ExTrAct-AML

Precision medicine aims at delivering the best treatment to an individual patient based on their unique characteristics. In recent years precision medicine approaches have largely been reliant on characterizing patients genomes to identify promising medicines for individuals and defined patient subgroups. These approaches have led to selected success stories. However, childhood cancers have not yet profited as much from such approaches, which is mainly due to their rarity and their distinct genetic profiles. One particular disease in need of more personalized treatments is pediatric acute myeloid leukemia (pedAML) a rare childhood blood cancer with poor outcome. Survival of pedAML patients has increased drastically over the recent decades by optimizing treatment in large international trials, but the improvements in survival rates have slowed down in recent years. Treating pedAML patients based on their genetic profiles can only partially improve this situation, because there are no targeting medicines for most mutations in this disease. To tackle this challenge and ultimately improve outcome in pedAML, we set up a scientific partnership between the St. Anna Childrens Cancer Research Institute and the CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences. In this team, we aim to perform comprehensive ex-vivo functional profiling of pedAML patients and their tumors. It allows us to test a many different medicines on individual patients by performing these tests on the tumor outside of the patients body. By additionally profiling the genetic makeup of the tumor and its internal signaling pathways, we aim to create a comprehensive map of promising medicines in pedAML where the genetic and signaling information allows us to understand why these medicines may work in individual patients and specific subgroups. We aim to first perform this profiling on samples from patients diagnosis, but then also characterize how these profiles change over time as patients are treated. With this approach, we hope to better understand the causes of poor treatment response in individual patients and specific subgroups. Additionally, we aim to establish this characterization as a tool to identify patients at risk early on and to be able to assign high-risk patients to promising trials early on, to improve their chances of survival.