Computational Approaches in Theranostics: Mining and Predicting Cancer Data

Abstract

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

Tumor microenvironment complexity and therapeutic implications at a glance

“…The loss of tissue integrity, carcinogenesis and further progress occurs as a consequence of reciprocal interactions between tumor cells with non-cellular (ECM) and cellular components of the TME [9,10]. Therefore, on the other side of the argument, interactions in reactive non-neoplastic cells, genetically-altered tumor cells, and ECM control the majority of the stages of tumorigenesis effectively including clonal evolution, cancer heterogeneity, epithelial-mesenchymaltransition (EMT), migration, invasion, development of metastasis, neovascularization, apoptosis and chemotherapeutic drug resistance [11][12][13][14].…”

Novel therapeutic targets for cancer metastasis

“…Many of the putative drug targets for metastasis have no known function or enzymatic activity and in silico analysis often finds these genes categorized into seemingly unrelated pathways such as lipid metabolism or vesicular transport [94][95][96][97].…”

Artificial intelligence and big data facilitated targeted drug discovery

“…With the development of life science and bioinformatics, more and more target structures have been analysed. Different from traditional drug research methods, big data mining is widely used in drug target research, such as using genetic algorithm and bagging-svm ensemble classifier to predict drug targets,27 mining and forecasting cancer-related database,28 and using genetic disease-related data to predict novel therapeutic targets by computational data mining methods 29…”