UK Society for Behavioural Medicine 9 th Annual Scientific Meeting

Transcription

1 UK Society for Behavioural Medicine 9 th Annual Scientific Meeting Behavioural Medicine: From Laboratory to Policy University of Oxford Examination Schools Monday 9 and Tuesday 10 December 2013 NPRI

2 UK Society for Behavioural Medicine 9 th Annual Scientific Meeting Behavioural Medicine: From Laboratory to Policy Parallel Session B Workshop NPRI

3 UK Society for Behavioural Medicine 9 th Annual Scientific Meeting Behavioural Medicine: From Laboratory to Policy Personalised genomics and prevention Jane Wardle, CRUK NPRI

4 Personalised genomics and prevention Symposium sponsored by Cancer Research UK s Population Research Committee

5 Translating the benefits of genomics for disease prevention: hype and hope Genomics has yielded many discoveries in the molecular basis of disease Visions of personalised treatment and prevention Better treatment outcomes Adding years to life (and life to years) Reduced health costs The reality of personalised genomics Small effect sizes Fears of genetic determinism Worries about prejudice and discrimination Public genetic literacy Incidental findings

6 Jane Wardle

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8 Collaboration between behavioural science and genomics for disease prevention Dr Saskia Sanderson (Mount Sinai) Psychological and behavioral effects of personal whole genome sequencing on healthy individuals Dr Nora Pashayan (UCL/University of Cambridge) Use of genomic information to stratify cancer screening schedules Susanne Meisel (University College London) Emotional and motivational responses to genetic test feedback for obesity risk. Dr Kat Arney (Cancer Research UK) Comment on issues of communication with the public and chair the discussion

9 NPRI UK Society for Behavioural Medicine 9 th Annual Scientific Meeting Behavioural Medicine: From Laboratory to Policy

10 Psychological and Behavioral Effects of Personal Whole Genome Sequencing on Healthy Individuals Saskia Sanderson, PhD Adjunct Assistant Professor Icahn School of Medicine at Mount Sinai New York, NY

11 What is whole genome sequencing? A technology that enables all or most of the DNA in an individual s entire genome to be sequenced or read Due to decreasing costs and utility, increasingly used for research and clinical purposes In future, consumers may be able to purchase personal whole genome sequencing for themselves outside of clinic

12 Types of information that may arise from personal whole genome sequencing Susceptibility to common, complex diseases Carrier status Physical traits Ancestry Rare variants of uncertain significance

13 The benefits of personal whole genome sequencing for healthy individuals include potential to identify disease risks earlier and engage in preventive behaviors or interventions to reduce those risks

14 The potential harms include raising fear, stress, anxiety by identifying rare variants that may have severe and threatening implications for the individual s health, but which are of uncertain significance

15 What is the behavioral impact of being informed from whole genome sequencing that you have an increased risk of a common, complex disease?

16 Health: good Lifestyle: cycles regularly Weight, BMI: a touch on the thin side No family history of type 2 diabetes

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18 Type 2 diabetes risk increased from 27% lifetime risk to 46% RiskOGram of type 2 diabetes: posttest probability calculated using 28 independent SNVs Chen et al (Cell, 2012)

19 RiskOGram of type 2 diabetes: posttest probability calculated using 28 independent SNVs Chen et al (Cell, 2012)

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21 RiskOGram of type 2 diabetes: posttest probability calculated using 28 independent SNVs Chen et al (Cell, 2012)

22 EXTERNAL STIMULI MESSAGE PROCESSING OUTCOMES PROCESS MESSAGE COMPONENTS Protection Motivation Message acceptance Danger Control Process Susceptibility Severity Response-efficacy Self-efficacy PERCEIVED THREAT (Susceptibility, Severity) Feedback loop Fear PERCEIVED EFFICACY (Response-efficacy, Self-efficacy) Defensive Motivation Message Rejection Fear Control Process No threat perceived (no response) Witte s Extended Parallel Process Model (EPPM) an extension of Roger s (1975) Protection Motivation Theory which grew out of the fear appeals literature

23 Personal genome sequencing X Lifestyle change and reduced disease risk Regular monitoring of biomarkers

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25 Impact of genetic testing on lifestyle: Cochrane Review Meta-analysis: 5 clinical studies on impact of genetic testing on smoking cessation Results: No differences, e.g. long-term (>6 months) smoking cessation (pooled OR 1.07, 95% CI 0.64 to 1.78) Conclusion: Evidence suggests no impact of genetic testing on smoking cessation Marteau TM et al. Effects of communicating DNA-based disease risk estimates on risk-reducing behaviours. Cochrane Database of Systematic Reviews 2010, Issue 10.

26 The 5 clinical studies on smoking cessation included in the meta-analysis First author and date published Genetic test used in study Disease Lerman et al (1997) CYP2D6 Lung cancer McBride et al (2002) GSTM1 Lung cancer Sanderson et al (2008) GSTM1 Lung cancer Ito et al (2006) L-myc Lung cancer & esophageal cancer Hishida et al (2010) L-myc Lung cancer & esophageal cancer Marteau TM et al. Effects of communicating DNA-based disease risk estimates on risk-reducing behaviours. Cochrane Database of Systematic Reviews 2010, Issue 10.

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28 EXTERNAL STIMULI MESSAGE PROCESSING OUTCOMES PROCESS MESSAGE COMPONENTS Protection Motivation Message acceptance Danger Control Process Susceptibility Severity Response-efficacy Self-efficacy PERCEIVED THREAT (Susceptibility, Severity) Feedback loop Fear PERCEIVED EFFICACY (Response-efficacy, Self-efficacy) Defensive Motivation Message Rejection Fear Control Process No threat perceived (no response) Witte s Extended Parallel Process Model (EPPM) an extension of Roger s (1975) Protection Motivation Theory which grew out of the fear appeals literature

29 What is the psychological impact of being informed from whole genome sequencing that you have a rare variant of uncertain significance?

30 We have to take seriously the very real possibility that whole genome sequencing may uncover something unexpected, ambiguous, and scary. Madeleine Price Ball, 15 Feb 2012 (

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33 Rare variant in the JAK2 gene At that point, Thakuria opened up a link to a 2010 study attached to the report. What it showed was that 14 of the 18 people with the variant developed cancer in their lifetimes. All of the 18 died within the study period. That s a very scary figure, Thakuria said. Information was starting to wash over me without really penetrating. I struggled to keep thinking of good questions for the team. Instead, I started asking myself questions: What am I doing here? What are these people telling me? I searched the faces arrayed around me, trying to see whether any of the researchers looked as panicked as I felt.

34 EXTERNAL STIMULI MESSAGE PROCESSING OUTCOMES PROCESS MESSAGE COMPONENTS Protection Motivation Message acceptance Danger Control Process Susceptibility Severity Response-efficacy Self-efficacy PERCEIVED THREAT (Susceptibility, Severity) Feedback loop Fear PERCEIVED EFFICACY (Response-efficacy, Self-efficacy) Defensive Motivation Message Rejection Fear Control Process No threat perceived (no response) Witte s Extended Parallel Process Model (EPPM) an extension of Roger s (1975) Protection Motivation Theory which grew out of the fear appeals literature

35 The meeting lasted almost two hours, and I left Church s office with Thakuria. We walked to a restaurant about halfway between Harvard Medical School and Fenway Park to sit and have a drink. I continued to quiz him on the relationship between the JAK2 variant and the diseases we d been talking about. Sitting on a barstool next to Thakuria and listening to him discuss the JAK2 variant, I felt reassured. It occurred to me that this wasn t how most people would receive the news of their results. As a reporter working on a story about genomics, I had access to experts that many people wouldn t. What will happen as more people get results from broad genome sequencing?

36 Even if we do a really good job of communicating individual common disease risks, we still face the challenge of VOLUME in whole genome sequencing As the volume of personalized information increases, the issue of who will be responsible for interpreting and explaining the assessments to individuals becomes more acute and suggests the need for training of a new class of genomic health care professional and development of novel ways to present the information. Patel et al (Genome Medicine 2013)

37 Four take-home messages 1. Even though common disease risk information from personal genome sequencing may be valuable, we have to be realistic in our expectations of the impact of that information, and realize that genetic risk information alone is usually not going to motivate people to change their lifestyle behaviors.

38 Four take-home messages 2. What might lead to protective health behavior changes is the cascade of events that may be triggered after genome sequencing, such as increased monitoring of biomarkers (e.g. glucose levels).

39 Four take-home messages 3. Personal genome sequencing with healthy individuals can and will also turn up rare variants that appear to be associated with rare diseases where the information is unexpected, ambiguous and scary. The possibility of these types of results arising should be part of the informed consent process upstream. How we can best communicate these types of results to people and help them manage them downstream remains an open question.

40 Four take-home messages 4. The personal genome sequencing results report is just the beginning of a long communication and health management process in which people will need access to a multitude of services and resources.

41 Acknowledgments Cancer Research UK UCL Jane Wardle Susan Michie Icahn School of Medicine at Mount Sinai, New York, NY Andrew Kasarskis Michael Linderman Eric Schadt

42 Thank you very much for your attention!

43 NPRI UK Society for Behavioural Medicine 9 th Annual Scientific Meeting Behavioural Medicine: From Laboratory to Policy

44 Polygenic risk stratification and personalised screening for cancers Nora Pashayan Senior Clinical Lecturer in Applied Health Research Cancer Research UK Clinician Scientist Fellow UCL Department of Applied Health Research UKSBM 9 th Annual Scientific Meeting Oxford, 9 th December 2013

45 All screening programmes do harm; some do good as well, and of these, some do more good than harm at reasonable cost Muir Gray

46 Outline At population level, is there a value in genetic profiling for common variants and risk stratification for personalised screening for cancer? Using case examples from modelling personalised screening for breast and prostate cancer on: Effectiveness Cost-effectiveness Reduction of harms of screening What further evidence would be needed before implementation of personalised screening programme?

47 Polygenic susceptibility Breast cancer loci Multiple variants SNP Locus Risk-allele frequency Odds ratio per allele* Variance Common 1 1p q q Small effect size 4 3p q p q q Total 0.28 * Based on individuals of European ancestry Multiplicative effect on risk (additive on logarithmic scale) Risk per variant=no. risk alleles*risk per allele Total risk= sum of the risk per variant

48 Frequency Distribution of polygenic risk For the currently known 76 loci for breast cancer susceptibility, the number of risk alleles that an individual can carry ranges from 0 to 152; with 3 76 unique combinations of alleles Each associated with different risk, and occurs at different frequency in population Risk

49 Distribution of polygenic risk (cont d) Log-normal distribution among the population and cases Population Population Cases Cases Pharoah et al, Nat Genet (1): log(rr) 2 = Mean - location of the distribution μ ' 2 μ σ = Standard deviation (SD) spread of the values around the mean = Variance Mean among cases: Variance among cases: 2 = variance among the population

50 Risk stratification In population-based screening, age is used to define the target population NHS Breast Screening Programme offers screening to women age 47 and over Screening age 47-73: covering 29% of the population of women, detecting 60% of breast cancers Implicit assumption that benefit-risk is surplus at this age

51 Risk stratification (cont d) In England, a 47 year old women has 2.5% chance of being diagnosed with breast cancer before age 57 Assume this is the absolute risk threshold at which screening becomes worthwhile Identify age at which 10-year absolute risk is 2.5% Whereby age at which screening is offered dependent on risk (determined by age, polygenic profile) This leads to reclassification of women into different risk groups

52 Age of eligibility for standard and personalised screening programmes % offered screening Age Standard screening Personalised screening* *Personalised screening based on 10-year absolute risk of 2.5% based on age and the currently known 76 known breast cancer variants

53 Reclassification: eligibility for screening Population of 100 women, years, by age group and risk threshold categories (i), that would be eligible for screening for breast cancer based on age (ii), and based on age and polygenic risk (based on 76 known breast cancer variants) (iii) (ii) Age-based screening: all women 47 years Eligible: = 66 women at low risk are also invited to screening (iii) Age and polygenic risk-based screening: all women with 10-year absolute risk 2% Eligible: = 44 younger women at high risk are also invited to screening

54 Reclassification: detectable cases Population of 100 women, years, with breast cancer by age group and risk threshold categories (i), that would be detectable by screening based on age (ii), and based on age and polygenic risk (based on 76 known breast cancer variants) (iii) (ii) Age-based screening: all women 47 years Potentially detectable cases: = 85 (iii) Age and polygenic risk-based screening: all women with 10-year absolute risk 2.5% Potentially detectable cases: = 70

55 Breast cancer: standard vs. personalised screening Population eligible for screening Cases potentially detectable NHS Breast screening Women years Personalised screening Women yrs. &10-yr AR 2.5% (based on the currently known 76 SNPs) 7.4x , x ,934 Reduction in no. of women eligible for screening per screening cycle (%) Difference in cases potentially detectable Currently known SNPs (explaining 15% FRR) 1,780,000 (24%) -425 Loci that explain 50% FRR 2,800,000 (38%) +31 Note: Estimates based on population and cancer registration in England

56 Prostate cancer: Personalised screening Age-based screening (aged years, 10-year absolute risk 2.0% or greater) vs. age and polygenic risk * based screening (absolute risk threshold of 2.0%): 19% fewer men to be offered screening while identifying 4% fewer potentially screen-detected cases Decision modelling Estimation of probabilities of overdiagnosis by polygenic risk * Polygenic profile based on the currently known 78 prostate cancer variants

57 Personalised screening: opportunities Personalised screening programme would improve the efficiency of screening programme and reduce the negative consequences of screening Efficiency improves with identification of further of genetic variants; and including additional information into the risk score

58 Challenges Feasibility: The set up and delivery of personalised screening programme is more complex than a programme with eligibility based on age alone Dynamic risk score ELSI and organisational complexities Acceptability to the public, health professionals and policy decision makers to use genetic profile in risk estimation Accessibility to ensure equitable delivery and uptake of personalised screening programme Public perception of genetic risk Communication of risk

59 Acknowledgements Funding: FP7 grant of the European Union Cancer Research UK Clinician Scientist Fellowship Mentors: Prof Paul Pharoah Prof Stephen Duffy

60 NPRI UK Society for Behavioural Medicine 9 th Annual Scientific Meeting Behavioural Medicine: From Laboratory to Policy

61 Genetic test feedback for susceptibility to weight gain motivational and behavioural effects Susie Meisel Health Behaviour Research Centre, UCL Cancer Research UK

62 Overview Background Genes and body weight Hypothetical Study Returning real FTO genetic test results to individuals Qualitative findings RCT

63 Genes and Body Weight Genes strongly implicated in the development of obesity FTO is the gene with the largest effect so far identified for common obesity Present in 2 variants (alleles) A, T Each higher-risk (A) allele of FTO adds about 1.6kg to body weight 20% higher lifetime risk of becoming overweight/obese Genetic tests for risk of weight gain are already available over the Internet Issue of body weight is familiar and emotive to many

64 Anticipated reactions to FTO feedback

65 What happens when we give people genetic test feedback for susceptibility to weight gain?

66 Methods and Participants DNA analysed from saliva Participants receive result letter in attachment Semi-structured interviews Analysed with Framework analysis and IPA

67 Reactions to a higher risk FTO result Result fits with private assumptions, giving an explanation for struggle with weight I still had one risk allele and that was kind of reassuring to me because I have always struggled to control my appetite, so I feel like, ok it s a combination of both, but it is in part biology and I am in a sense battling against my biology (P6, AT, overweight) Relief of self-blame and guilt it would just help understand yourself really. It makes me feel better that I have AT, at least it s got something to do with my genes and it s not just me being a pig. (P14, AT, obese)

68 Reactions to mismatches between genotype and phenotype It s not all down to genetics because it s down to the environment, the way you have been brought up and all that that has an influence on you (P20, TT, obese). And it doesn t make a lot of difference having it. Because if you have got it you are 3 kilos, 7.3 pound, it s a very small amount [ ]. I still think that there s something in the genes, but not in these particular ones, [ ] (P23, TT, obese).

69 Conclusions from qualitative work FTO feedback unlikely to have adverse psychological effects No evidence for genetic determinism Might be helpful for relieving guilt, self-blame Could increase motivation to control weight Could increase action to control weight However: multifactorial nature of obesity was emphasized, small, highly selected sample

70 Will the addition of genetic test feedback to generic weight control advice be more motivating for weight control than receiving generic weight control advice alone?

71 Methods single-centre, open, two-arm, parallel group, individuallyrandomised controlled trial Intervention: FTO Feedback & Weight control advice (FA); Control: Weight control advice only (AO) Self-reported motivation to control weight and adherence to tips after 1 month Please mark with an X the statement out of the next four that best describes you : I am not trying to control my weight, and I have no intention of doing so in the next month, I am not trying to control my weight, but I am thinking of doing something in the next month, I started to try to control my weight within the last month, I have been trying to control my weight for more than a month

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74 Participants Intervention FA ( n = 139) Control AO (n = 140) Gender male (n) 51.1% (71) 47.9% (67) Age (years) mean (SD) 20.2 (2.5) 20.9 (3.0) BMI kg/m kg/m 2 mean (SD) 21.2 (2.5) 21.4 (2.6) Normal weight (n) < % (128) 89.3% (125) Overweight/obese (n) >25 7.9% (11) 10.7% (15) Motivation weight control mean (SD) Range 1-3 No intention (n) Thinking about it (n) Started (n) 1.6 (0.8) 1.5 (0.7) 55.4% 23.0% 21.6% (77) (32) (30) 66.4% 15.7% 17.9% (93) (22) (25) FTO status (n) AA 13.7% (19) AT 39.6% (55) TT 46.8% (65)

75 Group differences in weight control intentions

76 Differences in weight control intentions by genotype

77 Group differences in behaviour change p = 0.847

78 Conclusions Positive effects on weight control intentions No decrease in weight control intentions with lower risk FTO result However, no effect on behaviour change Result in line with other research Limitations: Poor follow-up rates, self-selection, unmotivated population

79 Directions for future research Find out more about the personal value of genetic feedback why do people seek genetic testing? Find out more about reactions to discordant genetic test results Find out more about how people make sense of their genetic test results to improve communication Whole-Genome Sequencing is on the horizon will reactions be different?

80 I would like to thank Professor Jane Wardle Dr Ellen van Jaarsveld Dr Rebecca Beeken Professor Sir Steve O Rahilly Professor Sadaf Farooqi David Withers Study participants for funding this project

81 NPRI UK Society for Behavioural Medicine 9 th Annual Scientific Meeting Behavioural Medicine: From Laboratory to Policy