Elana Fertig
Johns Hopkins University, Oncology, Faculty Member
- Numerical Weather Prediction, Data Assimilation, Nonlinear dynamics, Chaos Theory, Systems Biology, Bioinformatics, and 30 moreCell Signaling, Oncology, Control Systems Engineering, Meteorology, Satellite remote sensing, Localization, Transcription Regulation, Parameter estimation, C. elegans, Ensemble Methods, Variational Methods, Cell Fate Transition, Ensemble Kalman Filter, Bias Correction, Biology, Molecular Biology, Medicine, Genetics, Mathematical Modelling, Systems Thinking, Microarray Data Analysis, Genomics, Gene expression, Numerical Linear Algebra, Grant Writing, RNA-seq Analysis, Kalman Filter, Transcription Factors, Synchronization, and Non-negative matrix factorizationedit
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The role of promoter methylation in the development of mucoepidermoid carcinoma (MEC) has not been fully explored. In this study, we investigated the epigenetic landscape of MEC. The Illumina HumanMethylation27 BeadChip array and... more
The role of promoter methylation in the development of mucoepidermoid carcinoma (MEC) has not been fully explored. In this study, we investigated the epigenetic landscape of MEC. The Illumina HumanMethylation27 BeadChip array and differential methylation analysis were utilized to screen for epigenetic alterations in 14 primary MEC tumors and 14 matched normal samples. Bisulfite sequencing was used to validate these results, with subsequent quantitative Methylation-Specific PCR (qMSP) to validate chloride intracellular channel protein 3 (CLIC3) in a separate cohort. Furthermore, CLIC3 immunohistochemical (IHC) staining was performed in another separate cohort of MEC. Finally, clinical and pathological characteristics were statistically analyzed for correlation with methylation status of CLIC3 and CLIC3 IHC H-scores by Wilcoxon rank sum, Kruskall-Wallis, and X(2) test tests. We obtained 6 significantly differentially methylated gene candidates demonstrating significant promoter hyper-...
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Lungs resected for adenocarcinomas often harbour minute discrete foci of cytologically atypical pneumocyte proliferations designated as atypical adenomatous hyperplasia (AAH). Evidence suggests that AAH represents an initial step in the... more
Lungs resected for adenocarcinomas often harbour minute discrete foci of cytologically atypical pneumocyte proliferations designated as atypical adenomatous hyperplasia (AAH). Evidence suggests that AAH represents an initial step in the progression to adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and fully invasive adenocarcinoma. Despite efforts to identify predictive markers of malignant transformation, alterations driving this progression are poorly understood. Here we perform targeted next-generation sequencing on multifocal AAHs and different zones of histologic progression within AISs and MIAs. Multiregion sequencing demonstrated different genetic drivers within the same tumour and reveal that clonal expansion is an early event of tumorigenesis. We find that KRAS, TP53 and EGFR mutations are indicators of malignant transition. Utilizing droplet digital PCR, we find alterations associated with early neoplasms in paired circulating DNA. This study provide...
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Background / Purpose: Coordinated changes in gene expression across samples result from the underlying biological processes distinguishing those samples. Whereas many microarray analysis techniques infer independent patterns across... more
Background / Purpose: Coordinated changes in gene expression across samples result from the underlying biological processes distinguishing those samples. Whereas many microarray analysis techniques infer independent patterns across samples, concurrent biological processes often regulate overlapping sets of genes. Main conclusion: We present an analysis technique, Coordinated Gene Activity in Pattern Sets (CoGAPS), which infers overlapping patterns in gene expression across samples and links these patterns to activity determined through gene set analysis. For example, CoGAPS inferred expected reduction in KIT pathway activity and an unexpected DNA damage response arising from imatinib treatment of gastrointestinal tumor cell lines.
Breast cancer is a heterogeneous disease, having multiple subtypes with different malignant phenotypes. The triple-negative breast cancer, or basal breast cancer, is highly aggressive, metastatic, and difficult to treat. Previously, we... more
Breast cancer is a heterogeneous disease, having multiple subtypes with different malignant phenotypes. The triple-negative breast cancer, or basal breast cancer, is highly aggressive, metastatic, and difficult to treat. Previously, we identified that key molecules (IL6, CSF2, CCL5, VEGFA, and VEGFC) secreted by tumor cells and stromal cells in basal breast cancer can promote metastasis. It remains to assess whether these molecules function similarly in other subtypes of breast cancer. Here, we characterize the relative gene expression of the five secreted molecules and their associated receptors (GP130, GMRA, GMRB, CCR5, VEGFR2, NRP1, VEGFR3, NRP2) in the basal, HER2 (human epidermal growth factor receptor 2) positive, luminal A, and luminal B subtypes using high throughput data from tumor samples in The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). IL6 and CCL5 gene expression are basal breast cancer specific, whereas high gene expression of GP130 was observed in luminal A/B. VEGFA/C and CSF2 mRNA are overexpressed in HER2 positive breast cancer, with VEGFA and CSF2 also overexpressed in basal breast cancer. Further study of the specific protein function of these factors within their associated cancer subtypes may yield personalized biomarkers and treatment modalities.
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Epidermal growth factor receptor (EGFR) is frequently overexpressed in head and neck squamous cell carcinoma (HNSCC) and cetuximab, a monoclonal antibody targeting this receptor, is widely used to treat these patients. In the following... more
Epidermal growth factor receptor (EGFR) is frequently overexpressed in head and neck squamous cell carcinoma (HNSCC) and cetuximab, a monoclonal antibody targeting this receptor, is widely used to treat these patients. In the following investigation, we examined the role of SMAD4 down-regulation in mediating epithelial-to-mesenchymal transition (EMT) and cetuximab resistance in HNSCC. We determined that SMAD4 down-regulation was significantly associated with increased cell motility, increased expression of vimentin, and cetuximab resistance in HNSCC cell lines. In the HNSCC genomic dataset obtained from The Cancer Genome Atlas, SMAD4 was altered in 20/279 (7%) of HNSCC via homozygous deletion, and nonsense, missense, and silent mutations. When SMAD4 expression was compared with respect to human papillomavirus (HPV) status, HPV-positive tumors had higher expression compared to HPV-negative tumors. Furthermore, higher SMAD4 expression also correlated with higher CDKN2A (p16) expression. Our data suggest that SMAD4 down-regulation plays an important role in the induction of EMT and cetuximab resistance. Patients with higher SMAD4 expression may benefit from cetuximab use in the clinic.
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Using high-throughput analyses and the TRANSFAC database, we characterized TF signatures of head and neck squamous cell carcinoma (HNSCC) subgroups by inferential analysis of target gene expression, correcting for the effects of DNA... more
Using high-throughput analyses and the TRANSFAC database, we characterized TF signatures of head and neck squamous cell carcinoma (HNSCC) subgroups by inferential analysis of target gene expression, correcting for the effects of DNA methylation and copy number. Using this discovery pipeline, we determined that human papillomavirus-related (HPV+) and HPV- HNSCC differed significantly based on the activity levels of key TFs including AP1, STATs, NF-κB, and p53. Immunohistochemical analysis confirmed that HPV- HNSCC is characterized by co-activated STAT3 and NF-κB pathways, and functional studies demonstrate that this phenotype can be effectively targeted with combined anti-NF-κB and anti-STAT therapies. These discoveries correlate strongly with previous findings connecting STATs, NF-κB, and AP1 in HNSCC. We identified 5 top-scoring pair biomarkers from STATs, NF-κB and AP1 pathways that distinguish HPV+ from HPV- HNSCC based on TF activity, and validated these biomarkers on TCGA and o...
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In head and neck squamous cell cancer (HNSCC), four intrinsic subtypes (or groups) have been identified, and each one possesses a unique biology that will require specific treatment strategies. We previously reported that mesenchymal... more
In head and neck squamous cell cancer (HNSCC), four intrinsic subtypes (or groups) have been identified, and each one possesses a unique biology that will require specific treatment strategies. We previously reported that mesenchymal (group 2) tumors exhibit reduced levels of Trop2 expression. In this study, we investigated the functional role of Trop2 in HNSCC and find that loss results in autocrine activation of the EGFR family member ErbB3 via neuregulin-1. Trop2 localizes to both the cell surface and cytosol of HNSCC cells and forms a complex with neuregulin-1, which is predominantly cytosolic. Inactivation of Trop2 increases the concentration of neuregulin-1 at the cell surface where it is cleaved to activate ErbB3. In primary HNSCC, detection of ErbB3 activation was limited to Trop2 negative tumors. An analysis of the Cancer Genome Atlas (TCGA) HNSCC dataset confirms enrichment for ErbB3 activity in mesenchymal tumors. Notably, Trop2 loss triggers sensitivity to anti-ErbB3 ant...
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Automated Genomics Analysis (AGA) is an interactive program to analyze high-throughput genomic data sets on a variety of platforms. An easy to use, point and click, guided pipeline is implemented to combine, define, and compare datasets,... more
Automated Genomics Analysis (AGA) is an interactive program to analyze high-throughput genomic data sets on a variety of platforms. An easy to use, point and click, guided pipeline is implemented to combine, define, and compare datasets, and customize their outputs. In contrast to other automated programs, AGA enables flexible selection of sample groups for comparison from complex sample annotations. Batch correction techniques are also included to further enable the combination of datasets from diverse studies in this comparison. AGA also allows users to save plots, tables and data, and log files containing key portions of the R script run for reproducible analyses. The link between the interface and R supports collaborative research, enabling advanced R users to extend preliminary analyses generated from bioinformatics novices.
Although p16 protein expression, a surrogate marker of oncogenic human papillomavirus (HPV) infection, is recognized as a prognostic marker in oropharyngeal squamous cell carcinoma (OPSCC), its prevalence and significance have not been... more
Although p16 protein expression, a surrogate marker of oncogenic human papillomavirus (HPV) infection, is recognized as a prognostic marker in oropharyngeal squamous cell carcinoma (OPSCC), its prevalence and significance have not been well established in cancer of the oral cavity, hypopharynx, or larynx, collectively referred as non-OPSCC, where HPV infection is less common than in the oropharynx. p16 expression and high-risk HPV status in non-OPSCCs from RTOG 0129, 0234, and 0522 studies were determined by immunohistochemistry (IHC) and in situ hybridization (ISH). Hazard ratios from Cox models were expressed as positive or negative, stratified by trial, and adjusted for clinical characteristics. p16 expression was positive in 14.1% (12 of 85), 24.2% (23 of 95), and 19.0% (27 of 142) and HPV ISH was positive in 6.5% (six of 93), 14.6% (15 of 103), and 6.9% (seven of 101) of non-OPSCCs from RTOG 0129, 0234, and 0522 studies, respectively. Hazard ratios for p16 expression were 0.63 ...
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Breast cancer metastasis involves lymphatic dissemination in addition to hematogenous spreading. Although stromal lymphatic vessels (LVs) serve as initial metastatic routes, roles of organ-residing LVs are underinvestigated. Here we show... more
Breast cancer metastasis involves lymphatic dissemination in addition to hematogenous spreading. Although stromal lymphatic vessels (LVs) serve as initial metastatic routes, roles of organ-residing LVs are underinvestigated. Here we show that lymphatic endothelial cells (LECs), a component of LVs within pre-metastatic niches, are conditioned by triple-negative breast cancer (TNBC) cells to accelerate metastasis. LECs within the lungs and lymph nodes, conditioned by tumour-secreted factors, express CCL5 that is not expressed either in normal LECs or in cancer cells, and direct tumour dissemination into these tissues. Moreover, tumour-conditioned LECs promote angiogenesis in these organs, allowing tumour extravasation and colonization. Mechanistically, tumour cell-secreted IL6 causes Stat3 phosphorylation in LECs. This pStat3 induces HIF-1α and VEGF, and a pStat3-pc-Jun-pATF-2 ternary complex induces CCL5 expression in LECs. This study demonstrates anti-metastatic activities of multiple repurposed drugs, blocking a self-reinforcing paracrine loop between breast cancer cells and LECs.
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Research Interests: Pharmacology, Bioinformatics, Evolutionary Biology, Pathology, Oncology, and 22 moreRadiology, Pharmacy, Informatics, Genomics, Phylogenetics, Computational Biology, Biotechnology, Cancer, Biomarkers, Biology, Proteomics, Biomedical informatics, Medicine, Bioscience, Phylogeny, Scientific, Microarray, Clinical Medicine, Biological, Genetic modification, Evidence Based, and Cancer Informatics
Patterns in time-course gene expression data can represent the biological processes that are active over the measured time period. However, the orthogonality constraint in standard pattern-finding algorithms, including notably principal... more
Patterns in time-course gene expression data can represent the biological processes that are active over the measured time period. However, the orthogonality constraint in standard pattern-finding algorithms, including notably principal components analysis (PCA), confounds expression changes resulting from simultaneous, non-orthogonal biological processes. Previously, we have shown that Markov chain Monte Carlo nonnegative matrix factorization algorithms are particularly adept at distinguishing such concurrent patterns. One such matrix factorization is implemented in the software package CoGAPS. We describe the application of this software and several technical considerations for identification of age-related patterns in a public, prefrontal cortex gene expression dataset.
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Tumor suppressor genes (TSGs) are commonly inactivated by somatic mutation and/or promoter methylation; yet, recent high-throughput genomic studies have not identified key TSGs inactivated by both mechanisms. We pursued an integrated... more
Tumor suppressor genes (TSGs) are commonly inactivated by somatic mutation and/or promoter methylation; yet, recent high-throughput genomic studies have not identified key TSGs inactivated by both mechanisms. We pursued an integrated molecular analysis based on methylation binding domain sequencing (MBD-seq), 450K Methylation arrays, whole exome sequencing, and whole genome gene expression arrays in primary head and neck squamous cell carcinoma (HNSCC) tumors and matched uvulopalatopharyngoplasty tissue samples (UPPPs). We uncovered 186 downregulated genes harboring cancer specific promoter methylation including PAX1 and PAX5 and we identified 10 key tumor suppressor genes (GABRB3, HOXC12, PARP15, SLCO4C1, CDKN2A, PAX1, PIK3AP1, HOXC6, PLCB1, and ZIC4) inactivated by both promoter methylation and/or somatic mutation. Among the novel tumor suppressor genes discovered with dual mechanisms of inactivation, we found a high frequency of genomic and epigenomic alterations in the PAX gene family of transcription factors, which selectively impact canonical NOTCH and TP53 pathways to determine cell fate, cell survival, and genome maintenance. Our results highlight the importance of assessing TSGs at the genomic and epigenomic level to identify key pathways in HNSCC, deregulated by simultaneous promoter methylation and somatic mutations.
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NOTCH1 mutations have been reported to occur in 10% to 15% of head and neck squamous cell carcinomas (HNSCC). To determine the significance of these mutations, we embarked upon a comprehensive study of NOTCH signaling in a cohort of 44... more
NOTCH1 mutations have been reported to occur in 10% to 15% of head and neck squamous cell carcinomas (HNSCC). To determine the significance of these mutations, we embarked upon a comprehensive study of NOTCH signaling in a cohort of 44 HNSCC tumors and 25 normal mucosal samples through a set of expression, copy number, methylation, and mutation analyses. Copy number increases were identified in NOTCH pathway genes, including the NOTCH ligand JAG1. Gene set analysis defined a differential expression of the NOTCH signaling pathway in HNSCC relative to normal tissues. Analysis of individual pathway-related genes revealed overexpression of ligands JAG1 and JAG2 and receptor NOTCH3. In 32% of the HNSCC examined, activation of the downstream NOTCH effectors HES1/HEY1 was documented. Notably, exomic sequencing identified 5 novel inactivating NOTCH1 mutations in 4 of the 37 tumors analyzed, with none of these tumors exhibiting HES1/HEY1 overexpression. Our results revealed a bimodal pattern of NOTCH pathway alterations in HNSCC, with a smaller subset exhibiting inactivating NOTCH1 receptor mutations but a larger subset exhibiting other NOTCH1 pathway alterations, including increases in expression or gene copy number of the receptor or ligands as well as downstream pathway activation. Our results imply that therapies that target the NOTCH pathway may be more widely suitable for HNSCC treatment than appreciated currently.
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Head and neck squamous cell carcinoma (HNSCC) is largely divided into two groups based on their etiology, human papillomavirus (HPV)-positive and –negative. Global DNA methylation changes are known to drive oncogene and tumor suppressor... more
Head and neck squamous cell carcinoma (HNSCC) is largely divided into two groups based on their etiology, human papillomavirus (HPV)-positive and –negative. Global DNA methylation changes are known to drive oncogene and tumor suppressor expression in primary HNSCC of both types. However, significant heterogeneity in DNA methylation within the groups results in different transcriptional profiles and clinical outcomes. We applied a meta-pathway analysis to link gene expression changes to DNA methylation in distinguishing HNSCC subtypes. This approach isolated specific epigenetic changes controlling expression in HPV− HNSCC that distinguish it from HPV+ HNSCC. Analysis of genes identified Hedgehog pathway activation specific to HPV− HNSCC. We confirmed that GLI1, the primary Hedgehog target, showed higher expression in tumors compared to normal samples with HPV− tumors having the highest GLI1 expression, suggesting that increased expression of GLI1 is a potential driver in HPV− HNSCC. Our algorithm for integration of DNA methylation and gene expression can infer biologically significant molecular pathways that may be exploited as therapeutics targets. Our results suggest that therapeutics targeting the Hedgehog pathway may be of benefit in HPV− HNSCC. Similar integrative analysis of high-throughput coupled DNA methylation and expression datasets may yield novel insights into deregulated pathways in other cancers.
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Abstract Epidermal growth factor receptor (EGFR)-targeted monoclonal antibodies (mAbs), such as cetuximab, execute their antitumor effect in vivo via blockade of receptor-ligand interactions and engagement of Fcγ receptors on immune... more
Abstract Epidermal growth factor receptor (EGFR)-targeted monoclonal antibodies (mAbs), such as cetuximab, execute their antitumor effect in vivo via blockade of receptor-ligand interactions and engagement of Fcγ receptors on immune effector cells which trigger antibody-dependent cell-mediated cytotoxicity (ADCC). We demonstrate that tumors counteract the in vivo antitumor activity of anti-EGFR mAbs by increasing tumor cell-autonomous expression of transforming growth factor-β (TGF-β).
While considerable progress has been made in the analysis of large systems containing a single type of coupled dynamical component (e.g., coupled oscillators or coupled switches), systems containing diverse components (e.g., both... more
While considerable progress has been made in the analysis of large systems containing a single type of coupled dynamical component (e.g., coupled oscillators or coupled switches), systems containing diverse components (e.g., both oscillators and switches) have received much less attention. We analyze large, hybrid systems of interconnected Kuramoto oscillators and Hopfield switches with positive feedback. In this system, oscillator synchronization promotes switches to turn on. In turn, when switches turn on, they enhance the synchrony of the oscillators to which they are coupled. Depending on the choice of parameters, we find theoretically coexisting stable solutions with either (i) incoherent oscillators and all switches permanently off, (ii) synchronized oscillators and all switches permanently on, or (iii) synchronized oscillators and switches that periodically alternate between the on and off states. Numerical experiments confirm these predictions. We discuss how transitions between these steady state solutions can be onset deterministically through dynamic bifurcations or spontaneously due to finite-size fluctuations.
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Numerous methodologies, assays, and databases presently provide candidate targets of transcription factors (TFs). However, TFs rarely regulate their targets universally. The context of activation of a TF can change the transcriptional... more
Numerous methodologies, assays, and databases presently provide candidate targets of transcription factors (TFs). However, TFs rarely regulate their targets universally. The context of activation of a TF can change the transcriptional response of targets. Direct multiple regulation typical to mammalian genes complicates direct inference of TF targets from gene expression data. We present a novel statistic that infers context-specific TF regulation based upon the CoGAPS algorithm, which infers overlapping gene expression patterns resulting from coregulation. Numerical experiments with simulated data showed that this statistic correctly inferred targets that are common to multiple TFs, except in cases where the signal from a TF is negligible relative to noise level and signal from other TFs. The statistic is robust to moderate levels of error in the simulated gene sets, identifying fewer false positives than false negatives. Significantly, the regulatory statistic refines the number of TF targets relevant to cell signaling in gastrointestinal stromal tumors (GIST) to genes consistent with the phosphorylation patterns of TFs identified in previous studies. As formulated, the proposed regulatory statistic has wide applicability to inferring set membership in integrated datasets. This statistic could be naturally extended to account for prior probabilities of set membership or to add candidate gene targets.
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Inference of Transcriptional Regulatory Networks (TRNs) provides insight into the mechanisms driving biological systems, especially mammalian development and disease. Many techniques have been developed for TRN estimation from indirect... more
Inference of Transcriptional Regulatory Networks (TRNs) provides insight into the mechanisms driving biological systems, especially mammalian development and disease. Many techniques have been developed for TRN estimation from indirect biochemical measurements. Although successful when initially tested in model organisms, these regulatory models often fail when applied to data from multicellular organisms where multiple regulation and gene reuse increase dramatically. Non-negative matrix factorization techniques were initially introduced to find non-orthogonal patterns in data, making them ideal techniques for inference in cases of multiple regulation. We review these techniques and their application to TRN analysis.
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Overview: Sequencing of the human genome was completed in 2001. Building on the technology and experience of whole-exome sequencing, numerous cancer genomes have been sequenced, including head and neck squamous cell carcinoma (HNSCC) in... more
Overview: Sequencing of the human genome was completed in 2001. Building on the technology and experience of whole-exome sequencing, numerous cancer genomes have been sequenced, including head and neck squamous cell carcinoma (HNSCC) in 2011. Although DNA sequencing data reveals a complex genome with numerous mutations, the biologic interaction and clinical significance of the overall genetic aberrations are largely unknown. Comprehensive analyses of the tumors using genomics and proteomics beyond sequencing data can potentially accelerate the rate and number of biomarker discoveries to improve biology-driven classification of tumors for prognosis and patient selection for a specific therapy. In this review, we will summarize the current genomic and proteomic technologies, general biomarker-discovery paradigms using the technology and published data in HNSCC---including potential clinical applications and limitations.
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Background: Aberrant activation of signaling pathways downstream of epidermal growth factor receptor (EGFR) has been hypothesized to be one of the mechanisms of cetuximab (a monoclonal antibody against EGFR) resistance in head and neck... more
Background: Aberrant activation of signaling pathways downstream of epidermal growth factor receptor (EGFR) has been hypothesized to be one of the mechanisms of cetuximab (a monoclonal antibody against EGFR) resistance in head and neck squamous cell carcinoma (HNSCC). To infer relevant and specific pathway activation downstream of EGFR from gene expression in HNSCC, we generated gene expression signatures using immortalized keratinocytes (HaCaT) subjected to ligand stimulation and transfected with EGFR, RELA/p65, or HRASVal12D.
Results: The gene expression patterns that distinguished the HaCaT variants and conditions were inferred using the Markov chain Monte Carlo (MCMC) matrix factorization algorithm Coordinated Gene Activity in Pattern Sets (CoGAPS). This approach inferred gene expression signatures with greater relevance to cell signaling pathway activation than the expression signatures inferred with standard linear models. Furthermore, the pathway signature generated using HaCaT-HRASVal12D further associated with the cetuximab treatment response in isogenic cetuximab-sensitive (UMSCC1) and -resistant (1CC8) cell lines.
Conclusions: Our data suggest that the CoGAPS algorithm can generate gene expression signatures that are pertinent to downstream effects of receptor signaling pathway activation and potentially be useful in modeling resistance mechanisms to targeted therapies.
Results: The gene expression patterns that distinguished the HaCaT variants and conditions were inferred using the Markov chain Monte Carlo (MCMC) matrix factorization algorithm Coordinated Gene Activity in Pattern Sets (CoGAPS). This approach inferred gene expression signatures with greater relevance to cell signaling pathway activation than the expression signatures inferred with standard linear models. Furthermore, the pathway signature generated using HaCaT-HRASVal12D further associated with the cetuximab treatment response in isogenic cetuximab-sensitive (UMSCC1) and -resistant (1CC8) cell lines.
Conclusions: Our data suggest that the CoGAPS algorithm can generate gene expression signatures that are pertinent to downstream effects of receptor signaling pathway activation and potentially be useful in modeling resistance mechanisms to targeted therapies.
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Complex network dynamics have been analyzed with models of systems of coupled switches or systems of coupled oscillators. However, many complex systems are composed of components with diverse dynamics whose interactions drive the system's... more
Complex network dynamics have been analyzed with models of systems of coupled switches or systems of coupled oscillators. However, many complex systems are composed of components with diverse dynamics whose interactions drive the system's evolution. We, therefore, introduce a new modeling framework that describes the dynamics of networks composed of both oscillators and switches. Both oscillator synchronization and switch stability are preserved in these heterogeneous, coupled networks. Furthermore, this model recapitulates the qualitative dynamics for the yeast cell cycle consistent with the hypothesized dynamics resulting from decomposition of the regulatory network into dynamic motifs. Introducing feedback into the cell-cycle network induces qualitative dynamics analogous to limitless replicative potential that is a hallmark of cancer. As a result, the proposed model of switch and oscillator coupling provides the ability to incorporate mechanisms that underlie the synchronized stimulus response ubiquitous in biochemical systems.
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Modeling of signal driven transcriptional reprogramming is critical for understanding of organism development, human disease, and cell biology. Many current modeling techniques discount key features of the biological sub-systems when... more
Modeling of signal driven transcriptional reprogramming is critical for understanding of organism development, human disease, and cell biology. Many current modeling techniques discount key features of the biological sub-systems when modeling multi-scale, organism level processes. We present a mechanistic hybrid model, GESSA, which integrates a novel pooled probabilistic Boolean network model of cell signaling and a stochastic simulation of transcription and translation responding to a diffusion model of extra-cellular signals. We apply the model to simulate the well studied cell fate decision process of the vulval precursor cells (VPCs) in C. elegans, using experimentally derived rate constants wherever possible and shared parameters to avoid overfitting. We demonstrate that GESSA recovers (1) the effects of varying scaffold protein concentration on signal strength, (2) amplification of signals in expression, (3) the relative external ligand concentration in a known geometry, and (4) feedback in biochemical networks. We demonstrate that setting model parameters based on wild-type and LIN-12 loss-of-function mutants in C. elegans leads to correct prediction of a wide variety of mutants including partial penetrance of phenotypes. Moreover, the model is relatively insensitive to parameters, retaining the wild-type phenotype for a wide range of cell signaling rate parameters.
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Cancer is a complex disease, resulting from system-wide interactions of biological processes rather than from any single underlying cause. The processes that drive all cancer development and progression have been termed the ‘hallmarks of... more
Cancer is a complex disease, resulting from system-wide interactions of biological processes rather than from any single underlying cause. The processes that drive all cancer development and progression have been termed the ‘hallmarks of cancer’. With the growth of large-scale measurements of numerous molecular and cellular properties, a new approach, cancer systems biology, to understanding the interrelationship between the hallmarks is presently being developed. Cancer systems biology focuses on systems-level analysis and presently strives to develop novel data integration and analysis techniques to model and infer cancer biology and treatment response.
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Summary: Coordinated Gene Activity in Pattern Sets (CoGAPS) provides an integrated package for isolating gene expression driven by a biological process, enhancing inference of biological processes from transcriptomic data. CoGAPS improves... more
Summary: Coordinated Gene Activity in Pattern Sets (CoGAPS) provides an integrated package for isolating gene expression driven by a biological process, enhancing inference of biological processes from transcriptomic data. CoGAPS improves on other enrichment measurement methods by combining a Markov chain Monte Carlo (MCMC) matrix factorization algorithm (GAPS) with a threshold-independent statistic inferring activity on gene sets. The software is provided as open source C++ code built on top of JAGS software with an R interface.
Availability: The R package CoGAPS and the C++ package GAPS-JAGS are provided open source under the GNU Lesser Public License (GLPL) with a users manual containing installation and operating instructions. CoGAPS is available through Bioconductor and depends on the rjags package available through CRAN to interface CoGAPS with GAPS-JAGS.
URL: http://www.cancerbiostats.onc.jhmi.edu/cogaps.cfm
Availability: The R package CoGAPS and the C++ package GAPS-JAGS are provided open source under the GNU Lesser Public License (GLPL) with a users manual containing installation and operating instructions. CoGAPS is available through Bioconductor and depends on the rjags package available through CRAN to interface CoGAPS with GAPS-JAGS.
URL: http://www.cancerbiostats.onc.jhmi.edu/cogaps.cfm
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... Kalman filter Elana Fertig Advisor: Brian R. Hunt November 1, 2006 ... Tellus, 58A:293-306., 2006. [2J MT Chahine, TS Pagano, and Coauthors. AIRS: Improved weather forecasting and providing new data on greenhouse gases. ...
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Many ensemble data assimilation schemes utilize spatial localization so that a small ensemble can capture the unstable degrees of freedom in the model state. These local ensemble-based schemes typically allow the analysis at a given... more
Many ensemble data assimilation schemes utilize spatial localization so that a small ensemble can capture the unstable degrees of freedom in the model state. These local ensemble-based schemes typically allow the analysis at a given location to depend only on observations near that location. Meanwhile, the location of satellite observations cannot be pinpointed in the same manner as conventional observations. We propose a technique to update the state at a given location by assimilating satellite radiance observations that are strongly correlated to the model state there. For satellite retrievals, we propose incorporating the observation error covariance matrix and selecting the retrievals that have errors correlated to observations near the location to be updated. Our selection techniques improve the analysis obtained when assimilating simulated satellite observations with a seven-layer primitive equation model, the SPEEDY model.