Multiobjective optimization identifies cancer-selective combination therapies
Otto I Pulkkinen1,2,3,4, Prson Gautam1, Ville Mustonen2,5
1Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
Abstract:
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.
Insights
This study presents a new optimization method to find the best drug combinations for advanced cancers resistant to single treatments. The approach identifies effective and safe combination therapies tailored to individual patients, improving cancer cell targeting.
Area of Science:
- Oncology
- Computational Biology
- Pharmacology
Background:
- Advanced cancers often develop resistance to single-drug therapies (monotherapies) by activating redundant signaling pathways.
- Identifying effective drug combinations is challenging due to the vast number of possibilities and the need for patient-specific approaches.
- Current methods often lack cost-effectiveness and systematic strategies for optimizing combinatorial treatments.
Purpose of the Study:
- To develop an exact multiobjective optimization method for identifying safe and effective cancer-selective drug combinations.
- To prioritize patient-specific combination therapies based on Pareto-optimization of therapeutic and nonselective effects.
- To demonstrate the method's applicability in optimizing combinatorial treatments for BRAF-V600E melanoma.
Main Methods:
- Developed an exact multiobjective optimization framework for combinatorial therapy selection.
- Utilized Pareto-optimization to balance therapeutic efficacy and nonselective effects across drug combinations.
- Applied the method to BRAF-V600E melanoma, predicting co-inhibition partners for vemurafenib.
- Experimentally validated predicted drug combinations in BRAF-V600E melanoma cell lines.
Main Results:
- The optimization method successfully predicted multiple effective co-inhibition partners for vemurafenib in BRAF-V600E melanoma.
- Experimental validation confirmed that combinatorial targeting of MAPK/ERK and compensatory pathways enhances selective inhibition of cancer cells.
- The approach demonstrated the potential of pairwise and third-order drug combinations to improve treatment outcomes.
- The method proved effective without requiring specific target information or genomic profiles.
Conclusions:
- The developed mechanism-agnostic optimization method offers a systematic and cost-effective approach to identify optimal combinatorial cancer therapies.
- This data-driven strategy is broadly applicable across various cancer types, supporting functional precision oncology.
- The findings suggest a paradigm shift towards optimizing patient-specific combination treatments beyond genetic dependency.
- Combinatorial therapies targeting redundant and compensatory pathways show promise for overcoming drug resistance in advanced cancers.
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