The Cancer Genome Atlas

July 14, 2011

Kenna M. Shaw, Ph.D.Deputy DirectorThe Cancer Genome Atlas Program

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TCGA: Core Objectives

Launched in 2006 as a pilot and expanded in 2009, the goals of TCGA are to:

•Establish the needed infrastructure, environment, community where the big fish swim together

•Develop a scalable “pipeline” beginning with highest quality samples

• Determine the feasibility of a large-scale, high throughput, systematic approach to identifying all of the relevant genetic alterations in cancer

•Systematically evaluate two cancers using a statistically-robust sample set (500 cancers and matched controls)

•Make the data publicly and broadly available to the cancer communities in a manner that protected patient privacy

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TCGA: “No Platform Left Behind”

25 forms of cancer

glioblastoma multiforme(brain)

squamous carcinoma(lung)

serouscystadenocarcinoma

(ovarian)

Etc. Etc. Etc.

Multiple data types

• Clinical diagnosis• Treatment history• Histologic diagnosis• Pathologic report/images• Tissue anatomic site• Surgical history• Gene expression/RNA

sequence• Chromosomal copy

number• Loss of heterozygosity• Methylation patterns• miRNA expression• DNA sequence• RPPA (protein)• Subset for Mass Spec

Biospecimen CoreResource with more

than 150 Tissue Source Sites

6 Cancer GenomicCharacterization

Centers

3 GenomeSequencing

Centers

7 Genome Data Analysis Centers

Data Coordinating Center

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TCGA: Lessons Learned from the Pilot

#1: It’s About the Pathways,

People!

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#2: Comparing Cancer Types is Like Comparing

Apples and Oranges

TCGA: Lessons Learned from the Pilot

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TCGA: Lessons Learned from the Pilot

#3: If you build it [a data portal], they will come.

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Making an Exhaustible Resource Inexhaustible

#4: The model CAN work and we can make it

happen.

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TCGA: Lessons Learned from the Pilot

#4: Slow and steady wins

the race.

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TCGA: Platforms- Then and Now

Platform Pilot Expansion

SNP/CNV Affy SNP 6.0Agilent CGH ArrayIllumina 1M Duo

Affy SNP 6.0Low Pass Sequencing*

Methylation Infinium Array Infinium Array

mRNA Agilent 244K ArrayAffy Human Exon ArrayAffy U133 Array

RNAseq

miRNA Agilent 8 x 15K Array RNAseq

Mutation 600-1000 genes DNAseq: 90% whole exomes

10% whole genomes

*- Not all samples currently receiving low pass sequencing for Copy Number/Rearrangement assays

More information on platforms and data available at: http:/tcga-data.nci.nih.gov/tcga/tcgaPlatformDesign.jsp

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TCGA Tumor Types

• AML• Breast Ductal• Breast Lobular/Breast Other• Bladder (pap and non-pap)• Cervical adeno & squamous• Colon• Clear cell kidney• DLBCL• Endometrial carcinoma• Esophageal adeno & squamous• Gastric adenocarcinoma• GBM• Head and Neck Squamous

• Hepatocellular• Lower Grade Glioma• Lung adeno• Lung squamous• Melanoma• Ovarian serous

cystadenocarcinoma• Papillary kidney• Pancreas• Prostate• Rectal• Sarcoma (dediff lipo, UPS,

leiomyosarcoma)• Thyroid

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Sample Criteria Limit ‘Askable’ Questions

• Primary tumor only (except for melanoma)• Malignant (no in situ cases)• Snap frozen, <60min from clamp to LN2• ~ 50-100 mg (aka no biopsies)• Pathology review of tissue sent to TCGA• No more than 20% necrosis ; ≥ 60% tumor

cells• No prior treatment• Normal tissue: Blood (buffy coat/white cells);

some adjacent normal tissue allowable but limited

• Clinical annotation• IRB approval for use in TCGA

10,000

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Tumor Project Progress

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Race & Ethnicity Data Summary

•Need to collaborate with biobanks that serve more diverse communities

•SNP data might be better ‘metric’ for some information due to a) limited success in getting data and b) concerns with self-reported data

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Acknowledgements

• Margi Sheth (Tumor Project Groups)• John Demchok (Clinical Data Quality Manager)• Martin Ferguson (Clinical Informatics)• Julie Gastier-Foster/Robert Penny (BCRs)• TCGA Research Network

Kenna Shaw: shawk@mail.nih.gov

First Annual TCGA Scientific Symposium: http://www.capconcorp.com/meeting/2011/TCGA/default.asp