Genomic Biomarkers for Personalized Medicine in Breast Cancer

Breast cancer is the most common malignant disease in Western women. Historically, breast cancer was perceived as a single disease with various clinicalpathological features, and therefore, “one drug fits all” approaches drove the treatment regimens. The advent of genomics studies has led to a new paradigm in which breast cancer is heterogeneous consisting of different diseases from the same organ site. For example, gene expression profiling analysis revealed that estrogen receptor (ER)-positive and ER negative breast cancer are two distinct diseases with different risk factors, clinical presentations, outcomes, and responses to systemic therapies. Consequently, the new paradigm demands a personalized strategy in cancer medicine, in which the selection of treatment regimens for each cancer patient will largely rely on assessment by predictive biomarkers and study of the anatomical and pathological features of the cancer

Systemic similarity analysis of compatibility drug-induced multiple pathway patterns _in vivo_

A major challenge in post-genomic research is to understand how physiological and pathological phenotypes arise from the networks of expressed genes and to develop powerful tools for translating the information exchanged between gene and the organ system networks. Although different expression modules may contribute independently to different phenotypes, it is difficult to interpret microarray experimental results at the level of single gene associations. The global effects and response pathways of small molecules in cells have been investigated, but the quantitative details of the activation mechanisms of multiple pathways _in vivo_ are not well understood. Similar response networks indicate similar modes of action, and gene networks may appear to be similar despite differences in the behaviour of individual gene groups. Here we establish the method for assessing global effect spectra of the complex signaling forms using Global Similarity Index (GSI) in cosines vector included angle. Our approach provides quantitative multidimensional measures of genes expression profile based on drug-dependent phenotypic alteration _in vivo_. These results make a starting point for identifying relationships between GSI at the molecular level and a step toward phenotypic outcomes at a system level to predict action of unknown compounds and any combination therapy

Proceedings of the First International Conference on Toxicogenomics Integrated with Environmental Sciences (TIES-2007)

The First International Conference on Toxicogenomics Integrated with Environmental Sciences (TIES-2007) was held at the North Carolina State University McKimmon Center in Raleigh, North Carolina on October 25th and 26th, 2007. Based on the presentations at the conference and the commitment or interest of the presenters to contribute a manuscript of their research, we compiled this collection of articles as proceedings of the conference and an in-depth topical review of the utility of bioinformatics in the fields of toxicogenomics and environmental genomics

Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data

DNA microarray technology provides a promising approach to the diagnosis and prognosis of tumors on a genome-wide scale by monitoring the expression levels of thousands of genes simultaneously. One problem arising from the use of microarray data is the difficulty to analyze the high-dimensional gene expression data, typically with thousands of variables (genes) and much fewer observations (samples), in which severe collinearity is often observed. This makes it difficult to apply directly the classical statistical methods to investigate microarray data. In this paper, total principal component regression (TPCR) was proposed to classify human tumors by extracting the latent variable structure underlying microarray data from the augmented subspace of both independent variables and dependent variables. One of the salient features of our method is that it takes into account not only the latent variable structure but also the errors in the microarray gene expression profiles (independent variables). The prediction performance of TPCR was evaluated by both leave-one-out and leave-half-out cross-validation using four well-known microarray datasets. The stabilities and reliabilities of the classification models were further assessed by re-randomization and permutation studies. A fast kernel algorithm was applied to decrease the computation time dramatically. (MATLAB source code is available upon request.

GOFFA: Gene Ontology For Functional Analysis – A FDA Gene Ontology Tool for Analysis of Genomic and Proteomic Data

BACKGROUND: Gene Ontology (GO) characterizes and categorizes the functions of genes and their products according to biological processes, molecular functions and cellular components, facilitating interpretation of data from high-throughput genomics and proteomics technologies. The most effective use of GO information is achieved when its rich and hierarchical complexity is retained and the information is distilled to the biological functions that are most germane to the phenomenon being investigated. RESULTS: Here we present a FDA GO tool named Gene Ontology for Functional Analysis (GOFFA). GOFFA first ranks GO terms in the order of prevalence for a list of selected genes or proteins, and then it allows the user to interactively select GO terms according to their significance and specific biological complexity within the hierarchical structure. GOFFA provides five interactive functions (Tree view, Terms View, Genes View, GO Path and GO TreePrune) to analyze the GO data. Among the five functions, GO Path and GO TreePrune are unique. The GO Path simultaneously displays the ranks that order GOFFA Tree Paths based on statistical analysis. The GO TreePrune provides a visual display of a reduced GO term set based on a user's statistical cut-offs. Therefore, the GOFFA visual display can provide an intuitive depiction of the most likely relevant biological functions. CONCLUSION: With GOFFA, the user can dynamically interact with the GO data to interpret gene expression results in the context of biological plausibility, which can lead to new discoveries or identify new hypotheses. AVAILABILITY: GOFFA is available through ArrayTrack software

Development of Estrogen Receptor Beta Binding Prediction Model Using Large Sets of Chemicals

We developed an ERβ binding prediction model to facilitate identification of chemicals specifically bind ERβ or ERα together with our previously developed ERα binding model. Decision Forest was used to train ERβ binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ERβ binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ERβ binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ERβ binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ERα prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ERβ or ERα