Label reusing based graph neural network for unbalanced classification of personalized driver genes in cancer

It is a big challenge to identify personalized driver genes (PDGs) for understanding tumor heterogeneity of cancer individual patients. From the perspective of machine learning, identifying PDGs is an inherent class imbalance issue due to the fewer known driver genes than most passenger ones. However, existing machine learning based methods including unsupervised and supervised learning based methods ignore the importance of limited well-established cancer tissue specific driver genes(CSDGs) for this class imbalance issue. Here we converted the PDG prediction issue to a semi-supervised classification task and a novel method (namely PersonalizedGNN) was developed to identify PDGs by using graph attention neural network and label reuse strategy in personalized gene interaction network (PGIN). PersonalizedGNN effectively utilizes the structure information of PGIN and the limited well-established CSDG information for achieving promising performance. Using the breast cancer and lung cancer datasets from The Cancer Genome Atlas, we validated our method and compared it with other advanced methods. PersonalizedGNN showed outstanding potential in identifying cancer driver genes in terms of prediction precision. Furthermore, we could discover subtype-specific de novo cancer driver genes and in vitro cell-based assays for a novel driver gene FZD7 in lung squamous cell carcinoma cells further validated the PersonalizedGNN. In summary, PersonalizedGNN offers a new effective perspective for discovering PDGs by considering information of prior known CSDGs in PGIN which help researchers understand tumor heterogeneity of cancer individual patients.