Loss of H3K27 methylation identifies poor outcomes in adult-onset acute leukemia.

BACKGROUND: Acute leukemia is an epigenetically heterogeneous disease. The intensity of treatment is currently guided by cytogenetic and molecular genetic risk classifications; however these incompletely predict outcomes, requiring additional information for more accurate outcome predictions. We aimed to identify potential prognostic implications of epigenetic modification of histone proteins, with a focus on H3K4 and H3K27 methylation marks in relation to mutations in chromatin, splicing and transcriptional regulators in adult-onset acute lymphoblastic and myeloid leukemia. RESULTS: Histone 3 lysine 4 di- and trimethylation (H3K4me2, H3K4me3) and lysine 27 trimethylation (H3K27me3) mark expression was evaluated in 241 acute myeloid leukemia (AML), 114 B-cell acute lymphoblastic leukemia (B-ALL) and 14T-cell ALL (T-ALL) patient samples at time of diagnosis using reverse phase protein array. Expression levels of the marks were significantly lower in AML than in B and T-ALL in both bone marrow and peripheral blood, as well as compared to normal CD34+ cells. In AML, greater loss of H3K27me3 was associated with increased proliferative potential and shorter overall survival in the whole patient population, as well as in subsets with DNA methylation mutations. To study the prognostic impact of H3K27me3 in the context of cytogenetic aberrations and mutations, multivariate analysis was performed and identified lower H3K27me3 level as an independent unfavorable prognostic factor in all, as well as in TP53 mutated patients. AML with decreased H3K27me3 demonstrated an upregulated anti-apoptotic phenotype. In ALL, the relative quantity of histone methylation expression correlated with response to tyrosine kinase inhibitor in patients who carried the Philadelphia cytogenetic aberration and prior smoking behavior. CONCLUSION: This study shows that proteomic profiling of epigenetic modifications has clinical implications in acute leukemia and supports the idea that epigenetic patterns contribute to a more accurate picture of the leukemic state that complements cytogenetic and molecular genetic subgrouping. A combination of these variables may offer more accurate outcome prediction and we suggest that histone methylation mark measurement at time of diagnosis might be a suitable method to improve patient outcome prediction and subsequent treatment intensity stratification in selected subgroups.

Optimizing VAP scars after childhood cancer treatment: a pilot study.

PURPOSE: Majority of pediatric cancer patients are treated with chemotherapy using Venous Access Ports (VAP). However, after surgical removal of the VAP prominent scars often remain and standard care is lacking. METHODS: Patients (N = 20) who were willing to participate were included prior to surgical removal of their VAP. All patients were off therapy at time of VAP removal. Patients had the option to either choose from Dermatix®, meridian color therapy (MCT), or no additional treatment (NAT). Assessment of scars was done prior to and 3, 6, and 12 months after surgical VAP removal using Patient and Observer Scar Assessment Scales (POSAS) questionnaires. To identify whether Dermatix® or MCT is associated with better scar healing than without additional treatment, Mann-Whitney U tests were used. RESULTS: After 12 months of follow-up, both patients and dermatologists noted VAP scars had healed better after MCT compared to those without treatment (P = 0.010 for both POSAS patient and POSAS observer). No significant differences were observed between VAP scars after Dermatix® use and those with no treatment. CONCLUSIONS: Scar healing after MCT significantly improved, whereas Dermatix® treatment showed no significant differences compared to NAT. To translate this to daily care, a larger prospective study is needed to validate these findings.

IDENTIFYING CANCER SPECIFIC METABOLIC SIGNATURES USING CONSTRAINT-BASED MODELS.

Cancer metabolism differs remarkably from the metabolism of healthy surrounding tissues, and it is extremely heterogeneous across cancer types. While these metabolic differences provide promising avenues for cancer treatments, much work remains to be done in understanding how metabolism is rewired in malignant tissues. To that end, constraint-based models provide a powerful computational tool for the study of metabolism at the genome scale. To generate meaningful predictions, however, these generalized human models must first be tailored for specific cell or tissue sub-types. Here we first present two improved algorithms for (1) the generation of these context-specific metabolic models based on omics data, and (2) Monte-Carlo sampling of the metabolic model ux space. By applying these methods to generate and analyze context-specific metabolic models of diverse solid cancer cell line data, and primary leukemia pediatric patient biopsies, we demonstrate how the methodology presented in this study can generate insights into the rewiring differences across solid tumors and blood cancers.