Refractoriness to initial chemotherapy and relapse after remission are the main obstacles to cure in T-cell Acute Lymphoblastic Leukemia (T-ALL). Biomarker guided risk stratification and targeted therapy have the potential to improve outcomes in high-risk T-ALL; however, cellular and genetic factors contributing to treatment resistance remain unknown. Previous bulk-genomic studies in T-ALL have implicated tumor heterogeneity as an unexplored mechanism for treatment failure. In this dissertation, I investigate the molecular basis of treatment resistance in T-ALL using both computational and experimental approaches: merging targeted single-cell genomic analysis on a focused group of patients with large-cohort bulk genomics and patient-specific drug-modeling experiments. First, I discuss our work investigating the molecular differences between treatment sensitive and treatment resistant T-ALL patients, in which we contextualized T-cell transformation within a newly created atlas of pediatric hematopoietic development to identify tumor subpopulations associated with treatment failure. Next, I discuss how we merged our single-cell data with large-cohort, bulk-genomic profiling, identifying driving mutations of different cell states and validating clinical utility of single-cell derived gene signatures. Finally, I describe our work in computationally nominating and experimentally studying new therapies to target high-risk cell populations. These data establish the first genetic signatures for rapid risk-stratification and no first targeted therapies for chemo-resistant T-ALL: highlighting the power of integrative genomics in uncovering novel angles to improve cancer therapy.