A research team from Columbia Engineering and the Irving Institute for Cancer Dynamics achieved a significant finding in cancer immunotherapy.
New York [US], January 25 (ANI): A research team from Columbia
Engineering and the Irving Institute for Cancer Dynamics achieved a significant
finding in cancer immunotherapy.
The team discovered a unique population of
immune cells that is important to the successful treatment of relapsed acute
myeloid leukemia (AML). This investigation was done in partnership with the
Dana Farber Cancer Institute (DFCI).
AML, which affects four out of 100,000
patients in the U.S. every 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 a
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 cellular therapy, raising the question: why do some patients
benefit from immunotherapy while others do not? The current treatment for
relapsed AML, donor lymphocyte infusion (DLI)--a therapy involving donor immune
cells--has a 5-year survival rate of only 24 per cent, according to research
conducted by Pfizer.
This new study finds that a unique
population of T cells found in patients who are responding to DLI might be the
key. These cells fight leukemia by boosting 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.
Utilizing the team's proprietary computational DIISCO approach,
the researchers discovered key interactions between the unique T cell
population and other immune cells may lead to patient remission. They also
traced these T cells back to the donor product. However, it was discovered that
the donor's immune cell composition has little to 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
cell interactions change over time with a focus on cancer and immune cells
profiled in clinical specimens.
The study's findings can lead to new
intervention options such as improving the immune environment before starting
the standard DLI treatment and exploring combinations of immunotherapies. This
will help patients who don't typically respond well to 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, who is a member of the Irving Institute for Cancer Dynamics, the
Herbert Irving Comprehensive Cancer Center, and Columbia's Data Science
Institute. "Our findings not only shed light on mechanisms underlying
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 PhD student in the
Azizi lab, who co-led this study with Katie Maurer at the Catherine Wu Lab at
Dana Farber-Cancer Institute. Park was also a co-developer of the DIISCO
algorithm.
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