Integration Of Gene Expression Data Research Articles

Abstract A14: Modeling drug combination sensitivity with Hopfield networks and transcriptomics data

Abstract The Hopfield neural network model is one of the simplest models able to mathematically implement Waddington’s interpretation of normal and anomalous cell phenotypes as dynamical attractors of epigenetic landscapes. Here, we propose a computational approach based on Hopfield’s associative memories that integrate gene expression data and gene interactome networks in (1) a model representing the dynamics and control of disease progression in multiple myeloma (MM), and (2) a model describing the control of angiogenesis. The MM model is built using single-cell RNA-seq data from bone marrow aspirates of MM patients as well as patients diagnosed with monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), two medical conditions that often progress to full MM. We identify different clusters of MGUS, SMM, and MM cells, map them to Hopfield associative memory patterns, and model the dynamics of transition between the different patterns. The model is then used to identify combinations of genes whose simultaneous inhibition is associated with delayed disease progression. In the angiogenesis control model, we use single-cell RNA-seq data to predict specific combinations of targets for inhibition that could induce a cellular transition from tip-like to stalk-like endothelial cells, inhibiting the formation of capillary sprouts in blood vessels. The model generates novel hypotheses for combinations that could complement standard VEGF inhibitors and lead to a more efficient control of angiogenesis in cancer. Citation Format: Carlo Piermarocchi, Sergii Domanskyi, Alex Hakansson, Giovanni Paternostro. Modeling drug combination sensitivity with Hopfield networks and transcriptomics data [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr A14.

OSskcm: an online survival analysis webserver for skin cutaneous melanoma based on 1085 transcriptomic profiles

BackgroundCutaneous melanoma is one of the most aggressive and lethal skin cancers. It is greatly important to identify prognostic biomarkers to guide the clinical management. However, it is technically challenging for untrained researchers to process high dimensional profiling data and identify potential prognostic genes in profiling datasets.MethodsIn this study, we developed a webserver to analyze the prognostic values of genes in cutaneous melanoma using data from TCGA and GEO databases. The webserver is named Online consensus Survival webserver for Skin Cutaneous Melanoma (OSskcm) which includes 1085 clinical melanoma samples. The OSskcm is hosted in a windows tomcat server. Server-side scripts were developed in Java script. The database system is managed by a SQL Server, which integrates gene expression data and clinical data. The Kaplan–Meier (KM) survival curves, Hazard ratio (HR) and 95% confidence interval (95%CI) were calculated in a univariate Cox regression analysis.ResultsIn OSskcm, by inputting official gene symbol and selecting proper options, users could obtain KM survival plot with log-rank P value and HR on the output web page. In addition, clinical characters including race, stage, gender, age and type of therapy could also be included in the prognosis analysis as confounding factors to constrain the analysis in a subgroup of melanoma patients.ConclusionThe OSskcm is highly valuable for biologists and clinicians to perform the assessment and validation of new or interested prognostic biomarkers for melanoma. OSskcm can be accessed online at: http://bioinfo.henu.edu.cn/Melanoma/MelanomaList.jsp.

Integrative phenotypic and gene expression data identify myostatin as a muscle growth inhibitor in Chinese shrimp Fenneropenaeus chinensis

Growth traits, largely determined by muscle growth, are the most critical economic traits in shrimp breeding. Myostatin (Mstn) is a conserved inhibitor of muscle growth in vertebrates, but until now solid evidence supporting a similar function of Mstn in invertebrates has been lacking. In the present study, we examined the Mstn expression along with growth trait data in a Fenneropenaeus chinensis population, to establish a potential correlation between Mstn and growth. The heritabilities of FcMstn expression, body weight at 190 days of culture, body weight and length at 230 days of culture, and average daily gain were estimated using 773 individuals and a thirteen-generation pedigree. The results showed FcMstn expression was negatively correlated with the growth traits, and the mean FcMstn expression in females was significantly lower than that of males, indicating Mstn negatively regulates muscle growth in shrimp, and its lower expression may underscore the faster growth of females. Low heritabilities were detected for FcMstn expression, suggesting that the expression of Mstn might be heritable in shrimp. These results provide strong support for a growth inhibitory function of Mstn in F. chinensis, and suggest a potential method for selective breeding of this species without substantial experimental resources and labor force.

Abstract GS2-06: Primary results of SOLTI-1402/CORALLEEN phase 2 trial of neoadjuvant ribociclib plus letrozole versus chemotherapy in PAM50 Luminal B early breast cancer: An open-label, multicenter, two-arm, randomized study

Abstract Background: Different approaches for treatment de-escalation are being investigated; however, the current ongoing phase III adjuvant trials with CDK4/6 inhibitors are not addressing the question if these drugs can replace multi-agent chemotherapy in high-risk early breast cancer. Here, we present the primary results of the CORALLEEN phase 2 trial, which evaluates the efficacy of ribociclib plus endocrine therapy (ET) as neoadjuvant treatment in patients with high-risk Luminal B disease. Methods: CORALLEEN is a parallel, multicenter, two-arm, randomized exploratory study in postmenopausal women with primary operable hormone receptor-positive (HR+)/HER2-negative breast cancer, Luminal B by Prosigna®. Other eligibility criteria include stage I-III operable breast cancer and ECOG 0-1. Patients were randomized 1:1 to receive either six 28-days cycles of ribociclib (600mg; 3-weeks-on/1-week-off) plus daily letrozole (2.5mg) or chemotherapy (CT): 4 cycles of AC (doxorubicin 60 mg/m2 and cyclophosphamide 600 mg/m2 every 21 days) followed by weekly paclitaxel (80 mg/m2) during 12 weeks. Baseline, Day 15 on-treatment, and surgical specimens were collected for molecular characterization and evaluation of response. The primary endpoint is the rate of PAM50 Risk of Relapse (ROR)-low disease at surgery in each arm. PAM50 ROR score integrates gene expression data, tumor size, and nodal status to define a low-risk group in the adjuvant setting (i.e. >90% distant relapse-free survival at 10 years). ROR-low was defined using the standard cutpoints as <40 points if pathologically node-negative (at surgery) and <15 points if 1-3 positive nodes (at surgery). The trial was designed to estimate the rate of ROR-low disease at surgery in each arm without a formal comparison. A total of 47 evaluable patients per arm and an expected ROR-low rate of 25%, would allow a precision of the estimate between 11.5% and 12.4%. Secondary endpoints included safety, intrinsic subtype at surgery, residual cancer burden (RCB), and Preoperative Endocrine Prognostic Index (PEPI). Results: From July 2017 to November 2018, 198 patients were screened with Prosigna® across 21 sites in Spain. From these, 106 (54%) patients with Luminal B disease were recruited, and 96 (90.6%) completed treatment as planned. Main baseline patient characteristics were similar between both treatment arms: mean age 64, mean tumor size 3.8 cm, N+ 39%, mean Ki67 33.2%, and mean ROR score 72.9 (86.8% were ROR-high). A total of 101 (95.3%) surgical samples were analyzed. ROR-low rates at surgery in the ribociclib+ET and CT arms were 48% (95%CI 33.7-62.6) and 47.1% (95%CI 32.9-61.5), respectively. Intrinsic subtype conversion to Luminal A at surgery occurred in 88% of patients in the ribociclib+ET arm and in 84.3% in the CT arm. The rates of RCB0/1 and PEPI 0 in the ribociclib+ET arm were 8% (95%CI 2.2-19.2) and 24% (95%CI 13.1-38.2), respectively. The rates of RCB0/1 and PEPI 0 in the CT arm were 11.8% (95%CI 4.4-23.9) and 17.6% (95%CI 8.4-30.9). Grade 3-4 toxicities were observed in 54.9% of the patients in the ribociclib+ET arm and 69.2% in the CT arm. Additional correlative molecular analyses will be presented. Conclusions: Neoadjuvant ribociclib and letrozole in high-risk Luminal B breast cancer achieves similar rates of ROR-low disease at surgery as multi-agent chemotherapy. Future studies in high-risk early breast cancer evaluating the survival outcomes and quality of life of this combination in the absence of cytotoxic therapy are justified. Citation Format: Joaquín Gavilá, Cristina Saura, Tomás Pascual, Cristina Hernando, Montserrat Muñoz, Laia Paré, Blanca González Farré, Pedro Fernandez, Patricia Galván, Xavier González Farré, Mafalda Oliveira, Miguel Gil Gil, Miriam Arumi, Neus Ferrer Tur, Alvaro Montaño, Yan Izarzugaza, Antonio Llombart Cussac, Raquel Bratos, Santiago González, Eduardo Martínez, Sergio Hoyos, Beatriz Rojas, Juan Antonio Virizuela, Vanesa Ortega, Rafael López, Pamela Céliz, Eva Ciruelos, Patricia Villagrasa, Aleix Prat. Primary results of SOLTI-1402/CORALLEEN phase 2 trial of neoadjuvant ribociclib plus letrozole versus chemotherapy in PAM50 Luminal B early breast cancer: An open-label, multicenter, two-arm, randomized study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr GS2-06.

Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma

BackgroundGrowing evidence has suggested that immune-related genes play crucial roles in the development and progression of hepatocellular carcinoma (HCC). Nevertheless, the utility of immune-related genes for evaluating the prognosis of HCC patients are still lacking. The study aimed to explore gene signatures and prognostic values of immune-related genes in HCC.MethodsWe comprehensively integrated gene expression data acquired from 374 HCC and 50 normal tissues in The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) analysis and univariate Cox regression analysis were performed to identify DEGs that related to overall survival. An immune prognostic model was constructed using the Lasso and multivariate Cox regression analyses. Furthermore, Cox regression analysis was applied to identify independent prognostic factors in HCC. The correlation analysis between immune-related signature and immune cells infiltration were also investigated. Finally, the signature was validated in an external independent dataset.ResultsA total of 329 differentially expressed immune‐related genes were detected. 64 immune‐related genes were identified to be markedly related to overall survival in HCC patients using univariate Cox regression analysis. Then we established a TF-mediated network for exploring the regulatory mechanisms of these genes. Lasso and multivariate Cox regression analyses were applied to construct the immune-based prognostic model, which consisted of nine immune‐related genes. Further analysis indicated that this immune-related prognostic model could be an independent prognostic indicator after adjusting to other clinical factors. The relationships between the risk score model and immune cell infiltration suggested that the nine-gene signature could reflect the status of tumor immune microenvironment. The prognostic value of this nine-gene prognostic model was further successfully validated in an independent database.ConclusionsTogether, our study screened potential prognostic immune-related genes and established a novel immune-based prognostic model of HCC, which not only provides new potential prognostic biomarkers and therapeutic targets, but also deepens our understanding of tumor immune microenvironment status and lays a theoretical foundation for immunotherapy.

A new method for mining information of co-expression network based on multi-cancers integrated data

BackgroundGene co-expression network is a favorable method to reveal the nature of disease. With the development of cancer, the way to build gene co-expression networks based on cancer data has been become a hot spot. However, there are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction.MethodsIn this paper, we introduce a new method for mining information of co-expression network based on multi-cancers integrated data, named PMN. We construct the network by combining the different types of relevant measures (linear and nonlinear rules) for different nodes based on integrated gene expression data of multi-cancers from The Cancer Genome Atlas (TCGA). For mining genes, we combine different properties (local and global characteristics) of the nodes.ResultsWe uncover more suspicious abnormally expressed genes and shared pathways of different cancers. And we have also found some proven genes and pathways; of course, there are some suspicious factors and molecules that need clinical validation.ConclusionsThe results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.

Identification of candidate cancer drivers by integrative Epi-DNA and Gene Expression (iEDGE) data analysis

The emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators – such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others – as predictive features of cancer outcome. However, identification of “driver genes” associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively. iEDGE first identifies the cis and trans gene expression signatures associated with the presence/absence of a particular epi-DNA alteration across samples. It then applies tests of statistical mediation to determine the cis genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway enrichment analysis to gain insights into the underlying regulatory networks. We used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers identified by iEDGE were found to be significantly enriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the analyses results are accessible at https://montilab.bu.edu/iEDGE. In summary, integrative analysis of SCNAs and gene expression using iEDGE successfully identified known cancer driver genes and putative cancer therapeutic targets across 19 cancer types in the TCGA. The proposed method can easily be applied to the integration of gene expression profiles with other epi-DNA assays in a variety of disease contexts.

Identification of gene specific cis-regulatory elements during differentiation of mouse embryonic stem cells: An integrative approach using high-throughput datasets.

Gene expression governs cell fate, and is regulated via a complex interplay of transcription factors and molecules that change chromatin structure. Advances in sequencing-based assays have enabled investigation of these processes genome-wide, leading to large datasets that combine information on the dynamics of gene expression, transcription factor binding and chromatin structure as cells differentiate. While numerous studies focus on the effects of these features on broader gene regulation, less work has been done on the mechanisms of gene-specific transcriptional control. In this study, we have focussed on the latter by integrating gene expression data for the in vitro differentiation of murine ES cells to macrophages and cardiomyocytes, with dynamic data on chromatin structure, epigenetics and transcription factor binding. Combining a novel strategy to identify communities of related control elements with a penalized regression approach, we developed individual models to identify the potential control elements predictive of the expression of each gene. Our models were compared to an existing method and evaluated using the existing literature and new experimental data from embryonic stem cell differentiation reporter assays. Our method is able to identify transcriptional control elements in a gene specific manner that reflect known regulatory relationships and to generate useful hypotheses for further testing.

Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease

Identifying methylation quantitative trait loci (meQTLs) and integrating them with disease-associated variants from genome-wide association studies (GWAS) may illuminate functional mechanisms underlying genetic variant-disease associations. Here, we perform GWAS of >415 thousand CpG methylation sites in whole blood from 4170 individuals and map 4.7 million cis- and 630 thousand trans-meQTL variants targeting >120 thousand CpGs. Independent replication is performed in 1347 participants from two studies. By linking cis-meQTL variants with GWAS results for cardiovascular disease (CVD) traits, we identify 92 putatively causal CpGs for CVD traits by Mendelian randomization analysis. Further integrating gene expression data reveals evidence of cis CpG-transcript pairs causally linked to CVD. In addition, we identify 22 trans-meQTL hotspots each targeting more than 30 CpGs and find that trans-meQTL hotspots appear to act in cis on expression of nearby transcriptional regulatory genes. Our findings provide a powerful meQTL resource and shed light on DNA methylation involvement in human diseases.

P13CMFA: Parsimonious 13C metabolic flux analysis.

Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are 13C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions. However, 13C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious 13C MFA (p13CMFA), an approach that runs a secondary optimization in the 13C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with 13C data. As proof of concept, we demonstrate how p13CMFA can be used to estimate intracellular flux distributions from 13C measurements and transcriptomics data. We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).

Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits

Transcriptome-wide association studies integrate gene expression data with common risk variation to identify gene-trait associations. By incorporating epigenome data to estimate the functional importance of genetic variation on gene expression, we generate a small but significant improvement in the accuracy of transcriptome prediction and increase the power to detect significant expression-trait associations. Joint analysis of 14 large-scale transcriptome datasets and 58 traits identify 13,724 significant expression-trait associations that converge on biological processes and relevant phenotypes in human and mouse phenotype databases. We perform drug repurposing analysis and identify compounds that mimic, or reverse, trait-specific changes. We identify genes that exhibit agonistic pleiotropy for genetically correlated traits that converge on shared biological pathways and elucidate distinct processes in disease etiopathogenesis. Overall, this comprehensive analysis provides insight into the specificity and convergence of gene expression on susceptibility to complex traits.

Stoichiometric gene-to-reaction associations enhance model-driven analysis performance: Metabolic response to chronic exposure to Aldrin in prostate cancer

BackgroundGenome-scale metabolic models (GSMM) integrating transcriptomics have been widely used to study cancer metabolism. This integration is achieved through logical rules that describe the association between genes, proteins, and reactions (GPRs). However, current gene-to-reaction formulation lacks the stoichiometry describing the transcript copies necessary to generate an active catalytic unit, which limits our understanding of how genes modulate metabolism. The present work introduces a new state-of-the-art GPR formulation that considers the stoichiometry of the transcripts (S-GPR). As case of concept, this novel gene-to-reaction formulation was applied to investigate the metabolic effects of the chronic exposure to Aldrin, an endocrine disruptor, on DU145 prostate cancer cells. To this aim we integrated the transcriptomic data from Aldrin-exposed and non-exposed DU145 cells through S-GPR or GPR into a human GSMM by applying different constraint-based-methods.ResultsOur study revealed a significant improvement of metabolite consumption/production predictions when S-GPRs are implemented. Furthermore, our computational analysis unveiled important alterations in carnitine shuttle and prostaglandine biosynthesis in Aldrin-exposed DU145 cells that is supported by bibliographic evidences of enhanced malignant phenotype.ConclusionsThe method developed in this work enables a more accurate integration of gene expression data into model-driven methods. Thus, the presented approach is conceptually new and paves the way for more in-depth studies of aberrant cancer metabolism and other diseases with strong metabolic component with important environmental and clinical implications.

Predicting Metabolism from Gene Expression in an Improved Whole-Genome Metabolic Network Model of Danio rerio.

Zebrafish is a useful modeling organism for the study of vertebrate development, immune response, and metabolism. Metabolic studies can be aided by mathematical reconstructions of the metabolic network of zebrafish. These list the substrates and products of all biochemical reactions that occur in the zebrafish. Mathematical techniques such as flux-balance analysis then make it possible to predict the possible metabolic flux distributions that optimize, for example, the turnover of food into biomass. The only available genome-scale reconstruction of zebrafish metabolism is ZebraGEM. In this study, we present ZebraGEM 2.0, an updated and validated version of ZebraGEM. ZebraGEM 2.0 is extended with gene-protein-reaction associations (GPRs) that are required to integrate genetic data with the metabolic model. To demonstrate the use of these GPRs, we performed an in silico genetic screening for knockouts of metabolic genes and validated the results against published in vivo genetic knockout and knockdown screenings. Among the single knockout simulations, we identified 74 essential genes, whose knockout stopped growth completely. Among these, 11 genes are known have an abnormal knockout or knockdown phenotype in vivo (partial), and 41 have human homologs associated with metabolic diseases. We also added the oxidative phosphorylation pathway, which was unavailable in the published version of ZebraGEM. The updated model performs better than the original model on a predetermined list of metabolic functions. We also determined a minimal feed composition. The oxidative phosphorylation pathways were validated by comparing with published experiments in which key components of the oxidative phosphorylation pathway were pharmacologically inhibited. To test the utility of ZebraGEM2.0 for obtaining new results, we integrated gene expression data from control and Mycobacterium marinum-infected zebrafish larvae. The resulting model predicts impeded growth and altered histidine metabolism in the infected larvae.

Machine learning approaches to predict lupus disease activity from gene expression data

The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms. Classifiers were evaluated by 10-fold cross-validation across three combined data sets or by training and testing in independent data sets, the latter of which amplified the effects of technical variation. A random forest classifier achieved a peak classification accuracy of 83 percent under 10-fold cross-validation, but its performance could be severely affected by technical variation among data sets. The use of gene modules rather than raw gene expression was more robust, achieving classification accuracies of approximately 70 percent regardless of how the training and testing sets were formed. Fine-tuning the algorithms and parameter sets may generate sufficient accuracy to be informative as a standalone estimate of disease activity.

Abstract 3474: Loss of function mutations in APOB and their clinical significance in hepatocellular carcinoma

Abstract APOB is major lipid binding proteins of low-density lipoprotein (LDL) particles and responsible for carrying fat molecules to cells within all tissues. High levels of APOB are known to be related to heart disease and vascular disease. Interestingly, recent The Cancer Genome Atlas (TCGA) study revealed that APOB is one of frequently mutated genes in multiple cancers including melanoma, liver cancer, stomach, esophageal, head and neck, uterine, and lung cancers (from >20 to 10%). Majority mutations are considered to cause loss of function as they are truncation mutations, suggesting that APOB might have tumor suppressive activity. However, because of its physiological roles in circulating fat molecules in our body, APOB has never been studied in cancer-related setting and thus its clinical relevance or biological roles of APOB in cancer development is currently unknown. Here we show that loss of APOB in hepatocellular carcinoma (HCC) is significantly associated with poor survival of HCC patients by applying comparative genomics approach that integrate genomic data from mouse and human HCC tumors. We further show that loss of APOB leads to shifting balance of lipid metabolisms favoring for tumor growth. For development of genomic signature reflecting hepatic APOB activity and test and validation of its association with prognosis, we used unsupervised approach combined with supervised prediction models by integrating gene expression data from mouse models and human HCC. When HCC patients were stratified into APOB-active and APOB-inactive subgroups, overall survival (OS) rate and recurrence free survival (RFS) rate of patients in APOB-inactive group is significantly lower than those in APOB-active (P < 0.001), indicating that APOB activity in HCC is significantly associated with prognosis of patients. This association was validated in 4 independent cohorts of HCC patients (n=88, 240, 242, and 371 in total of 941 patients). Gene network analysis revealed that many of the regulators involved in cell growth were activated in Apob-silenced livers. Many of the upregulated genes in Apob-silenced livers are direct downstream targets of Erbb2, Vegfa, and regulator of metastasis and cancer initiation cells Cd44. Consistent with activation of many tumor-promoting regulators, Tp53, a major tumor suppressor, was inactivated in Apob-silenced livers, suggesting that loss of Apob in the liver may provoke initiation of tumor development. More interestingly, proliferation of three liver cancer cells was significantly increased when expression of APOB was silenced by siRNAs, further suggesting functional roles of APOB in tumor development. For the first time, we showed that APOB inactivation has a clinical impact in HCC patients with significant alteration in regulators associated with tumorigenesis. Citation Format: Gena Lee, Yun Seong Jeong, Min Jun Kwak, Kim DoWon, Ju-Seog Lee, Yim Sun Young. Loss of function mutations in APOB and their clinical significance in hepatocellular carcinoma [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 3474.

Transcriptome Analysis of Rlm2-Mediated Host Immunity in the Brassica napus-Leptosphaeria maculans Pathosystem.

Our study investigated disease resistance in the Brassica napus-Leptosphaeria maculans pathosystem using a combination of laser microdissection, dual RNA sequencing, and physiological validations of large-scale gene sets. The use of laser microdissection improved pathogen detection and identified putative L. maculans effectors and lytic enzymes operative during host colonization. Within 24 h of inoculation, we detected large shifts in gene activity in resistant cotyledons associated with jasmonic acid and calcium signaling pathways that accelerated the plant defense response. Sequencing data were validated through the direct quantification of endogenous jasmonic acid levels. Additionally, resistance against L. maculans was abolished when the calcium chelator EGTA was applied to the inoculation site, providing physiological evidence of the role of calcium in B. napus immunity against L. maculans. We integrated gene expression data with all available information on cis-regulatory elements and transcription factor binding affinities to better understand the gene regulatory networks underpinning plant resistance to hemibiotrophic pathogens. These in silico analyses point to early cellular reprogramming during host immunity that are coordinated by CAMTA, BZIP, and bHLH transcription factors. Together, we provide compelling genetic and physiological evidence into the programming of plant resistance against fungal pathogens.

A dynamic programing approach to integrate gene expression data and network information for pathway model generation.

As large amounts of biological data continue to be rapidly generated, a major focus of bioinformatics research has been aimed toward integrating these data to identify active pathways or modules under certain experimental conditions or phenotypes. Although biologically significant modules can often be detected globally by many existing methods, it is often hard to interpret or make use of the results toward pathway model generation and testing. To address this gap, we have developed the IMPRes algorithm, a new step-wise active pathway detection method using a dynamic programing approach. IMPRes takes advantage of the existing pathway interaction knowledge in Kyoto Encyclopedia of Genes and Genomes. Omics data are then used to assign penalties to genes, interactions and pathways. Finally, starting from one or multiple seed genes, a shortest path algorithm is applied to detect downstream pathways that best explain the gene expression data. Since dynamic programing enables the detection one step at a time, it is easy for researchers to trace the pathways, which may lead to more accurate drug design and more effective treatment strategies. The evaluation experiments conducted on three yeast datasets have shown that IMPRes can achieve competitive or better performance than other state-of-the-art methods. Furthermore, a case study on human lung cancer dataset was performed and we provided several insights on genes and mechanisms involved in lung cancer, which had not been discovered before. IMPRes visualization tool is available via web server at http://digbio.missouri.edu/impres. Supplementary data are available at Bioinformatics online.

S858 SUZ12 INACTIVATION COOPERATES WITH JAK3 MUTANT SIGNALING TO DRIVE T‐ALL DEVELOPMENT

Background:The PRC2 complex, with core components EZH2, SUZ12 and EED, is responsible for writing the H3K27me3 histone mark that is associated with gene repression. Sequence data analysis of 419 T‐cell acute lymphoblastic leukemia (T‐ALL) cases demonstrated that 14.3% of the cases harbored mutations in EZH2 (7.2%), SUZ12 (6.2%) and/or EED (3.8%). A significant positive association was found between SUZ12 loss‐of function mutations and JAK3 activating mutations.Aims:The goals of our study were to assess whether SUZ12 inactivation cooperates with JAK3 mutations in driving T‐ALL development, and to unravel the mechanism of oncogenic cooperation.Methods:We applied CRISPR gene editing technology to mutate endogenous Suz12 in mouse cells. For cell culture experiments, we used primary pro‐T‐cells derived from Cas9 transgenic mice that were retrovirally transduced with an activating JAK3(M511I) mutant, a Suz12 guide RNA (Suz12gRNA) or the combination of both. To generate mouse models, we performed bone marrow transplantations with hematopoietic stem/progenitor cells from Cas9 transgenic mice that were transduced with JAK3(M511I), Suz12gRNA or both. These cells were injected in wild type recipient mice and leukemia development was followed. RNA‐seq, ATAC‐seq and ChIP‐seq was performed on leukemia cells. We performed a drug screen with 189 inhibitors targeting a variety of epigenetic factors and signaling molecules on the mouse leukemia cells ex vivo.Results:The combined expression of JAK3(M511I) with Suz12gRNA conferred IL7‐independent growth to ex vivo cultured pro‐T cells, whereas JAK3(M511I) or Suz12gRNA alone did not, indicating direct cooperation between mutant JAK3 signaling and Suz12 inactivation. Moreover, in mouse bone marrow transplant leukemia models, combined expression of JAK3(M511I) and Suz12gRNA resulted in accelerated leukemia development with a mean latency of 74 days, compared to JAK3(M511I) or Suz12gRNA alone which generated leukemias with longer latency (112 to 150 days, p < 0.001). JAK3(M511I)+Suz12gRNA leukemias were CD8+ or CD4+CD8+ T‐cell leukemias and were transplantable. Effective Suz12 gene editing was confirmed by sequence analysis, Western blotting and intracellular flow cytometry documenting very low Suz12 and H3K27me3 levels. Both in vitro and in vivo data thus showed that inactivation of Suz12 cooperates with JAK3(M511I). Integration of gene expression data with ChIP‐seq data for Suz12 and H3K27me3, revealed increased expression of Wnt signaling components and Wnt target genes, as well as activation of the PI3K‐AKT‐mTOR signaling pathway in the JAK3(M511I)+Suz12gRNA leukemia cells compared to JAK3(M511I) leukemia cells. In addition, a drug screen on JAK3(M511I)+Suz12gRNA leukemia cells revealed increased sensitivity for PI3K, mTOR and Wnt signaling inhibitors, which is in line with upregulation of Pik3cb, Akt3 and Wnt signaling genes detected by RNA‐seq and ChIP‐seq. Surprisingly, this pharmacological approach also uncovered an increased sensitivity of the JAK3(M511I)+Suz12gRNA leukemia cells to HDAC6 inhibitors compared to JAK3(M511I) leukemia cells.Summary/Conclusion:We have shown that Suz12 inactivation cooperates with JAK3 mutant signaling to drive T‐ALL through upregulation of Wnt and PI3K/mTOR pathways. Furthermore, we were also able to identify specific vulnerabilities of the JAK3/Suz12 mutant leukemia cells, as they showed increased sensitivity to Wnt, PI3K/mTOR and HDAC6 inhibitors.

2235-PUB: Detection and Validation of Hidden Driven Networks in Diabetic Microangiopathy

Aim: To find the molecular interact network specific for different diabetic microangiopathy complications that will direct us to research the mechanism involved in the pathological process of those complications. At present, the plentiful multiomics data of microvascular complications allow us to construct multidimentional regulator network for different microvascular complications from a integrated system perspective. In this study, we introduced hierarchical Bayesian Mode, drivenRN, to decode multi-regulation that form inner regulatory networks in different microvascular complications by combining multi-regulator and transcriptional kinetics into one single model framework, drivenRN can naturally integrate gene expression data and regulator-binding signals in order to identify the hidden driven networks and to detect the key function gene signature. Our study finds out the specific driven networks related to diabetic nephropathy, diabetic retinopathy and diabetic feet respectively. These complication specific driven networks not only offer a new kind biomarker for susceptible patients’ identification but also help us to research the pathological process of microvascular complications systematically. Then offer potential therapy targets and forewarning mechanisms for clinical practice. Figure 1. The Specific Driven Networks for Different Microvascular Complications of Diabetes Mellitus. Disclosure Y. Chen: None. H. Li: None. D. Guan: None. Funding National Key Research and Development Program of China (2017YFA0105803); National Natural Science Foundation of China (81770826); Sun Yat-sen University (2015015); Science and Technology Plan Projects of Guangdong Province (2016A050502010); Key Special Projects of Medical and Health Collaborative Innovation of Guangzhou City (201604020016); Special Scientific Research Project of Guangzhou City (2060404)

Detecting Diagnostic Biomarkers of Alzheimer's Disease by Integrating Gene Expression Data in Six Brain Regions.

Alzheimer's disease (AD) is a neurodegenerative and progressive disease, which often causes irreversible damages to the cerebrum. The pathogenesis of AD is far from being fully understood, while there are some popular hypotheses. So far, the diagnosis of AD relies only on clinical screening in the form of imaging techniques or cerebrospinal fluid analysis, which may lead to inaccurate evaluation and then cause the delay of suitable treatments. While molecular biomarkers provide promising alternatives of establishing correct relationships between genotypes and phenotypes of clinical symptoms. In this paper, we propose a machine-learning-based method of identifying potential diagnostic biomarkers of AD based on gene coexpression network by integrating gene expression profiles in six brain regions. After building an integrated gene coexpression network of multiple brain regions, we decompose the differential network into some subnetwork modules. The module candidates from these coexpressed gene communities are then identified by screening their discriminative powers in control from disease samples. The potential biomarkers are then validated by multiple cross-validations and functional enrichment analyses. If the biomarkers successfully pass clinical significance tests, they can be used as a reference for clinical diagnosis after wet-experimental validations.