  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 personalized gene interaction network (PGIN). However, most of
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 paper
proposed multi-objective optimization-based structural network control
principles (MONCP) by considering minimum driver nodes and maximum prior-known
drug-target information. To solve MONCP, a discrete multi-objective
optimization problem is formulated with many constrained variables, and a novel
evolutionary optimization model called LSCV-MCEA was developed by adapting a
multi-tasking framework and a rankings-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 BRCA cancer.
The study results show that multi-objective optimization can solve structural
network control principles effectively and offer a new perspective for
understanding tumor heterogeneity in cancer precision medicine.
