"Pediatric acute lymphoblastic leukemia (ALL) is the most common childhood cancer, diagnosed in about 3,000 American children per year. The study focused on the most common type called B-cell precursor ALL. The majority of these cases are cured with chemotherapy, but 10 to 20 percent of patients relapse. " "Prior research strongly suggested that cancer relapse may be driven by a few treatment-resistant cells that are present from the beginning of the diseases. The question is could those be identified and treated."
"Researchers analyzed samples from 60 children with pediatric acute lymphoblastic leukemia and compared them to samples from healthy patients. Using single-cell mass cytometry (also called CyTOF), the team looked at 35 proteins involved in B-cell development. Applying machine learning to that data, researchers identified six features of leukemia cells that could help predict relapse after treatment. "The research points to a way that would allow greater personalization in treatment for these pediatric cancer patients."
"Machine learning identified six features of expanded leukemic populations that were sufficient to predict patient relapse at diagnosis."
"The researchers tested bone marrow samples taken from 60 all patients at the time of their diagnosis. Each patient had three to 15 years of follow-up medical records available for analysis, including relapse information."
The method, used at the time of diagnosis, predicts which patients will relapse with 85 percent accuracy – a significant improvement over the traditional method.