Personalizing the Math: New Cancer Progression Model Individualizes the Numbers

A new study out this week aims to put a more individual spin on the mathematics behind tumor progression.

Niko Beerenwinkel and colleagues used a colon cancer case study to analyze how much particular gene mutations affect an individual’s tumor development. They found that current tumor progression models, which only looked at a few genes of interest, lacked the accuracy that a model based on a larger selection of gene mutations could provide.

Previously, doctors have relied heavily on standard mathematical formulas to derive predictions on how large individual tumors will grow in a particular amount of time. The article, which was published on November 9 in the Public Library of Science journal Computational Biology, may help explain the huge variation between individual tumors.

From the Public Library of Science:

Cancer progression proceeds stochastically from a single genetically altered cell to billions of invasive cells through a series of clonal expansions. According to their model, cancer progression is driven by mutations in many genes, each of which confers only a small selective advantage. It was found that the time it takes for a benign tumor to transform into a malignant tumor is dominated by the selective advantage per mutation and by the number of cancer genes, whereas tumor size and mutation rate have smaller impacts.

Despite my fear of calculus, I’ve always found tumor prediction models interesting. Computational biology systems just scream futuristic medicine to me, and I’ve seen them used in predicting tumor progression to locating genes of interest. Hopefully, physicians and researchers can harness the simulations to help find ways to manage and treat cancer as quickly as possible.

For more information about the new model system, see A new mathematical formula for cancer progression.