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

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

Analysis of whole-genome sequencing data from ICGC-PanCancer project

Cancer is one of the greatest health challenges of the 21st century and one of the deadliest diseases in the world. It is a group of different diseases which are caused by abnormal cell growth. In the human body, cell division and apoptosis are well regulated under normal circumstances so that the number of cells is in a dynamic balance. However, normal cells could transform into tumor cells because of genetic mutations. The tumorigenesis can happen in almost any cell of the human body. One of the central tools to address cancer is the profiling of cancer cell genomes and transcriptomes by next generation sequencing (NGS) and subsequent analysis by computational methods. The Pan-Cancer Analysis of Whole Genomes (PCAWG) project is the core project of the International Cancer Genome Consortium. This project provides massive amounts of cancer biological data for analysis. Include more than 2900 patients and 48 types of cancer samples. As part of this intensive effort, I have conducted a very detailed analysis on the molecular mechanisms of cancers. In particular, I conducted a comprehensive study of the relationship between genomic mutations and cancer development. These series of studies include the exploration of cancer driver genes, analysis of telomere maintenance mechanisms and data visualization at the cohort level. First, I explored potential cancer genes by performing statistical analysis of genomic point mutations, insertions and deletions, copy number variations and structural variations. Further, I analyzed the distribution of point mutations and structure variations in cancer genomes. Based on Knudson's two-hit hypothesis, I integrated point mutation and copy number variation information to construct a biallelic inactivation map of the cancer genome. With the biallelic inactivation information, I analyzed potential cancer drivers and applied this finding to synthetic lethality assays associated with cancer driver genes to uncover novel genetic targets that could be used to treat cancer patients with certain driver gene defects. In addition, I designed and improved the CaSINo model to score the relative mutation frequency of chromosomal sequences to screen for potential cancer driver mutations, which can be used not only in coding genes but also in non-coding regions. Moreover, I analyzed point mutations on promoters, trying to find those mutation sites that play a key role in the up-regulation of gene expression. Finally, I designed and improved a scoring method for copy number variation focality to explore the association of focal copy number variation with cancer driver genes at the cohort level. Second, as part of the PCAWG research projects, I analyzed the mechanisms of telomere maintenance in cancer cells. After analyzing the differences between alternative telomere lengthening and telomerase-positive samples, I designed a machine learning model based on repeat sequences, content, and mutation rate to determine whether an unknown cancer sample is an alternative lengthening of telomere (ALT) or telomerase-positive. Finally, for the massive data of the PCAWG project, I designed and implemented two bioinformatics visualization tools. TumorPrint is software in R and shell, which can be used to visualize genomic mutations and RNA-seq expression levels of a single gene or gene pairs, allowing users to quickly search for genes or gene pairs of interest. GenomeTornadoPlot is a software written in the R language for visualizing focal copy number variants of a single gene or adjacent paired genes, and can automatically calculate its copy number variation aggregation score

A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.

Although cancer genomes are replete with noncoding mutations, the effects of these mutations remain poorly characterized. Here we perform an integrative analysis of 930 tumor whole genomes and matched transcriptomes, identifying a network of 193 noncoding loci in which mutations disrupt target gene expression. These 'somatic eQTLs' (expression quantitative trait loci) are frequently mutated in specific cancer tissues, and the majority can be validated in an independent cohort of 3,382 tumors. Among these, we find that the effects of noncoding mutations on DAAM1, MTG2 and HYI transcription are recapitulated in multiple cancer cell lines and that increasing DAAM1 expression leads to invasive cell migration. Collectively, the noncoding loci converge on a set of core pathways, permitting a classification of tumors into pathway-based subtypes. The somatic eQTL network is disrupted in 88% of tumors, suggesting widespread impact of noncoding mutations in cancer

A filter-based feature selection approach for identifying potential biomarkers for lung cancer

Background: Lung cancer is the leading cause of death from cancer in the world and its treatment is dependant on the type and stage of cancer detected in the patient. Molecular biomarkers that can characterize the cancer phenotype are thus a key tool in planning a therapeutic response. A common protocol for identifying such biomarkers is to employ genomic microarray analysis to find genes that show differential expression according to disease state or type. Data-mining techniques such as feature selection are often used to isolate, from among a large manifold of genes with differential expression, those specific genes whose differential expression patterns are of optimal value in phenotypic differentiation. One such technique, Biomarker Identifier (BMI), has been developed to identify features with the ability to distinguish between two data groups of interest, which is thus highly applicable for such studies. Results: Microarray data with validated genes was used to evaluate the utility of BMI in identifying markers for lung cancer. This data set contains a set of 129 gene expression profiles from large-airway epithelial cells (60 samples from smokers with lung cancer and 69 from smokers without lung cancer) and 7 genes from this data have been confirmed to be differentially expressed by quantitative PCR. Using this data set, BMI was compared with various well-known feature selection methods and was found to be more successful than other methods in finding useful genes to classify cancerous samples. Also it is evident that genes selected by BMI (given the same number of genes and classification algorithms) showed better discriminative power than those from the original study. After pathway analysis on the selected genes by BMI, we have been able to correlate the selected genes with well-known cancer-related pathways. Conclusions: Our results show that BMI can be used to analyze microarray data and to find useful genes for classifying samples. Pathway analysis suggests that BMI is successful in identifying biomarker-quality cancer-related genes from the data

Integrated genomic and transcriptomic analyses of radiation-induced malignancies

Cancer is a genetic disease caused by an unregulated expansion of a clone of cells (Sompayrac, 2004). The genetic abnormalities in cancer are the consequences of defective DNA replication, repair, maintenance, and modification, genetic background, and exposure to mutagens (Alexandrov et al., 2013). Ionizing radiation (IR), a mutagen exposed to cancer patients during clinical radiotherapy (RT), can cause DNA damage, genomic instability, and mutagenesis (Sherborne et al., 2015). While RT has been effective in treating cancer, it increases the risk of second malignant neoplasm (SMN), a severe delayed complication associated with mainly pediatric cancer survivors many decades after the treatment of their first cancer (Robison & Hudson, 2014). As the mortality of patients with childhood cancer has been decreasing, cases of radiation-induced cancers has been increasing (Robison & Hudson, 2014). The considerable contribution by RT to SMN risk illustrate the need to characterize the genetic mechanism directly responsible for radiation-induced malignancies. To better our understanding of the mutational landscape of SMNs, our specific aims are to identify potential driver mutations implicated in radiation-induced malignancies through genome and transcriptome analysis and to assess whether genetic background, specifically germline polymorphisms and mutations in tumor suppressor gene TP53, has an impact on the formation of secondary malignancies

Characterization of novel epigenetic targets in ovarian cancer

Le cancer épithélial de l’ovaire (CEO) représente 4% de tous les cancers chez la femme et est la première cause de décès parmi les tumeurs gynécologiques. Dans la plupart part des cas le CEO est diagnostiqué dans les stades avancés de la maladie. Le traitement repose sur la chirurgie cytoréductive suivie de la chimiothérapie combinant les dérivés de platine et de taxanes avec un taux de réponse de plus de 80%, cependant, la plupart part des patientes font une récidive par l’émergence de la résistance. Les bases moléculaires du déclenchement et de la progression du cancer de l’ovaire sont encore mal connues empêchant ainsi le développement de nouvelles approches thérapeutiques et de diagnostique. Au cours d’un cancer, l’hyperméthylation des ilots CpG de certains promoteurs géniques conduit souvent à l’inactivation des gènes suppresseurs de tumeur. L’hypométhylation des ilots CpG de certains promoteurs est également impliquée dans la réactivation des proto-oncogènes et des gènes pro-métastatiques. Cependant l’hypométhlation de l’ADN dans le cancer de l’ovaire est très peu étudiée. En utilisant la méthode d’immunoprécipitation de l'ADN méthylée combinée à une analyse sur puce (MeDIP-chip), nous avons trouvé que l’hyperméthylation de l'ADN se produit dans les stades précoces du cancer ovarien, tandis que les stades avancés de la maladie sont liés à l’hypométhylation de l’ADN des oncogènes impliqués dans la progression de la tumeur, l’invasion/métastase et probablement dans la chimiorésistance. Cette approche épigénomique a conduit à l’identification de nouveaux oncogènes hypométhylés dans le CEO. Dans cette étude, RUNX2 est identifié comme un gène hypométhylé dans les cellules post-chimiothérapeutiques et GALNT3 et BCAT1 sont parmi les gènes hypométhylés identifiés particulièrement dans le CEO de type séreux. L’analyse fonctionnelle de ces trois gènes montre qu’ils sont associés à la prolifération (y compris le contrôle du cycle cellulaire pour GALNT3 et BCAT1), de la migration et de l'invasion cellulaire dans le CEO séreux, suggérant qu’ils ont un fort potentiel oncogène dans la progression de la tumeur et pourraient être des nouvelles cibles thérapeutiques au niveau du CEO.Epithelial ovarian cancer (EOC) accounts for 4% of all cancers in women and is the leading cause of death from gynecologic malignancies. Most of EOC cases are diagnosed at advanced stage, which is associated with poor outcome. Despite the good initial response to chemotherapy, recurrence occurs in the majority of patients, resulting in chemotherapy resistance leading to a fatal disease. The molecular basis of EOC initiation and progression is still poorly understood, thus hindering the development of new diagnostic and therapeutic strategies for more effective EOC treatment. In cancer, the hypermethylation of gene promoter CpG islands leads to inactivation of tumor suppressor genes, and CpG islands hypomethylation is associated with proto-oncogenes and pro-metastasis genes. Similar to all malignancies, aberrant DNA methylation occurs in EOC. However, DNA hypomethylation in ovarian cancer is very briefly studied. Using methylated DNA immunoprecipitation (MeDIP) coupled to CpG island tiling arrays, we found that DNA hypermethylation occurred in less invasive/early stages of ovarian tumorigenesis, while advanced disease was associated with DNA hypomethylation of a number of oncogenes, implicated in cancer progression, invasion/metastasis and probably chemoresistance. This epigenomic approach has led to the identification of a number of novel oncogenes hypomethylated in EOC. In this thesis study, RUNX2 gene was identified as hypomethyleted gene in post-chemotherapy primary cells cultures and GALNT3 gene and BCAT1 gene were among the genes identified to be notably hypomethylated in serous EOC tumors. Subsequent functional analyses of these three genes demonstrated that they were associated with EOC cell proliferation (including cell cycle control for GALNT3 and BCAT1), migration and invasion, suggesting that they have strong oncogenic potential in serous EOC progression and that they might be novel EOC therapeutic targets

Unlocking the power of cross-species genomic analyses: identification of evolutionarily conserved breast cancer networks and validation of preclinical models

The application of high-throughput genomic technologies has revealed that individual breast tumors display a variety of molecular features that require more personalized approaches to treatment. Several recent studies have demonstrated that a cross-species analytic approach provides a powerful means to filter through genetic complexity by identifying evolutionarily conserved genetic networks that are fundamental to the oncogenic process. Mouse-human tumor comparisons will provide insights into cellular origins of tumor subtypes, define interactive oncogenetic networks, identify potential novel therapeutic targets, and further validate as well as guide the selection of genetically engineered mouse models for preclinical testing

Identification and Characterization of Potential Novel Targets in Thyroid Carcinoma: Evidence of Non-Oncogene Addiction Unveiling Tumor Cell Vulnerabilities

Thyroid cancer is the most frequent endocrine malignancy, with an incidence constantly increasing. Despite well-differentiated thyroid tumors are generally cured by conventional therapy, a fraction of them relapses and progresses towards undifferentiated forms, characterized by a poor prognosis. Target therapies introduced in clinical testing are often unsuccessful; therefore, novel therapeutic strategies are needed. To identify critical nodal points for therapeutic intervention, our laboratory faced the “non-oncogene addiction” (NOA) paradigm, which asserts that the tumorigenic state relies on the activity of genes that are essential to support the phenotype of cancer cells but not required to the same degree for normal cell viability. By screening a siRNA library on normal and tumor thyroid cell lines, we identified 15 genes whose silencing interfered with the growth of tumor but not normal thyroid cells. The overall aim of this project was to validate NOA genes to identify thyroid tumor cell vulnerabilities and to explore their role in the regulation of thyroid tumor cell biology. Among the 15 hit genes, we selected MASTL, Cyclin D1 and COPZ1 for validation studies. We confirmed the growth inhibitory effect of their silencing in tumor but not normal thyroid cells and observed that these effects were common to a panel of thyroid tumor cell lines, irrespective of the histotype or genetic lesion. Functional studies on MASTL demonstrated that its depletion in thyroid tumor cells inhibited cell growth, led to mitotic catastrophe and induced DNA damage and cell death. COPZ1 depletion induced abortive autophagy, endoplasmic reticulum stress and apoptotic cell death in thyroid tumor cells, as well as tumor growth inhibition in vivo. Together, our studies identified MASTL and COPZ1 as vulnerability genes for thyroid tumor cells and provided the rationale for future studies aimed to explore their targeting for potential therapeutic intervention

Anti-Cancer Effects Of Tocotrienols In Nsclc

Lung cancer is one of the leading causes of death among cancers, with non-small cell lung cancer (NSCLC) accounting for 80-85% of all lung cancers and a five-year survival rate of 5 % at stage IIIB. Delta-tocotrienol (δT) including other tocotrienol isomers has been shown to exhibit anti-tumor activity via inhibition of different signaling pathways in tumors including NSCLC. Previously we reported that δT reduced cell invasion via inhibition of the Notch-1 and NF-κb pathway. Matrix metallopeptidase 9 (MMP9) dependent cell migration and invasion are key processes in cancer metastasis. Hence, its suppression is a promising strategy for cancer therapeutics. The objective of specific aim 01 was to investigate the possibility of MMP9 inhibition as the underlying mechanism behind the anti-metastatic effects of δT on NSCLC cell lines A549 and H1299. Effects of δT on cell migration, invasion, cell adhesion and aggregation capability were investigated. MMP-9 activity was determined using gel zymography. The various proteins, genes, and miR involved in the Notch-1 and uPA signal transduction pathways have been studied for anti-metastatic activity by RT-PCR and western blot. Our findings showed that δT reduced cell migration, invasion, and adhesion in a dose and time-dependent manner. δT significantly inhibited MMP-9 activity in gel zymography. Further, δT inhibited Notch-1 mediated NF-κb and urokinase plasminogen activator (uPA) pathways which lead to the down-regulated expression of MMP-9 and increased miR 451 expressions. Our data suggests that δT attenuates tumor aggressiveness, invasion, and metastasis by down-regulation of the MMP-9 gene via Notch1 and uPA pathways Further, the primary energy source of NSCLC is glutamine, and this cancer exhibits a high rate of glutamine dependency during its growth and development. Glutamine and essential amino acids (EAA) in NSCLC are reported to upregulate mTOR, a bioenergetics sensor which regulates cell growth, cell survival, and protein synthesis. SLC1A5/SLC7A5 transporters that allow glutamine and EAA to enter the proliferating tumors and send a regulatory signal to mTOR are novel concepts in cancer cell growth and development. Therefore, inhibiting glutamine uptake via blocking or downregulating glutamine transporters would be an excellent therapeutic target for NSCLC treatment. The Specific Aim:2 of this study, was to verify the metabolic dysregulation of glutamine and its derivatives in NSCLC using cellular 1H-NMR metabolomics approach while investigating the effect of δT on NSCLC growth and development, glutamine transporters, and mTOR pathway. Endometabolome of NSCLC was analyzed followed by cellular metabolomics analysis using SIMCA+ multivariate analyzing software. The results in metabolomics analysis showed significant inhibition in the uptake of glutamine, its derivatives; glutamate and glutathione, and some EAA in both cell lines with δT treatment providing their potential use as robust surrogate biomarkers for δT intervention in NSCLC. To further validation, NMR spectrums were quantified using Chenomx NMR Suite and metabolites were further analyzed using metaboanalyst 3.0 software. The results in metaboanalyst 3.0 indicated that δT directly impacted on the metabolism of glutamine and its derivatives where further validating results at previous analysis. Therefore, expression of glutamine transporters and mTOR pathway proteins were explored, and dose-dependent inhibition of glutamine transporters (SLC7A5 and SLC1A5) and mTOR pathway proteins (P-mTOR, mTOR, S6K, c-MYC and Bcl-XL) was evident in western blot analysis. Our findings suggest that δT works by inhibiting glutamine uptake into proliferating cells through glutamine transporter inhibition thus resulting in inhibition of cell proliferation and induction of apoptosis via downregulation of the mTOR pathway. As the specific aim, one and two reported that δT shows anti-cancer properties by targeting different anti-cancer mechanisms. However, the δT are presently not available in quantities required for animal or clinical studies. Therefore, the objective of specific aim 03 is to investigate the interactions and effects of commercially available tocotrienols mixture directly isolated from palm oil (a mixture of isomers) in adenocarcinoma (A549) and squamous cell carcinoma lung cancer (H520) cell lines. A dose-dependent decrease in all growth was observed in both cancer cell lines with the addition of tocotrienols by MTS and colonogenic assay. Furthermore, a significant reduction in cell migration and tumor invasiveness was seen in both cell lines. Additionally, a significant induction of apoptosis was observed in Annexin V stain in flow cytometry analysis. Since tocotrienols showed effects against proliferation, apoptosis, migration, and invasiveness, RT-PCR and western blot analysis were used to explore molecular mechanisms behind above regulations by testing the expression of Notch-1 and its downstream stream genes. A dose-dependent decrease in expression was observed in Notch-1, Hes-1, Survivin, MMP-9, VEGF, and Bcl-XL proteins. Also, we found a mechanism linking the NF-kB pathway and Notch-1 down-regulation from NF- Кb colorimetric assay. Thus, our data suggests that commercially available tocotrienols inhibit cell growth, migration, and tumor cell invasiveness via down-regulation of Notch 1 and NF -КB while inducing apoptosis. Hence, these commercially available tocotrienol rich capsule could be an effective therapeutic for lung cancer prevention as same as pure δT. These Anticancer effects and mechanisms of δT warrant further investigation of δT as a potential natural therapeutic approach to prevent NSCLC