Alicia Y. Zhou

PURPOSE: Germline mutations in DNA repair genes are present in approximately 10% of men with metastatic prostate cancer (mPC), and guidelines recommend genetic germline testing. Notable barriers exist, including access to genetic counseling, insurance coverage, and out-of-pocket costs. The GENTleMEN study was designed to determine the feasibility of an Internet-based, patient-driven germline genetic testing approach for men with mPC. PATIENTS AND METHODS: In this prospective cohort study, men with mPC provided informed consent via an Internet-based platform and completed a questionnaire including demographics and family cancer history. Supporting medical data were also collected. Genetic testing was performed using the Color Genomics 30-gene targeted panel of cancer predisposition genes on a mailed saliva sample. Men whose test results identified a germline pathogenic or likely pathogenic variant received results by phone or telehealth genetic counseling; other participants received results by email with an option for phone-based or telehealth genetic counseling. RESULTS: As of August 18, 2021, 816 eligible men were consented, of whom 68% (551) completed genetic testing, and 8.7% (48 of 551) were found to carry a pathogenic or likely pathogenic variant in a germline DNA repair gene: CHEK2 (17), BRCA2 (15), ATM (6), NBN1 (3), BRCA1 (2), PALB2 (2), PMS2 (2), and MSH6 (1). Participants were more likely to complete the testing process if they were non-Hispanic White, married, highly educated, or from a higher-income bracket. CONCLUSION: Here, we show the feasibility of delivering germline (inherited) genetic testing by a voluntary, patient-driven, Internet-based platform to men with mPC. Preliminary results show rates of germline DNA repair mutations, consistent with other cohorts. Although feasible for some, reduced steps for participation, more dedicated diverse outreach and participant support, and identification and addressing of additional barriers is needed to ensure equitable access and optimization.

Significance: As the COVID-19 pandemic drags on, we need strategies for reopening those schools that have remained closed. We developed stochastic network models to study the risks associated with returning to in-person learning during the COVID-19 pandemic, and to explore the value of mitigation measures in reducing these risks. Our models indicate that the risk of school outbreaks increases as community prevalence increases, and that secondary schools pose greater control challenges than primary schools. The models reveal that a number of measures can help substantially: dividing students into multiple cohorts who attend school on an alternating basis, frequently testing teachers and students, and vaccinating teachers and staff. Basic transmission control strategies such as mask use, social distancing, and ventilation remain essential.

During the initial stages of the COVID-19 pandemic, many workplaces and universities implemented institution-wide proactive testing programs of all individuals, ir-respective of symptoms. These measures have proven effective in mitigating outbreaks. As a greater fraction of the population becomes vaccinated, we need to understand what continued benefit, if any, proactive testing can contribute. Here, we address this problem with two distinct modeling approaches: a simple analytical model and a more simulation using the SEIRS+ platform. Both models indicate that proactive testing remains useful until a threshold level of vaccination is reached. This threshold depends on the transmissibility of the virus and the scope of other control measures in place. If a community is able to reach the threshold level of vaccination, testing can cease. Otherwise, continued testing will be an important component of disease control. Because it is usually difficult or impossible to precisely estimate key parameters such as the basic reproduction number for a specific workplace or other setting, our results are more useful for understanding general trends than for making precise quantitative predictions.

Publicly-available genetic databases promote data sharing and fuel scientific discoveries for the prevention, treatment, and management of disease. In 2018, we built Color Data, a user-friendly, open access database containing genotypic and self-reported phenotypic information from 50,000 individuals who were sequenced for 30 genes associated with hereditary cancer. In a continued effort to promote access to these types of data, we launched Color Data v2, an updated version of the Color Data database. This new release includes additional clinical genetic testing results from more than 18,000 individuals who were sequenced for 30 genes associated with hereditary cardiovascular conditions, as well as polygenic risk scores for breast cancer, coronary artery disease, and atrial fibrillation. In addition, we used self-reported phenotypic information to implement the following four clinical risk models: Gail Model for five-year risk of breast cancer, Claus Model for lifetime risk of breast cancer, simple office-based Framingham Coronary Heart Disease Risk Score for ten-year risk of coronary heart disease, and CHARGE-AF simple score for five-year risk of atrial fibrillation. These new features and capabilities are highlighted through two sample queries in the database. We hope that the broad dissemination of this data will help researchers continue to explore genotype-phenotype correlations and identify novel variants for functional analysis, enabling scientific discoveries in the field of population genomics.Database URL: https://data.color.com/

Publicly available genetic databases promote data sharing and fuel scientific discoveries for the prevention, treatment and management of disease. In 2018, we built Color Data, a user-friendly, open access database containing genotypic and self-reported phenotypic information from 50 000 individuals who were sequenced for 30 genes associated with hereditary cancer. In a continued effort to promote access to these types of data, we launched Color Data v2, an updated version of the Color Data database. This new release includes additional clinical genetic testing results from more than 18 000 individuals who were sequenced for 30 genes associated with hereditary cardiovascular conditions as well as polygenic risk scores for breast cancer, coronary artery disease and atrial fibrillation. In addition, we used self-reported phenotypic information to implement the following four clinical risk models: Gail Model for 5-year risk of breast cancer, Claus Model for lifetime risk of breast cancer, simple office-based Framingham Coronary Heart Disease Risk Score for 10-year risk of coronary heart disease and CHARGE-AF simple score for 5-year risk of atrial fibrillation. These new features and capabilities are highlighted through two sample queries in the database. We hope that the broad dissemination of these data will help researchers continue to explore genotype–phenotype correlations and identify novel variants for functional analysis, enabling scientific discoveries in the field of population genomics. Database URL: https://data.color.com/