Background The impact of CD8+ T cell differentiation along the paths of exhaustion and senescence states on clinical outcomes of patients with Acute myeloid leukemia (AML) after induction therapy has been characterized by immunophenotyping, gene expression, and functional studies. The results of the preceding studies show that the relative frequencies of naïve T cells, early memory T cells, terminally differentiated effector T cells (Term), exhausted cells and senescent-like cells are correlated with CR and non-response after induction therapy.
Methods We assumed that distinct profiles of T cell differentiation states before and after induction therapy for patients with AML, that is, the cellular states at initial diagnosis and complete remission are based on distinct dynamics of transcription factor network motifs. We selected seven transcription factors and PD-1 as key components of network motif controlling CD8+ T differentiation by literature review. Selected seven transcription factors (BATF, IRF4, NFATC1, TCF-1, EOMES, T-bet, TOX) and PD-1 were validated by examining whether the expressions of these encoding genes are effective for the classification between newly diagnosed patients and CR patients with AML by use of an artificial neural network model.
Results The expressions of the corresponding eight genes as input features were effective for classification between newly diagnosed patients and CR patients after induction therapy with high accuracy.
Conclusion The high accuracy of classification between newly diagnosed and complete remission (CR) patients after induction therapy suggests that these two disease states exhibit distinct expression patterns of the selected eight genes. The corresponding transcription factors may play a role in regulating CD8+ T cell differentiation in response to chronic antigen stimulation and its interruption by treatment in AML.
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