Department of Applied Health Research
Translating genomics into population-based
cancer screening strategies opportunities and challenges
Nora Pashayan
DAHR & HBRC Seminar
March 8th 2016
Today’s presentation

Is a different approach of screening needed?

How risk-stratification could be used in cancer screening
programmes?
 Polygenic risk score
 Polygenic risk score combined with non-genetic risk factors
 Implications on screening outcomes
Examples from prostate, breast and ovarian cancers

What are the challenges?
 Generating evidence
 Implementation
2
Benefit-harm balance of screening
‘All screening programmes do harm; some also do good, and of
these, some do more good than harm’*

Sir Muir Gray
Potential benefits
*
Potential harms
3
Gray et al. BMJ 2008:336(7642):480-483
Overdiagnosis

Detection of disease through screening that otherwise would
not have been diagnosed within a patient’s lifetime

Occurs in both indolent, non-progressive cancer, and
progressive cancer in an individual that dies before the cancer
progresses to manifest clinically

Epidemiological concept
4
Controversies in cancer screening
Screening for breast cancer
 Estimates of overdiagnosis vary widely
 The Independent UK Panel on Breast Cancer Screening estimated
that for every breast cancer death averted three cases are likely
to overdiagnosed *
Screening for prostate cancer by PSA
 In the European Randomised Study of Screening for Prostate
Cancer (ERSPC), at 13 years of follow up, for every cancer death
averted, 12 to 36 excess cases were detected **
 USPSTF recommends against screening
*
**
Independent UK Panel on Breast Cancer Screening. Lancet, 2012: 380(9855):1778-86
Auvinen et al. Clin Cancer Res, 2015:pii: clincanres.0941.2015
5
To improve benefit to harm balance…

Conventionally, age is used to define the target population
If breast screening programme
provided to women 47-73 years:
• 29% of the English women
population; accounting for
Population of women
• 60% of breast cancer cases
47-73
35-79
80+
Breast cancer cases (25/100,00 population)
Based on population size and number of cancer registrations
in England 2002-2006
6
Tumour progression is not homogeneous
Schematic diagram of tumour progression

The outcome of screening depends on the behaviour of the tumour

The outcome may be improved by varying age of start of screening and
frequency of screening
7
Diagram adapted from: Esserman et al. JAMA 2009: 302(15):1685-92
Shift in screening approach

A shift from ‘One size fits all’ to ‘Risk-tailored screening strategy’
(precision screening / risk-stratified screening/ personalised screening)
If it
• improves the benefit-harm balance of screening
• is cost-effective
• is acceptable to the users and to the providers
• is accessible to all
• is feasible to implement
8
Risk-tailored screening: two tiered screening
Risk-assessment
(using age, family history, genetic
profile, etc.)
Q. What factors to include?
Stratify population into
several groups
Q. How many?
Q. What thresholds?
-4
-2
0
Risk
2
4
Tailor screening to each
risk group
(different screening modality,
age for start / end of screening,
inter-screening interval)
Q. Which interventions work for different risk groups?
Diagram adapted from: Burton et al. IJPH 2012: 9(4)
9
Risk-assessment: at two different points
Pre-screening
*
Risk assessment of all
 Based on the risk
estimates, tailor screening

Post-screening
Baseline screening of all
 Based on the findings of the
baseline screening, tailor screening

Models of risk-tailored screening
 More intensified screening for those at higher than population
average risk e.g. (more often, start earlier age, stop later age)
 Less intensified screening for those at lower than population
average risk
 Fully stratified – covering the entire spectrum of risk
**
*
**
Dent et al. PH Genomics 2012 : doi: 10.1159/000345941
Chowdhury et al. Genet Med 2013: 15(6):423-32
10
Risk assessment
Risk score based on:

Non-genetic risk factors

Genetic risk factors

Common susceptibility variants at GWAS significance level

Common susceptibility variants at or below GWAS significance
level

Common variants plus the high penetrance alleles like in
BRCA1/2

Combination of genetic and non-genetic risk factors
11
Human genetic variation
Single nucleotide polymorphism (SNP)



Any two individuals share about 99.9% of their DNA sequence
Of the remaining 0.1%, about 80% consists of variation at single
nucleotides
A sequence variation may be described as polymorphic if occurs
with frequency ≥ 0.01


Most SNPs are bi-allelic
Allele – alternative form of a genetic marker

Genotype - the diploid combination of alleles at a particular locus in
an individual. For a SNP with two alleles (A, a) there are 3 possible
genotypes (AA, Aa, aa)
12
Genetic susceptibility to prostate and breast cancers




Common low penetrance variants,
(currently known 94 loci), explain ~15% of
excess Familial Relative Risk (FRR)
Subtype specific
Rare high penetrance alleles in BRCA1 and
BRCA2 explain ~15% of FRR
Rare moderate penetrance alleles in TP53,
LKB1, CDH1, PTEN, BRIP1, PALB2, ATM and
CHEK2 explain ~ 6% of the FRR

Common low penetrance variants,
currently known 100 loci, explain
~33% of the FRR
13
Polygenic susceptibility
Prostate cancer common susceptibility loci
SNP
Locus
Risk-allele
frequency
Risk ratio
per allele*
Variance**
1
2q31
0.94
1.30
0.008
2
2p15
0.19
1.15
0.002
3
2p21
0.23
1.08
0.002
4
3p12
0.11
1.18
0.002
5
3q21.3
0.28
1.12
0.002
7
4q24
0.55
1.09
0.004
8
4q22
0.66
1.10
0.003
9
4q22
0.46
1.08
0.007
10
6q25
0.29
1.17
0.013
…
100
Total

SNP: Normal variation in DNA sequence

At each locus, 2 possible alleles
- Risk allele
- Protective allele

Risk allele frequency >5%

Relative risk per allele carried <1.8

Alleles combine multiplicatively for each
locus on relative risk scale (or additively
on logarithmic scale)

Polygenic risk score: weighted sum of
the risk alleles
0.47
* Based on individuals of European ancestry
**Variance of the risk distribution due to an allele
is derived from RR conferred by each allele and
its population frequency
14
Polygenic risk distribution
3 loci  6 alleles  27 genotypes
Aabbcc
AaBbcc
AabbCc
aaBbCc
aabbCC
aaBBcc
Aabbcc
aaBbcc
aabbCc
aabbcc
AABbcc
AAbbCc
AaBbCc
AabbCC
AaBBcc
aaBBCc
aaBcCC
AABBcc
AABbCC
AAbbCC
AaBBCC
AaBbCC
aaBBCC
AABBCc
AABbCC
AaBBCC
AABBCC

For 94 SNPs there are 188 alleles and 394 possible genotype combinations

Each combination is associated with different risk and occurs at
different frequency in the population
15
Log-normal distribution of polygenic risk
2
Cases
Population


2
= Mean
= Variance

log(RR)
'
2
Mean among cases:     
Variance among cases:
 2 = variance among the population
16
Pharoah et al. Nat Genet 2002 31(1): 33-6
Distribution of polygenic risk –Prostate cancer
Risk distribution based on 100 Prostate cancer susceptibility loci
(polygenic variance 0.47)
Centile in pop
Population
-4
Cases
-2
0
log(RR)
2
4
RR
1st
0.16
10th
0.33
90th
1.90
99th
3.88
RR compared to population
average risk
17
Risk-stratification - case of prostate cancer
Proportion of prostate cancer cases explained by the proportion of the population
at highest risk (polygenic risk based on the known 100 prostate cancer loci)
1
50% FRR
Proportion of cases at highest risk
.9
•
AUC based on the
100 SNPs: 0.69
•
AUC based on 50%
Familial relative risk
RR (FRR): 0.74
.8
.7
.6
100 SNPS
.5
.4
.3
.2
.1
0
0
.1
.2
.3
.4
.5
.6
.7
.8
.9
1
Proportion of population at highest risk
AUC (area under the receiver operating characteristic curve), overall measure of predictive ability.
AUC values range from 0.5 (total lack of discrimination) to 1.0 (perfect discrimination)
18
Combined risk score
Discrimination – case of breast cancer
Assuming multiplicative interaction between genetic and non-genetic risk factors:

Models – Breast cancer
AUC
% Cases accounted by 50th
percentile of population at
highest risk
Limited non-genetic risk factors
0.58
62
Non-genetic risk factors
0.62
65
94 SNPs (15% FRR)
0.65
70
94 SNPs + non-genetic risk factors
0.68
75
94 SNPs + non-genetic risk factors + breast density
0.71
79
FRR 50% + non-genetic risk factors + breast density
0.73
81
Non-genetic risk factors*: menarche age, no. full tem pregnancies, oral contraceptive use,
benign lesion, family history, BMI, smoking and alcohol intake
Limited non-genetic risk factors**: age, pregnancies, OCP, FH
FRR: familial relative risk
•
•
•
*
Garcia-Closas et al, JNCI 2014; 106(11) | **Pharoah et al, NEJM 2008; 358(26):2996-803
19
Utility of risk-stratification - Examples
Preventative interventions

Ovarian cancer
Screening strategy


Breast cancer
Prostate cancer
20
Preventative intervention - Ovarian cancer
Ovarian cancer
• 18 common susceptibility loci at GWAS significance
• Polygenic variance of 0.09 and AUC=0.58
• Other known risk factors:
•
first-degree family history of ovarian cancer
endometriosis
parity
OCP use
tubal ligation
Decision modelling showed that risk reducing salpingo-oophorectomy is
cost-effective in post-menopausal women with ≥ 5% lifetime risk of
ovarian cancer (ICER <£15,000/QALY)*
*Manchanda et al. Gynaecol Oncol 2015; 139:487-94
21
Preventative intervention
Distribution of the lifetime risks of ovarian cancer based on the observed
combinations of the risk factors (polygenic and non-genetic)
Lifetime risk range:
0.35% to 8.78%
LR 5%
•
•
•
•
Population average LR 1.4%
The blue lines along the X axis represent the observed 214 combinations of risk factors and the height of the line
the frequency of the group
Lifetime risk based on the US population
7% of the population at <0.5% lifetime risk; and 1.5% of the population at > 4% lifetime risk
73% of women with lifetime risk >4% had no family history of ovarian cancer
Pearce et al. Cancer Epidemiol Biomarkers Prev 2015;24:671-676
22
Screening strategy - age of start of screening
Breast cancer
10-year absolute risk of developing breast cancer for women with and without
family history by polygenic risk percentiles
Women with Family History
Women without Family History
0.10
0.10
>80%
0.09
60-80%
60-80%
0.08
40-60%
0.07
20-40%
0.06
<20%
0.05
Reference
0.04
20-40%
0.06
<20%
0.05
Reference
0.04
0.03
0.03
0.02
0.02
0.01
0.01
0.00
0.00
20
25
30
35
40
45
50
55
60
Age (years)
•
40-60%
0.07
10 year risk
10 year risk
0.08
>80%
0.09
65
20
25
30
35
40
45
50
55
60
65
Age (years)
Reference: 2.5% 10-year absolute risk for developing breast cancer corresponds to risk
of UK women aged 47 , i.e. age of invitation to the UK NHS Breast Screening programme
Mavaddat et al. JNCI 2015: 107(5): djv036
23
Utility – screening programme efficiency

Screening eligibility based on age alone vs. based on absolute risk
(dependent on age and polygenic profile)

Risk threshold is equivalent to risk threshold for eligibility based on age
alone (10-year absolute risk of 2.5%)
Population eligible for
screening
Cases potentially
screen-detectable
NHS Breast screening (age-based)
Women 47-73 years*
7.44x106
22,359
Risk-stratified screening
Women 35-79 years & 10-yr AR  2.5%
(currently known 94 SNPs)
5.66x106
21,934
-1.78x106 (- 24% )
-423 (-2%)
Difference per screening round
Based on population and cancer registrations in England 2002-2006
* Currently NHSBSP covers women 50-70 years
Pashayan et al. BJC 2011: 104(10):1656-63
24
Reclassification: Eligibility for screening
Prostate cancer
Population of 100 men, 45-79 years, stratified by age group and risk threshold
categories (i), that would be eligible for screening based on age
(ii) and based on age and polygenic risk (iii)
(i)
(ii)
(iii)
< 55 years of age and 10-year absolute risk < 2%
 55 years of age and 10-year absolute risk < 2%
< 55 years of age and 10-year absolute risk  2%
 55 years of age and 10-year absolute risk ≥ 2%
Pashayan et al. BJC 2011; 104(10):1656-63
25
Reclassification: cases detectable
Prostate cancer
Population of 100 men with prostate cancer, 45-79 years, stratified by age
group and risk threshold categories (i), that would be detectable screening
based on age (ii) and based on age and polygenic risk (iii)
(i)
(ii)
(iii)
< 55 years of age and 10-year absolute risk < 2%
 55 years of age and 10-year absolute risk < 2%
< 55 years of age and 10-year absolute risk  2%
 55 years of age and 10-year absolute risk ≥ 2%
26
Polygenic risk and overdiagnosis - Prostate cancer
I. UK based study
 Total number of UK men 50-69 years: 15,747
- with screen-detected prostate cancer: 2,148 (ProtecT)
-
with clinically-detected prostate cancer: 5,991 (SEARCH, UKGPCS)
with no prostate cancer: 7,608 (ProtecT, SEARCH )

Polygenic risk score based on 66 prostate cancer SNPs

The observed prevalence of screen- detected prostate cancer is a combination
50
Pj  MSI j  oI' zj
of non-overdiagnosed
and overdiagnosed cancers
% Overdiagnosis
45
40
35
30
25
20
15
10
5
0
Q1
Q2
Q3
Q4
Quartiles of polygenic risk score
distribution in the population
Pashayan et al. Genet Med 2015: doi: 10.1038/gim.2014.192
27
Polygenic risk and overdiagnosis - Prostate cancer (2)
II. ERSPC – Finland section

Screening trial based on 71,502 men 55-67 years, followed up for 13 years,
screened with PSA every 4 years up to 3 rounds or age 71

Derived mean sojourn time and PSA test sensitivity from the incidence of
interval cancers
Using these estimates, calculated the expected number of non-overdiagnosed
cancers for each round of screening
Proportion of prostate cancers likely to be overdiagnosed derived from the
observed and expected cancers



Controls stratified to below and above 50th centile of polygenic risk distribution
derived based on 66 prostate cancer SNPs
Overall
% cancers overdiagnosed (95% CI)
42 (37-52)
Lower risk group
58 (54-65)
Higher risk group
37 (31-47)
28
Pashayan et al. BJC 2015: doi: 10.1038/bjc.2015.289
Targeted screening
100 men with screen-detected prostate cancer stratified by polygenic risk and
probability of overdiagnosis (i) and impact of targeting screening to men above
50th centile (higher) polygenic risk (ii) – based on ERSPC-Finland findings
i
ii
Lower polygenic risk overdiagnosed
Higher polygenic risk overdiagnosed
Lower polygenic risk Non-overdiagnosed
Higher polygenic risk Non-overdiagnosed
Targeting screening to men at higher polygenic risk:

50% less screening rounds

38% less overdiagnosed cacers

20% of non-overdiagnosed cancers missed
29
Implementation challenges
Chowdhury et al 2013, Genet Med 15(6):423-32 | http://www.phgfoundation.org/news/15513/
30
Key implementation issues

Feasibility: The set up and delivery of risk-stratified screening programme is
more complex than a programme with eligibility based on age alone
• Dynamic risk score
• Data safeguarding
• Ethical,legal, social implications (ELSI) and organisational complexities

Acceptability to the public, health professionals and policy decision makers
to use genetic profile in risk estimation

Risk perception and attitude of public and of professionals

Risk communication

Accessibility to ensure equitable delivery and uptake of personalised
screening programme

Healthcare professional training for interpretation of genetic risk score and
better understanding of advantages and challenges of stratified intervention

Evidence on that risk-stratified screening can do more good than harm at
affordable cost
31
Summary

There is a need for different approaches of screening to improve the
benefit to harm balance of screening and to better use of resources

Risk scores based on genetic and non-genetic risk factors could be
used for risk-stratification for risk-tailored screening

Risk-stratified screening programmes could improve the efficiency of
the screening programme

The set up and delivery of risk-stratified screening programme is more
complex than a programme with eligibility based on age alone

Robust evidence is needed on the effectiveness, cost-effectiveness of
such programmes and on best way of implementation
32
Acknowledgements
Funding
 Cancer Research UK training and clinician scientist fellowships
 EU-FP7 funded Collaborative Oncological Gene environment
Study (COGS)
Enabling
 Prof Paul Pharoah
 Prof Stephen Duffy
 Foundation for Genomics and Population Health (PHG
Foundation)
33