Phenotyping of pulmonary carcinoids and a Ki-67-based grading approach View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-03

AUTHORS

Tina Zahel, Sabine Krysa, Esther Herpel, Albrecht Stenzinger, Benjamin Goeppert, Peter Schirmacher, Hans Hoffmann, Philipp A. Schnabel, Arne Warth

ABSTRACT

Pulmonary carcinoids (PC) are separated into typical (TC) and atypical carcinoids (ATC). However, the biological behavior cannot be reliably predicted, and in small biopsies differential diagnosis can be challenging. To provide a basis for a grading approach, we analyzed mitoses and the proliferative index (PI; Ki-67) of 200 PC specimens (TC: n = 114; ATC: n = 86). To define suitable diagnostic and to screen for putative therapeutic markers, CD56, CD57, CD99, CD117, TTF-1, synaptophysin, chromogranin A, CK 18, KL-1, epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (Her-2/neu), somatostatin receptor subtype 2A (SSTR2A), thymidylate synthase (TS), and excision repair cross-complementation group 1 (ERCC-1) expression was analyzed. A combination of synaptophysin and cytokeratins is the most sensitive marker panel for PC with unclear histomorphology. Predictive phenotyping revealed that SSTR2A is expressed in >80% of all PC and may be used both, as a diagnostic marker for imaging approaches and as a predictive marker for octreotide-based therapies. We introduced a grading system distinguishing between PC with low and highly aggressive biological behavior similar to the grading system for gastrointestinal neuroendocrine tumors. The system is superior to the classical separation into TC and ATC. This study indicates that PI in addition to mitotic count may improve prediction of the biological behavior of PC and should be validated in prospective studies. More... »

PAGES

299-308

References to SciGraph publications

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00428-012-1194-2

    DOI

    http://dx.doi.org/10.1007/s00428-012-1194-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1048369072

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/22293945


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