The recruitment experience of a randomized clinical trial to aid young adult smokers to stop smoking without weight gain with interactive technology

AbstractMultiple recruitment strategies are often needed to recruit an adequate number of participants, especially hard to reach groups. Technology-based recruitment methods hold promise as a more robust form of reaching and enrolling historically hard to reach young adults. The TARGIT study is a randomized two-arm clinical trial in young adults using interactive technology testing an efficacious proactive telephone Quitline versus the Quitline plus a behavioral weight management intervention focusing on smoking cessation and weight change. All randomized participants in the TARGIT study were required to be a young adult smoker (18–35 years), who reported smoking at least 10 cigarettes per day, had a BMI < 40 kg/m2, and were willing to stop smoking and not gain weight. Traditional recruitment methods were compared to technology-based strategies using standard descriptive statistics based on counts and proportions to describe the recruitment process from initial pre-screening (PS) to randomization into TARGIT. Participants at PS were majority Black (59.80%), female (52.66%), normal or over weight (combined 62.42%), 29.5 years old, and smoked 18.4 cigarettes per day. There were differences in men and women with respect to reasons for ineligibility during PS (p < 0.001; ignoring gender specific pregnancy-related ineligibility). TARGIT experienced a disproportionate loss of minorities during recruitment as well as a prolonged recruitment period due to either study ineligibility or not completing screening activities. Recruitment into longer term behavioral change intervention trials can be challenging and multiple methods are often required to recruit hard to reach groups.ClinicalTrials.gov Identifier NCT01199185The NHLBI funded TARGIT as part of a U01 Cooperative Agreement and as such the study design was approved. They did not have input into the data collection, analysis, or the interpretation of the data or in the writing of this report

Mosaic PPM1D mutations are associated with predisposition to breast and ovarian cancer

Improved sequencing technologies offer unprecedented opportunities for investigating the role of rare genetic variation in common disease. However, there are considerable challenges with respect to study design, data analysis and replication. Using pooled next-generation sequencing of 507 genes implicated in the repair of DNA in 1,150 samples, an analytical strategy focused on protein-truncating variants (PTVs) and a large-scale sequencing case-control replication experiment in 13,642 individuals, here we show that rare PTVs in the p53-inducible protein phosphatase PPM1D are associated with predisposition to breast cancer and ovarian cancer. PPM1D PTV mutations were present in 25 out of 7,781 cases versus 1 out of 5,861 controls (P = 1.12 × 10-5), including 18 mutations in 6,912 individuals with breast cancer (P = 2.42 × 10-4) and 12 mutations in 1,121 individuals with ovarian cancer (P = 3.10 × 10-9). Notably, all of the identified PPM1D PTVs were mosaic in lymphocyte DNA and clustered within a 370-base-pair region in the final exon of the gene, carboxy-terminal to the phosphatase catalytic domain. Functional studies demonstrate that the mutations result in enhanced suppression of p53 in response to ionizing radiation exposure, suggesting that the mutant alleles encode hyperactive PPM1D isoforms. Thus, although the mutations cause premature protein truncation, they do not result in the simple loss-of-function effect typically associated with this class of variant, but instead probably have a gain-of-function effect. Our results have implications for the detection and management of breast and ovarian cancer risk. More generally, these data provide new insights into the role of rare and of mosaic genetic variants in common conditions, and the use of sequencing in their identification

Mosaic PPM1D mutations are associated with predisposition to breast and ovarian cancer

Improved sequencing technologies offer unprecedented opportunities for investigating the role of rare genetic variation in common disease. However, there are considerable challenges with respect to study design, data analysis and replication. Using pooled next-generation sequencing of 507 genes implicated in the repair of DNA in 1,150 samples, an analytical strategy focused on protein-truncating variants (PTVs) and a large-scale sequencing case-control replication experiment in 13,642 individuals, here we show that rare PTVs in the p53-inducible protein phosphatase PPM1D are associated with predisposition to breast cancer and ovarian cancer. PPM1D PTV mutations were present in 25 out of 7,781 cases versus 1 out of 5,861 controls (P = 1.12 × 10(-5)), including 18 mutations in 6,912 individuals with breast cancer (P = 2.42 × 10(-4)) and 12 mutations in 1,121 individuals with ovarian cancer (P = 3.10 × 10(-9)). Notably, all of the identified PPM1D PTVs were mosaic in lymphocyte DNA and clustered within a 370-base-pair region in the final exon of the gene, carboxy-terminal to the phosphatase catalytic domain. Functional studies demonstrate that the mutations result in enhanced suppression of p53 in response to ionizing radiation exposure, suggesting that the mutant alleles encode hyperactive PPM1D isoforms. Thus, although the mutations cause premature protein truncation, they do not result in the simple loss-of-function effect typically associated with this class of variant, but instead probably have a gain-of-function effect. Our results have implications for the detection and management of breast and ovarian cancer risk. More generally, these data provide new insights into the role of rare and of mosaic genetic variants in common conditions, and the use of sequencing in their identification

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

&lt;h2&gt;What's Changed&lt;/h2&gt; &lt;ul&gt; &lt;li&gt;Documentation and repo hierarchy refactoring by @sierra-moxon in https://github.com/biolink/biolink-model/pull/1418&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;Summary: 4.0.0 is a major release that includes many changes to the documentation for Biolink Model as well as the reorganization of the repository to support the new documentation structure and comply with LinkML best practices. The model itself has not changed significantly, but the documentation has been updated to reflect the current state of the model, and includes new visualizations of the model, additional text-based documentation, and a new gh-pages documentation layout.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Full Changelog&lt;/strong&gt;: https://github.com/biolink/biolink-model/compare/v3.6.0...v4.0.0&lt;/p&gt;Please cite the following works when using this software