| Project Detail |
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. |
