I’ve got algorithm: predicting tumor and autoimmune peptide targets for CD8+ T cells
Devin Dersh, Jonathan W. Yewdell
Devin Dersh, Jonathan W. Yewdell
Published November 14, 2016
Citation Information: J Clin Invest. 2016;126(12):4399-4401. https://doi.org/10.1172/JCI91302.
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Commentary

I’ve got algorithm: predicting tumor and autoimmune peptide targets for CD8+ T cells

Abstract

CD8+ T cells play a central role in eradicating intracellular pathogens, but also are important for noninfectious diseases, including cancer and autoimmunity. The ability to clinically manipulate CD8+ T cells to target cancer and autoimmune disease is limited by our ignorance of relevant self-peptide target antigens. In this issue of the JCI, Pearson et al. describe 25,270 MHC class I–associated peptides presented by a wide range of HLA A and B allomorphs expressed by 18 different B cell lines. Via extensive bioinformatic analysis, the authors make surprising conclusions regarding the selective nature of peptide generation at the level of individual gene products and create a predictive algorithm for disease-relevant self-peptides that will be of immediate use for clinical and basic immunological research.

Authors

Devin Dersh, Jonathan W. Yewdell

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Figure 1

Summary of experimental approach and salient conclusions.

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Summary of experimental approach and salient conclusions. As reported in...
As reported in this issue, Pearson et al. isolated B cells from 18 individuals, expressing a total of 27 MHC class I allomorphs, and identified surface-presented peptides using mild acid elution coupled with mass spectrometry. Simultaneously, personalized genetic databases were created using transcriptome and exome sequencing from each donor. Together, the peptide identification and sequencing information allowed for the mapping of MAPs at their source locations within the genome.