Bioinformatics identification of mutated HLA sequences to guide neoantigen-based immunotherapy

The Ph.D. candidate has developed and published an integrated HLA typing approach that can type both HLA class I and II alleles with up to 8-digits of resolution, with greater accuracy compared to the previous state-of ?the-art. The method uses a comprehensive library of reference alleles and a two-step Integer Linear Programming (ILP) algorithm incorporating ethnic-dependent allele frequencies that resolve certain typing ambiguities. The method displayed an overall accuracy of 98.73% for Class I and 96.37% for Class II alleles. We illustrate an improved integrative approach that outperforms existing tools and is able to predict HLA alleles with improved fidelity for both Class I and Class II alleles. The integrated approach, now a core component of the companies product offering, is being extended to detect somatic mutations in HLA region from tumor cells. The development of such a tool will be of pivotal importance to providing the rationale for designing therapeutic approaches that trigger specific antitumor responses. This work will allow us, and our partners, to characterize HLA mutations as a possible mechanism of immune evasion during cancer progression.

CURRENT OUTCOMES & IMPACTS The project has enabled NEC OncoImmunity AS to build a state-of-the-art software solution called the OncoHLA, which can identify immunogenic neoantigens from NGS data with improved precision due to the more accurate typing of the HLA background of the patient and in depth profiling of the mutation status of the HLA alleles within tumors. The developments realized during the project have helped NEC OncoImmunity to build and international and multidisciplinary development team and stablished a network of national and international collaborations CURRENT OUTCOMES & IMPACTS Designing personalized cancer vaccines with improved precision due to the technological advances of OncoHLA will lead to safer and more efficacious personalised treatments. Thereby improving health economics both nationally and internationally.

In many cancer types it has been shown that mutations in the HLA region lead to selective loss of HLA class I expression. However, loss of HLA expression as a mechanism of immune escape cannot be fully reconciled with the ability of NK cells, to target and destroy tumor cells that lack adequate HLA molecule presentation. Thus the phenotypic effect of HLA mutations may be more subtle, and may mediate immune escape through other means such as by modulating the repertoire of immunogenic noeantigens presented to T-cells. Until recently is was not technologically possible to comprehensively profile the HLA regions in the cancer context, however the recent advent of next-generation sequencing (NGS) technology offers an opportunity to finally address these questions. HLA typing using NGS offers the promising potential of both high-throughput and high-resolution characterization of the properties of HLA genes. However, there is a lack of bioinformatics software to accurately identify somatic mutations in this highly polymorphic region from NGS data. The accurate prediction of somatic mutations in human leukocyte antigen (HLA) genes using whole-exome sequencing (WES) is seriously impeded by the ultra-high polymorphism of the HLA loci. This high polymorphism rate prevents correct alignment of NGS reads to human reference genomes, and therefore subsequent prediction of somatic mutations in the HLA regions. In this project the cnadidate will develop a bioinformatics solution to identify and characterize somatically mutated HLA sequences from NGS data, and predict how these mutations modulate the repertoire of immunogenic noeantigens.