Predicting metastatic risk of gastrointestinal stromal tumors: Role of cell proliferation and cell cycle regulatory proteins

Gastrointestinal stromal tumors (GIST) are a heterogeneous group of neoplas ms whose biologic behavior is difficult to predict. The aim of this study i s to evaluate the prognostic value in GIST of some oncoproteins involved in regulation of cell proliferation. Tumor size, mitosis, necrosis, and p53, c-myc, and bcl-2 protein expression of 32 GIST were studied. Proliferative index was assessed with Ki67. The 32 cases were grouped into the following clinical categories: (1) clinically benign (BN) were defined as disease-fre e survival greater than 3 years (n=10); (2) clinically malignant (MN) in wh ich local recurrence or metastasis occurred regardless of the follow-up tim e (n=15); and (3) clinically indeterminate (LD) owing to follow-up <3 years without metastasis or local recurrence (n=seven). Discriminant analysis wa s used to allocate any tumor to one of the two prognostic groups (BN or MN) . In univariate analysis all six factors studied above proved to be of sign ificant prognostic value. Using a multivariate stepwise discriminant analys is to take into account the interrelationship between factors, we found tha t c-myc expression was the most important prognostic factor, followed, in o rder of statistical weight, by size and Ki67. These were combined to define a discriminant score ([10.75 x c-myc] + [0.39 x size] + [0.078 x Ki67]-15. 54=score), which was capable of correctly identifying tumors in our series whose known clinical behavior was BN or MN in 92% of the cases. The classif ication score was applied subsequently to the seven clinically ID cases: Th ree (42.9%) were predicted as BN, and four (57.1%) were predicted as MN. Bo th expression of oncoprotein c-myc and the proliferative index provide prog nostic information in GIST, in addition to morphologically established prog nostic factors such as size. These factors in a discriminant analysis prove d to be useful for the clinical classification of GIST into BN or MN and to predict the clinical outcome of clinically ID tumors.