Diffuse pleural mesothelioma (DPM) is a devastating lung cancer most commonly diagnosed at an advanced stage with a poor prognosis for patients. Therapies available to patients after diagnosis currently include surgical resection, radiotherapy, immunotherapy, and chemotherapy. However, these therapies only prolong life for about a year and a half on average. DPM patients desperately need effective therapies in the form of drugs, drug combinations, and miRNA-based therapies, that could lengthen overall survival and provide a better quality of life. I hypothesized that focusing on DPM tumor biology would streamline the process for discovering new therapies that will have a lasting impact for patients. I have applied systems biology methods to mine multiomic data from patient DPM tumors to discover new therapeutic options. I began by developing a somatic mutation integration pipeline, which created a comprehensive somatic mutational profile of DPM tumors from patient genomic and transcriptomic data. The somatic mutational profile was used in the generation of dpmSYGNAL, a disease-relevant gene regulatory network (GRN) trained on patient tumor multiomic data. I integrated this GRN with functional genomics screens performed on two low-passage primary DPM tumor cell lines and identified gene vulnerabilities that could be targeted by FDA-approved inhibitors and drug combinations. I also developed a pipeline to integrate miRNA target genes from biotinylated pulldowns with RNA-seq data from a study re-expressing the miRNA hsa-miR-497-5p in DPM cell lines. I determined that the re-expression of hsa-miR-497-5p had early pro-apoptotic effects and inhibited the cell cycle at later time points. The identification of inhibitors, combinations of inhibitors, and a therapeutic miRNA demonstrates that DPM biology can be used as a guide to discover new therapeutics for DPM.

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor function. Pathological mechanisms and clinical measures vary extensively from patient to patient, creating a spectrum of disease phenotypes with a poorly understood influence on individual outcomes like disease duration. The inability to ascertain patient phenotype has hindered clinical trial design and the development of more personalized and effective therapeutics. Wholistic analytical methods (‘-omics’) have provided unprecedented molecular resolution into cellular and system level disease processes and offer a foundation to better understand ALS disease variability. Building off initiatives by the New York Genome Center ALS Consortium and Target ALS groups, the goal of this work was to stratify a large patient cohort utilizing a range of bioinformatic strategies and bulk tissue gene expression (transcriptomes) from the brain and spinal cord. Central Hypothesis: Variability in the onset and progression of ALS is partially captured by molecular subgroups (subtypes) with distinct gene expression profiles and implicated pathologies. Work presented in this dissertation addresses the following: (Chapter 2): The use of unsupervised clustering and gene enrichment methods for the identification and characterization of patient subtypes in the postmortem cortex and spinal cord. Results obtained from this Chapter establish three ALS subtypes, identify uniquely dysregulated pathways, and examine intra-patient concordance between regions of the central nervous system. (Chapter 3): Patient subtypes from Chapter 2 are considered in the context of clinical outcomes, leveraging multiple survival models and gene co-expression analyses. Results from this Chapter establish a weak association between ALS subtype and clinical outcomes including disease duration and age at symptom onset. (Chapter 4): Utilizing differential expression analysis, ‘marker’ genes are defined and leveraged with supervised classification (“machine learning”) methods to develop a suite of classifiers design to stratify patients by subtype. Results from this Chapter provide postmortem marker genes for two of the three ALS subtypes and offer a foundation for clinical stratification. Significance: Knowledge gained from this research provides a foundation to stratify patients in the clinic and prior to enrollment in clinical trials, offering a path toward improved therapies.

Characterizing and identifying neuroinflammatory states is crucial in developing treatments for neurodegenerative diseases. Microglia, the resident immune cells of the brain, regulate inflammation and play a vital role in maintaining brain health by producing cytokines, performing phagocytosis, and inducing or reducing inflammation. These functional states can be described by specific patterns of gene expression called transcriptional programs, which are determined by the activity of a set of key transcription factors that have mostly been identified. Thus, an assay for transcription factor activity could reveal the state of the microglial cells and neuroinflammation across the brain. This research developed an assay that uses a transcription factor dependent reporter to indicate which transcriptional programs are activated in the cell when exposed to different stimuli. The prototype assay quantifies nuclear factor kappa B (NF-kB) response in cultured human cells. NF-kB is a well-characterized transcription factor associated with inflammatory pathways in most cells, including microglia. The reporter construct contains an NF-kB specific responsive element that can induce fluorescence/luminescence upon activation of the transcription factor. In an iterative refinement, a dual response fluorescent reporter was developed, which uses a secondary constitutively fluorescent reporter for built-in normalization of the responsive element for microscopy studies. With further refinement, this modular system will serve as a template for less understood transcriptional enhancers allowing for rapid, low-cost assays of neuroimmune regulators and potential in vivo applications in the study of neuroinflammation.

Glioblastoma multiforme (GBM) is an aggressive brain cancer without effectivetreatment options, leaving patient survival rates extremely low. HDAC1 knockdown was found to initiate an invasive phenotype in vivo, particularly within the BT145 human glioma stem cell (hGSC) line. Analysis through RNA sequencing (RNA-seq) gene expression and regulatory networks found both CEBPβ, a known transcription factor (TF) involved in cellular invasion, and the STAT3 pathway, a notorious genetic component of GBM, were differentially expressed in BT145 hGSCs after HDAC1 knockdown. Furthermore, overlap of genes regulated by CEBPβ and STAT3 indicate the CEBPβ/STAT3 pathway may be involved in the observed BT145- specific invasive phenotype. The SYstems Genetics Network AnaLysis (SYGNAL) pipeline was applied to construct sex-specific gene regulatory networks from The Cancer Genome Atlas (TCGA) GBM patient expression data. Unique bicluster eigengenes were discovered separately for all, female, and male patients. Through the application of these bicluster eigengenes to a GBM cohort with multiparametric magnetic resonance imaging (mpMRI) localized biopsies, sex-specific associations between bicluster expression, mpMRI readout, and hallmarks of cancer were determined. Distinctive cancer functions were revealed transcriptionally through bicluster expression, and connected to a unique mpMRI feature. Specifically, SPGRC mpMRI indicated a strong signal for both immune hallmarks (evading immune detection and tumor-promoting inflammation). At the same time, MD mpMRI displayed a tendency toward sustained angiogenesis, possibly signaling the formation of new blood vessels. Uncovering each mpMRI feature’s underlying biological processes enables improved GBM diagnosis and treatment utilizing an individualized, non-invasive approach.