TCGA Glioblastoma Pilot Project: Initial Report

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  • 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

    1. 1. TCGA Glioblastoma Pilot Project: Initial Report June 2008
    2. 2. 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 Alterations copy-number alterations translocations gene expression coding mutations methylation Integrate
    3. 3. 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
    4. 4. 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?
    5. 5. TCGA Components
    6. 6. (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
    7. 7. 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
    8. 8. Cancer genomes are complex Individual cancer genomes Integrate across many samples New analytical methods needed
    9. 9. 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
    10. 10. Copy-number alterations: Discordance in initial studies 208 97 144 Event counting n=141 n=37 n=178 Little overlap in early studies Include only some known genes Statistical significance 27 26 24 Many fewer events Highly concordant Includes all known genes
    11. 11. AmplificationsDeletions Copy-number alterations: ~30 in GBM
    12. 12. 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
    13. 13. Significantly mutated genes in GBM 600 genes x 86 GBM (non-hypermutated)
    14. 14. QuickTime™ and a TIFF (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
    15. 15. EGFR ERBB2 PI-3K Class I PI-3K Class I PDGFRA MET mutation, amplification in 45% mutation in 7% amplification In 13% amplification in 4% RASRASNF-1NF-1 AKTAKT FOXOFOXO PTENPTEN Proliferation Survival Translation Activated oncogenes MDM4MDM4 TP53TP53 MDM2MDM2 CDKN2A (ARF) CDKN2A (ARF) RB1RB1 RTK/RAS/PI-3K signaling network 86% RTK/RAS/PI-3K signaling network 86% P53 signaling 86% P53 signaling 86% Senescence Apoptosis CDK4CDK4 CDKN2A (P16/INK4A) CDKN2A (P16/INK4A) CDKN2BCDKN2B CDKN2CCDKN2C G1/S progression homozygous deletion in 51% RB signaling 77% 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% CDK6CDK6CCND2CCND2 amplification in 1% amplification in 2% Pathway Analysis in GBM
    16. 16. 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
    17. 17. 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?

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