Functional significance of Tumor Protein D52 amplification and overexpression in cancer

Tumor Protein D52 (TPD52) is an oncogene whose overexpression has been demonstrated in tumours of diverse cellular origins and associated with poor prognosis. Although the breadth and clinical significance of TPD52 overexpression in cancer is now well-established, there remains a long-standing deficit in our understanding of its functional significance. TPD52 is located on the frequently gained chromosome band 8q21, where we propose that it is an amplification target. We analysed TPD52 amplification in 995 cancer cell lines and then investigated whether this was broadly associated with altered lipid phenotypes in a selection of these cell lines. We demonstrated a significant positive association between TPD52 expression and lipid droplet staining, with increased lipid droplet number and size upon exogenous TPD52 overexpression in MDAMB231 cells. Furthermore, TPD52 interacted directly with the lipid droplet-associated proteins, adipophilin and TIP47. This suggests a role for TPD52 in intracellular lipid storage, which is important to cancer cells since their rapid proliferation is contingent upon having sufficient lipid for membrane synthesis. We therefore hypothesised that TPD52 overexpression could be applied clinically as a predictive marker for cancers likely to respond to drugs that interfere with lipid pathways. We performed predictive modelling using pharmacogenomic datasets to investigate for the first time whether TPD52 amplification and/or overexpression was associated with altered sensitivity to different drugs. These analyses identified several compounds for future validation, of which the lipid-relevant drugs fatostatin and brefeldin A were pursued in in vitro studies. Collectively, the findings presented in this thesis represent a significant step forward in our understanding of how TPD52 amplification and/or overexpression may contribute to the development of cancer at a molecular level, and how this could be applied to improve cancer treatment

WWOX, tumour suppressor and modifier gene, as a regulator of gene expression and apoptosis in ovarian cancer

WWOX is a tumour suppressor gene, as demonstrated by increased neoplasia incidence in Wwox-knock-out animals, and is frequently disrupted in human cancers. WWOX expression reconstitution abolishes xenograft growth of lung, pancreatic, breast, ovarian and prostate cancer cells. The tumoursuppressive function of WWOX is suggested to be related to pro-apoptotic effects or interactions with potentially oncogenic transcription factors leading to their cytoplasmic sequestration and trans-activity inhibition. In physiology WWOX might be involved in metabolism. Our group demonstrated that WWOX reconstitution in ovarian cancer cells inhibits xenograft growth but this was not accompanied by altered in vitro growth or apoptosis rates. Rather, WWOX transfection reduced cancer cell adhesion to extracellular matrix due to reduction of integrin α3 binding. Additionally, our group postulated that natural polymorphic variants of WWOX are linked to ovarian cancer clinicopathological features. The hypotheses behind this work were that WWOX regulates gene expression or promotes apoptosis in ovarian cancer. Further, this project aimed at confirming the associations of WWOX polymorphisms. I demonstrate no link between WWOX status and the subcellular localization of putative WWOX-binding transcription factors or the expression of their target genes in ovarian cancer cells. The phenotypic consequences of WWOX expression manipulation do not appear to be explained by transcriptional changes. I show that WWOX increases apoptosis rates in ovarian cancer cells exposed to the chemotherapy agent paclitaxel, but not cisplatin. This is independent of the anti-mitotic function of taxanes and unrelated to integrin regulation. Rather, WWOX promotes cell death during taxane-induced endoplasmic reticulum stress. I propose WWOX-driven cell death during endoplasmic reticulum stress might be also linked to anti-tumorigenic effects in vivo. A validation study did not confirm the link between WWOX genetic variants and ovarian cancer pathology. There is also no link between WWOX polymorphisms and bone metabolism, a trait affected in WWOX-knock-out animals

Exploring circulating biomarkers in patients with hepatocellular cancer

PhD ThesisHepatocellular cancer (HCC) is the sixth commonest cancer worldwide and the second leading cause of cancer mortality. The majority of patients present with advanced disease, where cure is not an option and palliative therapies are limited. What is more, biomarkers to stratify available therapies effectively are lacking. Tissue is not routinely obtained as diagnosis of HCC is largely reliant on imaging, as this is sensitive and specific in the majority of patients with cirrhosis and liver biopsy carries small but recognised risks - including tumour seeding and bleeding. Serum AFP lacks HCC diagnostic sensitivity and specific, but remains the only clinically useful serum biomarker – employed despite its limitations in surveillance programmes as well as to monitor HCC progression and to stratify patients for therapy. This highlights the need for alternative biomarkers for HCC management. Sampling patient blood is a quick, non-invasive and inexpensive method and may be regarded as a ‘liquid biopsy’ if it could deliver clinically relevant molecular information about the tumour or its microenvironment. Several liquid biopsy methods have been explored: circulating tumour cell (CTC) detection and characterisation; circulating tumour DNA (ctDNA) KRAS mutation detection; circulating immune cell counts and gene expression signatures of peripheral blood mononucleocytes (PBMCs). CTCs can be detected in patients with cancer and have the potential to provide diagnostic, prognostic and treatment stratifying information. A method for CTC detection using the Imagestream – an imaging fluorescent activated cells sorter with multi-channel immunofluorescence - was developed and optimised. A CD45-immunomagnetic depletion resulted in ≥95% reduction in WBCs with the maintenance of a recovery rate of 51.3-65.37% of CTCs. The remaining cell suspension was labelled with a panel of fluorescent antibodies to enable cell characterisation prior to running through the Imagestream. Between 1 and 1642 CTCs were detected in 65% of HCC patients. The presence of CTCs indicated a worse median overall survival (OS) in HCC patients (24.5 months vs 12 months, Log-Rank p>0.0001). Expression of biomarkers on CTCs was heterogeneous with CK being the most commonly detected biomarker, followed by DNA- ii PK. In some patients, CTCs observed were negative for all of the biomarkers in the panel but detectable on the basis of size and morphology. Clusters of CTCs and leucocyte interactions with CTCs were also observed. Studies on plasma ctDNA detected KRAS mutation in 2/38 (5%) of patients with HCC. Retrospective patient blood data analysis demonstrated that circulating neutrophils were the key circulating immune cell driving HCC progression. In two patient cohorts – Newcastle (n=583) and Hong Kong (n=585), circulating neutrophil counts were an independent predictor of prognosis. Furthermore, in the combined cohort (n=1168), the neutrophil count, either alone or combined with platelets and lymphocytes, was associated with significant differences in patients survival. In a small pilot cohort, gene expression analysis of PBMCs identified NFAT in the immune response as being the top altered pathway in patients with NAFLD/NASH-HCC. DNA extracts prepared from PBMCs in patients with NAFLD-NASH-HCC were more similar to samples derived from patients with HCV-HCC compared to non-cancerous NAFLD/NASH controls. These pilot data were encouraging, supporting a potential role for future application of liquid biopsy tools in the clinical setting for patients with HCC

The HBP1 tumor suppressor is a negative epigenetic regulator of MYCN driven neuroblastoma through interaction with the PRC2 complex

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Animal Modeling in Cancer

Dear Readers, Understanding the pathological mechanisms involved in human diseases and their possible treatment has been historically based on comparative analysis of diverse animal species that share a similar genetic, physiological and behavioural composition. The ancient Greeks were the first to use animals as models for anatomy and physiology, and this was consequently adopted by other cultures and led to important discoveries. In recent years, there have been many efforts to understand and fight cancer through new revolutionary personalized treatments and wider screenings that help diagnose and treat cancer. A fundamental part of this effort is to develop suitable cancer animal models that simulate the different disease variants and their progression. Ranging from tumor-derived xenografts to genetically engineered models, a wide variety of systems are applied for this purpose, and many technological breakthroughs are changing the way cancer is studied and analyzed. In this Special Issue, we collected a set of research articles and reviews that focus on the generation of cancer animal models that are used for understanding the disease and contribute to designing and testing new drugs for cancer prevention or treatment. Vladimir Korinek Collection Edito

92nd Annual Meeting of the Virginia Academy of Science: Proceedings

Full proceedings of the 92nd Annual Meeting of the Virginia Academy of Science, May 13-15, 2014, Virginia Commonwealth University, Richmond, Virgini

Altered expression of different GalNAc‑transferases is associated with disease progression and poor prognosis in women with high-grade serous ovarian cancer

Deposited from Open Access Butto

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

Molecular epidemiology of lung cancer in the Liverpool lung project (LLP) cohort

The primary aim of the project was to evaluate the epidemiological and genetic susceptibility factors associated with lung cancer, in the Liverpool Lung Project (LLP) population. The associated datasets available for research with the LLP dataset (questionnaire) were: Office of National Statistics (ONS), Health Episode Statistics (HES) data with comorbidity data, single nucleotide polymorphism (SNP) data of 570 cases from Liverpool, 3000 controls from the 1958 Birth Cohort. The epidemiological (HES) data was used to study the effect of Charlson (CCI) and Elixhauser comorbidity index (ECI) on the incidence of lung cancer using the Cox proportional hazard regression and use the same HES data to design a 5-year sex specific incidence model for lung cancer with crucial covariates. The ECI and CCI were significant in both univariate and multivariate analyses adjusted for age at the start of the study, sex and smoking pack years. The developed models had a good discriminatory power (AUCmale = 0.73; AUCfemale = 0.77) when internally validated using a 10-fold cross validation. The genetic data for the LLP lung cancer cases was used in several contexts: i) to identify SNPS associated with lung cancer under a range of allelic models (additive, dominant, recessive and genotypic), using the Wellcome trust 1958 Birth Cohort as a control dataset; ii) to identify SNPs associated with cause specific and overall survival in lung cancer patients, utilising the Cox proportional hazard model with adjustment for various covariates; and iii) to identify gene pathways that are associated with lung cancer survival using the random forest survival method. SNPs within the genes PRDM11, ZNF382 and HMGA2 were identified in the genome wide case-control study when using the additive, dominant or genotypic models, whereas the recessive model identified the gene ITIH2. Significant SNPs (p≤10-6) associated with cause-specific survival in early stage cases were rs10230420 (WIPF3), rs3746619 and rs3827103 (both in MC3R). In advanced stage cases, significant SNPs were rs1868110 (NEK10) and rs2206779 (AF357533). For the overall survival analysis, significant SNPs were rs10230420 (WIPF3), rs2056533 (ZBTB20) and rs6708630 (CYS1) in early stage cases, whereas rs1868110 (NEK10) and rs2206779 (AF357533) were significantly associated with overall survival in advanced stage NSCLC cases. The pathway analysis using the random survival forest method was undertaken on 18 pathways for both cause-specific and overall survival of lung cancer cases. The results were consistent with apoptosis, base excision repair and mismatch repair being pathways influencing survival