Digital karyotyping
`
`Tian-Li Wang*, Christine Maierhofer†, Michael R. Speicher†, Christoph Lengauer*, Bert Vogelstein*,
`Kenneth W. Kinzler*, and Victor E. Velculescu*‡
`
`*The Howard Hughes Medical Institute and The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University Medical Institutions,
`Baltimore, MD 21231; and †Institute of Human Genetics, Technical University Munich and GSF-Neuherberg, D-81675 Munich, Germany
`
`Contributed by Bert Vogelstein, October 9, 2002
`
`Alterations in the genetic content of a cell are the underlying cause
`of many human diseases, including cancers. We have developed a
`method, called digital karyotyping, that provides quantitative
`analysis of DNA copy number at high resolution. This approach
`involves the isolation and enumeration of short sequence tags
`from specific genomic loci. Analysis of human cancer cells by using
`this method identified gross chromosomal changes as well as
`amplifications and deletions,
`including regions not previously
`known to be altered. Foreign DNA sequences not present in the
`normal human genome could also be readily identified. Digital
`karyotyping provides a broadly applicable means for systematic
`detection of DNA copy number changes on a genomic scale.
`
`Somatic and hereditary variations in gene copy number can lead
`
`to profound abnormalities at the cellular and organismal levels.
`In human cancer, chromosomal changes, including the deletion of
`tumor suppressor genes and the amplification of oncogenes, are
`hallmarks of neoplasia (1). Single copy changes in specific chro-
`mosomes or smaller regions can result in a number of develop-
`mental disorders, including Down, Prader Willi, Angelman, and cri
`du chat syndromes (2). Current methods for the analysis of cellular
`genetic content include comparative genomic hybridization (CGH)
`(3), representational difference analysis (4), spectral karyotyping兾
`multiplex-fluorescence in situ hybridization (M-FISH) (5, 6), mi-
`croarrays (7–10), and traditional cytogenetics. Such techniques
`have aided in the identification of genetic aberrations in human
`malignancies and other diseases (11–14). However, methods em-
`ploying metaphase chromosomes have a limited mapping resolu-
`tion (⬇20 Mb; ref. 15), and therefore cannot be used to detect
`smaller alterations. Recent implementation of CGH to microarrays
`containing genomic or transcript DNA sequences provides im-
`proved resolution, but is currently limited by the number of
`sequences that can be assessed (16) or by the difficulty of detecting
`certain alterations such as homozygous deletions (9).
`To circumvent these limitations, we have developed a method
`that permits the comprehensive examination of cellular DNA
`content based on the quantitative analysis of short fragments of
`genomic DNA. This method is based on two concepts. First,
`short sequence tags (21 bp each) can be obtained from specific
`locations in the genome. These tags generally contain sufficient
`information to uniquely identify the genomic loci from which
`they were derived. Such tags are in principle related to those
`obtained in the serial analysis of gene expression (SAGE)
`approach (17, 18), but are obtained from genomic DNA, rather
`than from mRNA, and are isolated by using different methods.
`Second, populations of tags can be directly matched to the
`assembled genomic sequence, allowing observed tags to be
`sequentially ordered along each chromosome. Digital enumer-
`ation of tag observations along each chromosome can then
`be used to quantitatively evaluate DNA content with high
`resolution.
`
`Materials and Methods
`Digital Karyotyping Library Construction. Digital karyotyping was
`performed on DNA from colorectal cancer cell lines DiFi and
`Hx48, and from the lymphoblastoid cells of a normal individual
`(GM12911, obtained from Coriell Cell Repositories, Camden,
`NJ). Genomic DNA was isolated by using DNeasy or QIAamp
`
`DNA blood kits (Qiagen, Valencia, CA) and following the
`manufacturer’s protocols. For each sample, 1 ␮g of genomic
`DNA was sequentially digested with mapping enzyme SacI,
`ligated to 20–40 ng of biotinylated linker (5⬘-biotin-TTTG-
`CAGAGGTTCGTAATCGAGTTGGGTGAGC-3⬘, 5⬘-phos-
`phate-CACCCAACTCGATTACGAACCTCTGC-3⬘; Inte-
`grated DNA Technologies, Coralville, IA) by using T4 ligase
`(Invitrogen), and then digested with the fragmenting enzyme
`NlaIII. DNA fragments containing biotinylated linkers were
`isolated by binding to streptavidin-coated magnetic beads (Dy-
`nal, Oslo). The remaining steps were similar to those described
`for LongSAGE of cDNA (18). In brief, linkers containing MmeI
`recognition sites were ligated to captured DNA fragments, and
`tags were released with MmeI (University of Gdansk Center for
`Technology Transfer, Gdansk, Poland, and New England Bio-
`labs). The tags were ligated to form ditags, and the ditags were
`isolated, and then ligated to form concatemers, which were
`cloned into pZero (Invitrogen). The sequencing of concatemer
`clones was performed by using the Big Dye terminator v3.0 kit
`(Applied Biosystems) and analyzed with an SCE-9610 192-
`capillary electrophoresis system (SpectruMedix, State College,
`PA) or by contract sequencing at Agencourt (Beverly, MA).
`Digital karyotyping sequence files were trimmed by using PHRED
`sequence analysis software (CodonCode, Dedham, MA), and
`21-bp genomic tags were extracted by using the SAGE2000 soft-
`ware package, which identifies the fragmenting enzyme site
`between ditags, extracts intervening tags, and records them in a
`database. Detailed protocols for performing digital karyotyping
`and software for the extraction and analysis of genomic tags are
`available at www.digitalkaryotyping.org.
`
`Simulations. The theoretical sensitivity and specificity of digital
`karyotyping for copy number alterations was evaluated by using
`Monte Carlo simulations. For each alteration type, 100 simulations
`were performed as follows: Either 100,000 or 1,000,000 experimen-
`tal tags were randomly assigned to 730,862 equally spaced virtual
`tags in a genome containing a single randomly placed copy number
`alteration of a predefined size and copy number. Moving windows
`containing the same number of virtual tags as the simulated
`alteration were used to evaluate tag densities along the genome.
`Tag density values of ⬎4.9, ⬍0.1, ⬍0.6, and ⬎1.4 located within the
`area of amplifications, homozygous deletions, heterozygous losses,
`and subchromosomal gains, respectively, were considered true
`positives. Tag densities of these values in areas outside the altered
`region were considered false positives.
`
`Data Analysis. All tags adjacent to the NlaIII fragmenting enzyme
`(CATG) sites closest to SacI mapping enzyme sites were compu-
`tationally extracted from the human genome sequence (University
`of California, Santa Cruz, June 28, 2002 Assembly, http://
`genome.ucsc.edu/). Of the 1,094,480 extracted tags, 730,862 were
`
`Abbreviations: CGH, comparative genomic hybridization; EBV, Epstein–Barr virus; SAGE,
`serial analysis of gene expression.
`‡To whom correspondence should be addressed at: The Sidney Kimmel Comprehensive
`Cancer Center at Johns Hopkins, 1650 Orleans Street, Room 5M05, Baltimore, MD 21231-
`1001. E-mail: velculescu@jhmi.edu.
`
`16156 –16161 兩 PNAS 兩 December 10, 2002 兩 vol. 99 兩 no. 25
`
`www.pnas.org兾cgi兾doi兾10.1073兾pnas.202610899
`
`SEQUENOM EXHIBIT 1005
`
`

`

`GENETICS
`
`Schematic of the digital karyotyping approach. Colored boxes rep-
`Fig. 1.
`resent genomic tags. Small ovals represent linkers. Large blue ovals represent
`streptavidin-coated magnetic beads. See text for details.
`
`steps. DNA fragments containing biotinylated linkers are separated
`from the remaining fragments by using streptavidin-coated mag-
`netic beads (step 3). New linkers, containing a 6-bp site recognized
`by MmeI, a type IIS restriction endonuclease (18), are ligated to the
`captured DNA (step 4). The captured fragments are cleaved by
`MmeI, releasing 21-bp tags (step 5). Each tag is thus derived from
`the sequence adjacent to the fragmenting enzyme site that is closest
`to the nearest mapping enzyme site. Isolated tags are self-ligated to
`form ditags, PCR-amplified en masse, concatenated, cloned, and
`sequenced (step 6). As described for SAGE (17), the formation of
`ditags provides a robust method to eliminate potential PCR-
`induced bias during the procedure. Current automated sequencing
`technologies identify up to 30 tags per concatemer clone, allowing
`for the analysis of ⬇100,000 tags per day by using a single 384-
`capillary sequencing apparatus. Finally, tags are computationally
`extracted from sequence data and matched to precise chromosomal
`locations, and tag densities are evaluated over moving windows to
`detect abnormalities in DNA sequence content (step 7).
`The sensitivity and specificity of digital karyotyping for de-
`tecting genome-wide changes were expected to depend on
`several factors. First, the combination of mapping and fragment-
`ing enzymes determines the minimum size of the alterations that
`can be identified. For example, the use of SacI and NlaIII as
`mapping and fragmenting enzymes, respectively, was predicted
`to result in a total of 730,862 virtual tags (defined as all possible
`tags that could theoretically be obtained from the human
`genome). These virtual tags were spaced at an average of 3,864
`bp, with 95% separated by 4 bp to 46 kb. Practically, this
`resolution is limited by the number of tags actually sampled in
`a given experiment and the type of alteration present (Table 1).
`Monte Carlo simulations confirmed the intuitive concept that
`
`obtained from unique loci in the genome and were termed virtual
`tags. The experimentally derived genomic tags obtained from NLB,
`DiFi, and Hx48 cells were electronically matched to these virtual
`tags. The experimental tags with the same sequence as virtual tags
`were termed filtered tags and were used for subsequent analysis.
`The remaining tags corresponded to repeated regions, sequences
`not present in the current genome database release, polymorphisms
`at the tag site, or sequencing errors in the tags or in the genome
`sequence database. Tag densities for sliding windows containing N
`virtual tags were determined as the sum of experimental tags
`divided by the average number of experimental tags in similar sized
`windows throughout the genome. Tag densities were dynamically
`analyzed in windows ranging from 50 to 1,000 virtual tags. For
`windows of 1,000 virtual tags, DiFi tag densities were normalized
`to evaluated NLB tag densities in the same sliding windows to
`account for incomplete filtering of tags matching repetitive se-
`quences, and visualized by using tag density maps. For windows
`⬍1,000 virtual tags, a bitmap viewer was developed that specifically
`identified tag densities above or below defined thresholds.
`
`Quantitative PCR. Genome content differences between DiFi and
`normal cells were determined by quantitative real-time PCR
`performed on an iCycler apparatus (Bio-Rad). DNA content
`was normalized to that of Line-1, a repetitive element for which
`copy numbers per haploid genome are similar among all human
`cells (normal or neoplastic). Copy number changes per haploid
`genome were calculated by using the formula 2(Nt⫺Nline)⫺(Dt⫺Dline)
`where Nt is the threshold cycle number observed for an exper-
`imental primer in the normal DNA sample, Nline is the threshold
`cycle number observed for a Line-1 primer in the normal DNA
`sample, Dt is the average threshold cycle number observed for
`the experimental primer in DiFi, and Dline is the average
`threshold cycle number observed for a Line-1 primer in DiFi.
`Conditions for amplification were as follows: one cycle of 94°C
`for 2 min, followed by 50 cycles of 94°C for 20 sec, 57°C for 20
`sec, and 70°C for 20 sec. Threshold cycle numbers were obtained
`by using ICYCLER V 2.3 software. PCRs for each primer set were
`performed in triplicate and threshold cycle numbers were aver-
`aged. For analysis of homozygous deletions, the presence or
`absence of PCR products was evaluated by gel electrophoresis.
`PCR primers were designed by using Primer 3 (www-
`genome.wi.mit.edu/cgi-bin/primer/primer3㛭www.cgi) to span a
`100- to 200-bp nonrepetitive region and were synthesized by
`GeneLink (Hawthorne, NY). Primer sequences for each region
`analyzed in this study are included in Table 4, which is published
`as supporting information on the PNAS web site, www.pnas.org.
`
`Karyotyping and Comparative Genomic Hybridization. CGH was
`performed as described (19), and hybridization data were
`analyzed with Leica Microsystems (Deerfield, IL) imaging
`software. Karyotyping was performed with conventional
`procedures.
`
`Results
`Principles of Digital Karyotyping. The basic concepts of digital
`karyotyping have been implemented as described in Fig. 1.
`Genomic DNA is cleaved with a restriction endonuclease (mapping
`enzyme) that is predicted to cleave genomic DNA into several
`hundred thousand pieces, each, on average, ⬍10 kb in size (step 1).
`A variety of different endonucleases can be used for this purpose,
`depending on the resolution desired. In the current study, we used
`SacI, with a 6-bp recognition sequence. Biotinylated linkers are
`ligated to the DNA molecules (step 2) and then digested with a
`second endonuclease (fragmenting enzyme) that recognizes 4-bp
`sequences (step 3). As there are, on average, 16 fragmenting
`enzyme sites between every 2 mapping enzyme sites (46兾44), the
`majority of DNA molecules in the template are expected to be
`cleaved by both enzymes and, thereby, be available for subsequent
`
`Wang et al.
`
`PNAS 兩 December 10, 2002 兩 vol. 99 兩 no. 25 兩 16157
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`

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`Table 1. Theoretical detection of copy number alterations* by using digital karyotyping
`
`Size of alteration†
`
`Amplification, %
`(copy number ⫽ 10)
`
`Homozygous deletion, %
`(copy number ⫽ 0)
`
`Heterozygous loss, %
`(copy number ⫽ 1)
`
`Subchromosomal gain, %
`(copy number ⫽ 3)
`
`No. of base pairs No. of virtual tags
`
`100,000
`
`1,000,000
`
`100,000
`
`1,000,000
`
`100,000
`
`1,000,000
`
`100,000
`
`1,000,000
`
`100,000
`200,000
`600,000
`2,000,000
`4,000,000
`
`30
`50
`150
`500
`1,000
`
`100
`100
`100
`100
`100
`
`100
`100
`100
`100
`100
`
`0.06
`1
`96
`100
`100
`
`100
`100
`100
`100
`100
`
`0.008
`0.01
`0.07
`11
`99
`
`0.02
`3
`100
`100
`100
`
`0.006
`0.01
`0.05
`3
`97
`
`0.08
`0.7
`100
`100
`100
`
`*Copy number alteration refers to the gain or loss of chromosomal regions in the context of the normal diploid genome, where the normal copy number is 2.
`The limiting feature of these analyses was not sensitivity for detecting the alteration, as this was high in every case shown (⬎99% for amplifications and
`homozygous deletions and ⬎92% for heterozygous losses or subchromosomal gains). What was of more concern was the positive predictive value (PPV), that
`is, the probability that a detected mutation represents a real mutation. PPVs were calculated from 100 simulated genomes, using 100,000 or 1,000,000 filtered
`tags, and are shown in the table as percentages.
`†Size of alteration refers to the approximate size of the genomic alteration assuming an average of 3,864 bp between virtual tags.
`
`fewer tags are needed to detect high-copy-number amplifications
`than are needed to detect homozygous deletions or low-copy-
`number changes in similar sized regions (Table 1). Such simu-
`lations were used to predict the size of alterations that could be
`reliably detected given a fixed number of experimentally sam-
`pled tags. For example, the analysis of 100,000 tags would be
`expected to reliably detect a 10-fold amplification ⱖ100 kb,
`homozygous deletions ⱖ600 kb, or a single gain or loss of regions
`ⱖ4 Mb in size in a diploid genome (Table 1).
`
`Analysis of Whole Chromosomes. We characterized 210,245
`genomic tags from the lymphoblastoid cells of a normal indi-
`vidual (NLB) and 171,795 genomic tags from the colorectal
`cancer cell line (DiFi) by using the mapping and fragmenting
`enzymes described above. After filtering to remove tags that
`were within repeated sequences or were not present in the
`human genome (see Materials and Methods), we recovered a
`
`total of 111,245 and 107,515 filtered tags from the NLB and DiFi
`libraries, respectively. Tags were ordered along each chromo-
`some, and average chromosomal tag densities, defined as the
`number of detected tags divided by the number of virtual tags
`present in a given chromosome, were evaluated (Table 2).
`Analysis of the NLB data showed that the average tag density for
`each autosomal chromosome was similar, ⬇0.16 ⫾ 0.04. The
`small variations in tag densities were likely because of the
`incomplete filtering of tags matching repeated sequences that
`were not currently represented in the genome databases. The X
`and Y chromosomes had average densities about half this level,
`0.073 and 0.068, respectively, consistent with the normal male
`karyotype of these cells. Analysis of the DiFi data revealed a
`much wider variation in tag density, ranging from 0.089 to 0.27
`for autosomal chromosomes. In agreement with the origin of
`these tumor cells from a female patient (20), the tag density of
`the Y chromosome was 0.00. Estimates of chromosome number
`
`Table 2. Chromosome number analysis
`
`Chromosome
`
`No. of
`virtual tags
`
`No. of
`observed tags
`
`Tag density
`
`No. of
`observed tags
`
`Tag density
`
`Chromosome
`content*
`
`NLB
`
`DiFi
`
`1
`2
`3
`4
`5
`6
`7
`8
`9
`10
`11
`12
`13
`14
`15
`16
`17
`18
`19
`20
`21
`22
`X
`Y
`Total
`
`61,694
`61,944
`46,337
`41,296
`43,186
`41,633
`38,928
`35,033
`30,357
`37,320
`37,868
`30,692
`22,313
`23,378
`22,409
`23,028
`22,978
`18,431
`16,544
`20,585
`9,245
`12,579
`30,737
`2,347
`730,862
`
`10,090
`9,422
`6,732
`5,581
`6,216
`6,120
`5,836
`5,009
`4,909
`6,045
`6,081
`4,631
`3,012
`3,658
`3,581
`4,119
`4,298
`2,712
`3,271
`3,573
`1,465
`2,476
`2,249
`159
`111,245
`
`0.16
`0.15
`0.15
`0.14
`0.14
`0.15
`0.15
`0.14
`0.16
`0.16
`0.16
`0.15
`0.13
`0.16
`0.16
`0.18
`0.19
`0.15
`0.20
`0.17
`0.16
`0.20
`0.073
`0.068
`0.15
`
`6,991
`9,545
`7,379
`3,666
`4,136
`7,291
`9,875
`3,260
`4,861
`4,865
`5,432
`4,056
`5,197
`3,171
`4,159
`3,201
`3,145
`2,389
`3,589
`5,460
`1,036
`1,655
`3,147
`9
`107,515
`
`0.11
`0.15
`0.16
`0.089
`0.10
`0.18
`0.25
`0.093
`0.16
`0.13
`0.14
`0.13
`0.23
`0.14
`0.19
`0.14
`0.14
`0.13
`0.22
`0.27
`0.11
`0.13
`0.10
`0.00
`0.15
`
`1.4
`2.0
`2.2
`1.3
`1.3
`2.4
`3.4
`1.3
`2.0
`1.6
`1.8
`1.8
`3.5
`1.7
`2.3
`1.6
`1.5
`1.8
`2.2
`3.1
`1.4
`1.3
`1.4
`0.06
`2.0
`
`*DiFi chromosomal content is calculated for autosomal chromosomes as 2 times the ratio of DiFi tag densities to corresponding NLB tag
`densities, and for the X chromosome as the ratio of DIFI tag density to NLB tag density. Underlined values represent autosomal
`chromosome content ⬍1.5, while boldface values represent autosomal chromosome content ⬎3.
`
`16158 兩 www.pnas.org兾cgi兾doi兾10.1073兾pnas.202610899
`
`Wang et al.
`
`

`

`GENETICS
`
`Table 3. Quantitative analysis of amplifications and deletions
`
`Type of
`alteration
`
`Amplifications
`
`Deletions
`
`Location
`
`Chromosome 7:
`54.54–55.09 Mb
`Chromosome 13:
`30.36–32.72 Mb
`Chromosome 20:
`60.54–60.83 Mb
`Chromosome 18:
`49.34–51.67 Mb
`Chromosome 5:
`59.18–59.92 Mb
`Chromosome X:
`106.44–107.25 Mb
`
`Copy number*
`
`Digital
`karyotyping
`
`Quantitative
`PCR
`
`125
`
`139
`
`6.4
`
`5.4
`
`0
`
`0
`
`0
`
`5.7
`
`2.8
`
`0
`
`0
`
`0.4
`
`*Copy number values are calculated per haploid genome as described in
`Materials and Methods.
`
`containing 1,000 virtual tags (⬇4 Mb), as windows of this size were
`predicted to reliably detect such alterations (Table 1). For the NLB
`sample, tag density maps showed uniform content along each
`chromosome, with small variations (⬍1.5-fold) present over local-
`ized regions, presumably because of the overrepresentation of tags
`matching repeated sequences (data not shown). In contrast, the
`DiFi tag density map (normalized to the NLB data) revealed
`widespread changes, including apparent losses in large regions of
`5q, 8p, and 10q, and gains of 2p, 7q, 9p, 12q, 13q, and 19q (Fig. 2
`and Fig. 5, which is published as supporting information on the
`PNAS web site). These changes included regions of known tumor
`suppressor genes (21) and other areas commonly altered in colo-
`rectal cancer (11, 12, 22). These alterations were confirmed by
`chromosomal CGH analyses, which revealed aberrations that were
`largely consistent with digital karyotype analyses in both location
`and amplitude (Fig. 2 and Fig. 5).
`
`Analysis of Amplifications. To identify amplifications, which typ-
`ically involve regions much smaller than a chromosomal arm,
`average tag densities were dynamically calculated and visualized
`over sliding windows of different sizes. Although some relatively
`small alterations could be detected by using a 1,000 virtual tag
`window (Fig. 2), a window size of 50 virtual tags (⬇200 kb) was
`used for the detailed analyses of amplifications because it would
`be expected to provide a relatively high resolution and sensitivity
`for experimental data consisting of ⬇100,000 filtered tags (Table
`1). To visualize small alterations, we designed a bitmap-based
`viewer that allowed much higher resolution views than were
`possible with the standard chromosome maps such as commonly
`used for CGH. By using this strategy, three amplification events
`were observed in the DiFi genome, whereas none were observed
`in the lymphoblastoid DNA (Table 3). The most striking was a
`125-fold amplification located at position 54.54–55.09 Mb on
`chromosome 7p (Fig. 3A). Analysis of tags in this area resolved
`the boundaries of the amplified region to within 10 kb. Three
`genes were harbored within the amplicon, a predicted gene with
`no known function (DKFZP564K0822), the bacterial lantibiotic
`synthetase component C-like 2 (LANCL2) gene, and the epi-
`dermal growth factor receptor (EGFR) gene, an oncogenic
`tyrosine kinase receptor known to be amplified in DiFi cells (23).
`The second-highest amplification was a 6-fold change at position
`30.36–32.72 Mb on chromosome 13 (Fig. 2). This area, contain-
`ing eight genes, represents the apex of a broad region on 13q that
`is coamplified. Finally, a ⬍300-kb region within 2 Mb of the
`telomere of chromosome 20q appeared to be increased ⬎5-fold.
`Independent evaluation of the 7p, 13q, and 20q amplified regions
`by using quantitative PCR analyses of genomic DNA from DiFi
`
`Low-resolution tag density maps reveal many subchromosomal
`Fig. 2.
`changes. The upper graph in each set corresponds to the digital karyotype,
`while the lower graph represents CGH analysis. An ideogram of each normal
`chromosome is present under each set of graphs. For all graphs, values on the
`y axis indicate genome copies per haploid genome, and values on the x axis
`represent positions along the chromosome (Mb for the digital karyotype;
`chromosome bands for CGH). Digital karyotype values represent exponen-
`tially smoothed ratios of DiFi tag densities, using a sliding window of 1,000
`virtual tags normalized to the NLB genome. Chromosomal areas lacking
`digital karyotype values correspond to unsequenced portions of the genome,
`including heterochromatic regions. Note that using a window of 1,000 virtual
`tags does not permit accurate identification of alterations less than ⬇4 Mb,
`such as amplifications and homozygous deletions, and smaller windows need
`to be used to accurately identify these lesions (see Fig. 3 for an example).
`
`by using observed tag densities normalized to densities from
`lymphoblastoid cells suggested a highly aneuploid genetic con-
`tent, with ⱕ1.5 copies of chromosomes 1, 4, 5, 8, 17, 21, and 22,
`and ⱖ3 copies of chromosomes 7, 13, and 20 per diploid genome.
`These observations were consistent with CGH analyses (see
`below) and the previously reported karyotype of DiFi cells (20).
`
`Analysis of Chromosomal Arms. We next evaluated the ability of
`digital karyotyping to detect subchromosomal changes, partic-
`ularly gains and losses of chromosomal arms. Tag densities were
`analyzed along each chromosome by using sliding windows
`
`Wang et al.
`
`PNAS 兩 December 10, 2002 兩 vol. 99 兩 no. 25 兩 16159
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`

`

`Fig. 3. High-resolution tag density maps
`identify amplifications and deletions. (A)
`Amplification on chromosome 7. (Top) A
`bitmap viewer with the region containing
`the alteration encircled. The bitmap viewer
`is comprised of ⬇39,000 pixels representing
`tag density values at the chromosomal po-
`sition of each virtual tag on chromosome 7,
`determined from sliding windows of 50 vir-
`tual tags. Yellow pixels indicate tag densi-
`ties corresponding to copy numbers of
`⬍110, while black pixels correspond to copy
`numbers ⱖ110. (Middle) An enlarged view
`of the region of alteration. (Bottom) A
`graphical representation of the amplified
`region with values on the y axis indicating
`genome copies per haploid genome and
`values on the x axis representing positions
`along the chromosome in Mb. (B) Homozy-
`gous deletion on chromosome 5. Top, Mid-
`dle, and Bottom are similar to those for A
`except that the bitmap viewer for chromo-
`some 5 contains ⬇43,000 pixels, tag density
`values were calculated in sliding windows
`of 150 virtual tags, and yellow pixels indi-
`cate copy numbers ⬎0.1 while black pixels
`indicate copy numbers ⱕ0.1. (Bottom) Be-
`low the graph is a detailed analysis of the
`region containing the homozygous dele-
`tion in DiFi and Co52. For each sample,
`white dots indicate markers that were re-
`tained, while black dots indicate markers
`that were homozygously deleted. PCR prim-
`ers for each marker are listed in Table 4.
`
`they were located between the markers used for PCR analyses or
`because there were no genuine homozygous deletions. This latter
`possibility was not unexpected, given the positive predicted value
`(PPV) estimated for a window size of 150 virtual tags (Table 1) and
`the expectation that the PPV would be even lower in an aneuploid
`
`Identification of EBV DNA in NLB cells. NLB, genomic tags derived
`Fig. 4.
`from NLB cells after the removal of tags matching human genome sequences
`or tags matching DiFi cells. DiFi, genomic tags derived from DiFi cells after the
`removal of tags matching human genome sequences, or tags matching NLB
`cells. The number of observed tags matching EBV, other viral, or bacterial
`sequences is indicated on the vertical axis.
`
`cells revealed copy number gains similar to those observed by
`digital karyotyping (Table 3). CGH underestimated the fold
`amplification on 13q (Fig. 2). More importantly, CGH com-
`pletely failed to identify the amplification of chromosome 7p and
`20q because the ⬍0.5-Mb amplicons were below the level of
`resolution achievable with this technique.
`
`Analysis of Deletions. When a homozygous deletion occurs in a
`cancer cell, there are zero copies of the deleted sequences,
`compared with two copies in normal cells. This difference is far
`less than that observed with amplifications, wherein 10–200
`copies of the involved sequences are present in cancer cells
`compared with two copies in normal cells. Detection of homozy-
`gous deletions was therefore expected to be more difficult than
`the detection of amplifications. To assess the potential for
`detecting deletions, we first performed digital karyotyping on
`DNA from a cancer cell line (Hx48) known to have a homozy-
`gous deletion encompassing the SMAD4 and DCC genes on
`chromosome 18q (24). From a library of ⬇116,000 filtered tags,
`we were able to clearly identify this deletion on chromosome 18
`(Table 3). The size of this deletion was estimated to be 2.33 Mb
`from digital karyotyping and 2.48 Mb from PCR-based analysis
`of markers in the region.
`We next attempted to determine whether any deletions were
`present in DiFi cells. Using a window size of 150 virtual tags (600
`kb), we found evidence for four homozygous deletions in the DiFi
`genome but none in the NLB cells. These apparent deletions were
`on chromosomes 4p, 5q, 16q, and Xq, and were 782, 743, 487, and
`814 kb in size, respectively. Assessment of the regions on 4p and 16q
`by quantitative PCR did not confirm the deletions, either because
`
`16160 兩 www.pnas.org兾cgi兾doi兾10.1073兾pnas.202610899
`
`Wang et al.
`
`

`

`GENETICS
`
`genetic background. However, similar analyses did confirm the
`homozygous deletion at the 5q locus and showed a substantial
`reduction in genomic content at the chromosome X region in DiFi
`DNA (Fig. 3B; Table 3). Neither of these deletions was detected by
`conventional CGH analysis (Fig. 2). Further examination of the 5q
`locus by sequence-tagged site (STS) mapping demonstrated that
`the homozygous deletion was completely contained within the
`59.18–59.92 Mb area identified by digital karyotyping and was
`⬇450 kb in size (Fig. 3B). Analysis of 180 additional human
`colorectal tumors revealed an additional cell line (Co52) with an
`⬇350-kb homozygous deletion of the same region, suggesting the
`existence of a previously unknown tumor suppressor gene that may
`play a role in a subset of colorectal cancers.
`
`Detection of Foreign DNA Sequences. Digital karyotyping can, in
`principle, reveal sequences that are not normally present in
`human genomic DNA. The analysis of the library from NLB cells
`provided support for this conjecture. Like all lymphoblastoid
`lines, the NLB cells were generated through infection with
`Epstein–Barr virus (EBV) (25). EBV sequences persist in such
`lines in both episomal and integrated forms (26). To identify
`potential viral sequences in NLB cells, 210,245 unfiltered NLB
`tags were compared with virtual tags from the human genome,
`and to unfiltered DiFi tags. These comparisons yielded a subset of
`tags that had no apparent matches to the human genome and these
`were searched against virtual tags from all known viral or bacterial
`sequences. A total of 2,368 tags perfectly matched EBV or EBV-
`related primate herpes viruses, but no tags matched other viral or
`bacterial sequences (Fig. 4). Of the 100 virtual tags predicted to be
`found in the EBV genome, 94 (94%) were found among the NLB
`tags. A similar analysis of 171,795 unfiltered DiFi tags showed no
`matches to EBV or other microbial sequences (Fig. 4)
`
`Discussion
`Our data demonstrate that digital karyotyping can accurately
`identify regions whose copy number is abnormal, even in com-
`plex genomes such as that of humans. Whole chromosome
`changes, gains or losses of chromosomal arms, and interstitial
`amplifications or deletions were detected. All known genomic
`alterations in DiFi cells, including the amplification of EGFR on
`chromosome 7 and other gross chromosomal changes, were
`identified through digital karyotyping. Moreover, our analysis
`identified specific amplifications and deletions that had not
`been, to our knowledge, previously described by CGH or other
`methods in any human cancer. These analyses suggest that a
`potentially large number of undiscovered copy number alter-
`ations exist in cancer genomes and that many of these could be
`detected through digital karyotyping.
`
`Like all genome-wide analyses, digital karyotyping has limi-
`tations. First, the ability to measure tag densities over entire
`chromosomes depends on the accuracy and completeness of the
`genome sequence. Fortunately, ⬎94% of the human genome is
`available in draft form, and 95% of the sequence is expected to
`be in a finished state by the year 2003. Second, a small number
`of areas of the genome are expected to have a lower density of
`mapping enzyme restriction sites and could be incompletely
`evaluated by our approach. We estimate that ⬍5% of the
`genome would be incompletely analyzed by using the parameters
`used in the current study. Moreover, this problem could be
`overcome through the use of different mapping and fragmenting
`enzymes. Finally, digital karyotyping cannot reliably detect very
`small regions, on the order of several thousand base pairs or less,
`that are amplified or deleted.
`Nevertheless, it is clear from our analyses that digital karyotyping
`provides a heretofore unavailable picture of the DNA landscape of
`a cell. The approach should be immediately applicable to the
`analysis of human cancers, wherein the identification of homozy-
`gous deletions and amplifications has historically revealed genes
`important in tumor initiation and progression. In addition, one can
`envisage a variety of other applications for this technique. First, the
`approach could be used to identify previously undiscovered alter-
`ations in hereditary disorders. A potentially large number of such
`diseases are thought to occur because of deletions or duplications
`too small to be detected by conventional approaches. These diseases
`may be detectable with digital karyotyping, even in the absence of
`any linkage or other positional information. Second, mapping
`enzymes that are sensitive to DNA methylation (e.g., NotI) could
`be used to catalog genome-wide methylation changes in cancer, or
`diseases thought to be affected by genomic imprinting. Third, the
`approach could be as easily applied to the genomes of other
`organisms to search for genetic alterations responsible for specific
`phenotypes, or to identify evolutionary differences between related
`species. Finally, as the genome sequences of increasing numbers of
`microorganisms and viruses become available, the approach could
`be used to identify the presence of pathogenic DNA in infectious
`or neoplastic stat