Receptor-interacting protein kinase-3 (RIP3 or RIPK3) is an essential part of the cellular machinery that executes "programmed" or "regulated" necrosis. Here we show that programmed necrosis is activated in response to many chemotherapeutic agents and contributes to chemotherapy-induced cell death. However, we show that RIP3 expression is often silenced in cancer cells due to genomic methylation near its transcriptional start site, thus RIP3-dependent activation of MLKL and downstream programmed necrosis during chemotherapeutic death is largely repressed. Nevertheless, treatment with hypomethylating agents restores RIP3 expression, and thereby promotes sensitivity to chemotherapeutics in a RIP3-dependent manner. RIP3 expression is reduced in tumors compared to normal tissue in 85% of breast cancer patients, suggesting that RIP3 deficiency is positively selected during tumor growth/development. Since hypomethylating agents are reasonably well-tolerated in patients, we propose that RIP3-deficient cancer patients may benefit from receiving hypomethylating agents to induce RIP3 expression prior to treatment with conventional chemotherapeutics.

Purpose Molecular risk stratification of acute myeloid leukemia (AML) is largely based on genetic markers. However, epigenetic changes, including DNA methylation, deregulate gene expression and may also have prognostic impact. We evaluated the clinical relevance of integrating DNA methylation and genetic information in AML. Methods Next-generation sequencing analysis of methylated DNA identified differentially methylated regions (DMRs) associated with prognostic mutations in older (≥ 60 years) cytogenetically normal (CN) patients with AML (n = 134). Genes with promoter DMRs and expression levels significantly associated with outcome were used to compute a prognostic gene expression weighted summary score that was tested and validated in four independent patient sets (n = 355). Results In the training set, we identified seven genes (CD34, RHOC, SCRN1, F2RL1, FAM92A1, MIR155HG, and VWA8) with promoter DMRs and expression associated with overall survival (OS; P ≤ .001). Each gene had high DMR methylation and lower expression, which were associated with better outcome. A weighted summary expression score of the seven gene expression levels was computed. A low score was associated with a higher complete remission (CR) rate and longer disease-free survival and OS (P < .001 for all end points). This was validated in multivariable models and in two younger (< 60 years) and two older independent sets of patients with CN-AML. Considering the seven genes individually, the fewer the genes with high expression, the better the outcome. Younger and older patients with no genes or one gene with high expression had the best outcomes (CR rate, 94% and 87%, respectively; 3-year OS, 80% and 42%, respectively). Conclusion A seven-gene score encompassing epigenetic and genetic prognostic information identifies novel AML subsets that are meaningful for treatment guidance.

These findings support further investigation of AR-42 as part of a comprehensive therapeutic strategy for cancer cachexia.

Changes in the enzymatic activity of protein arginine methyltransferase (PRMT) 5 have been associated with cancer; however, the protein's role in acute myeloid leukemia (AML) has not been fully evaluated. Here, we show that increased PRMT5 activity enhanced AML growth in vitro and in vivo while PRMT5 downregulation reduced it. In AML cells, PRMT5 interacted with Sp1 in a transcription repressor complex and silenced miR-29b preferentially via dimethylation of histone 4 arginine residue H4R3. As Sp1 is also a bona fide target of miR-29b, the miR silencing resulted in increased Sp1. This event in turn led to transcription activation of FLT3, a gene that encodes a receptor tyrosine kinase. Inhibition of PRMT5 via sh/siRNA or a first-in-class small-molecule inhibitor (HLCL-61) resulted in significantly increased expression of miR-29b and consequent suppression of Sp1 and FLT3 in AML cells. As a result, significant antileukemic activity was achieved. Collectively, our data support a novel leukemogenic mechanism in AML where PRMT5 mediates both silencing and transcription of genes that participate in a 'yin-yang' functional network supporting leukemia growth. As FLT3 is often mutated in AML and pharmacologic inhibition of PRMT5 appears feasible, the PRMT5-miR-29b-FLT3 network should be further explored as a novel therapeutic target for AML.

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator–prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor T-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parametrize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit.

AbstractThe outcome of older (≥ 60 years) acute myeloid leukemia (AML) patients is poor, and novel treatments are needed. In a phase 2 trial for older AML patients, low-dose (20 mg/m2 per day for 10 days) decitabine, a DNA hypomethylating azanucleoside, produced 47% complete response rate with an excellent toxicity profile. To assess the genome-wide activity of decitabine, we profiled pretreatment and post treatment (day 25/course 1) methylomes of marrow samples from patients (n = 16) participating in the trial using deep-sequencing analysis of methylated DNA captured by methyl-binding protein (MBD2). Decitabine significantly reduced global methylation compared with pretreatment baseline (P = .001). Percent marrow blasts did not correlate with global methylation levels, suggesting that hypomethylation was related to the activity of decitabine rather than to a mere decrease in leukemia burden. Hypomethylation occurred predominantly in CpG islands and CpG island-associated regions (P ranged from .03 to .04) A significant concentration (P < .001) of the hypomehtylated CpG islands was found in chromosome subtelomeric regions, suggesting a differential activity of decitabine in distinct chromosome regions. Hypermethylation occurred much less frequently than hypomethylation and was associated with low CpG content regions. Decitabine-related methylation changes were concordant with those previously reported in distinct genes. In summary, our study supports the feasibility of methylome analyses as a pharmacodynamic endpoint for hypomethylating therapies.

DNMT3B encodes a DNA methyltransferase implicated in aberrant epigenetic changes contributing to leukemogenesis. We tested whether DNMT3B expression, measured by NanoString nCounter assay, associates with outcome, gene- and microRNA-expression and DNA methylation profiles in 210 older (≥60 years) adults with primary, cytogenetically normal AML (CN-AML). Patients were dichotomized into high versus low expressers using median cut. Outcomes were assessed in the context of known CN-AML prognosticators. Gene- and microRNA-expression, and DNA methylation profiles were analyzed using microarrays and MethylCap-sequencing, respectively. High DNMT3B expressers had fewer complete remissions (CR; P=0.002) and shorter disease-free (DFS; P=0.02) and overall (OS; P<0.001) survival. In multivariable analyses, high DNMT3B expression remained an independent predictor of lower CR rates (P=0.04) and shorter DFS (P=0.04) and OS (P=0.001). High DNMT3B expression associated with a gene-expression profile comprising 363 genes involved in differentiation, proliferation and survival pathways, but with only 4 differentially expressed microRNAs (miR-133b, miR-148a, miR-122, miR-409-3p) and no differential DNA methylation regions. We conclude that high DNMT3B expression independently associates with adverse outcome in older CN-AML patients. Gene-expression analyses suggest that DNMT3B is involved in the modulation of several genes, although the regulatory mechanisms remain to be investigated to devise therapeutic approaches specific for these patients.

Temporal dynamics of gene expression inform cellular and molecular perturbations associated with disease development and evolution. Given the complexity of high-dimensional temporal genomic data, an analytic framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Here we model temporal dynamics of the transcriptome of peripheral blood mononuclear cells in a two-dimensional state-space representing states of health and leukemia using time-sequential bulk RNA-seq data from a murine model of acute myeloid leukemia (AML). The statetransition model identified critical points that accurately predict AML development and identifies stepwise transcriptomic perturbations that drive leukemia progression. The geometry of the transcriptome state-space provided a biological interpretation of gene dynamics, aligned gene signals that are not synchronized in time across mice, and allowed quantification of gene and pathway contributions to leukemia development. Our statetransition model synthesizes information from multiple cell types in the peripheral blood and identifies critical points in the transition from health to leukemia to guide interpretation of changes in the transcriptome as a whole to predict disease progression. Significance: These findings apply the theory of state transitions to model the initiation and development of acute myeloid leukemia, identifying transcriptomic perturbations that accurately predict time to disease development. See related commentary by Kuijjer, p. 3072