Individual prediction of therapy benefit and selecting patients for ICT based on molecular biological profiles appear to be feasible in the future.

We defined specific protein patterns with ProteinChip technology using bioinformatic evaluation software, which allowed differentiation between nonmetastasized and metastasized bladder tumor samples with high sensitivity and specificity. We identified 4 differentially expressed proteins. Thus, it seems possible to identify patients at high metastasized risk even at a clinically localized stage, leading to individual therapy decisions.

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Molecular biological tumor markers and prognostic parameters are necessary for differential diagnosis, individual prognosis, and therapy in patients with renal cell tumors. By using high throughput technologies, it is possible to characterize tumor samples comprehensively. Based on specific genetic alterations, histopathological subtypes were defined as independent tumor entities. Genetic characteristics can be used for diagnosis of primary tumor samples and also of biopsies. Furthermore, specific molecular patterns of metastatic tumors are known, allowing the determination of the primary tumor's metastatic potential. The specific protein patterns of serum samples of tumor patients were analyzed, and several candidate proteins have been identified. One of these is SAA-1, which is elevated in patients with clear cell renal cell carcinomas (RCC). New therapeutic options are now available for patients with metastatic RCC. Therefore, it is necessary to select the best therapy for each patient and to detect therapy resistance very early. Biomarkers in tumor tissue and serum were found to correlate with therapy response.