The Leukemic Stem Cell Niche: Adaptation to “Hypoxia” versus Oncogene Addiction

Previous studies based on low oxygen concentrations in the incubation atmosphere revealed that metabolic factors govern the maintenance of normal hematopoietic or leukemic stem cells (HSC and LSC). The physiological oxygen concentration in tissues ranges between 0.1 and 5.0%. Stem cell niches (SCN) are placed in tissue areas at the lower end of this range (“hypoxic” SCN), to which stem cells are metabolically adapted and where they are selectively hosted. The data reported here indicated that driver oncogenic proteins of several leukemias are suppressed following cell incubation at oxygen concentration compatible with SCN physiology. This suppression is likely to represent a key positive regulator of LSC survival and maintenance (self-renewal) within the SCN. On the other hand, LSC committed to differentiation, unable to stand suppression because of addiction to oncogenic signalling, would be unfit to home in SCN. The loss of oncogene addiction in SCN-adapted LSC has a consequence of crucial practical relevance: the refractoriness to inhibitors of the biological activity of oncogenic protein due to the lack of their molecular target. Thus, LSC hosted in SCN are suited to sustain the long-term maintenance of therapy-resistant minimal residual disease

Distinct DNA Methylation and Expression Profiles Underlie CMML Responsiveness to Decitabine and Uncover Novel Mechanism of Resistance

Abstract Myelodysplastic syndromes (MDS) and the related disorder chronic myelomonocytic leukemia (CMML) are characterized by abnormal DNA hypermethylation and the DNA methyltransferase inhibitors (DMTis) azacytidine and decitabine (DAC) are frequently used as frontline therapy in these patients. However, DMTis are ineffective for ~50% of the patients who must still undergo treatment for at least 6 months before they can be deemed resistant. Therefore, it is of critical importance to identify baseline molecular differences associated with DMTi sensitivity that can help (i) to improve patient risk-stratification at diagnosis and (ii) identify the underlying mechanisms of resistance to these agents. Previous efforts to identify baseline DNA methylation differences at promoter regions between DMTi responders and non-responders have not been successful, so we hypothesized that any potential differences would be located distally from promoter regions. For this purpose we studied 40 CMML patients at diagnosis, all of whom had been uniformly treated with DAC 20 mg/m2/day x 5 days as frontline therapy. After 6 cycles of therapy patients were classified as responders (n=19, hematological improvement or better), or non-responders (n=21, stable or progressive disease). Mutational analysis showed no significant differences in the frequency of mutations in TET2, ASXL1, DNMT3A, RUNX1, TP53, JAK2, KIT, KRAS, EZH2, IDH1/2 and spliceosome genes. Using Enhanced Reduced Representation Bisulfite Sequencing (ERRBS) we analyzed the baseline methylation status at ~3M CpG sites across the genome of 39/40 CMML patients. We identified 158 statistically significant differentially methylated regions (DMRs) (FDR<0.1 and methylation difference ≥25%) between the two groups. DAC-sensitive patients displayed both regions of higher methylation as well as regions with lower methylation compared to DAC-resistant patients. As predicted, DMRs were depleted at promoters (DMRs 9% vs. Background [BG] 21%, p-value: 3.4×10-5) and CpG islands (DMRs 8% vs. BG 25%, p-value: 1.5×10-8). Further analysis showed that hypermethylated DMRs were enriched at intronic regions (Hyper DMRs 58% vs. BG 33%, p-value: 3.7×10-6) while hypomethylated DMRs were enriched at intergenic regions (Hypo DMRs 49% vs. BG 38%, p-value: 2.6×10-2). Moreover, hypermethylated DMRs were significantly enriched for enhancer regions, and in particular, enhancers located within gene bodies (hyper DMRs 38% vs. BG 18%, p-value: 2.3×10-5). KEGG pathway analysis showed a significant enrichment of DMRs in the MAPK signaling pathway (FDR<0.01). Next, using a support vector machine algorithm with 10-fold cross validation we were able to develop a classifier capable of predicting response to DAC with high level of accuracy (ROC AUC: 0.99) based solely on the DNA methylation status at diagnosis of 17 genomic regions. Three different random splits of the cohort into training and test sets achieved correct predictions for 85.7%, 89.47%, and 100% of cases, respectively, demonstrating the accuracy and potential utility of such a classifier. Finally, RNA-seq analysis identified 53 differentially expressed genes between responders (n=8) and non-responders (n=6) at diagnosis. Genes implicated in cell cycle and DNA replication were overexpressed in responders. By contrast, very few genes were overexpressed at the time of diagnosis in primary resistant patients. Among these were CXCL4 and CXCL7 which, given their reported contributions to cell cycle arrest and chemoresistance, were tested for their functional roles in DAC resistance. Pre-treatment of normal CD34+ cells for 72 h with 10nM DAC significantly reduced colony formation (p<0.05) but the addition of 50ng/mL of CXCL4 and CXCL7 restored colony formation to that of untreated cells. Moreover, treatment of primary CMML cells with 10 nM DAC for 72h significantly reduced viability of these cells, while concomitant treatment with 50ng/mL of CXCL4 and CXCL7 was sufficient to abrogate this effect. Taken together, our findings demonstrate that (i) specific DNA methylation profiles targeting non-promoter regulatory regions are associated with DAC sensitivity, (ii) these differences can be harnessed for the development of clinical biomarkers predictive of response and (iii) we identified a novel mechanism of resistance to DAC mediated through two chemokines that are exclusively overexpressed in non-responders. Disclosures No relevant conflicts of interest to declare

Are somatic mutations predictive of response to erythropoiesis stimulating agents in lower risk myelodysplastic syndromes?

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Molecular Determinants of Decitabine Response in Chronic Myelomonocytic Leukemia

Abstract The nucleoside analog (na) 5-aza-2’-deoxycytidine (dac) has good, but heterogeneous, efficacy in the therapy of chronic myelomonocytic leukemia (cmml). Given that no standard therapy has been identified so far for cmml, there is urgent need to identify molecular markers that could support the choice of dac therapy and identify patients more prone to respond. Recently, we demonstrated that in mds patients the expression of uck1, involved in the activation of azacitidine, may influence the clinical response to this treatment (valencia et al, leukemia 2013). In the present study, we assessed the role of dac metabolizing enzymes as well as genetic alterations common to cmml patients in response to dac in a uniformly and prospectively treated cohort of cmml patients. Methods: Patients Forty cmml patients were treated with dac 20mg/m2/day for 5 days every 28 d. DNA and rna were extracted from bm mononuclear cells of 19 pts defined as responders to dac (r), and 21 as non-responders (nr). Hematological response was evaluated according to iwg 2006 criteria. Gene mutation. the fifteen most frequently mutated genes in cmml were sequenced at a mean depth of coverage of 520x (range 169–714x). Functional studies. the role of two main genes involved in dac metabolism: dck (dac activation) and dctd (dac deactivation) was evaluated by silencing both genes with sirnas. The experiments were performed in the mds-skm1 cell line and in primary cells of 6 cmml cases exposed in vitro to dac 10um for 48h. Gene expression. the expression level of genes hent1, hent2, dctd, hcnt3, cn-ii, dck and cda, involved in dac metabolism was evaluated by qrt-pcr in 38/40 cmml patients and by rnaseq in 14/40 patients. CDNA libraries were sequenced using the hiseq 2000. The counts of endogenous genes were normalized by ercc spike-in library controls, and differential expression analysis was performed using edger (v3.4.2) glm model. The differentially expressed genes were identified at the fdr cutoff of 0.05 and absolute fold change greater than 2. Results: in skm-1 cells and in cmml primary mononuclear cells, dctd silencing increased dac- induced apoptosis (annexin v-positive cells 20.2%±0.8% vs control 13.8%±0.5%; p=0.01). dck silencing led to a decrease in dac-induced apoptosis (annexin v-positive cells 8.8%±0.1% vs control 13.8%±0.5%; p=0.05). We could not detect by rnaseq a significant difference in the expression levels of na metabolizing enzymes between responders and non responders to dac. qrt-pcr confirmed these observations. The mutational frequencies (figure) in this cohort of cmml cases were: srsf2 50%, tet2 44.7%, asxl1 39.4%, nras 18.4%, runx1 and dnmt3a 10.5%, u2af1 and tp53 7.8%, kras, jak2 and kit 5.2%, ezh2, idh1, idh2 and sf3b1 2.6%. No single genetic alteration was significantly associated with shorter overall survival or resistance to dac. Conclusions: although we could clearly demonstrate that in vitro expression of dac metabolizing enzymes influenced response to dac, clinical resistance does not appear to be directly correlated with the expression of genes involved in dac metabolism nor to specific gene mutations and is likely determined by other clinical/molecular variables that remain to be identified. Figure 1 Figure 1. Disclosures Finelli: Janssen: Speaker, Speaker Other; Novartis: Speaker, Speaker Other; Celgene: Research Funding, Speaker Other

Specific molecular signatures predict decitabine response in chronic myelomonocytic leukemia

Myelodysplastic syndromes and chronic myelomonocytic leukemia (CMML) are characterized by mutations in genes encoding epigenetic modifiers and aberrant DNA methylation. DNA methyltransferase inhibitors (DMTis) are used to treat these disorders, but response is highly variable, with few means to predict which patients will benefit. Here, we examined baseline differences in mutations, DNA methylation, and gene expression in 40 CMML patients who were responsive or resistant to decitabine (DAC) in order to develop a molecular means of predicting response at diagnosis. While somatic mutations did not differentiate responders from nonresponders, we identified 167 differentially methylated regions (DMRs) of DNA at baseline that distinguished responders from nonresponders using next-generation sequencing. These DMRs were primarily localized to nonpromoter regions and overlapped with distal regulatory enhancers. Using the methylation profiles, we developed an epigenetic classifier that accurately predicted DAC response at the time of diagnosis. Transcriptional analysis revealed differences in gene expression at diagnosis between responders and nonresponders. In responders, the upregulated genes included those that are associated with the cell cycle, potentially contributing to effective DAC incorporation. Treatment with CXCL4 and CXCL7, which were overexpressed in nonresponders, blocked DAC effects in isolated normal CD34(+) and primary CMML cells, suggesting that their upregulation contributes to primary DAC resistance