Identifying cancer biomarkers by network-constrained support vector machines

<p>Abstract</p> <p>Background</p> <p>One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.</p> <p>Results</p> <p>We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis.</p> <p>Conclusions</p> <p>We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.</p

Motif-guided sparse decomposition of gene expression data for regulatory module identification

<p>Abstract</p> <p>Background</p> <p>Genes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.</p> <p>Results</p> <p>We propose a novel approach, motif-guided sparse decomposition (mSD), to identify gene regulatory modules by integrating gene expression data and DNA sequence motif information. The mSD approach is implemented as a two-step algorithm comprising estimates of (1) transcription factor activity and (2) the strength of the predicted gene regulation event(s). Specifically, a motif-guided clustering method is first developed to estimate the transcription factor activity of a gene module; sparse component analysis is then applied to estimate the regulation strength, and so predict the target genes of the transcription factors. The mSD approach was first tested for its improved performance in finding regulatory modules using simulated and real yeast data, revealing functionally distinct gene modules enriched with biologically validated transcription factors. We then demonstrated the efficacy of the mSD approach on breast cancer cell line data and uncovered several important gene regulatory modules related to endocrine therapy of breast cancer.</p> <p>Conclusion</p> <p>We have developed a new integrated strategy, namely motif-guided sparse decomposition (mSD) of gene expression data, for regulatory module identification. The mSD method features a novel motif-guided clustering method for transcription factor activity estimation by finding a balance between co-regulation and co-expression. The mSD method further utilizes a sparse decomposition method for regulation strength estimation. The experimental results show that such a motif-guided strategy can provide context-specific regulatory modules in both yeast and breast cancer studies.</p

Co-Inhibition of BCL-W and BCL2 Restores Antiestrogen Sensitivity through BECN1 and Promotes an Autophagy-Associated Necrosis

BCL2 family members affect cell fate decisions in breast cancer but the role of BCL-W (BCL2L2) is unknown. We now show the integrated roles of the antiapoptotic BCL-W and BCL2 in affecting responsiveness to the antiestrogen ICI 182,780 (ICI; Fulvestrant Faslodex), using both molecular (siRNA; shRNA) and pharmacologic (YC137) approaches in three breast cancer variants; MCF-7/LCC1 (ICI sensitive), MCF-7/LCC9 (ICI resistant), and LY2 (ICI resistant). YC137 inhibits BCL-W and BCL2 and restores ICI sensitivity in resistant cells. Co-inhibition of BCL-W and BCL2 is both necessary and sufficient to restore sensitivity to ICI, and explains mechanistically the action of YC137. These data implicate functional cooperation and/or redundancy in signaling between BCL-W and BCL2, and suggest that broad BCL2 family member inhibitors will have greater therapeutic value than targeting only individual proteins. Whereas ICI sensitive MCF-7/LCC1 cells undergo increased apoptosis in response to ICI following BCL-W±BCL2 co-inhibition, the consequent resensitization of resistant MCF-7/LCC9 and LY2 cells reflects increases in autophagy (LC3 cleavage; p62/SQSTM1 expression) and necrosis but not apoptosis or cell cycle arrest. Thus, de novo sensitive cells and resensitized resistant cells die through different mechanisms. Following BCL-W+BCL2 co-inhibition, suppression of functional autophagy by 3-methyladenine or BECN1 shRNA reduces ICI-induced necrosis but restores the ability of resistant cells to die through apoptosis. These data demonstrate the plasticity of cell fate mechanisms in breast cancer cells in the context of antiestrogen responsiveness. Restoration of ICI sensitivity in resistant cells appears to occur through an increase in autophagy-associated necrosis. BCL-W, BCL2, and BECN1 integrate important functions in determining antiestrogen responsiveness, and the presence of functional autophagy may influence the balance between apoptosis and necrosis

Identification of microbial DNA in human cancer

<p>Abstract</p> <p>Background</p> <p>Microorganisms have been associated with many types of human diseases; however, a significant number of clinically important microbial pathogens remain to be discovered.</p> <p>Methods</p> <p>We have developed a genome-wide approach, called Digital Karyotyping Microbe Identification (DK-MICROBE), to identify genomic DNA of bacteria and viruses in human disease tissues. This method involves the generation of an experimental DNA tag library through Digital Karyotyping (DK) followed by analysis of the tag sequences for the presence of microbial DNA content using a compiled microbial DNA virtual tag library.</p> <p>Results</p> <p>To validate this technology and to identify pathogens that may be associated with human cancer pathogenesis, we used DK-MICROBE to determine the presence of microbial DNA in 58 human tumor samples, including brain, ovarian, and colorectal cancers. We detected DNA from Human herpesvirus 6 (HHV-6) in a DK library of a colorectal cancer liver metastasis and in normal tissue from the same patient.</p> <p>Conclusion</p> <p>DK-MICROBE can identify previously unknown infectious agents in human tumors, and is now available for further applications for the identification of pathogen DNA in human cancer and other diseases.</p

The pERK of being a target: Kinase regulation of the orphan nuclear receptor ERRγ

Estrogen-related receptors (ERRs) are orphan members of the nuclear receptor superfamily that are important regulators of mitochondrial metabolism with emerging roles in cancer. In the absence of an endogenous ligand, ERRs are reliant upon other regulatory mechanisms that include protein/protein interactions and post-translational modification, though the cellular and clinical significance of this latter mechanism is unclear. We recently published a study in which we establish estrogen-related receptor gamma (ERRγ) as a target for extracellular signal-regulated kinase (ERK), and show that regulation of ERRγ by ERK has important consequences for the function of this receptor in cellular models of estrogen receptor-positive (ER+) breast cancer. In this Research Highlight, we discuss the implications of these findings from a molecular and clinical perspective

ERRβ splice variants differentially regulate cell cycle progression

<div><p>Orphan receptors comprise nearly half of all members of the nuclear receptor superfamily. Despite having broad structural similarities to the classical estrogen receptors, estrogen-related receptors (ERRs) have their own unique DNA response elements and functions. In this study, we focus on 2 ERRβ splice variants, short form ERRβ (ERRβsf) and ERRβ2, and identify their differing roles in cell cycle regulation. Using DY131 (a synthetic agonist of ERRβ), splice-variant selective shRNA, and exogenous ERRβsf and ERRβ2 cDNAs, we demonstrate the role of ERRβsf in mediating the G1 checkpoint through p21. We also show ERRβsf is required for DY131-induced cellular senescence. A key novel finding of this study is that ERRβ2 can mediate a G2/M arrest in response to DY131. In the absence of ERRβ2, the DY131-induced G2/M arrest is reversed, and this is accompanied by p21 induction and a G1 arrest. This study illustrates novel functions for ERRβ splice variants and provides evidence for splice variant interaction.</p></div

ROS is a master regulator of in vitro matriptase activation.

Matriptase is a type II transmembrane serine protease that is widely expressed in normal epithelial cells and epithelial cancers. Studies have shown that regulation of matriptase expression and activation becomes deranged in several cancers and is associated with poor disease-free survival. Although the central mechanism of its activation has remained unknown, our lab has previously demonstrated that inflammatory conditions such as intracellular pH decrease strongly induces matriptase activation. In this investigation, we first demonstrate clear matriptase activation following Fulvestrant (ICI) and Tykerb (Lapatinib) treatment in HER2-amplified, estrogen receptor (ER)-positive BT474, MDA-MB-361 and ZR-75-30 or single ER-positive MCF7 cells, respectively. This activation modestly involved Phosphoinositide 3-kinase (PI3K) activation and occurred as quickly as six hours post treatment. We also demonstrate that matriptase activation is not a universal hallmark of stress, with Etoposide treated cells showing a larger degree of matriptase activation than Lapatinib and ICI-treated cells. While etoposide toxicity has been shown to be mediated through reactive oxygen species (ROS) and MAPK/ERK kinase (MEK) activity, MEK activity showed no correlation with matriptase activation. Novelly, we demonstrate that endogenous and exogenous matriptase activation are ROS-mediated in vitro and inhibited by N-acetylcysteine (NAC). Lastly, we demonstrate matriptase-directed NAC treatment results in apoptosis of several breast cancer cell lines either alone or in combination with clinically used therapeutics. These data demonstrate the contribution of ROS-mediated survival, its independence of kinase-mediated survival, and the plausibility of using matriptase activation to indicate the potential success of antioxidant therapy