Expanding the definition of MHC Class I peptide binding promiscuity to support vaccine discovery across cancers with CARMEN
Waleron, M. M.; Kallor, A. A.; Palkowski, A.; Daghir-Wojtkowiak, E.; Borole, P.; Kocikowski, M.; Polom, K.; Marino, F.; Monterde, B.; Mastromattei, M.; Venditti, D.; Stares, M.; The KATY Consortium, ; Singh, A.; Hupp, T.; Battail, C.; Laird, A.; Pesquita, C.; Zapata, L.; Symeonides, S. N.; Rajan, A.; Zanzotto, F. M.; Alfaro, J. A.
Show abstract
Promiscuity in T-cell antigen landscapes refers to the dual flexibility of peptides binding multiple MHC alleles and MHC alleles presenting diverse arrays of peptides. By understanding how neoantigens are shared across varied HLA backgrounds, promiscuity analysis can inform the selection of cancer-vaccine targets that reach a wider segment of the population and help refine patient stratification for diverse immunotherapies. We expand the concept of promiscuity to encompass peptides, MHC alleles, individuals, populations, and genomic regions. Our CARMEN database release harmonizes data from 72 publications (2,323 samples) across tissue types, with a focus on cancer. Using Gibbs clustering and dimensionality reduction (UMAP), we systematically map promiscuity and immunological versatility across these biological levels. Gene and mutation analysis reveals recurrent cancer mutations in highly promiscuous genomic regions, highly mutated cancer genes that avoid presented regions, and sheds light on genomic regions important to response to immunotherapy.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- APMAT analysis reveals the association between CD8 T cell receptors, cognate antigen, and T cell phenotype and persistence 97%
- Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells 97%
- NeoPrecis: Enhancing Immunotherapy Response Prediction through Integration of Qualified Immunogenicity and Clonality-Aware Neoantigen Landscapes 97%
Similar papers in this journal
- Integrative, high-resolution analysis of single cell gene expression across experimental conditions with PARAFAC2-RISE 95%
- Integrated single-cell analyses of affinity-tested B-cells enable the identification of a gene signature to predict antibody affinity. 95%
- Machine learning analysis of the T cell receptor repertoire identifies sequence features that predict self-reactivity 94%
Similar papers in this journal
- Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functions 96%
- Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions 96%
- NEST: Spatially-mapped cell-cell communication patterns using a deep learning-based attention mechanism 95%
Similar papers in this journal
- Functional Inference of Gene Regulation using Single-Cell Multi-Omics 95%
- Gene regulatory network inference from CRISPR perturbations in primary CD4+ T cells elucidates the genomic basis of immune disease 95%
- Impact of disease-associated chromatin accessibility QTLs across immune cell types and contexts 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.