New Approaches for Chemosensitivity Testing in Malignant Diseases

Abstract

Due to the irreplaceable role of chemotherapy in cancer treatment, research has focused on improving the efficacies of individual drugs and minimizing, or completely suppressing, their negative side effects. Based on long-term experience and the results of clinical trials, the selection of appropriate treatment is currently based on classical clinical diagnostic criteria, such as tumor size, grade, and the presence or absence of standard markers. However, complications arise due to variability between patients and tumor heterogeneity. Characterization of intratumoral heterogeneity and acquisition of more reliable drug performance indicators should improve personalized therapy. Development and selection of suitable models are therefore important issues in cancer research focused on predicting sensitivity to therapy. This work provides an overview of various chemosensitivity tests that have been previously employed and those that are currently used. Great emphasis is placed on comparing 2D and 3D cell culture models, since their importance and popularity are increasing. Particular attention is paid to in vivo systems, which have significantly improved recently and are tested in clinical trials to predict responses to therapy. This work provides a brief overview of chemosensitivity tests, focusing on the importance of individual tests and their application in decision-making and patient stratification to improve the clinical responses of patients and the development of targeted personalized therapy.Key words: cell culture techniques - personalized medicine - drug screening - biological models - tumor cell lines - carcinoma - cytotoxicity assays This work was supported by the project MEYSNPS I-LO1413 and GACR 17-05838S. The authors declare they have no potential conflicts of interest concerning drugs, products, or services used in the study. The Editorial Board declares that the manuscript met the ICMJE recommendation for biomedical papers.Submitted: 21. 9. 2017Accepted: 20. 12. 2017.