PROGNOSTIC VALUE OF KARYOMETRIC AND CLINICAL CHARACTERISTICS IN RENAL-CELL CARCINOMA - QUANTITATIVE ASSESSMENT OF TUMOR HETEROGENEITY

Background. The variation in tumor cell differentiation within one ren al cell carcinoma, also termed tumor heterogeneity, renders visual tum or grading of these carcinomas difficult. Karyometric analysis enables description of nuclear characteristics of multiple tumor areas. Hence , karyometric analysis can be used to quantify tumor heterogeneity and thus may aid in a more objective grading of renal cell carcinoma. Met hods. In 121 patients with renal cell carcinoma (tumors in Internation al Union Against Cancer [UICC] stages I [5 cases], II [23 cases], III [33 cases], and IV [60 cases]), clinical and karyometric features were studied to obtain routinely applicable prognostic factors. Several pa rts of the tumor were analyzed to obtain a measure of tumor heterogene ity. Univariate and multivariate Cox regression analyses were used to determine the predictive value of karyometric features independent of tumor stage and other clinical characteristics. Results. The Cox univa riate regression analysis showed correlation of several clinical and k aryometric characteristics with survival. Of the clinical characterist ics, TNM stage, tumor size, weight reduction, and performance status w ere significantly associated with survival. The karyometric features, especially those measurements associated with tumor heterogeneity (e.g . differences in nuclear size or chromatin texture between tumor subpo pulations) were of value in predicting prognosis. In the Cox multivari ate regression analysis, the Robson and UICC stages proved to be the m ost powerful predictors of survival (P < 0.0001). Of the clinical feat ures, weight reduction and performance score were the only characteris tics offering additional information regarding tumor stage (P < 0.0001 ). From the karyometric analysis quantification of anisokaryosis in th e tumor at time of diagnosis offered additional prognostic information . Moreover, the differences of karyometric features within the tumor p resumably associated with tumor heterogeneity correlated with survival . Using the features from the multivariate analysis, prognostic groups could be defined. Conclusion. We conclude that karyometric analysis o ffers a useful means for quantifying tumor heterogeneity. Multivariate Cox analysis revealed additional value of a grading system based on k aryometric analysis to tumor stage. Karyometric analysis can be a usef ul tool for stratification of patient populations.