Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees

BACKGROUND: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann–Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. RESULTS: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… CONCLUSIONS: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence

Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees

[[abstract]]Background: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann-Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression.Results: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence.... Conclusions: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence

Data-Driven Computational Modeling of the State and Architecture of the Breast Cancer Kinome

The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies.Doctor of Philosoph

Cellular and molecular consequences of S100A4-induced motility in rat breast tumour Rama 37 cells

Since the first discovery of S100 members in 1965, their expressions have been affiliated with numerous biological functions in all cells of the body. However, in the recent years, S100A4, a member of this superfamily has emerged as the central target in generating new avenue for cancer therapy as its overexpression has been correlated with cancer patients’ mortality as well as established roles as motility and metastasis promoter. As it has no catalytic activity, S100A4 has to interact with its target proteins to regulate such effects. Up to date, more than 10 S100A4 target proteins have been identified but the mechanical process regulated by S100A4 to induce motility remains vague. In this work, we demonstrated that S100A4 overexpression resulted in actin filaments disorganisation, reduction in focal adhesions, instability of filopodia as well as exhibiting polarised morphology. However, such effects were not observed in truncated versions of S100A4 possibly highlighting the importance of C terminus of S100A4 target recognition. In order to assess some of the intracellular mechanisms that may be involved in promoting migrations, different strategies were used, including active pharmaceutical agents, inhibitors and knockdown experiments. Treatment of S100A4 overexpressing cells with blebbistatin and Y-27632, non muscle myosin IIA (NMMIIA) inhibitors, as well as knockdown of NMMIIA, resulted in motility enhancement and focal adhesions reduction proposing that NMMIIA assisted S100A4 in regulating cell motility but its presence is not essential. Further work done using Cos 7 cell lines, naturally lacking NMMIIA, further demonstrated that S100A4 is capable of regulating cell motility independent of NMMIIA, possibly through poor maturation of focal adhesion. Given that all these experiments highlighted the independency of NMMIIA towards migration, a protein that has been put at the forefront of S100A4-induced motility, we aimed to gather further understanding regarding the other molecular mechanisms that may be at play for motility. Using high throughput imaging (HCI), 3 compounds were identified to be capable of inhibiting S100A4-mediated migration. Although we have yet to investigate the underlying mechanism for their effects, these compounds have been shown to target membrane proteins and the externalisation of S100 proteins, for at least one of the compounds, leading us to speculate that preventing externalisation of S100A4 could potentially regulate cell motility

Regulation of gene expression in the immune system and in virally-transformed cells

The correct development and functioning of the immune system is critical for the defence of the host organism against pathogens and cancers. V(D)J recombination generates diversity of immunoglobulin (Ig) and T cell receptor (TCR) genes by the regulated joining of variable (V), diversity (D) and joining (J) gene segments. Tissue-specific enhancers in the DNA genome activate these genes to undergo recombina-tion by triggering non-coding transcription through the recombining gene segments, following interaction with the respective promoters. How this is achieved is un-known. The specificity of enhancer/promoter interactions was examined using the murine Igλ chain locus. The transcription factors that bind to the three main promoters were identified by DNase I footprinting. Of these, a factor termed E47 was shown to interact with IRF4 by co-immunoprecipitation experiments. The importance of these interactions was confirmed by mutagenesis where it was shown that mutations of any of the binding sites in DNA for the transcription factors or mutations in the amino acids involved in protein-protein interactions decreased the rate of transcrip-tion. Together, these studies suggest that IRF4/E47 interactions may play a key role in triggering locus activation. RNA-Seq data from HPV-positive samples and cell lines were analysed to identify putative biomarkers for cervical cancer. Infection with HPVs is the main cause for cervical cancer accounting for 10-15% of cancer-related deaths in women world-wide. It is established that HPVs escape the immune response over decades to es-tablish tumorigenesis but the specific mechanism is unknown. Virus integration into the host genome and deregulation of several genes may play a key role in promot-ing cancer; of particular interest are those transcripts that form the “surfacesome”. Among these, particular interest was given to connexin 26 (Cx26), which is classified as cancer-predisposition gene and was found to be commonly down regulated in all samples analysed. Recombinant adenoviruses expressing the two HPV16 oncogenes were generated and employed to transduce HaCaT cells to analyse Cx26 mRNA and protein levels coupled with dye transfer assays to study the structural behaviour of connexins. The data presented showed that E6 and E7 alter Cx26 protein expression by relocating Cx26 within the cytoplasm from the membrane-bound form. This was confirmed in the dye transfer assay where cell-cell communications were los