PMID- 30902818
OWN - NLM
STAT- MEDLINE
DCOM- 20200722
LR  - 20200722
IS  - 2326-6074 (Electronic)
IS  - 2326-6066 (Linking)
VI  - 7
IP  - 5
DP  - 2019 May
TI  - Performance Evaluation of MHC Class-I Binding Prediction Tools Based on an 
      Experimentally Validated MHC-Peptide Binding Data Set.
PG  - 719-736
LID - 10.1158/2326-6066.CIR-18-0584 [doi]
AB  - Knowing whether a protein can be processed and the resulting peptides presented 
      by major histocompatibility complex (MHC) is highly important for immunotherapy 
      design. MHC ligands can be predicted by in silico peptide-MHC class-I binding 
      prediction algorithms. However, prediction performance differs considerably, 
      depending on the selected algorithm, MHC class-I type, and peptide length. We 
      evaluated the prediction performance of 13 algorithms based on binding affinity 
      data of 8- to 11-mer peptides derived from the HPV16 E6 and E7 proteins to the 
      most prevalent human leukocyte antigen (HLA) types. Peptides from high to low 
      predicted binding likelihood were synthesized, and their HLA binding was 
      experimentally verified by in vitro competitive binding assays. Based on the 
      actual binding capacity of the peptides, the performance of prediction algorithms 
      was analyzed by calculating receiver operating characteristics (ROC) and the area 
      under the curve (A(ROC)). No algorithm outperformed others, but different 
      algorithms predicted best for particular HLA types and peptide lengths. The 
      sensitivity, specificity, and accuracy of decision thresholds were calculated. 
      Commonly used decision thresholds yielded only 40% sensitivity. To increase 
      sensitivity, optimal thresholds were calculated, validated, and compared. In 
      order to make maximal use of prediction algorithms available online, we developed 
      MHCcombine, a web application that allows simultaneous querying and output 
      combination of up to 13 prediction algorithms. Taken together, we provide here an 
      evaluation of peptide-MHC class-I binding prediction tools and recommendations to 
      increase prediction sensitivity to extend the number of potential epitopes 
      applicable as targets for immunotherapy.
CI  - (c)2019 American Association for Cancer Research.
FAU - Bonsack, Maria
AU  - Bonsack M
AUID- ORCID: 0000-0003-2087-7451
AD  - German Cancer Research Center (DKFZ), Immunotherapy and Immunoprevention, 
      Heidelberg, Germany.
AD  - German Center for Infection Research (DZIF), Molecular Vaccine Design, partner 
      site Heidelberg, Heidelberg, Germany.
AD  - Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
FAU - Hoppe, Stephanie
AU  - Hoppe S
AD  - German Cancer Research Center (DKFZ), Immunotherapy and Immunoprevention, 
      Heidelberg, Germany.
AD  - German Center for Infection Research (DZIF), Molecular Vaccine Design, partner 
      site Heidelberg, Heidelberg, Germany.
AD  - Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
FAU - Winter, Jan
AU  - Winter J
AD  - German Cancer Research Center (DKFZ), Immunotherapy and Immunoprevention, 
      Heidelberg, Germany.
AD  - Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
FAU - Tichy, Diana
AU  - Tichy D
AD  - German Cancer Research Center (DKFZ), Division of Biostatistics, Heidelberg, 
      Germany.
FAU - Zeller, Christine
AU  - Zeller C
AD  - German Cancer Research Center (DKFZ), Immunotherapy and Immunoprevention, 
      Heidelberg, Germany.
FAU - Kupper, Marius D
AU  - Kupper MD
AD  - German Cancer Research Center (DKFZ), Immunotherapy and Immunoprevention, 
      Heidelberg, Germany.
AD  - Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
FAU - Schitter, Eva C
AU  - Schitter EC
AD  - German Cancer Research Center (DKFZ), Immunotherapy and Immunoprevention, 
      Heidelberg, Germany.
AD  - Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
FAU - Blatnik, Renata
AU  - Blatnik R
AD  - German Cancer Research Center (DKFZ), Immunotherapy and Immunoprevention, 
      Heidelberg, Germany.
AD  - German Center for Infection Research (DZIF), Molecular Vaccine Design, partner 
      site Heidelberg, Heidelberg, Germany.
AD  - Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
FAU - Riemer, Angelika B
AU  - Riemer AB
AUID- ORCID: 0000-0002-5865-0714
AD  - German Cancer Research Center (DKFZ), Immunotherapy and Immunoprevention, 
      Heidelberg, Germany. a.riemer@dkfz.de.
AD  - German Center for Infection Research (DZIF), Molecular Vaccine Design, partner 
      site Heidelberg, Heidelberg, Germany.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20190322
PL  - United States
TA  - Cancer Immunol Res
JT  - Cancer immunology research
JID - 101614637
RN  - 0 (E6 protein, Human papillomavirus type 16)
RN  - 0 (Epitopes, T-Lymphocyte)
RN  - 0 (Histocompatibility Antigens Class I)
RN  - 0 (Ligands)
RN  - 0 (Oncogene Proteins, Viral)
RN  - 0 (Papillomavirus E7 Proteins)
RN  - 0 (Peptides)
RN  - 0 (Repressor Proteins)
RN  - 0 (oncogene protein E7, Human papillomavirus type 16)
SB  - IM
EIN - Cancer Immunol Res. 2019 Jul;7(7):1221. PMID: 31262774
MH  - *Algorithms
MH  - Epitopes, T-Lymphocyte/*metabolism
MH  - Histocompatibility Antigens Class I/*metabolism
MH  - Humans
MH  - Ligands
MH  - Oncogene Proteins, Viral/*metabolism
MH  - Papillomavirus E7 Proteins/*metabolism
MH  - Peptides/*metabolism
MH  - Protein Binding
MH  - Repressor Proteins/*metabolism
EDAT- 2019/03/25 06:00
MHDA- 2020/07/23 06:00
CRDT- 2019/03/24 06:00
PHST- 2018/09/14 00:00 [received]
PHST- 2018/12/19 00:00 [revised]
PHST- 2019/03/18 00:00 [accepted]
PHST- 2019/03/25 06:00 [pubmed]
PHST- 2020/07/23 06:00 [medline]
PHST- 2019/03/24 06:00 [entrez]
AID - 2326-6066.CIR-18-0584 [pii]
AID - 10.1158/2326-6066.CIR-18-0584 [doi]
PST - ppublish
SO  - Cancer Immunol Res. 2019 May;7(5):719-736. doi: 10.1158/2326-6066.CIR-18-0584. 
      Epub 2019 Mar 22.