Tumor model reveals how to best treat cancer

A mathematical model based on patient data suggests that taking a combination of anti-cancer drugs may offer the highest chance of a cure.

As medicine becomes increasingly personalized, more and more emphasis is being placed on the development of therapies that target specific cancer-causing mutations. But while many of these drugs are effective in the short term, and do extend patient lives, tumors tend to evolve resistance to them within a few months.

The key problem is that large tumors are genetically diverse. This means that for any given treatment, there is likely to be a small population of cells within the tumor that is resistant to the effects of the drug. When the drug is given to a patient, these cells will survive and multiply and this will lead ultimately to treatment failure. Given that a single drug is therefore highly unlikely to eradicate a tumor, combinations of two or more drugs may offer a higher chance of cure. This approach has been effective in the treatment of HIV as well as certain forms of leukemia.

Ivana Bozic, Johannes Reiter, Benjamin Allen and co-workers now present a mathematical model designed to predict the effects of combination targeted therapies on tumors, based on the data obtained from 20 melanoma (skin cancer) patients. Their model revealed that if even 1 of the 6.6 billion base pairs of DNA present in a human cell has undergone a mutation that confers resistance to each of two drugs, treatment with those drugs will not lead to sustained improvement for the majority of patients. This confirms the need to develop drugs that target distinct pathways.

The model also reveals that combination therapy with two drugs given simultaneously is far more effective than sequential therapy where the drugs are used one after the other. Indeed, the model of Bozic, Reiter, Allen and co-workers indicates that sequential treatment offers no chance of a cure, even when there are no cross-resistance mutations present, whereas combination therapy offers some hope of a cure, even in the presence of cross-resistance mutations.

By emphasizing the need to develop drugs that target distinct pathways, and to administer them in combination rather than sequentially, this study offers valuable advice for drug development and the design of clinical trials, as well as for clinical practice.

To find out more

Listen to Martin Nowak talk about the evolutionary dynamics of cancer in episode 2 of the eLife podcast.
Read the eLife research paper on which this eLife Digest is based: “Evolutionary dynamics of cancer in response to targeted combination therapy” (June 25, 2013).

eLife is an open-access journal that publishes outstanding research in the life sciences and biomedicine.

The main text on this page was reused (with modification) under the terms of a Creative Commons Attribution 3.0 International License. The original “eLife digest” can be found in the linked eLife research paper.