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Canadian Bioinformatics Workshops
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Module #: Title of Module
2
Anna Lapuk, PhD
Vancouver Prostate Centre
[email protected]
Module 3: Clinical genomics
and survival analysis
• Part I
– Pharmacogenetics-pharmacogenomics
– Whole Genome Association Studies (GWAS)
– Cancer Genomics
• Part II
– Clinical data
• Censoring
– Survival analysis
• Kaplan-Meier, log-rank, Cox-regression
Module 3: Clinical genomics
and survival analysis
inheritance in
variation in drug response
Module 3: Clinical genomics
and survival analysis
• Earliest experiments - 1950s – 1960s
• Large differences in response to a standard drug
doses
Drug
Genetic variation
effect
Short-acting muscle
relaxant
enzymatic hydrolysis
(dysfunctional BCHE form )
prolonged muscle
paralysis
antituberculosis
drug
enzymatic acetylation
(activity of an enzyme NAT2)
Plasma concentrations ->
increased risk for adverse
effects
Module 3: Clinical genomics
and survival analysis
• Cytochromes P450 family of microsomal drugmetabolizing 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)
Module 3: Clinical genomics
and survival analysis
Extensive metabolizers
Poor metabolizers
Ultrarapid metabolizers
Module 3: Clinical genomics
and survival analysis
Weinshilboum, 2006
• 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 (1/101/15 of a dose)
Module 3: Clinical genomics
and survival analysis
• catalyzes the S-methylation of thiopurine drugs
• drugs are cytotoxic and immunosuppressive agents that
are used to treat acute lymphoblastic leukemia of
childhood,
inflammatory
disease, and organ
First example
ofbowel
pharmacogenetic
transplant recipients
data into
the drug label
• 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 (1/101/15 of a dose)
Module 3: Clinical genomics
and survival analysis
Trimodal frequency distribution of TPMT activity level
Module 3: Clinical genomics
and survival analysis
Weinshilboum, 2006
•Monogenic Mendelian traits
•pharmacokinetics
•Polygenic traits
•Pharmacokinetics, pharmacodynamics
Module 3: Clinical genomics
and survival analysis
• 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)
Module 3: Clinical genomics
and survival analysis
• 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
Module 3: Clinical genomics
and survival analysis
Module 3: Clinical genomics
and survival analysis
Weinshilboum, 2006
• 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.
Module 3: Clinical genomics
and survival analysis
• ‘Statins’ are a class of HMGCoA reductase inhibitors
(cholesterol level)
• Serious side effect –
myopathy
• SLCO1B1 variants and statininduced 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
Module 3: Clinical genomics
and survival analysis
Weinshilboum, 2008
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).
Module 3: Clinical genomics
and survival analysis
NCI
•
•
•
•
•
•
Module 3: Clinical genomics
and survival analysis
Genomic aberration
Mutations
Transcription changes
Splicing changes
Epigenetic changes
Changes on the protein level
Sequence structure of 20q13 amplicon core
in breast cancer
Courtesy of Dr. Collins and Volik
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Oncogenes
Tumor Suppressor Genes
Module 3: Clinical genomics
and survival analysis
•
•
•
•
•
•
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.
Slamon et 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
Module 3: Clinical genomics
and survival analysis
Response Rate (%)
80
70
60
50
40
30
20
10
0
Clinical trials
Module 3: Clinical genomics
and survival analysis
Module 3: Clinical genomics
and survival analysis
Chin K et al, 2006
ERBB2
basal
Module 3: Clinical genomics
and survival analysis
luminal
Chin K et al, 2006
Luminal A
Luminal B
Basal
Luminal amplifiers
ERBB2
LumA/non-amplifiers
LumA/amplifiers
Module 3: Clinical genomics
and survival analysis
Chin K et al, 2006
Module 3: Clinical genomics
and survival analysis
PSA screen
INCIDENCE
Module 3: Clinical genomics
and survival analysis
MORTALITY
Canadian Cancer Society: Canadian Cancer Statistics 2009
Mammography
INCIDENCE
Module 3: Clinical genomics
and survival analysis
MORTALITY
Canadian Cancer Society: Canadian Cancer Statistics 2009
MALES
Module 3: Clinical genomics
and survival analysis
FEMALES
Canadian Cancer Society: Canadian Cancer Statistics 2009
Module 3: Clinical genomics
and survival analysis
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
Module 3: Clinical genomics
and survival analysis
Back in 20 minutes
Module 3: Clinical genomics
and survival analysis
Module 3: Privacy and Security
Sohrab Shah
Centre for Translational and Applied Genomics
Molecular Oncology Breast Cancer Research Program
BC Cancer Agency
[email protected]
34
Module Overview
• 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
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What are the issues related to genomic
data derived from clinical subjects?
Advances
in research
Protection
of donor
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Problem: genetic data is identifiable
• 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)
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Identifiable data and privacy law
• 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?
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How are the large scale data providers
dealing with the issue of patient security?
http://www.icgc.org/files/ICGC_April_29_2008.pdf
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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
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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
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The Cancer Genome Atlas
• 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).
http://cancergenome.nih.gov
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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
• 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.
44
BCCA Institutional Policy
• 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.
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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
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EGA policy
• 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.
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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
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Conclusions
• 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
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Comments?
50
Coffee Break
Back at: 10:50
Module 3: Survival Analysis
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