Details

Autor(en) / Beteiligte

• Allory, Y
• Bazille, C
• Vieillefond, A
• Molinié, V
• Cochand-Priollet, B
• Cussenot, O
• Callard, P
• Sibony, M

Titel

Profiling and classification tree applied to renal epithelial tumours

Ist Teil von

• Histopathology, 2008-01, Vol.52 (2), p.158-166

Ort / Verlag

Oxford, UK: Blackwell Publishing Ltd

Erscheinungsjahr

2008

Quelle

MEDLINE

Beschreibungen/Notizen

• Aims:  Selection of the relevant combination from a growing list of candidate immunohistochemical biomarkers constitutes a real challenge. The aim was to establish the minimal subset of antibodies to achieve classification on the basis of 12 antibodies and 309 renal tumours. Methods and results:  Seventy‐nine clear cell (CC), 88 papillary (PAP) and 50 chromophobe (CHRO) renal cell carcinomas, and 92 oncocytomas (ONCO) were immunostained for renal cell carcinoma antigen, vimentin, cytokeratin (CK) AE1–AE3, CK7, CD10, epithelial membrane antigen, α‐methylacyl‐CoA racemase (AMACR), c‐kit, E‐cadherin, Bcl‐1, aquaporin 1 and mucin‐1 and analysed by tissue microarrays. First, unsupervised hierarchical clustering performed with immunohistochemical profiles identified four main clusters—cluster 1 (CC 67%), 2 (PAP 98%), 3 (CHRO 67%) and 4 (ONCO 100%)—demonstrating the intrinsic classifying potential of immunohistochemistry. A series of classification trees was then automatically generated using Classification And Regression Tree software. The most powerful of these classification trees sequentially used AMACR, CK7 and CD10 (with 86% CC, 87% PAP, 79% CHRO and 78% ONCO correctly classified in a leave‐one‐out cross‐validation test). The classifier was also helpful in 22/30 additional cases with equivocal features. Conclusion:  The classification tree method using immunohistochemical profiles can be applied successfully to construct a renal tumour classifier.