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.

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.

Tuberculosis, which is caused by Mycobacterium tuberculosis, remains one of the leading causes of mortality worldwide. The C-type lectin DC-SIGN is known to be the major M. tuberculosis receptor on human dendritic cells. We reasoned that if DC-SIGN interacts with M. tuberculosis, as well as with other pathogens, variation in this gene might have a broad range of influence in the pathogenesis of a number of infectious diseases, including tuberculosis. We tested whether polymorphisms in CD209, the gene encoding DC-SIGN, are associated with susceptibility to tuberculosis through sequencing and genotyping analyses in a South African cohort. After exclusion of significant population stratification in our cohort, we observed an association between two CD209 promoter variants (−871G and −336A) and decreased risk of developing tuberculosis. By looking at the geographical distribution of these variants, we observed that their allelic combination is mainly confined to Eurasian populations. Our observations suggest that the two −871G and −336A variants confer protection against tuberculosis. In addition, the geographic distribution of these two alleles, together with their phylogenetic status, suggest that they may have increased in frequency in non-African populations as a result of host genetic adaptation to a longer history of exposure to tuberculosis. Further characterization of the biological consequences of DC-SIGN variation in tuberculosis will be crucial to better appreciate the role of this lectin in interactions between the host immune system and the tubercle bacillus as well as other pathogens.

The recent introduction of fluorescent bead-based technology, allowing the measurement of multiples analytes in a single 25–50 µl sample has revolutionized the study of cytokine responses. However, such multiplex approaches may compromise the ability of these assays to accurately measure actual cytokine levels. This study evaluates the performance of three commercially available multiplex cytokine fluorescent bead-based immunoassays (Bio-Rad's Cytokine 17-plex kit; LINCO Inc's 29-plex kit; and RnD System's Fluorokine-Multi Analyte Profiling (MAP) base kit A and B). The LINCO Inc kit was found to be the most sensitive assay for measuring concentrations of multiple recombinant cytokines in samples that had been spiked with serial dilutions of the standard provided by the manufacturer, followed respectively by the RnD Fluorokine-(MAP) and Bio-Rad 17-plex kits. A positive correlation was found in the levels of IFN-γ measured in antigen stimulated whole blood culture supernatants by the LINCO Inc 29-plex, RnD Fluorokine-(MAP) and RnD system IFN-γ Quantikine ELISA kits across a panel of controls and stimulated samples. Researchers should take the limitation of such multiplexed assays into account when planning experiments and the most appropriate use for these tests may currently be as screening tools for the selection of promising markers for analysis by more sensitive techniques.

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. 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. 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. 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.