A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia

Abstract

Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy resistance for intensively treated adult patients. We used two large gene expression data sets (n=856) to develop a predictor of therapy resistance, which was validated in an independent cohort analyzed by RNA sequencing (n=250). In addition to gene expression markers, standard clinical and laboratory variables as well as the mutation status of 68 genes were considered during construction of the model. The final predictor (PS29MRC) consisted of 29 gene expression markers and a cytogenetic risk classification. A continuous predictor is calculated as a weighted linear sum of the individual variables. In addition, a cut off was defined to divide patients into a high-risk and a low-risk group for resistant disease. PS29MRC was highly significant in the validation set, both as a continuous score (OR=2.39, P=8.63·10−9, AUC=0.76) and as a dichotomous classifier (OR=8.03, P=4.29·10−9); accuracy was 77%. In multivariable models, only TP53 mutation, age and PS29MRC (continuous: OR=1.75, P=0.0011; dichotomous: OR=4.44, P=0.00021) were left as significant variables. PS29MRC dominated all models when compared with currently used predictors, and also predicted overall survival independently of established markers. When integrated into the European LeukemiaNet (ELN) 2017 genetic risk stratification, four groups (median survival of 8, 18, 41 months, and not reached) could be defined (P=4.01·10−10). PS29MRC will make it possible to design trials which stratify induction treatment according to the probability of response, and refines the ELN 2017 classification.

Heterogeneity in refractory acute myeloid leukemia

“…2B. Many of the genes significantly differentially expressed at a false discovery rate (FDR) < 0.05 have previously been implicated in drug resistance across multiple cancers, including BAK1, PPP1R13L, and NFKBIE (19,(22)(23)(24).…”

“…In addition to cytogenetic and molecular factors, other risk factors for primary refractory disease include clinical variables such as older age and an antecedent hematological neoplasm (13)(14)(15). Recent studies have suggested that primary treatment failures might also be the result of AML clones harboring intrinsic properties of hematopoietic stem cells and quiescence, and gene expression signatures may be predictive of outcome (11,(16)(17)(18)(19).…”

Loss of ISWI ATPase SMARCA5 (SNF2H) in Acute Myeloid Leukemia Cells Inhibits Proliferation and Chromatid Cohesion

“…RNA-Seq data sets from AML patient samples were previously described including the informed consent and ethical issues [29][30][31]. Reads were mapped with STAR aligner version 2.7.2d using GRCh37 reference and annotation version 32 from GENCODE (www.gencodegenes.org).…”

In vivo inducible reverse genetics in patients’ tumors to identify individual therapeutic targets

“…Gene annotations were obtained from Ensembl (GRCh38.84/GRCm38.75). Analysis of RNA sequencing data followed standard recommendations (43). Statistical analysis was performed using the R 3.6.1 software package (R Core Team, 2019).…”