June 15, 2015 | by Sara Lewis
Many oncologists, not to mention their patients, might think that there's no place for mathematical analysis in the treatment of cancer. They might think that all treatment decisions are based on unique factors affecting individual patients, with no connection to other patients and their treatment regimens.
Russell Rockne, Ph.D., is determined to change that misconception. Rockne is a mathematical oncologist, which means he uses mathematics as the means of discovery in cancer research.
In addition to investigating questions of cancer biology, Rockne uses outcomes data from large groups of patients to create predictive mathematical models, or algorithms, in the hope of generating effective stand-alone or combination therapies for individual patients. The algorithms loaded with clinical data essentially create a more precise treatment map for individuals experiencing similar cancers.
He joins City of Hope as an assistant professor in the Department of Research Information Sciences, bringing with him a background in both science and art. Formerly, a postdoctoral researcher in mathematical oncology at Feinberg School of Medicine, Northwestern University, Rockne received his doctorate in mathematical biology, and masters in applied mathematics from the University of Washington, Seattle, and his bachelor’s in mathematics and fine art from the University of Colorado, Boulder.
In this interview, he explains the potential for a mathematical oncologist to – if not change the world – at least improve cancer treatment.
I apply mathematical methods and theories to the study of cancer. I’m part of an emerging field called mathematical oncology that tries to use mathematical models to understand and predict how cancer evolves and responds to therapy. I see myself as a bridge between theory, physical science and clinical/translational science, which in this setting means the treatment of cancer patients or the testing of drugs.
Cancer is a complex process that changes in space and in time. Even the “same” cancer can be different between two patients. Mathematics, as the language of the physical universe, is very good at describing and predicting phenomena that change over time. A major challenge in cancer research is that our data is a static snapshot of a dynamic process. I use mathematics to understand the dynamics.
In fact, theory and mathematical methods have not been considered a meaningful component of cancer research until roughly the past decade, and there have not been many real successes in translating mathematical models or theory to the clinic, except for what my mentors and colleagues have been trying recently to do.
There are only a few cancer centers across the country, including City of Hope, where we now have groups of mathematicians and theorists who come together to work with clinicians and cancer biologists to approach problems facing clinicians in cancer treatment.
We do this by building mathematical models that can change in space and in time that can be adapted to individual patient data with the goal of improving day-to-day patient care. Our overarching goal is to improve patient care and drug selection utilizing all components of the data we collect on the clinical scale, which is really the goal of mathematical oncology.
In essence, the kind of data (MRI, genomic, outcomes) that is now available to researchers is unprecedented in its scope and nature. Mathematical modeling enables us to synthesize and examine these complex data sets — how they interact or may be predictive in suggesting courses of treatment or even cures.
MRI (magnetic resonance imaging) data is particularly important in the treatment of brain cancer because it provides a useful image of the organ and disease in a situation where you can’t just go inside to take out or observe the cancer itself. The MRI basically takes a picture of the disease as it changes over time, and I can utilize that data to observe and model that growth in terms of the relative response of that patient to a particular therapy.
So now we can see how a particular patient is responding to a particular therapy, and this helps in understanding how much treatment is too little or too much for patients who display a similar disease profile as represented by the MRI data “picture.”
Well, one big factor in all of this is Steve Rosen (provost and chief scientific officer, director of Beckman Research Institute of City of Hope), who really values my approach to these issues. Additionally, I know I will have the opportunity to apply my methodology to other disease types, and cancers other than brain cancer.
Down the line, I’d also like to become involved with investigative clinical trials that are driven by mathematical hypotheses and approaches. For example, brain cancer trials where there are a low number of patients. Utilizing mathematical modeling, we might better understand which patients are going to respond before the trial starts or midway through the therapy.
What we have today is the intersection of mathematics and biology, and increasingly in the research setting, the push to have engineers and mathematicians talking to biologists, thus improving communication among the disciplines. While interdisciplinary training has generally been frowned upon, what I’m really talking about is a broadening of awareness, which can produce a deeper understanding of how the physical, mathematical, clinical and biological sciences are linked.
Follow City of Hope's Division of Mathematical Oncology on Twitter: @COHMathOnc.
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