Accurate Human Leukocyte Antigen Research Articles

A novel framework for human leukocyte antigen (HLA) genotyping using probe capture-based targeted next-generation sequencing and computational analysis

Human leukocyte antigen (HLA) genes play pivotal roles in numerous immunological applications. Given the immense number of polymorphisms, achieving accurate high-throughput HLA typing remains challenging. This study aimed to harness the human pan-genome reference consortium (HPRC) resources as a potential benchmark for HLA reference materials. We meticulously annotated specific four field-resolution alleles for 11 HLA genes (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1, -DRB3, -DRB4 and -DRB5) from 44 high-quality HPRC personal genome assemblies. For sequencing, we crafted HLA-specific probes and conducted capture-based targeted sequencing of the genomic DNA of the HPRC cohort, ensuring focused and comprehensive coverage of the HLA region of interest. We used publicly available short-read whole-genome sequencing (WGS) data from identical samples to offer a comparative perspective. To decipher the vast amount of sequencing data, we employed seven distinct software tools: OptiType, HLA-VBseq, HISAT genotype, SpecHLA, T1K, QzType, and DRAGEN. Each tool offers unique capabilities and algorithms for HLA genotyping, allowing comprehensive analysis and validation of the results. We then compared these results with benchmarks derived from personal genome assemblies. Our findings present a comprehensive four-field-resolution HLA allele annotation for 44 HPRC samples. Significantly, our innovative targeted next-generation sequencing (NGS) approach for HLA genes showed superior accuracy compared with conventional short-read WGS. An integrated analysis involving QzType, T1K, and DRAGEN was developed, achieving 100% accuracy for all 11 HLA genes. In conclusion, our study highlighted the combination of targeted short-read sequencing and astute computational analysis as a robust approach for HLA genotyping. Furthermore, the HPRC cohort has emerged as a valuable assembly-based reference in this realm.

SpecHLA enables full-resolution HLA typing from sequencing data

Reconstructing diploid sequences of human leukocyte antigen (HLA) genes, i.e., full-resolution HLA typing, from sequencing data is challenging. The high homogeneity across HLA genes and the high heterogeneity within HLA alleles complicate the identification of genomic source loci for sequencing reads. Here, we present SpecHLA, which utilizes fine-tuned reads binning and local assembly to achieve accurate full-resolution HLA typing. SpecHLA accepts sequencing data from paired-end, 10×-linked-reads, high-throughput chromosome conformation capture (Hi-C), Pacific Biosciences (PacBio), and Oxford Nanopore Technology (ONT). It can also incorporate pedigree data and genotype frequency to refine typing. In 32 Human Genome Structural Variation Consortium, Phase 2 (HGSVC2) samples, SpecHLA achieved 98.6% accuracy for G-group-resolution HLA typing, inferring entire HLA alleles with an average of three mismatches fewer, ten gaps fewer, and 590bp less edit distance than HISAT-genotype per allele. Additionally, SpecHLA exhibited a 2-field typing accuracy of 98.6% in 875 real samples. Finally, SpecHLA detected HLA loss of heterozygosity with 99.7% specificity and 96.8% sensitivity in simulated samples of cancer cell lines.

Efficient and accurate KIR and HLA genotyping with massively parallel sequencing data.

Killer cell immunoglobulin like receptor (KIR) genes and human leukocyte antigen (HLA) genes play important roles in innate and adaptive immunity. They are highly polymorphic and cannot be genotyped with standard variant calling pipelines. Compared with HLA genes, many KIR genes are similar to each other in sequences and may be absent in the chromosomes. Therefore, although many tools have been developed to genotype HLA genes using common sequencing data, none of them work for KIR genes. Even specialized KIR genotypers could not resolve all the KIR genes. Here we describe T1K, a novel computational method for the efficient and accurate inference of KIR or HLA alleles from RNA-seq, whole-genome sequencing, or whole-exome sequencing data. T1K jointly considers alleles across all genotyped genes, so it can reliably identify present genes and distinguish homologous genes, including the challenging KIR2DL5A/KIR2DL5B genes. This model also benefits HLA genotyping, where T1K achieves high accuracy in benchmarks. Moreover, T1K can call novel single-nucleotide variants and process single-cell data. Applying T1K to tumor single-cell RNA-seq data, we found that KIR2DL4 expression was enriched in tumor-specific CD8+ T cells. T1K may open the opportunity for HLA and KIR genotyping across various sequencing applications.

Benchmarking Bioinformatics Pipelines in Data-Independent Acquisition Mass Spectrometry for Immunopeptidomics

Immunopeptidomes are the peptide repertoires bound by the molecules encoded by the major histocompatibility complex [human leukocyte antigen (HLA) in humans]. These HLA–peptide complexes are presented on the cell surface for immune T-cell recognition. Immunopeptidomics denotes the utilization of tandem mass spectrometry to identify and quantify peptides bound to HLA molecules. Data-independent acquisition (DIA) has emerged as a powerful strategy for quantitative proteomics and deep proteome-wide identification; however, DIA application to immunopeptidomics analyses has so far seen limited use. Further, of the many DIA data processing tools currently available, there is no consensus in the immunopeptidomics community on the most appropriate pipeline(s) for in-depth and accurate HLA peptide identification. Herein, we benchmarked four commonly used spectral library-based DIA pipelines developed for proteomics applications (Skyline, Spectronaut, DIA-NN, and PEAKS) for their ability to perform immunopeptidome quantification. We validated and assessed the capability of each tool to identify and quantify HLA-bound peptides. Generally, DIA-NN and PEAKS provided higher immunopeptidome coverage with more reproducible results. Skyline and Spectronaut conferred more accurate peptide identification with lower experimental false-positive rates. All tools demonstrated reasonable correlations in quantifying precursors of HLA-bound peptides. Our benchmarking study suggests a combined strategy of applying at least two complementary DIA software tools to achieve the greatest degree of confidence and in-depth coverage of immunopeptidome data.

Extended genomic HLA typing identifies previously unrecognized mismatches in living kidney transplantation.

Antibody mediated rejection (ABMR) is the most common cause of long-term allograft loss in kidney transplantation (KT). Therefore, a low human leukocyte antigen (HLA) mismatch (MM) load is favorable for KT outcomes. Hitherto, serological or low-resolution molecular HLA typing have been adapted in parallel. Here, we aimed to identify previously missed HLA mismatches and corresponding antibodies by high resolution HLA genotyping in a living-donor KT cohort. 103 donor/recipient pairs transplanted at the University of Leipzig Medical Center between 1998 and 2018 were re-typed using next generation sequencing (NGS) of the HLA loci -A, -B, -C, -DRB1, -DRB345, -DQA1, -DQB1, -DPA1, and -DPB1. Based on these data, we compiled HLA MM counts for each pair and comparatively evaluated genomic HLA-typing with pre-transplant obtained serological/low-resolution HLA (=one-field) typing results. NGS HLA typing (=two-field) data was further used for reclassification of de novo HLA antibodies as "donor-specific". By two-field HLA re-typing, we were able to identify additional MM in 64.1% (n=66) of cases for HLA loci -A, -B, -C, -DRB1 and -DQB1 that were not observed by one-field HLA typing. In patients with biopsy proven ABMR, two-field calculated MM count was significantly higher than by one-field HLA typing. For additional typed HLA loci -DRB345, -DQA1, -DPA1, and -DPB1 we observed 2, 26, 3, and 23 MM, respectively. In total, 37.3% (69/185) of de novo donor specific antibodies (DSA) formation was directed against these loci (DRB345 ➔ n=33, DQA1 ➔ n=33, DPA1 ➔ n=1, DPB1 ➔ n=10). Our results indicate that two-field HLA typing is feasible and provides significantly more sensitive HLA MM recognition in living-donor KT. Furthermore, accurate HLA typing plays an important role in graft management as it can improve discrimination between donor and non-donor HLA directed cellular and humoral alloreactivity in the long range. The inclusion of additional HLA loci against which antibodies can be readily detected, HLA-DRB345, -DQA1, -DQB1, -DPA1, and -DPB1, will allow a more precise virtual crossmatch and better prediction of potential DSA. Furthermore, in living KT, two-field HLA typing could contribute to the selection of the immunologically most suitable donors.

Super high-resolution single-molecule sequence-based typing of HLA class I alleles in HIV-1 infected individuals in Ghana.

Polymorphisms in human leukocyte antigen (HLA) class I loci are known to have a great impact on disease progression in HIV-1 infection. Prevailing HIV-1 subtypes and HLA genotype distribution are different all over the world, and the HIV-1 and host HLA interaction could be specific to individual areas. Data on the HIV-1 and HLA interaction have been accumulated in HIV-1 subtype B- and C-predominant populations but not fully obtained in West Africa where HIV-1 subtype CRF02_AG is predominant. In the present study, to obtain accurate HLA typing data for analysis of HLA association with disease progression in HIV-1 infection in West African populations, HLA class I (HLA-A, -B, and -C) four-digit allele typing was performed in treatment-naïve HIV-1 infected individuals in Ghana (n = 324) by a super high-resolution single-molecule sequence-based typing (SS-SBT) using next-generation sequencing. Comparison of the SS-SBT-based data with those obtained by a conventional sequencing-based typing (SBT) revealed incorrect assignment of several alleles by SBT. Indeed, HLA-A*23:17, HLA-B*07:06, HLA-C*07:18, and HLA-C*18:02 whose allele frequencies were 2.5%, 0.9%, 4.3%, and 3.7%, respectively, were not determined by SBT. Several HLA alleles were associated with clinical markers, viral load and CD4+ T-cell count. Of note, the impact of HLA-B*57:03 and HLA-B*58:01, known as protective alleles against HIV-1 subtype B and C infection, on clinical markers was not observed in our cohort. This study for the first time presents SS-SBT-based four-digit typing data on HLA-A, -B, and -C alleles in Ghana, describing impact of HLA on viral load and CD4 count in HIV-1 infection. Accumulation of these data would facilitate high-resolution HLA genotyping, contributing to our understanding of the HIV-1 and host HLA interaction in Ghana, West Africa.

Recent Advances of Human Leukocyte Antigen (HLA) Typing Technology Based on High-Throughput Sequencing.

The major histocompatibility complex (MHC) in humans is a genetic region consisting of cell surface proteins located on the short arm of chromosome 6. This is also known as the human leukocyte antigen (HLA) region. The HLA region consists of genes that exhibit complex genetic polymorphisms, and are extensively involved in immune responses. Each individual has a unique set of HLAs. Donor-recipient HLA allele matching is an important factor for organ transplantation. Therefore, an established rapid and accurate HLA typing technology is instrumental to preventing graft-verses-host disease (GVHD) in organ recipients. As of recent, high-throughput sequencing has allowed for an increase read length and higher accuracy and throughput, thus achieving complete and high-resolution full-length typing. With more advanced nanotechnology used in high-throughput sequencing, HLA typing is more widely used in third-generation single-molecule sequencing. This review article summarizes some of the most widely used sequencing typing platforms and evaluates the latest developments in HLA typing kits and their clinical applications.

Concurrent use of two independent methods prevents erroneous HLA typing of deceased organ donors – An important strategy for patient safety and accurate virtual crossmatching for broader sharing

Comprehensive and accurate human leukocyte antigen (HLA) typing within a short turnaround time is a crucial initial step for allocating deceased donor organs for transplantation. Erroneous HLA typing of deceased donors can be catastrophic and result in recipient death, failed transplant, and organ wastage due to inappropriately matched donors. The real-time polymerase chain reaction method is widely used as the sole method for HLA typing of deceased donors because of its simplified workflow. Herein, we have reported cases of four deceased donors showing discrepant HLA typing discovered using two independent methods concurrently. The HLA typing of these donors could have been erroneously reported if a single method had been used, which would have profound patient safety implications. In one case, the drop out of HLA-DR7 using a single method could have resulted in harmful organ allocation if the organ was transplanted after a virtual crossmatch to a sensitized candidate showing strong donor-specific HLA-DR7 antibodies. In conclusion, this case series suggests that concurrent dual typing is essential for accurate HLA typing of deceased donors. This strategy is vital because precise HLA typing is critical for accurate virtual crossmatching, which facilitates continuous distribution and broader geographic sharing of the deceased donor organ.

Accurate imputation of human leukocyte antigens with CookHLA

The recent development of imputation methods enabled the prediction of human leukocyte antigen (HLA) alleles from intergenic SNP data, allowing studies to fine-map HLA for immune phenotypes. Here we report an accurate HLA imputation method, CookHLA, which has superior imputation accuracy compared to previous methods. CookHLA differs from other approaches in that it locally embeds prediction markers into highly polymorphic exons to account for exonic variability, and in that it adaptively learns the genetic map within MHC from the data to facilitate imputation. Our benchmarking with real datasets shows that our method achieves high imputation accuracy in a wide range of scenarios, including situations where the reference panel is small or ethnically unmatched.

State-of-the-art genome inference in the human MHC.

The Major Histocompatibility Complex (MHC) on the short arm of chromosome 6 is associated with more diseases than any other region of the genome; it encodes the antigen-presenting Human Leukocyte Antigen (HLA) proteins and is one of the key immunogenetic regions of the genome. Accurate genome inference and interpretation of MHC association signals have traditionally been hampered by the region's uniquely complex features, such as high levels of polymorphism; inter-gene sequence homologies; structural variation; and long-range haplotype structures. Recent algorithmic and technological advances have, however, significantly increased the accessibility of genetic variation in the MHC; these developments include (i) accurate SNP-based HLA type imputation; (ii) genome graph approaches for variation-aware genome inference from next-generation sequencing data; (iii) long-read-based diploid de novo assembly of the MHC; (iv) cost-effective targeted MHC sequencing methods. Applied to hundreds of thousands of samples over the last years, these technologies have already enabled significant biological discoveries, for example in the field of autoimmune disease genetics. Remaining challenges concern the development of integrated methods that leverage haplotype-resolved de novo assembly of the MHC for the development of improved MHC genotyping methods for short reads and the construction of improved reference panels for SNP-based imputation. Improved genome inference in the MHC can crucially contribute to an improved genetic and functional understanding of many immune-related phenotypes and diseases.

In silico tools for accurate HLA and KIR inference from clinical sequencing data empower immunogenetics on individual-patient and population scales.

Immunogenetic variation in humans is important in research, clinical diagnosis and increasingly a target for therapeutic intervention. Two highly polymorphic loci play critical roles, namely the human leukocyte antigen (HLA) system, which is the human version of the major histocompatibility complex (MHC), and the Killer-cell immunoglobulin-like receptors (KIR) that are relevant for responses of natural killer (NK) and some subsets of T cells. Their accurate classification has typically required the use of dedicated biological specimens and a combination of in vitro and in silico efforts. Increased availability of next generation sequencing data has led to the development of ancillary computational solutions. Here, we report an evaluation of recently published algorithms to computationally infer complex immunogenetic variation in the form of HLA alleles and KIR haplotypes from whole-genome or whole-exome sequencing data. For both HLA allele and KIR gene typing, we identified tools that yielded >97% overall accuracy for four-digit HLA types, and >99% overall accuracy for KIR gene presence, suggesting the readiness of in silico solutions for use in clinical and high-throughput research settings.

Variation in the Human Leukocyte Antigen system and risk for endemic Burkitt lymphoma in northern Uganda.

Endemic Burkitt lymphoma (eBL) is an aggressive childhood B-cell lymphoma associated with Plasmodium falciparum (Pf) malaria and Epstein-Barr virus (EBV) infections. Variation in the Human Leukocyte Antigen (HLA) system is suspected to play a role, but assessments using less accurate serology-based HLA typing techniques in small studies yielded conflicting results. We studied 200 eBL cases and 400 controls aged 0-15years enrolled in northern Uganda and typed by accurate high-resolution HLA sequencing methods. HLA results were analyzed at one- or two-field resolution. Odds ratios and 95% confidence intervals (aOR, 95% CI) for eBL risk associated with common HLA alleles versus alleles that were rare (<1%)ordiffered by <2% between the cases and controls as the reference category, were estimated using multiple logistic regression adjusting for age, sex, microgeography, region, malaria positivity and treatment history, and genetic variants associated with eBL. Compared to the controls, eBL cases had a lower frequency of HLA-A*02 (aOR=0·59, 95% CI 0·38-0·91), HLA-B*41 (aOR=0·36, 95% CI 0·13-1·00), and HLA-B*58 alleles (aOR=0·59, 95% CI 0·36-0·97). eBL cases had a lower frequency of HLA-DPB1 homozygosity (aOR=0·57, 95% CI 0·40-0·82) but a higher frequency of HLA-DQA1 homozygosity (aOR=2·19, 95% CI 1·42-3·37). Our results suggest that variation in HLA may be associated with eBL risk.

HLA Typing from RNA Sequencing and Applications to Cancer.

The human leukocyte antigen (HLA) complex is necessary for antigen presentation and regulates both innate and adaptive immune responses. In the context of cancer and treatment therapies, the HLA locus plays a critical role in tumor recognition and tolerance mechanisms. In silico HLA class I and class II typing, as well as expression quantification from next-generation RNA sequencing, can therefore have great potential clinical applications. However, HLA typing from short-read data is a challenging task given the high polymorphism and homology at the HLA locus. In this chapter, we present our highly accurate HLA typing solution, arcasHLA. We provide a detailed outline for practitioners using our protocol to perform HLA typing and demonstrate the applicability of arcasHLA in several clinical samples from tumors.

Novel HLA Typing Method Identifies HLA Alleles Associated with Pediatric ITP

Background: Human leukocyte antigen (HLA) genes on chromosome 6p21 encode the major histocompatibility complex (MHC). MHC molecules are expressed on all nucleated cells and are essential to the immune system's ability to distinguish healthy native cells from foreign or abnormal ones. Specialized antigen presenting cells display MHC molecules loaded with foreign peptides to initiate T cell responses. HLA sequencing variability is critical to function; studies have shown even single allelic changes can alter the ability of MHC molecules to recognize cognate T cell receptors. Given the role of MHC molecules in identifying cells as "self", associations between HLA sequence variation and autoimmunity is a research focus. Like other autoimmune disorders, association between ITP and HLA genes has been investigated. Prior studies are limited by outdated HLA typing methods, genetically homogenous study populations, and lack of validation studies. Preexisting exome data has historically not been used to provide accurate HLA sequencing information given the polymorphic, highly repetitive sequence in this region. This study applied a novel bioinformatics pipeline, xHLA, to an existing set of whole exome sequencing in a cohort of chronic pediatric ITP patients. xHLA, reported by Xie et al. in 2017, yields high-resolution, two field HLA typing for HLA-A, -B, -C, -DQB1, -DPB1, and -DRB1. A prior genetic analysis of chronic ITP patient whole exome data identified a missense variant in BTNL2, a gene which plays a role in peripheral regulatory T cell induction. This variant (rs143211074) is upregulated in chronic ITP versus healthy controls (minor allele frequency= 4.17% in ITP v. 0.54% in controls, p=1.43x10-4). Linkage of rs143211074 and HLA alleles was examined in this study. Methods: Whole exome sequencing data for a cohort of 272 chronic ITP patients was processed using the xHLA method. In order to eliminate sequencing variability related to ethnicity alone, initial analyses were focused on Caucasians only (n=170), using Eigenvectors for principle components analysis. The frequency of individual HLA alleles and HLA haplotypes for Caucasians from the National Marrow Donor Program (NMDP) Be The Match Registry® were compared to the ITP cohort. HLA haplotypes were inferred using the NMDP HaploStats program. A chi-squared test was utilized to compare nonparametric categorical data using GraphPad Prism version 8.0.1 for Windows, GraphPad Software, San Diego, California, USA, www.graphpad.com. A Bonferroni correction was applied and a p-value of &lt;0.05 was statistically significant. Results: 12 HLA alleles had increased frequency in the chronic ITP cohort versus controls. (Table 1) The BTNL2 variant, rs143211074, was found in linkage disequilibrium with the HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype. Of the 10 patients heterozygous for the variant allele, 80% had the HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype. Both patients homozygous for rs143211074 were also homozygous for the HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype. In the entire cohort, this haplotype was identified in 3.63% of ITP patients, and only in 1.27% of controls. Our group recently published that a positive direct antiglobulin test is associated with chronic ITP and need for second line treatments. Of the patients with the HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype with DAT testing performed (n=9), 66% had a positive DAT test and 33% had a negative DAT result. Of those without the HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype who had DAT testing, 17.2% had a positive DAT and 82.8% had a negative test (p=0.0026). Conclusions: HLA alleles are seen at increased frequency in a cohort of Caucasian chronic ITP patients. The HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype is linked to a variant previously identified in BTNL2. Coupled with prior data suggesting that a positive DAT result is associated with chronic disease and use of second line agents, this presence of the HLA-DRB1*04:02, HLA-DQB1*03:02 haplotype may also be linked to refractory disease. Confirmation of the accuracy of the xHLA method in a cohort of patients with both exome data and traditional HLA typing is underway. In the future, a separate cohort of chronic and acute ITP patients will undergo next generation sequencing and have similar HLA typing performed as an independent validation set. Table 1 Disclosures Despotovic: Novartis: Research Funding; Amgen: Research Funding; Dova: Honoraria.

HLA*LA-HLA typing from linearly projected graph alignments.

SummaryHLA*LA implements a new graph alignment model for human leukocyte antigen (HLA) type inference, based on the projection of linear alignments onto a variation graph. It enables accurate HLA type inference from whole-genome (99% accuracy) and whole-exome (93% accuracy) Illumina data; from long-read Oxford Nanopore and Pacific Biosciences data (98% accuracy for whole-genome and targeted data) and from genome assemblies. Computational requirements for a typical sample vary between 0.7 and 14 CPU hours per sample.Availability and implementationHLA*LA is implemented in C++ and Perl and freely available as a bioconda package or from https://github.com/DiltheyLab/HLA-LA (GPL v3).Supplementary information Supplementary data are available at Bioinformatics online.

Improved accuracy of clinical HLA genotyping by next-generation DNA sequencing affects unrelated donor search results for hematopoietic stem cell transplantation

Matching of human leukocyte antigen (HLA) genes is critically important in hematopoietic stem cell transplantation (HSCT). HLA genes are highly polymorphic and HLA matching has historically been limited by technologies that are unable to unambiguously determine HLA genotypes. Next generation DNA sequencing (NGS) overcomes these limitations by enabling near full-gene sequences with phase determination for heterozygous alleles. Here we examine the efficacy and utility of HLA-NGS in the clinical setting. In a 54-sample validation study and 955 patient samples subsequently tested using HLA-NGS, we observed significant improvement in the ability to unambiguously identify HLA genotypes in both the validation (97.3%) and clinical (97.4%) sample cohorts compared to previous standard-of care HLA genotyping methods. We modeled the clinical impact of this improved diagnostic ability by comparing National Marrow Donor Program (NMDP) search results for 56 patients using HLA-NGS genotypes and simulated standard-of-care HLA genotypes. Surprisingly, we observed significant differences in 7.1% of NMDP searches, with improved unambiguous HLA genotyping correlating with improved prediction of finding well-matched and partially-matched unrelated HSCT donors. These data demonstrate that HLA-NGS can provide highly accurate and unambiguous HLA genotyping that facilitates donor selection for allogeneic HSCT.

Abstract 2348: Low-cost and accurate human leukocyte antigen (HLA) class I typing of The Cancer Genome Atlas on the Seven Bridges Cancer Genomics Cloud

Abstract The Cancer Genome Atlas (TCGA)network has generated more than2.5petabytes of genomic dataover the last decade, with petabyte-scale additions of dataexpected inthe coming years. Access and analysis of this informationonalocal compute environment is challenging due tothe volume of dataand lack of sufficient computing resources at many research organizations. The Cancer Genomics Cloud Pilot project from the National Cancer Institute (NCI)has helped indemocratizing access toTCGA by co-localizing datawith computational resources onthe cloud. Funded as part of this project, the SevenBridges Cancer Genomics Cloud (CGC)hosts nearly 5petabytes of public datafrom TCGA, the Simons Genome Diversity Project, the Therapeutically Applicable Research toGenerate Effective Treatments (TARGET)initiative, The Cancer Imaging Archive (TCIA), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). The CGCprovides academic researchers with asecure, scalable, cloud-based cancer research platform that includes collaborative tools for accessing, uploading, analyzing, and visualizing data. The platform uses resource descriptionframeworks, dataharmonization, and metadatacurationtofacilitate effective querying, and bioinformatics tools are implemented onthe CGCusing the CommonWorkflow Language (CWL), an emerging standard for describing computational workflows, tosupport computational reproducibility. Since its launch in2016, the CGChas enabled researchers from around the world tounderstand humangenetics and cancer biology through the analysis of large public datasets and private datainacloud computing environment. Inthis poster, we present anexample of analysis of TCGAdataonthe CGC. The OptiType tool for HumanLeukocyte Antigenclass I typing was used toprofile 8,872RNA-Seq samples present inthe TCGAdataset. All samples were accurately processed within2days using arobust, fault-tolerant, and cost-efficient CWL descriptionof OptiType that enabled analysis for less than50cents per sample onaverage. This case study demonstrates how cloud computing resources canfacilitate the successful analysis of large cohorts of datausing custom pipelines in a robust, scalable, and reproducible manner. Citation Format: Raunaq Malhotra, Alexandar Krasnitz, Anurag Sethi, Erik Lehnert, Elizabeth H. Williams, Davis-Dusenbery N. Brandi. Low-cost and accurate human leukocyte antigen (HLA) class I typing of The Cancer Genome Atlas on the Seven Bridges Cancer Genomics Cloud [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2348.

Kourami: graph-guided assembly for novel human leukocyte antigen allele discovery

Accurate typing of human leukocyte antigen (HLA) is important because HLA genes play important roles in immune responses and disease genesis. Previously available computational methods are database-matching approaches and their outputs are inherently limited by the completeness of already known types, making them unsuitable for discovery of novel alleles. We have developed a graph-guided assembly technique for classical HLA genes, which can construct allele sequences given high-coverage whole-genome sequencing data. Our method delivers highly accurate HLA typing, comparable to the current state-of-the-art methods. Using various data, we also demonstrate that our method can type novel alleles.

Accuracy of Programs for the Determination of Human Leukocyte Antigen Alleles from Next-Generation Sequencing Data.

The human leukocyte antigen (HLA) genes code for proteins that play a central role in the function of the immune system by presenting peptide antigens to T cells. As HLA genes show extremely high genetic polymorphism, HLA typing at the allele level is demanding and is based on DNA sequencing. Determination of HLA alleles is warranted as HLA alleles are major genetic risk factors in autoimmune diseases and are matched in transplantation. Here, we compared the accuracy of several published HLA-typing algorithms that are based on next-generation sequencing (NGS) data. As genome sequencing is becoming increasingly common in research, we wanted to test how well HLA alleles can be deduced from genome data produced in studies with objectives other than HLA typing and in platforms not especially designed for HLA typing. The accuracies were assessed using datasets consisting of NGS data produced using an in-house sequencing platform, including the full 4 Mbp HLA segment, from 94 stem cell transplantation patients and exome sequences from 63 samples of the 1000 Genomes collection. In the patient dataset, none of the software gave perfect results for all the samples and genes when programs were used with the default settings. However, we found that ensemble prediction of the results or modifications of the settings could be used to improve accuracy. For the exome-only data, most of the algorithms did not perform very well. The results indicate that the use of these algorithms for accurate HLA allele determination is not straightforward when based on NGS data not especially targeted to the HLA typing and their accurate use requires HLA expertise.

Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution

SummaryImmune evasion is a hallmark of cancer. Losing the ability to present neoantigens through human leukocyte antigen (HLA) loss may facilitate immune evasion. However, the polymorphic nature of the locus has precluded accurate HLA copy-number analysis. Here, we present loss of heterozygosity in human leukocyte antigen (LOHHLA), a computational tool to determine HLA allele-specific copy number from sequencing data. Using LOHHLA, we find that HLA LOH occurs in 40% of non-small-cell lung cancers (NSCLCs) and is associated with a high subclonal neoantigen burden, APOBEC-mediated mutagenesis, upregulation of cytolytic activity, and PD-L1 positivity. The focal nature of HLA LOH alterations, their subclonal frequencies, enrichment in metastatic sites, and occurrence as parallel events suggests that HLA LOH is an immune escape mechanism that is subject to strong microenvironmental selection pressures later in tumor evolution. Characterizing HLA LOH with LOHHLA refines neoantigen prediction and may have implications for our understanding of resistance mechanisms and immunotherapeutic approaches targeting neoantigens.Video