Data-driven science and diversity in the All of Us Research Program

The National Institutes of Health's All of Us Research Program is an accessible platform that hosts genomic and phenotypic data to be collected from 1 million participants in the United States. Its mission is to accelerate medical research and clinical breakthroughs with a special emphasis on diversity.

An Overview of Cancer in the First 315,000 All of Us Participants

Introduction

The NIH All of Us Research Program will have the scale and scope to enable research for a wide range of diseases, including cancer. The program’s focus on diversity and inclusion promises a better understanding of the unequal burden of cancer. Preliminary cancer ascertainment in the All of Us cohort from two data sources (self-reported versus electronic health records (EHR)) is considered.

Materials and methods

This work was performed on data collected from the All of Us Research Program’s 315,297 enrolled participants to date using the Researcher Workbench, where approved researchers can access and analyze All of Us data on cancer and other diseases. Cancer case ascertainment was performed using data from EHR and self-reported surveys across key factors. Distribution of cancer types and concordance of data sources by cancer site and demographics is analyzed.

Results and discussion

Data collected from 315,297 participants resulted in 13,298 cancer cases detected in the survey (in 89,261 participants), 23,520 cancer cases detected in the EHR (in 203,813 participants), and 7,123 cancer cases detected across both sources (in 62,497 participants). Key differences in survey completion by race/ethnicity impacted the makeup of cohorts when compared to cancer in the EHR and national NCI SEER data.

Conclusions

This study provides key insight into cancer detection in the All of Us Research Program and points to the existing strengths and limitations of All of Us as a platform for cancer research now and in the future.

The All of Us Research Program: Data quality, utility, and diversity

The All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools.

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The All of Us Research Program has released data for over 315,000 participants

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Demonstration projects support the utility and validity of the All of Us dataset

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The cloud-based Researcher Workbench provides secure, low-cost compute power

The engagement of participants in the research process and broad availability of data to diverse researchers are essential elements in building precision medicine equitably available for all. The NIH has established the ambitious All of Us Research Program to build one of the most diverse health databases in history with tools to support research to improve human health. Here, we present the initial launch of the Researcher Workbench with data types including surveys, physical measurements, and electronic health record data with validation studies to support researcher use of this novel platform. Broad access for researchers to data like these is a critical step in returning value to participants seeking to support the advancement of precision medicine and improved health for all.

NIH Primary and Secondary Prevention Research in Humans: a Portfolio Analysis of Study Designs Used in 2012-2019

We can learn a great deal about the research questions being addressed in a field by examining the study designs used in that field. This manuscript examines the research questions being addressed in prevention research by characterizing the distribution and trends of study designs included in primary and secondary prevention research supported by the National Institutes of Health through grants and cooperative agreements, together with the types of prevention research, populations, rationales, exposures, and outcomes associated with each type of design. The Office of Disease Prevention developed a taxonomy to classify new extramural NIH-funded research projects and created a database with a representative sample of 14,523 research projects for fiscal years 2012-2019. The data were weighted to represent the entirety of the extramural research portfolio. Leveraging this dataset, the Office of Disease Prevention characterized the study designs proposed in NIH-funded primary and secondary prevention research applications. The most common study designs proposed in new NIH-supported prevention research applications during FY12-19 were observational designs (63.3%, 95% CI 61.5%-65.0%), analysis of existing data (44.5%, 95% CI: 42.7-46.3), methods research (23.9%, 95% CI: 22.3-25.6), and randomized interventions (17.2%, 95% CI: 16.1%-18.4%). Observational study designs dominated primary prevention research, while intervention designs were more common in secondary prevention research. Observational designs were more common for exposures that would be difficult to manipulate (e.g., genetics, chemical toxin, and infectious disease (not pneumonia/influenza or HIV/AIDS)), while intervention designs were more common for exposures that would be easier to manipulate (e.g., education/counseling, medication/device, diet/nutrition, and healthcare delivery). Intervention designs were not common for outcomes that are rare or have a long latency (e.g., cancer, neurological disease, Alzheimer's disease) and more common for outcomes that are more common or where effects would be expected earlier (e.g., healthcare delivery, health related quality of life, substance use, and medication/device). Observational designs and analyses of existing data dominated, suggesting that much of the prevention research funded by NIH continues to focus on questions of association and on questions of identification of risk and protective factors. Randomized and non-randomized intervention designs were included far less often, suggesting that a much smaller fraction of the NIH prevention research portfolio is focused on questions of whether interventions can be used to modify risk or protective factors or to change some other health-related biomedical or behavioral outcome. The much heavier focus on observational studies is surprising given how much we know already about the leading risk factors for death and disability in the USA, because those risk factors account for 74% of the county-level mortality in the USA, and because they play such a vital role in the development of clinical and public health guidelines, whose developers often weigh results from randomized trials much more heavily than results from observational studies. Improvements in death and disability nationwide are more likely to derive from guidelines based on intervention research to address the leading risk factors than from additional observational studies.

Geographic Variation in Obesity at the State Level in the All of Us Research Program

INTRODUCTION

National obesity prevention strategies may benefit from precision health approaches involving diverse participants in population health studies. We used cohort data from the National Institutes of Health All of Us Research Program (All of Us) Researcher Workbench to estimate population-level obesity prevalence.

METHODS

To estimate state-level obesity prevalence we used data from physical measurements made during All of Us enrollment visits and data from participant electronic health records (EHRs) where available. Prevalence estimates were calculated and mapped by state for 2 categories of body mass index (BMI) (kg/m2): obesity (BMI >30) and severe obesity (BMI >35). We calculated and mapped prevalence by state, excluding states with fewer than 100 All of Us participants.

RESULTS

Data on height and weight were available for 244,504 All of Us participants from 33 states, and corresponding EHR data were available for 88,840 of these participants. The median and IQR of BMI taken from physical measurements data was 28.4 (24.4- 33.7) and 28.5 (24.5-33.6) from EHR data, where available. Overall obesity prevalence based on physical measurements data was 41.5% (95% CI, 41.3%-41.7%); prevalence of severe obesity was 20.7% (95% CI, 20.6-20.9), with large geographic variations observed across states. Prevalence estimates from states with greater numbers of All of Us participants were more similar to national population-based estimates than states with fewer participants.

CONCLUSION

All of Us participants had a high prevalence of obesity, with state-level geographic variation mirroring national trends. The diversity among All of Us participants may support future investigations on obesity prevention and treatment in diverse populations.

Pediatric data from the All of Us research program: demonstration of pediatric obesity over time

OBJECTIVE

To describe and demonstrate use of pediatric data collected by the All of Us Research Program.

MATERIALS AND METHODS

All of Us participant physical measurements and electronic health record (EHR) data were analyzed including investigation of trends in childhood obesity and correlation with adult body mass index (BMI).

RESULTS

We identified 19 729 participants with legacy pediatric EHR data including diagnoses, prescriptions, visits, procedures, and measurements gathered since 1980. We found an increase in pediatric obesity diagnosis over time that correlates with BMI measurements recorded in participants' adult EHRs and those physical measurements taken at enrollment in the research program.

DISCUSSION

We highlight the availability of retrospective pediatric EHR data for nearly 20 000 All of Us participants. These data are relevant to current issues such as the rise in pediatric obesity.

CONCLUSION

All of Us contains a rich resource of retrospective pediatric EHR data to accelerate pediatric research studies.

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

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.

Antibodies to SARS-CoV-2 in All of Us Research Program Participants, January 2-March 18, 2020

Background

With limited severe acute respiratory syndrome coronavirus (SARS-CoV-2) testing capacity in the United States at the start of the epidemic (January–March 2020), testing was focused on symptomatic patients with a travel history throughout February, obscuring the picture of SARS-CoV-2 seeding and community transmission. We sought to identify individuals with SARS-CoV-2 antibodies in the early weeks of the US epidemic.

Methods

All of Us study participants in all 50 US states provided blood specimens during study visits from 2 January to 18 March 2020. Participants were considered seropositive if they tested positive for SARS-CoV-2 immunoglobulin G (IgG) antibodies with the Abbott Architect SARS-CoV-2 IgG enzyme-linked immunosorbent assay (ELISA) and the EUROIMMUN SARS-CoV-2 ELISA in a sequential testing algorithm. The sensitivity and specificity of these ELISAs and the net sensitivity and specificity of the sequential testing algorithm were estimated, along with 95% confidence intervals (CIs).

Results

The estimated sensitivities of the Abbott and EUROIMMUN assays were 100% (107 of 107 [95% CI: 96.6%–100%]) and 90.7% (97 of 107 [83.5%–95.4%]), respectively, and the estimated specificities were 99.5% (995 of 1000 [98.8%–99.8%]) and 99.7% (997 of 1000 [99.1%–99.9%]), respectively. The net sensitivity and specificity of our sequential testing algorithm were 90.7% (97 of 107 [95% CI: 83.5%–95.4%]) and 100.0% (1000 of 1000 [99.6%–100%]), respectively. Of the 24 079 study participants with blood specimens from 2 January to 18 March 2020, 9 were seropositive, 7 before the first confirmed case in the states of Illinois, Massachusetts, Wisconsin, Pennsylvania, and Mississippi.

Conclusions

Our findings identified SARS-CoV-2 infections weeks before the first recognized cases in 5 US states.

New NIH Primary and Secondary Prevention Research During 2012-2019

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.

Training the Twenty-First Century Cancer Epidemiologist

To assess and advance training of twenty-first century cancer epidemiologists, the National Cancer Institute (NCI) sought to obtain a snapshot of the cancer epidemiology training landscape by conducting a survey across academic institutions and cancer centers, focusing on four key training areas driving current cancer epidemiology research ("drivers"): (1) collaboration, (2) novel methods/technologies, (3) multilevel analysis, and (4) knowledge integration. Complementary to the survey, we conducted a portfolio analysis of active NCI-funded training grants. In the present report, we provide our findings from this effort and contribute to the on-going conversation regarding the training of next-generation cancer epidemiologists. Analyses and insights gained from conversations with leaders/educators across 24 academic institutions/cancer centers and the portfolio analysis of training grants echoed contemporaneous conversation that cancer epidemiology training must adapt to meet the needs of the changing research environment. Currently, with the exception of novel methods/technologies, cancer epidemiology trainees receive the majority of their training in collaboration, multilevel approaches, and knowledge integration/translation either informally, ad hoc, or not at all; exposure to these identified drivers varied considerably by institution, mentor, and other external as well as internal factors.

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

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.

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

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.

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

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.

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.

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

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.

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

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.

Reproducible Research Practices and Transparency across the Biomedical Literature

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.

Examination of recent trends in reproducibility and transparency practices in biomedical research reveals an ongoing lack of access to full datasets and detailed protocols for both clinical and non-clinical studies.

There is increasing interest in the scientific community about whether published research is transparent and reproducible. Lack of replication and non-transparency decreases the value of research. Several biomedical journals have started to encourage or require authors to submit detailed protocols, full datasets, and disclose information on funding and potential conflicts of interest. In this study, we investigate the reproducibility and transparency practices across the full spectrum of published biomedical literature from 2000–2014. We identify an ongoing lack of access to full datasets and detailed protocols for both clinical and non-clinical biomedical investigation. We also map the availability of information on funding and conflicts of interest in this literature. The results from this study provide baseline data to compare future progress in improving these indicators in the scientific literature. We believe that this information may be essential to sensitize stakeholders in science about the need for improving reproducibility and transparency practices.

SEER cancer registry biospecimen research: yesterday and tomorrow

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.

Abstract 3698: The landscape of published cancer meta-analyses: a descriptive look from 2008-2013

Cancer epidemiology has undergone rapid growth in the past two decades. This rapid expansion of scientific literature in cancer epidemiology has required scientists to increasingly look towards meta-analyses to help navigate the “sea of literature”. Knowledge integration has facilitated the movement of translation of scientific findings into clinical applications, evidence-based recommendations and population health impact. Performing meta-analysis has allowed scientists to filter through large datasets and translate these scientific or clinical findings effectively. To our knowledge, there has never been a systematic attempt to compile meta-analyses in cancer. Here, we describe the landscape of cancer meta-analyses including publication production stratified by year, cancer outcome measures, study design, cancer type, journal and country leaders in the field.

To ascertain human cancer meta-analytical studies published in the scientific literature during the years 2008 through 2013, we searched PubMed using a text query and the Human Genome Epidemiology (HuGE) Navigator. Two reviewers read through abstracts to determine whether the articles met inclusion criteria. Articles were included if there was a meta-analytical analysis of a human cancer measurement and met one of the following outcome criteria: 1) risk of disease, 2) diagnostic measure, 3) prognostic measure, 4) treatment of disease, including long-term survival and/or adverse events and 5) screening for disease. Coding of articles involved extracting information from the Medline version of each PubMed article reference (i.e. Journal title, year of publication, first author location and cancer-site mesh terms), and by manually reviewing abstracts for classification on the human cancer outcome measurement and other manually coded information.

A total of 4,007 cancer meta-analyses were captured by PubMed from 2008 to 2013. Our systematic review of cancer meta-analyses publications indicates a rapid growth of cancer meta-analyses worldwide with 20% production in the past 6-year time period. Furthermore, PLoS One and Asian Pacific Journal of Cancer Prevention were recorded as the top two publishers in the research field. Genomic, clinical and modifiable cancer risk factors were captured as the top leading cancer meta-analyses studies. Observational studies were captured as the leading meta-analyses study design. In the past five years there was an increased wide distribution of meta-analyses on all cancer types.

Citation Format: Ximena V. Qadir, Mindy Clyne, Tram K. Lam, Muin J. Khoury, Sheri D. Schully. The landscape of published cancer meta-analyses: a descriptive look from 2008-2013. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3698. doi:10.1158/1538-7445.AM2015-3698

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

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.

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

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.

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

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.

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

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.

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

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.

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

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.

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

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.

Abstract 307: The use of biospecimens in cancer population science research

Background: Over the past two decades, researchers have increasingly used human biospecimens to help evaluate hypotheses related to disease risk, outcomes and treatment options. We conducted an analysis of the population science cancer research grants funded by NCI in order to gain a more comprehensive understanding of the biospecimens involved in those studies. Recognizing that there are many pre-existing collections of biospecimens, we investigated the cost and time efficiencies observed with studies involving the use of existing biospecimens versus collecting new specimens. Methods: Data available for 1,018 extramural, peer-reviewed grants (active as of July 2012) supported by the Division of Cancer Control and Population Sciences, (NCI Division that supports cancer control and population science extramural research grants) were analyzed. Results: 455 of the grants were determined to involve biospecimens. The most common specimen types included were DNA (66% of grants involved DNA), whole blood (51%), serum or plasma (40%), and tissue (39%). Grants that involved the use of existing biospecimens resulted in greater cost (serum/plasma studies were 4.2 times less expensive) and time efficiencies (1.4 times more publications per year resulted) than grants that collected new biospecimens. Conclusions: Clearly, there is an opportunity for NCI to promote future sharing. We are currently working to better catalogue our funded resources and make data available to the extramural community. Further work is being done to investigate possible trends based on year of grant award.

Citation Format: Danielle M. Carrick, Eliza Mette, Brittany Hoyle, Scott D. Rogers, Elizabeth M. Gillanders, Sheri D. Schully, Leah E. Mechanic. The use of biospecimens in cancer population science research. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 307. doi:10.1158/1538-7445.AM2014-307

Abstract 254: How can epidemiology become more effective in reducing the burden of cancer in the 21st century? An analysis of NCI-funded grants and the scientific literature

Background: Concurrent with an NCI sponsored 2012 workshop on the future of cancer epidemiology, a set of “drivers” were identified to accelerate the field of cancer epidemiology across the translational research continuum in the 21st century: (i) emerging technologies; (ii) multi-level analyses and interventions; (iii) knowledge integration from basic, clinical and population sciences; and (iv) collaboration and team science.

Objective: To map the evolution of identified “drivers” and key translational phases (T0-T4) in the past decade.

Methods: We analyzed grants funded by the NCI's Epidemiology and Genomics Research Program and published literature for 2000, 2005, and 2010. For each year, we evaluated the aims of all new and competing grants and randomly selected 100 cancer epidemiology articles from PubMed. We used two-sample t-tests to compare differences between “drivers” and multivariate logistic regression to investigate the relationship between multi-institutional collaboration and the remaining “drivers”.

Results: Our results show a significant shift from single-institution studies that focused on traditional questionnaire-based epidemiology studies to technology-driven, multi-disciplinary consortia-driven studies for both NCI grants and published literature. Compared to grants that were single-institution-based, consortia grants were significantly more likely to incorporate key contemporary technologies (OR= 3.53; 95% CI=1.44-8.61; p-value = 0.005) and engaged in multi-level analyses (OR =2.27; 95% CI=1.06-4.86, p-value=0.035). The vast majority of grants (82%) and publications (86%) analyzed were discovery (T0) or characterization (T1) research suggesting a critical need for more T2-T4 translational studies. Our evaluation also indicates a dearth of research in two areas: 1) multi-level analyses that takes into account the combination of molecular, individual, social and environmental determinants and 2) knowledge integration that evaluates the robustness and interpretation of scientific evidence derived from basic, clinical and population sciences.

Summary: Cancer epidemiology is at the cusp of a paradigm shift–propelled by a need to accelerate the pace of translating scientific discoveries to impart population health benefits. While multi-institutional and technology-drive collaboration is happening, our evaluation of funded grants and published literature in the first decade of the 21st century provide concrete evidence that concerted efforts to incorporate other key elements that influence the future of cancer epidemiology are warranted for the discipline to meet the challenges of this changing landscape.

Citation Format: Tram K. Lam, Christine Q. Chang, Scott D. Rogers, Muin J. Khoury, Sheri D. Schully. How can epidemiology become more effective in reducing the burden of cancer in the 21st century? An analysis of NCI-funded grants and the scientific literature. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 254. doi:10.1158/1538-7445.AM2014-254

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

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.

Prioritizing genomic applications for action by level of evidence: a horizon-scanning method

As evidence accumulates on the use of genomic tests and other health-related applications of genomic technologies, decision makers may increasingly seek support in identifying which applications have sufficiently robust evidence to suggest they might be considered for action. As an interim working process to provide such support, we developed a horizon-scanning method that assigns genomic applications to tiers defined by availability of synthesized evidence. We illustrate an application of the method to pharmacogenomics tests.

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

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.

Potential increased risk of cancer from commonly used medications: an umbrella review of meta-analyses

Several commonly used medications have been associated with increased cancer risk in the literature. Here, we evaluated the strength and consistency of these claims in published meta-analyses. We carried out an umbrella review of 74 meta-analysis articles addressing the association of commonly used medications (antidiabetics, antihyperlipidemics, antihypertensives, antirheumatics, drugs for osteoporosis, and others) with cancer risk where at least one meta-analysis in the medication class included some data from randomized trials. Overall, 51 articles found no statistically significant differences, 13 found some decreased cancer risk, and 11 found some increased risk (one reported both increased and decreased risks). The 11 meta-analyses that found some increased risks reported 16 increased risk estimates, of which 5 pertained to overall cancer and 11 to site-specific cancer. Six of the 16 estimates were derived from randomized trials and 10 from observational data. Estimates of increased risk were strongly inversely correlated with the amount of evidence (number of cancer cases) (Spearman's correlation coefficient = -0.77, P < 0.001). In 4 of the 16 topics, another meta-analysis existed that was larger (n = 2) or included better controlled data (n = 2) and in all 4 cases there was no statistically significantly increased risk of malignancy. No medication or class had substantial and consistent evidence for increased risk of malignancy. However, for most medications we cannot exclude small risks or risks in population subsets. Such risks are unlikely to be possible to document robustly unless very large, collaborative studies with standardized analyses and no selective reporting are carried out.

The geometric increase in meta-analyses from China in the genomic era

Meta-analyses are increasingly popular. It is unknown whether this popularity is driven by specific countries and specific meta-analyses types. PubMed was used to identify meta-analyses since 1995 (last update 9/1/2012) and catalogue their types and country of origin. We focused more on meta-analyses from China (the current top producer of meta-analyses) versus the USA (top producer until recently). The annual number of meta-analyses from China increased 40-fold between 2003 and 2011 versus 2.4-fold for the USA. The growth of Chinese meta-analyses was driven by genetics (110-fold increase in 2011 versus 2003). The HuGE Navigator identified 612 meta-analyses of genetic association studies published in 2012 from China versus only 109 from the USA. We compared in-depth 50 genetic association meta-analyses from China versus 50 from USA in 2012. Meta-analyses from China almost always used only literature-based data (92%), and focused on one or two genes (94%) and variants (78%) identified with candidate gene approaches (88%), while many USA meta-analyses used genome-wide approaches and raw data. Both groups usually concluded favorably for the presence of genetic associations (80% versus 74%), but nominal significance (P<0.05) typically sufficed in the China group. Meta-analyses from China typically neglected genome-wide data, and often included candidate gene studies published in Chinese-language journals. Overall, there is an impressive rise of meta-analyses from China, particularly on genetic associations. Since most claimed candidate gene associations are likely false-positives, there is an urgent global need to incorporate genome-wide data and state-of-the art statistical inferences to avoid a flood of false-positive genetic meta-analyses.

Comparative effectiveness research in cancer genomics and precision medicine: current landscape and future prospects

A major promise of genomic research is information that can transform health care and public health through earlier diagnosis, more effective prevention and treatment of disease, and avoidance of drug side effects. Although there is interest in the early adoption of emerging genomic applications in cancer prevention and treatment, there are substantial evidence gaps that are further compounded by the difficulties of designing adequately powered studies to generate this evidence, thus limiting the uptake of these tools into clinical practice. Comparative effectiveness research (CER) is intended to generate evidence on the "real-world" effectiveness compared with existing standards of care so informed decisions can be made to improve health care. Capitalizing on funding opportunities from the American Recovery and Reinvestment Act of 2009, the National Cancer Institute funded seven research teams to conduct CER in genomic and precision medicine and sponsored a workshop on CER on May 30, 2012, in Bethesda, Maryland. This report highlights research findings from those research teams, challenges to conducting CER, the barriers to implementation in clinical practice, and research priorities and opportunities in CER in genomic and precision medicine. Workshop participants strongly emphasized the need for conducting CER for promising molecularly targeted therapies, developing and supporting an integrated clinical network for open-access resources, supporting bioinformatics and computer science research, providing training and education programs in CER, and conducting research in economic and decision modeling.

Transforming epidemiology for 21st century medicine and public health

In 2012, the National Cancer Institute (NCI) engaged the scientific community to provide a vision for cancer epidemiology in the 21st century. Eight overarching thematic recommendations, with proposed corresponding actions for consideration by funding agencies, professional societies, and the research community emerged from the collective intellectual discourse. The themes are (i) extending the reach of epidemiology beyond discovery and etiologic research to include multilevel analysis, intervention evaluation, implementation, and outcomes research; (ii) transforming the practice of epidemiology by moving toward more access and sharing of protocols, data, metadata, and specimens to foster collaboration, to ensure reproducibility and replication, and accelerate translation; (iii) expanding cohort studies to collect exposure, clinical, and other information across the life course and examining multiple health-related endpoints; (iv) developing and validating reliable methods and technologies to quantify exposures and outcomes on a massive scale, and to assess concomitantly the role of multiple factors in complex diseases; (v) integrating "big data" science into the practice of epidemiology; (vi) expanding knowledge integration to drive research, policy, and practice; (vii) transforming training of 21st century epidemiologists to address interdisciplinary and translational research; and (viii) optimizing the use of resources and infrastructure for epidemiologic studies. These recommendations can transform cancer epidemiology and the field of epidemiology, in general, by enhancing transparency, interdisciplinary collaboration, and strategic applications of new technologies. They should lay a strong scientific foundation for accelerated translation of scientific discoveries into individual and population health benefits.

A review of NCI's extramural grant portfolio: identifying opportunities for future research in genes and environment in cancer

BACKGROUND

Genetic and environmental factors jointly influence cancer risk. The NIH has made the study of gene-environment (GxE) interactions a research priority since the year 2000.

METHODS

To assess the current status of GxE research in cancer, we analyzed the extramural grant portfolio of the National Cancer Institute (NCI) from Fiscal Years 2007 to 2009. Publications attributed to selected grants were also evaluated.

RESULTS

From the 1,106 research grants identified in our portfolio analysis, a random sample of 450 grants (40%) was selected for data abstraction; of these, 147 (33%) were considered relevant. The most common cancer type was breast (20%, n = 29), followed by lymphoproliferative (10%, n = 14), colorectal (9%, n = 13), melanoma/other skin (9%, n = 13), and lung/upper aerodigestive tract (8%, n = 12) cancers. The majority of grants were studies of candidate genes (68%, n = 100) compared with genome-wide association studies (GWAS) (8%, n = 12). Approximately one-third studied environmental exposures categorized as energy balance (37%, n = 54) or drugs/treatment (29%, n = 43). From the 147 relevant grants, 108 publications classified as GxE or pharmacogenomic were identified. These publications were linked to 37 of the 147 grant applications (25%).

CONCLUSION

The findings from our portfolio analysis suggest that GxE studies are concentrated in specific areas. There is room for investments in other aspects of GxE research, including, but not limited to developing alternative approaches to exposure assessment, broadening the spectrum of cancer types investigated, and conducting GxE within GWAS.

IMPACT

This portfolio analysis provides a cross-sectional review of NCI support for GxE research in cancer.

Provocative questions in cancer epidemiology in a time of scientific innovation and budgetary constraints

In a time of scientific and technological developments and budgetary constraints, the National Cancer Institute's (NCI) Provocative Questions Project offers a novel funding mechanism for cancer epidemiologists. We reviewed the purposes underlying the Provocative Questions Project, present information on the contributions of epidemiologic research to the current Provocative Questions portfolio, and outline opportunities that the cancer epidemiology community might capitalize on to advance a research agenda that spans a translational continuum from scientific discoveries to population health impact.

"Drivers" of translational cancer epidemiology in the 21st century: needs and opportunities

Cancer epidemiology is at the cusp of a paradigm shift--propelled by an urgent need to accelerate the pace of translating scientific discoveries into health care and population health benefits. As part of a strategic planning process for cancer epidemiologic research, the Epidemiology and Genomics Research Program (EGRP) at the National Cancer Institute (NCI) is leading a "longitudinal" meeting with members of the research community to engage in an on-going dialogue to help shape and invigorate the field. Here, we review a translational framework influenced by "drivers" that we believe have begun guiding cancer epidemiology toward translation in the past few years and are most likely to drive the field further in the next decade. The drivers include: (i) collaboration and team science, (ii) technology, (iii) multilevel analyses and interventions, and (iv) knowledge integration from basic, clinical, and population sciences. Using the global prevention of cervical cancer as an example of a public health endeavor to anchor the conversation, we discuss how these drivers can guide epidemiology from discovery to population health impact, along the translational research continuum.

Abstract 46: A review of the gene-environment interaction literature in cancer: What do we know?

Risk of cancer is determined by a complex interplay of genetic and environmental factors. Therefore, the study of gene-environment interactions has been an active area of research for several years. To examine the state of the science in the field of gene-environment interactions research in cancer, we performed a systematic review of the published literature. A total of 3019 articles were identified using the gene-environment or pharmacogenomic flags from the HuGE literature finder and CancerGAMAdb, curated databases of genetic association studies, from January 1, 2001 to January 31, 2010. From these articles, 243 articles were determined to be relevant based on the inclusion criteria of at least 1000 cases in the interaction studied, examining the combination of genes and environment, and investigating the interaction as related to cancer risk. Information from these articles was abstracted regarding cancer type, environmental exposure variables, genetic variables, and estimates of interaction effects. In these papers, over 3500 interactions were investigated. The majority of these interactions were examined in colon, rectal, or colorectal cancer types (40%) followed by breast cancer (30%). Most of these interactions were explored using candidate gene polymorphisms (82%) compared with polymorphisms identified from genome wide association studies (GWAS). The most common environmental exposure categories observed were “energy balance” (41%), e.g. BMI or diet, followed by “Lifestyle” (21%), e.g. smoking or alcohol intake. Further analysis regarding commonly observed interactions, analytical tests performed, number of statistically significant interactions and magnitudes of interactions is on-going. In conclusion, observations of published literature suggest that opportunity exists for more of an agnostic approach to the study of gene-environment interactions and exploring alternative environmental exposures.

Citation Format: Leah E. Mechanic, Naoko I. Simonds, Armen Ghazarian, Camilla B. Benedicto, Sheri D. Schully, Gary L. Ellison, Elizabeth M. Gillanders. A review of the gene-environment interaction literature in cancer: What do we know? [abstract]. In: Proceedings of the AACR Special Conference on Post-GWAS Horizons in Molecular Epidemiology: Digging Deeper into the Environment; 2012 Nov 11-14; Hollywood, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2012;21(11 Suppl):Abstract nr 46.

Knowledge integration in cancer: current landscape and future prospects

Knowledge integration includes knowledge management, synthesis, and translation processes. It aims to maximize the use of collected scientific information and accelerate translation of discoveries into individual and population health benefits. Accumulated evidence in cancer epidemiology constitutes a large share of the 2.7 million articles on cancer in PubMed. We examine the landscape of knowledge integration in cancer epidemiology. Past approaches have mostly used retrospective efforts of knowledge management and traditional systematic reviews and meta-analyses. Systematic searches identify 2,332 meta-analyses, about half of which are on genetics and epigenetics. Meta-analyses represent 1:89-1:1162 of published articles in various cancer subfields. Recently, there are more collaborative meta-analyses with individual-level data, including those with prospective collection of measurements [e.g., genotypes in genome-wide association studies (GWAS)]; this may help increase the reliability of inferences in the field. However, most meta-analyses are still done retrospectively with published information. There is also a flurry of candidate gene meta-analyses with spuriously prevalent "positive" results. Prospective design of large research agendas, registration of datasets, and public availability of data and analyses may improve our ability to identify knowledge gaps, maximize and accelerate translational progress or-at a minimum-recognize dead ends in a more timely fashion.

Multilevel research and the challenges of implementing genomic medicine

Advances in genomics and related fields promise a new era of personalized medicine in the cancer care continuum. Nevertheless, there are fundamental challenges in integrating genomic medicine into cancer practice. We explore how multilevel research can contribute to implementation of genomic medicine. We first review the rapidly developing scientific discoveries in this field and the paucity of current applications that are ready for implementation in clinical and public health programs. We then define a multidisciplinary translational research agenda for successful integration of genomic medicine into policy and practice and consider challenges for successful implementation. We illustrate the agenda using the example of Lynch syndrome testing in newly diagnosed cases of colorectal cancer and cascade testing in relatives. We synthesize existing information in a framework for future multilevel research for integrating genomic medicine into the cancer care continuum.

Building the evidence base for decision making in cancer genomic medicine using comparative effectiveness research

The clinical utility is uncertain for many cancer genomic applications. Comparative effectiveness research (CER) can provide evidence to clarify this uncertainty. The aim of this study was to identify approaches to help stakeholders make evidence-based decisions and to describe potential challenges and opportunities in using CER to produce evidence-based guidance. We identified general CER approaches for genomic applications through literature review, the authors' experiences, and lessons learned from a recent, seven-site CER initiative in cancer genomic medicine. Case studies illustrate the use of CER approaches. Evidence generation and synthesis approaches used in CER include comparative observational and randomized trials, patient-reported outcomes, decision modeling, and economic analysis. Significant challenges to conducting CER in cancer genomics include the rapid pace of innovation, lack of regulation, and variable definitions and evidence thresholds for clinical and personal utility. Opportunities to capitalize on CER methods in cancer genomics include improvements in the conduct of evidence synthesis, stakeholder engagement, increasing the number of comparative studies, and developing approaches to inform clinical guidelines and research prioritization. CER offers a variety of methodological approaches that can address stakeholders' needs and help ensure an effective translation of genomic discoveries.

Knowledge integration at the center of genomic medicine

Three articles in this issue of Genetics in Medicine describe examples of "knowledge integration," involving methods for generating and synthesizing rapidly emerging information on health-related genomic technologies and engaging stakeholders around the evidence. Knowledge integration, the central process in translating genomic research, involves three closely related, iterative components: knowledge management, knowledge synthesis, and knowledge translation. Knowledge management is the ongoing process of obtaining, organizing, and displaying evolving evidence. For example, horizon scanning and "infoveillance" use emerging technologies to scan databases, registries, publications, and cyberspace for information on genomic applications. Knowledge synthesis is the process of conducting systematic reviews using a priori rules of evidence. For example, methods including meta-analysis, decision analysis, and modeling can be used to combine information from basic, clinical, and population research. Knowledge translation refers to stakeholder engagement and brokering to influence policy, guidelines and recommendations, as well as the research agenda to close knowledge gaps. The ultrarapid production of information requires adequate public and private resources for knowledge integration to support the evidence-based development of genomic medicine.

Frontiers in cancer epidemiology: a challenge to the research community from the Epidemiology and Genomics Research Program at the National Cancer Institute

The Epidemiology and Genomics Research Program (EGRP) at the National Cancer Institute (NCI) is developing scientific priorities for cancer epidemiology research in the next decade. We would like to engage the research community and other stakeholders in a planning effort that will include a workshop in December 2012 to help shape new foci for cancer epidemiology research. To facilitate the process of defining the future of cancer epidemiology, we invite the research community to join in an ongoing web-based conversation at http://blog-epi.grants.cancer.gov/ to develop priorities and the next generation of high-impact studies.