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  • Perspective
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Executable cancer models: successes and challenges

Nature Reviews Cancer volume 20, pages 343–354 (2020)Cite this article

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Abstract

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field.

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Fig. 1: Computational modelling iterative cycle.
Fig. 2: Exploring cancer therapies using a computational model.
Fig. 3: Possible digital avatar for a cancer patient.

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Acknowledgements

The authors thank the many collaborators who have grounded the development of computational models with their experimental and clinical data. They thank G. Evan, N. Piterman, M. Vardi, B. Cook and A. Herbert for many fruitful discussions over the years. They further thank A. Herbert for critical reading of the manuscript. J.F. acknowledges funding from Cancer Research UK and The Mark Foundation for Cancer Research, and start-up funds from the University College London Cancer Institute.

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Authors and Affiliations

  1. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Matthew A. Clarke & Jasmin Fisher

  2. UCL Cancer Institute, University College London, London, UK

    Jasmin Fisher

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J.F. conceived the idea for this article and structured the manuscript. J.F. and M.A.C. researched data for the article and wrote, reviewed and edited the manuscript. J.F. conceived the figures. J.F. and M.A.C. designed the figures.

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Correspondence to Jasmin Fisher.

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Glossary

Abstraction

A model at a certain level of description simplifying lower-level details in a principled way, preserving key properties of the system behaviour.

Adaptive therapy

The application of cancer treatment in a manner that quickly responds to changes in the disease rather than following a fixed protocol, with the goal of managing the cancer and maintaining a limited tumour burden, rather than attempting to totally eliminate the disease.

Algorithm

A step-by-step sequence of basic operations required to produce a desired result in a discrete system.

Attractors

States towards which a system tends to evolve from a wide variety of starting conditions.

Compositionality

The ability to combine separate component models into a larger overall system model.

Computer programs

Collections of instructions that perform a specific algorithm when executed by a computer.

Concurrency

The parallel execution of multiple interacting computer programs.

Continuous models

Models with an infinite number of states, which may also be called analogue models.

Discrete model

A model with a countable number of states, which represent the remembered history of the system modelled. Discrete models are to be contrasted with continuous models.

Formal verification

A method to prove or disprove the correctness of computer programs with respect to a certain formal specification or property, by treating the program as a mathematical structure and proving theorems about it.

Instruction set architecture

An abstract description of a computer processer at the level required by programmers, including those writing compilers for high-level programming languages (for example, Python).

Level of abstraction

A hierarchy of abstractions where higher levels of abstraction are placed at the top and more detailed concepts underneath.

Logic gates

A representation of a Boolean logical operation, combining binary inputs to produce a binary output based on operations such as AND, OR or XOR (exclusive OR).

Model checking

A means of checking whether a program meets a given specification using automated theorem proving (a branch of mathematical logic dealing with proving mathematical theorems by computer programs).

Modularity

The focus on keeping components of a model or a computer program in discrete units, allowing them to be flexibly put together in different combinations.

Non-determinism

The abstraction of a complex behaviour showing more than one possible output (for example, phenotype) for a given input (for example, genotype).

Phase planes

2D visualizations of the behaviour of a system of differential equations where each axis shows one variable of the equations. These are often used to aid the visualization of the long-term behaviour of these systems. Higher dimensional visualizations are referred to as a phase space. For example, the Lotka–Volterra equations model the change in predator and prey populations over time. Plotting the number of predators or prey against time would show oscillatory behaviour, but plotting prey versus predator would reveal a closed loop in the phase plane, revealing the balanced trade-off in the number of predators and prey.

Program synthesis

A technique that automatically constructs a computer program that satisfies a given high-level specification.

Search space

The set of possible states or solutions through which an algorithm must search to find the optimum solution to some problem.

Specification

A set of known behaviours that a model must be able to produce in the correct circumstances in order to be considered valid.

State machine

An abstract model of a discrete system. A state machine can only be in exactly one of a countable (often finite) number of states at any given time. The machine can change from one state to another in response to some external (input) events (input signals); the change from one state to another is called a (state) transition and may give rise to external (output) signals.

Testing

The process of checking the consistency of a program with a given specification by comparing inputs with outputs across multiple runs of the program.

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Clarke, M.A., Fisher, J. Executable cancer models: successes and challenges. Nat Rev Cancer 20, 343–354 (2020). https://doi.org/10.1038/s41568-020-0258-x

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