PREDICT THE DRUG · Using ex vivo functional genomics as biomarker to predict the effectivity of Venetoclax in patients with acute leukemias

For decades, oncologists treating cancer patients desire biomarkers that predict drug sensitivity on a patient individual level; unfortunately, the challenge remains unsolved until today. As treating clinicians are unable to identify the patients who benefit most and despite advanced multi-omics diagnostics, the majority of cancer patients are treated according to risk groups. As a result, patients receive inactive drugs without benefit, but adverse effects and European healthcare systems lose significant resources without gain. During my ERC CoG work, we developed a completely new test principle which has the potential to bridge the gap. The group of “targeted” anticancer drugs causes tumor cells to die by inhibiting a single specific signalling molecule; we used the CRISPR/Cas9 mediated gene knockout to mimic the activity of such targeted drugs. In proof-of-principle work, we used patient-derived xenograft (PDX) leukemia models to show that the knockout of a target gene in vitro was able to predict which patient´s PDX model responded to treatment with the respective drug in vivo. Here, we plan to optimize our test system by studying knockout of BCL-2 to predict sensitivity to the BCL-2 targeting drug Venetoclax. The planned work harbours the potential of a major breakthrough to solve a long-standing challenge in anti-cancer treatment, namely to predict which individual patient´s tumor responds to which targeted.