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.

Mechanism of resistance to tyrosine kinase inhibitors in philadelphia-positive acute lymphblastic leukaemia (all): from genetic alterations to impaired RNA editing

The Ph chromosome is the most frequent cytogenetic aberration associated with adult ALL and it represents the single most significant adverse prognostic marker. Despite imatinib has led to significant improvements in the treatment of patients with Ph+ ALL, in the majority of cases resistance developed quickly and disease progressed. Some mechanisms of resistance have been widely described but the full knowledge of contributing factors, driving both the disease and resistance, remains to be defined. The observation of rapid development of lymphoblastic leukemia in mice expressing altered Ikaros (Ik) isoforms represented the background of this study. Ikaros is a zinc finger transcription factor required for normal hemopoietic differentiation and proliferation, particularly in the lymphoid lineages. By means of alternative splicing, Ikaros encodes several proteins that differ in their abilities to bind to a consensus DNA-binding site. Shorter, DNA nonbinding isoforms exert a dominant negative effect, inhibiting the ability of longer heterodimer partners to bind DNA. The differential expression pattern of Ik isoforms in Ph+ ALL patients was analyzed in order to determine if molecular abnormalities involving the Ik gene could associate with resistance to imatinib and dasatinib. Bone marrow and peripheral blood samples from 46 adult patients (median age 55 yrs, 18-76) with Ph+ ALL at diagnosis and during treatment with imatinib (16 pts) or dasatinib (30 pts) were collected. We set up a fast, high-throughput method based on capillary electrophoresis technology to detect and quantify splice variants. 41% Ph+ ALL patients expressed high levels of the non DNA-binding dominant negative Ik6 isoform lacking critical N-terminal zinc-fingers which display abnormal subcellular compartmentalization pattern. Nuclear extracts from patients expressed Ik6 failed to bind DNA in mobility shift assay using a DNA probe containing an Ikaros-specific DNA binding sequence. In 59% Ph+ ALL patients there was the coexistence in the same PCR sample and at the same time of many splice variants corresponded to Ik1, Ik2, Ik4, Ik4A, Ik5A, Ik6, Ik6 and Ik8 isoforms. In these patients aberrant full-length Ikaros isoforms in Ph+ ALL characterized by a 60-bp insertion immediately downstream of exon 3 and a recurring 30-bp in-frame deletion at the end of exon 7 involving most frequently the Ik2, Ik4 isoforms were also identified. Both the insertion and deletion were due to the selection of alternative splice donor and acceptor sites. The molecular monitoring of minimal residual disease showed for the first time in vivo that the Ik6 expression strongly correlated with the BCR-ABL transcript levels suggesting that this alteration could depend on the Bcr-Abl activity. Patient-derived leukaemia cells expressed dominant-negative Ik6 at diagnosis and at the time of relapse, but never during remission. In order to mechanistically demonstrated whether in vitro the overexpression of Ik6 impairs the response to tyrosine kinase inhibitors (TKIs) and contributes to resistance, an imatinib-sensitive Ik6-negative Ph+ ALL cell line (SUP-B15) was transfected with the complete Ik6 DNA coding sequence. The expression of Ik6 strongly increased proliferation and inhibited apoptosis in TKI sensitive cells establishing a previously unknown link between specific molecular defects that involve the Ikaros gene and the resistance to TKIs in Ph+ ALL patients. Amplification and genomic sequence analysis of the exon splice junction regions showed the presence of 2 single nucleotide polymorphisms (SNPs): rs10251980 [A/G] in the exon2/3 splice junction and of rs10262731 [A/G] in the exon 7/8 splice junction in 50% and 36% of patients, respectively. A variant of the rs11329346 [-/C], in 16% of patients was also found. Other two different single nucleotide substitutions not recognized as SNP were observed. Some mutations were predicted by computational analyses (RESCUE approach) to alter cis-splicing elements. In conclusion, these findings demonstrated that the post-transcriptional regulation of alternative splicing of Ikaros gene is defective in the majority of Ph+ ALL patients treated with TKIs. The overexpression of Ik6 blocking B-cell differentiation could contribute to resistance opening a time frame, during which leukaemia cells acquire secondary transforming events that confer definitive resistance to imatinib and dasatinib.

Molecular strategies for genetic diversity analysis and development of markers linked to resistance traits in apple

The goal of many plant scientists’ research is to explain natural phenotypic variation in term of simple changes in DNA sequence. DNA-based molecular markers are extensively used for the construction of genome-wide molecular maps and to perform genetic analysis for simple and complex traits. The PhD thesis was divided into two main research lines according to the different approaches adopted. The first research line is to analyze the genetic diversity in an Italian apple germplasm collection for the identification of markers tightly linked to targeted genes by an association genetic method. This made it possible to identify synomym and homonym accessions and triploids. The fruit red skin color trait has been used to test the reliability of the genetic approaches in this species. The second line is related to the development of molecular markers closely linked to the Rvi13 and Rvi5 scab resistance genes, previously mapped on apple’s chromosome 10 and 17 respectively by using the traditional linkage mapping method. Both region have been fine-mapped with various type of markers that could be used for marker-assisted selection in future breeding programs and to isolate the two resistance genes.