2021 AACR: Pan-cancer survey of HLA loss of heterozygosity using a robustly validated NGS based machine learning algorithm
HLA loss of heterozygosity (LOH) is increasingly being recognized as an important immune escape mechanism in response to checkpoint inhibitor therapy. HLA LOH reduces the repertoire of neoantigens displayed on the cell surface of cancer cells, limiting the efficacy of the immune system to detect and eliminate them. Though highly accurate HLA LOH detection algorithms are needed to allow clinical utility, the field lacks the robust, allele-specific validation approaches required to demonstrate performance. Moreover, algorithms of unknown sensitivity and specificity have led to significant discrepancies in the estimated occurrence of HLA LOH as an immune escape mechanism across tumor types. To address these challenges, we have developed a machine learning algorithm to detect HLA LOH called DASH (Deletion of Allele-Specific HLAs) that is integrated into ImmunoID NeXT.
The ImmunoID NeXT platform provides joint tumor genomics and immune profiling from a paired tumor/normal sample. Through augmenting coverage of the HLA locus, ImmunoID NeXT also provides the data to accurately type HLA alleles, detect somatic mutations and probe copy number deletions in this highly polymorphic region.