A research team from Columbia Engineering and the Irving Institute for Cancer Dynamics made a significant breakthrough in cancer immunotherapy. The team discovered a unique population of immune cells that is critical for the successful treatment of acute myeloid leukemia (AML). The investigation was conducted in partnership with the Dana Farber Cancer Institute (DFCI).
AML, which affects about four out of 100,000 patients in the U.S. each year, according to the National Cancer Institute, is a type of cancer that attacks the bone marrow before moving to infect the blood. The current treatment plan involves targeted chemotherapy followed by a stem cell transplant. Unfortunately, up to 40% of these patients relapse and have a median survival of six months after transplant. At that stage, the only hope for remission is through immunotherapy.
Role of immune cells in fighting cancer
Led by Elham Azizi, associate professor of biomedical engineering at Columbia University, the research revealed how coordinated immune networks in leukemia bone marrow microenvironments influence responses to cellular therapy, raising questions: why some patients benefit from immunotherapy. While others don’t? According to research conducted by Pfizer, the current treatment for AML, donor lymphocyte infusion (DLI), a therapy involving donor immune cells—has a 5-year survival rate of only 24 percent.
This new study finds that a unique population of T cells found in patients who respond to DLI may be important. These cells fight leukemia by enhancing the immune response. Additionally, studies show that patients with a healthier, more active and diverse immune environment in the bone marrow are better able to support these cells and their cancer-fighting abilities.
Using the team’s proprietary computational DIASCO approach, researchers discovered important interactions between unique T cell populations and other immune cells that can lead to patient remission. They traced these T cells back to the donor product. However, it turned out that the donor’s immune cell composition had no impact on patient success. In fact, the success of this treatment is determined by the immune environment of the patient. DIISCO is a machine learning method used to analyze how cell interactions change over time, with a focus on cancer and immune cells, which are profiled in clinical samples.
A step toward better cancer immunotherapy
The study findings may lead to new intervention options such as improving the immune environment before starting standard DLI treatment and exploring combinations of immunotherapies. This will help patients who typically do not respond well to find an individual option that works for them.
“This research exemplifies the power of combining computational and experimental methods through close collaboration to answer complex biological questions and uncover unexpected insights,” said Azizi, who is based at the Irving Institute for Cancer Dynamics. , is a member of the Herbert Irving Comprehensive Cancer Center and Columbia. Data Science Institute. “Our findings not only shed light on the mechanisms underlying successful immunotherapy responses in leukemia, but also provide a roadmap for developing effective treatments guided by innovative machine learning tools.”
“Seeing our findings validated through functional experiments is incredibly exciting and provides real hope for improving cancer immunotherapy,” said Cameron Park, a PhD student in the Azizi lab. -Cancer Institute. Park was also a co-developer of the Dysko algorithm.
In the future of this particular research, the team plans to explore interventions that can increase the effectiveness of DLI by modifying the tumor microenvironment. Although exciting, there is much more work to do before the team can head to clinical trials with the hope of improving outcomes for patients with AML.
