Inference of high resolution HLA types using genome-wide RNA or DNA sequencing reads

Bai, Yu; Ni, Min; Cooper, Blerta; Wei, Yi; Fury, Wen

doi:10.1186/1471-2164-15-325

Cited by 105 publications

(105 citation statements)

References 46 publications

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“…HLA alleles were called with PHLAT from exome sequencing data [56]. For each tumor, epitope predictions were made by considering interaction between confidently called HLA alleles and single-residue missense alterations (SNV) or protein-altering indel alterations.…”

Section: Methodsmentioning

confidence: 99%

Genomic landscape of high-grade meningiomas

et al. 2017

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High-grade meningiomas frequently recur and are associated with high rates of morbidity and mortality. To determine the factors that promote the development and evolution of these tumors, we analyzed the genomes of 134 high-grade meningiomas and compared this information with data from 595 previously published meningiomas. High-grade meningiomas had a higher mutation burden than low-grade meningiomas but did not harbor any significantly mutated genes aside from NF2. High-grade meningiomas also possessed significantly elevated rates of chromosomal gains and losses, especially among tumors with monosomy 22. Meningiomas previously treated with adjuvant radiation had significantly more copy number alterations than radiation-induced or radiation-naïve meningiomas. Across serial recurrences, genomic disruption preceded the emergence of nearly all mutations, remained largely uniform across time, and when present in low-grade meningiomas correlated with subsequent progression to a higher grade. In contrast to the largely stable copy number alterations, mutations were strikingly heterogeneous across tumor recurrences, likely due to extensive geographic heterogeneity in the primary tumor. While high-grade meningiomas harbored significantly fewer overtly targetable alterations than low-grade meningiomas, they contained numerous mutations that are predicted to be neoantigens, suggesting that immunologic targeting may be of therapeutic value.

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Section: Methodsmentioning

confidence: 99%

Genomic landscape of high-grade meningiomas

et al. 2017

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“…We compare the performance of HLA*PRG with PHLAT [14] and HLAreporter [13], two state-of-the-art algorithms that support HLA class I and class II (Table 1). For the Platinum samples, we find that PHLAT also correctly infers all 36 alleles, whereas HLAreporter only reports 16 alleles (of which 14 are correct).…”

Section: Resultsmentioning

confidence: 99%

High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs

et al. 2016

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Genetic variation at the Human Leucocyte Antigen (HLA) genes is associated with many autoimmune and infectious disease phenotypes, is an important element of the immunological distinction between self and non-self, and shapes immune epitope repertoires. Determining the allelic state of the HLA genes (HLA typing) as a by-product of standard whole-genome sequencing data would therefore be highly desirable and enable the immunogenetic characterization of samples in currently ongoing population sequencing projects. Extensive hyperpolymorphism and sequence similarity between the HLA genes, however, pose problems for accurate read mapping and make HLA type inference from whole-genome sequencing data a challenging problem. We describe how to address these challenges in a Population Reference Graph (PRG) framework. First, we construct a PRG for 46 (mostly HLA) genes and pseudogenes, their genomic context and their characterized sequence variants, integrating a database of over 10,000 known allele sequences. Second, we present a sequence-to-PRG paired-end read mapping algorithm that enables accurate read mapping for the HLA genes. Third, we infer the most likely pair of underlying alleles at G group resolution from the IMGT/HLA database at each locus, employing a simple likelihood framework. We show that HLA*PRG, our algorithm, outperforms existing methods by a wide margin. We evaluate HLA*PRG on six classical class I and class II HLA genes (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1) and on a set of 14 samples (3 samples with 2 x 100bp, 11 samples with 2 x 250bp Illumina HiSeq data). Of 158 alleles tested, we correctly infer 157 alleles (99.4%). We also identify and re-type two erroneous alleles in the original validation data. We conclude that HLA*PRG for the first time achieves accuracies comparable to gold-standard reference methods from standard whole-genome sequencing data, though high computational demands (currently ~30–250 CPU hours per sample) remain a significant challenge to practical application.

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“…Reads were retrieved from the BAM files, converted to FastQ format, and combined at the sample level. Two independent pipelines were used for HLA allele genotyping identification and inferences, ATHLATES (35) and PHLAT (36). Reads were aligned to all HLA cDNA and genomic sequences from the HLA/ImMunoGeneTics (IMGT) database using Burrows-Wheeler Aligner (in PHLAT pipeline) or novoalign (in ATHLATES pipeline) (Novocraft Technologies, www.novocraft.com).…”

Section: Methodsmentioning

confidence: 99%

Density of immunogenic antigens does not explain the presence or absence of the T-cell–inflamed tumor microenvironment in melanoma

Spranger

Luke

Bao

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

351

267

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Melanoma metastases can be categorized by gene expression for the presence of a T-cell–inflamed tumor microenvironment, which correlates with clinical efficacy of immunotherapies. T cells frequently recognize mutational antigens corresponding to nonsynonymous somatic mutations (NSSMs), and in some cases shared differentiation or cancer–testis antigens. Therapies are being pursued to trigger immune infiltration into non–T-cell–inflamed tumors in the hope of rendering them immunotherapy responsive. However, whether those tumors express antigens capable of T-cell recognition has not been explored. To address this question, 266 melanomas from The Cancer Genome Atlas (TCGA) were categorized by the presence or absence of a T-cell–inflamed gene signature. These two subsets were interrogated for cancer–testis, differentiation, and somatic mutational antigens. No statistically significant differences were observed, including density of NSSMs. Focusing on hypothetical HLA-A2+binding scores, 707 peptides were synthesized, corresponding to all identified candidate neoepitopes. No differences were observed in measured HLA-A2 binding between inflamed and noninflamed cohorts. Twenty peptides were randomly selected from each cohort to evaluate priming and recognition by human CD8+T cells in vitro with 25% of peptides confirmed to be immunogenic in both. A similar gene expression profile applied to all solid tumors of TCGA revealed no association between T-cell signature and NSSMs. Our results indicate that lack of spontaneous immune infiltration in solid tumors is unlikely due to lack of antigens. Strategies that improve T-cell infiltration into tumors may therefore be able to facilitate clinical response to immunotherapy once antigens become recognized.

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Inference of high resolution HLA types using genome-wide RNA or DNA sequencing reads

Cited by 105 publications

References 46 publications

Genomic landscape of high-grade meningiomas

Genomic landscape of high-grade meningiomas

High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs

Density of immunogenic antigens does not explain the presence or absence of the T-cell–inflamed tumor microenvironment in melanoma

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