Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme

Abstract

Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma 'omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in gene expression, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms associated with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas. We performed comparative network analysis between patients with long-and short-term survival. Seven pathways were identified as associated with survival, all of them involved in immune signaling; differential regulation of PD1 signaling was validated to correspond with outcome in an independent dataset from the German Glioma Network. In this pathway, transcriptional repression of genes for which treatment options are available was lost in short-term survivors; this was independent of mutational burden and only weakly associated with T-cell infiltration. Collectively, these results provide a new way to stratify glioblastoma patients that uses network features as biomarkers to predict survival. They also identify new potential therapeutic interventions, underscoring the value of analyzing gene regulatory networks in individual cancer patients. Statement of SignificanceGenome-wide network modeling of individual glioblastomas identifies dysregulation of PD1 signaling in patients with poor prognosis, indicating this approach can be used to understand how gene regulation influences cancer progression.'omics landscape for many different cancer types. Although this has somewhat improved our understanding of the biology underlying the development and progression of cancer(1), it has only led to direct improvement of patient survival for a limited subset of cancer types. For most cancers, Research.

The Network Zoo: a multilingual package for the inference and analysis of biological networks

“…Many of the netZoo tools share common methodological and computational cores and over the years we have used combinations of these tools to explore the regulatory features driving biological states [36, 37]. Harmonizing the implementation of these tools to create a unified resource, netZoo, facilitates interoperability and the seamless integration in a pipeline that connects network inference with downstream analyses (Figure 1) to generate hypotheses and actionable biological insights.…”

Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data

“…LIONESS is based on the assumption that edges estimated in an “aggregate” network model are a linear combination of edges specific to each of the input samples. This allows for the estimation of individual sample edge weights using a linear equation, these can then be used for sample-specific network analysis, as done previously [33, 34, 35].…”

gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit

“…LIONESS ( 13 ) builds on these methods and uses linear interpolation to infer individual regulatory networks for each sample in a population. We have used LIONESS with PANDA to infer networks for individuals in large studies, after which the networks are treated as inferred measurements and compared between relevant subgroups ( 15 , 19 , 20 ).…”