Cancer for dummies: A mathematical introduction to cancer

Explaining cancer growth and treatment from a mathematical point of view

Quick disclaimer

So what causes cancer and how can we prevent it?

Tumor formation and progression

The Bozic Model

Model for cancer formation and growth. Bozic et al [1]
Cancer branching model. Chowell et al [2]

Cancer growth

Table with cancer simulations. Chowell et al [2]

Based on these facts, which cancer do you think poses a greater threat: a fast cancer, or a slow cancer?

Cancer progression in fast (left) and slow (right) cancers. Simulations by author.
Composition of tumor at time of detection of fast (left) and slow (right) growing cancers. Simulations by author.
Number of subclones with acquired resistance. Chowell et al [2]

Cancer treatment


Resisting resistance

Based on what we think we know so far, what do you think is more effective. Constant maximum dosage, or intermittent drug treatment?

Drug resistance acquisition with different therapies. Zhang et al [3]
Photo by Ben Stern on Unsplash

Adaptative therapy

Different therapeutic strategies and the clinical outcome. Zhang et al [3]
A summary of the status of the 11 patients in the pilot trial (a) and 16 patients in the contemporaneous cohort. Zhang et al [3]

Multiple resistances

Photo by Greg Rakozy on Unsplash
Response to different therapies. Left (mono therapy). Center (with dual resistors present in original population). Right (with no dual resistors present in original population). Bozic et al [4]

Genetic vs Epi-Genetic Mutations

Traditional genotype to phenotype mapping. Huang et al [5]
Updated genotype to phenotype mapping. Huang et al [5]

Modelling cancer

Photo by Myriam Jessier on Unsplash

Closing statements


Geeky, introverted MSc in Computer Science and passionate rock climber. Data lover wanting to share his interest through writing.

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