|
|
Proc Natl Acad Sci U S A. 2003 July 8; 100(14): 8418–8423. Published online 2003 June 26. doi: 10.1073/pnas.0932692100. | PMCID: PMC166244 |
Copyright © 2003, The National Academy of
Sciences Medical Sciences Repeated observation of breast tumor subtypes in independent gene
expression data sets Therese Sørlie, * Robert Tibshirani, † Joel Parker, ‡ Trevor Hastie, § J. S. Marron, ¶ Andrew Nobel, ¶ Shibing Deng,  Hilde Johnsen, ** Robert Pesich, * Stephanie Geisler, †† Janos Demeter, * Charles M. Perou, ‡‡‡ Per E. Lønning, †† Patrick O. Brown, §§ Anne-Lise Børresen-Dale, ** and David Botstein *¶¶Departments of *Genetics, †Health, Research and Policy, and Statistics, §Statistics and Health, and §§Biochemistry and Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305; Departments of ‡Genetics and ‡‡Pathology and Laboratory Medicine, Lineberger Comprehensive Cancer Center, and Departments of ¶Statistics and Biostatistics, University of North Carolina, Chapel Hill, NC 27599; ††Department of Medicine, Section of Oncology, Haukeland University Hospital, 5021 Bergen, Norway; and **Department of Genetics, Norwegian Radium Hospital, 0310 Oslo, Norway Characteristic patterns of gene expression measured by DNA microarrays have
been used to classify tumors into clinically relevant subgroups. In this
study, we have refined the previously defined subtypes of breast tumors that
could be distinguished by their distinct patterns of gene expression. A total
of 115 malignant breast tumors were analyzed by hierarchical clustering based
on patterns of expression of 534 “intrinsic” genes and shown to
subdivide into one basal-like, one ERBB2-overexpressing, two
luminal-like, and one normal breast tissue-like subgroup. The genes used for
classification were selected based on their similar expression levels between
pairs of consecutive samples taken from the same tumor separated by 15 weeks
of neoadjuvant treatment. Similar cluster analyses of two published,
independent data sets representing different patient cohorts from different
laboratories, uncovered some of the same breast cancer subtypes. In the one
data set that included information on time to development of distant
metastasis, subtypes were associated with significant differences in this
clinical feature. By including a group of tumors from BRCA1 carriers
in the analysis, we found that this genotype predisposes to the basal tumor
subtype. Our results strongly support the idea that many of these breast tumor
subtypes represent biologically distinct disease entities. Gene expression profiling has come into use as a way of defining, at the
molecular level, the phenotypes of many kinds of tumors. In the past, we have
studied genome-wide expression patterns in several cancers including lymphoma,
breast, lung, liver, ovarian cancer and soft tissue sarcomas
( 1– 5).
One of the common features of these studies has been the emergence, through
hierarchical clustering analysis, of tumor subtypes with distinct gene
expression patterns for each of these cancers. The differences in gene
expression patterns among these subtypes are likely to reflect basic
differences in the cell biology of the tumors. On this basis, one might
consider these molecular subtypes as separable diseases. The molecular differences between the tumor subtypes are often accompanied
by differences in clinical features, such as statistically robust differences
in relapse-free and overall survival
( 1,
3,
6,
7). When an alternative
approach (i.e., analysis supervised by outcome data) was used, many studies
found handfuls of individual genes whose expression is associated with
prognosis
( 8– 13).
These genes define potential prognostic molecular markers without respect to
the biological diversity represented by the subtypes. Previously we reported a molecular taxonomy of breast cancer based on
variation in global gene expression patterns measured by cDNA microarrays
( 5,
6). In this report, we attempt
to reconcile the definition of those subtypes, and the accompanying
differences in disease outcome. Tumor samples from two independent studies of response to chemotherapy of
locally advanced breast cancer
( 14,
15) were analyzed on cDNA
microarrays as described ( 6).
Details of these and the additional tissue samples analyzed in this study can
be found in Table 2, which is published as supporting information on the PNAS
web site,
www.pnas.org.
Altogether, 122 tissue samples were included in the analysis, of which 77
carcinomas and 7 nonmalignant tissues were previously published. Data for all
experiments are stored in the Stanford Microarray Database
( http://genome-www.stanford.edu/MicroArray/)
and can be accessed at
( http://genome-www.stanford.edu/breast_cancer/).
In addition to these samples, we reanalyzed published data from two
independent studies: van't Veer et al.
( 13) and West et al.
( 16). An “intrinsic” gene list was selected (534 genes represented by
552 clones; 500 of these correspond to a single unique UniGene cluster in
source
( http://source.stanford.edu)
( 17), consisting of those
genes whose expression varied the least in successive samples from the same
patient's tumor but which showed the most variation among tumors of different
patients. For each data set, as many of these genes as possible were used for
clustering (534 for the Norway/Stanford cohorts, 461 for the van't Veer et
al. cohort, and 242 for the West et al. cohort). Centroids (i.e., profiles consisting of the average expression for each of
the 500 genes) were computed for each of the five classes found in the
Norway/Stanford data. To be conservative, only those tumors that showed the
highest correlation with each other within a subtype were used for this
calculation (Table 3, which is published as supporting information on the PNAS
web site). We then computed the correlation of each sample from the two
additional published data sets to each of these five centroids. Class prediction was performed by using prediction analysis of microarrays
(PAM), which is a variant of nearest-centroid classification with an automated
gene selection step integrated into the algorithm
( 18). During cross-validation,
a parameter Δ was iteratively increased, and a value that balanced
prediction accuracy with a minimal set of genes was chosen for the final
model. This value of Δ was used for training on the entire
Norway/Stanford set and predicting the class of each sample in the van't Veer
et al. and West et al. data sets (Table 4, which is
published as supporting information on the PNAS web site). Details of all
analyses are discussed in Supporting Materials and Methods, which is
published as supporting information on the PNAS web site. To evaluate the performance of the 231-gene metastasis predictor published
by van't Veer et al.
( 13) on patients from the
Norway cohort, we used 77 of the 231 genes (77 genes overlapped with the
Norway/Stanford data set) and performed a 10-fold cross validation
leave-one-out analysis on the data presented in van't Veer et al.
This “training” resulted in 81% accuracy on their own data set,
which is similar to the accuracy reported by the authors in a separate
validation ( 13). We then
applied this predictor to the Norway cohort (consisting of locally advanced
breast tumors from two patient series) by using PAM as was described for the
class-predictions above. Univariate Kaplan–Meier analysis was performed
by using winstat for excel (R. Fitch Software, Staufen,
Germany). Identification and Validation of Tumor Subtypes in Independent Data
Sets. To test the generality of previously proposed subtypes of breast
cancer described in Sørlie et al.
( 6), we analyzed three
independent data sets: an extended Norway/Stanford cohort and two published
data sets, those of van't Veer et al.
( 13) and West et al.
( 16). The genes used for
clustering were obtained from an “intrinsic” gene list comprising
534 genes derived from 45 repeated samples in the extended Norway/Stanford
cohort. For a comparable analysis of the two other data sets, we used as many
of the same genes as possible. Expression centroids were calculated for the
core members of each of the five subclasses in the Norway/Stanford data, and
correlations with the centroid were calculated for each sample from the two
other data sets. Core samples for each class are color-coded similarly in all
dendrograms. Table 1 summarizes
the distribution of the tumor samples from the different data sets across the
subtypes. In an additional, supervised analysis, we trained a predictor on the
Norway/Stanford data and used PAM to predict the tumor subtype of a given
individual sample in the two other data sets. | Table 1.Distribution of tumors (%) from three different studies across five
tumor subtypes |
Subclassification of Breast Tumors in the Norway/Stanford Data. Our
previously published work contained data from 85 tissue samples, 84 of which
were reanalyzed in this study. We added 38 more tumor samples from patients
with locally advanced breast cancer, which totals 122 breast tissue samples
analyzed in this study. The samples fell into five major subgroups,
characterized by distinct variation in gene expression pattern, as was
previously seen ( 5,
6) (for the complete cluster
diagram, see and Fig. 6,
which is published as supporting information on the PNAS web site). Although
the list of genes that was previously used and the gene list that forms the
basis for this classification only have ≈200 genes in common, the subtypes
are essentially the same and anchored by the same major gene clusters
(). Similarly, with few exceptions, the
samples that clustered together in the past still clustered together here (86%
within the same groups). The major distinction seen was between the tumors showing high expression
of luminal epithelial specific genes including the ESR1
() and all
other tumors showing low or no expression of these genes. The basal subtype
(red) was the most homogeneous cluster of tumors, as reflected by the
relatively short branches linking the tumors in this cluster (node correlation
>0.4) and the deep branch separating it from the other subtypes
(). Breast Cancer Data from van't Veer et al. Gene expression data
(log 10 ratios) were available for 24,480 genes in a set of 117
tumors from young breast cancer patients
( 13). Hierarchical clustering
was used, exactly as described above for the Norway/Stanford data, to display
the expression patterns of 461 “intrinsic” genes in the 97 tumor
samples that were obtained from patients diagnosed with sporadic cancer
(). Individual dendrogram
branches are colored according to the strongest correlation of the
corresponding tumor with the subtype centroid as defined for the
Norway/Stanford samples. As in the Norway/Stanford data, the clearest discrimination was between
tumors that expressed genes in the luminal A/ESR1 cluster at high levels
() and the
tumors that were negative for these genes and exhibited expression profiles
characteristic of either the basal, the ERBB2+ or the luminal B subtypes
(). It is noteworthy that all samples
that showed the strongest correlation with the basal subtype (red branches)
are all contained within the left branch of the dendrogram in a tight cluster.
The luminal A/luminal B distinction, though less clear than the basal/luminal
distinction, is also seen, with many of the luminal B tumors clustering near
each other on the right branch of the dendrogram. BRCA1 Mutations Are Associated with Basal-Type Tumors. The
van't Veer et al. study included tumors from 18 carriers of
BRCA1 mutations and two carriers of BRCA2 mutations. We did
not include this familial subset in the analysis above in order not to risk
sample bias in estimating the frequency of different tumor subtypes
( Table 1). When we included
these 20 tumors along with the 97 samples, we saw little difference in the
overall pattern, except for the striking result that all of the tumors from
patients carrying BRCA1 mutations fell within the basal subgroup
(). This indicates that a
mutation in the BRCA1 gene predisposes for the basal tumor subtype,
which is associated with lack of expression of the estrogen receptor and poor
prognosis. As also reported previously, none of the BRCA1 tumors
showed evidence of ERBB2 amplification
( 20). A distinct expression
profile in BRCA1 tumors was also noted by van't Veer et al.
as well as by others ( 21). Two
BRCA1 tumors were reported by the authors to be estrogen receptor
positive (sample 86 and sample 95); these two nevertheless clustered on the
left branch in the dendrogram that contains the basal tumors, but were present
on less correlated dendrogram branches. van't Veer et al. also
analyzed tumor samples from two BRCA2 carriers, both of which showed
luminal A expression patterns. Breast Cancer Data Set from West et al. Expression levels of a total
of 7,129 genes were measured in 49 breast tumor samples by West et
al. ( 16) using Affymetrix
oligonucleotide arrays. Data were transformed to a compatible format by
normalizing to the median experiment (see supporting information for details).
Expression values for 242 “intrinsic” genes were used in a
hierarchical cluster analysis exactly as was done for the Norway/Stanford and
the van't Veer et al. data sets. Correlation coefficients to the five
subtype centroids were calculated for each of the 49 tumors, and the branches
of the dendrogram were colored according to the nearest centroid
(). Again, the main discrimination seen was between the tumors that highly
expressed genes in the luminal A/ESR1 cluster and those that were clearly
negative for these genes (). Data for only about half of the genes from the
intrinsic gene list were found in this study, which may explain why a luminal
A/luminal B distinction was not seen in this data set. Two tumors showed
strong correlation to the ERBB2+ class
(), but they
did not cluster together in this analysis. Prediction of Tumor Subtypes By Using Norway/Stanford Data As a Training
Set. Hierarchical clustering analysis is a powerful technique for class
discovery; however, we wished also to apply a more supervised analysis that
could make a prediction as to what subtype a single sample would belong to
when considered only by itself. To accomplish this goal, we used the 115
Norway/Stanford tumor samples and the overlapping “intrinsic”
genes for both data sets, respectively, as a training set to develop a breast
tumor class predictor using PAM (see Supporting Materials and Methods
for details). When we compared these calculations with the results of the
hierarchical clustering described above, there was strong agreement, ranging
from 79% to 89% for both the van't Veer et al. and West et
al. data sets (Table 4). We note that prediction accuracies reported
above are somewhat optimistic, as some of the genes used as predictors were
used to define the test set groups in the first place. Tumor Subtypes Are Associated with Significant Difference in Clinical
Outcome. In our previous work, the expression-based tumor subtypes were
associated with a significant difference in overall survival as well as
disease-free survival for the patients suffering from locally advanced breast
cancer and belonging to the same treatment protocol
( 6). To investigate whether
these subtypes were also associated with a significant difference in outcome
in other patient cohorts, we performed a univariate Kaplan–Meier
analysis with time to development of distant metastasis as a variable in the
data set comprising the 97 sporadic tumors taken from van't Veer et
al. As shown in ,
the probability of remaining disease-free was significantly different between
the subtypes; patients with luminal A type tumors lived considerably longer
before they developed metastatic disease, whereas the basal and ERBB2+ groups
showed much shorter disease-free time intervals. Although the methodological
differences prevent a definitive interpretation, it is notable that the order
of severity of clinical outcome associated with the several subtypes is
similar in the two dissimilar cohorts. We could not carry out a similar
analysis in the West et al. data because the necessary follow-up data
were not provided. Breast Tumor Subtypes Represent Distinct Biological Entities. Gene
expression studies have made it clear that there is considerable diversity
among breast tumors, both biologically and clinically
( 5,
6,
13,
16,
19,
21,
22). This is not a new idea,
as epidemiological studies previously had inferred the existence of two or
more subpopulations of breast cancer
( 23). A straightforward
interpretation of the recurrent appearance of several different patterns of
gene expression among tumors of similar anatomical origin is to regard each as
representing a different biological entity. One possible basis for the
consistent differences in these patterns between tumor subtypes might be that
they originate from different cell types. Our findings provide some support
for this interpretation, as we found breast tumor subtypes with patterns of
gene expression similar to those of luminal epithelial cells (the cells that
line the duct and give rise to the majority of breast cancers) and patterns of
at least one other subtype (termed basal) that resembles the pattern found in
basal epithelial cells of the normal mammary gland (characterized by
expression of cytokeratins 5/6 and 17). If indeed luminal and basal tumor subtypes are distinct biological
entities, then the cognate expression patterns should be detectable in other
genome-scale studies of breast cancer. As shown above, we found strong
evidence for the universality of a distinction between basal-like and
luminal-like subtypes in two additional, independent data sets comprising
different patient populations whose gene expression profiles had been
determined by using different microarray technology platforms. We found
considerable evidence, in one of the studies, for the distinction between the
luminal A and B subtypes. The fact that we could make these distinctions for
the basal and luminal subtypes (less so for the luminal B subtype vis a
vis luminal A) means that the substantial differences in the
characteristics of the patients (e.g., age and tumor stage) are less important
determinants of tumor expression phenotypes than intrinsic biology. The statistical nature of the definition, the differences in the expression
technologies, and, more importantly, the limited number of intrinsic genes
held in common in particular for the West et al. data set, probably
suffices to account for the failure to find coherent clusters for every
subtype in each of the cohorts examined. Another expectation from the concept that the tumor subtypes represent
different biological entities is that genetic predispositions to breast cancer
might give rise preferentially to certain subtypes. This expectation is amply
fulfilled by our finding in the data of van't Veer et al., which
shows that the women carrying BRCA1-mutated alleles all had tumors
with the basal-like gene expression pattern. Tumor Subtypes and Clinical Outcome. Consistent with the results
previously found in our data
( 6), we also found differences
in clinical outcome associated with the different tumor subtypes in the data
set produced by van't Veer et al. The outcomes, as measured here in
time to development of distant metastasis, were strikingly similar to what we
found previously: worst for basal (and ERBB2+), best for luminal A, and
intermediate for luminal B subtypes. Recently, two reports corroborating the
poor outcome of the basal subtype solely based on immunohistochemistry with
antibodies against keratins 5 and 17 and Skp2, strongly supports our results
( 24,
25). The finding that our gene
cluster profile was of similar prognostic importance in the van't Veer et
al. cohort as among our patients is remarkable, taking into account
differences regarding disease stage (locally advanced versus stage I
primaries) and patient age, but in particular, the fact that the Norwegian
patients had presurgical chemotherapy and all patients expressing
ESR1 received adjuvant endocrine treatment, whereas the patients from
van't Veer et al. in general did not receive any systemic adjuvant
treatment. The observation that BRCA1 mutations are strongly associated with
a basal tumor phenotype indicates a particularly poor prognosis for these
patients. BRCA1-associated breast cancers are usually highly
proliferative and TP53-mutated, and usually lack expression of
ESR1 and ERBB2
( 20,
26). Status of BRCA1
in familial cancers has failed to be an independent prognostic factor in
several studies (reviewed in ref.
27), and is complicated by
confounding factors such as frequent screening and early diagnosis. Molecular Marker Identification. In a mixture of biologically
distinct subtypes, it may well be that individual markers derived by
supervised analysis will under-perform what is possible if tumor subtypes were
separated before searching, in a supervised fashion, for prognostic
indicators. Indeed, when we tested the prognostic impact of the 231 markers
published by van't Veer et al. on the Norwegian cohort, we found that
they performed less well (47%) in predicting recurrences within 5 years (see
Materials and Methods). This may in part be due to differences in the
patient cohorts and treatments as discussed above. Both van't Veer et al. and West et al. showed the ability
of gene expression profiles to classify tumors into clinically relevant groups
and to predict outcome by using supervised statistical analyses. Both reports,
however, showed only how the gene expression signatures discriminated tumors
based on previously known molecular and clinical parameters, such as
ESR1 status, lymph node status, and time to the development of
distant metastasis. We have taken a less supervised approach and showed that
there are several subtypes of tumors, which may be the result of alterations
in different and independent regulatory pathways. The basal subtype was
repeatedly recognized as a distinct group in each of three independent data
sets, and should be considered as a separate disease with respect to treatment
and follow up. The other subtypes are less clear, and require refinement of
their molecular definition before they can be reliably defined and
diagnosed. To conclude, classification of breast cancer based on gene expression
profiling captures the molecular complexity of tumors. It is for this reason
that we believe that the patterns that distinguish subtypes appear to provide
a more refined stratification of the patients compared with individual tumor
markers. These results imply that the status of the transcriptional programs
in the tumor cells and the underlying genetic alterations are major
determinants of the tumorigenic potential and ultimately the disease outcome
for the patient. We thank Mike Fero and Stanford Functional Genomics Facility for the
production of microarrays, and Michael Whitfield for help with analysis of the
data produced by using Affymetrix arrays. This research was supported by
National Cancer Institute Grants CA77097 and CA85129 (to D.B. and P.O.B.) and
CA58223-09A1 and CA097769-01 (to C.M.P.). T.S. is a postdoctoral fellow of the
Norwegian Cancer Society. P.O.B. is an Associate Investigator of the Howard
Hughes Medical Institute. 1. Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S.,
Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al.
(2000. ) Nature
403,
503-511. [PubMed] 2. Chen, X., Cheung, S. T., So, S., Fan, S. T., Barry, C., Higgins,
J., Lai, K. M., Ji, J., Dudoit, S., Ng, I. O., et al.
(2002. ) Mol. Biol. Cell
13,
1929-1939. [PubMed] 3. Garber, M. E., Troyanskaya, O. G., Schluens, K., Petersen, S.,
Thaesler, Z., Pacyna-Gengelbach, M., van de Rijn, M., Rosen, G. D., Perou, C.
M., Whyte, R. I., et al. (2001. ) Proc. Natl.
Acad. Sci. USA 98,
13784-13789. [PubMed] 4. Nielsen, T. O., West, R. B., Linn, S. C., Alter, O., Knowling, M.
A., O'Connell, J. X., Zhu, S., Fero, M., Sherlock, G., Pollack, J. R., et
al. (2002. ) Lancet
359,
1301-1307. [PubMed] 5. Perou, C. M., Sørlie, T., Eisen, M. B., van de Rijn, M.,
Jeffrey, S. S., Rees, C. A., Pollack, J. R., Ross, D. T., Johnsen, H., Akslen,
L. A., et al. (2000. ) Nature
406,
747-752. [PubMed] 6. Sørlie, T., Perou, C. M., Tibshirani, R., Aas, T., Geisler,
S., Johnsen, H., Hastie, T., Eisen, M. B., van de Rijn, M., Jeffrey, S. S.,
et al. (2001. ) Proc. Natl. Acad. Sci.
USA 98,
10869-10874. [PubMed] 7. Wigle, D. A., Jurisica, I., Radulovich, N., Pintilie, M., Rossant,
J., Liu, N., Lu, C., Woodgett, J., Seiden, I., Johnston, M., et al.
(2002. ) Cancer Res.
62,
3005-3008. [PubMed] 8. Beer, D. G., Kardia, S. L., Huang, C. C., Giordano, T. J., Levin,
A. M., Misek, D. E., Lin, L., Chen, G., Gharib, T. G., Thomas, D. G., et
al. (2002. ) Nat. Med.
8, 816-824. [PubMed] 9. Jenssen, T. K., Kuo, W. P., Stokke, T. & Hovig, E.
(2002. ) Hum. Genet.
111,
411-420. [PubMed] 10. Pomeroy, S. L., Tamayo, P., Gaasenbeek, M., Sturla, L. M., Angelo,
M., McLaughlin, M. E., Kim, J. Y., Goumnerova, L. C., Black, P. M., Lau, C.,
et al. (2002. ) Nature
415,
436-442. [PubMed] 11. Rosenwald, A., Wright, G., Chan, W. C., Connors, J. M., Campo, E.,
Fisher, R. I., Gascoyne, R. D., Muller-Hermelink, H. K., Smeland, E. B.,
Giltnane, J. M., et al. (2002. ) N. Engl. J.
Med. 346,
1937-1947. [PubMed] 12. Shipp, M. A., Ross, K. N., Tamayo, P., Weng, A. P., Kutok, J. L.,
Aguiar, R. C., Gaasenbeek, M., Angelo, M., Reich, M., Pinkus, G. S., et
al. (2002. ) Nat. Med.
8, 68-74. [PubMed] 13. van't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart,
A. A., Mao, M., Peterse, H. L., van der, K. K., Marton, M. J., Witteveen, A.
T., et al. (2002. ) Nature
415,
530-536. [PubMed] 14. Geisler, S., Lonning, P. E., Aas, T., Johnsen, H., Fluge, O.,
Haugen, D. F., Lillehaug, J. R., Akslen, L. A. & Borresen-Dale, A. L.
(2001. ) Cancer Res.
61,
2505-2512. [PubMed] 15. Geisler, S., Børresen-Dale, A.-L., Johnsen, H., Aas, T.,
Geisler, J., Akslen, L. A., Anker, G. & Lønning, P. E.
(2003. ) Clin. Cancer Res., in
press. 16. West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S.,
Spang, R., Zuzan, H., Olson, J. A., Jr., Marks, J. R. & Nevins, J. R.
(2001. ) Proc. Natl. Acad. Sci. USA
98,
11462-11467. [PubMed] 17. Diehn, M., Sherlock, G., Binkley, G., Jin, H., Matese, J. C.,
Hernandez-Boussard, T., Rees, C. A., Cherry, J. M., Botstein, D., Brown, P.
O., et al. (2003. ) Nucleic Acids
Res. 31,
219-223. [PubMed] 18. Tibshirani, R., Hastie, T., Narasimhan, B. & Chu, G.
(2002. ) Proc. Natl. Acad. Sci. USA
99,
6567-6572. [PubMed] 19. van de Vijver, M. J., He, Y. D., van't Veer, L. J., Dai, H., Hart,
A. A., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton,
M. J., et al. (2002. ) N. Engl. J.
Med. 347,
1999-2009. [PubMed] 20. Grushko, T. A., Blackwood, M. A., Schumm, P. L., Hagos, F. G.,
Adeyanju, M. O., Feldman, M. D., Sanders, M. O., Weber, B. L. & Olopade,
O. I. (2002. ) Cancer Res.
62,
1481-1488. [PubMed] 21. Hedenfalk, I., Duggan, D., Chen, Y., Radmacher, M., Bittner, M.,
Simon, R., Meltzer, P., Gusterson, B., Esteller, M., Kallioniemi, O. P.,
et al. (2001. ) N. Engl. J. Med.
344,
539-548. [PubMed] 22. Gruvberger, S., Ringner, M., Chen, Y., Panavally, S., Saal, L. H.,
Borg, A., Ferno, M., Peterson, C. & Meltzer, P. S. (2001. )
Cancer Res. 61,
5979-5984. [PubMed] 23. Fox, M. S. (1979. ) J. Am. Med.
Assoc. 241,
489-494. 24. Signoretti, S., Di Marcotullio, L., Richardson, A., Ramaswamy, S.,
Isaac, B., Rue, M., Monti, F., Loda, M. & Pagano, M. (2002. )
J. Clin. Invest. 110,
633-641. [PubMed] 25. van de Rijn, M., Perou, C. M., Tibshirani, R., Haas, P.,
Kallioniemi, O., Kononen, J., Torhorst, J., Sauter, G., Zuber, M., Kochli, O.
R., et al. (2002. ) Am. J. Pathol.
161,
1991-1996. [PubMed] 26. Armes, J. E., Trute, L., White, D., Southey, M. C., Hammet, F.,
Tesoriero, A., Hutchins, A. M., Dite, G. S., McCredie, M. R., Giles, G. G.,
et al. (1999. ) Cancer Res.
59,
2011-2017. [PubMed] 27. Kennedy, R. D., Quinn, J. E., Johnston, P. G. & Harkin, D. P.
(2002. ) Lancet
360,
1007-1014. [PubMed]
|
This article has been 
