Global mapping of drug synergy landscapes using massively parallel Cas13d genetic interaction screening
Cancer therapies rely on additive or synergistic drug combinations to enhance efficacy and reduce toxicity. However, systematic discovery of synergies has been impeded by the immense number of possible drug combinations, which cannot be tested pharmacologically. Here we address this issue by developing screening methodology that enables global mapping of genetic interactions within the druggable genome. The approach leverages Cas13d/CasRx based RNA knockdown to mimic drug effects, such as partial target inhibition or dosage-dependent synergism. Performing nearly one million pairwise genetic perturbations in pooled fitness screens, we queried all combinatorial on-target effects of anti-cancer drugs in clinical use or development. Through a series of technical and analytical innovations, we evolved this system to reconcile massively parallel screening with scalable experimental throughput, which allowed us to delineate drug synergy landscapes in several tumor types with high clinical need, including malignancies of the pancreas, intestine, lung and breast. We uncovered 7,981 genetic interactions and used preclinical models to demonstrate broad relevance of new discoveries, including synergies between ?-secretase and BET inhibition or Irinotecan and Bcl2 targeting in pancreatic cancer. Our work creates an unprecedented data resource for drug repurposing and rapid clinical translation and paves the way for pan-cancer exploration of the druggable genome and its enormous combinatorial space.
Cancer therapies rely on additive or synergistic drug combinations to enhance efficacy and reduce toxicity. However, systematic discovery of synergies has been impeded by the immense number of possible drug combinations, which cannot be tested pharmacologically. Here we address this issue by developing screening methodology that enables global mapping of genetic interactions within the druggable genome. The approach leverages Cas13d/CasRx based RNA knockdown to mimic drug effects, such as partial target inhibition or dosage-dependent synergism. Performing nearly one million pairwise genetic perturbations in pooled fitness screens, we queried all combinatorial on-target effects of anti-cancer drugs in clinical use or development. Through a series of technical and analytical innovations, we evolved this system to reconcile massively parallel screening with scalable experimental throughput, which allowed us to delineate drug synergy landscapes in several tumor types with high clinical need, including malignancies of the pancreas, intestine, lung and breast. We uncovered 7,981 genetic interactions and used preclinical models to demonstrate broad relevance of new discoveries, including synergies between ?-secretase and BET inhibition or Irinotecan and Bcl2 targeting in pancreatic cancer. Our work creates an unprecedented data resource for drug repurposing and rapid clinical translation and paves the way for pan-cancer exploration of the druggable genome and its enormous combinatorial space.
Klinikum rechts der Isar, II. Medizinische Klinik