Optimized low-dose combinatorial drug treatment boosts selectivity and efficacy of colorectal carcinoma treatment

Marloes Zoetemelk, George M. Ramzy, Magdalena Rausch, Thibaud Koessler, Judy R. van Beijnum, Andrea Weiss, Valentin Mieville, Sander R. Piersma, Richard R. de Haas, C. line Delucinge-Vivier, Axel Andres, Christian Toso, Alexander A. Henneman, Simone Ragusa, Tatiana V. Petrova, Mylène Docquier, Thomas A. McKee, Connie R. Jimenez, Youssef Daali, Arjan W. GriffioenLaura Rubbia-Brandt, Pierre-Yves Dietrich, Patrycja Nowak-Sliwinska*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


The current standard of care for colorectal cancer (CRC) is a combination of chemotherapeutics, often supplemented with targeted biological drugs. An urgent need exists for improved drug efficacy and minimized side effects, especially at late-stage disease. We employed the phenotypically driven therapeutically guided multidrug optimization (TGMO) technology to identify optimized drug combinations (ODCs) in CRC. We identified low-dose synergistic and selective ODCs for a panel of six human CRC cell lines also active in heterotypic 3D co-culture models. Transcriptome sequencing and phosphoproteome analyses showed that the mechanisms of action of these ODCs converged toward MAP kinase signaling and cell cycle inhibition. Two cell-specific ODCs were translated to in vivo mouse models. The ODCs reduced tumor growth by ~80%, outperforming standard chemotherapy (FOLFOX). No toxicity was observed for the ODCs, while significant side effects were induced in the group treated with FOLFOX therapy. Identified ODCs demonstrated significantly enhanced bioavailability of the individual components. Finally, ODCs were also active in primary cells from CRC patient tumor tissues. Taken together, we show that the TGMO technology efficiently identifies selective and potent low-dose drug combinations, optimized regardless of tumor mutation status, outperforming conventional chemotherapy.
Original languageEnglish
Pages (from-to)2894-2919
Number of pages26
JournalMolecular oncology
Issue number11
Early online date2020
Publication statusPublished - 1 Nov 2020

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