Growth factor independence 1 (GFI1) is a DNA-binding transcription factor and a key regulator of hematopoiesis. GFI1-36N is a germ line variant, causing a change of serine (S) to asparagine (N) at position 36. We previously reported that the GFI1-36N allele has a prevalence of 10% to 15% among patients with acute myeloid leukemia (AML) and 5% to 7% among healthy Caucasians and promotes the development of this disease. Using a multiomics approach, we show here that GFI1-36N expression is associated with increased frequencies of chromosomal aberrations, mutational burden, and mutational signatures in both murine and human AML and impedes homologous recombination (HR)-directed DNA repair in leukemic cells. GFI1-36N exhibits impaired binding to N-Myc downstream-regulated gene 1 (Ndrg1) regulatory elements, causing decreased NDRG1 levels, which leads to a reduction of O6-methylguanine-DNA-methyltransferase (MGMT) expression levels, as illustrated by both transcriptome and proteome analyses. Targeting MGMT via temozolomide, a DNA alkylating drug, and HR via olaparib, a poly-ADP ribose polymerase 1 inhibitor, caused synthetic lethality in human and murine AML samples expressing GFI1-36N, whereas the effects were insignificant in nonmalignant GFI1-36S or GFI1-36N cells. In addition, mice that received transplantation with GFI1-36N leukemic cells treated with a combination of temozolomide and olaparib had significantly longer AML-free survival than mice that received transplantation with GFI1-36S leukemic cells. This suggests that reduced MGMT expression leaves GFI1-36N leukemic cells particularly vulnerable to DNA damage initiating chemotherapeutics. Our data provide critical insights into novel options to treat patients with AML carrying the GFI1-36N variant.

Artifact chimeric reads are enriched in next-generation sequencing data generated from formalin-fixed paraffin-embedded (FFPE) samples. Previous work indicated that these reads are characterized by erroneous split-read support that is interpreted as evidence of structural variants. Thus, a large number of false-positive structural variants are detected. To our knowledge, no tool is currently available to specifically call or filter structural variants in FFPE samples. To overcome this gap, we developed 2 R packages: SimFFPE and FilterFFPE.

To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.

This study assessed the efficacy and tolerability of intravenous ibuprofen in the improvement of post-operative pain control and the reduction of opioid usage. Patients were randomly divided into placebo, ibuprofen 400 mg and ibuprofen 800 mg groups. All patients received patient-controlled intravenous morphine analgesia after surgery. The first dose of study drugs was administered intravenously 30 min before the end of surgery and then every 6 hours, for a total of 8 doses after surgery. The primary endpoint of this study was the mean amount of morphine used during the first 24 hours after surgery. Morphine use was reduced significantly in the ibuprofen 800 mg group compared with the placebo group (P = 0.04). Tramadol use was reduced significantly in the ibuprofen 400 mg and ibuprofen 800 mg groups compared with the placebo group (P < 0.01). The area under the curve of visual analog scale pain ratings was not different between groups. Safety assessments and side effects were not different between the three groups. Intravenous ibuprofen 800 mg was associated with a significant reduction in morphine requirements, and it was generally well tolerated for postoperative pain management in patients undergoing radical cervical cancer surgery.

Growth Factor Independence 1 (GFI1) is a transcription factor with an important role in the regulation of development of myeloid and lymphoid cell lineages and was implicated in the development of myelodysplastic syndrome (MDS) and acute myeloid leukaemia (AML). Reduced expression of GFI1 or presence of the GFI1-36N (serine replaced with asparagine) variant leads to epigenetic changes in human and murine AML blasts and accelerated the development of leukaemia in a murine model of human MDS and AML. We and other groups previously showed that the GFI1-36N allele or reduced expression of GFI1 in human AML blasts is associated with an inferior prognosis. Using GFI1-36S, -36N -KD, NUP98-HOXD13-tg mice and curcumin (a natural histone acetyltransferase inhibitor (HATi)), we now demonstrate that expansion of GFI1-36N or -KD, NUP98-HODXD13 leukaemic cells can be delayed. Curcumin treatment significantly reduced AML progression in GFI1-36N or -KD mice and prolonged AML-free survival. Of note, curcumin treatment had no effect in GFI1-36S, NUP98-HODXD13 expressing mice. On a molecular level, curcumin treatment negatively affected open chromatin structure in the GFI1-36N or -KD haematopoietic cells but not GFI1-36S cells. Taken together, our study thus identified a therapeutic role for curcumin treatment in the treatment of AML patients (homo or heterozygous for GFI1-36N or reduced GFI1 expression) and possibly improved therapy outcome.

Cancer metabolism influences multiple aspects of tumorigenesis and causes diversity across malignancies. Although comprehensive research has extended our knowledge of molecular subgroups in medulloblastoma (MB), discrete analysis of metabolic heterogeneity is currently lacking. This study seeks to improve our understanding of metabolic phenotypes in MB and their impact on patients' outcomes.