Maximizing Cancer Immunotherapy Success Rates – A Look into the Future

Immunotherapy offers several advantages over chemotherapy with regard to cancer treatment, such as reduced toxicity and lesser patient discomfort. Yet it has marked shortcomings, as over fifty percent of patients do not respond to immunotherapy. The sophisticated mathematical model designed by Cristini can serve as a decision-making tool for patients that are responsive to immunotherapy. The model has predicted outcomes for patients with higher than 90% accuracy and can be used to tailor treatments to engineer better outcomes.

Patient data that is readily available via standard of care protocols can be plugged into the mathematical model to yield specific information regarding the best treatment strategies for the patient. Furthermore, the model can predict the probability of success for the specific treatment strategy for individual patients. Therefore, the app that can be potentially developed based on this mathematical model can be a valuable tool for patients to make important decisions about their course of cancer treatments. Cancer patients are often in a vulnerable state of mind and amenable to input and guidance from their physicians. This app may serve to provide extended sets of information to review, help patients understand the rationale behind their treatment plans, and allow them to participate in their health care journey. Aggressive cancer therapies currently used do not accomplish complete tumor cell eradication, and immunotherapy is typically used as a last resort when standard clinical approaches are unsuccessful. Cristini suggests an approach that entails identifying the strength of treatment intervention necessary for the complete eradication of tumors on a patient-specific basis. The tailored treatments suggested by the model, based on available patient data, can be used in conjunction with other treatments such as radiation or chemotherapy. Cristini suggests, based on the mathematical model, that immunotherapy should be the treatment of choice from the initial outset. While accounting for the key biological aspects of immunotherapy, this model is derived from the first principles of conservation laws of physics. Cristini set out to design this translational mathematical model by asking questions, such as what are the factors driving the clinical response in patients receiving checkpoint inhibitor therapy? Using key parameters of tumor growth rate, tumor-immune infiltration and immunotherapy-mediated amplification of anti-tumor response, this model sheds light on why mechanistically clinical responses vary amongst patients. Complex interactions exist between the immune system and the tumor microenvironment. Cristini emphasizes the importance of understanding the four-dimensional tissue architecture within the tumor environment that dictates elements such as viscosity, the extent of free mobility of drugs and antibodies, and the feasibility of immune cells to travel into the tumor lesions. Understanding the physical processes occurring within the tumor environment helps in modifying parameters to obtain desired clinical outcomes with immunotherapy treatments. “Tumors are physical systems and they obey fundamental laws of physics.” said Cristini. “Irrespective of genetics, cancer cells still need to obey the conservation laws of physics since they belong to the physical realm. What is unique about this mathematical model is that it is historically and fundamentally based on patient data. According to our model, what actually matters is the specific combination of all the measurable quantities which we call superparameters. This mathematical model can yield unique and valuable information – which cannot be learned empirically. It would be like finding a needle in a haystack.” The most important application of the model is engineering desired outcomes by modulating variables such as the quantity of antibody administration during immunotherapy and the extent of penetration of immune cells in tumor lesions. Drugs can be used to change the behavior of certain parameters such as the amount of antibody that binds to the tumor cell. Cristini’s research is based on mechanistically understanding the clinical outcomes and systematically using these modeling tools effectively as novel biomarkers to design better treatments and achieve better outcomes. In the near future, these multi-institutional research efforts at the Texas Medical Center are likely to lead to optimized treatment strategies for cancer patients and significant improvements in survival rates and quality of life.