A framework for assessing clinical trial site readiness.

Clinical trial processes are unnecessarily inefficient and costly, slowing the translation of medical discoveries into treatments for people living with disease. To reduce redundancies and inefficiencies, a group of clinical trial experts developed a framework for clinical trial site readiness based on existing trial site qualifications from sponsors. The site readiness practices are encompassed within six domains: research team, infrastructure, study management, data collection and management, quality oversight, and ethics and safety. Implementation of this framework for clinical trial sites would reduce inefficiencies in trial conduct and help prepare new sites to enter the clinical trials enterprise, with the potential to improve the reach of clinical trials to underserved communities. Moreover, the framework holds benefits for trial sponsors, contract research organizations, trade associations, trial participants, and the public. For novice sites considering future trials, we provide a framework for site preparation and the engagement of stakeholders. For experienced sites, the framework can be used to assess current practices and inform and engage sponsors, staff, and participants. Details in the supplementary materials provide easy access to key regulatory documents and resources. Invited perspective articles provide greater depth from a systems, DEIA (diversity, equity, inclusion, and accessibility) and decentralized trials perspective.

Advancing translational science education.

In this communication, the authors offer considerations for how the scientific community can capitalize on decades of translational science advances and experiential knowledge to develop new education opportunities for a diverse and highly skilled translational science workforce. Continued advancement of the field of translational science will require new education approaches that distill key concepts in translational science from past and ongoing research initiatives and teach this foundational knowledge to current and future translational scientists. These key concepts include generalizable scientific and operational principles to guide translational science, as well as evidence-informed practices. Inspired by this approach, the National Center for Advancing Translational Sciences (NCATS) has developed an initial set of guiding principles for translational science generated via case studies of multiple highly successful translational science initiatives, and is now teaching them via new education activities that aim to reach a broad scientific audience interested in translational science. Our goal with this review is to prompt continued conversation with the translational science community regarding capitalizing on our collective translational science knowledge to advance core content for translational science education and disseminating this content to a broad range of scientific audiences.

The IDeaS initiative: pilot study to assess the impact of rare diseases on patients and healthcare systems.

BACKGROUND: Rare diseases (RD) are a diverse collection of more than 7-10,000 different disorders, most of which affect a small number of people per disease. Because of their rarity and fragmentation of patients across thousands of different disorders, the medical needs of RD patients are not well recognized or quantified in healthcare systems (HCS). METHODOLOGY: We performed a pilot IDeaS study, where we attempted to quantify the number of RD patients and the direct medical costs of 14 representative RD within 4 different HCS databases and performed a preliminary analysis of the diagnostic journey for selected RD patients. RESULTS: The overall findings were notable for: (1) RD patients are difficult to quantify in HCS using ICD coding search criteria, which likely results in under-counting and under-estimation of their true impact to HCS; (2) per patient direct medical costs of RD are high, estimated to be around three-fivefold higher than age-matched controls; and (3) preliminary evidence shows that diagnostic journeys are likely prolonged in many patients, and may result in progressive, irreversible, and costly complications of their disease CONCLUSIONS: The results of this small pilot suggest that RD have high medical burdens to patients and HCS, and collectively represent a major impact to the public health. Machine-learning strategies applied to HCS databases and medical records using sentinel disease and patient characteristics may hold promise for faster and more accurate diagnosis for many RD patients and should be explored to help address the high unmet medical needs of RD patients.

Diversity and inclusion for the All of Us research program: A scoping review.

The All of Us Research Program (All of Us) is a national effort to accelerate health research by exploring the relationship between lifestyle, environment, and genetics. It is set to become one of the largest research efforts in U.S. history, aiming to build a national resource of data from at least one million participants. All of Us aims to address the need for more diversity in research and set the stage for that diversity to be leveraged in precision medicine research to come. This paper describes how the program assessed demographic characteristics of participants who have enrolled in other U.S. biomedical research cohorts to better understand which groups are traditionally represented or underrepresented in biomedical research. We 1) reviewed the enrollment characteristics of national cohort studies like All of Us, and 2) surveyed the literature, focusing on key diversity categories essential to the program's enrollment aims. Based on these efforts, All of Us emphasizes enrollment of racial and ethnic minorities, and has formally designated the following additional groups as historically underrepresented: individuals-with inadequate access to medical care; under the age of 18 or over 65; with an annual household income at or below 200% of the federal poverty level; who have a cognitive or physical disability; have less than a high school education or equivalent; are intersex; identify as a sexual or gender minority; or live in rural or non-metropolitan areas. Research accounting for wider demographic variability is critical. Only by ensuring diversity and by addressing the very barriers that limit it, can we position All of Us to better understand and tackle health disparities.

The "All of Us" Research Program.

Knowledge gained from observational cohort studies has dramatically advanced the prevention and treatment of diseases. Many of these cohorts, however, are small, lack diversity, or do not provide comprehensive phenotype data. The All of Us Research Program plans to enroll a diverse group of at least 1 million persons in the United States in order to accelerate biomedical research and improve health. The program aims to make the research results accessible to participants, and it is developing new approaches to generate, access, and make data broadly available to approved researchers. All of Us opened for enrollment in May 2018 and currently enrolls participants 18 years of age or older from a network of more than 340 recruitment sites. Elements of the program protocol include health questionnaires, electronic health records (EHRs), physical measurements, the use of digital health technology, and the collection and analysis of biospecimens. As of July 2019, more than 175,000 participants had contributed biospecimens. More than 80% of these participants are from groups that have been historically underrepresented in biomedical research. EHR data on more than 112,000 participants from 34 sites have been collected. The All of Us data repository should permit researchers to take into account individual differences in lifestyle, socioeconomic factors, environment, and biologic characteristics in order to advance precision diagnosis, prevention, and treatment.

Development of the Initial Surveys for the All of Us Research Program.

BACKGROUND: The All of Us Research Program is building a national longitudinal cohort and collecting data from multiple information sources (e.g., biospecimens, electronic health records, and mobile/wearable technologies) to advance precision medicine. Participant-provided information, collected via surveys, will complement and augment these information sources. We report the process used to develop and refine the initial three surveys for this program. METHODS: The All of Us survey development process included: (1) prioritization of domains for scientific needs, (2) examination of existing validated instruments, (3) content creation, (4) evaluation and refinement via cognitive interviews and online testing, (5) content review by key stakeholders, and (6) launch in the All of Us electronic participant portal. All content was translated into Spanish. RESULTS: We conducted cognitive interviews in English and Spanish with 169 participants, and 573 individuals completed online testing. Feedback led to over 40 item content changes. Lessons learned included: (1) validated survey instruments performed well in diverse populations reflective of All of Us; (2) parallel evaluation of multiple languages can ensure optimal survey deployment; (3) recruitment challenges in diverse populations required multiple strategies; and (4) key stakeholders improved integration of surveys into larger Program context. CONCLUSIONS: This efficient, iterative process led to successful testing, refinement, and launch of three All of Us surveys. Reuse of All of Us surveys, available at http://researchallofus.org, may facilitate large consortia targeting diverse populations in English and Spanish to capture participant-provided information to supplement other data, such as genetic, physical measurements, or data from electronic health records.

Correction: A collaborative translational research framework for evaluating and implementing the appropriate use of human genome sequencing to improve health.

[This corrects the article DOI: 10.1371/journal.pmed.1002631.].

A collaborative translational research framework for evaluating and implementing the appropriate use of human genome sequencing to improve health.

In a Policy Forum, Muin Khoury and colleagues discuss research on the clinical application of genome sequencing data.

Neuroepigenomics: Resources, Obstacles, and Opportunities.

Long-lived post-mitotic cells, such as the majority of human neurons, must respond effectively to ongoing changes in neuronal stimulation or microenvironmental cues through transcriptional and epigenomic regulation of gene expression. The role of epigenomic regulation in neuronal function is of fundamental interest to the neuroscience community, as these types of studies have transformed our understanding of gene regulation in post-mitotic cells. This perspective article highlights many of the resources available to researchers interested in neuroepigenomic investigations and discusses some of the current obstacles and opportunities in neuroepigenomics.

Community resources and technologies developed through the NIH Roadmap Epigenomics Program.

This chapter describes resources and technologies generated by the NIH Roadmap Epigenomics Program that may be useful to epigenomics researchers investigating a variety of diseases including cancer. Highlights include reference epigenome maps for a wide variety of human cells and tissues, the development of new technologies for epigenetic assays and imaging, the identification of novel epigenetic modifications, and an improved understanding of the role of epigenetic processes in a diversity of human diseases. We also discuss future needs in this area including exploration of epigenomic variation between individuals, single-cell epigenomics, environmental epigenomics, exploration of the use of surrogate tissues, and improved technologies for epigenome manipulation.

Supplementing high-density SNP microarrays for additional coverage of disease-related genes: addiction as a paradigm.

Commercial SNP microarrays now provide comprehensive and affordable coverage of the human genome. However, some diseases have biologically relevant genomic regions that may require additional coverage. Addiction, for example, is thought to be influenced by complex interactions among many relevant genes and pathways. We have assembled a list of 486 biologically relevant genes nominated by a panel of experts on addiction. We then added 424 genes that showed evidence of association with addiction phenotypes through mouse QTL mappings and gene co-expression analysis. We demonstrate that there are a substantial number of SNPs in these genes that are not well represented by commercial SNP platforms. We address this problem by introducing a publicly available SNP database for addiction. The database is annotated using numeric prioritization scores indicating the extent of biological relevance. The scores incorporate a number of factors such as SNP/gene functional properties (including synonymy and promoter regions), data from mouse systems genetics and measures of human/mouse evolutionary conservation. We then used HapMap genotyping data to determine if a SNP is tagged by a commercial microarray through linkage disequilibrium. This combination of biological prioritization scores and LD tagging annotation will enable addiction researchers to supplement commercial SNP microarrays to ensure comprehensive coverage of biologically relevant regions.

Thyroid nodules, polymorphic variants in DNA repair and RET-related genes, and interaction with ionizing radiation exposure from nuclear tests in Kazakhstan.

Risk factors for thyroid cancer remain largely unknown except for ionizing radiation exposure during childhood and a history of benign thyroid nodules. Because thyroid nodules are more common than thyroid cancers and are associated with thyroid cancer risk, we evaluated several polymorphisms potentially relevant to thyroid tumors and assessed interaction with ionizing radiation exposure to the thyroid gland. Thyroid nodules were detected in 1998 by ultrasound screening of 2997 persons who lived near the Semipalatinsk nuclear test site in Kazakhstan when they were children (1949-1962). Cases with thyroid nodules (n = 907) were frequency matched (1:1) to those without nodules by ethnicity (Kazakh or Russian), gender and age at screening. Thyroid gland radiation doses were estimated from fallout deposition patterns, residence history and diet. We analyzed 23 polymorphisms in 13 genes and assessed interaction with ionizing radiation exposure using likelihood ratio tests (LRT). Elevated thyroid nodule risks were associated with the minor alleles of RET S836S (rs1800862, P = 0.03) and GFRA1 -193C>G (rs not assigned, P = 0.05) and decreased risk with XRCC1 R194W (rs1799782, P trend = 0.03) and TGFB1 T263I (rs1800472, P = 0.009). Similar patterns of association were observed for a small number of papillary thyroid cancers (n = 25). Ionizing radiation exposure to the thyroid gland was associated with significantly increased risk of thyroid nodules (age and gender adjusted excess odds ratio/Gy = 0.30, 95% CI 0.05-0.56), with evidence for interaction by genotype found for XRCC1 R194W (LRT P value = 0.02). Polymorphisms in RET signaling, DNA repair and proliferation genes may be related to risk of thyroid nodules, consistent with some previous reports on thyroid cancer. Borderline support for gene-radiation interaction was found for a variant in XRCC1, a key base excision repair protein. Other pathways such as genes in double-strand break repair, apoptosis and genes related to proliferation should also be pursued.

An ontology-driven semantic mashup of gene and biological pathway information: application to the domain of nicotine dependence.

OBJECTIVES: This paper illustrates how Semantic Web technologies (especially RDF, OWL, and SPARQL) can support information integration and make it easy to create semantic mashups (semantically integrated resources). In the context of understanding the genetic basis of nicotine dependence, we integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base. METHODS: We use an ontology-driven approach to integrate two gene resources (Entrez Gene and HomoloGene) and three pathway resources (KEGG, Reactome and BioCyc), for five organisms, including humans. We created the Entrez Knowledge Model (EKoM), an information model in OWL for the gene resources, and integrated it with the extant BioPAX ontology designed for pathway resources. The integrated schema is populated with data from the pathway resources, publicly available in BioPAX-compatible format, and gene resources for which a population procedure was created. The SPARQL query language is used to formulate queries over the integrated knowledge base to answer the three biological queries. RESULTS: Simple SPARQL queries could easily identify hub genes, i.e., those genes whose gene products participate in many pathways or interact with many other gene products. The identification of the genes expressed in the brain turned out to be more difficult, due to the lack of a common identification scheme for proteins. CONCLUSION: Semantic Web technologies provide a valid framework for information integration in the life sciences. Ontology-driven integration represents a flexible, sustainable and extensible solution to the integration of large volumes of information. Additional resources, which enable the creation of mappings between information sources, are required to compensate for heterogeneity across namespaces. RESOURCE PAGE: http://knoesis.wright.edu/research/lifesci/integration/structured_data/JBI-2008/

Similar prevalence of founder BRCA1 and BRCA2 mutations among Ashkenazi and non-Ashkenazi men with breast cancer: evidence from 261 cases in Israel, 1976-1999.

To evaluate the potential contribution of mutations in the BRCA1 and BRCA2 genes to male breast cancer (MBC), we expanded a previous study to screen a total of 261 Israeli men diagnosed with breast carcinoma. A total of 21 BRCA2 6174delT and 8 BRCA1 185delAG mutations were found. Similar frequencies of BRCA1 and BRCA2 mutation carriers were found among Ashkenazi (12.8%) and non-Ashkenazi Jews (9.1%). The combined prevalence of BRCA1/BRCA2 founder mutations among Ashkenazi Jewish men is slightly higher than for women, due to a higher frequency of BRCA2 mutations.