Abstract
Over the last decade, next-generation sequencing (NGS) methods have become increasingly used in various areas of human genomics. In routine clinical care, their use is already implemented in oncology to profile the mutational landscape of a tumor, as well as in rare disease diagnostics. However, its utilization in pharmacogenomics is largely lacking behind. Recent population-scale genome data has revealed that human pharmacogenes carry a plethora of rare genetic variations that are not interrogated by conventional array-based profiling methods and it is estimated that these variants could explain around 30% of the genetically encoded functional pharmacogenetic variability.
To interpret the impact of such variants on drug response a multitude of computational tools have been developed, but, while there have been major advancements, it remains to be shown whether their accuracy is sufficient to improve personalized pharmacogenetic recommendations in robust trials. In addition, conventional short-read sequencing methods face difficulties in the interrogation of complex pharmacogenes and high NGS test costs require stringent evaluations of cost-effectiveness to decide about reimbursement by national healthcare programs. Here, we illustrate current challenges and discuss future directions toward the clinical implementation of NGS to inform genotype-guided decision-making.
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Acknowledgments
The work in the authors’ laboratory is funded by the Swedish Research Council [grant agreement numbers: 2016-01153, 2016-01154, and 2019-01837], by the EU/EFPIA/OICR/McGill/KTH/Diamond Innovative Medicines Initiative 2 Joint Undertaking (EUbOPEN grant number 875510), and by the European Union’s Horizon 2020 research and innovation program Ubiquitous Pharmacogenomics (grant agreement number 668353).
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Zhou, Y., Lauschke, V.M. (2022). Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy. In: Cascorbi, I., Schwab, M. (eds) Precision Medicine. Handbook of Experimental Pharmacology, vol 280. Springer, Cham. https://doi.org/10.1007/164_2022_596
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