This author interview is by Dr Roger Day, of University of Pittsburgh.  Dr Day's full paper, What Tumor Dynamics Modeling Can Teach us About Exploiting the Stem-Cell View for Better Cancer Treatment, is available for download in Cancer Informatics.

Please summarise for readers the content of your article.

The cancer stem cell hypothesis is that in human solid cancers only a small proportion of the cells, the cancer stem cells (CSCs), are self-renewing; the vast majority of the cancer cells are unable to sustain tumor growth indefinitely on their own. In recent years, discoveries have led to the concentration, if not isolation, of putative CSCs. The evidence has mounted that CSCs do exist and are important. This knowledge may promote better understanding of treatment resistance, create opportunities to test agents against CSCs, and open up promise for a fresh approach to cancer treatment. The first clinical trials of new anti-CSC agents are completed, and many others follow. Excitement is mounting that this knowledge will lead to major improvements, even breakthroughs, in treating cancer. However, exploitation of CSC science to help patients requires different thinking.

Decades ago, studying the population dynamics of tumor development was fairly active, but it tapered off. The era of high-throughput assays led people away from thinking about cancer heterogeneity dynamically. Bringing CSC science to the clinical testing raises two novel challenges that tumor dynamics modeling can address: how to formulate the most effective combination treatment schedule, and how to design a clinical trial to test that schedule.

This article leverages tumor dynamics modeling to extract some guidance in designing anti-CSC treatment regimens and the clinical trials that test them. The best approaches are not initially obvious. However, simulations show what results to expect when a patient is treated with a well-chosen schedule combining standard chemotherapies and anti-CSC treatments, in contrast to results from more familiar but poorly chosen schedules. Simulations also show how the effects of good and bad anti-CSC treatment regimens should expect to manifest in clinical trials results. This sheds light on selecting primary clinical endpoints.

How did you come to be involved in your area of study?

As a doctoral candidate at the Harvard School of Public Health, I was privileged to work with Emil "Tom" Frei III, Steve Lagakos, and Marvin Zelen. Dr. Lagakos directed me to the dynamic modeling of cancer, which became my dissertation research. It was the early 1980's.  Many years later, an growing awareness of the cancer stem cell hypothesis was boosted by acquaintance with Vera and Al Donnenberg. The serendipitous connection between the two topics motivated me to pursue the questions addressed here.

What was previously known about the topic of your article?

There has been vigorous activity on the biology of cancer stem cells, and identification of possible agents to target them.  There were ongoing and a small number of completed clinical trials, and also some interesting efforts at mathematical modeling cancer stem cell dynamics.

How has your work in this area advanced understanding of the topic?

My early modeling work led to the "worst drug rule", a fascinating and initially counter-intuitive way of optimizing combinations of cancer treatments. The design of anti-cancer strategies when one of the agents targets stem cells is a scenario that cries out for the application of "worst drug rule" considerations. My hope is that researchers excited about testing anti-CSC ideas in clinical trials will pick up on the implications described here, design better trials as a result, and get quicker to valuable treatments for cancer patients.

What do you regard as being the most important aspect of the results reported in the article?

The clinical trials proposed so far do not exploit the "worst drug rule" idea or show awareness of the opportunities if the rule is properly applied. This article brings the two ideas together and explains the strategic implications for cancer treatment.

In addition, the question arises how to assess whether a strategy against cancer stem cells is working for patients in a clinical trial. The dynamics modeling demonstrates pitfalls in the usual approach to cancer treatment evaluation in clinical trials. While the article does not solve these problems, it presents a framework for moving towards more appropriate clinical trial designs.

share on