Allele-specific control of rodent and human lncRNA KMT2E-AS1 promotes hypoxic endothelial pathology in pulmonary hypertension

Hypoxic reprogramming of vasculature relies on genetic, epigenetic, and metabolic circuitry, but the control points are unknown. In pulmonary arterial hypertension (PAH), a disease driven by hypoxia inducible factor (HIF)-dependent vascular dysfunction, HIF-2α promoted expression of neighboring genes, long noncoding RNA (lncRNA) histone lysine N-methyltransferase 2E-antisense 1 (KMT2E-AS1) and histone lysine N-methyltransferase 2E (KMT2E). KMT2E-AS1 stabilized KMT2E protein to increase epigenetic histone 3 lysine 4 trimethylation (H3K4me3), driving HIF-2α-dependent metabolic and pathogenic endothelial activity. This lncRNA axis also increased HIF-2α expression across epigenetic, transcriptional, and posttranscriptional contexts, thus promoting a positive feedback loop to further augment HIF-2α activity. We identified a genetic association between rs73184087, a single-nucleotide variant (SNV) within a KMT2E intron, and disease risk in PAH discovery and replication patient cohorts and in a global meta-analysis. This SNV displayed allele (G)-specific association with HIF-2α, engaged in long-range chromatin interactions, and induced the lncRNA-KMT2E tandem in hypoxic (G/G) cells. In vivo, KMT2E-AS1 deficiency protected against PAH in mice, as did pharmacologic inhibition of histone methylation in rats. Conversely, forced lncRNA expression promoted more severe PH. Thus, the KMT2E-AS1/KMT2E pair orchestrates across convergent multi-ome landscapes to mediate HIF-2α pathobiology and represents a key clinical target in pulmonary hypertension.

Creation of a Curated Database of Experimentally Determined Human Protein Structures for the Identification of Its Targetome

Assembling an "integrated structural map of the human cell" at atomic resolution will require a complete set of all human protein structures available for interaction with other biomolecules - the human protein structure targetome - and a pipeline of automated tools that allow quantitative analysis of millions of protein-ligand interactions. Toward this goal, we here describe the creation of a curated database of experimentally determined human protein structures. Starting with the sequences of 20,422 human proteins, we selected the most representative structure for each protein (if available) from the protein database (PDB), ranking structures by coverage of sequence by structure, depth (the difference between the final and initial residue number of each chain), resolution, and experimental method used to determine the structure. To enable expansion into an entire human targetome, we docked small molecule ligands to our curated set of protein structures. Using design constraints derived from comparing structure assembly and ligand docking results obtained with challenging protein examples, we here propose to combine this curated database of experimental structures with AlphaFold predictions and multi-domain assembly using DEMO2 in the future. To demonstrate the utility of our curated database in identification of the human protein structure targetome, we used docking with AutoDock Vina and created tools for automated analysis of affinity and binding site locations of the thousands of protein-ligand prediction results. The resulting human targetome, which can be updated and expanded with an evolving curated database and increasing numbers of ligands, is a valuable addition to the growing toolkit of structural bioinformatics.

The divergence of mean phenotypes under persistent directional selection

Numerous organismal traits, particularly at the cellular level, are likely to be under persistent directional selection across phylogenetic lineages. Unless all mutations affecting such traits have large enough effects to be efficiently selected in all species, gradients in mean phenotypes are expected to arise as a consequence of differences in the power of random genetic drift, which varies by approximately five orders of magnitude across the Tree of Life. Prior theoretical work examining the conditions under which such gradients can arise focused on the simple situation in which all genomic sites affecting the trait have identical and constant mutational effects. Here, we extend this theory to incorporate the more biologically realistic situation in which mutational effects on a trait differ among nucleotide sites. Pursuit of such modifications leads to the development of semi-analytic expressions for the ways in which selective interference arises via linkage effects in single-effects models, which then extend to more complex scenarios. The theory developed clarifies the conditions under which mutations of different selective effects mutually interfere with each others' fixation and shows how variance in effects among sites can substantially modify and extend the expected scaling relationships between mean phenotypes and effective population sizes.

Computational repurposing of therapeutic small molecules from cancer to pulmonary hypertension

Cancer therapies are being considered for treating rare noncancerous diseases like pulmonary hypertension (PH), but effective computational screening is lacking. Via transcriptomic differential dependency analyses leveraging parallels between cancer and PH, we mapped a landscape of cancer drug functions dependent upon rewiring of PH gene clusters. Bromodomain and extra-terminal motif (BET) protein inhibitors were predicted to rely upon several gene clusters inclusive of galectin-8 (LGALS8). Correspondingly, LGALS8 was found to mediate the BET inhibitor–dependent control of endothelial apoptosis, an essential role for PH in vivo. Separately, a piperlongumine analog’s actions were predicted to depend upon the iron-sulfur biogenesis gene ISCU. Correspondingly, the analog was found to inhibit ISCU glutathionylation, rescuing oxidative metabolism, decreasing endothelial apoptosis, and improving PH. Thus, we identified crucial drug-gene axes central to endothelial dysfunction and therapeutic priorities for PH. These results establish a wide-ranging, network dependency platform to redefine cancer drugs for use in noncancerous conditions.

Frataxin deficiency promotes endothelial senescence in pulmonary hypertension

The dynamic regulation of endothelial pathophenotypes in pulmonary hypertension (PH) remains undefined. Cellular senescence is linked to PH with intracardiac shunts; however, its regulation across PH subtypes is unknown. Since endothelial deficiency of iron-sulfur (Fe-S) clusters is pathogenic in PH, we hypothesized that a Fe-S biogenesis protein, frataxin (FXN), controls endothelial senescence. An endothelial subpopulation in rodent and patient lungs across PH subtypes exhibited reduced FXN and elevated senescence. In vitro, hypoxic and inflammatory FXN deficiency abrogated activity of endothelial Fe-S-containing polymerases, promoting replication stress, DNA damage response, and senescence. This was also observed in stem cell-derived endothelial cells from Friedreich's ataxia (FRDA), a genetic disease of FXN deficiency, ataxia, and cardiomyopathy, often with PH. In vivo, FXN deficiency-dependent senescence drove vessel inflammation, remodeling, and PH, whereas pharmacologic removal of senescent cells in Fxn-deficient rodents ameliorated PH. These data offer a model of endothelial biology in PH, where FXN deficiency generates a senescent endothelial subpopulation, promoting vascular inflammatory and proliferative signals in other cells to drive disease. These findings also establish an endothelial etiology for PH in FRDA and left heart disease and support therapeutic development of senolytic drugs, reversing effects of Fe-S deficiency across PH subtypes.

Matrix stiffening induces a pathogenic QKI-miR-7-SRSF1 signaling axis in pulmonary arterial endothelial cells

Pulmonary arterial hypertension (PAH) refers to a set of heterogeneous vascular diseases defined by elevation of pulmonary arterial pressure (PAP) and pulmonary vascular resistance (PVR), leading to right ventricular (RV) remodeling and often death. Early increases in pulmonary artery stiffness in PAH drive pathogenic alterations of pulmonary arterial endothelial cells (PAECs), leading to vascular remodeling. Dysregulation of microRNAs can drive PAEC dysfunction. However, the role of vascular stiffness in regulating pathogenic microRNAs in PAH is incompletely understood. Here, we demonstrated that extracellular matrix (ECM) stiffening downregulated miR-7 levels in PAECs. The RNA-binding protein quaking (QKI) has been implicated in the biogenesis of miR-7. Correspondingly, we found that ECM stiffness upregulated QKI, and QKI knockdown led to increased miR-7. Downstream of the QKI-miR-7 axis, the serine and arginine-rich splicing factor 1 (SRSF1) was identified as a direct target of miR-7. Correspondingly, SRSF1 was reciprocally upregulated in PAECs exposed to stiff ECM and was negatively correlated with miR-7. Decreased miR-7 and increased QKI and SRSF1 were observed in lungs from patients with PAH and PAH rats exposed to SU5416/hypoxia. Lastly, miR-7 upregulation inhibited human PAEC migration, whereas forced SRSF1 expression reversed this phenotype, proving that miR-7 depended upon SRSF1 to control migration. In aggregate, these results define the QKI-miR-7-SRSF1 axis as a mechanosensitive mechanism linking pulmonary arterial vascular stiffness to pathogenic endothelial function. These findings emphasize implications relevant to PAH and suggest the potential benefit of developing therapies that target this miRNA-dependent axis in PAH.

SCUBE1 Controls BMPR2-Relevant Pulmonary Endothelial Function: Implications for Diagnostic Marker Development in Pulmonary Arterial Hypertension

Utilizing publicly available ribonucleic acid sequencing data, we identified SCUBE1 as a BMPR2-related gene differentially expressed between induced pluripotent stem cell-endothelial cells derived from pulmonary arterial hypertension (PAH) patients carrying pathogenic BMPR2 mutations and control patients without mutations. Endothelial SCUBE1 expression was decreased by known triggers of PAH, and its down-regulation recapitulated known BMPR2-associated endothelial pathophenotypes in vitro. Meanwhile, SCUBE1 concentrations were reduced in plasma obtained from PAH rodent models and patients with PAH, whereas plasma concentrations were tightly correlated with hemodynamic markers of disease severity. Taken together, these data implicate SCUBE1 as a novel contributor to PAH pathogenesis with potential therapeutic, diagnostic, and prognostic applications.

A Comprehensive Study of the Bridge Site and Substrate Relaxation Asymmetry for Methanethiol Adsorption on Au(111) at Low Coverage

We use dispersion-corrected density functional theory to explore the bridge-site asymmetry for methanethiol adsorbed on Au(111) with two different S-C bond orientations. We attribute the asymmetry to the intrinsic character of the Au(111) surface rather than the adsorbate. The preference for bridge-fcc versus bridge-hcp SCH3 adsorption sites is controlled by the S-C bond orientation. The system energy difference favors the bridge-fcc site by 8.1 meV on the unrelaxed Au(111) surface. Relaxing the Au substrate increased this energy difference to 26.1 meV. This asymmetry is also reflected in the atomic displacement of the relaxed Au surface. Although in both cases, the bridge-site Au atoms shift away from the fcc 3-fold hollow site, the motion is greater for the bridge-fcc allowing a more favorable geometry for the sulfur atom to bond to the bridging atoms. We confirm that the adsorption energy is strongly dependent on the S-C bond orientation and position, which can be understood in terms of a simple coordination geometry model. This work has important implications for alkanethiol surface diffusion and the structure of their self-assembled monolayers.

Systems Analysis of the Human Pulmonary Arterial Hypertension Lung Transcriptome

Pulmonary arterial hypertension (PAH) is characterized by increased pulmonary artery pressure and vascular resistance, typically leading to right heart failure and death. Current therapies improve quality of life of the patients but have a modest effect on long-term survival. A detailed transcriptomics and systems biology view of the PAH lung is expected to provide new testable hypotheses for exploring novel treatments. We completed transcriptomics analysis of PAH and control lung tissue to develop disease-specific and clinical data/tissue pathology gene expression classifiers from expression datasets. Gene expression data were integrated into pathway analyses. Gene expression microarray data were collected from 58 PAH and 25 control lung tissues. The strength of the dataset and its derived disease classifier was validated using multiple approaches. Pathways and upstream regulators analyses was completed with standard and novel graphical approaches. The PAH lung dataset identified expression patterns specific to PAH subtypes, clinical parameters, and lung pathology variables. Pathway analyses indicate the important global role of TNF and transforming growth factor signaling pathways. In addition, novel upstream regulators and insight into the cellular and innate immune responses driving PAH were identified. Finally, WNT-signaling pathways may be a major determinant underlying the observed sex differences in PAH. This study provides a transcriptional framework for the PAH-diseased lung, supported by previously reported findings, and will be a valuable resource to the PAH research community. Our investigation revealed novel potential targets and pathways amenable to further study in a variety of experimental systems.

Sex-specific gene and pathway modeling of inherited glioma risk

Background

To date, genome-wide association studies (GWAS) have identified 25 risk variants for glioma, explaining 30% of heritable risk. Most histologies occur with significantly higher incidence in males, and this difference is not explained by currently known risk factors. A previous GWAS identified sex-specific glioma risk variants, and this analysis aims to further elucidate risk variation by sex using gene- and pathway-based approaches.

Methods

Results from the Glioma International Case-Control Study were used as a testing set, and results from 3 GWAS were combined via meta-analysis and used as a validation set. Using summary statistics for nominally significant autosomal SNPs (P < 0.01 in a previous meta-analysis) and nominally significant X-chromosome SNPs (P < 0.01), 3 algorithms (Pascal, BimBam, and GATES) were used to generate gene scores, and Pascal was used to generate pathway scores. Results were considered statistically significant in the discovery set when P < 3.3 × 10-6 and in the validation set when P < 0.001 in 2 of 3 algorithms.

Results

Twenty-five genes within 5 regions and 19 genes within 6 regions reached statistical significance in at least 2 of 3 algorithms in males and females, respectively. EGFR was significantly associated with all glioma and glioblastoma in males only and a female-specific association in TERT, all of which remained nominally significant after conditioning on known risk loci. There were nominal associations with the BioCarta telomeres pathway in both males and females.

Conclusions

These results provide additional evidence that there may be differences by sex in genetic risk for glioma. Additional analyses may further elucidate the biological processes through which this risk is conferred.

Abstract A32: Comprehensive analysis of molecular pathogenesis of multiple myeloma by genetic ancestry

Introduction: Multiple Myeloma (MM) is a complex malignancy of plasma cells triggered by immunoglobulin gene rearrangements and well-described hyperdyploidy, accounting for slightly more than 10% of all hematologic cancers. MM is the most common hematologic malignancy in African American population with conspicuous racial disparities in both, mortality rates and incidence in cancer compared to European American. This observation evolved into a central hypothesis that MM has distinct biological differences across different ethnicities with yet unidentified race specific markers of tumor heterogeneity. Clear understanding of these molecular differences among ethnic minorities with MM will fulfill a major unmet medical need and eliminate racial disparity.

Methods: We acquired data from the Multiple Myeloma Research Foundation (MMRF) initiated comprehensive longitudinal study (CoMMpass) with an overall goal to profile 1,000 multiple myeloma patients at diagnosis, with multiple follow-up points throughout the course of the disease. To generate population subgroups based on genetic ancestry, we used a population stratification principle component analysis (PCA) and STRUCTURE to stratify myeloma patients by Ancestry Informative Markers. These well-established methods have allowed us to avoid confounders associated with self-reporting, and thus stratify the myeloma samples by genetic ancestry mapped along with anchor populations developed by 1000 genome project. We then assessed mutational frequencies as a function of PCA for each ancestry group using complex bioinformatics algorithms.

Results: We confirmed known commonly mutated genes in MM including KRAS, NRAS, FAM46C, and DIS3. Among the most striking and novel observations in our preliminary analysis of CoMMpass data using genetic ancestry and PCA was a significant difference in the frequency of nonsilent mutations in TP53, with a frequency of 7.1% (33/464) in patients clustering within the European ancestry compared to none (0/142) in African ancestry populations. Further analysis of enrichment of differentially mutated key factors within the TP53 pathway showed ATM as another gene with a significantly (p= 0.019) higher mutation frequency in EA PCA 4.7% (22/464) compared to AA PCA 0.7% (1/142). Analysis of clinical outcomes data showed poorer overall survival in patients harboring TP53 alterations. Furthermore, a comprehensive mutation analysis across samples identified a novel candidate PTCHD3 (p = 7.07E-06) with a significantly higher mutation occurrence in patients of African ancestry. Moreover, the frequency of copy number alterations known to be associated with poor prognosis revealed notable, but not significant (p=0.259) lower frequency of 1q gain in tumors from African compared to European descent. Lastly, we also observed a significant (p=0.0157) two-fold increase in early age of onset of MM in patients of African descent compared to those of European descent.

Conclusion: CoMMpass has constructed a fruitful discovery environment at nexus of high-resolution next generation deep sequencing with detailed clinical data allowing to elucidate potential ancestral drivers of MM paving the way to personalized treatments. Ultimately, these data may help us further delineate the influence of percent admixture on biological factors that drive differences in incidence and outcomes among multi-ethnic MM patients.

Citation Format: Zarko Manojlovic, Austin Christofferson, Gil Speyer, Seungchan Kim, Winnie Liang, Mary Derome, Daniel Auclair, David Craig, Jonathan Keats, John Carpten. Comprehensive analysis of molecular pathogenesis of multiple myeloma by genetic ancestry [abstract]. In: Proceedings of the AACR International Conference: New Frontiers in Cancer Research; 2017 Jan 18-22; Cape Town, South Africa. Philadelphia (PA): AACR; Cancer Res 2017;77(22 Suppl):Abstract nr A32.

Abstract 1083: Synergistic drug combination prediction through drug differential dependency network analysis

In an effort to discover strategies which identify effective drug combinations, we analyzed 39 of the 480 compounds screened in the Cancer Therapeutics Response Portal (CTRP) where combinations of two compounds were tested against 860 cancer cell lines; this enabled a comparison of the drug sensitivity of the combinations versus that of the individual compounds. More than half of the drug combinations (n=21) did not significantly improve the drug sensitivity, compared to individual compounds alone. In fact, some of the combinations showed reduced drug sensitivity. In EDDY-CTRP* analysis, the Cancer Cell Line Encyclopedia (CCLE) RNAseq data and CTRP compound response measurements were analyzed to discover both 1) pathways enriched with differential dependencies between sensitive and non-sensitive cell lines for each compound and 2) the mediators of cell line response to a drug. A mediator is a gene in a pathway that plays a significantly different role between sensitive and non-sensitive conditions. The significance is assessed for either essentiality, measured as a node’s centrality change, or specificity, measured as the difference in condition specific edges. These drug-pathway-mediator connections are predicted to reveal crucial molecular determinants of drug sensitivity that otherwise are hidden in the complexities of the molecular networks of the cell (Speyer et al., PSB 22:497-508, 2017). We further investigated whether mediators identified for single compounds could predict sensitivity to drug combinations. This analysis revealed that if two single compounds share the same specificity mediators, i.e. the genes with the most significant re-wiring of gene dependencies between sensitive and non-sensitive cell lines, combination of these two compounds correlate with improved sensitivity. The converse was also found: compounds that do not share mediators rarely show synergy. Further analysis and empirical testing of predicted combinations promises to prioritize synergistic drug combinations. We believe that this methodology may predict synergistic drug combinations from cancer cell line drug screening data. Supported by NIH U01CA168397.

*available at http://biocomputing.tgen.org/software/EDDY/CTRP

Citation Format: Seungchan Kim, Gil Speyer, Harshil Dhruv, Jeff Kiefer, Michael Berens. Synergistic drug combination prediction through drug differential dependency network analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1083. doi:10.1158/1538-7445.AM2017-1083

Abstract 1177: Targeting NEDD8 to uncover an exceptional responder molecular subtype in glioblastoma

Neddylation is a post-translational mechanism that marks proteins for degradation through activity of NEDD8 Activating Enzyme (NAE). NAE blocks cullin-RING ligases from initiating proteosomal degradation of select substrates including cell cycle regulators and apoptosis modulators. MLN4924, or Pevonedistat, targets NAE and inhibits Neddylation and induces apoptosis in sensitive cells. We have discovered, in a cohort of glioblastoma PDX models, an exceptional responder to MLN4924 (GBM102). Most pertinently the effects we observe are in a PDX cultured as 3D neurospheres that more closely resemble the true tumor architecture, heterogeneity, and “stem-like” phenotype characteristic of tumor growth. We have leveraged RNAseq expression data from Cancer Cell Line Encyclopedia (CCLE) and Pevonedistat response data from The Cancer Therapeutics Response Portal (CTRP) to apply a network-based analysis to identify pathways enriched with differential dependencies between cell lines sensitive and non-sensitive to MLN4924. The analysis also identifies potential mediating genes that appear to play critical roles in such differential dependency networks. Identified differential networks and mediators provide insight for cellular mechanisms underlying drug response. Additionally, we also investigated the efficacy of MLN4924 against orthotopic glioma PDX models (GBM102 and GBM116) in vivo to validate our findings in vitro. Thus genomic characterization of patient samples may lead to the identification of a molecular signature which is associated with a subset of GBMs vulnerable to MLN4924. As the treatment options for GBM are extremely limited, this may highlight a novel alternative opportunity to treat a select fraction of patients with this aggressive disease.

Citation Format: Lauren K. Hartman, Darren Finlay, Peiwen Pan, Seungchan Kim, Gil Speyer, Jeff Kiefer, Harshil Dhruv, Kristiina Vuori, Michael Berens. Targeting NEDD8 to uncover an exceptional responder molecular subtype in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1177. doi:10.1158/1538-7445.AM2017-1177

Abstract 1142: Novel target discovery for glioblastoma using chemical biology fingerprinting

The most common adult brain tumor is Glioblastoma Multiforme (GBM), an extremely aggressive cancer with only scant treatment options. Even with standard of care most patients present with a recurrence and the median survival is only circa 15 months. The need, therefore, for new therapeutic targets and treatment options is pressing. Here we describe here a multipronged approach to identifying said targets. We present an established methodology for the isolation and culture of patient derived GBM samples that retain the “stem-like” fraction thought to underlie resistance and recurrence. Furthermore we show genomically that these samples represent specific subtypes of the disease yet still form distinct groups in unbiased clustering analysis. Thus we have multiple representative patient derived cultures that are suitable for our drug discovery and chemical biology analyses. Using a process we term Chemical Biology Fingerprinting (CBF) we utilize small focused, and clinically relevant, chemical collections in order to identify patterns of chemovulnerabilities across multiple samples. This allows an unbiased yet cancer relevant sub-stratification and the identification of agents, and therefore targets, which may be relevant for GBM patient subtypes. Indeed our use of the highly annotated NCI CTD2 Informer Set of chemicals allows ready drug-to-target mapping and facilitates data sharing across the CTD2 network. Moreover, already defined subgroups can be clustered to find agents, or groups of agents, that show selective activity against traditional classifications (e.g. proneural, mesenchymal etc.). Finally our strategy is permissive for the identification of “exceptional responders”. That is, individual patient samples that respond to a specific drug whilst most samples are refractory. In sum we demonstrate generation of patient derived models and identify specific, and novel, drugs that may be relevant for specific GBM subtypes. Supported by NIH U01CA168397

Citation Format: Darren Finlay, Pedro Aza-Blanc, Harshil Dhruv, Alexey Eroshkin, Craig Hauser, Jeff Kiefer, Seungchan Kim, Tao Long, Robert G. Oshima, Sen Peng, Gil Speyer, Michael Berens, Kristiina Vuori. Novel target discovery for glioblastoma using chemical biology fingerprinting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1142. doi:10.1158/1538-7445.AM2017-1142

Contextualization of drug-mediator relations using evidence networks

Background

Genomic analysis of drug response can provide unique insights into therapies that can be used to match the “right drug to the right patient.” However, the process of discovering such therapeutic insights using genomic data is not straightforward and represents an area of active investigation. EDDY (Evaluation of Differential DependencY), a statistical test to detect differential statistical dependencies, is one method that leverages genomic data to identify differential genetic dependencies. EDDY has been used in conjunction with the Cancer Therapeutics Response Portal (CTRP), a dataset with drug-response measurements for more than 400 small molecules, and RNAseq data of cell lines in the Cancer Cell Line Encyclopedia (CCLE) to find potential drug-mediator pairs. Mediators were identified as genes that showed significant change in genetic statistical dependencies within annotated pathways between drug sensitive and drug non-sensitive cell lines, and the results are presented as a public web-portal (EDDY-CTRP). However, the interpretability of drug-mediator pairs currently hinders further exploration of these potentially valuable results.

Methods

In this study, we address this challenge by constructing evidence networks built with protein and drug interactions from the STITCH and STRING interaction databases. STITCH and STRING are sister databases that catalog known and predicted drug-protein interactions and protein-protein interactions, respectively. Using these two databases, we have developed a method to construct evidence networks to “explain” the relation between a drug and a mediator. 

Results

We applied this approach to drug-mediator relations discovered in EDDY-CTRP analysis and identified evidence networks for ~70% of drug-mediator pairs where most mediators were not known direct targets for the drug. Constructed evidence networks enable researchers to contextualize the drug-mediator pair with current research and knowledge. Using evidence networks, we were able to improve the interpretability of the EDDY-CTRP results by linking the drugs and mediators with genes associated with both the drug and the mediator.

Conclusion

We anticipate that these evidence networks will help inform EDDY-CTRP results and enhance the generation of important insights to drug sensitivity that will lead to improved precision medicine applications.

DIFFERENTIAL PATHWAY DEPENDENCY DISCOVERY ASSOCIATED WITH DRUG RESPONSE ACROSS CANCER CELL LINES

The effort to personalize treatment plans for cancer patients involves the identification of drug treatments that can effectively target the disease while minimizing the likelihood of adverse reactions. In this study, the gene-expression profile of 810 cancer cell lines and their response data to 368 small molecules from the Cancer Therapeutics Research Portal (CTRP) are analyzed to identify pathways with significant rewiring between genes, or differential gene dependency, between sensitive and non-sensitive cell lines. Identified pathways and their corresponding differential dependency networks are further analyzed to discover essentiality and specificity mediators of cell line response to drugs/compounds. For analysis we use the previously published method EDDY (Evaluation of Differential DependencY). EDDY first constructs likelihood distributions of gene-dependency networks, aided by known genegene interaction, for two given conditions, for example, sensitive cell lines vs. non-sensitive cell lines. These sets of networks yield a divergence value between two distributions of network likelihoods that can be assessed for significance using permutation tests. Resulting differential dependency networks are then further analyzed to identify genes, termed mediators, which may play important roles in biological signaling in certain cell lines that are sensitive or non-sensitive to the drugs. Establishing statistical correspondence between compounds and mediators can improve understanding of known gene dependencies associated with drug response while also discovering new dependencies. Millions of compute hours resulted in thousands of these statistical discoveries. EDDY identified 8,811 statistically significant pathways leading to 26,822 compound-pathway-mediator triplets. By incorporating STITCH and STRING databases, we could construct evidence networks for 14,415 compound-pathway-mediator triplets for support. The results of this analysis are presented in a searchable website to aid researchers in studying potential molecular mechanisms underlying cells' drug response as well as in designing experiments for the purpose of personalized treatment regimens.

Abstract 1520: Identifying differential dependency networks accounting for response to NEDD8-inhibitor in large-scale cancer cell line data

The Cancer Cell Line Encyclopedia (CCLE) houses molecular profiles of ∼800 human long term cell lines spanning several different histological types of cancer. In addition, Cancer Therapeutics Response Portal (CTRP) provides drug response measurements for 481 small molecules. Integration of these data enables investigation of the molecular correlates of drug response (sensitivity and resistance). In this current effort, we studied the NEDDylation small molecule inhibitor, MLN4924, in the context of genomic data to uncover novel mechanistic correlates of drug response across the panel of cell lines. We recently reported (Jung and Kim 2016 NAR) development of a robust computational method that shows promise to identify novel insights when applied to multi-dimensional data sets as outlined above. The Evaluation of Differential Dependency (EDDY) employs Bayesian networks to represent statistically distinct differences in relationships between genes within a specific biological pathway as queried between two conditions, in this instance, cell lines that are sensitive and those that are non-sensitive to MLN4924. While EDDY has been successfully employed in the analysis of specific diseases such as TCGA adrenocortical carcinoma, its statistical rigor incurred a prohibitive computational load to assess conditional differences across larger datasets. Recent computational enhancements to EDDY enable processing of larger datasets in reasonable time while maintaining sensitivity. The capability of analyzing broader pan-cancer datasets such as CCLE has enabled EDDY to become more capable in identifying general trends across disease subtypes. Specifically, we demonstrate the enhanced EDDY in analysis of MLN4924 response across the CCLE data set combined with CTRP data set.

Initial outcomes from EDDY point to both anticipated and unanticipated biological determinants of response. For example, it is noted that specific oncogenic pathways, such as those centered on PIK3CA, appear to show differential dependencies in the sensitive and non-sensitive cell lines. We also observe genes and candidate pathways related to apoptotic mechanisms that may reveal mechanistic insights to predicting drug response. Specifically, genes and pathways associated with certain apoptotic mechanisms around mitochondrial proteins and glutathione peroxidase may serve as unique determinants of drug response. Multidimensional data analyzed by EDDY uncovers candidate mechanisms of vulnerability to specific small molecule inhibitors, which may guide development of predictive models for treatment planning when using agents with highly context-dependent efficacies. Supported by NIH U01CA168397

Citation Format: Gil Speyer, Harshil Dhruv, Jeff Kiefer, Stuart Schreiber, Paul Clemons, Michael E. Berens, Seungchan Kim. Identifying differential dependency networks accounting for response to NEDD8-inhibitor in large-scale cancer cell line data. [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 1520.

KNOWLEDGE-ASSISTED APPROACH TO IDENTIFY PATHWAYS WITH DIFFERENTIAL DEPENDENCIES

We have previously developed a statistical method to identify gene sets enriched with condition-specific genetic dependencies. The method constructs gene dependency networks from bootstrapped samples in one condition and computes the divergence between distributions of network likelihood scores from different conditions. It was shown to be capable of sensitive and specific identification of pathways with phenotype-specific dysregulation, i.e., rewiring of dependencies between genes in different conditions. We now present an extension of the method by incorporating prior knowledge into the inference of networks. The degree of prior knowledge incorporation has substantial effect on the sensitivity of the method, as the data is the source of condition specificity while prior knowledge incorporation can provide additional support for dependencies that are only partially supported by the data. Use of prior knowledge also significantly improved the interpretability of the results. Further analysis of topological characteristics of gene differential dependency networks provides a new approach to identify genes that could play important roles in biological signaling in a specific condition, hence, promising targets customized to a specific condition. Through analysis of TCGA glioblastoma multiforme data, we demonstrate the method can identify not only potentially promising targets but also underlying biology for new targets.

Periodic scarred States in open quantum dots as evidence of quantum Darwinism

Scanning gate microscopy (SGM) is used to image scar structures in an open quantum dot, which is created in an InAs quantum well by electron-beam lithography and wet etching. The scanned images demonstrate periodicities in magnetic field that correlate to those found in the conductance fluctuations. Simulations have shown that these magnetic transform images bear a strong resemblance to actual scars found in the dot that replicate through the modes in direct agreement with quantum Darwinism.

Imaging scarred states in quantum dots

We have used the scanning gate microscopy technique to image scar structures in an open quantum dot, fabricated in an InAs quantum well and defined by electron beam lithography. These are shown to have a periodicity in magnetic field that correlates with that found in the conductance fluctuations. Simulations have shown that these magnetic transform images bear a strong resemblance to actual scars found in the dots.

Conduction switching of photochromic molecules

We report a theoretical study of single molecule conduction switching of photochromic dithienylethene molecules. The light-induced intramolecular transformation drives a swapping of the highest occupied molecular orbital and lowest unoccupied molecular orbital between two distinct conjugated paths. The shuffling of single and double bonds produces a significant conductance change when the molecule is sandwiched between metal electrodes. We model the switching event using quantum molecular dynamics and the conductance changes using Green's function electronic transport theory. We find large on-off conductance ratios (between 10 and over 100) depending on the side group outside the switching core.

Viral contamination of a mosquito cell line, Aedes albopictus, associated with syncytium formation

Viral contamination associated with syncytium formation in two sbulines of Singh's Aedes albopictus cell cultures was investigated. Electron microscopy of the syncytia revealed the presence of five different types of virus-like particles, which morphologically resembled the parvo-, picorna-, toga-, and orbi-, and bacterial viruses. When a virus-free subline of the A. albopictus cells (SL3) was inoculated with extracts of the syncytium-forming A. albopictus cells, the parvo-, toga-, and orbi-type viral agents were consistently observed. Among these three agents, the togavirus-type agent is most likely responsible for the syncytium induction. Serological examination of the infected cell extract indicated that at least one of three virus-like agents, presumably the togavirus-type agent, was related to Chikungunya. O'nyong-nyong, and Western equine encephalomyelitis viruses (alphaviruses of the Togaviridae), but separable from these.