Abstract Loss of human leukocyte antigen (HLA) is of increasing interest as a mechanism of cancer immune evasion and biomarker for cancer immunotherapy response. Each cancer patient has six class I HLA alleles that are capable of presenting a set of tumor-specific neoantigens. However, HLA alleles are often deleted in tumors, resulting in a loss of heterozygosity (LOH). When LOH occurs, neoantigen presentation is significantly impaired, potentially facilitating tumor immune evasion. Given the biologic impact of HLA LOH, there is a need for robust algorithms that can detect allele-specific HLA LOH in tumor samples. Here, we describe a novel computational approach to detect HLA LOH from exome sequencing, demonstrate the robustness of the method, and apply the method to calculate the frequencies of HLA LOH in key cancer types. We performed exome sequencing with augmented HLA region capture on the ImmunoID NeXT platform for tumor and normal samples of 184 patients across several cancer types and identified 430 nonhomozygous HLA genes. Next, we extracted the reads mapping to a custom HLA database and mapped them on to the patient-specific HLA alleles. For each allele, we calculated two key features: 1) the tumor b-allele frequency normalized by the native b-allele frequency and 2) the allele-specific tumor to normal coverage ratio. Using these two features, along with tumor purity and ploidy values, we trained a random forest model on a subset of the HLA genes (n=300). While standard copy-number variant (CNV) tools are unable to detect LOH in the polymorphic HLA genes, they can accurately measure deletions in their flanking regions, which we used to validate the accuracy of our allele-specific HLA LOH algorithm. In our test set (n=130), we found a high concordance between our allele-specific deletion calls and the generic deletion calls (94% accuracy, 0.85 F1 Score). When we constrained our test set to samples with high tumor content (>50%, n=40), we saw even stronger concordance (98% accuracy, 0.95 F1 Score). The only discordant call was a focal deletion within an HLA gene that was detected by our algorithm but missed by the CNV tool. Next, we ran our algorithm on patient samples of different cancer indications. For non-small cell lung cancer, we found a high frequency of patients affected by LOH (35%, 9 of 26), which is similar to frequencies previously reported in the literature. Furthermore, we found a lower frequency of melanoma tumors with LOH (15%, 7 of 48). In conclusion, we developed a novel algorithm to call allele-specific HLA LOH on the ImmunoID NeXT exome sequencing platform that augments coverage in the polymorphic HLA locus and demonstrated overall robust performance. The relatively high frequency of LOH events we detected in the melanoma and lung cancer samples suggests the importance of LOH analysis to inform cancer immunotherapy biomarker studies and personalized cancer therapies that depend on neoantigen presentation. Citation Format: Rachel Marty Pyke, Charles Abbott, Simo V. Zhang, Datta Mellacheruvu, John West, Richard Chen, Sean Michael Boyle. HLA allele-specific loss of heterozygosity detection using augmented exome capture approach [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr A19.
Read full abstract
Apr 1, 2020
Sophia Tsabouri + 4
Open Access
