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Biomedical subjects

Holly Dressman

Publications and source records attributed to Holly Dressman.

12 recordsLinked to original sources
PubMed2006

Profiling of CD4+, CD8+, and CD4+CD25+CD45RO+FoxP3+ T cells in patients with malignant glioma reveals differential expression of the immunologic transcriptome compared with T cells from healthy volunteers.

PURPOSE: Analyses of T-cell mRNA expression profiles in glioblastoma multiforme has not been previously reported but may help to define and characterize the immunosuppressed phenotype in patients with this type of cancer. EXPERIMENTAL DESIGN: We did microarray studies that have shown significant and fundamental differences in the expression profiles of CD4(+) and CD8(+) T cells and immunosuppressive CD4(+)CD25(+)CD45RO(+)FoxP3(+) regulatory T cells (T(reg)) from normal healthy volunteers compared with patients with newly diagnosed glioblastoma multiforme. For these investigations, we isolated total RNA from enriched CD4(+) and CD8(+) T cell or T(reg) cell populations from age-matched individuals and did microarray analyses. RESULTS: ANOVA and principal components analysis show that the various T cell compartments exhibit consistently similar mRNA expression profiles among individuals within either healthy or brain tumor groups but reflect significant differences between these groups. Compared with healthy volunteers, CD4(+) and CD8(+) T cells from patients with glioblastoma multiforme display coordinate down-regulation of genes involved in T cell receptor ligation, activation, and intracellular signaling. In contrast, T(regs) from patients with glioblastoma multiforme exhibit increased levels of transcripts involved in inhibiting host immunity. CONCLUSION: Our findings support the notion that key differences between expression profiles in T-cell populations from patients with glioblastoma multiforme results from differential expression of the immunologic transcriptome, such that a limited number of genes are principally important in producing the dysregulated T-cell phenotype.

Adult↗
PubMed2006

Genetic analysis of intracranial tumors in a murine model of glioma demonstrate a shift in gene expression in response to host immunity.

For the study of malignant glioma, we have previously characterized a highly tumorigenic murine astrocytoma, SMA-560, which arose spontaneously in an inbred, immunocompetent VM/Dk mouse. Using this cell line as a model of murine glioma, we performed DNA microarray analysis of autologous normal murine astroctyes (NMA) and SMA-560 tumor cells grown in monolayer culture or intracranially in syngeneic immunocompetent or immunocompromised hosts in order to determine whether tumors grown in vitro recreate the complex genetic regulation that occurs in vivo. Our findings support our hypothesis that glioma phenotype in vitro may be quite different in vivo and significantly altered by in situ growth factors and other invading cell populations.

Animals↗
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↗
PubMed2005

DIG--a system for gene annotation and functional discovery.

SUMMARY: We describe a database and information discovery system named DIG (Duke Integrated Genomics) designed to facilitate the process of gene annotation and the discovery of functional context. The DIG system collects and organizes gene annotation and functional information, and includes tools that support an understanding of genes in a functional context by providing a framework for integrating and visualizing gene expression, protein interaction and literature-based interaction networks.

Chromosome Mapping↗
PubMed2004

Gene expression phenotypes of atherosclerosis.

OBJECTIVE: Fulfilling the promise of personalized medicine by developing individualized diagnostic and therapeutic strategies for atherosclerosis will depend on a detailed understanding of the genes and gene variants that contribute to disease susceptibility and progression. To that end, our group has developed a nonbiased approach congruent with the multigenic concept of complex diseases by identifying gene expression patterns highly associated with disease states in human target tissues. METHODS AND RESULTS: We have analyzed a collection of human aorta samples with varying degrees of atherosclerosis to identify gene expression patterns that predict a disease state or potential susceptibility. We find gene expression signatures that relate to each of these disease measures and are reliable and robust in predicting the classification for new samples with >93% in each analysis. The genes that provide the predictive power include many previously suspected to play a role in atherosclerosis and additional genes without prior association with atherosclerosis. CONCLUSIONS: Hence, we are reporting a novel method for generating a molecular phenotype of disease and then identifying genes whose discriminatory capability strongly implicates their potential roles in human atherosclerosis.

Aorta, Thoracic↗
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↗
PubMed2003

Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction.

Genomic data, particularly genome-scale measures of gene expression derived from DNA microarray studies, has the potential for adding enormous information to the analysis of biological phenotypes. Perhaps the most successful application of this data has been in the characterization of human cancers, including the ability to predict clinical outcomes. Nevertheless, most analyses have used gene expression profiles to define broad group distinctions, similar to the use of traditional clinical risk factors. As a result, there remains considerable heterogeneity within the broadly defined groups and thus predictions fall short of providing accurate predictions for individual patients. One strategy to resolve this heterogeneity is to make use of multiple gene expression patterns that are more powerful in defining individual characteristics and predicting outcomes than any single gene expression pattern. Statistical tree-based classification systems provide a framework for assessing multiple patterns, that we term metagenes, selecting those that are most capable of resolving the biological heterogeneity. Moreover, this framework provides a mechanism to combine multiple forms of data, both genomic and clinical, to most effectively characterize individual patients and achieve the goal of personalized predictions of clinical outcomes.

Breast Neoplasms↗
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

Neoadjuvant comparisons of aromatase inhibitors and tamoxifen: pretreatment determinants of response and on-treatment effect.

Adjuvant endocrine therapy reduces the risk of relapse and death from early stage hormone receptor positive breast cancer. However, tamoxifen is only partially effective because of the development of tumor resistance. Aromatase inhibitors (letrozole, anastrozole and exemestane) are also prone to the development of resistance but the pharmacologic action (estrogen deprivation) is distinct and so different mechanisms may be responsible. The problem of endocrine resistance can be directly studied in patients by examining the relationship between predictive tumor biomarkers and clinical outcome. In an example of a prospectively planned biomarker study, tumor samples were examined from a randomized trial of neoadjuvant endocrine treatment in which letrozole proved more effective than tamoxifen in terms of the rate of breast conservation and tumor regression. Interestingly letrozole was more effective at all levels of ER expression and was particularly more efficacious than tamoxifen for tumors that expressed HER1 and/or HER2 (with ER). This suggests that HER1/2 predicts primary tamoxifen resistance and relative sensitivity to potent estrogen deprivation, perhaps because HER1/2 signaling promotes the partial agonist effects of tamoxifen. A Phase 2 study of neoadjuvant letrozole is now underway to focus on gene expression profiling as a mechanism to further investigate the transcriptional programs that underlie resistance and sensitivity to estrogen deprivation. Expression profiles taken at baseline and after 1 month of therapy reveal dramatic reductions in the expression from genes responsible for DNA replication and synthesis, cell cycle progression, suppression of apoptosis and tissue invasion. When enough profiles have been generated it should be possible to detect complex interaction patterns that correctly reclassify ER+ disease into treatment responsive and resistant categories with high probability.

Aromatase Inhibitors↗
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↗