Approximately 7 % of renal cell tumors are reported to be "unclassified" renal cell carcinoma (RCC) under the current (morphology-based) classification. Genetic lesions characteristic for RCC subtypes can be identified by virtual karyotyping with single nucleotide polymorphisms (SNP) microarrays. In this study, we examined whether virtual karyotypes could be used to better classify a cohort of morphologically challenging/unclassified RCC. Tumor resection specimens from 21 patients were profiled by virtual karyotyping with Affymetrix 10K 2.0 or 250K Nsp SNP mapping arrays and were also evaluated independently by a panel of 7 genito-urinary pathologists. Tumors were classified by the established pattern of genomic imbalances based on a reference cohort of 98 cases with classic morphology and compared with the morphologic diagnosis of the pathologist panel. Virtual karyotyping analysis identified recognized patterns of chromosomal imbalances in all but 1 (16/17 or 94%) cases with successful analysis. Four cases failed owing to low DNA quality. All cases with a panel diagnosis of unclassified RCC and cases in which a majority diagnosis was not reached were classified by their virtual karyotypes. In 1 case, the molecular-based diagnosis was in disagreement with the majority diagnosis. One case with a majority diagnosis of oncocytoma showed a novel genomic pattern not previously identified in the classic morphology cohort. We conclude that virtual karyotypes generated by SNP arrays are a valuable tool for increasing diagnostic accuracy in morphologically challenging or unclassified renal neoplasms. We consider that this technique is a feasible and practical approach for resolving difficult-to-diagnose renal tumors in clinical practice.
- Chromosomal imbalance
- Renal cell carcinoma, unclassified
- SNP array
- Virtual karyotype
ASJC Scopus subject areas
- Pathology and Forensic Medicine