Towards a new gold standard – NGS corrections to sanger SBT genotyping results

Melista, Efi; Rigo, Krisztina; Pasztor, Agnes; Christiansen, Mette; Bertinetto, Francesca; Meintjes, Peter; Hague, Tim

doi:10.1016/j.humimm.2015.07.205

Cited by 3 publications

(3 citation statements)

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“…Different MHC haplotypes have different binding affinity with peptides, so it is crucial to accurately genotype the patient’s HLA alleles before peptides-MHC binding affinity prediction. Polymerase chain reaction-sequencing based typing (PCR-SBT) is the gold standard for HLA genotyping ( 47 ). However, there are a large number of alleles in HLA, and the polymorphisms of alleles are outside the analysis region, or the alleles are heterozygous, which may lead to ambiguous genotyping results.…”

Section: Feature Extraction Algorithms In Neoantigen Predictionmentioning

confidence: 99%

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

Chen

Gao

et al. 2023

Front. Oncol.

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The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

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Section: Feature Extraction Algorithms In Neoantigen Predictionmentioning

confidence: 99%

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

Chen

Gao

et al. 2023

Front. Oncol.

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“…Therefore, it is crucial to know the HLA type before ranking peptides. The gold standard for HLA allotype determination is clinical HLA typing by sequence-specific PCR [ 101 , 102 ]. However, currently available HLA typers based on WES/RNA-seq data provide a high enough accuracy rate and can also be used for HLA allotype identification when a clinical HLA type is unavailable.…”

Section: Genomics-based Approaches and Current Bioinformatics Pipementioning

confidence: 99%

Main Strategies for the Identification of Neoantigens

Gopanenko

Кособокова

Косоруков

2020

Cancers

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Genetic instability of tumors leads to the appearance of numerous tumor-specific somatic mutations that could potentially result in the production of mutated peptides that are presented on the cell surface by the MHC molecules. Peptides of this kind are commonly called neoantigens. Their presence on the cell surface specifically distinguishes tumors from healthy tissues. This feature makes neoantigens a promising target for immunotherapy. The rapid evolution of high-throughput genomics and proteomics makes it possible to implement these techniques in clinical practice. In particular, they provide useful tools for the investigation of neoantigens. The most valuable genomic approach to this problem is whole-exome sequencing coupled with RNA-seq. High-throughput mass-spectrometry is another option for direct identification of MHC-bound peptides, which is capable of revealing the entire MHC-bound peptidome. Finally, structure-based predictions could significantly improve the understanding of physicochemical and structural features that affect the immunogenicity of peptides. The development of pipelines combining such tools could improve the accuracy of the peptide selection process and decrease the required time. Here we present a review of the main existing approaches to investigating the neoantigens and suggest a possible ideal pipeline that takes into account all modern trends in the context of neoantigen discovery.

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“…The data provided by SBT sequencing are comprehensive, and this method represents the gold standard in HLA genotyping. Nevertheless, a new sequencing method named next-generation sequencing (NGS) has been developed [ 29 ]. It produces massive and detailed sequencing data, offering a broad view of the genes in their entirety [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

HLA Gene Polymorphisms in Romanian Patients with Chronic Lymphocytic Leukemia

Tizu,

Calenic,

Hârza

et al. 2024

Genetics Research

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Background and Objectives. Numerous genome-wide association studies have highlighted that chronic lymphocytic leukemia (CLL) is a lymphoproliferative disorder with an important genetic component. An important number of genes have been implicated in CLL etiology, with some of the most important being human leukocyte antigen (HLA) genes. The aim of this study was to analyze the association of CLL with certain HLA alleles in the Romanian population and to compare our results with previous findings. Materials and Methods. This study included 66 patients with CLL, diagnosed between 2020 and 2022, and 100 healthy controls. HLA class I and class II genes (HLA-A/B/C, HLA-DQA1/DQB1/DPA1/DPB1, and HLA-DRB1/3/4/5) were investigated using next-generation sequencing technology. Results. Several HLA alleles were strongly associated with CLL. The most important finding was that HLA-DRB1∗04:02:01 (p=0.001, OR = 1.05) and HLA-DRB3∗02:01:01 (p=0.009, OR = 1.03) have a predisposing role in CLL development. Moreover, we identified that HLA-A∗24:02:01 0.01 (p=0.01, OR = 0.38), HLA-DQA1∗05:05:01 (p=0.01, OR = 0.56), HLA-DQB1∗03:02:01 (p=0.03, OR = 0.40), and HLA-DRB4∗01:03:01 (p=0.03, OR = 0.54 alleles have protective roles. Correlations between HLA expression and gender showed that women had a higher expression of protective HLA alleles when compared to men. Conclusions. Our data are the first to indicate that in Romanian patients with CLL, the HLA-A∗24:02:01 and HLA-DQA1∗05:05:01 alleles have a protective role against CLL development, whereas HLA-DRB1∗04:02:01 and HLA-DRB3∗02:01:01alleles are positively associated with CLL.

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Towards a new gold standard – NGS corrections to sanger SBT genotyping results

Cited by 3 publications

References 0 publications

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

Main Strategies for the Identification of Neoantigens

HLA Gene Polymorphisms in Romanian Patients with Chronic Lymphocytic Leukemia

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