BACKGROUND: The most common malignant renal tumors of childhood are Wilms tumor (WT), clear cell sarcoma of the kidney (CCSK), cellular mesoblastic nephroma (CMN), and rhabdoid tumor of the kidney (RTK). Because these tumors present significant diagnostic difficulties, the goal was to define diagnostically useful signatures based on gene expression. PROCEDURES: Gene expression analysis using oligonucleotide arrays was performed on a training set of 47 tumors (10 CCSKs, 9 CMNs, 8 RTKs, and 20 WTs). Classifiers were developed for each tumor type using variations of compound covariate class predictor. The classifiers were applied to an independent test set of 72 tumors (3 CMN, 7 CCSK, 4 RTK, and 58 WT). Central review diagnosis was utilized as the gold standard. Correlation with the institutional diagnosis and qualitative estimation of confidence levels at the time of central review were noted. RESULTS: Within the training set, classifiers resulted in no errors when >10 genes were utilized. Top genes in each classifier were verified using quantitative reverse transcription-polymerase chain reaction (RT-PCR). Applying the classifiers to the test set, 71 of 72 tumors were correctly classified with a confidence level of >99%. The exception was incorrectly classified by the gold standard. In comparison, by histopathology 31% of the non-WT were not accurately classified by the local institution, and 29% were classified with <95% confidence on central review. CONCLUSIONS: Classifiers based on gene expression provide diagnostic confidence and accuracy greater than that of pathologic analysis alone. Tumors that show ambiguous gene expression profiles are those that are also pathologically and molecularly ambiguous and merit further analysis. (c) 2006 Wiley-Liss, Inc.
BACKGROUND: The most common malignant renal tumors of childhood are Wilms tumor (WT), clear cell sarcoma of the kidney (CCSK), cellular mesoblastic nephroma (CMN), and rhabdoid tumor of the kidney (RTK). Because these tumors present significant diagnostic difficulties, the goal was to define diagnostically useful signatures based on gene expression. PROCEDURES: Gene expression analysis using oligonucleotide arrays was performed on a training set of 47 tumors (10 CCSKs, 9 CMNs, 8 RTKs, and 20 WTs). Classifiers were developed for each tumor type using variations of compound covariate class predictor. The classifiers were applied to an independent test set of 72 tumors (3 CMN, 7 CCSK, 4 RTK, and 58 WT). Central review diagnosis was utilized as the gold standard. Correlation with the institutional diagnosis and qualitative estimation of confidence levels at the time of central review were noted. RESULTS: Within the training set, classifiers resulted in no errors when >10 genes were utilized. Top genes in each classifier were verified using quantitative reverse transcription-polymerase chain reaction (RT-PCR). Applying the classifiers to the test set, 71 of 72 tumors were correctly classified with a confidence level of >99%. The exception was incorrectly classified by the gold standard. In comparison, by histopathology 31% of the non-WT were not accurately classified by the local institution, and 29% were classified with <95% confidence on central review. CONCLUSIONS: Classifiers based on gene expression provide diagnostic confidence and accuracy greater than that of pathologic analysis alone. Tumors that show ambiguous gene expression profiles are those that are also pathologically and molecularly ambiguous and merit further analysis. (c) 2006 Wiley-Liss, Inc.
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