Leukemia is the most common pediatric cancer in Qatar. Similar to global statistics, it accounts for over 40% of new pediatric cancer cases, therefore it is a national priority to better understand leukemia and develop new methods that aid patient stratification and prognosis.

Most pediatric leukemia patients respond well to current treatment regimes, for example, Acute Lymphocytic Leukemia (ALL) has close to a 90% five-year overall survival rate. In contrast, Acute Myeloid Leukemia (AML) has a higher relapse rate and much lower (~65%) overall survival rate. It is difficult to predict which patients will relapse because there are only a few recurrent mutations that are associated with poorer prognosis (e.g. FLT3, WT1). Furthermore, in pediatric cancers, it is equally important to identify patients with a good prognosis to avoid overtreatment. In order to achieve remission, all AML patients currently receive high doses of chemotherapeutic drugs, such as daunorubicin and cytarabine, which have considerable long-term adverse effects, thereby reducing the quality of life and increasing morbidity and mortality. Better patient stratification is needed to achieve risk-directed optimal intensity and a combination of therapy.

Here the team focuses on AML to investigate what causes 35-40% of pediatric AML cases to relapse conferring poor outcomes.

This project employs a multi-omics approach to elucidate how somatic mutations and long-range regulatory interactions contribute to relapse in pediatric AML patients carrying distinct chromosomal rearrangements. They will access patient samples from the pediatric AML ‘MyeChild’ drug trial in the UK and from the pediatric oncology unit in Sidra. The team will explore whether transcriptional and long-range regulatory interaction patterns discriminate against pediatric AML subtypes, and if the regulatory landscape differs in children compared to adults. They will also examine the changes in gene expression regulation and the regulatory elements involved in disease progression. The team will explore whether the use of next-generation artificial intelligence methods could predict whether the patient is likely to relapse.