Improving the communication of multifactorial cancer risk assessment results for different audiences: a co-design process.

BACKGROUND: Multifactorial cancer risk prediction tools, such as CanRisk, are increasingly being incorporated into routine healthcare. Understanding risk information and communicating risk is challenging and healthcare professionals rely substantially on the outputs of risk prediction tools to communicate results. This work aimed to produce a new CanRisk report so users can directly access key information and communicate risk estimates effectively. METHODS: Over a 13-month period, we led an 8-step co-design process with patients, the public, and healthcare professionals. Steps comprised 1) think aloud testing of the original CanRisk report; 2) structured feedback on the original report; 3) literature review; 4) development of a new report prototype; 5) first round of structured feedback; 6) updating the new report prototype; 7) second round of structured feedback; and 8) finalising and publishing the new CanRisk report. RESULTS: We received 56 sets of feedback from 34 stakeholders. Overall, the original CanRisk report was not suitable for patients and the public. Building on the feedback, the new report has an overview of the information presented: section one summarises key information for individuals; sections two and three present information for healthcare professionals in different settings. New features also include explanatory text, definitions, graphs, keys and tables to support the interpretation of the information. DISCUSSION: This co-design experience shows the value of collaboration for the successful communication of complex health information. As a result, the new CanRisk report has the potential to better support shared decision-making processes about cancer risk management across clinical settings.

Validation of the BOADICEA model for epithelial tubo-ovarian cancer risk prediction in UK Biobank.

BACKGROUND: The clinical validity of the multifactorial BOADICEA model for epithelial tubo-ovarian cancer (EOC) risk prediction has not been assessed in a large sample size or over a longer term. METHODS: We evaluated the model discrimination and calibration in the UK Biobank cohort comprising 199,429 women (733 incident EOCs) of European ancestry without previous cancer history. We predicted 10-year EOC risk incorporating data on questionnaire-based risk factors (QRFs), family history, a 36-SNP polygenic risk score and pathogenic variants (PV) in six EOC susceptibility genes (BRCA1, BRCA2, RAD51C, RAD51D, BRIP1 and PALB2). RESULTS: Discriminative ability was maximised under the multifactorial model that included all risk factors (AUC = 0.68, 95% CI: 0.66-0.70). This model was well calibrated in deciles of predicted risk with calibration slope=0.99 (95% CI: 0.98-1.01). Discriminative ability was similar in women younger or older than 60 years. The AUC was higher when analyses were restricted to PV carriers (0.76, 95% CI: 0.69-0.82). Using relative risk (RR) thresholds, the full model classified 97.7%, 1.7%, 0.4% and 0.2% women in the RR < 2.0, 2.0 ≤ RR < 2.9, 2.9 ≤ RR < 6.0 and RR ≥ 6.0 categories, respectively, identifying 9.1 of incident EOC among those with RR ≥ 2.0. DISCUSSION: BOADICEA, implemented in CanRisk ( www.canrisk.org ), provides valid 10-year EOC risks and can facilitate clinical decision-making in EOC risk management.

Implementing Multifactorial Risk Assessment with Polygenic Risk Scores for Personalized Breast Cancer Screening in the Population Setting: Challenges and Opportunities.

Risk-stratified breast screening has been proposed as a strategy to overcome the limitations of age-based screening. A prospective cohort study was undertaken within the PERSPECTIVE I&I project, which will generate the first Canadian evidence on multifactorial breast cancer risk assessment in the population setting to inform the implementation of risk-stratified screening. Recruited females aged 40-69 unaffected by breast cancer, with a previous mammogram, underwent multifactorial breast cancer risk assessment. The adoption of multifactorial risk assessment, the effectiveness of methods for collecting risk factor information and the costs of risk assessment were examined. Associations between participant characteristics and study sites, as well as data collection methods, were assessed using logistic regression; all p-values are two-sided. Of the 4246 participants recruited, 88.4% completed a risk assessment, with 79.8%, 15.7% and 4.4% estimated at average, higher than average and high risk, respectively. The total per-participant cost for risk assessment was CAD 315. Participants who chose to provide risk factor information on paper/telephone (27.2%) vs. online were more likely to be older (p = 0.021), not born in Canada (p = 0.043), visible minorities (p = 0.01) and have a lower attained education (p < 0.0001) and perceived fair/poor health (p < 0.001). The 34.4% of participants requiring risk factor verification for missing/unusual values were more likely to be visible minorities (p = 0.009) and have a lower attained education (p ≤ 0.006). This study demonstrates the feasibility of risk assessment for risk-stratified screening at the population level. Implementation should incorporate an equity lens to ensure cancer-screening disparities are not widened.

Exploring the barriers to and facilitators of implementing CanRisk in primary care: a qualitative thematic framework analysis.

BACKGROUND: The CanRisk tool enables the collection of risk factor information and calculation of estimated future breast cancer risks based on the multifactorial Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model. Despite BOADICEA being recommended in National Institute for Health and Care Excellence (NICE) guidelines and CanRisk being freely available for use, the CanRisk tool has not yet been widely implemented in primary care. AIM: To explore the barriers to and facilitators of the implementation of the CanRisk tool in primary care. DESIGN AND SETTING: A multi-methods study was conducted with primary care practitioners (PCPs) in the East of England. METHOD: Participants used the CanRisk tool to complete two vignette-based case studies; semi-structured interviews gained feedback about the tool; and questionnaires collected demographic details and information about the structural characteristics of the practices. RESULTS: Sixteen PCPs (eight GPs and eight nurses) completed the study. The main barriers to implementation included: time needed to complete the tool; competing priorities; IT infrastructure; and PCPs' lack of confidence and knowledge to use the tool. Main facilitators included: easy navigation of the tool; its potential clinical impact; and the increasing availability of and expectation to use risk prediction tools. CONCLUSION: There is now a greater understanding of the barriers and facilitators that exist when using CanRisk in primary care. The study has highlighted that future implementation activities should focus on reducing the time needed to complete a CanRisk calculation, integrating the CanRisk tool into existing IT infrastructure, and identifying appropriate contexts in which to conduct a CanRisk calculation. PCPs may also benefit from information about cancer risk assessment and CanRisk-specific training.

Incorporating Alternative Polygenic Risk Scores into the BOADICEA Breast Cancer Risk Prediction Model.

BACKGROUND: The multifactorial risk prediction model BOADICEA enables identification of women at higher or lower risk of developing breast cancer. BOADICEA models genetic susceptibility in terms of the effects of rare variants in breast cancer susceptibility genes and a polygenic component, decomposed into an unmeasured and a measured component - the polygenic risk score (PRS). The current version was developed using a 313 SNP PRS. Here, we evaluated approaches to incorporating this PRS and alternative PRS in BOADICEA. METHODS: The mean, SD, and proportion of the overall polygenic component explained by the PRS (α2) need to be estimated. $\alpha $ was estimated using logistic regression, where the age-specific log-OR is constrained to be a function of the age-dependent polygenic relative risk in BOADICEA; and using a retrospective likelihood (RL) approach that models, in addition, the unmeasured polygenic component. RESULTS: Parameters were computed for 11 PRS, including 6 variations of the 313 SNP PRS used in clinical trials and implementation studies. The logistic regression approach underestimates $\alpha $, as compared with the RL estimates. The RL $\alpha $ estimates were very close to those obtained by assuming proportionality to the OR per 1 SD, with the constant of proportionality estimated using the 313 SNP PRS. Small variations in the SNPs included in the PRS can lead to large differences in the mean. CONCLUSIONS: BOADICEA can be readily adapted to different PRS in a manner that maintains consistency of the model. IMPACT: : The methods described facilitate comprehensive breast cancer risk assessment.

Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence.

BACKGROUND: BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) for breast cancer and the epithelial tubo-ovarian cancer (EOC) models included in the CanRisk tool (www.canrisk.org) provide future cancer risks based on pathogenic variants in cancer-susceptibility genes, polygenic risk scores, breast density, questionnaire-based risk factors and family history. Here, we extend the models to include the effects of pathogenic variants in recently established breast cancer and EOC susceptibility genes, up-to-date age-specific pathology distributions and continuous risk factors. METHODS: BOADICEA was extended to further incorporate the associations of pathogenic variants in BARD1, RAD51C and RAD51D with breast cancer risk. The EOC model was extended to include the association of PALB2 pathogenic variants with EOC risk. Age-specific distributions of oestrogen-receptor-negative and triple-negative breast cancer status for pathogenic variant carriers in these genes and CHEK2 and ATM were also incorporated. A novel method to include continuous risk factors was developed, exemplified by including adult height as continuous. RESULTS: BARD1, RAD51C and RAD51D explain 0.31% of the breast cancer polygenic variance. When incorporated into the multifactorial model, 34%-44% of these carriers would be reclassified to the near-population and 15%-22% to the high-risk categories based on the UK National Institute for Health and Care Excellence guidelines. Under the EOC multifactorial model, 62%, 35% and 3% of PALB2 carriers have lifetime EOC risks of <5%, 5%-10% and >10%, respectively. Including height as continuous, increased the breast cancer relative risk variance from 0.002 to 0.010. CONCLUSIONS: These extensions will allow for better personalised risks for BARD1, RAD51C, RAD51D and PALB2 pathogenic variant carriers and more informed choices on screening, prevention, risk factor modification or other risk-reducing options.

Comprehensive epithelial tubo-ovarian cancer risk prediction model incorporating genetic and epidemiological risk factors.

BACKGROUND: Epithelial tubo-ovarian cancer (EOC) has high mortality partly due to late diagnosis. Prevention is available but may be associated with adverse effects. A multifactorial risk model based on known genetic and epidemiological risk factors (RFs) for EOC can help identify women at higher risk who could benefit from targeted screening and prevention. METHODS: We developed a multifactorial EOC risk model for women of European ancestry incorporating the effects of pathogenic variants (PVs) in BRCA1, BRCA2, RAD51C, RAD51D and BRIP1, a Polygenic Risk Score (PRS) of arbitrary size, the effects of RFs and explicit family history (FH) using a synthetic model approach. The PRS, PV and RFs were assumed to act multiplicatively. RESULTS: Based on a currently available PRS for EOC that explains 5% of the EOC polygenic variance, the estimated lifetime risks under the multifactorial model in the general population vary from 0.5% to 4.6% for the first to 99th percentiles of the EOC risk distribution. The corresponding range for women with an affected first-degree relative is 1.9%-10.3%. Based on the combined risk distribution, 33% of RAD51D PV carriers are expected to have a lifetime EOC risk of less than 10%. RFs provided the widest distribution, followed by the PRS. In an independent partial model validation, absolute and relative 5-year risks were well calibrated in quintiles of predicted risk. CONCLUSION: This multifactorial risk model can facilitate stratification, in particular among women with FH of cancer and/or moderate-risk and high-risk PVs. The model is available via the CanRisk Tool (www.canrisk.org).

CanRisk Tool-A Web Interface for the Prediction of Breast and Ovarian Cancer Risk and the Likelihood of Carrying Genetic Pathogenic Variants.

BACKGROUND: The CanRisk Tool (https://canrisk.org) is the next-generation web interface for the latest version of the BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) state-of-the-art risk model and a forthcoming ovarian cancer risk model. METHODS: The tool captures information on family history, rare pathogenic variants in cancer susceptibility genes, polygenic risk scores, lifestyle/hormonal/clinical features, and imaging risk factors to predict breast and ovarian cancer risks and estimate the probabilities of carrying pathogenic variants in certain genes. It was implemented using modern web frameworks, technologies, and web services to make it extensible and increase accessibility to researchers and third-party applications. The design of the graphical user interface was informed by feedback from health care professionals and a formal evaluation. RESULTS: This freely accessible tool was designed to be user friendly for clinicians and to boost acceptability in clinical settings. The tool incorporates a novel graphical pedigree builder to facilitate collection of the family history data required by risk calculations. CONCLUSIONS: The CanRisk Tool provides health care professionals and researchers with a user-friendly interface to carry out multifactorial breast and ovarian cancer risk predictions. It is the first freely accessible cancer risk prediction program to carry the CE marking. IMPACT: There have been over 3,100 account registrations, and 98,000 breast and ovarian cancer risk calculations have been run within the first 9 months of the CanRisk Tool launch.

Evaluating clinician acceptability of the prototype CanRisk tool for predicting risk of breast and ovarian cancer: A multi-methods study.

BACKGROUND: There is a growing focus on the development of multi-factorial cancer risk prediction algorithms alongside tools that operationalise them for clinical use. BOADICEA is a breast and ovarian cancer risk prediction model incorporating genetic and other risk factors. A new user-friendly Web-based tool (CanRisk.org) has been developed to apply BOADICEA. This study aimed to explore the acceptability of the prototype CanRisk tool among two healthcare professional groups to inform further development, evaluation and implementation. METHOD: A multi-methods approach was used. Clinicians from primary care and specialist genetics clinics in England, France and Germany were invited to use the CanRisk prototype with two test cases (either face-to-face with a simulated patient or via a written vignette). Their views about the tool were examined via a semi-structured interview or equivalent open-ended questionnaire. Qualitative data were subjected to thematic analysis and organised around Sekhon's Theoretical Framework of Acceptability. RESULTS: Seventy-five clinicians participated, 21 from primary care and 54 from specialist genetics clinics. Participants were from England (n = 37), France (n = 23) and Germany (n = 15). The prototype CanRisk tool was generally acceptable to most participants due to its intuitive design. Primary care clinicians were concerned about the amount of time needed to complete, interpret and communicate risk information. Clinicians from both settings were apprehensive about the impact of the CanRisk tool on their consultations and lack of opportunities to interpret risk scores before sharing them with their patients. CONCLUSIONS: The findings highlight the challenges associated with developing a complex tool for use in different clinical settings; they also helped refine the tool. This prototype may not have been versatile enough for clinical use in both primary care and specialist genetics clinics where the needs of clinicians are different, emphasising the importance of understanding the clinical context when developing cancer risk assessment tools.

Correction: BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors.

This has now been corrected in both the PDF and HTML versions of the Article. The authors regret this error.

BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors.

PURPOSE: Breast cancer (BC) risk prediction allows systematic identification of individuals at highest and lowest risk. We extend the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk model to incorporate the effects of polygenic risk scores (PRS) and other risk factors (RFs). METHODS: BOADICEA incorporates the effects of truncating variants in BRCA1, BRCA2, PALB2, CHEK2, and ATM; a PRS based on 313 single-nucleotide polymorphisms (SNPs) explaining 20% of BC polygenic variance; a residual polygenic component accounting for other genetic/familial effects; known lifestyle/hormonal/reproductive RFs; and mammographic density, while allowing for missing information. RESULTS: 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). CONCLUSION: This comprehensive model should enable high levels of BC risk stratification in the general population and women with family history, and facilitate individualized, informed decision-making on prevention therapies and screening.

pedigreejs: a web-based graphical pedigree editor.

Motivation: The collection, management and visualization of clinical pedigree (family history) data is a core activity in clinical genetics centres. However, clinical pedigree datasets can be difficult to manage, as they are time consuming to capture, and can be difficult to build, manipulate and visualize graphically. Several standalone graphical pedigree editors and drawing applications exist but there are no freely available lightweight graphical pedigree editors that can be easily configured and incorporated into web applications. Results: We developed 'pedigreejs', an interactive graphical pedigree editor written in JavaScript, which uses standard pedigree nomenclature. Pedigreejs provides an easily configurable, extensible and lightweight pedigree editor. It makes use of an open-source Javascript library to define a hierarchical layout and to produce images in scalable vector graphics (SVG) format that can be viewed and edited in web browsers. Availability and implementation: The software is freely available under GPL licence (https://ccge-boadicea.github.io/pedigreejs/). Contact: tjc29@cam.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

BamView: visualizing and interpretation of next-generation sequencing read alignments.

UNLABELLED: So-called next-generation sequencing (NGS) has provided the ability to sequence on a massive scale at low cost, enabling biologists to perform powerful experiments and gain insight into biological processes. BamView has been developed to visualize and analyse sequence reads from NGS platforms, which have been aligned to a reference sequence. It is a desktop application for browsing the aligned or mapped reads [Ruffalo, M, LaFramboise, T, Koyutürk, M. Comparative analysis of algorithms for next-generation sequencing read alignment. Bioinformatics 2011;27:2790-6] at different levels of magnification, from nucleotide level, where the base qualities can be seen, to genome or chromosome level where overall coverage is shown. To enable in-depth investigation of NGS data, various views are provided that can be configured to highlight interesting aspects of the data. Multiple read alignment files can be overlaid to compare results from different experiments, and filters can be applied to facilitate the interpretation of the aligned reads. As well as being a standalone application it can be used as an integrated part of the Artemis genome browser, BamView allows the user to study NGS data in the context of the sequence and annotation of the reference genome. Single nucleotide polymorphism (SNP) density and candidate SNP sites can be highlighted and investigated, and read-pair information can be used to discover large structural insertions and deletions. The application will also calculate simple analyses of the read mapping, including reporting the read counts and reads per kilobase per million mapped reads (RPKM) for genes selected by the user. AVAILABILITY: BamView and Artemis are freely available software. These can be downloaded from their home pages: http://bamview.sourceforge.net/; http://www.sanger.ac.uk/resources/software/artemis/. Requirements: Java 1.6 or higher.

Chado controller: advanced annotation management with a community annotation system.

SUMMARY: We developed a controller that is compliant with the Chado database schema, GBrowse and genome annotation-editing tools such as Artemis and Apollo. It enables the management of public and private data, monitors manual annotation (with controlled vocabularies, structural and functional annotation controls) and stores versions of annotation for all modified features. The Chado controller uses PostgreSQL and Perl. AVAILABILITY: The Chado Controller package is available for download at http://www.gnpannot.org/content/chado-controller and runs on any Unix-like operating system, and documentation is available at http://www.gnpannot.org/content/chado-controller-doc The system can be tested using the GNPAnnot Sandbox at http://www.gnpannot.org/content/gnpannot-sandbox-form CONTACT: valentin.guignon@cirad.fr; stephanie.sidibe-bocs@cirad.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data.

MOTIVATION: High-throughput sequencing (HTS) technologies have made low-cost sequencing of large numbers of samples commonplace. An explosion in the type, not just number, of sequencing experiments has also taken place including genome re-sequencing, population-scale variation detection, whole transcriptome sequencing and genome-wide analysis of protein-bound nucleic acids. RESULTS: We present Artemis as a tool for integrated visualization and computational analysis of different types of HTS datasets in the context of a reference genome and its corresponding annotation. AVAILABILITY: Artemis is freely available (under a GPL licence) for download (for MacOSX, UNIX and Windows) at the Wellcome Trust Sanger Institute websites: http://www.sanger.ac.uk/resources/software/artemis/.

GeneDB--an annotation database for pathogens.

GeneDB (http://www.genedb.org) is a genome database for prokaryotic and eukaryotic pathogens and closely related organisms. The resource provides a portal to genome sequence and annotation data, which is primarily generated by the Pathogen Genomics group at the Wellcome Trust Sanger Institute. It combines data from completed and ongoing genome projects with curated annotation, which is readily accessible from a web based resource. The development of the database in recent years has focused on providing database-driven annotation tools and pipelines, as well as catering for increasingly frequent assembly updates. The website has been significantly redesigned to take advantage of current web technologies, and improve usability. The current release stores 41 data sets, of which 17 are manually curated and maintained by biologists, who review and incorporate data from the scientific literature, as well as other sources. GeneDB is primarily a production and annotation database for the genomes of predominantly pathogenic organisms.

BamView: viewing mapped read alignment data in the context of the reference sequence.

SUMMARY: BamView is an interactive Java application for visualizing the large amounts of data stored for sequence reads which are aligned against a reference genome sequence. It supports the BAM (Binary Alignment/Map) format. It can be used in a number of contexts including SNP calling and structural annotation. BamView has also been integrated into Artemis so that the reads can be viewed in the context of the nucleotide sequence and genomic features. AVAILABILITY: BamView and Artemis are freely available (under a GPL licence) for download (for MacOSX, UNIX and Windows) at: http://bamview.sourceforge.net/

Blumeria graminis interactions with barley conditioned by different single R genes demonstrate a temporal and spatial relationship between stomatal dysfunction and cell death.

Hypersensitive response (HR) against Blumeria graminis f. sp. hordei infection in barley (Hordeum vulgare) was associated with stomata "lock-up" leading to increased leaf water conductance (g(l)). Unique spatio-temporal patterns of HR formation occurred in barley with Mla1, Mla3, or MlLa R genes challenged with B. graminis f. sp. hordei. With Mla1, a rapid HR, limited to epidermal cells, arrested fungal growth before colonies initiated secondary attacks. With Mla3, mesophyll HR preceded that in epidermal cells whose initial survival supported secondary infections. With MlLa, mesophyll survived and not all attacked epidermal cells died immediately, allowing colony growth and secondary infection until arrested. Isolines with Mla1, Mla3, or MlLa genes inoculated with B. graminis f. sp. hordei ranging from 1 to 100 conidia mm(2) showed abnormally high g(l) during dark periods whose timing and extent correlated with those of each HR. Each isoline showed increased dark g(l) with the nonpathogen B. graminis f. sp. avenae which caused a single epidermal cell HR. Guard cell autofluorescence was seen only after drying of epidermal strips and closure of stomata suggesting that locked open stomata were viable. The data link stomatal lock-up to HR associated cell death and has implications for strategies for selecting disease resistant genotypes.

DNAPlotter: circular and linear interactive genome visualization.

UNLABELLED: DNAPlotter is an interactive Java application for generating circular and linear representations of genomes. Making use of the Artemis libraries to provide a user-friendly method of loading in sequence files (EMBL, GenBank, GFF) as well as data from relational databases, it filters features of interest to display on separate user-definable tracks. It can be used to produce publication quality images for papers or web pages. AVAILABILITY: DNAPlotter is freely available (under a GPL licence) for download (for MacOSX, UNIX and Windows) at the Wellcome Trust Sanger Institute web sites: http://www.sanger.ac.uk/Software/Artemis/circular/