CINdex: A Bioconductor Package for Analysis of Chromosome Instability in DNA Copy Number Data.

The CINdex Bioconductor package addresses an important area of high-throughput genomic analysis. It calculates the chromosome instability (CIN) index, a novel measurement that quantitatively characterizes genome-wide copy number alterations (CNAs) as a measure of CIN. The advantage of this package is an ability to compare CIN index values between several groups for patients (case and control groups), which is a typical use case in translational research. The differentially changed cytobands or chromosomes can then be linked to genes located in the affected genomic regions, as well as pathways. This enables in-depth systems biology-based network analysis and assessment of the impact of CNA on various biological processes or clinical outcomes. This package was successfully applied to analysis of DNA copy number data in colorectal cancer as a part of multi-omics integrative study as well as for analysis of several other cancer types. The source code, along with an end-to-end tutorial, and example data are freely available in Bioconductor at http://bioconductor.org/packages/CINdex/.

DNA copy numbers profiles in affinity-purified ovarian clear cell carcinoma.

PURPOSE: Advanced ovarian clear cell carcinoma (CCC) is one of the most aggressive ovarian malignancies, in part because it tends to be resistant to platinum-based chemotherapy. At present, little is known about the molecular genetic alterations in CCCs except that there are frequent activating mutations in PIK3CA. The purpose of this study is to comprehensively define the genomic changes in CCC based on DNA copy number alterations. EXPERIMENTAL DESIGN: We performed 250K high-density single nucleotide polymorphism array analysis in 12 affinity-purified CCCs and 10 CCC cell lines. Discrete regions of amplification and deletion were also analyzed in additional 21 affinity-purified CCCs using quantitative real-time PCR. RESULTS: The level of chromosomal instability in CCC as defined by the extent of DNA copy number changes is similar to those previously reported in low-grade ovarian serous carcinoma but much less than those in high-grade serous carcinoma. The most remarkable region with DNA copy number gain is at chr20, which harbors a potential oncogene, ZNF217. This discrete amplicon is observed in 36% of CCCs but rarely detected in serous carcinomas regardless of grade. In addition, homozygous deletions are detected at the CDKN2A/2B and LZTS1 loci. Interestingly, the DNA copy number changes observed in fresh CCC tissues are rarely detected in the established CCC cell lines. CONCLUSIONS: This study provides the first high resolution, genome-wide view of DNA copy number alterations in ovarian CCC. The findings provide a genomic landscape for future studies aimed at elucidating the pathogenesis and developing new target-based therapies for CCCs.

Analysis of DNA copy number alterations in ovarian serous tumors identifies new molecular genetic changes in low-grade and high-grade carcinomas.

Ovarian serous carcinoma, the most common and lethal type of ovarian cancer, is thought to develop from two distinct molecular pathways. High-grade (HG) serous carcinomas contain frequent TP53 mutations, whereas low-grade (LG) carcinomas arise from serous borderline tumors (SBT) and harbor mutations in KRAS/BRAF/ERBB2 pathway. However, the molecular alterations involved in the progression from SBT to LG carcinoma remain unknown. In addition, the extent of deletion of tumor suppressors in ovarian serous carcinomas has not been well studied. To further address these two issues, we assessed DNA copy number changes among affinity-purified tumor cells from 37 ovarian serous neoplasms including SBT, LG, and HG tumors using high-density 250K single nucleotide polymorphism arrays. Chromosomal instability index as measured by changes in DNA copy number was significantly higher in HG than in LG serous carcinomas. Hemizygous ch1p36 deletion was common in LG serous carcinomas but was rarely seen in SBT. This region contains several candidate tumor suppressors including miR-34a. In contrast, in HG serous carcinomas, significant numbers of amplifications and deletions, including homozygous deletions, were identified. Among homozygous deletions, loci containing Rb1, CDKN2A/B, CSMD1, and DOCK4 were most common, being present in 10.6%, 6.4%, 6.4%, and 4.3%, respectively, in independent 47 affinity-purified HG serous carcinomas. Except for the CDKN2A/B region, these homozygous deletions were not present in either SBT or LG tumors. Our study provides a genome-wide homozygous deletion profile in HG serous carcinomas, which can serve as a molecular foundation to study tumor suppressors in ovarian cancer.

Gene selection for multiclass prediction by weighted Fisher criterion.

Gene expression profiling has been widely used to study molecular signatures of many diseases and to develop molecular diagnostics for disease prediction. Gene selection, as an important step for improved diagnostics, screens tens of thousands of genes and identifies a small subset that discriminates between disease types. A two-step gene selection method is proposed to identify informative gene subsets for accurate classification of multiclass phenotypes. In the first step, individually discriminatory genes (IDGs) are identified by using one-dimensional weighted Fisher criterion (wFC). In the second step, jointly discriminatory genes (JDGs) are selected by sequential search methods, based on their joint class separability measured by multidimensional weighted Fisher criterion (wFC). The performance of the selected gene subsets for multiclass prediction is evaluated by artificial neural networks (ANNs) and/or support vector machines (SVMs). By applying the proposed IDG/JDG approach to two microarray studies, that is, small round blue cell tumors (SRBCTs) and muscular dystrophies (MDs), we successfully identified a much smaller yet efficient set of JDGs for diagnosing SRBCTs and MDs with high prediction accuracies (96.9% for SRBCTs and 92.3% for MDs, resp.). These experimental results demonstrated that the two-step gene selection method is able to identify a subset of highly discriminative genes for improved multiclass prediction.

Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data.

MOTIVATION: Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-fitting, and may not provide acceptable prediction accuracy. RESULTS: Based on Fisher linear discriminant analysis, we designed and implemented an MLP optimization scheme for a two-layer MLP that effectively optimizes the initialization of MLP parameters and MLP architecture. The optimized MLP consistently demonstrated its ability in easing the curse of dimensionality in large microarray datasets. In comparison with a conventional MLP using random initialization, we obtained significant improvements in major performance measures including Bayes classification accuracy, convergence properties and area under the receiver operating characteristic curve (A(z)). SUPPLEMENTARY INFORMATION: The Supplementary information is available on http://www.cbil.ece.vt.edu/publications.htm

Phenotypic-specific gene module discovery using a diagnostic tree and caBIG VISDA.

For the critical task of gene module discovery in genomic research, we present a model-based hierarchical data clustering and visualization algorithm, visual statistical data analyzer (VISDA), which effectively exploits human-data interaction to improve the clustering outcome. Guided by a diagnostic tree, we apply VISDA to a muscular dystrophy dataset that contains a number of different phenotypic conditions. We then superimpose existing knowledge of gene regulation and gene function (ingenuity pathway analysis) to analyze the clustering results and generate novel hypotheses for further research on muscular dystrophies.