Acute myeloid leukemias with UBTF tandem duplications are sensitive to menin inhibitors.

ABSTRACT: UBTF tandem duplications (UBTF-TDs) have recently emerged as a recurrent alteration in pediatric and adult acute myeloid leukemia (AML). UBTF-TD leukemias are characterized by a poor response to conventional chemotherapy and a transcriptional signature that mirrors NUP98-rearranged and NPM1-mutant AMLs, including HOX-gene dysregulation. However, the mechanism by which UBTF-TD drives leukemogenesis remains unknown. In this study, we investigated the genomic occupancy of UBTF-TD in transformed cord blood CD34+ cells and patient-derived xenograft models. We found that UBTF-TD protein maintained genomic occupancy at ribosomal DNA loci while also occupying genomic targets commonly dysregulated in UBTF-TD myeloid malignancies, such as the HOXA/HOXB gene clusters and MEIS1. These data suggest that UBTF-TD is a gain-of-function alteration that results in mislocalization to genomic loci dysregulated in UBTF-TD leukemias. UBTF-TD also co-occupies key genomic loci with KMT2A and menin, which are known to be key partners involved in HOX-dysregulated leukemias. Using a protein degradation system, we showed that stemness, proliferation, and transcriptional signatures are dependent on sustained UBTF-TD localization to chromatin. Finally, we demonstrate that primary cells from UBTF-TD leukemias are sensitive to the menin inhibitor SNDX-5613, resulting in markedly reduced in vitro and in vivo tumor growth, myeloid differentiation, and abrogation of the UBTF-TD leukemic expression signature. These findings provide a viable therapeutic strategy for patients with this high-risk AML subtype.

Epigenetic regulator genes direct lineage switching in MLL/AF4 leukemia.

The fusion gene MLL/AF4 defines a high-risk subtype of pro-B acute lymphoblastic leukemia. Relapse can be associated with a lineage switch from acute lymphoblastic to acute myeloid leukemia, resulting in poor clinical outcomes caused by resistance to chemotherapies and immunotherapies. In this study, the myeloid relapses shared oncogene fusion breakpoints with their matched lymphoid presentations and originated from various differentiation stages from immature progenitors through to committed B-cell precursors. Lineage switching is linked to substantial changes in chromatin accessibility and rewiring of transcriptional programs, including alternative splicing. These findings indicate that the execution and maintenance of lymphoid lineage differentiation is impaired. The relapsed myeloid phenotype is recurrently associated with the altered expression, splicing, or mutation of chromatin modifiers, including CHD4 coding for the ATPase/helicase of the nucleosome remodelling and deacetylation complex. Perturbation of CHD4 alone or in combination with other mutated epigenetic modifiers induces myeloid gene expression in MLL/AF4+ cell models, indicating that lineage switching in MLL/AF4 leukemia is driven and maintained by disrupted epigenetic regulation.

CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis.

MOTIVATION: Centrality analysis involves a series of ambiguities in that there are numerous well-known centrality measures with differing algorithms for establishing which nodes in a network are essential. There is no clearly preferred measure or means of deciding which measure is most germane to a given network with respect to node essentiality vis-à-vis topological features. Our aim here was to develop an instrument that enables comparisons among potentially appropriate centrality measures to be made with respect to network structure and thereby to support the identification of the most informative measure according to dimensional reduction methods. METHODS: The Central Informative Nodes in Network Analysis (CINNA) package introduced herein gathers all required functions for centrality analysis in weighted/unweighted and directed/undirected networks. Then, it compares, assorts and visualizes centrality measures to select which best describes the node importance. AVAILABILITY AND IMPLEMENTATION: CINNA is available in CRAN, including a tutorial. URL: https://cran.r-project.org/web/packages/CINNA/index.html.

IMMAN: an R/Bioconductor package for Interolog protein network reconstruction, mapping and mining analysis.

BACKGROUND: Reconstruction of protein-protein interaction networks (PPIN) has been riddled with controversy for decades. Particularly, false-negative and -positive interactions make this progress even more complicated. Also, lack of a standard PPIN limits us in the comparison studies and results in the incompatible outcomes. Using an evolution-based concept, i.e. interolog which refers to interacting orthologous protein sets, pave the way toward an optimal benchmark. RESULTS: Here, we provide an R package, IMMAN, as a tool for reconstructing Interolog Protein Network (IPN) by integrating several Protein-protein Interaction Networks (PPINs). Users can unify different PPINs to mine conserved common networks among species. IMMAN is designed to retrieve IPNs with different degrees of conservation to engage prediction analysis of protein functions according to their networks. CONCLUSIONS: IPN consists of evolutionarily conserved nodes and their related edges regarding low false positive rates, which can be considered as a gold standard network in the contexts of biological network analysis regarding to those PPINs which is derived from.

Valproic acid inhibits the protective effects of stromal cells against chemotherapy in breast cancer: Insights from proteomics and systems biology.

Interaction between tumor and stromal cells is beginning to be decoded as a contributor to chemotherapy resistance. Here, we aim to take a system-level approach to explore a mechanism by which stromal cells induce chemoresistance in cancer cells and subsequently identify a drug that can inhibit such interaction. Using a proteomic dataset containing quantitative data on secretome of stromal cells, we performed multivariate analyses and found that bone-marrow mesenchymal stem cells (BM-MSCs) play the most protective role against chemotherapeutics. Pathway enrichment tests showed that secreted cytokines from BM-MSCs activated 4 signaling pathways including Janus kinase-signal transducer and activator of transcription, phosphatidylinositol 3-kinase-protein kinase B, and mitogen-activated protein kinase, transforming growth factor-ß in cancer cells collectively leading to nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) transcription factor activation. Based on the data from integrated Library of Integrated Network-Based Cellular Signatures (iLINCs) program, we found that among different drugs, valproic acid (VA) affected the expression of 34 genes within the identified pathways that are activated by stromal cells. Our in vitro experiments confirmed that VA inhibits NF-kB activation in cancer cells. In addition, analyzing gene expression data in patients taking oral VA showed that this drug decreased expression of antioxidant enzymes culminating in increased oxidative stress in tumor cells. These results suggest that VA confines the protective role of stromal cells by inhibiting the adaptation mechanisms toward oxidative stress which is potentiated by stromal cells. Since VA is an already prescribed drug manifesting anticancer effects, this study provides a mechanistic insight for combination of VA with chemotherapy in the clinical setting.

Nanoparticle-mediated targeting of the fusion gene RUNX1/ETO in t(8;21)-positive acute myeloid leukaemia.

A hallmark of acute myeloid leukaemias (AMLs) are chromosomal rearrangements that give rise to novel leukaemia-specific fusion genes. Most of these fusion genes are both initiating and driving events in AML and therefore constitute ideal therapeutic targets but are challenging to target by conventional drug development. siRNAs are frequently used for the specific suppression of fusion gene expression but require special formulations for efficient in vivo delivery. Here we describe the use of siRNA-loaded lipid nanoparticles for the specific therapeutic targeting of the leukaemic fusion gene RUNX1/ETO. Transient knockdown of RUNX1/ETO reduces its binding to its target genes and alters the binding of RUNX1 and its co-factor CBFß. Transcriptomic changes in vivo were associated with substantially increased median survival of a t(8;21)-AML mouse model. Importantly, transient knockdown in vivo causes long-lasting inhibition of leukaemic proliferation and clonogenicity, induction of myeloid differentiation and a markedly impaired re-engraftment potential in vivo. These data strongly suggest that temporary inhibition of RUNX1/ETO results in long-term restriction of leukaemic self-renewal. Our results provide proof for the feasibility of targeting RUNX1/ETO in a pre-clinical setting and support the further development of siRNA-LNPs for the treatment of fusion gene-driven malignancies.

The MLL-Menin Interaction is a Therapeutic Vulnerability in NUP98-rearranged AML.

Chromosomal translocations involving the NUP98 locus are among the most prevalent rearrangements in pediatric acute myeloid leukemia (AML). AML with NUP98 fusions is characterized by high expression of HOXA and MEIS1 genes and is associated with poor clinical outcome. NUP98 fusion proteins are recruited to their target genes by the mixed lineage leukemia (MLL) complex, which involves a direct interaction between MLL and Menin. Here, we show that therapeutic targeting of the Menin-MLL interaction inhibits the propagation of NUP98-rearrranged AML both ex vivo and in vivo. Treatment of primary AML cells with the Menin inhibitor revumenib (SNDX-5613) impairs proliferation and clonogenicity ex vivo in long-term coculture and drives myeloid differentiation. These phenotypic effects are associated with global gene expression changes in primary AML samples that involve the downregulation of many critical NUP98 fusion protein-target genes, such as MEIS1 and CDK6. In addition, Menin inhibition reduces the expression of both wild-type FLT3 and mutated FLT3-ITD, and in combination with FLT3 inhibitor, suppresses patient-derived NUP98-r AML cells in a synergistic manner. Revumenib treatment blocks leukemic engraftment and prevents leukemia-associated death of immunodeficient mice transplanted with NUP98::NSD1 FLT3-ITD-positive patient-derived AML cells. These results demonstrate that NUP98-rearranged AMLs are highly susceptible to inhibition of the MLL-Menin interaction and suggest the inclusion of AML patients harboring NUP98 fusions into the clinical evaluation of Menin inhibitors.

A systematic survey of centrality measures for protein-protein interaction networks.

BACKGROUND: Numerous centrality measures have been introduced to identify "central" nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures. RESULTS: We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network's topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities. CONCLUSIONS: The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.