Abstract 4420: Polytect, a robust algorithm to detect somatic mutations in polymorphic MHC/HLA genes
Authors: Michael Brodie Mumphrey, Abhijit Parolia, Chandan Kumar-Sinha, Marcin Cieslik
Journal: Cancer Research (2020)

Abstract Background: The immune system is able to recognize tumor cells through the presentation of mutant peptides via the major histocompatibility complex (MHC). Somatic mutations and loss of heterozygosity of MHC genes are a known mechanism of escape from this immune surveillance. Classical methods to identify somatic mutations involve alignment of sequencing reads to a standard reference genome, but this approach fails for the MHC genes. High sequence divergence between haplotypes and the GRCh38 reference, in addition to a large number of closely-related homologs, results in poor alignment and difficulty in variant detection. As a result, there is a lack of validated tools that can reliably identify mutations in the MHC genes, leaving this area understudied. Methods: We developed Polytect, a genomics tool that can call somatic mutations in highly polymorphic genes. Polytect accurately partitions reads across homologues, and aligns them against references that are dynamically constructed based on an individual's haplotype. This minimizes alignment errors, and enables the use of traditional variant callers. Reads are accurately partitioned among genes by exploring the space of alternative alignments to all haplotypes and pseudogenes. For the MHC, references are dynamically selected from the IMGT database of known haplotypes using a novel algorithm that models alignment to closely related sequences as a linear system. This allows us to solve for the true underlying haplotypes for each patient. Personalized references are then created by imputing germline mutations onto the selected haplotypes. Once reads are aligned to the personalized reference, somatic mutations can be called using existing methods. Results: In a set of validation experiments, selected references had a median levenshtein distance of 0 SNPs from the known ground truth sequences. However, suboptimal reference selection (&amp;gt; 15 SNPs) was more likely for genes with worse haplotype representation in IMGT and those with extremely variable intron lengths. Polytect achieved a sensitivity of 88%, detecting 65 out of 74 MHC-Class 1 mutation from a gold-standard dataset. We applied Polytect to a cohort of 706 patients with metastatic cancers from the MI-ONCOSEQ study. We found non-synonymous mutations in the MHC class 1 genes in 7% of tumors, a rate higher than the 3.3% reported in a previous study looking at primary tumors. We also applied Polytect to the previously uninvestigated MHC class 2 genes and found mutations in 6% of tumors. MHC mutations were most common in squamous cell carcinomas (40%, n = 20), melanomas (33%, n = 6), and colorectal cancers (25%, n = 8), compared to, for example, (10%, n=256) in prostate cancer. Conclusions: Polytect is an accurate and efficient algorithm to detect somatic mutations in polymorphic genes. Across a large cohort of metastatic tumors Polytect revealed a high prevalence (13%) of mutations in MHC class 1 and class 2 genes. Citation Format: Michael Brodie Mumphrey, Abhijit Parolia, Chandan Kumar-Sinha, Marcin Cieslik. Polytect, a robust algorithm to detect somatic mutations in polymorphic MHC/HLA genes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4420.