Patent Publication Number: US-2021172025-A1

Title: Personalized tumor biomarkers

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation of U.S. non-provisional application Ser. No. 15/950,863, filed Apr. 11, 2018, which is a continuation of U.S. non-provisional application Ser. No. 14/790,833, filed Jul. 2, 2015, issued on May 1, 2018, as U.S. Pat. No. 9,957,572, which is a division of U.S. non-provisional application Ser. No. 13/579,964, filed Jan. 7, 2013, now abandoned, which is a 35 U.S.C. § 371 national phase application of PCT application number PCT/US2011/25152, filed Feb. 17, 2011, which claims the benefit of and priority to U.S. provisional patent application Ser. No. 61/305,589, filed Feb. 18, 2010, the content of each of which is incorporated by reference herein in its entirety. 
    
    
     This invention was made with government support under grants CA121113, CA057345, CA62924, and CA043460 awarded by the National Institutes of Health. The government has certain rights in the invention. 
    
    
     TECHNICAL FIELD OF THE INVENTION 
     This invention is related to the area of cancer detection and management. In particular, it relates to identification and use of somatic rearrangements as markers of a person&#39;s cancer. 
     BACKGROUND OF THE INVENTION 
     A nearly universal feature of human cancer is the widespread rearrangement of chromosomes as a result of chromosomal instability (1). Such structural alterations begin to occur at the earliest stages of tumorigenesis and persist throughout tumor development. The consequences of chromosomal instability can include copy number alterations (duplications, amplifications and deletions), inversions, insertions, and translocations (2). Historically, the ability to detect such alterations has been limited by the resolution of genetic analyses. However, a number of more recent approaches including high density oligonucleotide arrays and high throughput sequencing have allowed detection of changes at much higher resolution (3-15). 
     Tumor-specific (somatic) chromosomal rearrangements have the potential to serve as highly sensitive biomarkers for tumor detection. Such alterations are not present in normal cells and should be exquisitely specific. Rearrangement-associated biomarkers therefore offer a reliable measure that would be useful for monitoring tumor response to specific therapies, detecting residual disease after surgery, and for long-term clinical management. Recurrent somatic structural alterations, such as those involving the BCR-ABL oncogene (the target of the Philadelphia chromosome translocation), immunoglobulin (Ig) genes, T cell receptor (TCR) genes, and the retinoic acid receptor alpha (RARα) gene, have been shown to be useful as diagnostic markers in certain hematopoietic malignancies (16-20). However, recurrent structural alterations do not generally occur in most solid tumors. There is a continuing need in the art to develop tools for diagnosing and monitoring cancers. 
     SUMMARY OF THE INVENTION 
     According to one aspect of the invention a method is provided for identifying a personalized tumor marker for a cancer patient. A mate-paired library is made from tumor DNA of the patient. Mate pairs of the library comprise two genomic tags that are co-linear but not contiguous in a segment of the tumor DNA. Sequence of a plurality of mate pairs of the library is determined. Regions of copy number differences among regions in the tumor DNA of the patient are determined. Mate paired tags which map within a region of copy number difference or spanning a boundary of copy number difference are identified as potential markers of a tumor-specific DNA rearrangement in the cancer patient. 
     According to another aspect of the invention a method is provided for assessing or detecting tumor in a patient. A DNA fragment is amplified using a template from the patient&#39;s tissues or body fluids and primers that span a patient-specific, tumor-specific rearrangement breakpoint. The rearrangement breakpoint is between genes involved in rearrangements in &lt;1% of tumors of patients with the same type of tumor. The amount or proportion of amplified DNA fragment in the patient&#39;s tissue or body fluid is determined. 
     Another aspect of the invention is another method of identifying a personalized tumor marker for a cancer patient. Sequence of two ends of each of a plurality of fragments of DNA from the cancer patient is determined. Regions of copy number differences among regions in the tumor DNA of the patient are determined. Fragments of the plurality of fragments which map within a region of copy number difference or spanning a boundary of copy number difference are identified as potential markers of a tumor-specific DNA rearrangement in the cancer patient. 
     A further aspect of the invention is another method of identifying a personalized tumor marker for a cancer patient. A plurality of mate paired tags of a library of mate paired tags is tested by comparing to non-tumor DNA or to sequence of non-tumor DNA. Each of the mate paired tags comprises two genomic tags that are co-linear but not contiguous in a segment of tumor DNA of the cancer patient. A tumor-specific DNA rearrangement is identified if the two genomic tags of a mate paired tag are at different locations or in a different orientation within a chromosome or on different chromosomes of non-tumor DNA compared to tumor DNA. 
     Yet another aspect of the invention is another method of identifying a personalized tumor marker for a cancer patient. Two ends of a plurality of fragments of tumor DNA of the cancer patient are tested by comparing to non-tumor DNA or to sequence of non-tumor DNA. A tumor-specific DNA rearrangement is identified if the ends of a fragment are at different locations or in a different orientation within a chromosome or on different chromosomes of non-tumor DNA compared to tumor DNA. 
     Still another aspect of the invention is a method of screening for a cancer in a human. A plurality of mate paired tags of a library of mate paired tags is tested by comparing to normal DNA or to sequence of normal DNA. Each of the mate paired tags comprises two genomic tags that are co-linear but not contiguous in a segment of DNA in the blood of the human. A DNA rearrangement is identified if the two genomic tags of a mate paired tag are at different locations or in a different orientation within a chromosome or on different chromosomes of normal DNA compared to blood DNA. The presence of a DNA rearrangement suggests the presence of a cancer in the human. 
     A further aspect of the invention is a method of screening for a cancer in a human. Two ends of a fragment of blood DNA of the human are tested by comparing to normal DNA or to sequence of normal DNA. A DNA rearrangement is identified if the ends are at different locations or in a different orientation within a chromosome or on different chromosomes of normal DNA compared to blood DNA. The presence of a DNA rearrangement suggests the presence of a cancer in the human. 
     An additional aspect of the invention is a kit for monitoring presence or amount of a breakpoint in a somatic DNA rearrangement in tumor DNA of a patient. The kit may comprise one or more pairs of amplification primers. Each pair is complementary to priming sites on opposite sides of a breakpoint. The priming sites are separated by less than 200 basepairs in the tumor DNA. The DNA rearrangement occurs in &lt;1% of tumors of patients with the same type of tumor. 
     These and other embodiments which will be apparent to those of skill in the art upon reading the specification provide the art with methods for detecting and monitoring cancers in the body. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1 . Schematic of “Personalized Analysis of Rearranged Ends (PARE)” approach. 
       The method is based on next generation mate-paired analysis of, e.g., resected tumor DNA to identify individualized tumor-specific rearrangements. Such alterations are used to develop PCR based quantitative analyses for personalized tumor monitoring of plasma samples or other bodily fluids. 
         FIGS. 2A and 2B . Detection of tumor-specific rearrangements in breast and colorectal cancers. Two representative rearrangements are shown for each tumor sample. PCR amplification across breakpoint regions is indicated in ( FIG. 2A ) and the genomic coordinates for a representative mate-pair of each rearrangement are listed in ( FIG. 2B ). 
         FIG. 3 . Detection of tumor specific rearrangements in mixtures of tumor and normal DNA. Decreasing amounts of tumor DNA were mixed with increasing amounts of normal tissue DNA (300 ng total) and were used as template molecules for PCR using chromosomes 4/8 translocation specific primers (top) or chromosome 3 control primers (see Example 1 for additional information). 
         FIG. 4A-4B . Detection of tumor-specific rearrangements in plasma of cancer patients.  FIG. 4A . The identified chromosome 4/8 and 16 rearrangements were used to design PCR primers spanning breakpoints and used to amplify rearranged DNA from tumor tissue and plasma from patients Hx402 and Hx403, respectively. A plasma sample from an unrelated healthy individual was used a control for both rearrangements.  FIG. 4B . Plasma samples from patient Hx402 were analyzed at different time points using digital PCR to determine the fraction of genomic equivalents of plasma DNA containing the chromosome 4/8 rearrangement. The fraction of rearranged DNA at day 137 was 0.3%, consistent with residual metastatic lesions present in the remaining lobe of the liver. 
         FIG. 5  ( Figure S1 .) Flow chart of approach used to identify rearranged sequences 
         FIG. 6  ( Figure S2 .) Comparison of Digital Karyotyping, Illumina SNP array, and SOLiD sequencing results on chromosome 8. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     We have found that any structural alteration identified in an individual&#39;s tumor can be used as a tumor marker, even if it is not found in tumors of the same type in other individuals and even if it is not a “driver”—causing a selective growth advantage—but merely a “passenger.” Moreover, such markers can be used to detect tumor and or quantify the tumor burden in an individual by assessment of blood. 
     Somatic rearrangements are a focus of the present invention. Such rearrangements are used as markers of a tumor. In particular, the boundaries of the rearrangments can be detected and used as a quantitative or qualitative indicator of the tumor. Because the boundaries are unique to the tumor DNA, they should be exquisitely specific markers of the tumor. Somatic rearrangements can be detected using any method known in the art. One particularly useful method is a technique called digital karyotyping. This technique identifies changes in copy number across regions or windows in the genome. Other methods may employ commercially available arrays to detect regions of copy number differences among regions of a genome. The copy number differences reflect a rearrangement, such as a deletion or amplification, and an amplification can further harbor other rearrangements within it. Once a somatic rearrangement is identified, one or more of its boundaries (also referred to as breakpoints) can be identified and that boundary can be a very specific marker for the tumor. Identifying a boundary can be accomplished by a number of techniques. 
     In one technique mate-paired genomic tags are tested to determine different copy numbers of one member of the pair compared to the other. A different copy number between two members suggests that the tags span a rearrangement breakpoint or boundary. The mate-pairs are typically derived from a single fragment that is processed to yield two smaller portions that can be more readily sequenced or analyzed. An intervening segment is typically removed, leaving the two smaller portions linked on a single molecule in the same orientation that they were found in the tumor genome. 
     A similar technique does not involve mate-pairs but involves sequencing and/or analyzing two different portions or ends of a single fragment of genomic DNA from a tumor. The two portions or ends may be separated by any distance, from immediately adjacent up to 1 kb, 1.5 kb, 2 kb, or 3 kb, for example. The ends may not be the literal ends of a fragment, but may be close to the ends or merely two non-overlapping portions. The sequence of the two ends may be determined separately, for example from either end, or the sequence can be determined in one direction and analyzed for separate, non-overlapping segments of differing copy numbers. 
     Amplification primers are known in the art and typically comprise between 15 and 50 nucleotides which are complementary to a template. A pair of primers is complementary to opposite strands of a template and can amplify a double stranded fragment that contains the two primer sequences in addition to sequences which are between them on the template. From 0 to 10, 20, 50, 100, 200, 500, 1000, 1500, or 2000 basepairs or nucleotides may lie between the two primer-complementary sequences on the template. According to the invention, each primer will hybridize to opposite sides of a rearrangement boundary. These primers are also referred to as spanning or flanking the breakpoint, because the amplicon that they generate will span and/or flank the breakpoint. Optionally, a primer may contain the boundary junction. Primers need not be 100% complementary to template, but may incorporate other bases or sequences of bases for other purposes, such as to facilitate purification or downstream processing. 
     Once tumor-specific breakpoints are ascertained for an individual patient, primers can be prepared and shipped elsewhere for use. For example pairs or panels of pairs of primers can be packaged in a single or divided container. The primers can be in any suitable condition, including in solution, dried, freeze dried, at room temperature, on wet ice, and on dry ice. Additional components may be included in the kits, for example other reagents for performing the monitoring or assessing with the primers. Additional components may include a polymerase for amplification, reagents for preparing template from cancer cells, normal cells, or body fluids, control primers, control templates, labeled or unlabelled deoxyribonucleotides. 
     In order to identify or confirm a rearrangement in tumor DNA, tumor sequences can be compared to a reference sequence, for example in a database, or to a sequence from normal DNA of the same or a related individual. Two mate-paired tags or two fragment ends that map to different locations on a chromosome or to different chromosomes or to differently oriented sequences on the same chromosome indicate a rearrangement. The comparison can be done in silico or in vitro. 
     Breakpoints in a rearrangement are places where two sequences are joined in a tumor DNA that are not joined in normal or reference DNA. Thus the breakpoint refers to an inferred break that occurred in order to join the sequences that are found in the tumor DNA. Breakpoints are also referred to as boundaries of a rearrangement. Normal DNA may be obtained from lymphocytes or a buccal swab, for example. In cases where the subject has a diagnosed tumor, normal DNA can be obtained from any non-tumor tissue, including a matched tissue from the same organ. 
     The breakpoints which are of interest in the present methods are those which are not known to be associated with or causative of leukemia, lymphoma, sarcoma, or prostate cancers. The breakpoints which are associated with or causative of those cancers typically occur in a high proportion of such tumors, often between the same or a limited number of genes or gene loci. The rearrangements used in the present methods are more idiosyncratic, occurring between the same genes or gene loci in less than 1%, less than 0.1%, or less than 0.01% of the patients with the same type of tumor. 
     Assays using tumor-specific primers can be used for a variety of purposes. For example, patients can be monitored over time to see if a tumor is in remission or is progressing. The assay can be used before, during, and/or after a therapy regimen. The assay can be used to assess surgical efficacy. Tumor margins can be assessed to guide the extent of surgical resection. The assay can be used to monitor for relapse or recurrence. 
     Using the tumor rearrangement-specific primers to conduct assays, one can obtain qualitative or quantitative results. The quantitative results can be absolute amounts or relative amounts, for example, compared to a non-rearranged sequence on the same or a different chromosome. Assays can be conducted using the rearrangement-specific primers and tissues or body fluids from a subject. Suitable body fluids include whole blood, serum, and plasma, which are collectively referred to as blood. Other body fluids which may be used are saliva, sputum, and stool, for example. One or more pairs of primers can be used to amplify and assay for one or more tumor-specific rearrangements in a single patient. Using a panel of rearrangements markers may mitigate against any possible loss of marker during tumor growth and progression. 
     The results shown below in the Examples demonstrate that massively parallel sequencing can be used to develop personalized biomarkers based on somatic rearrangements. We were able to identify tumor-specific markers in each of the six breast and colorectal cancer cases analyzed. Moreover, we demonstrated that the identified breakpoints can be used to detect tumor DNA in the presence of large quantities of normal DNA and in patient plasma. These results highlight the sensitivity and specificity of the approach and suggest broad clinical utility of the methods disclosed here, collectively referred to as PARE. 
     Virtually all tumors of clinical consequence are thought to have rearranged DNA sequences resulting from translocations and copy number alterations and these sequences are not present in normal human plasma or non-tumor tissues. A recent genome-wide analysis of 24 breast cancers showed that all analyzed samples contained at least one genomic rearrangement that could be detected by next generation sequencing (24). From a technical perspective, PARE-derived clinical assays should have no false positives: the PCR amplification of aberrant fusions of DNA sequences that are normally thousands of base pairs apart or on different chromosomes should not occur using non-tumor DNA as a template. In contrast, approaches that rely on monitoring of residual disease by analysis of somatic single base alterations in specific genes are limited by polymerase error rates at the bases of interest (25). The PCR process generates background single base mutations that are identical to bona fide mutations, but does not generate false-positive rearrangements with carefully chosen primers. Because of the higher signal-to-noise ratio thereby obtained, PARE theoretically permits more sensitive monitoring of tumor burden. 
     The PARE approach, however, is not without limitations. Although somatic alterations in oncogenes and tumor suppressor genes persist throughout the clonal evolution of a tumor, it is conceivable that some rearranged sequences could be lost during tumor progression. The identification of several PARE biomarkers, each specific for different chromosomal regions, would mitigate this concern, as it is unlikely that all such markers would be lost in any particular patient. Another limitation is the cost of identifying a patient-specific alteration. In this prototype study, we obtained an average of 194.7 million reads per patient, resulting in ˜200 tags in each 3 kb bin. The current cost for such an assay is $5,000, which is expensive for general clinical use. This cost is a consequence of the high physical coverage and the inefficiencies associated with stringent mapping of 25 bp sequence data to the human genome. As read quality and length continue to improve, less stringent mapping criteria and lower physical coverage will permit analyses similar to those in this study but with substantially less sequencing effort. Moreover, the cost of massively parallel sequencing, which has decreased substantially over the last two years, continues to spiral downwards. Finally, there are clinical settings where the fraction of any DNA from tumors, including rearranged sequences, in the patient plasma is exceedingly small and undetectable. To be detectable by PARE, there must be at least one rearrangement template molecule in the plasma sample analyzed. When disease-burden is this light, PARE may yield false negative results. Larger studies will be needed to confirm particular clinical uses of PARE and its prognostic capabilities. 
     Despite these caveats, there are numerous potential applications of PARE. These include the more accurate identification of surgical margins free of tumor and the analysis of regional lymph nodes as well as the measurement of circulating tumor DNA following surgery, radiation, or chemotherapy. Short term monitoring of circulating tumor DNA may be particularly useful in the testing of new drugs, as it could provide an earlier indication of efficacy than possible through conventional diagnostic methods such as CT scanning. Given current enthusiasm for the personalized management of cancer patients, PARE affords a timely method for uniquely sensitive and specific tumor monitoring. 
     The above disclosure generally describes the present invention. All references disclosed herein are expressly incorporated by reference. A more complete understanding can be obtained by reference to the following specific examples which are provided herein for purposes of illustration only, and are not intended to limit the scope of the invention. 
     Example 1—Materials and Methods 
     Clinical Samples and Cell Lines 
     DNA samples were obtained from early passage xenografts and cell lines of breast and colorectal cancers as described (26). Normal DNA samples were obtained from matched normal tissue. Plasma samples were collected from colorectal cancer patients Hx402 and Hx403 and from an unrelated normal control. All samples were obtained in accordance with the Health Insurance Portability and Accountability Act (HIPAA). 
     Digital Karyotyping and Illumina BeadChip Arrays 
     A Digital Karyotyping library for colorectal cancer cell line Co84C was constructed as previously described (6). In summary, 17 bp genomic DNA tags were generated using the NlaIII and SacI restriction enzymes. The experimental tags obtained were concatenated, cloned and sequenced. Previously described software was used to extract the experimental tags from the sequencing data. The sequences of the experimental tags were compared to the predicted virtual tags extracted from the human genome reference sequence. Amplifications were identified using sliding windows of variable sizes and windows with tag density ratios≥6 were considered to represent amplified regions. 
     The Illumina Infinium II Whole Genome Genotyping Assay employing the BeadChip platform was used to analyze the colorectal cancer cell line Co84C at 317k SNP loci from the Human HapMap collection. This assay is a two step procedure; first the sample is hybridized to a 50 nucleotide oligo, then the SNP position is interrogated by a two-color fluorescent single base extension. Image files and data normalization were processed as previously described (10). Amplifications were defined as regions having at least one SNP with a Log R ratio≥1.4, at least one in ten SNPs with a Log R ratio≥1, and an average Log R ratio of the entire region of ≥0.9. 
     SOLiD Library Preparation and Sequencing 
     Mate-pair libraries were generated for the SOLiD platform as described (15). In brief, genomic DNA was sheared into ˜1.4 kb fragments and used as template in emulsion PCR. Fragments were coupled to beads via an adapter sequence and clonally amplified. A 3′ modification of the DNA fragments allowed for covalent attachment to a slide. Sequencing primers hybridized to the adapter sequence and four fluorescently labeled di-base probes were used in ligation-based sequencing. Each nucleotide is sequenced twice in two different ligation reactions, resulting in two base encoding which has been shown to reduce sequencing artifacts. 
     Sequence data was mapped to the human genome reference sequence (hg18) using the Corona SOLiD software pipeline. All 25 bp tags (for both individual tag and mate-paired tag analyses) were required to match the reference genome uniquely and without mismatches. 
     Analysis of Single Tags for Copy Number Alterations 
     The SOLiD tags were filtered and the remaining tags were grouped by genomic position in non-overlapping 3 kb bins. A tag density ratio was calculated for each bin by dividing the number of tags observed in the bin by the average number of tags expected to be in each bin (based on the total number of tags obtained for chromosomes 1-22 for each library divided by 849,434 total bins). The tag density ratio thereby allowed a normalized comparison between libraries containing different numbers of total tags. A control group of SOLiD libraries made from the four matched normal samples from Table 1 and two itional normal samples (CEPH sample NA07357 and NA18507 used to define areas of germline copy number variation or which contained a large fraction of repeated or low complexity sequences. Any bin where at least 2 of the normal libraries had a tag density ratio of &lt;0.25 or &gt;1.75 was removed from further analysis. 
     Homozygous deletions were identified as three or more consecutive bins with tag ratios&lt;0.25 and at least one bin with a tag ratio&lt;0.005. Amplifications were identified as three or more consecutive bins with tag ratios&gt;2.5 and at least one bin with a tag ratio&gt;6. Single copy gains and losses were identified through visual inspection of tag density data for each sample. 
     Analysis of Mate-paired Tags 
     Mate-paired tags mapping the reference genome uniquely and without mismatches were analyzed for aberrant mate-pair spacing, orientation and ordering and categorized in 13 three letter data formats (27). Mate pairs from the same chromosome that map at appropriate distances (˜1.4 kb) and in the appropriate orientation and ordering are categorized as AAA. Mate pairs mapping to different chromosomes are categorized as C**. For the analysis of translocations of the PARE approach, we focused on C** mate pairs, while for analysis of rearrangements adjacent to copy number alterations, we chose all non-AAA (including C**) mate pairs for further analysis. 
     PARE Identification and Confirmation of Candidate Rearrangements 
     To identify candidate translocations, we grouped C** mate pair tags in 1 kb bins and looked for bin-pairs which were observed ≥5 times in the tumor sample but which were not observed in matched normal sample. For identification of candidate rearrangements associated with copy number alterations, we analyzed the 10 kb boundary regions of amplifications, homozygous deletions, or lower copy gains and losses for neighboring non-AAA tags observed &gt;2 times in the tumor but not matched normal sample. In the case of Hx402 and Hx403 the analysis of rearrangements adjacent to copy number alterations was performed in the absence of SOLiD libraries from normal tissue. 
     Mate pair tag sequences associated with a candidate rearrangement were used as target sequences for primer design using with Primer3 (28). When primers could not be designed from tag sequences alone, adjacent genomic sequence up to 100 bp was used for primer design. Importantly, the observed rearranged tag ordering and orientation was used for Primer3 queries. Primers were used for PCR on tumor and matched normal samples as previously described (26). The candidate rearrangement was confirmed if a PCR product of the expected size was seen in the tumor, but not the matched normal sample. Sanger sequencing of PCR products was used to identify sequence breakpoint in a subset of cases. 
     Detection of PARE Biomarker in Human Plasma 
     To determine the sensitivity of rearranged biomarkers in the presence of normal DNA, serial dilutions of tumor:normal DNA mixtures were used as templates for PCR using primers for the chromosome 4/8 translocation in Hx402. The tumor DNA dilution began at 1:125 tumor:normal and continued as a one-in-five serial dilution until reaching 1:390, 625 tumor:normal mixture. PCR was performed for each of the six tumor:normal DNA mixtures and for the normal DNA control, using translocation specific primers as well as control primers from chromosome 3. 
     One ml of human plasma samples were obtained from patients Hx402 and Hx403 and from a control individual and DNA was purified as described (29). Whole genome amplification of plasma DNA was performed by ligation of adaptor sequences and PCR amplification with universal primers from the Illumina Genomic DNA Sample Prep Kit. 
     Primers designed to amplify &lt;200 bp fragments spanning each PARE rearrangement were used in PCR from total plasma DNA using patient or control samples. Digital PCR of plasma DNA dilutions from patient Hx402 using rearrangement specific and control primers were used to quantitate the fraction mutated DNA molecules. 
     Example 2 
     Description of the Approach 
     The PARE approach, shown schematically in  FIG. 1 , in one embodiment employs the identification of patient-specific rearrangements in tumor samples. To determine the feasibility of identifying such alterations using next generation sequencing approaches, we initially analyzed four tumor samples (two colon and two breast tumors) and their matched normal tissue samples using the Applied Biosystems SOLiD System (Table 1). Genomic DNA from each sample was purified, sheared and used to generate libraries with mate-paired tags ˜1.4 kb apart. Libraries were digitally amplified by emulsion polymerase chain reaction (PCR) on magnetic beads (21) and 25 bp mate-paired tags were sequenced using the sequencing-by-ligation approach (15, 22). An average of 198.1 million 25 bp reads were obtained for each sample where each read aligned perfectly and was uniquely localized in the reference human genome (hg18), resulting in 4.95 Gb mappable sequence per sample. An average of 40 million mate-paired reads where both tags were perfectly mapped to the reference human genome were obtained for each sample. The total amount of genome base-pairs covered by the mate-paired analysis (i.e. distance between mate-paired tags x number of mate-paired tags) was 53.6 Gb per sample, or a 18-fold physical coverage of the human genome. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Summary of mate-paired tag libraries 
               
            
           
           
               
               
               
            
               
                   
                 Single tag analyses 
                 Mate-paired tag analyses 
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                   
                   
                 Number of tags 
                   
                 Expected 
                 Number of mate- 
                 Distance 
                 Total physical 
                 Expected 
               
               
                   
                 Number of 
                 matching 
                 Total bases 
                 coverage 
                 paired tags matching 
                 between mate- 
                 coverage by mate- 
                 genome 
               
               
                 Samples 
                 beads* 
                 human genome 
                 sequenced (bp) 
                 per 3 kb bin 
                 human genome 
                 paired tags (bp) 
                 paired tags (bp) 
                 coverage 
               
               
                   
               
            
           
           
               
            
               
                 Colon Cancer 
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 Co108 tumor 
                 526,209,780 
                 121,527,707 
                 3,038,192,675 
                 122 
                 21,899,809 
                 1,371 
                 30,024,693,714 
                 10.0 
               
               
                 Co108 normal 
                 328,599,033 
                 86,032,253 
                 2,150,806,325 
                 86 
                 11,694,361 
                 1,254 
                 14,665,530,804 
                 4.9 
               
               
                 Co84 tumor 
                 677,137,128 
                 256,065,437 
                 6,401,635,925 
                 256 
                 58,678,410 
                 1,488 
                 87,292,060,006 
                 29.1 
               
               
                 Co84 normal 
                 486,663,520 
                 218,280,146 
                 5,457,003,650 
                 218 
                 59,019,031 
                 1,384 
                 81,690,396,379 
                 27.2 
               
               
                 Hx402 tumor 
                 523,745,015 
                 198,342,749 
                 4,958,568,725 
                 198 
                 43,457,431 
                 1,629 
                 70,789,547,653 
                 23.6 
               
               
                 Hx403 tumor 
                 475,658,760 
                 164,061,938 
                 4,101,548,450 
                 164 
                 37,123,395 
                 1,705 
                 63,295,388,475 
                 21.1 
               
            
           
           
               
            
               
                 Breast cancer 
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 B7 tumor 
                 840,979,999 
                 281,027,274 
                 7,025,681,850 
                 281 
                 27,548,989 
                 1,220 
                 33,604,662,404 
                 11.2 
               
               
                 B7 normal 
                 705,704,265 
                 253,482,262 
                 6,337,056,550 
                 253 
                 57,878,644 
                 1,404 
                 81,271,654,770 
                 27.1 
               
               
                 B5 tumor 
                 444,249,217 
                 147,612,941 
                 3,690,323,525 
                 148 
                 29,961,045 
                 1,193 
                 35,730,144,651 
                 11.9 
               
               
                 B5 normal 
                 549,237,156 
                 220,669,795 
                 5,516,744,875 
                 221 
                 53,611,974 
                 1,205 
                 64,591,276,025 
                 21.5 
               
               
                   
               
               
                 *Number of beads corresponds to the number of magnetic beads containing clonally amplified DNA fragments and represents the maximal number of raw sequnece reads for each run. 
               
            
           
         
       
     
     Example 3 
     Identification of Somatic Rearrangements 
     Two methods were used to identify somatic rearrangements from these data ( FIG. 5 ). The first approach involved searching for tags whose mate-pairs were derived from different chromosomes (interchromosomal rearrangements). The high physical coverage of breakpoints provided by the ˜40 million mate-paired sequences per sample (Table 1) suggested that a large fraction of such translocations could be identified. End sequences from such mate-paired tags were grouped into 1 kb bins and those bin pairs that were observed at least 5 times were analyzed further. The requirement for ≥5 occurrences minimized the chance that the presumptive fusion sequences represent incorrect mapping to the reference genome or artifacts of library construction. Comparison with SOLiD libraries made from the matched normal samples reduced the possibility that the fusion sequences represented rare germline variants rather than somatic events. 
     The second approach combined mate-paired tag data with copy number alterations identified by analyses of individual 25 bp tags. Tumor-specific copy number alterations are often associated with de novo rearrangements (23) and the boundaries of such alterations would be expected to contain novel junctions not present in the human genome. To identify somatic copy number gains, losses, high-amplitude amplifications and homozygous deletions, tags were grouped into non-overlapping 3 kb bins. Normalized tag densities, defined as the number of tags per bin divided by average number of tags per bin, were determined for all 3 kb bins in each sample. Bins that displayed tag density ratios&gt;1.75 or &lt;0.25 in two or more normal tissue samples (corresponding to &lt;6% of all bins) were discarded from the analysis. This eliminated confounding regions of common germline copy number variation and resulted in 892,567 bins that were analyzed in each tumor sample. Comparison of 256 million reads from colorectal tumor sample Co84 with Illumina arrays containing ˜1 million SNP probes and with a ˜1 million Digital Karyotyping (DK) tag library obtained with Sanger sequencing showed high concordance for copy number alterations among the three platforms ( FIG. 6  and Table S1). With the higher resolution afforded by the SOLiD data, we were able to identify additional copy number changes not detected with the other methods (Table S2). Boundary regions of copy number alteration were analyzed to identify mate-paired tags corresponding to rearranged DNA sequences. These included fusion of DNA sequences that have inappropriate spacing, order or orientation on the same chromosome (intrachromosomal rearrangements) or inappropriate joining of sequences from different chromosomes (interchromosomal rearrangements). 
     
       
         
           
               
             
               
                 TABLE S1 
               
               
                   
               
               
                 Comparison of SOLiD sequencing, Illumina SNP arrays, and Digital 
               
               
                 Karyotyping for analysis of copy number alterations 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 Digital Karyotyping 
                 Illumina SNP Arrays 
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                   
                 Tumor 
                   
                 Left 
                 Right 
                   
                 Tag Density 
                 Left 
                 Right 
               
               
                   
                 Sample 
                 Chr 
                 Boundary 
                 Boundary 
                 Size (bp) 
                 Ratio* 
                 Boundary 
                 Boundary 
               
               
                   
               
               
                 Amplification 
                 Co84C 
                 6 
                 41,273,307 
                 43,008,812 
                 1,735,506 
                 9.1 
                 41,419,345 
                 42,485,546 
               
               
                 Amplification 
                 Co84C 
                 8 
                 127,618,526 
                 128,009,287 
                 390,762 
                 19.2 
                 127,621,008 
                 127,995,012 
               
               
                 Amplification 
                 Co84C 
                 8 
                 128,750,189 
                 128,857,861 
                 107,673 
                 8.3 
                 128,750,181 
                 128,848,183 
               
               
                 Amplification 
                 Co84C 
                 8 
                 129,473,672 
                 129,667,129 
                 193,458 
                 13.8 
                 129,472,209 
                 129,677,099 
               
               
                 Amplification 
                 Co84C 
                 11 
                 34,337,207 
                 35,266,401 
                 929,195 
                 33.0 
                 34,359,268 
                 35,265,359 
               
               
                 Amplification 
                 Co84C 
                 13 
                 109,096,557 
                 109,553,930 
                 457,374 
                 9.2 
                 109,108,212 
                 109,557,712 
               
               
                 Amplification 
                 Co84C 
                 15 
                 88,545,070 
                 89,258,106 
                 713,037 
                 26.2 
                 88,561,995 
                 89,253,599 
               
               
                 Amplification 
                 Co84C 
                 19 
                 34,570,450 
                 34,641,949 
                 71,500 
                 7.9 
                 34,561,976 
                 34,641,548 
               
               
                 Amplification 
                 Co84C 
                 19 
                 34,956,853 
                 35,344,522 
                 387,670 
                 14.3 
                 34,966,463 
                 35,321,409 
               
               
                 Amplification 
                 Co84C 
                 19 
                 36,274,262 
                 36,388,331 
                 114,070 
                 6.2 
                 36,281,540 
                 36,385,232 
               
               
                 Amplification 
                 Co84C 
                 19 
                 54,500,237 
                 54,643,655 
                 143,419 
                 8.4 
                 54,520,709 
                 54,622,533 
               
               
                   
               
            
           
           
               
               
               
            
               
                   
                 Illumina SNP Arrays 
                 SOLiD sequencing 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                   
                   
                   
                 Log R 
                 Left 
                 Right 
                   
                 Tag Density 
               
               
                   
                   
                 Size (bp) 
                 Ratio* 
                 Boundary 
                 Boundary 
                 Size (bp) 
                 Ratio* 
               
               
                   
                   
               
               
                   
                 Amplification 
                 1,066,202 
                 1.9 
                 41,418,000 
                 42,537,000 
                 1,119,001 
                 16.4 
               
               
                   
                 Amplification 
                 374,005 
                 2.7 
                 127,617,000 
                 128,010,000 
                 393,001 
                 150.0 
               
               
                   
                 Amplification 
                 98,003 
                 2.0 
                 128,748,000 
                 128,859,000 
                 111,001 
                 43.1 
               
               
                   
                 Amplification 
                 204,891 
                 3.4 
                 129,471,000 
                 129,678,000 
                 207,001 
                 116.6 
               
               
                   
                 Amplification 
                 906,092 
                 3.0 
                 34,338,000 
                 35,268,000 
                 930,001 
                 91.2 
               
               
                   
                 Amplification 
                 449,501 
                 2.3 
                 109,107,000 
                 109,557,000 
                 450,001 
                 33.6 
               
               
                   
                 Amplification 
                 691,605 
                 3.6 
                 88,542,000 
                 88,953,000 
                 411,001 
                 93.2 
               
               
                   
                   
                   
                   
                 88,983,000 
                 89,118,000 
                 135,001 
                 32.8 
               
               
                   
                   
                   
                   
                 89,133,000 
                 89,166,000 
                 33,001 
                 84.8 
               
               
                   
                   
                   
                   
                 89,208,000 
                 89,256,000 
                 48,001 
                 50.3 
               
               
                   
                 Amplification 
                 79,573 
                 2.2 
                 34,548,000 
                 34,641,000 
                 93,001 
                 33.9 
               
               
                   
                 Amplification 
                 354,947 
                 2.6 
                 34,956,000 
                 35,346,000 
                 390,001 
                 36.8 
               
               
                   
                 Amplification 
                 103,693 
                 2.5 
                 36,273,000 
                 36,396,000 
                 123,001 
                 21.2 
               
               
                   
                 Amplification 
                 101,825 
                 2.1 
                 54,498,000 
                 54,636,000 
                 138,001 
                 41.8 
               
               
                   
                   
               
               
                   
                 *Values for Tag Density Ratios and Log R Ratios represent observed maximum values for amplifications. 
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE S2 
               
             
            
               
                   
               
               
                 Putative copy number alterations identified by SOLiD sequencing in Co84 
               
               
                 that were not identified by Illumina SNP arrays or Digital Karyotyping 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                 Tag Density 
               
               
                 Alteration Type 
                 Chromosome 
                 Left Boundary 
                 Right Boundary 
                 Size (bp) 
                 Ratio* 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Homozygous deletion 
                 1 
                 83,388,000 
                 83,532,000 
                 144,001 
                 0.0 
               
               
                 Amplification 
                 1 
                 151,188,000 
                 151,194,000 
                 6,001 
                 11.2 
               
               
                 Amplification 
                 1 
                 159,393,000 
                 159,414,000 
                 21,001 
                 9.7 
               
               
                 Amplification 
                 1 
                 172,101,000 
                 172,107,000 
                 6,001 
                 18.1 
               
               
                 Amplification 
                 1 
                 179,910,000 
                 179,916,000 
                 6,001 
                 17.4 
               
               
                 Amplification 
                 1 
                 200,238,000 
                 200,256,000 
                 18,001 
                 9.6 
               
               
                 Amplification 
                 1 
                 204,188,000 
                 204,186,000 
                 18,001 
                 13.2 
               
               
                 Homozygous deletion 
                 4 
                 9,804,000 
                 9,813,000 
                 9,001 
                 0.0 
               
               
                 Homozygous deletion 
                 4 
                 69,066,000 
                 69,171,000 
                 105,001 
                 0.0 
               
               
                 Homozygous deletion 
                 4 
                 147,138,000 
                 147,147,000 
                 9,001 
                 0.0 
               
               
                 Amplification 
                 5 
                 31,749,000 
                 31,755,000 
                 6,001 
                 12.3 
               
               
                 Homozygous deletion 
                 5 
                 114,279,000 
                 114,288,000 
                 9,001 
                 0.0 
               
               
                 Homozygous deletion 
                 7 
                 38,358,000 
                 38,364,000 
                 6,001 
                 0.0 
               
               
                 Amplification 
                 8 
                 145,898,000 
                 145,725,000 
                 27,001 
                 11.5 
               
               
                 Homozygous deletion 
                 10 
                 66,978,000 
                 66,984,000 
                 6,001 
                 0.0 
               
               
                 Homozygous deletion 
                 13 
                 108,681,000 
                 108,687,000 
                 6,001 
                 0.0 
               
               
                 Amplification 
                 13 
                 110,139,000 
                 110,157,000 
                 18,001 
                 22.5 
               
               
                 Homozygous deletion 
                 16 
                 54,357,000 
                 54,378,000 
                 21,001 
                 0.0 
               
               
                 Homozygous deletion 
                 16 
                 59,112,000 
                 59,130,000 
                 18,001 
                 0.0 
               
               
                 Amplification 
                 17 
                 76,467,000 
                 76,482,000 
                 15,001 
                 17.8 
               
               
                 Homozygous deletion 
                 18 
                 14,268,000 
                 14,289,000 
                 21,001 
                 0.0 
               
               
                 Amplification 
                 19 
                 50,271,000 
                 50,277,000 
                 6,001 
                 9.3 
               
               
                 Amplification 
                 20 
                 25,404,000 
                 25,428,000 
                 24,001 
                 13.1 
               
               
                 Homozygous deletion 
                 X 
                 49,050,000 
                 49,059,000 
                 9,001 
                 0.0 
               
               
                 Homozygous deletion 
                 X 
                 121,650,000 
                 121,734,000 
                 84,001 
                 0.0 
               
               
                   
               
               
                 *Values for Tag Density Ratios represent observed maximum values for amplifications. 
               
            
           
         
       
     
     Through these two approaches, we identified 57 regions containing putative somatic rearrangements, with an average of 14 rearrangements per sample (Table 2). Of these, an average of seven represented interchromosomal rearrangements and seven represented intrachromosomal rearrangements. For confirmation, we designed primers to 42 of the paired-end regions and used them for PCR spanning the putative breakpoints. Thirty-five of these (83%) yielded PCR products of the expected size in the tumor samples but not in the normal samples ( FIG. 2A-2B , Table S3). Sanger sequencing of seven PCR products confirmed the rearrangements in all cases tested. Though there was variation in the number of detected alterations per sample (range 7 to 21), all four tumor samples were found to have at least 4 bona fide somatic rearrangements through this approach. 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 Summary of rearrangements idenitified in tumor samples 
               
            
           
           
               
               
               
               
            
               
                   
                 Rearrange- 
                   
                   
               
               
                   
                 ment type 
                   
                 Confirmed 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Intra- 
                 Inter- 
                 Total 
                 Tested 
                 somatic 
               
               
                   
                 chromo- 
                 chromo- 
                 rearrange- 
                 rearrange- 
                 rearrange- 
               
               
                 Sample 
                 somal 
                 somal 
                 ments 
                 ments 
                 ments 
               
               
                   
               
            
           
           
               
            
               
                 Tumor and normal libraries 
               
            
           
           
               
               
               
               
               
               
            
               
                 B5 
                 7 
                 4 
                 11 
                 7 
                 5 (71%) 
               
               
                 B7 
                 17 
                 4 
                 21 
                 16 
                 15 (94%)  
               
               
                 Co84 
                 0 
                 7 
                 7 
                 6 
                 4 (67%) 
               
               
                 Co108 
                 6 
                 12 
                 18 
                 13 
                 11 (85%)  
               
            
           
           
               
            
               
                 Tumor libraries 
               
            
           
           
               
               
               
               
               
               
            
               
                 Hx402 
                 7 
                 2 
                 9 
                 9 
                 4 (44%) 
               
               
                 Hx403 
                 17 
                 0 
                 17 
                 12 
                 7 (58%) 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 53 
               
             
            
               
                   
               
               
                 Confirmed somatic rearrangements in breast and colorectal cancer samples* 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Forward tag 
                 Reverse tag 
                   
                   
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 Sample 
                 Chromosome 
                 Position 
                 Chromosome 
                 Position 
                 Type 
                 Primer 1 
                 Primer 2 
               
               
                   
               
               
                 B5C 
                  3 
                   52,638,626 
                  3 
                   52,573,088 
                 AAC 
                 AAGTTTTTCAAGCTTTACCTGAAGT 
                 TATATTGGAAGAATAGAAATGAATGG 
               
               
                   
               
               
                 B5C 
                  4 
                   93,109,700 
                  4 
                  -93,105,085 
                 BAC 
                 AGCCAAGTGCAATTCTCCAG 
                 GCACACTGTTTGCAGGAATG 
               
               
                   
               
               
                 B5C 
                 11 
                   57,713,780 
                  8 
                  -48,889,516 
                 C** 
                 GCCACCTTTCTTTCTTTCTGA 
                 AAGCTTTGTTTGGTTGTTCTCA 
               
               
                   
               
               
                 B5C 
                 18 
                   19,141,985 
                 20 
                  -29,591,944 
                 C** 
                 TGGCTTTCAAAACCCACTG 
                 TCCTTTCTGCCCATTAGGG 
               
               
                   
               
               
                 B5C 
                 22 
                   48,743,603 
                  2 
                 -104,047,142 
                 C** 
                 TCATGGTTTATCCACGGTGT 
                 CACACCGCATTCACACAAAC 
               
               
                   
               
               
                 B7C 
                  1 
                  -96,237,189 
                  7 
                   65,542,257 
                 C** 
                 TCAAAACAGAAAGCATTAGGC 
                 CGCATCCAAAGTATTAATAGCAA 
               
               
                   
               
               
                 B7C 
                  2 
                  197,428,606 
                  2 
                  113,761,988 
                 AAC 
                 AACTCCTCCCACCTCAAAATC 
                 CCAAATTGCCTGCTTAAGAGAT 
               
               
                   
               
               
                 B7C 
                  2 
                  -32,084,286 
                  3 
                  185,241,029 
                 C** 
                 TGCTACCAATACTTCCCACTTG 
                 TACCGTCCTCCAGGCATGT 
               
               
                   
               
               
                 B7C 
                  2 
                  114,604,628 
                 18 
                   53,562,784 
                 C** 
                 GGAGAAAACCCTGGTTATTTTTA 
                 TCCCTCATCAGAGCAAATCA 
               
               
                   
               
               
                 B7C 
                  3 
                 -115,579,348 
                  3 
                 -115,651,310 
                 AAC 
                 AAATTGGGAAGGATCATACTGAC 
                 TCTGAACATGCCTGATCTCATC 
               
               
                   
               
               
                 B7C 
                  4 
                      785,983 
                  4 
                      733,804 
                 AAC 
                 CTGAACTCCTGGGCTGAA 
                 TTGCTAAGTGATGCTACCTGTG 
               
               
                   
               
               
                 B7C 
                  5 
                  107,405,959 
                  5 
                  107,231,803 
                 AAC 
                 CCTGGCCCCTTAGGTAAGAT 
                 TGAAGAATCCTTCTAGTGATGGAA 
               
               
                   
               
               
                 B7C 
                  5 
                   38,284,430 
                 10 
                  -44,715,202 
                 C** 
                 TGCAGCTTTTCTCTGTCTTCA 
                 CTGCCAGTCCAAACTGGTG 
               
               
                   
               
               
                 B7C 
                  6 
                  106,401,376 
                  6 
                   90,853,847 
                 AAC 
                 TGCTGTTTCAAATTCCTACAGTC 
                 TGAAATTAGGACCTGGAGCAC 
               
               
                   
               
               
                 B7C 
                  6 
                  101,933,981 
                  6 
                  102,444,426 
                 ABC 
                 GCCAGGTAACATGCTCACTTT 
                 GATGCAGGAAGTTGACAGCA 
               
               
                   
               
               
                 B7C 
                  9 
                   22,003,033 
                  9 
                   21,761,298 
                 AAC 
                 GGGCTAAGCTTAAGAGTCTGG 
                 GCCATGTGCAAGTCAAGAAG 
               
               
                   
               
               
                 B7C 
                 11 
                   -6,436,033 
                 11 
                   -6,519,897 
                 AAC 
                 TCTGCCGGCATACTGGAC 
                 TAAGGGCGATGTGAACAAGG 
               
               
                   
               
               
                 B7C 
                 12 
                   65,950,588 
                 12 
                   65,923,399 
                 AAC 
                 GCCCTATTTTCAGAGAAAGTGGTA 
                 AACATCTCTTCCTTTTGAAGATCC 
               
               
                   
               
               
                 B7C 
                 13 
                   60,438,525 
                 13 
                   52,159,979 
                 AAC 
                 AATTTGCTCTCATCGTATTGTGT 
                 AGCTGAATCAAAATTTCCAATG 
               
               
                   
               
               
                 B7C 
                 X 
                   31,583,118 
                 X 
                   31,179,704 
                 AAC 
                 CTGAATCTCTTTCCAGCAAAAT 
                 AATGGGTTAAGCAGTTTAGGG 
               
               
                   
               
               
                 Co108C 
                  2 
                  191,184,628 
                  5 
                 -104,930,827 
                 C** 
                 TAGCATGCACCACTTTAGGC 
                 AAAGGTTAAAGGACTGTTTTAAGTTG 
               
               
                   
               
               
                 Co108C 
                  2 
                   78,849,963 
                  6 
                  -13,299,323 
                 C** 
                 GGTTCTGGAGGGTTGGAGA 
                 GTTAAGATCAACATTTTTGTTTCAAG 
               
               
                   
               
               
                 Co108C 
                  2 
                   -7,268,710 
                  6 
                   13,299,385 
                 C** 
                 TATGCCACCATCGCTTAGGT 
                 TCCCAGTGCAATAAAACCAA 
               
               
                   
               
               
                 Co108C 
                  2 
                 -141,266,018 
                 13 
                   96,916,170 
                 C** 
                 GGTGTTCTCTCTCCCATACCA 
                 CGATCTATACACCACCCCACA 
               
               
                   
               
               
                 Co108C 
                  3 
                  -60,400,269 
                  3 
                  -60,437,489 
                 AAC 
                 TGCTTTTAGTTTTGGGTACGG 
                 GCTGATTTGTTTATACCCAGTGC 
               
               
                   
               
               
                 Co108C 
                  3 
                  -60,365,933 
                  3 
                  -60,498,861 
                 AAC 
                 ATCCTCGGACTGGACTGAGA 
                 AACCCCATCCTGAAGCTACC 
               
               
                   
               
               
                 Co108C 
                  3 
                   60,573,034 
                  3 
                   60,472,593 
                 AAC 
                 GGGTTATCTCAAAAGGGCAGA 
                 GCTCTCAATTTGTGTGATTTGG 
               
               
                   
               
               
                 Co108C 
                  4 
                   81,934,151 
                 15 
                   54,039,041 
                 C** 
                 TGTGTTCCTCTCCTCTTAAGCAT 
                 GACTACAAATGGCCCAGACTC 
               
               
                   
               
               
                 Co108C 
                  6 
                  -13,299,291 
                  5 
                  157,523,537 
                 C** 
                 ATCCCCACATTCCCAACC 
                 CCCAGCCATATGTTGGTTTA 
               
               
                   
               
               
                 Co108C 
                  6 
                   13,299,271 
                  2 
                  -20,956,947 
                 C** 
                 GTATTTGTTCATGTTTGTTAGGTGTT 
                 TCAATGGGGGAGAGAGAGC 
               
               
                   
               
               
                 Co108C 
                 13 
                   34,581,537 
                 10 
                   67,756,452 
                 C** 
                 ACGTGTGTATTGGGGGTAGC 
                 CCAGATGGCTGGGTTAAATAAA 
               
               
                   
               
               
                 Co84C 
                  8 
                  128,442,121 
                 19 
                   49,144,200 
                 C** 
                 AGCTAGGTGGAGAATTTGTCG 
                 GGCTTCTGTAGAGTGCACATGA 
               
               
                   
               
               
                 Co84C 
                 11 
                   34,790,251 
                 13 
                  109,267,462 
                 C** 
                 AAGGAGATTGGTTATTGTGGAAA 
                 CTGCAGGAACTGTCTCATTCTT 
               
               
                   
               
               
                 Co84C 
                 11 
                  -34,405,644 
                 15 
                   88,736,701 
                 C** 
                 TGCTGAATCATTCTCCCAACT 
                 TGGTGATTCCACTGAGGTGA 
               
               
                   
               
               
                 Co84C 
                 15 
                  -89,096,347 
                  8 
                  127,747,412 
                 C** 
                 GCATTCTAAAGATGAAGTCCCATT 
                 GGAAACCGTTAGTGGAAAAGTC 
               
               
                   
               
               
                 Hx402x 
                  8 
                   96,971,644 
                  4 
                  156,043,548 
                 C** 
                 CAGGTGATATACCAAAGAAAATTAGG 
                 TTTGGGTTCAGTTCTATTTGAAGA 
               
               
                   
               
               
                 Hx402x 
                  5 
                 -100,413,406 
                  5 
                 -137,521,052 
                 AAC 
                 AGTCAACGCCCTAGCATGG 
                 TGGGCATGAGCAAGATATTC 
               
               
                   
               
               
                 Hx402x 
                  8 
                 -144,771,376 
                  8 
                 -144,787,051 
                 AAC 
                 AATCACGTTGGGTGACTGTG 
                 GTGACAGGCTGGGTGTCC 
               
               
                   
               
               
                 Hx402x 
                 14 
                  -85,526,541 
                 14 
                  -85,560,400 
                 AAC 
                 TGAAGGTTGAGTTGCCAGTG 
                 TGTATGAAACATTGTAGAGGCTGT 
               
               
                   
               
               
                 Hx403x 
                  1 
                  119,547,240 
                  1 
                 -119,550,445 
                 BBC 
                 AGGAGGAAAGCAACACATAGAG 
                 GGTGATTTTCAATGCATATTTCA 
               
               
                   
               
               
                 Hx403x 
                  5 
                  -27,160,637 
                  5 
                   27,150,736 
                 BBC 
                 AATTACCACAACTCCCAGCAG 
                 CAAAAGATTTCCAAATGCAGGT 
               
               
                   
               
               
                 Hx403x 
                 11 
                   66,674,459 
                 11 
                   66,662,814 
                 AAC 
                 TGAATCAGAAAGTCTGGCAGT 
                 CACTTGAGAATCAATGATATGCAG 
               
               
                   
               
               
                 Hx403x 
                 16 
                    6,343,641 
                 16 
                    6,727,736 
                 AAC 
                 CCTAGCCCTTTGTTCCCTGT 
                 TTTGTGTACCTAGACATTCATCCAA 
               
               
                   
               
               
                 Hx403x 
                 16 
                    6,574,321 
                 16 
                    6,759,729 
                 AAC 
                 GCAGAGAACAGCAGAAAAGTTG 
                 AGCCAAGATCAAGCCACAGA 
               
               
                   
               
               
                 Hx403x 
                 16 
                   26,579,136 
                 16 
                  -26,582,595 
                 BBC 
                 TTCTCTTTCTCTGCCTTCAGTG 
                 TTGATGATTTAGAAACTCTAGCCTGT 
               
               
                   
               
               
                 Hx403x 
                 17 
                   34,622,352 
                 17 
                  -34,624,284 
                 BBA 
                 GGCTCCCCTCTCCATTCC 
                 CTGCTGACGTGCTGGTCTT 
               
               
                   
               
               
                 *A single representative mate pair is shown for each rearrangement. Forward and reverse tags and their genomic coordinates correspond to F3 and R3 SOLID mate pair tags. The type of rearrangement corresponds to the categories described in http://www3.appliedbiosystems.com/cms/groups/mcb_marketing/documents/generaldocuments/cms_058717.pdf. AAC corresponds to mate pairs spanning deletions; codes starting with B denote incorrect strand orientation; codes containing a B at the middle position denote incorrect ordering; and C** corresponds to interchromosomal translocations. Primers 1 and 2 correspond to primers used for confirming tumor-specific rearranged sequences. 
               
            
           
         
       
     
     Further examination revealed that rearrangements could be readily identified with high confidence even in the absence of data from matched normal DNA by using the copy number and mate-pair coupled approach. Elimination of analysis of the matched normal would reduce the cost and simplify the identification of rearrangements. To test this strategy, two additional tumor samples (Hx402 and Hx403) were then analyzed through the SOLiD approach, but without generation of matching normal DNA libraries. We found that it was possible to identify putative rearrangements resulting in inter- and intrachromosomal rearrangements at the border of copy number variations with high specificity even in the absence of a matched normal library. We were able to identify 11 confirmed somatic alterations (4 and 7 in Hx402 and Hx403, respectively) out of 21 candidate changes tested (Table S3). 
     Example 4 
     Development of PARE Biomarkers from Rearranged Sequences 
     Each of the rearranged sequences identified through PARE was unique, as no identical rearrangement was found in any of the other five tumor samples. To determine the utility of these rearranged sequences to serve as potential biomarkers, we designed PCR assays to detect them in the presence of increasing amounts of normal DNA. These conditions simulate detection of tumor DNA from patient blood or other bodily fluids where tumor DNA comprises a minority of total DNA. PCR products representing a rearranged region from each of the six dilutions of tumor DNA could be identified, even in mixtures of DNA containing 1 cancer genome equivalent among 390,000 normal genome equivalents ( FIG. 3 ). Furthermore, no background PCR products were discernable when DNA from normal tissues was used as control. 
     To determine whether the rearranged sequences could actually be detected in clinical samples, we evaluated circulating DNA from plasma samples of patients Hx402 and Hx403. The sample from patient Hx403 was obtained prior to surgery while the samples from patient Hx402 were obtained prior to and after surgery. A chromosome 4:8 translocation associated with an amplification was used in tumor Hx402 and an intrachromosomal rearrangement associated with a homozygous deletion of chromosome 16 was used in tumor Hx403. PCR amplification of plasma DNA using primers spanning the breakpoints produced products of the expected sizes only in the plasma samples from patients with disease and not in plasma from healthy controls ( FIG. 4A ). Sequencing of the PCR products from plasma DNA identified the identical breakpoints observed in the tumor DNA samples. 
     Example 5 
     Detection of PARE Biomarker in Human Plasma 
     To determine the sensitivity of rearranged biomarkers in the presence of normal DNA, serial dilutions of tumor:normal DNA mixtures were used as templates for PCR using primers for the chromosome 4/8 translocation in Hx402. The tumor DNA dilution began at 1:125 tumor:normal and continued as a one-in-five serial dilution until reaching 1:390,625 tumor:normal mixture. PCR was performed for each of the six tumor:normal DNA mixtures and for the normal DNA control, using translocation specific primers as well as control primers from chromosome 3. 
     One ml of human plasma samples were obtained from patients Hx402 and Hx403 and from a control individual and DNA was purified as described (29). Whole genome amplification of plasma DNA was performed by ligation of adaptor sequences and PCR amplification with universal primers from the Illumina Genomic DNA Sample Prep Kit. 
     Primers designed to amplify &lt;200 bp fragments spanning each PARE rearrangement were used in PCR from total plasma DNA using patient or control samples. Digital PCR of plasma DNA dilutions from patient Hx402 using rearrangement specific and control primers were used to quantitate the fraction mutated DNA molecules. 
     REFERENCES 
     The disclosure of each reference cited is expressly incorporated herein.
     1. C. Lengauer, K. W. Kinzler, B. Vogelstein, Genetic instabilities in human cancers. Nature 396, 643-649 (1998).   2. F. Mitelman, B. Johansson, F. Mertens, The impact of translocations and gene fusions on cancer causation. Nat Rev Cancer 7, 233-245 (2007).   3. D. Pinkel, R. Segraves, D. Sudar, S. Clark, I. Poole, D. Kowbel, C. Collins, W. L. Kuo, C. Chen, Y. Zhai, S. H. Dairkee, B. M. Ljung, J. W. Gray, D. G. Albertson, High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat Genet 20, 207-211. (1998).   4. R. Lucito, J. Healy, J. Alexander, A. Reiner, D. Esposito, M. Chi, L. Rodgers, A. Brady, J. Sebat, J. Troge, J. A. West, S. Rostan, K. C. Nguyen, S. Powers, K. Q. Ye, A. Olshen, E. Venkatraman, L. Norton, M. Wigler, Representational oligonucleotide microarray analysis: a high-resolution method to detect genome copy number variation. Genome Res 13, 2291-2305 (2003).   5. D. A. Peiffer, J. M. Le, F. J. Steemers, W. Chang, T. Jenniges, F. Garcia, K. Haden, J. Li, C. A. Shaw, J. Belmont, S. W. Cheung, R. M. Shen, D. L. Barker, K. L. Gunderson, High-resolution genomic profiling of chromosomal aberrations using Infinium whole-genome genotyping. Genome Res 16, 1136-1148 (2006).   6. T. L. Wang, C. Maierhofer, M. R. Speicher, C. Lengauer, B. Vogelstein, K. W. Kinzler, V. E. Velculescu, Digital karyotyping. Proc Natl Acad Sci USA 99, 16156-16161 (2002).   7. T. L. Wang, L. A. Diaz, Jr., K. Romans, A. Bardelli, S. Saha, G. Galizia, M. Choti, R. Donehower, G. Parmigiani, M. Shih Ie, C. Iacobuzio-Donahue, K. W. Kinzler, B. Vogelstein, C. Lengauer, V. E. Velculescu, Digital karyotyping identifies thymidylate synthase amplification as a mechanism of resistance to 5-fluorouracil in metastatic colorectal cancer patients. Proc Natl Acad Sci USA 101, 3089-3094 (2004).   8. C. Di, S. Liao, D. C. Adamson, T. J. Parrett, D. K. Broderick, Q. Shi, C. Lengauer, J. M. Cummins, V. E. Velculescu, D. W. Fults, R. E. McLendon, D. D. Bigner, H. Yan, Identification of OTX2 as a medulloblastoma oncogene whose product can be targeted by all-trans retinoic acid. Cancer Res 65, 919-924 (2005).   9. K. Nakayama, N. Nakayama, B. Davidson, H. Katabuchi, R. J. Kurman, V. E. Velculescu, M. Shih Ie, T. L. Wang, Homozygous deletion of MKK4 in ovarian serous carcinoma. Cancer Biol Ther 5, 630-634 (2006).   10. R. J. Leary, J. C. Lin, J. Cummins, S. Boca, L. D. Wood, D. W. Parsons, S. Jones, T. Sjoblom, B. H. Park, R. Parsons, J. Willis, D. Dawson, J. K. Willson, T. Nikolskaya, Y. Nikolsky, L. Kopelovich, N. Papadopoulos, L. A. Pennacchio, T. L. Wang, S. D. Markowitz, G. Parmigiani, K. W. Kinzler, B. Vogelstein, V. E. Velculescu, Integrated analysis of homozygous deletions, focal amplifications, and sequence alterations in breast and colorectal cancers. Proc Natl Acad Sci USA 105, 16224-16229 (2008).   11. D. Y. Chiang, G. Getz, D. B. Jaffe, M. J. O&#39;Kelly, X. Zhao, S. L. Carter, C. Russ, C. Nusbaum, M. Meyerson, E. S. Lander, High-resolution mapping of copy-number alterations with massively parallel sequencing. Nat Methods 6, 99-103 (2009).   12. J. O. Korbel, A. E. Urban, J. P. Affourtit, B. Godwin, F. Grubert, J. F. Simons, P. M. Kim, D. Palejev, N. J. Carriero, L. Du, B. E. Taillon, Z. Chen, A. Tanzer, A. C. Saunders, J. Chi, F. Yang, N. P. Carter, M. E. Hurles, S. M. Weissman, T. T. Harkins, M. B. Gerstein, M. Egholm, M. Snyder, Paired-end mapping reveals extensive structural variation in the human genome. Science 318, 420-426 (2007).   13. P. J. Campbell, P. J. Stephens, E. D. Pleasance, S. O&#39;Meara, H. Li, T. Santarius, L. A. Stebbings, C. Leroy, S. Edkins, C. Hardy, J. W. Teague, A. Menzies, I. Goodhead, D. J. Turner, C. M. Clee, M. A. Quail, A. Cox, C. Brown, R. Durbin, M. E. Hurles, P. A. Edwards, G. R. Bignell, M. R. Stratton, P. A. Futreal, Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing. Nat Genet 40, 722-729 (2008).   14. C. A. Maher, C. Kumar-Sinha, X. Cao, S. Kalyana-Sundaram, B. Han, X. Jing, L. Sam, T. Barrette, N. Palanisamy, A. M. Chinnaiyan, Transcriptome sequencing to detect gene fusions in cancer. Nature 458, 97-101 (2009).   15. K. J. McKernan, H. E. Peckham, G. L. Costa, S. F. McLaughlin, Y. Fu, E. F. Tsung, C. R. Clouser, C. Duncan, J. K. Ichikawa, C. C. Lee, Z. Zhang, S. S. Ranade, E. T. Dimalanta, F. C. Hyland, T. D. Sokolsky, L. Zhang, A. Sheridan, H. Fu, C. L. Hendrickson, B. Li, L. Kotler, J. R. Stuart, J. A. Malek, J. M. Manning, A. A. Antipova, D. S. Perez, M. P. Moore, K. C. Hayashibara, M. R. Lyons, R. E. Beaudoin, B. E. Coleman, M. W. Laptewicz, A. E. Sannicandro, M. D. Rhodes, R. K. Gottimukkala, S. Yang, V. Bafna, A. Bashir, A. MacBride, C. Alkan, J. M. Kidd, E. E. Eichler, M. G. Reese, F. M. De La Vega, A. P. Blanchard, Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding. Genome Res 19, 1527-1541 (2009).   16. D. Grimwade, J. V. Jovanovic, R. K. Hills, E. A. Nugent, Y. Patel, R. Flora, D. Diverio, K. Jones, H. Aslett, E. Batson, K. Rennie, R. Angell, R. E. Clark, E. Solomon, F. Lo-Coco, K. Wheatley, A. K. Burnett, Prospective minimal residual disease monitoring to predict relapse of acute promyelocytic leukemia and to direct pre-emptive arsenic trioxide therapy. J Clin Oncol 27, 3650-3658 (2009).   17. M. Bregni, S. Siena, A. Neri, R. Bassan, T. Barbui, D. Delia, G. Bonadonna, R. Dalla Favera, A. M. Gianni, Minimal residual disease in acute lymphoblastic leukemia detected by immune selection and gene rearrangement analysis. J Clin Oncol 7, 338-343 (1989).   18. V. H. van der Velden, E. R. Panzer-Grumayer, G. Cazzaniga, T. Flohr, R. Sutton, A. Schrauder, G. Basso, M. Schrappe, J. M. Wijkhuijs, M. Konrad, C. R. Bartram, G. Masera, A. Biondi, J. J. van Dongen, Optimization of PCR-based minimal residual disease diagnostics for childhood acute lymphoblastic leukemia in a multi-center setting. Leukemia 21, 706-713 (2007).   19. T. Lion, Minimal residual disease. Curr Opin Hematol 6, 406-411 (1999).   20. T. Hughes, M. Deininger, A. Hochhaus, S. Branford, J. Radich, J. Kaeda, M. Baccarani, J. Cortes, N. C. Cross, B. J. Druker, J. Gabert, D. Grimwade, R. Hehlmann, S. Kamel-Reid, J. H. Lipton, J. Longtine, G. Martinelli, G. Saglio, S. Soverini, W. Stock, J. M. Goldman, Monitoring CIVIL patients responding to treatment with tyrosine kinase inhibitors: review and recommendations for harmonizing current methodology for detecting BCR-ABL transcripts and kinase domain mutations and for expressing results. Blood 108, 28-37 (2006).   21. D. Dressman, H. Yan, G. Traverso, K. W. Kinzler, B. Vogelstein, Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations. Proc Natl Acad Sci USA 100, 8817-8822 (2003).   22. J. Shendure, G. J. Porreca, N. B. Reppas, X. Lin, J. P. McCutcheon, A. M. Rosenbaum, M. D. Wang, K. Zhang, R. D. Mitra, G. M. Church, Accurate multiplex polony sequencing of an evolved bacterial genome. Science 309, 1728-1732 (2005).   23. G. R. Bignell, T. Santarius, J. C. Pole, A. P. Butler, J. Perry, E. Pleasance, C. Greenman, A. Menzies, S. Taylor, S. Edkins, P. Campbell, M. Quail, B. Plumb, L. Matthews, K. McLay, P. A. Edwards, J. Rogers, R. Wooster, P. A. Futreal, M. R. Stratton. Genome Research 17:1296-1303 (2007).   24. P. J. Stephens, D. J. McBride, M. L. Lin, I. Varela, E. D. Pleasance, J. T. Simpson, L. A. Stebbings, C. Leroy, S. Edkins, L. J. Mudie, C. D. Greenman, M. Jia, C. Latimer, J. W. Teague, K. W. Lau, J. Burton, M. A. Quail, H. Swerdlow, C. Churcher, R. Natrajan, A. M. Sieuwerts, J. W. Martens, D. P. Silver, A. Langerød, H. E. Russnes, J. A. Foekens, J. S. Reis-Filho, L. van&#39;t Veer, A. L. Richardson, A. L. Børresen-Dale, P. J. Campbell, P. A. Futreal, M. R. Stratton. Complex landscapes of somatic rearrangement in human breast cancer genomes. Nature 462, 1005-1010 (2009).   25. M. Li, F. Diehl, D. Dressman, B. Vogelstein, K. W. Kinzler, BEAMing up for detection and quantification of rare sequence variants. Nat Methods 3, 95-97 (2006).   26. T. Sjoblom, S. Jones, L. D. Wood, D. W. Parsons, J. Lin, T. D. Barber, D. Mandelker, R. J. Leary, J. Ptak, N. Silliman, S. Szabo, P. Buckhaults, C. Farrell, P. Meeh, S. D. Markowitz, J. Willis, D. Dawson, J. K. Willson, A. F. Gazdar, J. Hartigan, L. Wu, C. Liu, G. Parmigiani, B. H. Park, K. E. Bachman, N. Papadopoulos, B. Vogelstein, K. W. Kinzler, V. E. Velculescu, The consensus coding sequences of human breast and colorectal cancers. Science 314, 268-274 (2006).   27. SOLiD Data format and File Definitions Guide. http://www3.appliedbiosystems. com/cms/groups/mcbmarketing/documents/generaldocuments/cms058717.pdf   28. Primer 3 v. 0.4.0. http://frodo.wi.mit.edu/primer3/29. F. Diehl, K. Schmidt, M. A. Choti, K. Romans, S. Goodman, M. Li, K. Thornton, N. Agrawal, L. Sokoll, S. A. Szabo, K. W. Kinzler, B. Vogelstein, L. A. Diaz, Jr., Circulating mutant DNA to assess tumor dynamics. Nat Med 14, 985-990 (2008).