Trends in Brain Cancer Research (Horizons in Cancer Research)

Functional Imaging for Cancer Diagnosis
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Mathematical oncology: exploiting maths for cancer research

Researchers continue to learn about the biology of cancer and how it affects the body. They are developing innovative new treatments with the hopes of extending life and one day finding cures. Hard copies of the Breakthroughs in Bioscience and Horizons in Bioscience series are available upon request.

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https://knotmingstowma.tk Epigenetics: Looking Beyond Our DNA - Our physical appearance and susceptibility to disease were once thought to be hard-wired within our DNA, but now scientists are starting to decipher how biological and environmental factors influence health disease. Mathematics plays an increasingly prominent role in cancer research, with applications ranging from theoretical studies to clinical trials designed using mathematical models.

As such, mathematical oncology — defined as the use of mathematics in cancer research — has gained momentum in recent years with the rapid accumulation of clinical data and applications of mathematical methodologies.

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The journal Physical Biology has now published The Mathematical Oncology Roadmap , a collection of 11 essays that provide a forward-looking view and demonstrate specific areas of focus within this unique field of research. Introducing the roadmap, Russell Rockne from City of Hope National Medical Center, explains that its dominant theme is the personalization of medicine through mathematics, modelling and simulation — achieved primarily through the use of patient-specific clinical data.

The collection begins with a demonstration of the role of mathematical oncology in personalizing medicine, via patient-specific modelling, analysis of patient-specific clinical data and patient-specific adaptive therapies. The next contribution emphasizes the importance of setting standards for data and mathematical models, to ensure interoperability and ultimately to develop useful tools for studying and treating cancer. This could be achieved using families of models where the optimal model s is selected and used to update patient-specific predictions over time. Another important challenge is improving the early detection of cancer.

Here, the authors propose applying mathematical models of cancer to evaluate and predict the efficacy of screening strategies.

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The ultimate goal: to produce clinically actionable, personalized cancer screening recommendations. The next three contributions examine the evolution of cancer.

The authors discuss: mathematical modelling using large population sizes to simulate tumour evolution and predict the evolution of resistant cells; applying a single-cell view to examine cancer heterogeneity and evolution; and accurate representation of metabolism in cancer progression. Mathematics can also be used to model patient-specific responses to radiation therapy. This is followed by a look at evolutionary therapy, an entirely new pillar of cancer treatment in which treatment schedule and dose are mathematically designed to reduce the possibility of resistance.

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A fitness landscape is a mathematical concept that enables prediction and interpretation of the temporal process of evolution. The roadmap identifies three critical milestones along the path to mathematically designed cancer treatment: obtaining accurate, rigorous and reproducible predictions of cancer progression; avoiding and mitigating therapeutic resistance; and merging mechanistic knowledge-based mathematical models with machine learning. Rockne notes that government agencies, such as the Federal Drug Administration FDA in the USA, have begun to recognize modelling and simulation as forms of valid scientific evidence in the review and approval process.

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