The diagnosis of the hematologic cancers presents a dauntingchallenge. The many stages of normal hematopoietic differentiationgive rise to a number of biologically and clinically distinctcancers. Inherited DNA-sequence variants do not appear to havea prominent causative role; rather, these diverse cancers aretypically initiated by acquired alterations to the genome ofthe cancer cell, such as chromosomal translocations, mutations,and deletions. The diagnosis of the hematologic cancers is commonlybased on morphologic evaluation supplemented by analysis ofa few molecular markers. However, in some diagnostic categoriesdefined in this fashion, the response of patients to treatmentis markedly heterogeneous, arousing the suspicion that therecan be several molecularly distinct diseases within the samemorphologic category.
Gene-expression profiling is a genomics technique that has provedeffective in deciphering this biologic and clinical diversity.The approach relies on the fact that only a fraction of thegenes encoded in the genome of each cell are expressed —that is, actively transcribed into messenger RNA (mRNA) (Figure 1A).The abundance of mRNA for each gene depends on a cell'slineage and stage of differentiation, on the activity of intracellularregulatory pathways, and on the influence of extracellular stimuli.To a large extent, the complement of mRNAs in a cell dictatesits complement of proteins, and consequently, gene expressionis a major determinant of the biology of normal and malignantcells.
Figure 1. Differential Expression of Messenger RNA (mRNA) by Different Types of Cells (Panel A) and Gene-Expression Profiling Using DNA Microarrays (Panel B).
In Panel A, different types of cells, exemplified by a myocyte and a lymphocyte, express a distinct set of mRNAs from their genomes. Although the myocyte and lymphocyte possess the same inherited genomic DNA, distinct regulatory networks inside each cell cause different genes to be expressed as mRNA. The genes that encode myosin and immunoglobulin are among the most differentially expressed genes between these two types of cells. The mRNAs for other genes may be present in both types of cells, but at different levels, which may also affect the biology of the cells. Panel B shows the technique of gene-expression profiling, which uses DNA microarrays. First, mRNA is extracted from a cell and copied enzymatically to create a fluorescent complementary DNA (cDNA) probe representing the expressed genes in the cell. This probe is then incubated on the surface of a DNA microarray, which contains spots of DNA derived from thousands of distinct human genes. During the incubation, each cDNA molecule in the probe hybridizes to the microarray spot that represents its respective gene. The extent of hybridization of fluorescent cDNAs to each microarray spot is quantitated with use of a scanning fluorescence microscope. The levels of expression of more than 20,000 genes — in this example, the genes for myosin and immunoglobulin — can be measured in a single DNA-microarray experiment.
In the process of expression profiling, robotically printedDNA microarrays are used to measure the expression of tens ofthousands of genes at a time; this creates a molecular profileof the RNA in a tumor sample1 (Figure 1B). A variety of analytictechniques are used to classify cancers on the basis of theirgene-expression profiles.2,3 There are two general approaches.In an unsupervised approach, pattern-recognition algorithmsare used to identify subgroups of tumors that have related gene-expressionprofiles (Figure 2A). In a supervised approach, statisticalmethods are used to relate gene-expression data and clinicaldata (Figure 2B). These methods have revealed unexpected subgroupswithin the diagnostic categories of the hematologic cancersthat are based on morphology and have demonstrated that theresponse to therapy is dictated by multiple independent biologicfeatures of a tumor. This is not a comprehensive review of hematologiccancers; rather, it will provide examples of how gene-expressionprofiling has been used to provide a framework for the moleculardiagnosis of these cancers.
Figure 2. Molecular Diagnosis of Cancer by Gene-Expression Profiling with the Use of Unsupervised (Panel A) and Supervised (Panel B) Pattern-Recognition Algorithms.
Panel A shows the discovery of cancer subgroups with the use of an unsupervised pattern-recognition algorithm. The expression of genes, as determined by DNA-microarray analysis, is depicted in a tabular format. Each row represents data for a particular human gene, and each column represents the expression of genes in a single biopsy sample (arrows). Highly expressed genes are shown in shades of red, and less highly expressed genes are shown in shades of green, according to the color scale shown. Before the analysis, no pattern is apparent (left-hand panel). A mathematical algorithm, termed "hierarchical clustering,"2 is applied to the gene-expression data to search for a pattern (right-hand panel). This algorithm first rearranges the genes (in rows) so that genes with related patterns of expression are clustered. The algorithm next rearranges the samples (in columns) so that samples that have related expression of these genes are clustered. In this example, the hierarchical-clustering algorithm identified a clear subgroup of three tumor samples (on the far right-hand side) whose pattern of gene expression is distinct. Panel B shows how a supervised statistical algorithm is used to identify genes with patterns of expression that predict the clinical outcome. For each gene on the microarray, expression data from tumors are correlated with overall survival data from the corresponding patients. The example shows two genes with patterns of expression that are correlated with survival after chemotherapy for diffuse large-B-cell lymphoma.4 A high level of expression of gene A is associated with extended survival, whereas a high level of expression of gene B is associated with short survival. Neither gene has a pattern of expression that is perfectly correlated with survival, illustrating that the clinical outcome is independently influenced by multiple molecular and clinical variables.4
Molecular Diagnosis of Non-Hodgkin's Lymphoma
Diffuse Large-B-Cell Lymphoma
Some cases of diffuse large-B-cell lymphoma respond well tomultiagent chemotherapy,5 but this lymphoma nonetheless remainsa perplexing clinical puzzle, since roughly 60 percent of casesare incurable. This observation raises the possibility thatthis single diagnostic category may harbor more than one moleculardisease.
The gene-expression profiles of lymph-node–biopsy specimensfrom patients with morphologically identical diffuse large-B-celllymphoma show pronounced variability, with no common set ofgenes expressed in all cases.4,6,7 To make sense of this variability,genes were classified into expression signatures8 — thatis, groups of genes with similar patterns of expression in aset of samples. Some signatures include genes expressed in aparticular type of cell or stage of differentiation, whereasother signatures include genes expressed during a particularbiologic response, such as cellular proliferation or the activationof a cellular signaling pathway.
One gene-expression signature that varies markedly among diffuselarge-B-cell lymphomas is the germinal-center B-cell signature.4,6This signature characterizes B cells that are responding toa foreign antigen within the germinal-center microenvironmentof secondary lymphoid organs. Among biopsy samples from patientswith diffuse large-B-cell lymphoma, three biologically and clinicallydistinct subgroups have been identified4,6 (Figure 3A). Thegerminal-center B-cell–like subgroup (approximately 50percent of cases) has high levels of expression of germinal-centerB-cell signature genes, whereas the other two subgroups of diffuselarge-B-cell lymphoma — termed activated B-cell–likeand type 3 — do not. The activated B-cell–like subgroup(approximately 30 percent of cases) instead expresses genesthat are induced by mitogenic stimulation of blood B cells.The type 3 subgroup does not express genes characteristic ofthe other two subgroups and may yet be found to be heterogeneous.These findings suggest that the subgroups of diffuse large-B-celllymphoma arise from different stages of normal B-cell development.
Figure 3. Examples of Molecularly and Clinically Distinct Subgroups of Lymphoma (Panel A) and Leukemia (Panel B).
Panel A shows the levels of expression of 57 genes that distinguish three subgroups of diffuse large-B-cell lymphoma4: germinal-center B-cell–like (orange), type 3 (purple), and activated B-cell–like (blue). The Kaplan–Meier curve shows that overall survival differs among the subgroups after chemotherapy. Panel B shows 39 genes that are differentially expressed in two subgroups of B-cell chronic lymphocytic leukemia,9 one with unmutated (wild-type) immunoglobulin genes (purple) and one with somatically mutated immunoglobulin genes (blue). The Kaplan–Meier curve shows that the two subgroups differ with respect to the time to initial treatment after diagnosis.
The notion that the gene-expression subgroups represent pathogeneticallydistinct types of diffuse large-B-cell lymphoma has been stronglysupported by analysis of recurring chromosomal abnormalitiesin this cancer.4,10 The t(14;18) translocation involving theBCL2 gene and the amplification of the c-rel gene on chromosome2p are recurrent oncogenic events in germinal-center B-cell–likediffuse large-B-cell lymphoma, but they never occur in the othersubgroups. Activation of the nuclear factor-B signaling pathwayis a feature of the activated B-cell–like subgroup butnot the other subgroups, and interference with this pathwayselectively kills this type of diffuse large-B-cell lymphoma.11
The subgroups defined with the use of gene-expression signaturesare clinically distinct as well: patients with the germinal-centerB-cell–like form have a higher rate of overall survivalfive years after chemotherapy than do patients in the othersubgroups4,6 (Figure 3A). This clinical distinction based ongene-expression profiles was evident even after the patientswere classified according to the International Prognostic Index,4,6a well-established predictor of outcome in diffuse large-B-celllymphoma.12
Predicting the Clinical Outcome
The example of diffuse large-B-cell lymphoma demonstrates howan unsupervised analysis of gene-expression data can revealclinically distinct subgroups of tumors. In the complementary,supervised approach, clinical data are used to identify geneswhose patterns of expression are correlated with the lengthof survival after diagnosis or with the likelihood that therapywill be curative. This approach has been used to develop robustpredictors of prognosis in mantle-cell lymphoma13 and diffuselarge-B-cell lymphoma.4,7
Mantle-cell lymphoma constitutes approximately 8 percent ofcases of non-Hodgkin's lymphomas but a much larger fractionof deaths from lymphoma, since current therapy is not curative.The length of survival among patients with mantle-cell lymphomais quite variable, ranging from less than 1 year to more than10 years.13 Gene-expression profiling revealed a strong associationbetween the expression of genes in the "proliferation" signatureand survival in mantle-cell lymphoma.13 The proliferation signatureincludes genes that are more highly expressed in dividing cellsthan in quiescent cells (Figure 4A). The quartile of patientswith the highest level of proliferation-signature expressionhad a median survival of 6.7 years, whereas the quartile withthe lowest level of expression had a median survival of 0.8year (Figure 4A). The variable survival of patients with mantle-celllymphoma is therefore largely dictated by a single aspect oftumor biology, the rate of cell division, which can be quantitatedby gene-expression profiling.
Figure 4. Use of the Proliferation Gene-Expression Signature to Predict the Clinical Outcome in Mantle-Cell Lymphoma (Panel A) and the Development of a Multivariate Gene-Expression–Based Predictor of Survival after Chemotherapy for Diffuse Large-B-Cell Lymphoma (Panel B).
Panel A shows the use of the proliferation gene-expression signature to predict the length of survival in patients with mantle-cell lymphoma. Elevated levels of expression of genes in the proliferation gene-expression signature in a biopsy specimen of mantle-cell lymphoma was associated with short survival.13 The relative level of expression of the proliferation-signature genes is represented by the color bars; the biopsy samples are ordered from left to right according to the increasing relative expression of the proliferation-signature genes. The levels of expression of 20 proliferation-signature genes were averaged, and the resulting average was used to subdivide patients with mantle-cell lymphoma into four quartiles. The Kaplan–Meier plot illustrates the striking differences in the length of survival among these four risk groups. In Panel B, the biopsy specimens of diffuse large-B-cell lymphoma are ordered as in Figure 3A according to their assignment to the three subgroups. A supervised analysis of gene-expression data identified four gene-expression signatures and one single gene — BMP6 — with patterns of expression that correlated with clinical outcome.4 A high level of expression of a gene or signature within a tumor was associated with a favorable or poor outcome after chemotherapy, as indicated. The colored bars represent the relative levels of expression of each signature or gene in each of the biopsy specimens according to the scale shown. The levels of expression of the signatures represent averages of data from multiple genes in each signature. These five patterns of gene expression vary independently of one another. Since each of these patterns correlates with the clinical outcome, multiple biologic attributes of the tumors must influence the clinical outcome. A linear combination of these five gene-expression components is used to assign a gene-expression outcome-predictor score for each patient. Patients are ranked according to their outcome-predictor scores and divided into quartiles. The Kaplan–Meier plot demonstrates the ability of the gene-expression–based outcome predictor to classify patients with diffuse large-B-cell lymphoma into prognostic groups. MHC denotes major histocompatibility complex. Data are adapted from Lymphoma/Leukemia Molecular Profiling Project studies of gene expression and clinical outcome in patients with diffuse large-B-cell lymphoma and mantle-cell lymphoma.4,13
Although the subgroups of diffuse large-B-cell lymphoma havedistinct survival rates, the statistical approach of supervisedanalysis identified additional molecular differences among thetumors that can account for much of the remaining heterogeneityin survival4,7 (Figure 4B). This approach demonstrated thatat least five distinct features of diffuse large-B-cell lymphomasinfluence the response to chemotherapy.4 Specifically, the levelsof expression of the germinal-center B-cell signature, the proliferationsignature, the major-histocompatibility-complex (MHC) classII signature, and the lymph-node signature were predictive ofthe clinical outcome, as was the level of expression of BMP6,a gene that does not belong to a defined expression signature.As in mantle-cell lymphoma, expression of the proliferationsignature predicted a poor outcome. Predictive genes in twoother signatures suggest that the host immune response has animportant role in curative responses to chemotherapy. Expressionof the lymph-node–signature genes reflects the nontumorcells in the diffuse large-B-cell lymphoma–biopsy specimen,including activated macrophages, natural killer cells, and stromalcells. A high level of expression of these genes predicts afavorable clinical outcome, suggesting that this reactive immuneresponse is beneficial. The MHC class II signature includesgenes encoding components of this critical antigen-presentation–proteincomplex, and decreased expression of these genes predicts apoor outcome. These findings suggest that some tumors may evadethe immune response by down-regulating their antigen-presentationcapacity.
These expression signatures can be combined to form a multivariatepredictor of survival after chemotherapy for diffuse large-B-celllymphoma.4 With the use of this approach, half the patientscan be placed into a favorable-risk group, with a five-yearsurvival rate of more than 70 percent; one quarter can be assignedto a poor-risk group, with a five-year survival rate of 15 percent;and the remaining patients are in an intermediate-risk group,with a five-year survival rate of 34 percent (Figure 4B).
Molecular Diagnosis of Leukemias
Acute Leukemias
The molecular diagnosis of leukemias began with the recognitionand analysis of recurrent chromosomal translocations.14,15 Thegenes discovered at the translocation break points have drawnattention to critical regulatory pathways in hematopoietic cellsthat can cause cancer when they are dysregulated. In many acuteleukemias, translocations fuse genes that reside on the twopartner chromosomes, creating a chimeric gene with novel oncogenicproperties.
Chromosomal translocations have been used to identify patientswith acute leukemia with distinct clinical outcomes.16,17 Inacute myeloid leukemia (AML), for instance, the presence ofa t(8;21) translocation or a chromosome 16 inversion identifiespatients with a comparatively good prognosis, whereas the t(9;22)translocation is associated with a poor outcome.17 It is importantto note that chromosomal translocations have been used to identifypatients who will benefit from intensifying the dose of chemotherapy.18,19,20
Despite these prognostic and therapeutic successes, chromosomaltranslocations account for only part of the varied clinicalbehavior of acute leukemia, for several reasons. First, othergenetic aberrations can be functionally equivalent to a translocation,21,22thus diminishing the prognostic power of a translocation asa single variable. Second, additional oncogenic abnormalitiesmay accumulate in a leukemia that alter its responsiveness totherapy. For example, mutations in the gene encoding the flt3receptor tyrosine kinase have been associated with responseto treatment in patients with AML.23,24,25,26 Furthermore, flt3mutations that activate the kinase are present in some casesof acute lymphoblastic leukemia (ALL) with a t(4;14) translocation,rendering them susceptible to killing by flt3 inhibitors.27Finally, a sizable fraction of the acute leukemias have noneof the defined recurrent translocations.16,17
Gene-expression profiling has been used as an alternative approachto mapping chromosomal translocations. In pediatric B-cell ALL,gene-expression signatures have been identified that correlatewith six different chromosomal abnormalities.28,29 These gene-expressionsignatures can be combined with the use of statistical algorithmsto predict chromosomal abnormalities with 96 to 100 percentaccuracy.29 Likewise, in adult AML, a gene-expression–basedpredictor has been created that can identify three differentchromosomal translocations with a high rate of accuracy.30 Gene-expressionpredictors can also identify patients with AML who have isolatedtrisomy 8.31 These encouraging results demonstrate that DNAmicroarrays can be used to diagnose most chromosomal abnormalitiesin acute leukemias and could potentially substitute for themultiple diagnostic tests for these abnormalities that are currentlyrequired.
An oncogene likely to be causally related to T-cell ALL canbe dysregulated by chromosomal translocations in some casesbut by alternative mechanisms in others.22 For example, theHOX11 oncogene is involved in recurrent but infrequent translocationsin T-cell ALL, but gene-expression profiling revealed that somecases of T-cell ALL overexpress HOX11 without any detectablechromosomal abnormalities in this gene. All leukemias that overexpressHOX11 have a common gene-expression signature, suggesting thatthey are biologically similar. Most important, patients withleukemias that overexpress HOX11 have a favorable outcome, ascompared with patients with other types of T-cell ALL, whetheror not the overexpression is due to translocation, indicatingthe clinical superiority of expression profiling22 over identificationof the translocation.
Two adverse events after the treatment of acute leukemias arerelapse and the development of secondary leukemias. In B-cellALL, gene-expression profiling at the time of diagnosis providedinformation that could predict which patients would relapseand which would remain in continuous complete remission.29 Interestingly,no patterns of gene expression have been found to predict relapsein all subtypes of ALL. Rather, relapse was predicted by theexpression of different genes in each leukemic subtype, emphasizingonce again their divergent biologic characteristics. SecondaryAML arises as a consequence of treatment in some patients withALL, and this complication could also be predicted on the basisof gene-expression profiling in the subgroup of B-cell ALL withthe t(12;21) translocation.29 Although these predictors of clinicaloutcome will need to be validated in independent data sets,these findings suggest that treatment stratification based ongene-expression profiling can be initiated at the time of theinitial diagnosis of ALL.
Chronic Lymphocytic Leukemia
The most common leukemia in humans — chronic lymphocyticleukemia (CLL) — is an indolent but inexorable diseasewith no cure. Studies of immunoglobulin gene mutations in CLLcells raised the intriguing hypothesis that CLL might be twodistinct diseases.32,33 The presence of somatic mutations inthe immunoglobulin genes of CLL cells defined a group of patientswho had stable or slowly progressing disease requiring lateor no treatment. By contrast, the absence of immunoglobulingene mutations in CLL cells defined a group of patients whohad a progressive clinical course requiring early treatment.These two subtypes of CLL may also differ with respect to oncogenicmechanisms, since deletion of the ATM locus on chromosome 11qis associated with the absence of immunoglobulin gene mutationsin CLL34,35,36 and with shortened survival in some patients.37
Despite these clinical and molecular differences between thesubtypes of CLL, gene-expression profiling revealed that CLLcells express a common gene-expression signature that differentiatesthis form of leukemia from other lymphoid cancers and from normallymphoid subpopulations.9,38 This signature is shared by allcases of CLL, irrespective of the immunoglobulin gene mutationstatus, suggesting that CLL should be considered a single diseaseentity.
Nonetheless, given the clear clinical differences between thetwo subtypes of CLL, a hunt was made for genes that correlatedwith this distinction.9,38 Roughly 160 genes were found whoselevels of expression differed significantly between the twosubtypes9 (Figure 3B). Expression of the single most discriminatinggene, ZAP-70, distinguished these two subtypes with 93 percentaccuracy.9,39 Whereas analysis of the immunoglobulin gene sequencewould be a challenging and expensive test to introduce intoroutine clinical practice, a quantitative reverse-transcriptase–polymerase-chain-reactionassay or protein-based assay for the expression of ZAP-70 isfeasible.39,40
Translating Molecular Diagnosis into a Clinical Reality
What form of technology will be used for the molecular diagnosisof cancer in the future? Our experience with gene-expressionprofiling has taught us two clear lessons: multiple genes needto be studied to distinguish most types of cancer, and quantitativemeasurement of molecular differences among tumors results inclinically important diagnostic and prognostic distinctions.An important goal will therefore be to develop a platform forroutine clinical diagnosis that can quantitatively measure theexpression of a few hundred genes. Such a diagnostic platformwould allow us quickly to translate what we have learned aboutimportant molecular subgroups within each hematologic cancer.As we design new clinical trials, however, we must include genomic-scalegene-expression profiling in order to identify the genes thatinfluence the response to the agents under investigation. Inthis fashion, we can iteratively refine the molecular diagnosisof the hematologic cancers on the basis of new advances in treatmentand thus eventually reach the goal of tailored therapies formolecularly defined diseases.
Supported by intramural research funds from the National CancerInstitute.
I am indebted to my colleagues in the Lymphoma/Leukemia MolecularProfiling Project for their collaboration and for stimulatingdiscussions regarding molecular diagnosis in hematologic cancers.
Source Information
From the Metabolism Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Md.
Address reprint requests to Dr. Staudt at the Metabolism Branch, NCI, Bldg. 10, Rm. 4N114, NIH, 9000 Rockville Pike, Bethesda, MD 20892, or at lstaudt{at}mail.nih.gov.
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Troen, G., Nygaard, V., Jenssen, T.-K., Ikonomou, I. M., Tierens, A., Matutes, E., Gruszka-Westwood, A., Catovsky, D., Myklebost, O., Lauritzsen, G., Hovig, E., Delabie, J.
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Mounier, N., Gisselbrecht, C., Briere, J., Haioun, C., Feugier, P., Offner, F., Recher, C., Stamatoullas, A., Morschhauser, F., Macro, M., Thieblemont, C., Sonet, A., Fabiani, B., Reyes, F.
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Kelloff, G. J., Bast, R. C. Jr., Coffey, D. S., D'Amico, A. V., Kerbel, R. S., Park, J. W., Ruddon, R. W., Rustin, G. J. S., Schilsky, R. L., Sigman, C. C., Woude, G. F. V.
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Celis, J. E., Gromov, P., Gromova, I., Moreira, J. M. A., Cabezon, T., Ambartsumian, N., Grigorian, M., Lukanidin, E., thor Straten, P., Guldberg, P., Bartkova, J., Bartek, J., Lukas, J., Lukas, C., Lykkesfeldt, A., Jaattela, M., Roepstorff, P., Bolund, L., Orntoft, T., Brunner, N., Overgaard, J., Sandelin, K., Blichert-Toft, M., Mouridsen, H., Rank, F. E.
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A correction has been published: N Engl J Med 2003;348(25):2588.
B signaling pathway is a feature of the activated B-cell–like subgroup but not the other subgroups, and interference with this pathway selectively kills this type of diffuse large-B-cell lymphoma.11 
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