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Publication
Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
The complexity of cancer biology and its clinical manifestation are driven by genetic, epigenetic, transcriptomic, proteomic and metabolomic alterations, supported by genomic instability as well as by environmental conditions and lifestyle factors. Although novel therapeutic modalities are being introduced, efficacious cancer therapy is not achieved due to the frequent emergence of distinct mechanisms of multidrug resistance (MDR). Advanced technologies with the potential to identify and characterize cancer MDR could aid in selecting the most efficacious therapeutic regimens and prevent inappropriate treatments of cancer patients. Herein, we aim to present technological tools that will enhance our ability to surmount drug resistance in cancer in the upcoming decade. Some of these tools are already in practice such as next-generation sequencing. Identification of genes and different types of RNAs contributing to the MDR phenotype, as well as their molecular targets, are of paramount importance for the development of new therapeutic strategies aimed to enhance drug response in resistant tumors. Other techniques known for many decades are in the process of adaptation and improvement to study cancer cells' characteristics and biological behavior including atomic force microscopy (AFM) and live-cell imaging. AFM can monitor in real-time single molecules or molecular complexes as well as structural alterations occurring in cancer cells induced upon treatment with various antitumor agents. Cell tracking methodologies and software tools recently progressed towards quantitative analysis of the spatio-temporal dynamics of heterogeneous cancer cell populations and enabled direct monitoring of cells and their descendants in 3D cultures. Besides, novel 3D systems with the advanced mimicking of the in vivo tumor microenvironment are applicable to study different cancer biology phenotypes, particularly drug-resistant and aggressive ones. They are also suitable for investigating new anticancer treatment modalities. The ultimate goal of using phenotype-driven 3D cultures for the investigation of patient biopsies as the most appropriate in vivo mimicking model, can be achieved in the near future.
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