“Topological Significance” Analysis of Gene Expression and Proteomic Profiles from Prostate Cancer Cells Reveals Key Mechanisms of Androgen Response

Background

The problem of prostate cancer progression to androgen independence has been extensively studied. Several studies systematically analyzed gene expression profiles in the context of biological networks and pathways, uncovering novel aspects of prostate cancer. Despite significant research efforts, the mechanisms underlying tumor progression are poorly understood. We applied a novel approach to reconstruct system-wide molecular events following stimulation of LNCaP prostate cancer cells with synthetic androgen and to identify potential mechanisms of androgen-independent progression of prostate cancer.

Methodology/Principal Findings

We have performed concurrent measurements of gene expression and protein levels following the treatment using microarrays and iTRAQ proteomics. Sets of up-regulated genes and proteins were analyzed using our novel concept of “topological significance”. This method combines high-throughput molecular data with the global network of protein interactions to identify nodes which occupy significant network positions with respect to differentially expressed genes or proteins. Our analysis identified the network of growth factor regulation of cell cycle as the main response module for androgen treatment in LNCap cells. We show that the majority of signaling nodes in this network occupy significant positions with respect to the observed gene expression and proteomic profiles elicited by androgen stimulus. Our results further indicate that growth factor signaling probably represents a “second phase” response, not directly dependent on the initial androgen stimulus.

Conclusions/Significance

We conclude that in prostate cancer cells the proliferative signals are likely to be transmitted from multiple growth factor receptors by a multitude of signaling pathways converging on several key regulators of cell proliferation such as c-Myc, Cyclin D and CREB1. Moreover, these pathways are not isolated but constitute an interconnected network module containing many alternative routes from inputs to outputs. If the whole network is involved, a precisely formulated combination therapy may be required to fight the tumor growth effectively.     

Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers

MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.     

Network Signatures of Survival in Glioblastoma Multiforme

To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included “protein kinase cascade,” “IκB kinase/NFκB cascade,” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.     

An Integrative -omics Approach to Identify Functional Sub-Networks in Human Colorectal Cancer

Emerging evidence indicates that gene products implicated in human cancers often cluster together in “hot spots” in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a “proteomics-first” approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to “seed” a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC) from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC. Cross-classification experiments to predict disease class show excellent performance using only a few sub-networks, underwriting the strength of the proposed approach in discovering relevant and reproducible sub-networks.     

The impact on high‐grade serous ovarian cancer of obesity and lipid metabolism‐related gene expression patterns: the underestimated driving force affecting prognosis

To investigate whether specific obesity/metabolism‐related gene expression patterns affect the survival of patients with ovarian cancer. Clinical and genomic data of 590 samples from the high‐grade ovarian serous carcinoma (HGOSC) study of The Cancer Genome Atlas (TCGA) and 91 samples from the Australian Ovarian Cancer Study were downloaded from the International Cancer Genome Consortium (ICGC) portal. Clustering of mRNA microarray and reverse‐phase protein array (RPPA) data was performed with 83 consensus driver genes and 144 obesity and lipid metabolism‐related genes. Association between different clusters and survival was analyzed with the Kaplan–Meier method and a Cox regression. Mutually exclusive, co‐occurrence and network analyses were also carried out. Using RNA and RPPA data, it was possible to identify two subsets of HGOSCs with similar clinical characteristics and cancer driver mutation profiles (e.g. TP53), but with different outcome. These differences depend more on up‐regulation of specific obesity and lipid metabolism‐related genes than on the number of gene mutations or copy number alterations. It was also found that CD36 and TGF‐ß are highly up‐regulated at the protein levels in the cluster with the poorer outcome. In contrast, BSCL2 is highly up‐regulated in the cluster with better progression‐free and overall survival. Different obesity/metabolism‐related gene expression patterns constitute a risk factor for prognosis independent of the therapy results in the Cox regression. Prognoses were conditioned by the differential expression of obesity and lipid metabolism‐related genes in HGOSCs with similar cancer driver mutation profiles, independent of the initial therapeutic response.     

Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets

The Cancer Genome Atlas (TCGA) projects have advanced our understanding of the driver mutations, genetic backgrounds, and key pathways activated across cancer types. Analysis of TCGA datasets have mostly focused on somatic mutations and translocations, with less emphasis placed on gene amplifications. Here we describe a bioinformatics screening strategy to identify putative cancer driver genes amplified across TCGA datasets. We carried out GISTIC2 analysis of TCGA datasets spanning 14 cancer subtypes and identified 461 genes that were amplified in two or more datasets. The list was narrowed to 73 cancer-associated genes with potential “druggable” properties. The majority of the genes were localized to 14 amplicons spread across the genome. To identify potential cancer driver genes, we analyzed gene copy number and mRNA expression data from individual patient samples and identified 40 putative cancer driver genes linked to diverse oncogenic processes. Oncogenic activity was further validated by siRNA/shRNA knockdown and by referencing the Project Achilles datasets. The amplified genes represented a number of gene families, including epigenetic regulators, cell cycle-associated genes, DNA damage response/repair genes, metabolic regulators, and genes linked to the Wnt, Notch, Hedgehog, JAK/STAT, NF-KB and MAPK signaling pathways. Among the 40 putative driver genes were known driver genes, such as EGFR, ERBB2 and PIK3CA. Wild-type KRAS was amplified in several cancer types, and KRAS-amplified cancer cell lines were most sensitive to KRAS shRNA, suggesting that KRAS amplification was an independent oncogenic event. A number of MAP kinase adapters were co-amplified with their receptor tyrosine kinases, such as the FGFR adapter FRS2 and the EGFR family adapter GRB7. The ubiquitin-like ligase DCUN1D1 and the histone methyltransferase NSD3 were also identified as novel putative cancer driver genes. We discuss the patient tailoring implications for existing cancer drug targets and we further discuss potential novel opportunities for drug discovery efforts.     

Comparative analyses identify molecular signature of MRI-classified SVZ-associated glioblastoma

Glioblastoma (GBM) is a highly aggressive brain cancer with limited therapeutic options. While efforts to identify genes responsible for GBM have revealed mutations and aberrant gene expression associated with distinct types of GBM, patients with GBM are often diagnosed and classified based on MRI features. Therefore, we seek to identify molecular representatives in parallel with MRI classification for group I and group II primary GBM associated with the subventricular zone (SVZ). As group I and II GBM contain stem-like signature, we compared gene expression profiles between these 2 groups of primary GBM and endogenous neural stem progenitor cells to reveal dysregulation of cell cycle, chromatin status, cellular morphogenesis, and signaling pathways in these 2 types of MRI-classified GBM. In the absence of IDH mutation, several genes associated with metabolism are differentially expressed in these subtypes of primary GBM, implicating metabolic reprogramming occurs in tumor microenvironment. Furthermore, histone lysine methyltransferase EZH2 was upregulated while histone lysine demethylases KDM2 and KDM4 were downregulated in both group I and II primary GBM. Lastly, we identified 9 common genes across large data sets of gene expression profiles among MRI-classified group I/II GBM, a large cohort of GBM subtypes from TCGA, and glioma stem cells by unsupervised clustering comparison. These commonly upregulated genes have known functions in cell cycle, centromere assembly, chromosome segregation, and mitotic progression. Our findings highlight altered expression of genes important in chromosome integrity across all GBM, suggesting a common mechanism of disrupted fidelity of chromosome structure in GBM.     

A statistical method for identifying differential gene-gene co-expression patterns

MOTIVATION: To understand cancer etiology, it is important to explore molecular changes in cellular processes from normal state to cancerous state. Because genes interact with each other during cellular processes, carcinogenesis related genes may form differential co-expression patterns with other genes in different cell states. In this study, we develop a statistical method for identifying differential gene-gene co-expression patterns in different cell states. RESULTS: For efficient pattern recognition, we extend the traditional F-statistic and obtain an Expected Conditional F-statistic (ECF-statistic), which incorporates statistical information of location and correlation. We also propose a statistical method for data transformation. Our approach is applied to a microarray gene expression dataset for prostate cancer study. For a gene of interest, our method can select other genes that have differential gene-gene co-expression patterns with this gene in different cell states. The 10 most frequently selected genes, include hepsin, GSTP1 and AMACR, which have recently been proposed to be associated with prostate carcinogenesis. However, genes GSTP1 and AMACR cannot be identified by studying differential gene expression alone. By using tumor suppressor genes TP53, PTEN and RB1, we identify seven genes that also include hepsin, GSTP1 and AMACR. We show that genes associated with cancer may have differential gene-gene expression patterns with many other genes in different cell states. By discovering such patterns, we may be able to identify carcinogenesis related genes.     

Integrative subtype discovery in glioblastoma using iCluster

Large-scale cancer genome projects, such as the Cancer Genome Atlas (TCGA) project, are comprehensive molecular characterization efforts to accelerate our understanding of cancer biology and the discovery of new therapeutic targets. The accumulating wealth of multidimensional data provides a new paradigm for important research problems including cancer subtype discovery. The current standard approach relies on separate clustering analyses followed by manual integration. Results can be highly data type dependent, restricting the ability to discover new insights from multidimensional data. In this study, we present an integrative subtype analysis of the TCGA glioblastoma (GBM) data set. Our analysis revealed new insights through integrated subtype characterization. We found three distinct integrated tumor subtypes. Subtype 1 lacks the classical GBM events of chr 7 gain and chr 10 loss. This subclass is enriched for the G-CIMP phenotype and shows hypermethylation of genes involved in brain development and neuronal differentiation. The tumors in this subclass display a Proneural expression profile. Subtype 2 is characterized by a near complete association with EGFR amplification, overrepresentation of promoter methylation of homeobox and G-protein signaling genes, and a Classical expression profile. Subtype 3 is characterized by NF1 and PTEN alterations and exhibits a Mesenchymal-like expression profile. The data analysis workflow we propose provides a unified and computationally scalable framework to harness the full potential of large-scale integrated cancer genomic data for integrative subtype discovery.     

An integrated approach to identify bimodal genes associated with prognosis in câncer

Bimodal gene expression (where a gene expression distribution has two maxima) is associated with phenotypic diversity in different biological systems. A critical issue, thus, is the integration of expression and phenotype data to identify genuine associations. Here, we developed tools that allow both: i) the identification of genes with bimodal gene expression and ii) their association with prognosis in cancer patients from The Cancer Genome Atlas (TCGA). Bimodality was observed for 554 genes in expression data from 25 tumor types. Furthermore, 96 of these genes presented different prognosis when patients belonging to the two expression peaks were compared. The software to execute the method and the corresponding documentation are available at the Data access section.     

An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes

Background

Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play “driver” roles in tumorigenesis, whereas others are only “passengers”.

Results

Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene expression data to identify driver methylation aberrations of cancer. Applying this approach to breast cancer data, we identified many novel cancer driver genes and found that some of the identified driver genes were subtype-specific for basal-like, luminal-A and HER2+ subtypes of breast cancer.

Conclusion

The proposed framework proved effective in identifying cancer driver genes from genome-wide gene methylation and expression data of cancer. These results may provide new molecular targets for potential targeted and selective epigenetic therapy.     

RAD51AP1 promotes progression of ovarian cancer via TGF-β/Smad signalling pathway

Ovarian cancer (OC) is one of the leading causes of female deaths. However, the molecular pathogenesis of OC has still remained elusive. This study aimed to explore the potential genes associated with the progression of OC. In the current study, 3 data sets of OC were downloaded from the GEO database to identify hub gene. Somatic mutation data obtained from TCGA were used to analyse the mutation. Immune cells were used to estimate effect of the hub gene to the tumour microenvironment. RNA-seq and clinical data of OC patients retrieved from TCGA were used to investigate the diagnostic and prognostic values of hub gene. A series of in vitro assays were performed to indicate the function of hub gene and its possible mechanisms in OC. As a result, RAD51AP1 was found as a hub gene, which expression higher was mainly associated with poor survival in OC patients. Up-regulation of RAD51AP1 was closely associated with mutations. RAD51AP1 up-regulation accompanied by accumulated Th2 cells, but reduced CD4 + T cells and CD8 + T cells. Nomogram demonstrated RAD51AP1 increased the accuracy of the model. Down-regulation of RAD51AP1 suppressed proliferation, migration and invasion capabilities of OC cells in vitro. Additionally, scatter plots showed that RAD51AP1 was positively correlated with genes in TGF-β/Smad pathway. The above-mentioned results were validated by RT-qPCR and Western blotting. In conclusion, up-regulation of RAD51AP1 was closely associated with mutations in OC. RAD51AP1 might represent an indicator for predicting OS of OC patients. Besides, RAD51AP1 might accelerate progression of OC by TGF-β/Smad signalling pathway.     

Hypoxic colorectal cancer-secreted exosomes deliver miR-210-3p to normoxic tumor cells to elicit a protumoral effect

Hypoxia, the most common feature in the tumor microenvironment, is closely related to tumor malignant progression and poor patient’s prognosis. Exosomes, initially recognized as cellular “garbage dumpsters”, are now known to be important mediums for mediating cellular communication in tumor microenvironment. However, the mechanisms of hypoxic tumor cell-derived exosomes facilitate colorectal cancer progression still need further exploration. In the present study, we found that exosomes from hypoxic colorectal cancer cells (H-Exos) promoted G1-S cycle transition and proliferation while preventing the apoptosis of colorectal cancer cells by transmitting miR-210-3p to normoxic tumor cells. Mechanistic investigation indicated that miR-210-3p from H-Exos elicited its protumoral effect via suppressing CELF2 expression. A preclinical study further confirmed that H-Exos could promote tumorigenesis in vivo. Clinically, the expression of miR-210-3p in circulating plasma exosomes was markedly upregulated in colorectal cancer patients, which were closely associated with multiple unfavorable clinicopathological features. Taken together, these results suggest that hypoxia may stimulate colorectal cancer cells to secrete miR-210-3p-enriched exosomes in tumor microenvironment, which elicit protumoral effects by inhibiting CELF2 expression. These findings provide new insights on the mechanism of colorectal cancer progression and potential therapeutic targets for colorectal cancer.