Gene Expression Differences between Enriched Normal and Chronic Myelogenous Leukemia Quiescent Stem/Progenitor Cells and Correlations with Biological Abnormalities

In comparing gene expression of normal and CML CD34+ quiescent (G0) cell, 292 genes were downregulated and 192 genes upregulated in the CML/G0 Cells. The differentially expressed genes were grouped according to their reported functions, and correlations were sought with biological differences previously observed between the same groups. The most relevant findings include the following. (i) CML G0 cells are in a more advanced stage of development and more poised to proliferate than normal G0 cells. (ii) When CML G0 cells are stimulated to proliferate, they differentiate and mature more rapidly than normal counterpart. (iii) Whereas normal G0 cells form only granulocyte/monocyte colonies when stimulated by cytokines, CML G0 cells form a combination of the above and erythroid clusters and colonies. (iv) Prominin-1 is the gene most downregulated in CML G0 cells, and this appears to be associated with the spontaneous formation of erythroid colonies by CML progenitors without EPO

Genetic Analysis of the Early Natural History of Epithelial Ovarian Carcinoma

The high mortality rate associated with epithelial ovarian carcinoma (EOC) reflects diagnosis commonly at an advanced stage, but improved early detection is hindered by uncertainty as to the histologic origin and early natural history of this malignancy.Here we report combined molecular genetic and morphologic analyses of normal human ovarian tissues and early stage cancers, from both BRCA mutation carriers and the general population, indicating that EOCs frequently arise from dysplastic precursor lesions within epithelial inclusion cysts. In pathologically normal ovaries, molecular evidence of oncogenic stress was observed specifically within epithelial inclusion cysts. To further explore potential very early events in ovarian tumorigenesis, ovarian tissues from women not known to be at high risk for ovarian cancer were subjected to laser catapult microdissection and gene expression profiling. These studies revealed a quasi-neoplastic expression signature in benign ovarian cystic inclusion epithelium compared to surface epithelium, specifically with respect to genes affecting signal transduction, cell cycle control, and mitotic spindle formation. Consistent with this gene expression profile, a significantly higher cell proliferation index (increased cell proliferation and decreased apoptosis) was observed in histopathologically normal ovarian cystic compared to surface epithelium. Furthermore, aneuploidy was frequently identified in normal ovarian cystic epithelium but not in surface epithelium.Together, these data indicate that EOC frequently arises in ovarian cystic inclusions, is preceded by an identifiable dysplastic precursor lesion, and that increased cell proliferation, decreased apoptosis, and aneuploidy are likely to represent very early aberrations in ovarian tumorigenesis

A classification model for distinguishing copy number variants from cancer-related alterations

<p>Abstract</p> <p>Background</p> <p>Both somatic copy number alterations (CNAs) and germline copy number variants (CNVs) that are prevalent in healthy individuals can appear as recurrent changes in comparative genomic hybridization (CGH) analyses of tumors. In order to identify important cancer genes CNAs and CNVs must be distinguished. Although the Database of Genomic Variants (DGV) contains a list of all known CNVs, there is no standard methodology to use the database effectively.</p> <p>Results</p> <p>We develop a prediction model that distinguishes CNVs from CNAs based on the information contained in the DGV and several other variables, including segment's length, height, closeness to a telomere or centromere and occurrence in other patients. The models are fitted on data from glioblastoma and their corresponding normal samples that were collected as part of The Cancer Genome Atlas project and hybridized to Agilent 244 K arrays.</p> <p>Conclusions</p> <p>Using the DGV alone CNVs in the test set can be correctly identified with about 85% accuracy if the outliers are removed before segmentation and with 72% accuracy if the outliers are included, and additional variables improve the prediction by about 2-3% and 12%, respectively. Final models applied to data from ovarian tumors have about 90% accuracy with all the variables and 86% accuracy with the DGV alone.</p

Bayesian DNA copy number analysis

<p>Abstract</p> <p>Background</p> <p>Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents.</p> <p>Bayesian Piecewise Constant Regression (BPCR) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve (BRC) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments.</p> <p>Results</p> <p>We derived an improved version of the BPCR (called mBPCR) and BRC, changing the segment number estimator and the boundary estimator to enhance the fitting procedure. We also proposed an alternative estimator of the variance of the segment levels, which is useful in case of data with high noise. Using artificial data, we compared the original and the modified version of BPCR and BRC with other regression methods, showing that our improved version of BPCR generally outperformed all the others. Similar results were also observed on real data.</p> <p>Conclusion</p> <p>We propose an improved method for DNA copy number estimation, mBPCR, which performed very well compared to previously published algorithms. In particular, mBPCR was more powerful in the detection of the true position of the breakpoints and of small aberrations in very noisy data. Hence, from a biological point of view, our method can be very useful, for example, to find targets of genomic aberrations in clinical cancer samples.</p

Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications.

Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA binding domain sequencing (MBD-seq). We applied all four methods to biological replicates of human embryonic stem cells to assess their genome-wide CpG coverage, resolution, cost, concordance and the influence of CpG density and genomic context. The methylation levels assessed by the two bisulfite methods were concordant (their difference did not exceed a given threshold) for 82% for CpGs and 99% of the non-CpG cytosines. Using binary methylation calls, the two enrichment methods were 99% concordant and regions assessed by all four methods were 97% concordant. We combined MeDIP-seq with methylation-sensitive restriction enzyme (MRE-seq) sequencing for comprehensive methylome coverage at lower cost. This, along with RNA-seq and ChIP-seq of the ES cells enabled us to detect regions with allele-specific epigenetic states, identifying most known imprinted regions and new loci with monoallelic epigenetic marks and monoallelic expression

A comparison of genomic copy number calls by Partek Genomics Suite, Genotyping Console and Birdsuite algorithms to quantitative PCR

<p>Abstract</p> <p>Background</p> <p>Copy number variants are >1 kb genomic amplifications or deletions that can be identified using array platforms. However, arrays produce substantial background noise that contributes to high false discovery rates of variants. We hypothesized that quantitative PCR could finitely determine copy number and assess the validity of calling algorithms.</p> <p>Results</p> <p>Using data from 29 Affymetrix SNP 6.0 arrays, we determined copy numbers using three programs: Partek Genomics Suite, Affymetrix Genotyping Console 2.0 and Birdsuite. We compared array calls at 25 chromosomal regions to those determined by qPCR and found nearly identical calls in regions of copy number 2. Conversely, agreement differed in regions called variant by at least one method. The highest overall agreement in calls, 91%, was between Birdsuite and quantitative PCR. Partek Genomics Suite calls agreed with quantitative PCR 76% of the time while the agreement of Affymetrix Genotyping Console 2.0 with quantitative PCR was 79%.</p> <p>Conclusions</p> <p>In 38 independent samples, 96% of Birdsuite calls agreed with quantitative PCR. Analysis of three copy number calling programs and quantitative PCR showed Birdsuite to have the greatest agreement with quantitative PCR.</p

DiNAMIC: a method to identify recurrent DNA copy number aberrations in tumors

Motivation: DNA copy number gains and losses are commonly found in tumor tissue, and some of these aberrations play a role in tumor genesis and development. Although high resolution DNA copy number data can be obtained using array-based techniques, no single method is widely used to distinguish between recurrent and sporadic copy number aberrations

A response to Yu et al. "A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array", BMC Bioinformatics 2007, 8: 145

<p>Abstract</p> <p>Background</p> <p>Yu et al. (BMC Bioinformatics 2007,8: 145+) have recently compared the performance of several methods for the detection of genomic amplification and deletion breakpoints using data from high-density single nucleotide polymorphism arrays. One of the methods compared is our non-homogenous Hidden Markov Model approach. Our approach uses Markov Chain Monte Carlo for inference, but Yu et al. ran the sampler for a severely insufficient number of iterations for a Markov Chain Monte Carlo-based method. Moreover, they did not use the appropriate reference level for the non-altered state.</p> <p>Methods</p> <p>We rerun the analysis in Yu et al. using appropriate settings for both the Markov Chain Monte Carlo iterations and the reference level. Additionally, to show how easy it is to obtain answers to additional specific questions, we have added a new analysis targeted specifically to the detection of breakpoints.</p> <p>Results</p> <p>The reanalysis shows that the performance of our method is comparable to that of the other methods analyzed. In addition, we can provide probabilities of a given spot being a breakpoint, something unique among the methods examined.</p> <p>Conclusion</p> <p>Markov Chain Monte Carlo methods require using a sufficient number of iterations before they can be assumed to yield samples from the distribution of interest. Running our method with too small a number of iterations cannot be representative of its performance. Moreover, our analysis shows how our original approach can be easily adapted to answer specific additional questions (e.g., identify edges).</p

Association between Ancestry-Specific 6q25 Variants and Breast Cancer Subtypes in Peruvian Women

Background: Breast cancer incidence in the United States is lower in Hispanic/Latina (H/L) compared with African American/ Black or Non-Hispanic White women. An Indigenous American breast cancer-protective germline variant (rs140068132) has been reported near the estrogen receptor 1 gene. This study tests the association of rs140068132 and other polymorphisms in the 6q25 region with subtype-specific breast cancer risk in H/Ls of high Indigenous American ancestry. Methods: Genotypes were obtained for 5,094 Peruvian women with (1,755) and without (3,337) breast cancer. Associations between genotype and overall and subtype-specific risk for the protective variant were tested using logistic regression models and conditional analyses, including other risk-associated polymorphisms in the region. Results: We replicated the reported association between rs140068132 and breast cancer risk overall [odds ratio (OR), 0.53; 95% confidence interval (CI), 0.47-0.59], as well as the lower odds of developing hormone receptor negative (HR-) versus HR+ disease (OR, 0.77; 95% CI, 0.61-0.97). Models, including HER2, showed further heterogeneity with reduced odds for HR+HER2+ (OR, 0.68; 95% CI, 0.51-0.92), HR-HER2+ (OR, 0.63; 95% CI, 0.44-0.90) and HR-HER2- (OR, 0.77; 95% CI, 0.56-1.05) compared with HR+HER2-. Inclusion of other risk-associated variants did not change these observations. Conclusions: The rs140068132 polymorphism is associated with decreased risk of breast cancer in Peruvians and is more protective against HR- and HER2+ diseases independently of other breast cancer-associated variants in the 6q25 region. Impact: These results could inform functional analyses to understand the mechanism by which rs140068132-G reduces risk of breast cancer development in a subtype-specific manner. They also illustrate the importance of including diverse individuals in genetic studies.National Institutes of HealthRevisión por pare