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Graham Ball

Graham Ball

Nottingham Trent University, UK

Title: A machine learning based approach identifies a powerful 3-gene prognosticator in an acute myeloid leukemia multi-cohort study

Biography

Biography: Graham Ball

Abstract

Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy with variable response to treatment. Recurring cytogenetic abnormalities and gene mutations are important prognosticators. However, 50-70% of AML cases harbor either normal or risk-indeterminate karyotypes. The identification of better biomarkers of clinical outcome is therefore necessary to inform tailored therapeutic decisions. We applied an artificial neural network (ANN) based machine learning approach to a discovery cohort of 641 adults with newly diagnosed AML. ANN analysis identified a parsimonious 3-gene expression signature predictive of survival, which comprised CALCRL, CD109 and LSP1. We computed a prognostic index (PI) from these markers using normalized gene expression levels and b-values from subsequently created Cox proportional hazards models with coupled with clinically established prognosticators. Our 3-gene PI separated the adult patients in each ELN cytogenetic risk category into subgroups with different survival probabilities and identified patients with very high-risk features, such as those with a high PI and either FLT3-ITD or non-mutated NPM1. The ability of the 3-gene PI to stratify survival was validated in two independent adult cohorts (n=221 subjects). Our ANN derived 3-gene signature applied to cox proportional hazards models by way of validation refined the accuracy of patient stratification and improved outcome prediction.