Approximately 8% to 19% of patients with acute myeloid leukemia (AML) have isocitrate dehydrogenase-2 (IDH2) mutations, which occur at active site arginine residues R140 and R172. IDH2 mutations produce an oncometabolite, 2-hydroxyglutarate (2-HG), which leads to DNA and histone hypermethylation and impaired hematopoietic differentiation. Enasidenib is an oral inhibitor of mutant-IDH2 proteins. This first-in-human phase 1/2 study evaluated enasidenib doses of 50 to 650 mg/d, administered in continuous 28-day cycles, in patients with mutant-IDH2 hematologic malignancies. Overall, 214 of 345 patients (62%) with relapsed or refractory (R/R) AML received enasidenib, 100 mg/d. Median age was 68 years. Forty-two patients (19.6%) attained complete remission (CR), 19 patients (10.3%) proceeded to an allogeneic bone marrow transplant, and the overall response rate was 38.8% (95% confidence interval [CI], 32.2-45.7). Median overall survival was 8.8 months (95% CI, 7.7-9.6). Response and survival were comparable among patients with IDH2-R140 or IDH2-R172 mutations. Response rates were similar among patients who, at study entry, were in relapse (37.7%) or were refractory to intensive (37.5%) or nonintensive (43.2%) therapies. Sixty-six (43.1%) red blood cell transfusion–dependent and 53 (40.2%) platelet transfusion–dependent patients achieved transfusion independence. The magnitude of 2-HG reduction on study was associated with CR in IDH2-R172 patients. Clearance of mutant-IDH2 clones was also associated with achievement of CR. Among all 345 patients, the most common grade 3 or 4 treatment-related adverse events were hyperbilirubinemia (10%), thrombocytopenia (7%), and IDH differentiation syndrome (6%). Enasidenib was well tolerated and induced molecular remissions and hematologic responses in patients with AML for whom prior treatments had failed. The study is registered at www.clinicaltrials.gov as #NCT01915498.

Specific microRNA (miRNA) signatures have been associated with different cytogenetic subtypes in acute leukemias. This finding prompted us to investigate potential associations between genetic abnormalities in multiple myeloma (MM) and singular miRNA expression profiles. Moreover, global gene expression profiling was also analyzed to find correlated miRNA gene expression and select miRNA target genes that show such correlation. For this purpose, we analyzed the expression level of 365 miRNAs and the gene expression profiling in 60 newly diagnosed MM patients, selected to represent the most relevant recurrent genetic abnormalities. Supervised analysis showed significantly deregulated miRNAs in the different cytogenetic subtypes as compared with normal PC. It is interesting to note that miR-1 and miR-133a clustered on the same chromosomal loci, were specifically overexpressed in the cases with t(14;16). The analysis of the relationship between miRNA expression and their respective target genes showed a conserved inverse correlation between several miRNAs deregulated in MM cells and CCND2 expression level. These results illustrate, for the first time, that miRNA expression pattern in MM is associated with genetic abnormalities, and that the correlation of the expression profile of miRNA and their putative mRNA targets is useful to find statistically significant protein-coding genes in MM pathogenesis associated with changes in specific miRNAs.

BackgroundAnalysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co-transcribed genes. However, most studies done at global “omic” scale are not focused on human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. Moreover, the technical noise present in genome-wide expression microarrays is another well reported problem that many times is not addressed with robust statistical methods, and the estimation of errors in the data is not provided.Methodology/Principal FindingsHuman genome-wide expression data from a controlled set of normal-healthy tissues is used to build a confident human gene coexpression network avoiding both pathological and technical noise. To achieve this we describe a new method that combines several statistical and computational strategies: robust normalization and expression signal calculation; correlation coefficients obtained by parametric and non-parametric methods; random cross-validations; and estimation of the statistical accuracy and coverage of the data. All these methods provide a series of coexpression datasets where the level of error is measured and can be tuned. To define the errors, the rates of true positives are calculated by assignment to biological pathways. The results provide a confident human gene coexpression network that includes 3327 gene-nodes and 15841 coexpression-links and a comparative analysis shows good improvement over previously published datasets. Further functional analysis of a subset core network, validated by two independent methods, shows coherent biological modules that share common transcription factors. The network reveals a map of coexpression clusters organized in well defined functional constellations. Two major regions in this network correspond to genes involved in nuclear and mitochondrial metabolism and investigations on their functional assignment indicate that more than 60% are house-keeping and essential genes. The network displays new non-described gene associations and it allows the placement in a functional context of some unknown non-assigned genes based on their interactions with known gene families.Conclusions/SignificanceThe identification of stable and reliable human gene to gene coexpression networks is essential to unravel the interactions and functional correlations between human genes at an omic scale. This work contributes to this aim, and we are making available for the scientific community the validated human gene coexpression networks obtained, to allow further analyses on the network or on some specific gene associations.The data are available free online at http://bioinfow.dep.usal.es/coexpression/.

Lenalidomide yields sustained RBC-TI in 26.9% of RBC transfusion-dependent patients with lower-risk non-del(5q) myelodysplastic syndromes ineligible for or refractory to erythropoiesis-stimulating agents. Response to lenalidomide was associated with improved HRQoL. Treatment-emergent adverse event data were consistent with the known safety profile of lenalidomide.

Lipid metabolism plays an essential role in carcinogenesis due to the requirements of tumoral cells to sustain increased structural, energetic and biosynthetic precursor demands for cell proliferation. We investigated the association between expression of lipid metabolism-related genes and clinical outcome in intermediate-stage colon cancer patients with the aim of identifying a metabolic profile associated with greater malignancy and increased risk of relapse. Expression profile of 70 lipid metabolism-related genes was determined in 77 patients with stage II colon cancer. Cox regression analyses using c-index methodology was applied to identify a metabolic-related signature associated to prognosis. The metabolic signature was further confirmed in two independent validation sets of 120 patients and additionally, in a group of 264 patients from a public database. The combined analysis of these 4 genes, ABCA1, ACSL1, AGPAT1 and SCD, constitutes a metabolic-signature (ColoLipidGene) able to accurately stratify stage II colon cancer patients with 5-fold higher risk of relapse with strong statistical power in the four independent groups of patients. The identification of a group of 4 genes that predict survival in intermediate-stage colon cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy, and avoids the toxic and unnecessary chemotherapy in patients classified as low-risk group.