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Application of Microarrays to Neurological Disease

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Application of Microarrays to Neurological Disease BASIC SCIENCE SEMINARS IN NEUROLOGY SECTION EDITOR: HASSAN M. FATHALLAH-SHAYKH, MD Application of Microarrays to Neurological Disease Lisa-Marie Sturla, PhD; Ana Fernandez-Teijeiro, MD, PhD; Scott L. Pomeroy, MD, PhD odern microarray-based functional genomics holds great promise for revealing novel molecular and cellular mechanisms of disease. First introduced commercially in 1996, microarrays have been used widely to monitor the expression of thousands of genes in biological samples, as described in the following paragraphs. Other mi- Mcroarray-based genomic applications are also in development, including comparative genomic hy- bridization, on-chip sequencing, and novel drug discovery. For example, DNA array-based com- parative genomic hybridization identifies chromosomal gains and losses with greatly improved resolution compared with conventional methods that use metaphase chromosomes as hybridiza- tion targets.1 This increase in resolution will continue to improve as the technology advances. More- over, microarrays provide a better platform for automation than is possible with standard meta- phase techniques. Where genetic mutations and aberrations are already well characterized, microarrays can be customized to be effectively used as a diagnostic and prognostic tool.2,3 In the field of drug discovery, microarrays have the potential to dramatically enhance progress, being used at all stages from target discovery (through validation of new molecular targets and understanding modes of action) to predicting patient response.4 These devices are beginning to revolu- the application of microarray technology tionize how scientists explore the opera- and emerging data analysis techniques to tion of normal cells in the body and the pediatric brain tumors.8 Using microar- molecular aberrations that underlie medi- rays that monitor the expression of more cal disorders. DNA microarrays, which are than 6800 genes, we endeavored to de- based on well-established principles of finitively differentiate a group of embryo- nucleic acid hybridization, simulta- nal tumors whose diagnosis on the basis neously interrogate thousands of genes.5-7 of morphologic features remains contro- The actual mechanics of data capture from versial and to predict outcome in the most raw material are ever-improving and well common of these tumors, medulloblas- documented, and it is the analysis and dis- toma, for which patient response to treat- covery of meaningful gene expression pat- ment is unpredictable. terns within these data to which we now There are 2 general approaches to data must turn our attention. analysis: supervised and unsupervised. Un- Analytical approaches to gene ex- supervised methods are applied to the en- pression analysis using a cancer classifi- tire gene expression data set without any cation model are illustrated in the recent previous knowledge of sample classifica- article by Pomeroy et al.8 Several impor- tion, allowing an impartial assessment of the tant clinical questions were answered via underlying features within a data set. Two examples of unsupervised methods are prin- From the Division of Neuroscience, Department of Neurology, Children’s Hospital, cipal component analysis and self- Harvard Medical School, Boston, Mass (Drs Sturla, Fernandez-Teijeiro, and Pomeroy); organizing maps (SOMs). Principal com- and the Unidad de Oncologia Pediatrica, Hospital de Cruces-Baracaldo, Basque ponent analysis allowed us to differentiate Country, Spain (Dr Fernandez-Teijeiro). at a molecular level between the different (REPRINTED) ARCH NEUROL / VOL 60, MAY 2003 WWW.ARCHNEUROL.COM 676 ©2003 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 brain tumor types and normal cer- primitive neuroectodermal tumors) in sonic hedgehog (shh)–related pro- ebellum (Figure 1). The marker a data set, may miss more subtle dis- teins are highly expressed in desmo- genes responsible for this distinc- tinctions. We found this to be true for plastic medulloblastomas, suggest- tion supported the conclusion that outcome prediction. Neither princi- ing that they arise as a consequence medulloblastomas are derived from pal component analysis nor SOMs of dysregulated shh signaling. Thus, cerebellar granule cell precursors and identified prognostically significant microarray analysis can identify gene that they are molecularly distinct from subgroups of medulloblastomas, so expression profiles that signify an ac- supratentorial primitive neuroecto- we turned to supervised analysis. Ex- tivated regulatory pathway or inter- dermal tumors. This argues against pression profiles were obtained from acting molecular processes leading to the hypothesis that medulloblasto- 60 children with medulloblastomas a known cellular response. mas are a subset of primitive neuro- who received similar treatment and There are, of course, limita- ectodermal tumors, differing only in whose outcome was known. Super- tions to any approach that involves their location in the cerebellum. Self- vised methods were used to “learn” the generation of such a large amount organizing maps are ideally suited for the distinction between survivors of data for each of a relatively small exploratory data analysis in the gen- and patients who failed treatment group of samples. One of the most sig- erally large and complex data sets gen- (Figure 3). Using take-one-out cross- nificant risks is finding statistically sig- erated in the study of a particular dis- validation, gene expression patterns nificant associations by chance. Con- ease, in our case brain tumors. Using predict survival with substantially sequently, identification of gene SOMs, we identified 2 distinct bio- more accuracy than current clinical expression patterns that may under- logical subtypes of medulloblasto- risk criteria. Several supervised analy- lie the pathogenesis of brain tumors mas with low and high ribosomal pro- sis methods showed a similar degree requires validation. Validation of the tein expression (Figure 2). Electron of accuracy, including k-nearest expression of single genes can be done microscopy subsequently con- neighbor, support vector machines, using well-established techniques firmed that these differences in ribo- and structural pattern localization such as Northern or Western blot- somal gene expression were re- analysis by sequential histograms. ting, as well as immunohistochemis- flected at a cellular level by differences Supervised methods were also try or in situ hybridization. Hypoth- in ribosome biogenesis. Although this used to successfully classify classic eses that arise from the interpretation was not an expected result, it pro- and desmoplastic medulloblasto- of significant patterns of gene expres- vided us with an interesting thera- mas (histologically confirmed by a sion can be tested in a variety of ways. peutic target. Sirolimus and its ana- single neuropathologist). These al- For example, we used electron mi- logues are currently under clinical gorithms allowed us not only to clas- croscopy to demonstrate that tu- investigation in tumors reliant on the sify tumors and predict outcome but mors with increased coordinate ex- PI3K signaling pathway and ribo- also to discover previously un- pression of ribosomal proteins have some biogenesis.9 known relationships between coor- high numbers of free ribosomes. Our This approach, although useful dinate gene expression and tumor gene expression–based outcome pre- in its ability to pull out prominent characteristics. For example, we dem- dictions must be validated in an in- structure (eg, medulloblastoma vs onstrated that the genes encoding dependent, prospective cohort of A B 0.000 0.000 Comp3 Comp3 4.000 13.000 0.000 4.000 Comp2 Comp2 18.000 0.000 0.000 –4.000 Comp1 0.000 Comp1 Medulloblastoma –4.000 –13.000 Malignant Glioma –18.000 Rhabdoid Tumor (Extrarenal) Atypical Rhabdoid Tumor (CNS) Normal Cerebellum PNETs Figure 1. Principal component analysis, with axes representing the 3 principal components (Comp) (linear combinations of genes) accounting for most of the data variance, using all genes exhibiting variation across the data set (A) and using the top 10 genes most highly associated with each tumor class (B). CNS indicates central nervous system; PNETs, primitive neuroectodermal tumors. (REPRINTED) ARCH NEUROL / VOL 60, MAY 2003 WWW.ARCHNEUROL.COM 677 ©2003 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 Class 0 Class 1 40S RIBOSOMAL PROTEIN S19 40S RIBOSOMAL PROTEIN S17 Metallopanstimulin 1 RPL 12 Ribosomal Protein L12 Ribosomal Protein S18 Enhancer of Rudimentary Homolog mRNA RPS5 Ribosomal Protein S5 RPL31 Ribosomal Protein L31 RPL18 Ribosomal Protein L18 Ribosomal Protein S7 Type II Inosine Monophosphate Dehydrogenase Ribosomal Protein S10 MRNA RpS8 Gene for Ribosomal Protein S8 60S RIBOSOMAL PROTEIN L23 CAG-isl 7 (Trinucleotide Repeat-Containing Sequence) RPS 14 Gene (Ribosomal Protein S14) 60S RIBOSOMAL PROTEIN L13 40S RIBOSOMAL PROTEIN S15A Human Ribosomal Protein S24 LAMR1 Laminin Receptor (2H5 Epitope) Ribosomal Protein L27a mRNA Alpha-Tubulin mRNA LCR1 HOMOLOG SnRNP Core Protein Sm D2 mRNA RPL8 Ribosomal Protein L8 5-Aminoimidazole-4-Carboxamide-1-Beta-D-Ribonucleotide Transformylase/Inosinicase RPL7A Ribosomal Protein L7a RPS25 Ribosomal Protein S25 RPL35a Ribosomal Protein L35a RPS21 Ribosomal Protein S21 60S RIBOSOMAL PROTEIN L18A HSPB1 Heat Shock 27kD Protein 1 RPS28 Ribosomal Protein S28 RPS9 Ribosomal Protein S9 Alpha-Tubulin
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