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.

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.

Background/Objectives: Serum proteomic analysis of deeply-phenotyped samples, biological pathway modeling and network analysis were performed to elucidate the inflammatory and neurodegenerative processes of multiple sclerosis (MS) and identify sensitive biomarkers of MS disease activity (DA). Methods: Over 1100 serum proteins were evaluated in >600 samples from three MS cohorts to identify biomarkers of clinical and radiographic (gadolinium-enhancing lesions) new MS DA. Protein levels were analyzed and associated with presence of gadolinium-enhancing lesions, clinical relapse status (CRS), and annualized relapse rate (ARR) to create a custom assay panel. Results: Twenty proteins were associated with increased clinical and radiographic MS DA. Serum neurofilament light chain (NfL) showed the strongest univariate correlation with radiographic and clinical DA measures. Multivariate modeling significantly outperformed univariate NfL to predict gadolinium lesion activity, CRS and ARR. Discussion: These findings provide insight regarding correlations between inflammatory and neurodegenerative biomarkers and clinical and radiographic MS DA.