Researchers from the Johns Hopkins Kimmel Cancer Center and the Johns Hopkins University School of Medicine have developed a method to assess which patients with metastatic triple-negative breast cancer could benefit from immunotherapy. Immunotherapy is used to boost the body’s immune system to attack cancer cells, but only some patients respond to treatment. To identify responsive patients, predictive biomarkers are used, which indicate the response to immunotherapy, but existing biomarkers have limited accuracy. The team employed a mathematical model called quantitative systems pharmacology to generate virtual patients with triple-negative breast cancer and performed simulations with the immunotherapy drug pembrolizumab to identify biomarkers that predict treatment response.
Using data from virtual patient simulations, researchers assessed the performance of 90 biomarkers to predict treatment outcomes. They found that pretreatment biomarkers, taken before treatment initiation, had limited predictive ability, while on-treatment biomarkers, taken after treatment initiation, were better predictors of outcomes. Some commonly used biomarkers, such as PD-L1 expression and lymphocyte presence in the tumor, were more accurate when assessed before treatment initiation. Non-invasive biomarkers, such as immune cell counts in the blood, showed comparable predictive performance to tumor-based biomarkers, suggesting a less-invasive way to predict treatment response. Changes in tumor diameter, measured by CT scans early in treatment, could also predict treatment response.
To validate their findings, investigators performed a virtual clinical trial selecting patients based on changes in tumor diameter at two weeks after the start of treatment. The simulated response rates increased more than two-fold, emphasizing the potential use of non-invasive biomarkers when collecting tumor biopsy samples is not feasible. By identifying predictive biomarkers, researchers aim to avoid overtreatment in patients who respond well without immunotherapy and undertreatment in those who do not respond well. This method could help in designing future clinical studies and may be replicated in other cancer types, shedding light on better patient selection for immunotherapy in metastatic breast cancer.
In a previous study, the team developed a computational model specifically focused on late-stage breast cancer where the tumor has spread to various parts of the body. The current work was supported by the National Institutes of Health and involved data from several clinical and experimental studies, which were used to develop and validate the computational model. The findings are expected to aid in patient selection for therapy and could be applied to other cancer types. Study co-authors include researchers from Johns Hopkins and Kaiser Permanente. The research was supported by the NSF and managed by The Johns Hopkins University in accordance with its conflict-of-interest policies.