Predicting Efficacy of Anti-cancer Immunotherapies

Despite the substantial advances, immunotherapy still presents notable challenges. While these therapies have been highly effective against certain types of cancer, more than 50 percent of cancer patients fail to respond to immunotherapies. Among patients who do respond, it often occurs more slowly than more traditional treatment regimens, making it challenging to determine when to alter the clinical approach. Several promising biomarkers for immunotherapy response have been identified to help alleviate this issue, but they are often specific to a certain family of drug and disease combinations and may require extensive and invasive diagnostic testing.

A team of researchers, led by Houston Methodist's Zhihui Wang, PhD, associate research professor of mathematics in medicine and Vittorio Cristini, PhD, professor of mathematics in medicine and have developed, analyzed and validated a mathematical model that can quantify the sensitivity of a cancer cell type to a specific immunotherapeutic drug. The new analytic tool employs inputs that are already being measured in cancer patients to help optimize treatment approaches based on the individual’s specific disease and immune health, thus enhancing the chances for selecting successful treatments from a wide variety of cancer-immunotherapy drug combinations. The model establishes a framework for engineering personalized treatment strategies., The specifics of the mathematical model were published in Nature Biomedical Engineering in collaboration with researchers at MD Anderson Cancer Center.

They found that model-derived measures that quantify the presence and health of the immune presence within the tumor and the resulting kill rate of cancer cells by immunotherapy-activated immune cells could be combined into a single measure that was highly correlated with long-term tumor burden, thus providing a unique numerical score of the strength of response of that cancer to the specific drug. These results were further validated with data from 177 additional patients treated with one of the most common checkpoint inhibitor immunotherapies, anti-CTLA4 or anti-PD1/PDL1 monotherapies.

Without the need for new technology or extensive training, the mathematical model can potentially provide a much-needed bedside tool that can easily be implemented alongside other clinical diagnostics, enabling health care providers to identify the treatment intervention needed to eliminate the disease on a patient-specific basis and to adjust the treatment plan accordingly early in the course of clinical intervention.