New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

Stem cell pathways in the basal-like breast carcinoma: the role of beta-catenin and HIF-1alpha

Basal-like tumor is an aggressive breast carcinoma subtype that displays an expression signature similar to that of the basal/myoepithelial cells of the breast tissue. Basal-like carcinoma are characterized by over-expression of the Epidermal Growth Factor receptor (EGFR), high frequency of p53 mutations, cytoplasmic/nuclear localization of beta-catenin, overexpression of the Hypoxia inducible factor (HIF)-1alpha target Carbonic Anhydrase isoenzime 9 (CA9) and a gene expression pattern similar to that of normal and cancer stem cells, including the over-expression of the mammary stem cell markers CD44. In this study we investigated the role of p53, EGFR, beta-catenin and HIF-1alpha in the regulation of stem cell features and genes associated with the basal-like gene expression profile. The findings reported in this investigation indicate that p53 inactivation in ductal breast carcinoma cells leads to increased EGFR mRNA and protein levels. In our experimental model, EGFR overexpression induces beta-catenin cytoplasmatic stabilization and transcriptional activity and, by that, leads to increased aggressive features including mammosphere (MS) forming and growth capacity, invasive potential and overexpression of the mammary stem cell gene CD44. Moreover we found that EGFR/beta-catenin axis promotes hypoxia survival in breast carcinoma cells via increased CA9 expression. Indeed beta-catenin positively regulates CA9 expression upon hypoxia exposure. Interestingly we found that beta-catenin inhibits HIF-1alpha transcriptional activity. Looking for the mechanism, we found that CA9 expression is promoted by HIF-1alpha and cytoplasmatic beta-catenin further increased it post-transcriptionally, via direct mRNA binding and stabilization. These data reveal a functional beta-catenin/HIF-1alpha interplay among hallmarks of basal-like tumors and unveil a new functional role for cytoplasmic beta-catenin in the phenotype of such tumors. Therefore it can be proposed that the interplay here described among EGFR/beta-catenin and HIF-1alpha may play a role in breast cancer stem cell survival and function.