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Written by | Chunxiao
Targeting tumor antigens that bind to MHC (Major Histocompatibility Complex) molecules holds great promise
for T cell therapy and immunotherapy.
Previous studies have found that post-translational modifications (PTMs) such as phosphorylation, citrullination, or glycosylation regulate antigen presentation and recognition [1].
However, with more than 200 different types of PTM and the difficulties of detection technology, whether and to what extent PTM-driven changes have expanded the cancer antigen target panorama remains to be explored
.
Current methods for discovering neoantigens mainly rely on genomic or transcriptome data [2] combined with HLA I (human leukocyte antigen class I) computational prediction tools
.
Because this method is only suitable for identifying pre-translational levels, they do not give direct information
about the modification status of the peptide.
There is also a mass spectrometry-based immunopeptiomic analysis method
.
However, since each additional modification increases exponentially in the number of theoretical possible peptides for identification, the vast majority of PTMs to date have not been detected
in the immunopeptide set.
To address these challenges, Yifat Merbl's research group from the Department of Immunology at the Weizmann Institute of Science in Israel recently published a title in the journal Nature Biotechnology Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors, a research paper that developed the protein modification integrated search engine PROMISE (Protein Modification Integrated Search Engine), which can detect multiple PTMs without prior biochemical enrichment
.
By analyzing data generated by different combinations of PTMs, including patient-derived tumor samples and cancer cell lines, the authors identified thousands of new modified HLA I-binding peptides
.
In addition, the authors also compared the immune peptide group of colorectal cancer in mice with the immune peptide group of healthy tissue, and found tumor-specific peptides
.
Finally, cancer site-specific modifications
were revealed by extending the analysis to breast cancer cohorts from the clinical proteome.
This study provides insight into PTM changes caused by cancer status and broadens PTM's understanding
of defining tumor-host interactions.
PROMISE relies on ultrafast MSFragger 35 software to compare theoretically derived peptides with those captured in the instrument and search for HLA immunopeptideomics data for multiple modification types (Figure 1).
Modifications identified by PROMISE indicate alterations
that represent the protein PTM in the cell.
To take full advantage of the potential of PROMISE, the authors divided the spectra into different groups based on the modification state, and calculated by PeptideProphet 43 that the peptide spectrum match was higher
in the modified peptide group.
Although the amino acid composition of the immunopeptide group is similar between standard search and PROMISE, PROMISE enriches modified amino acids
when comparing modified and unmodified peptide subgroups.
Next, the authors analyzed the length distribution of each modification type and observed that acetylation, citrullineation, dimethylation, SUMoylation, and ubiquitination levels were all longer
than in the unmodified subgroup.
Figure 1: PROMISE Assays for HLA I-Binding Peptide Modifications
To understand the effect of PTM in the formation of cancer immunopeptideomes, the authors examined a rich library
of peptides.
All PTMs were ordered according to the distribution of amino acid residues and the correlation of background, focusing on the possibility of altering HLA binding preference or T cell receptor (TCR) recognition PTM motifs, the binding of peptides to HLA I molecules depends on the structure and biochemical properties of peptides and HLA I, and TCR recognition motifs are determined
by HLA I peptide complexes.
The biochemical binding properties of specific HLA haplotypes are the strongest determinants of peptide motif order
.
To test whether the detected PTM driver motifs correlate with a specific haplotype, the authors re-analyzed the monoallele HLA immunopeptideomics data
from the references.
To focus on the most prominent features, the authors define a "site score": enrichment at anchor location is a positive score, while enrichment in the middle of the peptide is a negative score
.
Clustering of biological PTMs and haplotypes included in the dataset by locus scoring revealed that the same PTM may have different effects on peptide-MHC-TCR interactions for different haplotypes, in addition to the discovery of several HLAs associated with human diseases (e.
g.
, HLA-A*03:01 associated with multiple sclerosis and HLA-B*51:01 associated with Behcet's disease
。
The authors evaluated the new PTM binding motifs
using structural modeling.
The interaction between the predicted MHC and the modified peptide was found to be very strong, indicating that they were more stable than the unmodified peptide, and PROMISE predicted the same result
.
To test whether the peptides identified by PROMISE are specific to cancerous tissue, the authors performed immunopepteptidomics on colon cancer cells in MC 38 mice and compared
the results with the tissue immunopeptideomics data from healthy mice in the reference [3].
The PROMISE analysis revealed that 36% of the 2803 peptides had at least one PTM
.
When comparing modified peptides from the MC 38 immunopeptide group with modified peptides in healthy tissue, the authors found a cancer-specific subgroup that, through manual annotation and spectral similarity scores, confirmed that all matched the identification results, including peptides with N-terminal acetylation, citrullineation, dimethylation, methylation, phosphorylation, and SUMO modification (Figure 2).
The authors also analyzed immunopeptideomics data
from triple-negative breast cancer and control cohorts.
A total of 2771 modified peptides were identified in this cohort, and the evaluation found that several modifications in the tumor immunopeptide group included significant reductions in carbamoyl methylation and citrullineation frequency, and cystylated peptide frequencies increased
significantly in the tumor immunopeptide group.
In addition, 27 phosphorylated peptides were found to appear only in tumor tissue and not in controls, and 42% of the sites identified in immunopeptideomics and phosphorylated proteomics were phosphorylated
.
Figure 2: PROMISE assay identification of the MC38 immunopeptide group
By developing PROMISE, the authors systematically analyzed the PTM landscape in the immunopeptide group and identified thousands of modified peptides in different cancers, expanding the antigen field and revealing disease-specific modification targets, a discovery that may have broad implications for T-cell-mediated therapies for cancer, and in addition to cancer, this method can also be used to expand the understanding of PTM-driven HLA in infectious diseases, autoimmune diseases, and neuro-related disease pathology
。 Since PTM is more unstable than mutations, efforts will be needed to standardize
its detection if PTM becomes a routine complement to immunopeptideomics analysis.
Original link:
style="line-height: 1.
75em;margin-left: 8px;margin-right: 8px;margin-bottom: 0px;">Platemaker: Eleven
References
1.
Ramarathinam, S.
H.
, Croft, N.
P.
, Illing, P.
T.
, Faridi, P.
& Purcell, A.
W.
Employing proteomics in the study of antigen presentation: an update.
Expert Rev.
Proteomics 15, 637–645 (2018).
2.
Karasaki, T.
et al.
Prediction and prioritization of neoantigens: integration of RNA sequencing data with whole-exome sequencing.
Cancer Sci.
108, 170–177 (2017).
3.
Schuster, H.
et al.
A tissue-based draft map of the murine MHC class I immunopeptidome.
Sci.
Data 5, 180157 (2018).
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