On the Comparison of State- and Transition-based Analysis of Biological Relevance in Gene Co-expression Networks

Traditional correlation network analysis typically involves creating a network using gene expression data and then identifying biologically relevant clusters from that network by enrichment with Gene Ontology or pathway information. When one wants to examine these networks in a dynamic way - such as between controls versus treatment or over time - a snapshot approach is taken by comparing network structures at each time point. The biological relevance of these structures are then reported and compared. In this research, we examine the same snapshot networks but focus on the enrichment of changes in structure to determine if these results give any more insight into the mechanisms behind observed phenotypes. Our main hypothesis is that more information, particularly related to potential dynamic changes, can be obtained through transition­-based analysis of biological networks. To test this hypothesis, we compare gene expression data from the mouse hippocampus at three different time points: young, middle-aged, and aged, and compare the traditional state-based approach to the dynamic transition-based enrichment approach. In this study we use a clustering approach (SPICi) designed specifically for clustering of large biological networks. The results of this study verify an inconsistency between traditional and dynamic structure identification approaches through biological enrichment. These results highlight an intriguing issue for those performing, critiquing, and using network based approaches in their research - that a black box or workflow type of approach typically used in network based research can be supplemented with a transition­-based approach to support movement from in silico to in vivo experimentation of target genes

SWIM: A computational tool to unveiling crucial nodes in complex biological networks

SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called "fight-club hubs", characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called "switch genes", appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer

Networks from gene expression time series: characterization of correlation patterns

This paper describes characteristic features of networks reconstructed from gene expression time series data. Several null models are considered in order to discriminate between informations embedded in the network that are related to real data, and features that are due to the method used for network reconstruction (time correlation).Comment: 10 pages, 3 BMP figures, 1 Table. To appear in Int. J. Bif. Chaos, July 2007, Volume 17, Issue

Efficient parameter search for qualitative models of regulatory networks using symbolic model checking

Investigating the relation between the structure and behavior of complex biological networks often involves posing the following two questions: Is a hypothesized structure of a regulatory network consistent with the observed behavior? And can a proposed structure generate a desired behavior? Answering these questions presupposes that we are able to test the compatibility of network structure and behavior. We cast these questions into a parameter search problem for qualitative models of regulatory networks, in particular piecewise-affine differential equation models. We develop a method based on symbolic model checking that avoids enumerating all possible parametrizations, and show that this method performs well on real biological problems, using the IRMA synthetic network and benchmark experimental data sets. We test the consistency between the IRMA network structure and the time-series data, and search for parameter modifications that would improve the robustness of the external control of the system behavior

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

Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution.

Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies

Inference algorithms for gene networks: a statistical mechanics analysis

The inference of gene regulatory networks from high throughput gene expression data is one of the major challenges in systems biology. This paper aims at analysing and comparing two different algorithmic approaches. The first approach uses pairwise correlations between regulated and regulating genes; the second one uses message-passing techniques for inferring activating and inhibiting regulatory interactions. The performance of these two algorithms can be analysed theoretically on well-defined test sets, using tools from the statistical physics of disordered systems like the replica method. We find that the second algorithm outperforms the first one since it takes into account collective effects of multiple regulators