% 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{Srinivasan:128367,
      author       = {H. Srinivasan$^*$ and Y. Allory and M. Sill$^*$ and D.
                      Vordos and M. S. S. Alhamdani$^*$ and F. Radvanyi and J.
                      Hoheisel$^*$ and C. Schröder$^*$},
      title        = {{P}rediction of recurrence of non muscle-invasive bladder
                      cancer by means of a protein signature identified by
                      antibody microarray analyses.},
      journal      = {Practical proteomics},
      volume       = {14},
      number       = {11},
      issn         = {1615-9853},
      address      = {Weinheim},
      publisher    = {Wiley VCH55771},
      reportid     = {DKFZ-2017-04384},
      pages        = {1333 - 1342},
      year         = {2014},
      abstract     = {About $70\%$ of newly diagnosed cases of bladder cancer are
                      low-stage, low-grade, non muscle-invasive. Standard
                      treatment is transurethral resection. About $60\%$ of the
                      tumors will recur, however, and in part progress to become
                      invasive. Therefore, surveillance cystoscopy is performed
                      after resection. However, in the USA and Europe alone, about
                      54 000 new patients per year undergo repeated cystoscopies
                      over several years, who do not experience recurrence.
                      Analysing in a pilot study resected tumors from patients
                      with (n = 19) and without local recurrence (n = 6) after a
                      period of 5 years by means of an antibody microarray that
                      targeted 724 cancer-related proteins, we identified 255
                      proteins with significantly differential abundance. Most are
                      involved in the regulation and execution of apoptosis and
                      cell proliferation. A multivariate classifier was
                      constructed based on 20 proteins. It facilitates the
                      prediction of recurrence with a sensitivity of $80\%$ and a
                      specificity of $100\%.$ As a measure of overall accuracy,
                      the area under the curve value was found to be $91\%.$ After
                      validation in additional sample cohorts with a similarly
                      long follow-up, such a signature could support decision
                      making about the stringency of surveillance or even
                      different treatment options.},
      keywords     = {Proteome (NLM Chemicals)},
      cin          = {C060 / B070},
      ddc          = {540},
      cid          = {I:(DE-He78)C060-20160331 / I:(DE-He78)B070-20160331},
      pnm          = {312 - Functional and structural genomics (POF3-312)},
      pid          = {G:(DE-HGF)POF3-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:24610664},
      doi          = {10.1002/pmic.201300320},
      url          = {https://inrepo02.dkfz.de/record/128367},
}