Analysis of population specific transcriptomic variations among patients with acute lymphoblastic leukemia

Abstract

Acute Lymphoblastic Leukemia (ALL) is the most common childhood cancer globally, accounting for 25% of cancer diagnoses in children under 15 years of age and 72% of all cases of childhood leukemia. It is also a clinically and biologically heterogeneous malignancy with striking variation in incidence, progression, and outcome based upon racial/ethnic groups.While this research has made progress, current studies are constrained to a large degree by the lack of representation of minority populations in genomic and transcriptomic datasets, which impairs the reliable discovery of race-related gene expression signatures. In addition, the functional impact of these transcriptomic differences on upstream biological pathways (e.g., cell cycle regulation, immune signaling, drug metabolism) is poorly defined, and hence, the merging of transcriptomics with genetic ancestry and clinical covariates to distinguish genetic from social determinants of health is limited.This study provides a comprehensive transcriptomic analysis of acute lymphoblastic leukemia (ALL) patient cohorts, emphasizing population-specific patterns of molecular subtype distribution, differential gene expression, and druggable target profiles. Using gene set-based clustering and nearest neighbor scores, the research delineates molecular groupings aligned with progenitor cell origin and highlights critical associations between transcriptomic signatures and clinical variables such as relapse, age, and race. I employed bioinformatic methods to analyze patterns of differential gene expression and molecular pathway enrichment to detect unique molecular signatures associated with different racial ancestries. Special focus was given to genes that play a role in leukemogenesis, immunological regulation, and medication metabolism because they may be responsible for racial differences. Notably, unique subtypes and druggable targets were identified among African patients, such as the CEBP subtype and mitochondrial gene signatures, suggesting therapeutic vulnerabilities and protective mechanisms exclusive to this population. Druggability assessments further reveal distinct landscapes across racial groups, with implications for targeted therapies and precision medicine strategies. The study contextualizes these findings within persistent racial disparities in ALL outcomes, advocating for inclusive molecular profiling and tailored therapeutic approaches to improve health equity and treatment efficacy. Limitations include sample size and reliance on ivexisting drug databases, with recommendations for future functional studies and expanded cohorts to validate the impact of population-specific transcriptomic features on ALL prognosis and management. This work underscores the necessity of integrating genomics, clinical annotation, and population diversity for optimizing precision oncology in ALL.