Abstract

Introduction Wild-type FLT3 AML presents significant therapeutic challenges due to limited treatment options. The QUIWI trial investigated quizartinib (Quiz) combined with standard chemotherapy, showing promising results. To understand drug response and refractoriness, we conducted a correlative analysis, exploring differential expression patterns among distinct patient groups. We reasoned that the molecular fingerprints of good responders to Quiz plus chemotherapy would be different with respect to patients who respond only to standard chemotherapy, helping to elucidate critical molecular mechanisms associated with Quiz response. Our study provides a basis for personalized therapeutic strategies in FLT3-ITD negative AML. Methods We performed RNA sequencing (RNAseq) analysis on a subset of patients from the clinical trial. RNA was extracted using standard methods, followed by assessment of nucleic acid integrity (TapeStation) and quantification (Qubit). Total mRNA sequencing was performed using polyA RNAseq with TruSeq technology. A total of 206 adequate samples from bone marrow and peripheral blood were sequenced, out of which 55 cases were FLT3-ITD and were discarded for this analysis. For this analysis, only samples with a minimum of 20% blasts were considered. The sequences were aligned to the GRCh37 reference genome using the Hisat algorithm. Differential expression was assessed with the DESeq2 algorithm, using Blast proportion to adjust for disparities among the samples. P-values were adjusted for multiple testing using the FDR method. OS was defined as time from start of screening to death. Gene ontology and pathways analysis were performed using the WebGestalt portal. Results In this study, we compared the gene expression profiles of Quiz versus placebo-treated patients categorized as good responders, defined arbitrarily as those who continue alive and have a minimum follow-up of 6 months after diagnosis. Out of 82 eligible patients, we identified 100 (q-value < 0.05) and 172 (q-value < 0.1) differentially expressed genes between the Quiz and Placebo groups. Notably, 132 genes were overexpressed in the Quiz group, while 40 genes showed overexpression in the placebo group. Moreover, we observed a highly significant overexpression of ribosomal genes in the Quiz group (17 overlaps, KEGG pathways q-value < 2.2 x 10 -16), and a significant overexpression of genes related to the attenuation of the heat shock protein transcriptional response, which is involved in protein folding, in the placebo group (Reactome q-value, 0.008; gene ids: HSPA1A, HSPA1B and DNAJB1). Additionally, the Quiz group exhibited a significant overexpression of transmembrane receptor protein tyrosine phosphatase-related genes (Gene Ontology Molecular Function q-value, 0.02; gene ids: PTPRB, PTPRG and PTPRU). Conclusion This correlative analysis of the Quiwi trial revealed critical molecular mechanisms associated with Quiz response. The overexpression of ribosomal and transmembrane receptor protein tyrosine phosphatase-related genes in good responders within the Quiz group, along with the overexpression of the heat shock pathway genes in the placebo group, aligns with existing knowledge about the FLT3 pathway biology and the molecular determinants to tyrosine-kinase inhibitors. These findings offer potential biomarkers for personalized therapeutic approaches to improve clinical outcomes in this challenging AML population.

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