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

Renal cell carcinoma is characterized by an accumulation of complex chromos omal alterations during tumor progression. Chromosome 3p deletions are know n 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 deve lopment 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 oncogene sis process of 116 conventional (clear cell) renal carcinomas. This provide s a means to analyze and model cancer development processes based on a more dynamic model, including the presence of multiple pathways, as compared wi th the fixed linear model first proposed by Vogelstein st 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 m odel derived from the distance-based method is consistent with the establis hed theory that -3p is an important early event in conventional (clear cell ) renal cancer and supports the prediction made from the branching tree tha t -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 para meters were -9p and -13q. The distance-based tree clarifies that -8p (prese nt in 12% of tumors) is a late event, largely independent of other events. In summary, tree modeling of comparative genomic hybridization data provide d new information on the interrelationships of genetic changes in renal can cer and their possible order, as well as a clustering of these events. Usin g tree analysis, one can derive a more in-depth understanding of the renal cancer development process than is possible by simply focusing on the frequ encies of genetic events in a given cancer type.