Childhood Cancer Data Initiative Annual Symposium (Abstract Registration): Submission #49

Abstract: As an aggressive hematopoietic malignancy, acute myeloid leukemia (AML) is defined by aberrant clonal expansion of abnormal myeloid progenitor cells, typically leading to bone marrow failure and compromised hematopoiesis. Characterized by morphological, molecular, and genetic alterations, pediatric AML encompasses multiple distinct subtypes that would exhibit subtype-specific responses to treatment and prognosis, underscoring the critical need of accurately identifying AML subtypes for effective clinical management and tailored therapeutic approaches. Traditional wet lab approaches such as immunophenotyping, cytogenetic analysis, morphological analysis, or molecular profiling to identify AML subtypes are labor-intensive, costly, and time-consuming. To address these challenges, we propose AttentionAML, a novel attention-based deep learning framework for accurately categorizing AML subtypes based on transcriptomic data from the Childhood Cancer Data Initiative (CCDI) system. Benchmarking tests based on 1,661 AML patients suggested that AttentionAML outperformed state-of-the-art methods across all evaluated metrics (accuracy: 0.96, precision: 0.96, recall of 0.96, F1-score: 0.96, and Matthews correlation coefficient: 0.96). Furthermore, we also demonstrated the superiority of AttentionAML over conventional approaches in terms of AML patient clustering visualization and subtype-specific gene marker characterization. We believe AttentionAML will bring remarkable positive impacts on downstream AML risk stratification and personalized treatment design. To enhance its impact, a user-friendly Python package implementing AttentionAML is publicly available at our lab's Github repository at https://github.com/wan-mlab/AttentionAML.