Prediction of Survival in Diffuse Large-B-Cell Lymphoma Based on the Expression of Six Genes
Izidore S. Lossos, M.D., Debra K. Czerwinski, B.A., Ash A. Alizadeh, M.D., Ph.D., Mark A. Wechser, Ph.D., Rob Tibshirani, Ph.D., David Botstein, Ph.D., and Ronald Levy, M.D.
Background Several gene-expression signatures can be used topredict the prognosis in diffuse large-B-cell lymphoma, butthe lack of practical tests for a genome-scale analysis hasrestricted the use of this method.
Methods We studied 36 genes whose expression had been reportedto predict survival in diffuse large-B-cell lymphoma. We measuredthe expression of each of these genes in independent samplesof lymphoma from 66 patients by quantitative real-time polymerase-chain-reactionanalyses and related the results to overall survival.
Results In a univariate analysis, genes were ranked on the basisof their ability to predict survival. The genes that were thestrongest predictors were LMO2, BCL6, FN1, CCND2, SCYA3, andBCL2. We developed a multivariate model that was based on theexpression of these six genes, and we validated the model intwo independent microarray data sets. The model was independentof the International Prognostic Index and added to its predictivepower.
Conclusions Measurement of the expression of six genes is sufficientto predict overall survival in diffuse large-B-cell lymphoma.
The most common type of lymphoma in adults, diffuse large-B-celllymphoma, has an annual incidence in the United States of morethan 25,000 cases and accounts for 30 to 40 percent of casesof non-Hodgkin's lymphomas.1 Combination chemotherapy has transformeddiffuse large-B-cell lymphoma from a universally fatal diseaseto a potentially curable one, but less than half of all patientsare cured.2 The International Prognostic Index (IPI), a well-establishedpredictor of outcome in diffuse large-B-cell lymphoma, is basedon five clinical characteristics (age, tumor stage, serum lactatedehydrogenase concentration, performance status, and numberof extranodal disease sites).3 However, the outcome in patientswith diffuse large-B-cell lymphoma who have identical IPI valuesvaries considerably. New molecular methods may make risk-adjustedtherapies possible for diffuse large-B-cell lymphoma in a waysimilar to the current practice in acute leukemia.
The relation between prognosis and the molecular features ofdiffuse large-B-cell lymphoma has been investigated with theuse of genome-scale expression profiles assessed by DNA microarrays.4,5,6There are a variety of techniques for analyzing microarray data,but the two general types are unsupervised and supervised. Withthe unsupervised approach, microarray data are analyzed withoutthe use of external information such as clinical data or survivaltime. In contrast, with the supervised approach, the aim isto identify genes whose expression correlates with some externalvariables. With both unsupervised4 and supervised5,6 methods,microarray studies of diffuse large-B-cell lymphomas showedthat gene-expression signatures were associated with clinicaloutcomes.
Alizadeh et al.,4 with lymphochip complementary DNA (cDNA) microarrays,showed that overall survival after chemotherapy was significantlylonger among patients with diffuse large-B-cell lymphoma thathad high levels of expression of genes characteristic of normalgerminal-center B cells than among patients whose tumors hadlow levels of expression of these same genes. Two genes specificallyexpressed in the germinal-center B cell, BCL6 and HGAL, havebeen shown to predict overall survival, independently of theIPI, in unrelated groups of patients studied with the use ofother methods.7,8,9 However, another germinal-center B-cellmarker, CD10, did not predict survival in diffuse large-B-celllymphoma, suggesting that the outcome is associated with theexpression of only some genes in germinal-center B-cell signatures.8
Supervised analysis of gene-expression data in relation to overallsurvival has also made possible the construction of models topredict the outcome in diffuse large-B-cell lymphoma. Shippet al.5 derived a 13-gene predictive model, which was independentof the IPI, from a cohort of 58 patients whose lymphomas wereanalyzed by oligonucleotide microarrays. Only 3 of these 13genes were present in the data analyzed by Alizadeh and colleagues,4and of those 3, only 2 were associated with survival. Rosenwaldet al.6 used supervised analysis of gene-array data from 160patients with diffuse large-B-cell lymphoma to derive a predictivemodel based on the expression of 17 genes and applied this modelto a set of such lymphomas from 80 other patients.
There is no overlap among the genes in the models derived byShipp et al. and Rosenwald et al.5,6 Technical differences,the composition of the microarrays used, and different algorithmsused for constructing predictive models may underlie this disparity.In addition, every predictive model must be validated in anindependent cohort of patients to confirm that it works generallyand not just for the group of patients from which it was derived.10,11Therefore, it remains unclear which method and which model bestcapture the molecular, histopathological, and clinical heterogeneityof diffuse large-B-cell lymphoma. Also, since microarrays arenot yet readily available in clinical laboratories, more practicalassays for gene expression are needed.
We used quantitative reverse-transcriptase polymerase chainreaction (RT-PCR) to measure the expression of 36 genes in diffuselarge-B-cell lymphomas from 66 patients. We then built a predictivemodel based on the genes that were correlated with overall survival,either positively or negatively, and validated the model byapplying it to the microarray data from Shipp et al.5 and Rosenwaldet al.6 in order to determine whether it had predictive valuethat was independent of the method of measuring gene expression(i.e., quantitative RT-PCR, cDNA microarrays, or oligonucleotidemicroarrays). Our goal was to devise a model that was technicallysimple and applicable for routine clinical use.
Methods
Tumor Specimens
During diagnostic procedures at Stanford University MedicalCenter from 1975 to 1995, we obtained tumor specimens from patientswith newly diagnosed diffuse large-B-cell lymphoma. Specimenswere stored frozen, as previously reported.7,8 The diagnosisof diffuse large-B-cell lymphoma according to the revised European–Americanlymphoma classification12 was confirmed on reevaluation of allspecimens before their inclusion in this study. All the tumorshad the histologic appearance of centroblastic large-B-celllymphomas with a diffuse pattern and no residual follicles.All patients were treated with a regimen that included an anthracycline(cyclophosphamide, doxorubicin, vincristine, and prednisone[CHOP] or CHOP-like regimens) and were followed up at StanfordUniversity Hospital. Primary diffuse large-B-cell lymphoma specimensfrom a total of 66 patients fulfilled the criteria for inclusionin the study. Information on the tumor stage was obtained forall the patients according to the Ann Arbor system of staginglymphomas. We were able to determine the IPI score for 58 ofthe patients. Written informed consent was obtained from allpatients, and the study was approved by the institutional reviewboard of Stanford University Medical Center.
RNA Isolation and Real-Time PCR
Isolation of RNA, its quantification, and the RT reactions wereperformed according to established methods.7,13 Expression ofmessenger RNA (mRNA) for 36 genes that we tested (Table 1, aswell as Table A in the Supplementary Appendix [available withthe full text of this article at www.nejm.org]) and 2 endogenouscontrol genes was measured in each biopsy specimen of diffuselarge-B-cell lymphoma by real-time PCR (with TaqMan Gene ExpressionAssays products on an ABI PRISM 7900 HT Sequence Detection System,Applied Biosystems).13 For each gene, two to four sets of TaqManprobes and primers were tested. The probes contain a 6-carboxy-fluoresceinphosphoramidite (FAM dye) label at the 5' end of the gene anda minor groove binder and nonfluorescent quencher at the 3'end and are designed to hybridize across exon junctions. Theassays are supplied with primers and probe concentrations of900 nM and 250 nM, respectively. For each gene, the assay withthe highest amplification efficiency was selected for this study;the TaqMan probes and primer sequences are presented in TableA in the Supplementary Appendix. No fluorescent signal was generatedby these assays when genomic DNA was used as a substrate, whichconfirms that the assays measured only mRNA.
Table 1. Sources of Evidence for a Panel of 36 Genes Whose Expression Predicts Survival in Diffuse Large-B-Cell Lymphoma.
Phosphoglycerate kinase 1 (PGK1) and glyceraldehyde-3-phosphatedehydrogenase (GAPDH) were used as the endogenous RNA and cDNAquantity controls (P/N 4326318E and P/N 4326317E, respectively;Applied Biosystems). We chose PGK1 and GAPDH on the basis ofan analysis of their relatively constant expression in diffuselarge-B-cell lymphoma.13 Since the normalization to the endogenouscontrol genes PGK1 and GAPDH led to similar results and conclusions,we present only the data normalized to PGK1 expression. Forcalibration and generation of standard curves, we used cDNAderived from the Raji cell line of human B-cell lymphoma, cDNAprepared from Universal Human Reference RNA (Stratagene), orboth. The cDNA prepared from Universal Human Reference RNA wasused for genes that were not abundant in the Raji cell line(CCND1, CCND2, SLA, NR4A3, CD44, PLAU, and FN1). To controlfor possible variations among PCR runs performed on differentdays, the expression of all the analyzed and endogenous controlgenes was assessed in the Raji cell line before, midway through,and on completion of the analysis of all the specimens of diffuselarge-B-cell lymphoma. The assays were highly reproducible,with coefficient of variation less than 0.16 among these threeruns for all the genes assessed in the Raji cell line.
Statistical Analysis
The normalized gene-expression values were log-transformed (ona base 2 scale), in a manner similar to the transformation ofarray-based hybridization data. Overall survival time was calculatedfrom the date of diagnosis until death or the last follow-upcontact. We estimated survival curves by the Kaplan–Meierproduct-limit method and compared them using the log-rank test.To construct a model for the prediction of survival, univariateCox proportional-hazards analysis was performed, with overallsurvival as the dependent variable.27 Subsequently, genes withan absolute univariate z score greater than 1.5 or less than–1.5 were analyzed in a multivariate Cox proportional-hazardsregression model, with overall survival as the dependent variable.The individual components of the IPI and the overall score wereincluded in the model. Two-sided P values of less than 0.05were considered to indicate statistical significance. In thefinal model for the prediction of survival, we multiplied thelog-transformed normalized expression value measured for eachgene by a factor of z, a score derived from the multivariateanalysis (see the Supplementary Appendix for a description ofthis method).
To validate the usefulness of this model, we applied it to twoindependent, previously published sets of gene-expression datafor diffuse large-B-cell lymphoma that were derived from DNA-microarrayanalysis5,6 (see the Supplementary Appendix). These data setswere compared without shifting of the means or other scalingof the raw gene-expression data.
Results
Selection of a Panel of Genes for Quantitative RT-PCR
We selected a group of 36 genes for this study (Table 1). Theexpression of each of these genes, measured either individuallyor in large data sets derived from microarrays, has been foundto predict survival in diffuse large-B-cell lymphoma. We appliedsignificance analysis of microarrays28 — a supervisedmethod for the identification of genes with a statisticallysignificant association with survival — to the data setof Alizadeh et al.4 in order to identify genes that may havebeen missed in the unsupervised analyses.
We measured the expression of each of the 36 genes and of the2 internal-control genes for input mRNA (PGK1 and GAPDH) byquantitative RT-PCR. We determined the expression of each genein each of the 66 specimens of lymphoma relative to its expressionin a sample of RNA used as a reference.13 (For raw data, seethe Supplementary Appendix. The primer and probe sets for eachof the genes in this study are shown in Table A.)
A Model for Predicting Survival in Diffuse Large-B-Cell Lymphoma
We first performed a univariate analysis of the expression datafor the 36 genes, with overall survival as a dependent variable(Figure 1). We ranked the genes on the basis of their predictivepower (univariate z score). A negative z score was associatedwith longer overall survival, and a positive z score was associatedwith shorter overall survival. From this ranking, we selectedan optimal number of genes for use in constructing a predictivemodel. Including more genes, even with some redundancy, wouldhave tended to make the predictive model perform better in independentvalidation analyses, but a smaller number of genes would makethe model more practical. By inspection, the conventional cutoffvalue for z of ±2.0 (P<0.05) would have yielded onlyone gene, LM02 (Figure 1). Therefore, we picked the z valueof ±1.5 (P=0.13), which allowed the six genes to be included.(Other z values were tried after the fact, and the results areshown for comparison in the Supplementary Appendix.) The sixgenes that exceeded the z value of 1.5 in the univariate analysiswere LMO2, BCL6, FN1, CCND2, SCYA3, and BCL2.
Figure 1. Univariate Analysis of Expression of 36 Genes with Overall Survival as a Dependent Variable.
The genes are ranked on the basis of their predictive power (univariate z score), with a negative score associated with longer overall survival and a positive score associated with shorter overall survival. The dashed lines represent an absolute univariate z score of ±1.5. The prediction model is based on the weighted expression of six genes and is expressed by the following equation: mortality-predictor score = (–0.0273xLMO2) + (–0.2103xBCL6) + (–0.1878xFN1) + (0.0346xCCND2) + (0.1888xSCYA3) + (0.5527xBCL2).
When we performed a multivariate Cox regression analysis withoverall survival as a dependent variable, none of these genesindependently predicted overall survival at a statisticallysignificant level. This is not surprising, since the genes areinterrelated (e.g., BCL6 is known to down-regulate the expressionof CCND2 and SCYA3).25 Another multivariate Cox regression analysiswas then performed, which included the six genes as well aseach of the components of the IPI. This analysis showed thatonly the serum lactate dehydrogenase concentration was an independentpredictor of overall survival (P = 0.004).
Since we intended to construct a model that would be independentof and not overlap the IPI, we did not use the serum lactatedehydrogenase concentration in the model, because it is alreadya component of the IPI score. Instead, we constructed a modelthat is based on the relative contributions of each of the sixgenes in the multivariate analysis, as described in the followingequation: mortality-predictor score = (–0.0273xLMO2) +(–0.2103xBCL6) + (–0.1878 xFN1) + (0.0346xCCND2)+ (0.1888xSCYA3) + (0.5527xBCL2). For example, the negativeweighting value assigned to LMO2 indicates that higher expressioncorrelates with longer survival. The positive value for CCND2indicates that higher expression correlates with shorter survival.
We ranked the patients with known IPI scores according to theirmortality-predictor scores and divided them into three groupsaccording to whether they had a low, medium, or high risk ofdeath (low risk, lower than 0.063; medium risk, from –0.063to <0.093; and high risk, 0.093 or higher). Table 2 showsthe clinical characteristics of the patients according to theserisk groups. The rates of overall survival at five years inthe low-risk, medium-risk, and high-risk groups were 65 percent,49 percent, and 15 percent, respectively (P = 0.004). The meansurvival times were 8.7 years (95 percent confidence interval,4.9 to not reached), 7.1 years (95 percent confidence interval,3.3 to not reached), and 3.8 years (95 percent confidence interval,1.8 to 5.0), respectively (Figure 2).
Panel A shows Kaplan–Meier estimates of overall survival in the 66 patients with diffuse large-B-cell lymphoma, analyzed by quantitative reverse-transcriptase polymerase chain reaction with TaqMan probe-based assays. The dotted lines represent 95 percent confidence intervals. Panel B shows Kaplan–Meier curves for overall survival in the three groups (at low, medium, and high risk of death) as defined by a prediction model based on the weighted expression of six genes (LMO2, BCL6, FN1, CCND2, SCYA3, and BCL2). According to log-likelihood estimates, P=0.001 for the model based on a continuous variable, and P=0.02 for the model based on the three discrete groups shown in the figure.
To test the validity of this model, we applied it to publishedmicroarray gene-expression data from Shipp et al.5 (Figure 3Aand Figure 3B) and from Rosenwald et al.6 (Figure 3C and Figure 3D).These tests confirmed the ability of the model to predictsurvival. In the smaller cohort study of patients with diffuselarge-B-cell lymphoma, reported by Shipp et al.,5 the overallsurvival in the medium-risk group was similar to that in thehigh-risk group. However, the medium-risk group did have anintermediate risk in the larger cohort of patients that Rosenwaldet al. analyzed.6
Figure 3. Validation of the Performance of the Six-Gene Model with the Use of Data from Oligonucleotide Microarrays (Panels A and B) and cDNA Microarrays (Panels C and D).
Panel A shows Kaplan–Meier estimates of overall survival in all 58 patients with diffuse large-B-cell lymphoma reported by Shipp et al.,5 and Panel B Kaplan–Meier estimates of overall survival in the 58 patients after subdivision into three groups (at low, medium, and high risk of death) on the basis of the six-gene model for prediction. The dotted lines represent 95 percent confidence intervals. According to log-likelihood estimates, P=0.02 for the model as a continuous variable, and P=0.31 for the model as a class. Similar analyses of the data on the 240 patients with diffuse large-B-cell lymphoma reported by Rosenwald et al.6 are shown in Panels C and D. P<0.001 for the model based on a continuous variable and for the model based on the three discrete groups shown in the figure.
We next investigated whether our model could add prognosticvalue beyond that of the IPI. Among patients in our sample whowere at high risk for death according to the IPI, the six-genemodel could further subdivide the patients into those likelyto have longer survival and those likely to have shorter survival,in a manner similar to what we observed in the entire groupof 66 patients (P=0.006) (data not shown). But of our 66 patients,too few were in the IPI group with the lowest risk of deathfor our findings to achieve statistical significance. We thereforetested the added value of the six-gene model by analyzing thelarger data set reported by Rosenwald et al.6 (Figure 4). Weused that study group's three subdivisions, which were basedon the IPI (low, medium, and high). In some of these groups,the number of patients was small. But in each stratum of theIPI, we could identify a group with an especially low probabilityof survival (Figure 4, blue lines). Thus, by identifying thepatients who had either medium-risk or high-risk scores on theIPI along with a high-risk expression profile (the bottom, orhighest-risk, group), it was possible to identify the groupof approximately 30 percent of all patients with diffuse large-B-celllymphoma who had especially short survival.
Figure 4. The Six-Gene Model and the International Prognostic Index.
The Kaplan–Meier estimates show overall survival for groups of patients with low-risk (Panel A), medium-risk (Panel B), and high-risk (Panel C) scores on the International Prognostic Index, as reported by Rosenwald et al.,6 after subdivision into three groups (at low, medium, and high risk for death) on the basis of the six-gene model for prediction. According to log-likelihood estimates, P=0.01, P=0.002, and P=0.16 for the model based on a continuous variable applied to the low-risk, medium-risk, and high-risk groups, respectively, and P=0.02, P=0.003, and P=0.01, respectively, for the model based on the three discrete groups shown in the figure.
Recently, Barrans et al.9 reported that immunohistochemicalanalysis of antibodies to germinal-center markers and Bcl-2,in combination with the IPI, could improve risk stratificationamong patients with diffuse large-B-cell lymphoma. Colomo etal.29 could not confirm this result but did demonstrate thepredictive power of expression of the Bcl-2 protein. Comparisonof the predictive power of our model of the expression of sixgenes with that of a gene-expression model based on only BCL6(a germinal-center marker) and BCL2 showed that the six-genemodel predicted overall survival better among patients in ourcohort and in the cohorts studied by Shipp et al.5 and Rosenwaldet al.6 (see the Supplementary Appendix).
Discussion
As new therapies for lymphoma become available, it will be increasinglyimportant to identify patients who do not benefit from currenttreatments and who may be candidates for early treatment withthese new approaches. The IPI has proved useful for identifyingsuch patients with diffuse large-B-cell lymphoma,3 but we foundthat the molecular characteristics of lymphoma can add furtherpredictive power. Simultaneous analysis of the expression ofthousands of genes in diffuse large-B-cell lymphoma with theuse of cDNA and oligonucleotide microarrays by several groupshas provided a rich source of data that can be correlated withthe clinical outcome.4,5,6 Each of these studies has producedlists of genes for use in the stratification of the risk ofdeath among patients with diffuse large-B-cell lymphoma; however,validating such models in other, unrelated groups of patientsis essential. In addition, more convenient methods for the measurementof gene expression need to be developed.
The aim of our study was to identify a small group of geneswhose expression predicts survival in patients with diffuselarge-B-cell lymphoma and can be readily measured. To this end,we evaluated the prognostic significance of 36 genes that werechosen on the basis of previous reports of their prognosticpotential and on the basis of our own analysis of the existingmicroarray data. The results of this evaluation allowed us todesign a model consisting of six genes that predicts overallsurvival in patients with diffuse large-B-cell lymphoma. Themodel assigned the 66 patients in our series and the 58 and240 patients with lymphomas analyzed by Shipp et al.5 and Rosenwaldet al.,6 respectively, to three prognostic groups. Our methodstratified all three groups of patients according to risk andwas independent of the IPI. Furthermore, our 6-gene model wasas robust as the 17-gene model developed by Rosenwald et al.6and was also independent of the IPI in its ability to predictthe outcome (data not shown). Moreover, our six-gene model couldbe applied to the data sets derived from the measurement ofgene expression by three methods30,31,32 without the need forshifting or scaling of the expression data to match their meanand variance levels.33
The genes in our model occur in the germinal-center B-cell signature(LMO2 and BCL6), the activated B-cell signature (BCL2,CCND2,SCYA3), and the lymph-node signature (FN1).4,6 However, sincemany of the other genes in these signatures have no independentpredictive power in our model, the model we propose probablyrefines these signatures by identifying the genes with the highestlevel of independent prognostic power.
In this study, the expression of LMO2, BCL6, and FN1 correlatedwith prolonged survival. LMO234 has an important role in erythropoiesisand angiogenesis35,36 and is the most frequent site of chromosomaltranslocation in childhood T-cell acute lymphoblastic leukemia.34It is not expressed in normal T lymphocytes,37 but it is expressedat high levels in germinal-center lymphocytes.4LMO2 has alsobeen implicated in T-cell leukemia, developing after retrovirus-basedgene therapy of X-linked severe combined immunodeficiency.37The relationship between its ability to cause T-cell leukemiaand its correlation with prolonged survival in diffuse large-B-celllymphoma is unclear.
The BCL6 gene encodes a transcriptional repressor,38,39,40 isnormally expressed in B cells and CD4+ T cells within the germinalcenter, and controls germinal-center formation and T-cell–dependentimmune responses.41,42,43 It is expressed in non-Hodgkin's lymphomasthat originate from germinal-center B cells. BCL6 expressionhas been reported to predict survival in patients with diffuselarge-B-cell lymphoma,8 and our findings confirm this observation.
Fibronectin 1 (FN1), an extracellular glycoprotein, is a ligandfor the integrin family of cell-adhesion receptors that regulatecytoskeletal organization. The expression of FN1, by hepatocytes,stromal fibroblasts, and some tumor cells,44 has been associatedwith metastasis.45FN1 is a component of the lymph-node signaturein diffuse large-B-cell lymphoma.6 Its expression may reflectthe presence of mesenchymal cells in the biopsy specimen ora response of the lymph node to tumor cells, since we foundonly low levels of FN1 transcripts in the purified lymphomacells (data not shown).
In our study, the expression of BCL2,CCND2, and SCYA3 correlatedwith short survival. These three genes are included in the activatedB-cell–like signature that we and others have associatedwith short survival.4 The Bcl-2 protein is present at low levelsin normal germinal-center B cells but at increased levels insome non-Hodgkin's lymphoma cells that have a t(14;18) translocation.4,46Bcl-2 prevents apoptosis, and elevated levels of this protein,as detected immunohistologically, serve as an independent markerof a poor prognosis in patients with diffuse large-B-cell lymphoma.20,21,22,23
CCND2 encodes a protein belonging to the cyclin family, whosemembers are characterized by dramatic periodicity in the amountof protein that is present during the cell cycle. CCND2 controlsthe progression of the cell cycle from the G1 phase to the Sphase by serving as a mediator of S-phase commitment and DNAsynthesis.47 Overexpression of CCND2 occurs in chronic lymphocyticleukemia and mantle-cell lymphoma.48
SCYA3, otherwise known as MIP-1-alpha or CCL3, is a CC chemokinethat recruits a variety of cells to sites of inflammation.49Its function in B-cell lymphomas is unknown, but it is expressedmainly in the activated B-cell–like subgroup of diffuselarge-B-cell lymphoma.4 The promoter regions of the CCND2 andSCYA3 genes contain high-affinity binding sites for Bcl-6, andthe expression of these two genes is repressed by Bcl-6.25 Thisobservation underscores the relations among the individual genesthat we and others have implicated as determining the prognosisfor patients with diffuse large-B-cell lymphoma.
Diffuse large-B-cell lymphoma is heterogeneous and may requirea risk-adjusted approach to therapy. For simplicity, one canfocus on the high-risk group in our model, because patientsin this group have an especially poor prognosis with respectto long-term survival in the three data sets that we examined.Even among patients with a low risk of death according to theIPI, our six-gene model identified patients with a five-yearsurvival rate of only 57 percent. Among patients with a mediumor high clinical risk of death (medium or high scores on theIPI), the five-year survival rate in the high-risk group inour model is less than 27 percent. This group, representingapproximately one third of all patients with diffuse large-B-celllymphoma, may require a different therapeutic approach fromthat used in other patients. The six-gene model and quantitativePCR can most likely be used to identify patients who may benefitfrom new treatments.
Supported by grants (CA33399 and CA34233) from the Public HealthService, National Institutes of Health.
Source Information
From the Division of Oncology, Department of Medicine (I.S.L., D.K.C., R.L.), the Department of Genetics (A.A.A., D.B.), and the Departments of Health Research and Policy and Statistics (R.T.), Stanford University Medical Center, Stanford, Calif.; the Division of Hematology–Oncology, Department of Medicine, University of Miami, Miami (I.S.L.); and Applied Biosystems, Foster City, Calif. (M.A.W.).
Address reprint requests to Dr. Levy at Stanford University School of Medicine, Division of Oncology, Rm. 1105, Stanford, CA 94305-5151, or at levy{at}stanford.edu.
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