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Utilization of genomic signatures to identify phenotype-specific drugs.

ABSTRACT: Genetic and genomic studies highlight the substantial complexity and heterogeneity of human cancers and emphasize the general lack of therapeutics that can match this complexity. With the goal of expanding opportunities for drug discovery, we describe an approach that makes use of a phenotype-based screen combined with the use of multiple cancer cell lines. In particular, we have used the NCI-60 cancer cell line panel that includes drug sensitivity measures for over 40,000 compounds assayed on 59 independent cells lines. Targets are cancer-relevant phenotypes represented as gene expression signatures that are used to identify cells within the NCI-60 panel reflecting the signature phenotype and then connect to compounds that are selectively active against those cells. As a proof-of-concept, we show that this strategy effectively identifies compounds with selectivity to the RAS or PI3K pathways. We have then extended this strategy to identify compounds that have activity towards cells exhibiting the basal phenotype of breast cancer, a clinically-important breast cancer characterized as ER-, PR-, and Her2- that lacks viable therapeutic options. One of these compounds, Simvastatin, has previously been shown to inhibit breast cancer cell growth in vitro and importantly, has been associated with a reduction in ER-, PR- breast cancer in a clinical study. We suggest that this approach provides a novel strategy towards identification of therapeutic agents based on clinically relevant phenotypes that can augment the conventional strategies of target-based screens.

SUBMITTER: Mori S

PROVIDER: S-EPMC2729377 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature

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Utilization of genomic signatures to identify phenotype-specific drugs.

Mori Seiichi S Chang Jeffrey T JT Andrechek Eran R ER Potti Anil A Nevins Joseph R JR

PloS one 20090828 8

Genetic and genomic studies highlight the substantial complexity and heterogeneity of human cancers and emphasize the general lack of therapeutics that can match this complexity. With the goal of expanding opportunities for drug discovery, we describe an approach that makes use of a phenotype-based screen combined with the use of multiple cancer cell lines. In particular, we have used the NCI-60 cancer cell line panel that includes drug sensitivity measures for over 40,000 compounds assayed on 5 ...[more]

PMID: 19714244

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Project description:BackgroundThe highly dimensional data produced by functional genomic (FG) studies makes it difficult to visualize relationships between gene products and experimental conditions (i.e., assays). Although dimensionality reduction methods such as principal component analysis (PCA) have been very useful, their application to identify assay-specific signatures has been limited by the lack of appropriate methodologies. This article proposes a new and powerful PCA-based method for the identification of assay-specific gene signatures in FG studies.ResultsThe proposed method (PM) is unique for several reasons. First, it is the only one, to our knowledge, that uses gene contribution, a product of the loading and expression level, to obtain assay signatures. The PM develops and exploits two types of assay-specific contribution plots, which are new to the application of PCA in the FG area. The first type plots the assay-specific gene contribution against the given order of the genes and reveals variations in distribution between assay-specific gene signatures as well as outliers within assay groups indicating the degree of importance of the most dominant genes. The second type plots the contribution of each gene in ascending or descending order against a constantly increasing index. This type of plots reveals assay-specific gene signatures defined by the inflection points in the curve. In addition, sharp regions within the signature define the genes that contribute the most to the signature. We proposed and used the curvature as an appropriate metric to characterize these sharp regions, thus identifying the subset of genes contributing the most to the signature. Finally, the PM uses the full dataset to determine the final gene signature, thus eliminating the chance of gene exclusion by poor screening in earlier steps. The strengths of the PM are demonstrated using a simulation study, and two studies of real DNA microarray data--a study of classification of human tissue samples and a study of E. coli cultures with different medium formulations.ConclusionWe have developed a PCA-based method that effectively identifies assay-specific signatures in ranked groups of genes from the full data set in a more efficient and simplistic procedure than current approaches. Although this work demonstrates the ability of the PM to identify assay-specific signatures in DNA microarray experiments, this approach could be useful in areas such as proteomics and metabolomics.

Project description:HIV-1 entry is a receptor-mediated process directed by the interaction of the viral envelope with the host cell CD4 molecule and one of two co-receptors, CCR5 or CXCR4. The amino acid sequence of the third variable (V3) loop of the HIV-1 envelope is highly predictive of co-receptor utilization preference during entry, and machine learning predictive algorithms have been developed to characterize sequences as CCR5-utilizing (R5) or CXCR4-utilizing (X4). It was hypothesized that while the V3 loop is predominantly responsible for determining co-receptor binding, additional components of the HIV-1 genome may contribute to overall viral tropism and display sequence signatures associated with co-receptor utilization.The accessory protein Tat and the HlV-1 long terminal repeat (LTR) were analyzed with respect to genetic diversity and compared by Jensen-Shannon divergence which resulted in a correlation with both mean genetic diversity as well as the absolute difference in genetic diversity between R5- and X4-genome specific trends. As expected, the V3 domain of the gp120 protein was enriched with statistically divergent positions. Statistically divergent positions were also identified in Tat amino acid sequences within the transactivation and TAR-binding domains, and in nucleotide positions throughout the LTR. We further analyzed LTR sequences for putative transcription factor binding sites using the JASPAR transcription factor binding profile database and found several putative differences in transcription factor binding sites between R5 and X4 HIV-1 genomes, specifically identifying the C/EBP sites I and II, and Sp site III to differ with respect to sequence configuration for R5 and X4 LTRs.These observations support the hypothesis that co-receptor utilization coincides with specific genetic signatures in HIV-1 Tat and the LTR, likely due to differing transcriptional regulatory mechanisms and selective pressures applied within specific cellular targets during the course of productive HIV-1 infection.

Project description:Purpose: Gene expression signatures developed to measure the activity of oncogenic signaling pathways have been used to dissect the heterogeneity of tumor samples and to predict sensitivity to various cancer drugs that target components of the relevant pathways, thus potentially identifying therapeutic options for subgroups of patients. To facilitate broad use, including in a clinical setting, the ability to generate data from formalin-fixed, paraffin-embedded (FFPE) tissues is essential. Experimental Design: Patterns of pathway activity in matched fresh-frozen and FFPE xenograft tumor samples were generated using the MessageAmp Premier methodology in combination with assays using Affymetrix arrays. Results generated were compared with those obtained from fresh-frozen samples using a standard Affymetrix assay. In addition, gene expression data from patient matched fresh-frozen and FFPE melanomas were also utilized to evaluate the consistency of predictions of oncogenic signaling pathway status. Results: Significant correlation of pathway activity predictions was observed between paired fresh-frozen and FFPE xenograft tumor samples. In addition, significant concordance of pathway activity predictions was also observed between patient matched fresh-frozen and FFPE melanomas. Conclusion: Reliable and consistent predictions of oncogenic pathway activities can be obtained from FFPE tumor tissue samples. The ability to reliably utilize FFPE patient tumor tissue samples for genomic analyses will lead to a better understanding of the biology of disease progression and, in the clinical setting, will provide tools to guide the choice of therapeutics to those most likely to be effective in treating a patient’s disease. 8 replicates of HMECs infected with adenovirus expressing GFP, 8 replicates of HMECs infected with adenovirus expressing RAS, 6 replicates of HMECs infected with adenovirus expressing MYC, 25 fresh-frozen melanoma xenografts, 25 FFPE melanoma xenografts, 6 FFPE human melanoma

Project description:Lactobacillus rhamnosus is a bacterial species commonly colonizing the gastrointestinal (GI) tract of humans and also frequently used in food products. While some strains have been studied extensively, physiological variability among isolates of the species found in healthy humans or their diet is largely unexplored. The aim of this study was to characterize the diversity of carbohydrate utilization capabilities of human isolates and food-derived strains of L. rhamnosus in relation to their niche of isolation and genotype. We investigated the genotypic and phenotypic diversity of 25 out of 65 L. rhamnosus strains from various niches, mainly human feces and fermented dairy products. Genetic fingerprinting of the strains by amplified fragment length polymorphism (AFLP) identified 11 distinct subgroups at 70% similarity and suggested niche enrichment within particular genetic clades. High-resolution carbohydrate utilization profiling (OmniLog) identified 14 carbon sources that could be used by all of the strains tested for growth, while the utilization of 58 carbon sources differed significantly between strains, enabling the stratification of L. rhamnosus strains into three metabolic clusters that partially correlate with the genotypic clades but appear uncorrelated with the strain's origin of isolation. Draft genome sequences of 8 strains were generated and employed in a gene-trait matching (GTM) analysis together with the publicly available genomes of L. rhamnosus GG (ATCC 53103) and HN001 for several carbohydrates that were distinct for the different metabolic clusters: l-rhamnose, cellobiose, l-sorbose, and ?-methyl-d-glucoside. From the analysis, candidate genes were identified that correlate with l-sorbose and ?-methyl-d-glucoside utilization, and the proposed function of these genes could be confirmed by heterologous expression in a strain lacking the genes. This study expands our insight into the phenotypic and genotypic diversity of the species L. rhamnosus and explores the relationships between specific carbohydrate utilization capacities and genotype and/or niche adaptation of this species.

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Utilization of genomic signatures to identify phenotype-specific drugs.

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Utilization of genomic signatures to identify phenotype-specific drugs.

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