Identification and functional characterization of a Siglec-7 counter-receptor on K562 cells.

Sialic acid (Sia)-binding immunoglobulin-like lectin 7 (Siglec-7) is an inhibitory receptor primarily expressed on natural killer (NK) cells and monocytes. Siglec-7 is known to negatively regulate the innate immune system through Sia binding to distinguish self and nonself; however, a counter-receptor bearing its natural ligand remains largely unclear. Here, we identified a counter-receptor of Siglec-7 using K562 hematopoietic carcinoma cells presenting cell surface ligands for Siglec-7. We affinity-purified the ligands using Fc-ligated recombinant Siglec-7 and diSia-dextran polymer, a strong inhibitor for Siglec-7. We then confirmed the counter-receptor for Siglec-7 as leukosialin (CD43) through mass spectrometry, immunoprecipitation, and proximity labeling. Additionally, we demonstrated that the cytotoxicity of NK cells toward K562 cells was suppressed by overexpression of leukosialin in a Siglec-7-dependent manner. Taken together, our data suggest that leukosialin on K562 is a counter-receptor for Siglec-7 on NK cells and that a cluster of the Sia-containing glycan epitope on leukosialin is key as trans-ligand for unmasking the cis-ligand.

Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation.

Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.