Year : 2019 | Volume
: 15 | Issue : 2 | Page : 269--271
Translational epidemiology: The powerful tool for precision cancer medicine
Zhenming Fu1, Rui Zhang1, Ping Li2, Mingfang Jia3,
1 Cancer Center, Renmin Hospital of Wuhan University, Wuhan, China
2 Cancer Center, Renmin Hospital of Wuhan University; Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
3 Department of Health Management, Renmin Hospital of Wuhan University, Wuhan, China
Dr. Zhenming Fu
Cancer Center, Remin Hospital of Wuhan University, Wuhan 430060
Although this is an exciting time for translational medicine, systematic approaches and strategies to conduct translational research are sparse. We highlight in this editorial the opportunities to collaborate across disciplines and to forge new interdisciplinary collaborative ventures from the perspective of epidemiology. We specifically outline some feasible research areas, wherein Translational Epidemiology may readily speed up the translation of research for Precision Medicine.
|How to cite this article:|
Fu Z, Zhang R, Li P, Jia M. Translational epidemiology: The powerful tool for precision cancer medicine.J Can Res Ther 2019;15:269-271
|How to cite this URL:|
Fu Z, Zhang R, Li P, Jia M. Translational epidemiology: The powerful tool for precision cancer medicine. J Can Res Ther [serial online] 2019 [cited 2020 Sep 24 ];15:269-271
Available from: http://www.cancerjournal.net/text.asp?2019/15/2/269/255088
Biomedical research, including epidemiology research, has long been criticized that most research findings are not translational. That is, research has limited direct impact on improving patient care and public health. To reduce, remove, or bypass costly and time-consuming bottlenecks in the translational research pipeline, the National Institutes of Health established the National Center for Advancing Translational Sciences in the fiscal year 2012. Numerous facilities for translational research have been built around the world. Although this is an exciting time for translational medicine, systematic approaches and strategies to conduct translational research are sparse. Therefore, in this editorial, we highlight the opportunities to collaborate across disciplines and to forge new interdisciplinary collaborative ventures from the perspective of epidemiology.
Epidemiologists investigate research questions at the macroenvironment, individual, and molecular or biology levels. It seems that epidemiology has natural advantages to act as the bridge of gaps for translational cancer research. However, current epidemiology research tends to focus on social and environmental hypotheses, thus limiting its ability to integrate clinical and biological factors. Recently, there are enormous efforts in the epidemiology community trying to fill the gaps, to leverage epidemiological and clinical studies of cancer outcomes,, and finally, to transform epidemiology for 21st-century medicine and public health. Therefore, the prototype for translational epidemiology is emerging. However, it is often believed that sophisticated infrastructures are needed and only the top institutions have the ability to support such cutting-edge research. We specifically outline some feasible research areas, wherein epidemiology studies may readily speed up the translation of science in an average medical center.
Pan Life-Span Epidemiological Study
There is an increasing number of large-scale cohort studies and cohort consortiums with long-term follow-up. Many cohorts provide unique opportunities to address the effects of various demographic, lifestyle, genomic, molecular, clinical, and psychosocial factors on cancer outcomes. For example, the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial is a large-scale, population-based, randomized trial with extensive follow-up. By collecting biologic materials and risk factor information from trial participants before the diagnosis of disease, an ongoing PLCO component, the Etiology and Early Marker Studies (EEMS) are being added. These large-scale biologically based cohort studies present explosive opportunities for population-based research of disease etiology and early detection markers. PLCO data and EEMS biospecimens are available to all qualified researchers through a peer-review process. Efforts can be undertaken to link the epidemiologic data with electronic medical and health records to further address patients' outcomes. These efforts thus enable us to systemically study the research question across the whole lifespan of cancer patients. The study could cover topics from cancer susceptibility, gene–environment (G×E) interaction in cancer initiation, promotion, progression to treatment, and finally, the survival of patients. Therefore, a single study can be used to estimate cancer risk, evaluate treatment selection, and predict treatment response and survival outcomes. The findings can be extremely translational and will impact cancer prevention and management directly. The Precision Medicine Initiative (https://allofus.nih.gov/) Cohort Program is such a landmark longitudinal research effort that aims to engage the 1 million or more US participants to improve our ability to prevent and treat disease based on individual differences in lifestyle, environment, and genetics. If integrated with the Moonshot study, unprecedented potential for translational research will certainly appear. There are many other resources that provide such opportunities, for example, the cancer registries in the Nordic countries.
Pharmacogenetic Epidemiology Studies
Pharmacoepidemiology studies can be nested within these lifespan cohorts. Recent advances in genomic research have demonstrated a substantial role for genomic factors in predicting the response to cancer therapies. As the numbers of cancer survivors in these cohorts and many large-scale clinical trials for chemotherapy continue to grow, researches investigating the factors that affect cancer outcomes are maturing. These outcomes include but are not limited to cancer treatment response, side effects, disease recurrence, survival outcomes, and the late effects of cancer treatments. Researchers can seek to understand why individuals respond differently to drug therapy, in terms of both adverse effects and treatment efficacy. To advance the fields of cancer pharmacogenomics and pharmacoepidemiology, it may be ideal to initiate with the first genotype and analyze the association of outcomes with single-nucleotide polymorphisms in 139 drug metabolism genes identified through the PharmGKB database (http://www.pharmgkb.org/). Of course, with the rapid development of biomarkers and new technologies, novel tools such as various Omics and next-generation sequencing (NGS) for gene-drug interactions should be utilized and developed. However, sophisticated infrastructures are needed, and many clinical institutions may not have the ability to support this cutting-edge research.
Personalized medicine is a rapidly advancing field that is informed by each person's unique clinical, molecular, genomic, and environmental information. All these areas broadly fall into the domain of epidemiology. Hence, epidemiology can serve a pivotal role in personalized medicine research. The first step in determining targets for personalized cancer prevention and treatment is to identify clean phenotypes and distinct genotypes. Cancer is usually a collection of heterogeneous subtypes; for example, colorectal cancer is not a single disease, but a complex multifactorial disease. As a result, the genotype–phenotype relationship is complicated by significant heterogeneity, which is large because of gene–gene, G×E, and environment–environment interactions in all phases of carcinogenesis, progression, and also treatment. Traditional cancer epidemiology studies do not fully take account of these heterogeneities, partially because when it comes to subset analysis, the sample size is always an issue. Recently, established cohort consortiums make subset analysis a reality. Furthermore, most cancers are known to arise etiologically from neither exclusively genetic nor solely environmental factors but through a combination of the two., However, genome-wide association studies mostly do not consider environmental/risk factors. Thus, it will be rewarding to thoroughly investigate the interaction of both factors for distinct clinical/molecular subsets of specific cancer to identify new targets for personalized cancer prevention and treatment.
Mendelian Randomization Analysis (Mra) Studying G×e Interactions
One frustrating obstacle that prevents epidemiological findings from translating to intervention is that a large proportion of the findings from observational studies demonstrate associations rather than causations. To speed up the transition to application, novel tools for proving the causation has experienced the bottleneck effect. MRA, based on the principle of Mendel's law of independent assortment, is developed for this purpose., It combines genetic and classical epidemiological analysis exposures to reduce or even eliminate potential biases in the associations, thus inferring causality. However, the application of MRA in epidemiology has substantial limitations, which is predominantly due to the lack of good genetic factors as proxies for environmental exposures of interest. To overcome this limitation, there have been some novel approaches in which combined genetic risk categories based on the putative genetic pathways were used as the proxies.,, With the development of MRA, a more promising method termed “novel Mendelian randomization” has been developed to exploit the impact of long-term exposure differences on disease risk using combined genetic instruments to account for the potential biases due to confounding and reverse causation.
It is important to mention some pivotal research techniques that are routinely or will potentially be utilized in these driver research areas of current translational epidemiologic studies of all designs. We are experiencing a Renaissance in biomedical research primarily driven by next-generation biotechnologies, such as various “omics” and NGS genomics. Although genomic discoveries are at the forefront of next-generation omics/sequencing technologies, their implementation into the clinical arena has been painstakingly slow mainly because of high reaction costs and the unavailability of computational tools for large-scale data analysis. Given advances in biotechnology, bioinformatics, and computational/systems biology, there are unprecedentedly rich opportunities in big data and genome-editing technology to contribute to translation and precision medicine.
Translational cancer research is interdisciplinary and transdisciplinary by nature. Numerous suggestions and recommendations have been made for multidisciplinary collaborations and partnerships to identify and fill the knowledge gaps. Much less attention has been paid to how scientists should be prepared for transdisciplinary research., In fact, multidisciplinary training is a prerequisite for next-generation researchers who want to be fully capable of conducting translational cancer research. Next-generation epidemiologists (NGEs) may have to obtain comprehensive knowledge of cancer epidemiology, molecular/genetic biology, statistics, and oncology or pathology. Thus, an ideal NGE might be an oncologist with rigorous training in molecular genetics and epidemiology. For example, an integrative transdisciplinary science, molecular pathological epidemiology, is emerging. Of course, there should be many kinds of NGEs with different multidisciplinary expertise.
Nonetheless, knowledge integration is a key. Although the requirement of this kind of cross-training sounds prohibitive, this is a dynamic time for NGEs to play a critical role in personalized medicine and translational research. More integrated interdisciplinary methods such as those of molecular pathological epidemiology will certainly be future approaches of science. We may have to embark upon the exciting challenges to become an NGE and function fully as a translational researcher.
Financial support and sponsorship
This study was partially supported by grants 81472971 and 81773555 from the National Science Foundation of China.
Conflicts of interest
There are no conflicts of interest.
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