Acute Lymphoblastic Leukemia is the most frequent cancer type in children. Although 80% of patients are cured with the standard treatment, a fraction of patients still relapse and die because of the disease. Standard treatment of the disease will be determined by patient risk group. We use mathematical modeling to improve patient stratification, identify relapse risk and design personalized treatments by better using the large amount of data available, with a special focus on flow cytometry data.

Additionally, we are currently working on pediatric B-ALL from a genomic perspective. We are trying to assess the role of aneuploidy and chromosomal instability in pediatric B-ALL initiation and progression. Understanding its dynamics from a computational point of view can help in patient stratification.