Selecting control genes for RT-QPCR using public microarray data.

Background: Gene expression analysis has emerged as a major biological research area, with real-time quantitative reverse transcription PCR (RT-QPCR) being one of the most accurate and widely used techniques for expression profiling of selected genes. In order to obtain results that are comparable across assays, a stable normalization strategy is required. In general, the normalization of PCR measurements between different samples uses one to several control genes (e. g. housekeeping genes), from which a baseline reference level is constructed. Thus, the choice of the control genes is of utmost importance, yet there is not a generally accepted standard technique for screening a large number of candidates and identifying the best ones. Results: We propose a novel approach for scoring and ranking candidate genes for their suitability as control genes. Our approach relies on publicly available microarray data and allows the combination of multiple data sets originating from different platforms and/or representing different pathologies. The use of microarray data allows the screening of tens of thousands of genes, producing very comprehensive lists of candidates. We also provide two lists of candidate control genes: one which is breast cancer-specific and one with more general applicability. Two genes from the breast cancer list which had not been previously used as control genes are identified and validated by RT-QPCR. Open source R functions are available at http://www.isrec.isb-sib.ch/similar to vpopovic/research/ Conclusion: We proposed a new method for identifying candidate control genes for RT-QPCR which was able to rank thousands of genes according to some predefined suitability criteria and we applied it to the case of breast cancer. We also empirically showed that translating the results from microarray to PCR platform was achievable.

Modular analysis of gene expression data with R.

SUMMARY: Large sets of data, such as expression profiles from many samples, require analytic tools to reduce their complexity. The Iterative Signature Algorithm (ISA) is a biclustering algorithm. It was designed to decompose a large set of data into so-called 'modules'. In the context of gene expression data, these modules consist of subsets of genes that exhibit a coherent expression profile only over a subset of microarray experiments. Genes and arrays may be attributed to multiple modules and the level of required coherence can be varied resulting in different 'resolutions' of the modular mapping. In this short note, we introduce two BioConductor software packages written in GNU R: The isa2 package includes an optimized implementation of the ISA and the eisa package provides a convenient interface to run the ISA, visualize its output and put the biclusters into biological context. Potential users of these packages are all R and BioConductor users dealing with tabular (e.g. gene expression) data. AVAILABILITY: http://www.unil.ch/cbg/ISA CONTACT: sven.bergmann@unil.ch

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Identification of molecular apocrine breast tumours by microarray analysis.

Previous microarray studies on breast cancer identified multiple tumour classes, of which the most prominent, named luminal and basal, differ in expression of the oestrogen receptor alpha gene (ER). We report here the identification of a group of breast tumours with increased androgen signalling and a 'molecular apocrine' gene expression profile. Tumour samples from 49 patients with large operable or locally advanced breast cancers were tested on Affymetrix U133A gene expression microarrays. Principal components analysis and hierarchical clustering split the tumours into three groups: basal, luminal and a group we call molecular apocrine. All of the molecular apocrine tumours have strong apocrine features on histological examination (P=0.0002). The molecular apocrine group is androgen receptor (AR) positive and contains all of the ER-negative tumours outside the basal group. Kolmogorov-Smirnov testing indicates that oestrogen signalling is most active in the luminal group, and androgen signalling is most active in the molecular apocrine group. ERBB2 amplification is commoner in the molecular apocrine than the other groups. Genes that best split the three groups were identified by Wilcoxon test. Correlation of the average expression profile of these genes in our data with the expression profile of individual tumours in four published breast cancer studies suggest that molecular apocrine tumours represent 8-14% of tumours in these studies. Our data show that it is possible with microarray data to divide mammary tumour cells into three groups based on steroid receptor activity: luminal (ER+ AR+), basal (ER- AR-) and molecular apocrine (ER- AR+).

GuavaH: a compendium of host genomic data in HIV biology and disease.

BACKGROUND: There is an ever-increasing volume of data on host genes that are modulated during HIV infection, influence disease susceptibility or carry genetic variants that impact HIV infection. We created GuavaH (Genomic Utility for Association and Viral Analyses in HIV, http://www.GuavaH.org), a public resource that supports multipurpose analysis of genome-wide genetic variation and gene expression profile across multiple phenotypes relevant to HIV biology. FINDINGS: We included original data from 8 genome and transcriptome studies addressing viral and host responses in and ex vivo. These studies cover phenotypes such as HIV acquisition, plasma viral load, disease progression, viral replication cycle, latency and viral-host genome interaction. This represents genome-wide association data from more than 4,000 individuals, exome sequencing data from 392 individuals, in vivo transcriptome microarray data from 127 patients/conditions, and 60 sets of RNA-seq data. Additionally, GuavaH allows visualization of protein variation in ~8,000 individuals from the general population. The publicly available GuavaH framework supports queries on (i) unique single nucleotide polymorphism across different HIV related phenotypes, (ii) gene structure and variation, (iii) in vivo gene expression in the setting of human infection (CD4+ T cells), and (iv) in vitro gene expression data in models of permissive infection, latency and reactivation. CONCLUSIONS: The complexity of the analysis of host genetic influences on HIV biology and pathogenesis calls for comprehensive motors of research on curated data. The tool developed here allows queries and supports validation of the rapidly growing body of host genomic information pertinent to HIV research.

Transcriptional response to cardiac injury in the zebrafish: systematic identification of genes with highly concordant activity across in vivo models.

BACKGROUND: Zebrafish is a clinically-relevant model of heart regeneration. Unlike mammals, it has a remarkable heart repair capacity after injury, and promises novel translational applications. Amputation and cryoinjury models are key research tools for understanding injury response and regeneration in vivo. An understanding of the transcriptional responses following injury is needed to identify key players of heart tissue repair, as well as potential targets for boosting this property in humans. RESULTS: We investigated amputation and cryoinjury in vivo models of heart damage in the zebrafish through unbiased, integrative analyses of independent molecular datasets. To detect genes with potential biological roles, we derived computational prediction models with microarray data from heart amputation experiments. We focused on a top-ranked set of genes highly activated in the early post-injury stage, whose activity was further verified in independent microarray datasets. Next, we performed independent validations of expression responses with qPCR in a cryoinjury model. Across in vivo models, the top candidates showed highly concordant responses at 1 and 3 days post-injury, which highlights the predictive power of our analysis strategies and the possible biological relevance of these genes. Top candidates are significantly involved in cell fate specification and differentiation, and include heart failure markers such as periostin, as well as potential new targets for heart regeneration. For example, ptgis and ca2 were overexpressed, while usp2a, a regulator of the p53 pathway, was down-regulated in our in vivo models. Interestingly, a high activity of ptgis and ca2 has been previously observed in failing hearts from rats and humans. CONCLUSIONS: We identified genes with potential critical roles in the response to cardiac damage in the zebrafish. Their transcriptional activities are reproducible in different in vivo models of cardiac injury.

Nuclear Factor I genomic binding associates with chromatin boundaries.

BACKGROUND: The Nuclear Factor I (NFI) family of DNA binding proteins (also called CCAAT box transcription factors or CTF) is involved in both DNA replication and gene expression regulation. Using chromatin immuno-precipitation and high throughput sequencing (ChIP-Seq), we performed a genome-wide mapping of NFI DNA binding sites in primary mouse embryonic fibroblasts. RESULTS: We found that in vivo and in vitro NFI DNA binding specificities are indistinguishable, as in vivo ChIP-Seq NFI binding sites matched predictions based on previously established position weight matrix models of its in vitro binding specificity. Combining ChIP-Seq with mRNA profiling data, we found that NFI preferentially associates with highly expressed genes that it up-regulates, while binding sites were under-represented at expressed but unregulated genes. Genomic binding also correlated with markers of transcribed genes such as histone modifications H3K4me3 and H3K36me3, even outside of annotated transcribed loci, implying NFI in the control of the deposition of these modifications. Positional correlation between + and - strand ChIP-Seq tags revealed that, in contrast to other transcription factors, NFI associates with a nucleosomal length of cleavage-resistant DNA, suggesting an interaction with positioned nucleosomes. In addition, NFI binding prominently occurred at boundaries displaying discontinuities in histone modifications specific of expressed and silent chromatin, such as loci submitted to parental allele-specific imprinted expression. CONCLUSIONS: Our data thus suggest that NFI nucleosomal interaction may contribute to the partitioning of distinct chromatin domains and to epigenetic gene expression regulation.NFI ChIP-Seq and input control DNA data were deposited at Gene Expression Omnibus (GEO) repository under accession number GSE15844. Gene expression microarray data for mouse embryonic fibroblasts are on GEO accession number GSE15871.

Biomarkers of human gastrointestinal tract regions.

Dysregulation of intestinal epithelial cell performance is associated with an array of pathologies whose onset mechanisms are incompletely understood. While whole-genomics approaches have been valuable for studying the molecular basis of several intestinal diseases, a thorough analysis of gene expression along the healthy gastrointestinal tract is still lacking. The aim of this study was to map gene expression in gastrointestinal regions of healthy human adults and to implement a procedure for microarray data analysis that would allow its use as a reference when screening for pathological deviations. We analyzed the gene expression signature of antrum, duodenum, jejunum, ileum, and transverse colon biopsies using a biostatistical method based on a multivariate and univariate approach to identify region-selective genes. One hundred sixty-six genes were found responsible for distinguishing the five regions considered. Nineteen had never been described in the GI tract, including a semaphorin probably implicated in pathogen invasion and six novel genes. Moreover, by crossing these genes with those retrieved from an existing data set of gene expression in the intestine of ulcerative colitis and Crohn's disease patients, we identified genes that might be biomarkers of Crohn's and/or ulcerative colitis in ileum and/or colon. These include CLCA4 and SLC26A2, both implicated in ion transport. This study furnishes the first map of gene expression along the healthy human gastrointestinal tract. Furthermore, the approach implemented here, and validated by retrieving known gene profiles, allowed the identification of promising new leads in both healthy and disease states.

Increased expression of urokinase-type plasminogen activator mRNA determines adverse prognosis in ErbB2-positive primary breast cancer.

PURPOSE: To evaluate and validate mRNA expression markers capable of identifying patients with ErbB2-positive breast cancer associated with distant metastasis and reduced survival. PATIENTS AND METHODS: Expression of 60 genes involved in breast cancer biology was assessed by quantitative real-time PCR (qrt-PCR) in 317 primary breast cancer patients and correlated with clinical outcome data. Results were validated subsequently using two previously published and publicly available microarray data sets with different patient populations comprising 295 and 286 breast cancer samples, respectively. RESULTS: Of the 60 genes measured by qrt-PCR, urokinase-type plasminogen activator (uPA or PLAU) mRNA expression was the most significant marker associated with distant metastasis-free survival (MFS) by univariate Cox analysis in patients with ErbB2-positive tumors and an independent factor in multivariate analysis. Subsequent validation in two microarray data sets confirmed the prognostic value of uPA in ErbB2-positive tumors by both univariate and multivariate analysis. uPA mRNA expression was not significantly associated with MFS in ErbB2-negative tumors. Kaplan-Meier analysis showed in all three study populations that patients with ErbB2-positive/uPA-positive tumors exhibited significantly reduced MFS (hazard ratios [HR], 4.3; 95% CI, 1.6 to 11.8; HR, 2.7; 95% CI, 1.2 to 6.2; and, HR, 2.8; 95% CI, 1.1 to 7.1; all P < .02) as compared with the group with ErbB2-positive/uPA-negative tumors who exhibited similar outcome to those with ErbB2-negative tumors, irrespective of uPA status. CONCLUSION: After evaluation of 898 breast cancer patients, uPA mRNA expression emerged as a powerful prognostic indicator in ErbB2-positive tumors. These results were consistent among three independent study populations assayed by different techniques, including qrt-PCR and two microarray platforms.

GLUT3 is induced during epithelial-mesenchymal transition and promotes tumor cell proliferation in non-small cell lung cancer.

BACKGROUND: Alterations in glucose metabolism and epithelial-mesenchymal transition (EMT) constitute two important characteristics of carcinoma progression toward invasive cancer. Despite an extensive characterization of each of them separately, the links between EMT and glucose metabolism of tumor cells remain elusive. Here we show that the neuronal glucose transporter GLUT3 contributes to glucose uptake and proliferation of lung tumor cells that have undergone an EMT. RESULTS: Using a panel of human non-small cell lung cancer (NSCLC) cell lines, we demonstrate that GLUT3 is strongly expressed in mesenchymal, but not epithelial cells, a finding corroborated in hepatoma cells. Furthermore, we identify that ZEB1 binds to the GLUT3 gene to activate transcription. Importantly, inhibiting GLUT3 expression reduces glucose import and the proliferation of mesenchymal lung tumor cells, whereas ectopic expression in epithelial cells sustains proliferation in low glucose. Using a large microarray data collection of human NSCLCs, we determine that GLUT3 expression correlates with EMT markers and is prognostic of poor overall survival. CONCLUSIONS: Altogether, our results reveal that GLUT3 is a transcriptional target of ZEB1 and that this glucose transporter plays an important role in lung cancer, when tumor cells loose their epithelial characteristics to become more invasive. Moreover, these findings emphasize the development of GLUT3 inhibitory drugs as a targeted therapy for the treatment of patients with poorly differentiated tumors.

Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis.

BACKGROUND: Histologic grade in breast cancer provides clinically important prognostic information. However, 30%-60% of tumors are classified as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profiles of breast cancers and whether such profiles could be used to improve histologic grading. METHODS: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identified differentially expressed genes in a training set of 64 estrogen receptor (ER)-positive tumor samples by comparing expression profiles between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to define the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan-Meier analysis. All statistical tests were two-sided. RESULTS: We identified 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1-3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confidence interval = 2.25 to 5.78; P < .001, log-rank test). CONCLUSIONS: Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence. This approach may improve the accuracy of tumor grading and thus its prognostic value.

Integrative analysis of RUNX1 downstream pathways and target genes.

BACKGROUND: The RUNX1 transcription factor gene is frequently mutated in sporadic myeloid and lymphoid leukemia through translocation, point mutation or amplification. It is also responsible for a familial platelet disorder with predisposition to acute myeloid leukemia (FPD-AML). The disruption of the largely unknown biological pathways controlled by RUNX1 is likely to be responsible for the development of leukemia. We have used multiple microarray platforms and bioinformatic techniques to help identify these biological pathways to aid in the understanding of why RUNX1 mutations lead to leukemia. RESULTS: Here we report genes regulated either directly or indirectly by RUNX1 based on the study of gene expression profiles generated from 3 different human and mouse platforms. The platforms used were global gene expression profiling of: 1) cell lines with RUNX1 mutations from FPD-AML patients, 2) over-expression of RUNX1 and CBFbeta, and 3) Runx1 knockout mouse embryos using either cDNA or Affymetrix microarrays. We observe that our datasets (lists of differentially expressed genes) significantly correlate with published microarray data from sporadic AML patients with mutations in either RUNX1 or its cofactor, CBFbeta. A number of biological processes were identified among the differentially expressed genes and functional assays suggest that heterozygous RUNX1 point mutations in patients with FPD-AML impair cell proliferation, microtubule dynamics and possibly genetic stability. In addition, analysis of the regulatory regions of the differentially expressed genes has for the first time systematically identified numerous potential novel RUNX1 target genes. CONCLUSION: This work is the first large-scale study attempting to identify the genetic networks regulated by RUNX1, a master regulator in the development of the hematopoietic system and leukemia. The biological pathways and target genes controlled by RUNX1 will have considerable importance in disease progression in both familial and sporadic leukemia as well as therapeutic implications

Transcriptional analysis of left-sided colitis, pancolitis, and ulcerative colitis-associated dysplasia

BACKGROUND: It is unknown why patients with extensive ulcerative colitis (UC) have a higher risk of colorectal cancer compared with patients with left-sided UC. This study characterizes the inflammatory processes in left-sided UC, pancolitis, and UC-associated dysplasia at the transcriptional level to identify potential biomarkers and transcripts of importance for the carcinogenic behavior of chronic inflammation. METHODS: The Affymetrix GeneChip Human Genome U133 Plus 2.0 was applied on colonic biopsies from UC patients with left-sided UC, pancolitis, dysplasia, and controls. Reverse transcription polymerase chain reaction and immunohistochemistry were performed for validating selected transcripts in the initial cohort and in 2 independent cohorts of patients with UC. Microarray data were analyzed by principal component analysis, and reverse transcription polymerase chain reaction and immunohistochemistry data by the Wilcoxon's rank-sum test. RESULTS: The principal component analysis results revealed separate clusters for left-sided UC, pancolitis, dysplasia, and controls. Close clustering of dysplastic and pancolitic samples indicated similarities in gene expression. Indeed, 101 and 656 parallel upregulated and downregulated transcripts, respectively, were identified in specimens from dysplasia and pancolitis. Validation of selected transcripts hereof identified insulin receptor alpha (INSRA) and MAP kinase interacting serine/threonine kinase 2 (MKNK2) with an enhanced expression in dysplasia compared with left-sided UC and controls, whereas laminin γ2 (LAMC2) was found with a lower expression in dysplasia compared with the remaining 3 groups. CONCLUSIONS: This study demonstrates pancolitis and left-sided UC as distinct inflammatory processes at the transcriptional level, and identifies INSRA, MKNK2, and LAMC2 as potential critical transcripts in the inflammation-driven preneoplastic process of UC.

Functional analysis: evaluation of response intensities--tailoring ANOVA for lists of expression subsets.

Background: Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms. Results: As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets. Conclusions: The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect.

Identification and modelling of human breast cancer subtypes

Résumé Le cancer du sein est le cancer le plus commun chez les femmes et est responsable de presque 30% de tous les nouveaux cas de cancer en Europe. On estime le nombre de décès liés au cancer du sein en Europe est à plus de 130.000 par an. Ces chiffres expliquent l'impact social considérable de cette maladie. Les objectifs de cette thèse étaient: (1) d'identifier les prédispositions et les mécanismes biologiques responsables de l'établissement des sous-types spécifiques de cancer du sein; (2) les valider dans un modèle ín vivo "humain-dans-souris"; et (3) de développer des traitements spécifiques à chaque sous-type de cancer du sein identifiés. Le premier objectif a été atteint par l'intermédiaire de l'analyse des données d'expression de gènes des tumeurs, produite dans notre laboratoire. Les données obtenues par puces à ADN ont été produites à partir de 49 biopsies des tumeurs du sein provenant des patientes participant dans l'essai clinique EORTC 10994/BIG00-01. Les données étaient très riches en information et m'ont permis de valider des données précédentes des autres études d'expression des gènes dans des tumeurs du sein. De plus, cette analyse m'a permis d'identifier un nouveau sous-type biologique de cancer du sein. Dans la première partie de la thèse, je décris I identification des tumeurs apocrines du sein par l'analyse des puces à ADN et les implications potentielles de cette découverte pour les applications cliniques. Le deuxième objectif a été atteint par l'établissement d'un modèle de cancer du sein humain, basé sur des cellules épithéliales mammaires humaines primaires (HMECs) dérivées de réductions mammaires. J'ai choisi d'adapter un système de culture des cellules en suspension basé sur des mammosphères précédemment décrit et pat décidé d'exprimer des gènes en utilisant des lentivirus. Dans la deuxième partie de ma thèse je décris l'établissement d'un système de culture cellulaire qui permet la transformation quantitative des HMECs. Par la suite, j'ai établi un modèle de xénogreffe dans les souris immunodéficientes NOD/SCID, qui permet de modéliser la maladie humaine chez la souris. Dans la troisième partie de ma thèse je décris et je discute les résultats que j'ai obtenus en établissant un modèle estrogène-dépendant de cancer du sein par transformation quantitative des HMECs avec des gènes définis, identifiés par analyse de données d'expression des gènes dans le cancer du sein. Les cellules transformées dans notre modèle étaient estrogène-dépendantes pour la croissance, diploïdes et génétiquement normales même après la culture cellulaire in vitro prolongée. Les cellules formaient des tumeurs dans notre modèle de xénogreffe et constituaient des métastases péritonéales disséminées et du foie. Afin d'atteindre le troisième objectif de ma thèse, j'ai défini et examiné des stratégies de traitement qui permettent réduire les tumeurs et les métastases. J'ai produit un modèle de cancer du sein génétiquement défini et positif pour le récepteur de l'estrogène qui permet de modéliser le cancer du sein estrogène-dépendant humain chez la souris. Ce modèle permet l'étude des mécanismes impliqués dans la formation des tumeurs et des métastases. Abstract Breast cancer is the most common cancer in women and accounts for nearly 30% of all new cancer cases in Europe. The number of deaths from breast cancer in Europe is estimated to be over 130,000 each year, implying the social impact of the disease. The goals of this thesis were first, to identify biological features and mechanisms --responsible for the establishment of specific breast cancer subtypes, second to validate them in a human-in-mouse in vivo model and third to develop specific treatments for identified breast cancer subtypes. The first objective was achieved via the analysis of tumour gene expression data produced in our lab. The microarray data were generated from 49 breast tumour biopsies that were collected from patients enrolled in the clinical trial EORTC 10994/BIG00-01. The data set was very rich in information and allowed me to validate data of previous breast cancer gene expression studies and to identify biological features of a novel breast cancer subtype. In the first part of the thesis I focus on the identification of molecular apacrine breast tumours by microarray analysis and the potential imptìcation of this finding for the clinics. The second objective was attained by the production of a human breast cancer model system based on primary human mammary epithelial cells {HMECs) derived from reduction mammoplasties. I have chosen to adopt a previously described suspension culture system based on mammospheres and expressed selected target genes using lentiviral expression constructs. In the second part of my thesis I mainly focus on the establishment of a cell culture system allowing for quantitative transformation of HMECs. I then established a xenograft model in immunodeficient NOD/SCID mice, allowing to model human disease in a mouse. In the third part of my thesis I describe and discuss the results that I obtained while establishing an oestrogen-dependent model of breast cancer by quantitative transformation of HMECs with defined genes identified after breast cancer gene expression data analysis. The transformed cells in our model are oestrogen-dependent for growth; remain diploid and genetically normal even after prolonged cell culture in vitro. The cells farm tumours and form disseminated peritoneal and liver metastases in our xenograft model. Along the lines of the third objective of my thesis I defined and tested treatment schemes allowing reducing tumours and metastases. I have generated a genetically defined model of oestrogen receptor alpha positive human breast cancer that allows to model human oestrogen-dependent breast cancer in a mouse and enables the study of mechanisms involved in tumorigenesis and metastasis.