Technology - Genomics

Microarrays Mediabook: Learning to Read the Dots.

Campbell, AM, 2006. At http://gcat.davidson.edu/Pirelli/index.htm.

Intro. Comments:

Undergraduates can get up to speed on gene chips with the Microarrays MediaBook, a snazzy animated tutorial hosted by biology professor A. Malcolm Campbell of Davidson College in North Carolina. Using the example of yeast cells growing with and without oxygen, the site leads readers through the basics of making and interpreting microarrays. Students can then dig deeper into techniques for analyzing results. They can learn about hierarchical clustering to identify genes that might work together and the significance of fold changes, the alteration in gene activity compared with controls.

Predicting lung cancer by detecting aberrant promoter methylation in sputum.

Palmisano, WA et al., 2000.  Cancer Res. 60(21):5954-5958.

Abstract

Despite the promise of using DNA markers for the early detection of cancer, none has proven universally applicable to the most common and lethal forms of human malignancy. Lung carcinoma, the leading cause of tumor-related death, is a key example of a cancer for which mortality could be greatly reduced through the development of sensitive molecular markers detectable at the earliest stages of disease. By increasing the sensitivity of a PCR approach to detect methylated DNA sequences, we now demonstrate that aberrant methylation of the p16 and/or O6-methylguanine-DNA methyltransferase promoters can be detected in DNA from sputum in 100% of patients with squamous cell lung carcinoma up to 3 years before clinical diagnosis. Moreover, the prevalence of these markers in sputum from cancer-free, high-risk subjects approximates lifetime risk for lung cancer. The use of aberrant gene methylation as a molecular marker system seems to offer a potentially powerful approach to population-based screening for the detection of lung cancer, and possibly the other common forms of human cancer.

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Identification of the Genetic Basis for Complex Disorders by Use of Pooling-Based Genomewide Single-Nucleotide-Polymorphism Association Studies.

Pearson, JV et al., 2007. Am. J. Hum. Genet. 80(1): 126-139.

Abstract:

We report the development and validation of experimental methods, study designs, and analysis software for pooling-based genomewide association (GWA) studies that use high-throughput single-nucleotide-polymorphism (SNP) genotyping microarrays. We first describe a theoretical framework for establishing the effectiveness of pooling genomic DNA as a low-cost alternative to individually genotyping thousands of samples on high-density SNP microarrays. Next, we describe software called "GenePool," which directly analyzes SNP microarray probe intensity data and ranks SNPs by increased likelihood of being genetically associated with a trait or disorder. Finally, we apply these methods to experimental case-control data and demonstrate successful identification of published genetic susceptibility loci for a rare monogenic disease (sudden infant death with dysgenesis of the testes syndrome), a rare complex disease (progressive supranuclear palsy), and a common complex disease (Alzheimer disease) across multiple SNP genotyping platforms. On the basis of these theoretical calculations and their experimental validation, our results suggest that pooling-based GWA studies are a logical first step for determining whether major genetic associations exist in diseases with high heritability.

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Comment

This is a well illustrated and logical presentation the method known as Genome Wide Association studies.  GWA studies use a combination of genomics technologies plus population genetics combined with patient clinical data to identify gene sets that are involved in the complex diseases.  This paper uses Alzheimer Disease and Progressive Supranuclear Palsy as examples.

Microarray Analysis and Tumor Classification

Quackenbush, J, 2006, N. Engl. J. Med. 354(23):2463-2472

Intro Paragraphs

DNA microarray analysis was first described in the mid-1990s as a means to probe the expression of thousands of genes simultaneously and was quickly adopted by the research community for the study of a wide range of biologic processes. Most of the early studies had a simple and powerful design: to compare two biologic classes in order to identify the differential expression of the genes in them — genes with potential relevance to a wide range of biologic processes, such as the progression of cancer, the causes of asthma, heart disease, and neuropsychiatric disorders and the analysis of factors associated with infertility.

Soon after microarrays were introduced, many researchers realized that the technique could be used to find new subclasses in disease states and identify biologic markers (biomarkers) associated with disease and that even the expression patterns of the genes could be used to distinguish subclasses of disease. This realization resulted in a proliferation of searches for patterns of expression that could be used to classify types of tumors and predict the outcome and response to chemotherapy. An example is the Netherlands breast-cancer study, which sought to distinguish between patients who had the same stage of disease but a different response to treatment and a different overall outcome. The study was motivated by the observation that the best clinical predictors of metastasis, including lymph-node status and histologic grade, did not adequately predict clinical outcome, with the result that many patients receive chemotherapy or hormonal therapy regardless of whether they need this additional treatment. The study searched for gene-expression signatures that would indicate which patients would benefit from adjuvant chemotherapy. By profiling tumors of young patients who had received only surgical treatment and searching for correlations with clinical outcome, a signature of poor prognosis consisting of 70 genes was identified and was predictive of a short interval to distant metastasis in patients with tumors that were lymph-node–negative. The analysis showed that microarray-based signatures could outperform clinically based predictions of outcome in identifying patients who would benefit most from adjuvant therapy. These initial results led to a more extensive study that showed that the 70-gene classification profile was a more powerful predictor of disease outcome in young patients with breast cancer than were standard systems based on clinical and histologic criteria.

Journal Link  |  PMID

Comments

This is an excellent, well-illustrated review of the use of gene expression microarrays in the diagnosis and staging of cancers.  In addition to figures which give a good technical overview there is a glossary table defining the various types of 'omics' and the differences in their applications to clinical situations.

Genomic medicine: bringing biomarkers to clinical medicine

Seo, D & Ginsburg, GS, 2005. Curr. Opin. Chem. Biol. 9(4) 381-6

Abstract

An important by-product of sequencing the human genome has been the development of a novel 'toolbox' for biomarker discovery and development. Genomic medicine is an emerging discipline in the genome sciences that integrates these tools to interrogate genomic variation in well-defined populations in order to develop predictors of disease susceptibility, progression and drug response. Several important classes of biomarkers result from these analyses which, when translated to clinical medicine and drug development, will have an important impact on human health and disease. This review highlights both the opportunities and challenges in bringing biomarkers into clinical medicine.

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Comment

Brief description of the biomarker toolbox and listing of promising applications in pharmacogenomics and disease classes. One figure depicts the application to acute coronary syndrome. Tables summarize other potential applications.

Primer on medical genomics. Part III: Microarray experiments and data analysis

Tefferi, A et al., 2002. Mayo Clin. Proc. 77 (9): 927-940

Abstract

Genomics has been defined as the comprehensive study of whole sets of genes, gene products, and their interactions as opposed to the study of single genes or proteins. Microarray technology is one of many novel tools that are allowing global and high-throughput analysis of genes and gene products. In addition to an introduction on underlying principles, the current review focuses on the use of both complementary DNA and oligodeoxynucleotide microarrays in gene expression analysis. Genome-wide experiments generate a massive amount of data points that require systematic methods of analysis to extract biologically useful information. Accordingly, the current educational communication discusses different methods of data analysis, including supervised and unsupervised clustering algorithms. Illustrative clinical examples show clinical applications, including (1) identification of candidate genes or pathological pathways (ie, elucidation of pathogenesis); (2) identification of "new" molecular classes of diseases that may be relevant in disease reclassification, prognostication, and treatment selection (ie, class discovery); and (3) use of expression profiles of known disease classes to predict diagnosis and classification of unknown samples (ie, class prediction). The current review should serve as an introduction to the subject for clinician investigators, physicians and medical scientists in training, practicing clinicians, and other students of medicine.

Journal Link  |  PMID

Comment

Rather thorough description of the types of DNA microarrays, their construction, use and analysis of expression levels. Description of two classic examples of using expression microarray data to classify non-Hodgkin lymphoma and acute leukemia. Description of expression microarray data to distinguish BRAC1 from BRCA2 related breast cancer. Useful figures of the microarray data.