CaPSSA: visual evaluation of cancer biomarker genes for patient stratification and survival analysis using mutation and expression data.

SUMMARY: Predictive biomarkers for patient stratification play critical roles in realizing the paradigm of precision medicine. Molecular characteristics such as somatic mutations and expression signatures represent the primary source of putative biomarker genes for patient stratification. However, evaluation of such candidate biomarkers is still cumbersome and requires multistep procedures especially when using massive public omics data. Here, we present an interactive web application that divides patients from large cohorts (e.g. The Cancer Genome Atlas, TCGA) dynamically into two groups according to the mutation, copy number variation or gene expression of query genes. It further supports users to examine the prognostic value of resulting patient groups based on survival analysis and their association with the clinical features as well as the previously annotated molecular subtypes, facilitated with a rich and interactive visualization. Importantly, we also support custom omics data with clinical information. AVAILABILITY AND IMPLEMENTATION: CaPSSA (Cancer Patient Stratification and Survival Analysis) runs on a web-browser and is freely available without restrictions at http://www.kobic.re.kr/capssa/. The source code is available on https://github.com/yjjang/capssa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of tumor suppressor miRNAs by integrative miRNA and mRNA sequencing of matched tumor-normal samples in lung adenocarcinoma.

The roles of miRNAs in lung cancer have not yet been explored systematically at the genome scale despite their important regulatory functions. Here, we report an integrative analysis of miRNA and mRNA sequencing data for matched tumor-normal samples from 109 Korean female patients with non-small-cell lung adenocarcinoma (LUAD). We produced miRNA sequencing (miRNA-Seq) and RNA-Seq data for 48 patients and RNA-Seq data for 61 additional patients. Subsequent differential expression analysis with stringent criteria yielded 44 miRNAs and 2322 genes. Integrative gene set analysis of the differentially expressed miRNAs and genes using miRNA-target information revealed several regulatory processes related to the cell cycle that were targeted by tumor suppressor miRNAs (TSmiR). We performed colony formation assays in A549 and NCI-H460 cell lines to test the tumor-suppressive activity of downregulated miRNAs in cancer and identified 7 novel TSmiRs (miR-144-5p, miR-218-1-3p, miR-223-3p, miR-27a-5p, miR-30a-3p, miR-30c-2-3p, miR-338-5p). Two miRNAs, miR-30a-3p and miR-30c-2-3p, showed differential survival characteristics in the Tumor Cancer Genome Atlas (TCGA) LUAD patient cohort indicating their prognostic value. Finally, we identified a network cluster of miRNAs and target genes that could be responsible for cell cycle regulation. Our study not only provides a dataset of miRNA as well as mRNA sequencing from the matched tumor-normal samples, but also reports several novel TSmiRs that could potentially be developed into prognostic biomarkers or therapeutic RNA drugs.

Mito-TEMPO improves development competence by reducing superoxide in preimplantation porcine embryos.

Mito-TEMPO is a well-known mitochondria-specific superoxide scavenger. However, the effect of Mito-TEMPO on porcine embryo development, to our knowledge, has not been studied yet. In the present study, porcine embryos were classified into two groups (G1 and G2) based on the cytoplasm lipid contents at the zygote stage. The development of blastocysts derived from G2 zygotes was reduced (G2:16.2 ± 7.9% vs G1: 26.5 ± 5.9%; 1.6-fold, p < 0.05) compared to those from G1 zygotes. In G2 embryos, the proportion of TUNEL-positive cells was also higher than that of G1 embryos. Superoxide in G2 embryos was significantly increased compared to that in G1 embryos. Mitochondrial membrane potential and ATP production were lower in G2 embryos than in G1 embryos. Phosphorylation of Drp1 at Ser 616 increased in G1 embryos during the cleavage stages compared to that in the zygote but was not significantly different in G2 embryos. Then, the effects of Mito-TEMPO were investigated in G2 embryos. Blastocyst formation rate (G2: 19.1 ± 5.1% vs G2 + Mito-TEMPO: 28.8 ± 4.0%; 1.5-fold, p < 0.05) and mitochondrial aggregation were recovered after superoxide reduction by Mito-TEMPO treatment. Thus, we showed that Mito-TEMPO improves blastocyst development by superoxide reduction in porcine embryos in vitro.

An integrated clinical and genomic information system for cancer precision medicine.

BACKGROUND: Increasing affordability of next-generation sequencing (NGS) has created an opportunity for realizing genomically-informed personalized cancer therapy as a path to precision oncology. However, the complex nature of genomic information presents a huge challenge for clinicians in interpreting the patient's genomic alterations and selecting the optimum approved or investigational therapy. An elaborate and practical information system is urgently needed to support clinical decision as well as to test clinical hypotheses quickly. RESULTS: Here, we present an integrated clinical and genomic information system (CGIS) based on NGS data analyses. Major components include modules for handling clinical data, NGS data processing, variant annotation and prioritization, drug-target-pathway analysis, and population cohort explorer. We built a comprehensive knowledgebase of genes, variants, drugs by collecting annotated information from public and in-house resources. Structured reports for molecular pathology are generated using standardized terminology in order to help clinicians interpret genomic variants and utilize them for targeted cancer therapy. We also implemented many features useful for testing hypotheses to develop prognostic markers from mutation and gene expression data. CONCLUSIONS: Our CGIS software is an attempt to provide useful information for both clinicians and scientists who want to explore genomic information for precision oncology.

Korean Variant Archive (KOVA): a reference database of genetic variations in the Korean population.

Despite efforts to interrogate human genome variation through large-scale databases, systematic preference toward populations of Caucasian descendants has resulted in unintended reduction of power in studying non-Caucasians. Here we report a compilation of coding variants from 1,055 healthy Korean individuals (KOVA; Korean Variant Archive). The samples were sequenced to a mean depth of 75x, yielding 101 singleton variants per individual. Population genetics analysis demonstrates that the Korean population is a distinct ethnic group comparable to other discrete ethnic groups in Africa and Europe, providing a rationale for such independent genomic datasets. Indeed, KOVA conferred 22.8% increased variant filtering power in addition to Exome Aggregation Consortium (ExAC) when used on Korean exomes. Functional assessment of nonsynonymous variant supported the presence of purifying selection in Koreans. Analysis of copy number variants detected 5.2 deletions and 10.3 amplifications per individual with an increased fraction of novel variants among smaller and rarer copy number variable segments. We also report a list of germline variants that are associated with increased tumor susceptibility. This catalog can function as a critical addition to the pre-existing variant databases in pursuing genetic studies of Korean individuals.

MONGKIE: an integrated tool for network analysis and visualization for multi-omics data.

BACKGROUND: Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. RESULTS: Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. CONCLUSION: We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. .

Positive feedback loop between Sox2 and Sox6 inhibits neuronal differentiation in the developing central nervous system.

How a pool of undifferentiated neural progenitor cells is maintained in the developing nervous system is an issue that remains unresolved. One of the key transcription factors for self-renewal of these cells is Sox2, the forced expression of which has been shown to inhibit neuronal differentiation in vivo. To dissect the molecular mechanisms of Sox2 activity, a ChIP-on-chip assay has been carried out for Sox2, and multiple candidate direct target genes have been isolated. In this report, we provide evidence indicating that Sox6, which like Sox2 belongs to the SRY-related HMG box transcription factor family, is a bona-fide direct regulatory target of Sox2. In vivo, Sox6 expression is seen with a temporal lag in Sox2-positive neural precursor cells in the ventricular zone, and Sox2 promotes expression of Sox6 as a transcriptional activator. Interestingly, gain- and loss-of-function assays indicate that Sox6 in turn is required for the maintenance of Sox2 expression, suggesting that a positive feedback loop, which functions to inhibit premature neuronal differentiation, exists between the two transcription factors.

A high-dimensional, deep-sequencing study of lung adenocarcinoma in female never-smokers.

BACKGROUND: Deep sequencing techniques provide a remarkable opportunity for comprehensive understanding of tumorigenesis at the molecular level. As omics studies become popular, integrative approaches need to be developed to move from a simple cataloguing of mutations and changes in gene expression to dissecting the molecular nature of carcinogenesis at the systemic level and understanding the complex networks that lead to cancer development. RESULTS: Here, we describe a high-throughput, multi-dimensional sequencing study of primary lung adenocarcinoma tumors and adjacent normal tissues of six Korean female never-smoker patients. Our data encompass results from exome-seq, RNA-seq, small RNA-seq, and MeDIP-seq. We identified and validated novel genetic aberrations, including 47 somatic mutations and 19 fusion transcripts. One of the fusions involves the c-RET gene, which was recently reported to form fusion genes that may function as drivers of carcinogenesis in lung cancer patients. We also characterized gene expression profiles, which we integrated with genomic aberrations and gene regulations into functional networks. The most prominent gene network module that emerged indicates that disturbances in G2/M transition and mitotic progression are causally linked to tumorigenesis in these patients. Also, results from the analysis strongly suggest that several novel microRNA-target interactions represent key regulatory elements of the gene network. CONCLUSIONS: Our study not only provides an overview of the alterations occurring in lung adenocarcinoma at multiple levels from genome to transcriptome and epigenome, but also offers a model for integrative genomics analysis and proposes potential target pathways for the control of lung adenocarcinoma.

hiPathDB: a human-integrated pathway database with facile visualization.

One of the biggest challenges in the study of biological regulatory networks is the systematic organization and integration of complex interactions taking place within various biological pathways. Currently, the information of the biological pathways is dispersed in multiple databases in various formats. hiPathDB is an integrated pathway database that combines the curated human pathway data of NCI-Nature PID, Reactome, BioCarta and KEGG. In total, it includes 1661 pathways consisting of 8976 distinct physical entities. hiPathDB provides two different types of integration. The pathway-level integration, conceptually a simple collection of individual pathways, was achieved by devising an elaborate model that takes distinct features of four databases into account and subsequently reformatting all pathways in accordance with our model. The entity-level integration creates a single unified pathway that encompasses all pathways by merging common components. Even though the detailed molecular-level information such as complex formation or post-translational modifications tends to be lost, such integration makes it possible to investigate signaling network over the entire pathways and allows identification of pathway cross-talks. Another strong merit of hiPathDB is the built-in pathway visualization module that supports explorative studies of complex networks in an interactive fashion. The layout algorithm is optimized for virtually automatic visualization of the pathways. hiPathDB is available at http://hiPathDB.kobic.re.kr.

Clinical validation of colorectal cancer biomarkers identified from bioinformatics analysis of public expression data.

PURPOSE: Identification of novel biomarkers of cancer is important for improved diagnosis, prognosis, and therapeutic intervention. This study aimed to identify marker genes of colorectal cancer (CRC) by combining bioinformatics analysis of gene expression data and validation experiments using patient samples and to examine the potential connection between validated markers and the established oncogenes such as c-Myc and K-ras. EXPERIMENTAL DESIGN: Publicly available data from GenBank and Oncomine were meta-analyzed leading to 34 candidate marker genes of CRC. Multiple case-matched normal and tumor tissues were examined by RT-PCR for differential expression, and 9 genes were validated as CRC biomarkers. Statistical analyses for correlation with major clinical parameters were carried out, and RNA interference was used to examine connection with major oncogenes. RESULTS: We show with high confidence that 9 (ECT2, ETV4, DDX21, RAN, S100A11, RPS4X, HSPD1, CKS2, and C9orf140) of the 34 candidate genes are expressed at significantly elevated levels in CRC tissues compared to normal tissues. Furthermore, high-level expression of RPS4X was associated with nonmucinous cancer cell type and that of ECT2 with lack of lymphatic invasion while upregulation of CKS2 was correlated with early tumor stage and lack of family history of CRC. We also demonstrate that RPS4X and DDX21 are regulatory targets of c-Myc and ETV4 is downstream to K-ras signaling. CONCLUSIONS: We have identified multiple novel biomarkers of CRC. Further analyses of their function and connection to signaling pathways may reveal potential value of these biomarkers in diagnosis, prognosis, and treatment of CRC.

GARNET--gene set analysis with exploration of annotation relations.

BACKGROUND: Gene set analysis is a powerful method of deducing biological meaning for an a priori defined set of genes. Numerous tools have been developed to test statistical enrichment or depletion in specific pathways or gene ontology (GO) terms. Major difficulties towards biological interpretation are integrating diverse types of annotation categories and exploring the relationships between annotation terms of similar information. RESULTS: GARNET (Gene Annotation Relationship NEtwork Tools) is an integrative platform for gene set analysis with many novel features. It includes tools for retrieval of genes from annotation database, statistical analysis & visualization of annotation relationships, and managing gene sets. In an effort to allow access to a full spectrum of amassed biological knowledge, we have integrated a variety of annotation data that include the GO, domain, disease, drug, chromosomal location, and custom-defined annotations. Diverse types of molecular networks (pathways, transcription and microRNA regulations, protein-protein interaction) are also included. The pair-wise relationship between annotation gene sets was calculated using kappa statistics. GARNET consists of three modules--gene set manager, gene set analysis and gene set retrieval, which are tightly integrated to provide virtually automatic analysis for gene sets. A dedicated viewer for annotation network has been developed to facilitate exploration of the related annotations. CONCLUSIONS: GARNET (gene annotation relationship network tools) is an integrative platform for diverse types of gene set analysis, where complex relationships among gene annotations can be easily explored with an intuitive network visualization tool (http://garnet.isysbio.org/ or http://ercsb.ewha.ac.kr/garnet/).