Tatiana Nikolskaya

An Integrated Genomic Analysis of Human Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is the most common and lethal type of brain cancer. To identify the genetic alterations in GBMs, we sequenced 20,661 protein coding genes, determined the presence of amplifications and deletions using high-density oligonucleotide arrays, and performed gene expression analyses using next-generation sequencing technologies in 22 human tumor samples. This comprehensive analysis led to the discovery of a variety of genes that were not known to be altered in GBMs. Most notably, we found recurrent mutations in the active site of isocitrate dehydrogenase 1 (IDH1) in 12% of GBM patients. Mutations in IDH1 occurred in a large fraction of young patients and in most patients with secondary GBMs and were associated with an increase in overall survival. These studies demonstrate the value of unbiased genomic analyses in the characterization of human brain cancer and identify a potentially useful genetic alteration for the classification and targeted therapy of GBMs.

The genomic landscapes of human breast and colorectal cancers.

Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene “mountains” and a much larger number of gene “hills” that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.

The JAK2/STAT3 signaling pathway is required for growth of CD44+CD24– stem cell–like breast cancer cells in human tumors

Intratumor heterogeneity is a major clinical problem because tumor cell subtypes display variable sensitivity to therapeutics and may play different roles in progression. We previously characterized 2 cell populations in human breast tumors with distinct properties: CD44+CD24- cells that have stem cell-like characteristics, and CD44-CD24+ cells that resemble more differentiated breast cancer cells. Here we identified 15 genes required for cell growth or proliferation in CD44+CD24- human breast cancer cells in a large-scale loss-of-function screen and found that inhibition of several of these (IL6, PTGIS, HAS1, CXCL3, and PFKFB3) reduced Stat3 activation. We found that the IL-6/JAK2/Stat3 pathway was preferentially active in CD44+CD24- breast cancer cells compared with other tumor cell types, and inhibition of JAK2 decreased their number and blocked growth of xenografts. Our results highlight the differences between distinct breast cancer cell types and identify targets such as JAK2 and Stat3 that may lead to more specific and effective breast cancer therapies.

Regulation of In Situ to Invasive Breast Carcinoma Transition

The transition of ductal carcinoma in situ (DCIS) to invasive carcinoma is a poorly understood key event in breast tumor progression. Here, we analyzed the role of myoepithelial cells and fibroblasts in the progression of in situ carcinomas using a model of human DCIS and primary breast tumors. Progression to invasion was promoted by fibroblasts and inhibited by normal myoepithelial cells. Molecular profiles of isolated luminal epithelial and myoepithelial cells identified an intricate interaction network involving TGFbeta, Hedgehog, cell adhesion, and p63 required for myoepithelial cell differentiation, the elimination of which resulted in loss of myoepithelial cells and progression to invasion.

Integrated analysis of homozygous deletions, focal amplifications, and sequence alterations in breast and colorectal cancers

We have performed a genome-wide analysis of copy number changes in breast and colorectal tumors using approaches that can reliably detect homozygous deletions and amplifications. We found that the number of genes altered by major copy number changes, deletion of all copies or amplification to at least 12 copies per cell, averaged 17 per tumor. We have integrated these data with previous mutation analyses of the Reference Sequence genes in these same tumor types and have identified genes and cellular pathways affected by both copy number changes and point alterations. Pathways enriched for genetic alterations included those controlling cell adhesion, intracellular signaling, DNA topological change, and cell cycle control. These analyses provide an integrated view of copy number and sequencing alterations on a genome-wide scale and identify genes and pathways that could prove useful for cancer diagnosis and therapy.

Pathway Mapping Tools for Analysis of High Content Data

The complexity of human biology requires a systems approach that uses computational approaches to integrate different data types. Systems biology encompasses the complete biological system of metabolic and signaling pathways, which can be assessed by measuring global gene expression, protein content, metabolic profiles, and individual genetic, clinical, and phenotypic data. High content screening assays can also be used to generate systems biology knowledge. In this review, we will summarize the pathway databases and describe biological network tools used predominantly with this genomics, proteomics, and metabolomics data but which are equally as applicable for high content screening data analysis. We describe in detail the integrated data-mining tools applicable to building biological networks developed by GeneGo, namely, MetaCore and MetaDrug.

Cell type-specific DNA methylation patterns in the human breast

Cellular identity and differentiation are determined by epigenetic programs. The characteristics of these programs in normal human mammary epithelium and their similarity to those in stem cells are unknown. To begin investigating these issues, we analyzed the DNA methylation and gene expression profiles of distinct subpopulations of mammary epithelial cells by using MSDK (methylation-specific digital karyotyping) and SAGE (serial analysis of gene expression). We identified discrete cell-type and differentiation state-specific DNA methylation and gene expression patterns that were maintained in a subset of breast carcinomas and correlated with clinically relevant tumor subtypes. CD44+ cells were the most hypomethylated and highly expressed several transcription factors with known stem cell function including HOXA10 and TCF3. Many of these genes were also hypomethylated in BMP4-treated compared with undifferentiated human embryonic stem (ES) cells that we analyzed by MSDK for comparison. Further highlighting the similarity of epigenetic programs of embryonic and mammary epithelial cells, genes highly expressed in CD44+ relative to more differentiated CD24+ cells were significantly enriched for Suz12 targets in ES cells. The expression of FOXC1, one of the transcription factors hypomethylated and highly expressed in CD44+ cells, induced a progenitor-like phenotype in differentiated mammary epithelial cells. These data suggest that epigenetically controlled transcription factors play a key role in regulating mammary epithelial cell phenotypes and imply similarities among epigenetic programs that define progenitor cell characteristics.

Atherosclerosis and liver inflammation induced by increased dietary cholesterol intake: a combined transcriptomics and metabolomics analysis

BackgroundIncreased dietary cholesterol intake is associated with atherosclerosis. Atherosclerosis development requires a lipid and an inflammatory component. It is unclear where and how the inflammatory component develops. To assess the role of the liver in the evolution of inflammation, we treated ApoE*3Leiden mice with cholesterol-free (Con), low (LC; 0.25%) and high (HC; 1%) cholesterol diets, scored early atherosclerosis and profiled the (patho)physiological state of the liver using novel whole-genome and metabolome technologies.ResultsWhereas the Con diet did not induce early atherosclerosis, the LC diet did so but only mildly, and the HC diet induced it very strongly. With increasing dietary cholesterol intake, the liver switches from a resilient, adaptive state to an inflammatory, pro-atherosclerotic state. The liver absorbs moderate cholesterol stress (LC) mainly by adjusting metabolic and transport processes. This hepatic resilience is predominantly controlled by SREBP-1/-2, SP-1, RXR and PPARα. A further increase of dietary cholesterol stress (HC) additionally induces pro-inflammatory gene expression, including pro-atherosclerotic candidate genes. These HC-evoked changes occur via specific pro-inflammatory pathways involving specific transcriptional master regulators, some of which are established, others newly identified. Notably, several of these regulators control both lipid metabolism and inflammation, and thereby link the two processes.ConclusionWith increasing dietary cholesterol intake the liver switches from a mainly resilient (LC) to a predominantly inflammatory (HC) state, which is associated with early lesion formation. Newly developed, functional systems biology tools allowed the identification of novel regulatory pathways and transcriptional regulators controlling both lipid metabolism and inflammatory responses, thereby providing a rationale for an interrelationship between the two processes.

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors

IntroductionAs part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints.MethodsWe used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set.ResultsA ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models.ConclusionsWe showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.

Identifying disease-specific genes based on their topological significance in protein networks

BackgroundThe identification of key target nodes within complex molecular networks remains a common objective in scientific research. The results of pathway analyses are usually sets of fairly complex networks or functional processes that are deemed relevant to the condition represented by the molecular profile. To be useful in a research or clinical laboratory, the results need to be translated to the level of testable hypotheses about individual genes and proteins within the condition of interest.ResultsIn this paper we describe novel computational methodology capable of predicting key regulatory genes and proteins in disease- and condition-specific biological networks. The algorithm builds shortest path network connecting condition-specific genes (e.g. differentially expressed genes) using global database of protein interactions from MetaCore. We evaluate the number of all paths traversing each node in the shortest path network in relation to the total number of paths going via the same node in the global network. Using these numbers and the relative size of the initial data set, we determine the statistical significance of the network connectivity provided through each node. We applied this method to gene expression data from psoriasis patients and identified many confirmed biological targets of psoriasis and suggested several new targets. Using predicted regulatory nodes we were able to reconstruct disease pathways that are in excellent agreement with the current knowledge on the pathogenesis of psoriasis.ConclusionThe systematic and automated approach described in this paper is readily applicable to uncovering high-quality therapeutic targets, and holds great promise for developing network-based combinational treatment strategies for a wide range of diseases.