Construction of evolutionary tree models for renal cell carcinoma from comparative genomic hybridization data.

Renal cell carcinoma is characterized by an accumulation of complex chromosomal alterations during tumor progression. Chromosome 3p deletions are known to occur early in the carcinogenesis, but the nature of subsequent events, their interrelationships, and their sequence is poorly understood, as one usually only obtains a single "view" of the dynamic process of tumor development in a particular cancer patient. To address this limitation, we used comparative genomic hybridization analysis in combination with a distance-based and a branching-tree method to search for tree models of the oncogenesis process of 116 conventional (clear cell) renal carcinomas. This provides a means to analyze and model cancer development processes based on a more dynamic model, including the presence of multiple pathways, as compared with the fixed linear model first proposed by Vogelstein et al. (N. Engl. J. Med., 319: 525-532, 1988) for colorectal cancer. The most common DNA losses involved 3p (61%), 4q (50%), 6q (40%), 9p (35%), 13q (37%), and Xq (21%). The most common gains were seen at chromosome 17p and 17q (20%). The tree model derived from the distance-based method is consistent with the established theory that -3p is an important early event in conventional (clear cell) renal cancer and supports the prediction made from the branching tree that -4q is another important early event. Both tree models suggest that there may be two groups of clear cell renal cancers: one characterized by -6q, +17q, and + 17p, and another by -9p, -13q, and -18q. Putative prognostic parameters were -9p and -13q. The distance-based tree clarifies that -8p (present in 12% of tumors) is a late event, largely independent of other events. In summary, tree modeling of comparative genomic hybridization data provided new information on the interrelationships of genetic changes in renal cancer and their possible order, as well as a clustering of these events. Using tree analysis, one can derive a more in-depth understanding of the renal cancer development process than is possible by simply focusing on the frequencies of genetic events in a given cancer type.