Quantitative gene expression assessment identifies appropriate cell line models for individual cervical cancer pathways

<p>Abstract</p> <p>Background</p> <p>Cell lines have been used to study cancer for decades, but truly quantitative assessment of their performance as models is often lacking. We used gene expression profiling to quantitatively assess the gene expression of nine cell line models of cervical cancer.</p> <p>Results</p> <p>We find a wide variation in the extent to which different cell culture models mimic late-stage invasive cervical cancer biopsies. The lowest agreement was from monolayer HeLa cells, a common cervical cancer model; the highest agreement was from primary epithelial cells, C4-I, and C4-II cell lines. In addition, HeLa and SiHa cell lines cultured in an organotypic environment increased their correlation to cervical cancer significantly. We also find wide variation in agreement when we considered how well individual biological pathways model cervical cancer. Cell lines with an anti-correlation to cervical cancer were also identified and should be avoided.</p> <p>Conclusion</p> <p>Using gene expression profiling and quantitative analysis, we have characterized nine cell lines with respect to how well they serve as models of cervical cancer. Applying this method to individual pathways, we identified the appropriateness of particular cell lines for studying specific pathways in cervical cancer. This study will allow researchers to choose a cell line with the highest correlation to cervical cancer at a pathway level. This method is applicable to other cancers and could be used to identify the appropriate cell line and growth condition to employ when studying other cancers.</p

Mechanisms of Cell Cycle Control Revealed by a Systematic and Quantitative Overexpression Screen in S. cerevisiae

Regulation of cell cycle progression is fundamental to cell health and reproduction, and failures in this process are associated with many human diseases. Much of our knowledge of cell cycle regulators derives from loss-of-function studies. To reveal new cell cycle regulatory genes that are difficult to identify in loss-of-function studies, we performed a near-genome-wide flow cytometry assay of yeast gene overexpression-induced cell cycle delay phenotypes. We identified 108 genes whose overexpression significantly delayed the progression of the yeast cell cycle at a specific stage. Many of the genes are newly implicated in cell cycle progression, for example SKO1, RFA1, and YPR015C. The overexpression of RFA1 or YPR015C delayed the cell cycle at G2/M phases by disrupting spindle attachment to chromosomes and activating the DNA damage checkpoint, respectively. In contrast, overexpression of the transcription factor SKO1 arrests cells at G1 phase by activating the pheromone response pathway, revealing new cross-talk between osmotic sensing and mating. More generally, 92%–94% of the genes exhibit distinct phenotypes when overexpressed as compared to their corresponding deletion mutants, supporting the notion that many genes may gain functions upon overexpression. This work thus implicates new genes in cell cycle progression, complements previous screens, and lays the foundation for future experiments to define more precisely roles for these genes in cell cycle progression

Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data

To detect functional somatic mutations in tumor samples, whole-exome sequencing (WES) is often used for its reliability and relative low cost. RNA-seq, while generally used to measure gene expression, can potentially also be used for identification of somatic mutations. However there has been little systematic evaluation of the utility of RNA-seq for identifying somatic mutations. Here, we develop and evaluate a pipeline for processing RNA-seq data from glioblastoma multiforme (GBM) tumors in order to identify somatic mutations. The pipeline entails the use of the STAR aligner 2-pass procedure jointly with MuTect2 from genome analysis toolkit (GATK) to detect somatic variants. Variants identified from RNA-seq data were evaluated by comparison against the COSMIC and dbSNP databases, and also compared to somatic variants identified by exome sequencing. We also estimated the putative functional impact of coding variants in the most frequently mutated genes in GBM. Interestingly, variants identified by RNA-seq alone showed better representation of GBM-related mutations cataloged by COSMIC. RNA-seq-only data substantially outperformed the ability of WES to reveal potentially new somatic mutations in known GBM-related pathways, and allowed us to build a high-quality set of somatic mutations common to exome and RNA-seq calls. Using RNA-seq data in parallel with WES data to detect somatic mutations in cancer genomes can thus broaden the scope of discoveries and lend additional support to somatic variants identified by exome sequencing alone

Interobserver reliability of classification and characterization of proximal humeral fractures: a comparison of two and three-dimensional CT

Interobserver reliability for the classification of proximal humeral fractures is limited. The aim of this study was to test the null hypothesis that interobserver reliability of the AO classification of proximal humeral fractures, the preferred treatment, and fracture characteristics is the same for two-dimensional (2-D) and three-dimensional (3-D) computed tomography (CT). Members of the Science of Variation Group--fully trained practicing orthopaedic and trauma surgeons from around the world--were randomized to evaluate radiographs and either 2-D CT or 3-D CT images of fifteen proximal humeral fractures via a web-based survey and respond to the following four questions: (1) Is the greater tuberosity displaced? (2) Is the humeral head split? (3) Is the arterial supply compromised? (4) Is the glenohumeral joint dislocated? They also classified the fracture according to the AO system and indicated their preferred treatment of the fracture (operative or nonoperative). Agreement among observers was assessed with use of the multirater kappa (&kappa;) measure. Interobserver reliability of the AO classification, fracture characteristics, and preferred treatment generally ranged from &quot;slight&quot; to &quot;fair.&quot; A few small but statistically significant differences were found. Observers randomized to the 2-D CT group had slightly but significantly better agreement on displacement of the greater tuberosity (&kappa; = 0.35 compared with 0.30, p &lt; 0.001) and on the AO classification (&kappa; = 0.18 compared with 0.17, p = 0.018). A subgroup analysis of the AO classification results revealed that shoulder and elbow surgeons, orthopaedic trauma surgeons, and surgeons in the United States had slightly greater reliability on 2-D CT, whereas surgeons in practice for ten years or less and surgeons from other subspecialties had slightly greater reliability on 3-D CT. Proximal humeral fracture classifications may be helpful conceptually, but they have poor interobserver reliability even when 3-D rather than 2-D CT is utilized. This may contribute to the similarly poor interobserver reliability that was observed for selection of the treatment for proximal humeral fractures. The lack of a reliable classification confounds efforts to compare the outcomes of treatment methods among different clinical trials and reports

The accessible chromatin landscape of the human genome

DNaseI hypersensitive sites (DHSs) are markers of regulatory DNA and have underpinned the discovery of all classes of cis-regulatory elements including enhancers, promoters, insulators, silencers, and locus control regions. Here we present the first extensive map of human DHSs identified through genome-wide profiling in 125 diverse cell and tissue types. We identify ~2.9 million DHSs that encompass virtually all known experimentally-validated cis-regulatory sequences and expose a vast trove of novel elements, most with highly cell-selective regulation. Annotating these elements using ENCODE data reveals novel relationships between chromatin accessibility, transcription, DNA methylation, and regulatory factor occupancy patterns. We connect ~580,000 distal DHSs with their target promoters, revealing systematic pairing of different classes of distal DHSs and specific promoter types. Patterning of chromatin accessibility at many regulatory regions is choreographed with dozens to hundreds of co-activated elements, and the trans-cellular DNaseI sensitivity pattern at a given region can predict cell type-specific functional behaviors. The DHS landscape shows signatures of recent functional evolutionary constraint. However, the DHS compartment in pluripotent and immortalized cells exhibits higher mutation rates than that in highly differentiated cells, exposing an unexpected link between chromatin accessibility, proliferative potential and patterns of human variation

Extensive Evolutionary Changes in Regulatory Element Activity during Human Origins Are Associated with Altered Gene Expression and Positive Selection

Understanding the molecular basis for phenotypic differences between humans and other primates remains an outstanding challenge. Mutations in non-coding regulatory DNA that alter gene expression have been hypothesized as a key driver of these phenotypic differences. This has been supported by differential gene expression analyses in general, but not by the identification of specific regulatory elements responsible for changes in transcription and phenotype. To identify the genetic source of regulatory differences, we mapped DNaseI hypersensitive (DHS) sites, which mark all types of active gene regulatory elements, genome-wide in the same cell type isolated from human, chimpanzee, and macaque. Most DHS sites were conserved among all three species, as expected based on their central role in regulating transcription. However, we found evidence that several hundred DHS sites were gained or lost on the lineages leading to modern human and chimpanzee. Species-specific DHS site gains are enriched near differentially expressed genes, are positively correlated with increased transcription, show evidence of branch-specific positive selection, and overlap with active chromatin marks. Species-specific sequence differences in transcription factor motifs found within these DHS sites are linked with species-specific changes in chromatin accessibility. Together, these indicate that the regulatory elements identified here are genetic contributors to transcriptional and phenotypic differences among primate species

Quantitative gene expression assessment identifies appropriate cell line models for individual cervical cancer pathways-1

<p><b>Copyright information:</b></p><p>Taken from "Quantitative gene expression assessment identifies appropriate cell line models for individual cervical cancer pathways"</p><p>http://www.biomedcentral.com/1471-2164/8/117</p><p>BMC Genomics 2007;8():117-117.</p><p>Published online 10 May 2007</p><p>PMCID:PMC1878486.</p><p></p>es, whereas numbered labels represent biological replicates. The primary separation occurs between cell lines (dashed bar) and cervical tissue (solid bar). All GOG samples and CHTN samples #1, 2, 8, 12 and 13 were invasive cervical cancer biopsies. CHTN samples #6, 10, and 11 were normal cervix. Most replicates clustered together, indicating the data was of high quality. Spots present on the microarray that had a median intensity over background of at least 150 and were present in 80% of the arrays were included in the analysis, resulting in 8,338 genes. B: Singular value decomposition of transcriptional profiles reveals general relationships among the samples, positioned here as the projections among the first 3 singular components (accounting for 40% of the variance, [see Additional file ]. Again, cell lines were separated from cervical tissue

Quantitative gene expression assessment identifies appropriate cell line models for individual cervical cancer pathways-4

<p><b>Copyright information:</b></p><p>Taken from "Quantitative gene expression assessment identifies appropriate cell line models for individual cervical cancer pathways"</p><p>http://www.biomedcentral.com/1471-2164/8/117</p><p>BMC Genomics 2007;8():117-117.</p><p>Published online 10 May 2007</p><p>PMCID:PMC1878486.</p><p></p>ey, while the pathways where only one or two cell lines are adequate models are white. The pathway example "RNA Processing" indicates some cell lines were anti-correlated and therefore a quantitative analysis was needed to identify better models that could be used to study this pathway. Error bars were generated from the correlation of a single cell line for each pathway and calculating the standard deviation. The pathways shown here represented a minimum of four cell lines or growth conditions. Numbers in parenthesis indicate how many cell lines were used to calculate the correlation. B: The highest and lowest pathway correlations between normal cervix and cervical cancer. The JNK cascade has a high correlation between normal and tumor, and is modeled well by most cell lines (Figure 5A). Mitosis and a number of other pathways involved in growth and regulation show poor correlation in their gene expression between normal and tumor, as expected. Numbers in parenthesis indicate how many genes were used to calculate the Pearson correlation coefficient

Quantitative gene expression assessment identifies appropriate cell line models for individual cervical cancer pathways-2

<p><b>Copyright information:</b></p><p>Taken from "Quantitative gene expression assessment identifies appropriate cell line models for individual cervical cancer pathways"</p><p>http://www.biomedcentral.com/1471-2164/8/117</p><p>BMC Genomics 2007;8():117-117.</p><p>Published online 10 May 2007</p><p>PMCID:PMC1878486.</p><p></p>vical cancer. Cell lines were cultured in ATCC recommended media as monolayers. SiHa and HeLa cell lines were also cultured in different media as well as in an organotypic environment. In addition to the organotypic culture, a control was used that left out the fibroblasts, which prevented the epithelial cell line to stack in 3-dimensions. The primary, C4-I, and C4-II cell lines had the highest correlation to cervical cancer and therefore were the better general models of cervical cancer out of the cell lines we tested. Changing the media from MEM to DMEM increased the correlation to cervical cancer for the HeLa and SiHa cell lines, as well as culturing them in an organotypic environment. Error bars were derived from the standard deviation of the correlation of a cell line against each individual patient biopsy