Inibizione selettiva del gene MYCN mediante PNA (acidi peptido nucleici) anti-gene nel rabdomiosarcoma umano

MYCN oncogene amplification/expression is a feature of many childhood tumors, and some adult tumors, and it is associated with poor prognosis. While MYC expression is ubiquitary, MYCN has a restricted expression after birth and it is an ideal target for an effective therapy. PNAs belong to the latest class of nucleic acid-based therapeutics, and they can bind chromosomal DNA and block gene transcription (anti-gene activity). We have developed an anti-gene PNA that targets specifically the MYCN gene to block its transcription. We report for the first time MYCN targeted inhibition in Rhabdomyosarcoma (RMS) by the anti-MYCN-PNA in RMS cell lines (four ARMS and four ERMS) and in a xenograft RMS mouse model. Rhabdomyosarcoma is the most common pediatric soft-tissue sarcoma, comprising two main subgroups [Alveolar (ARMS) and Embryonal (ERMS)]. ARMS is associated with a poorer prognosis. MYCN amplification is a feature of both the ERMS and ARMS, but the MYCN amplification and expression levels shows a significant correlation and are greater in ARMS, in which they are associated with adverse outcome. We found that MYCN mRNA and protein levels were higher in the four ARMS (RH30, RH4, RH28 and RMZ-RC2) than in the four ERMS (RH36, SMS-CTR, CCA and RD) cell lines. The potent inhibition of MYCN transcription was highly specific, it did not affect the MYC expression, it was followed by cell-growth inhibition in the RMS cell lines which correlated with the MYCN expression rate, and it led to complete cell-growth inhibition in ARMS cells. We used a mutated- PNA as control. MYCN silencing induced apoptosis. Global gene expression analysis (Affymetrix microarrays) in ARMS cells treated with the anti-MYCN-PNA revealed genes specifically induced or repressed, with both genes previously described as targets of N-myc or Myc, and new genes undescribed as targets of N-myc or Myc (mainly involved in cell cycle, apoptosis, cell motility, metastasis, angiogenesis and muscle development). The changes in the expression of the most relevant genes were confirmed by Real-Time PCR and western blot, and their expression after the MYCN silencing was evaluated in the other RMS cell lines. The in vivo study, using an ARMS xenograft murine model evaluated by micro-PET, showed a complete elimination of the metabolic tumor signal in most of the cases (70%) after anti-MYCN-PNA treatment (without toxicity), whereas treatment with the mutated-PNA had no effect. Our results strongly support the development of MYCN anti-gene therapy for the treatment of RMS, particularly for poor prognosis ARMS, and of other MYCN-expressing tumors.

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.

Transcriptional functions of DNA Topoisomerases at a genome-wide scale and a single gene levels

The DNA topology is an important modifier of DNA functions. Torsional stress is generated when right handed DNA is either over- or underwound, producing structural deformations which drive or are driven by processes such as replication, transcription, recombination and repair. DNA topoisomerases are molecular machines that regulate the topological state of the DNA in the cell. These enzymes accomplish this task by either passing one strand of the DNA through a break in the opposing strand or by passing a region of the duplex from the same or a different molecule through a double-stranded cut generated in the DNA. Because of their ability to cut one or two strands of DNA they are also target for some of the most successful anticancer drugs used in standard combination therapies of human cancers. An effective anticancer drug is Camptothecin (CPT) that specifically targets DNA topoisomerase 1 (TOP 1). The research project of the present thesis has been focused on the role of human TOP 1 during transcription and on the transcriptional consequences associated with TOP 1 inhibition by CPT in human cell lines. Previous findings demonstrate that TOP 1 inhibition by CPT perturbs RNA polymerase (RNAP II) density at promoters and along transcribed genes suggesting an involvement of TOP 1 in RNAP II promoter proximal pausing site. Within the transcription cycle, promoter pausing is a fundamental step the importance of which has been well established as a means of coupling elongation to RNA maturation. By measuring nascent RNA transcripts bound to chromatin, we demonstrated that TOP 1 inhibition by CPT can enhance RNAP II escape from promoter proximal pausing site of the human Hypoxia Inducible Factor 1 (HIF-1) and c-MYC genes in a dose dependent manner. This effect is dependent from Cdk7/Cdk9 activities since it can be reversed by the kinases inhibitor DRB. Since CPT affects RNAP II by promoting the hyperphosphorylation of its Rpb1 subunit the findings suggest that TOP 1inhibition by CPT may increase the activity of Cdks which in turn phosphorylate the Rpb1 subunit of RNAP II enhancing its escape from pausing. Interestingly, the transcriptional consequences of CPT induced topological stress are wider than expected. CPT increased co-transcriptional splicing of exon1 and 2 and markedly affected alternative splicing at exon 11. Surprisingly despite its well-established transcription inhibitory activity, CPT can trigger the production of a novel long RNA (5’aHIF-1) antisense to the human HIF-1 mRNA and a known antisense RNA at the 3’ end of the gene, while decreasing mRNA levels. The effects require TOP 1 and are independent from CPT induced DNA damage. Thus, when the supercoiling imbalance promoted by CPT occurs at promoter, it may trigger deregulation of the RNAP II pausing, increased chromatin accessibility and activation/derepression of antisense transcripts in a Cdks dependent manner. A changed balance of antisense transcripts and mRNAs may regulate the activity of HIF-1 and contribute to the control of tumor progression After focusing our TOP 1 investigations at a single gene level, we have extended the study to the whole genome by developing the “Topo-Seq” approach which generates a map of genome-wide distribution of sites of TOP 1 activity sites in human cells. The preliminary data revealed that TOP 1 preferentially localizes at intragenic regions and in particular at 5’ and 3’ ends of genes. Surprisingly upon TOP 1 downregulation, which impairs protein expression by 80%, TOP 1 molecules are mostly localized around 3’ ends of genes, thus suggesting that its activity is essential at these regions and can be compensate at 5’ ends. The developed procedure is a pioneer tool for the detection of TOP 1 cleavage sites across the genome and can open the way to further investigations of the enzyme roles in different nuclear processes.

IGF system, CD99 and tumor-specific chromosomal translocations: evaluation of their cross-talk and definition of their role in Ewing sarcoma and prostate cancer

Aberrant expression of ETS transcription factors, including FLI1 and ERG, due to chromosomal translocations has been described as a driver event in initiation and progression of different tumors. In this study, the impact of prostate cancer (PCa) fusion gene TMPRSS2-ERG was evaluated on components of the insulin-like growth factor (IGF) system and the CD99 molecule, two well documented targets of EWS-FLI1, the hallmark of Ewing sarcoma (ES). The aim of this study was to identify common or distinctive ETS-related mechanisms which could be exploited at biological and clinical level. The results demonstrate that IGF-1R represents a common target of ETS rearrangements as ERG and FLI1 bind IGF-1R gene promoter and their modulation causes alteration in IGF-1R protein levels. At clinical level, this mechanism provides basis for a more rationale use of anti-IGF-1R inhibitors as PCa cells expressing the fusion gene better respond to anti-IGF-1R agents. EWS-FLI1/IGF-1R axis provides rationale for combination of anti-IGF-1R agents with trabectedin, an alkylator agent causing enhanced EWS-FLI1 occupancy on the IGF-1R promoter. TMPRSS2-ERG also influences prognosis relevance of IGF system as high IGF-1R correlates with a better biochemical progression free survival (BPFS) in PCa patients negative for the fusion gene while marginal or no association was found in the total cases or TMPRSS2-ERG-positive cases, respectively. This study indicates CD99 is differentially regulated between ETS-related tumors as CD99 is not a target of ERG. In PCa, CD99 did not show differential expression between TMPRSS2-ERG-positive and –negative cells. A direct correlation was anyway found between ERG and CD99 proteins both in vitro and in patients putatively suggesting that ERG target genes comprehend regulators of CD99. Despite a little trend suggesting a correlation between CD99 expression and a better BPFS, no clinical relevance for CD99 was found in the field of prognostic biomarkers.