Using computational tools, researchers from the Johns Hopkins Kimmel Cancer Center and the Johns Hopkins University School of Medicine developed a method to assess which patients with metastatic triple-negative breast cancer might benefit from immunotherapy. The work by computer scientists and clinicians was published Oct. 28 in the Proceedings of the National Academy of Sciences.
Immunotherapy is used to try to boost the body’s own immune system to attack cancer cells. However, only some patients respond to the treatment, explains lead study author Theinmozhi Arulraj, Ph.D., a postdoctoral fellow at Johns Hopkins: “It’s really important to identify the patients for whom it will work, because the toxicity of these treatments is high.”
To clarify this, studies have tested whether the presence or absence of certain cells, or the expression of various molecules in the tumor, can indicate whether a particular patient will respond to immunotherapy. These molecules are called prognostic biomarkers and are useful in choosing the right treatment for patients, explains senior study author Aleksander Popel, Ph.D., professor of biomedical engineering and oncology at the Johns Hopkins University School of Medicine.
Unfortunately, existing prognostic biomarkers have limited accuracy in identifying patients who will benefit from immunotherapy. In addition, a large-scale evaluation of characteristics predicting treatment response would require the collection of tumor biopsies and blood samples from many patients and would involve performing several analyses, which is very difficult.”
Aleksander Popel, Ph.D., professor of biomedical engineering and oncology, Johns Hopkins University School of Medicine
So the team used a mathematical model called quantitative systems pharmacology to create 1,635 virtual patients with metastatic, triple-negative breast cancer and performed simulations of treatment with the immunotherapy drug pembrolizumab. They then fed this data into powerful computational tools, including statistics and machine learning-based approaches, to look for biomarkers that accurately predict treatment response. They focused on identifying which patients would and would not respond to treatment.
Using the partially synthetic data generated from the virtual clinical trial, the researchers evaluated 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 treatment began, called pre-treatment biomarkers, had limited ability to predict treatment outcomes. However, measurements from patients taken after treatment began, 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 starting treatment than after starting treatment.
The researchers also examined the accuracy of measurements that do not require invasive biopsies, such as the number of immune cells in the blood, in predicting treatment outcomes, finding that some blood-based biomarkers performed comparable to tumor- or lymph node-based biomarkers in identifying a subset of patients who respond to treatment. This potentially suggests a less invasive way of predicting response.
Measurements of changes in tumor diameter can be easily obtained with CT scans and also could prove prognostic, says Popel: “This, measured very early within two weeks of starting treatment, had a great potential to identify who would responded if treatment was continued.”
To validate the findings, the researchers conducted a mock clinical trial with patients selected based on the change in tumor diameter two weeks after starting treatment. “Simulated response rates more than doubled—from 11% to 25%—which is very remarkable,” says Arulraj. “This highlights the potential for non-invasive biomarkers as an alternative, in cases where collection of tumor biopsy specimens is not feasible.”
“Prognostic biomarkers are critical as we develop optimized strategies for triple-negative breast cancer so as to avoid over-treating patients expected to do well without immunotherapy and under-treating those who do not respond well to immunotherapy,” adds co-author of the Cesar study. Santa-Maria, MD, associate professor of oncology and breast medical oncologist at Johns Hopkins Kimmel Cancer Center specializing in breast cancer immunotherapy and immune biomarkers. “The complexity of the tumor microenvironment makes biomarker discovery a clinical challenge, but technologies that leverage in-silico [computer-based] Modeling has the potential to capture such complexities and help select patients for treatment.”
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 types of cancer.
Previously, the team used an in-house modeling framework and developed a computational model with a particular focus on late-stage breast cancer, where the tumor has already spread to different parts of the body. This was posted on Advances in Science last year. The team used data from various clinical and experimental studies to develop and fully validate this computational model.
The current work was supported by the National Institutes of Health (grant R01CA138264). Part of the work was performed at the Advanced Research Computing core facility at Hopkins, which is supported by the National Science Foundation under grant OAC1920103.
Co-authors of the study are Hanwen Wang, Atul Deshpande, Ravi Varadhan, Elizabeth Jaffee and Elana Fertig of Johns Hopkins. and Leisha Emens of Kaiser Permanente in South Sacramento, California.
Popel is a consultant to Incyte and J&J/Janssen, and is a co-founder and consultant to AsclepiX Therapeutics. He also receives research funding from Merck. The terms of these arrangements are administered by Johns Hopkins University in accordance with its conflict of interest policies.
