Subnetwork State Functions Define Dysregulated Subnetworks in Cancer

Abstract

Emerging research demonstrates the potential of protein-protein interaction (PPI) networks in uncovering the mechanistic bases of cancers, through identification of interacting proteins that are coordinately dysregulated in tumorigenic and metastatic samples. When used as features for classification, such coordinately dysregulated subnetworks improve diagnosis and prognosis of cancer considerably over single-gene markers. However, existing methods formulate coordination between multiple genes through additive representation of their expression profiles and utilize fast heuristics to identify dysregulated subnetworks, which may not be well suited to the potentially combinatorial nature of coordinate dysregulation. Here, we propose a combinatorial formulation of coordinate dysregulation and decompose the resulting objective function to cast the problem as one of identifying subnetwork state functions that are indicative of phenotype. Based on this formulation, we show that coordinate dysregulation of larger subnetworks can be bounded using simple statistics on smaller subnetworks. We then use these bounds to devise an efficient algorithm, Crane, that can search the subnetwork space more effectively than existing algorithms. Comprehensive cross-classification experiments show that subnetworks identified by Crane outperform those identified by additive algorithms in predicting metastasis of colorectal cancer (CRC).

Integrative approaches for finding modular structure in biological networks

“…For example, active modules showing characteristic patterns of gene expression correlated with specific disease phenotypes can yield valuable biomarkers for disease classification 62,95,96 . Module-based biomarkers achieve greater predictive power and reproducibility over single gene markers, as demonstrated for the classification of numerous human disorders including Alzheimer’s 97 , diabetes 36,98-100 and several forms of cancers including breast cancers 45,55,62,99,101,102 , ovarian cancer 73,103,104 , glioblastomas 67,70,73,74 , and others 39,72,95,105,106 . Because active modules can reveal pathway-centric insights reinforced by multiple lines of evidence, they naturally provide mechanistic explanations for complex traits and multi-genic diseases like cancer.…”

Epithelial Mesenchymal Transition Network-Based Feature Engineering in Lung Adenocarcinoma Prognosis Prediction Using Multiple Omic Data

“…The second motivation is discretization, which has advantages of removing noise, decreasing computational complexity, and presenting tractable biological interpretations. Although discretization can lose information, it is shown to be effective in a few biological studies such as identifying dysregulated pathways [54], identifying subnetwork biomarkers [24,55], and cancer classification [56]. Based on the two motivations, we included the following feature sets in the experiments:…”

Biologically inspired intelligent decision making

“…The CRANE algorithm is an example of such an approach, which employs an ANN to perform classifications based on identified disregulated PPI subnetworks 58 . The expression levels of the genes in the subnetworks form the inputs to the ANN.…”