Novel Computational Tool Can Predict Immunotherapy Outcomes for Patients With Triple-Negative Breast Cancer

Researchers have developed a novel method to assess which patients with triple-negative breast cancer (TNBC) could benefit from immunotherapy. The research was conducted by Johns Hopkins Kimmel Cancer Center and the Johns Hopkins University School of Medicine and published this week in the Proceedings of the National Academy of Sciences.

“Unfortunately, existing predictive biomarkers have limited accuracy in identifying patients who will benefit from immunotherapy,” senior study author Aleksander Popel, PhD, a professor of biomedical engineering and oncology at the Johns Hopkins University School of Medicine via a press release. “Moreover, a large-scale assessment of characteristics that predict treatment response would require the collection of tumor biopsies and blood samples from many patients and would involve performing several assays, which is very challenging.”

Therefore, the researchers implemented a mathematical model called quantitative systems pharmacology. The model was able to generate 1,635 virtual patients with metastatic, TNBC and perform treatment simulations with the immunotherapy pembrolizumab. Subsequently, the researchers inputed these data into  computational tools to identify biomarkers that accurately predict the treatment response. Using the partially synthetic data produced by the virtual clinical trial, researchers then analyzed the performance of 90 biomarkers alone and in double, triple and quadruple combinations.

The research found that measurements from tumor biopsies or blood samples taken from pretreatment biomarkers had limited ability to predict treatment outcomes. However, measurements from on-treatment biomarkers were better predictive of outcomes. Moreover, and perhaps surprisingly, the research 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 before the start of treatment as opposed to after the start of treatment.

As noted by Dr. Popel, measurements of changes in tumor diameter can be readily obtained by CT scans, and may also prove predictive: “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 their findings, investigators also 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,” said lead study author Theinmozhi Arulraj, PhD, a postdoctoral fellow at Johns Hopkins. “This emphasizes the potential for noninvasive biomarkers as an alternative, in cases where collecting tumor biopsy samples is not feasible.”