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Yudong D He

Publications and source records attributed to Yudong D He.

12 recordsLinked to original sources
PubMed2006

Identification of potential pharmacological and toxicological targets differentiating structural analogs by a combination of transcriptional profiling and promoter analysis in LS-180 and Caco-2 adenocarcinoma cell lines.

Detecting and understanding the potential for off-target pharmacological effects is critical in the optimization of lead compounds in drug discovery programs. Compound-mediated activation of the pregnane X receptor (PXR; NR1I2), a key regulator for drug metabolism genes, is often monitored to avoid potential drug-drug interactions. Two structural analogs, MRL-1 and MRL-2, were determined to be equivalent PXR activators in trans-activation assays. To differentiate these two PXR activators, their transcriptional effects were examined in PXR-sufficient (LS180) and PXR-deficient (Caco-2) adenocarcinoma cell lines. Both compounds regulated drug-management genes (e.g. CYP3A4, CYP2B6, UGT1A1 and ABCB1) in LS180 cells, but not in PXR-deficient Caco-2 cells. The potency of MRL-1 and MRL-2 on PXR activation was again equivalent as revealed by a set of 113 genes that were regulated by four prototypical PXR agonists (rifampicin, ritonavir, troglitazone and dexamethasone) in the LS180 cells. The specificity of the PXR signature genes was supported by the enrichment of putative PXR binding sites uncovered by sequence-based promoter analyses. Interestingly, an additional off-target activity of MRL-2 was suggested where sterol response element binding protein binding sites were found enriched in a subset of PXR signature genes. These genes, involved in cholesterol and fatty acid synthesis, were significantly regulated by ritonavir, chlorpromazine and MRL-2, which were linked to the manifestation of phospholipidosis. The present study demonstrates the utility of our approach in the differentiation and selection of lead compounds for drug development.

Benzopyrans↗
PubMed2006

Genomic approach to biomarker identification and its recent applications.

This paper discusses selected activities, issues, and challenges in recent development of analytical methods and applications in biomarker identification and validation using state-of-the-art genomic approaches. Molecular profiling via genomics, proteomics, and metabonomics has opened new windows to study disease states and biological systems. It has also provided exciting opportunities for novel applications in clinical research as well as in drug discovery and development. In the past several years, we have witnessed enormous progress resulting particularly from gene expression profiling of mRNA or transcriptomics. After a brief review on technology advances in gene expression profiling using microarrays, I mainly discuss recent developments of the genomic approaches to biomarker identification and validation in two major types of applications. The first type involves examples in cancer diagnostics and prognostics based on tumor gene expression profiling, whereas the second type involves biomarker applications in drug discovery and development. The focus will be on analytical methods and algorithms that have been developed in recent years facilitating biomarker discovery and application by leveraging genome-wide expression profiles derived from microarrays. Technical issues in experimental design, data processing, error modeling, quality control, figures of merit for performance evaluation, and meta-analysis related to biomarker discovery and application are also discussed. A case study of disease outcome prognosis for breast cancer patients based on tumor expression pattern is presented before closing remarks.

Biomarkers↗
PubMed2005

A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients.

Breast cancer comprises a group of distinct subtypes that despite having similar histologic appearances, have very different metastatic potentials. Being able to identify the biological driving force, even for a subset of patients, is crucially important given the large population of women diagnosed with breast cancer. Here, we show that within a subset of patients characterized by relatively high estrogen receptor expression for their age, the occurrence of metastases is strongly predicted by a homogeneous gene expression pattern almost entirely consisting of cell cycle genes (5-year odds ratio of metastasis, 24.0; 95% confidence interval, 6.0-95.5). Overexpression of this set of genes is clearly associated with an extremely poor outcome, with the 10-year metastasis-free probability being only 24% for the poor group, compared with 85% for the good group. In contrast, this gene expression pattern is much less correlated with the outcome in other patient subpopulations. The methods described here also illustrate the value of combining clinical variables, biological insight, and machine-learning to dissect biological complexity. Our work presented here may contribute a crucial step towards rational design of personalized treatment.

Age Factors↗
PubMed2005

A protocol for building and evaluating predictors of disease state based on microarray data.

MOTIVATION: Microarray gene expression data are increasingly employed to identify sets of marker genes that accurately predict disease development and outcome in cancer. Many computational approaches have been proposed to construct such predictors. However, there is, as yet, no objective way to evaluate whether a new approach truly improves on the current state of the art. In addition no 'standard' computational approach has emerged which enables robust outcome prediction. RESULTS: An important contribution of this work is the description of a principled training and validation protocol, which allows objective evaluation of the complete methodology for constructing a predictor. We review the possible choices of computational approaches, with specific emphasis on predictor choice and reporter selection strategies. Employing this training-validation protocol, we evaluated different reporter selection strategies and predictors on six gene expression datasets of varying degrees of difficulty. We demonstrate that simple reporter selection strategies (forward filtering and shrunken centroids) work surprisingly well and outperform partial least squares in four of the six datasets. Similarly, simple predictors, such as the nearest mean classifier, outperform more complex classifiers. Our training-validation protocol provides a robust methodology to evaluate the performance of new computational approaches and to objectively compare outcome predictions on different datasets.

Algorithms↗
PubMed2005

Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival.

Based on the hypothesis that features of the molecular program of normal wound healing might play an important role in cancer metastasis, we previously identified consistent features in the transcriptional response of normal fibroblasts to serum, and used this "wound-response signature" to reveal links between wound healing and cancer progression in a variety of common epithelial tumors. Here, in a consecutive series of 295 early breast cancer patients, we show that both overall survival and distant metastasis-free survival are markedly diminished in patients whose tumors expressed this wound-response signature compared to tumors that did not express this signature. A gene expression centroid of the wound-response signature provides a basis for prospectively assigning a prognostic score that can be scaled to suit different clinical purposes. The wound-response signature improves risk stratification independently of known clinico-pathologic risk factors and previously established prognostic signatures based on unsupervised hierarchical clustering ("molecular subtypes") or supervised predictors of metastasis ("70-gene prognosis signature").

Breast Neoplasms↗
PubMed2004

T lymphocyte activation gene identification by coregulated expression on DNA microarrays.

High-capacity methods for assessing gene function have become increasingly important because of the increasing number of newly identified genes emerging from large-scale genome sequencing and cDNA cloning efforts. We investigated the use of DNA microarrays to identify uncharacterized genes specifically involved in human T cell activation. Activation of human peripheral blood T lymphocytes induced significant changes in hundreds of transcripts, but most of these were not unique to T cell activation. Variation of experimental parameters and analysis techniques allowed better enrichment for gene expression changes unique to T cell activation. Best results were achieved by identification of genes that were most highly coregulated with the T-cell-specific transcript interleukin 2 (IL2) in a "compendium" of experiments involving both T cells and other cell types. Among the genes most highly coregulated with IL2 were many genes known to function during T cell activation, together with ESTs of unknown function. Four of these ESTs were extended to novel full-length clones encoding T-cell-regulated proteins with predicted functions in GTP metabolism, cell organization, and signal transduction.

Base Sequence↗
PubMed2004

Activators of the rat pregnane X receptor differentially modulate hepatic and intestinal gene expression.

Ligand-mediated activation of the pregnane X receptor (PXR, NR1I2) is postulated to affect both hepatic and intestinal gene expression, because of the presence of this nuclear receptor in these important drug metabolizing organs; as such, activation of this receptor may elicit the coordinated regulation of PXR target genes in both tissues. Induction of hepatic and intestinal drug metabolism can contribute to the increased metabolism of drugs, and can result in adverse or undesirable drug-drug interactions. 2(S)-((3,5-bis(Trifluoromethyl)benzyl)-oxy)-3(S)phenyl-4-((3-oxo-1,2,4-triazol-5-yl)methyl)morpholine (L-742694) is a potent activator of the rat PXR and was characterized for its effects on hepatic and intestinal gene expression in female Sprague-Dawley rats by DNA microarray analysis. Transcriptional profiling in liver and small intestine revealed that L-742694 and dexamethasone (DEX) induced the prototypical battery of PXR target genes in liver, including CYP3A, Oatp2, and UGT1A1. In addition, both DEX and L-742694 induced common gene expression profiles that were specific to liver or small intestine, but there was a distinct lack of coordinated gene expression of genes common to both tissues. This pattern of gene regulation occurred in liver and small intestine independent of PXR, constitutive androstane receptor, or hepatic nuclear factor-4alpha expression, suggesting that other factors are involved in controlling the extent of coordinated gene expression in response to a PXR agonist. Overall, these results suggest that ligand-mediated activation of PXR and induction of hepatic, rather than small intestinal, drug metabolism genes would contribute to the increased metabolism of orally administered pharmaceuticals.

Animals↗
PubMed2003

Effects of atmospheric ozone on microarray data quality.

A data anomaly was observed that affected the uniformity and reproducibility of fluorescent signal across DNA microarrays. Results from experimental sets designed to identify potential causes (from microarray production to array scanning) indicated that the anomaly was linked to a batch process; further work allowed us to localize the effect to the posthybridization array stringency washes. Ozone levels were monitored and highly correlated with the batch effect. Controlled exposures of microarrays to ozone confirmed this factor as the root cause, and we present data that show susceptibility of a class of cyanine dyes (e.g., Cy5, Alexa 647) to ozone levels as low as 5-10 ppb for periods as short as 10-30 s. Other cyanine dyes (e.g., Cy3, Alexa 555) were not significantly affected until higher ozone levels (> 100 ppb). To address this environmental effect, laboratory ozone levels should be kept below 2 ppb (e.g., with filters in HVAC) to achieve high quality microarray data.

Artifacts↗
PubMed2003

Microarray standard data set and figures of merit for comparing data processing methods and experiment designs.

MOTIVATION: There is a very large and growing level of effort toward improving the platforms, experiment designs, and data analysis methods for microarray expression profiling. Along with a growing richness in the approaches there is a growing confusion among most scientists as to how to make objective comparisons and choices between them for different applications. There is a need for a standard framework for the microarray community to compare and improve analytical and statistical methods. RESULTS: We report on a microarray data set comprising 204 in-situ synthesized oligonucleotide arrays, each hybridized with two-color cDNA samples derived from 20 different human tissues and cell lines. Design of the approximately 24 000 60mer oligonucleotides that report approximately 2500 known genes on the arrays, and design of the hybridization experiments, were carried out in a way that supports the performance assessment of alternative data processing approaches and of alternative experiment and array designs. We also propose standard figures of merit for success in detecting individual differential expression changes or expression levels, and for detecting similarities and differences in expression patterns across genes and experiments. We expect this data set and the proposed figures of merit will provide a standard framework for much of the microarray community to compare and improve many analytical and statistical methods relevant to microarray data analysis, including image processing, normalization, error modeling, combining of multiple reporters per gene, use of replicate experiments, and sample referencing schemes in measurements based on expression change. AVAILABILITY/SUPPLEMENTARY INFORMATION: Expression data and supplementary information are available at http://www.rii.com/publications/2003/HE_SDS.htm

Base Sequence↗
PubMed2002

A gene-expression signature as a predictor of survival in breast cancer.

BACKGROUND: A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy. METHODS: Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses. RESULTS: Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome. CONCLUSIONS: The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria.

Adult↗
PubMed2002

Gene expression profiling predicts clinical outcome of breast cancer.

Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.

Adult↗