Multicellular network-informed survival model for identification of drug targets of gliomas

Increasing evidence suggests that communication between tumor cells (TCs) and tumor-associated macrophages (TAMs) plays a substantial role in promoting progression of low-grade gliomas (LGG). Hence, it is becoming critical to model TAM-TC interplay and interrogate how the crosstalk affects prognosis of LGG patients. This paper proposed a translational research pipeline to construct the multicellular interaction gene network (MIGN) for identification of druggable targets to develop novel therapeutic strategies. Firstly, we selected immunotherapy-related feature genes (IFGs) for TAMs and TCs using RNA-seq data of glioma mice from preclinical trials. After translating the IFGs to human genome, we constructed TAM- and TC- associated networks separately, using a training set of 524 human LGGs. Subsequently, clustering analysis was performed within each network, and the concordance measure K-index was adopted to correlate gene clusters with patient survival. The MIGN was built by combining the clusters highly associated with survival in TAM- and TC-associated networks. We then developed a MIGN-based survival model to identify prognostic signatures comprised of ligands, receptors and hub genes. An independent cohort of 172 human LGG samples was leveraged to validate predictive accuracy of the signature. The areas under time-dependent ROC curves were 0.881, 0.867, and 0.839 with respect to 1-year, 3-year, and 5-year survival rates respectively in the validation set. Furthermore, literature survey was conducted on the signature genes, and potential clinical responses to targeted drugs were evaluated for LGG patients, further highlighting potential utilities of the MIGN signature to develop novel immunotherapies to extend survival of LGG patients.