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Combine and conquer: challenges for targeted therapy combinations in early phase trials

Abstract

Our increasing understanding of cancer biology has led to the development of molecularly targeted anticancer drugs. The full potential of these agents has not, however, been realised, owing to the presence of de novo (intrinsic) resistance, often resulting from compensatory signalling pathways, or the development of acquired resistance in cancer cells via clonal evolution under the selective pressures of treatment. Combinations of targeted treatments can circumvent some mechanisms of resistance to yield a clinical benefit. We explore the challenges in identifying the best drug combinations and the best combination strategies, as well as the complexities of delivering these treatments to patients. Recognizing treatment-induced toxicity and the inability to use continuous pharmacodynamically effective doses of many targeted treatments necessitates creative intermittent scheduling. Serial tumour profiling and the use of parallel co-clinical trials can contribute to understanding mechanisms of resistance, and will guide the development of adaptive clinical trial designs that can accommodate hypothesis testing, in order to realize the full potential of combination therapies.

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Figure 1: Clinical impact of drug combinations on the tumour.
Figure 2: The challenge of optimizing drug dosing in combination regimens.
Figure 3: The future: proposed clinical trial designs to overcome resistance.

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J.S.L. and U.B. researched data for the article, wrote the manuscript, made a substantial contribution to discussion of content and reviewed/edited the manuscript before submission.

Corresponding authors

Correspondence to Juanita S. Lopez or Udai Banerji.

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Competing interests

The authors declare no competing financial interests.

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Glossary

Additivity

Refers to the scenario wherein each drug in a combination regimen has some clinical activity, and the effect on the tumour is equal to the sum of activities of both drugs.

Basket trials

Represent a new and evolving form of clinical trial design and are predicated on the hypothesis that the presence of a molecular marker predicts response to a targeted therapy, independently of tumour histology or anatomical tumour site.

Computational models

An important tool for increasing understanding of pathological signalling networks and prioritizing drug targets to test experimentally. Through model-based simulations, one can predict the relative importance of various proteins in the network, the presence of signal amplification, and the role of feedback and crosstalk in treatment responses.

Mild antagonism

Refers to the scenario in which one or all drugs in a combination regimen have some clinical activity, but the sum of the clinical activity of the combination is less than the sum of activity of each individual drug.

Synergy

Refers to the scenario whereby one or all drugs in a combination regimen have some clinical activity, but the sum of the clinical activity of the combination is greater than the effect of each drug.

Synthetic lethality

Originates from studies in Drosophila model systems, in which a combination of mutations in two or more separate genes leads to cell death, and exploits inherent differences between cancer cells and normal cells. In the drug-development setting, this paradigm has expanded to included scenarios in which drugs as single agents have minimal effects, but have significant antitumour activity when used in combination.

Systems biology

Is the computational and mathematical modelling of complex biological systems. In the context of drug development, these approaches aim to advance the prediction of effective drug combinations and the most-common strategies include computational modelling, gene-signature analysis, functional genomics, and high-throughput drug combination screening.

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Lopez, J., Banerji, U. Combine and conquer: challenges for targeted therapy combinations in early phase trials. Nat Rev Clin Oncol 14, 57–66 (2017). https://doi.org/10.1038/nrclinonc.2016.96

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