NETWORK ANALYTICS FOR THE MIRNA REGULOME AND MIRNA-DISEASE INTERACTIONS

miRNAs are non-coding RNAs of approx. 22 nucleotides in length that inhibit gene expression at the post-transcriptional level. By virtue of this gene regulation mechanism, miRNAs play a critical role in several biological processes and patho-physiological conditions, including cancers. miRNA behavior is a result of a multi-level complex interaction network involving miRNA-mRNA, TF-miRNA-gene, and miRNA-chemical interactions; hence the precise patterns through which a miRNA regulates a certain disease(s) are still elusive. Herein, I have developed an integrative genomics methods/pipeline to (i) build a miRNA regulomics and data analytics repository, (ii) create/model these interactions into networks and use optimization techniques, motif based analyses, network inference strategies and influence diffusion concepts to predict miRNA regulations and its role in diseases, especially related to cancers. By these methods, we are able to determine the regulatory behavior of miRNAs and potential causal miRNAs in specific diseases and potential biomarkers/targets for drug and medicinal therapeutics

Molecular Portraits of Cancer Evolution and Ecology

Research on the molecular lesions that drive cancers holds the translational promise of unmasking distinct disease subtypes in otherwise pathologically identical patients. Yet clinical adoption is hindered by the reproducibility crisis for cancer biomarkers. In this thesis, a novel metric uncovered transcriptional diversity within individual non-small cell lung cancers, driven by chromosomal instability. Existing prognostic biomarkers were confounded by tumour sampling bias, arising from this diversity, in ~50% of patients assessed. An atlas of consistently expressed genes was derived to address this diagnostic challenge, yielding a clonal biomarker robust to sampling bias. This diagnostic based on cancer evolutionary principles maintained prognostic value in a metaanalysis of >900 patients, and over known risk factors in stage I disease, motivating further development as a clinical assay. Next, in situ RNA profiles of immune, fibroblast and endothelial cell subsets were generated from cancerous and adjacent non-malignant lung tissue. The phenotypic adaptation of stromal cells in the tumour microenvironment undermined the performance of existing molecular signatures for cell-type enumeration. Transcriptome-wide analysis delineated ~10% of genes displaying cell-type-specific expression, paving the way for high-fidelity signatures for the accurate digital dissection of tumour ecology. Lastly, the impact of branching, Darwinian evolution on the detection of epistatic interactions was evaluated in a pan-cancer analysis. The clonal status of driver genes was associated with the proportion of significant epistatic findings in 44-78% of the cancer-types assessed. Integrating the clonal architecture of tumours in future analyses could help decipher evolutionary dependencies. This work provides pragmatic solutions for refining molecular portraits of cancer in the light of their evolutionary and ecological features, moving the needle for precision cancer diagnostics

Artificial intelligence for neurodegenerative experimental models

INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. HIGHLIGHTS: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery

Cancer proteogenomics : connecting genotype to molecular phenotype

The central dogma of molecular biology describes the one-way road from DNA to RNA and finally to protein. Yet, how this flow of information encoded in DNA as genes (genotype) is regulated in order to produce the observable traits of an individual (phenotype) remains unanswered. Recent advances in high-throughput data, i.e., ‘omics’, have allowed the quantification of DNA, RNA and protein levels leading to integrative analyses that essentially probe the central dogma along all of its constituent molecules. Evidence from these analyses suggest that mRNA abundances are at best a moderate proxy for proteins which are the main functional units of cells and thus closer to the phenotype. Cancer proteogenomic studies consider the ensemble of proteins, the so-called proteome, as the readout of the functional molecular phenotype to investigate its influence by upstream events, for example DNA copy number alterations. In typical proteogenomic studies, however, the identified proteome is a simplification of its actual composition, as they methodologically disregard events such as splicing, proteolytic cleavage and post-translational modifications that generate unique protein species – proteoforms. The scope of this thesis is to study the proteome diversity in terms of: a) the complex genetic background of three tumor types, i.e. breast cancer, childhood acute lymphoblastic leukemia and lung cancer, and b) the proteoform composition, describing a computational method for detecting protein species based on their distinct quantitative profiles. In Paper I, we present a proteogenomic landscape of 45 breast cancer samples representative of the five PAM50 intrinsic subtypes. We studied the effect of copy number alterations (CNA) on mRNA and protein levels, overlaying a public dataset of drug- perturbed protein degradation. In Paper II, we describe a proteogenomic analysis of 27 B-cell precursor acute lymphoblastic leukemia clinical samples that compares high hyperdiploid versus ETV6/RUNX1-positive cases. We examined the impact of the amplified chromosomes on mRNA and protein abundance, specifically the linear trend between the amplification level and the dosage effect. Moreover, we investigated mRNA-protein quantitative discrepancies with regard to post-transcriptional and post-translational effects such as mRNA/protein stability and miRNA targeting. In Paper III, we describe a proteogenomic cohort of 141 non-small cell lung cancer clinical samples. We used clustering methods to identify six distinct proteome-based subtypes. We integrated the protein abundances in pathways using protein-protein correlation networks, bioinformatically deconvoluted the immune composition and characterized the neoantigen burden. In Paper IV, we developed a pipeline for proteoform detection from bottom-up mass- spectrometry-based proteomics. Using an in-depth proteomics dataset of 18 cancer cell lines, we identified proteoforms related to splice variant peptides supported by RNA-seq data. This thesis adds on the previous literature of proteogenomic studies by analyzing the tumor proteome and its regulation along the flow of the central dogma of molecular biology. It is anticipated that some of these findings would lead to novel insights about tumor biology and set the stage for clinical applications to improve the current cancer patient care

Spatial and genomic analysis of the glioblastoma tumor microenvironment

Glioblastoma (GBM) is an aggressive brain cancer with devastating outcomes and few effective treatments. Although immunotherapy has shown promise in treating a variety of cancers, it is still unclear if and how it can be effectively used in GBM. Elucidating this will require a better understanding of the mechanistic role of immune cells and their interactions in the GBM tumor microenvironment. This thesis utilizes recent technological developments in cancer genomics and imaging to study the mechanisms underlying immunotherapy and the tumor microenvironment. First, we will provide background on our current understanding of GBM, its immune microenvironment, as well as modern sequencing and imaging methods. Second, we will present a longitudinal study of GBM patients before and after treatment with PD-1 immunotherapy. Only a small fraction of GBM patients respond to this type of therapy, so we perform genomic, transcriptomic, and spatial analyses to compare the molecular features of these rare responders versus non-responders. We show that clinical response to PD-1 immunotherapy in GBM is associated with specific molecular alterations and immune infiltration profiles that reflect the tumor’s clonal evolution during treatment. The most common infiltrating immune cells in GBM are macrophages, which are implicated in a wide variety of pro-tumor and anti-tumor roles. We then focus on this specific immune population by analyzing single-cell expression data from GBM tumors. We identify a novel macrophage subpopulation characterized by expression of the scavenger receptor MARCO, which drives tumor progression in GBM and is altered over the course of PD-1 immunotherapy. Next, we demonstrate that the methods we have developed for GBM are applicable to understanding the tumor microenvironments of other cancers as well. We analyze a cohort of melanoma cases to show that transcriptomic and imaging features can be combined to create a biomarker that stratifies patients into different risk groups. Finally, while most of the image analysis described so far has utilized histopathology, we include two appendices where we demonstrate new ways to process and analyze Magnetic Resonance Imaging (MRI) in GBM

Discovery and characterization of novel non-coding 3′ UTR mutations in NFKBIZ and their functional implications in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a very heterogenous disease that has historically been divided into two subtypes driven by distinct molecular mechanisms. The activated B-cell (ABC) subtype of DLBCL has the worst overall survival and is characterized by activation of the NF-κB signaling pathway. Although many genetic alterations have been identified in DLBCL, there remain cases with few or no known genetic drivers. This suggests that there are still novel drivers of DLBCL yet to be discovered. In this thesis I aimed to leverage whole genome sequencing data to identify novel regions of the genome that were recurrently mutated, with a specific focus on non-coding regions. Through this analysis we identified numerous novel putative driver mutations within the non-coding genome. One of the most highly recurrently mutated regions was in the 3′ untranslated region (UTR) of the NFKBIZ gene. Amplifications of this gene have been previously discovered in ABC DLBCL and this gene is known to activate NF-κB signaling. Therefore, we hypothesized that these 3′ UTR mutations were acting as drivers in DLBCL. The remaining portion of this thesis is focused on the functional characterization of NFKBIZ 3′ UTR mutations and how they drive DLBCL and contribute to treatment resistance. To this end, I induced NFKBIZ 3′ UTR mutations into DLBCL cell lines and determined that they cause both elevated mRNA and protein expression. These mutations conferred a selective growth advantage to DLBCL cell lines both in vitro and in vivo and overexpression of NFKBIZ in primary germinal center B-cells also provided cells a growth advantage. Lastly, I found that NFKBIZ-mutant cell lines were more resistant to a selection of targeted therapeutics (ibrutinib, idelalisib and masitinib). Taken together, this thesis highlights the importance of surveying the entire cancer genome, including non-coding regions, when searching for novel drivers. I demonstrated that mutations in the 3′ UTR of a gene can act as driver mutations conferring cell growth advantages and treatment resistance. This work also implicates NFKBIZ 3′ UTR mutations as potentially useful biomarkers for predicting treatment response and informing on the most effective treatment options for patients

Multi-omics characterization of pancreatic neuroendocrine neoplasms

Pancreatic neuroendocrine neoplasms (PNENs) are biologically and clinically heterogeneous neoplasms in which pathogenic alterations are often indiscernible. Treatments for PNENs are insufficient in part due to lack of alternatives once current options are exhausted. Despite previous efforts to characterize PNENs at the molecular level, there remains a lack of molecular subgroups and molecular features with clinical utility for PNENs. In this work, I describe the identification and characterization of four molecularly distinct subgroups from primary PNEN specimens using whole-exome sequencing, RNA-sequencing and global proteome profiling. A Proliferative subgroup with molecular features of proliferating cells was associated with an inferior overall survival probability. A PDX1-high subgroup consisted of PNENs demonstrating genetic and transcriptomic indications of NRAS or HRAS activation. An Alpha cell-like subgroup, enriched in PNENs with deleterious MEN1 and DAXX mutations, bore transcriptomic similarity to pancreatic α-cells and harbored proteomic cues of dysregulated metabolism involving glutamine and arginine. Lastly, a Stromal/Mesenchymal subgroup exhibited increased expression and activation of the Hippo signaling pathway effectors YAP1 and WWTR1 that are of emerging interest as potentially actionable targets in other cancer types. Whole-genome and whole-transcriptome analysis of PNEN metastases identified novel molecular events likely contributing to pathogenesis, including one case presumably driven by MYCN amplification. In agreement with the findings in primary PNENs, four of the metastatic PNENs displayed a substantial Alpha cell-like subgroup signature and all harboured concurrent mutations in MEN1 and DAXX. Collectively, the identified subgroups present a potential stratification scheme that facilitates the identification of therapeutic vulnerabilities amidst PNEN heterogeneity to improve the effective management of PNENs

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality