Multiobjective Optimization-Based Network Control Principles for Identifying Personalized Drug Targets With Cancer

It is a big challenge to develop efficient models for identifying personalized drug targets (PDTs) from high-dimensional personalized genomic profile of individual patients. Recent structural network control principles have introduced a new approach to discover PDTs by selecting an optimal set of driver genes in a personalized gene interaction network (PGIN). However, most of the current methods only focus on controlling the system through a minimum driver-node set and ignore the existence of multiple candidate driver-node sets for therapeutic drug target identification in PGIN. Therefore, this article proposed multiobjective optimization-based structural network control principles (MONCPs) by considering minimum driver nodes and maximum prior-known drug-target information. To solve MONCP, a discrete multiobjective optimization problem is formulated with many constrained variables, and a novel evolutionary optimization model called large-scale constrained variables-based multitasking cooperative evolutionary algorithm (LSCV-MCEA) was developed by adapting a multitasking framework and a ranking-based fitness function method. With genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, the effectiveness of LSCV-MCEA was validated. The experimental results indicated that compared with other advanced methods, LSCV-MCEA can more effectively identify PDTs with the highest area under the curve score for predicting clinically annotated combinatorial drugs. Meanwhile, LSCV-MCEA can more effectively solve MONCP than other evolutionary optimization methods in terms of algorithm convergence and diversity. Particularly, LSCV-MCEA can efficiently detect disease signals for individual patients with breast invasive carcinoma cancer. The study results show that multiobjective optimization can solve structural network control principles effectively and offer a new perspective for understanding tumor heterogeneity in cancer precision medicine.