Gene Expressionbased Classification Of Malignant Gliomas Correlates Better With

gene expression-based classification of malignant gliomas correlates better with survival than histological classification1 catherine l. n

Gene expression-based classification of malignant gliomas
correlates better with survival than histological classification1
Catherine L. Nutt, D. R. Mani, Rebecca A. Betensky, Pablo Tamayo, J.
Gregory Cairncross, Christine Ladd, Ute Pohl, Christian Hartmann,
Margaret E. McLaughlin, Tracy T. Batchelor, Peter M. Black, Andreas
von Deimling, Scott L. Pomeroy,
Todd R. Golub2 and David N. Louis2
Molecular Neuro-Oncology Laboratory and Molecular Pathology Unit,
Department of Pathology and Neurosurgical Service [C.L.N., U.P., C.H.,
T.T.B., D.N.L.] and Brain Tumor Center, Department of Neurology
[T.T.B.], Massachusetts General Hospital and Harvard Medical School,
Boston, Massachusetts 02114; Whitehead Institute/Massachusetts
Institute of Technology Center for Genome Research, Cambridge,
Massachusetts 02139 [D.R.M., P.T., C.L., T.R.G.]; Department of
Biostatistics, Harvard School of Public Health, Boston, Massachusetts
02115 [R.A.B.]; Department of Oncology and Clinical Neurological
Sciences, University of Western Ontario and London Regional Cancer
Centre, London, Ontario N6A 4L6, Canada [J.G.C.]; Department of
Pathology [M.E.M.] and Neurosurgery [P.M.B.], Brigham and Women’s
Hospital and Division of Neuroscience, Department of Neurology,
Children’s Hospital [S.L.P.], Boston, Massachusetts 02115; Department
of Neuropathology, Charité Hospital, Humboldt University, Berlin,
Germany [A.vD.]; Dana-Farber Cancer Institute and Harvard Medical
School, Boston, Massachusetts 02114 [T.R.G.]
Running Title: Microarray-based classification of high grade gliomas
Key Words: microarray, glioblastoma, oligodendroglioma, diagnosis,
histology
1 This work was supported in part by NIH CA57683 (D.N.L.); Affymetrix
and Bristol-Myers Squibb (Whitehead Institute/MIT Center for Genome
Research); NIH NS35701 (S.L.P.); and Canadian Institutes of Health
Research MOP37849 (J.G.C.).
2Address reprint requests to: David N. Louis, Molecular Pathology
Laboratory, CNY7, Massachusetts General Hospital, 149 13th St.,
Charlestown, MA 02129. Phone: (617) 726-5690.
Fax: (617) 726-5079. E-mail: [email protected]
Todd R. Golub, Whitehead Institute / Massachusetts Institute of
Technology Center for Genome Research, Building 300, 1 Kendall Square,
Cambridge, Massachusetts 02139. E-mail: [email protected]
3Central Brain Tumor Registry of the United States.
http://www.cbtrus.org
4The abbreviations used are: CCNU,
1-(2-chloroethyl)-3-cyclohexyl-1-nitrosourea; k-NN, k-nearest
neighbor; S2N, signal-to-noise; WHO, World Health Organization.
5This complete set of data is available at
http://www-genome.wi.mit.edu/cancer/pub/glioma
6http://www-genome.wi.mit.edu/cancer/software/software.html
7http://www.r-project.org
ABSTRACT
In modern clinical neuro-oncology, histopathological diagnosis affects
therapeutic decisions and prognostic estimation more than any other
variable. Among high grade gliomas, for example, histologically
classic glioblastomas and anaplastic oligodendrogliomas follow
markedly different clinical courses. Unfortunately, many malignant
gliomas are diagnostically challenging; these non-classic lesions are
difficult to classify by histological features, generating
considerable interobserver variability and limited diagnostic
reproducibility. The resulting tentative pathological diagnoses create
significant clinical confusion. We investigated whether gene
expression profiling, coupled with class prediction methodology, could
be used to classify high grade gliomas in a manner more objective,
explicit and consistent than standard pathology. Microarray analysis
was used to determine the expression of approximately 12,000 genes in
a set of 50 gliomas: 28 glioblastomas and 22 anaplastic
oligodendrogliomas. Supervised learning approaches were used to build
a two-class prediction model based on a subset of 14 glioblastomas and
7 anaplastic oligodendrogliomas with classic histology. A 20-feature k-nearest
neighbor model correctly classified 18 out of the 21 classic cases in
leave-one-out cross validation when compared to pathological
diagnoses. This model was then used to predict the classification of
clinically common, histologically non-classic samples. When tumors
were classified according to pathology, the survival of patients with
non-classic glioblastoma and non-classic anaplastic oligodendroglioma
was not significantly different (p=0.19). However, class distinctions
according to the model were significantly associated with survival
outcome (p=0.05). This class prediction model was capable of
classifying high grade, non-classic glial tumors objectively and
reproducibly. Moreover, the model provided a more accurate predictor
of prognosis in these non-classic lesions than did pathological
classification. These data suggest that class prediction models, based
on defined molecular profiles, classify diagnostically challenging
malignant gliomas in a manner that better correlates with clinical
outcome than does standard pathology.
INTRODUCTION
Malignant gliomas are the most common primary brain tumor and result
in an estimated 13,000 deaths each year in the United States.3 Glial
tumors are classified histologically, with pathological diagnosis
affecting prognostic estimation and therapeutic decisions more than
any other variable. Among high grade gliomas, anaplastic
oligodendrogliomas have a more favorable prognosis than glioblastomas
(1). Moreover, whereas glioblastomas are resistant to most available
therapies, anaplastic oligodendrogliomas are often chemosensitive,
with approximately two-thirds of cases responding to procarbazine,
CCNU4 and vincristine (2, 3). Paradoxically, recognition of the
clinical importance of diagnosing anaplastic oligodendroglioma has
blurred the histopathological line separating glioblastoma and
oligodendroglioma; to ensure that patients are not deprived of
effective chemotherapy, pathologists have loosened their criteria for
anaplastic oligodendroglioma. Indeed, this diagnostic promiscuity has
recently been described as a “contagion" (4). As such, there is a
critical need for an objective, clinically relevant method of glioma
classification.
The most widely used histological system of brain tumor classification
is that of the WHO (1). Gliomas are classified according to defined
histological features characteristic of the presumed normal cell of
origin. Tumors of classic histology clearly display these features and
resemble typical depictions in standard textbooks (5, 6); these
cases would be diagnosed similarly by nearly all pathologists.
Unfortunately, there are situations in which the WHO classification
system is problematic, primarily because pathological diagnosis
remains subjective (7); for example, intratumoral histological
variability is common and high grade gliomas can display little
cellular differentiation, thus lacking defining histological features.
The diagnosis of tumors with such non-classic histology is often
controversial. Consequently, diagnostic accuracy and reproducibility
are jeopardized and significant interobserver variability can occur.
Coons et al. found that complete diagnostic concordance among four
neuropathologists reviewing gliomas over four sessions peaked at 69%
(8). Giannini et al., in a study of seven neuropathologists and six
surgical pathologists scoring histological features of
oligodendroglioma, found that agreement for identifying features
ranged from 0.05 to 0.80, confirming that numerous classification
parameters are not easily reproduced (9).
To develop more objective approaches to glioma classification, recent
investigations have focused on molecular genetic analyses. Sasaki et
al. demonstrated loss of chromosome 1p in 86% of oligodendrogliomas
with classic histology and maintenance of both 1p alleles in 73% of
“oligodendrogliomas” with astrocytic features (10). Interestingly,
tumor genotype more closely predicted chemosensitivity, demonstrating
an ability of tumor genotype to augment standard pathology. Burger et
al. also demonstrated close correlation between classic low grade
oligodendroglioma appearance and allelic losses of 1p and 19q (11).
In gene expression studies, Lu et al. suggested that expression of
oligodendrocyte lineage genes (Olig1 and 2) might augment
identification of oligodendroglial tumors (12). Similarly, Popko et
al. found three of four myelin transcripts significantly more often in
oligodendrogliomas than in astrocytomas (13).
The advent of expression microarray techniques now allows simultaneous
analysis of thousands of genes. We hypothesized that this approach
could identify molecular markers capable of refining the current
method of malignant glioma classification. We therefore investigated
whether gene expression profiling, coupled with the computational
methodology of class prediction (14), could be used to define
subgroups of high grade glioma in a manner more objective, explicit
and consistent than standard pathology. To this end, a subset of
gliomas with classic histology was used to build a class prediction
model and this model was then utilized to predict the classification
of samples with non-classic histology.
MATERIALS AND METHODS
Glioma tissue samples
These investigations have been approved by the Massachusetts General
Hospital Institutional Review Board. Tissue samples were collected
from Canadian Brain Tumor Tissue Bank (London, Ontario, Canada),
Massachusetts General Hospital (Boston, Massachusetts), Brigham and
Women’s Hospital (Boston, Massachusetts), and Charité Hospital
(Berlin, Germany). Samples were collected immediately following
surgical resection, snap frozen, and stored at -80˚C. Hematoxylin and
eosin-stained frozen sections were reviewed histologically for every
specimen (DNL); samples containing significant regions of normal cell
contamination (greater than 10%) and/or excessively large amounts of
necrotic material were excluded. Using these criteria, 50 high grade
glioma samples were selected (Table 1): 28 glioblastomas and 22
anaplastic oligodendrogliomas; all were primary tumors sampled prior
to therapy. All cases had been diagnosed at the primary hospital by
board certified neuropathologists. Original pathology slides were
obtained and reviewed centrally by two additional neuropathologists
(DNL, MEM) for diagnostic confirmation and selection of the classic
tumor subset. Anaplastic oligodendrogliomas designated as having
classic histopathology exhibited relatively evenly distributed,
uniform and rounded nuclei and frequent perinuclear halos (10). In
contrast, classic glioblastomas were characterized by irregularly
distributed, pleomorphic and hyperchromatic nuclei, sometimes with
conspicuous eosinophilic cytoplasm. The classic subset of tumors were
cases diagnosed similarly by all examining pathologists and each case
resembled typical depictions in standard textbooks (5, 6). A total
of 21 classic tumors were selected and the remaining 29 samples were
considered non-classic tumors, lesions for which diagnosis might be
controversial. Of the 21 classic tumors, 14 were glioblastomas and 7
were anaplastic oligodendrogliomas.
Gene expression profiling
Tissues were homogenized in guanidinium isothiocyanate and RNA was
isolated using a CsCl gradient. RNA integrity was confirmed by gel
electrophoresis. For each sample, fifteen micrograms of total RNA were
used to generate biotinylated cRNAs, which were hybridized overnight
to Affymetrix U95Av2 GeneChips as described previously (14, 15).
Based on prior experience, one array per sample provided reproducible
results with a sample set of the size used in this study (14, 16).
Arrays were scanned on Affymetrix scanners and data was collected
using GENECHIP software (Affymetrix, Santa Clara, California). Scan
quality was assured based on a priori quality control criteria which
included the absence of visible microarray artifacts (e.g. scratches)
and significant differences in microarray intensity, and the presence
of greater than 30% “present” calls for the approximately 12,600 genes
and ESTs on the U95Av2 GeneChips.
Class prediction methodology
The subset of classic gliomas was used to build a class prediction
model. This model was then used to predict the classification of the
non-classic samples. Raw expression values were normalized by linear
scaling so that mean array intensity for active (“present”) genes was
identical for all scans.5 Data filtration settings were based on prior
studies (14, 16). Intensity thresholds were set at 20 and 16,000
units. Gene expression data was subjected to a variation filter that
excluded genes showing minimal variation across the samples; genes
whose expression levels varied less than 100 units between samples,
and genes whose expression varied less than 3-fold between any two
samples, were removed. The variation filters excluded 2/3 of the
genes, leaving approximately 3,900 genes for building class prediction
models. Further feature (gene) selection was effected, as described
previously (14, 16), using the S2N statistic. Signal-to-noise ratio
ranks genes based on their correlation to each of the two class
distinctions (i.e., classic glioblastoma and classic anaplastic
oligodendroglioma). In addition, the significance of the highly ranked
genes was confirmed by random permutation testing; the sample
classification labels were permuted and the S2N ratio was recomputed
to compare the true gene correlations to what would have been expected
by chance. Five different k-NN class prediction models were built,
utilizing different gene numbers (10, 20, 50, 100 and 250 genes),
using GeneCluster.6 Training error (on the classic cases) for these k-NN
models was determined using leave-one-out cross validation, where one
sample is withheld and the class membership of this withheld sample is
predicted using a model built upon the remaining samples. Class
prediction for the withheld sample was the majority class membership
of the k (k = 3 in these experiments) closest “neighboring” samples
based on the Euclidean distance between the sample under consideration
and samples used in training the k-NN model. This process was repeated
for each sample in the training set and a cumulative training error
was calculated. Finally, a k-NN model was built using all 21 classic
cases (with no samples left out), which was then used to predict
classification of the remaining gliomas based on the class labels of
the k nearest neighbors of each sample.
Survival analyses: Statistical methods
Survival distributions were compared between groups defined by
pathology or gene expression profiling using permutation logrank
tests, computed by drawing 50,000 samples from the relevant
permutation distribution. The statistical programming language, R,7
was used to compute permutation p-values. Kaplan-Meier plots were
generated with GraphPad Prism (Version 3.02, GraphPad Software, San
Diego, California).
RESULTS AND DISCUSSION
Training of the k-NN class prediction models. We investigated whether
gene expression profiling could be used to define subgroups of high
grade glioma more objectively and consistently than standard
pathology. To this end, we examined the expression profile of 14
glioblastomas and 7 anaplastic oligodendrogliomas with classic
histology (Fig.1A). Features (genes) correlating with each of the two
class distinctions were ranked according to S2N as described;
diagrammatic results for the top 50 features of each class are
illustrated (Fig 1B; the complete list of genes is available online5).
Since the expression profiles demonstrated robust class distinctions,
we proceeded to construct five k-NN class prediction models. The
number of features used in the models was chosen to give a range of
prediction accuracy; increasing the number of genes in a model can
improve prediction accuracy by providing additional biologically
relevant input and affording robust signals against noise, whereas
using too many genes can increase inaccuracy by generating excess
noise. Models were built using 10, 20, 50, 100 or 250 features and the
training error for each model was calculated using leave-one-out cross
validation (Table 2). Although accuracy of the models was comparable,
the 20-feature k-NN model was chosen for further study as it predicted
most accurately the class distinctions of the classic glioma training
set (18/21 correct calls; 86 % accuracy).
The 20 features used for prediction in this model correspond to 19
genes due to the presence of redundant probe sets (Table 3). Genes
highly correlated with glioblastoma included a mixture of metabolic,
structural, and signaling proteins. In particular, Rho GTPases (ARHC)
and MAP kinases are members of Ras signal transduction pathways known
to play a role in tumorigenesis and cell migration (17, 18). A large
proportion of genes highly correlated with anaplastic
oligodendroglioma were found to be involved in protein translation and
ribosome biogenesis; translation factors have been implicated
previously as effectors of tumorigenesis (19). Paradoxically,
ribosomal protein-encoding genes were found recently to be correlated
with poor outcome in medulloblastoma (16). These models thus provide
a substantial number of features that correlate with glioma class
distinction, but determination of the biological and clinical
significance of these genes requires additional studies.
Training “errors” of the class prediction model. Although a class
prediction was made for all 21 classic gliomas using the model, such
techniques typically classify some samples with more confidence than
others. For this reason, confidence values were calculated for all
predictions (Table 4). Of the three “errors” within the classic
training set, one prediction was made with relative high confidence
(“Brain_CO_4”; ranked 9 out of 21) and two were classified as low
confidence predictions (“Brain_CG_5” and “Brain_CG_10”; ranked 16 and
18, respectively). “Brain_CO_4”, a classic anaplastic
oligodendroglioma, displayed a gene expression profile strikingly more
similar to that of glioblastoma (Fig. 1B) and was classified as a
glioblastoma with relative high confidence in all five k-NN models
examined (mean confidence value of 0.17). Reexamination of reports
from the initial diagnosis and slides from the central pathology
review gave no justification for a histological classification of
glioblastoma. Although some evidence of nuclear pleomorphism and
hyperchromasia was noted in the original pathology report, the
presence of prominent perinuclear halos and a fine capillary network
indicated a classic anaplastic oligodendroglioma. Furthermore, glial
fibrillary acidic protein, an astrocytic marker, was not expressed in
the neoplastic cells. Notably, however, although the histological
features of “Brain_CO_4” were consistent with anaplastic
oligodendroglioma, clinical data suggested a course more
characteristic of a glioblastoma, with survival of only seven months
from diagnosis.
Independent validation of class prediction through survival analysis.
The prediction model classified 18 of 21 classic gliomas identically
to the pathological classification during leave-one-out cross
validation. The discrepancies in tumor classification could be the
result of a class prediction model “error” or a diagnostic “error”;
preliminary examination of the clinical behavior of “Brain_CO_4”
suggested that the class prediction model provided more pertinent
tumor classification. Ideally, the designation of “error” requires
independent validation. Differences in survival between patients with
glioblastomas and those with anaplastic oligodendrogliomas have been
well documented (1); consequently, as an independent validation of
the gene expression prediction model, prediction model classifications
were compared to pathological diagnoses with respect to survival. When
the classic gliomas were sorted according to pathology, a clear
distinction was found between survival of patients with glioblastoma
and those with anaplastic oligodendroglioma (Fig. 2). Although this
comparison was not statistically significant (n= 21, P=0.210), most
likely due to the small sample size and relatively short follow-up
time on three of the seven anaplastic oligodendrogliomas,
statistically significant differences in survival were seen within the
pathologically defined classes when all glioblastomas and anaplastic
oligodendrogliomas were compared (n=50, P=0.009; data not shown).
Remarkably however, when the classic gliomas were sorted using class
distinctions according to the model, survival differences were
statistically significant (n=21, P=0.031; Fig. 2). These results
demonstrate that, even within high grade gliomas of classic histology,
the biologically and clinically relevant information afforded by the
genetic profiles augments that provided by pathology alone.
Furthermore, the clinical outcome data suggest that the discrepancies
in tumor classification are more likely due to a diagnostic “error”
than a class prediction model “error”.
Class prediction of non-classic high grade gliomas. Next, we examined
the ability of this model to classify the common, non-classic high
grade gliomas that currently cause such clinical uncertainty regarding
therapy and prognosis (Fig. 3A). The ability to identify these lesions
in a uniform and reproducible manner would facilitate more accurate
therapeutic decisions and prognostic estimation, allowing for improved
clinical management of individual patients. The prediction model
classifications were compared to pathological diagnoses with respect
to survival. When these diagnostically challenging tumors were
classified according to pathology, survival of patients with
non-classic glioblastoma was not significantly different from that of
patients with non-classic anaplastic oligodendroglioma (n=29, P=0.194;
Fig. 3B). These results demonstrate clearly the difficulty in
distinguishing these challenging cases in a clinically relevant manner
based exclusively on histological parameters. In contrast, class
distinctions according to the gene expression-based model trained on
the classic gliomas were statistically significant (P=0.051), giving
much better separation between the anaplastic oligodendroglioma and
glioblastoma survival curves (Fig. 3B). Thus, gene expression profiles
have a remarkable ability to distinguish histologically ambiguous
glioblastomas and anaplastic oligodendrogliomas in a clinically
relevant manner. Indeed, gene expression profiles provide a more
objective and accurate predictor of prognosis in high grade
non-classic gliomas than does traditional histology. In addition, the
ability to distinguish histologically ambiguous gliomas enables
appropriate therapies to be tailored to specific tumor subtypes,
sparing patients who would not respond from unnecessary treatments.
Moreover, uniform and reproducible classification of these non-classic
lesions would provide improved stratification of patients in clinical
trials and molecular marker studies.
Summary. We investigated whether gene expression profiling, coupled
with the computational methodology of class prediction, could be used
to define subgroups of high grade glioma in a manner more objective,
explicit and consistent than standard pathology. Not only was this
method effective at classifying high grade gliomas objectively and
reproducibly, it also appeared to provide a more accurate predictor of
prognosis. Although the training sample sets for these models were
selected based on classic histological features, the biologically and
clinically relevant information afforded by the genetic profiles
greatly augments that provided by pathology alone. These data
therefore suggest that class prediction models, based on defined
molecular profiles, classify diagnostically challenging malignant
gliomas in a manner that better correlates with clinical outcome than
does standard pathology.
ACKNOWLEDGMENTS
The authors thank Magdalena Zlatescu and Loc Pham for valuable
assistance with collecting patient data; Marcela White and Jennifer
Roy for accessing tissue samples and information; Lisa Sturla for
technical assistance; members of the Program in Cancer Genomics,
Whitehead Institute/MIT Center for Genome Research for valuable
discussions; and Anat Stemmer-Rachamimov for critical review of the
manuscript.
REFERENCES
1. Kleihues, P. and Cavenee, W. K. World Health Organization
Classification of Tumours of the Nervous System. Lyon: WHO/IARC, 2000.
2. Cairncross, J. G. and Macdonald, D. R. Successful chemotherapy for
malignant oligodendroglioma. Ann Neurol, 23: 360-364, 1988.
3. Cairncross, J. G., Ueki, K., Zlatescu, M. C., Lisle, D. K.,
Finkelstein, D. M., Hammond, R. R., Silver, J. S., Stark, P. C.,
Macdonald, D. R., Ino, Y., Ramsay, D. A., and Louis, D. N. Specific
chromosomal losses predict chemotherapeutic response and survival in
patients with anaplastic oligodendrogliomas. J Natl Cancer Inst, 90:
1473-1479, 1998.
4. Burger, P. C. What is an oligodendroglioma? Brain Pathol, 12:
257-259, 2002.
5. Ironside, J. W., Moss, T. H., Louis, D. N., Lowe, J. S., and
Weller, R. O. Diagnostic Pathology of Nervous System Tumours. London:
Churchill Livingstone, 2002.
6. Burger, P. C., Scheithauer, B. W., and Vogel, F. S. Surgical
Pathology of the Nervous System and its Coverings, 4 edition, p. 592.
London: Churchill Livingstone, 2002.
7. Louis, D. N., Holland, E. C., and Cairncross, J. G. Glioma
classification: a molecular reappraisal. Am J Path, 159: 779-786,
2001.
8. Coons, S. W., Johnson, P. C., Scheithauer, B. W., Yates, A. J., and
Pearl, D. K. Improving diagnostic accuracy and interobserver
concordance in the classification and grading of primary gliomas.
Cancer, 79: 1381-1393, 1997.
9. Giannini, C., Scheithauer, B. W., Weaver, A. L., Burger, P. C.,
Kros, J. M., Mork, S., Graeber, M. B., Bauserman, S., Buckner, J. C.,
Burton, J., Riepe, R., Tazelaar, H. D., Nascimento, A. G., Crotty, T.,
Keeney, G. L., Pernicone, P., and Altermatt, H. Oligodendrogliomas:
Reproducibility and prognostic value of histologic diagnosis and
grading. J Neuropathol Exp Neurol, 60: 248-262, 2001.
10. Sasaki, H., Zlatescu, M. C., Betensky, R. A., Johnk, L., Cutone,
A., Cairncross, J. G., and Louis, D. N. Histopathological-molecular
genetic correlations in referral pathologist-diagnosed low-grade
"oligodendroglioma". J Neuropathol Exp Neurol, 61: 58-63, 2002.
11. Burger, P. C., Minn, A. Y., Smith, J. S., Borell, T. J., Jedlicka,
A. E., Huntley, B. K., Goldthwaite, P. T., Jenkins, R. B., and
Feuerstein, B. G. Losses of chromosomal arms 1p and 19q in the
diagnosis of oligodendroglioma. A study of paraffin-embedded sections.
Mod Pathol, 14: 842-853, 2001.
12. Lu, Q. R., Park, J. K., Noll, E., Chan, J. A., Alberta, J., Yuk,
D., Alzamora, M. G., Louis, D. N., Stiles, C. D., Rowitch, D. H., and
Black, P. M. Oligodendrocyte lineage genes (OLIG) as molecular markers
for human glial brain tumors. Proc Natl Acad Sci USA, 98: 10851-10856,
2001.
13. Popko, B., Pearl, D. K., Walker, D. M., Comas, T. C., Baerwald, K.
D., Burger, P. C., Scheithauer, B. W., and Yates, A. J. Molecular
markers that identify human astrocytomas and oligodendrogliomas. J
Neuropathol Exp Neurol, 61: 329-338, 2002.
14. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek,
M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri,
M. A., Bloomfield, C. D., and Lander, E. S. Molecular classification
of cancer: class discovery and class prediction by gene expression
monitoring. Science, 286: 531-537, 1999.
15. Bhattacharjee, A., Richards, W. G., Staunton, J., Li, C., Monti,
S., Vasa, P., Ladd, C., Beheshti, J., Bueno, R., Gillette, M., Loda,
M., Weber, G., Mark, E. J., Lander, E. S., Wong, W., Johnson, B. E.,
Golub, T. R., Sugarbaker, D. J., and Meyerson, M. Classification of
human lung carcinomas by mRNA expression profiling reveals distinct
adenocarcinoma subclasses. Proc Natl Acad Sci USA, 98: 13790-13795,
2001.
16. Pomeroy, S. L., Tamayo, P., Gaasenbeek, M., Sturla, L. M., Angelo,
M., McLaughlin, M. E., Kim, J. Y. H., Goumnerova, L. C., Black, P. M.,
Lau, C., Allen, J. C., Zagzag, D., Olson, J. M., Curran, T., Wetmore,
C., Biegel, J. A., Poggio, T., Mukherjee, S., Rifkin, R., Califano,
A., Stolovitzky, G., Louis, D. N., Mesirov, J. P., Lander, E. S., and
Golub, T. R. Prediction of central nervous system embryonal tumour
outcome based on gene expression. Nature, 415: 436-442, 2002.
17. Boettner, B. and Van Aelst, L. The role of Rho GTPases in disease
development. Gene, 286: 155-174, 2002.
18. Ridley, A. J. Rho GTPases and cell migration. J Cell Sci, 114:
2713-2722, 2001.
19. Clemens, M. J. and Bommer, U.-A. Translational control: the cancer
connection. Int J Biochem Cell Biol, 31: 1-23, 1999.

Table 1 Summary of Clinical Parameters for the High Grade Glioma
Dataset
Pathological diagnosis and survival from date of intial diagnosis are
given for all patients.
For living patients, survival is given to time of last follow-up.
GBM, glioblastoma; AO, anaplastic oligodendroglioma
Sample Name
Pathology
Vital Status
Survival (Days)
Brain_CG_1
Classic GBM
Dead
308
Brain_CG_2
Classic GBM
Dead
281
Brain_CG_3
Classic GBM
Dead
501
Brain_CG_4
Classic GBM
Dead
670
Brain_CG_5
Classic GBM
Alive
729
Brain_CG_6
Classic GBM
Dead
21
Brain_CG_7
Classic GBM
Alive
630
Brain_CG_8
Classic GBM
Dead
263
Brain_CG_9
Classic GBM
Dead
219
Brain_CG_10
Classic GBM
Dead
408
Brain_CG_11
Classic GBM
Dead
242
Brain_CG_12
Classic GBM
Dead
323
Brain_CG_13
Classic GBM
Dead
213
Brain_CG_14
Classic GBM
Dead
97
Brain_NG_1
Non-classic GBM
Dead
1375
Brain_NG_2
Non-classic GBM
Alive
1644
Brain_NG_3
Non-classic GBM
Dead
406
Brain_NG_4
Non-classic GBM
Dead
308
Brain_NG_5
Non-classic GBM
Dead
177
Brain_NG_6
Non-classic GBM
Dead
103
Brain_NG_7
Non-classic GBM
Alive
992
Brain_NG_8
Non-classic GBM
Dead
41
Brain_NG_9
Non-classic GBM
Alive
1354
Brain_NG_10
Non-classic GBM
Dead
276
Brain_NG_11
Non-classic GBM
Dead
519
Brain_NG_12
Non-classic GBM
Dead
368
Brain_NG_13
Non-classic GBM
Dead
157
Brain_NG_14
Non-classic GBM
Dead
1162
Brain_CO_1
Classic AO
Alive
231
Brain_CO_2
Classic AO
Alive
1674
Brain_CO_3
Classic AO
Alive
1604
Brain_CO_4
Classic AO
Dead
215
Brain_CO_5
Classic AO
Alive
359
Brain_CO_6
Classic AO
Alive
171
Brain_CO_7
Classic AO
Dead
272
Brain_NO_1
Non-classic AO
Dead
63
Brain_NO_2
Non-classic AO
Alive
585
Brain_NO_3
Non-classic AO
Alive
1804
Brain_NO_4
Non-classic AO
Dead
916
Brain_NO_5
Non-classic AO
Dead
793
Brain_NO_6
Non-classic AO
Dead
803
Brain_NO_7
Non-classic AO
Dead
559
Brain_NO_8
Non-classic AO
Alive
1137
Brain_NO_9
Non-classic AO
Alive
1100
Brain_NO_10
Non-classic AO
Dead
498
Brain_NO_11
Non-classic AO
Alive
795
Brain_NO_12
Non-classic AO
Dead
790
Brain_NO_13
Non-classic AO
Dead
789
Brain_NO_14
Non-classic AO
Alive
439
Brain_NO_15
Non-classic AO
Alive
638
Table 2 Training Error of k-NN Models
Class prediction models were built using 10, 20, 50, 100 or 250
features and the training error for each model was calculated using
leave-one-out cross validation.
Number of Features
Error
10 features
4/21
20 features
3/21
50 features
5/21
100 features
4/21
250 features
6/21
Table 3 Features of the 20-feature k-NN Class Prediction Model
Genes highly correlated with the class distinction of either GBM or AO
in the 20-feature k-NN class prediction model. Affymetrix feature
numbers, fold increase in gene expression (GBM>AO; AO>GBM), accession
numbers and gene identifications are shown.
GBM, glioblastoma; AO, anaplastic oligodendroglioma
Class Correlation
Feature Number
Fold Increase
Accession
Number
Gene Description
GBM
34091_s_at
2.55
Z19554
VIM: vimentin
GBM
630_at
4.83
L39874
DCTD: dCMP deaminase
GBM
631_g_at
2.80
L39874
DCTD: dCMP deaminase
GBM
39691_at
1.80
AB007960
SH3GLB1: SH3-domain GRB2-like endophilin B1
GBM
160039_at
5.57
NM_002747
MAPK4: mitogen-activated protein kinase 4
GBM
35016_at
1.89
M13560
CD74: CD74 antigen (invariant polypeptide of major histocompatibility
complex, class II antigen-associated)
GBM
38791_at
1.78
D29643
DDOST: dolichyl-diphosphooligosaccharide
protein glycosyltransferase
GBM
1395_at
2.10
L25081
ARHC: ras homolog gene family, member C
GBM
37542_at
2.41
D86961
LHFPL2: lipoma HMGIC fusion partner-like 2
GBM
935_at
1.49
L12168
CAP: adenylyl cyclase-associated protein
AO
33619_at
2.20
L01124
RPS13: ribosomal protein S13
AO
34679_at
2.64
X02596
BCR: breakpoint cluster region
AO
37573_at
3.96
AF007150
ANGPTL2: angiopoietin-like 2
AO
33677_at
1.81
M94314
RPL24: ribosomal protein L24
AO
326_i_at
2.03
HG1800-HT1823
RPS20: Ribosomal Protein S20
AO
41325_at
2.43
AF006823
KCNK3: potassium channel, subfamily K,
member 3 (TASK-1)
AO
38681_at
1.76
U62962
EIF3S6: eukaryotic translation initiation factor 3,
subunit 6 (48kD)
AO
41792_at
2.16
L78207
ABCC8: ATP-binding cassette, sub-family C (CFTR/MRP), member 8
AO
37249_at
3.40
AF079529
PDE8B: phosphodiesterase 8B
AO
37953_s_at
2.77
U78181
ACCN2: amiloride-sensitive cation channel 2, neuronal
Table 4 Summary of Training Sample Set Class Predictions
Set includes the 21 classic high grade gliomas. The “call” is the
classification given by the 20-feature k-NN model during leave-one-out
cross validation and appears along with the confidence value. “Errors”
are those tumors whose classification differed from the pathological
classification.
GBM, glioblastoma; AO, anaplastic oligodendroglioma
Sample Name
Call
Confidence
Pathology
“Error”
Brain_CG_8
GBM
0.677
GBM
Brain_CG_11
GBM
0.610
GBM
Brain_CG_3
GBM
0.558
GBM
Brain_CG_4
GBM
0.524
GBM
Brain_CG14
GBM
0.455
GBM
Brain_CG_2
GBM
0.445
GBM
Brain_CO_5
AO
0.377
AO
Brain_CO_1
AO
0.234
AO
Brain_CO_4
GBM
0.224
AO
*
Brain_CG_1
GBM
0.182
GBM
Brain_CO_6
AO
0.166
AO
Brain_CG_9
GBM
0.158
GBM
Brain_CO_2
AO
0.143
AO
Brain_CO_7
AO
0.141
AO
Brain_CG_6
GBM
0.101
GBM
Brain_CG_5
AO
0.028
GBM
*
Brain_CO_3
AO
0.023
AO
Brain_CG_10
AO
0.021
GBM
*
Brain_CG_13
GBM
0.008
GBM
Brain_CG_12
GBM
0.006
GBM
Brain_CG_7
GBM
0.000
GBM
FIGURE LEGENDS
Fig. 1. Characterization of classic high grade gliomas. A,
Histological features of classic high grade gliomas. “Brain_CG_3”
(top), classic glioblastoma featuring cells with copious eosinophilic
cytoplasm and fibrillary processes; “Brain_CG_7” (middle), classic
glioblastoma illustrating pleomorphic and spindled cells; “Brain_CO_1”
(bottom), classic anaplastic oligodendroglioma illustrating
monomorphic cells with rounded nuclei and perinuclear halos. B,
Classification of high grade gliomas by gene expression. Genes were
ranked by the S2N metric according to their correlation with the
classic glioblastoma (GBM) versus classic anaplastic oligodendroglioma
(AO) distinction. Results are shown for the top 50 genes of each
distinction. Each column represents a single glioma sample and each
row represents a single gene. For each gene, red indicates a high
level of expression relative to the mean; blue indicates a low level
of expression relative to the mean. The standard deviation from the
mean is indicated (). Asterisk indicates “Brain_CO_4” sample.
Fig. 2. Survival curves of patients with the 14 classic glioblastomas
(dashed line) and 7 classic anaplastic oligodendrogliomas (solid line)
used to train the 20-feature k-NN class prediction model. Survival
curves were plotted according to classifications based on either
traditional pathology or the class prediction model. When classic
tumors were sorted according to pathology, a clear distinction was
found between survival of patients with glioblastoma and those with
anaplastic oligodendroglioma, although this comparison was not
significantly different (P=0.210). Survival curves generated using
class distinctions according to the class prediction model were
significantly different (P=0.031).
Fig. 3. Characterization of non-classic high grade gliomas. A,
Histological features of non-classic high grade gliomas. “Brain_NG_1”
(top), non-classic glioblastoma with region having microgemistocytes
that raise the differential diagnosis of anaplastic oligodendroglioma;
“Brain_NG_3” (middle), non-classic glioblastoma with an area of
rounded cells that resemble oligodendroglioma and more spindled cells
that resemble glioblastoma; “Brain_NO_14” (bottom), non-classic
anaplastic oligodendroglioma with a region displaying the typical
branching vasculature and calcification (arrowhead) of
oligodendroglioma, but with more spindled cells. A, Survival curves of
patients with the 14 non-classic glioblastomas (dashed line) and 15
non-classic anaplastic oligodendrogliomas (solid line). Survival
curves were plotted according to classifications based on either
traditional pathology or the class prediction model trained on the
classic gliomas. When tumors were classified according to pathology,
survival of patients with non-classic glioblastoma was not
significantly different from that of patients with non-classic
anaplastic oligodendroglioma (P=0.194). In contrast, class
distinctions according to the class prediction model were
significantly different (P=0.051).
25