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Gene expression profiling in MS: what is the clinical relevance?
Reflection & Reaction Gene expression profiling in MS: what is the clinical relevance?
THE LANCET Neurology Vol 3 May 2004
of the important sources of sample variation include: (1) Intraindividual variation in PBMC gene expression due to diurnal and seasonal cycles, ongoing infection or allergy, and other environmental factors. (2) Interindividual variation owing to the genetic heterogeneity of the human population. (3) Disease stage variation (relapsing-remitting vs secondary progressive). (4) Disease state variation (active inflammation versus clinical and radiographical quiescence). (5) Tech-
Rights were not granted to include this image in electronic media. Please refer to the printed journal.
Microarrays may hold the key
nical differences such as sample preparation, non-specific crosshybridisation, and differences between microarray platforms. Although systematic variation due to technical problems can be minimised or compensated for, biological variation is a much more complex problem. Reducing biological variability of samples will require the study of large numbers of rigorously and extensively phenotyped individuals that have been sampled systematically. In addition, the adoption of standards for publication— such as validation of class-predicting gene sets (the MS “molecular fingerprint”) by use of a population independent to the one used to derive the profile—will be critical for the production of robust tools. These suggestions are easily made but will require major investments in sample collections, clinical phenotype databases, and, in particular, informatics tool design. Nonetheless, the bar must be set high to advance this area of research efficiently and to deliver on the promise of microarray technology. The initial microarray experiments done in small numbers of patients with
http://neurology.thelancet.com
Alfred Pasieka/Science Photo Library
The ability to analyse changes in the expression of every gene in the human genome, and each of its isoforms, is nearly a reality and has captivated the imagination of researchers and clinicians studying human diseases, including multiple sclerosis (MS). Several platforms for the generation of gene expression data now exist, which are commonly referred to as “microarrays”. These tools consist of thousands of complementary DNA samples or oligonucleotides arrayed in grids so that the exact location of each gene probe is known. With these microarrays, expression data can be quickly generated from any biological sample of interest containing RNA. Two recent reviews of ongoing gene discovery efforts have summarised the use of gene expression analysis to identify molecules in MS plaques that may have a role in the pathogenesis of the disease.1,2 In order to develop a practical diagnostic tool and continue the gene discovery effort, several large studies have been initiated to examine gene-expression patterns in the peripheral blood of patients with MS. One small study that was recently published illustrates many of the issues surrounding the development of geneexpression profiles for clinical use.3 Achiron and co-workers3 analysed 26 samples of peripheral blood from patients with MS and identified a collection of genes that were differentially expressed between patients and control individuals. The researchers then used these data to develop a reference gene-expression profile or “molecular fingerprint” of MS. This study has several limitations—the small sample size, the fact that samples were pooled irrespective of disease state and treatment modality, and the lack of validation in an independent sample set. Nevertheless, the results suggest that we may be able to identify patients with MS on the basis of gene expression profiling of fresh peripheral blood mononuclear cells (PBMCs). There are, however, several obstacles to the refinement of microarray data for clinical use. One major issue relates to variation among samples that is not due to the disease itself. Some
MS have shown that there may be differences in gene expression between patients with MS and control individuals and between different phases of the illness (relapse versus remission) in both the CNS and in PBMCs.1–3 However, we now need to improve the analysis of the existing data. The true power of gene expression patterns will come from identifying not only single genes but also molecular pathways, the expression and presumably function of which are different in MS and in the subtypes of the disease. Many of these changes may be subtle and may not be significant on a single gene basis. Furthermore, molecular pathways may be more amenable to therapeutic intervention than single genes because they offer a broader and deeper pool of targets for subtle modulation of a pathway’s activity. Many refinements of the data mining process are beginning to emerge;4 these more efficient and creative analyses will allow for the systematic study of molecular pathways. Such efforts will not only enrich the pool of drug targets but also yield diagnostic and prognostic tools to help clinicians assess what remains largely a clinically defined entity. Studies of gene-expression patterns in large, wellcharacterised populations of patients with MS have been initiated and will yield the breakthroughs needed to refine and validate gene expression profiling as a clinically useful tool in MS. Philip L De Jager and David A Hafler Brigham & Women’s Hospital and Harvard Medical School, Boston and the Broad Institute, Massachusetts Institute of Technology and Harvard University, Cambridge, USA. Email [email protected] Conflict of interest
We have no conflicts of interest. References 1 2 3
4
Steinman L and Zamvil S. Transcriptional analysis of targets in multiple sclerosis. Nat Rev Immunol 2003; 3: 483–92. Lock CB and Heller RA. Gene microarray analysis of multiple sclerosis lesions. Trends Mol Med 2003; 9: 535–41. Achiron A, Gurevich M, Friedman N, Kaminski N, and Mandel M. Blood transcriptional signatures of multiple sclerosis: unique gene expression of disease activity. Ann Neurol 2004; 55: 410–17. Butte A. The use and analysis of microarray data. Nat Rev Drug Discov 2002; 1: 951–60.
269
For personal use. Only reproduce with permission The Lancet Publishing Group.
THE LANCET Neurology Vol 3 May 2004
of the important sources of sample variation include: (1) Intraindividual variation in PBMC gene expression due to diurnal and seasonal cycles, ongoing infection or allergy, and other environmental factors. (2) Interindividual variation owing to the genetic heterogeneity of the human population. (3) Disease stage variation (relapsing-remitting vs secondary progressive). (4) Disease state variation (active inflammation versus clinical and radiographical quiescence). (5) Tech-
Rights were not granted to include this image in electronic media. Please refer to the printed journal.
Microarrays may hold the key
nical differences such as sample preparation, non-specific crosshybridisation, and differences between microarray platforms. Although systematic variation due to technical problems can be minimised or compensated for, biological variation is a much more complex problem. Reducing biological variability of samples will require the study of large numbers of rigorously and extensively phenotyped individuals that have been sampled systematically. In addition, the adoption of standards for publication— such as validation of class-predicting gene sets (the MS “molecular fingerprint”) by use of a population independent to the one used to derive the profile—will be critical for the production of robust tools. These suggestions are easily made but will require major investments in sample collections, clinical phenotype databases, and, in particular, informatics tool design. Nonetheless, the bar must be set high to advance this area of research efficiently and to deliver on the promise of microarray technology. The initial microarray experiments done in small numbers of patients with
http://neurology.thelancet.com
Alfred Pasieka/Science Photo Library
The ability to analyse changes in the expression of every gene in the human genome, and each of its isoforms, is nearly a reality and has captivated the imagination of researchers and clinicians studying human diseases, including multiple sclerosis (MS). Several platforms for the generation of gene expression data now exist, which are commonly referred to as “microarrays”. These tools consist of thousands of complementary DNA samples or oligonucleotides arrayed in grids so that the exact location of each gene probe is known. With these microarrays, expression data can be quickly generated from any biological sample of interest containing RNA. Two recent reviews of ongoing gene discovery efforts have summarised the use of gene expression analysis to identify molecules in MS plaques that may have a role in the pathogenesis of the disease.1,2 In order to develop a practical diagnostic tool and continue the gene discovery effort, several large studies have been initiated to examine gene-expression patterns in the peripheral blood of patients with MS. One small study that was recently published illustrates many of the issues surrounding the development of geneexpression profiles for clinical use.3 Achiron and co-workers3 analysed 26 samples of peripheral blood from patients with MS and identified a collection of genes that were differentially expressed between patients and control individuals. The researchers then used these data to develop a reference gene-expression profile or “molecular fingerprint” of MS. This study has several limitations—the small sample size, the fact that samples were pooled irrespective of disease state and treatment modality, and the lack of validation in an independent sample set. Nevertheless, the results suggest that we may be able to identify patients with MS on the basis of gene expression profiling of fresh peripheral blood mononuclear cells (PBMCs). There are, however, several obstacles to the refinement of microarray data for clinical use. One major issue relates to variation among samples that is not due to the disease itself. Some
MS have shown that there may be differences in gene expression between patients with MS and control individuals and between different phases of the illness (relapse versus remission) in both the CNS and in PBMCs.1–3 However, we now need to improve the analysis of the existing data. The true power of gene expression patterns will come from identifying not only single genes but also molecular pathways, the expression and presumably function of which are different in MS and in the subtypes of the disease. Many of these changes may be subtle and may not be significant on a single gene basis. Furthermore, molecular pathways may be more amenable to therapeutic intervention than single genes because they offer a broader and deeper pool of targets for subtle modulation of a pathway’s activity. Many refinements of the data mining process are beginning to emerge;4 these more efficient and creative analyses will allow for the systematic study of molecular pathways. Such efforts will not only enrich the pool of drug targets but also yield diagnostic and prognostic tools to help clinicians assess what remains largely a clinically defined entity. Studies of gene-expression patterns in large, wellcharacterised populations of patients with MS have been initiated and will yield the breakthroughs needed to refine and validate gene expression profiling as a clinically useful tool in MS. Philip L De Jager and David A Hafler Brigham & Women’s Hospital and Harvard Medical School, Boston and the Broad Institute, Massachusetts Institute of Technology and Harvard University, Cambridge, USA. Email [email protected] Conflict of interest
We have no conflicts of interest. References 1 2 3
4
Steinman L and Zamvil S. Transcriptional analysis of targets in multiple sclerosis. Nat Rev Immunol 2003; 3: 483–92. Lock CB and Heller RA. Gene microarray analysis of multiple sclerosis lesions. Trends Mol Med 2003; 9: 535–41. Achiron A, Gurevich M, Friedman N, Kaminski N, and Mandel M. Blood transcriptional signatures of multiple sclerosis: unique gene expression of disease activity. Ann Neurol 2004; 55: 410–17. Butte A. The use and analysis of microarray data. Nat Rev Drug Discov 2002; 1: 951–60.
269
For personal use. Only reproduce with permission The Lancet Publishing Group.