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`Human Molecular Genetics, 2009, Vol. 18, Review Issue 2
`doi:10.1093/hmg/ddp396
`
`R163–R168
`
`Cancer genome sequencing: a review
`Elaine R. Mardis and Richard K. Wilson
`
`The Genome Center, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA
`
`Received July 5, 2009; Revised and Accepted August 17, 2009
`
`A genomic era of cancer studies is developing rapidly, fueled by the emergence of next-generation sequen-
`cing technologies that provide exquisite sensitivity and resolution. This article discusses several areas
`within cancer genomics that are being transformed by the application of new technology, and in the process
`are dramatically expanding our understanding of this disease. Although, we anticipate that there will be
`many exciting discoveries in the near future, the ultimate success of these endeavors rests on our ability
`to translate what is learned into better diagnosis, treatment and prevention of cancer.
`
`INTRODUCTION
`
`In this past year, remarkable advances in our understanding of
`the mutational profiles and other disease-specific alterations of
`cancer genomes have been reported. In general,
`the field
`of cancer genomics has been impacted most profoundly by
`the application of next-generation sequencing technology,
`which has tremendously accelerated the pace of discovery
`while dramatically reducing the cost of data production.
`Hence,
`there has been a rapid progression from targeted
`gene re-sequencing using PCR and Sanger sequencing to
`either
`targeted, whole genome, or whole transcriptome
`sequencing using these massively parallel sequencing plat-
`forms,
`coupled with the
`requisite bioinformatics-based
`approaches to analyze the data. Within this brief timeframe,
`studies examining all known genes in a few samples to
`those examining hundreds of genes in hundreds of samples,
`to whole genome sequencing and analysis of a matched
`tumor/normal pair have been reported. There remains much
`to be learned about this complex disease, of course, but our
`fundamental understanding of which genes are mutated in
`cancer cells,
`the pathways that are impacted by these
`mutations, and how these data inform our models of cancer
`biology will undoubtedly evolve rapidly in the near future.
`
`STRUCTURAL VARIATION STUDIES
`
`A well-known characteristic of cancer genomes is that they are
`frequently altered in their gross chromosomal structure by
`amplification, deletion,
`translocation and/or
`inversion of
`chromosomal segments. Such alterations often, of course, con-
`comitantly alter genes in a number of ways that may be critical
`
`to cancer onset or progression. As such, important develop-
`ments in obtaining increasingly more detailed genome-wide
`characterizations of
`structural variation (SV)
`in tumor
`genomes have been described recently.
`Initially,
`these
`studies were conducted using signal strength-based analyses
`on high-density SNP array data sets, where tumor and
`normal genomic DNA were compared and any large-scale
`amplification or deletion signals were detected as continuous
`blocks of SNPs with higher than (amplification) or lower
`than (deletion) the normalized signal strength (1). The genes
`in these regions often are re-sequenced to identify mutations
`or are assayed for evidence of altered gene expression levels
`that correlate with a detected copy number alteration. Weir
`et al. (2) provided a powerful example of this approach
`using 384 lung adenocarcinoma samples in which they ident-
`ified a novel candidate proto-oncogene (NKX2-1/TITF1) in an
`amplified region of chromosome 14.
`Complementary to array-based methods, next-generation
`sequencing-based approaches are being applied to the SV
`problem at a higher level of resolution and complexity.
`Korbel et al. (3) first demonstrated that paired-end reads
`from next-generation sequencing platforms can be aligned to
`the genome and examined algorithmically to identify putative
`SV. Their approach was based on the identification of
`anomalously mapping read pairs that align several standard
`deviations outside the well-defined size range of the library
`itself. Read pairs that mapped too close together, too far
`apart, in an unpredicted orientation, or across chromosomes
`gave the indication of potential insertions, deletion, inversions
`or translocations in the sequenced genome. By these methods,
`we can obtain a much more precise view of genome-wide SV
`than by array-based analysis methods. Several groups have
`
`
`To whom correspondence should be addressed at: Department of Genetics, Washington University School of Medicine, 4444 Forest Park Boulevard,
`St. Louis, MO 63108, USA. Tel: þ1 3142861805; Fax: þ1 3142861810; Email: emardis@wustl.edu
`
`# The Author 2009. Published by Oxford University Press. All rights reserved.
`For Permissions, please email: journals.permissions@oxfordjournals.org
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`Human Molecular Genetics, 2009, Vol. 18, Review Issue 2
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`this
`of
`implementations
`advanced
`described
`recently
`approach; utilizing low coverage of a cancer genome with
`paired end reads (4,5). These methods fit nicely into a para-
`digm of whole genome sequencing followed by mutation
`discovery. Here, a small investment in paired end reads at
`light coverage can profile the extent of SV across a large
`number of tumor samples as a first step. This type of analysis
`not only identifies common copy number and structural variant
`loci, but can also allows a calculation of the deeper sequence
`coverage that will be required to characterize focal mutations
`(e.g. single nucleotide and small
`in/dels) in each tumor
`genome, since large-scale amplification (for example) will
`inflate the sequence coverage requirement. One can then
`obtain this deeper coverage with the same libraries used to
`produce the initial data set.
`
`TARGETED GENE SEQUENCING STUDIES
`
`The combination of PCR and Sanger sequencing to discover
`mutations in tumor genomes has proven a powerful initial
`approach, as evidenced by several recent studies that we
`describe below. Although studies using this method have tar-
`geted limited numbers of genes and successfully identified key
`somatic mutations in cancer genomes, the method recently has
`been applied to characterize hundreds of genes as well as the
`entire ‘exome’ (all known protein coding exons). In particular,
`two articles published in the same 2008 issue of Nature (6,7)
`demonstrated how targeted gene re-sequencing and variant
`detection can contribute significantly to our understanding of
`the types of genes carrying somatic mutations in a given
`cancer type [here,
`lung adenocarcinoma and glioblastoma
`multiforme (GBM)] by discovering novel genes mutated in
`each tumor type. In these studies, by virtue of sequencing
`large numbers of the same tumor type (based on pathological
`examination, tumor stage and grade), the results highlighted
`the cellular pathways putatively impacted by these mutations.
`Both articles arrived at important correlative conclusions by
`integrating the somatic mutation data with the results from
`other genome-wide characterizations of the same samples,
`such as array-based gene expression data, genome structure
`perturbation data [e.g. loss-of-heterozygosity (LOH), amplifi-
`cation or deletion of large chromosomal segments], and
`clinical data elements (e.g. outcome, response to therapy,
`etc.). For example, MAPK signaling, P53 signaling, cell
`cycle regulation and mTOR pathways are targeted in lung
`adenocarcinoma samples by combinations of point mutation,
`copy number amplification and deletion and LOH (7).
`Similarly, Vogelstein and colleagues have extended their
`initial efforts to characterize mutations by screening most of
`the known coding genes in the genome in several tumor
`types (8,9), to also include information about gene expression
`using next-generation sequencing of serial analysis of gene
`expression tags, and about genome copy number alterations
`from genotyping arrays. Their analyses combine data about
`somatically mutated genes with data about copy number
`alterations to identify candidate cancer genes (‘CAN-genes’),
`thereby generating evidence for mutations that are driving
`carcinogenesis
`(‘drivers’) versus having no impact on
`tumor growth (‘passengers’). Gene expression data inform
`
`the pathways analysis, by reflecting epigenetic alterations
`not detectable by sequencing or copy number analyses.
`This combined approach,
`in a study of GBM samples,
`resulted in the discovery of several commonly mutated
`genes, some impacting novel pathways. Among these was
`the surprising identification of an IDH1 mutation that was
`found in 18/149 (12%) cases, all occurring at
`the same
`residue (R132) (10). Using clinical data, several interesting
`correlations
`regarding the IDH1 mutation were made;
`namely that this mutation was more prevalent in younger
`GBM patients (mean age of 33 versus 53 years of age),
`more prevalent in patients developing secondary GBMs (that
`develop from low grade gliomas) and predicted a significantly
`improved prognosis (median overall survival of 3.8 versus 1.1
`years). In a follow-on study, this group evaluated the IDH1
`R132 and related IDH2 R172 mutation prevalence in a much
`wider
`range of
`tumor
`types that
`included 445 central
`nervous system (CNS) tumors and 494 non-CNS tumors
`(11). Here, the previously observed improved outcome for
`GBM patients carrying the IDH1 mutation was confirmed
`and extended to those carrying mutated IDH2 (median
`
`overall survival of 31 versus 15 months, at P ¼ 0.002), and
`
`for patients with anaplastic astrocytomas (median overall sur-
`vival of 65 versus 20 months, P , 0.001). An evaluation of the
`impact of one IDH1 mutation (R132H) and three IDH2
`mutations (R172G, K and M) on the function of the resulting
`proteins showed severely diminished activity in NADPH pro-
`duction relative to the wild-type enzymes.
`
`TRANSCRIPTOME CHARACTERIZATION
`
`As more detailed profiling of the cancer genome has devel-
`oped, the need for a full understanding of how these somatic
`alterations are manifest in the genes expressed by tumors
`has become pertinent. As in genome characterization,
`the
`use of next-generation sequencing of RNA extracted from
`tumor cells (‘RNA-seq’) produces a comprehensive data set
`for complete transcriptome characterization, as well as corre-
`lation to known genomic changes such as structural and
`copy number alterations, focused in/dels and single nucleotide
`mutations. Not only does this approach greatly expand the
`dynamic range of gene expression level data beyond the sen-
`sitivity limits of microarrays (12), but also it provides data
`that can be further mined in a number of ways (13) to
`enhance the understanding of the transcriptome in cancer.
`For example, RNA-seq data can identify allele-specific
`expression in the context of known mutations, verify the
`impact of a nonsense mutation, or provide a means of
`finding mutations in tumors as illustrated recently in ovarian
`tumors (14). Here, four granulosa-cell tumors (GCT) of the
`ovary were analyzed using whole transcriptome paired-end
`RNA sequencing, demonstrating that all four GCTs had a mis-
`sense point mutation in the FOXL2 gene. This gene encodes a
`transcription factor known to be crucial in granulosa cell
`development, and since the same mutation was determined
`to be present
`in additional GCTs of the same adult-type
`tumors, it is a potential driver mutation.
`These data also can be analyzed to detect alternative splice
`isoforms and fusion transcripts (15), as illustrated recently in a
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`very clever approach by Maher et al. (16) that identified both
`known and novel fusion transcripts in prostate cancer samples.
`This approach utilized a combination of two next-generation
`platforms to produce sequence reads that were combined to
`identify fusion transcripts from cancer cell lines. In particular,
`RNA-seq data from a longer-read technology (Roche/454) first
`identified putative fusion transcripts by virtue of their align-
`ment characteristics to the transcriptome, and then a second
`RNA-seq data set from short read length platform (Illumina
`Genome Analyzer) was aligned to the putative fusion tran-
`script reads to provide support for their presence. Using this
`paradigm, Maher et al. successfully identified known and
`novel fusion transcripts in the prostate cancer cell
`lines
`LnCaP and VCaP, and subsequently in RNA from several
`prostate tumor samples.
`RNA-seq also can build evidence for novel genes that
`previously have not been annotated due to lack of ESTs or
`were missed by in silico prediction (13,17). Hence, further
`development of methods that elucidate the complexity of the
`transcriptome in cancer will both support and enrich our
`understanding of the cancer genome and cancer biology.
`In addition to mRNA, the study of microRNAs (miRNAs)
`and their roles in regulating the expression of specific genes
`in both healthy and cancerous cells is rapidly expanding
`our comprehension about this aspect of cell biology (18).
`A recent study by Uziel et al. (19) demonstrated the inter-
`action
`between miRNA overexpression
`and
`a well-
`characterized signaling pathway, Sonic Hedgehog/Patched
`(SHH/PTCH) in medulloblastoma (MB). Having determined
`the overexpression of nine genes in the miR-17 – 92 cluster
`in an MB mouse model with constitutively activated SHH/
`PTCH signaling pathway, this group then tested and demon-
`strated similar miR-17 – 92 cluster upregulation in a subset
`of human MB tumors with constitutively activated SHH/
`PTCH. This study provided the first evidence that the SHH/
`PTCH signaling pathway and miR-17 – 92 functionally interact
`and contribute to both murine and human MB development.
`Similarly, Wyman et al. (20) and Nygaard et al. (21)
`demonstrated detection of novel miRNAs and miRNAs with
`differential expression in ovarian and breast cancer, respect-
`ively, using Roche/454 sequencing and miRNA discovery
`bioinformatics pipelines. Building upon these studies and
`others, numerous groups are now proposing miRNAs as
`prognostic or diagnostic markers for a variety of cancer
`types (22 – 25).
`
`WHOLE GENOME SEQUENCING
`
`The most significant impact of next-generation sequencing on
`cancer genomics has been the ability to re-sequence, analyze
`and compare the matched tumor and normal genomes of a
`single patient. With the significantly reduced cost of sequen-
`cing and tremendously enhanced throughput,
`it
`is now
`within the realm of possibility to sequence multiple patient
`samples of a given cancer type. Such efforts require not
`only data generation, but also the careful development of
`analytical tools and pipelines, supported by validation efforts
`that feedback into the analytical process, to enhance the sensi-
`tivity and specificity of variant discovery. Due to the complex
`
`nature of genome variation, the entire spectrum of potential
`mutations requires consideration, including germline suscepti-
`bility loci,
`somatic single nucleotide and small
`indel
`mutations, copy number alterations and structural variants.
`To-date, one publication has outlined such a study, describing
`the results obtained from sequencing and analysis of an acute
`myeloid leukemia genome (26). Several key concepts have
`emerged from this approach, including the use of high-density
`SNP genotype data to estimate genome sequence coverage by
`tracking the accuracy of sequence-based SNP calls at hetero-
`zygous loci, a step-wise approach to somatic single nucleotide
`variant discovery, and the use of read counts to establish the
`prevalence of somatic variants in the tumor cell population.
`(21-fold
`The basic analytical approach aligned tumor
`haploid coverage) and normal (14-fold haploid coverage)
`sequence reads to the reference human genome using the
`Maq alignment algorithm (27). As coverage accumulated
`during the generation of tumor and germline reads, Maq was
`used to call variant positions across the genome, and those
`calls were compared with the heterozygous loci determined
`from the overlapping set of SNP array genotype calls ident-
`ified by both Illumina and Affymetrix genotyping arrays.
`Sequence coverage was considered sufficient for mutation dis-
`covery once heterozygous calls from sequence data were made
`for .95% of these orthogonally determined heterozygous
`SNP positions. This approach toward monitoring genome
`coverage is now a cornerstone of our cancer genome
`re-sequencing pipeline.
`Somatic mutation discovery requires a number of steps to
`eliminate from consideration all known sequence variants,
`typically by (1) comparison with other sequenced genomes
`(via dbSNP) and to other resources for variant discovery
`such as the 1000 Genomes Project (www.1000genomes.org),
`followed by (2) comparison at
`remaining variant
`sites
`between the tumor and the normal genome. The approach
`also takes into consideration two primary measures of
`quality in order to distinguish high- from low-quality variants
`in the latter comparison. These primary measures include first,
`a cumulative base-calling quality value that is summed from
`the individual quality values of each base identifying the puta-
`tive variant (assigned by the Illumina analysis pipeline) and
`second, a mapping quality value assigned by Maq that
`indicates the genome-wide uniqueness of each aligned read.
`Nonetheless, false positives do occur in this analysis, as do
`false negatives. False positives tend to result from incorrect
`interpretation of one or more data elements considered by
`the multicomponent analysis algorithm, often due to non-
`unique read placement or to a missing variant call in the
`matched normal sequence. The false negatives are harder to
`evaluate, but mainly appear to be due to lack of sufficient
`read support for a true variant in the tumor. On one hand a
`reasonably high false positive rate is desired so true mutations
`are not missed, but on the other it is important to known which
`predictions are incorrect. Because of this, performing an
`orthogonal validation step using PCR-directed sequencing or
`genotyping to establish false from true positives for all puta-
`tive somatic variants in genes or in regulatory/conserved
`regions of the genome should be done.
`One of the key aspects of evaluating somatic mutations in
`cancer genomes is that the collective sequencing read pool
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`Figure 1. Summary of readcount data obtained for ten somatic mutations and two validated SNPs in the AML primary tumor, AML relapse tumor and normal
`skin specimens. As described in the text, all heterozygous mutations were determined by readcount data to be present at around 50% prevalence in the tumor cells
`with the exception of the FLT3 internal tandem duplication mutant. The variant alleles in the primary and relapse tumor samples are statistically different from
`that of the skin sample for all mutations. Note that the normal skin sample was contaminated with leukemic cells containing the somatic mutations, as the
`patient’s white blood cell count was 105 000 when the skin punch biopsy was obtained.
`
`represents a census of the genomic DNA contributed from all
`cancer cells used for DNA isolation. One challenge of this
`pooled approach is to determine what proportion of those
`cells carried each identified mutation. Information about the
`prevalence of any mutation in a cell population allows one
`to infer how early in the path toward cancer development
`that particular mutation occurred. The digital nature of next-
`generation sequencing allows us to evaluate this prevalence,
`since each read in the sequenced pool of fragments represents
`a single original DNA fragment from that cancer cell census.
`For example, since many mutations will present as heterozy-
`gous, we expect that 50% of the reads in a pure tumor cell
`population will contain the variant. Obviously, this proportion-
`ality will be influenced by the percentage of tumor cells in a
`sample, so a correction factor is applied based either on esti-
`mates from pathology review or by a more precise measure
`that calculates the percentage of normal reads present in the
`tumor read population at known/validated somatic sites in
`that tumor genome (L. Ding, personal communication). This
`type of analysis was applied to the first AML genome sequence,
`demonstrating that all somatic mutations were found in virtually
`all of the cells of the tumor, except for the FLT3 internal
`tandem duplication (Fig. 1), which is known from mouse
`models to not be an initiating mutation in AML (28).
`We recently published our findings from sequencing a
`second AML genome and matched normal (29), where we
`employed the aforementioned concepts,
`identifying nine
`single nucleotide somatic variants in genes,
`two genic
`indels, and 54 somatic single nucleotide variants in known
`regulatory or highly conserved regions of
`the genome.
`Although none of the novel somatic variants identified in the
`first AML genome were recurrent among 187 other AML
`tumor genomes tested, one mutation found in the second
`AML genome analysis proved to be recurrent in 8.2% of
`
`those samples. This gene was IDH1, mutated at the exact
`R132 site also identified in GBM (10), as described earlier.
`Unlike Parsons et al., however, our correlation analysis
`among the 187 AML patients, combined with the clinical
`data, indicated that in AML, the IDH1 mutations portend a
`significantly worse outcome by Kaplan – Meier analysis for
`those patients who have normal cytogenetics and lack the
`NPMc and FLT3 mutations (Fig. 2). This finding demonstrates
`the power of the genomics approach, and highlights how new
`insights into cancer biology will result from further cancer
`genome sequencing.
`
`CANCER GENOME SEQUENCING: THE FUTURE
`
`One clear trend in cancer genome sequencing is that the con-
`tinuing advance of next-generation technology in terms of data
`capacity per instrument run and read length will accelerate the
`rate of sequencing whole genomes, at ever-decreasing costs.
`Since next-generation platforms can produce data to character-
`ize gene expression, methylation, histone packaging, transcrip-
`tion factor and other regulatory protein binding positions, and
`so on, we can build data sets that quite comprehensively
`characterize a broad spectrum of genomic alterations among
`sets of tumor samples.
`A key question is what the planned sequencing of hundreds
`of tumors might reveal? For example,
`it is not yet clear
`whether the cancer-critical somatic alterations we identify
`will be found to recurrently affect specific genes, or if the
`combination of recurrent and ‘private’ mutations will define
`each cancer genome and hence, its treatment. We also need
`to understand the potential role of inherited genomic variation
`in shaping the onset of cancer and its outcomes, which is one
`reason sequencing a matched normal sample from each patient
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`Figure 2. Kaplan – Meier survival analysis of AML patients. Overall survival is shown for patients with (blue) or without (IDH1) mutations. (A) Entire AML
`cohort. (B) AML patients with normal cytogenetics. (C) AML patients with normal cytogenetics and non-mutated NPM1. (D) AML patients with normal cyto-
`genetics, non-mutated NPM1 and non-mutated FLT3. Differences were assessed by Log rank analysis.
`
`is so important. Determining the genomic landscape of
`hundreds of tumors ultimately will dictate whether each
`cancer genome will require a full genome variation profile
`as a diagnostic component of individualized treatment. It is
`imperative also to focus some genome characterization
`efforts toward elucidating the genomic changes that dis-
`tinguish primary from metastatic disease.
`Once we understand the genomic landscape of cancer, what
`should follow? Whereas genome-wide characterization of
`tumors likely will yield important clues about the genes that
`play a role in carcinogenesis or metastasis, we must be pre-
`pared to follow-up on these clues by carrying out functional
`screens of altered genes with commensurately high-throughput
`capabilities. Functional screening would aim to identify those
`somatic alterations that are initiating carcinogenesis, or pro-
`moting metastasis, thereby establishing candidate genes and
`their protein products for targeted therapy development or
`testing, as well as for diagnostic/prognostic assay develop-
`ment. Luo et al. (30) have published such one approach,
`employing pooled short hairpin RNA (shRNA) screening para-
`digms of cancer cell lines that identified genes essential for
`growth and related phenotypes in these cells, as well as
`genes involved in the response of cancer cells to tumoricidal
`agents. Lynda Chin and colleagues (31) recently published
`an elegant example of a complete genomics-to-function
`paradigm, first identifying a genomic region at 5p13 that
`was commonly amplified in several cancer
`types (lung,
`ovarian, prostate, breast, melanoma), and then using integrated
`analysis of this region to pinpoint the Golgi-associated protein
`GOLPH3 for further study. Using a variety of clues from the
`results of in vitro shRNA knock-down of GOLPH3 in cell
`lines that either did or did not contain the 5p13 amplification,
`
`to in vivo GOLPH3 overexpression in these same cell lines,
`to clues from yeast genetics that linked GOLPH3 to the trans-
`Golgi network and ultimately as a determinant of rapamycin
`sensitivity as a regulator of mTOR, the study established
`GOLPH3 as a first-in-class Golgi oncoprotein. This result
`further emphasizes the need for multiple lines of evidence to
`support functional and mechanistic roles for the genomic
`alterations we are finding in cancer genomics today.
`
`ACKNOWLEDGEMENTS
`
`The authors wish to acknowledge Drs Devin Locke and
`Li Ding, for their critical reading of the manuscript.
`
`Conflicts of Interest statement. None declared.
`
`FUNDING
`
`We thank the National Human Genome Research Institute for
`support of this research via U54-HG003079 (R.K.W.).
`
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