Opportunities and Challenges for Selected Emerging Technologies in Cancer Epidemiology: Mitochondrial, Epigenomic, Metabolomic, and Telomerase Profiling
+Author Affiliations
- Corresponding Author:
Mukesh Verma, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, NIH, 6130 Executive Boulevard, Room 5100, Bethesda, MD 20892. Phone: 301-594-7344; Fax: 301-435-6609; E-mail: vermam@mail.nih.gov
Abstract
Remarkable progress has been made in the last decade in new methods for biologic measurements using sophisticated technologies that go beyond the established genome, proteome, and gene expression platforms. These methods and technologies create opportunities to enhance cancer epidemiologic studies. In this article, we describe several emerging technologies and evaluate their potential in epidemiologic studies. We review the background, assays, methods, and challenges and offer examples of the use of mitochondrial DNA and copy number assessments, epigenomic profiling (including methylation, histone modification, miRNAs, and chromatin condensation), metabolite profiling (metabolomics), and telomere measurements. We map the volume of literature referring to each one of these measurement tools and the extent to which efforts have been made at knowledge integration (e.g., systematic reviews and meta-analyses). We also clarify strengths and weaknesses of the existing platforms and the range of type of samples that can be tested with each of them. These measurement tools can be used in identifying at-risk populations and providing novel markers of survival and treatment response. Rigorous analytic and validation standards, transparent availability of massive data, and integration in large-scale evidence are essential in fulfilling the potential of these technologies. Cancer Epidemiol Biomarkers Prev; 22(2); 189–200. ©2012 AACR.
Introduction
Tremendous progress has been made recently in the development and use of sophisticated technologies for enhancing biologic measurements beyond the classic platforms of genomics, proteomics, and gene expression profiling. The advent of these tools offers unique opportunities and challenges for their use in human studies, and cancer epidemiology may benefit from incorporating such measurements. In this review, we assess the landscape of this emerging literature and discuss several of these methods. We specifically address mitochondrial DNA and copy number assessments, epigenomic profiling (including assessments of methylation patterns, histone modification, miRNAs, and chromatin condensation), metabolite profiling (metabolomics), and telomere measurements. For each measurement platform, we offer a background introduction, describe the main assays and methods, and list the main remaining challenges. Finally, we overview the use of these methods in the cancer epidemiology literature, the types of samples they can be used on, and their overall strengths and weaknesses.
Overview of the literature landscape
Table 1 shows the advent of these measurement platforms in the overall literature and also focused on cancer, human studies, and specific types of designs. As shown, the volume of publications is still relatively limited compared with the massive literature on genomics/genetics and gene expression profiling, but many of these measurements already have as large literatures as proteomics with several tens of thousands of papers overall, and several thousands of articles focused on cancer in particular. Methylation and telomere-related articles have an especially strong cancer focus, with approximately 40% of the literature focusing on cancer (as compared with 13% of the overall PubMed). Moreover, 78% to 85% of the cancer literature on all these platforms is on humans. Their use in traditional epidemiologic studies is still relatively limited, accounting for a small fraction of this rapidly expanding literature, with only methylation-related epidemiologic studies exceeding 1,000. Many systematic reviews have also started being published, but meta-analyses remain uncommon, with only a few dozen being available. Most of these meta-analyses focus on single markers, and they almost ubiquitously depend on published summary data. This raises concerns about the breadth of
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