Genetics, Personalized Medicine, and Clinical Epidemiology
Expectations, validity, and reality in gene expression profiling

https://doi.org/10.1016/j.jclinepi.2010.02.018Get rights and content

Abstract

Objective

To provide a critical overview of gene expression profiling methodology and discuss areas of future development.

Results

Gene expression profiling has been used extensively in biological research and has resulted in significant advances in the understanding of the molecular mechanisms of complex disorders, including cancer, heart disease, and metabolic disorders. However, translating this technology into genomic medicine for use in diagnosis and prognosis faces many challenges. In addition, gene expression profile analysis is frequently controversial, because its conclusions often lack reproducibility and claims of effective dissemination into translational medicine have, in some cases, been remarkably unjustified. In the last decade, a large number of methodological and technical solutions have been offered to overcome the challenges.

Study Design and Setting

We consider the strengths, limitations, and appropriate applications of gene expression profiling techniques, with particular reference to the clinical relevance.

Conclusion

Some studies have demonstrated the ability and clinical utility of gene expression profiling for use as diagnostic, prognostic, and predictive molecular markers. The challenges of gene expression profiling lie with the standardization of analytic approaches and the evaluation of the clinical merit in broader heterogeneous populations by prospective clinical trials.

Introduction

What is new?

  • DNA microarrays became a widely used tool in the biomedical research and testing ground for novel statistical methodologies.

  • Many biological, technological, statistical and informatics challenges exist.

  • Gene expression profiling, when used properly, offers the potential for development of clinically-relevant biomarkers.

Microarray technology has become a widely used tool for genome-wide gene expression profiling, where expression levels of thousands of genes are measured at once. It is hoped that, by analyzing patterns of gene expression (e.g., profiling), scientists will be able to better understand the molecular etiology of multifactorial disorders, such as obesity, diabetes, heart disease, or cancer. Microarray technology offers an opportunity to pinpoint a few genes that may be the “key players” in the observable biological phenomena as well as to view a “big picture” and reveal important multigene interactions and understand changes at the level of molecular pathways and networks.

However, major challenges exist. Successful microarray experiment requires proper planning and sound experimental design that accounts for various sources of variability; careful sampling and preparation of biological material; thorough array processing, hybridization, scanning, and image analysis. The application of different analytical approaches to these massive data sets can result in different outcomes. Additionally, comparison of results obtained using different microarray platforms remains a challenge because of various informatic issues. Thus, the success of microarray studies depends not only on the quality of experimental design and data but also on the statistical and bioinformatic methods of analysis. Herein, we discuss advances in gene expression–profiling studies over the last decade and identify areas that need further research. There are many related fields where transcriptional data are used that we do not cover, such as eQTL studies, systems biology, or genome-wide association studies [1], [2], [3].

Section snippets

Expectations of gene expression–profiling studies

DNA microarray experiments are typically designed to achieve one or several of the following objectives: (1) to identify individual genes (transcripts) whose expression is correlated with a phenotypic trait, such as response to treatment; (2) to identify multiple genes that are interactively involved in regulatory networks and in mediating biological phenomena or disease pathogenesis; (3) to discover potential molecular targets for drug development; and (4) to identify molecular markers that

Clinical and therapeutic merits of gene expression profiling

Gene expression profiling is changing the approach to discovery of biomarkers in clinical research. Gene expression profiling of disease suggests reliance on the characteristic genomic “signatures” (groups of genes that can discriminate disease samples from healthy samples) with prognostic and predictive implications in clinical settings rather than on traditional clinical prognostic assessment. For example, investigators from The Netherlands Cancer Institute in Amsterdam constructed a gene

Challenges of gene expression–profiling studies

Gene expression profiling seems to be valuable in the discovery of molecular markers for potential use in diagnosis or as therapeutic targets. However, translating this technology into genomic medicine is still a work in progress. To better understand strengths and limitations of gene expression–profiling techniques, we need to understand the biological, technological, statistical, and informatic challenges and caveats.

Integration of microarray studies

Genomic scale profiling of gene expression has become a routine method in various areas of biological research. Of major concern is the reproducibility and validity of published results that complicate comparisons of results from multiple studies [76]. Validating the results is difficult or almost impossible—related studies often lead to contradictory results, even if they investigate the same biological phenomena. Interexperiment variation can also affect the fundamental comparison of results.

Acknowledgments

This work was supported in part by NIH/NIA P01AG025532 (K.K.) and NIH/NIDDK P30DK056336 (D.B.A.). K. Kim and S. O. Zakharkin contributed equally.

References (91)

  • K. Ikeo et al.

    CIBEX: center for information biology gene expression database

    C R Biol

    (2003)
  • R. Bellazzi et al.

    Predictive data mining in clinical medicine: current issues and guidelines

    Int J Med Inform

    (2008)
  • N.S. Baliga

    Systems biology. The scale of prediction

    Science

    (2008)
  • T.A. Manolio et al.

    The HapMap and genome-wide association studies in diagnosis and therapy

    Annu Rev Med

    (2009)
  • K. Kim et al.

    Genes and networks expressed in perioperative omental adipose tissue are correlated with weight loss from Roux-en-Y gastric bypass

    Int J Obes (Lond)

    (2008)
  • L.J. van't Veer et al.

    Gene expression profiling predicts clinical outcome of breast cancer

    Nature

    (2002)
  • M.J. van de Vijver et al.

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

    N Engl J Med

    (2002)
  • M.P. Jansen et al.

    Molecular classification of tamoxifen-resistant breast carcinomas by gene expression profiling

    J Clin Oncol

    (2005)
  • K. Iwao-Koizumi et al.

    Prediction of docetaxel response in human breast cancer by gene expression profiling

    J Clin Oncol

    (2005)
  • C. Cheadle et al.

    Stability regulation of mRNA and the control of gene expression

    Ann N Y Acad Sci

    (2005)
  • L. Thorrez et al.

    Using ribosomal protein genes as reference: a tale of caution

    PLoS One

    (2008)
  • S.L. Karsten et al.

    Gene expression analysis of neural cells and tissues using DNA microarrays

    Curr Protoc Neurosci

    (2008)
  • D.E. Malarkey et al.

    New insights into functional aspects of liver morphology

    Toxicol Pathol

    (2005)
  • Y. Niyaz et al.

    Noncontact laser microdissection and pressure catapulting: sample preparation for genomic, transcriptomic, and proteomic analysis

    Methods Mol Med

    (2005)
  • F. Medeiros et al.

    Tissue handling for genome-wide expression analysis: a review of the issues, evidence, and opportunities

    Arch Pathol Lab Med

    (2007)
  • S. Paik et al.

    Technology insight: application of molecular techniques to formalin-fixed paraffin-embedded tissues from breast cancer

    Nat Clin Pract Oncol

    (2005)
  • V. Popovici et al.

    Selecting control genes for RT-QPCR using public microarray data

    BMC Bioinform

    (2009)
  • R.A. Moor et al.

    Prion protein misfolding and disease

    Curr Opin Struct Biol

    (2009)
  • J.E. Larkin et al.

    Independence and reproducibility across microarray platforms

    Nat Methods

    (2005)
  • R.A. Irizarry et al.

    Multiple-laboratory comparison of microarray platforms

    Nat Methods

    (2005)
  • T. Bammler et al.

    Standardizing global gene expression analysis between laboratories and across platforms

    Nat Methods

    (2005)
  • R. Singh et al.

    An integrated reaction-transport model for DNA surface hybridization: implications for DNA microarrays

    Ann Biomed Eng

    (2009)
  • X. Li et al.

    Selection of optimal oligonucleotide probes for microarrays using multiple criteria, global alignment and parameter estimation

    Nucleic Acids Res

    (2005)
  • R. Wernersson et al.

    Probe selection for DNA microarrays using OligoWiz

    Nat Protoc

    (2007)
  • C.I. Dumur et al.

    Evaluation of quality-control criteria for microarray gene expression analysis

    Clin Chem

    (2004)
  • C. Li et al.

    Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection

    Proc Natl Acad Sci USA

    (2001)
  • E. Hubbell et al.

    Robust estimators for expression analysis

    Bioinformatics

    (2002)
  • R.A. Irizarry et al.

    Exploration, normalization, and summaries of high density oligonucleotide array probe level data

    Biostatistics

    (2003)
  • K. Shedden et al.

    Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data

    BMC Bioinform

    (2005)
  • S.O. Zakharkin et al.

    Sources of variation in Affymetrix microarray experiments

    BMC Bioinform

    (2005)
  • Y.H. Yang et al.

    Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation

    Nucleic Acids Res

    (2002)
  • T.B. Kepler et al.

    Normalization and analysis of DNA microarray data by self-consistency and local regression

    Genome Biol

    (2002)
  • D. Edwards

    Non-linear normalization and background correction in one-channel cDNA microarray studies

    Bioinformatics

    (2003)
  • G.C. Tseng et al.

    Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects

    Nucleic Acids Res

    (2001)
  • D.M. Rocke et al.

    A model for measurement error for gene expression arrays

    J Comput Biol

    (2001)
  • Cited by (50)

    • A novel 4-gene signature model simultaneously predicting malignant risk of oral potentially malignant disorders and oral squamous cell carcinoma prognosis

      2021, Archives of Oral Biology
      Citation Excerpt :

      |logFC| > 2 and p < 0.05 were considered as the threshold to screen out DEGs (Guo et al., 2006; Shi et al., 2008). Considering the bias of GEP data (Kim, Zakharkin, & Allison, 2010), the common DEGs obtained from the 3 GEO datasets were overlapped to get candidate feature genes for the predictive model to make screened DEGs more repeatable. Firstly, the exhaustive method was used to list all possible combinations of candidate genes.

    • Developing the Evidence to Support Clinical Use of Genomics

      2017, Genomic and Precision Medicine: Foundations, Translation, and Implementation: Third Edition
    • Lung cancer transcriptomes refined with laser capture microdissection

      2014, American Journal of Pathology
      Citation Excerpt :

      A major implication of this finding is that many or most of the published lung cancer–derived transcriptomes likely contain the confounding factor of cellular heterogeneity. Thus, it seems that many previous studies are necessarily nonreplicable, because the cellular admixing differs from sample to sample.14–17 To determine the effect of cell selection by LCM on transcriptomes, distinct from that of the preamplification step, we performed a separate set of expression microarray experiments.

    View all citing articles on Scopus
    1

    Currently at Science and Technology, Cadbury, Whippany, NJ, USA.

    View full text