A Multilayered and Clinically-Informed Integration of the Transcriptome, Phenome, and Radiome in Multifactorial Disorder Assessment

Researchers continue to struggle in deciphering the underlying molecular machinery of complex, multifactorial, and comorbid medical disorders. Integrating multiple layers of data –from genomic to exposomic – and evaluating their combinatorial effect on the phenome can mitigate limitations of simple differential analyses and ultimately help uncover causal factors.In my dissertation work, I specifically focus on the integration of transcriptomic data with other data types that have a high clinical translatability such as phenomic and radiomic characteristics. I apply a multi-layered transcriptome-phenome-radiome integrative framework to two use case scenarios to demonstrate its benefits and drawbacks. For use case scenario 1, I perform a multi-level analysis of RNA sequencing collected from in-house human placental decidual samples of various modes of parturition in late-stage pregnancy. I highlight differences in gene expression, co-expression, and alternative splicing and identify tissue- and labor-specific enrichment. I then incorporate dense prognostic and maternal and fetal phenomic information to derive genes and biological processes associated with premature and ceased labor. I demonstrate how an integrative framework successfully allows us to extract biologically relevant information that would have otherwise been missed through hypothesis-driven or monolayer differential analysis. For use case scenario. 2, I generate isoform-level information from RNA sequencing collected from The Cancer Genome Atlas (TCGA) GBM tumors. Using additional layers of the transcriptome, I filter for tumor-enriched genes to subtract microenvironment effects. I then incorporate 2 forms of quantitative morphologic radiomic features to extract exon inclusion-radiophenotype correlates. Through functional annotation, I highlight the underlying biological differences between tumor phenotypes. I demonstrate how an integrative framework provides exploratory insights into the biology of a GBM tumor yet fails to reveal significant associations due to data quality and analytical limitations. The potential applications of a multi-layered and clinically-informed integration of the transcriptome, phenome, and radiome extend far beyond the immediate rejoice of joining systems biology efforts in the integration of “big data”. Through a synergistic coupling of functional molecular indexes, phenotypic characterization, and dense prognostic traits, it enables an in-depth and comprehensive investigation of multifactorial disorders. In the process, it uses a converged data- and hypothesis-mediated approach to balance the benefit of a comprehensive analysis approach and an elaborate mechanistic depiction of etiology. By incorporating individual-level information (from phenomic and radiomic traits) into population-level findings (from transcriptomic analyses), it poses as a promising contributor to the personalized and precision medicine initiatives of modern medicine

A Multilayered and Clinically-Informed Integration of the Transcriptome, Phenome, and Radiome in Multifactorial Disorder Assessment

Researchers continue to struggle in deciphering the underlying molecular machinery of complex, multifactorial, and comorbid medical disorders. Integrating multiple layers of data –from genomic to exposomic – and evaluating their combinatorial effect on the phenome can mitigate limitations of simple differential analyses and ultimately help uncover causal factors.In my dissertation work, I specifically focus on the integration of transcriptomic data with other data types that have a high clinical translatability such as phenomic and radiomic characteristics. I apply a multi-layered transcriptome-phenome-radiome integrative framework to two use case scenarios to demonstrate its benefits and drawbacks. For use case scenario 1, I perform a multi-level analysis of RNA sequencing collected from in-house human placental decidual samples of various modes of parturition in late-stage pregnancy. I highlight differences in gene expression, co-expression, and alternative splicing and identify tissue- and labor-specific enrichment. I then incorporate dense prognostic and maternal and fetal phenomic information to derive genes and biological processes associated with premature and ceased labor. I demonstrate how an integrative framework successfully allows us to extract biologically relevant information that would have otherwise been missed through hypothesis-driven or monolayer differential analysis. For use case scenario. 2, I generate isoform-level information from RNA sequencing collected from The Cancer Genome Atlas (TCGA) GBM tumors. Using additional layers of the transcriptome, I filter for tumor-enriched genes to subtract microenvironment effects. I then incorporate 2 forms of quantitative morphologic radiomic features to extract exon inclusion-radiophenotype correlates. Through functional annotation, I highlight the underlying biological differences between tumor phenotypes. I demonstrate how an integrative framework provides exploratory insights into the biology of a GBM tumor yet fails to reveal significant associations due to data quality and analytical limitations. The potential applications of a multi-layered and clinically-informed integration of the transcriptome, phenome, and radiome extend far beyond the immediate rejoice of joining systems biology efforts in the integration of “big data”. Through a synergistic coupling of functional molecular indexes, phenotypic characterization, and dense prognostic traits, it enables an in-depth and comprehensive investigation of multifactorial disorders. In the process, it uses a converged data- and hypothesis-mediated approach to balance the benefit of a comprehensive analysis approach and an elaborate mechanistic depiction of etiology. By incorporating individual-level information (from phenomic and radiomic traits) into population-level findings (from transcriptomic analyses), it poses as a promising contributor to the personalized and precision medicine initiatives of modern medicine

“RADIOTRANSCRIPTOMICS”: A synergy of imaging and transcriptomics in clinical assessment

Recent advances in quantitative imaging and “omics” technology have generated a wealth of mineable biological “big data”. With the push towards a P4 “predictive, preventive, personalized, and participatory” approach to medicine, researchers began integrating complementary tools to further tune existing diagnostic and therapeutic models. The field of radiogenomics has long pioneered such multidisciplinary investigations in neuroscience and oncology, correlating genotypic and phenotypic signatures to study structural and functional changes in relation to altered molecular behavior. Given the innate dynamic nature of complex disorders and the role of environmental and epigenetic factors in pathogenesis, the transcriptome can further elucidate serial modifications undetected at the genome level. We therefore propose “radiotranscriptomics” as a new member of the P4 medicine initiative, combining transcriptome information, including gene expression and isoform variation, and quantitative image annotations

An inflammatory landscape for preoperative neurologic deficits in glioblastoma

Introduction: Patients with glioblastoma (GBM), one of the most aggressive forms of primary brain tumors, exhibit a wide range of neurologic signs, ranging from headaches to neurologic deficits and cognitive impairment, at first clinical presentation. While such variability is attributed to inter-individual differences in increased intracranial pressure, tumor infiltration, and vascular compromise, a direct association with disease stage, tumor size and location, edema, and necrotic cell death has yet to be established. The lack of specificity of neurologic symptoms often confounds the diagnosis of GBM. It also limits clinicians' ability to elect treatment regimens that not only prolong survival but also promote symptom management and high quality of life. Methods: To decipher the heterogeneous presentation of neurologic symptoms in GBM, we investigated differences in the molecular makeup of tumors from patients with and without preoperative neurologic deficits. We used the Ivy GAP (Ivy Glioblastoma Atlas Project) database to integrate RNA sequencing data from histologically defined GBM tumor compartments and neurologic examination records for 41 patients. We investigated the association of neurologic deficits with various tumor and patient attributes. We then performed differential gene expression and co-expression network analysis to identify a transcriptional signature specific to neurologic deficits in GBM. Using functional enrichment analysis, we finally provided a comprehensive and detailed characterization of involved pathways and gene interactions. Results: An exploratory investigation of the association of tumor and patient variables with the early development of neurologic deficits in GBM revealed a lack of robust and consistent clinicopathologic prognostic factors. We detected significant differences in the expression of 728 genes (FDR-adjusted p-value ≤ 0.05 and relative fold-change ≥ 1.5), unique to the cellular tumor (CT) anatomical compartment, between neurologic deficit groups. Upregulated differentially expressed genes in CT were enriched for mesenchymal subtype-predictive genes. Applying a systems approach, we then identified co-expressed gene sets that correlated with neurological deficit manifestation (FDR-adjusted p-value < 0.1). Collectively, these findings uncovered significantly enriched immune activation, oxidative stress response, and cytokine-mediated proinflammatory processes. Conclusion: Our study posits that inflammatory processes, as well as a mesenchymal tumor subtype, are implicated in the pathophysiology of preoperative neurologic deficits in GBM

Inflammatory and neurodegenerative serum protein biomarkers increase sensitivity to detect clinical and radiographic disease activity in multiple sclerosis.

The multifaceted nature of multiple sclerosis requires quantitative biomarkers that can provide insights related to diverse physiological pathways. To this end, proteomic analysis of deeply-phenotyped serum samples, biological pathway modeling, and network analysis were performed to elucidate inflammatory and neurodegenerative processes, identifying sensitive biomarkers of multiple sclerosis disease activity. Here, we evaluated the concentrations of &gt; 1400 serum proteins in 630 samples from three multiple sclerosis cohorts for association with clinical and radiographic new disease activity. Twenty proteins were associated with increased clinical and radiographic multiple sclerosis disease activity for inclusion in a custom assay panel. Serum neurofilament light chain showed the strongest univariate correlation with gadolinium lesion activity, clinical relapse status, and annualized relapse rate. Multivariate modeling outperformed univariate for all endpoints. A comprehensive biomarker panel including the twenty proteins identified in this study could serve to characterize disease activity for a patient with multiple sclerosis