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TCGA Glioblastoma Pilot Project: Initial Report
June 2008
NCI-NHGRI Partnership
Cancer Genomics: Ultimate Goal
Create comprehensive public catalog
of all genomic alterations present at significant frequency
for all major cancer types NCAB Report Feb 2005
Genomic Alterationscopy-number alterationstranslocationsgene expressioncoding mutations methylation
Integrate
NCI-NHGRI Partnership
TCGA Pilot Project Launched, 4Q2006
Cancers: • Glioblastoma multiforme• Squamous cell lung cancer• Ovarian cancer (serous cystadenomacarcinoma)
Goal: • Assemble high-quality samples of each type
• Characterize tumor genome by various approaches
• Rapidly share data with scientific community
• Compare and improve technologies
• Integrate and analyze data to illuminate genetic basis of cancers
Key questions posed at start of project
1. Can samples of adequate quality and quantity be assembled?
2. Can high-quality, high-throughput data be generated with current platforms?
3. How sensitive, specific and comparable are current platforms?
4. How can diverse data sets be integrated -- and what can be learned from integration?
5. Can recurrent events be distinguished from random background noise?
6. Can we identify new genes associated with cancer types?
7. Can we identify new subtypes of cancer?
8. Does new knowledge suggest therapeutic implications?
9. Can a network project drive technology progress in cancer?
TCGA Components
(1) >80% tumor cell content
(2) <40% necrosis
(3) Matched normal DNA sample
(4) Clinical annotation
(5) Appropriate informed consent
Current collection > 200 high quality tumors
GBM Samples: Strict Criteria
TCGA GBM: Center Overview
Broad/DFCI
Harvard LBNL MSKCC JHU/USC Stanford UNC SequencingBroad, WU, Baylor
SNP 6.0
Copy Number
HTA
RNA Expression
aCGH
Copy Number
Exon Array
RNAExpression
aCGH
Copy Number
GoldenGate
Methylation
Infinium
Copy Number
2 color arrays
RNA, miRNAExpression
PCR >ABI
SomaticMutations
Glioblastoma samples
Cross-platform Data Integration, Comparison
Cancer genomes are complex
Individual cancer genomes Integrate across many samples
New analytical methods needed
PresentationsCameron Brennan GBM and Genome Characterization
Stephen Baylin The GBM Epigenome
Rick Wilson Identifying mutations in GBM and
application of next gen sequencing technologies
Charles Perou The Challenge of Integrative Analysis
Eric Lander Summary and Discussion
Copy-number alterations: Discordance in initial studies
208 97
144
Event counting
n=141
n=37n=178
Little overlap in early studiesInclude only some known genes
Statistical significance
27
26 24
Many fewer eventsHighly concordantIncludes all known genes
AmplificationsDeletions
Copy-number alterations: ~30 in GBM
Coding mutations: Assessing statistical significance
Total across S samples
total bases
Random mutations: 2 x 10-6 per base 0.3% per typical coding region
Cancer-related mutations: Aim to detect 3-5% per typical coding region
Significantly mutated genes in GBM
600 genes x 86 GBM (non-hypermutated)
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
ProNeural Normal-like MesenchymalEGFR
Integrated analysis defines four subtypes in GBM
• Copy-number alteration
• RNA Expression
• DNA sequencing
• Methylation
EGFR ERBB2
PI-3KClass I
PI-3KClass I
PDGFRA MET
mutation, amplificationin 45%
mutationin 7%
amplificationIn 13%
amplificationin 4%
RASRASNF-1NF-1
AKTAKT
FOXOFOXO
PTENPTEN
ProliferationSurvival
Translation
Activated oncogenes
MDM4MDM4
TP53TP53
MDM2MDM2
CDKN2A(ARF)
CDKN2A(ARF)
RB1RB1
RTK/RAS/PI-3Ksignaling network
86%
RTK/RAS/PI-3Ksignaling network
86%
P53signaling
86%
P53signaling
86%
Senescence Apoptosis
CDK4CDK4
CDKN2A(P16/INK4A)
CDKN2A(P16/INK4A) CDKN2BCDKN2B CDKN2CCDKN2C
G1/S progression
homozygousdeletion in 51%
RBsignaling
77%
RBsignaling
77%
homozygousdeletion in 47%
homozygousdeletion in 2%
homozygousdeletion in 49%
amplification in 14%
amplification in 6%
mutation, homozygous
deletion in 35%
amplification in 17%
homozygous deletion,mutation in 11%
mutation in 2%
amplification in 2%
mutation in 2% mutationin 15%
mutation, homozygousdeletion in 17%
mutation, homozygousdeletion in 36%
CDK6CDK6CCND2CCND2 amplification in 1%
amplification in 2%
Pathway Analysis in GBM
454 Solexa, ABI SOLiD
~200bp
400 K / run
400 Mb
30+bp reads
~40 M / run
8-12 Gb
~700 bp
~100/run
70 K
ABI 3730XL
Next-generation sequencing technology
Read length Read number
Total bases
+
others
Costs decreasing rapidly
Key questions posed at start of project
1. Can samples of adequate quality and quantity be assembled?
2. Can high-quality, high-throughput data be generated with current platforms?
3. How sensitive, specific and comparable are current platforms?
4. How can diverse data sets be integrated -- and what can be learned from integration?
5. Can recurrent events be distinguished from random background noise?
6. Can we identify new genes associated with cancer types?
7. Can we identify new subtypes of cancer?
8. Does new knowledge suggest therapeutic implications?
9. Can a network project drive technology progress in cancer?
