Unknown

Dataset Information

0

A Recurrent Mutation in Anaplastic Lymphoma Kinase with Distinct Neoepitope Conformations.


ABSTRACT: The identification of recurrent human leukocyte antigen (HLA) neoepitopes driving T cell responses against tumors poses a significant bottleneck in the development of approaches for precision cancer therapeutics. Here, we employ a bioinformatics method, Prediction of T Cell Epitopes for Cancer Therapy, to analyze sequencing data from neuroblastoma patients and identify a recurrent anaplastic lymphoma kinase mutation (ALK R1275Q) that leads to two high affinity neoepitopes when expressed in complex with common HLA alleles. Analysis of the X-ray structures of the two peptides bound to HLA-B*15:01 reveals drastically different conformations with measurable changes in the stability of the protein complexes, while the self-epitope is excluded from binding due to steric hindrance in the MHC groove. To evaluate the range of HLA alleles that could display the ALK neoepitopes, we used structure-based Rosetta comparative modeling calculations, which accurately predict several additional high affinity interactions and compare our results with commonly used prediction tools. Subsequent determination of the X-ray structure of an HLA-A*01:01 bound neoepitope validates atomic features seen in our Rosetta models with respect to key residues relevant for MHC stability and T cell receptor recognition. Finally, MHC tetramer staining of peripheral blood mononuclear cells from HLA-matched donors shows that the two neoepitopes are recognized by CD8+ T cells. This work provides a rational approach toward high-throughput identification and further optimization of putative neoantigen/HLA targets with desired recognition features for cancer immunotherapy.

SUBMITTER: Toor JS 

PROVIDER: S-EPMC5797543 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

altmetric image

Publications


The identification of recurrent human leukocyte antigen (HLA) neoepitopes driving T cell responses against tumors poses a significant bottleneck in the development of approaches for precision cancer therapeutics. Here, we employ a bioinformatics method, Prediction of T Cell Epitopes for Cancer Therapy, to analyze sequencing data from neuroblastoma patients and identify a recurrent anaplastic lymphoma kinase mutation (<i>ALK</i> R1275Q) that leads to two high affinity neoepitopes when expressed i  ...[more]

Similar Datasets

| S-EPMC4907642 | biostudies-literature
2006-11-22 | GSE6184 | GEO
| S-EPMC3481340 | biostudies-literature
| S-EPMC5615338 | biostudies-literature
| S-EPMC5830391 | biostudies-literature
| S-EPMC8271116 | biostudies-literature
| S-EPMC10789887 | biostudies-literature
| S-EPMC6984433 | biostudies-literature
| S-EPMC3299366 | biostudies-literature
| S-EPMC5664077 | biostudies-literature