The promise of gene expression analysis in hematopoetic malignancies

Review

The promise of gene expression analysis in hematopoetic malignancies

Jerald P. Radich �

Clinical Research Division, Program in Genetics and Genomics, Fred Hutchinson Cancer Research Center, D4-100, 1100 Fairview Ave N.,Seattle, WA 98109, USA

Received 19 October 2001; received in revised form 15 January 2002; accepted 15 January 2002

Keywords: Gene expression; Leukemia; Microarray

1. Introduction

The last two decades have brought tremendousadvances and insight concerning the biology of can-cer. It is amazing that only 20 years have passedsince the discovery of the ras oncogene, and barelya decade from the discovery of the tumor suppressoroncogenes (Rb, p53). Diligent (and relentless) pursuitof oncogene and suppressor functions has linkedtheir activity through the cell cycle, revealing an ex-traordinarily complex, intertwined system that beau-tifully balances proliferation, di¡erentiation, and celldeath.

How can something so complex be studied? Theclassic reductionist approach, manipulating a gene byin£uencing expression, construction of transgenicmodels, etc., is a time-tested and exquisite methodto glimpse a piece of the cellular machinery ^ butonly a small, discrete piece. To begin to understandthe bigger picture, we need a di¡erent approach ^ abigger, more complex, approach.

While the scienti¢c questions we can ask rhetori-cally are almost limitless, the answers we can ap-proach experimentally are not, and are largely con-trolled by the technology at hand. Sometimes a newtechnical method can give a ¢eld a jump start. A

recent example was the advent of the polymerasechain reaction (PCR). In a short time this method-ology caused an explosion of discovery in basic andapplied biology, ranging from gene mapping andgene discovery to diagnostics. A potentially equaltechnological feat is the recent invention of micro-array gene expression platforms, which allows for thesimultaneous interrogation of the gene expressionlevels of literally tens of thousands of genes. Whilein its infancy, studies that apply gene expression‘pro¢ling’ have the potential to gain huge insightinto many facets of cancer biology, including diag-nostics, the unveiling of complex pathways, and themechanism of drug resistance, to name a few.

This review will discuss the studies and promise ofarray analysis in hematological malignancies, andoutline the potential and the pitfalls of this excitingnew ¢eld.

2. Methodology

The ¢ner points of the methodology of gene ex-pression studies have been detailed elsewhere. I willreview it here brie£y.

2.1. Basic principles

The basic aim of this method is to compare thegene expression of thousands of genes from two cell

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states. Thus, these comparisons may be: (a) a cellbefore and after an environmental exposure (e.g.hypoxia, hormone, chemotherapeutic agent); (b) anormal cell to a leukemic cell ; (c) leukemic cells atdi¡erent phases of progression or di¡erentiation.

In this method, labeled mRNA is directly hybrid-ized to a microarray chip, and the determination ofthe expression of all genes or potential genes (anexpression sequence tag, or EST) determined by thesignal intensity of the biotin, radioactive, or £uores-cent label coupled to the RNA. Moreover, relativelevels of gene expression can be measured. If RNAfrom sample ‘A’ is labeled with a green £uoro-chrome, and the RNA from a comparison sample‘B’ is labeled with red, the mixing of the two samples,followed by hybridization and exposure to light, willyield a color intensity for each gene on the chip pro-portional to the relative expression of that gene inthe two samples. For example, if gene No. 1 is ex-pressed in sample ‘A’ but not expressed in sample‘B’, that spot would emit a green signal. Similarly,a gene that is not expressed in ‘A’ and is expressed in‘B’ would emit a red signal, and genes expressing atdi¡erent levels will emit a signal proportional to thecombination of green and red £uorochromes.

2.2. Platform design

There are three basic platform designs. Each relieson an addressable probe of a gene or EST. A nucle-otide sequence complementary to a speci¢c gene se-quence is directly a⁄xed to a solid platform, and thisplatform supports the hybridization of RNA (labeledas outlined above). The approach pioneered by thebiotechnology company A¡ymetrix uses photolithog-raphy to chemically synthesize in situ millions ofcopies of V20-mer oligonucleotides complementaryto a speci¢c gene [1]. For each ‘perfect match’ oligo-nucleotide spot, scores of separate base pair mis-matched oligonucleotides are tiled to control fortrue ‘perfect’ hybridization vs. a false positive frombackground hybridization. A variation on this ap-proach is used by Rosetta Inpharmatics, which usesa single 60-mer oligonucleotide to probe for geneexpression [2]. The increased nucleotide lengthcoupled with stringent hybridization conditions al-lows for excellent sensitivity of expression (down to10 copies of mRNA) and speci¢city.

An alternative approach is the attachment ofcDNA clones to a solid platform [3]. These clonesare either purchased commercially already on a plat-form, or PCR-ampli¢ed and applied robotically atindividual array facilities. The attractive feature ofthis approach is that array ‘chips’ can be custommade, adding whatever genes an investigator wants.The downside of the customized approach is three-fold: (1) the stringency of quality control may vary;(2) purchased clones may not be what they wereadvertised; and (3) the design of chips to includeonly the genes that are ‘important’ may exclude net-works of genes that, unbeknownst to the investiga-tor, really are important.

The potential applications of these di¡erent plat-forms are not mutually exclusive. For example, theA¡ymetrix system uses biotin labeling, and thus, thetwo di¡erent cell states (e.g. leukemia cell vs. normalcell) must be hybridized to di¡erent platforms, read,and compared computationally. This is obviouslymore costly than systems that use di¡erent £uores-cent labeling, mix RNA, and determine gene expres-sion ratios directly. On the other hand, the A¡yme-trix approach is well suited for experimentalconditions that use rare samples. This is because aseries of controls (for example, the gene expressionpro¢le of primitive CD34+ hematopoetic cells) canbe pro¢led, and potentially used again and again as acomparative control to a variety of other gene ex-pression pro¢les (unfortunately, future changes inarray platform design by the manufacturer can foilsuch a plan).

2.3. Statistical analysis

The goal of array analysis is simply: to identifygenes up- or down-regulated in one cell state vs.another. In reality, the identi¢cation of complex ex-pression patterns in a background of up to 25 000data levels is, in the least, a vexing problem. Themathematical approaches, including hierarchicalclustering, self-organizing maps, and regression mod-eling, are above the scope of this review [4^6]. Su⁄ceit to say that the arrival of microarray analysis hasspawned a considerable interest among statisticians.There is still substantial work to be done beforeagreement prevails on the ‘best’ type of analysesfor a given experiment, how one should combine

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array pro¢le data with clinical data, and even theseemingly banal question of how statistical ‘power’should be approached in an array experiment.

2.4. Potential pitfalls

There has been little published about the potentialvariation of expression array methodology. Thislikely stems from both the expense of the experi-ments, as well as the temptation to jump to biolog-ical questions before the inherently less interestingissues of the performance and reliability of the tech-nique have been completely addressed. Thus, little ispublished as to: (1) head-to-head comparisons of thedi¡erent techniques; (2) biological variation in geneexpression due to tissue handling (time from acqui-sition to processing; in£uence of techniques such asfreezing and £ow sorting); and (3) robustness of var-ious statistical methods to determine gene expressionpatterns. Without consideration of these issues it ishard to determine, for example, which changes ingene expression are caused by the biology of system,versus that caused by biologically irrelevant manip-ulations (e.g. time to RNA isolation). Investigators,granting agencies, and peer-reviewed journals shouldbe sensitive to these issues.

3. Potential applications

For each of the potential applications below, wewill give an example of a relevant clinical or biolog-ical problem that is current insolvable by currentmethodology or knowledge.

3.1. Creating new diagnostic classi¢cation based ongene expression

One of the basic dilemmas in oncology is thatsimilar appearing cancers behave quite di¡erently.There are a few molecular markers that aid in de¢n-ing poor and good risk groups (for example, thePhiladelphia chromosome in acute lymphoblasticleukemia (ALL) denotes a particularly poor progno-sis), but the vast majority of hematological malig-nancies do not have a clear molecular subtype thatstrongly predicts behavior. The ability to predict clin-ical behavior is especially important in the hemato-

logical malignancies, since stem cell transplantationis an e¡ective, but toxic, therapeutic alternative. Theability to determine which patients would respond toconventional chemotherapy prior to actually institut-ing therapy would be a huge bene¢t in directing theappropriate patients to the most potentially e¡ectivetreatment.

Golub et al. have used expression arrays to estab-lish models of ‘class prediction’ (a diagnostic para-digm to assign cases to a known designated categoryof disease) and ‘class discovery’ (attempting to createnew diagnostic classi¢cation based on gene expres-sion) in 27 ALL and 11 acute myeloid leukemia(AML) cases [7]. For these pioneering studies heused the 6817 gene set developed by A¡ymetrix. Ap-proximately 1000 genes were highly correlated withAML or ALL, and of these, 50 could be used as adiagnostic predictor of AML or ALL. The diagnosticset was used to classify 34 new cases, and in 29 themodel correctly classi¢ed the cases as AML or ALL.The genes in the predictor set were not merely line-age-speci¢c genes. Thus, the oncogenes Myb andE2A were preferentially expressed in ALL cases,while HoxA9 was up-regulated in AML. Unexpectedgenes were also expressed ^ for example, fat metab-olism genes leptin and adipsin were high in AML,but not ALL. A self-organizing map analysis wasperformed to discover new classes of leukemia. Inthis analysis, diseases are forced into an arbitrarilyde¢ned number of categories. In both ‘2-node’ and‘4-node’ models, some classes contained both AMLand ALL cases. That is, some ALL cases were moresimilar to AML than to other ALL cases, and viceversa. These data strongly suggest that the underly-ing biology of some leukemias is more similar thansuggested by their routine pathological class. This isobviously a very rich lesson, and portends a newfrontier of diagnostics.

Di¡use large B-cell lymphomas (DLBCLs) are anextremely heterogeneous group of lymphomas withfairly unpredictable response to therapy. Conven-tional pathological examination is largely unable toclearly di¡erentiate between the various subclasses ofdisease that present with diverse natural histories.Allzadeh et al. attempted to rede¢ne the classi¢cationDLBCL, studying 42 cases, compared to various B-cell lines, puri¢ed B-cell populations, and cases ofCLL and follicular NHL [8]. DLBCL cases fell into

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two categories: those that demonstrated ‘germinalcenter’ gene expression patterns, and those thatshowed ‘activated B-cell’ expression patterns. Thisclassi¢cation scheme seemed quite relevant to diseaseoutcome. Cases demonstrating germinal center ex-pression patterns had a survival of approximately60%, compared to only 10% in cases demonstratingthe activated B-cell expression pattern. Moreover, incases that would be classi¢ed as ‘low risk’ by stan-dard clinical^pathological criteria, the pattern ofgene expression still had a profound impact on sur-vival. Those ‘low risk’ patients with germinal centerexpression patterns had a survival of approximately75%, compared to 40% in activated B-cell expressionpatterns. These are the ¢rst data clearly linking ex-pression data to disease outcome, and demonstratethe potential power of microarray analysis to detectdi¡erent subgroups of disease.

Myelodysplastic syndrome (MDS) is a stem celldisorder characterized by either progressive cytope-nias that result in bleeding and infectious complica-tions or the evolution to AML. The molecular path-ways involved in these di¡erent outcomes areunclear. It would be important to understand thebiology of myelodysplasia and the leukemic evolu-tion of this disease as well as predicting outcome inpatients in their early stage of disease. Two papershave recently begun to probe the molecular under-pinnings of disease progression in MDS. Miyazato etal. studied gene expression of £ow-separated blastpopulations in AML and MDS [9]. They used thecell surface marker AC113 to £ow sort a large pop-ulation of blast cells and used this selected cell pop-ulation as the RNA source. Three patients withMDS-associated AML, two patients with RAEB,and three patients with de novo AML were com-pared by expression array pro¢ling. Almost half thegenes in these samples were transcriptionally silent,but some genes were found to be speci¢cally associ-ated with either MDS or AML. Those genes foundto be highly MDS-speci¢c were those including theDLK gene, the Tec gene, and the inositol triphos-phate 1,4,5-receptor type 1. DLK was especially im-portant because it was a transmembrane protein be-longing to the super family of epidermal growthfactor like proteins, distantly related to the delta/ser-rate-notch family signaling molecules. Validation ofDLK expression by reverse transcription (RT)-PCR

found that the relative abundance of DLK mRNAwas elevated in 55% of MDS patients compared toonly 10% of AML patients. Genes speci¢c to AMLincluded opioid receptor delta 1, leptin receptors,and members of the solute carrier family 1. Lee etal. [10] compared gene expression in ¢ve patientswith various stages of MDS and found that progres-sion was associated with down-regulation of genes(n = 9) involved in di¡erentiation (e.g. globin genes,actin), and up-regulation of genes (n = 18) associatedwith proliferation (e.g. c-myb, p23). While these ¢nd-ings are not altogether surprising, they nonethelessportray the genes that may become diagnostic ortherapeutic targets.

A similar clinical dilemma is involved in predictingthe progression of chronic myeloid leukemia (CML).Most patients who have this disease are diagnosed ina chronic phase that is characterized by an expansionof relatively normal myeloid lineage cells. Howevereventually this disease will progress to a blastic evo-lution that resembles treatment resistant AML. Un-fortunately, there is no way at this time to tell wherean individual patient lies on the natural history time-line moving from chronic to blast phase. Therefore,all patients in the chronic phase usually tend to betreated similarly, with the de¢nitive therapy beingstem cell transplantation. While transplantation iscurative in many patients, it also places some pa-tients who are at a relatively low risk of transforma-tion at a high risk of treatment-related complica-tions. If we could understand the genes involved inthe progression of CML we could better tailor ther-apy to each patient’s risk of progression.

Our lab in collaboration with Rossetta Inphar-matics is evaluating the genes associated with trans-formation of CML. We have thus far comparedpools of CML chronic phase with blast crisis andlooked at individual cases of chronic phase with blastcrisis against a chronic phase pool (Cancer Res. 42,Abstract # 2303, p. 428, 2001). This has allowed usto (1) detect the variation of gene expression inchronic phase patients, and (2) outline the genes as-sociated with progression to blast crisis. Thus farusing a 25 000 spot oligonucleotide array we havefound approximately 100 genes which could be po-tential predictors of progression to blast crisis. More-over, we have found some patients whose gene ex-pression pro¢les in chronic phase actually appear

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quite similar to blast crisis despite a similar patho-logical diagnosis. In addition, these patients behavelike blast crisis, with early relapse after transplanta-tion. Thus, while these studies are quite preliminary,they are very suggestive that gene pro¢les will be ableto help us classify the true state of CML patients andthus allow us to better guide targeted drug therapy.

3.2. Pathway discovery and new targets of therapy

While in the above applications patterns of geneexpression are used to rede¢ne disease classi¢cationschemes, another approach is to use microarrays todetermine the downstream e¡ects of speci¢c geneperturbations, and to identify a smaller set of genesthat may be potential targets of new therapeutic ap-proaches. A potentially powerful application of arraytechnology is the discovery of normal or biologicalpathways. For example, Virtaneva et al. studied thegene expression pattern of AML cases with the tris-omy 8 abnormality, and of AML cases with normalcytogenetics, to that of normal CD34+ cells [11].AML cells had abnormal expression of approxi-mately a third of all 6606 genes surveyed. Of these,60 genes were consistently up- or down-regulated inAML samples compared to normal CD34+ cells. Ingeneral, however, the di¡erent subtypes of AMLshared only a subset of abnormal gene expressionpatterns, along the theme of abnormal cell adhesion.AML cells from trisomy 8 cases over-expressed geneson chromosome 8, clearly demonstrating a gene dos-age e¡ect of these malignancies. Moreover, apoptosisregulating genes were signi¢cantly down-regulated intrisomy 8 AML cases compared to AML cases withnormal cytogenetics. These ¢ndings suggest a funda-mental di¡erence in the leukemic pathways of theseleukemias, and may explain the clinical observationthat trisomy 8 AML cases are generally poorly re-sponsive to cytarabine-based chemotherapy, since thee¡ect of these agents appears to depend on the in-duction of apoptosis. Surprisingly in this study nosigni¢cant di¡erences in gene expression were ob-served in functions such as transcription, cell cycle,or signal transduction in the leukemic cells comparedto normal cells.

The promise of targeted therapy has been raisedby the introduction of the tyrosine kinase inhibitorSTI571 (a.k.a., Gleevec), which in CML inhibits the

abnormal kinase function of the Bcr-Abl chimericprotein. Unfortunately, the genetic underpinningsof most malignancies are unknown. One hope isthat microarray analyses may uncover new genesand pathways that can be exploited as new therapeu-tic targets. Hofmann et al. used microarray technol-ogy to study the apoptotic pathways in mantle celllymphoma [12]. Gene expression patterns fromlymph node preparations from MCL patients werecompared to non-malignant hyperplastic lymph no-des from non-lymphoma patients. A¡ymetrix arrayswith 5600 genes were used as the probe. RNA from¢ve MCL patients was compared to four normallymph nodes. In 5000 genes, 42 were signi¢cantlydown-regulated in the lymphoma samples. Many ofthese genes were involved in apoptotic pathways notthought to be prominent in lymphoma cells. TheBCL2 pathway altered as expected, but so was theFas-associated death domain (FADD) gene, suggest-ing the Fas cascade involvement was more than 10-fold down-regulated in MCL. Furthermore, death-associated protein 6 (DAXX) gene, the caspase 2(CASP2) gene, and the RIPK1 containing adapterwith death domain (RAIDD) gene which are alsoinvolved in pro-apoptotic pathways were also signi¢-cantly decreased in the MCL samples. Thus the mi-croarray analysis revealed several di¡erent anti-apo-ptotic pathways a¡ected in the MCL tumors thatwould have been di⁄cult to recognize by generalmolecular approaches. The work has major thera-peutic implications as it shows the di⁄culty of tar-geting only one apoptotic pathway for interventionin this disease.

Furthermore, the application of microarrays maydiscover new targets where various biological path-ways intersect. For example, mutations in the rasGTPase occur in 20^30% of AML cases [13^15]. Re-cently, activating mutations in the Flt3 receptor ty-rosine kinase have been found in 20^30% of AMLcases [16,17]. Stirewalt et al. examined a single cohortof AML cases and found mutations in either Flt3 orras in a remarkable s 50% of cases [18]. There wereonly a few patients with both Flt3 and ras mutations,suggesting that these two mutations act through arelated (if not the same) pathway. Comparisons ofthe gene expression patterns of ras and Flt3-mutatedAML cells may disclose similar abnormalities of ex-pression, and reveal biological ‘choke points’ com-

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mon to leukemias. As the net is broadened to includeother leukemias, a discrete number of choke pointsmay be discovered that link many di¡erent genotypesof leukemia. These common points of abnormal ex-pression could become the targets for novel thera-pies.

3.3. New markers of disease

Many patients with leukemia achieve a remissionafter induction chemotherapy, but most relapse.Conventional pathology is a fairly insensitive methodto detect low abundant leukemia cells. The study ofminimal residual disease (MRD) uses very sensitivetechniques, such as £ow cytometry or the PCR, todetect leukemia cells down to a level of one leukemiacell in a background of 1000^1 000 000 normal cells.The detection of MRD has been correlated with re-lapse in CML and ALL, and to a lesser degree AML.The detection of MRD is fairly straightforward inCML, since virtually all cases harbor the Bcr-Abltranslocation that thus serves as a sensitive and spe-ci¢c target for PCR assays. However, the detectionof molecular markers in other leukemias is moreproblematic. For example in ALL, leukemia-speci¢cPCR must be designed to the clonal immunoglobulingene rearrangement for each patient. In AML, sensi-tive assays can be performed for the t(8,21), t(15,17),and inv. 16 genetic lesions, but these only accountfor 20^30% of all AML cases. However, microarraytechniques may discover gene(s) which when abnor-mally over-expressed are markers of leukemia. Re-cently Chen et al. compared the gene expression pro-¢les of four ALL cases to normal CD19+, CD10+lymphoid precursors [19]. They found 334 of 4132genes were signi¢cantly over-expressed (1.5-fold andabove) in the ALL cases. Of these, they concentratedon seven genes for which antibody reagents wereavailable. One antigen, CD58+, appeared appropri-ately sensitive to be used as a marker of MRD. Thus,even in a strategy biased for genes in which antibod-ies existed, potentially new diagnostic reagents weredeveloped. We have performed similar preliminaryexperiments targeting RT-PCR as the method fordeveloping MRD assays.

Even a search through a limited set of genes mayreveal new markers of disease. Wellmann et al.studied 31 hematopoietic cell lines using an array

with merely 588 genes [20]. The known lymphoma-associated gene clusterin was found to be expressedin anaplastic large cell lymphoma (ALCL) cell lines,but in no other cell lines examined. Clusterin expres-sion was then probed in 198 lymphoma cases usingimmunohistochemistry assays and Western blotting.Clusterin was indeed found in all 36 ALCL cases,and was quite speci¢c, being positive in only twoof the 162 other lymphoma cases. This ¢nding makesclusterin a potential diagnostic tool for the character-ization of lymphomas, and is an instructive casestudy of the general approach of using microarrays.

We have begun a similar approach to ¢nd newmarkers of MRD in AML and ALL, respectively,comparing gene expression in pools of normal bonemarrow to the published data set of Golub et al., andhave found a small subset of genes (approximately25) consistently elevated in AML and ALL, and notexpressed in normal bone marrow. These types ofmarkers will likely lack the speci¢city of transloca-tions (which should absolutely not be found in nor-mal cells), but with quantitative PCR they may beperfectly adequate to herald relapse in patients be-lieved to be in remission. For example, abnormalexpression of WT1, HER2neu, and PGP 9.5 hasbeen suggested to be a marker of MRD in AML,breast cancer, and neuroblastoma, respectively [21^23].

4. Other interesting issues

There are myriad other interesting clinical ques-tions that might be potentially addressed by micro-array studies. The most exciting, perhaps, is the issueof treatment response, and the potential that geneexpression patterns found at diagnosis can have inguiding the selection of the most appropriate ther-apy. That is, can microarrays be used to detect thegenes that might predict treatment response? If so,can we predict which patients will respond best toone regimen, versus another? In addition, gene ex-pression studies may provide insight into the mech-anisms of disease resistance. Are the gene expressionpatterns found at relapse di¡erent than at diagnosis?If so, this implies that resistance is associated withacquired genetic lesions, or selection of a minoritysubclone of leukemia cells that gradually increases

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following therapy after the majority chemotherapysensitive population is eliminated. On the contrary,if gene expression patterns at diagnosis and relapseare identical, it suggests that a primary resistance ofthe original population exists, so that relapse is sim-ply due to sub-optimal killing of the original leuke-mia population. The ¢rst scenario might not be re-solved by more intense doses of chemotherapy,whereas the latter might. These types of clinicallyrelevant questions can be approached by microarrayexperiments, and the answers promise to change thepractice of oncology.

5. Conclusion

The above discussion has used distinct categoriesof applications that are obviously quite arti¢cial andsomewhat contrived. Obviously, many of the abovecategorizations overlap: thus, genes involved in newpathways are potential new therapeutic targets; newclassi¢cation schemes provide new diagnosticmarkers, etc. It is likely that the application of mi-croarray technology will change our experimentaland clinical practice. At the very least, the methodis a valuable screening tool to evaluate the role ofthousands of genes in the lab (think of it as a hyper-active Northern blot). At its best (and with the fur-ther involvement of mathematicians), gene expres-sion pro¢les may be linked to speci¢c treatmentregimens. Like any new technology, there will beperiods of disappointment after the initial excitement(recall when the problem of contamination was dis-covered in the early days of PCR), but even withthese bumps, the world of wholesale gene pro¢lingpromises to be an exciting ride.

Acknowledgements

Supported in part by National Institute of HealthGrants CA-18029 and CA-85053.

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