Subtyping of renal cortical neoplasms in fine needle aspiration biopsies using a decision tree based on genomic alterations detected by fluorescence in situ hybridization

Assessing Genomic Copy Number Alterations as Best Practice for Renal Cell Neoplasia: An Evidence-Based Review from the Cancer Genomics Consortium Workgroup

Renal cell neoplasia are heterogeneous with diverse histology, genetic alterations, and clinical behavior that are diagnosed mostly on morphologic features. The Renal Cell Neoplasia Workgroup of the Cancer Genomics Consortium systematically evaluated peer-reviewed literature on genomic studies of renal cell carcinoma (RCC), including clear cell RCC, papillary RCC, chromophobe RCC, and the translocation RCC involving TFE3, TFEB and MITF rearrangements, as well as benign oncocytoma, which together comprise about 95% of all renal cell neoplasia. The Workgroup curated recurrent copy number alterations (CNAs), copy-neutral loss-of-heterozygosity (cnLOH), rearrangements, and mutations, found in each subtype and assigned clinical relevance according to established criteria. In clear cell RCC, loss of 3p has a disease-initiating role and most likely also in progression with mutations detected in VHL and other genes mapped to this arm, and loss of 9p and/or 14q has well-substantiated prognostic utility. Gain of chromosomes 7 and 17 are hallmark CNAs of papillary RCC, but patterns of other CNAs as detected by chromosomal microarray analysis (CMA) afford sub-classification into Type 1 and 2 with prognostic value, and for further sub-stratification of Type 2. Inherent chromosome loss in chromophobe RCC as detected by CMA is useful for distinguishing the eosinophilic variant from benign oncocytoma which in contrast exhibits few CNAs or rearranged CCND1, but share mitochondrial DNA mutations. In morphologically atypical RCCs, rearrangement of TFE3 and TFEB should be considered in the differential diagnosis, portending an aggressive RCC subtype. Overall, this evidence-based review provides a validated role for assessment of CNAs in renal cell neoplasia in the clinical setting to assist in renal cell neoplasm diagnosis and sub-classification within subtypes that is integral to the management of patients, from small incidentally found renal masses to larger surgically resected specimens, and simultaneously identify the presence of key alterations portending outcome in malignant RCC subtypes.

Adult Renal Neoplasms: Cytology, Immunohistochemistry, and Cytogenetic Characteristics

Tissue sampling of renal masses is traditionally performed using percutaneous sonographic or CT guidance core biopsy (CB) with or without touch preparation cytology and/or fine-needle aspiration cytology (FNAC). The combined used of CB and FNAC is expanding in clinical practice, especially in small renal masses and plays a pivotal role in therapeutic decision making. Grouping the renal neoplasms in differential diagnostic groups helps in choosing specific immunohistochemical markers and reaching an accurate diagnosis.

A genomic algorithm for the molecular classification of common renal cortical neoplasms: development and validation

PURPOSE

Accurate discrimination of benign oncocytoma and malignant renal cell carcinoma is useful for planning appropriate treatment strategies for patients with renal masses. Classification of renal neoplasms solely based on histopathology can be challenging, especially the distinction between chromophobe renal cell carcinoma and oncocytoma. In this study we develop and validate an algorithm based on genomic alterations for the classification of common renal neoplasms.

MATERIALS AND METHODS

Using TCGA renal cell carcinoma copy number profiles and the published literature, a classification algorithm was developed and scoring criteria were established for the presence of each genomic marker. As validation, 191 surgically resected formalin fixed paraffin embedded renal neoplasms were blindly submitted to targeted array comparative genomic hybridization and classified according to the algorithm. CCND1 rearrangement was assessed by fluorescence in situ hybridization.

RESULTS

The optimal classification algorithm comprised 15 genomic markers, and involved loss of VHL, 3p21 and 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q and 20q, and chromosomes 3, 7 and 12. On histological rereview (leading to the exclusion of 3 specimens) and using histology as the gold standard, 58 of 62 (93%) clear cell, 51 of 56 (91%) papillary and 33 of 34 (97%) chromophobe renal cell carcinomas were classified correctly. Of the 36 oncocytoma specimens 33 were classified as oncocytoma (17 by array comparative genomic hybridization and 10 by array comparative genomic hybridization plus fluorescence in situ hybridization) or benign (6). Overall 93% diagnostic sensitivity and 97% specificity were achieved.

CONCLUSIONS

In a clinical diagnostic setting the implementation of genome based molecular classification could serve as an ancillary assay to assist in the histological classification of common renal neoplasms.