An Integrated Genomic Analysis of Human Glioblastoma Multiforme
`D. Williams Parsons,1,2* Siân Jones,1* Xiaosong Zhang,1* Jimmy Cheng-Ho Lin,1* Rebecca J. Leary,1*
`Philipp Angenendt,1* Parminder Mankoo,3 Hannah Carter,3 I-Mei Siu,4 Gary L. Gallia,4 Alessandro Olivi,4
`Roger McLendon,5 B. Ahmed Rasheed,5 Stephen Keir,5 Tatiana Nikolskaya,6 Yuri Nikolsky,7 Dana A.
`Busam,8 Hanna Tekleab,8 Luis A. Diaz Jr.,1 James Hartigan,9 Doug R. Smith,9 Robert L. Strausberg,8 Suely
`Kazue Nagahashi Marie,10 Sueli Mieko Oba Shinjo,10 Hai Yan,5 Gregory J. Riggins,4 Darell D. Bigner,5
`Rachel Karchin,3 Nick Papadopoulos,1 Giovanni Parmigiani,1 Bert Vogelstein,1† Victor E. Velculescu,1†
`Kenneth W. Kinzler1†
`1Ludwig Center for Cancer Genetics and Therapeutics, and The Howard Hughes Medical Institute at Johns Hopkins Kimmel
`Cancer Center, Baltimore, MD 21231, USA. 2Department of Pediatrics, Section of Hematology-Oncology, Baylor College of
`Medicine, Houston TX 77030, USA. 3Department of Biomedical Engineering, Institute of Computational Medicine, Johns
`Hopkins Medical Institutions, Baltimore, MD 21218, USA. 4Department of Neurosurgery, Johns Hopkins Medical Institutions,
`Baltimore, MD 21231, USA. 5Department of Pathology, Pediatric Brain Tumor Foundation, and Preston Robert Tisch Brain
`Tumor Center at Duke University Medical Center, Durham, NC 27710, USA. 6Vavilov Institute for General Genetics, Moscow
`B333, 117809, Russia. 7GeneGo, Inc., St. Joseph, MI 49085, USA. 8J. Craig Venter Institute, Rockville, MD 20850, USA.
`9Agencourt Bioscience Corporation, Beverly, MA 01915, USA. 10Department of Neurology, School of Medicine, University of
`São Paulo, São Paulo, Brazil.
`
`*These authors contributed equally to this work.
`
`†To whom correspondence should be addressed. E-mail: bertvog@gmail.com (B.V.); velculescu@jhmi.edu (V.E.V.);
`kinzlke@welch.jhu.edu (K.W.K.)
`
`Glioblastoma multiforme (GBM) is the most common and
`lethal type of brain cancer. To identify the genetic
`alterations in GBMs, we sequenced 20,661 protein coding
`genes, determined the presence of amplifications and
`deletions using high-density oligonucleotide arrays, and
`performed gene expression analyses using next-generation
`sequencing technologies in 22 human tumor samples. This
`comprehensive analysis led to the discovery of a variety of
`genes that were not known to be altered in GBMs. Most
`notably, we found recurrent mutations in the active site of
`isocitrate dehydrogenase 1 (IDH1) in 12% of GBM
`patients. Mutations in IDH1 occurred in a large fraction
`of young patients and in most patients with secondary
`GBMs, and were associated with an increase in overall
`survival. These studies demonstrate the value of unbiased
`genomic analyses in the characterization of human brain
`cancer and identify a potentially useful genetic alteration
`for the classification and targeted therapy of GBMs.
`Malignant gliomas are the most frequent and lethal cancers
`originating in the central nervous system. The most
`biologically aggressive subtype is glioblastoma multiforme
`(GBM; WHO grade IV astrocytoma), a tumor associated with
`a dismal prognosis (1). The current standard of care for GBM
`patients—surgical resection followed by adjuvant radiation
`
`therapy and chemotherapy with the oral alkylating agent
`temozolomide—produces a median survival of only 15
`months (2). Historically, GBMs have been categorized into
`two groups (“primary” and “secondary”) on the basis of
`clinical presentation (3). Secondary GBMs are defined as
`cancers that have clinical, radiologic, or histopathologic
`evidence of malignant progression from a preexisting lower-
`grade tumor, while primary GBMs have no such history and
`present at diagnosis as advanced cancers (4). Clinical
`differences have been reported between the two groups, with
`secondary GBMs occurring less frequently (~5% of GBMs)
`and predominantly in younger patients (median age ~45 years
`vs. ~60 years for primary GBM) (5, 6). The histopathologic
`findings of primary and secondary GBMs are
`indistinguishable, and the prognosis does not appear to be
`significantly different after adjustment for age (5, 6).
`Substantial research effort has focused on the
`identification of genetic alterations in GBMs that might help
`define subclasses of GBM patients with differing prognoses
`and/or response to specific therapies (7). Distinctions between
`the genetic lesions found in primary and secondary GBMs
`have been made, with TP53 mutations occurring more
`commonly in secondary GBMs and EGFR amplifications and
`PTEN mutations occurring more frequently in primary GBMs
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`(6, 8, 9); however, none of these alterations are sufficiently
`specific to distinguish between primary and secondary
`GBMs. This issue is further confounded by the possibility
`that a fraction of GBMs designated as primary tumors may
`follow a sequence of genetic events similar to that of
`secondary lesions but not come to clinical attention until
`malignant progression to a GBM has occurred.
`The comprehensive elucidation of genetic alterations in
`GBMs could provide novel targets that might be used for
`diagnostic, prognostic, or therapeutic purposes as well as
`identify subgroups of patients that preferentially respond to
`particular targeted therapies. The determination of the human
`genome sequence and improvements in sequencing and
`bioinformatic technologies have recently permitted genome-
`wide sequence analyses in human cancers. We have
`previously studied the genomes of 11 breast and 11 colorectal
`cancers by determining the sequence of the more than 18,000
`Consensus Coding Sequence (CCDS) and Reference
`Sequence (RefSeq) genes (10, 11). Here, we have analyzed
`20,661 protein coding genes in 22 human GBM samples. To
`complement these sequencing data, we have also performed a
`genome-wide analysis of focal copy number alterations,
`including amplifications and homozygous deletions, using
`high-density oligonucleotide microarrays on the same GBM
`tumors. Finally, we have examined the expression profiles of
`these same samples using serial analysis of gene expression
`(SAGE) and next-generation sequencing technologies.
`
`Sequencing strategy. We extended our previous
`sequencing strategy for identification of somatic mutations to
`include 23,219 transcripts from 20,661 genes (fig. S1). These
`included 2783 additional genes from the Ensembl databases
`that were not present in the CCDS or RefSeq databases
`analyzed in the previous studies (10, 11). In addition, we
`redesigned PCR primers for regions of the genome that (i)
`were difficult to PCR amplify in prior studies; or (ii) were
`found to share significant identity with other human or mouse
`sequences. The combination of these new, redesigned, and
`existing primers sequences resulted in a total of 208,311
`primer pairs (table S1) that were successfully used for
`sequence analysis of the coding exons of these genes.
`Twenty-two GBM samples (table S2) were selected for
`PCR sequence analysis, consisting of 7 samples extracted
`directly from patient tumors and 15 samples passaged in nude
`mice as xenografts. In the first stage of this analysis, called
`the Discovery Screen, the primer pairs were used to amplify
`and sequence 175,471 coding exons and adjacent intronic
`splice donor and acceptor sequences in 22 GBM samples and
`one matched normal sample. The data were assembled for
`each amplified region and evaluated using stringent quality
`criteria (12), resulting in successful amplification and
`sequencing of 95.0% of targeted amplicons and 93.0% of
`
`targeted bases in the 22 tumors (Table 1). A total of 689 Mb
`of sequence data was generated in this fashion. The amplicon
`traces were analyzed using automated approaches to identify
`changes in the tumor sequences that were not present in the
`reference sequences of each gene. Alterations present in the
`normal control sample and in single nucleotide polymorphism
`(SNP) databases were then removed from further analyses.
`The remaining sequence traces of potential alterations were
`visually inspected to remove false-positive mutation calls
`generated by the automated software. All exons containing
`putative mutations were then reamplified and sequenced in
`both the affected tumor and the matched normal DNA
`sample. This process allowed us to confirm the presence of
`the mutation in the tumor sample and determine whether the
`alteration was somatic (i.e. tumor-specific) or was present in
`the germline. All putative somatic mutations were examined
`computationally and experimentally to confirm that the
`alterations did not arise through the aberrant coamplification
`of related gene sequences (12).
`
`Analysis of sequence alterations. Analysis of the
`identified somatic mutations revealed that one tumor (Br27P),
`from a patient previously treated with radiation therapy and
`temozolomide, had 17-fold more alterations than any of the
`other 21 patients (table S3). The mutation spectrum of this
`sample was also dramatically different from those of the other
`GBM patients (12) and was consistent with previous
`observations of a hypermutation phenotype in glioma samples
`of patients treated with temozolomide (13, 14). After
`removing Br27P from consideration, we found that 685 genes
`(3.3% of the 20,661 genes analyzed) contained at least one
`nonsilent somatic mutation. The vast majority of these
`alterations were single-base substitutions (94%), while the
`others were small insertions, deletions, or duplications (Table
`1). The 993 somatic mutations were observed to be
`distributed relatively evenly among the 21 remaining tumors
`(table S3), with a mean of 47 mutations per tumor,
`representing 1.51 mutations per Mb of GBM tumor genome
`sequenced. The six DNA samples extracted directly from
`patient tumors had smaller numbers of mutations than those
`obtained from xenografts, likely because of the masking
`effect of nonneoplastic cells in the former. It has previously
`been shown that cell lines and xenografts provide the optimal
`template DNA for cancer genome sequencing analyses (15)
`and that they faithfully represent the alterations present in the
`original tumors (16). Both the total number and frequency of
`sequence alterations in GBMs were substantially smaller than
`the number and frequency of such alterations observed in
`colorectal or breast cancers, and slightly less than in
`pancreatic cancers (10, 11, 17). The most likely explanation
`for this difference is the reduced number of cell generations
`in glial cells prior to the onset of neoplasia (18).
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`We further evaluated a set of 21 mutated genes identified
`in the Discovery Screen in a second screen, called a
`Prevalence Screen, comprising an additional 83 GBMs with
`well-documented clinical histories (table S2). The 21 genes
`selected were mutated in at least two Discovery Screen
`tumors and had mutation frequencies of >10 mutations per
`Mb of tumor DNA sequenced. Nonsilent somatic mutations
`were identified in 16 of these 21 genes in the additional tumor
`samples (table S4). The mutation frequency of all analyzed
`genes in the Prevalence Screen was 24 mutations per Mb of
`tumor DNA, markedly increased from the overall mutation
`frequency in the Discovery Screen of 1.5 mutations per Mb
`(P < 0.001, binomial test). Additionally, the observed ratio of
`nonsilent to silent mutations among mutations in the
`Prevalence Screen was 14.8:1, substantially higher than the
`3.1:1 ratio that was observed in the Discovery Screen (P <
`0.001, binomial test). The increased mutation frequency and
`higher fraction of nonsilent mutations suggested that genes
`mutated in the Prevalence Screen were enriched for genes
`that actively contributed to tumorigenesis.
`In addition to the frequency of mutations in a gene, the
`type of mutation can provide information useful for
`evaluating its potential role in disease (19). The likely effect
`of missense mutations can be assessed through evaluation of
`the mutated residue by evolutionary or structural means. To
`evaluate missense mutations, we developed an algorithm (LS-
`MUT) that employs machine learning of 58 predictive
`features based on evolutionary conservation and the physical-
`chemical properties of amino acids involved in the alteration
`(12). Approximately 15% of the missense mutations
`evaluated were predicted to have a statistically significant
`effect on protein function when assessed by this method
`(table S3). We also were able to make structural models of
`244 of the 870 missense mutations identified in this study
`(20). In each case, the model was based on x-ray
`crystallography or nuclear magnetic resonance spectroscopy
`of the normal protein or a closely related homolog. This
`analysis showed that 35 of the missense mutations are located
`close to a domain interface or substrate-binding site and thus
`are likely to impact protein function [(links to structural
`models available in (12)].
`
`Analysis of copy number changes. The same tumors
`were then evaluated for copy number alterations through
`genomic hybridization of DNA samples to Illumina SNP
`arrays containing ~1 million probes (21). We have recently
`developed a sensitive and specific approach for the
`identification of focal amplifications resulting in 12 or more
`copies per nucleus (6-fold or greater amplification compared
`to the diploid genome) as well as deletions of both copies of a
`gene (homozygous deletions) using such arrays (22). Unlike
`larger chromosomal aberrations, such focused alterations can
`
`be used to identify underlying candidate genes in these
`regions.
`We identified a total of 147 amplifications (table S5) and
`134 homozygous deletions (table S6) in the 22 samples used
`in the Discovery Screen (Table 1). Although the number of
`amplifications was similar in samples extracted from patient
`tumors and those that had been passaged as xenografts, the
`latter samples allowed detection of a larger number of
`homozygous deletions (average of 8.0 deletions per sample in
`the xenografts versus 2.2 per sample in the tumors). These
`observations are consistent with previous reports
`documenting the difficulty of identifying homozygous
`deletions in samples containing contaminating normal DNA
`(23) and highlight the importance of using purified human
`tumor cells, such as those present in xenografts or cell lines,
`for genomic analyses.
`
`Integration of sequencing, copy number and expression
`analyses. Mutations that arise during tumorigenesis may
`provide a selective advantage to the tumor cell (driver
`mutations) or have no net effect on tumor growth (passenger
`mutations). The mutational data obtained from sequencing
`and analysis of copy number alterations were integrated in
`order to identify GBM candidate cancer genes (CAN-genes)
`that are most likely to be drivers and therefore worthy of
`further investigation. To determine if a gene was likely to
`harbor driver mutations, we compared the number and type of
`mutations observed (including sequence changes,
`amplifications and homozygous deletions) and determined the
`probability that these alterations would result from passenger
`mutation rates alone (12) (fig. S1).
`The CAN-genes, together with their passenger
`probabilities, are listed in table S7. The CAN-genes included
`several with well-established roles in gliomas, including
`TP53, PTEN, CDKN2A, RB1, EGFR, NF1, PIK3CA and
`PIK3R1 (24–34). Of these genes, the most frequently altered
`were CDKN2A (altered in 50% of GBMs), TP53, EGFR, and
`PTEN (altered in 30 to 40%), NF1, CDK4 and RB1 (altered in
`12 to 15%), and PIK3CA and PIK3R1 (altered in 8 to 10%)
`(Table 2). Overall, these frequencies, which are similar to or
`in some cases higher than those previously reported, validate
`the sensitivity of our approach for detecting somatic
`alterations.
`Through analysis of additional gene members within cell
`signaling pathways affected by these genes, we identified
`alterations of critical genes in the TP53 pathway (TP53,
`MDM2, MDM4), the RB1 pathway (RB1, CDK4, CDKN2A),
`and the PI3K/PTEN pathway (PIK3CA, PIK3R1, PTEN,
`IRS1). These alterations affected pathways in a majority of
`tumors (64%, 68%, and 50%, respectively) and in all cases
`but one, mutations within each tumor affected only a single
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`member of each pathway in a mutually exclusive manner (P <
`0.05) (Table 3).
`Systematic analyses of functional gene groups and
`pathways contained within the well-annotated MetaCore
`database (35) identified enrichment of alterations in a variety
`of cellular processes in GBMs, including additional members
`of the TP53 and PI3K/PTEN pathways. Many of the
`pathways identified were similar to core signaling pathways
`found to be altered in pancreas, colorectal, and breast tumors,
`such as those regulating control of cellular growth, apoptosis
`and cell adhesion (17, 22, 36). However, several pathways
`were enriched only in GBMs. These included channels
`involved in transport of sodium, potassium and calcium ions
`as well as nervous system-specific cellular pathways such as
`synaptic transmission, transmission of nerve impulses, and
`axonal guidance (table S8). Mutations in these latter
`pathways may represent a subversion of normal glial cell
`processes to promote dysregulated growth and invasion.
`Gene expression patterns can inform the analysis of
`pathways because they can reflect epigenetic alterations not
`detectable by sequencing or copy number analyses. To
`analyze the transcriptome of GBMs, we performed SAGE
`(serial analysis of gene expression) (37, 38) on all GBM
`samples for which RNA was available (total of 18 samples)
`as well as on two independent normal brain RNA controls.
`When combined with sequencing-by-synthesis methods (39–
`42), SAGE provides a highly quantitative and sensitive
`measure of gene expression. We first used the transcript
`analysis to help identify previously uncharacterized target
`genes from the amplified and deleted regions that were
`revealed by our study. In tables S5 and S6, a candidate target
`gene could be identified within several of these regions
`through the use of the mutational as well as transcriptional
`data. Second, we used the transcript analysis to help identify
`genes that were differentially expressed in GBMs compared
`to normal brain. A large number of genes (143) were
`expressed on average at 10-fold higher levels in the GBMs.
`Among the overexpressed genes, 16 encoded proteins that are
`predicted to be secreted or expressed on the cell surface,
`suggesting new opportunities for diagnostic and therapeutic
`applications. Finally, we assessed whether the gene sets
`implicated in the pathways enriched for genetic alterations
`were also altered through expression changes. Notably, the
`gene sets in these pathways were more highly enriched for
`differentially expressed genes than the remaining sets (P <
`0.001) (12). These expression data thus independently
`highlight the potential importance of these pathways in the
`development of GBMs.
`
`High frequency alterations of IDH1 in GBM. The CAN-
`gene list (table S7) included a number of individual genes that
`had not previously been linked to GBMs. The most frequently
`
`mutated of these genes, IDH1 on chromosome 2q33, encodes
`isocitrate dehydrogenase 1, which catalyzes the oxidative
`carboxylation of isocitrate to (cid:68)-ketoglutarate, resulting in the
`production of NADPH. Of the five isocitrate dehydrogenase
`proteins encoded in the human genome, four are localized to
`the mitochondria while only IDH1 is localized within the
`cytoplasm and peroxisomes (43). The IDH1 protein forms an
`asymmetric homodimer (44), and is thought to play a
`substantial role in cellular control of oxidative damage
`through generation of NADPH (45, 46). None of the other
`IDH genes were found to be genetically altered in our
`analysis.
`IDH1 was somatically mutated in five of the 22 GBM
`tumors in the Discovery Screen. Surprisingly, all five had the
`same heterozygous point mutation, a change of a guanine to
`an adenine at position 395 of the IDH1 transcript (G395A),
`leading to the replacement of an arginine with a histidine at
`amino acid residue 132 of the protein (R132H). In our prior
`study of colorectal cancers, this same codon was mutated in a
`single case through alteration of the adjacent nucleotide,
`resulting in a R132C amino acid change (10). Five GBMs
`evaluated in our Prevalence Screen were found to have
`heterozygous somatic R132H mutations and an additional
`two tumors had a third distinct somatic mutation affecting the
`same amino acid residue, R132S (fig. S2 and Table 4). In
`addition to the Discovery and Prevalence Screen samples, 44
`other GBMs were analyzed for IDH1 mutations , revealing
`six tumors with somatic mutations affecting R132. In total, 18
`of 149 GBMs (12%) analyzed had alterations in IDH1. The
`R132 residue is conserved in all known species and is
`localized to the substrate binding site, where it forms
`hydrophilic interactions with the alpha-carboxylate of
`isocitrate (Fig. 1) (44, 47).
`Several important observations were made about IDH1
`mutations and their potential clinical significance. First,
`mutations in IDH1 preferentially occurred in younger GBM
`patients, with a mean age of 33 years for IDH1-mutated
`patients, as opposed to 53 years for patients with wild-type
`IDH1 (P < 0.001, t test, Table 4). In patients under 35 years
`of age, nearly 50% (9 of 19) had mutations in IDH1. Second,
`mutations in IDH1 were found in nearly all of the patients
`with secondary GBMs (mutations in 5 of 6 secondary GBM
`patients, as compared to 7 of 99 patients with primary GBMs,
`P < 0.001, binomial test). Third, patients with IDH1
`mutations had a significantly improved prognosis, with a
`median overall survival of 3.8 years as compared to 1.1 years
`for patients with wild-type IDH1 (Fig. 2, P < 0.001, log-rank
`test). Although both younger age and mutated TP53 are
`known to be positive prognostic factors for GBM patients,
`this association between IDH1 mutation and improved
`survival was noted even in the subgroup of young patients
`with TP53 mutations (P < 0.02, log-rank test).
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`and the fact that all mutations observed to date were
`heterozygous (without any evidence of loss of the second
`allele through LOH). Interestingly, enzymatic studies have
`shown that in vitro engineered substitution of arginine at
`residue 132 with a different amino acid than observed in
`patients (glutamate) results in a catalytically inactive enzyme,
`suggesting a critical role for this residue (48). Further
`biochemical and molecular analyses will be needed to
`determine the effect of alterations of IDH1 on enzymatic
`activity and cellular phenotype.
`Regardless of the specific molecular consequences of
`IDH1 alterations, detection of mutations in IDH1 is likely to
`be clinically useful. Although significant effort has focused
`on the identification of characteristic genetic lesions in
`primary and secondary GBMs, the altered genes identified to
`date are not optimal for this purpose (5). Our study revealed
`IDH1 mutation to be a novel and potentially more specific
`marker for secondary GBM. One hypothesis is that IDH1
`alterations identify a biologically specific subgroup of GBM
`patients, including both patients who would be classified as
`having secondary GBMs as well as a subpopulation of
`primary GBM patients with a similar tumor biology and more
`protracted clinical course (Table 4). Interestingly, patients
`with IDH1 mutations had a very high frequency of TP53
`mutation and a very low frequency of mutations in other
`commonly altered GBM genes (Table 4). Patients with
`mutated IDH1 also had distinct clinical characteristics,
`including younger age and a significantly improved clinical
`prognosis (Table 4). It is conceivable that new treatments
`could be designed to take advantage of IDH1 alterations in
`these patients, as inhibition of a different IDH enzyme
`(mitochondrial IDH2) has recently been shown to result in
`increased sensitivity of tumor cells to a variety of
`chemotherapeutic agents (49). In summary, the discovery of
`IDH1 and other genes previously not known to play a role in
`human tumors (table S7) validates the utility of genome-wide
`genetic analysis of tumors in general and opens new avenues
`of basic and clinical brain tumor research.
`
`Discussion. The data resulting from this integrated
`analysis of mutations and copy number alterations have
`provided a novel view of the genetic landscape of
`glioblastomas. Like all large-scale genetic analyses, our study
`has limitations. We did not assess certain molecular
`alterations, including chromosomal translocations and
`epigenetic changes. However, our large scale expression
`studies should have identified any genes that were
`differentially expressed through these mechanisms (table S9).
`Additionally, we focused on copy number changes that were
`focal amplifications or homozygous deletions, as these have
`historically been most useful in identifying cancer genes. The
`array data we have generated can also be analyzed to
`determine loss of heterozygosity (LOH) or low-amplitude
`regions of copy number gains, but such changes cannot
`generally be used to pinpoint new candidate cancer genes.
`Finally, the samples directly extracted from patient tumors
`contained small amounts of contaminating normal tissue,
`which limited our ability to detect homozygous deletions and
`to a lesser extent, somatic mutations, in those specific tumors.
`Despite these limitations, our study provides a number of
`important genetic and clinical insights into GBMs. First, it
`revealed that some of the pathways known to be altered in
`GBMs affect a larger fraction of genes and patients than
`previously anticipated. A majority of the tumors analyzed had
`alterations in genes encoding components of each of the
`TP53, RB1, and PI3K pathways. The fact that all but one of
`the cancers with mutations in members of a pathway did not
`have alterations in other members of the same pathway
`suggests that such alterations are functionally equivalent in
`tumorigenesis. Second, these results have identified a variety
`of new genes and signaling pathways not previously
`implicated in GBMs (table S7 and S8). Some of these
`pathways were found to be altered in previous genome-wide
`analyses of pancreatic, breast and colorectal cancers and may
`represent core processes that underlie human tumorigenesis
`(17, 22, 36). A number of the signaling pathways mutated or
`altered through expression differences in GBMs appear to be
`involved in nervous system signaling processes and represent
`novel and potentially useful aspects of GBM biology.
`The comprehensive nature of our study allowed us to
`identify IDH1 as an unexpected target of genetic alteration in
`patients with GBM. All mutations in this gene resulted in
`amino acid substitutions at position 132, an evolutionarily
`conserved residue located within the isocitrate binding site
`(44). The recurrent nature of the mutations is reminiscent of
`activating alterations in oncogenes such as BRAF, KRAS, and
`PIK3CA. Our speculation that this sequence change is an
`activating mutation is strengthened by the absence of
`inactivating changes (e.g., frameshift or stop mutations), the
`absence of other alterations in key residues of the active site,
`
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`51. We thank N. Silliman, J. Ptak, L. Dobbyn, and M.
`Whalen for assistance with PCR amplification; D. Lister,
`L. J. Ehinger, D. L. Satterfield, J. D. Funkhouser, and P.
`Killela for assistance with DNA purification; T. Sjöblom
`with for assistance with database management; the
`Agencourt sequencing team for assistance with automated
`sequencing; and C.-S. Liu and the SoftGenetics team for
`their assistance with mutation detection analyses. This
`project was carried out under the auspices of the Ludwig
`Brain Tumor Initiative and was supported by The Virginia
`and D. K. Ludwig Fund for Cancer Research, NIH grants
`CA121113, NS052507, CA43460, CA 57345, CA62924,
`CA09547, 5P50-NS-20023, CA108786, CA11898, The
`Pew Charitable Trusts, The Pediatric Brain Tumor
`Foundation Institute, The Hirschhorn Foundation, Alex’s
`Lemonade Stand Foundation, The American Brain Tumor
`Association, The American Society of Clinical Oncology,
`The Brain Tumor Research Fund and Beckman Coulter
`Corporation. Under separate licensing agreements between
`the Johns Hopkins University and Genzyme, Beckman
`Coulter, and Exact Sciences Corporations, B.V., V.E.V.,
`and K.W.K. are entitled to a share of royalties received by
`the University on sales of products related to research
`described in this paper. These authors and the University
`own Genzyme and Exact Sciences stock, which is subject
`to certain restrictions under University policy. The terms
`of these arrangements are managed by the Johns Hopkins
`University in accordance with its conflict-of-interest
`policies.
`
`Supporting Online Material
`www.sciencemag.org/cgi/content/full/1164382/DC1
`Materials and Methods
`Figs. S1 and S2
`Tables S1 to S9
`References
`
`7 August 2008; accepted 27 August 2008
`Published online 4 September 2008;
`10.1126/science.1164382
`Include this information when citing this paper.
`
`Fig. 1. Structure of the active site of IDH1. The crystal
`structure of the human cytosolic NADP(+)–dependent IDH is
`shown in ribbon format (PDBID: 1T0L) (44). The active cleft
`of IDH1 consists of a NADP-binding site and the isocitrate-
`meta