Synthetic lethal interactions of DEAD/H-box helicases as targets for cancer therapy

DEAD/H-box helicases are implicated in virtually every aspect of RNA metabolism, including transcription, pre-mRNA splicing, ribosomes biogenesis, nuclear export, translation initiation, RNA degradation, and mRNA editing. Most of these helicases are upregulated in various cancers and mutations in some of them are associated with several malignancies. Lately, synthetic lethality (SL) and synthetic dosage lethality (SDL) approaches, where genetic interactions of cancer-related genes are exploited as therapeutic targets, are emerging as a leading area of cancer research. Several DEAD/H-box helicases, including DDX3, DDX9 (Dbp9), DDX10 (Dbp4), DDX11 (ChlR1), and DDX41 (Sacy-1), have been subjected to SL analyses in humans and different model organisms. It remains to be explored whether SDL can be utilized to identity druggable targets in DEAD/H-box helicase overexpressing cancers. In this review, we analyze gene expression data of a subset of DEAD/H-box helicases in multiple cancer types and discuss how their SL/SDL interactions can be used for therapeutic purposes. We also summarize the latest developments in clinical applications, apart from discussing some of the challenges in drug discovery in the context of targeting DEAD/H-box helicases

Epigenetic silencing of CREB3L1 by DNA methylation is associated with high-grade metastatic breast cancers with poor prognosis and is prevalent in triple negative breast cancers

Methylation within specific CpG regions of the CREB3L1 gene in different breast tumor subtypes. The relative methylation was plotted for each tumor subtype. Methylation in regions 2 and 3 show an inverse correlation with CREB3L1 mRNA expression (found in Fig. 6b), whereas methylation in regions 16, 19 and 20 show a direct correlation with CREB3L1 mRNA expression. For all panels: normal (n = 97), luminal (n = 357), human epidermal growth factor receptor 2 (HER2) amplified (n = 19), triple negative breast cancer (TNBC) (n = 113). Statistical differences were analyzed using post-hoc pairwise comparison: *p <0.05; **p <0.01; ***p <0.001. (PDF 97 kb

YHR150w and YDR479c encode peroxisomal integral membrane proteins involved in the regulation of peroxisome number, size, and distribution in Saccharomyces cerevisiae

The peroxin Pex24p of the yeast Yarrowia lipolytica exhibits high sequence similarity to two hypothetical proteins, Yhr150p and Ydr479p, encoded by the Saccharomyces cerevisiae genome. Like YlPex24p, both Yhr150p and Ydr479p have been shown to be integral to the peroxisomal membrane, but unlike YlPex24p, their levels of synthesis are not increased upon a shift of cells from glucose- to oleic acid–containing medium. Peroxisomes of cells deleted for either or both of the YHR150w and YDR479c genes are increased in number, exhibit extensive clustering, are smaller in area than peroxisomes of wild-type cells, and often exhibit membrane thickening between adjacent peroxisomes in a cluster. Peroxisomes isolated from cells deleted for both genes have a decreased buoyant density compared with peroxisomes isolated from wild-type cells and still exhibit clustering and peroxisomal membrane thickening. Overexpression of the genes PEX25 or VPS1, but not the gene PEX11, restored the wild-type phenotype to cells deleted for one or both of the YHR150w and YDR479c genes. Together, our data suggest a role for Yhr150p and Ydr479p, together with Pex25p and Vps1p, in regulating peroxisome number, size, and distribution in S. cerevisiae. Because of their role in peroxisome dynamics, YHR150w and YDR479c have been designated as PEX28 and PEX29, respectively, and their encoded peroxins as Pex28p and Pex29p

Genetic interactions reveal the evolutionary trajectories of duplicate genes

Duplicate genes show significantly fewer interactions than singleton genes, and functionally similar duplicates can exhibit dissimilar profiles because common interactions are ‘hidden' due to buffering.Genetic interaction profiles provide insights into evolutionary mechanisms of duplicate retention by distinguishing duplicates under dosage selection from those retained because of some divergence in function.The genetic interactions of duplicate genes evolve in an extremely asymmetric way and the directionality of this asymmetry correlates well with other evolutionary properties of duplicate genes.Genetic interaction profiles can be used to elucidate the divergent function of specific duplicate pairs

Integrating high-throughput genetic interaction mapping and high-content screening to explore yeast spindle morphogenesis

A combination of yeast genetics, synthetic genetic array analysis, and high-throughput screening reveals that sumoylation of Mcm21p promotes disassembly of the mitotic spindle

Systematic exploration of essential yeast gene function with temperature-sensitive mutants

Conditional temperature-sensitive (ts) mutations are valuable reagents for studying essential genes in the yeast Saccharomyces cerevisiae. We constructed 787 ts strains, covering 497 (~45%) of the 1,101 essential yeast genes, with ~30% of the genes represented by multiple alleles. All of the alleles are integrated into their native genomic locus in the S288C common reference strain and are linked to a kanMX selectable marker, allowing further genetic manipulation by synthetic genetic array (SGA)–based, high-throughput methods. We show two such manipulations: barcoding of 440 strains, which enables chemical-genetic suppression analysis, and the construction of arrays of strains carrying different fluorescent markers of subcellular structure, which enables quantitative analysis of phenotypes using high-content screening. Quantitative analysis of a GFP-tubulin marker identified roles for cohesin and condensin genes in spindle disassembly. This mutant collection should facilitate a wide range of systematic studies aimed at understanding the functions of essential genes

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

The CINs of Polo-Like Kinase 1 in Cancer

Polo-like kinase 1 (PLK1) is overexpressed near ubiquitously across all cancer types and dysregulation of this enzyme is closely tied to increased chromosomal instability and tumor heterogeneity. PLK1 is a mitotic kinase with a critical role in maintaining chromosomal integrity through its function in processes ranging from the mitotic checkpoint, centrosome biogenesis, bipolar spindle formation, chromosome segregation, DNA replication licensing, DNA damage repair, and cytokinesis. The relation between dysregulated PLK1 and chromosomal instability (CIN) makes it an attractive target for cancer therapy. However, clinical trials with PLK1 inhibitors as cancer drugs have generally displayed poor responses or adverse side-effects. This is in part because targeting CIN regulators, including PLK1, can elevate CIN to lethal levels in normal cells, affecting normal physiology. Nevertheless, aiming at related genetic interactions, such as synthetic dosage lethal (SDL) interactions of PLK1 instead of PLK1 itself, can help to avoid the detrimental side effects associated with increased levels of CIN. Since PLK1 overexpression contributes to tumor heterogeneity, targeting SDL interactions may also provide an effective strategy to suppressing this malignant phenotype in a personalized fashion

The EphB6 receptor is overexpressed in pediatric T cell acute lymphoblastic leukemia and increases its sensitivity to doxorubicin treatment

While impressive improvements have been achieved in T-ALL therapy, current treatment approaches fail in approximately 25% of patients and these patients have limited treatment options. Another significant group of patients is being overtreated, which causes long-lasting side effects. Identification of molecules controlling drug resistance in T-ALL is crucial for treatment optimisation in both scenarios. We report here the EphB6 receptor is frequently overexpressed in T-ALL. Remarkably, our observations indicate that EphB6 acts in T-ALL cells to enhance sensitivity to a DNA-damaging drug, doxorubicin, as interruption of EphB6 activity interferes with the efficiency of doxorubicin-induced eradication of T-ALL cells in cell culture and in xenograft animals. This effect relies on the protection of Akt kinase signaling, while Akt inhibition combined with doxorubicin application produces synergistic effects on the elimination of EphB6-deficient T-ALL cells. These data imply that EphB6 suppresses T-ALL resistance by interfering with Akt activity. Our observations highlight a novel role for EphB6 in reducing drug resistance of T-ALL and suggest that doxorubicin treatment should produce better results if personalised based on EphB6 levels. If successfully verified in clinical studies, this approach should improve outcomes for T-ALL patients resistant to current therapies and for patients, who are being overtreated.This work was sponsored by the Canadian Institutes of Health Research (CIHR) grant # 132191 and by the Saskatchewan Health Research Foundation (SHRF) grant # 2891. A.E.Z. was supported by SHRF Postdoctoral Fellowship

Molecular characterization of breast cancer cell lines through multiple omic approaches

Abstract Background Breast cancer cell lines are frequently used as model systems to study the cellular properties and biology of breast cancer. Our objective was to characterize a large, commonly employed panel of breast cancer cell lines obtained from the American Type Culture Collection (ATCC 30-4500 K) to enable researchers to make more informed decisions in selecting cell lines for specific studies. Information about these cell lines was obtained from a wide variety of sources. In addition, new information about cellular pathways that are activated within each cell line was generated. Methods We determined key protein expression data using immunoblot analyses. In addition, two analyses on serum-starved cells were carried out to identify cellular proteins and pathways that are activated in these cells. These analyses were performed using a commercial PathScan array and a novel and more extensive phosphopeptide-based kinome analysis that queries 1290 phosphorylation events in major signaling pathways. Data about this panel of breast cancer cell lines was also accessed from several online sources, compiled and summarized for the following areas: molecular classification, mRNA expression, mutational status of key proteins and other possible cancer-associated mutations, and the tumorigenic and metastatic capacity in mouse xenograft models of breast cancer. Results The cell lines that were characterized included 10 estrogen receptor (ER)-positive, 12 human epidermal growth factor receptor 2 (HER2)-amplified and 18 triple negative breast cancer cell lines, in addition to 4 non-tumorigenic breast cell lines. Within each subtype, there was significant genetic heterogeneity that could impact both the selection of model cell lines and the interpretation of the results obtained. To capture the net activation of key signaling pathways as a result of these mutational combinations, profiled pathway activation status was examined. This provided further clarity for which cell lines were particularly deregulated in common or unique ways. Conclusions These two new kinase or “Kin-OMIC” analyses add another dimension of important data about these frequently used breast cancer cell lines. This will assist researchers in selecting the most appropriate cell lines to use for breast cancer studies and provide context for the interpretation of the emerging results