PMID- 29104575
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20191120
IS  - 1664-3224 (Print)
IS  - 1664-3224 (Electronic)
IS  - 1664-3224 (Linking)
VI  - 8
DP  - 2017
TI  - 'Hotspots' of Antigen Presentation Revealed by Human Leukocyte Antigen 
      Ligandomics for Neoantigen Prioritization.
PG  - 1367
LID - 10.3389/fimmu.2017.01367 [doi]
LID - 1367
AB  - The remarkable clinical efficacy of the immune checkpoint blockade therapies has 
      motivated researchers to discover immunogenic epitopes and exploit them for 
      personalized vaccines. Human leukocyte antigen (HLA)-binding peptides derived 
      from processing and presentation of mutated proteins are one of the leading 
      targets for T-cell recognition of cancer cells. Currently, most studies attempt 
      to identify neoantigens based on predicted affinity to HLA molecules, but the 
      performance of such prediction algorithms is rather poor for rare HLA class I 
      alleles and for HLA class II. Direct identification of neoantigens by mass 
      spectrometry (MS) is becoming feasible; however, it is not yet applicable to most 
      patients and lacks sensitivity. In an attempt to capitalize on existing 
      immunopeptidomics data and extract information that could complement HLA-binding 
      prediction, we first compiled a large HLA class I and class II immunopeptidomics 
      database across dozens of cell types and HLA allotypes and detected hotspots that 
      are subsequences of proteins frequently presented. About 3% of the peptidome was 
      detected in both class I and class II. Based on the gene ontology of their source 
      proteins and the peptide's length, we propose that their processing may partake 
      by the cellular class II presentation machinery. Our database captures the global 
      nature of the in vivo peptidome averaged over many HLA alleles, and therefore, 
      reflects the propensity of peptides to be presented on HLA complexes, which is 
      complementary to the existing neoantigen prediction features such as binding 
      affinity and stability or RNA abundance. We further introduce two 
      immunopeptidomics MS-based features to guide prioritization of neoantigens: the 
      number of peptides matching a protein in our database and the overlap of the 
      predicted wild-type peptide with other peptides in our database. We show as a 
      proof of concept that our immunopeptidomics MS-based features improved neoantigen 
      prioritization by up to 50%. Overall, our work shows that, in addition to 
      providing huge training data to improve the HLA binding prediction, 
      immunopeptidomics also captures other aspects of the natural in vivo presentation 
      that significantly improve prediction of clinically relevant neoantigens.
FAU - Muller, Markus
AU  - Muller M
AD  - Vital-IT, Swiss Institute of Bioinformatics, Lausanne, Switzerland.
AD  - Swiss Institute of Bioinformatics, Lausanne, Switzerland.
FAU - Gfeller, David
AU  - Gfeller D
AD  - Swiss Institute of Bioinformatics, Lausanne, Switzerland.
AD  - Ludwig Cancer Research Center, University of Lausanne, Epalinges, Switzerland.
FAU - Coukos, George
AU  - Coukos G
AD  - Ludwig Cancer Research Center, University of Lausanne, Epalinges, Switzerland.
AD  - Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland.
FAU - Bassani-Sternberg, Michal
AU  - Bassani-Sternberg M
AD  - Ludwig Cancer Research Center, University of Lausanne, Epalinges, Switzerland.
AD  - Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland.
LA  - eng
PT  - Journal Article
DEP - 20171020
PL  - Switzerland
TA  - Front Immunol
JT  - Frontiers in immunology
JID - 101560960
PMC - PMC5654951
OTO - NOTNLM
OT  - antigen processing and presentation
OT  - cancer immunotherapy
OT  - human leukocyte antigen-binding prediction
OT  - immunopeptidomics
OT  - mass spectrometry
OT  - neoantigens
OT  - personalized cancer vaccines
EDAT- 2017/11/07 06:00
MHDA- 2017/11/07 06:01
PMCR- 2017/01/01
CRDT- 2017/11/07 06:00
PHST- 2017/07/31 00:00 [received]
PHST- 2017/10/05 00:00 [accepted]
PHST- 2017/11/07 06:00 [entrez]
PHST- 2017/11/07 06:00 [pubmed]
PHST- 2017/11/07 06:01 [medline]
PHST- 2017/01/01 00:00 [pmc-release]
AID - 10.3389/fimmu.2017.01367 [doi]
PST - epublish
SO  - Front Immunol. 2017 Oct 20;8:1367. doi: 10.3389/fimmu.2017.01367. eCollection 
      2017.