Fast and accurate HLA typing from short-read next-generation sequence data with xHLA

Xie, Chao; Yeo, Zhen Xuan; Wong, Marie; Piper, Jason; Long, Tao; Kirkness, Ewen F.; Biggs, William H.; Bloom, Ken; Spellman, Stephen R.; Vierra-Green, Cynthia; Brady, Colleen A.; Scheuermann, Richard H.; Telenti, Amalio; Howard, Sally; Brewerton, Suzanne; Turpaz, Yaron; Venter, J. Craig

doi:10.1073/pnas.1707945114

Cited by 130 publications

(106 citation statements)

References 27 publications

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“…Thus, from a total of 8,333 patients with exome sequencing, we successfully typed 7,929 patients at all three genes. To validate these HLA types, we also applied xHLA (Xie et al, 2017), which calls the beta alleles for HLA-DR, HLA-DP and HLA-DQ. We restricted our patient set to samples where both HLA-HD and xHLA completely agreed, leaving 5,942 patients (Table S1, Figure S2A).…”

Section: Resultsmentioning

confidence: 99%

“…Patients were assigned HLA-DP and -DQ types if they had successful typing for HLA-DPA1/HLA-DPB1 and HLA-DQA1/HLA-DQB1, respectively. Samples were validated by xHLA (Xie et al, 2017), run with default parameters, and only patients were all alleles agreed were included in the analysis (Table S1, Figure S2A). Allele frequencies were visualized with horizontal bar graphs (Figure S2B-F).…”

Section: Star Methodsmentioning

confidence: 99%

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Evolutionary Pressure against MHC Class II Binding Cancer Mutations

Pyke

Thompson

Salem

et al. 2018

Cell

173

100

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Summary: The anti-cancer immune response against mutated peptides of potential immunological relevance (neoantigens) is primarily attributed to MHC-I-restricted cytotoxic CD8+ T-cell responses. MHC-II-restricted CD4+ T-cells also drive anti-tumor responses, but their relation to neoantigen selection and tumor evolution has not been systematically studied. Modeling the potential of an individual’s MHC-II genotype to present 1,018 driver mutations in 5,942 tumors, we demonstrate that the MHC-II genotype constrains the mutational landscape during tumorigenesis in a manner complementary to MHC-I. Mutations poorly bound to MHC-II are positively selected during tumorigenesis, even more than mutations poorly bound to MHC-I. This emphasizes the importance of CD4+ T-cells in anti-tumor immunity. In addition, we observed less inter-patient variation in mutation presentation for MHC-II than for MHC-I. These differences were reflected by age at diagnosis, which was correlated with presentation by MHC-I only. Collectively, our results emphasize the central role of MHC-II presentation in tumor evolution.

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Section: Resultsmentioning

confidence: 99%

Section: Star Methodsmentioning

confidence: 99%

Evolutionary Pressure against MHC Class II Binding Cancer Mutations

Pyke

Thompson

Salem

et al. 2018

Cell

173

100

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“…We formulated the challenge of finding the correct HLA genotype from NGS data as an ILP problem. We applied extensions to previous ILP approaches performing HLA typing from NGS that allow for rare alleles and ethnic frequencies to be integrated into the objective function. The solution was achieved in two steps.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, for example, if the allele A*01:01 was selected as the most promising candidate in the first round, the new library was built containing A*01:01:01:01 , A*01:01:01:02 , A*01:01:02:01 , and so on. Although we used the same constraints applied to the ILP algorithm as previous approaches, the objective function in ILP implementation at this step deviated from previous approaches . We assume that a∈A is an allele and r∈R is a read.…”

Section: Methodsmentioning

confidence: 99%

“…21 The field has tried to overcome these challenges, and numerous of HLA typing tools that use NGS data have been developed during the last decade. 19,[22][23][24][25][26][27][28][29][30][31] All HLA typing algorithms can be divided into two major groups: those that perform de novo assembly of small reads into longer contigs (such as HLAreporter, 23 HLAminer, 24 and ATHLATES 25 ) and those methods that directly align reads against the reference HLA sequences (HLA*PRG, 19 OptiType, 26 Polysolver, 27 HLAscan, 28 HLA-HD, 29 xHLA, 30 and ALPHLARD 31 ). Each method has its own advantages and limitations, and recent studies have benchmarked the different tools in order to characterize their performance.…”

Section: Introductionmentioning

confidence: 99%

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Improved HLA typing of Class I and Class II alleles from next‐generation sequencing data

Sverchkova

Anzar

Stratford

et al. 2019

HLA

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Precise HLA genotyping is of great clinical importance, albeit a challenging bioinformatics endeavor because of the hyper polymorphism of the HLA region. The ever‐increasing availability of next‐generation sequencing (NGS) solutions has spurred the development of several computational methods for predicting HLA genotypes from NGS data. Although some of these tools genotype HLA Class I alleles reasonably well, there is a need to incorporate integrative parameters related to ethnicity frequency information, in order to improve performance for both Class I and Class II alleles. Here, we present a bioinformatics method that addresses some of the current shortfalls in HLA genotyping from NGS. First, reads that map to the HLA region is aligned against a comprehensive library of reference HLA alleles. The allele type was then subsequently determined on the basis of the distribution of aligned reads, and the prior probabilities of the ethnic frequencies of alleles. Three public NGS datasets were used to benchmark the approach against six similar tools. The method outlined in this manuscript displayed an overall accuracy of 98.73% for Class I and 96.37% for Class II alleles. We illustrate an improved integrative approach that outperforms existing tools and is able to predict HLA alleles with improved fidelity for both Class I and Class II alleles.

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Benchmarking HLA genotyping and clarifying HLA impact on survival in tumor immunotherapy

Zhou

Chen

et al. 2021

Molecular Oncology

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Human Leucocyte Antigen (HLA) genotyping gains intensive attention due to its critical role in cancer immunotherapy. It is still a challenging issue to generate reliable HLA genotyping results through in silico tools. In addition, the survival impact of HLA alleles in tumor prognosis and immunotherapy remains controversial. In this study, the benchmarking of HLA genotyping on TCGA is performed and a "Gun-Bullet" model which helps to clarify the survival impact of HLA allele is presented. The performance of HLA class I genotyping is generally better than class II. POLYSOLVER, OptiType and xHLA perform generally better at HLA class I calling with an accuracy of 0.954, 0.949 and 0.937, respectively. HLA-HD obtained the highest accuracy of 0.904 on HLA class II alleles calling. Each HLA-genotyping tool displayed specific error patterns. The ensemble HLA calling from the top-3 tools is superior to any individual one. HLA alleles show distinct survival impact among cancers. Cytolytic activity (CYT) was proposed as the underlying mechanism to interpret the survival impact of HLA alleles in the "Gun-Bullet" model for fighting cancer. A strong HLA allele plus a high tumor mutation burden (TMB) could stimulate intensive immune CYT, leading to extended survival. We established an up-to-now most reliable TCGA HLA benchmark dataset, composing of concordance alleles generated from eight prevalently used HLA genotyping tools. Our findings indicate that reliable HLA genotyping should be performed based on concordance alleles integrating multiple tools and incorporating TMB background with HLA genotype, which helps to improve the survival prediction compared to HLA genotyping alone.

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Fast and accurate HLA typing from short-read next-generation sequence data with xHLA

Cited by 130 publications

References 27 publications

Evolutionary Pressure against MHC Class II Binding Cancer Mutations

Evolutionary Pressure against MHC Class II Binding Cancer Mutations

Improved HLA typing of Class I and Class II alleles from next‐generation sequencing data

Benchmarking HLA genotyping and clarifying HLA impact on survival in tumor immunotherapy

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