Multiobjective optimization identifies cancer-selective combination therapies

Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the search space spanned by the therapeutic and nonselective effects of combinations. We demonstrate the performance of the method in the context of BRAF-V600E melanoma treatment, where the optimal solutions predicted a number of co-inhibition partners for vemurafenib, a selective BRAF-V600E inhibitor, approved for advanced melanoma. We experimentally validated many of the predictions in BRAF-V600E melanoma cell line, and the results suggest that one can improve selective inhibition of BRAF-V600E melanoma cells by combinatorial targeting of MAPK/ERK and other compensatory pathways using pairwise and third-order drug combinations. Our mechanism-agnostic optimization method is widely applicable to various cancer types, and it takes as input only measurements of a subset of pairwise drug combinations, without requiring target information or genomic profiles. Such data-driven approaches may become useful for functional precision oncology applications that go beyond the cancer genetic dependency paradigm to optimize cancer-selective combinatorial treatments.

[1]  C. I. Bliss.  THE TOXICITY OF POISONS APPLIED JOINTLY1 , 1939 .

[2]  Krister Wennerberg,et al.  Methods for High-Throughput Drug Combination Screening and Synergy Scoring , 2016, bioRxiv.

[3]  Krister Wennerberg,et al.  Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells , 2016, Molecular Cancer.

[4]  B. Al-Lazikani,et al.  Combinatorial drug therapy for cancer in the post-genomic era , 2012, Nature Biotechnology.

[5]  Andreas Bender,et al.  Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives. , 2016, Drug discovery today.

[6]  Ranadip Pal,et al.  Combination therapy design for maximizing sensitivity and minimizing toxicity , 2016, BMC Bioinformatics.

[7]  Amir K. Foroushani,et al.  Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen , 2019, Nature Communications.

[8]  Eduardo D Sontag,et al.  Mechanism-independent method for predicting response to multidrug combinations in bacteria , 2012, Proceedings of the National Academy of Sciences.

[9]  Uri Alon,et al.  Noise-precision tradeoff in predicting combinations of mutations and drugs , 2019, PLoS Comput. Biol..

[10]  Kevin B. Wood,et al.  Pairwise interactions and the battle against combinatorics in multidrug therapies , 2016, Proceedings of the National Academy of Sciences.

[11]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[12]  E. Cuppen,et al.  Portrait of a cancer: mutational signature analyses for cancer diagnostics , 2019, BMC Cancer.

[13]  P. Aloy,et al.  Quantification of Pathway Cross-talk Reveals Novel Synergistic Drug Combinations for Breast Cancer. , 2017, Cancer research.

[14]  P. Limonta,et al.  Estrogen Receptor β in Melanoma: From Molecular Insights to Potential Clinical Utility , 2016, Front. Endocrinol..

[15]  Jing Tang,et al.  What is synergy? The Saariselkä agreement revisited , 2015, Front. Pharmacol..

[16]  Michael T. M. Emmerich,et al.  A tutorial on multiobjective optimization: fundamentals and evolutionary methods , 2018, Natural Computing.

[17]  A. Ribas,et al.  Extended 5-Year Follow-up Results of a Phase Ib Study (BRIM7) of Vemurafenib and Cobimetinib in BRAF-Mutant Melanoma , 2019, Clinical Cancer Research.

[18]  Uri Alon,et al.  Prediction of drug cocktail effects when the number of measurements is limited , 2017, PLoS biology.

[19]  Ranadip Pal,et al.  A new approach for prediction of tumor sensitivity to targeted drugs based on functional data , 2013, BMC Bioinformatics.

[20]  Jing Tang,et al.  Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer , 2019, PLoS Comput. Biol..

[21]  T. Bortfeld,et al.  Improved planning time and plan quality through multicriteria optimization for intensity-modulated radiotherapy. , 2012, International journal of radiation oncology, biology, physics.

[22]  Uri Alon,et al.  Prediction of multidimensional drug dose responses based on measurements of drug pairs , 2016, Proceedings of the National Academy of Sciences.

[23]  J. Larkin,et al.  Vemurafenib: a new treatment for BRAF-V600 mutated advanced melanoma , 2012, Cancer management and research.

[24]  Olaf Sporns,et al.  A spectrum of routing strategies for brain networks , 2018, PLoS Comput. Biol..

[25]  Guozhi Yu,et al.  Antimicrobial combinations: Bliss independence and Loewe additivity derived from mechanistic multi-hit models , 2016, Philosophical Transactions of the Royal Society B: Biological Sciences.

[26]  P. Ascierto,et al.  Combined vemurafenib and cobimetinib in BRAF-mutated melanoma. , 2014, The New England journal of medicine.

[27]  Kam Y. J. Zhang,et al.  Discovery of a selective inhibitor of oncogenic B-Raf kinase with potent antimelanoma activity , 2008, Proceedings of the National Academy of Sciences.

[28]  Mark S. Cohen,et al.  The discovery of vemurafenib for the treatment of BRAF-mutated metastatic melanoma , 2016, Expert opinion on drug discovery.

[29]  Maarten Kamermans,et al.  A novel mechanism of cone photoreceptor adaptation , 2017, PLoS biology.

[30]  Larry Rubinstein,et al.  The National Cancer Institute ALMANAC: A Comprehensive Screening Resource for the Detection of Anticancer Drug Pairs with Enhanced Therapeutic Activity. , 2017, Cancer research.

[31]  Horst W. Hamacher,et al.  Inverse Radiation Therapy Planning: A Multiple Objective Optimisation Approach , 1999 .