OptiType: precision HLA typing from next-generation sequencing data

By András Szolek, Benjamin Schubert, C. Mohr, Marc Sturm, Magdalena Feldhahn, Oliver Kohlbacher. Bioinform., 2014

678 citations recorded by the source corpus.

Motivation: The human leukocyte antigen (HLA) gene cluster plays a crucial role in adaptive immunity and is thus relevant in many biomedical applications. While next-generation sequencing data are often available for a patient, deducing the HLA genotype is difficult because of substantial sequence similarity within the cluster and exceptionally high variability of the loci. Established approaches, therefore, rely on specific HLA enrichment and sequencing techniques, coming at an additional cost and extra turnaround time. Result: We present OptiType, a novel HLA genotyping algorithm based on integer linear programming, capable of producing accurate predictions from NGS data not specifically enriched for the HLA cluster. We also present a comprehensive benchmark dataset consisting of RNA, exome and whole-genome sequencing data. OptiType significantly outperformed previously published in silico approaches with an overall accuracy of 97% enabling its use in a broad range of applications. Contact: szolek@informatik.uni-tuebingen.de Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1d4162253d3e32a2b3a62d0f8faab4fba3386c10

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Immunology

In Molecular Biology

Citations in the corpus

Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer (2016, methodology): “To accurately infer a patient’s germline HLA 4-digit allele genotype, whole-exome-sequencing data from paired tumor/normal samples were first aligned to a reference allele set, which was then formulated as an integer linear programming optimization procedure to generate a final genotype (40).”
Genomic features of response to combination immunotherapy in patients with advanced non-small cell lung cancer (2018, background): “However, the sensitivity and specificity of PD-L1 expression is modest (Reck et al., 2016; Taube et al., 2014), which has prompted the search for additional predictive tools.”
Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures (2016, methodology): “A, HLA-B, and HLA-C alleles of each ccRCC TCGA patient using OptiType [62].”
Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution (2017, methodology): “HLA haplotypes from sequencing data (Shukla et al., 2015; Szolek et al., 2014) and utilizing previously published datasets (Brastianos et al., 2015; Jamal-Hanjani et al., 2017), we endeavored to address the prevalence and timing of HLA LOH in lung cancer and its potential impact on tumor evolution,…”
Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade (2016, methodology): “) Characterization of neoantigens HLA alleles were called from RNA-sequencing FASTQ files using Optitype (Szolek et al., 2014), selected for its high performance and for its applicability to RNA-sequencing data.”
Next-Generation Sequencing Technology: Current Trends and Advancements (2023, background): “As technology progresses, NGS is expected to play a pivotal role in areas such as single-cell genomics, long-read sequencing, epigenomics, and multi-omics integration, enabling a deeper understanding of cellular processes, disease mechanisms, and personalized treatment strategies.”
Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma (2019, methodology): “Specifically, Optitype (Szolek et al., 2014) was used to identify human leukocyte antigens (HLA) in the WES data. netMHCpan (Jurtz et al., 2017) was then used to predict HLA peptide binding affinity for somatic mutation-derived variant peptides with a length between 8-11 amino acids.”