The brain is a common site of metastatic disease in patients with breast cancer, which has few therapeutic options and dismal outcomes. The purpose of our study was to identify common and rare events that underlie breast cancer brain metastasis. We performed deep genomic profiling, which integrated gene copy number, gene expression and DNA methylation datasets on a collection of breast brain metastases. We identified frequent large chromosomal gains in 1q, 5p, 8q, 11q, and 20q and frequent broad-level deletions involving 8p, 17p, 21p and Xq. Frequently amplified and overexpressed genes included ATAD2, BRAF, DERL1, DNMTRB and NEK2A. The ATM, CRYAB and HSPB2 genes were commonly deleted and underexpressed. Knowledge mining revealed enrichment in cell cycle and G2/M transition pathways, which contained AURKA, AURKB and FOXM1. Using the PAM50 breast cancer intrinsic classifier, Luminal B, Her2+/ER negative, and basal-like tumors were identified as the most commonly represented breast cancer subtypes in our brain metastasis cohort. While overall methylation levels were increased in breast cancer brain metastasis, basal-like brain metastases were associated with significantly lower levels of methylation. Integrating DNA methylation data with gene expression revealed defects in cell migration and adhesion due to hypermethylation and downregulation of PENK, EDN3, and ITGAM. Hypomethylation and upregulation of KRT8 likely affects adhesion and permeability. Genomic and epigenomic profiling of breast brain metastasis has provided insight into the somatic events underlying this disease, which have potential in forming the basis of future therapeutic strategies.

The advent of high-throughput technology like microarrays has provided the platform for studying how different cellular components work together, thus created an enormous interest in mathematically modeling biological network, particularly gene regulatory network (GRN). Of particular interest is the modeling and inference on time-series data, which capture a more thorough picture of the system than non-temporal data do. We have given an extensive review of methodologies that have been used on time-series data. In realizing that validation is an impartible part of the inference paradigm, we have also presented a discussion on the principles and challenges in performance evaluation of different methods. This survey gives a panoramic view on these topics, with anticipation that the readers will be inspired to improve and/or expand GRN inference and validation tool repository.

Epidemiological data have suggested that African American (AA) persons are twice as likely to be diagnosed with multiple myeloma (MM) compared with European American (EA) persons. Here, we have analyzed a set of cytogenetic and genomic data derived from AA and EA MM patients. We have compared the frequency of IgH translocations in a series of data from 115 AA patients from 3 studies and 353 EA patients from the Eastern Cooperative Oncology Group (ECOG) studies E4A03 and E9487. We have also interrogated tumors from 45 AA and 196 EA MM patients for somatic copy number abnormalities associated with poor outcome. In addition, 35 AA and 178 EA patients were investigated for a transcriptional profile associated with high-risk disease. Overall, based on this cohort, genetic profiles were similar except for a significantly lower frequency of IgH translocations (40% vs 52%; P 5 .032) in AA patients. Frequency differences of somatic copy number aberrations were not significant after correction for multiple testing. There was also no significant difference in the frequency of high-risk disease based on gene expression profiling. Our study represents the first comprehensive comparisons of the frequency and distribution of molecular alterations in MM tumors between AA and EA patients. ECOG E4A03 is registered with ClinicalTrials.gov, number NCT00098475. ECOG E9487 is a companion validation set to the ECOG study E9486 and is registered with

Systems biology is a multi-disciplinary approach to the study of the interactions of various cellular mechanisms and cellular components. Owing to the development of new technologies that simultaneously measure the expression of genetic information, systems biological studies involving gene interactions are increasingly prominent. In this regard, reconstructing gene regulatory networks (GRNs) forms the basis for the dynamical analysis of gene interactions and related effects on cellular control pathways. Various approaches of inferring GRNs from gene expression profiles and biological information, including machine learning approaches, have been reviewed, with a brief introduction of DNA microarray experiments as typical tools for measuring levels of messenger ribonucleic acid (mRNA) expression. In particular, the inference methods are classified according to the required input information, and the main idea of each method is elucidated by comparing its advantages and disadvantages with respect to the other methods. In addition, recent developments in this field are introduced and discussions on the challenges and opportunities for future research are provided.

Despite numerous studies involving patient-derived xenograft (PDX) models, few studies have investigated the relationship between the ability of the tumor to engraft (tumorigenicity) and the clinical features of colorectal cancer (CRC). The aim of this study was to determine whether tumorigenicity correlates with clinical outcomes of CRC patients. We included 241 CRC patients who underwent radical surgery from 2010 to 2013. PDX models were established by implanting tumor fragments obtained from these patients into the subcutaneous layer of immunodeficient mice. Xenografts were successfully established from 62.2%. Successful engraftment was associated with advanced stage (p < 0.001) and moderate/poor differentiation (p = 0.029). Three-year disease-free survival (DFS) rates were lower for patients with tumorigenicity (p = 0.011). In stage III patients, tumorigenicity was an independent predictor of poor DFS (p = 0.034). In addition, mutation of TP53 was most frequently detected in stage III patients with tumorigenicity. Two models of stage IV disease without KRAS mutations showed high sensitivity to EGFR-targeted agents, while none of the models with KRAS mutations showed high sensitivity. In conclusion, PDX models may provide an effective preclinical tool for predicting cancer progression and could be used to further genomic and pharmacologic research on personalized treatments.

ContextAdrenocortical carcinomas (ACC) are a rare tumor type with a poor five-year survival rate and limited treatment options.ObjectiveUnderstanding of the molecular pathogenesis of this disease has been aided by genomic analyses highlighting alterations in TP53, WNT, and IGF signaling pathways. Further elucidation is needed to reveal therapeutically actionable targets in ACC.DesignIn this study, global DNA methylation levels were assessed by the Infinium HumanMethylation450 BeadChip Array on 18 ACC tumors and 6 normal adrenal tissues. A new, non-linear correlation approach, the discretization method, assessed the relationship between DNA methylation/gene expression across ACC tumors.ResultsThis correlation analysis revealed epigenetic regulation of genes known to modulate TP53, WNT, and IGF signaling, as well as silencing of the tumor suppressor MARCKS, previously unreported in ACC.ConclusionsDNA methylation may regulate genes known to play a role in ACC pathogenesis as well as known tumor suppressors.

Summary Zhong, Y., Jung, S., Pramanik, S. & Beaman, J. H.: Data model and comparison and query methods for interacting classifications in a taxonomic database. – Taxon 45: 223‐241. 1996. –ISSN 0040‐0262. An information‐theoretic view has been applied to biological classification to capture taxonomic concepts as taxonomic data entities and to develop a system for managing these concepts and the lineage relationships among them. In order to develop the data model, it has been necessary to apply explicit definitions to several taxonomic terms that generally have not been precisely defined and to coin and define several new terms and concepts. Methods are outlined for comparing interacting classifications and querying hierarchical taxonomic databases. A program/database system called HICLAS, which provides an X‐Window interface to query classification data, is available on the Internet.

Identifying differential features between conditions is a popular approach to understanding molecular features and their mechanisms underlying a biological process of particular interest. Although many tests for identifying differential expression of gene or gene sets have been proposed, there was limited success in developing methods for differential interactions of genes between conditions because of its computational complexity. We present a method for Evaluation of Dependency DifferentialitY (EDDY), which is a statistical test for differential dependencies of a set of genes between two conditions. Unlike previous methods focused on differential expression of individual genes or correlation changes of individual gene–gene interactions, EDDY compares two conditions by evaluating the probability distributions of dependency networks from genes. The method has been evaluated and compared with other methods through simulation studies, and application to glioblastoma multiforme data resulted in informative cancer and glioblastoma multiforme subtype-related findings. The comparison with Gene Set Enrichment Analysis, a differential expression-based method, revealed that EDDY identifies the gene sets that are complementary to those identified by Gene Set Enrichment Analysis. EDDY also showed much lower false positives than Gene Set Co-expression Analysis, a method based on correlation changes of individual gene–gene interactions, thus providing more informative results. The Java implementation of the algorithm is freely available to noncommercial users. Download from: http://biocomputing.tgen.org/software/EDDY.