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RAPPER Radiogenomics: Assessment of Polymorphisms for Predicting the Effects of Radiotherapy Today… l RAPPER: Hypothesis, aims, progress etc. l Candidate gene results l Genome-wide association study l Radiogenomics Consortium Study Rationale Radiotherapy dose is limited by the side effects/ toxicity to normal tissues 100% Probability l 50% Tumour control TCP Normal tissue complication NTCP Radiation dose (Gy) Study Rationale l 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 l There is an association between common genetic variation, reported by single nucleotide polymorphisms (SNPs) and individual patient variability in normal tissue toxicity. l Long-Term Aim: Develop genetic profiles that allow individualisation of radiotherapy l Radiation tolerant l Radiation sensitive Objectives l Collect toxicity data & EDTA blood samples l Radical radiotherapy patients l 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 Fe 07 bJ u 08 nO 08 ct Fe 08 bJ u 09 nO 09 ct Fe 09 b10 0 Month l l 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 l n=2,304 EDTA blood samples or lymphocytes l Extraction, quantification & normalisation from >99.6% samples (2296/2304) l Average DNA yield: 208 ± 73 µg (1SD) l l 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) l l Age of donor: weak but statistically significant association Time whole blood stored at -80°C: no association Toxicity Data Collection l l l Majority of RAPPER patients complete: l LENT SOMA and/or CTCAE questionnaires l Quality of Life (EORTC) l 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 l l CR-UK TRICC funding - £180,000 for genotyping 2005: 60 genes & 5 SNPs per gene l l l l Cell-cycle checkpoint control DNA damage recognition & repair Cytokine response Apoptosis l 2008: 120 genes & 10 SNPs per gene l Candidate or genome-wide? TGFβ1 Study l Gill Barnett (CR-UK/ RCR clinical training fellow) l TGFβ1 often implicated in late radiation toxicity l A number of small candidate gene studies showed a correlation between SNPs in TGFβ1 and late toxicity in breast cancer patients l l l Andreassen 03, 05, ( 06) Quarmby 03 SNPs studied were C-509T and L10P Linkage Disequilibrium l Genetic recombination: chromosomal crossover between paired chromosomes tends to occur at recombination hot-spots l Multiple SNPs inherited together as a single unit (haplotype); the presence of one SNP (tag-SNP) can predict the presence of several others l Tag-SNPs capture common variation in a given region and across whole genome (HapMap) l 250,000 - 500,000 tag-SNPs provide almost as much mapping information as all 10 million SNPs TGFβ1 Method l TGFβ1 tag-SNP (rs4803455) is strongly correlated with C-509T and L10P l 431 patients from the Cambridge IMRT Breast trial (largest cohort studied) l l Genotyping success rate >99% Late radiotherapy toxicity assessed at 2 years l Breast shrinkage (validated photographic technique) TGFβ1 Results l 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) l 943 breast IMRT patients l l l l 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: l l l l 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 l l A set of discriminatory endpoints selected to represent the spectrum of radiation toxicity For each toxicity endpoint, calculate: Z-score = (score – mean) / SD l STAT score for an individual is the average of the Z-scores for each endpoint Standardised Total Average Toxicity (STAT) Score l STAT score evaluates an individual s overall late toxicity burden l l l l 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 l l Treatment e.g. +/- breast boost Patient characteristics e.g. breast volume Fluidigm Results (II) l l 943 breast IMRT patients Significant associations for the following genes & overall toxicity (STAT score): l l l MRE11 SNP#1 (p=0.001) MRE11 SNP#2 (p=0.0009) ATM (p=0.003) Candidate Gene Summary l Numerous small-scale candidate gene studies have tried to show an association between SNPs and radiation toxicity l l Continue repeating experiments with other SNPs but: l l l l 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) l Whole genome approach l Investigate >10 million SNPs simultaneously using tag-SNPs (HapMap) l Requires no a priori knowledge of genes l Requires large sample sizes i.e. many thousands of patients Easton et al., 2007 – Nature paper l Breast cancer susceptibility GWAS l Phase I: 400 cases & 400 controls l l Phase II: 4000 cases & 4000 controls l l ~12,000 SNPs (top 5%) Phase III: 20,000 cases & 20,000 controls l l ~265,000 SNPs 30 SNPs International Consortium (BCAC) Easton et al., 2007 – Nature paper l Identified 7 SNPs with p-value < 10-7 l FGFR2 is associated with breast cancer risk l p-value = 2 x 10-76 l Within genes of unknown function & also non-coding regions l Strengthens argument of genome-wide approach RAPPER-GWAS Design l Phase I l l 2,000 patients using a standard panel of tag-SNPs (600,000) Phase II l 8,000 patients using a custom designed oligo array and top 5% of SNPs l Phased approach maintains power of the study but is mainly a cost reduction measure l Best design: genotype ALL samples for ALL SNPs l As more data are added more SNPs are confirmed by reaching significance RAPPER-GWAS Funded l l Funded by CR-UK in October 2009 Illumina Human CytoSNP-12 chip l l l l l 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 l Expect genotyping data return by May/June 2010 Radiogenomics Consortium l 17-18 November 2009, Manchester l Jointly led by Catharine West & Barry Rosenstein l Delegates from UK, USA, Denmark, Belgium, France, Spain, The Netherlands, Canada, Japan etc. l High quality toxicity data & DNA samples for radiosensitivity GWAS Radiogenomics Consortium l Set up an Organising Committee l Published announcements in Red & Green journals l TGFβ1 meta-analysis underway l Develop best practice guidelines for reporting radiogenomics studies l Next meeting: New York, 12-13 October 2010; as well as alternating sessions at ESTRO & ASTRO Acknowledgements l TRB group l Gill Barnett, Neil Burnet, Charlotte Coles, Alison Dunning, Paul Pharoah (University of Cambridge) l Søren Bentzen (University of Wisconsin) l 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 l MRE-11 forms part of the MRN complex which binds to DNA DSBs after ionizing radiation remains until damage is repaired l Effect of minor alleles of 2 SNPs are in different directions l l Only weak LD between them (r2 = 0.25) l Physically distant at opposite ends of the gene MRE-11SNPs (rs 569143 and rs 2155209) l 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) l l 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