Regulation of molecular processes in diffuse large b-cell lymphoma

The molecular understanding of diseases has advanced rapidly due to the use of gene expression profiling. However, these methods have been hampered by the limitation to use frozen tissue specimens. Formalin fixation and paraffin embedding (FFPE) is a standard procedure for long time storage of tissues. FFPE tissues are available in large numbers and are of value for molecular research with the main challenge of low RNA quality compared to fresh frozen (FF) tissues. This thesis showed important aspects on laboratory methods of gene expression using FFPE material analyzing gene regulation and environmental factors in patients with Diffuse Large B-cell Lymphoma (DLBCL). In Study I, we evaluated RNA extraction and gene expression of long-term preserved FFPE Non-small Cell Lung Cancer (NSCLC) specimens using quantitative PCR (qPCR) and microarray. High quality gene expression signatures could be recognized in long time stored FFPE tissues. According to the results of Study I, FFPE tissues were further used in Studies II, III and IV. Different countries of the world have varying prevalence of microbial infections. It should be of interest to study patient populations originating from regions with different infectious and environmental exposures with the same disease. Sweden and Egypt are countries defined as low and high endemic infectious disease areas respectively. DLBCL is the most common type of Non Hodgkin’s Lymphoma (NHL) and accounts for approximately 40% of newly diagnosed lymphomas worldwide. The ABC subgroup of DLBCL (ABC DLBCL) has a poor prognosis with short survival. NHL has been associated to viral infections as Epstein Barr virus (EBV) and Hepatitis viruses B and C (HBV, HCV). To understand if differences in environmental exposure are associated to the activated B-cell type (ABC) of DLBCL, we analyzed the expression of genes, regulatory factors and microbial agents of Swedish and Egyptian ABC DLBCL patients using microarrays. In Study II, we compared the global gene expression profiles of Swedish and Egyptian patients. Signal transducer and activators of transcription 3 and 5 (STAT3 and STAT5b) were differently expressed. STAT3 was significantly upregulated in Swedish compared to Egyptian patients and controls. The opposite expression patterns was demonstrated for STAT5b. The difference in STAT3 and STAT5b expression was confirmed at the protein level. Based on these results, we investigated microRNA (miRNA) expression profiles in Study III. miRNAs are non coding RNAs targeting mRNA modulating their expression at the post-transcriptional level. We found that miRNA-1234 (miR-1234) was significantly upregulated in Egyptian compared to Swedish patients. The expression level of miR-1234 correlated inversely to the expression of STAT3. Furthermore, the Stat3 protein was downregulated in cells transfected with miR-1234, suggesting that STAT3 might be a potential target for miR-1234. In Study IV, we analyzed the presence of microbial agents in Swedish and Egyptian ABC DLBCL patients using a microbial detection array (MDA). JC polyoma virus (JCV) was detected in both Swedish and Egyptian patients and the complete HBV genome in Egyptian patients. Study IV supports the notion that viral agents such as JCV and HBV may be involved in the tumorigenesis of DLBCL in high infectious disease regions. ABC DLBCL patients originating from areas with different environmental exposures have altered gene and miRNA expression profiles and a different viral load, which may be of importance for the development of ABC DLBCL. STAT3 may be regulated by miRNA and associated to the presence of viral infections. These results may be of potential importance for the development of STAT targeted therapy

Biomedical Data Integration in Cancer Genomics

Cancer is one of the leading causes of death in industrialized nations and its incidence is steadily increasing due to population aging. Cancer constitutes a group of diseases characterized by unwanted cellular growth which results from random genomic alterations and environmental exposure. Diverse genomic and epigenomic alterations separately and jointly regulate gene expression and stimulate and support neoplastic growth. More effective treatment, earlier and more accurate diagnosis, and improved management of cancer are important for public health and well-being. Technological improvements in data measurement, storing and transport capability are transforming cancer research to a data-intensive field. The large increases in the quality and quantity of data for the analysis and interpretation of experiments has made employing computational and statistical tools necessary. Data integration - the combination of different types of measurement data - is a valuable computational tool for cancer research because data integration improves the interpretability of data-driven analytics and can thereby provide novel prognostic markers and drug targets. I have developed two computational data integration tools for large-scale genomic data and a simulator framework for testing a specific type of data integration algorithm. The first computational method, CNAmet, enhances the interpretation of genomic analysis results by integrating three data levels: gene expression, copy-number alteration, and DNA methylation. The second computational method, GOPredict, uses a knowledge discovery approach to prioritize drugs for patient cohorts thereby stratifying patients into potentitally drug-sensitive subgroups. Using the simulator framework, we are able to compare the performance of integration algorithms which integrate gene copy-number data with gene expression data to find putative cancer genes. Our experimental results indicate in simulated, cell line, and primary tumor data that well-performing integration algorithms for gene copy-number and expression data use and process genomic data appropriately. Applying these methods to diffuse large B-cell lymphoma, integrative analysis of copy-number and expression data helps to uncover a gene with putative prognostic utility. Furthermore, analysis of glioblastoma brain cancer data with CNAmet suggests that a number of known cancer genes, including the epidermal growth factor receptor, are highly expressed due to co-occuring alterations in their promoter DNA methylation and copy-number. Finally, integration of publicly available molecular and literature data with GOPredict suggests that treating patients with FGFR inhibitors in breast cancer and CDK inhibitors in ovarian cancer could support standard drug therapies. Collectively, the methods developed here and their application to varied molecular cancer data sets illustrates the benefits of data integration in cancer genomics.Syöpä on yksi yleisimmistä kuolinsyistä teollisuusmaissa ja sen esiintyvyys kasvaa tasaisesti väestön vanhetessa. Syöpä käsittää joukon sairauksia, joiden yhteispiirteenä on ei-toivottu solujen uudiskasvu. Uudiskasvu on seurasta genomin sattumanvaraisista sekä ympäristövaikutteisista muutoksista. Monitahoiset genomiset ja epigenomiset muutokset yhdessä ja erikseen säätelevät ja ohjaavat geenien ilmentymistä sekä edesauttavat ja tukevat syövän kasvamista. Hoidon tehostaminen, aikaisempi ja osuvampi taudin määritys, ja parempi syövänhallinta ovat merkittäviä haasteita kansanterveydelle. Teknologinen kehitys tiedon mittauksessa, säilömisessä ja siirrossa on muuttanut syöpätutkimuksen dataintensiiviseksi alaksi. Aineistojen määrän ja laadun suuri kasvu on tehnyt laskennallisista ja tilastollisista menetelmistä välttämättömiä työkaluja. Data-integraatio - erilaisten mitta-aineistojen yhdistäminen - on syöpätutkimukselle arvokas laskennallinen työkalu, sillä sen käyttö parantaa aineistolähteisen tutkimuksen tulkintaa ja tällä tavoin edesauttaa uusien ennustetekijöiden ja lääkekohteiden tunnistamista. Olen kehittänyt kaksi laskennallista työkaluja suurien genomiaineistojen yhdistämiseen sekä aineistosimulaattorin erityyppisten genomisten aineistojen yhdistämisohjelmien koestamiseen. Ensimmäinen laskennallinen työkalu, CNAmet, parantaa genomiaineistojen analyysin tulkintaa yhdistämällä kolmea eri tyyppistä mittaustietoa: geeni-ilmentymän, kopiolukumuutosten ja DNA-metylaation. Toinen laskennallinen työkalu, GOPredict, käyttäen automaattista tiedonmääritystä panee lääkkeet tärkeysjärjestykseen potilaskohortissa ja täten tunnistaa mahdollisesti lääkeherkät potilasalijoukot. Aineistosimulaattorilla vertailemme eri yhdistämisalgoritmien suorityskykyä menetelmillä, jotka yhdistävät geenien kopiolukumittaustietoa ja ilmentymämittaustietoa löytääkseen mahdollisia syöpägeenejä. Kokeelliset tuloksemme simulaatio-, solulinja- ja kasvainaineistoissa osoittavat, että parhaat kopioluvun ja geeninilmentymistä yhdistävät työkalut käsittelevät kopiolukumittauksia oikealla tavalla. Kun näitä menetelmiä käytetään suurisoluiseen B-solulymfoomaan, geenien kopioluku- ja ilmentymätiedon yhdistäminen auttaa löytämään mahdollisen ennustetekijägeenin. Glioblastooma syöpäkasvaimien analysointi CNAmet-työkalulla antaa osviittaa, että osa tunnetuista syöpägeeneistä ilmenee voimakkaasti johtuen samanaikaisesti sattuvista muutoksista geenien promoottorien DNA-metylaatiossa ja geenien kopioluvussa. Lopuksi, avoimen molekulääristen ja kirjallisuusaineistojen yhdistäminen GOPredictillä antaa ymmärtää, että FGFR-estäjien käyttö rintasyövässä ja CDK-estäjien käyttö munasarjasyövässä saattaisi tukea vakiohoitoja. Kaiken kaikkiaan tässä työssä kehitetyt työkalut ja niiden käyttö monitahoisiin molekyläärisiin syöpäaineistoihin havainnollistavat data-integraation käytön hyödyllisyyden syöpägenomisten aineistojen käsittelyssä

Expression-based Pathway Signature Analysis (EPSA): Mining publicly available microarray data for insight into human disease

BackgroundPublicly available data repositories facilitate the sharing of an ever-increasing amount of microarray data. However, these datasets remain highly underutilized. Reutilizing the data could offer insights into questions and diseases entirely distinct from those considered in the original experimental design.MethodsWe first analyzed microarray datasets derived from known perturbations of specific pathways using the samr package in R to identify specific patterns of change in gene expression. We refer to these pattern of gene expression alteration as a "pathway signatures." We then used Spearman's rank correlation coefficient, a non-parametric measure of correlation, to determine similarities between pathway signatures and disease profiles, and permutation analysis to evaluate false discovery rate. This enabled detection of statistically significant similarity between these pathway signatures and corresponding changes observed in human disease. Finally, we evaluated pathway activation, as indicated by correlation with the pathway signature, as a risk factor for poor prognosis using multiple unrelated, publicly available datasets.ResultsWe have developed a novel method, Expression-based Pathway Signature Analysis (EPSA). We demonstrate that ESPA is a rigorous computational approach for statistically evaluating the degree of similarity between highly disparate sources of microarray expression data. We also show how EPSA can be used in a number of cases to stratify patients with differential disease prognosis. EPSA can be applied to many different types of datasets in spite of different platforms, different experimental designs, and different species. Applying this method can yield new insights into human disease progression.ConclusionEPSA enables the use of publicly available data for an entirely new, translational purpose to enable the identification of potential pathways of dysregulation in human disease, as well as potential leads for therapeutic molecular targets

miR-221/222: new insights in Burkitt Lymphoma

Burkitt Lymphoma (BL) is a highly aggressive B cell non Hodgkin lymphoma. It’s considered the fastest growing human tumor and it is commonly associated with EBV infection. It was the first type of cancer shown to have a chromosomal translocation that activates c-Myc to become an oncogene. This genetic rearrangement places myc that usually is on chromosome 8 under the immunoglobulin gene regulatory element on chromosome 14 resulting in tumor promoting effect. BL is a highly malignant B-cell neoplasm that occurs endemically in equatorial Africa and sporadically throughout the world. The endemic BL (eBL) is the pediatric form positive for EBV in most cases. The variant of BL affecting the rest of the world is the sporadic BL (sBL) which is found in older patients and it is considered EBV negative because only a minority is EBV infected. The World Health Organization recognizes also a third form, HIV-BL which develops in HIV positive patients. Although, until now the translocation 8-14 and its variants are considered the major mechanism for the pathogenesis of BL, other molecular mechanism such as microRNA expression profile have been used to characterize and classify different types of BL from other lymphoma malignancies. However, the differential expression of microRNAs between BL patients and healthy control has not been studied before. For this reason our goal is to investigate the functional role of microRNAs that are disregulated in BL patients compared to healthy (cancer free) individuals. MicroRNAs are noncoding RNA, 18-24 nucleotides long. They are transcribed in the nucleus as long primary transcripts, and then cut by Drosha and DGCR8 into 70 nucleotides long precursors (pre-miRNA). This Pre-Mir is exported to the cytoplasm by Exportin-5 and then cleaved into a mature dsRNA by Dicer. Only one strand of the duplex miRNA-miRNA* binds the target mRNA to modulate the gene expression through two principle mechanisms which are the degradation of mRNA or the inhibition of the protein translation. To gain further insight into the molecular pathology of BL, we performed miRNA expression profile using a set of 5 sporadic, and 2 endemic BL patients, compared to B cells from reactive lymph nodes of 9 healthy patients and 11 patients affected by mononucleosis. MiRNAs expression signature shows, among the group of downregulated miRNAs, miR-221 that usually is upregulated in solid tumors. This is the first microRNA profiling that has been done in BL using as negative control lymph nodes taken from reactive patients or patients affected by the EBV virus, whereas the literature shows microRNA profiles in BL using as negative control T cells or different type of B cell lymphomas like for example DLBL (diffuse large B cell lymphoma). To confirm the remarkable down-regulation of miR-221 a nanoString analysis in 2 different cohorts of BL cell lines was also performed. We observed a common trend of altered expression of microRNAs, highlighting once again the down-regulation of miR-221/222, suggesting a different role of these miRNA in liquid tumours compared to their well-known pro-tumorigenic function in epithelial tumors The down-modulation of miR-221/222 was also confirmed by the qRT-PCR method in a bigger cohort of BL cell lines compared to 4 normal B lymphoblast EBV transformed cell lines. The four cell lines representing the controls express high levels of miR-221 compared to the group that represents the BL cell lines where the miR-221 is lost. The same trend is shown for miR-222. We found that interesting considering the up-regulation of miR-221 and miR-222 previously confirmed in a lot of solid tumors by multiple studies, such as breast, liver and lung cancer. Here, we are investigating a different role of the cluster 221/222 in lymphomas that have a different process in carcinogenesis than solid tumors. To better understand the potential role of miR-221/222 in BL, we also analyzed their expression levels in EµMyc transgenic mouse model which has been considered for a decade a good in vivo model of BL. We investigated the expression of miR-221 and 222 in B cells extracted from both transgenic and wild type mice. The miRNA levels detected by qRT-PCR show a down-regulation in 80% of the transgenic samples when compared to normal B cells derived from the spleen of wild type mice littermates. Once we determined that miR-221 and 222 were down regulated in both human and mouse models, we wanted to understand what pathways both of the models had in common and how miR-221 and 222 play a role in these pathways. Therefore, in order to establish the effect of miR-221 and 222 in a human model, we transfected BL cell line Bjab, which lacks of miR-221 and 222 expression with mature miRNA. A gene expression analysis was then conducted on extracted RNA from treated Bjab cells collected 48 hours after transfection compared to its negative control collected at the same time point. We then performed a parallel gene expression profile on the mouse model using the RNA extracted from CD-19+ of the wild type spleen and the transgenic spleen of littermates. Then we picked the down-regulated gene of the human gene expression profiling and compared it to the gene expression profile of the mouse model. From this comparison, we found some genes that were up-regulated in the mouse model that were also down-regulated by miR-221 and 222 in the human model. One of these genes is DUSP6/MKP-3, a MAP kinase phosphatase that dephosphorylate phosphothreonine and phosphotyrosine within ERK pathway, playing a role in the induction of apoptosis. This dual specificity phosphatase has been found also acting as an oncogene but no further studies have been conducted, leaving its function in a contradictory background. The level of expression in BL cell lines of DUSP6 has been evaluated by qRT-PCR, compared to negative lymphoblastoid cell lines and results show an up-regulation of the mRNA in 80% of BL cell lines whereas it’s lost in the controls, suggesting an oncogenic role of this protein in BL but additional studies need to be performed to confirm this hypothesis. Since these new findings may highlight a different role of these miRNA in BL compared to their well-known pro-tumorigenic function in epithelial tumors we cross a miR-221/222 KO mouse with the well-known EµMyc transgenic mouse model. The miR-221/222 KO doesn’t show any particular phenotype but when we breed the KO with the transgenic EµMyc we observe an early development of the BL pathogenesis in 5 out of 8 miR-221/222 KO/ EµMyc tg positive and death at 3-4 months of age while the wild type miR-221/222/ EµMyc tg are still alive at 6 months of age without showing any enlarged lymph nodes. Unfortunately, even these preliminary results indicate that the loss of miR-221/222 can play an important role in the pathogenesis of BL, the number of wild type miR-221/222 are not enough for the statistical analysis; for this reason we are increasing the numbers of litters and we need further investigations. Our findings indicate that miR-221/222 can be critical mediators for BL pathogenesis and together with other important genetics alteration such as translocation of MYC can lead to the aggressive phenotype that this B cell malignancy usually shows. These results highlight the potential role of this cluster of microRNAs to be a good tool of diagnosis and prognosis for BL

Computational study of cancer

In my thesis, I focused on integrative analysis of high-throughput oncogenomic data. This was done in two parts: In the first part, I describe IntOGen, an integrative data mining tool for the study of cancer. This system collates, annotates, pre-processes and analyzes large-scale data for transcriptomic, copy number aberration and mutational profiling of a large number of tumors in multiple cancer types. All oncogenomic data is annotated with ICD-O terms. We perform analysis at different levels of complexity: at the level of genes, at the level of modules, at the level of studies and finally combination of studies. The results are publicly available in a web service. I also present the Biomart interface of IntOGen for bulk download of data. In the final part, I propose a methodology based on sample-level enrichment analysis to identify patient subgroups from high-throughput profiling of tumors. I also apply this approach to a specific biological problem and characterize properties of worse prognosis tumor in multiple cancer types. This methodology can be used in the translational version of IntOGen

Computational Integrative Analysis of Biological Networks in Cancer

Cancer is one of the most lethal diseases. By 2030, deaths caused by cancers are estimated to reach 13 million per year worldwide. Cancer is a collection of related diseases distinguished by uncontrolled cell division that is driven by genomic alterations. Cancer is heterogeneous and shows an extraordinary genomic diversity between patients with transcriptionally and histologically similar cancer subtypes, and even between tumors from the same anatomical position. The heterogeneity poses great challenges in understanding cancer mechanisms and drug resistance; this understanding is critical for precise prognosis and improved treatments. Emergence of high-throughput technologies, such as microarrays and next-generation sequencing, has motivated the investigation of cancer cells on a genome-wide scale. Over the last decade, an unprecedented amount of high-throughput data has been generated. The challenge is to turn such a vast amount of raw data into clinically valuable information to benefit cancer patients. Single omics data have failed to fully uncover mechanisms behind cancer phenotypes. Accordingly, integrative approaches have been introduced to systematically analyze and interpret multi-omics data, among which network-based integrative approaches have achieved substantial advances in basic biological studies and cancer treatments. In this thesis, the development and application of network-based integrative methods are included to address challenges in analyzing cancer samples. Two novel methods are introduced to integrate disparate omics data and biological networks at the single-patient level: PerPAS, which takes pathway topology into account and integrates gene expression and clinical data with pathway information; and DERA, which elevates gene expression analysis to the network level and identifies network-based biomarkers that provide functional interpretation. The performance of both methods was demonstrated using biological experiment data, and the results were validated in independent cohorts. The application part of this thesis focuses on understanding cancer mechanisms and identifying clinical biomarkers in breast cancer and diffuse large B-cell lymphoma using PerPAS, DERA, and an existing method SPIA. Our experimental results provided insights into underlying cancer mechanisms and potential prognostic biomarkers for breast cancer, and identified therapeutic targets for diffuse large B-cell lymphoma. The potential of the therapeutic targets was verified in in vitro experiments.癌症是一种复杂的疾病,也是现今最致命的疾病之一。据推算未来二十年后, 在世界范围内, 每年将有一千三百万人死于癌症。癌症是异质性疾病,表现出极大的基因组多样性。取自不同病人但属于相似亚组的基因组样品呈现出显著的差异性, 甚至取自同一个病人同一个位置的基因组样品也是具有差异性。理解癌症致病机理和发展过程才能更好地提供精确诊断及治疗。 高通量技术的出现激发了系统分析学和计算工具的发展。但是单一平台的数据不足以全面揭示癌症机理, 导致理解癌症机理一直是个极大的挑战。基于网络的整合方法的出现促进了基础生物的研究和病人的诊治。这篇论文包括两个部分: 整合方法的开发与应用。在开发新的整合方法方面, 我们研发了新的整合方法来应对整合数据的挑战并回答癌症研究中的问题。两个新开发的整合方法有: 1) PerPAS, 是一个体化治疗分析工具, 支持单个病人样品的分析, 并且能整合信号通路和基因表达数据。2) DERA, 是一个整合细胞网络和基因表达数据的工具。它能把基因表达数据的分析提升到网络层面并能进行单个样品的分析。这两种新型方法的可用性已经在生物数据应用中得以展示, 并且用独立数据验证了发现的结果。 整合方法的应用部分集中在全面整合分析mRNA, miRNA, 信号通路数据, 并在弥漫大B细胞淋巴瘤中识别出新的治疗靶点。在此方法的应用下, 我们发现了几个调控重要的临床存活的细胞通路的靶点。并且这些靶点的可靠性已经被实验验证

Dissection of Complex Genetic Correlations into Interaction Effects

Living systems are overwhelmingly complex and consist of many interacting parts. Already the quantitative characterization of a single human cell type on genetic level requires at least the measurement of 20000 gene expressions. It remains a big challenge for theoretical approaches to discover patterns in these signals that represent specific interactions in such systems. A major problem is that available standard procedures summarize gene expressions in a hard-to-interpret way. For example, principal components represent axes of maximal variance in the gene vector space and thus often correspond to a superposition of multiple different gene regulation effects (e.g. I.1.4). Here, a novel approach to analyze and interpret such complex data is developed (Chapter II). It is based on an extremum principle that identifies an axis in the gene vector space to which as many as possible samples are correlated as highly as possible (II.3). This axis is maximally specific and thus most probably corresponds to exactly one gene regulation effect, making it considerably easier to interpret than principle components. To stabilize and optimize effect discovery, axes in the sample vector space are identified simultaneously. Genes and samples are always handled symmetrically by the algorithm. While sufficient for effect discovery, effect axes can only linearly approximate regulation laws. To represent a broader class of nonlinear regulations, including saturation effects or activity thresholds (e.g. II.1.1.2), a bimonotonic effect model is defined (II.2.1.2). A corresponding regression is realized that is monotonic over projections of samples (or genes) onto discovered gene (or sample) axes. Resulting effect curves can approximate regulation laws precisely (II.4.1). This enables the dissection of exclusively the discovered effect from the signal (II.4.2). Signal parts from other potentially overlapping effects remain untouched. This continues iteratively. In this way, the high-dimensional initial signal (II.2.1.1) can be dissected into highly specific effects. Method validation demonstrates that superposed effects of various size, shape and signal strength can be dissected reliably (II.6.2). Simulated laws of regulation are reconstructed with high correlation. Detection limits, e.g. for signal strength or for missing values, lie above practical requirements (II.6.4). The novel approach is systematically compared with standard procedures such as principal component analysis. Signal dissection is shown to have clear advantages, especially for many overlapping effects of comparable size (II.6.3). An ideal test field for such approaches is cancer cells, as they may be driven by multiple overlapping gene regulation networks that are largely unknown. Additionally, quantification and classification of cancer cells by their particular set of driving gene regulations is a prerequisite towards precision medicine. To validate the novel method against real biological data, it is applied to gene expressions of over 1000 tumor samples from Diffuse Large B-Cell Lymphoma (DLBCL) patients (Chapter III). Two already known subtypes of this disease (cf. I.1.2.1) with significantly different survival following the same chemotherapy were originally also discovered as a gene expression effect. These subtypes can only be precisely determined by this effect on molecular level. Such previous results offer a possibility for method validation and indeed, this effect has been unsupervisedly rediscovered (III.3.2.2). Several additional biologically relevant effects have been discovered and validated across four patient cohorts. Multivariate analyses (III.2) identify combinations of validated effects that can predict significant differences in patient survival. One novel effect possesses an even higher predictive value (cf. III.2.5.1) than the rediscovered subtype effect and is genetically more specific (cf. III.3.3.1). A trained and validated Cox survival model (III.2.5) can predict significant survival differences within known DLBCL subtypes (III.2.5.6), demonstrating that they are genetically heterogeneous as well. Detailed biostatistical evaluations of all survival effects (III.3.3) may help to clarify the molecular pathogenesis of DLBCL. Furthermore, the applicability of signal dissection is not limited to biological data. For instance, dissecting spectral energy distributions of stars observed in astrophysics might be useful to discover laws of light emission

Identification and characterisation of micrornas involved in the pathogenesis of HIV–associated non-Hodgkin's lymphoma

Background: Since its discovery about three decades ago, the Human Immunodeficiency Virus (HIV) has claimed over millions of lives globally. Although our understanding of the mode of transmission and action of this causative agent for the Acquired Immune Deficiency Syndrome (AIDS) has increased through research, and treatment regimens developed and improved, in certain parts of the world the pandemic continues to expand. Sub-Saharan Africa, which is the epicentre of this global health concern, accounts for approximately 66% of the total number of individuals affected, with South Africa enduring the heaviest burden. South Africa has the world's largest antiretroviral therapy (ART) programme and as such, HIV infected people are living longer, and consequently the incidence of HIV co-morbidities has increased dramatically. HIV/AIDS defining cancers are such co-morbidities with Non- Hodgkin's lymphomas (NHL) being the second most common HIV-associated cancer. Diffuse Large B-cell lymphoma (DLBCL) and Burkitt's lymphoma (BL) are the main subtypes and both present aggressively in HIV positive patients with rapid progression. The use of highly active antiretroviral therapy (HAART) has decreased the incidence of DLBCL in HIV positive patients, however the prevalence of these cancers still remain high in some settings. It has been suggested that the pathogenesis of these cancers in HIV infected individuals is complex and different to that in HIV uninfected individuals, with the possibility that the virus may have an oncogenic role. This has already been demonstrated in the case of the HIV/AIDSdefining cancer Kaposi Sarcoma. However, the same has not been unequivocally demonstrated in HIV-associated NHL. In light of this, the mechanisms through which viruses and viral components promote cellular transformation is an area of active research. One of these mechanisms manipulated by viruses is through the dysregulation of cellular microRNAs (miRNAs) which are small non-coding RNA molecules that are key regulators of gene expression. While they are essential for normal cellular functioning, their expression has been found to be deregulated in diseases including cancer. Several studies have described specific miRNA signatures for NHLs including for DLBCL and BL but none have been described for the HIV-association of these cancers. Aim: The aim of this project was to identify and characterise miRNAs involved in the pathogenesis of HIV-associated NHLs. This thesis reports on the changes in expression of miRNAs in B-cells exposed to an attenuated form (structurally intact but non-infectious) of HIV. Methods: We designed a custom miRNA microarray to identify deregulated miRNAs in the BL cell line Ramos that were exposed to HIV compared to microvesicle treated cells. It was initially planned to use both normal B-cells (L1439A) and BL cells for analysis but Ramos was selected due to technical reasons for this step. Thereafter we validated selected miRNAs by quantitative real-time PCR (qPCR) using single-tube TaqMan® Assays which was predominantly performed in the lymphoblastoid cell line L1439A, which is derived from a healthy donor. We then focused on further characterising the role of one miRNA in the development of HIV-associated NHL by using prediction programmes to predict its putative gene targets and then confirmed its target by using qPCR and western blot analyses. Results: Extensive and comprehensive analysis of the array data led to the identification of a large number of miRNAs which were differentially expressed, with 32 being selected for further studies. These 32 miRNAs include 16 upregulated and 16 downregulated miRNAs, and were selected because they displayed changes in expression by two or more folds. Thereafter, four miRNAs, namely miR-363-3p, miR-222-3p, miR-200c-3p and miR-575, were chosen for validation based on their reported involvement in cancer for validation. The results of two miRNAs (miR-575 (upregulated) (p<0.05) and miR-200c-3p (downregulated) (p<0.05)) were found to be consistent with the results obtained from the miRNA microarray whilst the other two were opposite to that result (both downregulated) (p<0.05). Using online tools as well as the published literature, several potential target genes of miR-575 were identified, namely DENND5A, CDK1, CSTA and ATAD5. One particular target, the BH3- like motif containing inducer of cell death (BLID), which is involved in apoptosis, has previously been confirmed as a gene target in non small cell lung cancer. Using qPCR, we found that BLID messenger RNA (mRNA) was downregulated in normal B-cells when exposed to HIV-1 AT-2. Unfortunately, the BLID protein could not be detected using western blot analysis despite several attempts at detecting varying concentrations of the protein and using two different positive control cell lines. Conclusion: The reverse correlation, between miR-575 and BLID mRNA expression in the same cell line and under the same treatment conditions, supports the notion that the downregulation of miR-575 may be physiologically relevant. However, this could not be further verified as the BLID protein could not be detected in the L1439A cells, even in the microvesicle treated control cells. Future studies should look at further characterisation of miR- 575 in the pathogenesis of HIV-associated NHLs by investigating other predicted gene targets of the miRNA. This will then be followed by loss and gain of function assays to confirm the miRNA:mRNA relationship. Furthermore, functional analyses, such as measure of apoptosis, expression of key regulators of the cell cycle, and other cellular events characteristic of cancer should be carried out to define the role of the miR-575 in the development of HIV-associated lymphoma

MicroRNA expression in B-cell lymphomas

Tesis doctoral inédita, leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de lectura: 04-06-201