Precise tumor diagnosis is the first step in cancer management since therapy generally stems from the initial tumor classification. While many tumor biopsies are diagnostic and form the cornerstone of cancer therapy, classification of tumor type and site of origin is a significant clinical challenge that is often underestimated. Distinguishing the most common metastatic adenocarcinomas (ovary, colon, kidney, breast, lung and stomach) from each other is one of the most vexing problems clinicians are facing today. In fact, it is estimated that up to 10% of all metastatic tumors have no defined primary site of origin.1 Moreover, adenocarcinomas represent 60% of all of unknown primary tumor types.2 The current standard of pathologic practice, using morphologic criteria and semi-quantitative immunohistochemical (IHC) analyses, is often limited in its capacity to define tumor type or site of origin. Thus, there is a clear need for the identification and validation of a classification model that will cleanly distinguish these histologically similar tumor types and improve our capacity to direct therapy.
Gene expression profiling is a powerful tool that has shown promise in its capacity to discriminate subpopulations of tumors from heterogeneous groups.3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 We recently developed a prototype multitumor classifier capable of interrogating up to 21 different tumor types with an accuracy of ∼88%.14 The success with this approach led us to test the hypothesis that similar classifiers could be developed based on protein expression.
Using 2-D gel analysis combined with MALDI mass spectrometry to simultaneously assess 1,400 protein spots, we developed global protein expression profiles for 77 primary adenocarcinomas representing 6 different organ sites. We used a series of Wilcoxon rank-sum tests to generate 6 lists of proteins that effectively separated their associated tumors from the other 5 tumor types. A neural network was then constructed to develop a classifier to identify all 6 tumor types with a high degree of overall accuracy in a leave-one-out cross validation (LOOCV). Proteins have been identified by mass spectrometry that may serve as novel biomarkers for each disease site.