"With the advent of Omics technologies capable of profiling substantial fractions of molecular entities from the genome down to the metabolome level a significant boost in understanding disease pathophysiology, consequently providing tailored diagnostics and therapy, was expected. With impressive results for selected diseases, cancer still sees significant shortcomings. One reason for this clinical situation is certainly the apparent heterogeneity of cancers, manifesting in clinical presentation and outcome, but also on the personalized molecular level.Within DIPROMON we selected bladder cancer as a prototypical example for the need of personalization efforts in therapy, and we plan of using this clinically highly challenging disease phenotype for establishing a general workflow for enabling personalization strategies towards rational decision on tailoring therapeutic interventions for defined patient groups. DIPROMON on its basis follows a computational Systems Biology approach for delineating statistical rules for patient segmentation, resting on patient-specific biomarker profiles interpreted in tight integration with patient-specific clinical data. DIPROMON will establish a workflow including quality control aspects for i) selecting biomarker panels for given clinical phenotypes, ii) providing modular instrumentation for measuring the profiles in a clinical setting, and iii) deriving statistics-driven rules for patient segmentation regarding optimal therapy. DIPROMON will finally validate the concept in bladder cancer."