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Individualized Medicine from Prewomb to Tomb

2014; Cell Press; Volume: 157; Issue: 1 Linguagem: Inglês

10.1016/j.cell.2014.02.012

1097-4172

Eric J. Topol,

BRCA gene mutations in cancer

That each of us is truly biologically unique, extending to even monozygotic, “identical” twins, is not fully appreciated. Now that it is possible to perform a comprehensive “omic” assessment of an individual, including one’s DNA and RNA sequence and at least some characterization of one’s proteome, metabolome, microbiome, autoantibodies, and epigenome, it has become abundantly clear that each of us has truly one-of-a-kind biological content. Well beyond the allure of the matchless fingerprint or snowflake concept, these singular, individual data and information set up a remarkable and unprecedented opportunity to improve medical treatment and develop preventive strategies to preserve health. That each of us is truly biologically unique, extending to even monozygotic, “identical” twins, is not fully appreciated. Now that it is possible to perform a comprehensive “omic” assessment of an individual, including one’s DNA and RNA sequence and at least some characterization of one’s proteome, metabolome, microbiome, autoantibodies, and epigenome, it has become abundantly clear that each of us has truly one-of-a-kind biological content. Well beyond the allure of the matchless fingerprint or snowflake concept, these singular, individual data and information set up a remarkable and unprecedented opportunity to improve medical treatment and develop preventive strategies to preserve health. In 2010, Eric Schmidt of Google said “The power of individual targeting—the technology will be so good it will be very hard for people to watch or consume something that has not in some sense been tailored for them” (Jenkins, 2010Jenkins, H.W. (2010). Google and the search for the future. Wall Street Journal, August 14, 2010. http://ow.ly/qglOu.Google Scholar). Although referring to the capability of digital technology, we have now reached a time of convergence of the digital and biologic domains. It has been well established that 0 and 1 are interchangeable with A, C, T, and G in books and Shakespeare sonnets and that DNA may represent the ultimate data storage system (Church et al., 2012Church G.M. Gao Y. Kosuri S. Next-generation digital information storage in DNA.Science. 2012; 337: 1628Crossref PubMed Scopus (681) Google Scholar, Goldman et al., 2013bGoldman N. Bertone P. Chen S. Dessimoz C. LeProust E.M. Sipos B. Birney E. Towards practical, high-capacity, low-maintenance information storage in synthesized DNA.Nature. 2013; 494: 77-80Crossref PubMed Scopus (574) Google Scholar). Biological transistors, also known as genetic logic gates, have now been developed that make a computer from a living cell (Bonnet et al., 2013Bonnet J. Yin P. Ortiz M.E. Subsoontorn P. Endy D. Amplifying genetic logic gates.Science. 2013; 340: 599-603Crossref PubMed Scopus (340) Google Scholar). The convergence of biology and technology was further captured by one of the protagonists of the digital era, Steve Jobs, who said “I think the biggest innovations of the 21st century will be at the intersection of biology and technology. A new era is beginning” (Issacson, 2011Issacson W. Steve Jobs. Simon & Schuster, New York2011Google Scholar). With whole-genome DNA sequencing and a variety of omic technologies to define aspects of each individual’s biology at many different levels, we have indeed embarked on a new era of medicine. The term “personalized medicine” has been used for many years but has engendered considerable confusion. A recent survey indicated that only 4% of the public understand what the term is intended to mean (Stanton, 2013Stanton, D. (2013). GFK survey. GFK, August 22, 2013. http://www.gfk.com/us/news-and-events/press-room/press-releases/Pages/Only-27-of-US-Consumers-Have-Heard-of-Personalized-Medicine.aspx.Google Scholar), and the hackneyed, commercial use of “personalized” makes many people think that this refers to a concierge service of medical care. Whereas “person” refers to a human being, “personalized” can mean anything from having monogrammed stationary or luggage to ascribing personal qualities. Therefore, it was not surprising that a committee representing the National Academy of Sciences proposed using the term “precision medicine” as defined by “tailoring of medical treatment to the individual characteristics of each patient” (National Research Council, 2011National Research CouncilToward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. The National Academies Press, Washington, D.C.2011Google Scholar). Although the term “precision” denotes the objective of exactness, ironically, it too can be viewed as ambiguous in this context because it does not capture the sense that the information is derived from the individual. For example, many laboratory tests could be made more precise by assay methodology, and treatments could be made more precise by avoiding side effects—without having anything to do with a specific individual. Other terms that have been suggested include genomic, digital, and stratified medicine, but all of these have a similar problem or appear to be too narrowly focused. The definition of individual is a single human being, derived from the Latin word individu, or indivisible. I propose individualized medicine as the preferred term because it has a useful double entendre. It relates not only to medicine that is particularized to a human being but also the future impact of digital technology on individuals driving their health care. There will increasingly be the flow of one’s biologic data and relevant medical information directly to the individual. Be it a genome sequence on a tablet or the results of a biosensor for blood pressure or another physiologic metric displayed on a smartphone, the digital convergence with biology will definitively anchor the individual as a source of salient data, the conduit of information flow, and a—if not the—principal driver of medicine in the future. Perhaps the most commonly used geographic information systems (GIS) are Google maps, which provide a layered approach to data visualization, such as viewing a location via satellite overlaid with street names, landmarks, and real-time traffic data. This GIS exemplifies the concept of gathering and transforming large bodies of data to provide exquisite temporal and location information. With the multiple virtual views, it gives one the sense of physically being on site. Although Google has digitized and thus created a GIS for the Earth, it is now possible to digitize a human being. As shown in Figure 1, there are multiple layers of data that can now be obtained for any individual. This includes data from biosensors, scanners, electronic medical records, social media, and the various omics that include DNA sequence, transcriptome, proteome, metabolome, epigenome, microbiome, and exposome. Going forward, I will use the term “panoromic” to denote the multiple biologic omic technologies. This term closely resembles and is adopted from panoramic, which refers to a wide-angle view or comprehensive representation across multiple applications and repositories. Or more simply, according to the Merriam-Webster definition of panoramic, it “includes a lot of information and covers many topics.” Thus the term panoromic may be well suited for portraying the concept of big biological data. The first individual who had a human GIS-like construct was Michael Snyder. Not only was his whole genome sequenced, he also collected serial gene expression, autoantibody, proteomic, and metabolomic (Chen et al., 2012Chen R. Mias G.I. Li-Pook-Than J. Jiang L. Lam H.Y. Chen R. Miriami E. Karczewski K.J. Hariharan M. Dewey F.E. et al.Personal omics profiling reveals dynamic molecular and medical phenotypes.Cell. 2012; 148: 1293-1307Abstract Full Text Full Text PDF PubMed Scopus (908) Google Scholar) samples. A portion of the data deluge that was generated is represented in the Circos plot of Figure 2 or an adoption of the London Tube map (Shendure and Lieberman Aiden, 2012Shendure J. Lieberman Aiden E. The expanding scope of DNA sequencing.Nat. Biotechnol. 2012; 30: 1084-1094Crossref PubMed Scopus (217) Google Scholar). The integrated personal omics profiling (iPOP) or “Snyderome,” as it became known, proved to be useful for connecting viral infections to markedly elevated glucose levels. With this integrated analysis in hand, Michael Snyder changed his lifestyle, eventually restoring normal glucose homeostasis. Since that report in 2012, Snyder and his team have proceeded to obtain further omic data, including whole-genome DNA methylation data at multiple time points, serial microbiome (gut, urine, nasal, skin, and tongue) sampling, and the use of biosensors for activity tracking and heart rhythm. Snyder also discovered that several extended family members had smoldering, unrecognized glucose intolerance, thereby changing medical care for multiple individuals. Of note, to obtain the data and process this first panoromic study, it required an armada of 40 experienced coauthors and countless hours of bioinformatics and analytical work. To give context to the digital data burden, it took 1 terabyte (TB) for DNA sequence, 2 TB for the epigenomic data, 0.7 TB for the transcriptome, and 3 TB for the microbiome. Accordingly, this first human GIS can be considered a remarkable academic feat and yielded key diagnostic medical information for the individual. But, it can hardly be considered practical or scalable at this juncture. With the cost of storing information continuing to drop substantially, the bottleneck for scalability will likely be automating the analysis. On the other hand, each omic technology can readily be undertaken now and has the potential of providing meaningful medical information for an individual. Perhaps the greatest technologic achievement in the biomedical domain has been the extraordinary progress in our ability to sequence a human genome over the past decade. Far exceeding the pace of Moore’s Law for the relentless improvement in transistor capacity, there has been a >4 log order (or 0.00007th) reduction in cost of sequencing (Butte, 2013Butte, A.J. (2013). Should healthy people have their genomes sequenced at this time? The Wall Street Journal, February 15, 2013. http://online.wsj.com/news/articles/SB10000872396390443884104577645783975993656?KEYWORDS=cost+of+whole+genome+sequencing.Google Scholar), with a cost in 2004 of ∼$28.8 million compared with the cost as low as $1,000 in 2014 (Hayden, 2014Hayden, E.C. (2014). Is the $1,000 genome for real? Nature News, January 15, 2014. http://www.nature.com/news/is-the-1-000-genome-for-real-1.14530.Google Scholar). However, despite this incomparable progress, there are still major limitations to how rapid, accurate, and complete sequencing can be accomplished. High-throughput sequencing involves chopping the DNA into small fragments, which are then amplified by PCR. Currently, it takes 3 to 4 days in our lab to do the sample preparation and sequencing at 30× to 40× coverage of a human genome. The read length of the fragments is now ∼250 base pairs for the most cost-effective sequencing methods, but this is still suboptimal in determining maternal versus paternal alleles, or what is known as phasing. Because so much of understanding diseases involves compound heterozygote mutations, cis-acting sequence variant combinations, and allele-specific effects, phasing the diploid genome, or what we have called “diplomics” (Tewhey et al., 2011Tewhey R. Bansal V. Torkamani A. Topol E.J. Schork N.J. The importance of phase information for human genomics.Nat. Rev. Genet. 2011; 12: 215-223Crossref PubMed Scopus (173) Google Scholar), is quite important. Recently, Moleculo introduced a method for synthetically stitching together DNA sequencing reads yielding fragments as long as 10,000 base pairs. These synthetic long reads are well suited for phasing. Unfortunately, the term “whole-genome sequencing” is far from complete because ∼900 genes, or 3%–4% of the genome, are not accessible (Marx, 2013Marx V. Next-generation sequencing: The genome jigsaw.Nature. 2013; 501: 263-268Crossref PubMed Scopus (39) Google Scholar). These regions are typically in centromeres or telomeres. Other technical issues that detract from accuracy include long sequences of repeated bases (homopolymers) and regions rich in guanine and cytosine. Furthermore, the accuracy for medical grade sequencing still needs to be improved. A missed call rate of 1 in 10,000, which may not seem high, translates into a substantial number of errors when considering the 6 billion bases in a diploid genome. These errors obfuscate rare but potentially functional variants. Beyond this issue, the accurate determination of insertions, deletions, and structural variants is impaired, in part due to the relatively short reads that are typically obtained. The Clinical Sequencing Exploratory Research (CSER) program at the National Institutes of Health is aimed at improving the accuracy of sequencing for medical applications (National Human Genome Research Institute, 2013National Human Genome Research Institute. (2013). Clinical sequencing exploratory research (CSER). http://www.genome.gov/27546194.Google Scholar). Despite these shortcomings, the ability to identify rare or low-frequency variants that are pathogenic has been a major outgrowth of high-throughput sequencing. Well beyond the genome scans and genome-wide association studies that identified common variants associated with most complex, polygenic diseases and human traits, sequencing leads to high definition of the uncommon variants that typically have much higher penetrance. For example, rare Mendelian conditions have seen a remarkable surge of definition of their genomic underpinnings (Boycott et al., 2013Boycott K.M. Vanstone M.R. Bulman D.E. MacKenzie A.E. Rare-disease genetics in the era of next-generation sequencing: discovery to translation.Nat. Rev. Genet. 2013; 14: 681-691Crossref PubMed Scopus (505) Google Scholar). In the first half of 2010, the basis for four rare diseases was published, but in the first half of 2012, that number jumped to 68 (Boycott et al., 2013Boycott K.M. Vanstone M.R. Bulman D.E. MacKenzie A.E. Rare-disease genetics in the era of next-generation sequencing: discovery to translation.Nat. Rev. Genet. 2013; 14: 681-691Crossref PubMed Scopus (505) Google Scholar). With the power of sequencing, it is anticipated that the molecular basis for most of the 7,000 known Mendelian diseases will be unraveled in the next few years. The exome consists of only ∼40 Mb, or 1.5% of the human genome. There is continued debate over the use of whole-exome sequencing compared with whole-genome sequencing, given the lower cost of sequencing an exome, that can be readily captured via kits from a few different companies (Agilent SureSelect, Illumina TruSeq, and Roche NimbleGen). Exome sequencing is typically performed at much deeper coverage, >100× (as compared with 30×–40× for whole genome), which enhances accuracy, and the interpretation of variants that affect coding elements is far more advanced compared with the rest of the genome. However, the collective output from genome-wide association studies of complex traits has indicated that ∼80% of the incriminated loci are in noncoding regions, outside the confines of genes (Koboldt et al., 2013Koboldt D.C. Steinberg K.M. Larson D.E. Wilson R.K. Mardis E.R. The next-generation sequencing revolution and its impact on genomics.Cell. 2013; 155: 27-38Abstract Full Text Full Text PDF PubMed Scopus (651) Google Scholar). It is fair to say that we have long underestimated the importance of the rest of the genome, but its high density of key regulatory features provides intricate and extraordinarily tight control over how genes operate. Recent whole-genome sequencing studies have identified many critical variants in noncoding portions of the genome (Khurana et al., 2013Khurana E. Fu Y. Colonna V. Mu X.J. Kang H.M. Lappalainen T. Sboner A. Lochovsky L. Chen J. Harmanci A. et al.1000 Genomes Project ConsortiumIntegrative annotation of variants from 1092 humans: application to cancer genomics.Science. 2013; 342: 1235587Crossref PubMed Scopus (271) Google Scholar). A typical whole human genome sequence will contain ∼3.5 million variants compared with the reference genome, predominantly composed of single-nucleotide polymorphisms but also including insertion-deletions, copy number variants, and other types of structural variants (Frazer et al., 2009Frazer K.A. Murray S.S. Schork N.J. Topol E.J. Human genetic variation and its contribution to complex traits.Nat. Rev. Genet. 2009; 10: 241-251Crossref PubMed Scopus (766) Google Scholar). Today, analysis of most of the 3.5 million variants is left with the “variant of unknown significant” (VUS) diagnosis. As more people get sequenced with the full range of disease phenotypes, the proportion of VUS will drop, and each sequence will become more informative. Figure 3 provides a theoretical plot of how further reduction of the cost of whole-genome sequencing will also be accompanied by large numbers of individuals undergoing sequencing. In 2014, still well under 100,000 people have had whole-genome sequencing with only a very limited number of phenotypes addressed. At some point in the future, sequence data get progressively more informative at a lower price point, thus establishing particular value of whole-genome sequencing. It is not just about getting a large number of people with diverse medical conditions and diverse ancestries sequenced. The drive to informativeness will clearly be enhanced by incorporating family genomic assessment, especially for determining whether rare variants are meaningful. Here, too much focus on the individual can result in a loss of context, back to our analogy of the Google map of maximal zoom obscuring understanding. By anchoring the genomics of family members, such as was done with the important discovery of PCSK9 rare variants (Hall, 2013Hall S.S. Genetics: a gene of rare effect.Nature. 2013; 496: 152-155Crossref PubMed Scopus (28) Google Scholar) in cholesterol metabolism, progress in genomic medicine will be catalyzed. At this juncture, however, it appears that exome and whole-genome sequencing provide complementary information. As the cost of whole-genome sequencing is further reduced, along with the availability of enhanced analytical tools for the nongene 98.5% content interpretation, exome sequencing may ultimately become obsolete. The ability to perform sequencing of individual cells has provided remarkable new insights about human biology and disease (Shapiro et al., 2013Shapiro E. Biezuner T. Linnarsson S. Single-cell sequencing-based technologies will revolutionize whole-organism science.Nat. Rev. Genet. 2013; 14: 618-630Crossref PubMed Scopus (753) Google Scholar, Battich et al., 2013Battich N. Stoeger T. Pelkmans L. Image-based transcriptomics in thousands of single human cells at single-molecule resolution.Nat. Methods. 2013; 10: 1127-1133Crossref PubMed Scopus (189) Google Scholar, Owens, 2012Owens B. Genomics: The single life.Nature. 2012; 491: 27-29Crossref PubMed Scopus (12) Google Scholar). The unexpected heterogeneity in DNA sequence from one cell to another, such as has been well documented in tumor tissue and even in somatic cells in healthy individuals, has enlightened us about intraindividual genomic variation. The concept of “mosaicism” has gained rapid acceptance—with multiple mechanisms—ranging from gamete formation, embryonic development, to somatic mutation in cells in adulthood, that account for why each of us has cells with different DNA sequences (Lupski, 2013Lupski J.R. Genetics. Genome mosaicism—one human, multiple genomes.Science. 2013; 341: 358-359Crossref PubMed Scopus (124) Google Scholar, Wang et al., 2012Wang J. Fan H.C. Behr B. Quake S.R. Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm.Cell. 2012; 150: 402-412Abstract Full Text Full Text PDF PubMed Scopus (377) Google Scholar, Macosko and McCarroll, 2012Macosko E.Z. McCarroll S.A. Exploring the variation within.Nat. Genet. 2012; 44: 614-616Crossref PubMed Scopus (17) Google Scholar, Poduri et al., 2013Poduri A. Evrony G.D. Cai X. Walsh C.A. Somatic mutation, genomic variation, and neurological disease.Science. 2013; 341: 1237758Crossref PubMed Scopus (404) Google Scholar). It remains unclear whether mosaicism has functional significance beyond being tied to certain congenital conditions and cancers, but this is an active area of research that is capitalizing on single-cell sequencing technology. This is especially the case in neuroscience in order to explain the observed frequent finding of transposons, which appear to involve between 80 and 300 unique insertions for each neuron and are potentially associated with neurologic diseases (Poduri et al., 2013Poduri A. Evrony G.D. Cai X. Walsh C.A. Somatic mutation, genomic variation, and neurological disease.Science. 2013; 341: 1237758Crossref PubMed Scopus (404) Google Scholar). Sperm, which tend to swim solo, are particularly well suited for single-cell genomics. This work has quantified recombination rates of ∼25 events per sperm, identified the hot spots where these events are most likely to occur, and determined genomic instability as reflected by the rate of de novo mutations (Wang et al., 2012Wang J. Fan H.C. Behr B. Quake S.R. Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm.Cell. 2012; 150: 402-412Abstract Full Text Full Text PDF PubMed Scopus (377) Google Scholar, Poduri et al., 2013Poduri A. Evrony G.D. Cai X. Walsh C.A. Somatic mutation, genomic variation, and neurological disease.Science. 2013; 341: 1237758Crossref PubMed Scopus (404) Google Scholar). Such de novo mutations, which increase in sperm with paternal age, are associated with autism, schizophrenia, and intellectual disability (Poduri et al., 2013Poduri A. Evrony G.D. Cai X. Walsh C.A. Somatic mutation, genomic variation, and neurological disease.Science. 2013; 341: 1237758Crossref PubMed Scopus (404) Google Scholar, Kong et al., 2012Kong A. Frigge M.L. Masson G. Besenbacher S. Sulem P. Magnusson G. Gudjonsson S.A. Sigurdsson A. Jonasdottir A. Jonasdottir A. et al.Rate of de novo mutations and the importance of father’s age to disease risk.Nature. 2012; 488: 471-475Crossref PubMed Scopus (1350) Google Scholar, Veltman and Brunner, 2012Veltman J.A. Brunner H.G. De novo mutations in human genetic disease.Nat. Rev. Genet. 2012; 13: 565-575Crossref PubMed Scopus (543) Google Scholar, de Ligt et al., 2012de Ligt J. Willemsen M.H. van Bon B.W. Kleefstra T. Yntema H.G. Kroes T. Vulto-van Silfhout A.T. Koolen D.A. de Vries P. Gilissen C. et al.Diagnostic exome sequencing in persons with severe intellectual disability.N. Engl. J. Med. 2012; 367: 1921-1929Crossref PubMed Scopus (1130) Google Scholar). Intriguing, and possibly revolutionary, single-cell methods using in situ sequencing protocols are set to offer precise spatial information in addition to linear sequence data. In situ sequencing holds the potential to resolve the spatial distribution of copy number variants, circular DNA, tumor heterogeneity, and RNA localization. A number of methods have been published in the last year, and progress is likely to accelerate in the near future. As opposed to the DNA sequence, which is relatively static, RNA reflects the dynamic state of the cell. Gene expression of a particular tissue of the whole genome has been available via microarrays for several years, but RNA sequencing (RNA-seq) is a relatively new tool that transcends simple expression by capturing data on gene fusions, alternative spliced transcripts, and posttranscriptional changes, along with the whole gamut of RNAs (including microRNA [e.g., miRNAseq], small RNA, lincRNA, ribosomal RNA, and transfer RNA). A particularly valuable metric related to RNA is the expression quantitative trait locus (eQTL). By having both genome-wide association study (GWAS) data and whole-genome gene expression at baseline with or without particular stimuli, functional genomic assessment has been enabled. For example, Westra et al., 2013Westra H.J. Peters M.J. Esko T. Yaghootkar H. Schurmann C. Kettunen J. Christiansen M.W. Fairfax B.P. Schramm K. Powell J.E. et al.Systematic identification of trans eQTLs as putative drivers of known disease associations.Nat. Genet. 2013; 45: 1238-1243Crossref PubMed Scopus (1189) Google Scholar used eQTLs and loci derived from GWAS to provide functional genomic, mechanistic insights for multiple complex traits, including lupus and type 1 diabetes. The proteome, metabolome, and autoantibody landscape can be assessed for an individual approaching the whole-genome level via recent advances in mass spectrometry and protein arrays. Using these techniques, posttranslational modifications of proteins, protein-protein interactions, or the small-molecule metabolites produced by these proteins can be revealed. Emerging technologies such as RNA-mediated oligonucleotide annealing, selection, and ligation sequencing (RASL-seq), barcoded small hairpin RNA (shRNA) libraries, and combinatorial antibody libraries provide inexpensive and efficient views of biology. Longer read sequencing provides the opportunity to sequence antibodies, which typically have variable and constant regions composed of ∼2,000 nucleotides. Perhaps no area of biology has received more attention in recent years than the microbiome. Just the gut microbiome has orders of magnitude more DNA content than germline human DNA and has markedly heightened diversity. Our commensal bacterial flora has been shown to play an important role in various medical conditions (Cho and Blaser, 2012Cho I. Blaser M.J. The human microbiome: at the interface of health and disease.Nat. Rev. Genet. 2012; 13: 260-270Crossref PubMed Scopus (2070) Google Scholar). From fecal samples using a 16S ribosomal amplicon sequencing method, the gut microbiome has been the subject of intensive prospective clinical assessment. It was determined that there were three major enterotypes of the intestinal microbiome based on the predominant bacterial species, such as Bacteroides, Ruminococcus, or Prevotella (Arumugam et al., 2011Arumugam M. Raes J. Pelletier E. Le Paslier D. Yamada T. Mende D.R. Fernandes G.R. Tap J. Bruls T. Batto J.M. et al.MetaHIT ConsortiumEnterotypes of the human gut microbiome.Nature. 2011; 473: 174-180Crossref PubMed Scopus (4428) Google Scholar). The resident species appear to be quite stable over an extended period of time and to be initially transmitted via the mother at childbirth (Faith et al., 2013Faith J.J. Guruge J.L. Charbonneau M. Subramanian S. Seedorf H. Goodman A.L. Clemente J.C. Knight R. Heath A.C. Leibel R.L. et al.The long-term stability of the human gut microbiota.Science. 2013; 341: 1237439Crossref PubMed Scopus (1257) Google Scholar). As the interface between genomics and the host’s environment, the microbiome clearly plays a pivotal role in defining each individual. The influence of the diet on the gut microbiome, such as the content of fiber, along with the underpinning of malnutrition, has been documented (Gordon et al., 2012Gordon J.I. Dewey K.G. Mills D.A. Medzhitov R.M. The human gut microbiota and undernutrition.Sci. Transl. Med. 2012; 4: 137ps12Crossref PubMed Scopus (129) Google Scholar, Ridaura et al., 2013Ridaura V.K. Faith J.J. Rey F.E. Cheng J. Duncan A.E. Kau A.L. Griffin N.W. Lombard V. Henrissat B. Bain J.R. et al.Gut microbiota from twins discordant for obesity modulate metabolism in mice.Science. 2013; 341: 1241214Crossref PubMed Scopus (2421) Google Scholar). For example, even an individual’s response to medications, such as digoxin (Haiser et al., 2013Haiser H.J. Gootenberg D.B. Chatman K. Sirasani G. Balskus E.P. Turnbaugh P.J. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta.Science. 2013; 341: 295-298Crossref PubMed Scopus (394) Google Scholar), or multiple drugs used for cancer, has been shown to be linked to the bacterial flora of the gut microbiome (Viaud et al., 2013Viaud S. Saccheri F. Mignot G. Yamazaki T. Daillère R. Hannani D. Enot D.P. Pfirschke C. Engblom C. Pittet M.J. et al.The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide.Science. 2013; 342: 971-976Crossref PubMed Scopus (1240) Google Scholar, Iida et al., 2013Iida N. Dzutsev A. Stewart C.A. Smith L. Bouladoux N. Weingarten R.A. Molina D.A. Salcedo R. Back T. Cramer S. et al.Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment.Science. 2013; 342: 967-970Crossref PubMed Scopus (1314) Google Scholar). There has been extraordinary progress in our ability to map the human epigenome from DNA methylation to histone modifications and chromatin structure (Ziller et al., 2013Ziller M.J. Gu H. Müller F. Donaghey J. Tsai L.T. Kohlbacher O. De Jager P.L. Rosen E.D. Bennett D.A. Bernstein B.E. et al.Charting a dynamic DNA methylation landscape of the human genome.Nature. 2013; 500: 477-481Crossref PubMed Scopus (921) Google Scholar, Rivera and Ren, 2013Rivera C.M. Ren B. Mapping human epigenomes.Cell. 2013; 155: 39-55Abstract Full Text Full Text PDF PubMed Scopus (382) Google Scholar). The prolific ENCODE project has provided troves of data detailing the role of regulatory elements such as enhancers and insulators and how they are tied to DNA methylation and histone changes (Dawson and Kouzarides, 2012Dawson M.A. Kouzarides T. Cancer epigenetics: from mechanism to therapy.Cell. 2012; 150: 12-27Abstract Full Text Full Text PDF PubMed Scopus (2063) Google Scholar). Like gene expression, epigenomic findings are highly cell-type specific, with more than 200 different cell types in the human body.