A research team has made a significant advancement in cancer immunotherapy, identifying a crucial group of immune cells that can greatly affect the treatment outcomes for patients with relapsed acute myeloid leukemia (AML).
The research team from Columbia Engineering and the Irving Institute for Cancer Dynamics has achieved a groundbreaking discovery in the realm of cancer immunotherapy. Their findings, published today in Science Immunology, highlight a particular group of immune cells that are vital for effective treatment in cases of relapsed acute myeloid leukemia (AML). This research was conducted in partnership with the Dana Farber Cancer Institute (DFCI).
According to the National Cancer Institute, AML is diagnosed in approximately four out of 100,000 individuals annually in the U.S. This cancer initially impacts the bone marrow and later spreads to the bloodstream. Standard treatment involves targeted chemotherapy followed by a stem cell transplant. Regrettably, around 40% of patients relapse post-transplant, with an average survival span of only six months. At this point, immunotherapy may be the only option for achieving remission.
The study, led by Elham Azizi, an associate professor of biomedical engineering at Columbia Engineering, investigates how immune networks within the microenvironments of leukemia-affected bone marrow affect responses to cellular therapies. This raises an important question: why do some patients experience positive outcomes from immunotherapy, while others do not? Current treatments for relapsed AML, like donor lymphocyte infusion (DLI)—which uses immune cells from a donor—only achieve a five-year survival rate of 24%, as noted by studies from Pfizer.
The findings from this new research suggest that a specific subset of T cells present in patients responding to DLI may be crucial. These T cells enhance the immune response against leukemia. Moreover, the research indicates that patients with a more robust, active, and diverse immune environment in their bone marrow are better equipped to support these T cells in their fight against cancer.
Using their innovative computational approach known as DIISCO, the researchers uncovered significant interactions between this unique T cell population and other immune cells that could lead to patient remission. They also traced these T cells back to the donor immune product. Nevertheless, the analysis revealed that the composition of the donor’s immune cells has minimal impact on the treatment’s success. Instead, it is primarily the patient’s immune environment that dictates the treatment outcome. DIISCO is a machine learning technique designed to evaluate how interactions between cells evolve over time, particularly focusing on cancer and immune cells analyzed in clinical samples.
The insights gained from this study could pave the way for new intervention strategies, such as enhancing the immune environment prior to the standard DLI treatment and experimenting with various combinations of immunotherapies. This will provide options for patients who typically do not respond well to treatments, allowing for a more personalized approach.
“This research showcases the effectiveness of integrating computational and experimental techniques through close collaboration to tackle complex biological challenges and reveal unexpected findings,” remarked Azizi, a member of the Irving Institute for Cancer Dynamics, the Herbert Irving Comprehensive Cancer Center, and Columbia’s Data Science Institute. “Our results not only illuminate the mechanisms that contribute to successful immunotherapy responses in leukemia but also offer a guide for creating effective treatments through advanced machine learning methodologies.”
“The validation of our findings through practical experiments is extremely thrilling and brings genuine hope for enhancing cancer immunotherapy,” stated Cameron Park, a doctoral student in Azizi’s lab, who co-led this study alongside Katie Maurer at the Dana Farber Cancer Institute’s Catherine Wu Lab. Park also played a key role in developing the DIISCO algorithm.
Looking ahead, the research team aims to investigate ways to augment the effectiveness of DLI while concentrating on modifying the tumor microenvironment. Although the progress is promising, there remains considerable work before the team can transition to clinical trials aimed at improving outcomes for patients with relapsed AML.
