NGS and the Hallmarks of Cancer

January 28, 2011 by Dan Koboldt

Massively parallel sequencing will be applied to hundreds or thousands of tumor genomes this year. Catalogues of somatic alterations in human cancers (e.g. COSMIC) will grow, perhaps as exponentially as dbSNP did in the past decade. Perhaps more importantly, we will begin to see cases where whole-genome or whole-exome sequencing of a patient’s tumor guides his or her treatment. Bridging the gaps between mutation discovery, biological interpretation, and clinical action, however, will be a substantial challenge. Hence the theme of this month’s posts, cancer biology and pathology.

Just over a decade ago, Douglas Hanahan and Robert A. Weinberg published the landmark article “The Hallmarks of Cancer” in the journal Cell. At the time, nearly a quarter-century of rapid advances had revealed a wealth of knowledge about this deadly disease. Although more than 100 subtypes of cancer had been described, Hanahan and Weinberg described six principal cellular traits shared by virtually all forms of human cancers. Collectively, these essential alterations in cell physiology dictate tumor development and growth.

Credit: Hanahan and Weinberg, Cell (2000) 100:57-70

Each of these six acquired capabilities – evasion of apoptosis, self-sufficiency in growth signals, insensitivity to growth inhibition signals, limitless replicative potential, sustained angiogenesis, and tissue invasion/metastasis – represents the successful circumvention of inherent anticancer defense mechanisms of cells and tissues.

Genomic Instability and Driver Mutations

Most of these acquired capabilities arise from somatic alterations – mutations, structural events, and epigenetic changes – which presents something of a dilemma. Thanks to a swath of fastidious DNA monitoring and repair enzymes, mutation is a rare event, and altering the critical genes to successfully acquire each capability is inefficient. For a single cell to achieve all of them in the span of a human lifetime is, well, statistically improbable. The authors suggest a seventh principle, not a hallmark of cancer but a universally enabling characteristic, to explain the means by which these six biological endpoints are reached: genomic instability.

Mutations that cause genomic instability are likely critical, early events in tumorigenesis. Studies have shown that mutations in DNA repair genes (e.g. ATM, RAD51, CHEK1) and, more recently, components of DNA methylation pathways (DNMT3A/DNMT3B) are recurrently mutated in human cancers, suggesting an important functional role in disease development.

Hallmark 1: Self-sufficiency in Growth Signals

Autonomous growth signaling was the first hallmark to be defined by cancer researchers, due in part to the large number of oncogenes that modulate it. Three common molecular strategies are used by tumors to provide self-sufficient growth stimulation:

Modulation of extracellular growth signals, for example, the production of PDGF and TGF-alpha by glioblastomas and sarcomas, respectively.
Alteration of the trans-cellular signal transducers (surface receptors), e.g. the up-regulation of EGFR in stomach/brain/breast tumors and HER-2 in mammary tumors.
Deregulation of the intracellular signaling pathways linked to transmembrane receptors, such as the Ras/Raf/mitogen activated protein kinase (MAPK) cascade.

The authors suspected that growth signaling pathways suffer deregulation in all human tumors. Ten years later, we know that this is largely true. Large-scale sequencing efforts have revealed that mutations in Ras-family genes (KRAS, NRAS, HRAS, etc.) and MAP kinase genes are frequent events in human cancers. In breast cancer, for example, PI3 kinase genes are among the most highly mutated, suffering alterations in as many as 40% of tumors.

Hallmark 2: Insensitivity to Antigrowth Signals

In normal tissue, both soluble factors and matrix-embedded inhibitors cooperate to maintain homeostasis by blocking cell growth. Much like growth stimulation, these signals are transduced to cells via transmembrane receptors and then into complex intracellular circuits. At the molecular level, most or all anti-proliferative signals are funneled through retinoblastoma (Rb) related proteins. When hypophosphorylated, Rb sequesters and alters the functions of E2F transcription factors, which normally serve to activate a number of genes required for transition from G0 to S-phase.

The best documented modulator of Rb signaling is TGFB, a soluble signaling molecule that suppresses cell growth. TGFB prevents the phosphorylation that activates Rb, thereby blocking the advance through G1. Tumor cells disrupt TGFB signaling by a number of mechanisms, including downregulation or alteration of its cellular receptor, or mutation of the key transducer of TGFB signaling, Smad4. One way or another, the anti-growth circuit converging on Rb is disrupted in a vast majority of human malignancies, virtually “defining the concept” of tumor suppressor loss in cancer.

Hallmark 3: Evasion of Apoptosis

I won’t dwell much on this topic, since much of it was covered in my post on Cancer Versus the Immune System. Simply put, evasion of apoptosis is a hallmark of many and perhaps all human cancers.

Hallmark 4: Limitless Replication Potential

Most types of mammalian cells carry intrinsic programs that limit their replication. Once cells reach a certain number of divisions, they stop growing, or senesce. In culture, human fibroblasts can be forced to keep dividing (by knocking out p53 and pRb tumor suppressors) beyond this point. These cells eventually enter a crisis state characterized by karyotypic disarray and massive cell death. A small fraction of cells, however, continue to grow and divide without limit, a trait known as immortalization.

The limit for most normal cell types is 60-70 divisions, after which they enter senescence. Obviously tumor cells surpass this limit, managing to grow and progress even while undergoing massive apoptosis. One key rate-limiting mechanism in cell division is the length of chromosome telomeres, which decreases by 50 to 100 base pairs with each consecutive cell division. At some point, telomere loss introduces massive genetic instability, and crisis ensues.

Many, if not all tumor cells address this issue by up-regulating telomerase, the enzyme that extends telomeres. This maintenance operation is a key component for enabling limitless replication potential in tumor cells.

Hallmark 5: Sustained Angiogenesis

Here’s something I didn’t know: virtually all cells must remain within 100 um of a capillary blood vessel to get the oxygen and nutrients they need to survive. You’d think that because of this limitation, rapidly proliferating cells must have an intrinsic ability to induce angiogenesis (blood vessel growth). It turns out, not so much. The ability to stimulate angiogenesis is not inherent in normal cells or developing neoplasias, and represents an acquired capability that successful tumors must achieve.

Like the other biological processes discussed in this review, angiogenesis is encouraged or prevented by a complex network of signaling molecules. Soluble factors, cell surface receptors, integrins, and cell adhesion molecules all play a role in the counter-balancing of blood vessel growth. Vascular endothelial growth factor (VEGF) and fibroblast growth factors (FGF1/FGF2), for example, are molecules that stimulate angiogenesis initiation. Thrombospondin-1 is an important inhibitor of this process.

Tumor cells encourage blood vessel invasion/growth by up-regulating inducers and suppressing inhibitors, often at the level of transcription. Loss of TP53, for example, causes thrombospondin-1 levels to fall, thereby reducing the latter’s inhibitory potential. Similarly, Ras activation and loss of the VHL tumor suppressor induce an up-regulation of the gene encoding VEGF. More recently, we’ve come to realize that the transmembrane receptors for angiogenesis-stimulating molecules (VEGFR, FGFR) are commonly mutated in human cancers.

Hallmark 6: Tissue Invasion and Metastasis

Eventually, tumor cells venture out from the primary lesion to colonize and grow in other, often distant parts of the body. Ultimately, it is these metastases that account for 90% of cancer deaths. Several families of proteins involved in tethering cells to their surrounding tissue are altered during this process. Perhaps the best-known of these are cell-cell adhesion molecules (CAMs) and integrins, which mediate cell-cell interactions and cell-matrix interactions, respectively. One example of the communication between cell and environment is offered by E-cadherin, which is expressed on the surface of epithelial cells. Bridging of E-cadherin receptors between adjacent cells triggers anti-growth signals within the cell (Lef/Tcf transcription factor activation) via cytoplasmic B-catenin. A number of epithelial cancers block this pathway, either by mutational inactivation of E-cadherin or B-catenin genes, transcriptional repression, or proteolysis of the extracellular E-cadherin domain.

Integrins have dozens of subtypes with distinct substrate preferences. Successful colonization by tumor cells at a distant site requires adaptation, which is often achieved through shifts in the spectrum of integrin alpha- and beta-subunits displayed by migrating cells. Support for this idea can be seen even in cell culture, where forcing expression of different integrin subunits can induce invasive and metastatic behavior. This aspect of tumor progression will be especially challenging to characterize, because there are large numbers of integrin genes and many, many unique heterodimeric receptors that can be generated by differential subunit expression.

Summary

It was clear even a decade ago that the innate defense mechanisms of cells to prevent transformation and metastasis are diverse and complex, and the processes by which tumor cells subvert those defenses are equally so. Nevertheless, Hanahan and Weinberg postulated that 10-20 years after the time of writing this review, diagnosis of virtually all somatic lesions within a tumor would be a routine procedure, as would comprehensive gene expression analysis. With such knowledge in hand, it would be possible to definitively test whether all tumor types behave according to a set of common rules like the ones outlined above. We aren’t quite able to provide those answers just yet, but given the rapid advances of next-gen sequencing, that day is soon coming.

References
Hanahan, Douglas, & Weinberg, Robert (2000). The Hallmarks of Cancer Cell, 100 (1), 57-70 DOI: 10.1016/S0092-8674(00)81683-9

Cancer Versus the Metabolism

January 11, 2011 by Dan Koboldt

A number of complex biological forces drive the mutation and selection of cancer cells – competitive growth, angiogenesis, immune system evasion, etc. A recent review in the journal Science has shed light on another key aspect of tumor development – the hijacking of the metabolism.

cancer-vs-metab-screenshot

Over 80 years ago, Otto Warburg observed that tumor cells exhibit a different metabolic pattern than normal (adult) cells: increased glucose uptake and aerobic glycolysis coupled with decreased oxidative phosphorylation. The pyruvate generated by glycolysis is converted to lactate and secreted from the cell. He suggested that this metabolic switch explained the cancer phenotype and possibly represented a causal event in tumorigenesis.

Warburg’s observations seemed to yield more questions than answers. Why would tumor cells favor a metabolism pathway that was energentically inefficient? How did it contribute to cancer development and growth? And how was the metabolic switch made? Remarkably, Warburg’s findings did not spur additional research, and understanding of the implications eluded scientists for decades. Yet research in the last 10 years has begun to answer some of these questions, and generated new interest in the “Warburg effect.”

Glucose, Glutamine, and the Metabolism

In this review, Levine and Puzio-Kuter provide an excellent overview of the signaling pathways and related components of cellular metabolism. It begins with the uptake of glucose and glutamine, which serve as substrates for a number of tasks in proliferating cells:

Generating energy, in the form of ATP
Regulating the redox potential (the ratio of oxidized to reduced molecules in the cell)
Control of reactive oxygen species (ROS) and their adverse effects on cellular components
Synthesis of the substrates for membranes, nucleic acids (purines and pyrimidines), and proteins.

In normal differentiated cells, the need for cell division is low. Glucose metabolism is therefore optimized for efficient energy generation: glycolysis in the cell linked to the tricarboxylic acid (TCA) cycle (also known as the “Krebs cycle”) in mitochondria.

The Switch for Rapid Growth

In times when rapid cell division is required, however – during development, wound healing, or for immune responses – cells need to synthesize substrates for membranes, nucleic acids, and proteins; in other words, the intermediates for cell growth. This means not metabolizing all glucose to carbon dioxide and water. One way that cells accomplish this by slowing the entry of pyruvate into the mitochondria. The last step of glycolysis is catalyzed by pyruvate kinase, which receives input about the energy status of the cell and the presence of these intermediates. This enzyme is normally stimulated in a feed-forward loop by glycolysis. However, cancer cells make a splice variant, the M2 (fetal) isoform of pyruvate kinase in which several amino acids are added, including a tyrosine. This gets phosphorylated in tumors with activated tyrosine kinase signaling, inhibiting the feed-forward loop. Thus the last step of glycolysis is slowed in cancer cells, and keeps those intermediates around for growth.

A side-effect of delaying the TCGA cycle is the accumulation of pyruvate in the cell. As it builds up, pyruvate is converted to lactate and secreted, in order to keep glycolysis active. The secreted lactate lowers the pH of the cellular matrix; in tumors, this may influence remodeling of the matrix and help stimulate blood vessel invasion (angiogenesis). The use of glycolysis by tumors can make them acidotic, encouraging the selection of motile cells that can break free of the basement membrane and metastasize.

Glucose and Glutamine Uptake by Cancer Cells

Proliferating cells, including cancerous ones, also require favorable energetics – that is, higher ATP/ADP and ATP/AMP ratios. Many tumors satisfy this need by increasing glucose uptake. Glucose is brought into the cell by several transmembrane glucose transporters (GLUT1 to GLUT9), and then phosphorylated by one of several hexokinase enzymes to keep it in the cell. MYC and HIF-1, transcription factors and oncogenes that are often amplified in tumors, stimulate transcription of GLUT transporters and hexokinase 2, thereby ensuring that more glucose is brought into and kept in the cell. Many but not all tumors exhibit increasd glucose uptake in this fashion, which can be observed by PET scans of radioactive F-19-2-deoxyglucose.

Cancer cells also take up excessive amounts of glutamine (via glutamine transporters), which is converted to glutamate by glutaminase-1 before entering the mitochondria. There, the TCA cycle produces malate and citrate, both of which leave the mitochondria. In the cytosol, malate is converted to pyruvate and NADPH, while citrate is processed to or alpha-ketoglutarate (producing NADPH), acetyl-CoA (for lipid synthesis), or oxaloacetate (for amino acid synthesis).

Running Too Hot: Managing Reactive Oxygen Species

The increased uptake of glucose and glutamine by cancer cells thus helps fuel cell growth and proliferation, but it comes at a cost: excessive reactive oxygen species (ROS), which damage membranes, proteins, and nucleic acids. The major eliminator of ROS in a cell is glutathionine (GSH). This molecule accepts an electron, and is converted to the oxidized form glutathionine sulfate (GSSG). The enzyme glutathionine reductase uses another molecule, NADPH, to reduce GSSG back to its “eliminator” form (GSH). NADPH can be generated either from the citrate output of the TCA cycle, or via the pentose phosphate pathway (PPP) if there’s plenty of glucose available. Thus, when a cell starts to run too hot from oxidative reactions (e.g. glycolysis), NADPH serves as a sort of cellular coolant.

Of course, high levels of ROS can be advantageous for cancer cells. Damage to nucleic acids can promote mutagenesis or genomic instability, helping stimulate cell proliferation and immune system evasion. Even so, ROS levels that are too high will induce oxidative damage-induced cell death in cancer cells. Levine and Puzio-Kuter suggest that ROS thus have therapeutic potential, as they might be exploited to selectively kill cancer cells.

The Roles of Oncogenes and Tumor Suppressor Genes

In the last decade, research has revealed a number of key oncogenes (myc, NFKB, AKT, EGFR, IGF1, etc.) that activate the Ras, MAPK, mTOR, and phosphatidylinositoal 3-kinase (PI3K) pathways. These pathways stimulate the transcription of a number of important players in the glycolysis and glutaminolysis pathways. Myc alone activates the transcription of over 1,000 genes related to cell growth and metabolism. For example, it enhances transcription of glutaminase-1, which produces glutamate from glutamine. Myc also represses miR-23a and miR-23b, which increases expression of their intended target, mitochondrial glutaminase-1.

The famous P53 tumor-suppressor protein has several important roles in the regulation of cellular metabolism:

Reduces glucose uptake, by repressing transcription of the GLUT1 and GLUT4 transporters.
Decreases glycolysis, by activating the TIGAR gene which leads to diversion of glucose through the PPP.
Increases use of the TCA cycle and oxidative phosphorylation in the mitochondria, by transcribing glutaminase-2 and cytochrome C oxidase
Controls ROS levels through regulation of genes such as Sestrins 1, 2, 3, and 4.

TP53 also stimulates transcription of PTEN, IGF1BP3, TSC2, and the beta sub-unit of AMPK, all of which are negative regulators of the PI3K-AKT and mTOR pathways. The loss of tumor suppressors TP53 and PTEN, along with concurrent activation of AKT and mTOR pathways, results in high hypoxia-induced factor (HIF) activity. As you might guess from its name, HIF is regulated by the cell’s response to hypoxia. The absence of oxygen allows HIF-1A and HIF-2A proteins to form dimers with their beta-counterparts (HIF-1B and HIF-2B), stabilizing the HIF transcription factor complex. HIF increases the transcription of hundreds of genes, promoting angiogenesis, cell migration, and survival. It also regulates genes involved in energy metabolism, including lactate dehydrogenase A and pyruvate dehydrogenase kinase, both of which keep pyruvate away from the mitochondria, and 9 of the 10 enzymes required for glycolysis.

Known Driver Mutations

Mutations in the genes that encode TCA cycle components (enzymes) have been linked to human cancers. Rare mutations in succinate dehydrogenase (SDH) and fumarate hydratase (FHY) are initiating events in familial paraganalioma and papillary renal cell cancer. In glioblastoma multiforme (GBM), mutations in the gene encoding isocitrate dehydrogenase 1 (IDH1) block the conversion of isocitrate to alpha-ketoglutarate and generation of NADPH. IDH1/IDH2 mutations are recurrent in gliomas and leukemias, and recently have been shown to produce a new enzymatic activity: the reduction of alpha-ketoglutarate to 2-hydrozyglutarate, an “oncometabolite”.

Clearly, the metabolic switch observed by Warburg is important for the development, growth, and survival of many cancers. While some of the mechanisms by which tumors induce the “Warburg effect” have been elucidated, it’s likely that many of them remain to be discovered. Consideration of a gene’s involvement in cell metabolism or its regulation therefore will be critical as we sift through the mutations of newly sequenced cancer genomes.

References
Levine AJ, & Puzio-Kuter AM (2010). The control of the metabolic switch in cancers by oncogenes and tumor suppressor genes. Science (New York, N.Y.), 330 (6009), 1340-4 PMID: 21127244

The Year of the Exome

December 29, 2010 by Dan Koboldt

Next-generation sequencing technologies have dramatically altered the landscapes of genetics and genomics. There has been considerable interest in applying NGS platforms to selected regions of the human genome. Targeted sequencing of just the coding regions of the human genome — the exome — is of particular interest, because these regions presumably harbor the lion’s share of relevant genetic variation. In 2010, low-cost, high-throughput exome sequencing was made possible.

Two companies emerged as the titans of exome capture for sequencing:

Nimblegen, whose SeqCap EZ Exome kit claims to yield >10x coverage for 90% of the exons from 18,000 genes.
Agilent, whose SureSelect All Exon kits target 40-50 megabases of CCDS exons.

So which one is better? That’s a difficult question to answer, particularly because most groups with early access to these technologies are bound by non-disclosure agreements. Based on information in the public domain, such as the 20+ articles this year that employed exome sequencing, there is no clear winner. Some studies used Nimblegen, some used Agilent, and all of them achieved some kind of scientific success, or else we wouldn’t have read them. Clearly both of the companies are working hard to improve their products, and to incorporate the suggestions/requests of customers into their products. Both platforms saw a “version 2” release this year with a larger target space and other improvements. At Personal Genomes I saw at least two posters for studies where 1,000 or more exomes would be (or have been) sequenced. One thing is clear: Agilent and Roche/Nimblegen are selling exome kits like crazy.

Many fruits of exome sequencing have already come to market. A search for publications with ‘exome’ in the title turned up dozens of entries – two thirds of which were research articles on exome sequencing, and the other third, news briefs or reviews discussing its potential. A significant portion of these were low-hanging fruit: rare diseases of suspected genetic origin for which the causal gene(s) had not been identified. You can recognize these because they often have “syndrome” in the name: Fowler syndrome [13], Miller Syndrome [17], Kabuki syndrome [16], Sensenbrenner syndrome [7], Brown-Vialetto-van Laere syndrome [9] were all figured out (genetically) by exome sequencing this year. Mutations in a number of genes were linked to other rare inherited disorders:

WDR62 (severe brain malformations) [2]
GPSM2 (nonsyndromic hearing loss) [23]
STIM1 (fatal classic Kaposi sarcoma) [5]
ACAD9 (complex I deficiency) [8]
VCP (familial ALS) [10]
ADIPOQ (insulin resistance atherosclerosis) [4]
PIGV (hyperphosphatasia mental retardation) [12]
ANGPTL3 (familial combined hyperlipidemia) [15]
TGM6 (spinocerebellar ataxias) [24]
FADD (autoimmune lymphoproliferative syndrome) [3]

You might think that given how rare these diseases are, the impact of such findings is not very significant. But to an investigator who’s spent his or her life studying a rare disease (or the family that has it), the possibility of finding the disease-causing gene in a single experiment is simply irresistible. Though the sample numbers are small, the ramifications of these discoveries are not. They enable everyone in the world with a rare disease, even if this only totals a handful of patients, to be efficiently genotyped for causal mutations. They shed light on new and unanticipated mechanisms of disease pathogenesis. They’ve even justified having CXXorfXX genes in the set of human genes (C20orf54 was shown to cause Brown-Vialetto-van Laere syndrome [9]).

Larger studies of more common, more complex phenotypes are already beginning to pop up. A collaboration between the University of Copenhagen (Denmark) and BGI (Shenzen) has sequenced the exomes of at least 250 individuals. A subset of these (n=50) were used to study adaptation to high altitude [26], while another 200 were the subject of a recent Nature Genetics paper [14] entitled “Resequencing of 200 human exomes identifies an excess of low-frequency non-synonymous coding variants,” (whose inline title could have just been “Duh”).

Thus, exome sequencing has already enabled significant advances in the understanding of [rare] human diseases. In the coming year, I expect we’ll see a dramatic scale-up as exome sequencing is applied to thousands of patients with cancer, diabetes, autism, and other common diseases. Who knows? Maybe 2011 will be the year of exome sequencing as well.

References

Bainbridge, M. N., M. Wang, et al. “Whole exome capture in solution with 3 Gbp of data.” Genome Biol 11(6): R62.
Bilguvar, K., A. K. Ozturk, et al. “Whole-exome sequencing identifies recessive WDR62 mutations in severe brain malformations.” Nature 467(7312): 207-10.
Bolze, A., M. Byun, et al. “Whole-exome-sequencing-based discovery of human FADD deficiency.” Am J Hum Genet 87(6): 873-81.
Bowden, D. W., S. S. An, et al. “Molecular basis of a linkage peak: exome sequencing and family-based analysis identify a rare genetic variant in the ADIPOQ gene in the IRAS Family Study.” Hum Mol Genet 19(20): 4112-20.
Byun, M., A. Abhyankar, et al. “Whole-exome sequencing-based discovery of STIM1 deficiency in a child with fatal classic Kaposi sarcoma.” J Exp Med 207(11): 2307-12.
Cirulli, E. T., A. Singh, et al. “Screening the human exome: a comparison of whole genome and whole transcriptome sequencing.” Genome Biol 11(5): R57.
Gilissen, C., H. H. Arts, et al. “Exome sequencing identifies WDR35 variants involved in Sensenbrenner syndrome.” Am J Hum Genet 87(3): 418-23.
Haack, T. B., K. Danhauser, et al. “Exome sequencing identifies ACAD9 mutations as a cause of complex I deficiency.” Nat Genet 42(12): 1131-4.
Johnson, J. O., J. R. Gibbs, et al. “Exome sequencing in Brown-Vialetto-van Laere syndrome.” Am J Hum Genet 87(4): 567-9; author reply 569-70.
Johnson, J. O., J. Mandrioli, et al. “Exome sequencing reveals VCP mutations as a cause of familial ALS.” Neuron 68(5): 857-64.
Kozlowski, P., M. de Mezer, et al. “Trinucleotide repeats in human genome and exome.” Nucleic Acids Res 38(12): 4027-39.
Krawitz, P. M., M. R. Schweiger, et al. “Identity-by-descent filtering of exome sequence data identifies PIGV mutations in hyperphosphatasia mental retardation syndrome.” Nat Genet 42(10): 827-9.
Lalonde, E., S. Albrecht, et al. “Unexpected allelic heterogeneity and spectrum of mutations in Fowler syndrome revealed by next-generation exome sequencing.” Hum Mutat 31(8): 918-23.
Li, Y., N. Vinckenbosch, et al. “Resequencing of 200 human exomes identifies an excess of low-frequency non-synonymous coding variants.” Nat Genet 42(11): 969-72.
Musunuru, K., J. P. Pirruccello, et al. “Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia.” N Engl J Med 363(23): 2220-7.
Ng, S. B., A. W. Bigham, et al. “Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome.” Nat Genet 42(9): 790-3.
Ng, S. B., K. J. Buckingham, et al. “Exome sequencing identifies the cause of a mendelian disorder.” Nat Genet 42(1): 30-5.
Otto, E. A., T. W. Hurd, et al. “Candidate exome capture identifies mutation of SDCCAG8 as the cause of a retinal-renal ciliopathy.” Nat Genet 42(10): 840-50.
Rosenfeld, J. A., A. K. Malhotra, et al. “Novel multi-nucleotide polymorphisms in the human genome characterized by whole genome and exome sequencing.” Nucleic Acids Res 38(18): 6102-11.
Summerer, D., N. Schracke, et al. “Targeted high throughput sequencing of a cancer-related exome subset by specific sequence capture with a fully automated microarray platform.” Genomics 95(4): 241-6.
Teer, J. K. and J. C. Mullikin “Exome sequencing: the sweet spot before whole genomes.” Hum Mol Genet 19(R2): R145-51.
Tennessen, J. A., J. Madeoy, et al. “Signatures of positive selection apparent in a small sample of human exomes.” Genome Res 20(10): 1327-34.
Walsh, T., H. Shahin, et al. “Whole exome sequencing and homozygosity mapping identify mutation in the cell polarity protein GPSM2 as the cause of nonsyndromic hearing loss DFNB82.” Am J Hum Genet 87(1): 90-4.
Wang, J. L., X. Yang, et al. “TGM6 identified as a novel causative gene of spinocerebellar ataxias using exome sequencing.” Brain 133(Pt 12): 3510-8.
Worthey, E. A., A. N. Mayer, et al. “Making a definitive diagnosis: Successful clinical application of whole exome sequencing in a child with intractable inflammatory bowel disease.” Genet Med.
Yi, X., Y. Liang, et al. “Sequencing of 50 human exomes reveals adaptation to high altitude.” Science 329(5987): 75-8.
Zhao, Q., E. F. Kirkness, et al. “Systematic detection of putative tumor suppressor genes through the combined use of exome and transcriptome sequencing.” Genome Biol 11(11): R114.

CSHL 2010: Genomes Get Personal

September 22, 2010 by Dan Koboldt

Last week I attended the third annual “Personal Genomes” meeting at Cold Spring Harbor. The meeting opened with a keynote talk by NHGRI director Eric Green, who reminded us that finding the pathway to genomic medicine is the central mission of NHGRI. He mentioned several of the past successful initiatives that have yielded key findings concerning human genetic variation and its relationship to phenotype: The HapMap Project (common variation), the ENCODE Project (functional variation), and the 1,000 Genomes Project (rare variation), to name a few. He showed the absolutely stunning growth of the NHGRI-hosted genome-wide association study (GWAS) catalog, which currently holds ~2,600 associations from 780 publications.

Dr. Green also discussed the dichotomy of genetic architecture underlying human diseases, and took the position that while we’ve made substantial progress studying rare, monogenic, mendelian disorders (predominantly caused by coding mutations), we face a more daunting task with common, complex, multigenic diseases because he believes that these arise from primarily noncoding mutations.

Theme 1: Human Mutation Rates

Several talks addressed the topic of mutation rate in human genomes. Donald Conrad, who will be joining the WashU Genetics Department next year, presented mutation rate as a quantitative trait based on 1,000 Genomes Project trio data. Three of the primary sources of variation in mutation rate are age (males have 3x-6x higher rates), environment, and genetic variation (e.g. inherited aging disorders).

Lee Hood gave an excellent keynote on “Systems Genetics and P4 Medicine”, part of which was a discussion of mutation rate. His group uses whole-genome sequencing (WGS) of family cohorts (in this case, the Miller syndrome family quartet), focusing on the ~2.3 GBP of non-repetitive reference sequence. Using the family information and inheritance modeling, they identify de novo mutations in the offspring, which manifest as errors of Mendelian inheritance. Validation using a custom capture array for 60,000 candidate sites followed by deep sequencing showed that only 1/1,000 “new” mutations in the offspring were real; the vast majority proved to be sequencing errors. That works out to a mutation rate of 1.1 x 10-8, or roughly 70 mutations per child.

Lynn Jorde (Univ. of Utah) later gave a talk on directly estimating human mutation rate by WGS, also using the Miller syndrome quartet. Sequencing by Complete Genomics yielded >50x fold coverage per subject; there were ~4 million positions in the 1.8 Gbp of “useful” reference sequence in which at least one subject differed from the reference. Only 330,000 or so SNPs were novel (not known to dbSNP), and 20% of these proved to be sequencing errors. More array validation, more calculations, and the same answer as given by Dr. Hood: a mutation rate of 1.1 x 10-8.

Theme 2: Personal Cancer Genomes

Cancer genomes were another focus of the meeting. Sean Grimmond (Univ. of Brisbane, Queensland, Australia) presented some of his group’s work on pancreatic cancer as part of the International Cancer Genome Consortium (ICGC). Pancreatic is one of the most deadly forms of cancer; about 90% of patients diagnosed die within one year. Brisbane has assembled a very nice workflow from sample collection to sequencing, that includes pathology review, tumor dissection, QA, and microarray analysis to determine tumor cellularity. The sequencing strategy (WGS, exome, and RNA-seq) differs between high-cellularity (70-100%) and low-cellularity (~30%) tumors. The ultimate deliverable is a “tumor report” documenting cellularity estimates, microarray findings, cytogenetics, what sequencing was done, and what mutations were found.

James Brugarolas (UT Southwestern Medical Center) described the genome evaluation and functional studies of a patient with clear cell renal carcinoma. I learned a bit more about this form of cancer – 85% of tumors prove to be the “clear cell” carcinoma; common lesions include 3p loss (VHL gene) and 5q35 gain. This particular tumor underwent Illumina whole-genome sequencing to 35x coverage; some 46 somatic mutations were validated. One of these was in a gene whose protein product complexes with mTOR, the central player in a known cancer pathway. The tumor was successfully xenografted to a mouse model; some 43/46 somatic mutations were retained, and all had higher frequencies (similar to our findings on basal-like breast cancer). The xenograft let them test a few different cancer drugs – erlotinib (an EGFR inhibitor that had no effect), sunitinib (the front-line therapy for these patients, also no effect), and others. Intriguingly, however, the tumor was sensitive to an mTOR inhibitor compound.

Rick Wilson (The Genome Center at Washington University) gave a talk on whole-genome sequencing of leukemia patients at WashU. Of the 50+ leukemia patients sequenced to date, most have less than 20 valid protein-altering mutations. For most patients, low-resolution cytogenetic screens are the paradigm for disease classification and treatment decisions. Favorable-risk patients (17% of cases) undergo light chemotherapy. For adverse-risk patients (22% of cases), an all0-matched bone marrow transplant is the standard of care. That leaves a large body of patients (~61%) with “intermediate” risk according to cytogenetics; here, the correct treatment decision is harder to make. Better stratification of intermediate-risk patients is the first goal. Dr. Wilson related a fascinating case study, a 39-year-old female with suspected acute promyelotic leukemia, in which rapid-turnaround WGS was able to provide an accurate diagnosis that was not obtained by conventional FISH, and ultimately guided her treatment.

Theme 3: Genome Regulation and Epigenetics

Peter Laird (Univ. Southern California, LA) led us out of the genome to the epigenome with his talk on mining the cancer methylome. He argued that the first steps in oncogenesis may be epigenetic changes, specifically, the dysrgeulation of genes due to abnormal methylation. Dr. Laird presented what he’s calling the first cancer methylome – a tumor sample and matched normal control that underwent bisulfite treatment and sequencing to ~30x coverage. As expected, bisulfite sequencing yielded very accurate estimates of DNA methylation (r=0.97 with Illumina Infinium) but was able to do so across the complete human genome with base-pair resolution.

Theme 4: Exome Sequencing

There is a ton of exome sequencing going on. I saw at least two posters describing “whole” exome sequencing in 1,000 cases and 1,000 controls. I put “whole” in quotes because it’s not true at this point; people really shouldn’t be going around saying that the “whole exome” was sequenced. It’s more like 80-90% of known genes. Rick Lifton spoke about some of the valuable applications of exome sequencing – finding dominant reproductive lethal mutations, unraveling recessive traits with high locus heterogeneity, characterizing somatic mutations in cancer, and identifying rare variants associated with common disease. He described recently published work in which recessive mutations in WDR62 were linked to severe brain malformations by exome sequencing. Matt Bainbridge gave a nice overview of the exome sequencing currently under way at Baylor. So yes, it turns out that groups outside of WashU are doing exome sequencing too.