Biomedical subjects

Erich Huang

Publications and source records attributed to Erich Huang.

7 recordsLinked to original sources

PubMed2006

Genomic prediction of locoregional recurrence after mastectomy in breast cancer.

PURPOSE: This study aims to explore gene expression profiles that are associated with locoregional (LR) recurrence in breast cancer after mastectomy. PATIENTS AND METHODS: A total of 94 breast cancer patients who underwent mastectomy between 1990 and 2001 and had DNA microarray study on the primary tumor tissues were chosen for this study. Eligible patient should have no evidence of LR recurrence without postmastectomy radiotherapy (PMRT) after a minimum of 3-year follow-up (n = 67) and any LR recurrence (n = 27). They were randomly split into training and validation sets. Statistical classification tree analysis and proportional hazards models were developed to identify and validate gene expression profiles that relate to LR recurrence. RESULTS: Our study demonstrates two sets of gene expression profiles (one with 258 genes and the other 34 genes) to be of predictive value with respect to LR recurrence. The overall accuracy of the prediction tree model in validation sets is estimated 75% to 78%. Of patients in validation data set, the 3-year LR control rate with predictive index more than 0.8 derived from 34-gene prediction models is 91%, and predictive index 0.8 or less is 40% (P = .008). Multivariate analysis of all patients reveals that estrogen receptor and genomic predictive index are independent prognostic factors that affect LR control. CONCLUSION: Using gene expression profiles to develop prediction tree models effectively identifies breast cancer patients who are at higher risk for LR recurrence. This gene expression-based predictive index can be used to select patients for PMRT.

Adult↗

PubMed2004

Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes.

We describe a comprehensive modeling approach to combining genomic and clinical data for personalized prediction in disease outcome studies. This integrated clinicogenomic modeling framework is based on statistical classification tree models that evaluate the contributions of multiple forms of data, both clinical and genomic, to define interactions of multiple risk factors that associate with the clinical outcome and derive predictions customized to the individual patient level. Gene expression data from DNA microarrays is represented by multiple, summary measures that we term metagenes; each metagene characterizes the dominant common expression pattern within a cluster of genes. A case study of primary breast cancer recurrence demonstrates that models using multiple metagenes combined with traditional clinical risk factors improve prediction accuracy at the individual patient level, delivering predictions more accurate than those made by using a single genomic predictor or clinical data alone. The analysis also highlights issues of communicating uncertainty in prediction and identifies combinations of clinical and genomic risk factors playing predictive roles. Implicated metagenes identify gene subsets with the potential to aid biological interpretation. This framework will extend to incorporate any form of data, including emerging forms of genomic data, and provides a platform for development of models for personalized prognosis.

Bayes Theorem↗

PubMed2004

Bayesian analysis of binary prediction tree models for retrospectively sampled outcomes.

Classification tree models are flexible analysis tools which have the ability to evaluate interactions among predictors as well as generate predictions for responses of interest. We describe Bayesian analysis of a specific class of tree models in which binary response data arise from a retrospective case-control design. We are also particularly interested in problems with potentially very many candidate predictors. This scenario is common in studies concerning gene expression data, which is a key motivating example context. Innovations here include the introduction of tree models that explicitly address and incorporate the retrospective design, and the use of nonparametric Bayesian models involving Dirichlet process priors on the distributions of predictor variables. The model specification influences the generation of trees through Bayes' factor based tests of association that determine significant binary partitions of nodes during a process of forward generation of trees. We describe this constructive process and discuss questions of generating and combining multiple trees via Bayesian model averaging for prediction. Additional discussion of parameter selection and sensitivity is given in the context of an example which concerns prediction of breast tumour status utilizing high-dimensional gene expression data; the example demonstrates the exploratory/explanatory uses of such models as well as their primary utility in prediction. Shortcomings of the approach and comparison with alternative tree modelling algorithms are also discussed, as are issues of modelling and computational extensions.

Algorithms↗

PubMed2003

Distinct gene expression phenotypes of cells lacking Rb and Rb family members.

The study of tumor suppressor gene function has been aided by the creation of discrete gene alterations in the mouse. One such example can be seen in the study of tumor suppressor gene function in general and the retinoblastoma (Rb) tumor suppressor in particular. Because the phenotype of a cell is a direct reflection of the gene activity within that cell, a comprehensive analysis of changes in gene activity resulting from the loss of Rb function has the potential to greatly enhance our understanding of Rb biology. We have used DNA microarray analysis to identify gene expression profiles in wild-type and Rb-null mouse embryo fibroblasts, as well as cells lacking other Rb family members, as an approach to developing a more complete understanding of Rb function. In so doing, we have identified gene expression phenotypes that characterize the loss of Rb function, that distinguish a Rb-null cell from a wild-type cell as well as a p107/p130-null cell, and that identify gene regulatory pathways unique to these events. Importantly, the Rb gene expression patterns can identify murine tumors that result from Rb loss of function. We suggest that this is an approach to the eventual understanding of gene regulatory pathways that define a phenotypic state, including those events that lead to tumor development.

Animals↗

PubMed2003

Gene expression predictors of breast cancer outcomes.

BACKGROUND: Correlation of risk factors with genomic data promises to provide specific treatment for individual patients, and needs interpretation of complex, multivariate patterns in gene expression data, as well as assessment of their ability to improve clinical predictions. We aimed to predict nodal metastatic states and relapse for breast cancer patients. METHODS: We analysed DNA microarray data from samples of primary breast tumours, using non-linear statistical analyses to assess multiple patterns of interactions of groups of genes that have predictive value for the individual patient, with respect to lymph node metastasis and cancer recurrence. FINDINGS: We identified aggregate patterns of gene expression (metagenes) that associate with lymph node status and recurrence, and that are capable of predicting outcomes in individual patients with about 90% accuracy. The metagenes defined distinct groups of genes, suggesting different biological processes underlying these two characteristics of breast cancer. Initial external validation came from similarly accurate predictions of nodal status of a small sample in a distinct population. INTERPRETATION: Multiple aggregate measures of profiles of gene expression define valuable predictive associations with lymph node metastasis and disease recurrence for individual patients. Gene expression data have the potential to aid accurate, individualised, prognosis. Importantly, these data are assessed in terms of precise numerical predictions, with ranges of probabilities of outcome. Precise and statistically valid assessments of risks specific for patients, will ultimately be of most value to clinicians faced with treatment decisions.

Adult↗

PubMed2003

Gene expression phenotypic models that predict the activity of oncogenic pathways.

High-density DNA microarrays measure expression of large numbers of genes in one assay. The ability to find underlying structure in complex gene expression data sets and rigorously test association of that structure with biological conditions is essential to developing multi-faceted views of the gene activity that defines cellular phenotype. We sought to connect features of gene expression data with biological hypotheses by integrating 'metagene' patterns from DNA microarray experiments in the characterization and prediction of oncogenic phenotypes. We applied these techniques to the analysis of regulatory pathways controlled by the genes HRAS (Harvey rat sarcoma viral oncogene homolog), MYC (myelocytomatosis viral oncogene homolog) and E2F1, E2F2 and E2F3 (encoding E2F transcription factors 1, 2 and 3, respectively). The phenotypic models accurately predict the activity of these pathways in the context of normal cell proliferation. Moreover, the metagene models trained with gene expression patterns evoked by ectopic production of Myc or Ras proteins in primary tissue culture cells properly predict the activity of in vivo tumor models that result from deregulation of the MYC or HRAS pathways. We conclude that these gene expression phenotypes have the potential to characterize the complex genetic alterations that typify the neoplastic state, whether in vitro or in vivo, in a way that truly reflects the complexity of the regulatory pathways that are affected.

Animals↗

PubMed2003

Gene expression profiling for prediction of clinical characteristics of breast cancer.

We have applied techniques of gene expression analysis to the analysis of human breast cancer by identifying metagene models with the capacity to discriminate breast tumors based on estrogen receptor (ER) status as well as the propensity for lymph node metastasis. We assess the utility and validity of these models in predicting status of tumors in cross-validation determinations. The practical value of such approaches relies on the ability not only to assess relative probabilities of clinical outcomes for future samples but also to provide an honest assessment of the uncertainties associated with such predictive classifications, based on the selection of gene subsets for each validation analysis. This latter point is of critical importance to the ability of applying these methodologies to clinical assessment of tumor phenotype. It is also clear from ER predictions that these analyses identify genes known to be involved in ER function but also identify new candidate genes involved in ER function. We believe these gene expression phenotypes have the potential to characterize the complex genetic alterations that typify the neoplastic state in a way that truly reflects the complexity of the regulatory pathways that are affected.

Breast Neoplasms↗