Identifying mutant-specific multi-drug combinations using comparative network reconstruction
Evert Bosdriesz,
João M. Fernandes Neto,
Anja Sieber,
René Bernards,
Nils Blüthgen,
Lodewyk F.A. Wessels
Affiliations
Evert Bosdriesz
Bioinformatics, Computer Science, VU Amsterdam, De Boelelaan 1111, Amsterdam 1081 HV, the Netherlands; Corresponding author
João M. Fernandes Neto
Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam 1066 CX, the Netherlands
Anja Sieber
Institute of Pathology, Charite Universitatsmedizin, Chariteplatz 1, Berlin 10117, Germany; IRI Life Sciences, Humboldt University of Berlin, Philippstraße 13, Berlin 10115, Germany
René Bernards
Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam 1066 CX, the Netherlands
Nils Blüthgen
Institute of Pathology, Charite Universitatsmedizin, Chariteplatz 1, Berlin 10117, Germany; IRI Life Sciences, Humboldt University of Berlin, Philippstraße 13, Berlin 10115, Germany; Berlin Institute of Health, Anna-Louisa-Karsch-Straße 2, Berlin 10178, Germany
Lodewyk F.A. Wessels
Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam 1066 CX, the Netherlands; Faculty of EEMCS, Delft University of Technology, Mekelweg 4, Delft 2628 CD, the Netherlands; Corresponding author
Summary: Targeted inhibition of aberrant signaling is an important treatment strategy in cancer, but responses are often short-lived. Multi-drug combinations have the potential to mitigate this, but to avoid toxicity such combinations must be selective and given at low dosages. Here, we present a pipeline to identify promising multi-drug combinations. We perturbed an isogenic PI3K mutant and wild-type cell line pair with a limited set of drugs and recorded their signaling state and cell viability. We then reconstructed their signaling networks and mapped the signaling response to changes in cell viability. The resulting models, which allowed us to predict the effect of unseen combinations, indicated that no combination selectively reduces the viability of the PI3K mutant cells. However, we were able to validate 25 of the 30 combinations that we predicted to be anti-selective. Our pipeline enables efficient prioritization of multi-drug combinations from the enormous search space of possible combinations.
