Loading…

Flash Player 9 (or above) is needed to view presentations.
We have detected that you do not have it on your computer. To install it, go here.

Like this presentation? Why not share!

TCGA Glioblastoma Pilot Project: Initial Report

on

  • 617 views

 

Statistics

Views

Total Views
617
Slideshare-icon Views on SlideShare
617
Embed Views
0

Actions

Likes
0
Downloads
7
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • All major cancer types?
  • Making it possible to do many more genes/cancers
  • Making it possible to do many more genes/cancers
  • MYCN: amplified in 1 sample, but doesn’t reach statistical significance in this sample size. Not amplified in these 43: PIK3CA, the broad gains on 8q (including MYC), or CCND2
  • Point: Operational excellence can sometimes be the enemy of flexibility, but we cannot allow ourselves to become too rigid.
  • Making it possible to do many more genes/cancers

TCGA Glioblastoma Pilot Project: Initial Report TCGA Glioblastoma Pilot Project: Initial Report Presentation Transcript

  • 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 Integrate Genomic Alterations copy-number alterations translocations gene expression coding mutations methylation
  • 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
    • Can samples of adequate quality and quantity be assembled?
    • Can high-quality, high-throughput data be generated with current platforms?
    • How sensitive, specific and comparable are current platforms?
    • How can diverse data sets be integrated -- and what can be learned from integration?
    • Can recurrent events be distinguished from random background noise?
    • Can we identify new genes associated with cancer types?
    • Can we identify new subtypes of cancer?
    • Does new knowledge suggest therapeutic implications?
    • 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
    • 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 Sequencing Broad, WU, Baylor SNP 6.0 Copy Number HTA RNA Expression aCGH Copy Number Exon Array RNA Expression aCGH Copy Number GoldenGate Methylation Infinium Copy Number 2 color arrays RNA, miRNA Expression PCR >ABI Somatic Mutations Glioblastoma samples Cross-platform Data Integration, Comparison
  • Cancer genomes are complex Individual cancer genomes Integrate across many samples New analytical methods needed
  • Presentations Cameron 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 Event counting n=141 n=37 n=178 Little overlap in early studies Include only some known genes 208 97 144 Statistical significance 27 26 24 Many fewer events Highly concordant Includes all known genes
  • Amplifications Deletions Copy-number alterations: ~30 in GBM
  • Coding mutations: Assessing statistical significance 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 Total across S samples total bases
  • Significantly mutated genes in GBM 600 genes x 86 GBM (non-hypermutated)
  • Integrated analysis defines four subtypes in GBM •  Copy-number alteration •  RNA Expression • DNA sequencing •  Methylation ProNeural Normal-like Mesenchymal EGFR
  • Pathway Analysis in GBM EGFR ERBB2 PI-3K Class I PDGFRA MET mutation, amplification in 45% mutation in 7% amplification In 13% amplification in 4% RAS NF-1 AKT FOXO PTEN Proliferation Survival Translation Activated oncogenes MDM4 TP53 MDM2 CDKN2A (ARF) RB1 RTK/RAS/PI-3K signaling network 86% P53 signaling 86% Senescence Apoptosis CDK4 CDKN2A (P16/INK4A) CDKN2B CDKN2C G1/S progression homozygous deletion in 51% RB signaling 77% homozygous deletion in 47% homozygous deletion in 2% homozygous deletion 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% mutation in 15% mutation, homozygous deletion in 17% mutation, homozygous deletion in 36% CDK6 CCND2 amplification in 1% amplification in 2%
  • 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
    • Can samples of adequate quality and quantity be assembled?
    • Can high-quality, high-throughput data be generated with current platforms?
    • How sensitive, specific and comparable are current platforms?
    • How can diverse data sets be integrated -- and what can be learned from integration?
    • Can recurrent events be distinguished from random background noise?
    • Can we identify new genes associated with cancer types?
    • Can we identify new subtypes of cancer?
    • Does new knowledge suggest therapeutic implications?
    • Can a network project drive technology progress in cancer?
  •