Doktorgradsstipendiaten har utviklet og publisert en integrert metode for typebestemmelse av både HLA-klasse-I- og klasse-II-alleler med oppløsning på opptil 8 sifre, med større nøyaktighet enn de tidligere beste metodene. Metoden bruker et omfattende bibliotek av referansealleler og en totrinns "Integer Linear Programming (ILP)"-algoritme som tar hensyn til etnisitetsavhengige allelefrekvenser for å håndtere visse tvetydigheter rundt typebestemmelsen. Metoden viser en samlet nøyaktighet på 98,73% for klasse-I- og 96,37% for klasse-II-alleler. Vi viser en forbedret, integrert tilnærming som overgår eksisterende verktøy, som også er i stand til å predikere HLA-alleler med forbedret sikkerhet for både klasse-I- og klasse-II-alleler. Den integrerte metoden, som nå er en kjernekomponent i selskapets produkttilbud, er i ferd med å bli utvidet til å kunne oppdage somatiske mutasjoner i HLA-regionen i kreftceller. Utviklingen av et slikt verktøy vil være av sentral betydning for å danne grunnlaget for å designe terapier som setter i gang spesifikke antitumorresponser. Dette arbeidet vil gjøre det mulig for oss, og våre partnere, å karakterisere HLA-mutasjoner som en mulig mekanisme for kreftcellers evne til å unnvike immunsystemet.
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.
