RPPA-profiling in pediatric acute leukemia: pattern recognition and therapeutic guidance

Despite impressive improvements in overall survival, (pediatric) acute leukemia remains a life-threatening disease. As further therapy intensification leads to increased toxicities, new treatment options are needed. Also, better risk stratification is important to identify low- and high-risk patients. The heterogeneity in the pathophysiology of acute leukemia, arising from the near infinite possible combinations of genetic and epigenetic events, creates a major challenge to personalized medicine. Because proteins (DNA > mRNA > proteins) are the central effectors of both genetic and external forces that drive cell function, we hypothesized that genetic and environmental variability coalesces into a more finite number of protein expression patterns, and that these expression patterns can potentially guide the design of individualized therapy, and aid in the process of risk stratification. In this thesis, we analysed protein expression in various subsets of acute leukemia patients. Our main findings concluded that 1) acute leukemia can be classified into subgroups of patients based on strong recurrent protein expression patterns, that 2) protein expression patterns are independently prognostic for outcome, and that 3) expression patterns can identify patients that benefit from additional bortezomib treatment. These results suggest a potential value for proteomics to augment the current classification systems, and to advance (individualized) therapy selection if patients can be classified in an early phase of their treatment. Future studies are needed to validate our findings and to explore whether proteins expressed with an expression significantly different from normal can form a hypothetical platform for personalized medicine.