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标题 "03_complexheatmap.7z" 暗示了这个压缩包可能包含与复杂热图（ComplexHeatmap）相关的材料。在这个上下文中，ComplexHeatmap 是一个 R 语言的包，用于绘制复杂的热图，它允许用户在单个图形中展示多层次的数据，并具有高度自定义的功能。描述中的信息没有提供额外的技术细节，但我们可以推测，这个压缩包可能包含了至少以下几个部分： 1. **PIIS1535610812003649.pdf**：这看起来像是一个学术论文或研究报告，可能详细介绍了复杂热图在生物信息学或数据分析中的应用。论文通常会包含理论背景、方法描述以及案例研究，是理解 ComplexHeatmap 包如何使用的宝贵资源。 2. **01_complexheatmap.R**：这是一个 R 脚本文件，很可能包含了使用 ComplexHeatmap 包创建热图的示例代码。通过阅读和运行这个脚本，我们可以学习如何加载数据、调整参数以及自定义热图的样式。 3. **.Rhistory**：这是 R 的历史记录文件，保存了用户在 R 会话中执行过的命令。尽管可能不是特别结构化，但它可以提供额外的代码示例和使用 ComplexHeatmap 的上下文。 4. **correction.Rproj**：这是一个 R 项目文件，用于组织 R 工作环境。它可能包含了项目的设置、工作目录以及依赖包的信息，这对于复现分析过程和管理代码版本非常有用。 5. **data**：这是一个文件夹，可能包含了用于创建热图的数据集。这些数据可能是模拟的，也可能是来自实际研究的，用于演示 ComplexHeatmap 包的功能。综合以上内容，这个压缩包为学习和实践 ComplexHeatmap 提供了一个全面的资源集合。用户可以通过阅读论文理解其背景和应用场景，通过 R 脚本学习编程技巧，利用 .Rhistory 文件获取额外的灵感，用 R 项目文件组织自己的工作流程，并借助数据进行实践操作。在学习过程中，用户应该重点关注数据预处理、热图布局设计、颜色映射、注释添加以及与其他 R 包的集成等方面，以全面掌握 ComplexHeatmap 的使用。

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Cancer Cell

Article

Hotspot Mutations in H3F3A

and IDH1 Deﬁne Distinct Epigenetic

and Biological Subgroups of Glioblastoma

Dominik Sturm,

1,42

Hendrik Witt,

1,7,42

Volker Hovestadt,

2,42

Dong-Anh Khuong-Quang,

11,42

David T.W. Jones,

Carolin Konermann,

Elke Pfaff,

Martje To

njes,

Martin Sill,

Sebastian Bender,

Marcel Kool,

Marc Zapatka,

Natalia Becker,

Manuela Zucknick,

Thomas Hielscher,

Xiao-Yang Liu,

Adam M. Fontebasso,

Marina Ryzhova,

Steffen Albrecht,

Karine Jacob,

Marietta Wolter,

Martin Ebinger,

Martin U. Schuhmann,

Timothy van Meter,

Michael C. Fru¨ hwald,

Holger Hauch,

Arnulf Pekrun,

Bernhard Radlwimmer,

Tim Niehues,

Gregor von Komorowski,

Matthias Du¨ rken,

Andreas E. Kulozik,

Jenny Madden,

Andrew Donson,

Nicholas K. Foreman,

Rachid Drissi,

Maryam Fouladi,

Wolfram Scheurlen,

Andreas von Deimling,

5,9

Camelia Monoranu,

Wolfgang Roggendorf,

Christel Herold-Mende,

Andreas Unterberg,

Christof M. Kramm,

rg Felsberg,

Christian Hartmann,

Benedikt Wiestler,

Wolfgang Wick,

Till Milde,

6,7

Olaf Witt,

6,7

Anders M. Lindroth,

Jeremy Schwartzentruber,

Damien Faury,

Adam Fleming,

Magdalena Zakrzewska,

Pawel P. Liberski,

Krzysztof Zakrzewski,

Peter Hauser,

Miklos Garami,

Almos Klekner,

Laszlo Bognar,

Sorana Morrissy,

Florence Cavalli,

Michael D. Taylor,

Peter van Sluis,

Jan Koster,

Rogier Versteeg,

Richard Volckmann,

Tom Mikkelsen,

Kenneth Aldape,

Guido Reifenberger,

V. Peter Collins,

Jacek Majewski,

Andrey Korshunov,

Peter Lichter,

Christoph Plass,

3,41,

Nada Jabado,

11,41,

and Stefan M. Pﬁster

1,7,41,

Division of Pediatric Neurooncology

Division of Molecular Genetics

Division of Epigenetics and Cancer Risk Factors

Division of Biostatistics

Clinical Cooperation Unit Neuropathology

Clinical Cooperation Unit Pediatric Oncology

German Cancer Research Center (DKFZ) Heidelberg, 69120 Heidelberg, Germany

Department of Pediatric Oncology, Hematology, and Immunology

Department of Neurosurgery

Department of Neuropathology

Department of Neurooncology

Heidelberg University Hospital, 69120 Heidelberg, Germany

Departments of Pediatrics and Human Genetics, McGill University

Division of Experimental Medicine, Montreal Children’s Hospital

McGill University Health Center Research Institute, Montreal, QC H3Z 2Z3, Canada

NN Burdenko Neurosurgical Institute, Moscow, 125047, Russia

Department of Pathology, Montreal Children’s Hospital, McGill University Health Center Research Institute, Montreal, QC H3H 1P3, Canada

Department of Neuropathology, Heinrich-Heine-University, 40225 Du

sseldorf, Germany

Department of Hematology and Oncology, Children’s University Hospital Tu

bingen, 72076 Tu

bingen, Germany

Department of Neurosurgery, University Hospital Tu

bingen, 76076 Tu

bingen, Germany

Virginia Commonwealth University, Richmond, VA 23298, USA

Pediatric Hospital, Klinikum Augsburg, 86156 Augsburg, Germany

Pediatric Hospital, Klinikum Heilbronn, 74078 Heilbronn, Germany

Prof.-Hess-Kinderklinik, Klinikum Bremen-Mitte, 28177 Bremen, Germany

Children’s Hospital, Helios Clinics, 47805 Krefeld, Germany

Children’s University Hospital, 68135 Mannheim, Germany

Department of Pediatrics, University of Colorado Denver, Aurora, CO 80045, USA

Division of Oncology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA

Cnopf’sche Kinderklinik, Nu

rnberg Children’s Hospital, 90419 Nu

rnberg, Germany

Department of Neuropathology, Institute of Pathology, University Wu

rzburg, 97080 Wu

rzburg, Germany

University Children’s Hospital, Martin Luther University Halle-Wittenberg, 06120 Halle, Germany

Institute of Pathology, Department of Neuropathology, Hannover Medical School, 30175 Hannover, Germany

McGill University and Genome Quebec Innovation Centre, Montreal, QC H3A 1A4, Canada

Department of Molecular Pathology and Neuropathology, Medical University of Lodz 92-216 Poland

Department of Neurosurgery, Polish Mother’s Memorial Hospital Research Institute, Lodz 93-338 Poland

Department of Paediatrics, Semmelweis University, Budapest H-1094 Hungary

Department of Neurosurgery, Medical and Health Science Center, University of Debrecen, H-4032 Debrecen, Hungary

Program in Developmental and Stem Cell Biology, Division of Neurosurgery, Arthur and Sonia Labatt Brain Tumour Research Centre,

Hospital for Sick Children, University of Toronto, Toronto, ON M4N 1X8, Canada

Department of Oncogenomics, AMC, University of Amsterdam, Amsterdam 1105 AZ, The Netherlands

Departments of Neurology and Neurosurgery, Henry Ford Hospital, Detroit, MI 48202, USA

Department of Neuro-Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

Division of Molecular Histopathology, Department of Pathology, University of Cambridge, Cambridge, CB2 0QQ, United Kingdom

Cancer Cell 22, 425–437, October 16, 2012 ª2012 Elsevier Inc. 425

Department of Human Genetics, McGill University, Montreal, QC H3Z 2Z3, Canada

These authors contributed equally to this work

*Correspondence: c.plass@dkfz.de (C.P.), nada.jabado@mcgill.ca (N.J.), s.pﬁster@dkfz.de (S.M.P.)

http://dx.doi.org/10.1016/j.ccr.2012.08.024

SUMMARY

Glioblastoma (GBM) is a brain tumor that carries a dismal prognosis and displays considerable heteroge-

neity. We have recently identiﬁed recurrent H3F3A mutations affecting two critical amino acids (K27 and

G34) of histone H3.3 in one-third of pediatric GBM. Here, we show that each H3F3A mutation deﬁnes an

epigenetic subgrou p of GBM with a distinct global methylation pattern, and that they are mutually exclusive

with IDH1 mutations, which characteriz e a third mutation-deﬁned subgroup. Three further epigenetic

subgroups were enr iched for hallmark genetic events of adult GBM and/or established transcriptomic signa-

tures. We also demonstrate that the two H3F3A mutations give rise to GBMs in separate anatomic compart-

ments, with differential regulation of transcription factors OLIG1, OLIG2, and FOXG1, possibly reﬂecting

different cellular origins.

INTRODUCTION

Glioblastoma (GBM; World Health Organization [WHO] grade IV),

the most common primary brain tumor, carries a universally

dismal prognosis in children and adults (Louis et al., 2007).

With evidence emerging recently of age-speciﬁc molecular and

genetic differences, it is now becoming apparent that pediatric

GBM is largely biologically distinct from adult GBM. Based on

similarities in recurrent genomic aberrations (Bax et al., 2010;

McLendon et al., 2008; Paugh et al., 2010; Qu et al., 2010; Schiff-

man et al., 2010; Zarghooni et al., 2010), it was long thought

that pediatric GBMs more closely resembled adult ‘‘secondary’’

GBM, which arise from a preceding lower-grade lesion. How-

ever, stepwise transformation from less-malignant gliomas into

GBM rarely occurs in children (Broniscer et al., 2007). Further-

more, IDH1 or IDH2 mutations, which are found in up to 98%

of adult secondary GBM, are very rare in childhood GBM

(<10%) (Antonelli et al., 2010; Balss et al., 2008; De Carli et al.,

2009; Paugh et al., 2010; Pollack et al., 2011; Schiffman et al.,

2010; Setty et al., 2010; Yan et al., 2009).

We recently identiﬁed two recurrent somatic mutations in the

H3F3A gene, affecting highly conserved residues of its encoded

protein, the replication-independent histone 3 variant H3.3, in

one-third of pediatric GBMs ( Schwartzentruber et al., 2012).

Mutations in a protein complex comprised of H3.3 and ATRX/

DAXX were detected in 45% of cases, and were shown to be

associated with TP53 mutations and alternative lengthening of

telomeres (ALT). The H3.3 mutations result in amino acid sub-

stitutions at K27 or G34—at or near residues targeted by key

post-translational modiﬁcations that regulate H3.3’s activity in

governing gene expression (Hyland et al., 2011)—and were

shown to be linked to distinct transcriptional proﬁles (Schwart-

zentruber et al., 2012). Methylation of K27 and K36 is also disrup-

ted by elevated levels of the onco-metabolite 2-hydroxyglutarate

(2-HG) resulting from gain-of-function mutations in IDH1

(Chowdhury et al., 2011; Xu et al., 2011), which was previously

shown to be associated with a distinct Glioma-CpG-Island

Methylator Phenotype (G-CIMP) (Noushmehr et al., 2010).

In the present study, we further investigate the heterogeneity

of glioblastoma across the entire age spectrum, and elucidate

the impact of H3F3A mutations on the GBM epigenome.

RESULTS

Integrated Molecular Classiﬁcation of Glioblastoma

We used an integrative approach based on epigenetic, copy-

number, expression, and genetic analyses to investigate the

heterogeneity of glioblastoma across all age groups. An over-

view of all GBM samples subjected to various analyses is given

in Figure S1A available online.

Signiﬁcance

GBM is the most common and also the most devastating brain tumor, with a 5-year survival rate below 10%. We present

strong evidence that GBM can be subclassiﬁed into multiple groups, indistinguishable by histological appearance, but

correlating with molecular-genetic factors as well as key clinical variables such as patient age and tumor location. We iden-

tiﬁed six epigenetic GBM subgroups displaying characteristic global DNA methylation patterns, harboring distinct hotspot

mutations, DNA copy-number alterations, and transcriptomic patterns. These ﬁndings may guide the identiﬁcation of inno-

vative subgroup-speciﬁc treatments, e.g., targeted epigenetic therapies for H3.3-mutated variants, and improve the design

of future clinical trials. Our study enables classiﬁcation of GBM across the entire age continuum into biologically meaningful

subgroups carrying clinical implications.

Cancer Cell

Epigenetic and Biological Subgroups of Glioblastoma

426 Cancer Cell 22, 425–437, October 16, 2012 ª2012 Elsevier Inc.

We investigated a cohort of GBMs from children (n = 59) and

adult patients (n = 77) for genome-wide DNA methylation

patterns using the Illumina 450k methylation array, and comple-

mented our data with unpublished proﬁles of 74 adult GBM

samples generated by The Cancer Genome Atlas (TCGA)

Consortium (McLendon et al., 2008)(Table S1). Consensus

clustering using the 8,000 most variant probes across the data

set robustly identiﬁed six distinct DNA methylation clusters

(Figures 1 and S1B). Based on correlations with mutational

status, DNA copy-number aberrations, and gene expression

signatures, as outlined below, we have labeled these subgroups

‘‘IDH,’’ ‘‘K27,’’ ‘‘G34,’’ ‘‘RTK I (PDGFRA),’’ ‘‘Mesenchymal,’’ and

‘‘RTK II (Classic).’’

A striking ﬁnding of this integrated analysis is that H3F3A K27

and G34 mutations were exclusively distributed to the K27

(18/18) and G34 (18/18) clusters, respectively (p < 0.001; Fisher’s

exact test) (Figure 1). The IDH group contained 88% of IDH1-

mutated tumors (23/26) (p < 0.001) and displayed concerted,

global hypermethylation (Figures 1, 2A, and 2B), thereby ex-

panding the previously described link between IDH1 mutation

and G-CIMP

tumors to a pediatric setting (Noushmehr et al.,

2010). In contrast, tumors in the G34 cluster speciﬁcally showed

widespread hypomethylation across the whole genome, and

especially in nonpromoter regions, when compared with all other

subgroups (Figures 2A and 2B). This suggests the existence of

a more global version of a CpG hypomethylator phenotype

(CHOP), as proposed for a small number of genes in gastric

cancer (Kaneda et al., 2002). More detailed mapping of differen-

tially methylated regions revealed that the hypomethylation

observed in H3F3A G34-mutated tumors was particularly prom-

inent at chromosome ends ( Figures 2C and 2D), potentially link-

ing subtelomeric demethylation to alternative lengthening of

telomeres, which is most frequently observed in this subgroup

(Schwartzentruber et al., 2012).

Of note, all mutations in H3F3A and IDH1 were mutually exclu-

sive (p < 0.001) ( Figure 1). To further test this observation, we

GBM samples (n = 210)

●

●●

●

●●

●

●●

●

●●

●

●●

●

●●

●

●●

●

Age

PDGFRA ampl.

EGFR ampl.

CDKN2A del.

Chr. 10 loss

Chr. 7 gain

TP53

H3F3A

IDH1

TCGA Expression

TCGA Methylation

Methylation cluster

Color scale

(beta-values)

0.5

G34R/V

MUT

K27M

Controls

MutationsCytogenetics

IDH K27 G34 RTK I 'PDGFRA' Mesenchymal RTK II 'Classic'

DNA methylation probes (n = 8,000)

G-CIMP

Cluster #2

Cluster #3

Proneural

Neural

Classical

Mesenchymal

TCGA subgroups

MethylationExpression

p < 0.001

Fetal normal brain (n=4)

Adult normal brain (n=2)

WGA-DNA (n=2)

M.SssI-DNA (n=2)

Unmethylated Methylated

Figure 1. Methylation Proﬁling Reveals the Existence of Six Epigenetic GBM Subgroups

Heatmap of methylation levels in six GBM subgroups identiﬁed by unsupe rvised k-means consensus clustering, and control samples as indicated. Each row

represents a probe; each column represents a sample. The level of DNA methylation (beta-value) is represented with a color scale as depicted. For each sample

(n = 210), patient age, subgroup association, mutational status, and cytogenetic aberrations are indicated.

See also Figure S1 and Tables S1 and S2.

Cancer Cell

Epigenetic and Biological Subgroups of Glioblastoma

Cancer Cell 22, 425–437, October 16, 2012 ª2012 Elsevier Inc. 427

0.2

0.4

0.0e+00 5.0e+06 1.0e+07 1.5e+07 2.0e+07 2.5e+07 3.0e+07

-0.04

0.00

0.04

Distance to chromosome end [bp]

Number of probes

(x 1,000)

Normalized DNA Methylation

Fraction CpG Island

G34

IDH

K27

RTK I

MES

RTK II

C D

***

IDH

K27

G34

RTK I

MES

RTK II

Control

0.35

0.40

0.45

0.50

0.55

0.60

Mean methylation (mean beta−value)

***

IDH

K27

G34

RTK I

MES

RTK II

Control

0.20

0.25

0.30

0.35

***

IDH

K27

G34

RTK I

MES

RTK II

Control

0.45

0.50

0.55

0.60

0.65

0.70

0.75

***

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Overall

Probabilty Fn(probes)

Hypomethylation

Hypermethylation

DNA methylation (beta-value)

0.0 0.2 0.4 0.6 0.8 1.0

Non-promoter

0.0

0.2

0.4

0.6

0.8

1.0

DNA methylation (beta-value)

K27

IDH

G34

RTK I

MES

RTK II

Control

0.0 0.2 0.4 0.6 0.8 1.0

Promoter

0.0

0.2

0.4

0.6

0.8

1.0

DNA methylation (beta-value)

K27

IDH

G34

RTK I

MES

RTK II

Control

-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

Chromosome end (4Mb)

IDH

K27

G34

RTK I

MES

RTK II

***

Mean methylation (mean beta−value)

Figure 2. Global DNA Methylation Patterns in GBM Subgroups

(A) Distinct patterns of global DNA methylation in GBM subgroups as identiﬁed by consensus clustering. The empirical cumulative distribution function for DNA

methylation levels (beta-values) is plotted individually for each subgroup.

(B) Overall DNA methylation levels (mean beta-values) of individual GBM methylation subgroups. Signiﬁcant differences (***p < 0.001; **p < 0.01; *p < 0.05) to IDH

and G34 subgroups are indicated.

(C) Upper panel: Probe density in respect of distance to chromosome end. The fraction of probes located within CpG-Islands (red line) remains stable. Lower

panel: Mean methylation value per subgroup within windows of 500kb, normalized to control samples. Individual samples are normalized by the mean overall

methylation value.

(D) Mean methylation value within 4 Mb to the chromo some end normalized to the mean overall methylation value and to control samples. Signiﬁcant differences