Bioinformatics for cancer immunology and immunotherapy

Abstract

Recent mechanistic insights obtained from preclinical studies and the approval of the first immunotherapies has motivated increasing number of academic investigators and pharmaceutical/biotech companies to further elucidate the role of immunity in tumor pathogenesis and to reconsider the role of immunotherapy. Additionally, technological advances (e.g., next-generation sequencing) are providing unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and ultimately enable individualized treatment. However, the increasing complexity of the generated data and the plethora of bioinformatics methods and tools pose considerable challenges to both tumor immunologists and clinical oncologists. In this review, we describe current concepts and future challenges for the management and analysis of data for cancer immunology and immunotherapy. We first highlight publicly available databases with specific focus on cancer immunology including databases for somatic mutations and epitope databases. We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling. Mathematical models are powerful tools that can predict and explain important patterns in the genetic and clinical progression of cancer. Therefore, a survey of mathematical models for tumor evolution and tumor-immune cell interaction is included. Finally, we discuss future challenges for individualized immunotherapy and suggest how a combined computational/experimental approaches can lead to new insights into the molecular mechanisms of cancer, improved diagnosis, and prognosis of the disease and pinpoint novel therapeutic targets.

Fig. 1

Data and information flow in cancer immunology research. The datasets are integrated from clinical observations, medical records, “omic” technologies, and the next-generation sequencing technology and analyzed by using bioinformatics methods. Cancer researchers are using these data to extract information for diagnosis, classification, prognosis, and therapeutic guidance. Furthermore, the multi-parametric data can lead to the improvement of the immunotherapy and can be exploited for patients benefit using individualized therapeutic cancer vaccines

Fig. 2

Databases for epitopes and calculation of the total number of epitopes. Shown are available databases and the number of entries in each database (see text for abbreviations). Since there is a considerable overlap between the databases, we have analyzed the data and as of to date identified the number of unique peptide sequences to be around 35,000. The number of entries per database refers only to human peptide sources