Synergistic Genes Research Articles

Review of molecular biological research on the treatment of membranous nephropathy with Tripterygium glycosides based on TCM theory.

To explore the mechanism of Tripterygium wilfordii polyglycoside (TWP) in the treatment of membranous nephropathy (MN) by network pharmacology. TCMSP and DrugBank databases were used to screen the main targets of the main active components of Tripterygium glycosides, and OMIM and Gene Cards databases were used to search the gene targets of MN. UniProt database was used to normalize all the targets to get the intersection targets of TGs and MNs. Synergistic genes were uploaded to the STRING platform to construct a protein-protein interaction network and screen related core targets. Gene Ontology and Kyoto Genome Encyclopedia analyses of core targets were performed using the DAVID database. AutoDockTools software was used to verify the molecular docking between the active components of TGs and the synergistic genes. We identified 126 potential targets for the active component of Tripterygium glycosides, 584 MN-associated disease targets, and 28 co-acting genes. It mainly involves AGE-RAGE signaling pathway, lipid and atherosclerosis, IL-17 signaling pathway, fluid shear stress and atherosclerosis, NF-kappa B signaling pathway and other pathways and biological pathways in diabetic complications. The active component of that Tripterygium glycosides and the active site of the synergistic core target can the bond energy is less than -5kJ/mol. Tripterygium glycosides can regulate the release of inflammatory factors to treat MN through multiple active components, multiple disease targets, multiple biological pathways and multiple pathways, which provides a basis for broadening the clinical use of traditional Chinese medicine in the treatment of MN.

QTL Mapping for Fiber Quality Based on Introgression Lines Population from G. hirsutum × G. tomentosum

As one of the most widely cultivated cotton species in China, upland cotton has moderate fiber quality and wide applicability, but its genetic basis is relatively narrow. To expand genetic diversity and improve fiber quality, in this study an introgression population (BC5S5) containing 107 lines was constructed by using G. hirsutum acc. 4105 as the recurrent parent and G. tomentosum as the donor parent. Using the specific-locus amplified fragment sequencing (SLAF-seq) strategy, 3157 high-throughput single nucleotide polymorphism (SNP) markers were obtained. Linkage analysis showed that a total of ninety-one QTLs related to fiber quality traits were detected in three environments, and the phenotypic variance explained (PVE) rates were 4.53–20.92%. Forty-six QTL (50.55%) synergistic genes were derived from G. tomentosum. Among them, qFS-A02-1 and qSCI-A02-1 were stably detected with a PVE of 9.8–16.71% and 14.78–20.92%, respectively. Within the candidate interval, Ghir_A02G012730, Ghir_A02G012790 and Ghir_A02G012830 were found to be possibly involved in cellulose and cell wall biosynthesis, with a relatively high expression during fiber development, 20 DPA and 25 DPA, which suggested that these three genes may be involved in the regulation of fiber strength traits, but their functions need further validation to determine the regulatory mechanism. Our research lays the foundation of fiber quality related to basic genetic research and breeding in cotton.

Transcriptome-wide analysis reveals core transcriptional regulators associated with culm development and variation in Dendrocalamus sinicus, the strongest woody bamboo in the world

Transcription factors (TFs) play indispensable roles in plant development and stress responses. As the largest woody bamboo species in the world, Dendrocalamus sinicus is endemic to Yunnan Province, China, and possesses two natural variants characterized by culm shape, namely straight or bent culms. Understanding the transcriptional regulation network of D. sinicus provides a unique opportunity to clarify the growth and development characteristics of woody bamboos. In this study, 10,236 TF transcripts belonging to 57 families were identified from transcriptome data of two variants at different developmental stages, from which we constructed a transcriptional regulatory network and unigene-coding protein-TFs interactive network of culm development for this attractive species. Gene function enrichment analysis revealed that hormone signaling and MAPK signaling pathways were two most enriched pathways in TF-regulated network. Based on PPI analysis, 50 genes interacting with nine TFs were screened as the core regulation components related to culm development. Of them, 18 synergistic genes of seven TFs, including nuclear cap-binding protein subunit 1, transcription factor GTE9-like, and ATP-dependent DNA helicase DDX11 isoform X1, involved in culm-shape variation. Most of these genes would interact with MYB, C3H, and ARF transcription factors. Six members with two each from ARF, C3H, and MYB transcription factor families and six key interacting genes (IAA3, IAA19, leucine-tRNA ligase, nuclear cap-binding protein subunit 1, elongation factor 2, and coiled-coil domain-containing protein 94) cooperate with these transcription factors were differentially expressed at development stage of young culms, and were validated by quantitative PCR. Our results represent a crucial step towards understanding the regulatory mechanisms of TFs involved in culm development and variation of D. sinicus.

OR07-3 Hormone-Controlled Cooperative Binding of Transcription Factors Within Enhancer Clusters Drives Synergistic Induction of Fasting-Regulated Genes

Abstract During fasting, hepatocytes produce glucose in response to hormonal signals. Glucagon and glucocorticoids are principal hormones secreted during fasting that cooperate in regulating glucose production via increasing hepatic gluconeogenesis. However, how these hormone signals are integrated in hepatocytes and translated to a biological output is unknown. We used genome-wide profiling of gene expression, enhancer dynamics and transcription factor binding in primary mouse hepatocytes to uncover the mode of cooperation between glucagon and corticosterone (the major glucocorticoid in mice). We found that compared to a single treatment with either hormone alone, a dual treatment initiates a pro-gluconeogenic gene program in hepatocytes by synergistically inducing gluconeogenic genes. We show that the mechanism driving synergistic gene expression is based on glucagon-mediated enhancer activation, leading to increased binding of the glucocorticoid receptor (GR) upon corticosterone stimulation. This was shown by glucagon-dependent increases in the active enhancer mark H3K27ac at GR binding sites (both signals were measured genome-wide via chromatin immunoprecipitation sequencing). Thus, glucagon-dependent enhancer activation, mediated by the glucagon-activated transcription factor CREB, assists GR loading to specific enhancers located near synergistic gluconeogenic genes. This 'assisted loading' mechanism is enhancer-specific as GR was able to efficiently bind other enhancers without assistance by CREB. Interestingly, we found that the glucagon-CREB axis does not only activate single enhancers but is also able to activate entire enhancer clusters. Indeed, CREB binding in one enhancer unit within a cluster, led to cluster-wide enhancer activation, thereby assisting the loading of GR in other enhancer units within the cluster that are unbound by CREB directly. These chromatin and gene expression changes collectively lead to synergistic glucose production from hepatocytes in the presence of glucagon and corticosterone. In summary, we show that hepatocytes integrate extracellular signals by an enhancer-specific mechanism: glucagon activates enhancers, thereby assisting the loading of GR upon stimulation by corticosterone. This chromatin-bound integration of endocrine signals leads to synergistic gene induction and a tailored response to fasting. Presentation: Saturday, June 11, 2022 12:00 p.m. - 12:15 p.m.

Mining synergistic genes for nutrient utilization and disease resistance in maize based on co-expression network and consensus QTLs.

Nutrient restrictions and large-scale emergence of diseases are threatening the maize production. Recent findings demonstrated that there is a certain synergistic interaction between nutrition and diseases pathways in model plants, however there are few studies on the synergistic genes of nutrients and diseases in maize. Thus, the transcriptome data of nitrogen (N) and phosphorus (P) nutrients and diseases treatments in maize, rice, wheat and Arabidopsis thaliana were collected in this study, and four and 22 weighted co-expression modules were obtained by using Weighted Gene Co-expression Network Analysis (WGCNA) in leaf and root tissues, respectively. With a total of 5252 genes, MFUZZ cluster analysis screened 26 clusters with the same expression trend under nutrition and disease treatments. In the meantime, 1427 genes and 22 specific consensus quantitative trait loci (scQTLs) loci were identified by meta-QTL analysis of nitrogen and phosphorus nutrition and disease stress in maize. Combined with the results of cluster analysis and scQTLs, a total of 195 consistent genes were screened, of which six genes were shown to synergistically respond to nutrition and disease both in roots and leaves. Moreover, the six candidate genes were found in scQTLs associated with gray leaf spot (GLS) and corn leaf blight (CLB). In addition, subcellular localization and bioinformatics analysis of the six candidate genes revealed that they were primarily expressed in endoplasmic reticulum, mitochondria, nucleus and plasma membrane, and were involved in defense and stress, MeJA and abscisic acid response pathways. The fluorescence quantitative PCR confirmed their responsiveness to nitrogen and phosphorus nutrition as well as GLS treatments. Taken together, findings of this study indicated that the nutrition and disease have a significant synergistic response in maize.

Bio-Electro-Enhancement and Synergistic Antibiotic Resistance Genes Abatement of Mariculture Wastewater Treatment by Electric Field Attached Membrane Bioreactor (E-MBR)

Bio-Electro-Enhancement and Synergistic Antibiotic Resistance Genes Abatement of Mariculture Wastewater Treatment by Electric Field Attached Membrane Bioreactor (E-MBR)

Abstract 1053: A large cellular screen charting the landscape of synergistic drug combinations in lung cancer

Abstract Introduction Non-small-cell lung cancer (NSCLC) is among the most common cancers, accounting for 85% of all lung cancer cases. Drug combinations are known to more effective than single drugs and can delay drug resistance. NSCLC has a poor prognosis and there are no approved targeted cancer drug combinations. Here we have conducted the first large-scale screen of drug combinations in NSCLC cell lines to identify new candidate synergistic combinations and map the landscape of synthetic lethal and synergistic pathways underlying them. Results We conducted the largest of its kind in vitro drug combination screening of 81 NSCLC cell-lines for 21 x 242 cancer drug combinations at 5 different doses. Our collection of approved and investigational drugs included mainly targeted drugs as well as some chemotherapy drugs. For each drug combination, we computed the synergy score using the Bliss model. Our key findings are as follows: 1. We identify drug combinations (like CHEK and WEE1 inhibitors) that are highly synergistic in many NSCLC cell lines. 2. Combinations of targeted therapies are overall more synergistic than chemotherapeutic drug combinations. 3. Synergistic combinations occur more within pathways. 4. We see that most drug combinations do not make cell lines more sensitive than the most sensitive cell line to the corresponding single agents across all the cell lines tested. 5. We identify a new class of drug combinations - called super-sensitizers - whose combined effects are larger than the effects observed for the individual drugs composing across all of the cohort cell-lines. 6. Combinations of multi-target drugs tend to be more synergistic than single-target drugs. 7. We identify and characterize a set of context-specific drug combinations that are differentially synergistic or differentially effective (i.e. viability after treatment) between wild-type vs mutant cell lines of various cancer drivers (KRAS, TP53, STK11, PIK3CA, EGFR). 8. Studying potential mechanistic underpinnings of drug synergism in NSCLC, we find that: (a) synergistic combinations target genes that have more proximal physical interactions, (b) some synergistic combinations target synthetic lethal combinations, (c) a subset of synergistic combinations have further support by NSCLC patients survival analysis. Conclusions We performed the first large in vitro drug combination screen in NSCLC. We identify top drug combinations that are highly synergistic and effective in many cell lines and characterize key biological properties and mechanisms underlying them. The screen results are provided in a user-friendly public data resource that can help facilitate the development of targeted drug combinations in lung cancer therapy. Citation Format: Nishanth Ulhas Nair, Adam Friedman, Patricia Greninger, Avinash D. Sahu, Ellen Murchie, Giovanna Stein Crowther, Joe McClanaghan, Joo Sang Lee, Daniel A. Haber, Jeffrey Engelman, Eytan Ruppin, Cyril Benes. A large cellular screen charting the landscape of synergistic drug combinations in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1053.

CRISPR-cas9: a powerful tool towards precision medicine in cancer treatment.

Cancer is a highly heterogeneous disease in term of molecular signature even though it is originated from the same tissue type. Cancer heterogeneity may occur during its development or treatment, which is the main cause resulting in drug resistance and recurrence. Precision medicine refers to matching the right medicine to the right patients based on their molecular signatures. Therefore, a thorough understanding of the mechanism of tumorigenesis and drug resistance is essential to precision medicine. CRISPR-cas9 system is a powerful tool for gene editing and CRISPR-based high-throughput screening has been widely applied especially in searching for tumor-driven or synergistic lethal genes aiming to overcome drug resistance. In this review, we describe the progress of CRISPR-cas9-based unbiased screening in precision medicine including identification of new drug targets, biomarkers and elucidation of mechanisms leading to drug resistance. The existing challenges as well as the future directions are also discussed.

Multiple down-regulated cytochrome P450 monooxygenase genes contributed to synergistic interaction between chlorpyrifos and imidacloprid against Nilaparvata lugens

Abstract Insecticide mixtures are an effective strategy in pest resistance management. The synergistic chlorpyrifos and imidacloprid mixture could significantly increase toxicity against rice pest, Nilaparvata lugens, despite their high levels of resistance. However, synergism mechanisms to explain this phenomenon remain unknown. Chlorpyrifos and imidacloprid at a 1:0.5 ratio showed significant synergism on N. lugens with a combination index value of 0.18 after topical exposure. We constructed a genetic database of the genes expressed in individual and synergistic chlorpyrifos and imidacloprid treatments of N. lugens using Illumina Hiseq™ X Ten, and 17 co-downregulated genes putatively involved in synergism were detected by comparative transcriptome analyses. Expression patterns of the 17 candidate synergistic genes matched with transcriptome sequencing data by quantitative real-time PCR analyses. Feeding of dsRNAs further reduced the expression levels of 10 of these candidate synergistic genes (from 1.68 to 4.13-fold). Nymphs fed with only dsRNAs of CYP4DE1, CYP6AY1v2, CYP353D1, and CYP439A1 experienced more high mortality rates (81.45–90.34%) to improve synergism between chlorpyrifos and imidacloprid. Multiple reductive expressed P450 genes were potentially associated with synergism of a mixture of chlorpyrifos and imidacloprid, as confirmed by comparative transcriptome analyses and RNAi assays. Our findings suggested that synergistic interactions between chlorpyrifos and imidacloprid might be controlled by P450s.

A one-step tRNA-CRISPR system for genome-wide genetic interaction mapping in mammalian cells

Mapping genetic interactions in mammalian cells is limited due to technical obstacles. Here we describe a method called TCGI (tRNA-CRISPR for genetic interactions) to generate a high-efficient, barcode-free and scalable pairwise CRISPR libraries in mammalian cells for identifying genetic interactions. We have generated a genome- wide library to identify genes genetically interacting with TAZ in cell viability regulation. Validation of candidate synergistic genes reveals the screening accuracy of 85% and TAZ-MCL1 is characterized as combinational drug targets for non-small cell lung cancer treatments. TCGI has dramatically improved the current methods for mapping genetic interactions and screening drug targets for combinational therapies.

Abstract 371: Synthetic lethal and resistance pathways in midostaurin-treated AML

Abstract Introduction: Mutations in fms-like tyrosine kinase 3 (FLT3) are common genomic abnormalities in adult acute myeloid leukemia (AML). When FLT3 is present as a dominant internal tandem duplication (ITD) AML clone, it is associated with poor treatment outcome and short overall survival (OS). A phase 3 study with the multi-kinase (including FLT3) inhibitor midostaurin improved OS of FLT3+ AML prompting widespread use, yet little is understood about resistance pathways and synergistic pharmacologic targets. We conducted a genome-wide CRISPR-KO (loss-of-function) screen in a FLT3+ AML cell line treated with midostaurin, validated results in vitro and in vivo, and identified distinct findings relative to limited CRISPR screening previously reported (Cancer Res 77:4402, 2017) with a very selective FLT3-ITD inhibitor (quizartinib). Methods: CRISPR pooled screens (GeCKOv2 library, >500 cells/sgRNA) of midostaurin versus vehicle were analyzed via MAGeCK. Gene essentiality was derived from guide abundance after 7-day selection of transduced cells compared to plasmid levels. In vitro validation included shRNA knockdown and/or inhibition followed by proliferation and clonogenic assays in cell lines and primary AML blasts. NSG mice (n=7/group) engrafted with MOLM-13-luciferase cells were treated with vehicle, single agents, or combination and evaluated for disease burden (IVIS) and survival (Kaplan-Meier). Results: We first confirmed our screening approach correlated well with previously reported essential genes (Cell Rep 17:1193, 2016; Cancer Cell 33:386, 2017;Genome Biol 16:281, 2015). We then identified >500 genes (FDR<2%) with significant depletion (synergistic) or enrichment (resistance) after midostaurin treatment. Top synergistic genes included BCL2, MEF2D, CCNG1, and RPS14, with no overlap to reported quizartinib results. We selected BCL2 for immediate validation based on demonstrated efficacy targeting this gene in AML. Indeed, shRNA knockdown of BCL2 sensitized FLT3+ AML cell lines to midostaurin. Similarly, combination treatment of FLT3+ cell lines and AML patient tumor samples with midostaurin and venetoclax displayed synergy at clinically relevant doses. In a xenograft model, combination therapy decreased disease burden and improved survival over single agents supporting synergy of these agents in vivo. Conclusion: FLT3 inhibitors represent the first targeted therapeutic to impact AML survival and herein we use genome-wide CRIPSR screening to demonstrate that resistance mechanisms of broad-spectrum kinase inhibitors are distinct from those previously reported with specific inhibitors (quizartinib). Furthermore, we validate the select target BCL2 genetically and with the therapeutic venetoclax demonstrating in vitro and in vivo synergy, thereby justifying pursuit of this combination in future clinical trials. Ongoing efforts include validation of other targets that will be presented. Citation Format: Lindsey T. Brinton, Katie Williams, Shelley Orwick, Larry Beaver, Daniel Canfield, Casey Cempre, Jordan Skinner, Ronni Wasmuth, Quais Hassan, John C. Byrd, Rosa Lapalombella, James S. Blachly. Synthetic lethal and resistance pathways in midostaurin-treated AML [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 371.

A data-driven interactome of synergistic genes improves network-based cancer outcome prediction.

Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment. Network-based outcome predictors (NOPs), which considers the cellular wiring diagram in the classification, hold much promise to improve performance, stability and interpretability of identified marker genes. Problematically, reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions. In this paper we turn the prediction problem around: instead of using a given biological network in the NOP, we aim to identify the network of genes that truly improves outcome prediction. To this end, we propose SyNet, a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data. To obtain SyNet, we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a NOP model. We evaluated SyNet and 11 other networks on a compendium dataset of >4000 survival-labelled breast cancer samples. For this purpose, we used cross-study validation which more closely emulates real world application of these outcome predictors. We find that SyNet is the only network that truly improves performance, stability and interpretability in several existing NOPs. We show that SyNet overlaps significantly with existing gene networks, and can be confidently predicted (~85% AUC) from graph-topological descriptions of these networks, in particular the breast tissue-specific network. Due to its data-driven nature, SyNet is not biased to well-studied genes and thus facilitates post-hoc interpretation. We find that SyNet is highly enriched for known breast cancer genes and genes related to e.g. histological grade and tamoxifen resistance, suggestive of a role in determining breast cancer outcome.

238 Transcriptome profiling of primary bovine macrophages from cows with Johne’s disease suggests a tolerance state induced by convergent signalling via NF-κB and its synergistic tolerized genes.

238 Transcriptome profiling of primary bovine macrophages from cows with Johne’s disease suggests a tolerance state induced by convergent signalling via NF-κB and its synergistic tolerized genes.

A computational systems approach identifies synergistic specification genes that facilitate lineage conversion to prostate tissue

To date, reprogramming strategies for generating cell types of interest have been facilitated by detailed understanding of relevant developmental regulatory factors. However, identification of such regulatory drivers often represents a major challenge, as specific gene combinations may be required for reprogramming. Here we show that a computational systems approach can identify cell type specification genes (master regulators) that act synergistically, and demonstrate its application for reprogramming of fibroblasts to prostate tissue. We use three such master regulators (FOXA1, NKX3.1 and androgen receptor, AR) in a primed conversion strategy starting from mouse fibroblasts, resulting in prostate tissue grafts with appropriate histological and molecular properties that respond to androgen-deprivation. Moreover, generation of reprogrammed prostate does not require traversal of a pluripotent state. Thus, we describe a general strategy by which cell types and tissues can be generated even with limited knowledge of the developmental pathways required for their specification in vivo.

Finding Synergy Networks From Gene Expression Data: A Fuzzy-Rule-Based Approach

Genes interact among themselves directly as well as indirectly, and thereby, a gene regulates the expression levels of other genes. In this work, our objective is to identify a special type of network called “synergy network.” We want to find synergistic gene pairs that interact via collaboration with respect to a disease and form a network of such synergistic genes. First, we discuss some issues related to existing information-theoretic methods of finding synergy networks and, then, propose a fuzzy-rule-based approach for discovery of synergy networks. We justify that fuzzy rule base is a natural choice to realize all the desired attributes of synergistic relations. To our knowledge, this is the first attempt to exploit fuzzy modeling for finding synergy networks. The system uses a set of human understandable rules that is generated at a low cost for every pair of genes. We apply our method on two prostate cancer datasets. We show that the proposed method is capable of discovering gene pairs that collaborate with each other with respect to prostate cancer. We demonstrate that our results are statistically significant. We also discuss the relevance of the identified genes to cancer biology.

Abstract 2657: Target specificity of epigenetic therapy in cancer

Abstract A major question facing the use of epigenetic therapies in cancer is specificity in modulating gene expression. In addition, combined targeting of DNA and histone methylation remains largely unexplored despite the promising synergistic effects observed from combining DNA methyltransferase inhibitors with HDAC inhibitors. To address these questions, we performed RNA-seq, DNA methylation analysis and ChIP-seq (H3K4me2, H3K9me2, and H3K27me3) to study the effects of inhibitors of DNA methyltransferases (DAC), histone deacetylases (Depsi), histone demethylases (KDM1A inhibitor S2101), and histone methylases (EHMT2 inhibitor UNC0638 and EZH2 inhibitor GSK343) in three different cancer models (colon cancer, breast cancer, and leukemia). In colon cancer cells (YB5), DAC affected 3% of the transcriptome and 93% of the effect was gene upregulation. DAC had a greater effect on genes expressed in normal tissues and silenced in cancer (443 genes) compared to genes that do not change in cancer (194 genes). 90% of DAC targets genes showed no promoter DNA methylation in normal colon but gained methylation in cancer. Depsi changed the expression of 35% of the transcriptome and showed little specificity for gene upregulation or silenced genes. S2101, UNC0638, and GSK343 had limited effects on their own (<1.5% of the transcriptome), but UNC0638 and GSK343 preferentially targeted genes with H3K9me2 or H3K27me3, respectively. DAC combined with histone methylation inhibitors led to synergistic gene upregulation while still maintaining specificity for DNA methylated and silenced genes. These synergistic genes had limited overlap, indicating the possibility to target distinct sets of genes based on different epigenetic therapy combinations. IPA analysis demonstrated that these genes are enriched in cancer pathways, and consistent with this analysis, the combination therapies were able to decrease cancer cell proliferation more effectively than monotherapy. Broadly similar results were seen with genome wide studies in both breast cancer (MCF7) and leukemia (HL-60) cells. These results demonstrate that DNA methyltransferase inhibitors preferentially target cancer relevant genes, and can be combined with inhibitors targeting histone methylation for synergistic effects while still maintaining specificity. Citation Format: Takahiro Sato, Matteo Cesaroni, Shoghag Panjarian, Anthony Tran, Jozef Madzo, Yasuyuki Okamoto, Hanghang Zhang, Xiaowei Chen, Jaroslav Jelinek, Jean-Pierre J. Issa. Target specificity of epigenetic therapy in cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2657.

Abstract 706: Chemotherapy and inflammation show a p65- and STAT3-dependent pro-tumorigenic potential

Abstract Tumor microenvironment (TME) plays a critical role in cancer progression and response to therapies. Therapeutic intervention strategies for cancer treatment can induce inflammation, resulting in changes in the TME. To investigate the response of cancer cells to chemotherapy in the context of an inflammatory microenvironment, we studied the effects driven by the chemotherapeutic drug doxorubicin, and the inflammatory cytokine TNFá. We previously demonstrated using expression microarrays experiments (validated also through qPCR) that the Doxo+TNFá combined treatment determined a strong up-regulation of migration-related genes as well as increased motility in breast cancer cells (MCF7). We confirmed the synergistic activation of a group of 6 different genes upon Doxo+TNFá combined treatment in different cell line models coming from different cancer types (A549-lung, U2OS-osteosarcoma). Moreover, we demonstrated that this effect was only partially p53-depenedent but strongly Doxo-dependent using p53 mutated MDA-MB-231 breast cancer cells or knocking-out p53 in MCF7 or U2OS cells through the cutting edge technology CRISPR/Cas9. We also demonstrated that the combined Doxo+TNFá treatment was not only able to disrupt the 3D architecture of mammary acini observed with MCF10A primary cells grown within matrigel, but also to stimulate the tube-forming potential of Human Umbilical Vein Endothelial Cells (HUVEC). Furthermore, a signature of Doxo+TNFá highly synergistic genes (DT-29) was shown to exhibit prognostic value for luminal breast cancer patients, with adverse outcome correlating with higher relative expression (based on Kaplan-Meier plotter tool). We further used available experimental data sets (RNA-seq measurements from Gene Expression Omnibus) both from breast cancer cell lines (luminal-like vs basal-like breast cancer cells) and breast cancer patients (ER positive vs Triple Negative Breast Cancer, TNBC and vs healthy adjacent tissues). The expression of DT-29 gene signature was analyzed and compared among the different groups of samples. The most statistically relevant genes differentially expressed among the different groups were involved in the STAT3-driven pathways and were prevalently highly expressed in TNBC patients and cell lines. In order to demonstrate a STAT3-dependent regulation of up-regulated genes upon Doxo+TNFá combined treatment, we pharmacologically inhibited STAT3 using Stattic or we knocked-out STAT3 using CRISPR/Cas9 in MCF7, MDA-MB-231 and U2OS cells. Results demonstrated a STAT3-dependent up-regulation for some of the selected genes and STAT3 inhibition resulted also in a reduced migration potential particularly in U2OS cells. We propose that the combined treatment with Doxo+TNFá can lead to the activation of specific gene expression programs that may impact on cancer phenotypes and potentially modify the efficacy of cancer therapy. Citation Format: Federica Alessandrini, Laura Pezze, Francesca Precazzini, Silvia D’Ambrosi, Dennis Pedri, Lia Pinto, Yari Ciribilli. Chemotherapy and inflammation show a p65- and STAT3-dependent pro-tumorigenic potential. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 706.

Discovery of synergistic genetic network: A minimum spanning tree-based approach.

Identification of gene interactions is one of the very well-known and important problems in the field of genetics. However, discovering synergistic gene interactions is a relatively new problem which has been proven to be as significant as the former in genetics. Several approaches have been proposed in this regard and most of them depend upon information theoretic measures. These approaches quantize the gene expression levels, explicitly or implicitly and therefore, may lose information. Here, we have proposed a novel approach for identifying synergistic gene interactions directly from the continuous expression levels, using a minimum spanning tree (MST)-based algorithm. We have used this approach to find pairs of synergistically interacting genes in prostate cancer. The advantages of our method are that it does not need any discretization and it can be extended straightway to find synergistically interacting sets of genes having three or more elements as per the requirement of the situation. We have demonstrated the relevance of the synergistic genes in cancer biology using KEGG pathway analysis and otherwise.

Abstract B42: Decitabine specifically targets genes that gain DNA methylation and lose expression in cancer and can be combined with histone methylation inhibitors for increased efficacy

Abstract A major question facing the use of epigenetic therapies in cancer is the specificity in reactivating gene expression. In addition, combined targeting of DNA and histone methylation remains largely unexplored despite the promising synergistic effects observed from combining DNA methyltransferase inhibitors with HDAC inhibitors. To address these questions, we performed RNA-seq in colon cancer (SW48) and breast cancer (MCF7) models treated with a DNA methyltransferase inhibitor (Decitabine/DAC) either alone or in combination with a LSD1 inhibitor (S2101), G9a inhibitor (UNC0638), EZH2 inhibitor (GSK343) or a HDAC inhibitor (Depsipeptide). Additionally, we performed ChIP-seq (H3K4me2, H3K9me2, and H3K27me3) and DNA methylation analysis (DREAM) on SW48 cells. Both low dose (100nM) and intermediate dose (1uM) DAC were very specific in upregulating genes with high levels of DNA methylation (86% and 78% of upregulated genes in SW48 cells, respectively). DAC alone and in combination with S2101, UNC0638, or GSK343 preferentially upregulated genes that were expressed in normal colon and silenced in SW48 cells (443, 718, 728, 1095 genes, respectively) compared to genes that didn't change expression (194, 343, 424, 479 genes, respectively). Additionally, the combination therapies specifically upregulated genes that were not DNA methylated in normal colon but gained methylation in cancer (91%, 85%, 87%, and 79% of upregulated genes, respectively). The combination therapies also preferentially upregulated genes that lost expression in cancer and were silenced by H3K9me2 and/or H3K27me3 (121, 204, 277, 380 genes, respectively) compared to genes that didn't change expression in cancer but were silenced by histone methylation (42, 82, 163, 167 genes, respectively). In contrast, Depsipeptide and the combination of Depsipeptide with DAC induced major expression changes in all genes (2167 and 2938 genes upregulated that were expressed in normal colon and silenced in SW48, compared to 1807 and 2389 genes upregulated with no change in expression, respectively). Furthermore, DAC in combination with S2101, UNC0638, or GSK343 induced a significant number of synergistic genes that were only upregulated with the combination therapy and not with either monotherapy (>490 genes for each inhibitor combination in both cancer models). These synergistic genes showed limited overlap between the three combination therapies (only 7.64% of genes commonly upregulated in YB5, 7.31% in MCF7), and formed distinct clusters in a hierarchical cluster analysis. IPA analysis demonstrated that these genes are enriched in critical cancer regulation pathways such as cell proliferation, cell death, and cell movement. Consistent with this analysis, the combination therapies were able to decrease cancer cell proliferation more effectively than monotherapy. Overall, these results demonstrate that DNA methyltransferase inhibitors preferentially target cancer relevant genes, and can be combined with epigenetic inhibitors targeting histone methylation to increase efficacy while still maintaining specificity. Citation Format: Takahiro Sato, Matteo Cesaroni, Anthony Tran, Jozef Madzo, Yasuyuki Okamoto, Hanghang Zhang, Shoghag Panjarian, Jaroslav Jelinek, Jean-Pierre Issa. Decitabine specifically targets genes that gain DNA methylation and lose expression in cancer and can be combined with histone methylation inhibitors for increased efficacy. [abstract]. In: Proceedings of the AACR Special Conference on Chromatin and Epigenetics in Cancer; Sep 24-27, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2016;76(2 Suppl):Abstract nr B42.

Abstract 419: Cooperative interactions between p53 and NFκB enhance cell plasticity

Abstract Breast cancer prevention, diagnosis and treatment have improved during recent years. However, despite these scientific and clinical improvements, breast cancer survival rate has not substantially increased, due to cancer recurrence and metastatic spread. It is therefore important to further investigate the mechanisms and causes responsible for breast cancer metastasis. The p53 and NFκB sequence-specific transcription factors (TFs) play crucial roles in cell proliferation and survival with critical, even if typically opposite, effects on cancer progression. To investigate a possible crosstalk between p53 and NFκB driven by chemotherapy-induced responses in the context of an inflammatory microenvironment, we performed a proof of concept study using MCF7 cells. Transcriptome analyses upon single or combined treatments with doxorubicin (Doxo) and the NFκB inducer Tumor Necrosis Factor-alpha (TNFα) were performed. Expression microarrays experiments identified a common set of synergistically up-regulated genes upon combined drug treatments in different cellular models of breast and lung carcinomas. This gene signature was enriched in inflammation, proliferation and metastasis gene functions, according to gene ontology and pathway analysis. Interestingly, the migratory phenotypes exhibited by our cell models significantly differed upon single and combined drug treatments and exhibited cell-type specificity. For a selected group of genes synergistically up-regulated upon Doxo+TNFα we were able to demonstrate by ChIP analysis a direct binding of p53 and NFκB and we uncovered LAMP3 and ETV7 as new p53-NFκB direct target genes. We also demonstrated that the combined Doxo+TNFα treatment was not only able to disrupt the 3D architecture of mammary acini observed with MCF10A primary cells grown within matrigel, but also to stimulate the tube-forming potential of Human Umbilical Vein Endothelial Cells (HUVEC). Furthermore, a signature of 29 Doxo+TNFα (DT-29) highly synergistic genes exhibited prognostic value for luminal breast cancer patients, with adverse outcome correlating with higher relative expression (based on Kaplan-Meier plotter tool). We further used available experimental data sets (RNA-seq measurements from Gene Expression Omnibus) both from breast cancer cell lines (luminal-like vs basal-like breast cancer cells) and breast cancer patients (ER positive vs Triple Negative Breast Cancer, TNBC and vs healthy adjacent tissues). The expression of DT-29 gene signature was analyzed and compared among the different groups of samples. The most statistically relevant genes differentially expressed among the different groups were involved in the STAT3-driven pathways and were prevalently highly expressed in TNBC patients and cell lines. We propose that the crosstalk between p53 and NFκB can lead to the activation of specific gene expression programs that may impact on cancer phenotypes and potentially modify the efficacy of cancer therapy. Citation Format: Federica Alessandrini, Vasundhara Sharma, Alessandra Bisio, Sara Zaccara, Alberto Inga, Yari Ciribilli. Cooperative interactions between p53 and NFκB enhance cell plasticity. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 419. doi:10.1158/1538-7445.AM2015-419