Gene ontology analysis of arthrogryposis (multiple congenital contractures).

In 2016, we published an article applying Gene Ontology Analysis to the genes that had been reported to be associated with arthrogryposis (multiple congenital contractures) (Hall & Kiefer, 2016). At that time, 320 genes had been reported to have mutations associated with arthrogryposis. All were associated with decreased fetal movement. These 320 genes were analyzed by biological process and cellular component categories, and yielded 22 distinct groupings. Since that time, another 82 additional genes have been reported, now totaling 402 genes, which when mutated, are associated with arthrogryposis (arthrogryposis multiplex congenita). So, we decided to update the analysis in order to stimulate further research and possible treatment. Now, 29 groupings can be identified, but only 19 groups have more than one gene.

Isolation and characterization of patient-derived CNS metastasis-associated stromal cell lines.

The functional role of human derived stromal cells in the tumor microenviornment of CNS metastases (CM) remain understudied. The purpose of the current study was to isolate and characterize stromal cells of the tumor microenvironment in CM. Four different patient-derived cell lines (PDCs) of stromal and one PDC of tumorigenic origin were generated from breast or lung CM. PDCs were analyzed by DNA/RNA sequencing, DNA methylation profiling, and immunophenotypic assays. The stromal derived PDCs were termed CNS metastasis-associated stromal cells (cMASCs). Functional analysis of cMASCs was tested by co-implanting them with tumorigenic cells in mice. cMASCs displayed normal genotypes compared with tumorigenic cell lines. RNA-seq and DNA methylation analyses demonstrated that cMASCs highly resembled each other, suggesting a common cell of origin. Additionally, cMASCs revealed gene expression signatures associated with cancer associated fibroblasts (CAFs), epithelial to mesenchymal transition, mesenchymal stem cells and expressed high levels of collagen. Functionally, cMASCs restricted tumor growth, and induced desmoplasia in vivo, suggesting that cMASCs may promote a protective host response to impede tumor growth. In summary, we demonstrated the isolation, molecular characterization and functional role of human derived cMASCs, a subpopulation of cells in the microenvironment of CM that have tumor inhibitory functions.

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.

Correction: Role of MicroRNA 1207-5P and Its Host Gene, the Long Non-Coding RNA Pvt1, as Mediators of Extracellular Matrix Accumulation in the Kidney: Implications for Diabetic Nephropathy.

[This corrects the article DOI: 10.1371/journal.pone.0077468.].

Arthrogryposis as a Syndrome: Gene Ontology Analysis.

Arthrogryposis by definition has multiple congenital contractures. All types of arthrogryposis have decreased in utero fetal movement. Because so many things are involved in normal fetal movement, there are many causes and processes that can go awry. In this era of molecular genetics, we have tried to place the known mutated genes seen in genetic forms of arthrogryposis into biological processes or cellular functions as defined by gene ontology. We hope this leads to better identification of all interacting pathways and processes involved in the development of fetal movement in order to improve diagnosis of the genetic forms of arthrogryposis, to lead to the development of molecular therapies, and to help better define the natural history of various types of arthrogryposis.

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.

Review of X-linked syndromes with arthrogryposis or early contractures-aid to diagnosis and pathway identification.

The following is a review of 50 X-linked syndromes and conditions associated with either arthrogryposis or other types of early contractures. These entities are categorized as those with known responsible gene mutations, those which are definitely X-linked, but the responsible gene has not been identified, and those suspected from family history to be X-linked. Several important ontology pathways for known disease genes have been identified and are discussed in relevance to clinical characteristics. Tables are included which help to identify distinguishing clinical features of each of the conditions.

Integrated genomic and epigenomic analysis of breast cancer brain metastasis.

The brain is a common site of metastatic disease in patients with breast cancer, which has few therapeutic options and dismal outcomes. The purpose of our study was to identify common and rare events that underlie breast cancer brain metastasis. We performed deep genomic profiling, which integrated gene copy number, gene expression and DNA methylation datasets on a collection of breast brain metastases. We identified frequent large chromosomal gains in 1q, 5p, 8q, 11q, and 20q and frequent broad-level deletions involving 8p, 17p, 21p and Xq. Frequently amplified and overexpressed genes included ATAD2, BRAF, DERL1, DNMTRB and NEK2A. The ATM, CRYAB and HSPB2 genes were commonly deleted and underexpressed. Knowledge mining revealed enrichment in cell cycle and G2/M transition pathways, which contained AURKA, AURKB and FOXM1. Using the PAM50 breast cancer intrinsic classifier, Luminal B, Her2+/ER negative, and basal-like tumors were identified as the most commonly represented breast cancer subtypes in our brain metastasis cohort. While overall methylation levels were increased in breast cancer brain metastasis, basal-like brain metastases were associated with significantly lower levels of methylation. Integrating DNA methylation data with gene expression revealed defects in cell migration and adhesion due to hypermethylation and downregulation of PENK, EDN3, and ITGAM. Hypomethylation and upregulation of KRT8 likely affects adhesion and permeability. Genomic and epigenomic profiling of breast brain metastasis has provided insight into the somatic events underlying this disease, which have potential in forming the basis of future therapeutic strategies.

Role of microRNA 1207-5P and its host gene, the long non-coding RNA Pvt1, as mediators of extracellular matrix accumulation in the kidney: implications for diabetic nephropathy.

Diabetic nephropathy is the most common cause of chronic kidney failure and end-stage renal disease in the Western World. One of the major characteristics of this disease is the excessive accumulation of extracellular matrix (ECM) in the kidney glomeruli. While both environmental and genetic determinants are recognized for their role in the development of diabetic nephropathy, epigenetic factors, such as DNA methylation, long non-coding RNAs, and microRNAs, have also recently been found to underlie some of the biological mechanisms, including ECM accumulation, leading to the disease. We previously found that a long non-coding RNA, the plasmacytoma variant translocation 1 (PVT1), increases plasminogen activator inhibitor 1 (PAI-1) and transforming growth factor beta 1 (TGF-ß1) in mesangial cells, the two main contributors to ECM accumulation in the glomeruli under hyperglycemic conditions, as well as fibronectin 1 (FN1), a major ECM component. Here, we report that miR-1207-5p, a PVT1-derived microRNA, is abundantly expressed in kidney cells, and is upregulated by glucose and TGF-ß1. We also found that like PVT1, miR-1207-5p increases expression of TGF-ß1, PAI-1, and FN1 but in a manner that is independent of its host gene. In addition, regulation of miR-1207-5p expression by glucose and TGFß1 is independent of PVT1. These results provide evidence supporting important roles for miR-1207-5p and its host gene in the complex pathogenesis of diabetic nephropathy.

A pilot study using next-generation sequencing in advanced cancers: feasibility and challenges.

PURPOSE: New anticancer agents that target a single cell surface receptor, up-regulated or amplified gene product, or mutated gene, have met with some success in treating advanced cancers. However, patients' tumors still eventually progress on these therapies. If it were possible to identify a larger number of targetable vulnerabilities in an individual's tumor, multiple targets could be exploited with the use of specific therapeutic agents, thus possibly giving the patient viable therapeutic alternatives. EXPERIMENTAL DESIGN: In this exploratory study, we used next-generation sequencing technologies (NGS) including whole genome sequencing (WGS), and where feasible, whole transcriptome sequencing (WTS) to identify genomic events and associated expression changes in advanced cancer patients. RESULTS: WGS on paired tumor and normal samples from nine advanced cancer patients and WTS on six of these patients' tumors was completed. One patient's treatment was based on targets and pathways identified by NGS and the patient had a short-lived PET/CT response with a significant reduction in his tumor-related pain. To design treatment plans based on information garnered from NGS, several challenges were encountered: NGS reporting delays, communication of results to out-of-state participants and their treating oncologists, and chain of custody handling for fresh biopsy samples for Clinical Laboratory Improvement Amendments (CLIA) target validation. CONCLUSION: While the initial effort was a slower process than anticipated due to a variety of issues, we demonstrate the feasibility of using NGS in advanced cancer patients so that treatments for patients with progressing tumors may be improved.

Elucidating potentially significant genomic regions involved in the initiation and progression of undifferentiated pleomorphic sarcoma.

Sarcomas are cancers that arise in soft tissues or bone and make up a small percentage of malignancies. In an effort to identify potential genetic targets for therapy, this study explores the genomic landscape of a metastatic undifferentiated pleomorphic sarcoma (UPS) with spindle cell morphology. Thick sections (50 µm) of formalin-fixed, paraffin-embedded tissue from a primary, recurrent, and metastatic tumor were collected and processed from a single patient for DNA content-based flow-sorting and analyses. Nuclei of diploid and aneuploid populations were sorted from the malignant tissues and their genomes interrogated with array comparative genomic hybridization. The third sample was highly degraded and did not contain any intact ploidy peaks in our flow assays. A 2.5N aneuploid population was identified in the primary and recurrent sample. We detected a series of shared and unique genomic aberrations in the sorted aneuploid populations. The patterns of aberrations suggest that two similar but independent clonal populations arose during the clinical history of this rare tumor. None of these aberrations were detected in the matching sorted diploid samples. The targeted regions of interest might play a role in UPS and may lead to clinical significance with further investigation.

Learning contextual gene set interaction networks of cancer with condition specificity.

BACKGROUND: Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients. RESULTS: In this study, we describe a novel method to discern molecular interactions specific to certain molecular contexts. Unlike conventional approaches to build modular networks of individual genes, our focus is to identify cancer-generic and subtype-specific interactions between contextual gene sets, of which each gene set share coherent transcriptional patterns across a subset of samples, termed contextual gene set. We then apply a novel formulation for quantitating the effect of the samples from each subtype on the calculated strength of interactions observed. Two cancer data sets were analyzed to support the validity of condition-specificity of identified interactions. When compared to an existing approach, the proposed method was much more sensitive in identifying condition-specific interactions even in heterogeneous data set. The results also revealed that network components specific to different types of cancer are related to different biological functions than cancer-generic network components. We found not only the results that are consistent with previous studies, but also new hypotheses on the biological mechanisms specific to certain cancer types that warrant further investigations. CONCLUSIONS: The analysis on the contextual gene sets and characterization of networks of interaction composed of these sets discovered distinct functional differences underlying various types of cancer. The results show that our method successfully reveals many subtype-specific regions in the identified maps of biological contexts, which well represent biological functions that can be connected to specific subtypes.

The BioIntelligence Framework: a new computational platform for biomedical knowledge computing.

Breakthroughs in molecular profiling technologies are enabling a new data-intensive approach to biomedical research, with the potential to revolutionize how we study, manage, and treat complex diseases. The next great challenge for clinical applications of these innovations will be to create scalable computational solutions for intelligently linking complex biomedical patient data to clinically actionable knowledge. Traditional database management systems (DBMS) are not well suited to representing complex syntactic and semantic relationships in unstructured biomedical information, introducing barriers to realizing such solutions. We propose a scalable computational framework for addressing this need, which leverages a hypergraph-based data model and query language that may be better suited for representing complex multi-lateral, multi-scalar, and multi-dimensional relationships. We also discuss how this framework can be used to create rapid learning knowledge base systems to intelligently capture and relate complex patient data to biomedical knowledge in order to automate the recovery of clinically actionable information.

Preclinical investigation of nanoparticle albumin-bound paclitaxel as a potential treatment for adrenocortical cancer.

BACKGROUND: Traditional drug discovery methods have a limited role in rare cancers. We hypothesized that molecular technology including gene expression profiling could expose novel targets for therapy in adrenocortical carcinoma (ACC), a rare and lethal cancer. SPARC (secreted protein acidic rich in cysteine) is an albumin-binding matrix-associated protein that is proposed to act as a mechanism for the increased efficacy of a nanoparticle albumin-bound preparation of the antimicrotubular drug Paclitaxel (nab-paclitaxel). METHODS: The transcriptomes of 19 ACC tumors and 4 normal adrenal glands were profiled on Affymetrix U133 Plus2 expression microarrays to identify genes representing potential therapeutic targets. Immunohistochemical analysis for target proteins was performed on 10 ACC, 6 benign adenomas, and 1 normal adrenal gland. Agents known to inhibit selected targets were tested in comparison with mitotane in the 2 ACC cell lines (H295R and SW-13) in vitro and in mouse xenografts. RESULTS: SPARC expression is increased in ACC samples by 1.56 ± 0.44 (µ ± SD) fold. Paclitaxel and nab-paclitaxel show in vitro inhibition of H295R and SW-13 cells at IC50 concentrations of 0.33 µM and 0.0078 µM for paclitaxel and 0.35 µM and 0.0087 µM for nab-paclitaxel compared with mitotane concentrations of 15.9 µM and 46.4 µM. In vivo nab-paclitaxel treatment shows a greater decrease in tumor weight in both xenograft models than mitotane. CONCLUSIONS: Biological insights garnered through expression profiling of ACC tumors suggest further investigation into the use of nab-paclitaxel for the treatment of ACC.

Kinome-wide siRNA screening identifies molecular targets mediating the sensitivity of pancreatic cancer cells to Aurora kinase inhibitors.

Aurora kinases are a family of mitotic kinases that play important roles in the tumorigenesis of a variety of cancers including pancreatic cancer. A number of Aurora kinase inhibitors (AKIs) are currently being tested in preclinical and clinical settings as anti-cancer therapies. However, the antitumor activity of AKIs in clinical trials has been modest. In order to improve the antitumor activity of AKIs in pancreatic cancer, we utilized a kinome focused RNAi screen to identify genes that, when silenced, would sensitize pancreatic cancer cells to AKI treatment. A total of 17 kinase genes were identified and confirmed as positive hits. One of the hits was the platelet-derived growth factor receptor, alpha polypeptide (PDGFRA), which has been shown to be overexpressed in pancreatic cancer cells and tumor tissues. Imatinib, a PDGFR inhibitor, significantly enhanced the anti-proliferative effect of ZM447439, an Aurora B specific inhibitor, and PHA-739358, a pan-Aurora kinase inhibitor. Further studies showed that imatinib augmented the induction of G2/M cell cycle arrest and apoptosis by PHA-739358. These findings indicate that PDGFRA is a potential mediator of AKI sensitivity in pancreatic cancer cells.

High-throughput siRNA screening as a method of perturbation of biological systems and identification of targeted pathways coupled with compound screening.

High-throughput RNA interference (HT-RNAi) is a powerful research tool for parallel, 'genome-wide', targeted knockdown of specific gene products. Such perturbation of gene product expression allows for the systematic query of gene function. The phenotypic results can be monitored by assaying for specific alterations in molecular and cellular endpoints, such as promoter activation, cell proliferation and survival. RNAi profiling may also be coupled with drug screening to identify molecular correlates of drug response. As with other genomic-scale data, methods of data analysis are required to handle the unique aspects of data normalization and statistical processing. In addition, novel techniques or knowledge-mining strategies are required to extract useful biological information from HT-RNAi data. Knowledge-mining strategies involve the novel application of bioinformatic tools and expert curation to provide biological context to genomic-scale data such as that generated from HT-RNAi data. Pathway-based tools, whether text-mining based or manually curated, serve an essential role in knowledge mining. These tools can be applied during all steps of HT-RNAi screen experiments including pre-screen knowledge gathering, assay development and hit confirmation and validation. Most importantly, pathway tools allow the interrogation of HT-RNAi data to identify and prioritize pathway-based biological information as a result of specific loss of gene function.

Validation of short interfering RNA knockdowns by quantitative real-time PCR.

RNA interference (RNAi) is a natural mechanism, that is triggered by the introduction of double-stranded RNA into a cell. The long double-stranded RNA is then processed into short interfering RNA (siRNA) that mediates sequence-specific degradation of homologous transcripts. This phenomenon can be exploited to experimentally trigger RNAi and downregulate gene expression by transfecting mammalian cells with synthetic siRNA. Thus, siRNAs can be designed to specifically silence the expression of genes bearing a particular target sequence. In this chapter, we present methods and procedures for validating the effects of siRNA-based gene silencing on target gene expression. To illustrate our approach, we use examples from our analysis of a Cancer Gene Library of 278 siRNAs targeting 139 classic oncogenes and tumor suppressor genes (Qiagen Inc., Germantown, MD). Specifically, this library was used for high-throughput RNAi phenotype analysis followed by gene expression analysis to validate gene silencing for siRNA that produced a phenotype. Methods and protocols are presented that illustrate how sequence-specific gene silencing of effective siRNAs are analyzed and validated by quantitative real-time PCR assays to measure the extent of target gene silencing, as well as effects on various gene expression end points.

EphB2 expression across 138 human tumor types in a tissue microarray: high levels of expression in gastrointestinal cancers.

PURPOSE: To comprehensively evaluate ephrin receptor B2 (EphB2) expression in normal and neoplastic tissues. EphB2 is a tyrosine kinase recently implicated in the deregulation of cell-to-cell communication in many tumors. EXPERIMENTAL DESIGN: EphB2 protein expression was analyzed by immunohistochemistry on tissue microarrays that included 76 different normal tissues, >4,000 samples from 138 different cancer types, and 1,476 samples of colon cancer with clinical follow-up data. RESULTS: We found most prominent EphB2 expression in the intestinal epithelium (colonic crypts) with cancer of the colorectum displaying the highest EphB2 positivity of all tumors. Positivity was found in 100% of 118 colon adenomas but in 33.3% of 45 colon carcinomas. EphB2 expression was also observed in 75 tumor categories, including serous carcinoma of the endometrium (34.8%), adenocarcinoma of the esophagus (33.3%), intestinal adenocarcinoma of the stomach (30.2%), and adenocarcinoma of the small intestine (70%). The occasional finding of strong EphB2 positivity in tumors without EphB2 positivity in the corresponding normal cells [adenocarcinoma of the lung (4%) and pancreas (2.2%)] suggests that deregulation of EphB2 signaling may involve up-regulation of the protein expression. In colon carcinoma, loss of EphB2 expression was associated with advanced stage (P < 0.0001) and was an indicator of poor overall survival (P = 0.0098). CONCLUSIONS: Our results provide an overview on the EphB2 protein expression in normal and neoplastic tissues. Deregulated EphB2 expression may play a role in several cancer types with loss of EphB2 expression serving as an indicator of the possible pathogenetic role of EphB2 signaling in the maintenance of tissue architecture of colon epithelium.

Nonsense-mediated decay microarray analysis identifies mutations of EPHB2 in human prostate cancer.

The identification of tumor-suppressor genes in solid tumors by classical cancer genetics methods is difficult and slow. We combined nonsense-mediated RNA decay microarrays and array-based comparative genomic hybridization for the genome-wide identification of genes with biallelic inactivation involving nonsense mutations and loss of the wild-type allele. This approach enabled us to identify previously unknown mutations in the receptor tyrosine kinase gene EPHB2. The DU 145 prostate cancer cell line, originating from a brain metastasis, carries a truncating mutation of EPHB2 and a deletion of the remaining allele. Additional frameshift, splice site, missense and nonsense mutations are present in clinical prostate cancer samples. Transfection of DU 145 cells, which lack functional EphB2, with wild-type EPHB2 suppresses clonogenic growth. Taken together with studies indicating that EphB2 may have an essential role in cell migration and maintenance of normal tissue architecture, our findings suggest that mutational inactivation of EPHB2 may be important in the progression and metastasis of prostate cancer.