A research team from Columbia Engineering and the Irving Cancer Dynamics Institute has reached significant discoveries in cancer immunotherapy. The team has revealed a unique population of immune cells, which is important for successful treatment of acute myelogenous (OML). This investigation was conducted in partnership with the Dana Farbert Cancer (DFCI).
AML, which affects four of the 100,000 patients annually, according to the National Cancer Institute, is a type of cancer that first attacks the bone marrow before moving to blood infection. The current treatment plan includes targeted chemotherapy followed by stem cell transplantation. Unfortunately, up to 40% of these patients recur after transplantation and have an average survival of six months. At this stage, the only hope for remission is immunotherapy.
The role of immune cells in the fight against cancer
Under the guidance of Elham Aziz, Associate Professor of the Biomedical Engineering Department Columbia Engineering, the study explores how coordinated immune networks in the bone marrow microsity in leukemia affect the reaction to cell therapy, raising the question: Why do some patients benefit? According to a Pfizer study, the current treatment of OML recurrence, donor lymphocytes infusion (DLI) – therapy involving donor immune cells – has 5 -year survival only 24 percent.
This new study shows that a unique T-cell population found in patients responding to DLI may be a key. These cells fight leukemia, enhancing the immune response. In addition, the study shows that patients with a healthier, more active and varied immune medium in the bone marrow are better able to support these cells and their ability to fight cancer.
Using the patented computing approach of the DIISCO team, the researchers found key interactions between the unique T-cell population and other immune cells that can lead to the patient’s remission. They also traced these T-cells to the donor product. However, it was found that the composition of the immune cells of the donor has virtually 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 method of machine learning used to analyze how cell interaction changes over time with cancer focus and immune cells profiled in clinical patterns.
Step to Improvement of Cancer Immunotherapy
The results of the study can lead to new intervention options, such as improving the immune environment before starting standard DLI treatment and studying immunotherapy combinations. This will help patients who usually respond badly, find the personalized option that they fit.
“This study shows the power of combining computing and experimental methods through close cooperation to answer complex biological questions and disclosure of unexpected ideas,” said Aziz, which is a member of the Irving Cancer Dynamics Institute, a comprehensive oncology center Herbert Irving and Colombian University. Institute of Science of Data. “Our conclusions not only shed light on the mechanisms that underpin a successful immunotherapy reaction in leukemia, but also give a roadmap to develop effective treatments through innovative machine learning instruments.”
“Seeing our findings confirmed by functional experiments is incredibly interesting and gives real hope for improving cancer’s immunotherapy,” said Cameron Park, Azizi Postgraduate Star, who, together with Katie Maurre, managed this study in the Catherine Wu Lab. -The institute of cancer. The park was also one of the developers of the DIISCO algorithm.
In the future of this particular study, the team plans to examine the measures that increase the efficiency of DLI, focusing on the modulation of the micro -industrial tumor. Although this is exciting, you need to do more work before the team will be able to head for clinical trials with the hope of improving the results for patients with OML recurrence.
