By james art 
New York [US]January 25 (ANI): A researcher Columbia Engineering and the Institute for Cancer Irving Dynamics has achieved a significant discovery in cancer immunotherapy.
The team has discovered a unique population of immune cells, which is important for the successful treatment of recurrent acute myeloid leukemia (AML). This investigation was done in partnership with the Dana Farber Cancer Institute (DFCI).
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AML, which affects four of the 100,000 patients in the United States every year, according to the National Institute of Cancer, is a type of cancer that first attacks the bone marrow before switching to blood infection. The current treatment plan includes targeted chemotherapy, followed by stem cell transplantation. Unfortunately, up to 40% of these patients have relapse after transplantation and have an average survival of six months. At this stage, the only hope for remission is through immunotherapy.
Elham Azizi, Assistant Professor of Biomedical Engineering at Columbia Engineering, the study examines how coordinated immune networks in bone marrow microsresses affect the answers to cell therapy, raising the question: Why do some patients benefit from immunotherapy? The current treatment for recurrent AML, infusion of donor lymphocytes (DLI) – a therapy involving donor immune cells – has a 5 -year survival of only 24 percent, according to a study conducted by Pfizer.
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This new study found that a unique population of T cells found in patients responding to DLI may be the key. These cells fight leukemia by enhancing the immune response. In addition, the study shows that patients with a healthier, more active and varied immune environment in the bone marrow are more capable of maintaining these cells and their cancer abilities.
Using the team’s own computing approach of the team, researchers have found key interactions between the unique T cell population and other immune cells can lead to the patient’s remission. They also track these T cells to the donor product. However, it was found that the composition of the donor immune cells had little or no effect on the patient’s 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 an emphasis on cancer and immune cells profiled in clinical samples.
The results of the study can lead to new intervention opportunities such as improving the immune medium before starting standard DLI treatment and studying combinations of immunotherapy. This will help patients who usually do not respond well to find a personalized option that works for them.
“This study is an example of the power of combining computing and experimental methods through close cooperation to answer complex biological questions and to reveal unexpected insights,” said Azizi, who is a member of the Irving Institute for Cancer Dynamics Complete Oncology Center and Colombian University of Data Science. “Not only do our discoveries shed light on the mechanisms that underlie the successful immunotherapy response to leukemia, but they also provide a road card to develop effective treatments, guided by innovative machine learning tools.”
“Let’s see our findings validated through functional experiments is incredibly exciting and offers real hope for improving immunotherapy against cancer,” said Cameron Park, a doctoral student at Azizi lab, who led this study with Katie Maurer at Catherine Wu Laboratory in Dana Farber – Institute of Cancer. The park was also a co -worker of the DIISCO algorithm.
In the future of this particular study, the team plans to study interventions that increase the efficiency of DLI while focusing on the modulation of the tumor micro -congregation. Although exciting, much more work must be done before the team can focus on clinical trials in the hope of improving the results for patients with AML relapse. (Ani)
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