogenic mutations in LRRK2 are within the enzymatic cores of the protein; therefore, many screens have focused on finding chemical modulators of this enzymatic activity. There are alternative screening approaches that could be taken to investigate compounds that modulate LRRK2 cellular functions. These screens are more often phenotypic screens. The preparation for a screen has to be rigorous and enable high-throughput accurate assessment of a compound's activity. The pipeline to beginning a drug screen and some LRRK2 inhibitor and phenotypic screens will be discussed

Fungal Origins of the Bicyclo[2.2.2]diazaoctane Ring System of Prenylated Indole Alkaloids

Over eight different families of natural products, consisting of nearly seventy secondary metabolites, which contain the bicyclo[2.2.2]diazaoctane ring system, have been isolated from various Aspergillus, Penicillium, and Malbranchea species. Since 1968, these secondary metabolites have been the focus of numerous biogenetic, synthetic, taxonomic, and biological studies, and, as such, have made a lasting impact across multiple scientific disciplines. This review covers the isolation, biosynthesis, and biological activity of these unique secondary metabolites containing the bridging bicyclo[2.2.2]diazaoctane ring system. Furthermore, the diverse fungal origin of these natural products is closely examined and, in many cases, updated to reflect the currently accepted fungal taxonomy

Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis

Most tumors exhibit increased glucose metabolism to lactate, however, the extent to which glucose-derived metabolic fluxes are used for alternative processes is poorly understood [1, 2]. Using a metabolomics approach with isotope labeling, we found that in some cancer cells a relatively large amount of glycolytic carbon is diverted into serine and glycine metabolism through phosphoglycerate dehydrogenase (PHGDH). An analysis of human cancers showed that PHGDH is recurrently amplified in a genomic region of focal copy number gain most commonly found in melanoma. Decreasing PHGDH expression impaired proliferation in amplified cell lines. Increased expression was also associated with breast cancer subtypes, and ectopic expression of PHGDH in mammary epithelial cells disrupted acinar morphogenesis and induced other phenotypic alterations that may predispose cells to transformation. Our findings show that the diversion of glycolytic flux into a specific alternate pathway can be selected during tumor development and may contribute to the pathogenesis of human cancer.National Institutes of Health (U.S.)National Cancer Institute (U.S.)Smith Family FoundationDamon Runyon Cancer Research FoundationBurroughs Wellcome Fun

Systematic Identification of Combinatorial Drivers and Targets in Cancer Cell Lines

There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance

Sense variability and typology of cultures

The article is concerned with the problem of justification of cultural (civilizational) typological diversity, which is being analyzed in the framework of variant types and meaning-making methods. Authors attempt to prove the leading role of these methods in shaping cultures’ idiosyncrasies, as well as a specific course of their evolution, and sustainability as culture-transforming programs. In the article, the criteria of distinguishing historical culture types set in the XIX-XX centuries by the most influential civilizational concepts are analyzed, and a new criterion is proposed. This is the criterion of the meaning-making method which is considered the most applicable to describe the core of any culture as an integrity of both experimental and textual ways of human existence re-examination. A number of culture (civilization) types were used as examples; the article observes how the key meaning-making model sculpts the civilization as a unique entity and defines it

High resolution genomic analysis of sporadic breast cancer using array-based comparative genomic hybridization

INTRODUCTION: Genomic aberrations in the form of subchromosomal DNA copy number changes are a hallmark of epithelial cancers, including breast cancer. The goal of the present study was to analyze such aberrations in breast cancer at high resolution. METHODS: We employed high-resolution array comparative genomic hybridization with 4,134 bacterial artificial chromosomes that cover the genome at 0.9 megabase resolution to analyze 47 primary breast tumors and 18 breast cancer cell lines. RESULTS: Common amplicons included 8q24.3 (amplified in 79% of tumors, with 5/47 exhibiting high level amplification), 1q32.1 and 16p13.3 (amplified in 66% and 57% of tumors, respectively). Moreover, we found several positive correlations between specific amplicons from different chromosomes, suggesting the existence of cooperating genetic loci. Queried by gene, the most frequently amplified kinase was PTK2 (79% of tumors), whereas the most frequently lost kinase was PTK2B (hemizygous loss in 34% of tumors). Amplification of ERBB2 as measured by comparative genomic hybridization (CGH) correlated closely with ERBB2 DNA and RNA levels measured by quantitative PCR as well as with ERBB2 protein levels. The overall frequency of recurrent losses was lower, with no region lost in more than 50% of tumors; the most frequently lost tumor suppressor gene was RB1 (hemizygous loss in 26% of tumors). Finally, we find that specific copy number changes in cell lines closely mimicked those in primary tumors, with an overall Pearson correlation coefficient of 0.843 for gains and 0.734 for losses. CONCLUSION: High resolution CGH analysis of breast cancer reveals several regions where DNA copy number is commonly gained or lost, that non-random correlations between specific amplicons exist, and that specific genetic alterations are maintained in breast cancer cell lines despite repeat passage in tissue culture. These observations suggest that genes within these regions are critical to the malignant phenotype and may thus serve as future therapeutic targets

Good practice guidelines for biomarker discovery from array data: a case study for breast cancer prognosis

BACKGROUND: Biomarker discovery holds the promise for advancing personalized medicine as the biomarkers can help match patients to optimal treatment to improve patient outcomes. However, serious concerns have been raised because very few molecular biomarkers or signatures discovered from high dimensional array data can be successfully validated and applied to clinical use. We propose good practice guidelines as well as a novel tool for biomarker discovery and use breast cancer prognosis as a case study to illustrate the proposed approach. RESULTS: We applied the proposed approach to a publicly available breast cancer prognosis dataset and identified small numbers of predictive markers for patient subpopulations stratified by clinical variables. Results from an independent cross-platform validation set show that our model compares favorably to other gene signature and clinical variable based prognostic tools. About half of the discovered candidate markers can individually achieve very good performance, which further demonstrate the high quality of feature selection. These candidate markers perform extremely well for young patient with estrogen receptor-positive, lymph node-negative early stage breast cancers, suggesting a distinct subset of these patients identified by these markers is actually at high risk of recurrence and may benefit from more aggressive treatment than cur-rent practice. CONCLUSION: The results show that by following good practice guidelines, we can identify highly predictive genes in high dimensional breast cancer array data. These predictive genes have been successfully validated using an independent cross-platform dataset

Characterization of adjacent breast tumors using oligonucleotide microarrays

BACKGROUND: Current methodology often cannot distinguish second primary breast cancers from multifocal disease, a potentially important distinction for clinical management. In the present study we evaluated the use of oligonucleotide-based microarray analysis in determining the clonality of tumors by comparing gene expression profiles. METHOD: Total RNA was extracted from two tumors with no apparent physical connection that were located in the right breast of an 87-year-old woman diagnosed with invasive ductal carcinoma (IDC). The RNA was hybridized to the Affymetrix Human Genome U95A Gene Chip(®) (12,500 known human genes) and analyzed using the Gene Chip Analysis Suite(®) 3.3 (Affymetrix, Inc, Santa Clara, CA, USA) and JMPIN(®) 3.2.6 (SAS Institute, Inc, Cary, NC, USA). Gene expression profiles of tumors from five additional patients were compared in order to evaluate the heterogeneity in gene expression between tumors with similar clinical characteristics. RESULTS: The adjacent breast tumors had a pairwise correlation coefficient of 0.987, and were essentially indistinguishable by microarray analysis. Analysis of gene expression profiles from different individuals, however, generated a pairwise correlation coefficient of 0.710. CONCLUSION: Transcriptional profiling may be a useful diagnostic tool for determining tumor clonality and heterogeneity, and may ultimately impact on therapeutic decision making

Integrated functional visualization of eukaryotic genomes

BACKGROUND: Increasing amounts of data from large scale whole genome analysis efforts demands convenient tools for manipulation, visualization and investigation. Whole genome plots offer an intuitive window to the analysis. We describe two applications that enable users to easily plot and explore whole genome data from their own or other researchers' experiments. RESULTS: STRIPE and GFFtool (General Feature Format Tool) are softwares designed to support integration, visualization and exploration of whole genome data from eukaryotic genomes. STRIPE, in addition to providing a highly customizable and interactive data plot, provides access to numerous well-selected databases with updated information on all genes of a genome. GFFtool provides a user-friendly solution to integrating experimental data with the genomic information available in public databases. They also obviate the need for users to maintain large annotation resources, as they link to well-known resources using standard gene and protein identifiers. CONCLUSION: The programs provide the user with broad genomic overviews of data distribution, fast access to data of interest, and the ability to navigate speedily from one resource to another, and gain a better understanding of result of whole genome analysis experiments

Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays

The detection of copy number variants (CNV) by array-based platforms provides valuable insight into understanding human diversity. However, suboptimal study design and data processing negatively affect CNV assessment. We quantitatively evaluate their impact when short-sequence oligonucleotide arrays are applied (Affymetrix Genome-Wide Human SNP Array 6.0) by evaluating 42 HapMap samples for CNV detection. Several processing and segmentation strategies are implemented, and results are compared to CNV assessment obtained using an oligonucleotide array CGH platform designed to query CNVs at high resolution (Agilent). We quantitatively demonstrate that different reference models (e.g. single versus pooled sample reference) used to detect CNVs are a major source of inter-platform discrepancy (up to 30%) and that CNVs residing within segmental duplication regions (higher reference copy number) are significantly harder to detect (P < 0.0001). After adjusting Affymetrix data to mimic the Agilent experimental design (reference sample effect), we applied several common segmentation approaches and evaluated differential sensitivity and specificity for CNV detection, ranging 39–77% and 86–100% for non-segmental duplication regions, respectively, and 18–55% and 39–77% for segmental duplications. Our results are relevant to any array-based CNV study and provide guidelines to optimize performance based on study-specific objectives