Characterization of differential genetic expression profiles in mesenchymal and epithelial components of uterine and ovarian carcinosarcomas.

e17104 Background: Gynecologic carcinosarcomas (GCS) are rare tumors with poor prognosis. Reasons include a high percentage of advanced stage at diagnosis and a low response to conventional treatments. GCS constitutes a model for research in both tumoral heterogeneity and the epithelial-mesenchymal transition (EMT) process. Our aim is to define molecular expression heterogeneity in GCS distinct morphologic components. Methods: A retrospective, single institution, IRB approved study of 13 patients diagnosed with GCS was undertaken. Total RNA was extracted from representative FFPE tissue blocks of both the epithelial and mesenchymal components. The expression profile for each component (n = 26) was determined using the GeneChip WT Pico Reagent Kit and the Clariom D Array (Affymetrix Inc., Santa Clara, CA, USA). Robust multi-array normalization (RMA) and t-statistics was used for detecting differentially expressed genes between the studied conditions. Genes with a p-value < 0.05 and with an absolute fold change (FC) value > 1.5 were selected as significant. Results: A total of 13 cases representing 26 distinct samples, 9 uterine (UCS) and 4 ovarian carcinosarcoma with a median age of 68 (range: 45-81), 38% presented FIGO IIIC-IV stage at diagnosis. Among UCS, 5 women had a previous personal history of breast cancer. A total of 101 genes appeared as differentially expressed between epithelial and mesenchymal components, highlighting 5 of them: HMGA2 (FC = 2.15, p = 0.04) and ERBB4 (FC = 2.14, p = 0.005) overexpressed in epithelial component and ANX2 (FC = 1.95, p = 0.0006), SPP1 (FC = 2.15, p = 0,005) and ERRFI1 (FC = 1.95, p = 0.001) overexpressed in mesenchymal component. Conclusions: This is the first expression profiling in GCS that helps identify candidate genes that show a distinct expression in mesenchymal and epithelial components that could have a potential prognostic and predictive role.

A Single-Array-Based Method for Detecting Copy Number Variants Using Affymetrix High Density SNP Arrays and its Application to Breast Cancer

Cumulative evidence has shown that structural variations, due to insertions, deletions, and inversions of DNA, may contribute considerably to the development of complex human diseases, such as breast cancer. High-throughput genotyping technologies, such as Affymetrix high density single-nucleotide polymorphism (SNP) arrays, have produced large amounts of genetic data for genome-wide SNP genotype calling and copy number estimation. Meanwhile, there is a great need for accurate and efficient statistical methods to detect copy number variants. In this article, we introduce a hidden-Markov-model (HMM)-based method, referred to as the PICR-CNV, for copy number inference. The proposed method first estimates copy number abundance for each single SNP on a single array based on the raw fluorescence values, and then standardizes the estimated copy number abundance to achieve equal footing among multiple arrays. This method requires no between-array normalization, and thus, maintains data integrity and independence of samples among individual subjects. In addition to our efforts to apply new statistical technology to raw fluorescence values, the HMM has been applied to the standardized copy number abundance in order to reduce experimental noise. Through simulations, we show our refined method is able to infer copy number variants accurately. Application of the proposed method to a breast cancer dataset helps to identify genomic regions significantly associated with the disease.

Array Normalization Algorithms Applied to Qualitative Electronic Noses

In a qualitative electronic nose, different gas concentrations of the training dataset will have a negative effect on the correct recognition rate of the system. In order to reduce or eliminate the impact of the factor of concentration on the qualitative electronic nose, array normalization algorithms are proposed. In this paper, six different array normalization algorithms were studied and compared in different application cases. All of these algorithms are effective in increasing the correct recognition rate of the qualitative electronic nose and different algorithms are biased in favor of different application directions. The algorithms I II and III are most commonly used ones because of their stableness, the algorithms with global compression are better than the ones with local compression when more sensors are used in a array.

An Empirical Evaluation of normalization Methods for MicroRNA Arrays in a Liposarcoma Study

Background Methods for array normalization, such as median and quantile normalization, were developed for mRNA expression arrays. These methods assume few or symmetric differential expression of genes on the array. However, these assumptions are not necessarily appropriate for microRNA expression arrays because they consist of only a few hundred genes and a reasonable fraction of them are anticipated to have disease relevance. Methods We collected microRNA expression profiles for human tissue samples from a liposarcoma study using the Agilent microRNA arrays. For a subset of the samples, we also profiled their microRNA expression using deep sequencing. We empirically evaluated methods for normalization of microRNA arrays using deep sequencing data derived from the same tissue samples as the benchmark. Results: In this study, we demonstrated array effects in microRNA arrays using data from a liposarcoma study. We found moderately high correlation between Agilent data and sequence data on the same tumors, with the Pearson correlation coefficients ranging from 0.6 to 0.9. Array normalization resulted in some improvement in the accuracy of the differential expression analysis. However, even with normalization, there is still a significant number of false positive and false negative microRNAs, many of which are expressed at moderate to high levels. Conclusions Our study demonstrated the need to develop more efficient normalization methods for microRNA arrays to further improve the detection of genes with disease relevance. Until better methods are developed, an existing normalization method such as quantile normalization should be applied when analyzing microRNA array data.