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Volume 147 Number 1 September 30, 2011

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Genomic Basis for Oncogenic Rearrangements Frontiers in Human Genetics

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Leading Edge Cell Volume 147 Number 1, September 30, 2011 IN THIS ISSUE SELECT 5

Cancer’s Epigenome

VOICES 9

Human Genome: What’s Been Most Surprising?

ANALYSIS 11

Genomics in Africa: Avoiding Past Pitfalls

M. Kaplan

COMMENTARIES 14

Genomics Reaches the Clinic: From Basic Discoveries to Clinical Impact

T.A. Manolio and E.D. Green

17

Genetics and Genomics to the Clinic: A Long Road ahead

D. Ginsburg

PREVIEWS 20

Translocation Mapping Exposes the Risky Lifestyle of B Cells

R.P. McCord and J. Dekker

22

Splicing up Pluripotency

B.R. Graveley

24

Unweaving the Autism Spectrum

C. Lord

ESSAY 26

A Blueprint for Advancing Genetics-Based Cancer Therapy

W.R. Sellers

PERSPECTIVES 32

Clan Genomics and the Complex Architecture of Human Disease

J.R. Lupski, J.W. Belmont, E. Boerwinkle, and R.A. Gibbs

44

Metagenomics and Personalized Medicine

H.W. Virgin and J.A. Todd

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Ubiquitination

PRIMER 57

Mapping Rare and Common Causal Alleles for Complex Human Diseases

S. Raychaudhuri

REVIEW 70

Modeling Human Disease in Humans: The Ciliopathies

G. Novarino, N. Akizu, and J.G. Gleeson

SNAPSHOT 248

Human Biomedical Genomics

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Articles Cell Volume 147 Number 1, September 30, 2011 81

The Lin28/let-7 Axis Regulates Glucose Metabolism

H. Zhu, N. Shyh-Chang, A.V. Segre, G. Shinoda, S.P. Shah, W.S. Einhorn, A. Takeuchi, J.M. Engreitz, J.P. Hagan, M.G. Kharas, A. Urbach, J.E. Thornton, R. Triboulet, R.I. Gregory, DIAGRAM Consortium, MAGIC Investigators, D. Altshuler, and G.Q. Daley

95

Translocation-Capture Sequencing Reveals the Extent and Nature of Chromosomal Rearrangements in B Lymphocytes

I.A. Klein, W. Resch, M. Jankovic, T. Oliveira, A. Yamane, H. Nakahashi, M. Di Virgilio, A. Bothmer, A. Nussenzweig, D.F. Robbiani, R. Casellas, and M.C. Nussenzweig

107

Genome-wide Translocation Sequencing Reveals Mechanisms of Chromosome Breaks and Rearrangements in B Cells

R. Chiarle, Y. Zhang, R.L. Frock, S.M. Lewis, B. Molinie, Y.-J. Ho, D.R. Myers, V.W. Choi, M. Compagno, D.J. Malkin, D. Neuberg, S. Monti, C.C. Giallourakis, M. Gostissa, and F.W. Alt

120

A DNA Repair Complex Functions as an Oct4/Sox2 Coactivator in Embryonic Stem Cells

Y.W. Fong, C. Inouye, T. Yamaguchi, C. Cattoglio, I. Grubisic, and R. Tjian

132

An Alternative Splicing Switch Regulates Embryonic Stem Cell Pluripotency and Reprogramming

M. Gabut, P. Samavarchi-Tehrani, X. Wang, V. Slobodeniuc, D. O’Hanlon, H.-K. Sung, M. Alvarez, S. Talukder, Q. Pan, E.O. Mazzoni, S. Nedelec, H. Wichterle, K. Woltjen, T.R. Hughes, P.W. Zandstra, A. Nagy, J.L. Wrana, and B.J. Blencowe

147

Selective Translation of Leaderless mRNAs by Specialized Ribosomes Generated by MazF in Escherichia coli

O. Vesper, S. Amitai, M. Belitsky, K. Byrgazov, A.C. Kaberdina, H. Engelberg-Kulka, and I. Moll

158

Regulatory Control of the Resolution of DNA Recombination Intermediates during Meiosis and Mitosis

J. Matos, M.G. Blanco, S. Maslen, J.M. Skehel, and S.C. West

173

Saturated Fatty Acids Induce c-Src Clustering within Membrane Subdomains, Leading to JNK Activation

R.G. Holzer, E.-J. Park, N. Li, H. Tran, M. Chen, C. Choi, G. Solinas, and M. Karin

185

Conformation-Sensing Antibodies Stabilize the Oxidized Form of PTP1B and Inhibit Its Phosphatase Activity

A. Haque, J.N. Andersen, A. Salmeen, D. Barford, and N.K. Tonks

199

Crystal Structure of the Mammalian GIRK2 K+ Channel and Gating Regulation by G Proteins, PIP2, and Sodium

M.R. Whorton and R. MacKinnon

(continued)

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A Pseudoatomic Model of the Dynamin Polymer Identifies a Hydrolysis-Dependent Powerstroke

J.S. Chappie, J.A. Mears, S. Fang, M. Leonard, S.L. Schmid, R.A. Milligan, J.E. Hinshaw, and F. Dyda

223

Beclin1 Controls the Levels of p53 by Regulating the Deubiquitination Activity of USP10 and USP13

J. Liu, H. Xia, M. Kim, L. Xu, Y. Li, L. Zhang, Y. Cai, H.V. Norberg, T. Zhang, T. Furuya, M. Jin, Z. Zhu, H. Wang, J. Yu, Y. Li, Y. Hao, A. Choi, H. Ke, D. Ma, and J. Yuan

235

Absence of CNTNAP2 Leads to Epilepsy, Neuronal Migration Abnormalities, and Core Autism-Related Deficits

O. Pen~agarikano, B.S. Abrahams, E.I. Herman, K.D. Winden, A. Gdalyahu, H. Dong, L.I. Sonnenblick, R. Gruver, J. Almajano, A. Bragin, P. Golshani, J.T. Trachtenberg, E. Peles, and D.H. Geschwind

CORRECTION 247

AKT/FOXO Signaling Enforces Reversible Differentiation Blockade in Myeloid Leukemias

S.M. Sykes, S.W. Lane, L. Bullinger, D. Kalaitzidis, R. Yusuf, B. Saez, F. Ferraro, F. Mercier, H. Singh, K.M. Brumme, € S.S. Acharya, C. Scholl, Z. Tothova, E.C. Attar, S. Frohling, R.A. DePinho, D.G. Gilliland, S.A. Armstrong, and D.T. Scadden

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On the cover: Chromosomal rearrangements disrupt the integrity of the genome and are involved in producing leukemias and lymphomas. Klein et al. (pp. 95–106) implement translocation capture sequencing (TC-Seq) to document genome-wide chromosomal rearrangements in primary B cells. Their results reveal that double-strand break location, transcriptional activity, chromosome territories, and activation-induced cytidine deaminase (AID) activity influence the extent of genome rearrangement and favor recurrent oncogenic translocations. The cover image depicts AID as a spider and a TC-Seq-generated translocation map to the immunoglobulin heavy-chain locus as its web. The cover was designed by Rafael Casellas and Ethan Tyler.

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Leading Edge

In This Issue Splicing Up ES Cell Pluripotency PAGE 132

Gabut et al. identify a splicing switch that leads to an ESC-specific form of the transcription factor FOXP1, with an altered DNA-binding domain. This isoform stimulates expression of pluripotency genes, represses differentiation genes, and is also required for efficient reprogramming.

Linking mTOR, let-7, and Diabetes PAGE 81

Connecting cellular energy sensing with type 2 diabetes (T2D), Zhu et al. show that muscle-specific overexpression of the let-7 miRNA-binding protein Lin28 ameliorates insulin resistance and glucose tolerance in mice via upregulation of mTOR signaling. Genome-wide association studies reveal that let-7 target genes are enriched for SNPs associated with T2D, extending the relevance of these findings to human disease.

Making Translocation Spots Hot PAGE 95 and PAGE 107

Aberrant fusions between the c-myc and IgH loci are frequently associated with B-cell lymphoma. In this issue, Klein et al. and Chiarle et al. report high-throughput techniques to investigate how these oncogenic rearrangements occur. By analyzing the repair of inducible DNA double-strand breaks in millions of primary B cells, the authors find that AID activity, chromosome topology, and transcription all conspire to increase the probability of translocation. The findings provide a molecular understanding of translocation hot spots.

DNA Repair Complex Meets Pluripotency Factors PAGE 120

Fong et al. uncover a stem cell-selective function of a DNA repair complex comprising XPC, RAD23B, and CETN2. This complex coactivates Oct4/Sox2-directed transcription of key pluripotency genes and is important for stem cell maintenance and reprogramming.

Remodeling Ribosomes under Stress PAGE 147

Vesper et al. show that, when under stress, E. coli generates functionally specialized ribosomes by truncating 16S rRNA with the endoribonuclease MazF. Moreover, MazF specifically removes 50 UTRs of distinct transcripts to form leaderless mRNAs, which are selectively translated by the altered ribosomes to adjust the translational program in response to stress.

Timed Triggers for Crossovers PAGE 158

The timely resolution of DNA recombination intermediates is essential for chromosome segregation. Matos et al. show that the activation of two crossover-promoting endonucleases is temporally coordinated with cell-cycle progression to enable the specialized meiotic and mitotic chromosome segregation programs. In meiosis, the nucleases are hyperactivated to promote crossovers, whereas in mitosis, their activities are restrained to favor noncrossover products. Cell 147, September 30, 2011 ª2011 Elsevier Inc. 1

Targeting Reversible Oxidation PAGE 185

The phosphatase PTP1B regulates insulin and leptin signaling. Haque et al. identify an antibody that selectively recognizes and stabilizes the oxidized, inactive form of PTP1B to promote insulin signaling. The findings indicate that the targeting of oxidized PTP1B is a promising therapeutic approach for diabetes and obesity.

Fatty Acids Congeal Signaling Pathways PAGE 173

What are the molecular mechanisms behind the ill effects of a high-fat diet? Holzer et al. provide evidence that saturated fatty acids (FA) trigger stress signaling and insulin resistance through their effects on membrane fluidity. Saturated FA, but not unsaturated FA, induce clustering of c-Src into membrane subdomains, which in turn, leads to JNK signaling and resistance to insulin. The findings provide a new model for how membrane composition can trigger different signaling cascades, distinct from a ligand-sensing mechanism.

Channel Gating by Coincidence PAGE 199

G protein-gated potassium channels control electrical excitability in cells in response to G protein activation and the signaling lipid PIP2. Whorton and MacKinnon report structures of wild-type and constitutively active mutant channels, providing mechanistic insights into multiligand regulation of channel gating. Their findings reveal that, in the absence of PIP2, G proteins appear to open only one of two sequential gates, whereas the presence of both molecules opens both gates, leading to ion conduction.

Stopping Autophagy on a Dime PAGE 223

Liu et al. report a small-molecule inhibitor of autophagy that blocks two ubiquitin-specific peptidases. Using the inhibitor, Liu et al. elucidate a regulatory pathway linking Beclin1 and p53 that is controlled by ubiquitination. As reduced levels of Beclin1 lead to reduced p53, these findings provide insights into how Beclin1 functions as a tumor suppressor.

New Model for Autism Treatment PAGE 235

Pen˜agarikano et al. report that mice lacking the neurexin Cntnap2, a gene strongly associated with autism and related neurodevelopmental disorders, exhibit striking neuropathological and behavioral parallels to humans with mutations in the equivalent gene. As in human autism patients, the repetitive, but not the social behavioral, deficits of the mice are rescued by the drug risperidone, suggesting promise for the model in facilitating the identification of new therapies.

Dynamin Powerstrokes PAGE 209

The dynamin GTPase mediates the release of clathrin-coated vesicles from the plasma membrane by assembling into collars around the necks of invaginated coated pits. Chappie et al. combine cryo-EM, X-ray diffraction, and crosslinking data to describe the structure and topology of assembled dynamin collars, suggesting that energy from dimerization and GTP hydrolysis is converted into large structural movements that drive membrane fission.

Cell 147, September 30, 2011 ª2011 Elsevier Inc. 3

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Leading Edge

Select Cancer’s Epigenome Large-scale cancer genomics projects are just starting to trickle into the literature, and the flow will only grow in size and impact in the upcoming year. Nevertheless, the studies so far are already reshaping our view of tumor progression, especially in terms of how cancer manipulates the epigenome for fast growth and adaptability. This Select discusses recent genomics articles that, collectively, offer a new mechanism for cancer evolution and highlight the rising importance of chromatin remodeling factors in cancer.

A Cellular Gala´pagos? Cancer cells have remarkable plasticity. They quickly respond to changes in their environment by switching phenotypes and even back-tracking out of differentiated states. Now, a landmark study by Hansen et al. (2011) uncovers a new mechanism driving cancer cells’ adaptability: increased stochasticity in DNA methylation levels across the genome. Methylating cytosines in promoters generally silences genes, and when a cytosine is immediately next to a guanine (i.e., ‘‘CpG’’ dinucleotide), the cytosine is often methylated. Thus, studies on DNA methylation in cancer have focused primarily on gene promoters and genomic regions with a high concentration of CpG dinucleotides. Now, Hansen et al. decide to take a broader approach and A multidimensional scaling of methylation for examine regions with lower CpG density. Using a custom-made Illumina microselected CpGs in cancer, showing the distribution array, the authors measure methylation levels at 384 sites in 290 tumor and of normal tissues (left) and cancer (right). A few normal samples, including colon, lung, thyroid, breast, and Wilms’ tumors. cancer samples fall within the normal range but do Compared to normal tissues, all five cancers display significant increases in not define a tight distribution themselves. Dotted methylation variability at 70%–92% of CpG sites—that is, the standard devialines indicate the typical variation for normal tissues. tions for methylation levels are strikingly higher in cancer than in normal tissue. Image courtesy of K.D. Hansen and W. Timp. The authors then use bisulfate sequencing on three matched colorectal tumors to characterize the methylation pattern on a genomic scale. This analysis reveals large blocks of hypomethylated DNA, which include genes associated with tumor heterogeneity and progression. Although 80% of the genes in these blocks are silenced in both cancer and normal samples, the variability in gene expression across samples is substantially higher in cancer than in normal tissue, as the authors observed for methylation levels. Together, these findings suggest a provocative new model for how cancer cells can adapt so quickly to fluctuating environments: increased randomness in methylation levels enhances the variability of gene expression across a uniform population of cells, allowing selective pressures to select for epigenetic configurations most suitable for a given condition. In other words, the epigenetic instability provides the opportunity for Darwin-like evolution to occur at the cellular level. Hansen, K.D., et al. (2011) Nat. Genet. 43, 768–775.

Massive Sequencing Strikes Suppressor Gold If epigenetic patterns, such as DNA and histone modifications, contribute to cancer’s evolution and tumorigenesis, then proteins involved in shaping the chromatin landscape should be key drivers in a broad range of cancers. Now, four independent exome sequencing studies identify multiple chromatin remodeling factors as tumor suppressors and oncogenes in non-Hodgkin lymphoma (Morin et al., 2011; Pasqualucci et al., 2011). Until now, only a few genetic drivers have been identified for the most common forms of non-Hodgkins lymphoma—diffuse large B cell lymphoma (DLBCL) and follicular lymphoma. To hunt for more tumor suppressors in lymphoma, Morin et al. (2011) sequence the genomes or exomes of 15 lymphoma cases (14 DLBCLs and 1 follicular lymphoma) and then perform RNA sequencing on these cases plus 113 more samples. Independently, Pasqualucci et al. combine exome sequencing and copy number analysis on six DLBCL cases. Both groups identify 100 genes recurrently mutated in multiple tumors, and both pools of genes are enriched with enzymes involved in DNA methylation and histone modifications, such as histone methyltransferases and acetyltransferases. In both studies, 10% of the lymphomas carry mutations in the MEF2B gene, which encodes a factor involved with histone acetylation. More importantly, Morin et al. find that 89% of the follicular lymphoma cases carry mutations in the MLL2 gene, and both groups observe MLL2 mutations in 24%–32% of the DLBCL

Genome-wide visualization of somatic mutation targets in non-Hodgkin lymphoma from Morin et al. (2011). Purple circles mark genes mutated in more than one case, with their diameters proportional to the number of cases with single-nucleotide variants observed. Image courtesy of R. Morin.

Cell 147, September 30, 2011 ª2011 Elsevier Inc. 5

tumors, making MLL2 the most common mutated gene in non-Hodgkin lymphoma. MLL2 encodes an H3K4-specific histone methyltransferase involved in gene activation. The mutation patterns of MLL2—that is, a high percentage of inactivating mutations— strongly indicate that MLL2 is a critical tumor suppressor in non-Hodgkin lymphoma. Moreover, both MLL2 and MEF2B display signs that selective pressures accelerated the acquisition of nonsynonymous point mutations, providing further evidence that these chromatin remodelers are core drivers of non-Hodkgin lymphoma. Morin, R.D., et al. (2011) Nature 476, 298–303. Pasqualucci, L., et al. (2011) Nat. Genet. 43, 830–837.

Remodeling Tumor Suppressor Theory Using a similar strategy as the two studies above on lymphoma, Gui et al. (2011) and Li et al. (2011) also identify chromatin remodelers as new tumor suppressors in bladder carcinoma and hepatitis C-associated liver cancers (HCC), respectively. In both studies, the authors start by sequencing the exomes for 10 different tumors (to 90-fold coverage); identify genes with nonsilent mutations in more than one tumor; and then sequence these genes in 100 more samples. For bladder cancer, 8 of the 49 most frequently mutated genes encode chromatin remodeler proteins, including a SWI/SNF-related gene (ARID1A), a histone demethylase (UTX), acetyltransferases (CREBBP and EP300), and methyltransferases (MLL and MLL3). For HCC, 18.2% of the samples harbor inactivating mutations in the ARID2 gene, which encodes a subunit of the chromatin remodeling complex PBAF Histology image of a hepatitis C-associ(polybromo and BRG1-associated factor). In both studies, the mutational patterns ated liver cancer case with an ARID2 mutaobserved for these seven chromatin remodelers strongly implicate these genes as tumor tion. Image courtesy of M. Torbenson. suppressors in bladder cancer or HCC. A major strength of the study by Hansen et al. (see first summary) is that the increased variability in methylation and gene expression was observed across diverse types of cancers, suggesting that this epigenetic instability is a general property of cancer. Now, a related trend seems to be arising from exome sequencing projects: disrupting enzymes that maintain the proper epigenetic landscape drives malignant transformation in diverse types of cancers, from non-Hodgkin lymphoma to bladder and liver cancers. Collectively, these studies also suggest that targeting chromatin remodeler genes may be a particularly productive approach for finding new tumor suppressors and oncogenes in future sequencing projects. Gui, Y. et al. (2011). Nat. Genet. 43, 875–878. Li, M., et al. (2011). Nat. Genet. 43, 828–829.

BRCA1 Breaks into Chromatin Remodeling Mutations in the breast cancer susceptibility gene 1 (BRCA1) increase the risk for developing breast and ovarian cancers up to 95%, and it is generally accepted that BRCA1 stops tumorigenesis by promoting the repair of DNA breaks through homologous recombination. Now, Zhu et al. (2011) challenge this classic view by presenting evidence that BRCA1 maintains genomic stability through the ubiquitination of histone H2A in heterochromatin. Centromeres are surrounded by heterochromatin, which contains large arrays of repeating sequences called satellite DNA. These repeats are thought to be noncoding and largely silenced by heterochromatin formation. The BRCA1 protein contains a domain with ubiquitin E3 ligase activity, and previous studies have shown that, in vitro, BRCA1 preferentially ubiquitinates H2A, a histone variant implicated in gene silencing. Now, Zhu et al. demonstrate that deleting BRCA1 disrupts the organization of heterochromatin, decreases levels of ubiquitinated H2A histones at heterochromatin, and increases the expression of transcripts from the satellite DNA. Next, the authors show that boosting transcription of the satellite DNA surprisingly causes many abnormalities seen in BRCA1-deficient cells, such as deficiencies in homologous recombination and impaired chromosomal segregation. Then, when the authors overexpress an H2A-ubiquitin fusion protein, the satellite DNA is resilenced and many of the BRCA1-linked defects improve, including p53-induced apoptosis, growth arrest, and impaired homologous recombination. Zhu and colleagues find that these satellite transcripts are also increased in breast cancer tumors from both humans and mice harboring BRCA1 mutations. Together, these findings suggest that BRCA1 exerts its tumor-suppressive effects by maintaining the integrity of heterochromatin. Although many questions still remain, such as how the satellite transcripts alter DNA stability and integrate with BRCA1’s known role at DNA breaks, the study may have unlocked an entirely new route for tumor evolution and adaptability. It also reminds us that, clearly, we are just beginning to become privy to the dangerous cabal that cancer has with chromatin and the epigenome. Zhu, Q., et al. (2011). Nature 477, 179–184.

The confocal image in the Sputnik satellite shows a cell overexpressing satellite DNA and with abnormally amplified centrosomes (red). At center is a mathematical deconvolution of the confocal image to resolve the tubulin fibers (green) and the individual chromosomes (blue). Image courtesy of Q. Zhu, J. Fitzpatrick, and J. Simon.

Michaeleen Doucleff Cell 147, September 30, 2011 ª2011 Elsevier Inc. 7

Leading Edge

Voices Human Genome: What’s Been Most Surprising? Let’s Remember the Chromosomes

Variation and Complexity

A Hidden Ecosystem

David Page

Vivian Cheung

David Haussler

Massachusetts Institute of Technology

University of Pennsylvania

University of California, Santa Cruz

What surprises me most about the state of the human genome in 2011 is how much of the sequence remains to be assembled accurately and how important that achievement would be for fulfilling the Human Genome Project’s original goal: a comprehensive reading of the book of life. Achieving this aim will require conquering the most structurally ornate and dynamic regions of the genome, including the essential but elusive elements (such as centromeres and telomeres) that are responsible for faithful transmission of the genome from one generation to the next. Students of human biology and medicine would finally be able to see the genome as an orchestra of chromosomes—not merely a ‘‘parts list’’ of genes. We would, at last, be positioned to address some of the longstanding mysteries of human biology and medicine, such as the fragile and tenuous nature of human reproduction, and we would understand why a sizeable proportion of all human conceptions—and half of spontaneously lost pregnancies—display dramatic anomalies of one or more chromosomes. Just as high-resolution crystal structures of macromolecular complexes enable unforeseen insights into function in health and disease, a complete and accurate assembly of the human genome will answer questions that we do not even know to ask.

The simplicity of A, C, G, and Ts as building blocks of the human genome is deceptive. Although various genomescale projects seek to identify functional units based on DNA sequences, it is surprisingly difficult to find genes and regulatory elements, and such information is critical for determining how sequence variants affect disease risks. DNA sequences are highly processed and modified during transcription and translation. The same DNA sequence can code for different proteins by alternate promoter usage or splicing, and base modification such as methylation and chromatin modifications also affect how DNA sequences are converted into functional units. In addition, DNA sequences are not always copied exactly into RNA and proteins; processes such as RNA editing lead to proteins that are not encoded by the underlying DNA sequences. Even after the proteins are synthesized, different modifications affect their functions. Although DNA is viewed as the template for transcripts and proteins, there is a lack of a direct relationship between DNA sequence and functional elements. A deeper understanding of the fundamental yet complex relationships between DNA and RNA (as well as proteins) is necessary to assess the functional significance of genome variation. An approach may involve studying individual variation in the transcriptome and proteome in addition to DNA polymorphisms to determine which and how genetic variants affect disease susceptibility and response to therapy.

The role of functional, noncoding DNA has been simultaneously the biggest surprise and the biggest mystery emerging from our first glimpse of the human genome. Comparative sequencing of the genomes of other species revealed that, since the inception of placental mammals, more than 5% of the human genome has been under selective constraint to preserve some presumably fitness-enhancing function. Careful gene structure analysis proved that only about 1.2% codes for protein. Many of the most dramatically constrained genome segments are noncoding. What does this ‘‘dark matter’’ do, and how did it evolve? Ample experimental evidence suggests that the bulk of the evolutionarily constrained segments will have gene regulatory functions either as DNA elements or as parts of noncoding RNA genes. It has become clear that these elements turn over and are reinvented anew on a much faster timescale than the protein-coding genes. The other big surprise is the mounting evidence that transposons—mobile elements within a cell’s DNA that are viewed by most as almost exclusively parasitic—play a critical role in this turnover. Previously viewed as a library of cellular information infested with a few parasites, the genome has turned out to be a lively and complex ecosystem unto itself, with many unusual denizens partaking in complex evolutionary alliances and battles that remain to be deciphered.

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Huge Heterogeneity

Richard Gibbs Baylor College of Medicine

It is remarkable how much of our perception of the human genome is shaped by the nature of its ‘‘accessible’’ portions. The regions that are unique, more easily finished, and accessible to highthroughput genotyping are naturally the best studied. This excludes many repetitive regions and segments that are otherwise recalcitrant, such as those with high GC content. Many interesting and disease-related alleles fall in these regions. It is a shock to hear from colleagues involved in medical diagnostics that, for some regions, ‘‘we ignore the reference sequence.’’ This is because there is enormous population heterogeneity, and the different alleles involve complex rearrangements. The assessment in the diagnostic laboratory is bootstrapped by all of the data from the patients that are tested with standard arrays and only modest dependence on the reference. Another surprise is that there are many more private, even ‘‘unique,’’ and potentially functionally significant variants in populations than we expected. This means that it will be a long time before all of the important alleles that contribute to human health have been discovered. And in general, when new sequences are examined, there will be newly discovered alleles for which the greatest challenge will be assessing their functional significance.

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Leading Edge

Analysis Genomics in Africa: Avoiding Past Pitfalls A landmark genomics project is taking shape in Africa that shifts the power and prominence to local scientists. If successful, the program will offer valuable insights into the inheritance of common diseases and reshape the paradigm of foreign-funded research. In the past few years, large-scale genomics studies have made one point clear: scientists must screen the genomes of diverse populations around the world to decipher the genetic basis of common diseases. But the relationship between genomicists and indigenous populations is often contentious. Although biomedical projects are more positively received than evolutionary studies, the prevailing structure still bears elements of neocolonialism; European and American scientists gather samples and return home to analyze and publish the results. This imbalance breeds skepticism for projects and limits participation. Starting this October, the US’s NIH and the UK’s Wellcome Trust are joining forces to restructure this paradigm by developing a genomics research network across Africa. Named The Human Heredity and Health in Africa Project, or H3Africa, the program plans to give African researchers an opportunity to bid for research grants to study diverse topics in genomics, from the human microbiome and pharmogenomics to the genetics of communicable and noncommunicable diseases. Those selected for funding will establish or enhance local research facilities in their home country to generate modern sequencing and phenotyping laboratories. These centers will also serve as training facilities and function within a network of clinics and bioinformatics laboratories. The hope is that the genetic work will prove useful in combating diseases that are specifically problematic in Africa but will also yield insight into the genes behind global diseases, like diabetes and cancer. The project naturally elicits the question of ‘‘why Africa.’’ Indeed, it would be less

expensive and politically far simpler to have western researchers continue to sample western genes, but that would yield an incomplete picture of human genetic variation. The reason is that Africa is a genetically special place. Why Africa In May 2009, a study published in Science sent shockwaves through the field of human genetics when it reported the most comprehensive analysis of African genetic diversity to date (Tishkoff et al., 2009, Science 324, 1035–1044). Led by Sarah Tishkoff at the University of Pennsylvania, the study looked at 1327 nuclear microsatellite and insertion/ deletion markers in 2,400 individuals— a diminutive scale and scope compared to sequencing and genome-wide association studies reported today. But what made Tishkoff’s study so extraordinary were the individuals themselves. Most genetic studies in African had sampled only a handful of groups and then generalized their findings. In contrast, Tishkoff and her team acquired samples from 113 distinct populations across the continent, from the Mozabite Berbers of Morocco to the hunter-gatherer San of the Kalahari Desert. The findings made it clear that Africa was home to the highest levels of human genetic diversity on the planet. The reason for this vast diversity is that migration has reduced genetic variation in populations outside of Africa. Just as people today move to take advantage of low housing prices or new jobs, ancient humans moved to find new forms of shelter and food. A major threat faced by these migratory populations was genetic isolation; inbreeding decreased genetic

diversity and increased the population’s vulnerability to disease. Today, human populations decrease in genetic diversity the further away they get from Africa because founder populations that were formed from other founder populations shrunk the gene pool further. For instance, a study last year reported the complete genome (or exome) sequences of four individuals from a hunter-gatherer population in Southern Africa called Khosians. Despite their geographic proximity, the Khosians were genetically quite far apart, with more differences in their genes than those observed between Europeans and Asians (Schuster et al., 2010, Nature 463, 943–947). Researchers keen to find genes that contribute to diseases are limiting the variations that they have available for study if they restrict their search to individuals outside of Africa. To date, however, genomic studies have focused almost exclusively on westerners. Approximately 96% of the subjects in genome-wide association studies so far are from European descent. Findings from these studies may not generalize to other populations, and they may also miss many alleles that are rare in Europeans but more frequent in other ethnic groups. ‘‘A lot of past work has been Eurocentric, and that is not good. We need to know much more about the genetics of humans worldwide, particularly Africa,’’ says geneticist Nick Patterson at the Broad Institute. Genetically, in comparison to most other human populations, Africans have more haplotypes (combinations of alleles in different loci) and lower levels of linkage disequilibrium (alleles on the same chromosome are more likely to be inherited independently of each other). ‘‘These allow for fine-mapping and better localization of risk variants in genomic loci,’’ says Charles Rotimi, president of the African Society of Human Genetics and director of the Center for Research on Genomics and Global Health at the NIH. But the reasons for going to Africa are not solely genetic ones. ‘‘In the developing countries that I’ve visited, there is a large community with tremendous intellectual capacity. We really want to reverse the history of not involving African communities and see to it that the people of Africa reap the health benefits that will

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Phenetic tree of human populations around the world. African branches are color coded according to language classifications. The two insets show principal components analysis created on the basis of individual genotype. Adapted from Tishkoff et al., 2009, Science 324, 1035–1044.

result from analysis of their genetics,’’ says Jane Peterson, associate director of extramural research at the NIH. ‘‘Over the years, it has been very frustrating working in genomics and seeing so few Africans participate. Creating a research environment where young African men and women can pursue this research on their own is everything,’’ says Dr Rotimi. Yet the path forward is not without obstacles. Colonial Ghosts Whether justified or not, a connection could be made between historic colonial mining of African resources and the western funding of projects that will mine African genomes. Indeed, Africans need not look to colonial times to have

concerns about misuse of collected biological resources. In 1983, blood samples were collected from natives of the Nuu-chah-nulth tribe on Vancouver Island by researchers. Rheumatoid arthritis was a serious problem among the indigenous population. The tribe had agreed with the scientists using their blood to search for genes associated with rheumatoid arthritis, with the hopes of alleviating pain in their community. The researchers were unable to find any genetic basis for the arthritis during the 1980s, and when the lead researcher behind this work moved to a new university in 1986, so did the Nuu-chah-nulth blood samples. Over time, the samples were shared with other researchers, and studies were conducted on them that did

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not involve rheumatoid arthritis at all. When the tribe ultimately discovered that studies, including one on viruses spread by intravenous drug use, were being conducted on their biological samples without their permission, tensions rose and an international debate emerged over the proper care of genetic samples. ‘‘This was all about expectations not being met and the people not being involved in the decisions to carry out secondary research after the initial research could not be completed,’’ explains Laura Arbour at the University of British Columbia, a medical geneticist who was not involved with the Nuu-chahnulth work. ‘‘People who live with health disparity want research money spent in ways that can make a difference for them. But research is a dynamic process.

Things change. Results may unexpectedly lead to a different path or the research intended cannot be completed. If new research is being proposed for their samples, they should be included in that discussion too,’’ she says. Making African Research African To avoid creating a Nuu-chah-nulth debacle and to promote independence from NIH support, keeping research inside of Africa is a priority for the new project. During the next five years, considerable funding—five million dollars a year from the NIH and eight million pounds a year from the Wellcome Trust—is going to be awarded in the form of grants to African researchers by an NIH-selected peer review team. However, collaboration with labs outside of Africa is undoubtedly going to happen, and this means that some African samples may need to leave the continent. Keenly aware that even sending some blood and DNA out of Africa might generate the wrong message, H3Africa is leaving control of biological materials collected in Africa to researchers from the continent. ‘‘African researchers will develop the policy for sample distribution,’’ says Peterson. Yet because the project is still in its infancy, the funders

behind H3Africa will not guarantee that an African biorepository for material storage and management will be built. ‘‘A biorepository remaining on African soil and under the governance of African researchers and other local stakeholders is an ideal way to reduce the risk of unapproved secondary research,’’ comments Dr Arbour. ‘‘But if the samples need to be analyzed elsewhere, it remains possible that the protective processes can still be effective as long as all parties are on board and the governance remains with the African partners,’’ she says. It also remains to be determined what diseases will get studied and to what extent they will include diseases that are specifically problematic in Africa, as the project’s future revolves around the research proposals that come in during the months ahead. The funding bodies behind H3Africa are not formally declaring any diseases that they are keen to see fought. ‘‘I would guess that we will see a mix chosen since one of the key criteria that will be used by the peer reviewers is medical relevance, and there are certainly a lot of highly relevant diseases shared by both Africa and the rest of the world,’’ says Peterson. But this silence from the funding agency is not stopping individuals from expressing what they would like to see studied.

‘‘A distressingly high proportion of young children in Africa pick up lethal diarrhea. I suspect that this has been persisting in Africa for a long time. Since it sets in before reproductive age and selection probably has been playing a role in selecting for infants that can survive the disease, there might be some genetic resistance there, and this may be well worth looking for,’’ says Patterson. As for Rotimi, he is keen to see sickle cell anemia dealt with. ‘‘In some areas of western Africa, heterozygotes can be as common as 20% of the population, while actual sickle cell patients (sufferers) can be up to 3%. Many sufferers never make it to medical facilities alive. In addition, it is often assumed that carriers are entirely normal, but we really do not know that! Research directed at understanding this disease, its complications, and treatment has not received enough scientific attention, and this needs to change,’’ says Rotimi. Indeed, the project looks well aimed to avoid past pitfalls, and with the right project proposals, it could have a profound impact on our understanding of disease inheritance and human evolution. But there is still a long way to go to narrow the gap between Africans’ genetic richness and their power to use it for their own benefit.

Matt Kaplan London, UK DOI 10.1016/j.cell.2011.09.018

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Leading Edge

Commentary Genomics Reaches the Clinic: From Basic Discoveries to Clinical Impact Teri A. Manolio1 and Eric D. Green1,* 1National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA *Correspondence: [email protected] DOI 10.1016/j.cell.2011.09.012

Today, more than ever, basic science research provides significant opportunities to advance our understanding about the genetic basis of human disease. Close interactions among laboratory, computational, and clinical research communities will be crucial to ensure that genomic discoveries advance medical science and, ultimately, improve human health. The potential for the burgeoning knowledge of genome structure and function to improve medical care has long been anticipated (Collins, 1999), but until very recently, the actual clinical application of genomics has been limited (Green and Guyer, 2011). Despite concerns about the pace of medically relevant genomic discoveries and the implementation of genomic medicine (clinical care based on or influenced by knowledge of a patient’s specific genomic variants) (Varmus, 2010), growing numbers of encouraging examples are now in hand. Early case reports of genomic-based diagnoses leading to altered treatment and an improved clinical course, facilitated by advancing genomic technologies such as whole-exome and -genome sequencing, illustrate the potential of genomically informed medicine for improving clinical care. Such reports also demonstrate the critical role that basic science approaches play in characterizing implicated variants and pointing toward more effective treatments. Here, we describe several recent successes in genomic medicine that illustrate the critical interplay between basic and translational researchers that will be required to make the routine use of genomic medicine a reality. The potential of whole-exome sequencing for identifying the genetic cause of a mysterious and disabling disease and, in some cases, for illuminating a path toward effective treatment was vividly demonstrated by the desperate case of a young boy with severe, intractable, and atypical inflammatory bowel disease (Worthey et al., 2011). He failed

to respond to conventional treatment and progressively worsened. The only treatment option remaining was hematopoietic stem cell transplantation. In the absence of a clear diagnosis, clinicians were concerned about subjecting the boy to an invasive procedure with unknown chances for survival. Wholeexome sequencing provided the key diagnostic clue: a nonsynonymous change in XIAP, a gene involved in apoptosis. Functional studies quickly confirmed that the mutation caused aberrant XIAP function. These findings, and the known morbidity risk of XIAP deficiency-related hemophagocytic lymphohistiocytosis, tipped the balance in favor of stem cell transplantation. The patient survived the procedure and is now well 1 year later (D. Dimmock, personal communication). There is little doubt among the clinical and laboratory teams that this course of treatment—a high-risk gamble justified only upon identification of the presumed causal mutation—saved the patient’s life. Genomic analysis is a cornerstone of the National Institutes of Health’s Undiagnosed Diseases Program (http:// rarediseases.info.nih.gov/Resources.aspx? PageID=31), and it recently proved invaluable for studying three families with severe, symptomatic arterial calcifications (St Hilaire et al., 2011). Several of the affected individuals had disabling intermittent claudication with extensive occlusion of the iliofemoral arterial system due to heavy calcification. One family was consanguineous (a third-cousin marriage), so genome-wide single-nucleotide polymorphism arrays were used

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to identify genomic regions that were homozygous in all affected siblings but heterozygous in the unaffected parents. The only such region included three genes implicated in cellular pathways potentially involved in calcification. One of these, NT5E, codes for the protein CD73, which is involved in the same pathway as a gene associated with generalized arterial calcification of infancy. Targeted sequencing of the affected siblings revealed a homozygous nonsense mutation in NT5E, and quantitative PCR analysis demonstrated decreased NT5E expression in cultured fibroblasts from two of the affected siblings. Studies of two other affected families detected missense and nonsense NT5E mutations in homozygous or compound heterozygous states. A series of elegant experiments revealed markedly reduced CD73 levels and absent CD73 enzymatic activity in fibroblasts from affected patients, with the latter rescued by transfection with a CD73-encoding lentiviral vector. Fibroblasts carrying the NT5E mutation also showed excessive staining for tissue-nonspecific alkaline phosphatase (TNAP), a key enzyme for calcification, as well as abundant calcium phosphate crystal formation. These phenotypes were ameliorated by CD73 transfection or treatment with either adenosine or an inhibitor of alkaline phosphatase. Elucidating the precise molecular defect in this condition enables consideration of treatments affecting other components of this calcification pathway and may shed light on potential treatments for ectopic tissue calcification in other disorders.

These two notable successes are encouraging, but it is sobering to recognize that whole-genome analysis has failed to reveal the cause of a rare genetic disease in the majority of cases studied to date. More robust approaches for genome analysis are being developed to study the thousands of genetic disorders for which the molecular basis remains unknown. Pharmacogenomics is another area where genomic discoveries can be leveraged to improve clinical care. Genotypetargeted treatment with clopidogrel represents a prototypic pharmacogenomic advance facilitated by basic science investigation of the effect of specific genetic variants. Clopidogrel is a widely prescribed antiplatelet drug that binds to the platelet P2Y12 receptor with wide interindividual variability in response (Roden and Shuldiner, 2010). Further study of clopidogrel’s mechanism of action showed that it is a pro-drug highly dependent on cytochrome P450 2C19 for activation. Up to 30% of individuals carrying CYP2C19 variants are unable to generate the active form, and inhibition of platelet aggregation was diminished in these individuals. Some, but not all, studies also point to an associated higher risk for thrombotic cardiovascular events among these CYP2C19 variant carriers. An alternative but more costly anti-P2Y12 drug does not require bioactivation, raising the potential for genotype-targeted selection of individuals needing the higher-cost alternative. Another common 2C19 polymorphism can actually increase clopidogrel metabolism, whereas the effects of several rarer 2C19 variants remain to be studied. Several pilot studies are now underway examining the effectiveness of pre-emptive genotyping in patients. The effects of other 2C19 variants, the role of genotyping versus phenotypic platelet inhibition assays, and the therapeutic potential of other antiplatelet drugs provide fertile ground for basic science investigations that can generate more effective treatments to reduce the risk of thrombotic events. Clinical outcome improvements like these, in conjunction with appropriate changes in physician and patient behavior, will be essential for promoting adoption of such genomic approaches in routine clinical care. Findings from these

and related studies will also inform policy development and regulatory oversight, as illustrated by the Food and Drug Administration’s promotion of druglabeling changes (www.fda.gov/drugs/ scienceresearch/researchareas/pharmaco genetics/ucm083378.htm). Other pharmacogenomic successes include the documented increased risk for life-threatening adverse reactions to carbamazepine in persons carrying the HLA-B*1502 allele and the reduction of that risk following genotyping and drug avoidance by carriers of the risk allele (Wilke and Dolan, 2011). HLA-mediated risk for adverse drug reactions remains poorly understood but is suspected to involve HLA-allele-specific presentation of key drug moieties to immune-activating cells—an area ripe for basic investigation. Another potent pharmacogenomic example is the BRAF kinase inhibitor vemurafenib. Patients with metastatic melanoma whose tumors carry the activating BRAF V600E somatic mutation respond dramatically, with improved rates of survival (Chapman et al., 2011). The potential for vemurafenib and other BRAF kinase inhibitors to improve health spans of other cancer patients carrying the BRAF V600E mutation is being investigated and may be an initial step in the long-anticipated classification of cancers based on molecular taxonomy rather than organ of origin and histopathology. These examples demonstrate the enormous potential of basic research to contribute key insights about the phenotypic consequences of disease-associated variants that, in turn, lead to changes in patient care. Although genetic variants of large effect with clear functional impact may be more readily identified in familial or isolated cases of severe disease, such as those described above, recent studies have demonstrated the important new areas of research catalyzed by studying smaller-effect loci identified in genome-wide association studies (GWAS) (Ernst et al., 2011). For example, 80% or more of the genomic regions implicated in the GWAS conducted to date are intronic or intergenic (http:// www.genome.gov/gwastudies), with disease-associated variants being significantly enriched in enhancer elements

(Ernst et al., 2011). Together, these findings make detailed investigation of genetically associated noncoding genomic regions a high priority, yet our functional understanding of noncoding regions is in its relative infancy and will be complicated by their greater genomic variation compared to coding regions (1000 Genomes Project Consortium, 2010). Variants in gene regulatory regions will undoubtedly prove to be important in disease causation, but their role in pathogenesis will also be complex and difficult to define compared to the coding variants represented by the recent discoveries described above and the overwhelming majority of disease-causing mutations reported to date. For genomic medicine to be successful, basic science advances are also needed to promote development of low-cost, rapid, and clinically available technologies for detecting genomic variants. Although the advent of genome-wide genotyping arrays revolutionized identification of disease-associated loci, this and other genomic technologies (such as genome sequencing) remain largely unavailable outside major research laboratories. The requisite data analysis and quality control will likely keep these technologies out of the typical clinical laboratory for some time. This is unfortunately also true for methods to detect variants in targeted genes recognized to have significant clinical implications. Although becoming increasingly available, whole-exome and -genome sequencing presents significant challenges with respect to data analysis, interpretation, and display. Robust yet easy-toutilize bioinformatic tools are urgently needed for analyzing genome sequence data, providing to clinicians only the information about genomic variants that is relevant to a patient’s care. Our ability to define the role of genomic variation in human disease is growing at an ever-accelerating pace. As revealed in the above examples, using these advances to directly improve patient care will require close interactions between the basic and clinical research communities. The insights resulting from genomic knowledge moving freely between the laboratory and the clinic hold great promise for the implementation of genomic medicine.

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ACKNOWLEDGMENTS

C., Testori, A., Maio, M., et al; BRIM-3 Study Group. (2011). N. Engl. J. Med. 364, 2507–2516.

We thank William Gahl, Geoffrey Ginsburg, Mark Guyer, Laura Rodriguez, Jeffery Schloss, and Marc Williams for helpful input in preparing this paper.

Collins, F.S. (1999). Ann. N Y Acad. Sci. 882, 42–55, discussion 56–65.

REFERENCES

Ernst, J., Kheradpour, P., Mikkelsen, T.S., Shoresh, N., Ward, L.D., Epstein, C.B., Zhang, X., Wang, L., Issner, R., Coyne, M., et al. (2011). Nature 473, 43–49.

1000 Genomes Project Consortium. (2010). Nature 467, 1061–1073.

Green, E.D., and Guyer, M.S.; National Human Genome Research Institute. (2011). Nature 470, 204–213.

Chapman, P.B., Hauschild, A., Robert, C., Haanen, J.B., Ascierto, P., Larkin, J., Dummer, R., Garbe,

Roden, D.M., and Shuldiner, A.R. (2010). Circulation 122, 445–448.

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St Hilaire, C., Ziegler, S.G., Markello, T.C., Brusco, A., Groden, C., Gill, F., Carlson-Donohoe, H., Lederman, R.J., Chen, M.Y., Yang, D., et al. (2011). N. Engl. J. Med. 364, 432–442. Varmus, H. (2010). N. Engl. J. Med. 362, 2028– 2029. Wilke, R.A., and Dolan, M.E. (2011). JAMA 306, 306–307. Worthey, E.A., Mayer, A.N., Syverson, G.D., Helbling, D., Bonacci, B.B., Decker, B., Serpe, J.M., Dasu, T., Tschannen, M.R., Veith, R.L., et al. (2011). Genet. Med. 13, 255–262.

Leading Edge

Commentary Genetics and Genomics to the Clinic: A Long Road ahead David Ginsburg1,* 1Howard Hughes Medical Institute, Departments of Internal Medicine, Human Genetics, and Pediatrics, and the Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA *Correspondence: [email protected] DOI 10.1016/j.cell.2011.09.013

Advances in genomic technology have produced an explosion of new information about the genetic basis for human disease, fueling extraordinarily high expectations for improved treatments. This perspective will take brief stock of what genetics/genomics have brought to clinical practice to date and what we might expect for the future.

Improved Diagnosis for Mendelian Genetic Disorders First the ‘‘good news’’: the contribution of modern genetics and genomics to the diagnosis of Mendelian genetic disorders has been nothing short of spectacular. The number of human single-gene disorders with a known molecular genetic cause has risen from less than 5 in 1982, to approximately 150 in 1990, and to nearly 3,000 in 2011 (http://omim.org). Precise DNA diagnosis in approved clinical testing laboratories is available for many of these disorders, and testing on a research basis can be obtained for a significant subset of the remaining diseases. Although ambiguity still remains for some sequence findings (e.g., ‘‘variants of unknown significance’’), definitive diagnosis can often be established by DNA testing, and once a familial mutation is known, testing of nearly perfect sensitivity and specificity is then available for other at-risk family members, including prenatal and preimplantation diagnoses. These advances have led to the near elimination of select autosomal-recessive diseases in specific populations, such as b-thalassemia in parts of the Mediterranean and TaySachs disease among Ashkenazi Jews (Zlotogora, 2009). Though the full power of genetic diagnosis has not yet been fully realized, major progress is clear, and the impact of DNA testing on single-gene disorders, particularly with the advent of next-generation sequencing, is likely to expand dramatically over the next decade.

Treatment for Mendelian Genetic Disorders Now for the ‘‘bad news’’: in contrast to the remarkable impact on diagnosis, the contribution of modern genetics and genomics to the treatment of most Mendelian genetic disorders has been, with a few notable exceptions, a disappointing failure. Among the 3,000 single-gene disorders for which the responsible gene has been identified, only a handful (<1%) have been translated directly into new therapies. Most of this success has been restricted to diseases due to enzyme deficiencies, such as hemophilia and the lysozomal storage diseases. However, for most genetic diseases, little has changed in treatment since the discovery of the responsible gene. Where modest improvement in therapy has been seen, this has generally been empiric and not the direct result of knowing the underlying genetic defect. Examples include muscular dystrophy, whose gene was identified in 1986, cystic fibrosis (1989), and Huntington disease (1993). Sickle cell anemia provides a particularly humbling example for the biomedical research community. Its molecular basis has been understood at the 5.5 A˚ level since 1960 (Perutz et al., 1960). However, current therapy is still largely empiric and not derived from a sophisticated understanding of hemoglobin structure and sickle cell anemia molecular pathogenesis. Chronic myelogenous leukemia (CML) offers a striking exception for which understanding genetic pathogenesis has

indeed had a striking impact on treatment. Imatinib (Gleevec), a targeted therapeutic against the unique fusion gene product (BCR/ABL) found in all CML patients, has profoundly altered the prognosis for patients with this disease. Despite the initial enthusiasm that designer drugs of this type would soon become a routine follow-on to the discovery of any pathogenic gene, few such therapies with an impact of this magnitude have yet emerged. It has become increasingly clear that the path for translation of basic genetic findings into new therapeutics is not an easy one. Although 3,000 Mendelian diseases can now be diagnosed by DNA testing, is this always of direct clinical value to the patient? Medical test ordering by physicians often follows a logic similar to Mallory’s decision to climb Mount Everest, simply ‘‘because it’s there.’’ This approach has made unrestrained diagnostic testing a key contributor to runaway health care costs. A transition toward evidence-based medicine will increasingly demand that diagnostic tests, including DNA testing, be restricted to those settings wherein a significant impact on treatment and medical management is anticipated. It is here that our disappointing success rate in translating genetic discovery into improved medical treatment dampens the significance of our triumph in the diagnostic arena. Unfortunately, a significant fraction of DNA diagnostic tests currently performed does not meet this standard of evidence-based medical justification.

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For example, one of the most commonly performed genetic diagnostic tests in the US today is for factor V Leiden, a common human polymorphism that predisposes to venous thrombosis. However, the results of this test are of limited or no value in guiding current medical therapy (Middeldorp, 2011). Though modern genetics and genomics have had a transforming impact on our understanding of Mendelian human genetic disorders, direct translation to improved treatment remains elusive for most patients. This relative failure to date should not be a cause for resignation or despair, but rather a call to redouble our efforts. The path from basic discovery to effective therapy is usually a long and arduous one, and generally unpredictable. Recent history simply demonstrates that identifying something as broken is much easier than fixing it. Complex Genetic Disorders Single-gene disorders are estimated to account for approximately 9% of childhood mortality and probably less than 2% of overall hospital admissions in the US (Korf et al., 2007). The vast majority of health care costs are devoted to the treatment of common complex disorders such as coronary artery disease, stroke, diabetes, hypertension, and cancer, which all appear to have large, heritable components. The ‘‘common disease/ common variant’’ hypothesis has been tested by genome-wide association studies (GWAS) for over 220 diseases or traits (Hindorff et al., 2011). Over 1,300 highly significant genetic risk loci have been identified, though nearly all with very modest effect (allelic odds ratios generally < 1.5) and in aggregate accounting for only a small fraction of the overall genetic risk (Manolio et al., 2009). Is information from GWAS clinically useful in guiding medical management or choice of therapy? At present, the clear consensus among clinical genetic experts is a resounding ‘‘no.’’ One prime example is type 2 diabetes, where extensive single-nucleotide polymorphism (SNP) testing adds little to the much greater predictive value of family history and body mass index (Lyssenko et al., 2008). The lack of demonstrated diagnostic or therapeutic utility has not dissuaded the enthusiastic direct-to-consumer (DTC)

marketing of whole-genome SNP analysis for self-diagnosis of risk for a broad array of diseases and common human traits. Though the technical validity of SNP calling seems high, risk prediction from similar data varies dramatically between different DTC companies, often in opposite directions (Ng et al., 2009). The potential negative impact of DTC genetic testing on medical practice and health care cost containment has led to calls for increased regulation and oversight. Despite limited direct clinical payoff to date, GWAS have nonetheless provided important advances in our understanding of several complex genetic diseases. Examples include entirely unanticipated insight into the role of complement factor genes in the pathogenesis of age-related macular degeneration (de Jong, 2006) and the discovery of the BCL11A gene as a critical regulator of fetal hemoglobin levels and the long-sought-after basis for the fetal to adult globin switch (Sankaran et al., 2009). In addition, the observation that common SNPs in aggregate generally account for <10%–20% of overall genetic risk for most complex diseases has sparked new efforts to identify the genetic basis for the remaining ‘‘missing’’ heritability (Manolio et al., 2009). Pharmacogenomics There has been considerable enthusiasm that the identification of common human genetic variants would accurately predict drug response and toxicity, allowing precise tailoring of individualized or ‘‘personalized’’ treatments for patients based on their specific disease and genetic makeup. Nonetheless, progress in this area remains limited and has not yet proven as straightforward as initially thought. Although there are several clear-cut associations of specific genetic variants with drug toxicity, genetic screening prior to treatment has not yet become a standard part of medical care. As in disease diagnostics, pharmacogenomic testing has often been greeted with naive enthusiasm and advocated well in advance of solid evidence for clinical utility. A case in point is CYP2/C9 and VKORC1 genotyping to predict response to warfarin, a widely used oral anticoagulant for which precise dosing is critical (too low a dose leads to inadequate efficacy as an anticoagulant and too high

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a dose to excessive bleeding). Common variants in these two genes account for 30% of the variability in the individual response to warfarin, leading the U.S. Food and Drug Administration to modify warfarin labeling to suggest CYP2/C9 and VKORC1 genotyping. However, this testing has not yet been demonstrated to be of practical clinical value in decreasing bleeding complications or increasing anticoagulant efficacy and is rarely used in clinical practice (Rosove and Grody, 2009). Where Do We Go from Here? The impact of next-generation sequencing on human genetics, both diagnostically and therapeutically, is likely to be transformational. With decreased cost and improved quality over the next 5–10 years, whole-genome sequencing may replace conventional newborn screening in much of the developed world. Initially, only those genes for which immediate diagnosis in a newborn is of clear value for directing medical management are likely to be evaluated, including the 20–30 genes comprising current newborn screening panels, with the rest of the sequence archived for later use. The list of such actionable Mendelian genetic disorders will undoubtedly expand with time. The availability of full genome sequences for large fractions of the patient population, together with implementation of a uniform electronic medical record, should enable an entirely new scale of genetic epidemiologic health outcomes research. Along with these advances, issues of informed consent and genetic privacy, as well as ethical considerations related to prenatal and preimplantation intervention, will pose increasingly complex challenges. The anticipated flood of sequencing data, along with the development of new computational and conceptual tools to analyze it, are likely to yield profound new insights into the pathogenesis of human disease, the diagnosis of complex genetic disorders, and the accurate prediction of drug efficacy and toxicity, leading to improved treatment selection. The near future promises to be a very exciting time for biomedical research, perhaps finally providing the tools required to realize the promise of translating basic scientific discovery into improved outcomes for patients.

ACKNOWLEDGMENTS D.G. is a Howard Hughes Investigator and also supported by National Institutes of Health grants RO1HL039693 and PO1HL057346. D.G. is also a member of the Board of Directors for Shire plc.

REFERENCES de Jong, P.T. (2006). N. Engl. J. Med. 355, 1474– 1485. Hindorff, L.A., Junkins, H.A., Hall, P.N., Mehta, J.P., and Manolio, T.A. (2011). http://www. genome.gov/gwastudies.

Korf, B.R., Rimoin, D.L., Connor, J.M., and Pyeritz, R.E. (2007). Nature and Frequency of Genetic Disease. In Emery and Rimoin’s Principles and Practice of Medical Genetics, D.L. Rimoin, J.M. Connor, R.E. Pyeritz, and B.R. Korf, eds. (Philadelphia: Churchill Livingstone Elsevier), pp. 49–52. Lyssenko, V., Jonsson, A., Almgren, P., Pulizzi, N., Isomaa, B., Tuomi, T., Berglund, G., Altshuler, D., Nilsson, P., and Groop, L. (2008). N. Engl. J. Med. 359, 2220–2232. Manolio, T.A., Collins, F.S., Cox, N.J., Goldstein, D.B., Hindorff, L.A., Hunter, D.J., McCarthy, M.I., Ramos, E.M., Cardon, L.R., Chakravarti, A., et al. (2009). Nature 461, 747–753.

Middeldorp, S. (2011). J. Thromb. Thrombolysis 31, 275–281. Ng, P.C., Murray, S.S., Levy, S., and Venter, J.C. (2009). Nature 461, 724–726. Perutz, M.F., Rossmann, M.G., Cullis, A.F., Muirhead, H., Will, G., and North, A.C. (1960). Nature 185, 416–422. Rosove, M.H., and Grody, W.W. (2009). Ann. Intern. Med. 151, 270–273, W95. Sankaran, V.G., Xu, J., Ragoczy, T., Ippolito, G.C., Walkley, C.R., Maika, S.D., Fujiwara, Y., Ito, M., Groudine, M., Bender, M.A., et al. (2009). Nature 460, 1093–1097. Zlotogora, J. (2009). Hum. Genet. 126, 247–253.

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Leading Edge

Previews Translocation Mapping Exposes the Risky Lifestyle of B Cells Rachel Patton McCord1 and Job Dekker1,* 1Programs in Systems Biology and Gene Function and Expression, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, 364 Plantation Street, Worcester, MA 01605-0103, USA *Correspondence: [email protected] DOI 10.1016/j.cell.2011.09.005

Recurrent chromosomal translocations can drive oncogenesis, but how they form has remained elusive. Now, Chiarle et al. (2011) and Klein et al. (2011) characterize the genome-wide spectrum of translocations that form from a single double-stranded break, revealing that specific loci have an intrinsic predisposition for frequent chromosomal rearrangements. In the face of damage from everyday metabolic processes or external ionizing radiation, a cell must maintain chromosomal integrity. The most dangerous event that it can face is a DNA doublestrand break (DSB), which can lead to chromosomal translocations (Mills et al., 2003). Chromosomal translocations are a common feature of many cancers, with specific, recurrent translocations occurring in nearly 40% of all human tumors (Shaffer and Pandolfi, 2006). Recurrent translocations can promote tumorigenesis by creating novel gene products or by altering the regulation of genes involved in cell proliferation and differentiation. For example, the Bcr/Abl translocation, observed in 90% of chronic myelogenous leukemia cases, creates a novel oncogene that is sufficient to transform cells in vitro. Why do recurrent translocations occur so frequently? They could result from random and rare events that are subsequently selected due to their proliferative potential. Alternatively, molecular processes could predispose specific loci to engage frequently in translocations. In this issue, Klein et al. and Chiarle et al. present powerful new methods to address such questions by capturing the genome-wide landscape of translocations (a translocatome), which result from a single defined DSB in primary cells in the absence of confounding effects of growth selection (Chiarle et al., 2011; Klein et al., 2011). The two methods induce a DSB at a defined position in the genome. After allowing the cells to repair this DSB for

a relatively short time, translocation junctions between the induced DSB and endogenous DSBs are identified by PCR amplification and deep sequencing. Both studies find that most cells repair the induced DSB by rejoining the ends without causing major genomic rearrangements. However, a significant fraction of cells join the induced DSB ends with endogenous DSBs elsewhere in the genome, creating intra- and interchromosomal rearrangements. Though these technologies can be widely applied to any cellular system, both groups use them to examine genomic rearrangements in murine B cells. These immune cells can give rise to lymphomas as a result of a recurrent interchromosomal translocation between the c-myc gene and the IgH locus (Ku¨ppers and Dalla-Favera, 2001). In these two studies, the authors conditionally introduce a DSB at either the IgH or the c-myc locus in B cells activated for class switch recombination (CSR), a process that generates a spectrum of endogenous DSBs. They find that B cells minimize the risk of deleterious translocations from all of these DSBs by repairing almost half of the induced DSBs locally, within 1 kb of the original break site. Even within 100 kb of the break site, some junctions can be attributed to the local repair process of resection and resealing. Outside of local repair, however, the authors observe a multitude of translocation partners of c-myc and IgH that could generate almost every chromosomal aberration imaginable, including di-centric and a-centric chromosomes.

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This diversity shows the power of capturing an early, unselected translocatome, as many of these rearrangements would have been lost if cells were required to undergo cell division. Importantly, these studies reveal that the frequency of translocations derived from a single DSB is 0.4%–1%. In a living organism, where millions of B cells per day experience endogenous DSBs during CSR, the observed translocation rate predicts that nearly 103 cells per day could form inadvertent translocations. With distinct yet similar experimental protocols, these two studies paint a picture of some of the underlying mechanisms that predispose certain regions to translocations in B cells (Figure 1). For instance, both groups find that translocations occur more frequently on the chromosome carrying the induced break, even up to 50 Mb away. Such a phenomenon cannot be explained by local repair activity but, rather, suggests that translocations between pairs of DSBs occurring on the same chromosome are strongly preferred over interchromosomal events. Given that loci on the same chromosome tend to be located closer to each other in the nucleus than loci on different chromosomes, the spatial organization of the genome may directly impact the formation of translocations. Despite the enrichment for intrachromosomal translocations, the authors find that interchromosomal translocations occur frequently, comprising 60% of the nonlocal repair events. Some chromosomal regions translocate so frequently that they are classified as ‘‘hot

Figure 1. Factors Predisposing Loci to Double-Strand Breaks and Translocations Genome-wide translocation mapping identifies translocations that form when a DNA double-strand break (DSB) is artificially introduced in a defined location by the I-SceI meganuclease (center). The majority of induced DSBs are repaired locally, but many form translocation junctions with endogenous DSBs. These endogenous DSBs, often mediated by AID activity, occur at transcription start sites (left), class switch recombination sites (top), and elsewhere throughout the genome (right).

spots.’’ Among these, the c-myc/IgH translocation is among the most frequent, regardless of whether the original break was introduced at c-myc or IgH. What mechanisms might explain the recurrent formation of certain translocations? Both studies find that many hot spots depend on activation-induced cytidine deaminase (AID). AID initiates CSR and somatic hypermutation by inducing DNA damage at immunoglobulin (Ig) loci (Yamane et al., 2011). Importantly, Klein et al. (2011) and Chiarle et al. (2011) show that this enzyme not only acts at canonical target loci, but also at additional sites throughout the genome, initiating DSBs that can lead to translocations. Thus, B cells pay a price for their programmed ability to rearrange

Ig loci by acquiring DNA damage elsewhere in the genome. Even in the absence of AID-dependent DSBs, there are numerous nonrandom translocations throughout the genome. Both studies find that translocations are much more likely to happen near transcription start sites (TSSs) of actively transcribed genes. Though this effect is pronounced when AID is present (AID induces DSBs at exposed cytosines revealed by stalled RNA polymerases; Pavri et al., 2010), transcription alone creates a risk of DSBs. Together, these studies show that transcription, the generation of breaks by AID, and physical linkage along the DNA all affect translocation frequency

(Figure 1). Importantly, the presence of translocation hot spots in the absence of selection suggests that recurrent translocations observed in disease may have mechanistic causes rather than solely being observed due to selection. Recurrent translocations in cancer may result from intrinsic mechanisms and thus may or may not be drivers of disease. The broad distribution of translocations, along with the likelihood that these rearrangements occur near gene promoters, explains the high risk of acquiring novel gene products and translocation-driven cancers. These studies highlight the molecular processes that lead to formation of DSBs at particular genomic sites. However, for a translocation to occur, two DSBs must also come into close proximity. How and when this contact occurs has long been debated in terms of two contrasting models. The ‘‘contact first’’ model proposes that only regions of chromosomes already in contact prior to DSB formation can form translocations, whereas the ‘‘breakage first’’ model hypothesizes that DSBs can move in the nuclear space to contact translocation partners (Meaburn et al., 2007). The results of the studies from Klein et al. (2011) and Chiarle et al. 2011 suggest that spatial genome organization may affect translocation frequency, but neither study resolves which of the two models applies. The tendency of translocations to occur on the same chromosome as the engineered break site suggests that close spatial proximity can contribute to formation of specific translocations. Indeed, other studies have reported that IgH and c-myc are closely juxtaposed even before a DSB occurs (Roix et al., 2003), possibly facilitating translocation formation when DSBs are introduced. However, the extent to which loci move, especially when CSR and somatic hypermutation are activated, remains unresolved. We now have the ability to comprehensively determine the initiating translocatomes; combining such techniques with other genome-wide assays that probe chromatin state and spatial conformation (Lieberman-Aiden et al., 2009) will contribute to a better understanding of the mechanisms that lead to recurrent formation of disease-causing genomic rearrangements.

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REFERENCES Chiarle, R., Zhang, Y., Frock, R.L., Lewis, S.M., Molinie, B., Ho, Y.-J., Myers, D.R., Choi, V.W., Compagno, M., Malkin, D.J., et al. (2011). Cell 147, this issue, 107–119. Klein, I.A., Resch, W., Jankovic, M., Oliveira, T., Yamane, A., Nakahashi, H., Di Virgilio, M., Bothmer, A., Nussenzweig, A., Robbiani, D.F., et al. (2011). Cell 147, this issue, 95–106. Ku¨ppers, R., and Dalla-Favera, R. (2001). Oncogene 20, 5580–5594.

Lieberman-Aiden, E., van Berkum, N.L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B.R., Sabo, P.J., Dorschner, M.O., et al. (2009). Science 326, 289–293. Meaburn, K.J., Misteli, T., and Soutoglou, E. (2007). Semin. Cancer Biol. 17, 80–90. Mills, K.D., Ferguson, D.O., and Alt, F.W. (2003). Immunol. Rev. 194, 77–95. Pavri, R., Gazumyan, A., Jankovic, M., Di Virgilio, M., Klein, I., Ansarah-Sobrinho, C., Resch, W.,

Yamane, A., Reina San-Martin, B., Barreto, V., et al. (2010). Cell 143, 122–133. Roix, J.J., McQueen, P.G., Munson, P.J., Parada, L.A., and Misteli, T. (2003). Nat. Genet. 34, 287–291. Shaffer, D.R., and Pandolfi, P.P. (2006). Nat. Med. 12, 14–15. Yamane, A., Resch, W., Kuo, N., Kuchen, S., Li, Z., Sun, H.W., Robbiani, D.F., McBride, K., Nussenzweig, M.C., and Casellas, R. (2011). Nat. Immunol. 12, 62–69.

Splicing up Pluripotency Brenton R. Graveley1,* 1Department of Genetics and Developmental Biology, University of Connecticut Stem Cell Institute, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, CT 06030, USA *Correspondence: [email protected] DOI 10.1016/j.cell.2011.09.004

In this issue of Cell, Gabut and colleagues (2011) identify a new splice variant of FOXP1 that directly regulates the expression of pluripotency genes. It endows human embryonic stem cells with their pluripotent nature and is required for the reprogramming of somatic cells to induced pluripotent stem cells. The past few years have seen remarkable progress in our understanding of the mechanistic basis of pluripotency, including the identification of key factors required for maintaining the pluripotent state of human embryonic stem cells (ESCs) (Chen et al., 2008; Kim et al., 2008; Silva et al., 2009). Moreover, one of the great breakthroughs of this decade was the discovery that a only few critical transcriptions factors, such as the combination of Oct4, Sox2, Klf4, and c-Myc, are sufficient to reprogram somatic cells into induced pluripotent stem (iPS) cells (Takahashi et al., 2007). These factors appear to activate a transcriptional network that endows cells with pluripotency (Samavarchi-Tehrani et al., 2010), but gene expression can be regulated by numerous processes other than transcription, including chromatin modifications, RNA stability, and preRNA splicing. How these processes contribute to pluripotency has been largely understudied in human ESCs. Now in this issue of Cell, Gabut et al. (2011) break this field wide open by identifying an alter-

native splicing ‘‘switch’’ at the top of the pluripotency transcriptional network. Alternative splicing—the process by which exons can be joined together in different patterns such that a single gene can give rise to multiple transcripts—is known to regulate key developmental decisions in a number of systems (Nilsen and Graveley, 2010). Perhaps the best known example is the sex-determination pathway in Drosophila (Salz, 2011). This pathway consists of five genes encoding premRNAs that are spliced in a sex-specific manner (Figure 1A). The genes are organized in a hierarchy in which the splicing of an upstream gene regulates that of downstream genes. The genes at the bottom of this hierarchy, dsx (doublesex) and fru (fruitless), encode transcription factors, and the male-specific and female-specific protein variants of each factor regulate distinct sets of target genes. Thus, these regulated splicing events act in a switch-like manner to specify nearly all aspects of sex determination and courtship behavior.

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To explore the role of alternative splicing in human ESC pluripotency, Gabut et al. use microarrays that can detect different splicing variants. These experiments reveal numerous splicing events that change as human ESCs differentiate into neural precursor cells, including one in the FOXP1 gene. This event involves a previously unannotated exon that is included in human ESCs but skipped in differentiated cells (Figure 1B). Strikingly, the exon’s sequence and its stem cell specificity is conserved in mouse, suggesting that it might play a significant role in stem cell biology. FOXP1 encodes a member of the forkhead family of transcription factors, which recognize particular DNA sequences through a ‘‘forkhead domain.’’ FOXP1 is an essential gene that is broadly expressed and required for the establishment of specific cell types. Fusions of FOXP1 with other genes or loss of FOXP1 function are associated with many different types of cancer (Wang et al., 2004; Dasen et al., 2008). Intriguingly, the

Figure 1. Alternative Splicing Regulatory Switches (A) A cascade of alternative splicing regulatory switches control sex determination in Drosophila; Sxl (Sex lethal), tra (transfer), msl-2 (male-specific lethal-2), dsx (doublesex), and fru (fruitless) genes are all differentially spliced in males and females. Sxl encodes a female-specific RNA-binding protein that autoregulates itself and represses the male-specific isoforms of tra and msl-2. The female-specific isoform of tra encodes a female-specific RNA-binding protein that activates expression of the female-specific isoforms of both dsx and fru. These isoforms encode female-specific transcription factors that activate expression of genes specifying female physical traits and sexual behavior. (B) An alternative splicing regulatory switch in FOXP1 regulates pluripotency and reprogramming (Gabut et al., 2011). FOXP1 contains two exons, 18 and 18b, which are spliced in a mutually exclusive manner. In embryonic stem cells (ESCs), exon 18b is included. This results in the production of FOXP1-ES, which binds to and activates pluripotency genes while simultaneously repressing differentiation genes. In differentiated cells, only exon 18 is included, resulting in the production of FOXP1, which activates the expression of differentiation genes.

ES-specific exon is located within the forkhead domain, suggesting that the FOXP1 splice variants may encode proteins with distinct DNA-binding specificities. To examine this possibility, Gabut and colleagues determine the DNA-binding specificity of both the traditional FOXP1 and the ES-specific FOXP1 (FOXP1-ES) with microarrays that contain oligonucleotides with all possible 8-mers. Whereas FOXP1 preferentially recognizes the sequence GTAAACA, FOXP1-ES preferentially binds to AATAAACA and CGATACAA. These results suggest that alternative splicing of FOXP1 could regulate the activation of distinct transcriptional programs. Next Gabut and colleagues use two complementary approaches to determine whether FOXP1 and FOXP1-ES control different sets of genes. First, they deplete either FOXP1 or FOXP1-ES by RNA interference (RNAi) and then sequence the resulting transcriptome. This allows them to identify genes that increase or decrease expression upon disruption of a specific FOXP1 isoform. Additionally, the authors use chromatin immunoprecipitation experiments to identify where FOXP1 and FOXP1-ES bind in the genome. The

results of these experiments are striking. FOXP1-ES, but not FOXP1, enhances expression of many pluripotency genes, including OCT4, NR5A2, and NANOG, by directly binding to their promoters. Simultaneously, FOXP1-ES represses the expression of genes that control differentiation. These intriguing observations prompt the authors to investigate the role and requirement for FOXP1-ES in stem cell pluripotency and reprogramming. Increasing the expression of FOXP1-ES, but not FOXP1, prevents differentiation of mouse ESCs under conditions that promote efficient differentiation. Conversely, depleting FOXP1-ES, but not FOXP1, inhibits reprogramming of mouse embyronic fibroblasts via the activation of Oct4, Klf4, c-Myc, and Sox2. The article by Gabut and colleagues is a landmark study that shifts the paradigm for mechanisms regulating embryonic stem cell pluripotency and reprogramming in mammals. Instead of a transcriptional network being at the top of the hierarchy, an alternative splicing switch can now be placed upstream of this network, as FOXP1-ES activates expression of plu-

ripotency genes and represses expression of differentiation genes. Despite changing our understanding of the regulatory network controlling pluripotency and reprogramming, this work also raises many questions to be addressed in future studies. For instance, what controls the FOXP1-FOXP1-ES splicing switch? What splicing factors are responsible for flipping this switch, and how are their expression and activities regulated? Answering these questions is like hunting down the ‘‘chicken-or-the egg’’ paradox, but they will ultimately uncover the master regulator of stem cell pluripotency.

REFERENCES Chen, X., Xu, H., Yuan, P., Fang, F., Huss, M., Vega, V.B., Wong, E., Orlov, Y.L., Zhang, W., Jiang, J., et al. (2008). Cell 133, 1106–1117. Dasen, J.S., De Camilli, A., Wang, B., Tucker, P.W., and Jessell, T.M. (2008). Cell 134, 304–316. Gabut, M., Samavarchi-Tehrani, P., Wang, X., Slobodeniuc, V., O’Hanlon, D., Sung, H.-K., Alvarez, M., Talukder, S., Pan, Q., Mazzoni, E.O., et al. (2011). Cell 147, this issue, 132–146. Kim, J., Chu, J., Shen, X., Wang, J., and Orkin, S.H. (2008). Cell 132, 1049–1061.

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Nilsen, T.W., and Graveley, B.R. (2010). Nature 463, 457–463.

Nagy, A., and Wrana, J.L. (2010). Cell Stem Cell 7, 64–77.

Salz, H.K. (2011). Curr. Opin. Genet. Dev. 21, 395–400.

Silva, J., Nichols, J., Theunissen, T.W., Guo, G., van Oosten, A.L., Barrandon, O., Wray, J., Yamanaka, S., Chambers, I., and Smith, A. (2009). Cell 138, 722–737.

Samavarchi-Tehrani, P., Golipour, A., David, L., Sung, H.K., Beyer, T.A., Datti, A., Woltjen, K.,

Takahashi, K., Tanabe, K., Ohnuki, M., Narita, M., Ichisaka, T., Tomoda, K., and Yamanaka, S. (2007). Cell 131, 861–872. Wang, B., Weidenfeld, J., Lu, M.M., Maika, S., Kuziel, W.A., Morrisey, E.E., and Tucker, P.W. (2004). Development 131, 4477–4487.

Unweaving the Autism Spectrum Catherine Lord1,* 1Department of Psychiatry, Weill-Cornell Medical College, New York, NY 10605, USA *Correspondence: [email protected] DOI 10.1016/j.cell.2011.09.017

Although genes associated with human autism spectrum disorders have been identified, bridging the gap between genetics and the patchwork of behavioral deficits associated with the disease remains an enormous challenge. Pen˜agarikano et al. (2011) now show that mice lacking CNTNAP2, a gene that causes a rare form of epilepsy associated with autistic features and language impairment, display similar phenotypes to their human counterparts, raising hopes that such models may speed the identification of neuronal circuitries underlying the core features of autism. Disorders that affect behavior, including both psychiatric conditions and developmental disabilities, provide challenging opportunities and pitfalls for neuroscientists. In autism, a three-domain model describing deficits in communication, social interaction, and fixated or repetitive behaviors and interests has proven useful as a ‘‘grammar’’ to represent the nature of the deficits and to yield reliable diagnoses (Figure 1A). This model does not, however, necessarily reflect functional relationships between behaviors (Gotham et al., 2007). Such limitations underlie both the strengths and weaknesses of bold, integrative approaches such as those found in this issue in Pen˜agarikano et al. (2011), which reports a comprehensive and ambitious series of experimental behavioral, neuropathological, and neurophysiological studies of CNTNAP2 knockout mice. CNTNAP2, a gene on chromosome 7q35, is of particular interest because it has been shown to cause a rare form of epilepsy (Strauss et al., 2006). These patients have severe intellectual disabilities and, like most individuals with severe cognitive deficits, are described as having

features of autism. Although it likely accounts for fewer than 1% of cases of autism spectrum disorder (ASD) (Sanders et al., 2011), CNTNAP2 has also been associated with specific language impairment (SLI), which is characterized by difficulties with grammatical aspects of language acquisition in the absence of related causes such as hearing loss (Bishop, 2010). CNTNAP2 is also a downstream target of FOXP2, one of the first genes to have been identified as a cause of SLI. In common with some human CNTNAP2 patients, CNTNAP2 mutant mice have epileptic seizures and display impaired migration of cortical projection neurons, cortical neuronal synchrony, and numbers of GABAergic interneurons. To quantify the behavioral impact of these anatomical and electrophysiological defects, the authors assess knockout and wild-type mice for behaviors considered analogous to the three domains of autism. Standards for interpreting behavioral data in mouse models have become more sophisticated (Silverman et al., 2010). However, leaps made from findings to interpretations are still often substantial (Minshew and McFadden, 2011). Pen˜a-

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garikano et al. (2011) report multiple measures and address several confounding factors such as potential olfactory impairment and the effect of sedation. Most striking, though not obviously anticipated, is that the mutant mice have deficits across diverse contexts and domains. On average, mice lacking CNTNAP2 make fewer social approaches and engage in less vocalization and nesting. In contrast, perseveration, grooming, and digging (used to indicate repetitive behaviors) are enhanced, as are overall levels of activity. Treatment with risperidone, an atypical antipsychotic drug licensed for the treatment of autism, increases nesting and decreases grooming, perseveration, and hyperactivity. However, risperidone has no effect on social approach or vocalization. The authors propose that these specific responses to pharmacological intervention are likely a result of the behaviors being driven by distinct neural circuits. Though the idea that social deficits and repetitive behaviors in autism are separable on an anatomical level is appealing, the absence of any attempt to address functional relationships between these deficits within individuals in

Figure 1. The Changing Landscape of Autism (A and B) The three-domain model of autism in the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) (A), compared with the two-domain model of DSM-V (B).

analyses of multilevel behavioral data makes conclusions about differences in circuits across these domains very questionable. For example, juvenile play is treated as an independent measure of social behavior from nesting and grooming, a repetitive behavior, and is analyzed separately, when these behaviors seem unlikely to be independent measures. On a broader level, associations among language delay, SLI, and ASD, which account for much of the interest in CNTNAP2, are also much more complex than are typically acknowledged in genetic studies (Bishop, 2010; Vernes et al., 2008). This is, in part, because simple, less interesting explanations, such as associations between genetic findings and overall level of ability or severity of ASD, are often not ruled out (Hus et al., 2007). Language difficulties in autism, in which delays in comprehension and onset are common, are not the same as those found in specific language deficit, in which grammatical aspects of expressive language are most affected. Thus, overlapping findings regarding CNTNAP2 in autism and SLI are not necessarily evidence of direct links between particular behavioral deficits and specific genetic loci. As is the case for similar parallels between autism and schizophrenia or ADHD, more developmental or functional models are required. This is most relevant for the findings of Pen˜agarikano et al. (2011) because recent conceptualizations of ASD contain only

two autism-specific domains (social communication; fixated/repetitive behaviors and interests), with language delays and structural language deficits now treated as separate (though important) diagnoses that may or may not co-occur with ASD, as is the case for intellectual disabilities (http://www.dsm5.org) (Figure 1B). Social aspects of communication are considered within a single social communication domain. Fixated interests, over- or underreactions to sensory input, and simple repetitive behaviors continue to be categorized within a single domain. However, in contrast to the lumping together of social communication deficits, there is mounting evidence that different types of repetitive/restricted behaviors are no more related to each other than they are to social communicative deficits (Richler et al., 2007). If mouse models are justified in terms of the analogs that they provide to human behavior (Silverman et al., 2010), the evolving specification of the nature of autism in humans must be considered. There is strong evidence that different genes are associated with ASD, but in almost all instances, these findings as yet have no clinical implications. Whereas standards in statistical genetics and for the biological aspects of the genetics are high, standards in genotype-phenotype studies for clarity in what is hypothesized, what is treated as a replication, and how different findings are considered in light of one another often seem comparatively low. Pen˜agarikano et al. (2011)

represents some of the most impressive research in this area through its attempts to integrate genetics, behavioral studies, and neural function and anatomy. It also highlights the need for more complex analyses, as well as for restraint in making claims about different behaviors that are not as simple or as distinct as represented. REFERENCES Bishop, D.V.M. (2010). Behav. Genet. 40, 618–629. Gotham, K., Risi, S., Pickles, A., and Lord, C. (2007). J. Autism Dev. Disord. 37, 613–627. Hus, V., Pickles, A., Cook, E.H., Jr., Risi, S., and Lord, C. (2007). Biol. Psychiatry 61, 438–448. Minshew, N., and McFadden, K. (2011). Autism Res. 4, 1–4. Pen˜agarikano, O., Abrahams, B.S., Herman, E.I., Winden, K.C., Gdalyahu, A., Dong, H., Sonnenblick, L.I., Gruver, R., Almajano, J., Bragin, A., et al. (2011). Cell 147, this issue, 235–246. Richler, J., Bishop, S.L., Kleinke, J.R., and Lord, C. (2007). J. Autism Dev. Disord. 37, 73–85. Sanders, S.J., Ercan-Sencicek, A.G., Hus, V., Luo, R., Murtha, M.T., Moreno-De-Luca, D., Chu, S.H., Morrow, M.P., Gupta, A.R., Thomson, S.A., et al. (2011). Neuron 70, 863–885. Silverman, J.L., Yang, M., Lord, C., and Crawley, J.N. (2010). Nat. Rev. Neurosci. 11, 490–502. Strauss, K.A., Puffenberger, E.G., Huentelman, M.J., Gottlieb, S., Dobrin, S.E., Parod, J.M., Stephan, D.A., and Morton, D.H. (2006). N. Engl. J. Med. 354, 1370–1377. Vernes, S.C., Newbury, D.F., Abrahams, B.S., Winchester, L., Nicod, J., Groszer, M., Alarco´n, M., Oliver, P.L., Davies, K.E., Geschwind, D.H., et al. (2008). N. Engl. J. Med. 359, 2337–2345.

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Leading Edge

Essay A Blueprint for Advancing Genetics-Based Cancer Therapy William R. Sellers1,* 1Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA *Correspondence: [email protected] DOI 10.1016/j.cell.2011.09.016

In the era of next-generation sequencing, there are significant challenges to harnessing cancer genome information to develop novel therapies. Key research thrusts in both academia and industry will speed this transition, and lessons learned for cancer will more broadly shape the process for genetic contributions to the therapy of disease more broadly. Introduction Breakthrough advances in the treatment of medical illness require the elucidation of the pathogenic mechanisms initiating and driving disease states. Cancer is largely a disease of the genome, and through recent technological advances, we are now able to separate the ‘‘diseased’’ cancer genome from the normal genome. As a consequence, the next decade should see dissection of disease-relevant somatic mutations and the completion of the ‘‘pathogenetic’’ landscape of cancer, paving the way for further therapeutic development. The discovery of key pathogenetic cancer alterations has already transformed the treatment of specific cancer types. The introduction of all-trans retinoic acid to the treatment of acute promyelocytic leukemia harboring translocations into the RARa gene has led to curative responses in the majority of patients (Huang et al., 1988). Treatment of chronic myelogenous leukemia, bearing the BCRABL fusion gene, with imatinib, an inhibitor of the Abelson kinase, has led to a staggering 80% decline in disease mortality (Figure 1). More recently, inhibitors targeting EGFR mutations in adenocarcinoma of the lung, BRAF mutations in melanoma, and ALK translocations in lung cancer highlight the potential to alter the course of previously untreatable disease (reviewed in Haber et al., 2011). These encouraging results strongly suggest that the next step toward the goal of curing cancer is to more fully exploit this genetic-therapeutic strategy. Though access to ever-increasing amounts of sequence information holds

great promise, there are at least five significant barriers to this goal. First, we do not yet have a complete picture of the genetic alterations comprising the disordered cancer genome. Second, the discovery of specific mutations that lead to cancer, so called ‘‘driver’’ mutations, is too often accompanied by an inability to create a relevant therapeutic molecule. In other words, there is a ‘‘druggability gap.’’ Third, most cancers, particularly those that are late stage, are genetically heterogeneous or capable of rapid genetic evolution. Either scenario can lead to the rapid emergence of therapeutic resistance. Fourth, as a corollary to the problem of resistance, combination therapy will be required to achieve therapeutic cure or long-lasting disease control. Finally, the ability to make more definitive predictions about clinical therapeutic outcome is severely limited by the lack of robust preclinical disease models. ‘‘Completing’’ the Human Cancer Genome The daunting complexity of the human cancer genome is amply illustrated by the emerging fully characterized cancer genomes. For example, a recent study looking at prostate cancer identified substantial variation between the patient tumors (Figure 2) (Berger et al., 2011). This complexity appears both as the diversity of genetic alterations across many cancer types and samples and the complexity within single tumors, in which the background mutation frequency (‘‘carrier’’ mutations) often exceeds the frequency of ‘‘driver’’ mutations. Thus, in order to distinguish the critical genetic

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alterations of therapeutic interest, repetitive mutations or repetitively mutated genes across many samples from individual tumor types must be identified and correlated with disease in a relevant manner. One can envision a broader goal requiring larger sample sets whereby we would define not only the cancer gene alterations, but also the cancer gene interactions. As examples, early efforts to describe the glioblastoma genome identified three core interacting genetic pathways in this disease (The Cancer Genome Atlas Research Network, 2008), whereas low-frequency single-gene mutations appear to, in aggregate, target the NFKB pathway in myeloma (Chapman et al., 2011). Therefore, the vision of ‘‘completeness’’ is to characterize cancer genomes at a sufficient scale so that genetic interactions can be used to directly define the key functional cancer pathways and potentially the therapeutic targets. To this end, the goal should be to describe three interaction attributes beyond single-gene mutation frequency, specifically: (1) to identify those genetic alterations that co-occur at a greater frequency than by chance alone and are hence cooperative, (2) to identify those genes in which alterations anticorrelate and thus provide a similar function or where co-mutation is incompatible with cancer cell progression, and (3) to identify genes whose coding sequence is notable for the absence of background mutations. In this latter case, the absence of background loss-of-function mutations could potentially define genes whose function is absolutely required for cancer cell

targets, it is likely that these resources will be of more value than the screening platforms themselves. In industry, time-dependent pressure to deliver molecules ready for clinical trials drives early-stage drug discovery efforts focused on tractable targets, including those with often tenuous links to disease pathogenesis over those focused on difficult targets with incontrovertible disease linkage. Perversely, this can reward a scenario of costly late-clinical failure based on the invalidation of the therapeutic hypothesis From Genetics to over a scenario of early-reTherapy: Bridging the search failure based on drugDruggability Gap gability. Notably, the current The identification of protein biotechnology venture-based kinases, activated by somatic funding model suffers from mutation, has led the recent Figure 1. CML Mortality Has Declined in the United States, and the the same short-term reward Annual Incidence Is Unchanged wave of therapeutic breakShown are the estimated US-based CML incidence and mortality rates for the structure. Somehow, a prothroughs outlined above. In years 1997, 1998, and 2000–2011. These data were abstracted from the portion of the biopharmasharp contrast is the realizaannual cancer statistics publications published by CA: A Cancer Journal for ceutical drug discovery retion that most of the known Clinicians’ in the years 1997, 1998, and 2000–2011 (Siegel et al., 2011). source must be dedicated critical oncogenes and tumor suppressors remain beyond the reach between BH3-containing proteins and over the longer term to the discovery of of current therapeutic modalities. Se- BCL2 family members belie the notion compounds that break new ground quencing projects have reaffirmed TP53 that such PPI targets are inherently intrac- against the most challenging of targets. To expand these efforts on the small moleas the most commonly mutated tumor table (Oltersdorf et al., 2005). Further broadening of the scope of cule front for particular targets, we need suppressor gene and the RAS oncogenes (K-RAS and N-RAS) as key drivers of druggable targets can be achieved research funding mechanisms in acadea variety of devastating cancers. Yet, the through a number of parallel mechanisms. mia or industry that are compatible with elucidation of p53 and RAS biology has In the arena of ‘‘typical’’ synthetic low- the 5–10 year timescale required for tacknot been coupled with major revolutions molecular weight molecules, we will ling difficult drug discovery problems. Alternative therapeutic modalities, in therapeutics based on these genetic benefit from increases in diversity and alterations. Similarly, the discovery of scale of available small-molecule li- including therapeutic siRNA, intracellular key oncogenic transcription factors, braries. In academia, renewed interest in peptide therapies, and gene therapy, including MYC, NMYC, ERG, and others, direct participation in drug discovery hold promise for a broad attack on the has not been paralleled by the develop- has enabled establishment of modest- druggability gap but are all beset by a ment of transcription factor inhibitors. To scale low-molecular weight screening common issue: difficult drug delivery. address this druggabilty gap, it is first facilities. Such efforts may focus on The challenge to the delivery of theranecessary that we challenge the notion phenotype-oriented drug discovery, and peutic siRNA remains essentially unof what is ‘‘undruggable’’ so that drug hopefully breakthroughs against difficult changed from that faced by antisense targets are not rejected outright based targets will follow. But centers must be oligonucleotides. The molecules are large on prior assumptions (or past failures). bolstered with robust medicinal chemistry (>10,000 MW) and highly charged, making Similar prejudices led to the premature resources, or it will become difficult to distribution across biological membranes conclusions that kinases and other ATP- make advances beyond the relatively problematic (Shim and Kwon, 2010). To utilizing enzymes were themselves un- weak nonspecific inhibitors typically iden- realize the promises for these novel theradruggable. The development of drugs dis- tified through screening. The most chal- peutic classes, a renewed focus on rupting protein-protein interactions (PPI) lenging drug discovery projects (e.g., systematic and robust approaches to the between IAPs and SMAC (Sun et al., PPIs) typically require structure-based delivery problem is warranted. In the short-term, certain key cancer 2004), between p53 and HDM2 (Vassilev guidance (X-ray and NMR) and an investet al., 2004), and, most notably, along ment in biophysical analysis platforms. genes are likely to remain relatively difficult the remarkably extended interface Indeed, in tackling challenging drug to drug. This is a notable problem for the survival. This so-called class of ‘‘never-mutated’’ genes will likely be conditioned by driver genetic events and could be an important new class of ‘‘conditional lethality’’ genes particularly relevant to therapeutic development. Advances in sequencing technology have created the opportunity to achieve these goals; however, high-priority efforts must now be made to address the remaining ratelimiting steps of tumor sample acquisition, data handling, and data analysis.

Cell 147, September 30, 2011 ª2011 Elsevier Inc. 27

Figure 2. The Chromosomal Rearrangements Found in Seven Prostate Cancer Genomes Copy number alterations are represented by the colors of the chromosome segments depicted along the inner ring (red, copy gain; blue, copy loss). Intrachromosomal rearrangements and interchromosomal translocations are shown with green and purple lines, respectively Reprinted by permission from Macmillan Publishers Ltd: Nature 470, 214–220, copyright 2011.

tumor suppressor genes, in which the gene product is often completely absent. Here, the hope is to exploit the concept of ‘‘synthetic lethality’’ or ‘‘conditional lethality’’ in order to define key therapeutic targets whose requirement for the maintenance of the cancer state is conditioned by a specific genetic event (Kaelin, 2005). Such conditional lethality nodes can be conceived of as existing within a pathway downstream of an oncogene or tumor suppressor or in a parallel pathway. In the case of downstream conditional lethality, preclinical and clinical proof of concept has been obtained from the inhibition of the SMO receptor downstream of tumor suppressor mutations in the PTCH gene (Von Hoff et al., 2009), from the inhibition of mTORC1 downstream of germline mutations in the TSC genes (Krueger et al., 2010), and from the inhibition of MEK downstream of mutant oncogenic BRAF in melanoma (Solit et al., 2006). In the case of parallel pathway conditional lethality, clinical proof of concept has been obtained from the inhibition of PARP1 in the context of BRCA1 loss-of-

function mutations in breast cancer (Farmer et al., 2005; Fong et al., 2009). In each case, the key finding is that mutantbearing tumors are far more susceptible to the relevant inhibitor than either nonmutant tumors or the normal host tissue. The tools for further discovery of such conditional lethal nodes are only just becoming available with improvements in larger-scale shRNA libraries. However, the robust realization of such discoveries remains plagued by the noise inherent in larger-scale shRNA screens and, in some cases, the overreliance on screens in isogenic cell line pairs that can often lead to cell line-specific hits. In this regard, the alternative approach of cell line panels should be considered (Brough et al., 2011). An investment in the development of highly validated shRNAs for each human and murine gene could go a long way in helping to reduce the notion of conditional lethality to practical discovery. The Development of Resistance The evolutionary nature of cancer and its mutable genome makes emergent thera-

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peutic resistance a serious and often unnerving problem. No doubt this is a major hurdle in moving from therapeutic efficacy to curative cancer therapy. Though there has been a notable lack of progress in defining clinically relevant mechanisms of resistance to classical cytotoxics, more rapid progress has been seen in understanding resistance to genetically directed targeted therapeutics. In particular, it is clear that, in the latter case, the preclinical discovery of resistance mechanisms can be directly predictive of the resistance features seen in patients. A first example was the elucidation of mechanisms of resistance to imatinib, in which the preclinical definition of resistance alleles through a random mutagenesis and selection process was, in retrospect, highly correlated with resistance alleles uncovered in patient samples (Azam et al., 2003; Shah et al., 2002). Similarly, clinically relevant mechanisms of resistance to EGFR, SMO, and RAF inhibitors have been uncovered through a variety of preclinical studies employing the relevant addicted cell line or animal

models. In parallel, the application of next-generation sequencing technologies to the discovery of very low-abundance pre-existing resistance alleles can allow confirmation of preclinical discoveries prior to treatment with relevant inhibitors. The utility of predictive preclinical resistance studies together with the advent of deep sequencing raise the possibility that resistance mechanisms could be identified before the clinical trials of novel therapeutics begin. In fact, ideally such an approach would be used to guide improvements in the first-generation inhibitors during lead optimization. For example, choices among distinct small-molecule scaffold classes with alternate binding modes to a target of interest could be prioritized based on the frequency and propensity of resistance mechanisms and upon which resistance alleles are detectable in patients prior to treatment. Presumably, this approach could accelerate the generation of bestin-class targeted therapeutics. The rapid emergence of resistance during targeted therapy has raised the specter of an endless chase of resistance alleles, with ever more specific inhibitors dealing with an increasing complex spectrum of resistance alleles and nongenetic tumor-adaptive responses. Though this so-called ‘‘whack-a-mole’’ approach can lead to the rapid clinical development of second-generation or third-generation inhibitors applied in mutation-specific settings, this will not be the ultimate strategy for achieving curative therapy. Rather, defining the emergent principles of resistance should be used to both (1) guide improvements to the first-line targeted therapeutics to gain-greater efficacy and (2) elucidate rationale combinations based on the understanding of escape mechanisms. What can we say about the principles of resistance to targeted therapeutics that are now evident from the study of ABL, EGFR, KIT, BRAF, and SMO inhibitors? The common theme is a fairly remarkable and consistent finding of persistent target and pathway addiction. Given the complexity of the cancer genome and the well-recognized mutability, one might have imagined that hundreds of distinct resistance mechanisms would have emerged in response to any given targeted therapy. Instead, the observation is that of a consistent pattern of resistance

mechanisms, acting in large part to restore the activity of the original ‘‘addicting’’ pathway. In the case of imatinibbased treatment of CML, the great majority of resistance alleles are found in the ABL kinase domain itself. Based on these data, ABL inhibitors capable of both directly suppressing such mutations and more potently inhibiting the wild-type kinase have substantially improved the molecular response rates (Kantarjian et al., 2010; Saglio et al., 2010). Lung adenocarcinomas bearing activating mutations in EGFR show dramatic response to catalytic EGFR inhibitors but relapse through direct mutations in the EGFR kinase domain or through coamplification of MET (Kobayashi et al., 2005; Pao et al., 2005). In both instances, a common feature is the restoration of downstream signaling through phosphoinositide-3 kinase. Based on these data, second-generation inhibitors, novel combinations with MET inhibitors and with PI3K inhibitors, are in development. Melanomas bearing activating mutations in BRAF similarly manifest significant responses to BRAF or MEK inhibitors, and through heterogeneous mechanisms, escape is manifest largely by reactivation of the MEK-ERK cascade. Finally, the study of resistance to the androgen receptor pathway inhibitors in prostate cancer for two decades has focused on so-called androgen-independent means of resistance. The recent clinical success of CYP17 inhibitors (Attard et al., 2008) and novel AR antagonists (Scher et al., 2010) has shown, however, that such ‘‘androgen-independent’’ tumors remain dependent on both AR and AR ligands. In all, the emerging data from these examples and others strongly support the notion that cancers remain highly dependent on these initial dominant oncogenic pathways and support the elaboration of improved inhibitors or of ‘‘vertical’’ pathway combinations, where inhibitors target the same pathway, as at least one mechanism by which we can improve the first-line treatment of cancer. Such combinations are also likely to be effective in the case of nongenetic or adaptive resistance mechanisms involving pharmacologic activation of homeostatic feedback loops exemplified by the finding of RTK activation induced by AKT inhibition (Chandarlapaty et al., 2011).

Toward the Discovery and Development of Curative Combinations The majority of curative cancer treatment regimens involve multidrug combinations. The emergence of therapeutic resistance together with the frequent incomplete response to primary therapy underscores the importance of developing novel highly efficacious combinations. Here, we face substantial challenges in both the research arena and the clinic. The preclinical discovery of combination therapies is significantly limited by experimental throughput and by the lack of a consistent understanding of which measures of preclinical combination activity might be predictive of clinical combination activity. The throughput of combination profiling in vitro might be solved through automation; however, animal costs rapidly become prohibitive if systematic in vivo combination testing is required. Measures of combination effect, i.e., the interaction between two drugs to produce a cellular outcome, have become increasingly sophisticated, yet there is little understanding of how such improved methods will relate to the clinic. Efforts to solve this latter problem are urgently needed. The clinical challenge is to move beyond the paradigm that requires the testing of new agents in combination with existing standard of care therapies (SOCs). This incremental strategy ignores the possibilities that novel and existing agents might be antagonistic and that two novel agents might be more effective as a combination without an SOC component. Fortunately, recognition that this paradigm is inadequate has led to early phase II clinical trials in which two novel agents are being tested in combination prior to the full demonstration of single-agent activity. Notably, the observation that the treatment of RASdriven tumors may require blockade of both the PI3K and RAS pathways (Engelman et al., 2008) has led to multiple trials of the ‘‘horizontal’’ combination of AKT or PI3K inhibitors in combination with MEK inhibitors. In addition, the ‘‘vertical’’ combination of MEK and RAF inhibitors is being tried in melanoma (Infante et al., 2011). If future results of such phase II trials were truly distinctive, including high partial and perhaps complete response rates in relatively unresponsive cancers, such combinations might be directly

Cell 147, September 30, 2011 ª2011 Elsevier Inc. 29

submitted as new drug applications or might proceed to phase III trials comparing new combinations against the accepted standard of care. The Development of Preclinical Disease Model Systems Until recently, preclinical therapeutic profiling was typically confined to the analysis of a handful of human cancer cell lines. As such, preclinical testing is conducted on fewer cancer cell lines than the number of human patients treated in even exploratory trials. Under such conditions, it is unreasonable to expect either robust preclinical prediction of the clinical utility of a candidate therapeutic or the preclinical discovery and validation of predictive biomarkers. In simple terms, the test sets are too small. Ideally, preclinical models would be of sufficient robustness and used at sufficient scale so that clinical failure would be reduced to zero. The complexity of human cancer makes it unlikely that this landmark will be attained; nonetheless, progress toward this goal will be of unquestionable benefit. The ‘‘required’’ elements of a robust preclinical infrastructure include molecularly defined model systems that are directly reflective of their human counterparts and sufficient model numbers for one to approach the disease diversity found in humans. Additionally, the preservation of stromal-epithelial interactions, in particular those occurring through ligand-receptor pairs, along with the ability to study cancer in the setting of a functioning immune system, are vitally important. Lastly, the ability to replicate disease progression and to examine sufficient intratumoral heterogeneity to enable the study of resistance are desirable for such an infrastructure. It is selfevident that no single class of preclinical models will satisfy such requirements. Moreover, each model system will have distinct advantages and weaknesses. Thus, the monolithic view that there is a single best system for the preclinical study of therapeutic effect is naive. It is reasonable, therefore, to take a multipronged approach with the paramount commonality that each model is related to human cancer through detailed molecular characterization. The in vitro study of cancer cell lines is the only current method for characterizing

therapeutic effect across hundreds of representative disease models derived from bona fide human tumors. The artificial nature of cell culture systems has many limitations, including a limit to the spectrum of human cancer that can be adequately represented. Nonetheless, cell-autonomous growth inhibitory effects can be robustly studied in many instances. Prior efforts at systematic profiling, including the NCI60, were limited by the small sample size and the lack of molecular characterization. Two efforts to rectify this deficit have now characterized both the molecular constituents and drug sensitivity of many hundreds of cell lines. The Cancer Cell Line Encyclopedia project (http:// www.broadinstitute.org/ccle) has completed the expression profiling, the copy number analysis, and the exon sequencing of 1600 genes across 949 cell lines available through commercial sources. The MGH-Sanger cell line project (Genomics of Drug Sensitivity in Cancer; http://www. cancerRxgene.org) has completed the expression and copy number analysis of 800 cancer cell lines, as well as exon sequencing of 65 genes commonly mutated in cancer. Full genomic exome sequencing will be completed by the end of 2011. These genomic parameters are being correlated with drug responses to a panel of 135 anticancer small molecules. Renewed interest in studying human tumors without the requirement for in vitro growth has led to the development of primary human tumor explants for propagation in immunocompromised animals. This approach allows for the development of tumor models characterized by molecular alterations that appear difficult to maintain during in vitro growth. In addition, the function of key developmental pathways (e.g., the Hedgehog pathway) are much more likely to be preserved. Finally, the role of the murine stroma in supporting the growth of human cancers can in some instances reflect the human stromal response. For example, in pancreatic cancers, tumor production of Hedgehog ligands leads to stromal Hhpathway activation. In turn, inhibition of stromal Hh pathway activation by SMO inhibitors leads to an antitumor response (Yauch et al., 2008). Genetically engineered mouse (GEM) models have the intrinsic advantage of preserving stromal-to-epithelial interac-

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tions and a competent immune system. GEM models are increasingly reflective of the genetic alterations seen in some human cancers and likely harbor a high degree of genetic heterogeneity such that relevant resistance mechanisms can be rapidly identified (Buonamici et al., 2010). These systems remain limited by the necessity of staging large numbers of mice for even rudimentary preclinical trials, and hence transplantable versions of GEM-derived tumors might provide a more tractable approach. Finally, the rapid advances in nextgeneration sequencing throughput make it worth revisiting spontaneous or carcinogen-induced murine cancers, along with cancers arising in mice with targeted disruption of DNA repair pathways. Here, tumors can be collected in large numbers, and as sequencing costs drop, the full complement of genetic alterations will be understood. Such models could then be accepted or rejected as representatives of human disease based on a genetic comparison. This might allow the rapid development of a large array of genetically complex transplantable murine tumors. The growing evidence that preclinical models, when used in robust numbers and when accurately characterized, can be helpful in guiding clinical development should renew investment in building these models as a commonly available resource for wider-spread use. Implications for Therapeutic Development in Nononcologic Diseases The treatment of the majority of noninfectious medical illness remains largely based on phenotype. For example, diabetic therapy is largely confined to the lowering of blood glucose, the most readily measured ‘‘phenotype’’ of this complex disease. The progress toward understanding genetic mechanisms of disease has been notable in those medical illnesses caused by Mendelian inherited genetic alterations; however, progress in defining causally associated genetic variants has been slower. Limitations to progress in the genetic definition of medical disease have come in two forms: (1) the need to more precisely define distinct subdisease phenotypes so that genetically diverse diseases are not admixed and (2) an incomplete ability

to analyze genetic variation, including rare variants across large sample sets. As is the case for cancer, we can see the near-term end of the second roadblock. And with sufficient attention to the first, the future discovery of new genetically defined pathways is likely. The discovery of complement pathway genetic variants as the largest attributable factor in the pathogenesis of macular degeneration is emblematic of the transformation that awaits (Edwards et al., 2005; Haines et al., 2005; Klein et al., 2005). Following the definition of diseasecausing genetic variants, the same challenges seen in cancer are likely to arise. First, the druggability gap will pose a substantial challenge. As an example, the identification of genetic alterations in hemoglobin has not led to a major advancement in medical therapy of sickle cell anemia and is reflective of the same difficulty faced in treating loss-of-function mutations in tumor suppressor genes. Though therapeutic resistance through a hypermutable genome is unlikely to be a common theme in nononcologic medical disease, adaptive resistance through pathway and endocrine remodeling likely suggests that combination therapy of medical illness will be essential. Lastly, though cancer models are not ideal, the situation in the medical disease area is far worse. For most commonly used therapeutic models (collageninduced arthritis, chemical-induced fibrosis), we have little to no evidence that they mimic and/or reflect the human disease. Furthermore, in most cases, only one model system is used at all. Again, the likelihood that this testing paradigm will be predictive of human therapeutic success is low. A key challenge in these areas is to take emerging genetic data derived from patient-based studies and create models (likely starting with GEMs) based on the most relevant genetic alterations both alone and in combination. The completion of the human genome project provided a roadmap to the eventual understanding of disease genomes. Genetic drivers of disease are now being elucidated and will allow us to transform the treatment of human illness. Though substantial, the hurdles to fully realizing this transformation can be overcome to drive the next stages of innovation and investment.

ACKNOWLEDGMENTS I am most grateful to my colleagues Levi Garraway, Chatanika Stoop, and Mariellen Gallagher for their critical comments and many suggestions. W.R.S. is an employee and shareholder of Novartis Pharmaceuticals.

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Cell 147, September 30, 2011 ª2011 Elsevier Inc. 31

Leading Edge

Perspective Clan Genomics and the Complex Architecture of Human Disease James R. Lupski,1,2,3,* John W. Belmont,1,2 Eric Boerwinkle,4,5 and Richard A. Gibbs1,5,* 1Department

of Molecular and Human Genetics of Pediatrics Baylor College of Medicine, Houston, TX 77030, USA 3Texas Children’s Hospital 4Human Genetics Center University of Texas Health Science Center at Houston, Houston, TX 77030-1501, USA 5The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA *Correspondence: [email protected] (J.R.L.), [email protected] (R.A.G.) DOI 10.1016/j.cell.2011.09.008 2Department

Human diseases are caused by alleles that encompass the full range of variant types, from singlenucleotide changes to copy-number variants, and these variations span a broad frequency spectrum, from the very rare to the common. The picture emerging from analysis of whole-genome sequences, the 1000 Genomes Project pilot studies, and targeted genomic sequencing derived from very large sample sizes reveals an abundance of rare and private variants. One implication of this realization is that recent mutation may have a greater influence on disease susceptibility or protection than is conferred by variations that arose in distant ancestors. Genetic Contributions to Disease Common chronic diseases such as diabetes, coronary heart disease, stroke, neuropsychiatric illness (including schizophrenia, autism, and developmental disabilities), chronic respiratory disease, and cancer account for an overwhelmingly large fraction of mortality, morbidity, and health care expenditure (http://www.cdc.gov/nchs/). These diseases disproportionately affect aging populations and burden the health care systems and economies of industrialized nations throughout the world. Understanding the underlying causes of such disorders is a key step toward enabling earlier and more precise diagnosis, prognosis, interventional therapy, and potentially prevention. Most common diseases are complex or multifactorial with both environmental and genetic contributions along with their nearly intractable interaction effects. In general, the environmental components are challenging to identify and quantitate. In contrast, as a result of the emergence of powerful genomic technologies, the analysis of the genetic components is becoming increasingly tractable and relatively inexpensive to investigate. These technical improvements have fueled a pipeline of discovery of the genes and variants that predispose to human maladies. The technical improvements have also impacted genetic diagnostics, as it is now practical to sequence an entire individual’s genome for less than the cost of a comprehensive set of whole-body imaging scans. Furthermore, the cost of whole-genome sequencing is rapidly becoming less expensive than current clinically implemented ‘‘multigene panel testing’’ for molecular diagnosis of disease traits with even modest genetic heterogeneity. Allele Frequency Distributions and Human Disease A ‘‘common disease/common variant’’ (CDCV) hypothesis has been popularized as an explanation for common disorders and 32 Cell 147, September 30, 2011 ª2011 Elsevier Inc.

has garnered much support (Reich and Lander, 2001). This model presupposes that different combinations of common alleles aggregate in specific individuals to increase disease risk. The CDCV hypothesis was a major intellectual impetus for the International Haplotype Mapping (HapMap) project and ultimately led to a proliferation of genome-wide association studies (GWAS) identifying regions influencing disease status or risk factor levels (http://www.genome.gov/gwastudies). As a consequence, insights into potential new pathways underlying common disease have emerged. Manolio et al. (2009) suggested that the genetic variance explained per se is of interest for what it might suggest about effective research paradigms. Accounting for the genetic variance is not the same as achieving utility and impact. If the goal of our shared research program is to achieve mechanistic understanding and lessen the impact of human disease, then the magnitude of the genetic effect is less important than the possible insight provided by the newly identified loci. Genes identified through their weakly acting common alleles may give important clues about pathways and/or be excellent targets for therapeutic and preventive strategies. However impressive the information gleaned from GWAS has been, the results have explained only a few percent of the apparent genetic variance contributing to common diseases. Furthermore, these studies have not yet delivered medically actionable variants that inform medical decision making by helping to establish an etiological diagnosis and lead to a more efficacious treatment or prevention plan. Both diagnostic utility and classification of pathogenicity are closely associated with the magnitude of each variant-specific effect. For most complex diseases, unless a variant clearly partitions the affected into distinct biological subgroups or can be incorporated into

risk-prediction models, there is likely to be limited diagnostic usefulness. Morrison et al. (2007) proposed the use of a composite ‘‘genetic risk score’’ for risk assessment. However, it remains to be determined whether variants identified by GWAS have a role as biomarkers in risk assessment and clinical decision making. Overall, these data do not support a simple additive version of the CDCV model as an explanation for the majority of the genetic component underlying risk for common disease. Research efforts have shifted to exploration of less frequent variants in common disorders. It has been noted for decades that the mutational changes that underlie rare and highly penetrant Mendelian disease may share features with genetic factors that underlie more common forms of the disease (Boerwinkle and Utermann, 1988; Goldstein and Brown, 2001). Clearly, the relationship between rare and common disease is not a simple one, but there are emerging examples wherein specific loci that cause ‘‘Mendelian disease’’ are contributing to the background risk to a parallel common disorder. Although a common disease/rare variant (CDRV) hypothesis is attractive, it demands tenable and complete explanations as to how the functional roles of individual alleles can work to produce the ultimate phenotypic effects. The models must consider the range of variant types, including single-base or simple-nucleotide variants (SNV), short insertions or deletions (indels), structural variants, and copynumber variants (CNV), the penetrance of individual alleles, and allelic and locus interactions (dominance and epistasis, respectively) and show how these all combine to produce the population frequency and the phenotypic complexity of different disorders. Fortunately, the current state of knowledge of key examples supports models that close the gap between complex and Mendelian traits. These examples show how mutations in single genes can fulfill the definition of Mendelian disease—but in different context are parts of the menu of causal contributors to complex disorders (Greeley et al., 2010; Voight et al., 2010). As we begin to observe instances wherein variation at more than one locus contributes to perturbations of networks and ultimate phenotype, the relevance of assessing genome-wide variation becomes more apparent. Thus, inferences about individual mutation burden by geneticists in the last century are now open to direct observation (Muller, 1950). Variation and Disease Susceptibility—We Are All Truly Unique The interplay between different types of variation and their contribution to disease are highly dependent on our understanding of the normal patterns of genetic variation. For rare variants, this has been a particular challenge, as highly accurate data need to be generated from many samples in order to properly determine the frequency and population distribution of the genetic variants. To illustrate this: the successful HapMap project (International HapMap 3 Consortium, 2010; International HapMap Consortium, 2005; Frazer et al., 2007) that provided an early survey of single-base variation across major human populations cataloged only a fraction of the genetic variation above a frequency of 5%. Even the 1000 Genomes Project pilot studies comprehensively captured only variation at greater than 1% frequency (1000 Genomes Project Consortium, 2010).

Our view of the site frequency spectrum of these rare variants (<1%) has been more influenced recently as a result of the generation of personal genome data using whole-exome sequencing and whole-genome sequencing (Gonzaga-Jauregui et al., 2011). The number of diploid human genome sequences available for analyses is growing rapidly. Remarkably, from the small number determined and publicly available to date, it is apparent that even more genetic variation exists between individuals than was previously expected (Ahn et al., 2009; Bentley et al., 2008; Kim et al., 2009; Levy et al., 2007; Lupski et al., 2010; Schuster et al., 2010; Wang et al., 2008; Wheeler et al., 2008). When compared with the haploid reference, each individual human genome on average contains some three and a half million SNV and about 1,000 CNV (>450 bp) (Conrad et al., 2010), many of which appear to be rare in the population from which the individual was sampled. In addition, each individual personal genome sequence still reveals 200,000–500,000 SNV that have not been observed in other publicly available personal genomes, many of which may be unique to that individual’s family or clan. In parallel, recent studies that deeply sequence relatively large samples (hundreds to thousands of individuals) show that the rate of identification of variants that have not been seen (private variants) continues unabated with every new individual sampled (Coventry et al., 2010; Turner et al., 2008). The extent of some of this nucleotide variation may have been anticipated from human genetic studies during the previous three decades that established that an SNV occurs about every 1 Kb; however, the extent of rare and ‘‘private’’ SNV was not anticipated, and the extent of CNV was unexpected. There are technical limitations to some of these studies—including a background of variation introduced in cultured cells—as well as in the mutation detection methods themselves, particularly for CNV in the 100 bp to 500 bp range, low-copy repeat sequences, and simple repeats. Nevertheless, the enormous extent of private variation has been clearly established. Rare Variants and New Mutation A number of factors may have led to the observed skewing of the allele frequency spectrum toward rare and private variants. The explosion of human populations in the current historical epoch could, by itself, account for the short branch lengths and low frequencies of the most distal segments of human variant genealogies (Boyko et al., 2008; Coventry et al., 2010; Turner et al., 2008). In addition, secular factors that have enabled the explosion of the population, such as abundant food supplies, improved sanitation, and routine vaccinations, may directly participate in the relaxation of the most important selective pressures that have constrained the population in the past. Even the widespread availability of minimal routine health care may be artificially slowing negative selection. Dramatic reductions in maternal death and infant mortality, properly celebrated in the last 100 years, may be influencing the distribution of genetic variation and contribute to relaxed selection. Finally, mutation rate, perhaps partially driven by increased paternal age (Crow, 2008) and undiscovered environmental factors, may contribute to the observed rare variant spectra. The conceptual shift to emphasizing studies of abundant, rare, and heterogeneous variants profoundly impacts our approaches Cell 147, September 30, 2011 ª2011 Elsevier Inc. 33

to studying the genetic architecture of human disease, leading to a genome-wide, versus a locus- or gene-specific, emphasis. Genetic architecture here refers to the types of variation (SNV, CNV, etc., both coding and noncoding), their allele frequency distribution (common, rare, intermediate), the size of an allele’s effects, and new mutation rates. For a given individual, what is important to know is not only the number and location of pathogenic variants taken one at a time but also the unique composition of his or her genome-wide mutational burden. If this is the case, then the risk conferred by any particular allele estimated from the population risk would be much less relevant than the personal risk emerging from the total mutational burden in each individual. The shift in emphasis to a whole-genome view changes how we should consider the way in which harmful combinations of mutant alleles assemble or accumulate in each genome. Each personal genome has a collection or ‘‘ecology’’ of deleterious and protective variations, which in combination (not necessarily in sum) dictate the health of the individual. Understanding this genome ecology will be a substantial challenge in human genetics and has ramifications for the extent to which genetic information can be maximized for medical utility. Each personal genome combines inherited alleles and new variation introduced by de novo mutation. Interestingly, CNV may contribute in a significant way, from both the novel combinations inherited from each parent and the new mutations. This is the very type of variation not fully taken into account when previous mutation models were being considered. Locusspecific mutation rates for SNV are 2.0–2.5 3 108 and have recently been shown to potentially differ in male versus female germ cells (Conrad et al., 2011); for CNV, new mutation rates can be substantially higher: between 106 and 104, 100 to 10,000 times more frequent than in SNV (Lupski, 2007a). The latter figures implicate CNV in sporadic traits (Lupski, 2007a) including birth defects (Lu et al., 2008) and highlight the contribution of new mutation to individual mutational burden (Potocki et al., 1999). Either new or recent (i.e., arising in close relatives or ‘‘clan members’’) de novo mutations could substantially contribute to phenotypic extremes, such as birth defects and disease. Although de novo CNV have been detectable now for some time with microarray technologies, identifying smaller de novo events (e.g., SNV) has become feasible only recently with the advent of large-scale DNA sequencing technologies. Recent exome sequencing studies of family trios with patients manifesting sporadic intellectual disability (previously more frequently referred to as mental retardation [MR]) identified a high frequency of de novo mutations in ‘‘MR genes’’ (Vissers et al., 2010). Such studies support established theory that if the mutational target is large (and hence the observed gene mutation rate is high), de novo mutations may account for a high incidence of disease even when the selection coefficient is close to 1.0. These early studies suggest a resolution to the question of why the frequency of neurodevelopmental disabilities is high despite near genetic lethality for such traits. Relatedly, sequencing studies of multiple ion channel genes in patients with epilepsy (Klassen et al., 2011) and of known autism susceptibility genes in subjects with highfunctioning autism (Schaaf et al., 2011) reveal many rare variants and also de novo mutations that may be contributing to disease. 34 Cell 147, September 30, 2011 ª2011 Elsevier Inc.

The concept of new mutation in X-linked lethal disorders was well established by Haldane (Haldane, 1935). However, the new mutation contribution to many human disease traits may be greater than anticipated (Hoischen et al., 2010), particularly for genetically heterogeneous conditions in which hundreds of genes could be involved but only one or a few loci are responsible in an individual patient. The developmental timing of new somatic mutations is perhaps underappreciated (Lupski, 2010) as previous studies have emphasized germline events. New mutations may occur in the germline, during any stage of development of the organism, in stem cells, or in differentiated somatic cells. Chromothripsis in cancer (Stephens et al., 2011) and complex genomic rearrangements (CGR) associated with selected genomic disorders (Liu et al., 2011) both illustrate the potential gene(s) alteration—complexities that can be brought about by new mutation CNV events. In each case, a single mutational event can result in a cataclysmic chromosomal catastrophe and alter the copy number or structure of several different genes. ‘‘Clan Genomics’’ Most sites of variation have low minor allele frequencies (that is, are rare) and are of recent origin, and therefore the major contributors to inherited disease susceptibility are likely to be those alleles that arose recently in an extended pedigree. Purifying natural selection is expected to eliminate highly deleterious variants before they reach a high frequency, such that disease risk alleles with large effects should be enriched at the lower frequencies (Marth et al., 2011). The idea that there are unique combinations of rare variants characteristic of a recent family lineage and that these combinations can have a causative role in disease is encapsulated by what we refer to as ‘‘clan genomics’’ (Figure 1). The population from which one comes and its collection of older common variants may have less influence on an individual’s disease susceptibility than the collection of recently arisen rare variants and de novo mutations (Figures 2A and 2B). The most important thing that an individual needs to consider in terms of their genetic variation with relation to disease susceptibility is therefore recent ‘‘genetic history’’ of their extended pedigree or clan. From the standpoint of delivering personalized genomic medicine, the medically actionable alleles are the ones of most interest; and these may be highly weighted toward recent rare variants. Nevertheless, the most important thing is not to focus disproportionately on specific variants, but rather to integrate across all classes of risk-associated variants. In some individuals, risk may be caused by an unusual combination of common variants, whereas in others it will be due to a smaller number of large effect rare variants. Mendelian Disease and Complex Traits Resequencing studies of genes that can cause rare Mendelian forms of common complex traits reveal that rare variants can contribute to hypertension (Ji et al., 2008; Wagner, 2008), hypercholesterolemia (Kotowski et al., 2006), hypertriglyceridemia (Romeo et al., 2009), and nonalcoholic fatty liver disease (Romeo et al., 2008) in the population at large. These examples inform models where individual alleles with high penetrance contribute to common complex traits. In addition, when GWAS signals

Figure 1. Clan Genomics Heat map and extended pedigree showing the conceptual relationship among de novo mutations leading to disease (red), recent mutations with moderate effects arising within a clan (yellow and green), and older common variants with small effects segregating in the population (blue). An individual’s genetic disease risk emerges from the collection of variants he or she has inherited from both parental lineages of distant ancestors (typically common and of individually small effect), more recent ancestors (rare, but potentially larger effect), and de novo mutations.

have identified variants for common traits, their molecular mechanistic underpinnings often support those already established by Mendelian forms of the condition (Sankaran et al., 2008, 2009; Vernimmen et al., 2009). The idea that genes responsible for Mendelian disease can also have a role in the common form of the same or a similar condition is not new. For example, the pioneering studies of Michael Brown and Joseph Goldstein showed that individuals with compound heterozygous mutations in the low-density lipoprotein receptor (LDLR) gene manifest the Mendelian disorder familial hypercholesterolemia (FH) (Brown and Goldstein, 1986; Goldstein and Brown, 1987). FH patients have extremely high cholesterol levels and can have coronary atherosclerotic heart disease and myocardial infarctions in their teenage years. Interestingly, the type of LDLR gene mutation predicts cardiovascular risk in children with familial hypercholesterolemia (Guardamagna et al., 2009). Heterozygous rare variant mutations at the LDLR locus can also cause the complex traits of early onset hypercholesterolemia, coronary atherosclerotic heart disease, and myocardial infarctions in carriers with disease manifesting in the fourth or fifth decades of life. Recessive Mendelian Mutations Can Increase Complex Disease Risk in Carriers Heterozygous carriers for recessive disease genes do not manifest the recessive disease but may be susceptible to a milder or related malady, which may consist of a complex trait with a similar phenotype. For example, heterozygote carriers of mutations in the ataxia telangiestasia locus are susceptible to breast

cancer (Athma et al., 1996), and similar heterozygous carrier susceptibilities are also manifest for other recessive human cancer predisposition syndromes (Heim et al., 1991). Carriers for mutations in the Gaucher disease causative gene, GBA encoding glucocerebrosidase, are at increased risk for Parkinson disease (Goker-Alpan et al., 2004; Sidransky et al., 2009). Heterozygous carriers of mutations in the cystic fibrosis transmembrane regulator gene, CFTR, can be susceptible to idiopathic pancreatitis (Cohn et al., 1998; Sharer et al., 1998; Weiss et al., 2005), chronic obstructive pulmonary disease (COPD) (Divac et al., 2004), and even chronic rhinosinusitis (Wang et al., 2000, 2005). Carriers of a-1-antitrypsin (AAT) deficiency can also be susceptible to COPD (Hersh et al., 2004; Poller et al., 1990). Interestingly, even such common traits as age-related macular degeneration (AMD) and carpal tunnel syndrome are associated with heterozygous carrier status for mutations in ABCA4, the gene responsible for Stargardt macular dystrophy (Bacq et al., 2009), and Charcot-Marie-Tooth neuropathy genes (Lupski et al., 2010), respectively (Figures 2C and 2D). In the latter case, haploinsufficiency due to either heterozygous SNV (Lupski et al., 2010) or heterozygous CNV (Del Colle et al., 2003) can convey the trait. Whereas most carrier states may have rare allele frequencies, others will actually have a significant carrier frequency in selected populations (e.g., CFTR 4% in European descendants). Genes and Single Loci Implicated in Mendelian Disease and in Complex Disease Risk In addition to variants that cause Mendelian disease-informing complex traits, there is a striking reciprocity of genes implicated by GWAS that are also known to underlie rare Mendelian diseases. For example, 11 of 30 genes associated with serum lipid levels are implicated in single-gene disorders of lipid metabolism (Kathiresan et al., 2009). We reviewed the current listing of annotated genes with significant associations in 891 GWAS studies (http://www.genome.gov/gwastudies/). We found that at least 268 genes implicated by GWAS are also known to bear mutations in rare single-gene disorders. Some of these associations are intuitive, such as those associated with biochemical traits and related inborn errors of metabolism. There are also a significant number of genes that underlie developmental disorders that harbor common variants affecting risk of cancer, body growth, and cardiovascular traits (Table S1 available online). This raises the testable hypothesis that genetic influences on human diseases can largely be accounted for by a subset of genes that play roles in a restricted set of pathways. Immune and inflammatory pathways provide a robust example as do those genes involved in lipid metabolism. It is important to note that in our survey, for most cases of GWAS the causal gene underlying a given GWAS signal is unknown. Whereas GWAS can indirectly implicate ‘‘Mendelian genes’’ in complex disease risk, different mutations of a single gene, or a CNV at a single locus, are directly implicated in complex disease risk. A poignant example of the different phenotypic consequences of distinct allelic variants at a locus is provided by the fragile X mental retardation 1 (FMR1) locus. Triplet repeat expansion of the CGG repeat element in the 50 untranslated region (UTR) of the FMR1 gene—an especially unstable form of indel mutation—causes severe X-linked mental retardation in both males and females. Alleles with lower numbers of CGG Cell 147, September 30, 2011 ª2011 Elsevier Inc. 35

Figure 2. Phenotypic Consequences of Allele Combinations This figure demonstrates ‘‘clan genomics,’’ wherein the combinations of alleles one inherits from his or her nearest relatives profoundly affect clinical outcome. In these illustrative pedigrees, different combinations of ABCA4 alleles can affect age-of-onset of Stargardt macular dystrophy (STGD; MIM 601691) (A), Mendelian versus multifactorial trait (i.e., Stargardt disease versus age-related macular degeneration [AMD]) (B), or retinal disease type (i.e., Stargardt disease versus retinitis pigmentosa [RP; MIM 601718]) (C). (D) Differing SH3TC2 alleles result in recessive Charcot-Marie-Tooth disease (CMT; MIM 601596), dominant axonal neuropathy, or the complex trait of carpal tunnel syndrome.

repeats (55–200 repeats; called premutation alleles), however, cause adult onset tremor/ataxia syndrome (FXTAS) in approximately 33% of males and 10% of females (Hagerman et al., 2004; Jacquemont et al., 2004). Thus, premutation variants that have been considered nonpathogenic can have phenotypic consequences for common complex traits such as tremor and ataxia. Rare CNV at different loci have also recently been associated with complex traits including Alzheimer disease (Rovelet-Lecrux et al., 2006), Parkinson disease (Farrer et al., 2004; Singleton et al., 2003), lupus glomerulonephritis (Aitman et al., 2006), Crohn disease (Fellermann et al., 2003, 2006; McCarroll et al., 2008), psoriasis (Hollox et al., 2008), pancreatitis (Le Mare´chal et al., 2006), and obesity (Bochukova et al., 2010). Many rare CNV have also been associated with intellectual disability (Stankiewicz and Beaudet, 2007) and with some forms of neuropsychiatric illness, including schizophrenia (Consortium, 2008; Lupski, 2008; McCarthy et al., 2009; Stefansson et al., 2008) and autism (Kumar et al., 2008; Shinawi et al., 2010; Weiss et al., 2008). Same Gene, Different Mutations, Diseases, and Modes of Inheritance A further illustrative example of the connection between Mendelian and complex traits is provided by variants at the MECP2 36 Cell 147, September 30, 2011 ª2011 Elsevier Inc.

locus and their contribution to disease. Heterozygous loss-offunction SNV in MECP2 result in the X-linked dominant trait of Rett Syndrome in girls (Amir et al., 1999); however, hemizygous loss-of-function mutations are thought to be lethal in males. Recently, duplication of CNV including MECP2 has been associated with an intellectual disability plus seizure disorder in males (Carvalho et al., 2009; del Gaudio et al., 2006; Friez et al., 2006; Meins et al., 2005; Van Esch et al., 2005) and autism spectrum disorder (Ramocki et al., 2009; Schaaf et al., 2011). Male patients with triplication of MECP2 have a more severe phenotype (del Gaudio et al., 2006). Of note, maternal carriers of the MECP2 duplication (CNV) appear more susceptible to psychiatric symptoms unrelated to having a child with a disability (Ramocki et al., 2009). Thus, at a single locus, the genetic variation can cause an X-linked dominant disorder in females and an X-linked recessive trait in males and can be associated with susceptibility to a common complex trait in carrier mothers. For mutations at a single locus, allelic interactions can profoundly affect clinical phenotype. At the ABCA4 locus, the disease severity is related to the residual activity of encoded transporter protein (Figures 2A–2C). Recessive Stargardt disease is caused by compound heterozygous mutations at this locus (Allikmets et al., 1997b). Homozygous or compound

Figure 4. Totality of Pathogenic Variants, Disease Severity, and Clan Genomics

Figure 3. Models of Disease Allele Transmission (A) In classical Mendelian disease, for a recessive, monogenic disease, at that single locus there is biallelic inheritance (highlighted in box). Examples could be either Stargardt macular dystrophy or cystic fibrosis, which are both due to point mutations in ATP-binding cassette (ABC) transporter genes. However, at some loci in the human genome, imprinting results in monoallelic expression, and the disease phenotype will occur in a manner dependent on the parent of origin of the specific mutation, either by deletion copy number variants (CNV) or uniparental disomy (UPD). The example given is the Angelman syndrome with point mutations in the UBE3A gene. The CMT1A locus (17p12) represents a triallelic locus whereby because of the duplication, there are three copies of the PMP22 gene. None of the copies have point mutations in them, but it takes three copies to convey the clinical phenotype. Other examples of disease allele transmission include interactions between two or potentially more genes. In the classic model of digenic inheritance, the phenotype of retinitis pigmentosa has been shown to be due to heterozygous point mutations in the ROM1 gene in combination with heterozygous point mutations at the RDS locus. Thus there is biallelic digenic inheritance. Note that a genomic deletion CNV renders a locus monoallelic, whereas a duplication CNV results in a triallelic locus. (B) Bardet-Biedl syndrome (BBS), traditionally thought of as a recessive trait, can sometimes result from three mutant alleles, two of which come from one locus, and one from another locus. This is an example of digenic triallelic inheritance. (C) A single pedigree illustrates triallelic inheritance for BBS. Standard pedigree symbols are used; filled squares, affected with BBS. Alleles segregating at two distinct loci (BBS2 and BBS6) are shown, one in each pedigree. WT, wild-type or normal allele.

heterozygous mutations, if both null, result in retinitis pigmentosa. Within a single pedigree or clan, different combinations of alleles can result in differing ages of onset (Lewis et al., 1999), completely different diseases (Shroyer et al., 2001a), or both a recessive Stargardt disease and susceptibility to a complex trait, age-related macular degeneration, due to a heterozygous carrier state (Figure 2) (Shroyer et al., 1999, 2001b).

Pedigrees of families segregating Charcot-Marie-Tooth (CMT) neuropathy, illustrating that disease severity is directly related to pathogenic mutational burden. (A–C) Mutations at two different CMT loci result in a more severe phenotype. These double heterozygotes may be due to either a single-nucleotide variant (SNV) + copy-number variant (CNV) (A and B) or two SNV (C) (Chung et al., 2005; Hodapp et al., 2006; Meggouh et al., 2005). (D) In a single family, disease results from homozygous MTMR2 mutation (likely related to consanguinity) or de novo CNV—the CMT1A duplication (PMP22) (Verny et al., 2004); an example of clan genomics.

Multiple Mutated Genes Underlying Clinical Phenotypes Rare point mutations (either functional noncoding SNV [Kurotaki et al., 2005] or coding SNV with incomplete penetrance [Shy et al., 2006]) in combination with a deletion CNV have been shown to contribute together to particular phenotypes. A combination of a rare deletion CNV with a de novo duplication CNV can also result in a phenotype that appears to be a complex trait (Potocki et al., 1999). Sometimes SNV mutations at two different loci, i.e., digenic inheritance, are required to manifest a trait segregating as a recessive disease, and the mutational load required may have a single mutant allele at each of the two loci (Kajiwara et al., 1994) (double heterozygous) or two mutant alleles at one locus and one at the other (triallelic inheritance) (Figure 3) (Katsanis et al., 2001). With respect to models for Mendelian transmission, a deletion CNV renders a locus monoallelic, whereas a duplication CNV results in a triallelic locus (Figure 3). It is now well established that even simple Mendelian traits can have modifier loci (Badano and Katsanis, 2002; Dipple and McCabe, 2000), demonstrating the potential importance of nonhomologous allelic interaction and epistasis. For example, severity of disease for CMT can be due to a combination of mutations at more than one CMT locus (Chung et al., 2005; Hodapp et al., 2006; Meggouh et al., 2005) (Figures 4A–4C). Cell 147, September 30, 2011 ª2011 Elsevier Inc. 37

Figure 5. Bridging the Gap between Chromosomal Syndromes and Mendelian Disorders (A) Chromosomal duplication mapping wherein chromosomally visible duplication abnormalities, as evidenced by altered G-banding patterns, are used to delineate the portion of the genome responsible for the reduced motor nerve conduction velocities that accompany the demyelinating form of Charcot-Marie-Tooth disease (CMT1A; MIM 118220). Several different chromosomal abnormalities have been reported in association with a CMT1 phenotype. Note, different chromosome 17 abnormalities including direct duplications, inverted duplications, and inherited as well as de novo translocations have been reported with complex phenotypes that include CMT. If the duplicated genomic interval encompasses the 17p12 dark G-band where the PMP22 gene maps (*), then the patient will have a demyelinating neuropathy, as evidenced by decreased motor nerve conduction velocities, as part of their clinical phenotype. (B) Submicroscopic genomic rearrangements associated with neuropathy. Vertical lines represent a ‘‘blow-up’’ of the genomic interval within 17p12 containing the PMP22 gene (filled rectangle). The horizontal parentheses delimit the rearranged interval for the common deletion (depicted by absence of vertical line) and duplication (two copies of gene and interval). To the right are rare-sized copy-number variants (CNV) depicting genomic deletion (green dots on array CGH) versus duplication (red dots on array). (C) shows genotype/phenotype correlations between PMP22 point mutations associated with neuropathy. The T118M missense amino acid substitution in PMP22 appears to be a reduced penetrance loss-of-function allele. As a heterozygous mutation it can result in a mild hereditary neuropathy with liability to pressure palsies (HNPP) phenotype in some individuals; as a homozygous allele it can convey a severe axonal neuropathy. Interestingly, when the T118M allele occurs in combination with the HNPP deletion, a severe demyelinating phenotype results. Of further interest, when the T118M allele occurs in combination with the CMT1A duplication, the loss-of-function missense amino substitution appears to mitigate some of the consequences of the gain-of-function duplication CNV.

Large CNV or Aneuploidy Can Simultaneously Affect Multiple Genes In contrast to the trans-genetics of Mendelism (Figures 2–4), genetic interactions occurring on the same chromosome or in cis (Figures 5A–5B) can also have profound consequences as exemplified at the alpha globin locus. For structural variants, the genomic mutational load can reflect the size of the CNV and inclusion of additional dosage-sensitive genes or genomic segments in cis (Bi et al., 2009; Lupski et al., 1991, 1992; Roa et al., 1996). Two extreme examples of this ‘‘cis-genetics’’ effect are segmental aneuploidy (Figure 5) and complete aneuploidy (e.g., trisomy 21) that convey complex phenotypes related to the size of the CNV and number of dosage-sensitive genes and/or genomic segments involved. For Down syndrome associated with trisomy 21, this includes an endophenotype of early onset Alzheimer disease; the amyloid precursor protein (APP) gene maps to chromosome 21, and duplications involving this gene have indeed been associated with Alzheimer disease (Rovelet-Lecrux et al., 2006). For intellectual disability, recent studies suggest the possibility that two independent CNV (El-Hattab et al., 2010; Girirajan et al., 2010; Potocki et al., 1999) can contribute to the ultimate phenotype, as shown in individual patients and as predicted by previous models (Lupski, 2007b). In aggregate these data show that rare variants and the genome-wide totality of pathogenic alleles contribute to complex traits (Allikmets, 2000; Allikmets et al., 1997a; Douros 38 Cell 147, September 30, 2011 ª2011 Elsevier Inc.

et al., 2008; Hersh et al., 2004; Lupski, 2007b; Poller et al., 1990; Wittrup et al., 1997, 2006). Unfortunately, such rare variants are not being accounted for in many current GWAS, and CNV and noncoding SNV are not detected by typical wholeexome sequencing approaches. A Unified Genetic Model for Human Disease In the past, focused, locus-specific, single-gene analyses have elucidated genetic etiologies for disease, but it is now emerging that whole-genome sequencing will produce a more complete assessment of genetic variation contributing to personal health. The genome of each individual contains the inherited contribution of common variants that segregate within the population, the inherited contributions of rare variants that emerged in recent history in the clan, the new combinations of such recently arising variants from both parents, and the new mutation contributions yielding the total mutational burden (Figure 1). Highly penetrant rare variants, and often de novo mutations, contribute medically actionable alleles to Mendelian disease and perhaps extremes of phenotypes in common disease. Common variants can contribute to medically actionable variants for pharmacogenomics traits. What emerges is a unified picture whereby previously distinct entities or categories of human diseases, chromosomal syndromes, genomic disorders, Mendelian traits, and common diseases or complex traits, can now be considered as part of one continuum (Figure 6), whereby common and rare variants

Figure 6. A Continuum for the Genetics of Human Disease The square (center) represents genomic variation that can influence the different categories of genetic disease. The circles represent the overlapping categories of human disease with darker regions depicting intersection with greater overlap in the underlying genetic influences on these given disease categories. A unified model for human genetic disease proposes that all major categories of disease with genetic influence—Mendelian disease, common disease or complex traits, genomic disorders, and chromosomal syndromes— can be explained by variation in DNA sequence (SNV) or copy number (CNV) from a ‘‘wild-type’’ diploid state. Whereas trans-genetic interactions at a single locus (alleles) or between loci may contribute to Mendelian disease and complex traits, cis-genetic interactions can be important to phenotypic manifestations in genomic disorders (CNV) and chromosomal syndromes (segmental aneuploidy). Digenic and triallelic inheritance bridge Mendelian traits and complex disease; each represents an oligogenic inheritance model.

context of complete individual genetic variation data, population genetics, and evolution. Genome-wide assays including whole-genome sequencing, copy-number arrays, and transcriptional profiling are among the current technologies that can be used to further explore and test the ‘‘genome-wide totality of pathogenic variants’’ hypothesis. These genome analysis methods can now generate a massive data flow, opening up to experimental exploration fundamental questions that have occupied the minds of generations of scientists and philosophers. Yet, such genome-wide experimental assays alone will be insufficient. Other challenges include: How many types of variants (repeat expansions, CNV between 100 and 500 bp, etc.) are we missing with current techniques? How will we validate the phenotypic effects of variants observed in a single individual or family? What analytical approaches should clinical genome sequencing projects adopt given the sheer complexity of some of the gene-disease associations described herein? How can we integrate disease risk emerging from common and rare variants in an individual genome? Can disease phenotypes be refined and redefined by molecular correlates such as gene expression, chromatin conformation, DNA methylation, and all of the other ‘omics? Can individual serial observation of molecular phenotypes, much as we currently do for routine lab measures such as glucose and lipids, show us stronger effects of underlying genetic variation that are otherwise poorly captured by crosssectional studies and lead us to yet new models? SUPPLEMENTAL INFORMATION

including de novo mutations in the context of environmental influences result in perturbation of the biological balance of a restricted set of networks activating final common pathways that ultimately cause disease. Even though there may be many loci that contribute to interindividual inherited susceptibility of a phenotype in a population, in any one individual rare or common variants from just a few may be responsible for the trait (i.e., oligogenic inheritance). Extreme genetic heterogeneity and the contributions of new mutation may underlie some of the apparent complexity of complex traits. A unified genetic model for human disease breaks down the artificial boundaries between categories of human disease (Figure 6). It views all human disease categories including complex traits, Mendelian disease, genomic disorders, and even chromosomal syndromes as representing a spectrum of phenotypic manifestations reflecting the totality of pathogenic variants: ancestral alleles, those arising in recent ancestors (clan), unique combinations inherited from parents, and de novo variants (Figure 1). A full accounting of individual mutational load genome-wide and expansion of the current genocentric, locusspecific model opens the door to reinvestigation of classic problems in human genetics. These challenges include understanding the molecular basis of incomplete penetrance and variable expressivity of monogenic traits, clinical manifestations of ‘‘recessive alleles’’ (i.e., weak semidominance), homologous allelic interaction and nonhomologous allelic interaction, and their effects on disease and health. This new synthesis is required to interpret the ecology of individual genomes in the

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Leading Edge

Perspective Metagenomics and Personalized Medicine Herbert W. Virgin1,* and John A. Todd2,* 1Department of Pathology and Immunology, Department of Molecular Microbiology, and Midwest Regional Center of Excellence for Biodefense and Emerging Infectious Diseases Research, Washington University School of Medicine, St. Louis, MO, 63110, USA 2Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0XY, UK *Correspondence: [email protected] (H.W.V.), [email protected] (J.A.T.) DOI 10.1016/j.cell.2011.09.009

The microbiome is a complex community of Bacteria, Archaea, Eukarya, and viruses that infect humans and live in our tissues. It contributes the majority of genetic information to our metagenome and, consequently, influences our resistance and susceptibility to diseases, especially common inflammatory diseases, such as type 1 diabetes, ulcerative colitis, and Crohn’s disease. Here we discuss how host-gene-microbial interactions are major determinants for the development of these multifactorial chronic disorders and, thus, for the relationship between genotype and phenotype. We also explore how genome-wide association studies (GWAS) on autoimmune and inflammatory diseases are uncovering mechanism-based subtypes for these disorders. Applying these emerging concepts will permit a more complete understanding of the etiologies of complex diseases and underpin the development of both next-generation animal models and new therapeutic strategies for targeting personalized disease phenotypes. Recent advances in diverse areas of science and technology make this a unique time to study the genetics and pathogenesis of complex diseases, such type 1 diabetes (T1D) and inflammatory bowel disease (IBD), which includes Crohn’s disease (CD) and ulcerative colitis (UC). These distinct diseases are now understood to share important common characteristics and aspects of their disease mechanisms. In all three diseases, the immune system damages tissues: T1D is likely an autoimmune disease, whereas CD and UC are likely caused by inappropriate inflammatory responses to components of our microbiome (see Box 1 for definition of key terms). Many genetic loci regulate the risk for each disease. Although a threshold dose of these susceptibility alleles provides the foundation for developing the disease, these alleles are not sufficient to cause the disease. It has been obvious for decades that complex gene-gene and gene-environment interactions govern these diseases, but not surprisingly, untangling this web of interactions has been extremely difficult (Figure 1). Despite the failure to identify single causal agents for each disease, there is strong evidence that microbes contribute to pathogenesis. Furthermore, genomewide association studies (GWAS), which use large study populations and careful replication of results, have effectively identified many important loci in the host that increase one’s risk for the disease, and these results have fundamentally altered how we conceptualize these diseases (Stappenbeck et al., 2011; Khor et al., 2011; Anderson et al., 2011; Franke et al., 2010; Todd, 2010). Correlation of GWAS data with genome-wide gene expression analyses (eQTLs), in combination with proteinprotein interaction data, is greatly assisting the identification of candidate causal genes within these loci (Anderson et al., 2011; Franke et al., 2010; Cotsapas et al., 2011; Rossin et al., 2011; Fehrmann et al., 2011). Recently, numerous approaches 44 Cell 147, September 30, 2011 ª2011 Elsevier Inc.

have been developed to start defining mechanisms for complex inflammatory diseases by using leads from GWAS and analyses of the microbiome. These promising approaches include the following: the introduction of mutations in GWAS-identified loci into the mouse genome (Cadwell et al., 2010; Bloom et al., 2011); the creation of induced pluripotent stem cells (iPSCs) from patients and their differentiation into relevant cell types (e.g., Rashid et al., 2010); and humanized mouse models in which the murine immune system is replaced by transplantation (e.g., Brehm et al., 2010; Esplugues et al., 2011) or human microbial communities are transplanted into formerly germ-free mice (Goodman et al., 2011). Currently the great challenges in this field are to (1) understand how both microbiome and GWAS-identified genes contribute to disease; (2) elucidate the molecular mechanisms by which causal genes act during pathogenesis; and (3) validate biomarkers and druggable pathways via genotype-phenotype studies (e.g., Dendrou et al., 2009; Bloom et al., 2011; Cadwell et al., 2010). By peering through the lens of recent studies on CD, UC, and T1D, this review seeks to delineate emerging concepts in research on complex inflammatory diseases and to comment on the implications of these concepts for the interpretation of genetic and pathogenetic data. Two concepts are emphasized and integrated herein: (1) that single disease diagnoses are unlikely to be single phenotypes and may instead be the sum of multiple mechanism-based disease subsets, and (2) that the interactions of individual microorganisms and their genomes with specific host genes or pathways underpin the relationship between genotype and phenotype in these complex diseases. In this view, disease genetics may be combinatorial with different host-gene-microbial interactions, contributing to the pathogenesis of disease in subsets of patients. These two interrelated

Box 1. Definition of Terms Dysbiosis: Most commonly refers to a disruption in the normal homeostatic and beneficial relationship between microbes and their host, including disruptions in microbial community structure and function. Alterations in microbial community structure, involving Bacteria, Archaea, and/or Eukarya, can occur in any body habitat but have been best described in the gut where they have been associated with a number of disease states including, for example, inflammatory bowel disease. GWAS: Analysis of common alleles (mostly single-nucleotide polymorphisms, SNPs) in a population that associates genetic loci with disease susceptibility. These loci contain ‘‘candidate’’ disease genes. Familial clustering: If a family member is diagnosed with a disease such as type 1 diabetes, ulcerative colitis, or Crohn’s disease, then the risk of other first-degree family members is much greater (perhaps as much as 50-fold for some multifactorial disorders) than that for a person taken at random from the general population. Familial clustering is caused by a combination of inherited genetic variants from the parents to the children and shared environmental factors within the families. Susceptibility variants are being discovered rapidly by GWAS, but the environmental factors remain unknown, although numerous candidates are recognized, most particularly a role for the microbiome and infections. Metagenetics: Approaching genetic and genomic studies by considering all of the genes in the metagenome as opposed to considering, in isolation, host genes or genes that confer particular properties (e.g., virulence or commensalism) upon an individual microbe. Importantly, the history of microbial inputs into the metagenomic profile of an individual is important for identifying the causes of complex disease, requiring expensive but essential longitudinal studies, including information from maternal and gestational exposures and phenotypes. Metagenome: As used here, metagenome is the sum of all genes and genetic elements and their modifications in the somatic and germ cells of a host plus all genes and genetic elements in all microorganisms that live on or in that host at a given time. The metagenome has transient elements (e.g., during infection with a pathogen) and more persistent elements (e.g., infection with latent eukaryotic virus; presence of commensal bacteria). Microbiome: As used here, the microbiome is the sum of all microbial organisms that live in or on the host at a given time. The microbiome includes members of Bacteria, Archaea, Eukarya, and the viruses of these organisms. In other articles this term may be used to refer to the genes of these organisms. Virome: The sum of all viruses living in the tissues of the host or infecting organisms in the microbiome. These viruses may be further divided into viruses that infect members of each of the three domains of life (e.g., bacterial virome or bacterial phages or the eukaryotic virome).

concepts, therefore, define T1D, CD, and UC as metagenetic (Box 1), rather than simply ‘‘genetic,’’ diseases. These concepts will guide the design and interpretation of future experiments that seek to dissect the pathophysiologic mechanisms underlying a number of complex diseases and to identify more effective approaches for their treatment and prevention. Host Genetic Grist for the Metagenetic Mill Recently, meta-analyses of GWAS of large cohorts of patients of European descent with UC or CD have been performed (Franke et al., 2010; Anderson et al., 2011; Khor et al., 2011). These

studies identified 98 loci, and candidate genes within these loci, that have a putative role in IBD. Similar studies of T1D identified 53 disease susceptibility loci (Barrett et al., 2009) (http:// www.t1dbase.org). Importantly, many disease susceptibility loci are shared among common autoimmune and inflammatory diseases, including T1D, Graves’ disease, celiac disease, CD, UC, psoriasis, rheumatoid arthritis, alopecia areata, multiple sclerosis, and systemic lupus erythematosus (Cotsapas et al., 2011; Khor et al., 2011). It is striking that T1D and CD share 13/52 (25%) risk loci outside the human leukocyte antigen (HLA) gene complex despite the fact that these diseases are neither thought to be related diseases nor reported to be shared within families more often than expected by chance (http://www. t1dbase.org). Notably, the candidate causal genes in these 13 susceptibility loci regulate immunity. These include (Khor et al., 2011) PTPN22, which is involved in T and B cell signaling; IL10, encoding a powerful cytokine that suppresses inflammatory responses (including in specialized T regulatory cells in the gut) (Maloy and Powrie, 2011); BACH2, which regulates B cell gene expression and possibly IgA production; TAGAP, which is involved in T cell activation; IKZF1, which negatively regulates B cells; IL2RA, which controls T regulatory lymphocyte development and function; GSDMB/GSDMA/ORMDL3, which is involved in stress responses; FUT2, which controls microbial susceptibility (Smyth et al., 2011; Franke et al., 2010; McGovern et al., 2010); and IL27, which suppresses inflammatory responses and regulates IL-10 signaling (Imielinski et al., 2009; Barrett et al., 2009). This is a remarkable concordance of involved genes for two unrelated diseases, indicating that different diseases can have common mechanistic components and that the immune system is key for both diseases. However, not withstanding all insights into disease mechanisms that the GWAS approach has already provided, the inheritance and the strong clustering of these multifactorial diseases within families (Box 1), which encompass both inherited genetic variants and intrafamilial environmental factors, remain only partially explained. Assuming a simple statistical model of gene interaction (Clayton, 2009), the numerous identified loci account for not more than 25% of the familial clustering of CD and UC (Anderson et al., 2011; Franke et al., 2010). This contrasts with T1D, in which the HLA effect is uniquely large and, together with 52 non-HLA loci, can account for almost all of the familial clustering (Clayton, 2009). For T1D, the massive effect of the HLA region, owing to functional polymorphisms in the HLA class II and class I genes, contributes almost 50% of familial clustering on its own (Clayton, 2009; Todd, 2010). There are, however, probably hundreds of non-HLA loci affecting the risk of CD, UC, and T1D that remain unmapped owing to their very small effect sizes (Barrett et al., 2009; Anderson et al., 2011; Franke et al., 2010). These putative loci will be difficult to map unless they contain rare mutations of higher penetrance, an occurrence that is just beginning to yield informative findings (Nejentsev et al., 2009; Rivas et al., 2011) and holds continued promise with the rapid use of high-throughput next-generation sequencing. In humans, the HLA locus contains a large number of genes encoding the major histocompatibility complex (MHC) molecules (which are responsible for presenting antigens to cells of the Cell 147, September 30, 2011 ª2011 Elsevier Inc. 45

Figure 1. Perfect Storms for Developing Crohn’s Disease and Type 1 Diabetes A series of overlapping events and phenotypes driven by metagenetic and environmental processes that, in sum, contribute to the development and pathogenesis of type 1 diabetes (A) and Crohn’s disease (B).

immune system), along with a number of other genes that modulate immune responses. The remarkable contribution of HLA variations (Todd, 2010) to T1D risk is an unusual feature of a common disease. Nevertheless, HLA genotypes that greatly predispose individuals to T1D are not sufficient to cause the disease because only 5% of high-risk HLA carriers develop T1D. HLAs are expressed by antigen-presenting cells (APCs), such as macrophages, B lymphocytes, and dendritic cells (DCs). DCs are highly potent APCs that reside in the pancreas and its islets (i.e., collections of insulin-producing beta cells and other endocrine cells) and could initiate the autoimmune destruction of beta cells by T cells (Calderon et al., 2011a, 2011b). Interestingly, the pancreatic lymph nodes, where DC priming of T cells for the induction of T1D may occur, also drain parts of the intestine, providing a site where the microbiome might influence the genesis of T1D (Turley et al., 2005; Wen et al., 2008). Because the insulin gene is one of the strongest non-HLA T1D susceptibility loci in the genome (Todd, 2010) (http://www.t1dbase.org), insulin and its precursors are likely primary autoantigens. These very strong associations with both HLA and this autoantigen gene are not a feature of CD or UC, in which no particular antigen is known to be targeted, hence their classification as inflammatory rather than autoimmune diseases. GWAS point to several other immunologic components of T1D etiology, including IL-2 production and receptor signaling (IL-2 gene, IL-2 receptors IL2RA [CD25] and IL2RB [CD132]; Todd, 2010), immune tolerance and T cell receptor signaling (PTPN2 [Long et al., 2011], PTPN22 [Arechiga et al., 2009; Bottini et al., 2006]), and recently, the immune response to viral infections and the type 1 interferon responses (IFIH1 [encoding MDA5], GPR183 [EBI2] [Heinig et al., 2010], TLR7 and TLR8, and FUT2 [Smyth et al., 2011]). 46 Cell 147, September 30, 2011 ª2011 Elsevier Inc.

Twenty-eight loci (28/71, 39%) of CD risk loci are shared with UC, indicating that a set of core mechanisms participate in these diseases (Figure 2) (Khor et al., 2011). These diseases genes implicate numerous processes in both CD and UC, including T cell differentiation and function, autophagy, endoplasmic reticulum stress, oxidative stress, and mucosal immune defenses, among others. There are important gene-gene and pathwaypathway interactions within this core set of processes. For example, the CD risk gene NOD2 links to autophagy through interactions with the Nod2 protein and with Atg16L1, induction of proinflammatory cytokines, control of bacterial infection, and sensing of pathogen-associated molecular patterns (Levine et al., 2011). Particularly notable are pathways involving the cytokines IL-23 and IL-12, which regulate the development of TH1 and TH17 CD4 T cells, and IL-10, which is essential in the function of certain regulatory T cells (Tregs) via its anti-inflammatory activity. Rare mutations in genes encoding IL-10 receptors confer susceptibility to early-onset IBD (Glocker et al., 2009). These genetic clues point to a key role for regulating the balance between pro- and anti-inflammatory T cells in CD and UC. The regulation of T cell differentiation is also a key target for hostgene-microbial interactions, as discussed below. Mechanism-Based Disease Subtypes GWAS have revealed a wealth of genes potentially involved in T1D, UC, and CD, but no single gene or set of genes is prognostic. How can we interpret this observation? Here, we argue for an important contributor to this observation—the concept that ‘‘diagnosis’’ does not equal ‘‘single phenotype.’’ Without a distinct phenotype, genetic results are often difficult to interpret. This basic principle comes into sharp focus as one considers current genetic and pathogenesis studies of CD, UC, and T1D.

Figure 2. Refining the Relationship between Genotype and Phenotype in Complex Inflammatory Diseases (A) Traditionally, a disease is considered as a single phenotype, with genes or loci conferring risk to two diseases shown as overlapping in a Venn diagram. (B) We propose a new view of the genotype-phenotype relationship in which different sets of loci are responsible for mechanistically distinct subtypes of diseases, and the sum of these subtypes constitutes the overall diagnosis. Here two disease subtypes are indicated for simplicity, but many such subtypes may exist, and sets of overlapping risk loci may be associated with these multiple mechanistically distinct disease phenotypes.

Why is a diagnosed ‘‘disease’’ an imprecise phenotype? It is not because patients have been misdiagnosed—the diagnoses of UC, CD, or T1D have stood the test of time to predict patient prognosis. However, we believe that there are many pathways to the same diagnosis. A diagnosis may be ‘‘clinically’’ precise but ‘‘mechanistically’’ imprecise. Thus, clinical diagnoses are poor phenotypes for genetic studies unless a single mechanism is responsible for the diagnosis, as in the case of a rare gene mutation in a monogenic disease. The complexity of GWAS results is consistent with the existence of multiple disease subtypes within T1D, UC, or CD, each based on a specific mechanism (Figure 2). Support for this idea comes from the observation that subsets of IBD patients respond differentially to mechanistically distinct interventions (Melmed and Targan, 2010). Why do diagnostic categories group different mechanistic processes under the same moniker? Over many decades, pathologists have lumped patients with similar but nonidentical clinical and pathological signs and symptoms into diagnostic categories that predict outcome and complications. Indeed, this has enormous value clinically, but it emphasizes similarities between patients in outcome rather than differences in pathways that lead to a common endpoint. Complex diseases are diagnosed by summing up multiple factors that may be causes or mere consequences of the disease process. Disease ‘‘diag-

nosis’’ does not require the presence in the tissue of all of the abnormalities that may be ‘‘classically’’ seen in a given disease (Gianani et al., 2010; Odze, 2003). For example, at the polar extreme, CD is easily distinguished from UC by its classical ileal involvement (i.e., involvement of tissue at the end of the small intestine), fissures, granulomas, transmural inflammation (i.e., inflammation through the entire intestinal wall), fat wrapping of the intestine, patchy pathology, skip lesions, and patient presentation with bowel strictures or percutaneous fistulae. However, like UC, CD can be restricted to the colon, and the inflammatory infiltrates of CD and UC overlap. UC can be patchy, and the patient presentations of the two diseases can overlap extensively. Similarly, the genetics, pathology, and pathogenesis of IBD may differ between young and old patients with the same diagnosis (Imielinski et al., 2009; Odze, 2003). Even when all classical aspects of a disease are present, the mechanism responsible for the pathology observed may differ from one person to another. Based on these considerations, it is no surprise that the genetics of T1D, CD, and UC are complex because different phenotypes may have been grouped into a single analysis. This putative mechanistic heterogeneity is reflected in sometimes subtle, but quantifiable, characteristics of the disease process and pathology. Taking such differences into account can be used to identify disease subtypes that are more recognizable as molecularly defined pathological conditions and that more closely relate to specific pathogenetic mechanisms underpinned by distinct sets of genetic risk loci (Figure 3). For example, variations in the ATG16L1 gene (i.e., hypomorphic expression in the mouse and homozygosity for the T300A variant in humans) result in abnormalities in Paneth cell granules and secretion (Cadwell et al., 2008, 2010). Paneth cells are innate immune epithelial cells positioned at the base of small intestinal crypts, where they secrete antimicrobial peptides and other factors that help shape the configuration of the intestine’s bacterial community. Abnormalities in Paneth cells are observed in the subset of CD patients homozygous for the T300A allele, thus defining a pathologic subtype of CD (Figure 2B). If one used criteria including Paneth cell abnormalities in CD diagnosis, the frequency of the ATG16L1 T300A allele would be higher in patients with the ‘‘Paneth cell subtype’’ of CD than in the CD population as a whole (Figure 3). If multiple risk loci contribute to such Paneth cell changes, one might be able to detect gene-gene interactions in this subset of patients compared to other subsets. A similar situation exists in T1D. Biopsy specimens of the pancreas are virtually impossible to obtain. Therefore, T1D is defined clinically by the downstream consequences of destroying the insulin-secreting b cells of the pancreatic islets, namely, high blood glucose and absolute insulin dependence, rather than by the mechanisms for their destruction. It is, therefore, possible that several different pathologic processes result in this disease. T1D patients diagnosed under age 10 years frequently exhibit islet inflammation or insulitis, whereas patients diagnosed over age 10 years exhibit insulitis less frequently. More recently, this histopathological heterogeneneity has become even more evident (Gianani et al., 2010), thanks to the Juvenile Diabetes Research Foundation nPOD project (http:// www.jdrfnpod.org). As for CD, the diagnosis of T1D may reflect the presence of more than one pathogenetic mechanism and, Cell 147, September 30, 2011 ª2011 Elsevier Inc. 47

patient outcome. We, therefore, argue for iterative high-precision phenotyping of patients into mechanism-based subtypes in future studies; this will allow more accurate interpretation of genetic, pathogenesis, outcome, and therapeutic studies (Figure 3). Such definitions must be iteratively reassessed as risk alleles are defined and disease mechanisms are delineated so that the field is not limited by inflexible definitions of disease that may obscure mechanistic heterogeneity. This type of approach is a necessary presage to so-called stratified or personalized medicine. The genetic and pathological complexity of T1D, CD, and UC is particularly well suited for testing whether iteratively redefining disease diagnoses can enhance the value of genetic and pathogenesis studies. Importantly, precision in disease categorization would make defining the impact of hostgene-microbial interactions on disease processes more robust.

Figure 3. The Iterative Redefinition of Mechanism-Based Disease Subtypes Here we present a conceptual workflow for breaking a broad disease diagnosis into its component subtypes by the iterative application of genetics and mechanistic studies. One output would be therapeutics based on disease subtype and patient stratification into groups more likely to respond to a given therapy or preventive strategy (A). (B) shows specific challenges for this process for type 1 diabetes and Crohn’s disease.

thus, represent more than one disease subtype, although in T1D the HLA effects are an essential common pathway. The concept that disease diagnoses include mechanismbased disease subtypes has many implications for interpreting human genetic studies and for understanding the relationship between the microbiome and genetic susceptibility, as discussed below. Including disease subtypes within a single diagnosis would decrease the power to define causal alleles and to detect gene-gene interactions that contribute to a single disease subtype. In this view, the difficulty of interpreting how multiple small genetic effects sum to predispose an individual to a clinical diagnosis may partly reflect insufficient precision in selection of specific phenotypes to study. It is important to recognize that it is the power and informativeness of GWAS themselves that drive the concept of mechanismbased disease subtypes (Figure 2). In the absence of candidate genetic mechanisms for defining disease subtypes, there is limited clinical utility in focusing on low-frequency characteristics or subtypes within a larger diagnostic category that predicts 48 Cell 147, September 30, 2011 ª2011 Elsevier Inc.

Host-Gene-Microbial Interactions in Metagenetics Metazoan organisms are complex communities that include a core organism in combination with a veritable zoo of other organisms that live on or in the body—our microbiome. The microbiome includes eukaryotic viruses, Eukarya, bacteria viruses, Bacteria, Archaea, and, for many, helminths (Virgin et al., 2009; Kau et al., 2011; Garrett et al., 2010b; Spor et al., 2011). The importance of understanding the microbiome has been repeatedly emphasized, giving rise to a large number of international human microbiome projects (e.g., https://commonfund.nih. gov/hmp/, http://www.metahit.eu/) that have focused initially on the bacterial component of the microbiome. The host plus non-host genes of this polyglot and interactive community constitute our metagenome (Box 1). A critical emerging concept is that bacterial and viral interactions in the pathogenesis of inflammatory disease occur in a host gene-specific fashion (see below; Virgin et al., 2009; Cadwell et al., 2010; Bloom et al., 2011; Elinav et al., 2011). Understanding the metagenome is, therefore, highly relevant to understanding T1D, UC, CD, and other common multifactorial diseases. Intestinal bacteria play a role in driving IBD, and emerging data support a similar view for T1D (Wen et al., 2008; Giongo et al., 2011; Roesch et al., 2009). The evidence that bacteria play a role in IBD includes two major observations: that surgical diversion of the fecal stream ameliorates inflammation (Sartor, 2008), and that antibiotics help some patients. In mouse models of colitis, viruses, bacteria, or both acting together can contribute to the pathology via signaling through innate immune sensors and regulation of pro- and anti-inflammatory cytokines (Levine et al., 2011; Maloy and Powrie, 2011; Khor et al., 2011). For many years, enterovirus infection has been associated with T1D (e.g., Yeung et al., 2011; Oikarinen et al., 2011; Stene et al., 2010), and the major sensor for enterovirus RNA is the T1D susceptibility gene IFIH1, encoding MDA5 (Nejentsev et al., 2009; McCartney et al., 2011). The mechanisms for these associations between components of the microbiome and T1D, CD, or UC have proven elusive. The lack of integration among scientific disciplines and among training programs, together with limitations in technology, has substantially limited the understanding of metagenomic contributions to disease. Adult mammals are permanently infected by many viruses, and they are populated by large site-specific bacterial and

Figure 4. Microbe Plus Gene Interactions Determine Inflammatory Bowel Disease Phenotypes (A) Two recent studies analyzed the capacity of two different strains of murine norovirus, MNV strain CR6 versus MNV strain CW3, to trigger phenotypes when orally inoculated into mice with a mutation in the Crohn’s disease risk gene Atg16L1 (Cadwell et al., 2008, 2010). This mutation results in decreased expression of Atg16L1 protein (hypomorphic, Atg16L1HM). Even though MNV CW3 and MNV CR6 are closely related, they have different effects on intestinal pathology in Atg16L1HM mice. Some of these interactions are observed only when mice are fed the chemical dextran sodium sulfate (DSS). (B) Two other studies analyzed the capacity of two different species of Bacteroides to trigger phenotypes in combination with mutations in the IL-10 receptor and T cell expression of a dominant-negative form of the TGF-b receptor (dnKO mice) (Bloom et al., 2011; Kang et al., 2008). dnKO mice are cured of their spontaneous colitis by treatment with antibiotics, but oral feeding of ‘‘cured’’ mice with fecal contents or specific bacteria reinduces disease. Even though Bacteroides thetaiotaomicron and Bacteroides sp. TP5 are closely related, they induce different forms of inflammation when fed to antibiotic-cured dnKO mice. In the same studies, dysbiosis (Box 1) with increases in the numbers of Enterobacteriaceae was noted in dnKO mice prior to curing the mice with antibiotics. However, E. coli inoculation did not trigger the pathologies seen with either Bacteroides species.

phage communities without overt negative effects (Virgin et al., 2009; Foxman and Iwasaki, 2011; Spor et al., 2011). Thus, the bacterial microbiota (and their phages) and the eukaryotic virome are two major (but not the only) contributors to the metagenome. The intestinal microbiome plays a critical role in mammalian physiology by synthesizing vitamins and harvesting energy from food (Spor et al., 2011; Kau et al., 2011). Further, the normal function of the innate immune system, which is critically involved in the pathogenesis of T1D, UC, and CD, is regulated by both chronic viral infections and resident bacterial communities (Barton et al., 2007; White et al., 2010; Virgin et al., 2009; Spor et al., 2011; Kau et al., 2011). The microbiome and metagenome vary from person to person based on host genetics, diet, exposure, geography (including westernization as approximated by the gross national product of a country), socioeconomic status, mode of delivery, gestational age at birth, breast feeding, antibiotic use, and additional factors (Virgin et al., 2009; Benson et al., 2010; Spor et al., 2011; Kau et al., 2011; Penders et al., 2006). Such variations could certainly provide environmental inputs that contribute to the incidence of T1D, UC, and CD, within the genetic foundation revealed by

GWAS (Bach, 2002; Vehik and Dabelea, 2011; Ehlers and Kaufmann, 2010). There are extensive interactions between host and non-host genes within the metagenome, and bacteria and eukaryotic viruses alter our physiology and fitness (Spor et al., 2011; Virgin et al., 2009; Hansen et al., 2010). These genetic interactions within the metagenome create a complex and poorly understood host-gene-microbial interaction matrix that can define phenotype. Host genes, such as those involved in innate and adaptive immunity (e.g., NOD2, NLRP6, HLA, TLR2, and MYD88), shape the bacterial microbiota (Spor et al., 2011; Elinav et al., 2011; Wen et al., 2008). Forward genetic screens in mice suggest that resistance to individual viruses involves hundreds of genes (Virgin et al., 2009), making it likely that many host genes regulate the microbiome (and thus the metagenome). Importantly, key interactions between members of the microbiome have been and are increasingly being reported. For example, murine norovirus can trigger an intestinal inflammatory process in mice with a mutation in Atg16L1. This process can be treated with antibiotics and is thus presumed to be bacteria dependent (Cadwell et al., 2010) (Figure 4). The existence of such interactions Cell 147, September 30, 2011 ª2011 Elsevier Inc. 49

Figure 5. A Metagenetic View of Developing Normal and Pathological Immune Responses This flowchart depicts stages in the development of normal immune responses or autoimmune and inflammatory diseases at which metagenetic interactions (i.e., gene-gene and gene-microbe interactions) might play a determining role. ‘‘Microbial products’’ refers to molecules that interact with host innate immune sensors and initiate inflammation.

could contribute to disease susceptibility and potentially explain a proportion of the familial clustering, the so-called ‘‘missing heritability’’ of multifactorial diseases.

indicates that it will be important to consider that the disease contributions of microbiome members (e.g., helminths and bacteria) are potentially dependent on each other. Metagenetic influences on disease could occur in various ways. Familial disease clustering may reflect intrafamilial behavioral and dietary factors that define the metagenome. A major influence on the human metagenome may be vertical transmission of the maternal microbiome. In the controlled environment of mouse colonies, the bacterial microbiota is clearly maternally inherited. Furthermore, this microbiome can have profound pathological effects in mice carrying specific mutations, such that studies of host-gene functions must now consider contributions of the metagenome (see below). The situation in humans is more complex (Hansen et al., 2011; Benson et al., 2010), although the importance of early environmental exposures has been well documented, including studies with mono- and dizygotic twins (Turnbaugh et al., 2009). Children delivered vaginally initially acquire a distinctly different intestinal bacterial microbiota than those born by caesarian section (Penders et al., 2006; Dominguez-Bello et al., 2010). In a meta-analysis, delivery by caesarean section increases the risk of T1D by 20% (Cardwell et al., 2008). An association with increased risk for T1D has also been reported for higher birth weight (Cardwell et al., 2010) and early infant diet (Pflu¨ger et al., 2010) (Figure 5). Furthermore, changing microbial exposures and infections likely has a major influence on the incidence of other diseases. The dramatic rise in the incidence of allergy, asthma, and T1D in the last 60 years correlates with vast improvements in healthcare and sanitation (Bach, 2002; Vehik and Dabelea, 2011; Ehlers and Kaufmann, 2010). For example, severe rhinovirus infection before the age of 3 years coupled to an asthma-predisposing inherited host genome, has been associated with increased risk of asthma (Foxman and Iwasaki, 2011). Thus, the metagenome 50 Cell 147, September 30, 2011 ª2011 Elsevier Inc.

Metagenetic Effects on Immunity and Autoimmunity GWAS point to a fundamental role for the immune system in the pathogenesis of T1D, UC, and CD. An emerging concept is that bacterial and viral interactions contribute to both normal immune physiologies and abnormal pathologic responses that occur in a host gene-specific fashion (Virgin et al., 2009; Cadwell et al., 2010; Bloom et al., 2011; Garrett et al., 2007; Elinav et al., 2011). The microbiome has significant effects on the development of the immune system (Lee et al., 2011; Mazmanian et al., 2008; Sartor, 2008; Ivanov et al., 2009) and on physiology, including susceptibilities to obesity and the metabolic syndrome (Kau et al., 2011). Serum IgE responses to antigenic challenge are lower in mice colonized with Clostridium, confirming that bacteria can have profound effects on systemic immune responses involved in allergy (Atarashi et al., 2011). Central nervous system (CNS) inflammation induced by autoantigen is limited in germ-free mice, but it can be restored by colonization with specific bacteria (Lee et al., 2011). In the non-obese diabetic (NOD) mouse model mouse model, autoimmune diabetes is regulated by a MyD88-dependent interaction of intestinal microbes with the innate immune system (Wen et al., 2008). Germ-free K/BxN T cell receptor transgenic mice are resistant to arthritis caused by autoantibodies to the selfantigen glucose-6-phosphate isomerase. When these animals are colonized with segmented filamentous bacteria (SFB), they regenerate TH17 responses in the small intestine, autoantibody production, and arthritis (Wu et al., 2010). Furthermore, the normal intestinal microbiome is essential for effective resistance to oral inoculation with Toxoplasma gondii and for generating appropriate CD8+ T cell responses to influenza (Ichinohe et al., 2011; Benson et al., 2009). The mechanisms responsible for these observations are under intensive investigation. One recent study shows that intestinal bacteria induce Tregs in an antigen-specific and T cell receptor-dependent fashion (Lathrop et al., 2011). This is a key observation because it provides a mechanism, in addition to thymic exposure to self-antigens, for how regulatory responses can be generated to blunt inflammation. Given the continuous

presence of the stimulating antigens for these Tregs in the normal intestinal microbiome, such cells could have profound effects on both intestinal and systemic immune responses, including responses to