Kudos to Marilyn Hockenberry and her entire team for their NINR-R01 Multi-PI subcontract proposal on a Baylor College of Medicine application entitled “Using Integrative Pharmacometabolomics to Predict Symptoms in Children Treated for Acute Lymphoblastic Leukemia.” This proposal is for a five-year period with a start date of September 1, 2017.

Abstract: While the cure rate for childhood acute lymphoblastic leukemia (ALL) has increased to >80% over the last three decades, efforts to predict and manage cancer treatment symptoms have not kept pace. Adverse symptoms during treatment often result in complications, treatment delays, and therapy dose reductions, all of which can negatively influence quality of life and jeopardize chances for long-term survival. Pain, nausea, and fatigue are often reported by more than one-third of patients, and fatigue is reported as one of the most distressing and persistent symptoms. Dynamic models are needed to explain the complexities of symptoms during cancer treatment. Because metabolites change rapidly in response to physiologic perturbations (e.g., anticancer agents), they represent proximal reporters of these complex phenotypes. Therefore, metabolomics holds great promise for monitoring response and toxicity to anticancer agents, but to date has been understudied. This is a unique and timely opportunity to apply an integrative metabolomic/genomic approach to predict symptom clusters in an ongoing, well-characterized cohort of children undergoing ALL therapy. The objective of this proposal is to increase our understanding of treatment-related symptoms and their ultimate impact on childhood ALL cure. The central hypothesis of the proposed research is that ALL therapy induces consistent and recognizable metabolomic alterations, ultimately leading to symptom clusters in these patients, which influence quality of life and may compromise long-term survival. We will use an integrative metabolomic/genomic approach to better characterize these symptom clusters. The aims of this study are to: 1) Determine the association between metabolomic profiles and symptoms experienced by children and adolescents during the most intensive phase of ALL therapy; 2)  Conduct a metabolite quantitative trait loci (mQTL) analysis in children and adolescents being treated for ALL to identify genetic loci that drive metabolic changes during cancer therapy; and 3) Integrate metabolomic profiles, genotypes, and phenotypic responses in a Risk Prediction Model to identify patients who are more likely to experience adverse symptom clusters during treatment for ALL, and who could be targeted for personalized therapy that could potentially improve their overall quality of life and chance for survival. This proposal has substantive translational potential to explore why children with ALL exhibit extreme variations in symptom burden, and lead to individualized therapy that avoids or reduces risk of adverse clinical outcomes and their ultimate impact on childhood leukemia care and the likelihood of long-term cure.