Computational tool developed to predict immunotherapy outcomes for patients with metastatic breast cancer
Using computational tools, 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.
The work by computational scientists and clinicians was published October 28 in the Proceedings of the National Academy of Sciences.
The team employed a mathematical model called quantitative systems pharmacology to generate 1,635 virtual patients with metastatic, triple-negative breast cancer and performed treatment simulations with the immunotherapy drug pembrolizumab. They then fed these data into powerful computational tools, including statistical and machine learning-based approaches, to look for biomarkers that accurately predict the treatment response. They focused on identifying which patients would and would not respond to treatment.
Using the partially synthetic data produced by the virtual clinical trial, researchers assessed the performance of 90 biomarkers alone and in double, triple and quadruple combinations. They found that measurements from tumor biopsies or blood samples taken before the start of treatment, called pretreatment biomarkers, had limited ability to predict treatment outcomes. However, measurements from patients taken after the start of treatment, called on-treatment biomarkers, were better predictive of outcomes. Surprisingly, they also found that some commonly used biomarker measurements, such as the expression of a molecule called PD-L1 and the presence of lymphocytes in the tumor, performed better when assessed before the start of treatment than after treatment initiation.
The researchers also looked at the accuracy of measurements that do not require invasive biopsies, such as immune cell counts in the blood, in predicting treatment outcomes, finding that some blood-based biomarkers performed comparably to tumor- or lymph node-based biomarkers in identifying a subset of patients who respond to treatment. This potentially suggests a less-invasive way to predict response.
Measurements of changes in tumor diameter can be readily obtained by CT scans, and also could prove predictive, senior study author Aleksander Popel, Ph.D. says: “This, measured very early within two weeks of treatment initiation, had a great potential to identify who would respond if the treatment were continued.”
To validate the findings, investigators performed a virtual clinical trial with patients selected based on change in tumor diameter at two weeks after the start of treatment. “The simulated response rates increased more than two-fold — from 11% to 25% — which is quite remarkable,” says lead study author Theinmozhi Arulraj, Ph.D. “This emphasizes the potential for noninvasive biomarkers as an alternative, in cases where collecting tumor biopsy samples is not feasible.”
Collectively, these new findings shed light on how to better select patients with metastatic breast cancer for immunotherapy. The researchers say these findings are expected to help design future clinical studies, and this method could be replicated in other cancer types.
