George Mulligan,1Constantine Mitsiades,2Barb Bryant,1Fenghuang Zhan,3Wee J. Chng,4Steven Roels,1Erik Koenig,1
Andrew Fergus,1Yongsheng Huang,3Paul Richardson,2William L. Trepicchio,1Annemiek Broyl,5Pieter Sonneveld,5
John D. Shaughnessy Jr,3P. Leif Bergsagel,4David Schenkein,1Dixie-Lee Esseltine,1Anthony Boral,1
and Kenneth C.Anderson2
1Millennium Pharmaceuticals, Cambridge, MA;2Dana-Farber Cancer Institute, Boston, MA;3Myeloma Institute for Research and Therapy, University of
Arkansas for Medical Sciences, Little Rock;4Mayo Clinic, Scottsdale,AZ;5Department of Hematology, Erasmus Medical Centre, Rotterdam, The Netherlands
The aims of this study were to assess the
feasibility of prospective pharmaco-
tional clinical trials of bortezomib in mul-
tiple myeloma and to develop predictive
classifiers of response and survival with
bortezomib. Patients with relapsed my-
eloma enrolled in phase 2 and phase 3
clinical trials of bortezomib and con-
sented to genomic analyses of pretreat-
ment tumor samples. Bone marrow aspi-
rates were subject to a negative-selection
procedure to enrich for tumor cells, and
sion profiling using DNA microarrays.
Data quality and correlations with trial
outcomes were assessed by multiple
groups. Gene expression in this dataset
was consistent with data published from
a single-center study of newly diagnosed
multiple myeloma. Response and sur-
vival classifiers were developed and
shown to be significantly associated with
outcome via testing on independent data.
The survival classifier improved on the
risk stratification provided by the Interna-
tional Staging System. Predictive models
and biologic correlates of response show
some specificity for bortezomib rather
than dexamethasone. Informative gene
expression data and genomic classifiers
that predict clinical outcome can be de-
rived from prospective clinical trials of
new anticancer agents. (Blood. 2007;109:
© 2007 by TheAmerican Society of Hematology
Multiple myeloma is an incurable malignancy that originates in the
antibody-secreting bone marrow plasma cells. Median survival is
approximately 3 to 4 years, but the clinical course is highly variable
and difficult to predict.1,2Therefore, there is a need to better define
patient-specific treatment strategies for the use of both standard and
Anumber of clinical and laboratory features provide prognostic
information, including age, performance status, tumor burden,
tumor proliferative index, and hemoglobin and platelet levels, as
well as serum ?-2 microglobulin, albumin, creatinine, lactic
dehydrogenase, and calcium.3-8Some of these factors relate to the
patient’s status, whereas others reflect aspects of the tumor. A
recent multivariate analysis of data from 10 000 patients identified
serum albumin and ?-2 microglobulin as a reliable prognostic tool,
referred to as the International Staging System (ISS).2The ISS is
valid for patients of different age groups and geographies, and with
respect to the 2 most common myeloma treatments of the past
decade, standard-dose chemotherapy and high-dose therapy (HDT)
followed by stem cell rescue.2However, therapeutic choices for
myeloma have become increasingly complex as new active agents
have emerged,9,10and their optimal use either alone or in combina-
tion with standard chemotherapy or HDT remains to be defined.As
indicated in the ISS study, standard clinical prognostic factors
were unable to reliably identify the highest risk patients most in
need of novel therapies (defined as those with ? 12 months overall
It is anticipated that genomics will help provide more precise
prognostic and predictive tools.11-13However, the practicality, utility,
Various molecular analyses suggest that myeloma, like other
cancers, is composed of distinct subtypes that have somewhat
different molecular pathologies and prognoses.11,16,17For instance,
cytogenetic studies reveal that approximately 60% to 80% of
myeloma cases exhibit rearrangements of the IGH heavy chain
locus, with 40% involving 5 recurrent translocations.11Patients
with the t(11;14)(q13;q32) translocation experience superior sur-
vival on treatment with HDT, whereas those with t(4;14)(p16;q32)
exhibit a relatively poor survival.18-25More specifically, although
t(4;14)(p16;q32) tumors initially respond to therapy, they relapse
quickly and are insensitive to alkylator salvage treatment.11,25
Deletion of chromosome 13 occurs in tumors with and without IgH
translocations26and is a significant poor prognostic factor, regard-
less of therapy or age.26-28
Submitted September 6, 2006; accepted December 6, 2006. Prepublished
online as Blood First Edition Paper, December 21, 2006; DOI 10.1182/blood-
Presented in poster form at the 47th annual meeting of theAmerican Society of
Hematology,Atlanta, GA, December 12, 2005.63
The online version of this article contains a data supplement.
An Inside Blood analysis of this article appears at the front of this issue.
The publication costs of this article were defrayed in part by page charge
payment. Therefore, and solely to indicate this fact, this article is hereby
marked ‘‘advertisement’’ in accordance with 18 USC section 1734.
© 2007 by TheAmerican Society of Hematology
3177 BLOOD, 15APRIL 2007?VOLUME 109, NUMBER 8
Furthermore, distinct gene expression patterns are associated
with most of the molecular subtypes of myeloma,14,29-33and these
patterns are now being associated with disease prognosis.14A
recent genomic analysis of 231 myeloma cases identified 8 distinct
tumor subtypes, defined via assessment of cyclin D status and other
genes frequently involved in IgH translocations (referred to as TC
subtypes, for Translocation and Cyclin D).33Despite these and
other molecular advances, it remains unclear how to match disease
subtypes appropriately with standard myeloma therapies or the use
of new agents.
To assess the technical feasibility of conducting prospective
pharmacogenomics research in myeloma and, if possible, to
develop and independently validate a genomic classifier of efficacy
to a specific single agent, we generated gene expression data during
the course of national and international phase 234,35and phase 336
clinical trials of a novel agent, the proteasome inhibitor bortezomib
(VELCADE; Millennium Pharmaceuticals, and Johnson & John-
son Pharmaceutical Research & Development). We report here the
microarray results from those trials. This is the first report
demonstrating the prospective development and independent vali-
dation of a genomic classifier that predicts clinical response
between myeloma patients treated with a new agent (bortezomib)
or an active control drug (high-dose dexamethasone; Dex).
Materials and methods
Sample collection, enrichment, data generation,
and array quality control
On collection of patients’ bone marrow aspirate, myeloma cells were
enriched via negative selection. The RosetteSep procedure (Stem Cell
Technologies, Vancouver, BC, Canada) uses a cocktail of cell-type–specific
antibodies (as described in Tai et al37) to deplete nonplasma cells (see
tal Materials link at the top of the online article). Myeloma cells were
collected and frozen. In the international studies, the first 2 samples from
each site were collected and subjected to RNAisolation so that feedback on
quantity and quality could be provided; ultimately, phase 2 and 3 trials
provided a similar percentage of informative samples. Control samples
included bone marrow–derived normal plasma cells (PCs), neutrophils,
T cells, and CD71? erythroid cells (AllCells, Berkeley, CA).
Total RNAwas isolated using a Qiagen RNAeasy isolation kit (Qiagen,
Valencia, CA) and quantified by spectrophotometry; samples with at least
0.5 ?g were labeled for gene expression profiling in 2 batches (Document
S1), using the Affymetrix GeneChip microarray system (Affymetrix, Santa
Clara, CA). A standard T7-based amplification protocol (Affymetrix) was
used to convert 2.0 ?g RNA(if available) to biotinylated cRNA. cRNAfor
each sample was hybridized to the U133A/B arrays in triplicate; operators,
chip lots, clinical sites, and scanners (GeneArray 3000; Affymetrix) were
controlled throughout. Data processing used Affymetrix MAS5.0. Quality
control metrics determined byAffymetrix and Millennium Pharmaceuticals
included the percentage present (? 25%), scale factor (? 14), ?-actin 3?/5?
ratio (? 15), and background (? 120) (Table S1). Samples falling outside
these metrics were excluded from subsequent analysis.
The myeloma purity score examines expression of genes described as
highly expressed in myeloma cells and their normal plasma precursor cells
(205692_s_at CD38 antigen [P45]; 201286_at syndecan-1 [SDC1];
201891_s_at ?-2 microglobulin [B2M]; 211528_x_at B2M) compared with
genes expressed highly in erythroid cells (37986_at erythropoietin receptor
[EPOR]; 209962_at EPOR; 205838_at glycophorinA[GYPA]), neutrophils
(203948_s_at myeloperoxidase [MPO]; 203591_s_at colony-stimulating
factor 3 receptor [CSFR3] [granulocyte]; 204039_at CCAAT/enhancer
binding protein ? [CEBPA]; 214 523_at CCAAT/enhancer binding protein
? [CEBPE]), or T cells (209603_at GATA binding protein 3 [GATA3];
209604_s_at GATA binding protein 4 [GATA4]; 205456_at CD3E antigen,
? polypeptide). Myeloma score ? expression of myeloma markers /
expression of (erythroid ? neutrophil ? T cell) markers. The data set is
available at Gene Expression Omnibus (http://www.ncbi.gov/geo/).
Clinical studies and efficacy
The APEX phase 3 trial (039) was conducted at 93 centers in the United
States, Canada, Europe, and Israel from June 2002 to October 2003.36A
total of 669 patients with myeloma who had relapsed following 1 to 3 prior
therapies were randomly assigned to treatment with bortezomib 1.3 mg/m2
or high-dose Dex; Dex patients who experienced progressive disease (PD)
were permitted to crossover to receive bortezomib in a companion study
(040). The 040 study also directly enrolled 263 patients who had more than
3 prior therapies; these “non-crossover” patients were also eligible for
patients with relapsed and refractory myeloma at 14 centers in the United
States.35Patients received bortezomib 1.3 mg/m2for no more than 8 cycles.
The CREST phase 2 trial (024) had a similar design, except the 54 enrolled
patients had either relapsed or refractory disease, and they received
bortezomib 1.0 or 1.3 mg/m2.34Phase 2 investigators had the option to add
Dex 20 mg if patients had suboptimal response; however, clinical and
genomic studies report activity of single-agent bortezomib by censoring
outcome data at the time of adding Dex.
Review boards at all participating institutions approved the studies; all
patients provided written informed consent. Additional consent was pro-
vided for pharmacogenomics analysis. The studies were conducted in
accordance with the Declaration of Helsinki and International Conference
on Harmonisation Good Clinical Practice guidelines.
Clinical response was treated as a categorical variable, whereas OS was
treated as a censored continuous-time variable. OS (days) was assessed
from the date patients received their first dose of study drug, without regard
to other subsequent therapies. Patients were classified as achieving
complete response (CR), partial response (PR), minimal response (MR), no
change (NC), or PD, using European Group for Bone Marrow Transplanta-
tion criteria.38In brief, PD requires 25% increase in paraprotein, whereas
MR, PR, and CR require at least 25%, 50%, and 100% decreases,
respectively. NC is the absence of response or progression but, in this study,
required at least 2 measures of stable disease. The efficacy data of the
genomics subset were manually reviewed to reconfirm classifications
Only the 9200 probe sets with strongest between-sample variance
relative to their in-sample replicate variance were retained for further
analysis (B.B., E.K., G.M., manuscript in preparation). Repeated
expression measurements on a given sample were summarized by the
log of their median value.
Gene set enrichment analysis (GSEA). Analysis used GSEAsoftware
(version 1.0; Broad Institute, http://www.broad.mit.edu/gsea/) and C2
curated functional gene sets from the Molecular Signature Database
(MSigDB)39(Document S1). Analysis was performed on the full set of
bortezomib samples from all trials, as well as on samples from individual
trials, including 039 bortezomib and 039 Dex separately. Gene sets
satisfying the default multiple hypothesis testing threshold (FDR q value
? .25) and having nominal P values no more than .025 were identified (56
associated with response [CR/PR/MR; R] and 16 associated with PD).
These were classified as to whether they also had a less stringent (nominal
P ? .05), but consistent, association with the phenotype in analysis of
samples from at least 2 individual trials. Gene sets were then ranked, first by
consistent association with phenotype, then by FDR q value.
Analysis of clinical response. Differential expression of genes with
respect to clinical response was assessed by 2-sided t test with unequal
variances. Predictive models were built using a linear predictor score40on
the top 100 differentially expressed genes. To assess the accuracy of the
predictive modeling method on the entire dataset, a standard bootstrap
3178 MULLIGAN et alBLOOD, 15APRIL 2007?VOLUME 109, NUMBER 8
procedure was used,41in which the data were repeatedly divided into
separate training and test sets. Each time, genes were selected, and a
predictive model was built on the training set; the model was then applied to
the test set to assess accuracy, sensitivity (Sn), and specificity (Sp). To
determine whether predictive accuracy differed significantly from what
might be expected at random, outcome values were repeatedly randomly
permuted among samples, and the bootstrap procedure was reapplied.
Empirical distributions of accuracies for true and permuted outcomes were
Analysis of OS. We used the Cox proportional hazards model to assess
strength of association of individual probe sets with OS. Predictive models
were built using the method of Bair and Tibshirani,42as follows. The 100
probe sets most strongly associated with outcome were selected for the
model. We computed the top 2 principal components of these genes’
expression on the training samples. Test data were mapped onto the space
defined by the principal component vectors, and Cox modeling was used to
assess strength of association of the transformed test data with outcome. For
visualization of the models using Kaplan-Meier curves, the linear predictor
score from the Cox model was used to divide test samples in equally sized
high- and low-risk groups.
Sample collection and genomic data generation in multicenter
The phase 2 and phase 3 clinical trials of bortezomib for the
treatment of myeloma included a research component to investi-
gate the feasibility of pharmacogenomics in a prospective setting;
tumor samples were provided from 89 centers in 12 different
countries. A pretreatment bone marrow aspirate was collected
during routine screening procedures. The percentage of tumor cells
in aspirates varied from approximately 5% to greater than 75%.All
samples were therefore subject to an enrichment procedure in an
effort to increase tumor content to at least 60% to 80%, a level
consistent with prior genomic studies of cancer biology and
before and after enrichment demonstrated that enrichment could
yield samples of 80% to 90% tumor cells (Figure 1A). FACS
analyses were not practical at all participating centers. Therefore,
we assessed sample purity via analysis of a myeloma purity score
derived from microarray data. Samples with low tumor cell purity
were excluded from further analyses (Figure 1B).
Sample attrition was observed at each step in the process of
generating gene expression data (Table 1). Approximately 60% of
tion. Of these samples, about 85% generated high-quality microar-
ray data, and 85% passed the assessment of tumor-cell enrichment
with the myeloma purity score. Results were generally consistent
between trials (Table 1). The bortezomib dataset consists of 169
patients evaluable for response and 188 evaluable for OS, whereas
the Dex dataset has 70 and 76 evaluable for response and OS,
respectively. The details for each trial are provided in Table S2.
For each trial we examined a series of clinical and prognostic
variables to ensure that the subsets of patients with genomic data
were representative of the general trial populations (Table 2). No
bias was observed with regard to age, sex, number of prior
therapies, or myeloma isotype. For some trials the response rate,
time to progression (TTP), or survival values of the genomics
subset were indicative of a worse outcome. Although serum
Figure 1. Bone marrow aspirate enrich-
ment procedure effectively depletes non-
tumor cells. (A) Bone marrow aspirate
samples before and after enrichment were
subject to CD138 staining and FACS analy-
sis. (B) Myeloma purity score is elevated in
control plasma cell samples (? 90% pure)
relative to bone marrow mononuclear cells
enriched patient samples of 84% and 91%
tumor purity by FACS analysis had scores of
35 and 28, respectively (blue arrows). A
score of at least 10 (at least 3-fold elevated
relative to the score for nonplasma cell
types) was set as a threshold for further
GENE EXPRESSION PROFILING IN BORTEZOMIB TRIALS 3179BLOOD, 15APRIL 2007?VOLUME 109, NUMBER 8
albumin and serum ?-2 microglobulin were elevated in the
genomics subset of trial 025, this was not observed in other trial
data. However, the genomics subset of each trial did exhibit a
higher baseline tumor burden in the bone marrow aspirate (Table
2), indicating that successful sampling is partly related to extent of
marrow disease. The data suggest that genomic subsets are
reasonable representations of study populations as a whole, except
for an overrepresentation of patients with greater tumor burden.
Because of differences in entry criteria, there are differences
between the trial populations in terms of median number of prior
therapies, time from diagnosis, and response rate (Table 1; Table
S3). For example, trial 025 enrolled relapsed patients who were
refractory to their last prior therapy (median number of prior lines
of therapy, 6), whereas trial 039 specified 1 to 3 prior lines of
therapy. Accordingly, the median number of prior lines of therapy
in genomics subsets of trials 025 and 039 are 6 and 2, respectively.
ray hybridizations were performed in 2 batches separated by 9
months. Replicate hybridizations allowed us to assess within-
patient reproducibility and between-patient variations prior to
selecting the approximately 9200 most differentially expressed
probe sets for further analysis.
Comparison of dataset with published myeloma biology
Our genomics approach differs from that of prior myeloma
studies14,29,30,45in that samples were collected at multiple sites and
subjected to a negative-selection procedure to enrich for tumor
cells. Therefore, we closely examined how the data might have
been influenced by demographic, clinical, and technical parame-
ters, using unsupervised hierarchical clustering. Figure 2A shows a
dendrogram of 264 myeloma patient samples and 6 normal plasma
cell control samples. Patients with different age, sex, and myeloma
isotype were randomly distributed (Figure 2A) across these groups.
Further, there was no significant clustering of samples that
originated at the same clinical center.
However, a nonrandom distribution was observed for clinical
study, number of prior therapies, array hybridization batch, my-
eloma purity score, and, consistent with a recent report,14myeloma
TC subtype. Because several of these factors are interrelated (eg,
patients from trial 039 had fewer prior therapies and their samples
were hybridized in one batch), it was difficult to discern which
factors influence the clustering. We investigated the influence of
prior therapies by examining the distribution of samples from trial
025, which have a varied number of prior therapies and were
hybridized in a single batch. In fact, patients from trial 025 in
groups 1 to 3 had fewer lines of prior therapy (mean ? 3.7) than
those in groups 4 to 5 (mean ? 5.1) (P ? .053), suggesting that
distribution of these samples is at least in part influenced by the
extent of prior therapy.
Analysis of gene expression patterns within this dataset re-
vealed several features in common with previously reported studies
of myeloma (Figure 2B). These include a reduced expression of
neous overexpression of cancer antigens, interferon-induced genes,
and genes involved in protein synthesis and proliferative path-
ways.29,30,46,47We also noted differential expression of various
genes related to protein secretion and endoplasmic reticulum stress,
as well as NF-?B transcription targets (Figure 2B).
Recently, a study highlighted the overexpression of D-type
cyclins and other common IgH translocation targets and suggested
that newly diagnosed myeloma comprises 8 distinct TC subtypes.33
These TC subtypes were also observed in this dataset of relapsed/
refractory patients collected from multiple clinical centers (Figure
3A). Notably, the frequencies of eachTC subtype were very similar
in the 2 different datasets (Figure 3B). Figure 3A highlights
additional hallmark features of myeloma gene expression, includ-
ing loss of FGFR3 expression in a subset of t(4;14)(p16;q32)–
positive samples48and correlation between overexpression of
c-MAF transcription factor and the c-MAF target gene cyclin D2.45
Together these observations indicate that the current genomic
dataset, derived from national and international clinical trials, is
consistent with previously described data.
We noted that a subset of samples express genes generally
detected in erythroid or myeloid lineages, including GYPA and
CD14 (Figure 2B). The origin of this expression pattern remains
unclear. However, such expression has been noted in both normal
plasma cells and myeloma cells after positive selection,14and in
such studies this expression was associated with better prognosis
on treatment with HDT.14
Can pretreatment gene expression predict response?
investigate whether information in pretreatment tumor samples could
predict whether patients would respond to bortezomib, we first used a
Table 1. Sample attrition in the process of generating gene expression data in the 024, 025, 039, and 040 trials, and the number of response-
and survival-evaluable samples obtained from each trial
Trial 024 Trial 025Trial 039 Trial 040
No. of patients
Disease/prior therapy entry criteria
Patients providing consent for
pharmacogenomics analysis,* no. (%)
Samples collected, no. (%)
Samples that passed RNA QC, no. (%)
Samples that passed Affymetrix hyb QC, no. (%)
Samples that passed purity analysis, no. (%)
Response evaluable, no.
Survival evaluable, no.
54 202 669263†
Phase 2Phase 2Phase 3
1-3 prior lines
Phase 3 companion
? 3 prior linesRelapsed or refractoryRelapsed and refractory
141 (71 bortezomib, 70 Dex)
156 (80 bortezomib, 76 Dex)
CR indicates complete response; PR, partial response; QC, quality control.
*In trials 039 and 040, based on the informed consent of all patients in trial; not all patients who consented had a sample collected; see following row (Samples collected).
‡Consent for whole genome analysis.
3180 MULLIGAN et al BLOOD, 15APRIL 2007?VOLUME 109, NUMBER 8
the influence of known and unknown confounding variables. To best
distinguish any predictive signal and interpret subsequent biology we
initially focused on patients who had either PD or R.Alinear predictor
classifier40to distinguish PD and R was developed in each training set
and evaluated on the held-out test data. As shown in Figure 4A, the
median test-set accuracy was 70.2% (mean?69.8%); this accuracy
exceeded the accuracy obtained when sample labels in the training set
Because data came from several multisite studies with different
patient populations, we next assessed whether a predictor devel-
oped with data from one study could be validated on another. Using
samples from the earliest trial (025), a classifier was developed to
distinguish PD and R, and bootstrap validation within trial 025
suggested the classifier should have significant accuracy on other
similar data (73% average accuracy, 95% of test sets showing
? 55% accuracy). However, this classifier exhibited an overall
accuracy of 55% (Sn ? 58%, Sp ? 47%; P ? .77) on testing in the
bortezomib arm of trial 039, and 57% (Sn ? 64%, Sp ? 48%;
P ? .41) in the Dex arm. Lack of significance with the samples
from trial 039 as an independent test set may relate to differences in
patient populations enrolled in these distinct trials (notably, the
higher response rate to bortezomib in trial 039), the relatively small
sample size of the training set, disease heterogeneity, or a
combination of these factors.
Table 2. Baseline and disease characteristics, and efficacy data in the total populations in the 025, 039, and 040 trials
and in the pharmacogenomics/nonpharmacogenomics cohorts from each trial
Variable Overall Nongenomics Genomics
P, genomics vs
Overall survival, d*
Time to progression, d*
Response rate, CR ? PR, %†
Response rate, CR ? PR ? MR, %†
Albumin level, g/L‡
Platelet count, ? 109/L‡
C-reactive protein level, mg/L‡
?-2 Microglobulin level, ?g/mL‡
Prior lines, n‡
Age at randomization, y‡
Plasma cells in bone marrow aspirate, %‡
Overall survival, d*
Time to progression, d*
Response rate, CR ? PR, %†
Response rate, CR ? PR ? MR, %†
Albumin level, g/L‡
Platelet count, ? 109/L‡
C-reactive protein level, mg/L‡
?-2 Microglobulin level, ?g/mL‡
Prior lines, n‡
Age at randomization, y‡
Plasma cells in bone marrow aspirate, %‡
Overall survival, d*
Time to progression, d*
Response rate, CR ? PR, %†
Response rate, CR ? PR ? MR, %†
Albumin level, g/L‡
Platelet count, ? 109/L‡
C-reactive protein level, mg/L?
?-2 Microglobulin level, ?g/mL?
Prior lines, n?
Age at randomization, y‡
Plasma cells in bone marrow aspirate, %‡
? 3 (4.00-4.00)
? 3 (4.00-4.00)
? 3 (4.00-4.00)
To convert ?-2 microglobulin level from micrograms per milliliter to nanomoles per liter, multiply micrograms per milligrams by 85.
CR indicates complete response; PR, partial response; MR, minimal response; NA, not available; IgG, immunoglobulin G myeloma subtype.
*Median time to event; 95% CI in parentheses; P value from log-rank test.
†P value from Fisher exact test.
‡Mean; 95% CI in parentheses; P value from Wilcoxon rank sum test.
§Data for patients receiving bortezomib or Dex.
?No more detailed information collected.
GENE EXPRESSION PROFILING IN BORTEZOMIB TRIALS3181 BLOOD, 15APRIL 2007?VOLUME 109, NUMBER 8
We next built a response classifier using data from both the 025
and 040 trials (67 samples from patients with R or PD) and tested it
on data from trial 039. As shown in Figure 4B, response in the
bortezomib arm was predicted with an overall accuracy of 75%,
(Sn ? 92%, Sp ? 33%; P ? .033). However, response prediction
for the Dex arm was 57% (Sn ? 79%, Sp ? 32%; P ? .53),
suggesting that the classifier has some specificity for bortezomib.
The 100 probe sets comprising this classifier are listed in Table S4.
Finally, we obtained similar results when these predictive analyses
included patients with NC, who were grouped with PD patients to
form a nonresponse (NR) category. Although an 025 trial NR
versus R classifier was unable to significantly predict outcome of
the test set from trial 039, an 025 ? 040 trials NR versus R
classifier exhibited 63% (P ? .03) and 54% (P ? .3) overall
accuracy when tested in the bortezomib and Dex arms of trial 039,
respectively (Table S5). Median bootstrap accuracy of the NR
Figure 2. Sample relationships are influenced by clinical and gene-expression characteristics. Two hundred sixty-four myeloma patient samples and 6 normal
plasma cell control samples were subject to unsupervised hierarchical clustering based on 9174 differentially expressed probe sets. Highly related branches (labeled groups
1-5) were identified by setting a fixed similarity metric (GeneMaths software;Applied Maths,Austin, TX) and requiring at least 12 samples for membership; unlabeled samples
comprise various smaller groups. (A) Patient attributes are encoded below the sample dendrogram.Attributes with nonrandom distribution (P ? .05) are indicated by asterisks.
Black is associated with age older than 60 years, female sex, IgG isotype, 1 or 2 prior therapies, hybridization batch 1 (trials 024, 025, and 040), and low purity score. White is
associated with age 60 years and younger, male sex, other isotypes, 3 or more prior therapies, hybridization batch 2 (trial 039), and high purity score. (B) An overview of the
9174 differentially expressed probe sets, with an expansion of specific functional groups.
3182 MULLIGAN et al BLOOD, 15APRIL 2007?VOLUME 109, NUMBER 8
versus R predictor was also 63% (mean ? 63.1%); this accuracy
exceeded that obtained when the sample labels were permuted
(median ? 49.2%, mean ? 49.4%, 95th percentile ? 62.7%; Fig-
ure S1). Similar accuracy was noted when the number of probe sets
used in the classifier was varied from 50 to 500 (data not shown).
Although we have used the permutation approach and the
Fisher exact test to establish that our predictions are significant, it is
also common to compare prediction accuracies with that of the best
constant model (which is 72% for trial 039 bortezomib arm, and
68% for the bootstrap of all bortezomib-treated patients). Our PD
versus R models do not significantly outperform the best constant
model. However, this appears to be due to the response classes
being unbalanced: a subsampling analysis (Figure S2) shows that
our prediction method significantly outperforms the best constant
model in the case of equal numbers of PD and R patients. The
more-balanced NR versus R prediction also shows significant
accuracy (63%) compared with the best constant model (46%; see
Document S1 for details).
In summary, we observed a statistically significant prediction of
response when data were combined across all the studies or from 2
studies, but not with the 025 study alone.
Genes and pathways associated with response
Anumber of the probe sets in the 025 ? 040 trials classifier (Table
S4) represent genes of known function. Among those overex-
pressed in PD are ribosomal (RPS7, RPS13), mitochondrial
(COX7C, UQCRH), ER stress (SERP1), DNA repair (APEX1,
REC14), and cancer-associated (NRAS, NPM1) genes. Those
overexpressed in patients achieving R include components of the
PI3 kinase pathway (PIK3R1, DAPP1) and other signaling mol-
ecules (TYROBP, RRAGC, LYK5).
We further examined the biology of bortezomib sensitivity by
applying GSEA,49an algorithm that correlates all approximately
18 000 genes represented on the arrays with a phenotype (R or PD)
and highlights known or experimentally annotated sets of genes
that are enriched in these phenotypes. This analysis included
bortezomib data from trials 024 and 039 as well as that of the
samples from 025 ? 040 trials. The most significant gene sets
relatively highly expressed in samples from responsive patients are
shown in Table 3. These include adhesion, cytokines, NF-?B
activity, and hypoxia gene sets. Gene sets elevated in samples from
patients classified as PD (Table 3) include protein synthesis,
mitochondrial function and RNAtranscription/splicing.Among the
Figure 3. All samples assigned to TC subtypes based on expression of D cyclins and translocation target genes (n ? 264). (A) The TC subtypes of 264 relapsed
myeloma samples are shown. The y-axis shows normalized expression level of each gene; subtypes were determined as in Bergsagel et al.33(B) A comparison of the TC
subtype frequency for relapsed patients in Millennium Pharmaceuticals (MPI) studies (green) and for newly diagnosed patients (blue) as defined at the University of
Figure 4. Prediction scores. (A) Data from all bortezomib-treated patients analyzed in bootstrap; the empirical distributions of prediction accuracies for all test sets are shown.
Note that the median value of the accuracies for the correctly labeled samples (70.2%) is higher than 95% of the accuracies for the permuted sample labels (95th
percentile ? 69.4%). Thus, the 2 distributions are significantly different. (B)Aclassifier for trials 025 and 040 was used to predict the response of patients receiving bortezomib
and patients receiving Dex in trial 039.Accuracy of response prediction for bortezomib-treated patients is significant (P ? .033; 75% overall accuracy) but not significant (P ?
.53; 57% overall accuracy) for patients treated with Dex. No significant accuracy is observed when all test samples are simply predicted as the most popular response category
(P ? .999).
GENE EXPRESSION PROFILING IN BORTEZOMIB TRIALS3183 BLOOD, 15APRIL 2007?VOLUME 109, NUMBER 8
Table 3. Gene sets associated with response (R) and progressive disease (PD)
Associated with R†
BRENTANI_CELL_ADHESION‡Cancer-related genes involved in cell adhesion and
Genes down-regulated in Egr2Lo/Lo mice (mutations in the
transcription factor Egr2) with expression altered after sciatic nerve
Genes associated with cellular adhesion that are differentially
expressed in endothelial cells of pig aortas from regions of disturbed
Intercellular signaling in the immune system occurs via secretion of
cytokines, which promote antigen-dependent B- and T-cell response
Genes up-regulated in K-ras knockdown vs control in a human cell line
Genes up-regulated in hepatoma induced by diethylnitrosamine
Cardiac myocytes have a variety of adrenergic receptors that induce
subtype-specific signaling effects
Genes down-regulated by MYC in 293T (transformed fetal renal cell)
Adrenergic receptors respond to epinephrine and norepinephrine
Genes up-regulated in human pulmonary endothelial cells under
hypoxic conditions or after exposure to AdCA5 (constitutively active
The local acute inflammatory response is mediated by activated
macrophages and mast cells or by complement activation
Genes up-regulated by NF-?B
Genes up-regulated by TNFA in Hc cells (normal hepatocyte)
Genes down-regulated in multiple myeloma cells with N-ras-activating
mutations versus those cocultured with bone marrow stromal cells
Genes up-regulated in multiple myeloma cells exposed to the pro-
proliferative cytokine IL-6 versus those cocultured with bone marrow
Genes up-regulated in hepatoma induced by ciprofibrate
Cancer-related genes also related to the cytoskeleton
0.046 0.0373 NC
cytokinePathway‡ 0.093 0.0149 NC
Associated with PD§
Genes involved in electron transport
Genes involved in mRNA processing
Genes involved in mRNA splicing
Genes down-regulated by interferon-? in colon, dermal, iliac, aortic,
and lung endothelial cells
Eukaryotic initiation factor 2 (EIF2) initiates translation by transferring
Met-tRNA to the 40S ribosome in a GTP-dependent process
Genes up-regulated in CD8?T cells undergoing homeostatic
proliferation (HP) versus naive CD8?T-cell populations; these
genes are not up-regulated versus effector or memory cell
Obsolete by GO; was not defined before being made obsolete
CELL_CYCLE_REGULATOR 0.2260.0596 0.1615
Top-scoring gene sets from GSEAanalysis of the full set of bortezomib samples are shown, along with corresponding FDR statistical scores.The nominal P values for the 2
arms of the 039 trial, used to assess the extent to which the gene set associations were treatment specific, are also shown.
NAindicates no description available; NC, no correlation with phenotype.
*Gene set name and description from Molecular Signature Database.39
†Top 20 gene sets are listed.
‡Gene sets showing generally consistent phenotype association based on analysis of individual trials are shown in ranked order.
§All 16 gene sets are listed.
3184MULLIGAN et alBLOOD, 15APRIL 2007?VOLUME 109, NUMBER 8
NF-?B targets correlated with R were IL8, IL15, CXCL5, CFLAR,
ICAM, and NFKB2, suggesting that expression of a subset of
NF-?B targets characterizes myeloma cells more sensitive to
bortezomib. This is consistent with various preclinical studies of
bortezomib’s mechanism of efficacy, showing inhibition of NF-?B
signaling and subsequent apoptosis of myeloma50,51and other
cells52,53on treatment with bortezomib.
Several gene sets elevated in samples from patients achieving R
encode adhesion molecules, indicating that more adhesive myelo-
mas may be sensitive to bortezomib. This interpretation is also
supported by preclinical experiments showing that fibronectin
adhesion increases sensitivity of myeloma cells to bortezomib54
ingly, on analysis of the smaller datasets from the 039 trial, several
gene sets highlighted in Table 3 are strongly correlated with R or
PD (P ? .05) in the bortezomib arm but not the Dex arm; these
include brentani cell adhesion and cytokine pathway (Table 3,
associated with R), and translation factors and ribosomal proteins
(Table 3, associated with PD). Such results imply that these
pathway observations are specific to bortezomib.
Can pretreatment gene expression predict survival?
The 039 randomized trial demonstrated superior OS with bort-
ezomib versus Dex (30 versus 24 months; P ? .027; 22-month
median follow-up, 44% events occurred).56A significant TTP and
survival advantage was also observed at a preplanned interim
analysis, at which time all patients were permitted to receive
bortezomib and 62% of the patients in the Dex arm subsequently
received single-agent bortezomib.36
We used gene expression data from patients in 025 ? 040 trials
to develop a survival classifier42that was then tested with data from
the 039 trial.As shown in Figure 5A, this gene expression classifier
stratified the patients in trial 039 receiving bortezomib into high-
and low-risk groups that were significantly associated with risk of
death (P ? .001). The classifier also effectively stratified the
patients enrolled in the Dex arm of trial 039 (P ? .001; Figure 5B).
It is possible this survival classifier and the underlying probe sets
may be prognostic of survival independent of the specific therapy
administered. However, there may be some specificity for bort-
ezomib (as observed with the response classifier) that is masked by
the subsequent use of bortezomib in the majority of patients
enrolled in the Dex arm.Additional analyses and comparisons with
other myeloma pharmacogenomics datasets will be required to
address these possibilities.
To determine whether the pretreatment gene expression pro-
vides data not already captured by prognostic clinical variables, we
assessed the survival of patients predicted to be high- or low-risk
by ISS.2These risk groups are relevant for various myeloma
therapies2and also discern high/low risk in the 039 trial patients
(data not shown). As shown in Figure 5C-D, the gene expression
classifier enables further, significant stratification in patients identi-
fied as low risk (ISS ? 1; Figure 5C) and high risk (ISS ? 2-3;
information are not redundant but are likely to be complementary.
The probe sets comprising this survival classifier (Table S6) do
not overlap with the response classifier. This is not surprising,
because the survival and response end points are only partially
related. Overexpression of adhesion-related genes (CDH1, CD36)
are correlated with longer survival, suggesting there may be
biologic consistencies, but a more detailed examination of response
and survival pathways will be required.
Clinical genomics offers great promise to improve cancer diagno-
sis, prognosis, and treatment selection. However, this type of
research requires large datasets derived from well-characterized,
Figure 5. Prediction of survival using Super PC. A survival
classifier based on 025 ? 040 trials was used to identify high- and
low-risk groups within an independent test dataset derived from
039 patients. Kaplan-Meier analyses of the actual survival of
these predicted high-/low-risk patient groups is shown for test set
(A) trial 039 bortezomib, (B) trial 039 Dex, (C) ISS ? 1 for patients
from 039 trial (bortezomib or Dex), (D) ISS ? 2 to 3 for patients
from 039 trial (bortezomib or Dex).
GENE EXPRESSION PROFILING IN BORTEZOMIB TRIALS3185 BLOOD, 15APRIL 2007?VOLUME 109, NUMBER 8
uniformly treated patients with appropriate end point data.12,42,57
This study describes a myeloma gene expression dataset derived
from large prospective clinical trials, and the lessons from this
research highlight both the challenges and advantages of the
implementation of similar research in the future.
The first challenge was sample attrition (Table 1). Inadequate
RNA, because of insufficient tumor sampling or RNAdegradation,
precluded use of approximately 50% of collected samples. Across
these myeloma trials, patient consent, sample acquisition, and data
generation/quality control produced only limited losses; however,
even small losses at each stage compounded the attrition issue.
Second, the necessary analysis of data from multiple clinical
trials and comparisons between trials were made more difficult
because of differences between trials. Patients in the phase 2 trials
had experienced more prior therapy and were less responsive than
patients enrolled in the phase 3 trial (Table 1; Table S3).34-36It will
be interesting to compare further these data from relapsed patients
with data from newly diagnosed myeloma. The near identical
frequency of TC subtypes in both relapsed and newly diagnosed
myeloma (Figure 3B) indicates the TC categories do not define any
subgroup of patients that is rapidly lost after first-line therapy; other
ways of comparing these datasets may reveal such high-risk patient
types. A final caveat to future studies is the time required for
prospective research. In this example, despite bortezomib’s
rapid advance to phase 3 trials in myeloma, more than 4 years
elapsed between the initial sample collection in phase 2 trials
and the genomic analysis of the updated survival data from the
phase 3 trial.
Despite such issues and differences in purity methodologies,
these clinical trials yield a myeloma dataset consistent with a
previous single-center study (Figure 3).33The data are primarily
derived from patients who were subsequently treated with bort-
ezomib but includes a subset of control patients whose treatment
We identified a pretreatment gene expression pattern and
predictive classifier that is significantly associated with subsequent
response to bortezomib but not Dex.Although the association with
response appears to be subtle, the significance is supported by
bootstrap analyses as well as testing of independent data.57,58This
comparison of predictive accuracy for bortezomib and Dex is not
complicated by the previously mentioned confounding variables
(extent of prior therapies and prognostic features) because the
independent test data derives from patients enrolled in a random-
ized study that controlled for the number of prior therapies and
?-2M levels in each study arm.36The apparent specificity suggests
that there are distinct subsets of patients sensitive to bortezomib or
Dex and that these subsets can be distinguished by pretreatment
tumor gene expression. A distinct genomic classifier based on OS
also showed statistical significance when tested with independent
data. However, at this time, we cannot determine whether this
association is specific for bortezomib treatment, because the
majority of patients in the Dex arm were subsequently treated with
An overview of the gene sets significantly associated with
response to bortezomib (Table 3) highlighted pathways, such as
NF-?B activity and cell adhesion, whose functions were already
clearly implicated as relevant to bortezomib activity in vitro.50-54
This overlap between genomic analyses of clinical specimens and
preclinical model systems is encouraging and suggests that some
preclinical systems may provide relevant information regarding the
drug sensitivity of patients. Many of the pathways associated with
PD regulate protein biosynthesis and mitochondrial function,
which could relate to protein load in secretory myeloma cells59,60or
the status of mitochondrial apoptotic pathways.61Consistent with
the response prediction, some of these pathways (eg, adhesion- and
cytokine-related gene sets) appeared to be bortezomib specific
when data for bortezomib versus Dex were compared (Table 3). It
will be important to induce and/or inhibit these pathways in model
systems to test whether their activity confers sensitivity or resis-
tance to bortezomib, Dex, and/or other anticancer agents.
discern risk groups within the high- as well as the low-risk ISS groups
(Figure 5). Studies in lymphoma have drawn similar conclusions.62
being investigated; it is hoped that merging these complementary data
will enable a better understanding of both clinical trial populations and
The predictive accuracy required of a clinical diagnostic for
myeloma treatment has not yet been defined. Requirements may
vary according to disease stage, therapeutic options (single-agent
versus combination regimens), and whether therapy is likely to
achieve disease control or cure. Although the classifier described
here is promising, further refinement is necessary before it can be
considered for clinical use in predicting patient response to
single-agent bortezomib in the relapsed setting. The 75% overall
accuracy (92% Sn, 33% Sp) might be improved with more patient
samples, or it may be that there is not adequate information in the
RNA levels of pretreatment, purified myeloma samples to make a
significantly more accurate prediction. Additional research is
needed to assess the relevance of these genomic predictors in newly
diagnosed myeloma and in the context of multiagent therapy that is
fundamental to more-effective treatment of myeloma. Key data for
such analyses will emerge from genomic research in other large
clinical trials, including Total Therapy 2 and 3,14,29as well as the
ongoing HOVON cooperative trial comparing vincristine, doxoru-
bicin, and Dex with bortezomib, doxorubicin, and Dex as induction
therapy in newly diagnosed patients. These analyses will help to
tions as well as those still in need of novel therapies.
We thank all patients participating in these clinical trials. We thank
MPMx, J. Brown, A. Bolt, S. Kim, A. Damokosh, G. Tucker-
Kellogg, M. Lane, M. Morrissey, J. Larsen, T. Hideshima, M.
Kauffman, and J.Adams. We also thank R. Roubenoff, H. Danaee,
T. Myers, andY. Meng for critical review of the manuscript.
This work was supported in part by Millennium Pharmaceuti-
cals and Johnson & Johnson Pharmaceutical Research and
Contribution: G.M., P.R., P.S., D.S., and K.C.A. designed the
research; E.K., P.R., P.S., and K.C.A. performed the research;
G.M., B.B., S.R., E.K.,A.F., W.L.T., D.S., and D.-L.E. contributed
vital new reagents or analytical tools; G.M., B.B., E.K., A.F., and
K.C.A. collected the data; G.M., C.M., B.B., F.Z., W.J.C., S.R.,
3186MULLIGAN et al BLOOD, 15APRIL 2007?VOLUME 109, NUMBER 8
E.K., Y.H., P.R., W.L.T., A. Broyl, P.S., J.D.S., P.L.B., D.-L.E., A.
Boral, and K.C.A. analyzed the data; and G.M., B.B., P.R., W.L.T.,
P.S., J.D.S., P.L.B.,A. Boral, and K.C.A. wrote the paper.
Conflict-of-interest disclosure: Several of the authors (G.M.,
B.B., F.Z., S.R., E.K., Y.H., W.L.T., D.S., D.-L.E., and A. Boral)
have declared a financial interest in Millennium Pharmaceuticals,
Inc, whose product was studied in the present work. Several of the
authors (G.M., B.B., S.R., E.K., A.F., W.L.T., D.-L.E., and A.
Boral) are currently employed by Millennium Pharmaceuticals,
Inc. D.S. is currently employed by Genentech. The remaining
authors declare no competing financial interests.
A list of the members of the APEX study group appears as
Correspondence: George Mulligan, Clinical Research/Transla-
tional Medicine, Millennium Pharmaceuticals, Inc, 40 Landsdowne
St, Cambridge, MA02139; e-mail: firstname.lastname@example.org.
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