AML with myelodysplasia-related changes (AML-MRC) is a subtype of AML known to have adverse prognosis. The karyotype abnormalities in AML-MRC have been well established; however, relatively little has been known about the role of gene mutation profiles by next generation sequencing. 177 AML patients (72 AML-MRC and 105 non-MRC AML) were analyzed by NGS panel covering 53 AML related genes. AML-MRC showed statistically significantly higher frequency of TP53 mutation, but lower frequencies of mutations in NPM1, FLT3-ITDLow, FLT3-ITDHigh, FLT3-TKD, NRAS, and PTPN11 than non-MRC AML. Supervised tree-based classification models including Decision tree, Random forest, and XGboost, and logistic regression were used to evaluate if the mutation profiles could be used to aid the diagnosis of AML-MRC. All methods showed good accuracy in differentiating AML-MRC from non-MRC AML with AUC (area under curve) of ROC ranging from 0.69 to 0.78. Additionally, logistic regression indicated 3 independent factors (age and mutations of TP53 and FLT3) could aid the diagnosis AML-MRC. Using weighted factors, a AML-MRC risk scoring equation was established for potential application in clinical setting: +1x(Age ≥ 65) + 3 x (TP53 mutation) - 2 x (FLT3 mutation). Using a cutoff score of 0, the accuracy of the risk score was 0.76 with sensitivity of 0.77 and specificity of 0.75 for predicting the diagnosis of AML-MRC. Further studies with larger sample sizes are warranted to further evaluate the potential of using gene mutation profiles to aid the diagnosis of AML-MRC.
Keywords: AML with MRC; FLT3; Machine learning; NGS; Supervised classification; TP53.
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