ABSTRACT This application is being submitted in response to the Notice of Special Interest (NOSI) identified as NOT-CA- 23-038. The development of tyrosine kinase inhibitors (TKIs), such as imatinib, for the treatment of chronic myeloid leukemia (CML) has dramatically improved patients’ life expectancy. In high-income nations, the life expectancy of a patient with CML approaches that of the general population. While high-income countries are moving towards a treatment paradigm that allows for TKI discontinuation in the setting of complete remission, these same outcomes are unfortunately not observed in Tanzania, despite good access of TKIs to CML patients through an established patient assistance program. Our group has previously shown that delayed diagnoses and presentations with advanced disease features contribute to the observed poor responses among patients with CML in Tanzania. Recent studies have shown that late CML diagnosis is associated with cumulative accumulation of more genetic lesions which can subsequently lead to treatment failure, disease progression, and eventual death. These mutations are most commonly identified in epigenetic modifier genes such as ASXL1, DNMT3A, TET2, IDH1, and IDH2. We hypothesize that the poor treatment responses observed in CML patients in Tanzania is due to a high burden of these myeloid leukemia mutations at the time of diagnosis. Therefore, this study aims to better understand mechanisms of TKI resistance contributed by these myeloid-associated mutations. We will conduct a prospective cohort study of newly diagnosed CML patients followed over a period of 12 months after imatinib initiation at Ocean Road Cancer Institute in Dar es Salaam, Tanzania. Presence of myeloid-leukemia associated mutations (ASXL1, DNMT3A, TET2, IDH1, and IDH2) will be assessed at diagnosis. Extracted genomic DNA (gDNA) will undergo next generation sequencing using commercially available myeloid leukemia panel on MiSeq (Illumina, San Diego, USA). Generalized Estimating Equations (GEE) will be used to investigate the influence of mutation status at diagnosis on molecular responses in the first 12 months of treatment as defined by the European Leukemia Net Guidelines. Other variables including sex, age, and Sokal score at diagnosis will be considered as covariates in the model. Identification of predictors of treatment resistance has a high potential to improve CML treatment guidelines in Tanzania and sub-Saharan Africa broadly, based on scientific evidence derived from African population.