Tools for genetics and genomics: Gene expression profiling
- Avrum Spira, MD, MSc
Avrum Spira, MD, MSc
- Chief, Section of Computational Biomedicine
- Professor of Medicine, Pathology and Bioinformatics
- Boston University School of Medicine
- Katrina Steiling, MD, MSc
Katrina Steiling, MD, MSc
- Assistant Professor of Medicine
- Boston University School of Medicine
The genetic basis for disease is determined by the inheritance of genes containing specific sequences of deoxyribonucleic acid (DNA). The phenotypic expression of these genes, through the synthesis of specific proteins, involves interaction with environmental signals that trigger activation of particular genes.
According to the central dogma of biology, ribonucleic acid (RNA) is transcribed from a DNA template; messenger RNA (mRNA) is then translated into protein (figure 1). Transcription and translation underlie gene expression.
Approximately 3 to 5 percent of genes are active in a particular cell, even though all cells have the same information contained in their DNA. Most of the genome is selectively repressed, a property that is governed by the regulation of gene expression, mostly at the level of transcription (ie, the production of messenger RNA from the DNA). In response to a cellular perturbation, changes in gene expression take place that result in the expression of hundreds of gene products and the suppression of others. This molecular heterogeneity can affect when and how a disease presents clinically in an individual with genetic predisposition to a condition and how individuals with a given disease will respond to specific treatments.
Analyses of gene expression can be clinically useful for disease classification, diagnosis, prognosis, and tailoring treatment to underlying genetic determinants of pharmacologic response.
This topic will focus on the role of mRNA in the cell, platforms for profiling mRNA expression, the challenges in interpreting the data from these analyses, and the emerging clinical applications of gene expression measurements. An overview of molecular genetics in clinical oncology is presented separately. (See "Principles of molecular genetics".)
- Brown TA. Genomes 3, 3rd ed, Garland Science, 2007.
- Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993; 75:843.
- Girard A, Sachidanandam R, Hannon GJ, Carmell MA. A germline-specific class of small RNAs binds mammalian Piwi proteins. Nature 2006; 442:199.
- Aravin A, Gaidatzis D, Pfeffer S, et al. A novel class of small RNAs bind to MILI protein in mouse testes. Nature 2006; 442:203.
- Khalil AM, Guttman M, Huarte M, et al. Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression. Proc Natl Acad Sci U S A 2009; 106:11667.
- Dvorák Z, Pascussi JM, Modrianský M. Approaches to messenger RNA detection - comparison of methods. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2003; 147:131.
- International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature 2004; 431:931.
- Alwine JC, Kemp DJ, Stark GR. Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proc Natl Acad Sci U S A 1977; 74:5350.
- Gall JG, Pardue ML. Formation and detection of RNA-DNA hybrid molecules in cytological preparations. Proc Natl Acad Sci U S A 1969; 63:378.
- Jin L, Lloyd RV. In situ hybridization: methods and applications. J Clin Lab Anal 1997; 11:2.
- Nolan T, Hands RE, Bustin SA. Quantification of mRNA using real-time RT-PCR. Nat Protoc 2006; 1:1559.
- VanGuilder HD, Vrana KE, Freeman WM. Twenty-five years of quantitative PCR for gene expression analysis. Biotechniques 2008; 44:619.
- Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995; 270:467.
- Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nat Genet 2002; 32 Suppl:490.
- Pease AC, Solas D, Sullivan EJ, et al. Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proc Natl Acad Sci U S A 1994; 91:5022.
- Nuwaysir EF, Huang W, Albert TJ, et al. Gene expression analysis using oligonucleotide arrays produced by maskless photolithography. Genome Res 2002; 12:1749.
- Wilhelm BT, Landry JR. RNA-Seq-quantitative measurement of expression through massively parallel RNA-sequencing. Methods 2009; 48:249.
- Quackenbush J. Microarray data normalization and transformation. Nat Genet 2002; 32 Suppl:496.
- Brazma A, Hingamp P, Quackenbush J, et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 2001; 29:365.
- R Development Core Team. R: A Language and Environment for Statistical Computing 2009. R Foundation for Statistical Computing. Available at: www.R-project.org (Accessed on December 14, 2009).
- The Mathworks I. The MathWorks - MATLAB and Simulink for Technical Computing 2009. Available at: www.mathworks.com (Accessed on December 14, 2009).
- Faith JJ, Hayete B, Thaden JT, et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 2007; 5:e8.
- Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Statist Soc 1995; 57:289.
- Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 2003; 100:9440.
- Bammler T, Beyer RP, Bhattacharya S, et al. Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods 2005; 2:351.
- Barrett T, Troup DB, Wilhite SE, et al. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res 2009; 37:D885.
- Dai M, Wang P, Boyd AD, et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res 2005; 33:e175.
- Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005; 102:15545.
- Mootha VK, Lindgren CM, Eriksson KF, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003; 34:267.
- Lamb J, Crawford ED, Peck D, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006; 313:1929.
- Dennis G Jr, Sherman BT, Hosack DA, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 2003; 4:P3.
- Ingenuity Systems. Ingenuity Pathway Analysis Software 2009. Available at: www.ingenuity.com (Accessed on December 14, 2009).
- Deng MC, Eisen HJ, Mehra MR, et al. Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling. Am J Transplant 2006; 6:150.
- Spira A, Beane JE, Shah V, et al. Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer. Nat Med 2007; 13:361.
- Beane J, Sebastiani P, Whitfield TH, et al. A prediction model for lung cancer diagnosis that integrates genomic and clinical features. Cancer Prev Res (Phila) 2008; 1:56.
- Whitney DH, Elashoff MR, Porta-Smith K, et al. Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy. BMC Med Genomics 2015; 8:18.
- Silvestri GA, Vachani A, Whitney D, et al. A Bronchial Genomic Classifier for the Diagnostic Evaluation of Lung Cancer. N Engl J Med 2015; 373:243.
- Alexander EK, Kennedy GC, Baloch ZW, et al. Preoperative diagnosis of benign thyroid nodules with indeterminate cytology. N Engl J Med 2012; 367:705.
- Calin GA, Sevignani C, Dumitru CD, et al. Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci U S A 2004; 101:2999.
- Lovat F, Valeri N, Croce CM. MicroRNAs in the pathogenesis of cancer. Semin Oncol 2011; 38:724.
- Esquela-Kerscher A, Slack FJ. Oncomirs - microRNAs with a role in cancer. Nat Rev Cancer 2006; 6:259.
- Calin GA, Dumitru CD, Shimizu M, et al. Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A 2002; 99:15524.
- Calin GA, Ferracin M, Cimmino A, et al. A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia. N Engl J Med 2005; 353:1793.
- Garzon R, Volinia S, Liu CG, et al. MicroRNA signatures associated with cytogenetics and prognosis in acute myeloid leukemia. Blood 2008; 111:3183.
- Yanaihara N, Caplen N, Bowman E, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006; 9:189.
- Yu SL, Chen HY, Chang GC, et al. MicroRNA signature predicts survival and relapse in lung cancer. Cancer Cell 2008; 13:48.
- Raponi M, Dossey L, Jatkoe T, et al. MicroRNA classifiers for predicting prognosis of squamous cell lung cancer. Cancer Res 2009; 69:5776.
- Fanini F, Vannini I, Amadori D, Fabbri M. Clinical implications of microRNAs in lung cancer. Semin Oncol 2011; 38:776.
- Boeri M, Pastorino U, Sozzi G. Role of microRNAs in lung cancer: microRNA signatures in cancer prognosis. Cancer J 2012; 18:268.
- Castañeda CA, Agullo-Ortuño MT, Fresno Vara JA, et al. Implication of miRNA in the diagnosis and treatment of breast cancer. Expert Rev Anticancer Ther 2011; 11:1265.
- Sandhu S, Garzon R. Potential applications of microRNAs in cancer diagnosis, prognosis, and treatment. Semin Oncol 2011; 38:781.
- Nair VS, Maeda LS, Ioannidis JP. Clinical outcome prediction by microRNAs in human cancer: a systematic review. J Natl Cancer Inst 2012; 104:528.
- RNA IN CELL FUNCTION
- MEASURING GENE EXPRESSION
- Expression profiling of single genes or small gene panels
- - Northern blot
- - Ribonuclease protection assay
- - In-situ hybridization
- - Real-time reverse transcription polymerase chain reaction
- - Spotted cDNA arrays
- Genome-wide gene expression profiling
- - Oligonucleotide arrays (microarrays)
- - Transcriptome sequencing
- Microarray analysis and interpretation
- - Normalization, quality assessment, and preprocessing
- Quality assessment
- - Data storage and analysis
- Data storage
- Data analysis
- - The multiple comparison problem
- - Biological interpretation
- Comparison with other microarray datasets
- Enrichment ranking
- OVERVIEW OF CLINICAL APPLICATIONS