Nov 2014 ouellette_windsor_icgc_final
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Presentation to the Department of Biology at the University of Windsor, Windsor, Ontario. The description and update of activities related to the International Cancer Genome Consortium (ICGC)
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Nov 2014 ouellette_windsor_icgc_final
- 1. A project status for the International Cancer Genome Consortium (ICGC). November 21th 2014 B.F. Francis Ouellette francis@oicr.on.ca • Senior Scientists & Associate Director, Informatics and Biocomputing, Ontario Institute for Cancer Research, Toronto, ON • Associate Professor, Department of Cell and Systems Biology, University of Toronto, Toronto, ON. @bf fo on
- 2. 2 You are free to: Copy, share, adapt, or re-mix; Photograph, film, or broadcast; Blog, live-blog, or post video of; This presentation. Provided that: You attribute the work to its author and respect the rights and licenses associated with its components. Slide Concept by Cameron Neylon, who has waived all copyright and related or neighbouring rights. This slide only ccZero. Social Media Icons adapted with permission from originals by Christopher Ross. Original images are available under GPL at; http://www.thisismyurl.com/free-downloads/15-free-speech-bubble-icons-for-popular-websites
- 3. 3 But first, a little about me … … an unfinished story!
- 15. from the National Centre for Biotechnology Information 15 (from the National Centre for Biotechnology Information)
- 16. 16 from the National Centre for Biotechnology Information
- 17. 17 from the National Centre for Biotechnology Information PANIC
- 18. 18
- 19. 19 PANIC
- 20. 20 PANIC
- 21. 1998 2001 2004
- 22. 22
- 25. 1999
- 32. 32
- 33. 33 International Cancer Genome Consortium: icgc.org
- 36. 36
- 38. 38 E-mail: course_info@bioinformatics.ca Web: http://bioinformatics.ca
- 39. 39 Cancer A Disease of the Genome Challenge in Treating Cancer: Every tumor is different Every cancer patient is different
- 40. 40 Large-Scale Studies of Cancer Genomes Johns Hopkins > 18,000 genes analyzed for mutations 11 breast and 11 colon tumors L.D. Wood et al, Science, Oct. 2007 Wellcome Trust Sanger Institute 518 genes analyzed for mutations 210 tumors of various types C. Greenman et al, Nature, Mar. 2007 TCGA (NIH) Multiple technologies brain (glioblastoma multiforme), lung (squamous carcinoma), and ovarian (serous cystadenocarcinoma). F.S. Collins & A.D. Barker, Sci. Am, Mar. 2007
- 41. 41 Lessons learned Heterogeneity within and across tumor types High rate of abnormalities (driver vs passenger) Sample quality matters Consent and controlled data access is complicated
- 42. 42 International Cancer Genome Consortium • Collect ~500 tumour/normal pairs from each of 50 different major cancer types; • Comprehensive genome analysis of each T/N pair: – Genome – Transcriptome – Methylome – Clinical data • Make the data available to the research community & public. Identify genome changes …GATTATTCCAGGTAT… …GATTATTGCAGGTAT… …GATTATTGCAGGTAT…
- 43. 43 Rationale for the ICGC • The scope is huge, such that no country can do it all. • Coordinated cancer genome initiatives will reduce duplication of effort for common and easy to acquire tumor samples and and ensure complete studies for many less frequent forms of cancer. • Standardization and uniform quality measures across studies will enable the merging of datasets, increasing power to detect additional targets. • The spectrum of many cancers varies across the world for many tumor types, because of environmental, genetic and other causes. • The ICGC will accelerate the dissemination of genomic and analytical methods across participating sites, and the user community
- 44. 44 International Cancer Genome Consortium (ICGC) Goals • Catalogue genomic abnormalities in tumors in 50 different cancer types and/or subtypes of clinical and societal importance across the globe • Generate complementary catalogues of transcriptomic and epigenomic datasets from the same tumors • Make the data available to research community rapidly with minimal restrictions to accelerate research into the causes and control of cancer 50 tumor types and/or subtypes 500 tumors + 500 controls per subtype 50,000 Human Genome Projects! Nature (2010) 464:993
- 45. 45 ICGC Goals, Structure, Policies & Guidelines http://goo.gl/sPGLQN
- 46. 46 Primary Goal: coordinate efforts to reach goals (50 tumours)
- 48. Primary Goal: be comprehensive 48 http://goo.gl/BE7KH1
- 49. 49 Analysis Data Types • Germline variants (SNPs) • Simple Somatic Mutations (SSM) • Copy Number Alterations (CNA) • Structural Variants (SV) • Gene Expression (micro-arrays and RNASeq) • miRNA Expression (RNASeq) • Epigenomics (Arrays and Methylation) • Splicing Variation (RNASeq) • Protein Expression (Arrays)
- 50. 50 Primary Goal: generate highest quality http://goo.gl/FXCvi9
- 51. 51
- 52. 52 Primary Goal: available to all
- 53. 53 Primary Goal: available to all
- 54. 54 ICGC Controlled Access Datasets • Detailed Phenotype and Outcome data Region of residence Risk factors Examination Surgery Radiation Sample Slide Specific histological features Analyte Aliquot Donor notes • Gene Expression (probe-level data) • Raw genotype calls • Gene-sample identifier links • Genome sequence files ICGC OA Datasets • Cancer Pathology Histologic type or subtype Histologic nuclear grade • Patient/Person Gender, Age range, Vital status, Survival time Relapse type, Status at follow-up • Gene Expression (normalized) • DNA methylation •Computed Copy Number and Loss of Heterozygosity • Newly discovered somatic variants http://goo.gl/w4mrV
- 55. 55 Secondary Goal: coordinate work to benefit productivity http://goo.gl/K5mHC3
- 57. 57 Secondary Goal: disseminate knowledge http://goo.gl/ObcZXy
- 58. 58 ICGC Goals, Structure, Policies & Guidelines http://goo.gl/sPGLQN
- 59. 59 Policy ICGC membership implies compliance with Core Bioethical Elements for samples used in ICGC Cancer Projects: http://goo.gl/TFrCmK http://goo.gl/nYx6YG
- 60. 60 POLICY: The members of the International Cancer Genomics Consortium (ICGC) are committed to the principle of rapid data release to the scientific community. http://goo.gl/TFrCmK
- 61. 61 Publication Policy • The individual research groups in the ICGC are free to publish the results of their own efforts in independent publications at any time (subject, of course, to any policies of any collaborations in which they may be participating).
- 62. 62 Moratorium: http://www.icgc.org/icgc/goals-structure-policies-guidelines/e3-publication-policy
- 64. 64 Where do you find that information? • We actually make it hard to find, but we are working on that! (this is an example of where ICGC would like to do what TCGA does!) • http://cancergenome.nih.gov/publications/publicatio nguidelines
- 65. 65 Where do you find that information? For ICGC data: • Need to find the policy! • http://icgc.org/icgc/goals-structure-policies-guidelines/ e3-publication-policy • Find text: • Find date: in README on FTP file • This is bad, we know it, and we are fixing it! • In doubt, contact us: info@icgc.org
- 66. 66 Policy on Intellectual Property • All ICGC members agree not to make claims to possible IP derived from primary data (including somatic mutations) and to not pursue IP protections that would prevent or block access to or use of any element of ICGC data or conclusions drawn directly from those data. http://goo.gl/TCMXCl
- 67. 67 ICGC Map – May 2014 72 projects launched
- 68. 68 DCC Activities DCC activities are split between two groups: • Software Development – DCC portal – Submission tool • Biocuration (which also includes Content Management) – Data level management – Submitter “handling” – Coordination with secretariat – User support http://dcc.icgc.org/team 68
- 69. 69 Data Validation VaVlaidliadtaiotinon (dictionary) Validation (across fields) Validation (across fields) Validation (across fields) indexing Happy Users http://goo.gl/1EcyR
- 72. 72 ICGC Biocuration • Helping submitters get their data to ICGC • Progress reporting (data audit) • Quality checks (coverage, correctness, etc.) • Helping users get to the data • Validate and check (and recheck) metadata on public repositories • Test and integrate with other public repositories via standard data formats, ontologies. • Documentation, documentation, and more documentation • Training 72
- 73. 73 ICGC datasets to date ICGC Data Portal Cumulative Donor Count for Member Projects 14,000 12,000 10,000 8000 6000 4000 2000 0 Number of Donors Release 7 Release 8 Release 10 Release 9 Release 11 Release 12 Release 14 Release 13 Release 15 Release 16 Release 17
- 74. ICGC dataset version 17 Sept 11th 2014 •Cancer types: 50 •Body sites: 18 •Donors: 12,232 •Specimens: 24, 661 •Simple somatic mutations: 9,871,477 •Mutated genes: 57,526
- 75. 75 Clinical Data Completeness Overall Donor Clinical Data Completeness Donor Tumour stage at diagnosis supplemental Donor relapse type Donor relapse interval Donor Tumour stage at diagnosis Donor Tumour staging system at diagnosis Donor vital status Donor region of residence Disease status last followup Donor interval of last followup Donor diagnosis ICG10 Donor Fields Donor survival time Donor age at last followup Donor age at diagnosis Donor sex Donor ID Average Percentage Completeness
- 76. 76 Clinical Data Completeness Overall Donor Clinical Data Completeness Donor Tumour stage at diagnosis supplemental Donor relapse type Donor relapse interval Donor Tumour stage at diagnosis Donor Tumour staging system at diagnosis Donor vital status Donor region of residence Disease status last followup Donor interval of last followup Donor diagnosis ICG10 Donor Fields Donor survival time Donor age at last followup Donor age at diagnosis Donor sex Donor ID Average Percentage Completeness
- 77. 77 Clinical Data Completeness Overall Specimen Clinical Data Completeness Specimen Biobank ID Specimen donor treatment type other Specimen Biobank Percentage cellularity Tumour Stage Supplemental Tumour Grade Supplemental Level of cellularity Tumour Grade Specimen type other Tumour Stage Tumour Grading System Tumour Stage System Digital Image of Stained Section Specimen available Tumour Histological Type Specimen storage Specimen processing Specimen Interval Specimen donor treatment type Specimen processing other Tumour confirmed Specimen storage other Specimen type Specimen ID Donor ID Specimen Fields 0 20 40 60 80 10 30 50 70 90 100 Average Percentage Completeness
- 78. 78 Clinical Data Completeness Overall Specimen Clinical Data Completeness Specimen Biobank ID Specimen donor treatment type other Specimen Biobank Percentage cellularity Tumour Stage Supplemental Tumour Grade Supplemental Level of cellularity Tumour Grade Specimen type other Tumour Stage Tumour Grading System Tumour Stage System Digital Image of Stained Section Specimen available Tumour Histological Type Specimen storage Specimen processing Specimen Interval Specimen donor treatment type Specimen processing other Tumour confirmed Specimen storage other Specimen type Specimen ID Donor ID Specimen Fields 0 20 40 60 80 10 30 50 70 90 100 Average Percentage Completeness
- 79. 79 DACO ICGC cgHUB EGA TCGA BAM Open Open BA M Germ Line + EGA id BA M BA M ERA
- 80. ICGC BAM/FASTQ TCGA BAM/FASTQ ICGC Open Data (includes TCGA Open Data) COSMIC Open Data
- 81. 81 Raw Data Availability at EGA by Project and Data Type • https://www.ebi.ac.uk/ega/organisations/EGAO00000000024
- 82. 82
- 83. 83
- 84. 84
- 85. 85 Select “Bladder Cancer – China”
- 86. 86 Select “Pancreatic cancer – Canada”
- 87. 87 … But where is the data?
- 88. 88
- 90. 90
- 91. 91
- 92. 92 Highlights of the new portal: dcc.icgc.org • Faceted searches capabilities for variants, genes and donors – Interactive data exploration fast and easy • Mutation aggregation & counts across donors and cancers – # of pancreatic cancers donors with mutation KRAS G12D • Standardized gene consequence across all projects • Genome browser • Data doewnload • Protein domains • Links to repositories
- 93. 93 KRAS search
- 94. 94 • Summary • Cancer type distribution • Other links (Cosmic, Entrez, etc) • Mutation profile in protein • Domains • Genomic Context • Mutation profile • Most common mutations
- 96. 96
- 97. 97
- 98. 98
- 99. 99 Donor • Donor ID • Primary site • Cancer Project • Gender • Tumor Stage • Vital Status • Disease Status • Release type • Age at diagnosis • Available data types • Analysis types
- 100. 100 Genes
- 101. 101 Mutations • Consequences • Type • Platform • Verification status
- 102. 102 Exporting data
- 103. 103 Exporting data
- 104. 104
- 105. 105 Exporting data
- 106. 106 Can do bulk download of the data …
- 107. 107 BIG DATA Validation ValidRaAtiWon DATA Meta DATA Interpreted data ✔ ✔ ✔ ✔ ✔
- 108. 108 DACO ICGC dbGaP EGA TCGA BAM Open Open ERA BA M Germ Line + EGA id BA M BA M
- 109. 109 ICGC Data Categories ICGC Open Access Datasets ICGC Controlled Access Datasets Cancer Pathology Histologic type or subtype Histologic nuclear grade Donor Gender Age range RNA expression (normalized) DNA methylation Genotype frequencies Somatic mutations (SNV, CNV and Structural Rearrangement) Detailed Phenotype and Outcome Data Patient demography Risk factors Examination Surgery/Drugs/Radiation Sample/Slide Specific histological features Protocol Analyte/Aliquot Gene Expression (probe-level data) Raw genotype calls (germline) Gene-sample identifier links Genome sequence files Most of the data in the portal is publically available without restriction. However, access to some data, like the germline mutations, requires authorization by the Data Access Compliance Office (DACO)
- 111. http://icgc.org/daco
- 112. 112 ICGC Controlled Access Datasets • Detailed Phenotype and Outcome data Region of residence Risk factors Examination Surgery Radiation Sample Slide Specific histological features Analyte Aliquot Donor notes • Gene Expression (probe-level data) • Raw genotype calls • Gene-sample identifier links • Genome sequence files ICGC OA Datasets • Cancer Pathology Histologic type or subtype Histologic nuclear grade • Patient/Person Gender, Age range, Vital status, Survival time Relapse type, Status at follow-up • Gene Expression (normalized) • DNA methylation •Computed Copy Number and Loss of Heterozygosity • Newly discovered somatic variants http://goo.gl/w4mrV
- 113. Identify yourself Fill out detail form which includes: • Contact and Project Information •Information Technology details and procedures for keeping data secure •Data Access Agreement All of these documents are put into a PDF file that you print and get your institution to sign off on your behalf
- 115. ‹#›
- 116. ‹#›
- 117. ‹#›
- 118. ‹#›
- 119. ‹#›
- 120. ‹#› DACO approved projects: > 160 groups - 75% academic (> 870 people)
- 121. 121 121 Nature 409:452 Bioinformatics Citizenship: What it means, and what does it cost?
- 122. 122 Important messages: • The ICGC portal is evolving and getting better all the time • Lots of data provided by the ICGC • Important to be good citizens of the scientific world • The idea behind all of this is to provide tools to help cure cancer • Need to respect policies and guidelines • There is help out there, and user feedback is *always* welcome.
- 123. 123 Acknowledgments DCC Software Developer Vincent Ferretti Daniel Chang Anthony Cros Jerry Lam Brian O'Connor Bob Tiernay Stuart Watt Shane Wilson Junjun Zhang ICGC Project leaders at the OICR: Tom Hudson John McPherson Lincoln Stein Jared Simpson Paul Boutros Vincent Ferretti Francis Ouellette Jennifer Jennings http://oicr.on.ca http://icgc.org Ouellette Lab Michelle Brazas Emilie Chautard Nina Palikuca Zhibin Lu Web Dev Joseph Yamada Angela Chao Daniel Gross Kamen Wu Kim Cullion Miyuki Fukuma Wen Xu Pipeline Development & Evaluation Morgan Taschuk Michael Laszloffy Peter Ruzanov ICGC DCC Biocuration Hardeep Nahal Marc Perry Research IT/Systems David Sutton, Bob Gibson Sam Maclennan David Magda Rob Naccarato Brian Ott Gino Yearwood EGA Justin Paschall Jeff Almeida-King Ilkka Lappalainen Jordi Rambla De Argila Marc Sitges Puy … and all the patients and their families that that are putting their hopes into our work!
- 124. 124 Informatics and Biocomputing at the OICR
- 126. 126
- 127. 127 http://icgc.org http://dcc.icgc.org http://docs.icgc.org info@icgc.org @bffo Video tutorial: https://vimeo.com/75522669