Background: Use of risk-reducing Salpingo-oophorectomy (RRSO) substantially reduces the risk of ovarian and breast cancer for women who carry a BRCA1/2 mutation. It is important to adjust for RRSO use in the estimation of BRCA1/2 penetrance of breast and ovarian cancer. Methods: We searched PubMed for penetrance estimates of breast and ovarian cancer from studies that genotyped individual patients and explicitly adjusted for RRSO use by censoring follow-up at the age of RRSO. We meta-analyzed penetrance estimates from 7 identified studies. We implemented the resulting penetrance estimates in a Mendelian risk prediction model as iplemented in the software package BRCAPRO, which we applied to estimate carrier probabilities in 2 BRCA cohorts. Results: Penetrance estimates by age 70 years for breast cancer were 64.6% (95% confidence interval [CI] = 59.5% to 69.4%) for BRCA1 mutation carriers and 61.0% (95% CI = 48.1% to 72.5%) for BRCA2 mutation carriers, and for ovarian cancer they were 48.3% (95% CI = 38.8% to 57.9%) and 20.0% (95% CI = 13.3% to 29.0%), respectively. When integrated into BRCAPRO, our estimates led to good calibration and different estimates of carrier probabilities for some individuals when evaluating the models in 2 cohorts. Conclusions: The report updates penetrance estimates for BRCA1/2-associated cancer. We report higher estimates than previously reported, which did not adjust for RRSO. Differential use of RRSO may partially explain heterogeneity in the currently available penetrance estimates. For some individuals, using our estimates in BRCAPRO may result in changes in estimated carrier probabilities, which warrants validation in future studies.

Background and Objectives Recent data suggest increasing global prevalence of multiple sclerosis (MS). Early diagnosis of MS reduces the burden of disability-adjusted life years and associated health care costs. Yet diagnostic delays persist in MS care and even within national health care systems with robust resources, comprehensive registries, and MS subspecialist referral networks. The global prevalence and characteristics of barriers to expedited MS diagnosis, particularly in resource-restricted regions, have not been extensively studied. Recent revisions to MS diagnostic criteria demonstrate potential to facilitate earlier diagnosis, but global implementation remains largely unknown. Methods The Multiple Sclerosis International Federation third edition of the Atlas of MS was a survey that assessed the current global state of diagnosis including adoption of MS diagnostic criteria; barriers to diagnosis with respect to the patient, health care provider, and health system; and existence of national guidelines or national standards for speed of MS diagnosis. Results Coordinators from 107 countries (representing approximately 82% of the world population), participated. Eighty-three percent reported at least 1 “major barrier” to early MS diagnosis. The most frequently reported barriers included the following: “lack of awareness of MS symptoms among general public” (68%), “lack of awareness of MS symptoms among health care professionals” (59%), and “lack of availability of health care professionals with knowledge to diagnose MS” (44%). One-third reported lack of “specialist medical equipment or diagnostic tests.” Thirty-four percent reported the use of only 2017 McDonald criteria (McD-C) for diagnosis, and 79% reported 2017 McD-C as the “most commonly used criteria.” Sixty-six percent reported at least 1 barrier to the adoption of 2017 McD-C, including “neurologists lack awareness or training” by 45%. There was no significant association between national guidelines pertaining to MS diagnosis or practice standards addressing the speed of diagnosis and presence of barriers to early MS diagnosis and implementation of 2017 McD-C. Discussion This study finds pervasive consistent global barriers to early diagnosis of MS. While these barriers reflected a lack of resources in many countries, data also suggest that interventions designed to develop and implement accessible education and training can provide cost-effective opportunities to improve access to early MS diagnosis.

Lynch syndrome (LS) is a hereditary cancer susceptibility condition associated with varying cancer risks depending on which of the five causative genes harbors a pathogenic variant; however, lifestyle and medical interventions provide options to lower those risks. We developed MyLynch, a patient-facing clinical decision support (CDS) web application that applies genetically-guided personalized medicine (GPM) for individuals with LS. The tool was developed in R Shiny through a patient-focused iterative design process. The knowledge base used to estimate patient-specific risk leveraged a rigorously curated literature review. MyLynch informs LS patients of their personal cancer risks, educates patients on relevant interventions, and provides patients with adjusted risk estimates, depending on the interventions they choose to pursue. MyLynch can improve risk communication between patients and providers while also encouraging communication among relatives with the goal of increasing cascade testing. As genetic panel testing becomes more widely available, GPM will play an increasingly important role in patient care, and CDS tools offer patients and providers tailored information to inform decision-making. MyLynch provides personalized cancer risk estimates and interventions to lower these risks for patients with LS.

To develop reparative therapies for neurological disorders like multiple sclerosis (MS), we need to better understand the physiology of loss and replacement of oligodendrocytes, the cells that make myelin and are the target of damage in MS.In vivotwo-photon fluorescence microscopy allows direct visualization of oligodendrocytes in the intact brain of transgenic mouse models, promising a deeper understanding of the longitudinal dynamics of replacing oligodendrocytes after damage. However, the task of tracking the fate of individual oligodendrocytes requires extensive effort for manual annotation and is especially challenging in three-dimensional images. While several models exist for annotating cells in two-dimensional images, few models exist to annotate cells in three-dimensional images and even fewer are designed for tracking cells in longitudinal imaging. Notably, existing options often come with a substantial financial investment, being predominantly commercial or confined to proprietary software. Furthermore, the complexity of processes and myelin formed by individual oligodendrocytes can result in the failure of algorithms that are specifically designed for tracking cell bodies alone. Here, we propose a fast, free, consistent, and unsupervised beta-mixture oligodendrocyte segmentation system (FAST) that is written in open-source software, and can segment and track oligodendrocytes in three-dimensional images over time with minimal human input. We showed that the FAST model can segment and track oligodendrocytes similarly to a blinded human observer. Although FAST was developed to apply to our studies on oligodendrocytes, we anticipate that it can be modified to study four-dimensionalin vivodata of any brain cell with associated complex processes.

Many pathogenic sequence variants (PSVs) have been associated with increased risk of cancers. Mendelian risk prediction models use Mendelian laws of inheritance to predict the probability of having a PSV based on family history, as well as specified PSV frequency and penetrance (agespecific probability of developing cancer given genotype). Most existing models assume penetrance is the same for any PSVs in a certain gene. However, for some genes (for example, BRCA1/2), cancer risk does vary by PSV. We propose an extension of Mendelian risk prediction models to relax the assumption that risk is the same for any PSVs in a certain gene by incorporating variant-specific penetrances and illustrating these extensions on two existing Mendelian risk prediction models, BRCAPRO and PanelPRO. Our proposed BRCAPRO-variant and PanelPRO-variant models incorporate variant-specific BRCA1/2 PSVs through the region classifications. Due to the sparsity of the variant information we classify BRCA1/2 PSVs into three regions; the breast cancer clustering region (BCCR), the ovarian cancer clustering region (OCCR), and an other region. Simulations were conducted to evaluate the performance of the proposed BRCAPRO-variant model compared to the existing BRCAPRO model which assumes the penetrance is the same for any PSVs in BRCA1 (and respectively BRCA2). Simulation results showed that the BRCAPRO-variant model was well calibrated to predict region-specific BRCA1/2 carrier status with high discrimination and accuracy on the region-specific level. In addition, we showed that the BRCAPRO-variant model achieved performance gains over the existing risk prediction models in terms of calibration without loss in discrimination and accuracy. We also evaluated the performance of the two proposed models, BRCAPRO-variant and PanelPRO-variant, on a cohort of 1,961 families from the Cancer Genetics Network (CGN). We showed that our proposed models provide region-specific PSV carrier probabilities with high accuracy, while the calibration, discrimination and accuracy of gene-specific PSV carrier probabilities were comparable to the existing gene-specific models. As more variant-specific PSV penetrances become available, we have shown that Mendelian risk prediction models can be extended to integrate the additional information, providing precise variant or region-specific PSV carrier probabilities and improving future cancer risk predictions.

To develop reparative therapies for multiple sclerosis (MS), we need to better understand the physiology of loss and replacement of oligodendrocytes, the cells that make myelin and the target of damage in MS. In vivo two-photon fluorescence microscopy allows direct visualization of oligodendrocytes in transgenic mouse models, and promises a deeper understanding of the longitudinal dynamics of replacing oligodendrocytes after damage. However, the task of tracking oligodendrocytes requires extensive human effort and is especially challenging in three-dimensional images. While several models exist for automatically annotating cells in two-dimensional images, few models exist to annotate cells in three-dimensional images and even fewer are designed for tracking cells in longitudinal imaging. Furthermore, the complexity of processes and myelin formed by individual oligodendrocytes can result in the failure of algorithms that are specifically designed for tracking cell bodies alone. Here, we propose a novel beta-mixture unsupervised oligodendrocyte segmentation system (BOSS) that can segment and track oligodendrocytes in three-dimensional images over time that requires minimal human input. We evaluated the performance of the BOSS model on a set of eight images obtained longitudinally. We showed that the BOSS model can segment and track oligodendrocytes similarly to a blinded human observer. Our method offers many advantages, as it does not require fully curated data, reduces computational time, and most importantly recapitulates cell dynamic patterns observed by manually tracking oligodendrocytes. Although BOSS was developed to apply to our studies on oligodendrocytes, we anticipate that it can be modified to study four-dimensional in vivo data of any brain cell.