ABSTRACT
Despite surgical advances and recent progress in adjuvant therapies, the prognosis for patients with malignant brain tumors such as glioblastoma multiforme has remained poor, and the neurological deterioration suffered by most patients as a consequence of tumor progression is dramatic and severe. In addition, malignant brain tumors have >>95% recurrence close to the primary site of initial resection. Unfortunately, standard imaging techniques do not permit the intraoperative identification of individual or small clusters of residual tumor cells, precluding their selective removal while sparing the surrounding normal brain tissue. In this report, we show that quantum dots (QDs) coupled to epidermal growth factor (EGF) or anti-EGF receptor receptor (EGFR, Her1) specifically and sensitively label glial tumor cells in cell culture, glioma mouse models, and human brain-tumor biopsies. A clear demarcation between brain and tumor tissue at the macroscopic as well as the cellular level is provided by the fluorescence emission of the QDs.
Subject(s)
Antibodies, Monoclonal/pharmacokinetics , Brain Neoplasms/metabolism , Brain Neoplasms/pathology , Epidermal Growth Factor/pharmacokinetics , Glioma/metabolism , Glioma/pathology , Microscopy, Fluorescence/methods , Quantum Dots , Animals , Cell Line, Tumor , Humans , Image Enhancement/methods , Mice , Molecular Probe Techniques , Sensitivity and SpecificityABSTRACT
We report the stepwise transformation of a linear peptide epitope recognized by the anti-transforming growth factor alpha monoclonal antibody Tab2 into peptomers and finally into peptoid analogs. The key experiment in this study is the substitution analysis in which each position of the peptide is exchanged by a set of different peptoid building blocks resulting in a peptidomimetic array. After probing the array toward antibody binding, the best binding peptomer spots were selected and subjected to a successive transformation. The best peptoid found in this study has a K(D) of 200 nM when binding to Tab2, which is only 8-fold higher than the starting peptide. Moreover, this approach permits to ask directly questions about the transformation of peptide lead structures into non-peptidic compounds in the context of protein recognition.
