Yudong D. He

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.

Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer

We describe a flexible system for gene expression profiling using arrays of tens of thousands of oligonucleotides synthesized in situ by an ink-jet printing method employing standard phosphoramidite chemistry. We have characterized the dependence of hybridization specificity and sensitivity on parameters including oligonucleotide length, hybridization stringency, sequence identity, sample abundance, and sample preparation method. We find that 60-mer oligonucleotides reliably detect transcript ratios at one copy per cell in complex biological samples, and that ink-jet arrays are compatible with several different sample amplification and labeling techniques. Furthermore, results using only a single carefully selected oligonucleotide per gene correlate closely with those obtained using complementary DNA (cDNA) arrays. Most of the genes for which measurements differ are members of gene families that can only be distinguished by oligonucleotides. Because different oligonucleotide sequences can be specified for each array, we anticipate that ink-jet oligonucleotide array technology will be useful in a wide variety of DNA microarray applications.

Rosetta error model for gene expression analysis

MOTIVATION In microarray gene expression studies, the number of replicated microarrays is usually small because of cost and sample availability, resulting in unreliable variance estimation and thus unreliable statistical hypothesis tests. The unreliable variance estimation is further complicated by the fact that the technology-specific variance is intrinsically intensity-dependent. RESULTS The Rosetta error model captures the variance-intensity relationship for various types of microarray technologies, such as single-color arrays and two-color arrays. This error model conservatively estimates intensity error and uses this value to stabilize the variance estimation. We present two commonly used error models: the intensity error-model for single-color microarrays and the ratio error model for two-color microarrays or ratios built from two single-color arrays. We present examples to demonstrate the strength of our error models in improving statistical power of microarray data analysis, particularly, in increasing expression detection sensitivity and specificity when the number of replicates is limited.

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.

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

Microarrays—the 21st century divining rod?

Artificial neural networks were used to decipher gene-expression signatures collected with DNA microarrays and to classify cancers into specific categories. Will this technology lead to better diagnostic tools and new therapeutic targets? (pages 673–679)

Profiling the Hepatic Effects of Flutamide in Rats: A Microarray Comparison with Classical AhR Ligands and Atypical CYP1A Inducers

The antiandrogen flutamide (FLU) is used primarily for prostate cancer and is an idiosyncratic hepatotoxicant that sometimes causes severe liver problems. To investigate FLUs overt hepatic effects, especially on inducible drug clearance-related gene networks, FLUs hepatic gene expression profile was examined in female Sprague-Dawley rats using ∼22,500 oligonucleotide microarrays. Rats were dosed daily for 3 days with FLU at 500, 250, 62.5, 31.3, and 15.6 mg/kg/day, and hepatic RNA was isolated. FLU resulted in the dose-dependent regulation of ∼350 genes. Employing a gene-response compendium, FLU was compared with three classical aryl hydrocarbon receptor (AhR) ligands, 3-methylcholanthrene, benzo[a]pyrene, and β-naphthoflavone, and four atypical CYP1A inducers, indole-3-carbinol (I3C), omeprazole (OME), chlorpromazine (CPZ), and clotrimazole (CLO). The FLU gene response was comparable with classical AhR ligands across a signature AhR ligand gene set that included CYP1A1 and other members of the AhR gene battery. Dose-related responses of CYP1 genes established a maximum response ceiling and discerned potency differences in atypical inducers. FLU had a sharp down-regulation of c-fos that was comparable with all the compounds except CPZ and CLO. FLU absorption, distribution, metabolism, and excretion (ADME) gene expression analysis revealed that FLU, as well as I3C and OME, induced CYP2B and CYP3A, distinguishing them from the classical AhR ligands. By using a compendium of gene expression profiles, FLU was shown to signal in rats similar to an AhR activator with additional CYP2B and CYP3A effects that most resembled the ADME gene expression pattern of the atypical CYP1A inducers I3C and OME.