Unraveling genotype-phenotype associations and predictive modeling of outcome in acute myeloid leukemia

Abstract

Acute myeloid leukemia (AML) comprises 32% of adult leukemia cases, with a 5-year survival rate of only 20-30%. Here, the immunophenotypic landscape of this heterogeneous malignancy is explored in a single-center cohort using a novel quantitative computational pipeline. For 122 patients who underwent induction treatment with intensive chemotherapy, leukemic cells were identified at diagnosis, computationally preprocessed, and quantitatively subtyped. Computational analysis provided a broad characterization of inter- and intra-patient heterogeneity, which would have been harder to achieve with manual bivariate gating. Statistical testing discovered associations between CD34, CD117, and HLA-DR expression patterns and genetic abnormalities. We found the presence of CD34⁺ cell populations at diagnosis to be associated with a shorter time to relapse. Moreover, CD34^- CD117⁺ cell populations were associated with a longer time to AML-related mortality. Machine learning (ML) models were developed to predict 2-year survival, European LeukemiaNet (ELN) risk category, and inv(16) or NPM1^mut, based on computationally quantified leukemic cell populations and limited clinical data, both readily available at diagnosis. We used explainable artificial intelligence (AI) to identify the key clinical characteristics and leukemic cell populations important for our ML models when making these predictions. Our findings highlight the importance of developing objective computational pipelines integrating immunophenotypic and genetic information in the risk stratification of AML.

Keywords: acute myeloid leukemia (AML); bioinformatics; computational cytometry; computational pipeline; explainable artificial intelligence; flow cytometry; machine learning models.