Core module biomarker identification with network exploration for breast cancer metastasis

<p>Abstract</p> <p>Background</p> <p>In a complex disease, the expression of many genes can be significantly altered, leading to the appearance of a differentially expressed "disease module". Some of these genes directly correspond to the disease phenotype, (i.e. "driver" genes), while others represent closely-related first-degree neighbours in gene interaction space. The remaining genes consist of further removed "passenger" genes, which are often not directly related to the original cause of the disease. For prognostic and diagnostic purposes, it is crucial to be able to separate the group of "driver" genes and their first-degree neighbours, (i.e. "core module") from the general "disease module".</p> <p>Results</p> <p>We have developed COMBINER: COre Module Biomarker Identification with Network ExploRation. COMBINER is a novel pathway-based approach for selecting highly reproducible discriminative biomarkers. We applied COMBINER to three benchmark breast cancer datasets for identifying prognostic biomarkers. COMBINER-derived biomarkers exhibited 10-fold higher reproducibility than other methods, with up to 30-fold greater enrichment for known cancer-related genes, and 4-fold enrichment for known breast cancer susceptible genes. More than 50% and 40% of the resulting biomarkers were cancer and breast cancer specific, respectively. The identified modules were overlaid onto a map of intracellular pathways that comprehensively highlighted the hallmarks of cancer. Furthermore, we constructed a global regulatory network intertwining several functional clusters and uncovered 13 confident "driver" genes of breast cancer metastasis.</p> <p>Conclusions</p> <p>COMBINER can efficiently and robustly identify disease core module genes and construct their associated regulatory network. In the same way, it is potentially applicable in the characterization of any disease that can be probed with microarrays.</p

A new approach for biomarker detection using fusion networks

In this thesis we propose a new approach for biomarker detection using single source and fusion networks. Our algorithm detects metastable regions with similar and high weights in networks. Standard methods for biomarker detection analyse the differentially expression, or other biological measurement, of genes, without taking any further biological knowledge into account. Network approaches include further insight into the analysis, nevertheless most of them are limited to the examination of one data type. With our fusion network algorithm we introduce a new and promising approach. We analyse breast cancer gene expression and methylation data using our fusion network approach. The proposed method detects known and novel biomarkers, which are highly supported by breast cancer literature, biological pathways, and classification power

Co-expression module analysis reveals biological processes, genomic gain, and regulatory mechanisms associated with breast cancer progression

<p>Abstract</p> <p>Background</p> <p>Gene expression signatures are typically identified by correlating gene expression patterns to a disease phenotype of interest. However, individual gene-based signatures usually suffer from low reproducibility and interpretability.</p> <p>Results</p> <p>We have developed a novel algorithm Iterative Clique Enumeration (ICE) for identifying relatively independent maximal cliques as co-expression modules and a module-based approach to the analysis of gene expression data. Applying this approach on a public breast cancer dataset identified 19 modules whose expression levels were significantly correlated with tumor grade. The correlations were reproducible for 17 modules in an independent breast cancer dataset, and the reproducibility was considerably higher than that based on individual genes or modules identified by other algorithms. Sixteen out of the 17 modules showed significant enrichment in certain Gene Ontology (GO) categories. Specifically, modules related to cell proliferation and immune response were up-regulated in high-grade tumors while those related to cell adhesion was down-regulated. Further analyses showed that transcription factors NYFB, E2F1/E2F3, NRF1, and ELK1 were responsible for the up-regulation of the cell proliferation modules. IRF family and ETS family proteins were responsible for the up-regulation of the immune response modules. Moreover, inhibition of the PPARA signaling pathway may also play an important role in tumor progression. The module without GO enrichment was found to be associated with a potential genomic gain in 8q21-23 in high-grade tumors. The 17-module signature of breast tumor progression clustered patients into subgroups with significantly different relapse-free survival times. Namely, patients with lower cell proliferation and higher cell adhesion levels had significantly lower risk of recurrence, both for all patients (<it>p </it>= 0.004) and for those with grade 2 tumors (<it>p </it>= 0.017).</p> <p>Conclusions</p> <p>The ICE algorithm is effective in identifying relatively independent co-expression modules from gene co-expression networks and the module-based approach illustrated in this study provides a robust, interpretable, and mechanistic characterization of transcriptional changes.</p

Deregulations of RNA Pol II Subunits in Cancer

Deregulated transcription is a well-known characteristic of cancer cells, with differentially expressed genes being a common feature of several cancers. Often, deregulated transcription is a consequence of alterations in transcription factors (TFs), which play a crucial role in gene expression and can act as tumour suppressors or proto-oncogenes. In eukaryotic organisms, transcription is carried out by three distinct RNA polymerase complexes: Pol I, Pol II, and Pol III. Pol II, specifically, is responsible for transcribing messenger RNA (mRNA), the protein coding part of the genome, as well as long non-coding RNAs (lncRNAs). While there is considerable research on the impact of specific deregulated transcription factors in cancer development, there is a lack of studies focusing on defects within the RNA polymerase complexes and their subunits. This review aims to shed light in particular on the Pol II complex and highlight the deregulation of its subunits that have a significant impact on tumour development, prognosis, and survival. By providing a comprehensive overview of our current understanding of Pol II subunits in cancer, this review emphasizes the importance of further research in this area. It suggests that exploring these subunits’ deregulations could lead to the identification of valuable biomarkers and potential therapeutic targets, making it a topic of collective interest

Yes-associated protein (YAP) in pancreatic cancer: at the epicenter of a targetable signaling network associated with patient survival.

Pancreatic ductal adenocarcinoma (PDAC) is generally a fatal disease with no efficacious treatment modalities. Elucidation of signaling mechanisms that will lead to the identification of novel targets for therapy and chemoprevention is urgently needed. Here, we review the role of Yes-associated protein (YAP) and WW-domain-containing Transcriptional co-Activator with a PDZ-binding motif (TAZ) in the development of PDAC. These oncogenic proteins are at the center of a signaling network that involves multiple upstream signals and downstream YAP-regulated genes. We also discuss the clinical significance of the YAP signaling network in PDAC using a recently published interactive open-access database (www.proteinatlas.org/pathology) that allows genome-wide exploration of the impact of individual proteins on survival outcomes. Multiple YAP/TEAD-regulated genes, including AJUBA, ANLN, AREG, ARHGAP29, AURKA, BUB1, CCND1, CDK6, CXCL5, EDN2, DKK1, FOSL1,FOXM1, HBEGF, IGFBP2, JAG1, NOTCH2, RHAMM, RRM2, SERP1, and ZWILCH, are associated with unfavorable survival of PDAC patients. Similarly, components of AP-1 that synergize with YAP (FOSL1), growth factors (TGFα, EPEG, and HBEGF), a specific integrin (ITGA2), heptahelical receptors (P2Y2R, GPR87) and an inhibitor of the Hippo pathway (MUC1), all of which stimulate YAP activity, are associated with unfavorable survival of PDAC patients. By contrast, YAP inhibitory pathways (STRAD/LKB-1/AMPK, PKA/LATS, and TSC/mTORC1) indicate a favorable prognosis. These associations emphasize that the YAP signaling network correlates with poor survival of pancreatic cancer patients. We conclude that the YAP pathway is a major determinant of clinical aggressiveness in PDAC patients and a target for therapeutic and preventive strategies in this disease

Network-Based Biomarker Discovery : Development of Prognostic Biomarkers for Personalized Medicine by Integrating Data and Prior Knowledge

Advances in genome science and technology offer a deeper understanding of biology while at the same time improving the practice of medicine. The expression profiling of some diseases, such as cancer, allows for identifying marker genes, which could be able to diagnose a disease or predict future disease outcomes. Marker genes (biomarkers) are selected by scoring how well their expression levels can discriminate between different classes of disease or between groups of patients with different clinical outcome (e.g. therapy response, survival time, etc.). A current challenge is to identify new markers that are directly related to the underlying disease mechanism

Functional and Mechanical Role of Splice Variant of Mucin4 (MUC4/X) and Trefoil Factors in Pancreatic Cancer Pathogenesis

Pancreatic Cancer (PC) is one of the vicious cancers as it ranks third in the race of leading cause of cancer-related death. Lack of early diagnostic marker, poor understanding of molecular mechanism of the disease and failure to conventional chemotherapy makes this disease dreadful. Mucin 4 (MUC4), a high molecular weight glycoprotein is one of the top differentially expressed molecules in PC while not expressed in normal pancreas. Accumulating evidence from our lab suggested its tumorigenic role in PC by increasing cell proliferation, invasion, chemotherapy resistance, tumor growth, and metastasis. Previously, our lab and other has identified 24 different splice variant of MUC4 among them MUC4/X is devoid of exon 2 and 3 and MUC4/Y is devoid of exon 2. Exon 2 encodes for the largest domain of MUC4 suggesting that MUC4/X is devoid of the largest domain of MUC4 which variable tandem repeat. Though lots of effort has been made to identify its role in PC, there is still a gap on understanding its splice variant in PC as splice variant has an invaluable role in tumor pathogenesis. Recently splice variant has emerged as one of the key players for tumorigenesis and MUC4 is one of the key players for PC pathogenesis, we aim to identify the functional and mechanical role of MUC4/X, a splice variant which is devoid of the largest domain of MUC4 yet contains all other functional domain, in PC pathogenesis. Thus, in this part of dissertation, we sought to identify the role of splice variant MUC4/X, a unique splice variant of wild-type MUC4 which contain all functional domain except largest tandem repeat. First, we identified that, MUC4/X in aberrantly expressed in poorly differentiated PC clinical sample. Then our invitro experimental evidence suggested overexpression of MUC4/X in PC cells is involved in increased cell proliferation, invasion and metastasis. Moreover, our orthotopic transplantation system also corroborated our in-vitro findings which showed increased volume of tumor and metastasis to distant organ. Using inducible tet-on system to overexpress MUC4/X in the presence of WT-MUC4 in CAPAN-1 cells, we identified that MUC4/X has increased cell proliferation and invasion suggesting their role as tumorigenic alone as well as in the presence of WT-MUC4. Our mechanical investigation indicate that overexpression of MUC4/X led to upregulation of integrin β1-FAK-ERK pathway which might be potential mechanism for MUC4/X mediated PC tumorigenesis. Lack of early effective diagnostic marker and resistance to chemotherapy are the major reasons for poor PC patient outcome. There is a pressing need to identify highly specific and sensitive biomarker as well as precise understanding of chemoresistance of PC. Trefoil factors (TFFs) are small secretory molecules mostly associated with mucin. Their primary role is to protect gastrointestinal tract partnering with mucin. Report on aberrant expression, potential as biomarker and role in tumorigenicity has conveyed for many cancers, however, their role in PC is still elusive. Recently they have emerged as a part of gene signature of classical subtype of PC, a subtype which showed gemcitabine resistance towards PC. As it is high time to identify effective biomarker and understanding the role of chemoresistance in PC, in this part of my thesis, we focused to evaluate TFFs diagnostic potential using a training and validation cohort of PC clinical sample. Here, we comprehensively investigated the diagnostic potential of all the member of trefoil family, i.e., TFF1, TFF2, and TFF3 (TFFs) in combination with CA19.9 for detection of PC. In silico analysis of publicly available datasets and expression analysis from human and spontaneous PC mouse model revealed a significantly increased expression of TFFs in precursor lesions and PC cases. Additionally, we performed a comprehensive analysis in the sample set (n= 377) comprising of independent training and validation set using ELISA consisted of benign controls (BC), chronic pancreatitis (CP), and various stages of PC. Our analysis revealed that TFF1 and TFF2 were significantly elevated in early stages of PC in comparison to BC (P Additionally, we also aim to identify the molecular landscape of TFFs role in gemcitabine resistance of PC which integrates analyzing publicly available cancer genome dataset, dissecting transcriptomic and signaling pathways and identification of biochemical interaction. From TCGA database analysis revealed a significant positive correlation between TFF1 and GR predictor of PC (P=0.0001). Our in vitro studies showed that SW1990-TFF1-KD cells induced apoptosis, reduced colony formation capacity and modulated many apoptotic regulators such increase of cleaved caspases and decrease of CIAP in the presence of gemcitabine. Furthermore, TFF1 was observed to be colocalized with MUC5AC, in human and mouse PC tissues suggesting their partnering are critical for PC pathogenesis. Interestingly, our chromatin immunoprecipitation indicates that 16 fold enrichment of GATA-6, an overexpressed transcription factor in classical subtype of PC, was observed on two distinct TFF1 promoter sites and GATA-6-siRNA repressed expression of TFF1. Moreover, protein-protein docking studies revealed the interaction of TFF1 with CXCR4 at Phe-172, Ser-122 and Glu-1 and TFF1 recombinant protein treatment in SW1990 cells increased CXCR4 mediated downstream signaling critical for GR. In this part, our overall data demonstrate that TFF1 may play a crucial role in gemcitabine resistance which is regulated by GATA6 and by interacting with MUC5AC

Recent Developments in Cancer Systems Biology

This ebook includes original research articles and reviews to update readers on the state of the art systems approach to not only discover novel diagnostic and prognostic biomarkers for several cancer types, but also evaluate methodologies to map out important genomic signatures. In addition, therapeutic targets and drug repurposing have been emphasized for a variety of cancer types. In particular, new and established researchers who desire to learn about cancer systems biology and why it is possibly the leading front to a personalized medicine approach will enjoy reading this book