Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 18 (12), e1700465

Immunopeptidomic Profiling of HLA-A2-Positive Triple Negative Breast Cancer Identifies Potential Immunotherapy Target Antigens


Immunopeptidomic Profiling of HLA-A2-Positive Triple Negative Breast Cancer Identifies Potential Immunotherapy Target Antigens

Nicola Ternette et al. Proteomics.


The recent development in immune checkpoint inhibitors and chimeric antigen receptor (CAR) T-cells in the treatment of cancer has not only demonstrated the potency of utilizing T-cell reactivity for cancer therapy, but has also highlighted the need for developing new approaches to discover targets suitable for such novel therapeutics. Here we analyzed the immunopeptidomes of six HLA-A2-positive triple negative breast cancer (TNBC) samples by nano-ultra performance liquid chromatography tandem mass spectrometry (nUPLC-MS2 ). Immunopeptidomic profiling identified a total of 19 675 peptides from tumor and adjacent normal tissue and 130 of the peptides were found to have higher abundance in tumor than in normal tissues. To determine potential therapeutic target proteins, we calculated the average tumor-associated cohort coverage (aTaCC) that represents the percentage coverage of each protein in this cohort by peptides that had higher tumoral abundance. Cofilin-1 (CFL-1), interleukin-32 (IL-32), proliferating cell nuclear antigen (PCNA), syntenin-1 (SDCBP), and ribophorin-2 (RPN-2) were found to have the highest aTaCC scores. We propose that these antigens could be evaluated further for their potential as targets in breast cancer immunotherapy and the small cohort immunopeptidomics analysis technique could be used in a wide spectrum of target discovery. Data are available via ProteomeXchange with identifier PXD009738.

Keywords: HLA-A2; aTaCC; breast cancer; immunopeptidome; mass spectrometry.


Figure 1
Figure 1
Characteristics of peptides isolated from HLA‐A2‐positive patient tumor and adjacent normal tissues. A) Numbers of HLA‐associated peptides identified from each patient sample. B) The average numbers of peptides identified from normal and tumor tissues from all patients. C) Length distribution of all identified peptides in normal and tumor tissue. D) Venn diagrams show the numbers of peptides identified in normal and tumor tissue for each patient. Percentage of peptides derived from normal tissue that are also present in the tumor sample of the same patient is shown for each graph.
Figure 2
Figure 2
Characteristics of peptides predicted to bind HLA‐A*0201 by NetMHCpan. A) Numbers of peptides with NetMHCpan rank score for HLA‐A*0201 binding ≤2 from each patient sample. B) Average numbers and C) length distribution of all such predicted HLA‐A*0201 binders. D) Motif analysis of predicted HLA‐A*0201 binders in comparison with the known IEDB motif. E) Overlap of HLA‐A*0201‐peptides identified in all five patients.

Similar articles

See all similar articles

Cited by 4 PubMed Central articles


    1. Higgins M. J., Baselga J., J. Clin. Invest. 2011, 121, 3797. - PMC - PubMed
    1. Bauer K. R., Brown M., Cress R. D., Parise C. A., Caggiano V., Cancer 2007, 109, 1721. - PubMed
    1. Andreopoulou E., Schweber S. J., Sparano J. A., McDaid H. M., Expert Opin. Pharmacother. 2015, 16, 983. - PMC - PubMed
    1. Caron E., Vincent K., Fortier M. H., Laverdure J. P., Bramoulle A., Hardy M. P., Voisin G., Roux P. P., Lemieux S., Thibault P., Perreault C., Mol. Syst. Biol. 2011, 7, 533. - PMC - PubMed
    1. Haen S. P., Rammensee H. G., Curr. Opin. Immunol. 2013, 25, 277. - PubMed

Publication types