EBI Industry programme TCGA Warren KIbbe November 2013
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This document discusses strategic objectives and activities of the National Cancer Institute's Center for Biomedical Informatics and Information Technology (NCI CBIIT). The key objectives are to reduce cancer risk, improve cancer outcomes, provide cancer information to the public, and enable precision oncology through data access and modeling. Specific activities mentioned include the Genomic Data Commons, cloud computing initiatives, clinical trials repositories, and the Tumor Genome Atlas project. TCGA has collected over 700 terabytes of genomic and clinical data on 20+ cancer types to date. The data provides a platform for understanding cancer drivers, molecular subtypes of cancers, and the implications of data sharing policies.
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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#