Division of Program Coordination Planning and Strategic Initiatives

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes.

Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible three-dimensional molecular and cellular atlas of the human body, in health and under various disease conditions.

Despite efforts to promote diversity in the biomedical workforce, there remains a lower rate of funding of National Institutes of Health R01 applications submitted by African-American/black (AA/B) scientists relative to white scientists. To identify underlying causes of this funding gap, we analyzed six stages of the application process from 2011 to 2015 and found that disparate outcomes arise at three of the six: decision to discuss, impact score assignment, and a previously unstudied stage, topic choice. Notably, AA/B applicants tend to propose research on topics with lower award rates. These topics include research at the community and population level, as opposed to more fundamental and mechanistic investigations; the latter tend to have higher award rates. Topic choice alone accounts for over 20% of the funding gap after controlling for multiple variables, including the applicant's prior achievements. Our findings can be used to inform interventions designed to close the funding gap.

Despite their recognized limitations, bibliometric assessments of scientific productivity have been widely adopted. We describe here an improved method to quantify the influence of a research article by making novel use of its co-citation network to field-normalize the number of citations it has received. Article citation rates are divided by an expected citation rate that is derived from performance of articles in the same field and benchmarked to a peer comparison group. The resulting Relative Citation Ratio is article level and field independent and provides an alternative to the invalid practice of using journal impact factors to identify influential papers. To illustrate one application of our method, we analyzed 88,835 articles published between 2003 and 2010 and found that the National Institutes of Health awardees who authored those papers occupy relatively stable positions of influence across all disciplines. We demonstrate that the values generated by this method strongly correlate with the opinions of subject matter experts in biomedical research and suggest that the same approach should be generally applicable to articles published in all areas of science. A beta version of iCite, our web tool for calculating Relative Citation Ratios of articles listed in PubMed, is available at https://icite.od.nih.gov.

Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.

This is the second publication of Clinical Development Plans from the National Cancer Institute, Division of Cancer Prevention and Control, Chemoprevention Branch and Agent Development Committee. The Clinical Development Plans summarize the status of promising chemopreventive agents regarding evidence for safety and chemopreventive efficacy in preclinical and clinical studies. They also contain the strategy for further development of these drugs, addressing pharmacodynamics, drug effect measurements, intermediate biomarkers for monitoring efficacy, toxicity, supply and formulation, regulatory approval, and proposed clinical trials. Sixteen new Clinical Development Plans are presented here: curcumin, dehydroepiandrosterone, folic acid, genistein, indole-3-carbinol, perillyl alcohol, phenethyl isothiocyanate, 9-cis-retinoic acid, 13-cis-retinoic acid, l-selenomethionine and 1, 4-phenylenebis(methylene)selenocyanate, sulindac sulfone, tea, ursodiol, vitamin A, and (+)-vorozole. The objective of publishing these plans is to stimulate interest and thinking among the scientific community on the prospects for developing these and future generations of chemopreventive drugs.

Following uncontrolled proliferation, a subset of primary tumour cells acquires additional traits/mutations to trigger phenotypic changes that enhance migration and are hypothesized to be the initiators of metastasis. This study reveals an adaptive mechanism that harnesses synergistic paracrine signalling via IL-6/8, which is amplified by cell proliferation and cell density, to directly promote cell migration. This effect occurs in metastatic human sarcoma and carcinoma cells- but not in normal or non-metastatic cancer cells-, and likely involves the downstream signalling of WASF3 and Arp2/3. The transcriptional phenotype of high-density cells that emerges due to proliferation resembles that of low-density cells treated with a combination of IL-6/8. Simultaneous inhibition of IL-6/8 receptors decreases the expression of WASF3 and Arp2/3 in a mouse xenograft model and reduces metastasis. This study reveals a potential mechanism that promotes tumour cell migration and infers a strategy to decrease metastatic capacity of tumour cells.

The field of regenerative medicine is approaching translation to clinical practice, and significant safety concerns and knowledge gaps have become clear as clinical practitioners are considering the potential risks and benefits of cell-based therapy. It is necessary to understand the full spectrum of stem cell actions and preclinical evidence for safety and therapeutic efficacy. The role of animal models for gaining this information has increased substantially. There is an urgent need for novel animal models to expand the range of current studies, most of which have been conducted in rodents. Extant models are providing important information but have limitations for a variety of disease categories and can have different size and physiology relative to humans. These differences can preclude the ability to reproduce the results of animal-based preclinical studies in human trials. Larger animal species, such as rabbits, dogs, pigs, sheep, goats, and non-human primates, are better predictors of responses in humans than are rodents, but in each case it will be necessary to choose the best model for a specific application. There is a wide spectrum of potential stem cell-based products that can be used for regenerative medicine, including embryonic and induced pluripotent stem cells, somatic stem cells, and differentiated cellular progeny. The state of knowledge and availability of these cells from large animals vary among species. In most cases, significant effort is required for establishing and characterizing cell lines, comparing behavior to human analogs, and testing potential applications. Stem cell-based therapies present significant safety challenges, which cannot be addressed by traditional procedures and require the development of new protocols and test systems, for which the rigorous use of larger animal species more closely resembling human behavior will be required. In this article, we discuss the current status and challenges of and several major directions for the future development of large animal models to facilitate advances in stem cell-based regenerative medicine.

The move from reading to writing the human genome offers new opportunities to improve human health. The United States National Institutes of Health (NIH) Somatic Cell Genome Editing (SCGE) Consortium aims to accelerate the development of safer and more-effective methods to edit the genomes of disease-relevant somatic cells in patients, even in tissues that are difficult to reach. Here we discuss the consortium's plans to develop and benchmark approaches to induce and measure genome modifications, and to define downstream functional consequences of genome editing within human cells. Central to this effort is a rigorous and innovative approach that requires validation of the technology through third-party testing in small and large animals. New genome editors, delivery technologies and methods for tracking edited cells in vivo, as well as newly developed animal models and human biological systems, will be assembled-along with validated datasets-into an SCGE Toolkit, which will be disseminated widely to the biomedical research community. We visualize this toolkit-and the knowledge generated by its applications-as a means to accelerate the clinical development of new therapies for a wide range of conditions.

INTRODUCTION: The metabolomics quality assurance and quality control consortium (mQACC) is enabling the identification, development, prioritization, and promotion of suitable reference materials (RMs) to be used in quality assurance (QA) and quality control (QC) for untargeted metabolomics research. OBJECTIVES: This review aims to highlight current RMs, and methodologies used within untargeted metabolomics and lipidomics communities to ensure standardization of results obtained from data analysis, interpretation and cross-study, and cross-laboratory comparisons. The essence of the aims is also applicable to other 'omics areas that generate high dimensional data. RESULTS: The potential for game-changing biochemical discoveries through mass spectrometry-based (MS) untargeted metabolomics and lipidomics are predicated on the evolution of more confident qualitative (and eventually quantitative) results from research laboratories. RMs are thus critical QC tools to be able to assure standardization, comparability, repeatability and reproducibility for untargeted data analysis, interpretation, to compare data within and across studies and across multiple laboratories. Standard operating procedures (SOPs) that promote, describe and exemplify the use of RMs will also improve QC for the metabolomics and lipidomics communities. CONCLUSIONS: The application of RMs described in this review may significantly improve data quality to support metabolomics and lipidomics research. The continued development and deployment of new RMs, together with interlaboratory studies and educational outreach and training, will further promote sound QA practices in the community.

Citation data have remained hidden behind proprietary, restrictive licensing agreements, which raises barriers to entry for analysts wishing to use the data, increases the expense of performing large-scale analyses, and reduces the robustness and reproducibility of the conclusions. For the past several years, the National Institutes of Health (NIH) Office of Portfolio Analysis (OPA) has been aggregating and enhancing citation data that can be shared publicly. Here, we describe the NIH Open Citation Collection (NIH-OCC), a public access database for biomedical research that is made freely available to the community. This dataset, which has been carefully generated from unrestricted data sources such as MedLine, PubMed Central (PMC), and CrossRef, now underlies the citation statistics delivered in the NIH iCite analytic platform. We have also included data from a machine learning pipeline that identifies, extracts, resolves, and disambiguates references from full-text articles available on the internet. Open citation links are available to the public in a major update of iCite (https://icite.od.nih.gov).

ABSTRACT: Chronic pain affects more than 50 million Americans. Treatments remain inadequate, in large part, because the pathophysiological mechanisms underlying the development of chronic pain remain poorly understood. Pain biomarkers could potentially identify and measure biological pathways and phenotypical expressions that are altered by pain, provide insight into biological treatment targets, and help identify at-risk patients who might benefit from early intervention. Biomarkers are used to diagnose, track, and treat other diseases, but no validated clinical biomarkers exist yet for chronic pain. To address this problem, the National Institutes of Health Common Fund launched the Acute to Chronic Pain Signatures (A2CPS) program to evaluate candidate biomarkers, develop them into biosignatures, and discover novel biomarkers for chronification of pain after surgery. This article discusses candidate biomarkers identified by A2CPS for evaluation, including genomic, proteomic, metabolomic, lipidomic, neuroimaging, psychophysical, psychological, and behavioral measures. Acute to Chronic Pain Signatures will provide the most comprehensive investigation of biomarkers for the transition to chronic postsurgical pain undertaken to date. Data and analytic resources generatedby A2CPS will be shared with the scientific community in hopes that other investigators will extract valuable insights beyond A2CPS's initial findings. This article will review the identified biomarkers and rationale for including them, the current state of the science on biomarkers of the transition from acute to chronic pain, gaps in the literature, and how A2CPS will address these gaps.

BACKGROUND/AIMS: Pragmatic clinical trials embedded within health care systems provide an important opportunity to evaluate new interventions and treatments. Networks have recently been developed to support practical and efficient studies. Pragmatic trials will lead to improvements in how we deliver health care and promise to more rapidly translate research findings into practice. METHODS: The National Institutes of Health (NIH) Health Care Systems Collaboratory was formed to conduct pragmatic clinical trials and to cultivate collaboration across research areas and disciplines to develop best practices for future studies. Through a two-stage grant process including a pilot phase (UH2) and a main trial phase (UH3), investigators across the Collaboratory had the opportunity to work together to improve all aspects of these trials before they were launched and to address new issues that arose during implementation. Seven Cores were created to address the various considerations, including Electronic Health Records; Phenotypes, Data Standards, and Data Quality; Biostatistics and Design Core; Patient-Reported Outcomes; Health Care Systems Interactions; Regulatory/Ethics; and Stakeholder Engagement. The goal of this article is to summarize the Biostatistics and Design Core's lessons learned during the initial pilot phase with seven pragmatic clinical trials conducted between 2012 and 2014. RESULTS: Methodological issues arose from the five cluster-randomized trials, also called group-randomized trials, including consideration of crossover and stepped wedge designs. We outlined general themes and challenges and proposed solutions from the pilot phase including topics such as study design, unit of randomization, sample size, and statistical analysis. Our findings are applicable to other pragmatic clinical trials conducted within health care systems. CONCLUSION: Pragmatic clinical trials using the UH2/UH3 funding mechanism provide an opportunity to ensure that all relevant design issues have been fully considered in order to reliably and efficiently evaluate new interventions and treatments. The integrity and generalizability of trial results can only be ensured if rigorous designs and appropriate analysis choices are an essential part of their research protocols.

Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper's eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community's early reaction to a paper.

Recent national reports and commentaries on the current status and needs of the U.S. biomedical research workforce have highlighted the limited career development opportunities for predoctoral and postdoctoral trainees in academia, yet little attention is paid to preparation for career pathways outside of the traditional faculty path. Recognizing this issue, in 2013, the U.S. National Institutes of Health (NIH) Common Fund issued a request for application titled “NIH Director's Biomedical Research Workforce Innovation Award: Broadening Experiences in Scientific Training (BEST).” These 5‐yr 1‐time grants, awarded to 17 single or partnering institutions, were designed to develop sustainable approaches to broaden graduate and postgraduate training, aimed at creating training programs that reflect the range of career options that trainees may ultimately pursue. These institutions have formed a consortium in order to work together to develop, evaluate, share, and disseminate best practices and challenges. This is a first report on the early experiences of the consortium and the scope of participating BEST programs. In this report, we describe the state of the U.S. biomedical workforce and development of the BEST award, variations of programmatic approaches to assist with program design without BEST funding, and novel approaches to engage faculty in career development programs. To test the effectiveness of these BEST programs, external evaluators will assess their outcomes not only over the 5 yr grant period but also for an additional 10 yr beyond award completion.—Meyers, F. J., Mathur, A., Fuhrmann, C. N., O'Brien, T. C., Wefes, I., Labosky, P. A., Duncan, D. S., August, A., Feig, A., Gould, K. L., Friedlander, M. J., Schaffer, C. B., Van Wart, A., Chalkley, R. The origin and implementation of the Broadening Experiences in Scientific Training programs: an NIH common fund initiative. FASEB J. 30, 507‐514 (2016). www.fasebj.org

The Extracellular RNA (exRNA) Communication Consortium, funded as an initiative of the NIH Common Fund, represents a consortium of investigators assembled to address the critical issues in the exRNA research arena. The overarching goal is to generate a multi‐component community resource for sharing fundamental scientific discoveries, protocols, and innovative tools and technologies. The key initiatives include (a) generating a reference catalogue of exRNAs present in body fluids of normal healthy individuals that would facilitate disease diagnosis and therapies, (b) defining the fundamental principles of exRNA biogenesis, distribution, uptake, and function, as well as development of molecular tools, technologies, and imaging modalities to enable these studies, (c) identifying exRNA biomarkers of disease, (d) demonstrating clinical utility of exRNAs as therapeutic agents and developing scalable technologies required for these studies, and (e) developing a community resource, the exRNA Atlas, to provide the scientific community access to exRNA data, standardized exRNA protocols, and other useful tools and technologies generated by funded investigators.

Nanotechnology will have great impact on how cancer is diagnosed and treated in the future. New technologies to detect and image cancerous changes and materials that enable new methods of cancer treatment will radically alter patient outcomes. The National Cancer Institute (NCI) Alliance for Nanotechnology in Cancer sponsors research in cancer prevention, diagnosis, and therapy and promotes translation of basic science discoveries into clinical practice. The Fourth Annual NCI Alliance Principal Investigator Meeting was held in Manhattan Beach, California October 20-22, 2009. Presented here are highlights from the research presentations at the meeting, in the areas of in vitro diagnostics, targeted delivery of anticancer and contrast enhancement agents, and nanotherapeutics and therapeutic monitoring.

PURPOSE: We report colon cancer survival rates that are conditioned on patients having already survived one or more years after diagnosis. These rates have more meaning clinically, because they consider those patients who have already survived a given period of time after treatment. METHODS: The life table method was used to compute conditional survival rates, using population-based data obtained from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute. Patients were diagnosed between 1983 and 1987 and followed up through 1994. Relative and observed survival rates are considered. RESULTS: Survival rates up to ten years after diagnosis are reported by stage of disease, gender, and race for colon cancer patients who survived from one to five years after diagnosis. Survival rates are also reported by lymph node involvement. CONCLUSIONS: Five-year and ten-year survival in colon cancer patients having already survived between one and five years after diagnosis continues to be influenced significantly by stage and race.

Abstract Despite their recognized limitations, bibliometric assessments of scientific productivity have been widely adopted. We describe here an improved method that makes novel use of the co-citation network of each article to field-normalize the number of citations it has received. The resulting Relative Citation Ratio is article-level and field-independent, and provides an alternative to the invalid practice of using Journal Impact Factors to identify influential papers. To illustrate one application of our method, we analyzed 88,835 articles published between 2003 and 2010, and found that the National Institutes of Health awardees who authored those papers occupy relatively stable positions of influence across all disciplines. We demonstrate that the values generated by this method strongly correlate with the opinions of subject matter experts in biomedical research, and suggest that the same approach should be generally applicable to articles published in all areas of science. A beta version of iCite, our web tool for calculating Relative Citation Ratios of articles listed in PubMed, is available at https://icite.od.nih.gov .