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Purpose: Prostate cancer (PCa) is highly heritable. No validated PCa risk model currently exists. We therefore sought to develop a genetic risk model that can provide personalised predicted PCa risks based on known moderate-to-high-risk pathogenic variants, low-risk common genetic variants and explicit cancer family history, and to externally validate the model in an independent prospective cohort. Patients and methods: We developed a risk model using a kin-cohort comprising individuals from 16,633 PCa families ascertained in the UK in 1993-2017 from the UK Genetic Prostate Cancer Study, and complex segregation analysis adjusting for ascertainment. The model was externally validated in 170,850 unaffected men (7,624 incident PCas) recruited in 2006-2010 to the independent UK Biobank prospective cohort study. Results: The most parsimonious model included the effects of pathogenic variants in BRCA2, HOXB13 and BRCA1 and a polygenic score based on 268 common low-risk variants. Residual familial risk was modelled by a hypothetical recessively inherited variant and a polygenic component whose standard deviation decreased log-linearly with age. The model predicted familial risks that were consistent with those reported in previous observational studies. In the validation cohort, the model discriminated well between unaffected men and men with incident PCas within 5yr (C-index=0.790, 95% CI 0.783-0.797) and 10yr (C-index=0.772, 95% CI 0.768-0.777). The 50% of men with highest predicted risks captured 86.3% of PCa cases within 10yr. Conclusion: This is the first validated risk model offering personalised PCa risks. The model will assist in counselling men concerned about their risk and can facilitate future risk-stratified population screening approaches.

Polygenic risk; Prediction; Breast cancer Riesgo poligénico; Predicción; Cáncer de mama Risc poligènic; Predicció; Càncer de mama 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. α 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 α⁠, as compared with the RL estimates. The RL α 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. This work has been supported by grants from Cancer Research UK (PPRPGM-Nov20\100002); the European Union's Horizon 2020 Research and Innovation Programme under grant agreement numbers 633784 (B-CAST) and 634935 (BRIDGES); the PERSPECTIVE I&I project which is funded by the Government of Canada through Genome Canada (#13529) and the Canadian Institutes of Health Research (#155865), the Ministère de l’Économie et de l'Innovation du Québec through Genome Québec, the Quebec Breast Cancer Foundation, the CHU de Quebec Foundation and the Ontario Research Fund; and by the NIHR Cambridge Biomedical Research Centre (BRC-1215–20014). BCAC is funded by the European Union's Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST respectively), and the PERSPECTIVE I&I project. Additional funding for BCAC is provided via the Confluence project which is funded with intramural funds from the NCI Intramural Research Program, NIH. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer Research UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSRSIIRI-701 grant, and the Quebec Breast Cancer Foundation. MT was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215–20014) and Cancer Research UK C22770/A31523 (International Alliance for Cancer Early Detection programme). The PRISMA study has been funded by Instituto de Salud Carlos III through the project " PI19/01195″ (Co-funded by European Regional Development Fund "A way to make Europe") and it received the institutional support of CERCA Program (Generalitat de Catalunya). The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

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 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.

Cell populations achieve size homoeostasis by coordinating cell growth and division. Direct (sizer) or indirect (adder, timer) models of size regulation have been proposed. Early experiments from 1970s showed that fission yeast implements an almost perfect sizer, whereas more recent data hint at looser regulation. It is unclear whether external conditions can affect regulation stringency and putative additional internal thresholds or timers regulating cell cycle progression. Moreover, it is currently unclear what causes different size heterogeneity levels observed by varying experimental conditions. In this work, I implemented an experimental and computational pipeline to investigate the aforementioned points. First, I created a cell cycle reporter strain and devised an imaging pipeline for analysing cells images, which I employed for acquiring high-throughput phenotypic data on cells grown in different experimental conditions (nitrogen sources). Second, I wrote cell cycle models and optimization scripts for extracting additional dynamic information from static experimental data, e.g. regarding cell size regulation. Apart from confirming that fission yeast adopts an imperfect sizer mechanism (at the G2/M transition), I unexpectedly observed that sizer stringency increases in faster-growing populations and is responsible for the reduction in heterogeneity of cell length at division. I also found that a constant adder regulates G1 elongation and a timer determines S duration. Fission yeast cells elongate asymmetrically; I found that elongation asymmetry depends on growth rate and on the stage in which cells divided. I also observed the presence of slower growing subpopulations, whose rate ranges between 15-40% of the overall growth rate and whose abundance increases in least favourable conditions. Finally, I found that the duration of the non-elongating phase between mitosis and cell division depends on growth rate, as well as the duration of cell cycle stages in the elongating phase and the duration of the mitotic phase. Open Access

The construction and analysis of networks is increasingly widespread in biological research. We have developed esyN ("easy networks") as a free and open source tool to facilitate the exchange of biological network models between researchers. esyN acts as a searchable database of user-created networks from any field. We have developed a simple companion web tool that enables users to view and edit networks using data from publicly available databases. Both normal interaction networks (graphs) and Petri nets can be created. In addition to its basic tools, esyN contains a number of logical templates that can be used to create models more easily. The ability to use previously published models as building blocks makes esyN a powerful tool for the construction of models and network graphs. Users are able to save their own projects online and share them either publicly or with a list of collaborators. The latter can be given the ability to edit the network themselves, allowing online collaboration on network construction. esyN is designed to facilitate unrestricted exchange of this increasingly important type of biological information. Ultimately, the aim of esyN is to bring the advantages of Open Source software development to the construction of biological networks.

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 NICE guidelines and CanRisk being freely available for use, the CanRisk tool has not yet been implemented widely in primary care Aim: The aim of this study was to explore the barriers and facilitators to the implementation of the CanRisk tool in primary care. Design and setting: A multi-methods study, conducted with primary care practitioners (PCPs) in the UK Methods: Three methods were employed. Participants completed two vignette-based case studies, a semi-structured interview and a questionnaire. Results: 16 PCPs completed the study. The main barriers to implementation were: the time needed to complete the tool, competing priorities, IT infrastructure, and PCPs’ lack of confidence and knowledge to use the tool. The 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: This more developed understanding of the barriers and facilitators to the use of CanRisk in primary care highlights 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.