Microarrays and Clinical Investigations

n engl j med

350;16

www.nejm.org april

15, 2004

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P E R S P E C T I V E

Microarrays and Clinical Investigations

Edison T. Liu, M.D., and Krishna R. Karuturi, Ph.D.

Two articles in this issue of the

Journal

tell a similarstory: primary acute myeloid leukemia (AML) maybe divided into subclasses according to gene-expres-sion profiles. Given the number of articles that de-scribe the correlative and predictive usefulness ofarray-based molecular classifications, especially inleukemias, these outcomes no longer elicit surprise.But perhaps they should — not only because of theirclinical usefulness, as outlined in the editorial byGrimwade and Haferlach (pages 1676–1678), butalso because of the rapid pace at which expressiongenomics is changing the conduct of clinical re-search.

Not too long ago, a single molecular marker,such as N-

myc

amplification, would be studied alonein the clinical setting, and its individual prognosticcapabilities extracted from the biologic din (e.g.,the French–American–British [FAB] classificationscheme and blast count) with the use of logistic-regression analysis. Large clinical studies involving400 to 1000 patients were often deemed necessaryfor statistical validation. A high level of quantitativeprecision was needed in that single laboratory testin order to uncover associations — not to mentionsatisfying downstream regulatory requirements.

By contrast, the two current articles — one byBullinger et al. (pages 1605–1616) and one by Valket al. (pages 1617–1628) — present what might beconsidered the genomic approach to the discoveryof biomarkers in clinical trials. In each case, thenumber of variables assessed (approximately 26,000genes and 13,000 genes, respectively) far exceededthe number of patients studied (116 and 285, re-spectively). The specific behavior of individual geneswas less informative than the composite movementof defined clusters of genes, and therefore a definedcluster was used as a biomarker; thus, quantitativevariations in individual markers of 20 to 30 percentare easily tolerated. The power of microarray tech-nology is its ability to use somewhat imprecise pat-terns of gene expression rather than exact thresh-olds of individual markers. Most intriguingly, the

clinical and biologic findings are remarkably robust.The results of several independent studies appear tobe consistent, even though relatively small sampleswere used for each study. This new genomic ap-proach to the discovery of biomarkers suggests thata succession of smaller studies, performed quicklyand possibly with the use of progressively bettertechnology, will outperform larger studies that arelocked into outdated approaches. In other words,agility trumps size.

1,2

During this period of dramatic transition, stu-dents of clinical investigation will inevitably be chal-lenged by completely new analytic approaches. Inmicroarray experimentation, the difficulty no long-er lies with the technology, but rather with the con-ceptual heterogeneity of the data analysis. Many in-vestigators are troubled by the “voodoo” nature ofarray analysis and their impression that each studyuses different analytic algorithms. Given these con-cerns, a more common-sense explanation of array-directed, genome-based analyses seems to be inorder.

1,3

Expression microarrays are devices containingtens of thousands of short DNA probes of specifiedsequences, arrayed in an orderly fashion and teth-ered to a flat surface. Each spot on the array corre-sponds to a particular probe, and each probe cor-responds to a short section of a gene. (Often, anindividual spot represents a gene; however, in someformats, several spots cover one gene.) RNA derivedfrom that gene may bind to the probe (or spot); thisis the core reaction of microarray analysis. In stud-ies like those described by Bullinger et al. and Valket al., leukemia RNA is labeled with fluorescent dye.After the labeled RNA is incubated with the mi-croarray and the unbound RNA is washed away, theamount of bound, labeled RNA is measured by theintensity of the fluorescent signal of the spot, whichis indicative of the relative expression of the corre-sponding gene.

Both groups of investigators applied a standardfilter to the expression data in order to eliminate as

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much “noise” as possible before any analysis wasperformed. Only those genes whose expression ac-tually varied among the tumors were included inthe analysis. Both groups of investigators used su-pervised and unsupervised approaches to the analy-sis of array data. A supervised analysis starts with agold-standard class definition — for example, a leu-kemia either has or does not have a t(8;21) translo-cation. Then the algorithm may ask for all genes thatare statistically associated with the translocation sta-tus. An unsupervised analysis does not use any apriori class definitions, instead simply seeking todetermine what structure is inherent in the data.Such an analysis is based on the question “whichgenes organize the leukemias into groups, with noreference to biologic outcomes?” A supervisedanalysis is more likely to uncover putative associ-ations between genes and the cytogenetic classbut may bias the outcome by forcing a model ontothe data.

In the current studies, the two groups of investi-gators sought to identify a core or minimal group ofgenes whose differential expression can be used toassign AMLs to distinct molecular and prognosticclasses. Both groups used what may be describedas an orthogonal approach to data analysis: theyused one method to derive a list of biologically im-portant genes, another to classify tumor clusters,and yet another to extract the genes that were themost effective classifiers; finally, they attempted toassociate these clusters with the clinical outcomeor to validate their results. Such an approach growsout of the inherent belief that no single analytic toolis better than the others and that each tool alonemay, in fact, introduce unrecognized bias.

Bullinger et al. first established, using unsuper-vised clustering, that the 6283 genes whose expres-sion varied the most among the leukemic samplescould be used to identify specific subgroups of AML.Subsequent analysis showed that these array-basedclusters correlated with known cytogenetic and mo-lecular aberrations. Using a supervised approach,called significance analysis of microarrays (SAM),they further identified genes whose individual ex-pression correlated statistically with survival time.On the basis of these SAM-selected genes, the leuke-mias were then partitioned into two groups with theuse of a different clustering approach (k-means clus-ter analysis). Standard Kaplan–Meier survival analy-sis confirmed the prognostic significance of thesetwo clusters: one is associated with a good outcome,and the other with a poor outcome.

This dichotomization of the training samplesinto a good-prognosis group and a bad-prognosisgroup was important for the next step. Since one oftheir goals was to identify the minimal set of genesthat can predict the prognosis of each AML, the in-vestigators applied a supervised method called pre-diction analysis of microarrays (PAM) on this train-ing set of samples. This approach identifies a set ofgenes whose expression is solidly associated withone prognostic class (in this case, a good or pooroutcome). In this manner, all genes were reclassi-fied according to their ability to separate the AMLsinto good-prognosis and poor-prognosis groups.Those genes that were less useful in discriminatingbetween these classes were eliminated.

The authors then applied a cross-validation pro-cess whereby the gene-prediction model is fit on thebasis of 90 percent of the samples and then testedon the remaining 10 percent. When this process isexecuted multiple times with adjustments, the out-put is the smallest list of genes that can be used topredict the class of an individual sample with theminimal error rate. This analysis resulted in a min-imal list of 133 prognostically relevant genes thatpredicted the outcome independently of known riskfactors. The authors went one step further and test-ed their prognostic-gene signature on a separatevalidation set of samples.

Valk et al. also used a prefiltered set of genesarising from their array experiments and clustered,in an unsupervised manner, 285 AML samples. Six-teen array-based subgroups could be identified withthe use of 2856 gene markers that were strongly cor-related with different cytogenetic and molecular ab-errations. The investigators then used SAM to nar-row the set of genes down to the 599 that bestdiscriminate the cytogenetic configurations. Apply-ing PAM to this gene set, they then also identified theminimal number of genes that could be effectivelyused as a molecular classifier in individual patients.Their predictors of cytogenetic subgroups also per-formed well on a validation sample set.

What have we learned from these two array stud-ies? First, despite the use of different microarrayplatforms, when such studies are performed prop-erly, their results are surprisingly robust. Second,the distinct expression profiles of leukemias arisefrom their associated genetic mutations (e.g., tu-mors with an

AML1–ETO

translocation have a differ-ent profile than those with a

PML–RAR

a

transloca-tion). Third, there seems to be a hierarchy of effectsof different genetic mutations on the gene-expres-




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sion signature of cancers (e.g., the

AML1–ETO

trans-location has a greater effect than

ras

mutations).Fourth, expression profiling with the use of thou-sands of probes can uncover linkages between mo-lecular subclasses and clinical outcomes that cannotbe identified by standard cytogenetic analysis or theuse of clinical variables.

What is the take-home message? Our usualthinking about biomarker discovery in clinical trialsis about to change dramatically. In the future, clin-ical investigations will consist of small trials with ahigh density of data, precise patient stratification

according to the expression profile, and highly tai-lored analysis of microarray data.

From the Genome Institute of Singapore, Singapore.

1.

Miller LD, Long PM, Wong L, Mukherjee S, McShane LM, LiuET. Optimal gene expression analysis by microarrays. CancerCell 2002;2:353-61.

2.

Yeoh EJ, Ross ME, Shurtleff SA, et al. Classification, subtypediscovery, and prediction of outcome in pediatric acute lympho-blastic leukemia by gene expression profiling. Cancer Cell 2002;1:133-43.

3.

Simon R. Diagnostic and prognostic prediction using geneexpression profiles in high-dimensional microarray data. Br JCancer 2003;89:1599-604.

Cytogenetics is the genetic analysis of cells, a disci-pline that has flourished since the chromosome-banding techniques introduced in 1969 by TorbjörnCaspersson and Lore Zech first provided a simpleand inexpensive way to gauge the number and as-sess the structural integrity of chromosomes. Chro-mosome banding (see Figure 1A) is probably themost commonly performed genetic test: it is usedabout 500,000 times each year in North America(most laboratories use G-banding, named after theGerman chemist Gustav Giemsa). It is used prena-tally and postnatally to screen for the presence ofconstitutional chromosomal aberrations associatedwith birth defects, as well as for the diagnosis anddifferential diagnosis of cancer — hematologic can-cers in particular.

The application of cytogenetics to the screeningof the genomes of patients has led to the linking ofspecific chromosomal abnormalities (and conse-quently genetic alterations) with specific diseases;such findings carry enormous implications for med-icine. The first such specific correlation was madewhen Jérôme Lejeune, in Paris, detected extra cop-ies of a small chromosome in patients with Down’ssyndrome. Other descriptions of constitutional ab-normalities, involving both autosomes and the sexchromosomes, followed in rapid sequence.

Such associations were also established in can-cer cells. The first report on the so-called Philadel-phia chromosome (described by Peter Nowell andDavid Hungerford in 1960 and characterized by

Janet Rowley in 1973), created by an exchange ofgenetic material between chromosomes 9 and 22,launched the highly prolific field of cancer cytoge-netics. (The Philadelphia chromosome reflects thespecific target of imatinib mesylate in the treat-ment of chronic myeloid leukemia.) This work notonly confirmed the relevance of cytogenetic abnor-malities as disease-initiating events, but also led tothe molecular cloning of numerous cancer-associ-ated genes, many of which sit at the junctions ofchromosomal translocations.

Cytogeneticists then adapted and combined mo-lecular cloning and hybridization and so arrived atfluorescence in situ hybridization, which has hadprofound effects on cytogenetic diagnostic testingand research. Specific fluorescently tagged DNAprobes could now be used to visualize, with dramat-ically increased resolution, small deletions and oth-er genetic aberrations, as well as to map the chromo-somal locations of genes. Complex clone librarieswere developed for the “painting” of entire chromo-somes or chromosome arms (see Figure 2), a tech-nique that is useful not only for the confirmation ofsuspected aberrations in clinical diagnosis, but alsofor basic research efforts aimed at elucidating theevolution and structure of genomes, relationshipsbetween structure and function, mechanisms ofDNA repair, and chromosome segregation.

Fluorescence in situ hybridization extendedthe application of cytogenetic diagnosis from chro-mosomes in metaphase to all stages of the cell cy-

Cytogenetics — In Color and DigitizedThomas Ried, M.D.




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Microarrays and Clinical Investigations