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キーワード:
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要旨:
HLA class I molecules reflect the health state of cells to cytotoxic T
cells by presenting a repertoire of endogenously derived peptides.
However, the extent to which the proteome shapes the peptidome is still
largely unknown. Here we present a high-throughput
mass-spectrometry-based workflow that allows stringent and accurate
identification of thousands of such peptides and direct determination of
binding motifs. Applying the workflow to seven cancer cell lines and
primary cells, yielded more than 22,000 unique HLA peptides across
different allelic binding specificities. By computing a score
representing the HLA-I sampling density, we show a strong link between
protein abundance and HLA-presentation (p < 0.0001). When analyzing
overpresented proteins - those with at least fivefold higher density
score than expected for their abundance - we noticed that they are
degraded almost 3 h faster than similar but nonpresented proteins (top
20% abundance class; median half-life 20.8h versus 23.6h, p < 0.0001).
This validates protein degradation as an important factor for HLA
presentation. Ribosomal, mitochondrial respiratory chain, and
nucleosomal proteins are particularly well presented. Taking a set of
proteins associated with cancer, we compared the predicted
immunogenicity of previously validated T-cell epitopes with other
peptides from these proteins in our data set. The validated epitopes
indeed tend to have higher immunogenic scores than the other detected
HLA peptides. Remarkably, we identified five mutated peptides from a
human colon cancer cell line, which have very recently been predicted to
be HLA-I binders. Altogether, we demonstrate the usefulness of combining
MS-analysis with immunogenesis prediction for identifying, ranking, and
selecting peptides for therapeutic use.
