12 research outputs found
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Computational prediction of MHC anchor locations guides neoantigen identification and prioritization
Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient's specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies
Using Confirmatory Factor Analysis to Evaluate Construct Validity of the Brief Pain Inventory (BPI)
Supplementary Figure S2 from Memory-like Differentiation, Tumor-Targeting mAbs, and Chimeric Antigen Receptors Enhance Natural Killer Cell Responses to Head and Neck Cancer
Supplementary Figure 2. HNSCC cell lines and patient samples express multiple inhibitory and activating surface ligands.</p
Supplementary Figure S6 from Memory-like Differentiation, Tumor-Targeting mAbs, and Chimeric Antigen Receptors Enhance Natural Killer Cell Responses to Head and Neck Cancer
Supplementary Figure 6. EphA2-CAR ML NK cells display enhanced functional responses against HPV positive and HPV negative HNSCC cell lines.</p
Supplementary Figure S7 from Memory-like Differentiation, Tumor-Targeting mAbs, and Chimeric Antigen Receptors Enhance Natural Killer Cell Responses to Head and Neck Cancer
Supplementary Figure 7. EphA2-CAR-ML NK cells require intracellular CAR signaling and have antigen specific enhanced response to HNSCC.</p
Supplementary Figure S1 from Memory-like Differentiation, Tumor-Targeting mAbs, and Chimeric Antigen Receptors Enhance Natural Killer Cell Responses to Head and Neck Cancer
Supplementary Figure 1. Memory-Like (ML) NK cells from normal donors exhibit improved ability to control HNSCC cell lines compared to conventional NK (cNK) cells.</p
Supplementary Figure S4 from Memory-like Differentiation, Tumor-Targeting mAbs, and Chimeric Antigen Receptors Enhance Natural Killer Cell Responses to Head and Neck Cancer
Supplementary Figure 4. Cetuximab does not impair in vitro growth of HNSCC cells or cause toxicity to mice in NSG xenograft models.</p
Supplementary Figure S5 from Memory-like Differentiation, Tumor-Targeting mAbs, and Chimeric Antigen Receptors Enhance Natural Killer Cell Responses to Head and Neck Cancer
Supplementary Figure 5. Expression of EphA2 on target cells and gating strategy for CAR-expressing NK cells</p