Canadian Bioinformatics Workshops

Canadian Bioinformatics Workshops www.bioinformatics.ca 1

Module #: Title of Module 2

Module 3: Part IClinical Genomics Anna Lapuk, PhD Vancouver Prostate Centre alapuk@prostatecentre.com

Module Overview • Part I • Pharmacogenetics-pharmacogenomics • Whole Genome Association Studies (GWAS) • Cancer Genomics • Part II • Clinical data • Censoring • Survival analysis • Kaplan-Meier, log-rank, Cox-regression

Pharmacogeneitics-Pharmacogenomics inheritance in variation in drug response

Origin and classical examples of biochemical pharmacogenetics Earliest experiments - 1950s – 1960s Large differences in response to a standard drug doses

CYP2D6 story • Cytochromes P450 family of microsomal drug-metabolizing enzymes • CYP2D6 catalyzes biotransformation of scores of drugs • Antidepressants • Antiarrhythmic drugs • Activates analgesic prodrug codeine • Genetic variation • nonsynonymous cSNPs associated with decreased activity • gene deletion • gene duplication (up to 13 copies)

CYP2D6 story Extensive metabolizers Poor metabolizers Ultrarapid metabolizers Weinshilboum, 2006

TPMT story catalyzes the S-methylation of thiopurine drugs drugs are cytotoxic and immunosuppressive agents that are used to treat acute lymphoblastic leukemia of childhood, inflammatory bowel disease, and organ transplant recipients narrow therapeutic index most serious thiopurine-induced toxicity is life-threatening myelosuppression (bone marrow suppression) Genetic polimorphisms inactivate TPMT (protein misfolding) - > increasing risk for myelosuppression (1/10-1/15 of a dose)

TPMT story First example of pharmacogenetic data into the drug label catalyzes the S-methylation of thiopurine drugs drugs are cytotoxic and immunosuppressive agents that are used to treat acute lymphoblastic leukemia of childhood, inflammatory bowel disease, and organ transplant recipients narrow therapeutic index most serious thiopurine-induced toxicity is life-threatening myelosuppression (bone marrow suppression) Genetic polimorphisms inactivate TPMT (protein misfolding) - > increasing risk for myelosuppression (1/10-1/15 of a dose)

TPMT story Trimodal frequency distribution of TPMT activity level Weinshilboum, 2006

Monogenic Mendelian traits • pharmacokinetics • Pharmacogenetics • Pharmacogenomics • Polygenic traits • Pharmacokinetics, pharmacodynamics

EGFR story EGFR is over-expressed in nonsmall cell lung cancer Gefitinib to inhibit EGFR. Best responders are women, never smoked, East Asian origin. Genetic variation in a ATP-binding site of this protein – gain-of-function somatic mutation (patients with mutation responded better)

Warfarin story Warfarin is the most widely prescribed oral anticoagulant Serious adverse effects – haemorrhage, undesired coagulation Predominantly metabolized by cytochrome P450 family member CYP2C9 Two common polymorphisms are associated with decreased activity of CYP2C9 (12% and 5% of wild type) Frequency of polymorphisms 8-12% and 6-10%. Pharmacokinetic genetic variation did not explain most of the variance in response VKORC1 – target of this drug. Series of haplotypes were associated with final dose of warfarin

Warfarin story Weinshilboum, 2006

GWAS genome-wide association study (GWA study, or GWAS) - also known as whole genome association study (WGA study) - is an examination of genetic variation across a given genome, designed to identify genetic associations with observable traits. require two groups of participants: subjects with the disease (cases) and subjects without (controls). Genotyping each individual and test the association of set of markers (SNPs) with the disease or trait. Saxena R et al Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science, 2007. Easton DF et al Genome-wide association study identifies novel breast cancer susceptibility loci. Nature, 2007. Zeggini E et al Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science, 2007 Scott LJ et al A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science, 2007. Wellcome Trust Case Control Consortium. Largest ever study of genetics of common diseases published today. Press release, 2007-06-06.

‘Statins’ story • ‘Statins’ are a class of HMG-CoA reductase inhibitors (cholesterol level) • Serious side effect – myopathy • SLCO1B1 variants and statin-induced myopathy—a genomewide study. The SEARCH Collaborative Group. N. Engl. J. Med. 2008 • 85 patients vs 90 controls (OR=16.9, p-value~10-9) • Validated in 10,269 patients (OR=2.6) • >60% could be attributed to this single variant allele Weinshilboum, 2008

Cancer Genomics Cancer genomics is the study of the human cancer genome. It is a search within "cancer families" and patients for the full collection of genes and mutations--both inherited and sporadic--that contribute to the development of a cancer cell and its progression from a localized cancer to one that grows uncontrolled and metastasizes (spreads throughout the body). NCI

Cancer Is A Polygenic Disease Genomic aberration Mutations Transcription changes Splicing changes Epigenetic changes Changes on the protein level

Sequence structure of 20q13 amplicon corein breast cancer Courtesy of Dr. Collins and Volik

Cancer is a heterogeneous disease Oncogenes Tumor Suppressor Genes

HER2 and trastuzumab story • The ERBB2 or HER2 receptor is a cell surface receptor tyrosine kinase (RTK), member of ERBB family. Overexpression results in activation of intracellular signalling through the Ras-Raf-ERK and PI3K-AKT pathways to promote cell division, cell growth and inhibit apoptosis. • Slamonet al 1987, Science – HER2 is overexpressed (2-20x) in 25-30% of breast cancers and is associated with shorter survival and relapse times. • 1990 – Genentech develops humanized monoclonal antibody against HER2 receptor. Effective only in few % of patients • Methods to detect HER2 amplifications with FISH and protein levels with IHC. • 1992 - Trastuzumab (Herceptin) clinical trials • Standard of care: • Test for HER2 expression status • Herceptin in combination with other drugs

Herceptin Therapy of HER2+ cancers Response Rate (%) Clinical trials

Predicting outcome of breast cancer patients based on the integrative analysis of genome copy number and expression data: Chin K et al, Cancer Cell 2006 Chin K et al, 2006

Expression signatures of breast tumors ERBB2 luminal basal Chin K et al, 2006

Recurrent amplifications and expression signature predict outcome Luminal A Luminal B Basal Luminalamplifiers ERBB2 LumA/non-amplifiers LumA/amplifiers Chin K et al, 2006

How well are we managing cancer?

Males: selected Cancers PSA screen INCIDENCE MORTALITY Canadian Cancer Society: Canadian Cancer Statistics 2009

Females: selected Cancers Mammography INCIDENCE MORTALITY Canadian Cancer Society: Canadian Cancer Statistics 2009

All Cancers Incidence rate & Mortality rates (CCS). MALES FEMALES Canadian Cancer Society: Canadian Cancer Statistics 2009

There’s a keen need in novel prognostic and therapeutic biomarkers

Part I: What Have We Learned? Modern technology and biological knowledge has transformed our perception of clinical intervention: from silver bullet to a personalized medicine. • Most diseases are polygenic traits • Increasing appreciation of genetic and genomic data for drug labels • Modern technology has enabled scanning of the whole genomes and transcriptomes for additional/better prognostic and therapeutic targets • Integration of multiple level data (genomic/transcription/etc.) increases power. • Still much to be done for putting principles of personalized medicine into practice: new biomarkers, new analytical methodology, new legislation. • Still a long way to go to manage cancer

Questions? Back in 20 minutes

Module 3: Privacy and Security Sohrab Shah Centre for Translational and Applied Genomics Molecular Oncology Breast Cancer Research Program BC Cancer Agency sshah@bccrc.ca 34

Module Overview 35 • Handling identifying data in research • Policies of 3 organisations • International cancer genome consortium • European genotyping archive • The cancer genome atlas project • Controlled or tiered access

What are the issues related to genomic data derived from clinical subjects? Advances in research Protection of donor 36

Problem: genetic data is identifiable 37 • Identity of the donor should remain anonymous to avoid: • embrassment • legal or financial consequences • stigmatization • discrimination • insurance, employment, loans, etc… • Lowrance and Collins, Science (2007)

Identifiable data and privacy law 38 • Controlled, conditional release • Not available for public release • Does this impact research? • Research has benefitted tremendously from freely accessible data • Human genome project • GenBank, Ensembl, etc… • Will this work in clinical genomics?

How are the large scale data providers dealing with the issue of patient security? http://www.icgc.org/files/ICGC_April_29_2008.pdf 39

Core Bioethical Elements Core Bioethical Elements: For prospective research, ICGC members should convey to potential participants, that: • The ICGC is a coordinated effort among related scientific research projects being carried on around the world • Participation in the ICGC and its component projects is voluntary • Samples and data collected will be used for cancer research, which may include whole genome sequencing • The patient’s care will not be affected by their decision regarding participation • The samples collected will be in limited quantities; access to them will be tightly controlled and will depend on the policy and practices of the ICGC-member project. At least a small percentage of the samples may be shared with international laboratories for the purposes of performing quality control studies • Data derived from the samples collected and data generated by the ICGC members will be made accessible to ICGC members and other international researchers through either an open or a controlled access database under terms and conditions that will maximize participant confidentiality • Those accessing data and samples will be required to affirm that they will not attempt to re-identify participants • There is a remote risk of being identified from data available on the databases 40

Core bioethical elements, cont’d • Once data is placed in open databases, that data cannot be withdrawn later • In controlled access databases the links to (local) data that can identify an individual will be destroyed upon withdrawal. Data previously distributed will continue to be used • ICGC members agree not to make claims to possible IP derived from primary data • No profit from eventual commercial products will be returned to subjects donating samples http://www.icgc.org/files/ICGC_April_29_2008.pdf 41

The Cancer Genome Atlas http://cancergenome.nih.gov 42 The TCGA Pilot Project anticipates that its data will be of high value in a number of research areas and will be used in many ways. Those include but are not limited to development of new analytical methods, identification of the genomic etiology of individual tumor types and subtypes, and development of new experimental diagnostic, therapeutic and preventive approaches and strategies for cancer. Thus, the TCGA Project recognizes that the data should be available to all users for any purpose, limited only by the need to avoid identifiability of the research participants (Lowrance and Collins, Science, August 3, 2007).

The Cancer Genome Atlas Project To ensure protection of genetic privacy for sample donors, data users will have to agree to certain conditions described in the TCGA Patient Protection Policy and Controlled Access Policy as to how the data will be used. For example, users will have to agree that they will share these data only with others who have also completed a data access agreement and that they will not patent discoveries in a way that prevents others from using the data (refer to IP policy ). This means that reviewers of a manuscript who need to see any controlled-access TCGA data underlying a result must also agree to these user access conditions before they can see these data. http://cancergenome.nih.gov 43

BCCA institutional policy 44 The BCCA has supported collection of de-identified data from more than 1000 individuals collected under BCCA’s study approved by the UBC Behavioural Research Ethics Board on November 6, 2008 Protocol # H05-60119, entitled: Formation of the Gynaecological Cancer Tissue Bank and on October 14, 2008 Protocol #H08-01411, entitled: Gene Fusions in Ovarian Carcinoma- “pilot funding from BC Cancer Foundation for OvCaRe” (“Study”). This well-characterized population provides a rare and valuable scientific resource.

BCCA Institutional Policy 45 Data collected by the Study have been stripped of all personal identifiers but the wealth of data available on them might make possible the individual identification of some Study participants. To protect the confidentiality and privacy of these Study participants, the Recipient who is granted access to these data must adhere to the requirements of this Data Access Agreement (“DAA”). Failure to comply with this Data Access Agreement could result in denial of further access to Study Data. Violation of the confidentiality requirements of this agreement is considered a breach of confidentiality and may leave requesting investigators liable to legal action on the part of Study participants, their families, or the Canadian Government.

The European Genome Archive http://www.ebi.ac.uk/ega Created to store and disseminate the data from the Welcome Trust Case Control Consoritum (17,000 cases) http://www.ebi.ac.uk/ega/bcms/ega/Documents/EGA_whitepaper.pdf 46

EGA policy 47 The EGA will provide the necessary security required to control access, and maintain patient confidentiality, while providing access to those researchers and clinicians authorized to view the data. In all cases, data access decisions will be made by the appropriate data access-granting organisation (DAO) and not by the EGA. The DAO will normally be the same organisation that approved and monitored the initial study protocol or a designate of this approving organisation.

Procedure to store data in EGA Encrypt data using a key known to you and EGA Upload data to EGA User requests data Committee notifies EGA and user is given a decryption key MTA is signed by user (or appropriate institutional rep) and returned to committee EGA informs committee of request and MTA is sent to user User downloads and decrypts data 48

Conclusions 49 • Genetic data is potentially identifiable • Researchers have a (legal) responsibility to safeguard the privacy of the donors • Several models have now been implemented • IGCG, TCGA, EGA

Comments? 50

Canadian Bioinformatics Workshops