Survival prediction optimization of acute myeloid leukaemia based on T-cell function-related genes and plasma proteins

Interactions between acute myeloid leukaemia (AML) cells and immune cells are postulated to corelate with outcomes of AML patients. However, data on T-cell function-related signature are not included in current AML survival prognosis models. We examined data of RNA matrices from 1611 persons with AML extracted from public databases arrayed in a training and three validation cohorts. We developed an eight-gene T-cell function-related signature using the random survival forest variable hunting algorithm. Accuracy of gene identification was tested in a real-world cohort by quantifying cognate plasma protein concentrations. The model had robust prognostic accuracy in the training and validation cohorts with five-year areas under receiver-operator characteristic curve (AUROC) of 0.67-0.76. The signature was divided into high- and low-risk scores using an optimum cut-off value. Five-year survival in the high-risk groups was 6%-23% compared with 42%-58% in the low-risk groups in all the cohorts (all p values <0.001). In multivariable analyses, a high-risk score independently predicted briefer survival with hazard ratios of death in the range 1.28-2.59. Gene set enrichment analyses indicated significant enrichment for genes involved in immune suppression pathways in the high-risk groups. Accuracy of the gene signature was validated in a real-world cohort with 88 pretherapy plasma samples. In scRNA-seq analyses most genes in the signature were transcribed in leukaemia cells. Combining the gene expression signature with the 2017 European LeukemiaNet classification significantly increased survival prediction accuracy with a five-year AUROC of 0.82 compared with 0.76 (p < 0.001). Our T-cell function-related risk score complements current AML prognosis models.