Gene Expression Profiling in Cancer

The contribution of modern-day genetics in designing efficient gene expression profiles for cancer is immense. The progress of technology and science in recent years provides the opportunity for discovery and application of new techniques for treating various diseases that affect humanity. Methods for finding and analyzing the profile of gene expression of infected cells give scientists the ability to develop more targeted and effective treatments, especially for diseases such as cancer. The development of gene expression profiling is one of the most important achievements in cancer genetics in our time. It is essentially the driving force behind personalized and precision medicine. This book highlights recent developments, applications, and breakthroughs in the field of gene expression profiling in cancer

Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria

The growth and survival of an organism in a particular environment is highly depends on the certain indispensable genes, termed as essential genes. Sulfate-reducing bacteria (SRB) are obligate anaerobes which thrives on sulfate reduction for its energy requirements. The present study used Oleidesulfovibrio alaskensis G20 (OA G20) as a model SRB to categorize the essential genes based on their key metabolic pathways. Herein, we reported a feedback loop framework for gene of interest discovery, from bio-problem to gene set of interest, leveraging expert annotation with computational prediction. Defined bio-problem was applied to retrieve the genes of SRB from literature databases (PubMed, and PubMed Central) and annotated them to the genome of OA G20. Retrieved gene list was further used to enrich protein–protein interaction and was corroborated to the pangenome analysis, to categorize the enriched gene sets and the respective pathways under essential and non-essential. Interestingly, the sat gene (dde_2265) from the sulfur metabolism was the bridging gene between all the enriched pathways. Gene clusters involved in essential pathways were linked with the genes from seleno-compound metabolism, amino acid metabolism, secondary metabolite synthesis, and cofactor biosynthesis. Furthermore, pangenome analysis demonstrated the gene distribution, where 69.83% of the 116 enriched genes were mapped under “persistent,” inferring the essentiality of these genes. Likewise, 21.55% of the enriched genes, which involves specially the formate dehydrogenases and metallic hydrogenases, appeared under “shell.” Our methodology suggested that semi-automated text mining and network analysis may play a crucial role in deciphering the previously unexplored genes and key mechanisms which can help to generate a baseline prior to perform any experimental studies

Role of mitochondria in early molecular diagnosis and prognosis of cancer

Background:Earlier clinical detection of cancer may improve survival as well as offer opportunities for less invasive treatment options. This thesis explores whether the mitochondria and its related genes in the nuclear genome can be used as novel methods for the diagnosis and prognosis of cancers.Aims and Methods:Paper I: To investigate if mitochondrial dysfunction (characterized by mtDNA copy number variations) is associated with prevalent, incident cancer and cancer mortality – droplet digital PCR (ddPCR).Paper II: To investigate the potential causal relationship between mitochondrial dysfunction (characterized by genetic predispositions in all mitochondrial-related genes) and common cancer risks – Mendelian randomization, colocalization.Paper III: To investigate mitochondrial mutations as potential biomarkers for the early diagnosis of breast cancer – whole mitochondrial genome sequencing, bioinformatics, ddPCR.Paper IV: To investigate the mitochondrial-related gene expression signature as a prognostic model to predict the clinical outcome for breast cancer patients – machine learning.Results and conclusions:Paper I: We found that mtDNA-CN was significantly associated with prevalent and incident cancer as well as cancer mortality. However, these associations were cancer-type specific and need further investigation.Paper II: We identified potential causal relationships between mitochondrial-related genes and breast, prostate and lung cancer. Furthermore, this study identified candidate genes that can be the targets of potential pharmacological agents for cancer prevention.Paper III: We comprehensively characterized the mtDNA mutation landscape of breast cancer biopsies and matched baseline whole blood samples. Notably, we have identified and validated mt.16093T>C mutation, which was associated with a 67% increased risk of developing breast cancer, and could potentially be used as early breast cancer diagnostic biomarkers.Paper IV: We built a novel 14 genes mitochondrial signature model that could be an independent prognostic predictor and together with clinical variables as an improved model for predicting overall earlystage of breast cancer survival

Deconvolute brain tumor genomic alterations based on DNA methylation

Molecular classification based on mutations, expression subtypes, and copy number variants has improved diagnosis and treatment decision-making for patients with brain tumors, particularly malignant gliomas. However, the association between epigenetic signature and genetic alterations is poorly understood. For example, mutation of isocitrate dehydrogenase (IDH) is associated with genome-wide hypermethylation of CpG islands in gliomas. But other subtype-associated alterations, including telomerase reverse transcriptase (TERT) promoter mutation, alpha thalassemia/mental retardation syndrome X-linked (ATRX) mutation, chromosome 1p19q co-deletion (chr1p19q codel), and gene expression subtypes, have yet to be associated with any epigenetic signature. Therefore, we hypothesized that DNA methylation signatures can classify gliomas based on these alterations and give insight into subgroup characteristics. Machine learning models, including elastic net and random forest, were used to predict somatic mutations of IDH, TERTp, and ATRX, chr1p19q codel, and gene expression subtype of gliomas. Data from the NOA-04 randomized phase III trial were used for external validation. In total, 926 cases from The Cancer Genome Atlas were included in this study. Prediction accuracies for IDH, TERTp, and ATRX mutations, and chr1p19q codel were 100%, 98.3%, 90.48%, and 99.21%, respectively in test set. Accuracy for gene expression subtype prediction was 72.2%. The methylation-based prediction models for both ATRX and chr1p19q codel statuses proved superior to conventional assays for these biomarkers. Similarly, characteristic alterations associated with gene expression subtypes were better discriminated using methylation compared to transcriptome-based classification. DNA methylation signatures accurately predicted somatic alterations and improved over existing classifiers. The established Unified Diagnostic Pipeline (UniD) is a rapid and cost-effective diagnostic platform of genomic alterations and gene expression subtypes at initial clinical diagnosis and improves over individual assays currently in clinical use. The significant relationship between genetic alterations and epigenetic signatures indicates the broad applicability of our approach to other malignancies

Evaluation of blood-based microRNAs toward clinical use as biomarkers in common and rare diseases

According to the GLOBOCAN project of the International Agency for Research on Cancer, the top three common cancer diseases worldwide in the year 2020 were breast, lung and colorectal cancer. These are usually diagnosed via imaging methods (e.g. computer tomography) or invasive methods (e.g. biopsy). However, these techniques are potentially risky and expensive and thus not accessible to all patients, resulting in most cancers being detected in an advanced stage. Since the discovery of small non-coding RNAs and specifically microRNAs and their role as gene regulators, many researchers investigate their association with disease development. In particular, researchers examine body fluid based microRNAs which could present potential cost-effective and minimally- or non-invasive alternatives to the previously described established diagnosis methods. This dissertation focuses on microRNAs and investigates their suitability as minimally-invasive blood-borne biomarkers for potential diagnostic purposes. More specifically, the goals of this work are (1) to implement a new method to predict novel microRNAs, (2) to understand stability and characteristics of these small non-coding RNAs, possibly relevant for the last goal, (3) to discover potential diagnostic biomarkers in common and rare diseases. The first goal was addressed by developing miRMaster, a web service to predict new microRNAs. The tool uses machine learning and high-throughput sequencing data to find microRNA candidates that follow the known biogenesis pathways. The second goal was pursued in four publications. First, we performed a large scale evaluation of miRMaster by generating a high-resolution map of the human small non-coding RNA transcriptome for which we analyzed and validated potential microRNA candidates. Next, we examined the influence of seasonal effects on microRNA expression profiles and observed the largest difference between spring and the other seasons. Additionally, we evaluated the evolutionary conservation of small non-coding RNAs in zoo animals and showed that the distribution of sncRNA classes varies across species, while common microRNA families are present in more diverse organisms than assumed so far. Furthermore, we analyzed if microRNAs are technically stable, and whether biological variation is preserved when using capillary dried blood spots as an alternative sample collection device to venous blood specimens. Finally, we investigated the suitability of microRNAs as biomarkers for two diseases: lung cancer and Marfan disease. We identified blood-borne biomarker candidates for lung cancer detection in a large-scale multi-center study via machine learning. For the rare Marfan disease we analyzed the paired messenger RNA and microRNA expression levels in whole-blood samples. This highlighted several significantly deregulated microRNAs and messenger RNAs, which we subsequently validated in an independent cohort. In summary, this thesis provides valuable results toward potential clinical use of microRNAs, and the herein described projects represent comprehensive analyses of them from different perspectives: starting with microRNA discovery, addressing various technical and biological questions and ending with the potential use as biomarkers.Nach Angaben des GLOBOCAN-Projekts der International Agency for Research on Cancer sind die drei häufigsten Krebserkrankungen weltweit im Jahr 2020 Brust-, Lungen- und Darmkrebs. Diese werden in der Regel durch bildgebende Verfahren (z.B. Computertomographie) oder invasive Methoden (z.B. Biopsie) diagnostiziert. Diese Verfahren sind jedoch potenziell risikoreich und teuer und daher nicht für alle Patienten zugänglich. Dies führt dazu, dass die meisten Krebsarten erst in einem fortgeschrittenen Stadium entdeckt werden. Seit der Entdeckung der kurzen nichtkodierenden RNAs und insbesondere der microRNAs und ihrer Rolle als Genregulatoren untersuchen viele Forscher ihren Zusammenhang mit der Krankheitsentwicklung. Insbesondere untersuchen die Forscher die in Körperflüssigkeiten vorkommenden microRNAs, die potenziell kosteneffiziente und minimal- oder nicht-invasive Alternativen zu den bisher beschriebenen etablierten Diagnosemethoden darstellen könnten. Diese Dissertation konzentriert sich auf microRNAs und untersucht deren Eignung als minimal-invasive blutbasierte Biomarker für potenzielle diagnostische Zwecke. Genauer gesagt sind die Ziele dieser Arbeit (1) die Implementierung einer neuen Methode zur Vorhersage neuartiger microRNAs, (2) das Verständnis über die Stabilität und Charakteristika dieser kurzen nicht-kodierenden RNAs, die möglicherweise für das nächste Ziel relevant sind, (3) die Entdeckung potenzieller diagnostischer Biomarker für verschiedene Anwendungen. Das erste Ziel wurde durch die Entwicklung von miRMaster verfolgt, einem Webdienst zur Vorhersage neuer microRNAs. Das Tool nutzt maschinelles Lernen und Hochdurchsatz-Sequenzierungsdaten, um microRNA-Kandidaten zu finden, die den bekannten Wege der Biogenese folgen. Das zweite Ziel wurde in vier Veröffentlichungen verfolgt. Zunächst führten wir eine groß angelegte Evaluierung von miRMaster durch, indem wir eine High-Resolution Map des menschlichen Transkriptoms kurzer nichtkodierender RNAs erstellten, für die wir potenzielle microRNA-Kandidaten analysierten und validierten. Anschließend untersuchten wir den Einfluss saisonaler Effekte auf die microRNA-Expressionsprofile und beobachteten den größten Unterschied zwischen dem Frühling und den anderen Jahreszeiten. Darüber hinaus untersuchten wir die evolutionäre Erhaltung kurzer nichtkodierender RNAs in Zoo-Tieren und zeigten, dass die Verteilung der kurzer nichtkodierenden RNA-Klassen zwischen den Arten variiert, während gemeinsame microRNA-Familien in verschiedeneren Organismen vorkommen als bisher angenommen. Darüber hinaus analysierten wir, ob microRNAs technisch stabil sind und ob die biologische Variation erhalten bleibt, wenn kapillares Trockenblut als alternatives Probenentnahmeverfahren zu venösen Blutproben verwendet werden. Schließlich untersuchten wir die Eignung von microRNAs als Biomarker für zwei Krankheiten: Lungenkrebs und Marfan-Krankheit. In einer groß angelegten multizentrischen Studie identifizierten wir mit Hilfe von maschinellem Lernen Biomarker-Kandidaten aus dem Blut für die Erkennung von Lungenkrebs. Für die seltene Marfan-Krankheit analysierten wir die gepaarten Expressionsniveaus von messengerRNA und microRNA in Vollblutproben. Dabei wurden mehrere signifikant deregulierte microRNAs und messengerRNAs festgestellt, die wir anschließend in einer unabhängigen Kohorte validierten. Zusammenfassend lässt sich sagen, dass diese Arbeit wertvolle Ergebnisse im Hinblick auf die potenzielle klinische Verwendung von microRNAs liefert. Die hier beschriebenen Projekte stellen umfassende Analysen aus verschiedenen Blickwinkeln dar: angefangen bei der Entdeckung von microRNAs, über verschiedene technische und biologische Fragen bis hin zur potenziellen Verwendung als Biomarker

Development of Extracellular Vesicle Isolation and Model Systems Toward Early Ovarian Cancer Diagnostics

Ovarian cancer (OC) is characterized by late stage discovery and low survivability. However, when diagnosed early (Stages I or II) the 5-year survival rate is 92% up from 29%.5 The extreme dichotomy in survivability is what makes OC a prime candidate for early diagnosis techniques. Exosomes, a subtype of extracellular vesicles, may bridge the gap between early and late diagnosis, but are lacking consistent isolation and detection technologies. Here poly(ethylene terephthalate) (PET) capillary channeled polymer (C-CP) fibers employing an HIC protocol are investigated as a novel exosome isolation method and a quick, inexpensive, and easy-to-use platform for OC diagnosis. The cell model system, immunoaffinity protocols, and biomarker identification tools developed here will aid in the refinement of a selective PET C-CP exosome isolation. The exosome isolation and diagnostic technique developed as a result of these investigations will allow for earlier and routine diagnosis of OC and save many women from one of the deadliest cancers

Transcriptome regulation network in Multiple sclerosis: Role of circular RNAs.

274 p.Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system, that leads to neurological disability. The disease course and clinical phenotype are highly variable and therefore, biomarkers that can aid in the clinical practice are needed. Previous studies have shown a dysregulation in the coding and non-coding RNAs and proposed some as biomarkers. However, still none of them have reached the clinical practice. Recently, circular RNAs (circRNAs) have emerged as new players in the transcriptome that hold a great potential as biomarkers thanks to the features endowed by their circularity. In this thesis, we have performed different approaches to characterise the circRNA expression in leukocytes, peripheral blood mononuclear cells and extracellular vesicles from MS patients. Results have revealed hundreds of circRNAs whose expression is changed in the disease, and we have proposed eleven of them as potential minimally-invasive biomarkers. Finally, our functional experiments indicate that circRNAs may act as regulators of the immune response. This thesis opens a new research line in MS for future investigations to further evaluate the biomarker potential and the function of circRNAs in MS

Developing Bottom-Up, Integrated Omics Methodologies for Big Data Biomarker Discovery

Indiana University-Purdue University Indianapolis (IUPUI)The availability of highly-distributed computing compliments the proliferation of next generation sequencing (NGS) and genome-wide association studies (GWAS) datasets. These data sets are often complex, poorly annotated or require complex domain knowledge to sensibly manage. These novel datasets provide a rare, multi-dimensional omics (proteomics, transcriptomics, and genomics) view of a single sample or patient. Previously, biologists assumed a strict adherence to the central dogma: replication, transcription and translation. Recent studies in genomics and proteomics emphasize that this is not the case. We must employ big-data methodologies to not only understand the biogenesis of these molecules, but also their disruption in disease states. The Cancer Genome Atlas (TCGA) provides high-dimensional patient data and illustrates the trends that occur in expression profiles and their alteration in many complex disease states. I will ultimately create a bottom-up multi-omics approach to observe biological systems using big data techniques. I hypothesize that big data and systems biology approaches can be applied to public datasets to identify important subsets of genes in cancer phenotypes. By exploring these signatures, we can better understand the role of amplification and transcript alterations in cancer

Knowledge Management Approaches for predicting Biomarker and Assessing its Impact on Clinical Trials

The recent success of companion diagnostics along with the increasing regulatory pressure for better identification of the target population has created an unprecedented incentive for the drug discovery companies to invest into novel strategies for stratified biomarker discovery. Catching with this trend, trials with stratified biomarker in drug development have quadrupled in the last decade but represent a small part of all Interventional trials reflecting multiple co-developmental challenges of therapeutic compounds and companion diagnostics. To overcome the challenge, varied knowledge management and system biology approaches are adopted in the clinics to analyze/interpret an ever increasing collection of OMICS data. By semi-automatic screening of more than 150,000 trials, we filtered trials with stratified biomarker to analyse their therapeutic focus, major drivers and elucidated the impact of stratified biomarker programs on trial duration and completion. The analysis clearly shows that cancer is the major focus for trials with stratified biomarker. But targeted therapies in cancer require more accurate stratification of patient population. This can be augmented by a fresh approach of selecting a new class of biomolecules i.e. miRNA as candidate stratification biomarker. miRNA plays an important role in tumorgenesis in regulating expression of oncogenes and tumor suppressors; thus affecting cell proliferation, differentiation, apoptosis, invasion, angiogenesis. miRNAs are potential biomarkers in different cancer. However, the relationship between response of cancer patients towards targeted therapy and resulting modifications of the miRNA transcriptome in pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have created an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. We present a novel SMARTmiR algorithm to predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer. The application of an optimised and fully automated version of the algorithm has the potential to be used as clinical decision support tool. Moreover this research will also provide a comprehensive and valuable knowledge map demonstrating functional bimolecular interactions in colorectal cancer to scientific community. This research also detected seven miRNA i.e. hsa-miR-145, has-miR-27a, has- miR-155, hsa-miR-182, hsa-miR-15a, hsa-miR-96 and hsa-miR-106a as top stratified biomarker candidate for cetuximab therapy in CRC which were not reported previously. Finally a prospective plan on future scenario of biomarker research in cancer drug development has been drawn focusing to reduce the risk of most expensive phase III drug failures

Machine Learning Methods for Effectively Discovering Complex Relationships in Graph Data

Graphs are extensively employed in many systems due to their capability to capture the interactions (edges) among data (nodes) in many real-life scenarios. Social networks, biological networks and molecular graphs are some of the domains where data have inherent graph structural information. Built graphs can be used to make predictions in Machine Learning (ML) such as node classifications, link predictions, graph classifications, etc. But, existing ML algorithms hold a core assumption that data instances are independent of each other and hence prevent incorporating graph information into ML. This irregular and variable sized nature of non-Euclidean data makes learning underlying patterns of the graph more sophisticated. One approach is to convert the graph information into a lower dimensional space and use traditional learning methods on the reduced space. Meanwhile, Deep Learning has better performance than ML due to convolutional layers and recurrent layers which consider simple correlations in spatial and temporal data, respectively. This proves the importance of taking data interrelationships into account and Graph Convolutional Networks (GCNs) are inspired by this fact to exploit the structure of graphs to make better inference in both node-centric and graph-centric applications. In this dissertation, the graph based ML prediction is addressed in terms of both node classification and link prediction tasks. At first, GCN is thoroughly studied and compared with other graph embedding methods specific to biological networks. Next, we present several new GCN algorithms to improve the prediction performance related to biomedical networks and medical imaging tasks. A circularRNA (circRNA) and disease association network is modeled for both node classification and link prediction tasks to predict diseases relevant to circRNAs to demonstrate the effectiveness of graph convolutional learning. A GCN based chest X-ray image classification outperforms state-of-the-art transfer learning methods. Next, the graph representation is used to analyze the feature dependencies of data and select an optimal feature subset which respects the original data structure. Finally, the usability of this algorithm is discussed in identifying disease specific genes by exploiting gene-gene interactions