EBI Industry programme TCGA Warren KIbbe November 2013
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Presentation to the EBI Industry Programme to highlight the TCGA project at the NCI
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EBI Industry programme TCGA Warren KIbbe November 2013
- 1. NCI CBIIT Re-engaged Warren Kibbe Warren.kibbe@nih.gov 240-276-7300 The views expressed are my own and not a reflection of DHHS or NCI policy
- 2. General strategic objectives • Reduce cancer risk • Improve cancer outcomes • Education and dissemination of information • Provide informative data and powerful examples
- 3. Broad strategic activities • Understand social media as a mechanism for communication, education, and improving lifestyle choices • Work productively with patient advocates • Understand risk factors leading to cancer • Model cancer initiation and progression • Enable precision oncology • Help build learning healthcare systems
- 4. Informatics strategic objectives • Lower barriers to data access, analysis and modeling • Promote agility, flexibility, data liquidity • Promote Open Access, Open Data, Open Source, Open Science • Promote semantic interoperability, standards, CDEs and Case Report Forms
- 5. Informatics strategic objectives • Promote mobile and BYOD for patient reported outcomes, education, surveillance, eligibility … • Use informatics to improve and lower barriers to clinical trials accrual • Use informatics to blur the distinction between care and research – support clinical standards in research • Identify and disseminate innovations and practices that make research more efficient and effective
- 6. A few specific activities • • • • • • Genomic Data Commons Cloud Pilot EVS, NCI Thesaurus, NCI Metathesaurus CDEs, Case Report Forms MPACT, MATCH, Exceptional Responders Integrated informatics for the cooperative groups • FDA Clinical Trials Repository – Janus – Collaboration between the FDA and NCI • RAS Initiative – hub at NCI Frederick
- 7. TCGA history • Initiated in 2005 • Collaboration of NHGRI and NCI to examine GBM, Lung and Ovarian cancer using genomic techniques in 2006. • Expanded to 20+ tumor types.
- 8. TCGA snapshot • Data collection will complete in Q3 2014 • As of October 2013, 700TB of data has been collated and integrated. • Anticipates 2.5 PB of data as of the end of Q3 2014 • Some tumor types are complete, others nearly complete, and still others are just getting to the point of submission
- 9. TCGA snapshot • Today there is a standardized analysis pipeline with standardized protocols • Today there is standardized consent and consenting process • Today there is a standardized data access policy
- 10. TCGA drivers • Providing high quality reference sets for 20+ tissue types • Providing a platform for systems biology and hypothesis generation • Providing a test bed for understanding the real world implications of consent and data access policies on genomic and clinical data.
- 11. Focus on the TCGA • The TCGA consortium slides
- 12. TCGA – Lessons from structural genomics# Jean Claude Zenklusen, Ph.D. Director TCGA Program Office National Cancer Institute
- 13. Tumor Project Progress 1200 Accepting AA cases only Goal of 500 reached 1000 Manuscript submitted or published Analysis underway 800 Sample acquisition phase ® Rare tumor project 600 400 200 0 13 ® ® ® ® ® ® ® ®
- 14. The Mutational Burden of Human Cancer# Childhood# cancers# Carcinogens# Increasing genomic# complexity# Mike Lawrence and Gaddy Getz
- 16. Frequent Activation of the PI(3)K Pathway in# Clear Cell Renal Carcinoma# PI(3)K aberrations (28% of cases)# Response of RCC# To Everolimus# Placebo# mTOR mutations# Everolimus# Progression-free survival# (months)# TCGA Nature 499:45 (2013)# Sato et al Nat Gen 45:860 (2013)# Hakimi et al Nat Gen 45:849 (2013)# Motzer et al Lancet 372:449 (2008)#
- 18. Four Molecular Subgroups of Endometrial Cancer# Defined by Integrative Analysis# POLE# (ultra-# mutated)# MSI# (hypermutated)# Copy-number low# (endometriod)# Copy-number high# (serous-like)# Mutations# Per Mb# PolE# MSI / MSH2# Copy ## PTEN# p53# Histology# TCGA Nature 497:67 (2013)#
- 19. Molecular Subgroups Refine Histological Diagnosis# Of Endometrial Carcinoma# POLE# (ultra-# mutated)# MSI# (hypermutated)# Copy-number low# (endometriod)# Copy-number high# (serous-like)# Mutations# Per Mb# PolE# MSI / MSH2# Copy ## PTEN# p53# Histology# Histology# Endometrioid# Serous# Serous# misdiagnosed# as endometrioid?# TCGA Nature 497:67 (2013)#
- 20. Molecular Diagnosis of Endometrial Cancer May# Influence Choice of Therapy# POLE# (ultra-# mutated)# MSI# (hypermutated)# Copy-number low# (endometriod)# Copy-number high# (serous-like)# Mutations# Per Mb# PolE# MSI / MSH2# Copy ## PTEN# p53# Histology# Surgery only?# Adjuvant# radiotherapy?# Adjuvant# chemotherapy?# TCGA Nature 497:67 (2013)#
- 21. NCI Cancer Genomics Data Commons Functionality# ... Genomic +# clinical data# GDC! NCI Genomics# Data Commons#
- 22. NCI Cancer Genomics Data Commons Functionality# ... Genomic +# clinical data# GDC! Cancer# information# donor# NCI Genomics# Data Commons#
- 24. ICGC BAM/FASTQ ICGC Open Data (includes TCGA Open Data) COSMIC Open Data TCGA BAM/FASTQ
- 25. Relationship of the Cancer Genomics Data Commons and NCI Clouds # Periodic Data Freezes GDC! NCI Cloud Computational Centers# NCI Genomics# Data Commons# Analysis Search / retrieve
- 26. Cancer Genomics Cloud Pilots
- 27. Essential Functions of a Genomics Data Commons# v v v Perform data quality control# Harmonize primary data across studies = realign all primary sequence data to the reference genome# Provide “gold standard” derived data: = mutations / copy number / digital gene expression #
- 28. Essential Functions of a Genomics Data Commons# v v v v Perform data quality control# Harmonize primary data across studies = realign all primary sequence data to the reference genome# Provide “gold standard” derived data: = mutations / copy number / digital gene expression # Permit integrative analysis across data types# Copy # gain# Copy # loss# Overexpressed# Under expressed# Mutated# Cancer Genome Diagnostic Report Jones et al. Genome Biol. 2010;11(8):R82.
- 29. Essential Functions of a Genomics Data Commons# v v v v v Perform data quality control# Harmonize primary data across studies = realign all primary sequence data to the reference genome# Provide “gold standard” derived data: = mutations / copy number / digital gene expression # Permit integrative analysis across data types# Enable integrative analysis across all cancer samples# TCGA PanCan Working Group# Giovanni Ciriello# Nikloaus Schultz# Chris Sander#
- 30. Utility of a Cancer Knowledge Base# Cancer# information# donor# Identify# low-frequency# cancer drivers# GDC! Define genomic# Compose clinical trial# determinants of response# cohorts sharing# to therapy# Targeted genetic lesions#