BACKGROUND: Success in precision medicine depends on the ability to accurately and rapidly identify master regulators of critical gene subnetworks. To achieve this goal requires a seamless computational target discovery platform that integrates global network visualization with identification of master regulatory subnetworks (MRS) and their key influencers.

METHODS: We have created NetZen - a computer cluster-capable computational suite allowing for 1) robust network generation with million edges; 2) network-based MRS identification; 3) hierarchical analysis with automated annotations; and 4) whole-network 3D visualization of all 20,000 genes. To identify cancer MRS, we applied NetZen to expression datasets of tumors and normal tissues, tumor-initiating cells (TICs), non-TIC cancer cells, and normal stem and non-stem cells from TCGA, GEO and internal sources. For validation, we introduced these subnetworks in normal cells to recapitulate TIC phenotypes in vivo or disrupted them in patients-derived TICs and mouse xenografts (PDX). To identify MRS of mortality, we applied NetZen to survival data in TCGA.

RESULTS: Networks of 28 human cancers were generated, integrating transcriptomics, microRNAs, proteomics, and clinical data. For cancers with TIC datasets, we identified TIC-specific MRS with clear-cut hierarchy controlling immune surveillance, EMT and developmental functions. Interestingly, in some cancers, the immune subnetwork including immune checkpoints was entirely encompassed in the developmental subnetwork. We present representative validation on glioblastoma TIC (GIC) MRS with features common in other cancers’ TICs. Exogenous introduction of this subnetwork in normal astrocytes forced them to become GICs and form tumors in mice. Disrupting this subnetwork in 10 independent PDXs profoundly blunted tumor growth and prolonged survival. Lastly, we present detailed MRS of mortality analysis of multiple cancer types and identify common pathways directly influencing outcomes.

DISCUSSION: NetZen provides a complete solution to big data analysis by effortlessly processing million-edge hierarchical networks with biological insights through automated annotations. The findings that the immune and developmental subnetworks were intimately linked suggest that immune escape by TICs is intrinsic of cellular transformation rather than a product of selection. Lastly, detailed network hierarchy provides a logical roadmap for cancer therapeutic development.

Citation Format: Son Le, Alberto Riva, Changwang Deng, David D. Tran. NetZen: A comprehensive network-based pathway and target discovery platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2456.