Identifying causal networks linking cancer processes and anti‐tumor immunity using Bayesian network inference and metagene constructs

Abstract

Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples.

New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data

“…BNOmics has been very useful in our own research projects and collaborations. Currently, there is a substantial interest in applying systems biology thinking and analysis methods to the large-scale omics data (Qi et al, 2014; Agostinho et al, 2015; Sherif et al, 2015; Marini et al, 2015; Yin et al, 2015b; Kaiser et al, 2016). However, the assortment of workable systems biology data analysis tools is very limited especially if the ultimate goal is reverse engineering of biological networks from the massive flat datasets.…”

Identification of Reverse Engineering Candidates utilizing Machine Learning and Aircraft Cannibalization Data

“…The update capability of Bayesian networks has resulted in application of the method in several domains to include dynamic risk assessment in the oil and gas industry (Li, Chen, & Zhu, 2016). Bayesian networks have also been applied in the medical community to investigate causality within cancer processes (Kaiser, Bland, & Klinke II, 2016).…”

New Analysis Framework Incorporating Mixed Mutual Information and Scalable Bayesian Networks for Multimodal High Dimensional Genomic and Epigenomic Cancer Data

“…Among the BN advantages are their probabilistic nature, model flexibility, ability to handle non-additive, higherorder, interactions, and ease of the result interpretation. However, applications of BNs to the TCGA (and TCGA-like) data (Gevaert et al, 2006;Xu et al, 2012Xu et al, , 2014Wang et al, 2013;Huang et al, 2015;Zhu et al, 2015;Kaiser et al, 2016;Wu et al, 2017) face two principal difficulties: combining mixed data types in a single analysis framework, and achieving sufficient (for genomic data) scalability, simultaneously. (These, of course, are the two fundamental, and interconnected, BN modeling challenges in general, not just in the TCGA application).…”