Cancer Data Science Pulse
When the Sum is Greater Than the Parts—Mathematical Modeling Informs Combining Radiation and Immunotherapy
You’ll be discussing the topic, “Computational and Mathematical Approaches to Modeling Immunotherapy-Radiotherapy Combinations,” in an upcoming seminar. Can you tell us what first interested you in this topic area?
At my core, I’m a physicist. My Ph.D. was in proton radiotherapy and its application in lung cancer. Toward the end of my Ph.D., I began to see that simply focusing on radiation wasn’t including the entire treatment process and impact on the tumor. We had many computational and theoretical models to examine how radiation interacts with cancer. We knew how to use radiotherapy to improve outcomes, but it was such a narrow view.
Radiation therapy is only a small part of cancer treatment, albeit the majority of cancer patients receive it at some point during their treatment. There’s chemotherapy and novel biological agents, which are becoming more and more successful, particularly in treating lung cancer. I began to realize that to truly improve outcomes after radiation therapy, we needed to consider these other agents and approaches. We also needed a way to quantitatively model these approaches to get the greatest benefit for effective treatment and fully understand the synergy that takes place between these different treatment methods.
You mention synergy between radiotherapy and immunotherapy. Are you looking at each approach individually or only as they interact with one another?
We’re not so much looking at the immunotherapy side per se, rather at how to modify radiation therapy regimen to synergize well with immunotherapy. We’re using data from patients who are receiving immunotherapy (such as checkpoint inhibitors) and applying models to help us better understand what happens to tumor response when we irradiate a highly localized area of the body.
Radiotherapy has several beneficial effects on anti-tumor immunity, but also has certain immune-suppressive aspects. Ultimately, the idea is to modify radiation therapy to deliver it in a way that’s less immunosuppressive, more lymphocyte sparing, to heighten the efficiency of the immunotherapy. We hope to apply what we learn to current therapies, like checkpoint inhibitors, but also to the next generation of immunotherapy agents, so we can continue to adapt radiation therapy and make the most of a multimodal approach to treatment.
Who should attend this webinar?
I’ll be discussing two research projects: one looking at how radiation interacts with the immune system and the other examining computational tools we’re using to calculate the radiation dose to parts of the immune system, in this case circulating lymphocytes.
I’d encourage anyone interested in this area of research to attend. Others who are considering using other combination-type therapies also might find this information useful.
Your background as a physicist doesn’t seem like a typical path for work in the bioinformatics and data field.
That’s true, it’s not typical for bioinformatics. However, in our field (radiation oncology), this is a more common path. Medical physicists are deeply embedded in the clinical delivery of radiotherapy to patients, from calibrating machinery to working with medical doctors to ensure patients receive the correct amount of radiation. Although I’m strictly on the research side of this specialty, the close contact to clinical medical physicists and oncologists is invaluable for this type of applied research.
I think physicists in general bring a unique perspective to cancer research. As physicists, we’re acutely interested in matter and energy—how they interact and the impact of those relationships. In looking at cancer treatments, I think it’s a natural progression to examine how different treatment modalities can all work together to impact cancer. So, it may not be a typical path, but I think it’s definitely helping me to view the biological effects of radiation therapy in a new way.
Were there any surprises that you encountered in your work on this topic?
One surprise I found was in a project simulating blood flow in the brain. We were looking at tracking radiation’s effects on brain tumors and circulating lymphocytes. As you’d expect, when irradiating the brain, circulating lymphocytes also get irradiated. However, I thought, because we were focused on the brain itself, we didn’t need to simulate those immune cells once they left the brain.
Once we started performing the simulations, we found our radiation treatments are quite long when compared with the average time circulating blood spends in the brain. This meant that blood circulating through the body came back to the brain multiple times during irradiation, making the exact return probability very important. That was a bit of a curve ball and one that I’ll discuss in full detail during my talk. It was definitely a key challenge for us. What started as a highly focused study on the brain turned out to be rather intimidating, as we then had to pivot our research to look at the entire human body. Yet, it was this change in direction that allowed us to gain a better understanding of how radiation impacted tumors in the brain.
Where do you see this topic headed in the next decade or so?
Radiation therapy will continue to have an important place in cancer treatment, and its role likely will expand. Right now, radiation is typically used in the early stages of disease, when the cancer is still more localized. I think in the coming years we’ll see this therapy used over the long term for treating metastatic disease in patients who are responding well to systemic therapy.
From a medication’s perspective, drugs are becoming more effective, not only immunotherapy drugs, but also agents targeted to specific gene mutations underlying cancer’s development. Some of this work is underway. We’re highly successful in treating cancers like melanoma or those with specific targetable mutations. Thanks to these advances, patients with metastasized disease are having better outcomes. Still, given the evolutionary nature of cancer, it’s likely that a few tumor cells will evolve and adapt. Those cells will learn to evade the immune system or render the drug that’s been designed to target them ineffective. Radiation isn’t currently part of the standard of care in these cases, and clinical studies are ongoing to define exactly which patients might benefit in which circumstances. Still, I do think radiation will prove to be beneficial in cases like these to help curb metastatic disease.
I also expect a multimodal approach will become even more commonplace in the earlier stages of cancer. When we think of using radiation and immunotherapy together in this setting, the goal posts for radiotherapy might shift. Right now, radiation therapy is used early in treatment, and the goal is to kill the greatest number of cells locally with minimal toxicity to surrounding tissues. This objective might change when we combine radiation with immunotherapy, with more focus on immune-activation and supporting the biological agent.
What satisfies you the most about this work?
We all work in cancer research. We’re all trying to address the same immense problem and that’s rewarding in itself. I also enjoy working with a really great team of people, especially students, and I like helping to prepare our postdocs for the next steps in their careers.
When it comes to the research, I relish the quantitative modeling aspects of the radiation field. So much of what I do is centered on optimizing how best to use this technology. Having the opportunity to build on radiation therapy by bringing in the biological aspect is very challenging and rewarding.
I think most of all though, I enjoy the chance to think “outside the box” as we look for new and better ways to combine existing treatments. Here, for example, this means tapping into the intradisciplinary aspects of two very different treatment modalities—radiotherapy and immunotherapy. We know that radiation has an impact on the immune response. Now, with the work we’re doing, I have a chance to clearly quantify that response using computational models.