Comprehensive prediction of tumor neoantigens with nextNEOpi

Immunotherapy has revolutionized cancer treatment by harnessing the immune system to target tumor cells expressing neoantigens. Neoantigens are peptides arising from tumor-specific aberrations that are presented by cancer cells and recognized by T cells. The computational prediction of cancer neoantigens from somatic mutations and other tumor-specific aberrations using patients' sequencing data is key for the investigation of anticancer immune responses and for the design of personalized immunotherapies. However, neoantigen prediction requires the implementation of complex computational pipelines to distill large-scale information from RNA and DNA sequencing data and derive neoantigen candidates together with associated features for their prioritization and selection. We previously developed nextNEOpi, a comprehensive and stand-alone bioinformatics pipeline that not only predicts class-I and -II neoantigens and fusion neoantigens, but also sheds light onto the tumor-immune cell interface, quantifying neoantigen clonality, immunogenicity, and tumor-specific metrics like tumor mutational burden and immune-cell receptor repertoire diversity. In this chapter, we showcase the main capabilities of the nextNEOpi pipeline by analyzing genomic and transcriptomic data generated from multiple biopsies collected from patients with lung cancer.