Novel immunogenic antigens increase classification accuracy in meningioma to 93.84

There is growing evidence that simultaneous analysis of multiple autoantibody reactions can be utilized for diagnosis of neoplasms. Using a set of 57 meningioma‐associated antigens, we recently separated meningioma patients from individuals without known disease with an accuracy of 90.3%. Here, we a...

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Bibliographic Details
Published in:International journal of cancer 2011-03, Vol.128 (6), p.1493-1501
Main Authors: Ludwig, Nicole, Keller, Andreas, Heisel, Sabrina, Leidinger, Petra, Rheinheimer, Stefanie, Andres, Claudia, Stephan, Bernhard, Steudel, Wolf‐Ingo, Donauer, Erich, Graf, Norbert, Burgeth, Bernhard, Weickert, Joachim, Lenhof, Hans‐Peter, Meese, Eckart
Format: Article
Language:English
Subjects:
Adolescent
Adult
Aged
Aged, 80 and over
antigens
Antigens, Neoplasm - classification
Antigens, Neoplasm - immunology
Antigens, Neoplasm - metabolism
Autoantibodies - blood
Autoantibodies - immunology
Biological and medical sciences
Biomarkers, Tumor - blood
brain tumor
Case-Control Studies
Child
Child, Preschool
diagnosis
Female
Gene Library
Glioma - blood
Glioma - genetics
Glioma - immunology
Humans
Male
Medical sciences
Meningeal Neoplasms - blood
Meningeal Neoplasms - genetics
Meningeal Neoplasms - immunology
meningioma
Meningioma - blood
Meningioma - genetics
Meningioma - immunology
Middle Aged
Neurology
Prognosis
Sensitivity and Specificity
tumor marker
Tumors
Tumors of the nervous system. Phacomatoses
Young Adult
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Summary:There is growing evidence that simultaneous analysis of multiple autoantibody reactions can be utilized for diagnosis of neoplasms. Using a set of 57 meningioma‐associated antigens, we recently separated meningioma patients from individuals without known disease with an accuracy of 90.3%. Here, we ask whether a largely increased set of immunogenic antigens can further improve this discrimination. We used an array with 1,827 human recombinant clones and measured reactivity of serum autoantibodies against the clones by a novel automated image analysis procedure. We were able to separate meningioma sera from sera of healthy controls with a specificity of 95.62%, a sensitivity of 91.83% and an accuracy of 93.84%. Of the analyzed clones, 23 in‐frame clones were highly informative for the classification of meningioma vs. normal sera as shown by their AUC values. These results demonstrate that the accuracy of a serum‐based diagnostic can be readily and considerably improved by screening extended sets of proteins.
ISSN:0020-7136
1097-0215
1097-0215
DOI:10.1002/ijc.25467