Emerging microfluidic and biosensor technologies for improved cancer theranostics

Microfluidics and biosensors have already demonstrated their potential in cancer research. Typical applications of microfluidic devices include the realistic modeling of the tumor microenvironment for mechanistic investigations or the real-time monitoring/screening of drug efficacy. Similarly, point-of-care biosensing platforms are instrumental for the early detection of predictive biomarkers and their accurate quantification. The combination of both technologies offers unprecedented advantages for the management of the disease, with an enormous potential to contribute to improving patient prognosis. Despite their high performance, these methodologies are still encountering obstacles for being adopted by the healthcare market, such as a lack of standardization, reproducibility, or high technical complexity. Therefore, the cancer research community is demanding better tools capable of boosting the efficiency of cancer diagnosis and therapy. During the last years, innovative microfluidic and biosensing technologies, both individually and combined, have emerged to improve cancer theranostics. In this chapter, we discuss how these emerging-and in some cases unconventional-microfluidics and biosensor technologies, tools, and concepts can enhance the predictive power of point-of-care devices and the development of more efficient cancer therapies.D.C. acknowledges the financial support from the Portuguese Foundation for Science and Technology (FCT) under the program CEEC Individual 2017 (CEECIND/00352/2017). D.C., C.M.A, and S.C.K. thank the support from the FCT under the scope of the project 2MATCH (PTDC/BTM-ORG/28070/2017) funded by the Programa Operacional Regional do Norte supported by European Regional Development Funds (ERDF). S.C.K. also acknowledges the FCT for the support provided through the BREAST-IT project (PTDC/BTM-ORG/28168/2017). Finally, all the authors thank the financial support from the EU Framework Programme for Research and Innovation Horizon 2020 on Forefront Research in 3D Disease Cancer Models as in vitro Screening Technologies (FoReCaST-no. 668983)