% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Bonsack:143345,
      author       = {M. Bonsack$^*$ and S. Hoppe$^*$ and J. Winter and D.
                      Tichy$^*$ and C. Zeller$^*$ and M. Küpper$^*$ and E. C.
                      Schitter$^*$ and R. Blatnik$^*$ and A. Riemer$^*$},
      title        = {{P}erformance evaluation of {MHC} class-{I} binding
                      prediction tools based on an experimentally validated
                      {MHC}-peptide binding dataset.},
      journal      = {Cancer immunology research},
      volume       = {7},
      number       = {5},
      issn         = {2326-6074},
      address      = {Philadelphia, Pa.},
      publisher    = {AACR},
      reportid     = {DKFZ-2019-00935},
      pages        = {719-736},
      year         = {2019},
      abstract     = {The question whether a protein can be processed and
                      resulting peptides presented by major histocompatibility
                      complex (MHC) is of high importance for immunotherapy
                      design. MHC ligands can be predicted by in silico
                      peptide-MHC class-I binding prediction algorithms. However,
                      there are considerable differences in prediction
                      performance, 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 8-11-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
                      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 (AROC). No algorithm
                      outperformed others, but different algorithms predicted best
                      for particular HLA types and peptide lengths. 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 online available prediction
                      algorithms, we developed MHCcombine, a web application that
                      allows simultaneous querying and output combination of up to
                      13 prediction algorithms. Taken together, we here provide an
                      evaluation of peptide-MHC class-I binding prediction tools
                      and recommendations how to increase prediction sensitivity
                      to extend the number of potential epitopes applicable as
                      targets for immunotherapy.},
      cin          = {F130 / C060},
      ddc          = {610},
      cid          = {I:(DE-He78)F130-20160331 / I:(DE-He78)C060-20160331},
      pnm          = {316 - Infections and cancer (POF3-316)},
      pid          = {G:(DE-HGF)POF3-316},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30902818},
      doi          = {10.1158/2326-6066.CIR-18-0584},
      url          = {https://inrepo02.dkfz.de/record/143345},
}