Mathematical Modeling Sheds Light on Viral Mimicry in Cancer

Understanding how immune cells recognize and respond to cancer cells is central to the development of cancer immunotherapies, such as checkpoint inhibition and cellular therapies, as well as approaches like cancer vaccines. Over the past ten years, Benjamin Greenbaum, from Memorial Sloan Kettering Cancer Center, has focused his research on the genetic changes in cancer that trigger immune system detection. His work examines how certain genetic patterns in cancer cells—known as pathogen-associated molecular patterns (PAMPs)—can appear foreign to the immune system, even though they originate from the patient’s own DNA.

PAMPs can develop from repetitive DNA sequences ordinarily inactive within the genome. While these elements constitute nearly half of human genetic material, their activation leads to the production of RNA molecules with similarities to viral genetic codes, a phenomenon termed "viral mimicry." Dr. Greenbaum’s laboratory is investigating the emergence and consequences of viral mimicry: what prompts it, how the immune system perceives it, and how it could influence the evolution of cancer cells. 

To address these questions, Dr. Greenbaum’s team, worked with international collaborators to create a mathematical model designed to quantify viral mimicry. Drawing on techniques from statistical physics and machine learning, they analyzed why certain viral mimics remain in the genome while others disappear as cancers evolve. Their findings, published in Cell Genomics, highlight that some classes of repetitive DNA are especially effective at mimicking viruses. This mimicry may help cells defend against viral threats or alert the immune system to cellular distress.

Dr. Greenbaum emphasizes that having accurate ways to assess viral mimicry will clarify how the innate immune system interacts with cells, particularly during cancer’s progression. As he notes, “Being able to quantify mimicry, to have good methods for assessing what turns it on or off, is going to help us understand how the innate immune system interacts with cells and impacts their evolution, including during cancer evolution.”

Earlier work by the group used these tools to show how pancreatic cancer cells can use repeated DNA elements, like retrotransposons, to avoid immune attacks. Their mathematical approach is expected to help reveal more about the relationship between innate immunity and cancer. Dr. Greenbaum states, “A better understanding of what activates the innate immune system can help us figure out how to improve immunotherapies. With cancer vaccines, it could help us learn how to make the vaccines more or less visible to the immune system so that we can better tune immune engagement.”