Artificial learning and multi-omics based analysis of large-scale cancer data for personalized survival predictions

Cancer is one of the leading causes of death worldwide. The high lethality of some of the sub-types of cancer increases the importance of correct diagnosis, complete follow-up and effective treatment. Survivability in cancer can be defined as the length of time that patients live after the diagnosis and/or the administration of a certain treatment. The estimation of survival, which is a critical topic in biomedicine, is possible using relevant indicators and historical patient data. Until lately, researchers mainly used clinical and demographic data of patients to model survivability, which generally resulted in low success, due to ignoring patient-specific molecular properties that affect both the response given to a treatment and the progression of the disease in general. In this study, we proposed a new computational method to predict the survival of cancer patients. For this purpose, we utilized multi-omics data of patients diagnosed with 1 of the 13 different types of cancer, which are obtained from Genomic Data Commons (GDC) data portal. We used mutation, copy number variation (CNV), gene expression, and miRNA expression as our input omic data types. In addition, we incorporated the clinical data and administered drug information of the patients, to our input features. We utilized the random forest algorithm and trained 13 tissue/cancer specific binary classification models. According to our results, models that use multiple types of omic data achieved better prediction performance, compared to the models using a single-omic. Among different types of omics data, mutation and gene expression features provided the highest prediction performance, in the majority of the tissues. This study contributes to the literature as a detailed investigation of different molecular data types for tissue specific prediction of cancer patient survival.