10,049 research outputs found

    INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

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    Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

    RAB25 expression is epigenetically downregulated in oral and oropharyngeal squamous cell carcinoma with lymph node metastasis

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    Oral and oropharyngeal squamous cell carcinoma (OOSCC) have a low survival rate, mainly due to metastasis to the regional lymph nodes. For optimal treatment of these metastases, a neck dissection is required; however, inaccurate detection methods results in under- and over-treatment. New DNA prognostic methylation biomarkers might improve lymph node metastases detection. To identify epigenetically regulated genes associated with lymph node metastases, genome-wide methylation analysis was performed on 6 OOSCC with (pN+) and 6 OOSCC without (pN0) lymph node metastases and combined with a gene expression signature predictive for pN+ status in OOSCC. Selected genes were validated using an independent OOSCC cohort by immunohistochemistry and pyrosequencing, and on data retrieved from The Cancer Genome Atlas. A two-step statistical selection of differentially methylated sequences revealed 14 genes with increased methylation status and mRNA downregulation in pN+ OOSCC. RAB25, a known tumor suppressor gene, was the highest-ranking gene in the discovery set. In the validation sets, both RAB25 mRNA (P = 0.015) and protein levels (P = 0.012) were lower in pN+ OOSCC. RAB25 mRNA levels were negatively correlated with RAB25 methylation levels (P < 0.001) but RAB25 protein expression was not. Our data revealed that promoter methylation is a mechanism resulting in downregulation of RAB25 expression in pN+ OOSCC and decreased expression is associated with lymph node metastasis. Detection of RAB25 methylation might contribute to lymph node metastasis diagnosis and serve as a potential new therapeutic target in OOSCC

    Virtual CGH: an integrative approach to predict genetic abnormalities from gene expression microarray data applied in lymphoma

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    Background: Comparative Genomic Hybridization (CGH) is a molecular approach for detecting DNA Copy Number Alterations (CNAs) in tumor, which are among the key causes of tumorigenesis. However in the post-genomic era, most studies in cancer biology have been focusing on Gene Expression Profiling (GEP) but not CGH, and as a result, an enormous amount of GEP data had been accumulated in public databases for a wide variety of tumor types. We exploited this resource of GEP data to define possible recurrent CNAs in tumor. In addition, the CNAs identified by GEP would be more functionally relevant CNAs in the disease pathogenesis since the functional effects of CNAs can be reflected by altered gene expression. Methods: We proposed a novel computational approach, coined virtual CGH (vCGH), which employs hidden Markov models (HMMs) to predict DNA CNAs from their corresponding GEP data. vCGH was first trained on the paired GEP and CGH data generated from a sufficient number of tumor samples, and then applied to the GEP data of a new tumor sample to predict its CNAs. Results: Using cross-validation on 190 Diffuse Large B-Cell Lymphomas (DLBCL), vCGH achieved 80% sensitivity, 90% specificity and 90% accuracy for CNA prediction. The majority of the recurrent regions defined by vCGH are concordant with the experimental CGH, including gains of 1q, 2p16-p14, 3q27-q29, 6p25-p21, 7, 11q, 12 and 18q21, and losses of 6q, 8p23-p21, 9p24-p21 and 17p13 in DLBCL. In addition, vCGH predicted some recurrent functional abnormalities which were not observed in CGH, including gains of 1p, 2q and 6q and losses of 1q, 6p and 8q. Among those novel loci, 1q, 6q and 8q were significantly associated with the clinical outcomes in the DLBCL patients (p \u3c 0.05). Conclusions: We developed a novel computational approach, vCGH, to predict genome-wide genetic abnormalities from GEP data in lymphomas. vCGH can be generally applied to other types of tumors and may significantly enhance the detection of functionally important genetic abnormalities in cancer research

    Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review

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    A variety of genome-wide profiling techniques are available to probe complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher-level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we provide a comparison among various modeling procedures for integrating genome-wide profiling data of gene copy number and transcriptional alterations and highlight common approaches to genomic data integration. A transparent benchmarking procedure is introduced to quantitatively compare the cancer gene prioritization performance of the alternative methods. The benchmarking algorithms and data sets are available at http://intcomp.r-forge.r-project.orgComment: PDF file including supplementary material. 9 pages. Preprin
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