PROJECT SUMMARY Relapse is the major cause of cancer related mortality in children with leukemia. Despite improvements in overall survival for children with B-cell progenitor acute lymphoblastic leukemia (ALL), for the 600 patients who will relapse each year, half will die of their disease. The high mortality of patients who relapse underscores the need for improved risk prediction and treatment strategies to prevent recurrent leukemia. Current approaches to relapse prediction are limited by insufficient accuracy, delayed prediction and the inability to make actionable treatment adjustments based on prediction information. To address these limitations, we applied a single-cell, high-parameter proteomic approach to ALL patient samples at the time of diagnosis, accurately predicting future relapse based on the presence of pre-B cells with activated signaling. This approach was 38% more accurate than standard of care relapse prediction methods. We propose that identifying relapse-predictive cells in ALL at the time of diagnosis using their distinguishing proteomic and genetic features will result in a clinical risk prediction model that is accurate, immediate, and actionable. This approach to relapse prediction will change the clinical paradigm of relapse risk in ALL to reduce the incidence of relapse itself. Using large multi-institutional, multimodal cohorts of molecularly and clinically annotated diagnostic patient samples, we will apply deep proteomic approaches to identify surface proteins uniquely expressed on relapse predictive pre-B cells enabling direct identification in a diagnosis sample. We will determine how genomic mutations associate with the presence of relapse predictive cells and examine their genomic mutational burden using single-cell exome sequencing. Finally, building on our data-driven, machine learning approaches, we will construct a diagnostic relapse predictor that is more accurate than standard of care models while informing on leukemia biology and targeted therapeutic options for patients at risk. This will enable a more precise approach to patient classification and treatment, reducing the number of children facing relapse and moving closer to precision medicine for children with ALL.