Decitabine Response Associated Gene Expression Patterns In Acute Myeloid Leukemia (AML)

The hypomethylating agent decitabine (DAC) represents a therapeutic option for acute myeloid leukemia (AML) patients who are not eligible for an intensive treatment regime. However, there are no biomarkers available yet that can predict patients who will likely benefit from this epigenetic therapy.

Therefore, we executed a gene expression analysis prior to the treatment of patients with DAC in order to evaluate gene expression patterns associated with response to DAC that ultimately might be used to predict DAC outcome. Patients had been entered in a multicenter phase II trial of DAC as first-line treatment of older AML patients judged unfit for induction chemotherapy (Lübbert et al. Haematologica 2011; NCT00866073).

Gene expression was profiled in selected DAC responders (n=17) and non-responders (n=19; non-response was defined as stable disease or progressive disease). These groups did not show significant differences regarding age, gender, performance status, blast counts and cytogenetics. Supervised data analysis strategies were applied to identify genes and gene patterns associated with DAC response.

While the study cohort comprised a heterogeneous group of AML patients, a class comparison analysis nevertheless could reveal a DAC response associated gene pattern comprising 301 genes at a significance level of p<0.05. This signature was enriched for genes belonging to pathways that are essential in immune response and tumor suppressor function. Among these genes that were significantly associated with no DAC response included IFI44L, IFI27, PDK4, MX1, FAS, and ITGB2; in contrast to SLC24A3, MUM1, TNFSF9, DBN1, ABAT, and DDX52, which were significantly higher expressed in patients that showed response to DAC treatment. Significantly over-expressed in the DAC non-responder group, the immune and inflammation-related genes IFI44L and IFI27 might reflect a hyperstimulated, but insufficient immune system as has been recently shown in myelofibrosis. As DAC was shown to have the capability to induce cancer testis antigens, thereby generating an efficient immune response with tumor cell lysis by CD8+ T-lymphocytes, an impaired immune system may prevent response to DAC. Furthermore, the non-response signature contained known poor prognostic markers such as PDK4, which has been associated with EVI1 and FLT3-ITD mediated signaling. In addition, we observed high expression of MX1 and FAS in the non-response group. Notably, both genes have been shown to be repressed by promoter hypermethylation in distinct AML subtypes and DAC treatment was able to upregulate their expression levels. In contrast, high pre-treatment expression levels might indicate that in the respective AML cases deregulated promotor methylation might not be the prominent pathomechanism, and thus these cases might less likely benefit from DAC treatment. Finally, we found ITGB2, encoding for an integral cell-surface protein participating in cell-surface mediated signaling, associated with DAC resistance. As recently ITGB3, another member of this integrin protein family, was shown mandatory for leukemogenesis, but not relevant for normal hematopoiesis, high expression of ITGB2 might also play a role in AML and point to leukemias where epigenetic deregulation at the DNA level seems to be a less prone pathomechanism. Among the group of genes linked with response to DAC treatment TNFSF9 can act as cytotoxic leukemic specific T-cell inducer, which has previously been correlated with unfavorable AML subtypes and poor outcome. However, due to the immunomodulation of DAC it seems that the poor prognostic impact of TNFSF9 might be overcome by DAC, thereby rendering TNFSF9 a positive marker for DAC response. In accordance, we found that several genes of the 4-1BB-dependent immune response pathway, including TNFSF9, were more highly expressed in DAC responding patients. Finally, MUM1 encodes also a gene important for interferon dependent immune response, thereby further underscoring a potential immunomodulation effect of DAC. In summary, we were able to elucidate a gene signature which could be used to predict response to DAC treatment in AML. While this gene expression pattern included many genes involved in the immune response, thereby suggesting that the DAC treatment effect is at least in part depending on immunomodulatory effects, further studies are warranted to evaluate the respective markers in larger AML cohorts.