Bioinformatics for cancer immunotherapy target discovery

Abstract

The mechanisms of immune response to cancer have been studied extensively and great effort has been invested into harnessing the therapeutic potential of the immune system. Immunotherapies have seen significant advances in the past 20 years, but the full potential of protective and therapeutic cancer immunotherapies has yet to be fulfilled. The insufficient efficacy of existing treatments can be attributed to a number of biological and technical issues. In this review, we detail the current limitations of immunotherapy target selection and design, and review computational methods to streamline therapy target discovery in a bioinformatics analysis pipeline. We describe specialized bioinformatics tools and databases for three main bottlenecks in immunotherapy target discovery: the cataloging of potentially antigenic proteins, the identification of potential HLA binders, and the selection epitopes and co-targets for single-epitope and multi-epitope strategies. We provide examples of application to the well-known tumor antigen HER2 and suggest bioinformatics methods to ameliorate therapy resistance and ensure efficient and lasting control of tumors.

Fig. 1

General overview of bioinformatics supported discovery of potential T cell-based immunotherapy targets in cancer tumors. Green operations are preliminary and final laboratory analyses; red operations are bioinformatics analyses performed using computational tools (described in further detail in the following sections); blue operations are cross-references with biological databases (likewise described in further detail in the following sections), and yellow operations denote intermediary outputs from the analyses and cross-referencing

Fig. 2

Protein–protein interaction (PPI) network for HER2 and interacting tumor antigens (from TANTIGEN). PPI networks may indicate possible compensatory activity such as that for HER2 and EGFR, making interaction networks useful for elucidating additional potential targets. Nodes represent proteins and edges correspond to functional interactions. Thicker edges signify higher confidence in the interaction. Image was generated using the STRING database