Abstract:
Although immunotherapy has recently proven effective in some cancers, most patients with metastatic cancer do not benefit from this therapy. A lack of understanding of the mechanisms that underpin a successful immunotherapeutic response hamper the development of novel effective treatment combinations. In addition, defining a reliable predictive biomarker is a challenging feature selection problem given the complex and dynamic nature of an effective anti-tumour immune response. Functional genomics is an increasingly common approach to the field of biomarker discovery in cancer immunotherapy. A variety of sequencing technologies are available and data can be analysed on multiple platforms, making it accessible to scientists in many disciplines. This review explores computational strategies that can be leveraged to analyse large amounts of high-dimensional sequencing data to yield biological insight into the processes involved in the immunotherapeutic response and to identify candidate biomarkers and drug targets for laboratory-based validation.
Functional genomics in cancer immunotherapy: Computational approaches for biomarker and drug discovery
This review explores computational strategies to yield biological insight into the processes involved in the immunotherapeutic response