A research team from Columbia Engineering and the Irving Cancer Dynamics Institute made a fundamental discovery in the field of cancer immunotherapy. In an article published today in Scientific immunologyThe team identified a specific population of immune cells that play a critical role in the successful treatment of recurrent acute myeloid leukemia (AML). This work was in collaboration with the Dana Farber Cancer Institute (DFCI).
AML, which affects four out of every 100,000 patients in the U.S. each year, according to the National Cancer Institute, is a type of cancer that first attacks the bone marrow before moving to infect the blood. The current treatment plan includes targeted chemotherapy followed by stem cell transplant. Unfortunately, up to 40% of these patients relapse after transplant and have a median survival of six months. At that stage, the only hope for remission is through immunotherapy.
Led by Elham Azizi, associate professor of biomedical engineering at Columbia Engineering, the research explores how coordinated immune networks in leukemia bone marrow microenvironments influence responses to cell therapy, raising the question: why do some patients benefit from immunotherapy while others do not? The current treatment for recurrent AML, donor lymphocyte infusion (DLI), a therapy that involves donor immune cells, has a 5-year survival rate of only 24%, according to research by Pfizer.
This new study finds that a unique population of T cells found in patients who respond to DLI could be the key. These cells fight leukemia by increasing the immune response. Additionally, the study shows 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 Diisco approach, the researchers discovered key interactions between the unique population of T cells and other immune cells can lead to patient remission. They also traced these T cells back to the donor product. However, the donor’s immune cell composition was found to have little or no effect on patient success. In fact, the success of this treatment is determined by the patient’s immune environment. Diisco is a machine learning method used to analyze how cellular interactions change over time with a focus on cancer and immune cells profiled in clinical samples.
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 don’t respond well find a personalized 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, a member of the Irving Cancer Dynamics Institute, Herbert Irving Comprehensive Cancer Center. and Columbia’s and Columbia’s Data Science Institute. “Our findings not only shed light on the mechanisms underlying the successful immunotherapy response 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 offers real hope for improving cancer immunotherapy,” said Cameron Park, a doctoral student in Azizi’s lab, who co-led this study with Katie Maurer. in Catherine Wu’s laboratory at the Dana Farber-Scancer Institute. Park was also a co-developer of the Diisco algorithm.
In the future of this particular research, the team plans to explore interventions that enhance the effectiveness of DLI while focusing on modulating the tumor microenvironment. Although exciting, much more work needs to be done before the team can head into clinical trials in hopes of improving outcomes for patients with recurrent AML.