Abstract A particular profile of gene expression can reflect an underlying molecular abnormality in malignancy. Distinct gene expression profiles and deregulated gene pathways can be driven by specific gene mutations and may shed light on the biology of the disease and lead to the identification of new therapeutic targets. We selected 143 cases from our large-scale gene expression profiling (GEP) dataset on bone marrow CD34+ cells from patients with myelodysplastic syndromes (MDS), for which matching genotyping data were obtained using next-generation sequencing of a comprehensive list of 111 genes involved in myeloid malignancies (including the spliceosomal genes SF3B1, SRSF2, U2AF1 and ZRSR2, as well as TET2, ASXL1and many other). The GEP data were then correlated with the mutational status to identify significantly differentially expressed genes associated with each of the most common gene mutations found in MDS. The expression levels of the mutated genes analyzed were generally lower in patients carrying a mutation than in patients wild-type for that gene (e.g. SF3B1, ASXL1 and TP53), with the exception of RUNX1 for which patients carrying a mutation showed higher expression levels than patients without mutation. Principal components analysis showed that the main directions of gene expression changes (principal components) tend to coincide with some of the common gene mutations, including SF3B1, SRSF2 and TP53. SF3B1 and STAG2 were the mutated genes showing the highest number of associated significantly differentially expressed genes, including ABCB7 as differentially expressed in association with SF3B1 mutation and SULT2A1 in association with STAG2 mutation. We found distinct differentially expressed genes associated with the four most common splicing gene mutations (SF3B1, SRSF2, U2AF1 and ZRSR2) in MDS, suggesting that different phenotypes associated with these mutations may be driven by different effects on gene expression and that the target gene may be different. We have also evaluated the prognostic impact of the GEP data in comparison with that of the genotype data and importantly we have found a larger contribution of gene expression data in predicting progression free survival compared to mutation-based multivariate survival models. In summary, this analysis correlating gene expression data with genotype data has revealed that the mutational status shapes the gene expression landscape. We have identified deregulated genes associated with the most common gene mutations in MDS and found that the prognostic power of gene expression data is greater than the prognostic power provided by mutation data. AP and MG contributed equally to this work. JB and PJC are co-senior authors. Disclosures: No relevant conflicts of interest to declare.