Publications by authors named "Sheri D Schully"

53 Publications

New NIH Primary and Secondary Prevention Research During 2012-2019.

Am J Prev Med 2021 Mar 18. Epub 2021 Mar 18.

Office of Disease Prevention, Division of Program Coordination Planning, and Strategic Initiatives, Office of the Director, NIH, Bethesda, Maryland.

Introduction: This manuscript characterizes primary and secondary prevention research in humans and related methods research funded by NIH in 2012‒2019.

Methods: The NIH Office of Disease Prevention updated its prevention research taxonomy in 2019‒2020 and applied it to a sample of 14,523 new extramural projects awarded in 2012-2019. All projects were coded manually for rationale, exposures, outcomes, population focus, study design, and type of prevention research. All results are based on that manual coding.

Results: Taxonomy updates resulted in a slight increase, from an average of 16.7% to 17.6%, in the proportion of prevention research awards for 2012‒2017; there was a further increase to 20.7% in 2019. Most of the leading risk factors for death and disability in the U.S. were observed as an exposure or outcome in <5% of prevention research projects in 2019 (e.g., diet, 3.7%; tobacco, 3.9%; blood pressure, 2.8%; obesity, 4.4). Analysis of existing data became more common (from 36% to 46.5%), whereas randomized interventions became less common (from 20.5% to 12.3%). Randomized interventions addressing a leading risk factor in a minority health or health disparities population were uncommon.

Conclusions: The number of new NIH awards classified as prevention research increased to 20.7% in 2019. New projects continued to focus on observational studies and secondary data analysis in 2018 and 2019. Additional research is needed to develop and test new interventions or develop methods for the dissemination of existing interventions, which address the leading risk factors, particularly in minority health and health disparities populations.
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http://dx.doi.org/10.1016/j.amepre.2021.01.006DOI Listing
March 2021

Predictive Analytics for Glaucoma Using Data From the All of Us Research Program.

Am J Ophthalmol 2021 Jan 23;227:74-86. Epub 2021 Jan 23.

UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California; Division of Health Services Research and Development, Veterans Affairs San Diego Healthcare System, La Jolla, California (L.O.-M.), USA.

Purpose: To (1) use All of Us (AoU) data to validate a previously published single-center model predicting the need for surgery among individuals with glaucoma, (2) train new models using AoU data, and (3) share insights regarding this novel data source for ophthalmic research.

Design: Development and evaluation of machine learning models.

Methods: Electronic health record data were extracted from AoU for 1,231 adults diagnosed with primary open-angle glaucoma. The single-center model was applied to AoU data for external validation. AoU data were then used to train new models for predicting the need for glaucoma surgery using multivariable logistic regression, artificial neural networks, and random forests. Five-fold cross-validation was performed. Model performance was evaluated based on area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall.

Results: The mean (standard deviation) age of the AoU cohort was 69.1 (10.5) years, with 57.3% women and 33.5% black, significantly exceeding representation in the single-center cohort (P = .04 and P < .001, respectively). Of 1,231 participants, 286 (23.2%) needed glaucoma surgery. When applying the single-center model to AoU data, accuracy was 0.69 and AUC was only 0.49. Using AoU data to train new models resulted in superior performance: AUCs ranged from 0.80 (logistic regression) to 0.99 (random forests).

Conclusions: Models trained with national AoU data achieved superior performance compared with using single-center data. Although AoU does not currently include ophthalmic imaging, it offers several strengths over similar big-data sources such as claims data. AoU is a promising new data source for ophthalmic research.
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http://dx.doi.org/10.1016/j.ajo.2021.01.008DOI Listing
January 2021

Assessment of Prevention Research Measuring Leading Risk Factors and Causes of Mortality and Disability Supported by the US National Institutes of Health.

JAMA Netw Open 2019 11 1;2(11):e1914718. Epub 2019 Nov 1.

Office of Disease Prevention, National Institutes of Health, North Bethesda, Maryland.

Importance: No studies to date have examined support by the National Institutes of Health (NIH) for primary and secondary prevention research in humans and related methods research that measures the leading risk factors or causes of death or disability as outcomes or exposures.

Objective: To characterize NIH support for such research.

Design And Setting: This serial cross-sectional study randomly sampled NIH grants and cooperative agreements funded during fiscal years 2012 through 2017. For awards with multiple subprojects, each was treated as a separate project. Study characteristics, outcomes, and exposures were coded from October 2015 through February 2019. Analyses weighted to reflect the sampling scheme were completed in March through June 2019. Using 2017 data from the Centers for Disease Control and Prevention and 2016 data from the Global Burden of Disease project, the leading risk factors and causes of death and disability in the United States were identified.

Main Outcomes And Measures: The main outcome was the percentage of the NIH prevention research portfolio measuring a leading risk factor or cause of death or disability as an outcome or exposure.

Results: A total of 11 082 research projects were coded. Only 25.9% (95% CI, 24.0%-27.8%) of prevention research projects measured a leading cause of death as an outcome or exposure, although these leading causes were associated with 74.0% of US mortality. Only 34.0% (95% CI, 32.2%-35.9%) measured a leading risk factor for death, although these risk factors were associated with 57.3% of mortality. Only 31.4% (95% CI, 29.6%-33.3%) measured a leading risk factor for disability-adjusted life-years lost, although these risk factors were associated with 42.1% of disability-adjusted life-years lost. Relatively few projects included a randomized clinical trial (24.6%; 95% CI, 22.5%-26.9%) or involved more than 1 leading cause (3.3%; 95% CI, 2.6%-4.1%) or risk factor (8.8%; 95% CI, 7.9%-9.8%).

Conclusions And Relevance: In this cross-sectional study, the leading risk factors and causes of death and disability were underrepresented in the NIH prevention research portfolio relative to their burden. Because so much is already known about these risk factors and causes, and because randomized interventions play such a vital role in the development of clinical and public health guidelines, it appears that greater attention should be given to develop and test interventions that address these risk factors and causes, addressing multiple risk factors or causes when possible.
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http://dx.doi.org/10.1001/jamanetworkopen.2019.14718DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6902772PMC
November 2019

A Machine Learning Approach to Identify NIH-Funded Applied Prevention Research.

Am J Prev Med 2018 12 25;55(6):926-931. Epub 2018 Oct 25.

Office of Disease Prevention, NIH, Rockville, Maryland.

Introduction: To fulfill its mission, the NIH Office of Disease Prevention systematically monitors NIH investments in applied prevention research. Specifically, the Office focuses on research in humans involving primary and secondary prevention, and prevention-related methods. Currently, the NIH uses the Research, Condition, and Disease Categorization system to report agency funding in prevention research. However, this system defines prevention research broadly to include primary and secondary prevention, studies on prevention methods, and basic and preclinical studies for prevention. A new methodology was needed to quantify NIH funding in applied prevention research.

Methods: A novel machine learning approach was developed and evaluated for its ability to characterize NIH-funded applied prevention research during fiscal years 2012-2015. The sensitivity, specificity, positive predictive value, accuracy, and F1 score of the machine learning method; the Research, Condition, and Disease Categorization system; and a combined approach were estimated. Analyses were completed during June-August 2017.

Results: Because the machine learning method was trained to recognize applied prevention research, it more accurately identified applied prevention grants (F1 = 72.7%) than the Research, Condition, and Disease Categorization system (F1 = 54.4%) and a combined approach (F1 = 63.5%) with p<0.001.

Conclusions: This analysis demonstrated the use of machine learning as an efficient method to classify NIH-funded research grants in disease prevention.
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http://dx.doi.org/10.1016/j.amepre.2018.07.024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251715PMC
December 2018

NIH Primary and Secondary Prevention Research in Humans During 2012-2017.

Am J Prev Med 2018 12 25;55(6):915-925. Epub 2018 Oct 25.

Office of Disease Prevention, NIH, Rockville, Maryland.

Introduction: This paper provides the first detailed analysis of the NIH prevention research portfolio for primary and secondary prevention research in humans and related methods research.

Methods: The Office of Disease Prevention developed a taxonomy of 128 topics and applied it to 11,082 projects representing 91.7% of all new projects and 84.1% of all dollars used for new projects awarded using grant and cooperative agreement activity codes that supported research in fiscal years 2012-2017. Projects were coded in 2016-2018 and analyzed in 2018.

Results: Only 16.7% of projects and 22.6% of dollars were used for primary and secondary prevention research in humans or related methods research. Most of the leading risk factors for death and disability in the U.S. were selected as an outcome in <5% of the projects. Many more projects included an observational study, or an analysis of existing data, than a randomized intervention. These patterns were consistent over time.

Conclusions: The appropriate level of support for primary and secondary prevention research in humans from NIH will differ by field and stage of research. The estimates reported here may be overestimates, as credit was given for a project even if only a portion of that project addressed prevention research. Given that 74% of the variability in county-level life expectancy across the U.S. is explained by established risk factors, it seems appropriate to devote additional resources to developing and testing interventions to address those risk factors.
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http://dx.doi.org/10.1016/j.amepre.2018.08.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251492PMC
December 2018

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

PLoS Med 2018 08 2;15(8):e1002631. Epub 2018 Aug 2.

Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, United States of America.

In a Policy Forum, Muin Khoury and colleagues discuss research on the clinical application of genome sequencing data.
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http://dx.doi.org/10.1371/journal.pmed.1002631DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6071954PMC
August 2018

Trends in published meta-analyses in cancer research, 2008-2013.

Cancer Causes Control 2017 01 29;28(1):5-12. Epub 2016 Nov 29.

Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, 6100 Executive Boulevard, Suite 2B03, Bethesda, MD, USA.

In order to capture trends in the contribution of epidemiology to cancer research, we describe an online meta-analysis database resource for cancer clinical and population research and illustrate trends and descriptive detail of cancer meta-analyses from 2008 through 2013. A total of 4,686 cancer meta-analyses met our inclusion criteria. During this 6-year period, a fivefold increase was observed in the yearly number of meta-analyses. Fifty-six percent of meta-analyses concerned observational studies, mostly of cancer risk, more than half of which were genetic studies. The major cancer sites were breast, colorectal, and digestive. This online database for Cancer Genomics and Epidemiology Navigator will be continuously updated to allow investigators to quickly navigate the meta-analyses emerging from cancer epidemiology studies and cancer clinical trials.
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http://dx.doi.org/10.1007/s10552-016-0830-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5219854PMC
January 2017

A standardized, evidence-based protocol to assess clinical actionability of genetic disorders associated with genomic variation.

Genet Med 2016 12 28;18(12):1258-1268. Epub 2016 Apr 28.

Center for Health Research, Kaiser Permanente Northwest, Portland, Oregon, USA.

Purpose: Genome and exome sequencing can identify variants unrelated to the primary goal of sequencing. Detecting pathogenic variants associated with an increased risk of a medical disorder enables clinical interventions to improve future health outcomes in patients and their at-risk relatives. The Clinical Genome Resource, or ClinGen, aims to assess clinical actionability of genes and associated disorders as part of a larger effort to build a central resource of information regarding the clinical relevance of genomic variation for use in precision medicine and research.

Methods: We developed a practical, standardized protocol to identify available evidence and generate qualitative summary reports of actionability for disorders and associated genes. We applied a semiquantitative metric to score actionability.

Results: We generated summary reports and actionability scores for the 56 genes and associated disorders recommended by the American College of Medical Genetics and Genomics for return as secondary findings from clinical genome-scale sequencing. We also describe the challenges that arose during the development of the protocol that highlight important issues in characterizing actionability across a range of disorders.

Conclusion: The ClinGen framework for actionability assessment will assist research and clinical communities in making clear, efficient, and consistent determinations of actionability based on transparent criteria to guide analysis and reporting of findings from clinical genome-scale sequencing.Genet Med 18 12, 1258-1268.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5085884PMC
http://dx.doi.org/10.1038/gim.2016.40DOI Listing
December 2016

Review of the Gene-Environment Interaction Literature in Cancer: What Do We Know?

Genet Epidemiol 2016 07 7;40(5):356-65. Epub 2016 Apr 7.

Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, United States of America.

Background: Risk of cancer is determined by a complex interplay of genetic and environmental factors. Although the study of gene-environment interactions (G×E) has been an active area of research, little is reported about the known findings in the literature.

Methods: To examine the state of the science in G×E research in cancer, we performed a systematic review of published literature using gene-environment or pharmacogenomic flags from two curated databases of genetic association studies, the Human Genome Epidemiology (HuGE) literature finder and Cancer Genome-Wide Association and Meta Analyses Database (CancerGAMAdb), from January 1, 2001, to January 31, 2011. A supplemental search using HuGE was conducted for articles published from February 1, 2011, to April 11, 2013. A 25% sample of the supplemental publications was reviewed.

Results: A total of 3,019 articles were identified in the original search. From these articles, 243 articles were determined to be relevant based on inclusion criteria (more than 3,500 interactions). From the supplemental search (1,400 articles identified), 29 additional relevant articles (1,370 interactions) were included. The majority of publications in both searches examined G×E in colon, rectal, or colorectal; breast; or lung cancer. Specific interactions examined most frequently included environmental factors categorized as energy balance (e.g., body mass index, diet), exogenous (e.g., oral contraceptives) and endogenous hormones (e.g., menopausal status), chemical environment (e.g., grilled meats), and lifestyle (e.g., smoking, alcohol intake). In both searches, the majority of interactions examined were using loci from candidate genes studies and none of the studies were genome-wide interaction studies (GEWIS). The most commonly reported measure was the interaction P-value, of which a sizable number of P-values were considered statistically significant (i.e., <0.05). In addition, the magnitude of interactions reported was modest.

Conclusion: Observations of published literature suggest that opportunity exists for increased sample size in G×E research, including GWAS-identified loci in G×E studies, exploring more GWAS approaches in G×E such as GEWIS, and improving the reporting of G×E findings.
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http://dx.doi.org/10.1002/gepi.21967DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4911236PMC
July 2016

Reproducible Research Practices and Transparency across the Biomedical Literature.

PLoS Biol 2016 Jan 4;14(1):e1002333. Epub 2016 Jan 4.

Department of Health Research and Policy, Stanford School of Medicine, Palo Alto, California, United States of America.

There is a growing movement to encourage reproducibility and transparency practices in the scientific community, including public access to raw data and protocols, the conduct of replication studies, systematic integration of evidence in systematic reviews, and the documentation of funding and potential conflicts of interest. In this survey, we assessed the current status of reproducibility and transparency addressing these indicators in a random sample of 441 biomedical journal articles published in 2000-2014. Only one study provided a full protocol and none made all raw data directly available. Replication studies were rare (n = 4), and only 16 studies had their data included in a subsequent systematic review or meta-analysis. The majority of studies did not mention anything about funding or conflicts of interest. The percentage of articles with no statement of conflict decreased substantially between 2000 and 2014 (94.4% in 2000 to 34.6% in 2014); the percentage of articles reporting statements of conflicts (0% in 2000, 15.4% in 2014) or no conflicts (5.6% in 2000, 50.0% in 2014) increased. Articles published in journals in the clinical medicine category versus other fields were almost twice as likely to not include any information on funding and to have private funding. This study provides baseline data to compare future progress in improving these indicators in the scientific literature.
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http://dx.doi.org/10.1371/journal.pbio.1002333DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4699702PMC
January 2016

Robustness of Next Generation Sequencing on Older Formalin-Fixed Paraffin-Embedded Tissue.

PLoS One 2015 29;10(7):e0127353. Epub 2015 Jul 29.

Division of Cancer Control and Population Sciences (DCCPS), National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20850, United States of America.

Next Generation Sequencing (NGS) technologies are used to detect somatic mutations in tumors and study germ line variation. Most NGS studies use DNA isolated from whole blood or fresh frozen tissue. However, formalin-fixed paraffin-embedded (FFPE) tissues are one of the most widely available clinical specimens. Their potential utility as a source of DNA for NGS would greatly enhance population-based cancer studies. While preliminary studies suggest FFPE tissue may be used for NGS, the feasibility of using archived FFPE specimens in population based studies and the effect of storage time on these specimens needs to be determined. We conducted a study to determine whether DNA in archived FFPE high-grade ovarian serous adenocarcinomas from Surveillance, Epidemiology and End Results (SEER) registries Residual Tissue Repositories (RTR) was present in sufficient quantity and quality for NGS assays. Fifty-nine FFPE tissues, stored from 3 to 32 years, were obtained from three SEER RTR sites. DNA was extracted, quantified, quality assessed, and subjected to whole exome sequencing (WES). Following DNA extraction, 58 of 59 specimens (98%) yielded DNA and moved on to the library generation step followed by WES. Specimens stored for longer periods of time had significantly lower coverage of the target region (6% lower per 10 years, 95% CI: 3-10%) and lower average read depth (40x lower per 10 years, 95% CI: 18-60), although sufficient quality and quantity of WES data was obtained for data mining. Overall, 90% (53/59) of specimens provided usable NGS data regardless of storage time. This feasibility study demonstrates FFPE specimens acquired from SEER registries after varying lengths of storage time and under varying storage conditions are a promising source of DNA for NGS.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0127353PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4519244PMC
April 2016

Evolution of the "drivers" of translational cancer epidemiology: analysis of funded grants and the literature.

Am J Epidemiol 2015 Apr 11;181(7):451-8. Epub 2015 Mar 11.

Concurrently with a workshop sponsored by the National Cancer Institute, we identified key "drivers" for accelerating cancer epidemiology across the translational research continuum in the 21st century: emerging technologies, a multilevel approach, knowledge integration, and team science. To map the evolution of these "drivers" and translational phases (T0-T4) in the past decade, we analyzed cancer epidemiology grants funded by the National Cancer Institute and published literature for 2000, 2005, and 2010. For each year, we evaluated the aims of all new/competing grants and abstracts of randomly selected PubMed articles. Compared with grants based on a single institution, consortium-based grants were more likely to incorporate contemporary technologies (P = 0.012), engage in multilevel analyses (P = 0.010), and incorporate elements of knowledge integration (P = 0.036). Approximately 74% of analyzed grants and publications involved discovery (T0) or characterization (T1) research, suggesting a need for more translational (T2-T4) research. Our evaluation indicated limited research in 1) a multilevel approach that incorporates molecular, individual, social, and environmental determinants and 2) knowledge integration that evaluates the robustness of scientific evidence. Cancer epidemiology is at the cusp of a paradigm shift, and the field will need to accelerate the pace of translating scientific discoveries in order to impart population health benefits. While multi-institutional and technology-driven collaboration is happening, concerted efforts to incorporate other key elements are warranted for the discipline to meet future challenges.
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http://dx.doi.org/10.1093/aje/kwu479DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4697447PMC
April 2015

The authors reply.

Am J Epidemiol 2015 Mar 18;181(5):361. Epub 2015 Feb 18.

Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA.

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http://dx.doi.org/10.1093/aje/kwv019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4351367PMC
March 2015

Leveraging biospecimen resources for discovery or validation of markers for early cancer detection.

J Natl Cancer Inst 2015 Apr 16;107(4). Epub 2015 Feb 16.

: Division of Cancer Control and Population Sciences (SDS, DMC, LEM, VPDR, MJK), Division of Cancer Prevention (SuS, BSK), and Division of Cancer Treatment and Diagnosis (LMM), National Cancer Institute, Bethesda, MD; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA (GLA, NU); Department of Medicine, University of North Carolina, Chapel Hill, NC (JAB, DFR); Department of Radiation Oncology, Johns Hopkins Medicine, Baltimore, MD (CDB); Center for Prostate Disease Research, Department of Defense, Rockville, MD (JC); Mount Sinai Hospital, Toronto, Ontario, Canada (EPD); Center for Health Research, Kaiser Permanente, Northwest, Portland, OR (KABG); Division of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA (SEH); Division of Research, Kaiser Permanente, Oakland, CA (LHK); Group Health Research Institute, Seattle, WA (EBL); American Society of Clinical Oncology, Alexandria, VA (RLS); Genomic Health, Inc., Redwood City, CA (StS); Biostatistics Center, Massachusetts General Hospital, Boston, MA (SJS); Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA (MJK).

Validation of early detection cancer biomarkers has proven to be disappointing when initial promising claims have often not been reproducible in diagnostic samples or did not extend to prediagnostic samples. The previously reported lack of rigorous internal validity (systematic differences between compared groups) and external validity (lack of generalizability beyond compared groups) may be effectively addressed by utilizing blood specimens and data collected within well-conducted cohort studies. Cohort studies with prediagnostic specimens (eg, blood specimens collected prior to development of clinical symptoms) and clinical data have recently been used to assess the validity of some early detection biomarkers. With this background, the Division of Cancer Control and Population Sciences (DCCPS) and the Division of Cancer Prevention (DCP) of the National Cancer Institute (NCI) held a joint workshop in August 2013. The goal was to advance early detection cancer research by considering how the infrastructure of cohort studies that already exist or are being developed might be leveraged to include appropriate blood specimens, including prediagnostic specimens, ideally collected at periodic intervals, along with clinical data about symptom status and cancer diagnosis. Three overarching recommendations emerged from the discussions: 1) facilitate sharing of existing specimens and data, 2) encourage collaboration among scientists developing biomarkers and those conducting observational cohort studies or managing healthcare systems with cohorts followed over time, and 3) conduct pilot projects that identify and address key logistic and feasibility issues regarding how appropriate specimens and clinical data might be collected at reasonable effort and cost within existing or future cohorts.
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http://dx.doi.org/10.1093/jnci/djv012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4342676PMC
April 2015

SEER cancer registry biospecimen research: yesterday and tomorrow.

Cancer Epidemiol Biomarkers Prev 2014 Dec;23(12):2681-7

Division of Cancer Control and Population Sciences, National Cancer Institute, NIH, Rockville, Maryland.

The National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) registries have been a source of biospecimens for cancer research for decades. Recently, registry-based biospecimen studies have become more practical, with the expansion of electronic networks for pathology and medical record reporting. Formalin-fixed paraffin-embedded specimens are now used for next-generation sequencing and other molecular techniques. These developments create new opportunities for SEER biospecimen research. We evaluated 31 research articles published during 2005 to 2013 based on authors' confirmation that these studies involved linkage of SEER data to biospecimens. Rather than providing an exhaustive review of all possible articles, our intent was to indicate the breadth of research made possible by such a resource. We also summarize responses to a 2012 questionnaire that was broadly distributed to the NCI intra- and extramural biospecimen research community. This included responses from 30 investigators who had used SEER biospecimens in their research. The survey was not intended to be a systematic sample, but instead to provide anecdotal insight on strengths, limitations, and the future of SEER biospecimen research. Identified strengths of this research resource include biospecimen availability, cost, and annotation of data, including demographic information, stage, and survival. Shortcomings include limited annotation of clinical attributes such as detailed chemotherapy history and recurrence, and timeliness of turnaround following biospecimen requests. A review of selected SEER biospecimen articles, investigator feedback, and technological advances reinforced our view that SEER biospecimen resources should be developed. This would advance cancer biology, etiology, and personalized therapy research. See all the articles in this CEBP Focus section, "Biomarkers, Biospecimens, and New Technologies in Molecular Epidemiology." Cancer Epidemiol Biomarkers Prev; 23(12); 2681-7. ©2014 AACR.
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http://dx.doi.org/10.1158/1055-9965.EPI-14-0490DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4256714PMC
December 2014

Prevalence and correlates of receiving and sharing high-penetrance cancer genetic test results: findings from the Health Information National Trends Survey.

Public Health Genomics 2015 ;18(2):67-77

Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Room 3E642, Bethesda, MD 20892-9761 (USA). Jennifer.taber @ nih.gov

Background/aims: The aim of this study was to explore the prevalence and correlates of receiving and sharing high-penetrance cancer genetic test results.

Methods: Participants completed the population-based, cross-sectional 2013 Health Information National Trends Survey. We examined sociodemographic characteristics of participants reporting having had BRCA1/2 or Lynch syndrome genetic testing, and sociodemographic and psychosocial correlates of sharing test results with health professionals and family members.

Results: Participants who underwent BRCA1/2 or Lynch syndrome genetic testing (n = 77; 2.42% of respondents) were more likely to be female and to have a family or personal history of cancer than those not undergoing testing. Approximately three-quarters of participants shared results with health professionals and three-quarters with their family; only 4% did not share results with anyone. Participants who shared results with health professionals reported greater optimism, self-efficacy for health management, and trust in information from their doctors. Participants who shared results with their family were more likely to be female and to have a personal history of cancer, and had greater self-efficacy for health management, perceived less ambiguity in cancer prevention recommendations, and lower cancer prevention fatalism.

Conclusions: We identified several novel psychosocial correlates of sharing genetic information. Health professionals may use this information to identify patients less likely to share information with at-risk family members.
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http://dx.doi.org/10.1159/000368745DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405401PMC
July 2015

The Cancer Genomics and Epidemiology Navigator: An NCI online tool to enhance cancer epidemiology research.

Cancer Epidemiol Biomarkers Prev 2014 Nov;23(11):2610-1

Epidemiology and Genomics Research Program, Bethesda, Maryland. Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, Georgia.

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http://dx.doi.org/10.1158/1055-9965.EPI-14-0902DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4221803PMC
November 2014

An overview of recommendations and translational milestones for genomic tests in cancer.

Genet Med 2015 Jun 23;17(6):431-40. Epub 2014 Oct 23.

1] Epidemiology and Genomics Research Program, National Cancer Institute, Rockville, Maryland, USA [2] Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA [3] Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA [4] Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, California, USA.

Purpose: To understand the translational trajectory of genomic tests in cancer screening, diagnosis, prognosis, and treatment, we reviewed tests that have been assessed by recommendation and guideline developers.

Methods: For each test, we marked translational milestones by determining when the genomic association with cancer was first discovered and studied in patients, and when a health application for a specified clinical use was successfully demonstrated and approved or cleared by the US Food and Drug Administration. To identify recommendations and guidelines, we reviewed the websites of cancer, genomic, and general guideline developers and professional organizations. We searched the in vitro diagnostics database of the US Food and Drug Administration for information, and we searched PubMed for translational milestones. Milestones were examined against type of recommendation, Food and Drug Administration approval or clearance, disease rarity, and test purpose.

Results: Of the 45 tests we identified, 9 received strong recommendations for their usage in clinical settings, 14 received positive but moderate recommendations, and 22 were not currently recommended. For 18 tests, two or more different sources had issued recommendations, with 67% concordance. Only five tests had Food and Drug Administration approval, and an additional five had clearance. The median time from discovery to recommendation statement was 14.7 years.

Conclusion: In general, there were no associations found between translational trajectory and recommendation category.Genet Med 17 6, 431-440.
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http://dx.doi.org/10.1038/gim.2014.133DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4686861PMC
June 2015

The next generation of large-scale epidemiologic research: implications for training cancer epidemiologists.

Am J Epidemiol 2014 Nov 18;180(10):964-7. Epub 2014 Sep 18.

There is expanding consensus on the need to modernize the training of cancer epidemiologists to accommodate rapidly emerging technological advancements and the digital age, which are transforming the practice of cancer epidemiology. There is also a growing imperative to extend cancer epidemiology research that is etiological to that which is applied and has the potential to affect individual and public health. Medical schools and schools of public health are recognizing the need to develop such integrated programs; however, we lack the data to estimate how many current training programs are effectively equipping epidemiology students with the knowledge and tools to design, conduct, and analyze these increasingly complex studies. There is also a need to develop new mentoring approaches to account for the transdisciplinary team-science environment that now prevails. With increased dialogue among schools of public health, medical schools, and cancer centers, revised competencies and training programs at predoctoral, doctoral, and postdoctoral levels must be developed. Continuous collection of data on the impact and outcomes of such programs is also recommended.
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http://dx.doi.org/10.1093/aje/kwu256DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4224365PMC
November 2014

The use of biospecimens in population-based research: a review of the National Cancer Institute's Division of Cancer Control and Population Sciences grant portfolio.

Biopreserv Biobank 2014 Aug;12(4):240-5

Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health , Rockville, Maryland.

Over the past two decades, researchers have increasingly used human biospecimens to evaluate hypotheses related to disease risk, outcomes and treatment. We conducted an analysis of population-science cancer research grants funded by the National Cancer Institute (NCI) to gain a more comprehensive understanding of biospecimens and common derivatives involved in those studies and identify opportunities for advancing the field. Data available for 1,018 extramural, peer-reviewed grants (active as of July 2012) supported by the Division of Cancer Control and Population Sciences (DCCPS), the NCI Division that supports cancer control and population-science extramural research grants, were analyzed. 455 of the grants were determined to involve biospecimens or derivatives. The most common specimen types included were whole blood (51% of grants), serum or plasma (40%), tissue (39%), and the biospecimen derivative, DNA (66%). While use of biospecimens in molecular epidemiology has become common, biospecimens for behavioral and social research is emerging, as observed in our analysis. Additionally, we found the majority of grants were using already existing biospecimens (63%). Grants that involved use of existing biospecimens resulted in lower costs (studies that used existing serum/plasma biospecimens were 4.2 times less expensive) and more publications per year (1.4 times) than grants collecting new biospecimens. This analysis serves as a first step at understanding the types of biospecimen collections supported by NCI DCCPS. There is room to encourage increased use of archived biospecimens and new collections of rarer specimen and cancer types, as well as for behavioral and social research. To facilitate these efforts, we are working to better catalogue our funded resources and make that data available to the extramural community.
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http://dx.doi.org/10.1089/bio.2014.0009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4150371PMC
August 2014

Evidence synthesis and guideline development in genomic medicine: current status and future prospects.

Genet Med 2015 Jan 19;17(1):63-7. Epub 2014 Jun 19.

1] Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, USA [2] Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

Purpose: With the accelerated implementation of genomic medicine, health-care providers will depend heavily on professional guidelines and recommendations. Because genomics affects many diseases across the life span, no single professional group covers the entirety of this rapidly developing field.

Methods: To pursue a discussion of the minimal elements needed to develop evidence-based guidelines in genomics, the Centers for Disease Control and Prevention and the National Cancer Institute jointly held a workshop to engage representatives from 35 organizations with interest in genomics (13 of which make recommendations). The workshop explored methods used in evidence synthesis and guideline development and initiated a dialogue to compare these methods and to assess whether they are consistent with the Institute of Medicine report "Clinical Practice Guidelines We Can Trust."

Results: The participating organizations that develop guidelines or recommendations all had policies to manage guideline development and group membership, and processes to address conflicts of interests. However, there was wide variation in the reliance on external reviews, regular updating of recommendations, and use of systematic reviews to assess the strength of scientific evidence.

Conclusion: Ongoing efforts are required to establish criteria for guideline development in genomic medicine as proposed by the Institute of Medicine.
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http://dx.doi.org/10.1038/gim.2014.69DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4272332PMC
January 2015

Multi-marker Solid Tumor Panels Using Next-generation Sequencing to Direct Molecularly Targeted Therapies.

PLoS Curr 2014 May 27;6. Epub 2014 May 27.

Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, NIH, Rockville, Maryland, USA.

In contemporary oncology practices there is an increasing emphasis on concurrent evaluation of multiple genomic alterations within the biological pathways driving tumorigenesis. At the foundation of this paradigm shift are several commercially available tumor panels using next-generation sequencing to develop a more complete molecular blueprint of the tumor. Ideally, these would be used to identify clinically actionable variants that can be matched with available molecularly targeted therapy, regardless of the tumor site or histology. Currently, there is little information available on the post-analytic processes unique to next-generation sequencing platforms used by the companies offering these tests. Additionally, evidence of clinical validity showing an association between the genetic markers curated in these tests with treatment response to approved molecularly targeted therapies is lacking across all solid-tumor types. To date, there is no published data of improved outcomes when using the commercially available tests to guide treatment decisions. The uniqueness of these tests from other genomic applications used to guide clinical treatment decisions lie in the sequencing platforms used to generate large amounts of genomic data, which have their own related issues regarding analytic and clinical validity, necessary precursors to the evaluation of clinical utility. The generation and interpretation of these data will require new evidentiary standards for establishing not only clinical utility, but also analytical and clinical validity for this emerging paradigm in oncology practice.
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http://dx.doi.org/10.1371/currents.eogt.aa5415d435fc886145bd7137a280a971DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4038678PMC
May 2014

Collaborative biomedicine in the age of big data: the case of cancer.

J Med Internet Res 2014 Apr 7;16(4):e101. Epub 2014 Apr 7.

PricewaterhouseCoopers LLP, McLean, VA, United States.

Biomedicine is undergoing a revolution driven by high throughput and connective computing that is transforming medical research and practice. Using oncology as an example, the speed and capacity of genomic sequencing technologies is advancing the utility of individual genetic profiles for anticipating risk and targeting therapeutics. The goal is to enable an era of "P4" medicine that will become increasingly more predictive, personalized, preemptive, and participative over time. This vision hinges on leveraging potentially innovative and disruptive technologies in medicine to accelerate discovery and to reorient clinical practice for patient-centered care. Based on a panel discussion at the Medicine 2.0 conference in Boston with representatives from the National Cancer Institute, Moffitt Cancer Center, and Stanford University School of Medicine, this paper explores how emerging sociotechnical frameworks, informatics platforms, and health-related policy can be used to encourage data liquidity and innovation. This builds on the Institute of Medicine's vision for a "rapid learning health care system" to enable an open source, population-based approach to cancer prevention and control.
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http://dx.doi.org/10.2196/jmir.2496DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4004150PMC
April 2014

Horizon scanning for translational genomic research beyond bench to bedside.

Genet Med 2014 Jul 9;16(7):535-8. Epub 2014 Jan 9.

1] Epidemiology and Genomics Research Program, National Cancer Institute, Bethesda, Maryland, USA [2] Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

Purpose: The dizzying pace of genomic discoveries is leading to an increasing number of clinical applications. In this report, we provide a method for horizon scanning and 1 year data on translational research beyond bench to bedside to assess the validity, utility, implementation, and outcomes of such applications.

Methods: We compiled cross-sectional results of ongoing horizon scanning of translational genomic research, conducted between 16 May 2012 and 15 May 2013, based on a weekly, systematic query of PubMed. A set of 505 beyond bench to bedside articles were collected and classified, including 312 original research articles; 123 systematic and other reviews; 38 clinical guidelines, policies, and recommendations; and 32 articles describing tools, decision support, and educational materials.

Results: Most articles (62%) addressed a specific genomic test or other health application; almost half of these (n = 180) were related to cancer. We estimate that these publications account for 0.5% of reported human genomics and genetics research during the same time.

Conclusion: These data provide baseline information to track the evolving knowledge base and gaps in genomic medicine. Continuous horizon scanning of the translational genomics literature is crucial for an evidence-based translation of genomics discoveries into improved health care and disease prevention.
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http://dx.doi.org/10.1038/gim.2013.184DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4079725PMC
July 2014

Translation of genetics research to clinical medicine: the National Heart, Lung, and Blood Institute perspective.

Circ Cardiovasc Genet 2013 Dec;6(6):634-9

Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, and Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD.

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http://dx.doi.org/10.1161/CIRCGENETICS.113.000227DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3957221PMC
December 2013

Epigenetic research in cancer epidemiology: trends, opportunities, and challenges.

Cancer Epidemiol Biomarkers Prev 2014 Feb 10;23(2):223-33. Epub 2013 Dec 10.

Authors' Affiliations: Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences; Division of Cancer Prevention, National Cancer Institute; Office of the Director, Information Resources and Services Branch, NIH, Bethesda, Maryland; and Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, Georgia.

Epigenetics is emerging as an important field in cancer epidemiology that promises to provide insights into gene regulation and facilitate cancer control throughout the cancer care continuum. Increasingly, investigators are incorporating epigenetic analysis into the studies of etiology and outcomes. To understand current progress and trends in the inclusion of epigenetics in cancer epidemiology, we evaluated the published literature and the National Cancer Institute (NCI)-supported research grant awards in this field to identify trends in epigenetics research. We present a summary of the epidemiologic studies in NCI's grant portfolio (from January 2005 through December 2012) and in the scientific literature published during the same period, irrespective of support from the NCI. Blood cells and tumor tissue were the most commonly used biospecimens in these studies, although buccal cells, cervical cells, sputum, and stool samples were also used. DNA methylation profiling was the focus of the majority of studies, but several studies also measured microRNA profiles. We illustrate here the current status of epidemiologic studies that are evaluating epigenetic changes in large populations. The incorporation of epigenomic assessments in cancer epidemiology studies has and is likely to continue to provide important insights into the field of cancer research.
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http://dx.doi.org/10.1158/1055-9965.EPI-13-0573DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3925982PMC
February 2014

A systematic review of cancer GWAS and candidate gene meta-analyses reveals limited overlap but similar effect sizes.

Eur J Hum Genet 2014 Mar 24;22(3):402-8. Epub 2013 Jul 24.

Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, USA.

Candidate gene and genome-wide association studies (GWAS) represent two complementary approaches to uncovering genetic contributions to common diseases. We systematically reviewed the contributions of these approaches to our knowledge of genetic associations with cancer risk by analyzing the data in the Cancer Genome-wide Association and Meta Analyses database (Cancer GAMAdb). The database catalogs studies published since January 1, 2000, by study and cancer type. In all, we found that meta-analyses and pooled analyses of candidate genes reported 349 statistically significant associations and GWAS reported 269, for a total of 577 unique associations. Only 41 (7.1%) associations were reported in both candidate gene meta-analyses and GWAS, usually with similar effect sizes. When considering only noteworthy associations (defined as those with false-positive report probabilities≤0.2) and accounting for indirect overlap, we found 202 associations, with 27 of those appearing in both meta-analyses and GWAS. Our findings suggest that meta-analyses of well-conducted candidate gene studies may continue to add to our understanding of the genetic associations in the post-GWAS era.
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http://dx.doi.org/10.1038/ejhg.2013.161DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3925284PMC
March 2014