Abstract 5130: Integrative approaches for the identification of therapeutic targets in basal like breast cancer from multi-level molecular data

Basal-like breast cancer is a molecular subtype of breast carcinoma accounting for up to 15% of cases and associated with a poor prognosis. There is no targeted therapy for this disease; since most basal-like breast cancers do not over-express HER2 and are negative for hormone receptors, they cannot be treated with trastuzumab antibody (anti-HER2) or anti-hormonal therapies. Finding therapeutic targets for the treatment of basal-like tumors is therefore a priority in breast cancer research. In this project, biopsies for the different subclasses of breast cancer (46 basal-like, 33 HER2+/ER-, 35 luminal A and 40 luminal B) were obtained from the Biological Center of Institut Curie and characterized by immuno-histochemistry. Using different microarray platforms, genome-wide profiles were obtained at the DNA, messenger RNA, and microRNA levels. Additionally, data were collected for a selection of 150 proteins using Reverse Phase Protein Arrays. We present the results of separate analyses of each of these molecular levels, and we introduce methods that integrate information across multiple layers of data to pinpoint potential drivers of the disease. In particular, we propose an approach to identify genome regions where copy number alterations correlate with transcriptome-wide perturbation of gene expression. Such correlation suggests that these regions may harbor genes playing an important role in carcinogenesis. Using criterions based on co-expression and on prior biological knowledge, the method determines the genes in these regions that are most likely to be cancer drivers and may therefore be relevant as therapeutic targets. Using HER2+ tumors to illustrate the approach, we show that the method is indeed able to designate the HER2 oncogene as a likely driver. With the addition of miRNA and protein data to these integrated studies, we hope that such approaches will suggest putative cancer driving genes as novel potential therapeutic targets.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5130. doi:1538-7445.AM2012-5130

Undifferentiated embryonic cell transcription factor 1 regulates ESC chromatin organization and gene expression

Previous reports showed that embryonic stem (ES) cells contain hyperdynamic and globally transcribed chromatin-properties that are important for ES cell pluripotency and differentiation. Here, we demonstrate a role for undifferentiated embryonic cell transcription factor 1 (UTF1) in regulating ES cell chromatin structure. Using chromatin immunoprecipitation-on-chip analysis, we identified >1,700 UTF1 target genes that significantly overlap with previously identified Nanog, Oct4, Klf-4, c-Myc, and Rex1 targets. Gene expression profiling showed that UTF1 knock down results in increased expression of a large set of genes, including a significant number of UTF1 targets. UTF1 knock down (KD) ES cells are, irrespective of the increased expression of several self-renewal genes, Leukemia inhibitory factor (LIF) dependent. However, UTF1 KD ES cells are perturbed in their differentiation in response to dimethyl sulfoxide (DMSO) or after LIF withdrawal and display increased colony formation. UTF1 KD ES cells display extensive chromatin decondensation, reflected by a dramatic increase in nucleosome release on micrococcal nuclease (MNase) treatment and enhanced MNase sensitivity of UTF1 target genes in UTF1 KD ES cells. Summarizing, our data show that UTF1 is a key chromatin component in ES cells, preventing ES cell chromatin decondensation, and aberrant gene expression; both essential for proper initiation of lineage-specific differentiation of ES cells.

Critical reasoning on causal inference in genome-wide linkage and association studies

Genome-wide linkage and association studies of tens of thousands of clinical and molecular traits are currently underway, offering rich data for inferring causality between traits and genetic variation. However, the inference process is based on discovering subtle patterns in the correlation between traits and is therefore challenging and could create a flood of untrustworthy causal inferences. Here we introduce the concerns and show that they are already valid in simple scenarios of two traits linked to or associated with the same genomic region. We argue that more comprehensive analysis and Bayesian reasoning are needed and that these can overcome some of the pitfalls, although not in every conceivable case. We conclude that causal inference methods can still be of use in the iterative process of mathematical modeling and biological validation.

DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules

BACKGROUND

Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differentially expressed genes between two conditions, the field of differential coexpression analysis is still relatively new. More specifically, there is so far no sensitive and untargeted method to identify gene modules (also known as gene sets or clusters) that are differentially coexpressed between two conditions. Here, sensitive and untargeted means that the method should be able to construct de novo modules by grouping genes based on shared, but subtle, differential correlation patterns.

RESULTS

We present DiffCoEx, a novel method for identifying correlation pattern changes, which builds on the commonly used Weighted Gene Coexpression Network Analysis (WGCNA) framework for coexpression analysis. We demonstrate its usefulness by identifying biologically relevant, differentially coexpressed modules in a rat cancer dataset.

CONCLUSIONS

DiffCoEx is a simple and sensitive method to identify gene coexpression differences between multiple conditions.

XGAP: a uniform and extensible data model and software platform for genotype and phenotype experiments

XGAP, a software platform for the integration and analysis of genotype and phenotype data.

We present an extensible software model for the genotype and phenotype community, XGAP. Readers can download a standard XGAP (http://www.xgap.org) or auto-generate a custom version using MOLGENIS with programming interfaces to R-software and web-services or user interfaces for biologists. XGAP has simple load formats for any type of genotype, epigenotype, transcript, protein, metabolite or other phenotype data. Current functionality includes tools ranging from eQTL analysis in mouse to genome-wide association studies in humans.

Defining gene and QTL networks

Current technologies for high-throughput molecular profiling of large numbers of genetically different individuals offer great potential for elucidating the genotype-to-phenotype relationship. Variation in molecular and phenotypic traits can be correlated to DNA sequence variation using the methods of quantitative trait locus (QTL) mapping. In addition, the correlation structure in the molecular and phenotypic traits can be informative for inferring the underlying molecular networks. For this, new methods are emerging to distinguish among causality, reactivity, or independence of traits based upon logic involving underlying QTL. These methods are becoming increasingly popular in plant genetic studies as well as in studies on many other organisms.

Who Are They? Identifying and Treating Adult Children of Alcoholics

1. Adult children of alcoholics are frequently found within the population of clients on inpatient psychiatric units. The percentage of this population is greater than the occurrence of ACoAs in the general population. 2. Nursing staff can assist these clients in a therapeutic way by becoming familiar with the types of issues these clients present and the behaviors they manifest. These issues should be reflected in treatment planning. 3. Anger, abuse, guilt, and grief form the core issues that ACoAs must deal with in therapeutic ways during psychiatric hospitalization.