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Baumgaertner, P.

Publications and source records attributed to Baumgaertner, P..

2 recordsLinked to original sources

The C-terminal extension landscape of naturally presented HLA-I ligands

HLA-I molecules play a central role in antigen presentation. They typically bind 9- to 12-mer peptides and their canonical binding mode involves anchor residues at the second and last positions of their ligands. To investigate potential non-canonical binding modes we collected in-depth and accurate HLA peptidomics datasets covering 54 HLA-I alleles and developed novel algorithms to analyze these data. Our results reveal frequent (442 unique peptides) and statistically significant C-terminal extensions for at least eight alleles, including the common HLA-A03:01, HLA-A31:01 and HLA-A68:01. High resolution crystal structure of HLA-A68:01 with such a ligand uncovers structural changes taking place to accommodate C-terminal extensions and helps unraveling sequence and structural properties predictive of the presence of these extensions. Scanning viral proteomes with the new C-terminal extension motifs identifies many putative epitopes and we demonstrate direct recognition by human CD8+ T cells of a C-terminally extended epitope from cytomegalovirus.

immunology

Simultaneous Enumeration Of Cancer And Immune Cell Types From Bulk Tumor Gene Expression Data

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research.

cancer biology