Download Overview presentation about RAPPER

RAPPER
Radiogenomics: Assessment of
Polymorphisms for Predicting the
Effects of Radiotherapy
Today…
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RAPPER: Hypothesis, aims, progress etc.
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Candidate gene results
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Genome-wide association study
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Radiogenomics Consortium
Study Rationale
Radiotherapy dose is limited by the side effects/
toxicity to normal tissues
100%
Probability
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50%
Tumour control
TCP
Normal tissue complication
NTCP
Radiation dose (Gy)
Study Rationale
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Some patients are more likely to experience side
effects than others i.e. there is individual variation
in tissue response.
Same
dose
Different
effect
Grade 1
Grade 5
Courtesy of Prof Ingela Turesson
Hypothesis
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There is an association between common genetic
variation, reported by single nucleotide
polymorphisms (SNPs) and individual patient
variability in normal tissue toxicity.
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Long-Term Aim: Develop genetic profiles that
allow individualisation of radiotherapy
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Radiation tolerant
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Radiation sensitive
Objectives
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Collect toxicity data & EDTA blood samples
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Radical radiotherapy patients
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Breast, prostate, gynaecological or rectal cancer
Overall Accrual
RAPPER Accrual
2,200 target
3000
Count
2500
Cumulative
Target
2000
1500
1000
500
Ju
nO 03
ct
Fe 03
bJ u 04
nO 04
ct
Fe 04
bJ u 05
nO 05
ct
Fe 05
bJ u 06
nO 06
ct
Fe 06
bJ u 07
nO 07
ct
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bJ u 08
nO 08
ct
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bJ u 09
nO 09
ct
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b10
0
Month
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Target achieved in October 2008: 2,245 samples
Current total March 2010: 2,713 samples
Accrual by Tumour Type
Prostate, 1179
Breast, 1143
Rectal, 133
Gynae, 258
Accrual by Trial
Cx Lymphocytes, 207
EXCITE, 38
PRECIOUS, 6
CHHIP, 828
IMRT, 1052
RT01, 240
Chr Prospective, 115
Retro Gynae, 25
Dose Esc, 57
Brachy, 49
RICE, 97
DNA Extraction
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n=2,304 EDTA blood samples or lymphocytes
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Extraction, quantification & normalisation from
>99.6% samples (2296/2304)
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Average DNA yield: 208 ± 73 µg (1SD)
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Yield range: 22-696 µg
Only require 25 µg for genotyping
DNA YieldDNAvs.
Transit Time
Yield vs. Time in Transit
Average DNA Yield (ug)
350
(n=1,208)
300
250
200
150
100
50
0
r= -0.17
0
p<0.01
1
2
3
4
5
Tim e blood in transit before freezing (days)
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Age of donor: weak but statistically significant association
Time whole blood stored at -80°C: no association
Toxicity Data Collection
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Majority of RAPPER patients complete:
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LENT SOMA and/or CTCAE questionnaires
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Quality of Life (EORTC)
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Confounders (patient- & treatment-related factors that
influence late toxicity)
Contributing RT trials may also use other clinical
scales to score late toxicity
Currently 2 year data stored on ~2,000 patients
Candidate Gene Approach
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CR-UK TRICC funding - £180,000 for genotyping
2005: 60 genes & 5 SNPs per gene
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Cell-cycle checkpoint control
DNA damage recognition & repair
Cytokine response
Apoptosis
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2008: 120 genes & 10 SNPs per gene
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Candidate or genome-wide?
TGFβ1 Study
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Gill Barnett (CR-UK/ RCR clinical training fellow)
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TGFβ1 often implicated in late radiation toxicity
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A number of small candidate gene studies
showed a correlation between SNPs in TGFβ1
and late toxicity in breast cancer patients
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Andreassen 03, 05, ( 06)
Quarmby 03
SNPs studied were C-509T and L10P
Linkage Disequilibrium
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Genetic recombination: chromosomal crossover
between paired chromosomes tends to occur at
recombination hot-spots
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Multiple SNPs inherited together as a single unit
(haplotype); the presence of one SNP (tag-SNP)
can predict the presence of several others
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Tag-SNPs capture common variation in a given
region and across whole genome (HapMap)
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250,000 - 500,000 tag-SNPs provide almost as
much mapping information as all 10 million SNPs
TGFβ1 Method
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TGFβ1 tag-SNP (rs4803455) is strongly
correlated with C-509T and L10P
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431 patients from the Cambridge IMRT Breast
trial (largest cohort studied)
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Genotyping success rate >99%
Late radiotherapy toxicity assessed at 2 years
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Breast shrinkage (validated photographic technique)
TGFβ1 Results
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No relationship found between the tag-SNP
rs4803455 and late radiation toxicity in breast
cancer patients (p=0.93)
Fluidigm Genotyping Method
1) Need 4 dynamic arrays
for each 384 well plate
containing DNA
Dynamic array
3) 96 DNA samples
with the sample mix
are loaded onto right
side of chip
2) 96 assays are
loaded into the left
side of the chip
Fluidigm (96 SNPs)
TGFβ1
TGFβ3
MLH1
CD44
APEX
Rad17
NEIL3
ID3
PTTG1
MSH2
SOD2
Rad21
GSTA1
RAD9A
ERCC2
REV3L
BAX
EPDR1
ABCA1
RAD51
XRCC1
OGG1
eNOS
LIG4
ERCC4
ALAD
COMT
PAH
TP53
XPC
XRCC3
ATM
MPO
LIG3
GSTP1
MAT1A
SART1
XRCC6
HIF1a
MRE11
XRCC5
CYP 2D6
MAD2L2
TGFβR3
NFE2L2
MAP3K7
SH3GL1
IL12RB2
CDKN1A
PRKDC
Fluidigm Results (I)
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943 breast IMRT patients
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91/96 SNPs gave good results
Average sample call rate 99.6%
Duplicate concordance 100% (33 duplicate samples
included)
Significant associations for the following genes &
individual endpoints:
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HIF1A and increased breast shrinkage (p=0.003)
XRCC3 and increased pigmentation (p=0.007)
LIG4 and poor overall cosmesis (p=0.009)
XRCC1 and decreased risk of telangiectasia
Standardised Total Average
Toxicity (STAT) Score
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A set of discriminatory endpoints selected to
represent the spectrum of radiation toxicity
For each toxicity endpoint, calculate:
Z-score = (score – mean) / SD
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STAT score for an individual is the average of the
Z-scores for each endpoint
Standardised Total Average
Toxicity (STAT) Score
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STAT score evaluates an individual s overall late
toxicity burden
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Different toxicity scales & pooling of endpoints
Different tumour sites
Missing data & rare toxicity grades
Using residuals, it can also correct for patient and
treatment related factors
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Treatment e.g. +/- breast boost
Patient characteristics e.g. breast volume
Fluidigm Results (II)
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943 breast IMRT patients
Significant associations for the following genes &
overall toxicity (STAT score):
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MRE11 SNP#1 (p=0.001)
MRE11 SNP#2 (p=0.0009)
ATM (p=0.003)
Candidate Gene Summary
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Numerous small-scale candidate gene studies
have tried to show an association between SNPs
and radiation toxicity
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Continue repeating experiments with other SNPs
but:
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Results have been contradictory
Don t fully understand normal tissue biology
Function of many genes is unknown
What about non-coding regions?
So…
Genome-Wide Association
Study (GWAS)
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Whole genome approach
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Investigate >10 million SNPs simultaneously
using tag-SNPs (HapMap)
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Requires no a priori knowledge of genes
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Requires large sample sizes i.e. many thousands
of patients
Easton et al., 2007 – Nature paper
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Breast cancer susceptibility GWAS
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Phase I: 400 cases & 400 controls
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Phase II: 4000 cases & 4000 controls
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~12,000 SNPs (top 5%)
Phase III: 20,000 cases & 20,000 controls
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~265,000 SNPs
30 SNPs
International Consortium (BCAC)
Easton et al., 2007 – Nature paper
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Identified 7 SNPs with p-value < 10-7
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FGFR2 is associated with breast cancer risk
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p-value = 2 x 10-76
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Within genes of unknown function & also
non-coding regions
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Strengthens argument of genome-wide approach
RAPPER-GWAS Design
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Phase I
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2,000 patients using a standard panel of tag-SNPs
(600,000)
Phase II
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8,000 patients using a custom designed oligo array
and top 5% of SNPs
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Phased approach maintains power of the study
but is mainly a cost reduction measure
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Best design: genotype ALL samples for ALL
SNPs
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As more data are added more SNPs are confirmed by
reaching significance
RAPPER-GWAS Funded
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Funded by CR-UK in October 2009
Illumina Human CytoSNP-12 chip
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300,000 tag-SNPs
Covers variation found in European populations
£140 per chip including processing
1,316 breast samples (includes RAPPER,
IMPORT & RACE samples)
Sent off in March 2010
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Expect genotyping data return by May/June 2010
Radiogenomics Consortium
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17-18 November 2009, Manchester
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Jointly led by Catharine West & Barry Rosenstein
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Delegates from UK, USA, Denmark, Belgium,
France, Spain, The Netherlands, Canada, Japan
etc.
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High quality toxicity data & DNA samples for
radiosensitivity GWAS
Radiogenomics Consortium
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Set up an Organising Committee
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Published announcements in Red & Green
journals
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TGFβ1 meta-analysis underway
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Develop best practice guidelines for reporting
radiogenomics studies
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Next meeting: New York, 12-13 October 2010; as
well as alternating sessions at ESTRO & ASTRO
Acknowledgements
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TRB group
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Gill Barnett, Neil Burnet, Charlotte Coles, Alison
Dunning, Paul Pharoah (University of Cambridge)
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Søren Bentzen (University of Wisconsin)
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RAPPER collaborators recruiting patients,
collecting samples & toxicity data
Working with residuals
Patients with
negative residuals
have less toxicity
than expected
Patients with positive
residuals have
greater than expected
toxicity
Patients with a residual of zero have
toxicity that can be accounted for by
patient / treatment factors
Possible relevance of MRE11
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MRE-11 forms part of the MRN complex which
binds to DNA DSBs after ionizing radiation remains until damage is repaired
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Effect of minor alleles of 2 SNPs are in different
directions
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Only weak LD between them (r2 = 0.25)
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Physically distant at opposite ends of the gene
MRE-11SNPs (rs 569143 and rs 2155209)
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Chosen from reported associated with incidence of
breast and bladder cancer and has been suggested as
a candidate gene for radiosensitivity
Possible relevance of ATM-P1054R
(rs1800057)
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Reported association between ATM -1054Arg
allele and increased micronuclei formation after IR
in breast cancer cell lines1
May also be a breast cancer susceptibility allele2