Recalculating Breast Cancer Risk and Exploring the Experience of Receiving Updated Breast Cancer Risk Estimates in Women With a Family History of Breast Cancer
Conditions: Breast Cancer Risk
Sponsor: University of Manchester

 
 
 
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RESEARCH PROTOCOL 
 
FH-Risk 2.0: Updating breast 
cancer risk estimates 
 
Recalculating breast cancer risk and exploring the experience of receiving 
updated breast cancer risk estimates in women with a family history of 
breast cancer 
Protocol version no: 
 
 
 
Version 4.0 
Protocol version date: 
 
 
 
18.08.2023 
IRAS project ID: 
 
 
 
 
290959 
Funder’s reference no: 
 
 
 
MR/N013751/1 
 
 
Version Number 
Author 
Effective Date 
Reason for Change 
4.0 
Victoria Woof, 
& Anthony 
Howell 
18/08/2023 
Change to add an additional 
participant procedures to the 
questionnaire arm (Part 4).  
 
This protocol has regard for the HRA guidance 
 
 
 
 
 
PROTOCOL VERSION 4.0, 18.08.2023 AUTHORISATION SIGNATURES 

 
 
 
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The undersigned confirm that the following protocol has been agreed and accepted and that the Chief 
Investigator agrees to conduct the study in compliance with the approved protocol and will adhere to 
the principles outlined in the Declaration of Helsinki, the Sponsor’s SOPs, and other regulatory 
requirement. 
 
I agree to ensure that the confidential information contained in this document will not be used for any 
other purpose other than the evaluation or conduct of the investigation without the prior written 
consent of the Sponsor. 
 
I also confirm that I will make the findings of the study publically available through publication or other 
dissemination tools without any unnecessary delay and that an honest accurate and transparent 
account of the study will be given; and that any discrepancies from the study as planned in this 
protocol will be explained. 
 
Chief Investigator: 
Name & Role:  
 
 
   Signature: 
 
 
Date authorised: 
     Prof Gareth Evans 
 
For and on behalf of the study sponsor:  
Name & Role:  
 
 
   Signature: 
 
 
Date authorised: 
    Ms Lynne Macrae 
 
 
 
 
 
 
 
 
 
 
 
 
 
RESEARCH TEAM AND STUDY CONTACTS: 

 
 
 
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Chief Investigator: 
Name: Professor Gareth Evans 
Address: University of Manchester, Department 
of Medical Genetics (6th Floor), St Mary’s 
Hospital, Oxford Road, Manchester M13 9PL 
UoM Email: gareth.d.evans@manchester.ac.uk  
MFT Email: gareth.evans@mft.nhs.uk   
Telephone: 0161 276 6206 
Fax: N/A 
 
Sponsor: 
Name: The University of Manchester  
Sponsor contact: Ms Lynne Macrae, Faculty 
Research Practice Governance Coordinator 
Address:  
Faculty of Biology, Medicine and Health  
5.012 Carys Bannister Building  
University of Manchester 
M13 9PL 
Email: FBMHethics@manchester.ac.uk 
Telephone: 0161 275 5436 
 
Lead R&D Trust contact: 
Name: Hayley Brooks 
Address: Manchester University NHS 
Foundation Trust, Wythenshawe Hospital. 
R&D email: R&D.applications@mft.nhs.uk 
Email: Hayley.brooks@mft.nhs.uk 
Telephone: 0161 291 2433 
 
Co-investigator(s): 
Name: Professor David French 
Address: Manchester Centre of Health Psychology, 
School of Health Sciences, The University of Manchester,  
Coupland 1, Oxford Street, Manchester, M13 9PL 
Email: david.french@manchester.ac.uk  
Telephone: 0161 275 2605 
 
Name: Miss Victoria Woof 
Address: Manchester Centre of Health Psychology, 
School of Health Sciences, The University of Manchester,  
Coupland 1, Oxford Street, Manchester, M13 9PL 
Email: victoria.woof@postgrad.manchester.ac.uk  
Telephone: 0161 275 2572 
 
Name: Dr Lorna McWilliams 
Address: Manchester Centre of Health Psychology, 
School of Health Sciences, The University of Manchester,  
Coupland 1, Oxford Street, Manchester, M13 9PL 
Email: lorna.mcwilliams@manchester.ac.uk  
Telephone: 0161 275 2555 
Fax: N/A 
 
Name: Professor Anthony Howell 
Address: Division of Cancer Sciences, University of 
Manchester, Manchester Cancer Research Centre, The 
Christie NHS Foundation Trust, 555 Wilmslow Road, 
M20 4GJ 
Email: anthony.howell@manchester.ac.uk   
Telephone: 0161 306 3248 
 
Name: Dr Sue Astley 
Address: Division of Informatics, Imaging and Data 
Sciences, University of Manchester, Stopford Building, 
Oxford Rd, Manchester M13 9PT 
Email: sue.astley@manchester.ac.uk 
Telephone: 0161 275 5162 
 
Name: Dr Elaine Harkness 
Address: Division of Informatics, Imaging and Data 
Sciences, University of Manchester, Stopford Building, 
Oxford Rd, Manchester M13 9PT 
Email: Elaine.Harkness@manchester.ac.uk 
 
Name: Dr Mayada Haydar 
Address: Nightingale Breast Screening Centre, 
Manchester University Foundation Trust, Southmoor 
Road. Manchester M239LT  
Email: mayada.haydar@mft.nhs.uk  
 
Name: Dr Caroline Parkin 
Address: Nightingale Breast Screening Centre, 
Manchester University Foundation Trust, Southmoor 
Road. Manchester M239LT  
Email: caroline.parkin2@mft.nhs.uk  
 
Name: Dr Sacha Howell 

 
 
 
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Address: Division of Cancer Sciences, Manchester 
Academic Health Science Centre, University of 
Manchester, M204BX 
Email: sacha.howell@manchester.ac.uk 
Telephone: 0161 446 3721 
 
 
 
 

 
 
 
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STUDY SUMMARY 
Study Title 
Recalculating breast cancer risk and exploring the experience of receiving updated 
breast cancer risk estimates in women with a family history of breast cancer 
Short title 
Updated breast cancer risk estimates 
Study 
Design 
Mixed methods, Quantitative/Qualitative 
Study 
participants 
Women who attend the Family History, Risk and Prevention Clinic (FHRPC) who were 
participants in the FH-Risk study (REC form 10/H1008/24) in 2010-2012 
Planned 
size of 
sample  
Part 1 (risk estimate recalculation on 954 women. All women (between the ages of 25 
and 60) will be invited to receive updated risk feedback): 
 
Recalculation of breast cancer risk will be performed on 954 women’s data from the 
FH-Risk study. Women in the FH-Risk study consented to: 
 
All necessary genetic tests on a blood DNA sample 
 
The measurement of mammographic density 
 
That all information gathered can be stored by the custodians for possible use 
in future ethically approved research studies. 
Recalculation will be based on SNPs313, high and moderate risk gene analysis, 
mammographic density and updated risk prediction models (Tyrer-Cuzickv8, 
BOADICEAV [CanRisk]). 
 
FH-Risk women still in follow up and those who have been discharged from the FHRPC 
will be given the opportunity to learn of their recalculated risk via a clinic 
consultation.  
 
Part 2 (interviews with 20 women):  
  
Up to 20cross-sectional interviews across the following groups: 
 
 women who have received an increased breast cancer risk estimate (by at least 
one category) since their initial estimate 
 women who have received a decreased (by at least one category) breast cancer 
risk estimate since their initial estimate 
Part 3 (think-alouds with 30 women):  
Up to 30 cross-sectional ‘think-aloud’ interviews across the following groups: 
 women who have received an increased breast cancer risk estimate (by at least 
one category) since their initial estimate 

 
 
 
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 woman who have received a breast cancer risk estimate that has not changed 
category since their initial estimate  
 women who have received a decreased (by at least one category) breast cancer 
risk estimate since their initial estimate. 
Part 4 (questionnaire study): 
Between 300 and 400 questionnaires sent to women who: 
 
have had a consultation about their updated breast cancer risk from Part 1 of 
this study. 
Study 
Period 
3 years 
Research 
Aims 
i) To determine change in NICE categories of risk (average, moderate and high risk) 
when new risk factors are added to standard breast cancer risk models. 
ii) To explore how much new risk models (including MD, SNP313 and mutation 
testing) change risk estimates previously communicated to women as part of the 
FH-Risk study. 
iii) To explore whether a change in breast cancer risk affects credibility of how risk is 
calculated and communicated to women, including trust in its use  
iv) To use the findings to understand how and in what format(s) changes to breast 
cancer risk estimates should be communicated in the future to enable women to 
properly understand change in risk, potential change in management and manage 
emotional sequelae  
v) To use the findings to create information materials to communicate changes in 
breast cancer risk and assess their usability 
vi) To use the findings to develop a questionnaire to be given to women who 
previously took part in the FH-Risk study and assess the impact of changed risk in 
a definitive study 
Funders & 
contact 
details 
NIHR Manchester Biomedical Research Centre Cancer Prevention and Early Detection 
Theme; Professor Gareth Evans, University of Manchester, Manchester Cancer 
Research Centre, 555 Wilmslow Road, Manchester M20 4GJ Email address: 
gareth.evans@mft.nhs.uk Tel: 0161 701 5104 
 
This study has been funded my an MRC PhD studentship, awarded to Miss Victoria 
Woof – MR/N013751/1 
 
 
 
 
 
 
 
 

 
 
 
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Table of Contents  
Section 
Contents 
Pg 
1.0 
STUDY PROTOCOL 
7 
1.1 
Research abstract 
7 
1.2 
Research background 
8 
1.3 
Rationale 
17 
2.0 
STUDY OBJECTIVES 
18 
2.1 
Aims 
18 
2.2 
Objectives 
18 
3.0 
STUDY DESIGN & PROTOCOL 
19 
3.1 
Part 1: Re-evaluation of breast cancer risk in the FH-Risk study population 
19 
3.2 
Part 1: Clinical consultations with FH-Risk women still in follow up at the FHRPC 
25 
3.3 
Part 1: Data management (risk recalculation) 
27 
3.4 
Part 2: Exploring the experiences of receiving updated breast cancer risk estimates in women with a 
family history of breast cancer: an interview study 
28 
3.5 
Part 3: Optimising and refining information materials for the communication of revised risk estimates: a 
‘think-aloud’ interview study 
32 
3.6 
Data management for Part 2 and 3 (qualitative interviews) 
35 
3.7 
Part 4: Communicating an updated breast cancer risk: A questionnaire study 
 
3.8 
Data management for Part 4 (questionnaire study) 
 
4.0 
CONSENT 
38 
5.0 
ETHICAL AND REGULATORY APPROVAL 
38 
6.0 
STATEMENT OF INDEMNITY 
40 
7.0 
SPONSOR DETAILS & STUDY CONTACT DETAILS 
40 
8.0 
FUNDING 
41 
9.0 
PUBLICATION POLICY 
41 
10.0 
REFERENCES 
41 

 
 
 
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11.0 
Participant Pathway 
50 
Appendices 
Appendix 1: FH-Risk Consent form 
51 
1.0 STUDY PROTOCOL 
1.1 Research abstract 
Estimation of breast cancer risk is important since it enables selection of a high and moderate risk 
population who benefit from more frequent breast screening and the introduction of targeted 
measures to reduce risk such as lifestyle change, chemoprevention and risk reducing surgery. 
Traditionally, risk is estimated by combining information concerning family history and non-familial 
factors, such as age of menarche and first pregnancy. Subsequent management is related to the 
degree of risk (high, moderate or average) according to NICE guidelines. Members of the research 
team and others have added mammographic density (MD) and breast cancer risk associated single 
nucleotide polymorphisms (SNPs) to risk models which improves the accuracy of risk estimation but 
which may change the original given risk and risk management given before the updated models 
became available. The objective of this study is to quantify change in risk and risk management when 
MD and SNPs are incorporated into two standard models (Tyrer-Cuzick v8 & BOADACEA V). A second 
objective of the study is to determine the psychological effects of change of risk and management. 
This study will use participant data from the Family History Risk (FH-Risk) study to recalculate risk. The 
FH-Risk study population consists of 954 women referred to the Family History Risk and Prevention 
Clinic (FHRPC) between 2000 and 2012 who gave informed consent for DNA testing and estimation of 
MD as part of the FH-Risk study which recruited between 2010 and 2012. Change in risk and 
management will be calculated by comparing given risk at the time of clinic entry compared with re-
estimated risk when MD and SNPs are added to the risk models according to NICE guidelines. Risk will 
be estimated retrospectively at the time of entry to the clinic and the time of latest follow up. All of 
those who took part in the FH-Risk study) will be given the opportunity to discuss their updated risk.   
 
Following the risk update consultation a sample of women will be asked whether they would like to 
take part in an interview to assess the psychological effects of the change, as well as their views and 
perceptions of the change. These interviews will inform the development of information materials for 
communicating recalculated risk. These materials will be appraised via ‘think aloud’ interviews with 
women from the FH-Risk study who have received their recalculated risk. The results of this study are 
likely to inform the next iteration of NICE management guidelines for Family History Clinics, as well as 
inform the creation of patient facing information materials to aid patient - healthcare professional 
communication.  The findings will also be used to develop a questionnaire to be given to women who 

 
 
 
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previously took part in the FH-Risk study (and who have been discharged from the clinic) to assess the 
psychological impact of changed risk in a definitive study. 
1.2 Research background 
Management of breast cancer risk: Family History Clinics 
 
In the 1980’s publicity concerning the rise in the incidence of breast cancer and the introduction of 
breast screening programmes increasingly led women with a family history of the disease, to seek 
advice concerning management of their personal risk of breast cancer. In response to primary care 
physicians and colleagues within our breast oncology service, we set up a referral Family History Risk 
& Prevention Clinic (FHRPC) Manchester, UK in 1987 with a cancer genetics component initiated in 
1990. The clinic serves the catchment population of a National Health Service Breast Screening Centre 
(the Nightingale Centre) which covers a total population of 1.8m, (just over half the population of 
Greater Manchester) although women at high risk may be specially referred from a population of 
approximately 5 million in the Northwest Region of England.   
 
During clinic appointments risk was explained, annual breast screening initiated and advice given 
concerning diet and lifestyle factors which might affect risk. Later genetic testing (1994), 
chemoprevention (1992) as part of the IBIS I clinical trial) and risk reducing surgery (1994) were 
introduced. Local (Evans 1994) and, later, national guidelines for management of women with a family 
history were published for the UK (Eccles 2000, NICE McIntosh 2004), the USA (Hoskins 1995, NCCN 
Merajver 2003). In the UK NICE guidelines indicate management should be stratified in the clinic. 
Women estimated to be at high risk (≥ 30% lifetime risk) are offered annual mammographic screening 
until 60, and chemoprevention or risk reducing surgery. Women at moderate risk (17-20% lifetime 
risk) are offered mammographic screening until 50 and are ‘considered’ (rather than ‘offered’ 
chemoprevention (Table 1)) but not considered for risk reducing surgery. Thus precise estimation of 
risk is important for management. Since the inception of the FHRPC in 1987, 49.7% of women were at 
high risk (n=5390) whereas 50.3% were at moderate risk (Howell 2020). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
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Table 1. Nice categories of lifetime BC risk and subsequent management dependant on risk 
 
 
 
 
 
 
Models for assessment of breast cancer risk.      
Early observations on the greater incidence of breast cancer in nuns and the increased effect on risk 
of older age of first birth (Ramazzini 1713, MacMahon 1970) and family history (Broca  1866, Lynch & 
Krush 1966) suggested that both reproductive/lifestyle and familial factors were related to risk of 
breast cancer (Brinton 1982). Models combining these risk factors were developed to estimate overall 
breast cancer risk in individuals (Gail 1989). 
 
The Gail (1989) model includes the risk factors shown in table 2 although only a limited first degree 
family history is used. In our clinic the expected number of cancers compared with the number 
observed was 0.48 using the Gail model (Amir 2003, Evans 2014). Before 2004, we used the Claus 
model modified by other risk factors which gave an E/O of 1.22. After 2004 the Tyrer -Cuzick model 
was used which includes a larger number of risk factors, including a more extensive family history and 
gave an E/O of 0.81 (Tyrer 2004). Recently even greater precision has been obtained by adding more 
risk factors including mammographic density and breast cancer risk associated single nucleotide 
polymorphisms (SNPs). (Tyrer-Cuzick model v8. 2017; http://www.ems-trials.org/riskevaluator/). 
 
Some models assess only family history in order to help predict the probability of a gene mutation. 
(Mutations are now called pathological variants [PVs]). The most widely used model to predict PVsl is 
BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (see 
Table 2) (Antoniou 2004). Very recently the group in Cambridge, who developed the early BOADICEA 
models added hormonal/lifestyle factors and also mammographic density and SNPs to the model (Lee 
2019).  
 
 
 
 
 
 
 
 
 

 
 
 
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Table 2: Known risk factors and their incorporation into existing risk models. The models which 
depend only on family history do not predict overall risk well. 
 
E/O Expected over observed cancer ratio (all models assessed underestimated cancer occurrence) 
OCP- Oral Contraceptive.   
 
 
RR at 
extremes 
Gail 
(1989) 
Claus 
(1994) 
BRCAPRO  
    (1998) 
Tyrer-
Cuzick 
V6 (2017) 
BOADICEA 1 
(2004) 
B0ADICEA V  
CanRisk (2019) 
 
Prediction  
Amir et al 
[validation study 
[E/O] 
 
0.48 
0.56 
0.49 
0.81 
 
Not assessed 
95% Confidence 
interval [30] 
 
(0.54-0.90) 
(0.59-0.99) 
(0.52-0.80) 
(0.85-1.41) 
 
Not assessed 
 
Personal information 
Age (20-70) 
30 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Body mass index 
2 
No 
No 
No 
Yes 
No 
Yes 
Alcohol intake (0-4 
units) daily 
1.24 
No 
No 
No 
No 
No 
 
Yes 
 
Hormonal / reproductive factors 
Age of menarche 
2 
Yes 
No 
No 
Yes 
No 
Yes 
Age of first live 
birth 
3 
Yes 
No 
No 
Yes 
No 
Yes 
Age of menopause 
4 
No 
No 
No 
Yes 
No 
Yes 
HRT use 
2 
No 
No 
No 
Yes 
No 
Yes 
OCP use 
1.24 
No 
No 
No 
No 
No 
Yes 
Breast feeding 
0.8 
No 
No 
No 
No 
No 
No 
Plasma oestrogen 
5 
No 
No 
No 
No 
No 
No 
Personal breast disease 
 
 
Breast biopsies 
2 
Yes 
No 
No 
Yes 
No 
No 
Atypical ductal 
hyperplasia 
3 
Yes 
No 
No 
Yes 
No 
No 
Lobular carcinoma 
in-situ 
4 
No 
No 
No 
Yes 
No 
No 
Breast density 
6 
No 
No 
No 
Yes 
No 
Yes 
Family History 
 
 
First degree 
relatives 
3 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Second degree 
relatives 
1.5 
No 
Yes 
Yes 
Yes 
Yes 
Yes 
Third degree 
relatives 
 
No 
No 
No 
No 
No 
Yes 
Age of onset of 
breast cancer 
3 
No 
Yes 
Yes 
Yes 
Yes 
Yes 
Bilateral breast 
cancer 
3 
No 
No 
Yes 
Yes 
Yes 
Yes 
Ovarian cancer 
1.5 
No 
No 
Yes 
Yes 
Yes 
Yes 
Male breast cancer 
3-5 
No 
No 
Yes 
No 
Yes 
Yes 

 
 
 
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Thus, since inception of the FHRPC, two major models have evolved. The Tyer-Cuzick model is widely 
used around the world. The CanRisk model evolved from the BOADICEA model (Antonionou 2004) was 
not published with the addition of non-family history risk factors until August 2019 but is already 
becoming widely used including in our own clinic (Lee 2019). In the clinics women are given their risks 
and are then followed up by annual mammography.  The question we wish to ask in the study outlined 
here is how much these new models change previously given risk and should NICE guidelines be 
changed to include them? Most women in the clinic will have been given risk according to the early 
modified Claus and Tyer-Cuzick models. Our preliminary information indicates that using the new 
models risk can be different and thus potentially alter management according to NICE guidelines 
(Evans 2017. Figure 1). In addition to managing changes in NICE categories we wish to assess the 
psychological effects of change in risk. 
 
The Tyrer-Cuzick model  
The Tyrer-Cuzick model was first published in 2004 (Tyrer 2004) and updates were produced in 2013 
(Version 7) and 2017 (Version 8). Statistically, the model is a combination of two approaches, 1) 
estimation from family pedigree data of the probability of carrying one or more high-risk mutations 
using segregation analysis, and then using the penetrance of a mutation to alter age-specific risk and, 
2) regression analyses by combination of a regression model derived from case-control or cohort 
studies of specific relative risks combined with absolute population-based incidence rates from cancer 
registries (Brentnall & Cuzick 2020).  
 
The model combines up to second degree family history and non-familial risk factors. These are family 
history, BRCA1 and BRCA2 in the family, age of menarche, age of first full term pregnancy, age of 
menopause, height, weight, use of HRT. Version 7 (2013) added Ashkenazi family history and V8 added 
polynomial risk scores (PRS) based on as many breast cancer risk associated SNPs that were available 
and mammographic density (measured either by visual analogue scale [VAS], Volpara or BIRADs). The 
model gives 10 year and lifetime risk until the age of 85. There is some evidence of a degree of under-
prediction in women at low risk and over-prediction at high risk (Brentnall and Cuzick 2020). The risk 
prediction is stable for at least 19 years (Brentnall 2020). Our studies indicate the potential change in 
risk when adding mammographic density (Brentnall 2015, Warwick 2014), a PRS (Cuzick 2017, Evans 
2017) and combining density and PRS (van Veen 2018, Brentnall 2020). 
 
The BOADICEA V (CanRisk) model 
The first BOADICEA model was published in 2004 based on family history alone (Antoniou 2004). 
BOADICEA V extends the BOADICEA BC risk prediction model to allow for consistent prediction of 
personal BC for unaffected women on the basis of their rare (high risk and moderate risk) BC genetic 

 
 
 
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susceptibility variants, common genetic variants, explicit family history and other known risk factors. 
The model incorporates the effects of truncating variants in BRCA1, BRCA2, PALB2, CHEK2, ATM and 
a PRS based on 313 single-nucleotide polymorphisms (SNPs) The other risk factors included in the 
model are MD, age at menarche, age at menopause, parity, age at first live birth, oral contraceptive 
use, HRT use, height, BMI, and alcohol intake (Lee 2019). Among all factors considered, the predicted 
UK BC risk distribution is widest for the PRS, followed by mammographic density. The highest BC risk 
stratification is achieved when all genetic and lifestyle/hormonal/reproductive/anthropomorphic 
factors are considered jointly. With all factors, the predicted lifetime risks for women in the UK 
population vary from 2.8% for the 1st percentile to 30.6% for the 99th percentile, with 14.7% of 
women predicted to have a lifetime risk of ≥17–<30% (moderate risk according to National Institute 
for Health and Care Excellence [NICE] guidelines) and 1.1% a lifetime risk of ≥30% (high risk). 
‘New’ risk factors incorporated into risk models – mammographic density & DNA tests 
1. Measurement of mammographic density 
The amount of dense tissue in proportion to the size of the breast, mammographic density is a strong 
breast cancer risk factor (figure 2). In the NIHR PROCAS (Predicting Risk of Cancer At Screening) study 
we assessed several methods for measuring the proportion of breast tissue in the breast in the NHS 
Breast Screening Programme. The importance of breast density for predicting risk is shown in figure 
1. The risk of breast cancer (odds ratio) was 4 fold higher in the top quintile of density compared with 
the lowest. We compared five methods for density measurement: visual analogue scales (VAS), an AI 
method based on convolutional neural networks (CNN) and a commercial measure, Densitas. These 
three measures give density as an area of dense tissue compared with the area of the breast. Two 
other commercial measures were used which measure the volume of breast dense tissue and the 
volume of the breast Volpara and Quantra. The percent density assessed by our radiologists using a 
0-100 visual analogue scale (VAS) was the optimal method but is cumbersome in practice. Our group 
has developed an automated method depending on convoluted neural networks (CNN. Ionescu 2017) 
which gives similar results to VAS and may be the method of choice in the future for estimating density 
for incorporating density into the risk models used in the clinic. The automated volumetric method, 
Volpara, performed well and we will also assess this method in the current study. The most widely 
used visual method for estimating density in breast radiology is the Breast Imaging-Reporting and Data 
System (BIRADs). It is a risk assessment and quality assurance tool developed by the American College 
of Radiology that provides a widely accepted lexicon and reporting schema for imaging of the breast. 
The BIRADs for density is shown in figure 3. The breast appearance is divided into four measures, (A) 
the breasts are almost entirely fatty, (B) there are scattered areas of fibroglandular density, (C) the 
breasts are heterogeneously dense, which may obscure small masses and (D) the breasts are 

 
 
 
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extremely dense, which lowers the sensitivity of the mammogram. The Tyrer-Cuzick model can use 
VAS, Volpara or BIRADs for risk calculation whereas BOADICEA V uses BIRADS only (T-C v8, BOADICEA 
V). 
 
Figure 2 Odds ratio of developing breast cancer by quintiles of percent density and method 
(Astley 2018, Ionescu 2019) (VAS – visual analogue scale. CNN – convoluted neural networks) 
 
Figure 3. BIRADs categories (see https://densitas.health/blog/breast-density-scales) 
 
2. DNA measurements of risk of breast cancer 
Genome wide association studies (GWAS) (Michailidou 2017, Turnbull 2010, Easton 2007, Mavaddat 
2019) reported a series of common genetic variants (SNPs) each carried by 28-44% of the population 
associated with a 1.07-1.26 relative risk of BC. The first seven breast cancer risk associated variants 
where published in 2007 (Easton 2007). At the time of the initiation of the FH-Risk study (see below 
for explanation of FH-Risk study) in 2010, details of 18 variants were available. As a result of studies 
from the Breast Cancer Associated Consortium (which includes data from Manchester) a total of 313 
variants are available and can be incorporated into the Tyrer-Cuzick v8 and BOADACEA V models 
(Mavaddat 2019). In an early report from the FH-Risk study we demonstrated that adding the results 

 
 
 
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of the 18 SNP score to the Tyrer-Cuzick model altered risk such that 25% of women increased risk and 
25% decreased risk indicating the importance of SNPs in defining risk in women at moderate and high 
risk of breast cancer (Evans 2017).  
 
PVs (mutations) in the TP53, BRCA1 and BRCA2 genes were shown to be associated with a high risk 
breast cancer in the 1990ies (Malkin 1990, Miki 1994, Wooster 1995). Since a number of potential 
breast cancer genes have been discovered. In order to delineate genes which are definitively related 
to breast cancer the Breast Cancer Association Consortium estimated the breast cancer risk 
association of 34 potentially important genes in  67,269  women diagnosed with breast cancer and   
59,299 controls from a large number of study groups including PROCAS and FH-Risk studies from 
Manchester (Dorling 2020). The genes reported with PVs which were associate with a greater than 
twofold increase in breast cancer risk are ATM, BRCA1, BRCA2, CHEK2, PALB2, BARD1, RAD51C, 
RAD51D and TP53.  All women in the FH-Risk study have been tested for SNP313 and all potential 
breast cancer genes. 
 
Family History Risk study (FH-Risk) 
The FH-Risk study aimed to assess, validate and improve breast cancer risk assessment models from 
the FHRPC at the Nightingale Centre in the North West of England (Evans et al., 2016). The FH-Risk 
study (funded by NIHR) recruited 954 women without a breast cancer diagnosis between November 
2010 and November 2012 in a nested case control study who were age-matched to women who had 
been diagnosed with breast cancer (Evans 2017). The women in the control group, who form the basis 
of the study outlined in this protocol, had their first FHRPC visit between ~2000 and 2012. At their first 
visit if during the period 1993-2010, risk was assessed using Claus tables modified by hormonal risk 
factors as this is more accurate for predicting risk compared with family history alone (Amir 2003, 
Evans 2013). Later in this period, the TC V6 model (Tyrer, Duffy, & Cuzick, 2004) was validated to be 
the most accurate available model to predict individual breast cancer risk and was therefore 
subsequently used in the FHRP. Women received individual lifetime risk estimates and counselling 
when attending the FHRPC for the first time. Those with lifetime risks of 1 in 4-6 (moderate risk) or >1 
in 3 (high risk) were included in the clinic annual screening programme according to NICE Guidelines. 
The women also provided blood samples to assess SNPs and other genetic information between 2010-
2012. They received the results related to SNP risk via letter (as the SNPs had not been fully validated 
at time of sending letters this simply indicated whether risk had not changed substantially with odds 
ratios of 0.5-2 or if below or above that risk had decreased or increased as a result of the DNA tests. 
No personalized feedback was given). The results of estimated MD were not fed back at the time. The 
research group were later able to validate that the use of 18 SNPs accurately moved 50% of women a 
whole risk category upwards or downwards (Evans et al., 2017).  

 
 
 
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Pilot study. Currently, the FHRPC clinic follows up approximately 4,000 women per year for 12 
monthly breast screening because of their risk. In a pilot study between September 2017 and August 
2018, a sub-sample of women in the FH-Risk study who attend the FHRPC for screening had their risks 
re-evaluated using the updated TC Version 8 (V8). In 106 women whose SNPs and MD were added to 
the TC model, their lifetime risk estimate of breast cancer decreased by one or more categories (i.e. 
from high to moderate or moderate to low) in 53 (50%) women compared with TC alone (no MD or 
SNP included). Forty (38%) women did not change risk category whilst 13 (12%) women had an 
increased risk estimate by one or more category. See Figure 3 for changes in estimates according to 
TC model alone, TC model and MD TC model, SNPs and MD.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3 Change in risk in 106 women from the FH-Risk study when mammographic density (MD) and 
polygenic risk score derived from single nucleotide polymorphisms (SNP18) were used in the Tyrer-
Cuzick (TC) model  (blue – TC alone, red – TC+MD, green – TC+MD+SNPs). 
 
These data highlight that substantial changes in risk can occur when MD and SNPs are added into the 
standard TC model. The women who participated in the study have not yet been offered the 
opportunity to receive their updated risk estimate. Similarly, the research team has the opportunity 
to provide women with feedback on a panel test of breast cancer related genes (for example, BRCA1, 
BRCA2, PALB2, CHECK2, ATM) that indicate mainly high lifetime risk of developing breast cancer.  
 
Psychological, cognitive and behavioural effects of breast cancer risk communication 
Risk prediction models have been found to support informed choices in women identified via FHRPCs 
as being at high risk of developing breast cancer in relation to aiding difficult decisions around risk 
reducing surgery (Evans, Baildam, et al., 2009; Evans, Lalloo, et al., 2009). One of the largest research 

 
 
 
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studies to explore the use of risk prediction in a population-level breast screening programme was 
conducted in Greater Manchester with over 56,000 women (Predicting Risk of Breast Cancer at 
Screening, PROCAS; Evans et al., 2016). One of the main reasons for the study was that only around 1 
in 6 women in the population who would receive a high-risk estimate (and thus was eligible to receive 
more frequent screening or discuss preventative measures) have been identified. One might think that 
because this figure is low, women are not interested in knowing their breast cancer risk. However, a 
key finding from Evans and colleagues (2015) was that 96% of women (excluding those who received 
an ‘average’ risk estimate; 2-4.99%) wanted to receive their risk upon receipt of a risk appointment 
invite. Similarly, 70% opted to have a face to face appointment to discuss their risk. Interestingly, those 
who received high risk estimates were more likely to attend a review appointment (74%) compared 
to women who received moderate (73%) or low risk estimates (55%).  
 
In a nested study, a sub-sample of women completed questionnaires assessing the psychological 
(anxiety, cancer worry, perceived risk of breast cancer) and behavioural (future screening intentions, 
lifestyle changes) effects of risk-stratification with or without a delay in receiving risk estimate 
feedback (French et al., 2018). There was no evidence that providing risk information influenced 
intentions to make lifestyle related changes although women who received a high-risk estimate were 
not included in this study. However, women were less anxious if they received their risk estimate 
compared to women in the control group who had not yet received their risk results. A UK survey also 
found that over two thirds of the sample (n=942 women) felt that genetic testing does more good 
than harm and that varying breast screening intervals is a good or very good idea (Meisel et al., 2015). 
However a recent review of qualitative research suggests that the acceptability of risk stratified 
screening depends on women’s perceived risk of breast cancer (Rainey, van der Waal, Wengström, et 
al., 2018). Cancer worry and perceptions of being at risk are associated with uptake of breast cancer 
prevention medication and surgery in high risk women. Meanwhile low perceived risk has been linked 
to scepticism around preventative healthcare in women with high risk of breast cancer (Salant, 
Ganschow, Olopade, & Lauderdale, 2006). Further, poor understanding of genetic testing and 
heightened anxiety can be experienced during risk assessment in women with a family history of 
breast cancer (Phelps, Wood, Bennett, Brain, & Gray, 2007). This suggests that women can experience 
uncertainty and misunderstanding during risk communication which could undermine trust and 
credibility of its use to support healthcare decision making. Similarly, studies exploring risk information 
recall  have shown that individuals tend to only remember the ‘gist’ of information about probabilities; 
this is therefore an important aspect to consider when communicating risk (Hopwood, Howell, Lalloo, 
& Evans, 2003). For example, a woman who received their risk of breast cancer may not recall what 
their risk was, particularly if a number of years have lapsed. Equally, a woman has to make an informed 
decision as to whether she would like to receive some or all risk estimate information for example, 

 
 
 
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genetic-related risk or risk information related to prevention of breast cancer (Kaphingst et al., 2016). 
Combined, these are important factors to consider regarding breast cancer risk communication in 
order to establish the likely harms and benefits of personalised breast cancer risk. However, there 
appears to be a paucity of research that has explored this. 
1.3 Rationale 
Women in the FHRPC were given partial or incomplete information about their breast cancer risk. We 
are now in a position to provide women in the FH-Risk study with a more accurate estimate of their 
risk due to use in the models of additional powerful risk factors. We can do this by using the updated 
Tyrer-Cuzick V8, MD (measured using Predictive VAS, BI-RADS and Volpara), SNP313 and genetic 
information for high and moderate risk genes. A recalculation of risk could result in women changing 
risk category, either up or down (some women will not have an altered risk estimate). The question 
we wish to ask in the study outlined here is how much these new models change previously given risk 
and should NICE guidelines be changed to include them? Most women in the clinic will have been 
given risk according to a modification of the Claus model (Amir 2003) and the early Tyer-Cuzick model. 
Our preliminary information indicated using the new models that risk can be different and thus may 
alter management according to NICE guidelines. As a result, women who still currently attend the 
FHRPC should be given the opportunity to learn of their revised risk estimate. This opportunity will be 
provided as part of this study. 
 
Furthermore, conducting initial explorative research using qualitative methods can facilitate the 
development of appropriate risk materials and communication strategies that are acceptable to 
women. This is particularly important given that data on risk perception can be difficult to interpret. 
If the purpose of providing change in risk estimates is to inform healthcare decisions, framing the 
change in risk i.e. losses versus gains, will also have an effect on risk perceptions (and also may change 
subsequent clinical management). Importantly, there appears to be no published studies that have 
examined the effect of changes in breast cancer risk estimates during follow up with women, yet this 
is a vital aspect of risk communication. Likewise, qualitative research with a smaller sample of the FH-
Risk case-control group can inform the development of definitive quantitative assessments to examine 
the psychological consequences of re-evaluated risk estimates in the remaining sample. This is crucial 
given that up to half of women may have a changed risk estimate higher or lower compared to the 
category that they initially received. Before any change in service that provides re-evaluated breast 
cancer risk estimates could be implemented, it is important to establish the likely harms and benefits 
of providing women with their personalized potential change in risk of breast cancer. 

 
 
 
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2.0 AIMS & OBJECTIVES OF THE STUDY 
2.1 Aims 
The aims of this study are: 
i) 
To determine change in NICE categories of risk (average, moderate and high risk) when new 
risk factors are added to standard breast cancer risk models. 
ii) To explore how much new risk models (including MD, SNP313 and mutation testing) change 
risk estimates previously communicated to women as part of their management plan at first 
attendance at the FHRPC.  
iii) To explore the experiences of risk estimate changing over time in women with a family history 
of breast cancer. This will be assessed by using the re-evaluated risk estimates using the TC v8 
and BOADICEA (with added MD, SNP313 and mutation testing) in a sub-sample of FH-Risk 
study participants and conducting qualitative interviews with women who choose to receive 
their re-evaluated breast cancer risk estimate. 
iv) To determine whether change in management differs between the three density measures in 
the Tyrer-Cuzick model. (BIRADs, Predictive VAS AND Volpara). 
v) To compare the degree of management change between the Tyrer-Cuzick (v8) and the 
BOADICEA model (v5) (Can-Risk). 
vi) To create information materials informed by participant interviews to communicate and 
inform women of their breast cancer risk re-evaluation and have these materials appraised by 
a sample of the FH-Risk women who have received their recalculated risk. 
2.2 Objectives 
Specific objectives are: 
i) 
To explore how much new risk models (including MD, SNP313 and mutation testing) 
change risk estimates, in women in the FH-Risk study, between their first referral and 
latest visit to the FHRPC. 
ii) To compare the risk estimations given by Tyrer-Cuzick v8 and Can-Risk (BOADICEA). 
iii) To compare the risk profiles given by TCv8 for different measures of mammographic 
density (BIRADS, Volpara and Predictive VAS) (BOADICEA V uses only the BIRADS to assess 
MD). 
iv) To determine the reproducibility and validity of two readers of BIRADs 
v) To explore how women recall being told about their initial personal breast cancer risk, 
including risk perceptions, psychological wellbeing and subsequent behaviour.  

 
 
 
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vi) To explore whether a change in breast cancer risk over time affects credibility of how risk 
is calculated and communicated to women, and trust in its use in the NHS. 
vii) To use the findings to understand how and in what format(s) changes to breast cancer risk 
over time should be communicated in the future to enable women to properly understand 
change in risk, potential change in management and manage emotional sequelae.  
viii) To develop information materials based on participant interviews and refine via further 
interviews with women. 
ix) To use the findings to develop an understanding of the optimal way to communicate 
changes in breast cancer risk to women who were part of the FH-Risk study but discharged 
from the clinic.  
x) To use the findings to develop a questionnaire to be given to women who previously took 
part in the FH-Risk study (who have been discharged from the clinic) to assess the 
psychological impact of changed risk. 
3.0 STUDY DESIGN & PROTOCOL 
3.1: Part 1: Re-evaluation of breast cancer risk in the FH-Risk study 
population. 
 
Participants in the FH-Risk study 
A total of 954 women were entered into the NIHR FH-Risk study which was designed to assess how 
standard breast cancer risk estimates based on familial and non-familial factors (components of the 
standard Tyrer-Cuzick risk model) might be altered by the addition of MD and the newly discovered 
(at the time) breast cancer SNPs. Women under follow up in the Manchester FHRPC were recruited 
between November 2010 and November 2012 to the FH-Risk study. Recruitment was from women 
who may have previously been referred (from approximately 2000 onward) or women newly referred 
to the clinic during the recruitment period between 2010 and 2012.     
 
Feedback of the results of how SNP18 affected risk was given in 2013. The risk change was given in 
general terms since the precise importance of the risk change was only partially clear and the 
algorithm was not yet fully validated. Feedback on MD was not given as previously suggested because 
analysis was not possible to compare with cancers at the time as the raw mammogram images 
required for processing MD were not retained. In 2017 v8 of the Tyrer-Cuzick model and in 2019 v5 of 
the BOADICEA model (CanRisk) was introduced which incorporated density measures and the 
assessment of SNPs.  
 
All women in the FH-Risk study were/are undergoing annual mammography. They were/are 
discharged to the NHSBSP at age 50 if moderate risk or at age 60 if high risk as per NICE clinical 

 
 
 
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guidelines. At present (October 2020) approx. 271 patients remain under annual follow up for 
mammography and should be given the opportunity to learn of their revised risk estimate, as this 
information could change their entitlement to risk reducing strategies. Women who have been 
discharged will also be given the opportunity to be informed on their updated breast cancer risk 
estimate. 
 
All FH-Risk women had blood taken for genetic studies. A copy of the consent form and the participant 
information sheet for FH-Risk is given in appendix 1. The following statements on the consent form 
are of high importance since women indicated: 
1. I consent to the storage of my DNA sample for future research. 
2. I consent to the gifting of my blood sample for future research. 
3. I agree to my existing mammograms and information from my Family History questionnaire(s) 
being used in this research. 
4. I consent to the transfer of coded data with no personal identifiers for the purpose of the 
current research study looking at common genetic changes associated with breast cancer risk 
to any sponsor of the research 
5. I agree that the information gathered can be stored by the custodians, University Hospital of 
South Manchester Foundation Trust, for possible use in future ethically approved research 
studies as described in the information sheet. 
6. Would you like to receive your updated breast cancer risk information? (Please tick one box 
below) 
Archived information that women consented to for the use in other ethically approved studies include: 
demographic details, family pedigree including any history of cancer (current age or age of death of 
any relative, type of cancer and age at diagnosis), reproductive history (age at menarche, age at first 
pregnancy, duration of episodes of lactation and age at menopause if applicable), history of benign 
breast disease (including number of benign biopsies), artificial oestrogen exposure (duration of oral 
contraceptive pill usage, hormone replacement or fertility drugs) and morphometric information (BMI 
is also assessable from self-reported height and weight estimates from the women). In addition, the 
database stores an absolute lifetime risk calculated using the modified Claus or Tyrer-Cuzick models. 
The database also contains information regarding breast cancer incidence until at least January 2015. 
This will be extended during the study to December 2020. Although MD was not included in the risk 
estimates provided as part of FH-Risk, women did consent to their MD being accessed, calculated and 
fed back to them and this data will be accessed under their original consent (See appendix 1).  
 

 
 
 
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Members of the research team who are consultants and who were also part of the FH-Risk research 
team have permission (as they are part of the clinical care team) to access the above data (including 
DNA data) which participants consented to in order to perform the recalculation of risk. 
 
Eligibility criteria 
Inclusion criteria for performing a recalculation of risk on the FH-Risk study population (approx.954) 
includes: 
1. Completed entry risk form prior to first attendance at the clinic (clinic standard questionnaire).  
2. Entry to the clinic between ~2000 and 2012. 
3. DNA collected from a blood sample between November 2010 and November 2012 during the 
period of recruitment to the FH-Risk Study. 
4. Signed informed consent to the FH-Risk study. 
5. Signed informed consent for the use of genetic and MD data in future ethically approved studies. 
Completions of a clinic standard questionnaire prior to the first visit to the FHRPC included details of 
family history, age of menarche and first full term pregnancy, number of children and their DoB, age 
of menopause, use of oral contraceptives and HRT, smoking history, self-reported weight, height, 
weight gain since the age of 20, alcohol intake, previous breast or ovary problems and breast biopsy 
results. 
METHODS 
Risk recalculation 
Risk recalculation will be performed on all 954 consented FH-Risk women’s data.  
Risk estimation models to be used to recalculate risk 
Calculation using Tyrer-Cuzick Version 8 
Phenotypic risk will be assessed using predicted remaining 10-year and lifetime risks to age 85 from 
the TC model V8 (Tyrer 2004; http://www.ems-trials.org/riskevaluator/). The following information 
from the original risk questionnaire collected before 2012 will be incorporated: age at baseline; first 
and second-degree relatives (age affected by breast and ovarian cancer or current age or age at 
death); age at first child, menarche and menopause; height and weight; and history of prior benign 
breast disease. Changes in these risk parameters will be assessed at subsequent clinical appointments 
and incorporated into the model. 
 
Version 8 of the TC Model differs from V6 in that risk is estimated until the age of 85 (from 73 or 80 
years) and because breast cancer risks have generally increased since V6 was developed (Tyrer et al., 
2004). Thus, women will have in any case somewhat higher risks than previously given at their initial 

 
 
 
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appointment. However they still may end up with a lower risk estimate than originally given when MD 
and SNPs are taken into account (Brentnall et al., 2015).  
 
Calculation using BOADICEA V – Can-Risk 
Collaborative work with the Cambridge team that have now produced a user-friendly risk algorithm 
called CanRisk from their BOADICEA programme, means we can now provide bespoke breast cancer 
10-year and remaining lifetime risks especially for those who carry pathogenic variants in moderate 
risk genes CHEK2 and ATM that are not provided for in Tyrer-Cuzick model (Archer et al 2020). CanRisk 
includes the same inputs as Tyrer-Cuzick (above) but also includes additional genes. Women will be 
provided the CanRisk output instead of Tyrer-Cuzick when they carry a pathogenic variant in any of 
the high or moderate risk genes. Comparisons will also be made to identify discrepancies in 10-year 
risks that would mean women were in different risk categories. 
 
Estimation of Mammographic Density 
Mammograms will be assessed as near as possible to the time of first referral to the clinic and at the 
time of discharge (last clinic mammogram) or at the time of last visit if still under review. They will be 
assessed by the three methods used in the Tyrer-Cuzick model, (BIRADs, Volpara and an automated 
modification of the Visual Analogue Scale (Predictive VAS).  
 
BI-RADS (Breast Imaging-Reporting and Data System) will be reported according the system 
developed by American College of Radiology (5th Version 2013). Breast density is reported on a four 
point scale, a. breast is almost entirely fatty, b. scattered areas of fibroglandular density, c. 
heterogeneously dense breast, may obscure small masses and d. extremely dense breasts, lower the 
sensitivity of mammography. All mammograms will be double read by two experienced readers and if 
there is disagreement between readers a consensus is then reached. Training for consistency of 
reading will be by assessment of a series of 100 pairs of mammograms taken less than two weeks 
apart as previously described (Astley 2020). A third reader will assess the images if there is a 
discrepancy in BIRADs category. The final result will be incorporated in the Tyrer-Cuzick and the 
CanRisk models together with additional data required for risk estimation for each model. 
 
Volpara Volumetric Density (Volpara Health Tech- nologies, Wellington, New Zealand) is a fully-
automated method to estimate percentage volumetric density that requires commercial software and 
digital mammography DICOM files ("FOR PROCESSING" type). VolparaTM from Matakina is able to 
process images from a range of manufacturers (Hologic, GE, Siemens and Fuji). The method, in which 
knowledge of tissue attenuation coefficients, the physics of the imaging process and information in 
the DICOM header, are used to compute glandular thickness at each pixel position. VolparaTM uses a 

 
 
 
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relative physics model which reduces the need for accurate imaging physics data, but depends on 
locating a suitable fatty reference area within the image. VolparaTM outputs the fibroglandular tissue 
volume, total breast tissue volume, percentage of density by volume, and a Volpara Density Grade. 
We will be using the most recent software. 
 
The Volpara algorithm determines the X-ray attenuation between the image detector and the X-ray 
source, based on the pixel signals in the images. A pixel signal corresponding to purely adipose tissue 
is used as a reference to which all other pixels are compared, in order to calculate the thickness of 
adipose versus fibroglandular tissue that must have been present between the detector and the X-ray 
source. As the pixel dimensions are reported in the image, the volumes of adipose and fibroglandular 
tissue are then summed across the entire breast. VBD is calculated as the ratio of fibroglandular tissue 
volume to total breast volume, expressed as a percentage. This quantitative VBD value is then mapped 
to a FDA cleared VDG (i.e. an automated equivalent of the BI-RADS 5th edition density categories) 
using pre-set thresholds. 
 
Predictive VAS – Members of the research team (Astley, Harkness) have built convolutional neural 
networks (CNN) to predict density VAS scores from full-field digital mammograms. The CNNs are 
trained using whole-image mammograms, each labelled with the average VAS score of two 
independent readers. Each CNN learns a mapping between mammographic appearance and VAS score 
so that at test time, they can predict VAS score for an unseen image. Networks were trained using 
67,520 mammographic images from 16,968 women and for model selection we used a dataset of 
73,128 images. There was no significant difference between reader VAS and predicted VAS for the 
prior test set (likelihood ratio chi square, p=0.134). The fully automated method shows promising 
results for cancer risk prediction and is comparable with human performance (Ionesco 2018). 
 
Determination of Polygenic risk Scores and Gene Testing                
Blood samples were taken from all women as part of the original FH-Risk study protocol, from which 
DNA was extracted, or pre-existing DNA samples (also previously extracted from blood) were used.  
Single Nucleotide Polymorphisms & Polygenic risk scores 
A polygenic risk score (PRS) will be used to provide an overall relative risk estimate. We calculate the 
odds ratios (OR) for each of the three SNP geno-types (no risk alleles, one risk allele and two risk 
alleles) from published per-allele ORs, assuming independence and normalizing by an assumed risk 
allele frequency 10 mL of extracted DNA 96-well plates. Two samples with known genotypes have 
been used as internal controls for each plate. Assays were carried out blindly at Genome Quebec 
(Montreal, Canada) and at the Sanger Centre (Cambridge). A PRS will be used to provide an overall 

 
 
 
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risk estimate based on 313 SNPs (Mavaddat et al 2019). Assay failures have been ignored in the SNP 
score by imputing a relative risk of 1.0 when they occurred. An overall SNP risk score for each woman 
in the original FH-Risk study was formed by multiplying the genotype ORs together for an overall PRS. 
Results of SNP313 are available already for all participating women. 
Genetic testing - Assay methods 
Blood samples were taken from all women as part of the original FH-Risk study protocol, from which 
DNA was extracted, or pre-existing DNA samples (also previously extracted from blood) were used. 
BRCA1/2 mutation testing was carried out when clinically indicated (the prior probability of identifying 
a mutation must have met the threshold of BRCA1/2 likelihood probability ≥10% in accordance with 
UK clinical guidance (NICE 2013), using the Manchester score, using DNA Sanger sequencing and 
multiple ligation-dependent probe amplification analysis of all exons and intronexon boundaries and 
more recently next generation sequencing). Relatives of those identified with BRCA1/2 mutations 
were offered cascade screening for the family-specific genetic mutation. All women were genotyped 
for 18 SNPs that have been shown to be associated with breast cancer risk in general European 
populations (FGFR2, CASP8, TOX3, MAP3K, 2q,CDKN2A, 10q22,COX11,NOTCH, 11q13, 10q21,SLC4A7, 
6q25.1, 8q24,RAD51L1,LSP1, 5p12, 10q). In brief, multiplex genotyping was performed using 
Sequenom iPlex Gold (Sequenom, San Diego, California, USA) and TaqMan assay (Life Technologies). 
Intra-plate duplicates and negative controls were included in all genotyping. Genotypes were verified 
by Scientific Data Systems (SDS) and MassARRAY TyperAnalyze. Since the study feedback in 2013-14 
further analysis (see above) has been carried out to perform a SNP313 PRS. All samples have also been 
sequenced for mutations in a panel of 35 genes including 14 that confer at least a moderate risk of 
breast cancer (Dorling et al 2020). Results of gene mutations in a panel of 35 genes are already 
available for all women who took part in the FH-Risk study.  
Statistical Analysis Plan 
The baseline anthropometric, familial and hormonal information from the FHRPC questionnaires will 
be entered into the breast cancer risk prediction models under consideration (TCv8 and CanRisk) and 
estimates of absolute 10 year and lifetime risk to age 80 risk obtained for each woman. The 
performance of the models will be compared in two ways; differences in prediction of individual risk 
and the change in category of risk (average, moderate and high) according to NICE guidelines. The 
follow-up period will be taken to be the time from initial referral and first clinic assessment with risk 
assessment to the most recent assessment or December 1st 2020, whichever is later 
1. Objectives 
Primary 
vii) To determine change in NICE categories of risk (average, moderate and high risk) when new 
risk factors are added to standard breast cancer risk models. 

 
 
 
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viii) To explore how much new risk models (including MD, SNP313 and mutation testing) change 
risk estimates previously communicated to women in the FH-Risk study, between their first 
referral and their latest visit to the FHRPC  
ix) To compare 10-year and lifetime risk estimates given by Tyrer-Cuzick (v8) and the BOADICEA 
model (v5) (Can-Risk)  
Secondary 
i) To compare the risk profiles for TCv8 for different measures of mammographic density 
(BIRADS, Volpara and predicted VAS) (BOADICEA V5 – CanRisk uses only the BIRADS to assess 
MD). 
ii) To determine the reproducibility and validity of two readers of BIRADs 
 
2. Methods 
Analysis will be based on those individuals who attend two risk appointments – at time of entry to the 
FHRPC clinic and at their latest FHRPC clinic appointment and who have corresponding risk scores.  
 
Primary endpoints:  
 
 
10-year and lifetime breast cancer risk and the corresponding NICE risk categories for two risk 
prediction models: i) TCv8 and ii) BOADICEA v5. 
 
Risk scores from TCv8 and BOADICEA will be assessed using different model inputs: i) classical 
factors alone, ii) classical factors plus mammographic density (BIRADS) and iii) classical factors 
plus mammographic density plus SNPs. 
Secondary endpoints:  
 
Differences in measures of mammographic density: i) visually assessed BIRADS, ii) Volpara and 
iii) predicted VAS using ‘for presentation’ and ‘for processing’ mammographic images and the 
relationship between these measures and 10-year and lifetime risk score in TCv8. 
3. Analysis 
The primary analysis will compare the risk categories and risk scores for the risk prediction models for 
women at entry to the clinic (or age 40) and at last follow-up in the clinic, and will compare the risk 
scores given by TCv8 and BOADICEA using different model inputs.  
   
i) 
Change in risk categories – NICE risk categories (average, moderate and high) will be 
compared by creating cross tabulations of risk categories at entry and at last follow-up – 
the number (and percentage) of women moving up and down risk categories will be 

 
 
 
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reported and the McNemar-Bowker test will be used to determine whether the change in 
risk categories is statistically significant between the two time points.  
 
ii) 
Change in 10-year and lifetime risk scores - change in continuous risk measures (10-year 
and lifetime risk estimates) will be estimated from TCv8 and BOADICEA risk prediction 
models.  Normality and skewness of 10-year and lifetime breast cancer risk scores will be 
assessed. Where there is evidence of non-normality scores will be transformed. 
Scatterplots and correlation coefficients will be used to examine the relationship between 
10-year and lifetime risk estimates at baseline and at latest follow-up.  
 
Descriptive statistics will be used to report change in risk scores from time at entry to 
latest follow-up and will be compared using a paired t-test.  Linear regression will be used 
to determine which risk factors (e.g. SNP313, MD) are associated with change in risk score. 
 
iii) 
Comparison between risk models - descriptive statistics will be used to report risk scores 
from TCv8 and the BOADICEA models and differences in risk scores compared. Models will 
be compared using a) classical risk factors alone, b) classical risk factors plus 
mammographic density, as measured by BIRADS, which can be included in both models 
and c) classical risk factors plus mammographic density and SNPs. Scatterplots and 
correlation coefficients will be used to examine the relationships between models and 
Bland-Altman plots used to show the agreement between the models.  
Secondary analysis will focus on the comparison between differences in mammographic density 
measures and the relationship with risk scores using TCv8.  
 
iv) 
Comparison of mammographic density methods and the relationship with TCv8 risk 
scores – scatterplots, intraclass correlation coefficents and Bland-Altman plots will be 
used to look at the relationship between different mammographic density methods. Risk 
scores will also be compared for TCv8 using different methods for measuring 
mammographic density to determine the impact this has on overall risk score. Changes in 
NICE risk categories will be compared using the different methods using the McNemar-
Bowker test and differences in the continuous risk measures will be compared using 
repeated measures analysis of variance (ANOVA).  
 
v) 
Reader reproducibility for BIRADS - reproducibility for readers assessing BIRADS score 
will be determined using inter-reader and intra-reader statistics, using the McNemar-
Bowker test to assess agreement within and between readers.  

 
 
 
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3.2 Part 1: Clinical consultations with FH-Risk women still in follow up at the 
FHRPC 
As indicated above approx. 271 women from the FH-Risk study are still in follow up at the FHRPC. A 
re-evaluated risk can have implications for these FH-Risk women still in follow-up, i.e. it could affect 
their access to risk reducing strategies. Therefore the clinical team/research team have a duty of care 
to give these women who were part of the FH-Risk study and who still attend the FHRPC the 
opportunity to learn of their recalculated breast cancer risk. There is also a large sample from the 
original FH-Risk study have been discharged from the clinic. We also have a duty of care to invite these 
women to receive their updated breast cancer risk estimate regardless of whether they are still under 
follow-up.   
 
 
Participants and recruitment 
We expect to send invitation letters to up to 954 women who still attend or have been discharged 
from the FHRPC for check-ups (at the Nightingale Centre, Wythenshawe Hospital, Manchester 
University NHS Foundation Trust; MFT).  Women still under follow-up will be invited after they have 
attended their annual mammogram and have received a clear result. Personal identifiable data will be 
accessed by the clinical care team who have legitimate access to patient information and the 
admin/research support office team based at MFT will send the invites out on behalf of the clinical 
team. As part of clinical follow-up these women have had their risk re-evaluated using the Tyrer-Cuzick 
Version 8. Women in the FH-Risk study who consented to the use of their data for future research 
purposes will be invited to consent to participate in a follow-up study and discuss their re-evaluated 
breast cancer risk at a clinic appointment (face to face or over the telephone). 
 
Eligibility criteria 
Inclusion criteria for invitation are: 
 
Previously given informed consent to participate in the FH-Risk study at the Nightingale 
Centre. 
 
Have an up to date clear mammogram. 
 
Who are still in follow-up at the FHRPC. 
 
Discharged from the family history clinic.  
 
Aged between 25 and 60. 
 
Have not developed breast cancer. 
 
Able to consent to participate in a research study. 
Exclusion criteria are: 
• 
Women who have received a diagnosis of breast cancer. 

 
 
 
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• 
Women who have not had a clear mammogram. 
• 
Women who did not consent to their FH-Risk data to be used in other ethically approved 
studies. 
• 
Women who lack the capacity to consent. 
Procedure 
Members of the clinical care team/authorised members of the research team with legitimate access 
to patient contact details will access contact details of eligible women in order to send invite letters 
out in the post to FH-Risk women to provide them with the opportunity to attend (in person or via the 
telephone) a clinic consultation to discuss their recalculated risk. The admin/research support office 
team based at MFT will send the invites out on behalf of the clinical team. Reminder invite letters will 
be sent out if deemed necessary (based on participant numbers). Women who are still under follow-
up will be invited after they have attended their annual mammogram and have received a clear result. 
With this invite women will receive a participant information sheet an expression of interest and 
consent to contact form and three copies of a consent form (note: consent forms will be sent in the 
post to be signed and returned during the COVID19 pandemic as it may not be possible to complete 
the clinic consultation in person; consent forms can be signed in person if clinical appointments are to 
take place; one consent form to be retained by the participant and two to be sent back for the research 
file and medical records). Interested women will be able to indicate that they would like more 
information about their risk on the expression of interest and consent to contact form. On this form 
women will also be asked to provide their contact details so a member of the team can arrange their 
clinical consultation. A statement on this form will also ask women whether they are happy for their 
contact details to be retained for the purpose of inviting them to future research, including interviews 
in Part 2 and 3 of this protocol. Women will be asked to send the consent form and expression of 
interest and consent to contact form back to the team in the pre-paid envelope provided. A suitable 
time and date will be arranged for their clinical consultation which will either take place in person or 
over the phone (over the phone likely during the COVID19 pandemic).  
 
On the consent form women will be indicate that they want their updated breast cancer risk estimate 
and their medical records can be accessed to calculate their risk. The clinical care team and the 
admin/research support office staff will provide the clinicians with the medical notes needed to 
calculate an updated risk before the appointment. Members of the research team required to 
calculate breast density will be provided with the appropriate data to do so.  
 
Once women have consented and had their updated risk consultation the clinical care team will inform 
their GP of their updated risk estimate. The GP will only be informed when consent to do so has been 
gathered from the woman. Two GP letters will be in use, (1) one letter for those still under follow-up 

 
 
 
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and discharged and (2) one letter for those who have been discharged from the clinic and require a 
re-referral.  
 
Following their consultation a summary letter will be sent to women, summarising the information 
that was spoken about in the appointment.  
 
During the consultation the clinician will discuss any possible changes to risk management, i.e. a 
change to screening frequency or chemoprevention. 
 
3.3 Part 1: Data Management (risk recalculation) 
We are collecting and storing data in accordance with the General Data Protection Regulation (GDPR) 
and Data Protection Act 2018 which legislate to protect participants’ personal information.   
 
Current storage of data from the FH-Risk study: 
 
All archived data from the FH-Risk study is stored on the MFT computer system by the FH-Risk study 
team (members of the present study team overlap with the FH-Risk study team). Clinical details are in 
the notes of each patient which are entered into the clinic database based in the clinical secretaries 
office. Consultants (who are members of the research team) currently have access to 271 women’s 
information from the FH-Risk study as these women are still in follow up at the FHRPC and attend for 
annual mammograms as part of their ongoing care. An anonymised database of all the data obtained 
from women in the FH-Risk study (n=954) is stored at MFT on the hospital’s computer system. SNP 
and genetic test results, Volpara and pVAS data will be/are stored on MFT computer databases. All 
these databases will be accessed to recalculate risk. All participants from the FH-Risk study have given 
informed consent for the use of their data in other ethically approved studies. The archived data from 
the FH-Risk study is stored in accordance with the FH-Risk study protocol, ethical approval and MFT 
sponsor conditions. UoM research team members will not have access to this data and archived data 
will remain at MFT.  
 
Storage of recalculated risks: 
 
Recalculated risk and risk information, i.e. SNP data and MD data are classed as clinical results and will 
be stored in patient FHRPC notes at the Nightingale Centre, stored on clinic computers on MFT’s 
secure computer system. Images to calculate mammographic density will be used by UoM staff which 
honorary contracts with MFT. These images will be anonymised for the purpose of this analysis and 
will then be de-anonymised when being fed back to the patient. This is so UoM clinical staff with 
honorary contracts at MFT do not see any patient identifiable data. Images will not be retained on 

 
 
 
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UoM systems. Written clinic notes for women who consent to a consultation will be stored at the 
Nightingale Centre in the FHRPC in secure cabinets.  
 
An anonymised database of all FH-Risk women’s previous and updated breast cancer risk estimates 
will be stored on the MFT secure computer system. Only the clinical care team will be able to link this 
data back to women via a unique participant number. 
 
A second anonymised database of women’s previous and updated breast cancer risk estimates (who 
are still in follow up) will be stored on the MFT secure computer system. Only the clinical care team 
will be able to link this data back to women via a unique participant number.  
 
Anonymised databases will be retained for at least 10 years after the study in accordance with UoM’s 
retention policy.  
 
Study databases/storage of consents/expression of interest and consent to contact details 
 
A database of contact details of women who took part in the FH-Risk study and who are still in clinical 
follow up at the FHRPC will be created by the clinical care team/members of the research team who 
have legitimate access. This will be used to keep track of who has been invited to receive a 
consultation. This database will be stored on MFT computers by the clinical care team/authorised 
members of the study team and password protected. This database will be deleted securely following 
the completion of the study.  
 
A separate database will be kept of women who have consented to taking part and information from 
their expression of interest and consent to contact form will be added to this database (i.e. level of 
feedback they require and consent to contact preferences for future research (interviews in part 2 and 
3)). The physical copy of this form will be destroyed once information has been transferred to the 
database. This database will be stored on MFT computers by the clinical care team/authorised 
members of the study team and password protected. Entries on this database will be deleted at the 
end of the study if a woman did not consent to being contacted about future research. Those who 
consented to being contacted about future research, their details will be retained in this database.  
All the above will be stored on the secure MFT computer network and will only be accessed by 
authorised members of the research team/clinical care staff.  
 
Consent forms will be stored at MFT by the clinical care team in a folder in a locked cabinet in secure 
offices. A second consent form will be stored in women’s medical notes. Consent forms will be stored 
for 2 years in accordance with UoM’s retention period.  
 

 
 
 
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3.4 Part 2: Exploring the experience of receiving updated breast cancer risk 
estimates in women with a family history of breast cancer: an interview study 
 
Participants and recruitment 
Twenty women who have consented to discuss their re-evaluated risk estimate at the FHRPC (see part 
1) and who have consented to be contacted about future research (namely the related interview 
studies in part 2 and 3) will be invited to participate in a semi-structured interview (over the telephone 
or via Zoom). Purposive sampling will be used to invite 20 women who have had their breast cancer 
risk estimate re-evaluated by the clinical team. The research team will invite 10 women who have a 
re-evaluated risk that has either increased by at least one category and 10 women who havedecreased 
by at least one category. Eligibility screening will be conducted before sending invitation letters by the 
breast cancer research team at MFT or by an eligible member of the research team with the requisite 
permissions. We estimate that a sample size of 20 will be sufficient to explore views around re-
evaluated risk estimate experiences from the perspective of women who have received a range of risk 
estimate changes. 
 
Eligibility criteria 
Inclusion criteria for invitation are: 
• 
Previously given informed consent to participate in the FH-Risk study at the Nightingale 
Centre. 
• 
Consented to/have received their re-evaluated risk at a clinic appointment (see Part 1). 
• 
Consented to be contacted about future research (namely interviews in Part 2 and 3). 
• 
Aged between 25 and 60. 
• 
Have not developed breast cancer. 
• 
Able to consent to participate in a research study. 
Exclusion criteria are: 
• 
Women who have received a diagnosis of breast cancer 
• 
Women who lack capacity to consent.  
Part 2 Study Procedure - Interviews 
Only women who consent to having a clinical appointment to discuss their re-evaluated risk estimate 
and consented to take part in future research will be invited to this interview study. The interview 
study will be mentioned to women at the end of their clinical appointment to assess their interest in 
taking part. As mentioned above women will complete an expression of interest and consent to 
contact form and a consent form prior to their clinical appointment. On these forms there is a 
statement which asks if the participant is happy for the research team to retain her contact details in 

 
 
 
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order to invite her to related future research and whether their contact details ONLY can be passed to 
a member of the research team based at UoM. Therefore information about this study will only be 
sent to women who consent to this statement. 
Inviting women to interview 
Part 1: Face to face consultation: 
If a woman has indicated that she would be happy to be contacted about taking part in future research 
(via the expression of interest and consent to contact form) the consultant (and member of the 
research team) will provide them with information about the interview study. The consultant will 
introduce the study and answer any questions. Interested women will be given a participant 
information sheet (PIS) to take home. This will be provided at the end of the appointment. If a woman 
is interested in taking part they will be reminded that the contact details they provided on the 
expression of interest and consent to contact form will be passed to the UoM research team member 
who will be in touch (providing they have consented to this). Women may also call the research team 
directly using the contact details within the PIS if she wishes to take part.   
 
Telephone consultation (and COVID): 
For telephone consultations (and for during COVID-19) the consultant at the end of the appointment 
will explain the interview study to the women and ask whether they would be interested in receiving 
further information (providing they have consented to being contacted about future research and 
consented for their contact details to be passed to the researcher conducting the interviews (UoM 
based)). For women who have consented on their expression of interest and consent to contact form 
to being contacted via post, an invite letter and a PIS will be sent out from the research team at 
MFT/UoM. Women will then be asked to contact the researcher conducting the interviews if they 
would like to take part. Reminder invite letters will be sent out if deemed necessary (based on 
participant numbers). For women who consented to be contacted via email or telephone the 
researcher conducting the interviews (UoM based) will contact them via these methods prior to 
providing them with the PIS and arranging an interview.  
 
Setting 
Depending on the interviewees’ preferences, interviews will either take place over the telephone or 
Zoom. Telephone and Zoom interviewing will be given as the only option during COVID-19. We may 
review whether it is possible to interview women face-to-face when COVID restrictions are lifted/less 
severe. Face to face interviewing is built in to this application.  
 
Materials and Methods 

 
 
 
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Telephone and Zoom interviewing will be given as the only option during COVID-19 restrictions. 
Should restrictions change and allow for face to face interviewing, women can choose to be 
interviewed at the University of Manchester, their homes or a private location of their choosing. The 
participant information sheet has been created with additional information in relation to in person 
interviewing and Covid-19.  
 
Audio recorded consent will be taken at the beginning of each interview. The researcher will read out 
the statements and ask whether the interviewee agrees. This will be recorded on an encrypted device 
separately from the study data. If interviews are able to take place in person the participant will be 
required to read and sign a physical copy of the consent form. With consent, interviews will be audio-
recorded using an encrypted device. The researcher may take notes during the interview and this will 
be used as field notes only and will be destroyed at the end of the study data analysis. When zoom is 
used we will not video record the session. Instead the audio recorder will be placed next to the 
computer speaker. This has worked for colleagues and reduces the amount of identifiable data 
produce by the study. 
 
A topic guide will be used to conduct the interviews to ensure that the same broad topics are covered, 
but leaving the participants free to talk about what is important to them and their own experiences. 
These may be iterative depending on on-going data collection. The main focus of the interviews 
however will be to gain an understanding of what the breast cancer risk estimates and possibly 
changed estimates mean to the women attending the FHRPC and how this may affect psychological 
wellbeing and future risk-related healthcare decisions. These will be piloted before use. Women may 
choose to bring notes to the interview in order to remember the risk estimate that they initially 
received and the re-evaluated risk. Interviews will be approximately 60 minutes in length. Non-
mandatory demographic details will also be collected; which will be use to describe the sample when 
the findings are written up. Consent to use each participant’s postcode to calculate an Index of 
Multiple Deprivation (IMD) will also be requested to use as a measure of deprivation when describing 
the study sample during write-up. Once this has been calculated the postcode will be destroyed. 
With consent, medical data with be accessed after the interview including women’s  initial risk and re-
evaluated risk estimates held within medical records at MFT (women will consent for the research 
team to access their medical records as indicated on the study consent form). Relevant medical data 
(for example, chemoprevention uptake, and frequency of mammogram attendance) will be extracted 
using a case report form (CRF). The CRF will be filled in by the consultant and will be provided to the 
researcher electronically without any personal identifiable data present. This will be provided as full 
data set for all the participants.  
 
Analysis 

 
 
 
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Data will be analysed using Reflexive Thematic Analysis following the rigorous guidelines suggested by 
Braun and Clarke (Braun & Clarke, 2019). A reflexive thematic analysis aims to explore patterns that 
are inherent across the data and represent these patterns into a meaningful, in-depth, rich analysis; 
whilst acknowledging the role of the researcher in the final thematic structure. In this analysis, data 
will be analysed inductively at a manifest level. Analysing data inductively and at a manifest level 
means the researchers are able to remain close to the data and represent the interviewees’ views and 
experiences, free from the influences of pre-existing theories and literature (Braun & Clarke, 2006). 
Additionally the theoretical position which will be used to inform this analysis will be essentialist. This 
approach will enable the researchers to report the experiences, meanings and realities of the 
participants as they appear in the data.  
 
Data will be coded by experienced qualitative researchers and the emerging code framework will be 
developed with the guidance of qualitative and thematic analysis experts. Coding will be conducted 
systematically and interactively including double coding of initial transcripts. Negative cases will be 
sought to test the emerging code framework and highlight its limits. Regular coding meetings will be 
held to refine the coding structure. Coding will continue until the team are satisfied that codes and 
themes adequately describe and capture the data and that saturation has been achieved. Data with 
be analysed using NVivo, a qualitative analysis package.  
 
The analysis from these interviews will be used to inform how best to assess the psychological and 
behavioural impact of receiving re-evaluated breast cancer risk estimates in the remaining FH-Risk 
sample in a prospective, longitudinal study. Additionally, the analysis will inform how best to 
communicate risk, discuss risk management (i.e. healthcare decisions) and manage emotional 
sequelae. Data from these interviews will also be used to inform the production of information 
materials related to breast cancer risk communication in the FHRPC (see part 2). 
 
3.5 Part 3: Optimising and refining information materials for the 
communication of revised risk estimates: a ‘think aloud’ interview study 
The findings from Part 2 will be used to develop information materials (i.e. letter and information 
leaflets) intended for women who have had their breast cancer risk re-calculated. Materials will be 
developed for the following groups: (i) women whose risk estimate has increased, (ii) women whose 
risk estimate has stayed the same and (iii) women whose risk estimate has decreased.  
Once these materials have been developed and been through an ethical amendment we will invite 
women from the FH-Risk study who have had received a revised risk estimate to appraise these 
materials. We will invite women from the following groups, (i) women whose risk estimate has 
increased, (ii) women whose risk estimate has stayed the same and (iii) women whose risk estimate 

 
 
 
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has decreased. Each of these groups of women will appraise the materials applicable to their risk 
status. The interview method used will be ‘think alouds’. ‘Think aloud’ interviews are a form of 
qualitative enquiry where the participant is required to read material out loud and provide their views 
and feedback as they naturally occur whilst reading. These interviews will be audio-recorded. 
Women from each group will be required to read a suite of information materials out loud and will be 
asked to reflect on the following whilst doing so: 
• 
The level of information, is there too little or too much 
• 
The accessibility of the information 
• 
The readability of the materials 
• 
The appearance of the materials 
A brief semi-structured element will end these interviews and will explore views on:  
• 
Whether there is any additional information that should be added 
• 
Whether the information aids women’s understanding with regards their new estimated 
risk 
• 
Whether the information provided would help women make a decision about risk 
reduction strategies and more frequent screening if moderate or high risk  
• 
How women would feel about receiving this information 
Participants and recruitment 
Thirty women who have consented to discuss their re-evaluated risk estimate at the FHRPC (see part 
1) and who have consented to be contacted about future research (namely the related interview 
studies in part 2and 3) will be invited to participate in a ‘think-aloud’ interview (over the telephone or 
via Zoom). Purposive sampling will be used to invite 30 women who have had their breast cancer risk 
estimate re-evaluated by the clinical team. The research team will invite 10 women who have a re-
evaluated risk that has either increased by at least one category, decreased by at least one category 
or their risk category has remained the same. Eligibility screening will be conducted before sending 
invitation letters by the breast cancer research team at MFT or by an eligible member of the research 
team with the requisite permissions. We estimate that a sample size of 30 (10 women from each 
group) will enable a thorough appraisal of materials. 
 
Eligibility criteria 
Inclusion criteria for invitation are: 
• 
Previously given informed consent to participate in the FH-Risk study at the Nightingale 
Centre. 
• 
Consented to and have received their re-evaluated risk at a clinic appointment (see Part 1). 
• 
Consented to be contacted about future research. 

 
 
 
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 
Who are still in follow-up at the FHRPC. 
• 
Aged between 25 and 60. 
• 
Have not developed breast cancer. 
• 
Able to consent to participate in a research study. 
 
Exclusion criteria are: 
• 
Women who have received a diagnosis of breast cancer. 
• 
Women who lack the capacity to consent. 
 
Part 3 Study Procedure 
Only women who consent to having a clinical appointment to discuss their re-evaluated risk estimate 
and consented to take part in future research will be invited to this interview study. The interview 
study will be mentioned to the women at the end of their clinical appointment to assess their interest 
in taking part. As mentioned above women will complete an expression of interest and consent to 
contact form and a consent form prior to their clinical appointment. On these forms there is a 
statement which asks if the participant is happy for the research team to retain her contact details in 
order to invite her to related future research and whether their contact details ONLY can be passed a 
member of the research team based at UoM. Therefore information about this study will only be sent 
to women who consent to this statement. Reminder invite letters will be sent out if deemed necessary 
(based on participant numbers). 
 
Inviting women to interview 
Inviting women to interview in Part 3 will follow the same format as Part 2. 
 
Setting 
The same settings for part 2 apply for part 3.  
 
Materials and Methods 
Telephone and Zoom interviewing will be given as the only option during COVID-19 restrictions. 
Should restrictions change and allow for face to face interviewing, women can choose to be 
interviewed at the University of Manchester, their homes or a private location of their choosing. The 
participant information sheet has been created with additional information in relation to in person 
interviewing and Covid-19.  
 
Audio recorded consent will be taken at the beginning of each interview. The researcher will read out 
the statements and ask whether the interviewee agrees. This will be recorded on an encrypted device 
separately from the study data. If interviews are able to take place in person the participant will be 
required to read and sign a physical copy of the consent form. With consent, interviews will be audio-

 
 
 
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recorded using an encrypted device. The researcher may take notes during the interview and this will 
be used as field notes only and will be destroyed at the end of the study data analysis. When zoom is 
used we will not video record the session. Instead the audio recorder will be placed next to the 
computer speaker. This has worked for colleagues and reduces the amount of identifiable data 
produce by the study. 
 
Women taking part in these interviews will be asked to read aloud the materials that are applicable 
to their risk status, i.e. risk has gone up, risk has stayed the same and risk has gone down. Materials 
could include letters and information leaflets. As interviews may take place over the telephone the 
researchers will post or email the materials to the participant prior to the interview taking place. If 
interviews take place over Zoom the researcher will either post or email the materials or share their 
screen with the participant so they are able to read the materials. Interviews will be approximately 60 
minutes in length.  
 
Following these interviews we will refine the materials based on the appraisals the women have 
provided. Once refined we will present the materials to a PPIE panel to request their feedback. 
Materials will be refined again following any PPIE feedback.  
 
A topic guide will be used for the semi-structured component of the interviews and will be piloted 
before use. At the point of consent we will ask participants to provide some non-mandatory 
demographical information; which will be used to describe the sample when the findings are written 
up. Consent to use each participant’s postcode to calculate an Index of Multiple Deprivation (IMD) will 
also be requested to use as a measure of deprivation when describing the study sample during write-
up. Once the deprivation score has been obtained the postcode will be destroyed.  
 
With consent, medical data with be accessed after the interview including women’s  initial risk and re-
evaluated risk estimates held within medical records at MFT (women will consent for the research 
team to access their medical records as indicated on the study consent form). Relevant medical data 
(for example, chemoprevention uptake, and frequency of mammogram attendance) will be extracted 
using a case report form (CRF). The CRF will be filled in by the consultant and will be provided to the 
researcher electronically without any personal identifiable data present. This will be provided as full 
data set for all the participants.  
 
Analysis 
A content analysis will be performed on the ‘think aloud’ data between each round of interviews. The 
conventional form of content analysis will be used where researchers explore the number of 
commonalities in the data. Thus, content analysis enables the researcher to systematically describe 

 
 
 
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the data by extracting the amount of times a point is raised by participants (Hsieh & Shannon 2005). 
Analysing in this way will allow the researcher to identify the commonalities and consistencies in 
participants’ comments in order to refine materials in line with these views (Hsieh & Shannon 2005). 
For the brief semi-structured section of the interviews a manifest, inductive thematic analysis will be 
used as described in study 1. Findings from these interviews will be used to inform changes to 
information materials.  
 
3.6 Data management for Part 2 and 3 (qualitative interviews) 
 
We are collecting and storing data in accordance with the General Data Protection Regulation (GDPR) 
and Data Protection Act 2018 which legislate to protect participants’ personal information.  
  
The clinical care team/authorised members of the research team in Part 1 of this study will provide 
the UoM researcher with the contact details of women who indicated that they would be happy for 
their details to be shared with the UoM researcher in order for them to contact her about future 
related and ethically approved research. Their consent to be contacted will be recorded on the 
expression of interest and consent to contact form issued in Part 1. The clinical care team/authorised 
members of the research team in Part 1 will either enter women’s details into an excel file and send 
the file password protected via secure email or will telephone the UoM researcher who will input the 
details into a password protected excel file stored on the University’s Research Storage Service (Isilon). 
Each participant taking part in Part 2 and 3 of this study will be provided with a reference code used 
to label which interview they took part in. Participants will also be given pseudonyms in place of their 
real names in transcripts and publications. A password protected file containing the reference codes 
and pseudonyms for each participant will be kept on the University’s secure server (p:drive) stored 
separately from study data. Study data (audio recordings and anonymised transcripts) will be kept on 
the University’s Research Storage Service (Isilon). Audio recordings will be stored separately from 
other study data. Excel files containing participants’ identifiable information will also be stored on 
Isilon. All files will be password protected. Only authorised members of the research team will be able 
to link this data together should participants want to withdraw their contribution. Files containing 
reference codes and pseudonyms will be deleted after a participants data has become part of the 
wider dataset, approximately 2 weeks after the interview. Contact details will only be retained if 
women consent to being contacted about future research. These details will be retained for up to 10 
years after the study has ended. If and when women are contacted about future research they will be 
given the option to have their details deleted from the database. For those who do not consent for 
their details to be retained, their details will be deleted after results have been written up for 
publication and participants who requested it have received a summary of the results. After this point 

 
 
 
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the research team will not be able to identify participants from the data. Only authorised members of 
the research team will have access to this data. This data will be kept strictly confidential.  
 
Forms containing demographical information will be added to a excel spreadsheet. Paper copies will 
be shredded by the University’s service for disposing of sensitive documents. This excel file will be 
password protected and stored on Isilon and will be destroyed when findings have been written up 
for publication. With consent, medical data with be accessed after the interview including women’s 
initial risk and re-evaluated risk estimates held within medical records at MFT (women will consent 
for the research team to access their medical records as indicated on the study consent form). 
Relevant medical data (for example, chemoprevention uptake, and frequency of mammogram 
attendance) will be extracted using a case report form (CRF). The CRF will be filled in by the consultant 
in part 1 and will be provided to the researcher electronically without any personal identifiable data 
present. This information will be added to the demographic information excel file. This excel file will 
be password protected and stored on Isilon and will be destroyed when findings have been written 
up for publication. 
 
Audio recordings of all interviews will be recorded on an encrypted audio device. Video footage from 
zoom will not be recorded. Interviews will take place in a private location and where possible 
identifiable information which could be captured on the recording will be avoided. Any identifiable 
data captured will be replaced during transcription. Once an interview is complete the researcher will 
store the device securely until uploading to the University’s secure server can be carried out (as soon 
as is feasible). The audio recording will then be uploaded to the University’s Research Storage server, 
password protected and encrypted. The researcher will listen to the recording to check the upload 
and will then permanently delete the recording off the device. Transcription is to take place via a 3rd 
party service, approved by the University. Audio files will be uploaded via a secure file upload. Audio 
files will be stored separately from other study data on the Research Storage server until the final 
analysis of the results have taken place. After this point audio files will be permanently destroyed. 
Anonymised transcripts will be stored for up to 10 years following publication. The production and 
storage of audio files and transcripts will be carried out in accordance with the University’s Standard 
Operating Procedure for secure handling of recordings and transcriptions. All audio recordings 
including audio recorded consent will be stored separately from other study data.  
 
Should participants wish not to be recorded but would still like to be interviewed the researcher will 
take notes. These will be typed up, password protected and stored on Isilon. Quotes from these notes 
will not be used. Original hand written notes will be shredded via the University’s shredding of 
confidential documents service 
 

 
 
 
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Recorded consent taken over the telephone/zoom will be stored securely, electronically and separate 
from the study data. These will be destroyed as per the policy for physical consent forms (for low risk 
studies: 2 years after the study has been completed) which is in line with University policy. Physical 
consent forms will be stored in a locked cabinet in the lead researchers locked office at the University 
for up to 2 years after the study has been completed. Consent forms will then be destroyed.  
 
Anonymised quotes will be used in research conferences, presentations and publications. 
Pseudonyms will be used with each quote used. Anonymised transcripts may be shared with reputable 
researchers and organisations (NHS and healthcare organisations) who are required to handle data in 
the same way as the University. These organisations will have to request to see this anonymised data 
via a data sharing agreement. No personal information will be shared and participants will not be able 
to be identified. 
 
In Parts 2 and 3, for the safety of the research staff, members of the study team who will be conducting 
the interviews will be required to fill out a lone working form detailing the address of the participant 
should they be interviewed at home. Only authorised members of the research team will have access 
to this information if needed. These forms will be destroyed when they are no longer required. 
 
Managing distress and disclosure of bad practice: 
We do not anticipate that the content of these interviews will cause any undue discomfort to 
participants. However, the discussion of a family history of breast cancer could potentially be 
distressing, as well as any changes to risk. We will follow the University’s managing distress policy 
should participants become distressed or uncomfortable. In the unlikely event of a disclosure of 
malpractice or gross misconduct, the researcher may be obliged to report such a disclosure, breaking 
confidentiality. 
 
3.7 Part 4: Communicating an updated breast cancer risk: A questionnaire 
study 
 
Questionnaire rationale and objectives 
It is well documented in the literature that women either under or over-estimate their risk of 
developing breast cancer even after the provision of a clinically-derived risk estimate. It is unknown 
however what women understand about their risk and how they describe their risk following an 
update which incorporates new strong independent risk factors.  
 

 
 
 
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In this questionnaire study we want to examine the relationship between objective risk (i.e. the 
updated clinical estimate) and women’s subjective understanding of their risk following the provision 
of an updated clinically-derived breast cancer risk estimate. With this we also want to examine 
women’s breast cancer risk knowledge, including whether they perceived a change to their risk and 
cancer worry. 
 
Specific objectives include: 
 
To examine whether there is a difference between objective and subjective risk, looking at 
first risk given in the first FH-Risk study, updated risk (provided in Part 1 of this study) and 
women’s perceived risk. 
 
To examine the predictors of subjective risk, including objective risk factors such as family 
history, PRS, breast density and weight.  
 
To examine whether women have perceived a change to their risk.  
 
To examine women’s breast cancer risk knowledge, including factors which have 
contributed to their risk.  
Participants and recruitment 
Women who opted to have their updated breast cancer risk and attended a consultation in Part 1 will 
be approached for this part of the study. All these women will be eligible apart from those who took 
part in interviews in Part 2 and 3 and those who did not consent to hearing about future related 
studies. Approximately 330-350 women will be eligible from Part 1. Women will be invited to take part 
in this follow-on questionnaire study via post. They will receive an invite letter, PIS, consent form and 
paper questionnaire, together with a pre-paid returning envelope.  
 
Sample size justification 
The sample size calculation is based on detecting a weak positive correlation coefficient between 
variables (r=0.2). Assuming this, a sample size of at least n=194 will be required to have 80% power 
(β=0.2) at α=0.05 significance. 
 
There are between 330 and 350 women eligible to complete this questionnaire from Part 1 of this 
study. We anticipate that asking 350 women to take part will result in responses from n=210 women, 
assuming a 60% response rate.  
 
Procedure 
Women will first be instructed to complete a copy of the consent form. They will receive two copies, 
one for their records and one for the study team. Following consent, women will be asked to complete 
the accompanying questionnaire. This should take no more than 10 minutes to complete. Once 

 
 
 
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complete women will be instructed to return their signed consent form and completed questionnaire 
in the pre-paid envelope provided. A reminder letter will be sent to women approximately 2 weeks 
after the first invite to gather additional questionnaires from those who may have forgotten to 
complete.  
 
Once questionnaire responses have been received this information will be inputted onto a database 
and linked with women’s data from Part 1 via their unique ID given to them in Part 1. This linkage will 
be carried out by members of the clinical care team at the hospital as they are the only ones who 
know how to link this data together. Once this data has been brought together their ID will be removed 
from the database and the dataset anonymised. This will happen approximately 2 weeks after 
receiving the questionnaire.  All consent forms will be stored securely at UoM in a locked cabinet in a 
locked office.  
 
Free text response box procedure: Since distributing questionnaires it has come to our attention that 
a minority of women are using the free text response box to pose questions to their clinician (who is 
the clinician on the study). Some of these questions require an answer and we would like to give these 
women the opportunity to speak with their clinician again as part of this study. We believe that we 
have a duty of care to provide this option. To do this, the researcher at the University who receives 
the completed questionnaires will provide women’s study IDs and question response via a secure 
database to the research/clinical team at the hospital. As the hospital is able to link IDs to contact 
information, the clinician will identify these women and a member of the research team (on behalf of 
the clinician on this project) will contact those with questions and give them the opportunity to speak 
with their clinician further if they still want to. If women want a further conversation, an appointment 
will be arranged. A summary letter will be posted to women following their conversations, which will 
be copied to the GP is appropriate.  
 
Materials and methods 
An invite letter, PIS, consent form and paper questionnaire (and pre-paid envelope) have been 
developed to distribute to women from Part 1 who attended a consultation and consented to hearing 
about future related research. Data will be gathered by a short questionnaire. Data from women’s 
involvement in Part 1 and the FH-Risk studies will also be gathered from those who consented for their 
data to be used in future related research.  
 
The questionnaire will include the following measures: 

 
 
 
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 
A measure of comparative risk using the measure proposed by Weinstein (1999; What does it 
mean to understand a risk? Evaluating risk comprehension. J Natl Cancer I Monogr 1999; 25: 
15-20). 
 
 A measure of absolute risk using lifetime risk thresholds for breast cancer as defined by NICE 
clinical guidelines. 
 
A measure developed by the research team assessing for a perceived change in breast cancer 
risk.  
 
A measure developed by the research team assessing for women’s knowledge about breast 
cancer risk factors.  
 
A measure of satisfaction regarding receiving an updated breast cancer risk estimate proposed 
by French et al (2004; The psychological costs of inadequate cervical smear test results. Br J 
Cancer. 2004;91:1887–1892).  
 
 One item from the Leman cancer worry scale (1991) about developing breast cancer 
(Psychological side effects of breast cancer screening. Health psychology, 10(4), 259).  
 
A free text response option to allow participants to identify anything important to them that 
the questionnaire may not have asked.  
 
Analysis 
Data will be analysed using correlational analysis and regression modelling. For example, correlational 
analysis will be used to assess the relationship between objective risk and women’s subjective 
understanding of their risk, as well as the correlation between objective and subjective risk and cancer 
worry. Regression modelling will be used to explore which risk factors women attribute to their risk. 
Descriptive statistics will also be used for the satisfaction measure.  
 
3.8 Data management for Part 4 (questionnaire study)  
In accordance with data protection law, The University of Manchester is the Data Controller for this 
project. This means that we are responsible for making sure personal information is kept secure, 
confidential and used only in the way the participant has been told it will be used. 
 
All data provided as part of the FH-Risk studies is stored on the secure NHS server on the hospital 
computer system by the study team. Notes from these studies are also stored in patient notes which 
are entered onto the clinic database based in the clinical secretary’s office. An pseudonymised dataset 
is also stored at MFT and is linked to personal identifiable data via a study ID link. Only members of 
the research team can link this data to personal information.  An anonymised database of all the data 
provided as part of the FH-Risk studies is also stored on the secure NHS server at the hospital, as well 

 
 
 
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as on secure servers at the University of Manchester. Women cannot be identified from this particular 
database. 
 
Consent forms will be stored at the University of Manchester in the researchers locked office in a 
locked cabinet for up to 5 years following the end of the study and will then be destroyed. We will 
only retain participant contact details if they consent to being informed about future related research. 
If they do not consent to their contact details being retained we will destroy them once the findings 
have been written up for publication. Only authorised members of the research team will have access 
to this data. A database will kept at Manchester University NHS Foundation Trust electronically of 
contact details for those who have taken part and who wish to be contacted again.   
 
Completed paper questionnaires will be securely destroyed once data has been inputted 
electronically. This data will be pseudonymised using the participant ID number assigned to women 
when they received their updated breast cancer risk estimate in Part 1. This participant ID is only 
known to authorised members of the research team. This ID number will be inserted on each 
questionnaire before dispatch. Once data from their involvement in the present questionnaire study 
has been added to the data from the FH-Risk studies women’s participant ID will be deleted from this 
database and the dataset anonymised, meaning we will not be able to identify any woman from this 
data. This will happen approximately 2 weeks after receiving the completed questionnaire.   
 
Electronic material including, the pseudonymised and eventual anonymised database with the FH-Risk 
study data and questionnaire responses will be stored at the University of Manchester and as well as 
at Manchester University NHS Foundation Trust (Nightingale Centre, Wythenshawe Hospital) on 
secure, password protected computers. Electronic files containing ‘personal identifiable information’ 
will be deleted when they are no longer needed. Files containing ‘personal identifiable information’ 
will not be linked to women’s questionnaire data.  
 
The study team at The University of Manchester will have access to personal information and they will 
anonymise this as soon as possible. Names and any other identifying information will be removed and 
replaced with the ID number assigned to women in Part 1. The research team will have access to the 
key that links this ID number to women’s personal information. Should women wish to opt out of us 
retaining this information, they can email the research team to request their removal. 
 
4.0 CONSENT 

 
 
 
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All potential participants in each part of this study will receive notification of the study either via invite 
letter (Part 1, 2,3 & 4), email or telephone (Part 2 & 3). With this invite participants will receive a PIS. 
This sheet will contain all the relevant information needed about the study to make an informed 
decision as to whether to take part. All participants in each part of this study will have at least 24 hours 
to consider whether they would like to take part. In Part 1 women will fill in an expression of interest 
and consent to contact form and post it back to the study team, together with their consent form 
(during COVID19) if they would like to have a clinical appointment to discuss their risk. The clinical 
care team/authorised members of the study team will then contact the women to make this 
appointment. If clinical appointments are able to take place face to face (due to COVID19 face to face 
consultations may not be available) women can sign the consent form and expression of interest and 
consent to contact form at the appointment. For interviews taking place over the phone or zoom (Part 
2 and 3), the researcher will read out each statement of the consent form and ask the participant if 
they agree. This will be audio recorded on an encrypted device and kept separate from study data. In 
part 4 (questionnaire study) women will receive a copy of the consent form and will be asked to 
complete this and post back in the pre-paid envelope provided with the completed questionnaire. 
Women in all parts of the study can contact the study team to ask questions at any time using the 
details on the PIS.  
 
5.0 ETHICAL AND REGULATORY APPROVAL 
The study will be conducted in full conformance with all relevant legal requirements and the principles 
of the Declaration of Helsinki, Good Clinical Practice (GCP) and the UK Policy Framework for Health 
and Social Care Research 2017. 
 
Research Ethics Committee (REC) and other Regulatory review & reports 
The study will receive ethical review and approval from the necessary regulatory bodies. Before the 
start of the study, a favourable opinion will be sought from a NHS REC and Health Research Authority.  
 
Amendments that require review by NHS REC will not be implemented until that review is in place and 
other mechanisms are in place to implement at site. All correspondence with the REC will be retained. 
The Chief Investigator will produce the annual report as required and notify the REC of the end of the 
study. An annual progress report (APR) will be submitted to the REC within 30 days of the anniversary 
date on which the favourable opinion was given, and annually until the study is declared ended. If the 
study is ended prematurely, the Chief Investigator will notify the REC, including reasons for the 
premature termination. A final report will be submitted with the results to the REC within one year 
after the end of the study. 
 

 
 
 
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Sponsor approval will be sought prior to the study commencing. 
Regulatory Review & Compliance 
Before any enrolment of participants into the study, the Principal Investigator will ensure that 
appropriate approvals from participating organisations (in this instance, Manchester University 
Foundation Trust, Wythenshawe Hospital) are in place. For any amendments, these will be handled in 
line with the sponsors and site management organisational policies.  
 
Peer review 
Originally this study was under review for funding from Prevent Breast Cancer. A part of that funding 
application peer review was received using the internal peer review system within the Faculty of 
Biology, Medicine and Health at The University of Manchester and when the funding application went 
under review by Prevent Breast Cancer. 
 
This study was also reviewed as PhD application by the MRC which was successfully funded and 
deemed scientific. 
 
Patient & Public Involvement 
The research team have strong links with a PPI group of women who have been involved in previous 
breast cancer risk-related studies that have taken place in recent years. This panel of women will be 
directly involved in the present research project and have contributed to the proposed design and 
reviewed this application. Co-applicant Dr McWilliams set up the PPI group and will support this aspect 
of the current application together with PhD student Miss Woof.  
 
A woman who received her breast cancer risk estimate including SNPs in a previous research study 
(Predicting Risk of Breast Cancer; PROCAS) provided feedback on this proposal including our study 
objectives which have been incorporated into the final draft. Suggestions were also made and 
incorporated to improve the lay summary and lay overview in this application. This will include 
reviewing study information to recruit women into the study before the study begins during ethical 
approvals through to disseminating study findings. The research team also have close links with the 
Public Programmes Team which form part of the NIHR Manchester Biomedical Research Centre and 
will utilise their membership panel and advice, where appropriate. For example, the lead applicant 
has successfully received funding from this group (along with co-applicant Prof French) to conduct a 
community engagement project in cervical screening following completion of a research project and 
would reapply for the project outlined in this protocol. Costs for PPI involvement during the project 
have been established. 
End of study 

 
 
 
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It is expected that the last participant to be recruited to all areas of this study will be by 1st June 2024. 
Before an end of study report is sent to HRA/REC participants will be provided with a summary of the 
results and dissemination of results via publications will be completed. If more time is needed for 
these activities an extension to the study end date will be applied for via an amendment.  
 
6.0 STATEMENT OF INDEMNITY  
The University has insurance available in respect of research involving human subjects that provides 
cover for legal liabilities arising from its actions or those of its staff or supervised students.  The 
University also has insurance available that provides compensation for non-negligent harm to 
research subjects occasioned in circumstances that are under the control of the University. 
 
7.0 SPONSOR DETAILS 
Sponsor Name: The University of Manchester 
Sponsor Contact: Ms Lynne Macrae 
Address: Faculty Research Practice Governance Coordinator 
Faculty of Biology, Medicine and Health  
5.012 Carys Bannister Building  
University of Manchester 
M13 9PL 
Email: FBMHethics@manchester.ac.uk 
Telephone: 0161 275 5436 
 
 
STUDY CONTACT DETAILS 
 
Study Contact: Miss Victoria Woof 
Postal Address: Manchester Centre of Health Psychology, 
School of Health Sciences,  
The University of Manchester,  
Coupland 1, Coupland Street,  
Manchester, M13 9PL 
Email: victoria.woof@postgrad.manchester.ac.uk 
Telephone: 0161 275 2572 
 
8.0 FUNDING 

 
 
 
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This study has been funded and supported by the Manchester NIHR Biomedical Research Centre and 
Medical Research Council via a PhD Studentship awarded to Miss Victoria Woof (MR/N013751/1). 
 
9.0 PUBLICATION POLICY 
Results will be written up in scientific journals and abstracts will be submitted for presentation of 
findings at national and international scientific meetings and conferences. A study of the main findings 
will be written with the PPI group and shared with participants who agreed to be contacted about this. 
The study contact should be contacted in the first instance regarding information about results and 
publication copies. 
 
10.0 REFERENCES 
  
Amir, E., Evans, D. G., Shenton, A., Lalloo, F., Moran, A., Boggis, C., ... & Howell, A. (2003). 
Evaluation of breast cancer risk assessment packages in the family history evaluation and 
screening programme. Journal of medical genetics, 40(11), 807-814.  
 
Amir, E., Freedman, O. C., Seruga, B., & Evans, D. G. (2010). Assessing women at high risk of 
breast cancer: a review of risk assessment models. JNCI: Journal of the National Cancer 
Institute, 102(10), 680-691. 
 
Antoniou, A. C., Pharoah, P. P. D., Smith, P., & Easton, D. F. (2004). The BOADICEA model of 
genetic susceptibility to breast and ovarian cancer. British journal of cancer, 91(8), 1580-
1590. 
 
Archer, S., Babb de Villiers, C., Scheibl, F., Carver, T., Hartley, S., Lee, A., ... & Tischkowitz, M. 
(2020). Evaluating clinician acceptability of the prototype CanRisk tool for predicting risk of 
breast and ovarian cancer: A multi-methods study. PloS one, 15(3), e0229999. 
 
Astley, S. M., Harkness, E. F., Sergeant, J. C., Warwick, J., Stavrinos, P., Warren, R., ... & Jain, 
A. (2018). A comparison of five methods of measuring mammographic density: a case-
control study. Breast Cancer Research, 20(1), 10. 
 
Bell, D. W., Varley, J. M., Szydlo, T. E., Kang, D. H., Wahrer, D. C., Shannon, K. E., ... & Birch, J. 
M. (1999). Heterozygous germ line hCHK2 mutations in Li-Fraumeni syndrome. Science, 
286(5449), 2528-2531. 
 
Boyd, N. F., Byng, J. W., Jong, R. A., Fishell, E. K., Little, L. E., Miller, A. B., ... & Yaffe, M. J. 
(1995). Quantitative classification of mammographic densities and breast cancer risk: results 
from the Canadian National Breast Screening Study. JNCI: Journal of the National Cancer 
Institute, 87(9), 670-675. 
 
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in 
Psychology, 3(2), 77-101.  
 

 
 
 
IRAS: 290959_FH-Risk 2.0: Updating breast cancer risk estimates_Research Protocol_v4.0_18.08.2023 
Page 50 of 61 
 
Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research 
in Sport, Exercise and Health, 11(4), 589-597. 
 
Brentnall, A. R., & Cuzick, J. (2020). Risk models for breast cancer and their validation. 
Statistical science: a review journal of the Institute of Mathematical Statistics, 35(1), 14. 
 
Brentnall, A. R., Cuzick, J., Buist, D. S., & Bowles, E. J. A. (2018). Long-term accuracy of breast 
cancer risk assessment combining classic risk factors and breast density. JAMA oncology, 
4(9), e180174-e180174.  
 
Brentnall, A. R., Harkness, E. F., Astley, S. M., Donnelly, L. S., Stavrinos, P., Sampson, S., ... & 
Beetles, U. (2015). Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail 
breast cancer risk models in a prospective UK screening cohort. Breast Cancer Research, 
17(1), 147. 
Brentnall, A. R., van Veen, E. M., Harkness, E. F., Rafiq, S., Byers, H., Astley, S. M., ... & Evans, 
D. G. R. (2020). A case–control evaluation of 143 single nucleotide polymorphisms for breast 
cancer risk stratification with classical factors and mammographic density. International 
journal of cancer, 146(8), 2122-2129. 
 
Brentnall, A. R., Warren, R., Harkness, E. F., Astley, S. M., Wiseman, J., Fox, J., ... & Evans, D. 
G. (2020). Mammographic density change in a cohort of premenopausal women receiving 
tamoxifen for breast cancer prevention over 5 years. Breast Cancer Research, 22(1), 1-10. 
 
Brinton, L. A., Hoover, R., & Fraumeni Jr, J. F. (1982). Interaction of familial and hormonal 
risk factors for breast cancer. Journal of the National Cancer Institute, 69(4), 817-822.  
 
Britt, K. L., Cuzick, J., & Phillips, K. A. (2020). Key steps for effective breast cancer prevention. 
Nature Reviews Cancer, 1-20. 
 
Broca, P. Taite des tumeurs Vol. 13 (Libraire De La Faculte De Medecine, 1866)  
Cancer Research UK. (2015). Retrieved 19/10/2018, 2018, from 
https://www.cancerresearchuk.org/health-professional/cancer-
statistics/incidence/common-cancers-compared#heading-Two 
 
Chlebowski, R. T., Aragaki, A. K., Anderson, G. L., Pan, K., Neuhouser, M. L., Manson, J. E., ... 
& Wactawski-Wende, J. (2020). Dietary modification and breast cancer mortality: long-term 
follow-up of the women’s health initiative randomized trial. Journal of Clinical Oncology, 
38(13), 1419-1428. 
 
Chlebowski, R. T., Luo, J., Anderson, G. L., Barrington, W., Reding, K., Simon, M. S., ... & 
Strickler, H. (2019). Weight loss and breast cancer incidence in postmenopausal women. 
Cancer, 125(2), 205-212. 
 
Chowdhury, S., Dent, T., Pashayan, N., Hall, A., Lyratzopoulos, G., Hallowell, N., ... & Burton, 
H. (2013). Incorporating genomics into breast and prostate cancer screening: assessing the 
implications. GeNeTICs in meDICINe, 15(6), 423-432. 
 
Clarke, C., Allen, J., Beesley, J., Bolla, M. K., Dennis, J., Dicks, E., & Eilber, U. (2017). 
Association analysis identifies 65 new breast cancer risk loci. 

 
 
 
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Page 51 of 61 
 
 
Claus, E. B., Risch, N. J., & THOMPSON, W. D. (1990). Age at onset as an indicator of familial 
risk of breast cancer. American journal of epidemiology, 131(6), 961-972. 
 
Claus, E. B., Risch, N., & Thompson, W. D. (1993). The calculation of breast cancer risk for 
women with a first degree family history of ovarian cancer. Breast cancer research and 
treatment, 28(2), 115-120. 
 
Claus, E. B., Risch, N., & Thompson, W. D. (1994). Autosomal dominant inheritance of early‐
onset breast cancer. Implications for risk prediction. Cancer, 73(3), 643-651.  
 
Cuzick, J., & Baum, M. (1985). Tamoxifen and contralateral breast cancer. The Lancet, 
326(8449), 282. 
Cuzick, J., Brentnall, A. R., Segal, C., Byers, H., Reuter, C., Detre, S., ... & Newman, W. G. 
(2017). Impact of a panel of 88 single nucleotide polymorphisms on the risk of breast cancer 
in high-risk women: results from two randomized tamoxifen prevention trials. Journal of 
Clinical Oncology, 35(7), 743. 
 
Cuzick, J., Warwick, J., Pinney, E., Duffy, S. W., Cawthorn, S., Howell, A., ... & Warren, R. M. 
(2011). Tamoxifen-induced reduction in mammographic density and breast cancer risk 
reduction: a nested case–control study. Journal of the National Cancer Institute, 103(9), 
744-752. 
 
Delaloge, S., Giorgio-Rossi, P., Balleyguier, C., Guindy, M., Burrion, J. B., & Guilbert, F. My 
Personal Breast Screening (MyPeBS). Retrieved 21/11/2018, from http://mypebs.eu/en/ 
 
Dorling L,  Carvalho S, Allen J, González-Neira A, Luccarini C, Wahlström C, Pooley KA, 
Parsons MT, Fortuno C, et al. Breast cancer risk genes: association analysis of rare coding 
variants in 34 genes in 60,466 cases and 53,461 controls. N Engl J Med 2020 in press 
 
Easton, D. F., Pooley, K. A., Dunning, A. M., Pharoah, P. D., Thompson, D., Ballinger, D. G., ... 
& Wareham, N. (2007). Genome-wide association study identifies novel breast cancer 
susceptibility loci. Nature, 447(7148), 1087-1093. 
 
Eccles, D. M., Evans, D. G. R., & Mackay, J. (2000). Guidelines for a genetic risk based 
approach to advising women with a family history of breast cancer. Journal of Medical 
Genetics, 37(3), 203-209.  
 
Eliassen, A. H., Colditz, G. A., Rosner, B., Willett, W. C., & Hankinson, S. E. (2006). Adult 
weight change and risk of postmenopausal breast cancer. Jama, 296(2), 193-201. 
 
Esserman, L. J. (2017). The WISDOM Study: breaking the deadlock in the breast cancer 
screening debate. NPJ Breast Cancer, 3(1), 1-7. 
 
Evans DG, Fentiman IS, McPherson K, Asbury D, Ponder BA, Howell A. Familial Breast Cancer                            
BMJ. 1994 Jan 15;308(6922):183-7 
 

 
 
 
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Page 52 of 61 
 
Evans DG, Gandhi A, Wiseley J, Woodward ER, Harvey J, Highton L, Murphy J, Barr L,  Lambe 
G, Howell SJ,  Lalloo F, Harkness EF, Howell A. Predictors of uptake of bilateral risk reducing 
mastectomy: an analysis of over 7000 women at high risk of breast cancer. Submitted  
 
Evans, D. G. R., & Howell, A. (2007). Breast cancer risk-assessment models. Breast cancer 
research, 9(5), 213.  
 
Evans, D. G. R., Baildam, A. D., Anderson, E., Brain, A., Shenton, A., Vasen, H. F., ... & Moller, 
P. (2009). Risk reducing mastectomy: outcomes in 10 European centres. Journal of medical 
genetics, 46(4), 254-258. 
 
Evans, D. G. R., Barwell, J., Eccles, D. M., Collins, A., Izatt, L., Jacobs, C., ... & Thomas, S. 
(2014). The Angelina Jolie effect: how high celebrity profile can have a major impact on 
provision of cancer related services. Breast Cancer Research, 16(5), 442. 
Evans, D. G. R., Donnelly, L. S., Harkness, E. F., Astley, S. M., Stavrinos, P., Dawe, S., ... & 
Harvie, M. N. (2016). Breast cancer risk feedback to women in the UK NHS breast screening 
population. British journal of cancer, 114(9), 1045-1052. 
 
Evans, D. G. R., Ingham, S., Dawe, S., Roberts, L., Lalloo, F., Brentnall, A. R., ... & Howell, A. 
(2014). Breast cancer risk assessment in 8,824 women attending a family history evaluation 
and screening programme. Familial Cancer, 13(2), 189-196.  
 
Evans, D. G. R., Lalloo, F., Ashcroft, L., Shenton, A., Clancy, T., Baildam, A. D., ... & Howell, A. 
(2009). Uptake of risk-reducing surgery in unaffected women at high risk of breast and 
ovarian cancer is risk, age, and time dependent. Cancer Epidemiology and Prevention 
Biomarkers, 18(8), 2318-2324. 
 
Evans, D. G., Astley, S., Stavrinos, P., Harkness, E., Donnelly, L. S., Dawe, S., ... & Wilson, M. 
(2016). Improvement in risk prediction, early detection and prevention of breast cancer in 
the NHS Breast Screening Programme and family history clinics: a dual cohort study. 
 
Evans, D. G., Brentnall, A. R., Harvie, M., Dawe, S., Sergeant, J. C., Stavrinos, P., ... & Buchan, 
I. (2014). Breast cancer risk in young women in the National Breast Screening Programme: 
implications for applying NICE guidelines for additional screening and chemoprevention. 
Cancer Prevention Research, 7(10), 993-1001.  
 
Evans, D. G., Brentnall, A., Byers, H., Harkness, E., Stavrinos, P., Howell, A., ... & FH-risk study 
Group. (2017). The impact of a panel of 18 SNPs on breast cancer risk in women attending a 
UK familial screening clinic: a case–control study. Journal of medical genetics, 54(2), 111-
113. 
 
Evans, D. G., French, D. P., Maxwell, A., Ulph, F., Harvie, M., Dobrashian, R., . . . van Staa, T. 
Predicting Risk of Cancer at Screening. from https://preventbreastcancer.org.uk/wp-
content/uploads/2018/09/PROCAS-2-Information-Sheet.pdf 
 
Evans, D. G., Harkness, E. F., Plaskocinska, I., Wallace, A. J., Clancy, T., Woodward, E. R., ... & 
Lalloo, F. (2017). Pathology update to the Manchester Scoring System based on testing in 
over 4000 families. Journal of Medical Genetics, 54(10), 674-681. 
 

 
 
 
IRAS: 290959_FH-Risk 2.0: Updating breast cancer risk estimates_Research Protocol_v4.0_18.08.2023 
Page 53 of 61 
 
French, D. P., Cameron, E., Benton, J. S., Deaton, C., & Harvie, M. (2017). Can 
communicating personalised disease risk promote healthy behaviour change? A systematic 
review of systematic reviews. Annals of Behavioral Medicine, 51(5), 718-729. 
 
French, D. P., Howell, A., & Evans, D. G. (2018). Psychosocial issues of a population approach 
to high genetic risk identification: Behavioural, emotional and informed choice issues. The 
Breast, 37, 148-153.  
 
French, D. P., Southworth, J., Howell, A., Harvie, M., Stavrinos, P., Watterson, D., ... & 
Donnelly, L. S. (2018). Psychological impact of providing women with personalised 10-year 
breast cancer risk estimates. British journal of cancer, 118(12), 1648-1657. 
 
Gail, M. H., Brinton, L. A., Byar, D. P., Corle, D. K., Green, S. B., Schairer, C., & Mulvihill, J. J. 
(1989). Projecting individualized probabilities of developing breast cancer for white females 
who are being examined annually. JNCI: Journal of the National Cancer Institute, 81(24), 
1879-1886. 
 
Gandhi A, Duxbury P, Murphy J, Foden P, Lalloo F, Clancy T, Wisely J, Howell A, Evans DG  
Patient Reported Outcome Measures in a Cohort of Patients at High Risk of Breast Cancer 
Treated by Bilateral Risk Reducing Mastectomy and Breast Reconstruction Submitted 
 
Harvie, M., Cohen, H., Mason, C., Mercer, T., Malik, R., Adams, J., ... & Howell, A. (2010). 
Adherence to a diet and exercise weight loss intervention amongst women at increased risk 
of breast cancer. The Open Obesity Journal. 
 
Harvie, M., Howell, A., & Evans, D. G. (2015). Can diet and lifestyle prevent breast cancer: 
what is the evidence?. American Society of Clinical Oncology Educational Book, 35(1), e66-
e73. 
 
Harvie, M., Wright, C., Pegington, M., McMullan, D., Mitchell, E., Martin, B., ... & Camandola, 
S. (2013). The effect of intermittent energy and carbohydrate restriction v. daily energy 
restriction on weight loss and metabolic disease risk markers in overweight women. British 
Journal of Nutrition, 110(8), 1534-1547. 
 
Henneman, L., van Asperen, C. J., Oosterwijk, J. C., Menko, F. H., Claassen, L., & 
Timmermans, D. R. (2020). Do Preferred Risk Formats Lead to Better Understanding? A 
Multicenter Controlled Trial on Communicating Familial Breast Cancer Risks Using Different 
Risk Formats. Patient preference and adherence, 14, 333. 
 
Hewitt, R. M., Pegington, M., Harvie, M., & French, D. P. (2020). How acceptable is a weight 
maintenance programme for healthy weight young women who are at increased risk of 
breast cancer?. Psychology & health, 35(7), 854-871. 
 
Himes, D. O., Root, A. E., Gammon, A., & Luthy, K. E. (2016). Breast cancer risk assessment: 
calculating lifetime risk using the Tyrer-Cuzick model. The Journal for Nurse Practitioners, 
12(9), 581-592. 
 

 
 
 
IRAS: 290959_FH-Risk 2.0: Updating breast cancer risk estimates_Research Protocol_v4.0_18.08.2023 
Page 54 of 61 
 
Hopper, J. L., Nguyen, T. L., Schmidt, D. F., Makalic, E., Song, Y. M., Sung, J., ... & Li, S. (2020). 
Going beyond conventional mammographic density to discover novel mammogram-based 
predictors of breast cancer risk. Journal of Clinical Medicine, 9(3), 627. 
 
Hopwood, P., Howell, A., Lalloo, F., & Evans, G. (2003). Do women understand the odds? 
Risk perceptions and recall of risk information in women with a family history of breast 
cancer. Public Health Genomics, 6(4), 214-223. 
 
Hoskins, K. F., Stopfer, J. E., Calzone, K. A., Merajver, S. D., Rebbeck, T. R., Garber, J. E., & 
Weber, B. L. (1995). Assessment and counseling for women with a family history of breast 
cancer: a guide for clinicians. Jama, 273(7), 577-585.  
 
Houlston, R. S., Lemoine, L., McCarter, E., Harrington, S., MacDermot, K., Hinton, J., ... & 
Slack, J. (1992). Screening and genetic counselling for relatives of patients with breast 
cancer in a family cancer clinic. Journal of medical genetics, 29(10), 691-694.  
Howell, A., Anderson, A. S., Clarke, R. B., Duffy, S. W., Evans, D. G., Garcia-Closas, M., ... & 
Harvie, M. N. (2014). Risk determination and prevention of breast cancer. Breast Cancer 
Research, 16(5), 1-19. 
 
Howell, A., Howell, S., Wilson, M., Maxwell, A., Astley S., Harvie, M., Pegington, M., Gandhi, 
A., Barr, L., Baildam, A., Harkness, E., Hopwood, P., Wisely, J., Lalloo, F., & Evans, G. Long 
term evaluation and treatment of women referred to a Breast Cancer Family History Risk 
and Prevention Clinic (Manchester UK 1987-2020). Cancers, Submitted  
 
Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. 
Qualitative health research, 15(9), 1277-1288 
 
Ingham, S. L., Warwick, J., Buchan, I., Sahin, S., O'Hara, C., Moran, A., ... & Evans, D. G. 
(2013). Ovarian cancer among 8005 women from a breast cancer family history clinic: no 
increased risk of invasive ovarian cancer in families testing negative for BRCA1 and BRCA2. 
Journal of medical genetics, 50(6), 368-372. 
 
Ionescu, G. V., Fergie, M., Berks, M., Harkness, E. F., Hulleman, J., Brentnall, A. R., ... & 
Astley, S. M. (2019). Prediction of reader estimates of mammographic density using 
convolutional neural networks. Journal of Medical Imaging, 6(3), 031405. 
 
Kaphingst, K. A., Ivanovich, J., Biesecker, B. B., Dresser, R., Seo, J., Dressler, L. G., ... & 
Goodman, M. S. (2016). Preferences for return of incidental findings from genome 
sequencing among women diagnosed with breast cancer at a young age. Clinical genetics, 
89(3), 378-384. 
 
Kim, S. J., Huzarski, T., Gronwald, J., Singer, C. F., Møller, P., Lynch, H. T., ... & Senter, L. 
(2018). Prospective evaluation of body size and breast cancer risk among BRCA1 and BRCA2 
mutation carriers. International journal of epidemiology, 47(3), 987-997. 
 
Kuchenbaecker, K. B., McGuffog, L., Barrowdale, D., Lee, A., Soucy, P., Dennis, J., ... & 
Mavaddat, N. (2017). Evaluation of polygenic risk scores for breast and ovarian cancer risk 
prediction in BRCA1 and BRCA2 mutation carriers. JNCI: Journal of the National Cancer 
Institute, 109(7). 

 
 
 
IRAS: 290959_FH-Risk 2.0: Updating breast cancer risk estimates_Research Protocol_v4.0_18.08.2023 
Page 55 of 61 
 
 
Lalloo, F., Baildam, A., Brain, A., Hopwood, P., Evans, D. G. R., & Howell, A. (2000). A 
protocol for preventative mastectomy in women with an increased lifetime risk of breast 
cancer. European journal of surgical oncology, 26(7), 711-713. 
 
Lalloo, F., Boggis, C. R. M., Evans, D. G. R., Shenton, A., Threlfall, A. G., & Howell, A. (1998). 
Screening by mammography, women with a family history of breast cancer. European 
Journal of Cancer, 34(6), 937-940. 
 
Lee, A. J., Cunningham, A. P., Tischkowitz, M., Simard, J., Pharoah, P. D., Easton, D. F., & 
Antoniou, A. C. (2016). Incorporating truncating variants in PALB2, CHEK2, and ATM into the 
BOADICEA breast cancer risk model. Genetics in Medicine, 18(12), 1190-1198. 
 
Lee, A., Mavaddat, N., Wilcox, A. N., Cunningham, A. P., Carver, T., Hartley, S., ... & Walter, F. 
M. (2019). BOADICEA: a comprehensive breast cancer risk prediction model incorporating 
genetic and nongenetic risk factors. Genetics in Medicine, 21(8), 1708-1718. 
 
Lo, L. L., Collins, I. M., Bressel, M., Butow, P., Emery, J., Keogh, L., ... & Mann, G. B. (2018). 
The iPrevent online breast cancer risk assessment and risk management tool: usability and 
acceptability testing. JMIR formative research, 2(2), e24. 
 
Lynch, H. T., & Krush, A. J. (1966). Heredity and breast cancer: implications for cancer 
control. Medical times, 94(5), 599. 
 
MacMahon, B., Cole, P., Lin, T. M., Lowe, C. R., Mirra, A. P., Ravnihar, B., ... & Yuasa, S. 
(1970). Age at first birth and breast cancer risk. Bulletin of the world health organization, 
43(2), 209. 
 
Malkin, D., Li, F. P., Strong, L. C., Fraumeni, J. F., Nelson, C. E., Kim, D. H., ... & Tainsky, M. A. 
(1990). Germ line p53 mutations in a familial syndrome of breast cancer, sarcomas, and 
other neoplasms. science, 250(4985), 1233-1238. 
 
Manchanda, R., Lieberman, S., Gaba, F., Lahad, A., & Levy-Lahad, E. (2020). Population 
Screening for Inherited Predisposition to Breast and Ovarian Cancer. Annual Review of 
Genomics and Human Genetics, 21. 
 
Mavaddat, N., Michailidou, K., Dennis, J., Lush, M., Fachal, L., Lee, A., ... & Yang, X. (2019). 
Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. The 
American Journal of Human Genetics, 104(1), 21-34. 
 
McIntosh, A., Shaw, C., Evans, G., Turnbull, N., Bahar, N., Barclay, M., ... & Hopwood, P. 
(2004). Clinical guidelines and evidence review for the classification and care of women at 
risk of familial breast cancer. London: National Collaborating Centre for Primary 
Care/University of Sheffield. 
 
Meisel, S. F., Pashayan, N., Rahman, B., Side, L., Fraser, L., Gessler, S., ... & Wardle, J. (2015). 
Adjusting the frequency of mammography screening on the basis of genetic risk: attitudes 
among women in the UK. The Breast, 24(3), 237-241. 
 

 
 
 
IRAS: 290959_FH-Risk 2.0: Updating breast cancer risk estimates_Research Protocol_v4.0_18.08.2023 
Page 56 of 61 
 
Merajver, S. D., & Milliron, K. (2003). Breast cancer risk assessment: A guide for clinicians 
using the NCCN Breast Cancer Risk Reduction Guidelines. Journal of the National 
Comprehensive Cancer Network, 1(2), 297-301. 
 
Michailidou, K., Lindström, S., Dennis, J., Beesley, J., Hui, S., Kar, S., ... & Bolla, M. K. (2017). 
Association analysis identifies 65 new breast cancer risk loci. Nature, 551(7678), 92. 
 
Miki, Y., Swensen, J., Shattuck-Eidens, D., Futreal, P. A., Harshman, K., Tavtigian, S., ... & 
Ding, W. (1994). A strong candidate for the breast and ovarian cancer susceptibility gene 
BRCA1. Science, 266(5182), 66-71.  
 
Muranen, T. A., Greco, D., Blomqvist, C., Aittomäki, K., Khan, S., Hogervorst, F., ... & Luben, 
R. (2017). Genetic modifiers of CHEK2* 1100delC-associated breast cancer risk. Genetics in 
Medicine, 19(5), 599-603. 
 
National Institute of Clinical Excellence. (2017). Familial breast cancer: classification, care 
and managing breast cancer and related risks in people with a family history of breast 
cancer (NICE Guideline CG164).  
 
Office for National Statistics. (2016). Cancer registration statistics, England.  
 
Ottman, R., King, M. C., Pike, M., & Henderson, B. (1983). Practical guide for estimating risk 
for familial breast cancer. The Lancet, 322(8349), 556-558. 
 
Pal Choudhury, P., Wilcox, A. N., Brook, M. N., Zhang, Y., Ahearn, T., Orr, N., ... & Swerdlow, 
A. J. (2020). Comparative validation of breast cancer risk prediction models and projections 
for future risk stratification. JNCI: Journal of the National Cancer Institute, 112(3), 278-285. 
 
Pashayan, N., Antoniou, A. C., Ivanus, U., Esserman, L. J., Easton, D. F., French, D., ... & 
Simard, J. (2020). Personalized early detection and prevention of breast cancer: ENVISION 
consensus statement. Nature Reviews Clinical Oncology, 1-19. 
 
Pharoah, P. D., Antoniou, A. C., Easton, D. F., & Ponder, B. A. (2008). Polygenes, risk 
prediction, and targeted prevention of breast cancer. New England Journal of Medicine, 
358(26), 2796-2803.  
 
Phelps, C., Wood, F., Bennett, P., Brain, K., & Gray, J. (2007). Knowledge and expectations of 
women undergoing cancer genetic risk assessment: a qualitative analysis of free-text 
questionnaire comments. Journal of genetic counseling, 16(4), 505-514. 
 
Rahman, N., Seal, S., Thompson, D., Kelly, P., Renwick, A., Elliott, A., ... & Jayatilake, H. 
(2007). PALB2, which encodes a BRCA2-interacting protein, is a breast cancer susceptibility 
gene. Nature genetics, 39(2), 165-167. 
 
Rainey, L., van der Waal, D., Jervaeus, A., Wengström, Y., Evans, D. G., Donnelly, L. S., & 
Broeders, M. J. (2018). Are we ready for the challenge of implementing risk-based breast 
cancer screening and primary prevention?. The Breast, 39, 24-32. 
 

 
 
 
IRAS: 290959_FH-Risk 2.0: Updating breast cancer risk estimates_Research Protocol_v4.0_18.08.2023 
Page 57 of 61 
 
Rainey, L., van der Waal, D., Wengström, Y., Jervaeus, A., & Broeders, M. J. (2018). Women’s 
perceptions of the adoption of personalised risk-based breast cancer screening and primary 
prevention: a systematic review. Acta Oncologica, 57(10), 1275-1283. 
 
Ramazzini B, De Morbis Artificum Diatriba. Diseases of Workers. The Latin text of 1713 
revised with translation. The University of Chicago Press, 1940  
 
Salant, T., Ganschow, P. S., Olopade, O. I., & Lauderdale, D. S. (2006). “Why take it if you 
don't have anything?” breast cancer risk perceptions and prevention choices at a public 
hospital. Journal of general internal medicine, 21(7), 779-785.  
Tabar, L., Larsson, L. G., Anderson, I., Duffy, S. W., Nystrom, L., Rutqvist, L. E., ... & Chen, H. 
H. (1996). Breast-cancer screening with mammography in women aged 40-49 years. 
International Journal of Cancer, 68(6), 693-699. 
 
Terry, M. B., Liao, Y., Whittemore, A. S., Leoce, N., Buchsbaum, R., Zeinomar, N., ... & Milne, 
R. L. (2019). 10-year performance of four models of breast cancer risk: a validation study. 
The Lancet Oncology, 20(4), 504-517. 
 
Turnbull, C., Ahmed, S., Morrison, J., Pernet, D., Renwick, A., Maranian, M., ... & Hughes, D. 
(2010). Genome-wide association study identifies five new breast cancer susceptibility loci. 
Nature genetics, 42(6), 504-507. 
 
Tyrer, J., Duffy, S. W., & Cuzick, J. (2004). A breast cancer prediction model incorporating 
familial and personal risk factors. Stat Med, 23(7), 1111-1130. doi: 10.1002/sim.1668 
 
Vachon, C. M., Pankratz, V. S., Scott, C. G., Haeberle, L., Ziv, E., Jensen, M. R., ... & Hack, C. C. 
(2015). The contributions of breast density and common genetic variation to breast cancer 
risk. Journal of the National Cancer Institute, 107(5), dju397.  
 
van Veen, E. M., Brentnall, A. R., Byers, H., Harkness, E. F., Astley, S. M., Sampson, S., ... & 
Evans, D. G. R. (2018). Use of single-nucleotide polymorphisms and mammographic density 
plus classic risk factors for breast cancer risk prediction. JAMA oncology, 4(4), 476-482. 
Volpara Technologies. Wellington, New Zealand.  
 
Vořechovský, I., Rasio, D., Luo, L., Monaco, C., Hammarström, L., Webster, A. D. B., ... & 
Croce, C. M. (1996). The ATM gene and susceptibility to breast cancer: analysis of 38 breast 
tumors reveals no evidence for mutation. Cancer research, 56(12), 2726-2732. 
 
Wang, C., Brentnall, A. R., Cuzick, J., Harkness, E. F., Evans, D. G., & Astley, S. (2017). A novel 
and fully automated mammographic texture analysis for risk prediction: results from two 
case-control studies. Breast Cancer Research, 19(1), 114. 
 
Warwick, J., Birke, H., Stone, J., Warren, R. M., Pinney, E., Brentnall, A. R., ... & Cuzick, J. 
(2014). Mammographic breast density refines Tyrer-Cuzick estimates of breast cancer risk in 
high-risk women: findings from the placebo arm of the International Breast Cancer 
Intervention Study I. Breast Cancer Research, 16(5), 451.  
 
Wolfe, J. N. (1976). Breast patterns as an index of risk for developing breast cancer. 
American Journal of Roentgenology, 126(6), 1130-1137.  

 
 
 
IRAS: 290959_FH-Risk 2.0: Updating breast cancer risk estimates_Research Protocol_v4.0_18.08.2023 
Page 58 of 61 
 
 
Wooster, R., Bignell, G., Lancaster, J., Swift, S., Seal, S., Mangion, J., ... & Barfoot, R. (1995). 
Identification of the breast cancer susceptibility gene BRCA2. Nature, 378(6559), 789-792.  
 
Zhang, X., Rice, M., Tworoger, S. S., Rosner, B. A., Eliassen, A. H., Tamimi, R. M., ... & Willett, 
W. C. (2018). Addition of a polygenic risk score, mammographic density, and endogenous 
hormones to existing breast cancer risk prediction models: A nested case–control study. 
PLoS medicine, 15(9), e1002644.

 
 
 
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11.0 Participant pathway 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
PART 1: FH-Risk women (954) invited to 
receive update risk feedback 
PART 1: Clinic team organise clinic 
PART 1: FH-Risk women send EofI/CtCf 
and consent forms to the clinic team 
PART 1: Women contacted for clinic apt 
PART 1: Women attend/have their clinic 
apt 
PART 1: Women reminded (if consented 
to be contacted) that they might receive 
info about an interview study. 
PART 1: Clinic team identify FH-
Risk in the FHRPC and send invites 
PART 1: Clinic team store consents 
and contact details 
PART 1: TH, GE, & SH provide the 
apt and clinic team file and store the 
data securely. 
PART 2/3: Women receive info about 
the interview OR ‘think aloud’ study 
via post or email or telephone 
PART 2/3: Women who consent will 
be interviewed over the phone or in 
person 
PART 2/3: UoM (VW) to obtain 
details from the clinic team of women 
who have consented for their details to 
be shared for Parts 2 and 3. 
PART 2/3: UoM (VW) to send 
interview/’think aloud’ information to 
women 
PART 2/3: UoM (VW) to complete 
interview/’think aloud’. 

 
 
 
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Appendix 1: FH-Risk Original Consent Form (2012) 
CONSENT FORM  
 
FHRisk: Assessment, validation and improvement of breast cancer risk assessment models 
in the family history clinic 
 
 
Please initial box 
 
 
1 
I have read and understand the Information Sheet dated 25th April 2012 (Version 1.0) for the above study.  I have had 
the opportunity to consider the information, ask questions and have had these answered satisfactorily. 
 
 
 
 
2 
I understand that my participation is voluntary and that I am free to withdraw at any time without giving any reason. I 
understand that withdrawal would not affect my medical care or my legal rights. 
 
 
 
 
3 
I understand that relevant sections of my medical notes and data collected during the study may be looked at by 
researchers and individuals from University Hospital of South Manchester NHS Trust, where it is relevant to my taking 
part in this research. I give permission for these individuals to have access to my records. 
 
 
 
 
 
 
4 
I agree to my existing mammograms and information from my Family History questionnaire(s) being used in this research. 
 
 
 
 
 
 
5 
I agree to give a sample of blood for research in this study. I understand how the sample will be collected and that giving 
a sample for this research is voluntary. I understand that I am free to withdraw my approval for use of the sample at any 
time, without giving any reason and without my medical care or legal rights being affected. 
 
 
 
 
 
6 
I consent to the gifting of my blood sample for future research. 
 
 
 
7 
I consent to the storage of my DNA sample for future research. 
 
 
 
 
 8 
I consent to the transfer of coded data with no personal identifiers for the purpose of the current research study looking 
at common genetic changes associated with breast cancer risk to any sponsor of the research 
 
 
 
 
 9 
I agree that the information gathered can be stored by the custodians, University Hospital of South Manchester 
Foundation Trust, for possible use in future ethically approved studies as described in the information sheet. 
 
 
 
 
10 
I give consent for my GP to be informed of my participation. 
 
 
 
11 
I agree to take part in the above study. 
 
 
 
 
 
 
12 
I understand that if I choose to receive feedback, I will have an appointment to discuss this, and my GP will be given this 
information. 
 
 
 
 
13 
 
Would you like to receive your updated breast cancer risk information? (Please tick one box below) 
 
YES                    NO  
 
 
 
Name of Participant 
 
Date 
 
Signature 
 
Witnessed by 
 
Date 
 
Signature 
 

 
 
 
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