JPWO2019009431A1 - Highly accurate method for identifying mutations in tumor cells - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method for identifying a mutation caused in a tumor cell with high accuracy by identifying a DNA mutation in a subject. A method for highly accurately discriminating a mutation occurring in a tumor cell and a mutation occurring in a normal cell among mutations detected in a subject, the mutation being identified in the DNA of the subject. And a step of collating the identified mutation with a database in which cancer-specific mutations are accumulated, wherein the identified mutation is a predetermined threshold case in the database as a mutation specific to the tumor. If the number or more is accumulated, it is determined that the mutation is derived from the tumor cell, and the collating step is included. [Selection diagram] None

Description

  The present invention provides a method for highly accurately discriminating a mutation generated in a tumor cell from a mutation generated in a normal cell among mutations detected in a subject, and particularly to identifying a DNA mutation in a subject. The present invention relates to a method for identifying a mutation caused in a tumor cell with high accuracy.

  Circulating tumor DNA (ctDNA) is cell-free DNA (cfDNA) in which genomic DNA of a cancer cell destroyed by apoptosis or immunity is leaked into the blood. It is expected to have a wide range of uses such as use as a marker and early detection and monitoring of drug resistance. Further, since ctDNA can be obtained from the blood of the subject, non-invasive diagnosis is possible. Therefore, although it is desired to apply ctDNA to cancer for diagnosis, since 1 ml of blood contains cfDNA derived from 1 to several thousand of fragments fragmented to 170 base pairs on average, it is derived from normal cells. It is extremely difficult to detect and quantify mutations that have occurred in cancer cells from the enormous amount of DNA.

  Digital PCR and next-generation sequencing (NGS) are used as techniques for detecting such mutations. However, since the mutations generated in cancer cells are present in the blood in a small amount, the sequencing error rate due to NGS becomes a serious problem. Therefore, the present inventors have introduced a molecular barcode sequence into the sequencing sequence in order to solve this problem (Patent Document 1). According to this molecular barcode technology, DNA fragments are often labeled with a random sequence of 10 to 15 bases, which makes it possible to distinguish the leads from individual molecules and to group the leads from each molecule. Thus, creating a consensus of reads will provide high quality DNA sequencing and allow the counting of sequenced molecules.

Patent No. 6125731

Schmitt MW, Kennedy SR, Salk JJ, Fox EJ, Hiatt JB, Loeb LA. Detection of ultra-rare mutations by next-generation sequencing. Proc Natl Acad Sci USA 2012; 109: 14508-13. Newman AM, Lovejoy AF, Klass DM, Kurtz DM, Chabon JJ, Scherer F, et al. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat Biotechnol 2016; 34: 547-55.

  However, even if the sequence can be accurately read by such a molecular barcode technique, the difference in the bases generated before the PCR such as the base substitution in the genomic DNA due to the DNA damage during the sample preparation is Cannot be detected. Also, with regard to somatic mutations that occur in low frequencies in normal tissues or in cell-free DNA in blood, it is difficult to distinguish between normal cell-derived DNA and tumor cell-derived DNA that is present in a very small number. There is.

  The double-stranded sequencing technology can distinguish a mutation (mutation) existing in two strands of DNA from a mutation (DNA damage) existing only in one strand (Non-Patent Document 1). .. Therefore, although comprehensive analysis of cancer patient-derived cfDNA using double-stranded sequencing technology is very useful for elucidating the cause of mutation, a large amount of DNA is required for double-stranded sequencing technology. Since it is necessary, it is not suitable for diagnostic use (Non-Patent Document 2). Therefore, it is desired to develop a more accurate method that can be used for diagnostic purposes in order to discriminate between mutations occurring in tumor cells and mutations occurring in normal cells in the mutation of cfDNA of a subject. There is.

  The present invention has been made in view of such circumstances, and an object of the present invention is to provide a method for identifying a mutation caused in a tumor cell with high accuracy by identifying a DNA mutation in a subject. ..

  In order to solve such a problem, the present inventors have focused on the characteristics of mutations accumulated in the cancer somatic mutation catalog (COSMIC), and as a result, found that most of the mutations observed in healthy subjects were , COSMIC, or only a few entries were found. Therefore, as a result of intensive research, we developed a filter that can eliminate mutants that are unlikely to be somatic mutations in cancer tissues, and analyze mutations in the DNA of test subjects using this filter to detect tumor cells. It was found that it was possible to discriminate between the mutations that occurred in (1) and the mutations that occurred in normal cells.

  Specifically, according to the first main aspect of the present invention, among mutations detected from a subject, a mutation occurring in a tumor cell and a mutation occurring in a normal cell are distinguished with high accuracy. A method of identifying a mutation in a subject's DNA, and matching the identified mutation to a database of accumulated cancer-specific mutations, wherein the identified mutation is the tumor And a predetermined threshold number of cases or more are accumulated in the database as a mutation specific to the tumor cell, the mutation is determined to be derived from the tumor cell, and the matching step is provided. It

  According to such an arrangement, it is intended to provide a method for identifying a mutation in a tumor cell or a normal cell by simply identifying a mutation in a subject's DNA. You can Further, according to such a configuration, the presence or absence of tumor cells in the subject can be confirmed by non-invasively and conveniently identifying the presence of the mutation in the subject, and through this, It can serve as a material for selecting a suitable treatment method.

  Moreover, according to one embodiment of the present invention, in the method according to the first main aspect of the present invention, the DNA can be cell-free DNA derived from blood.

  Furthermore, according to another embodiment of the present invention, in the method according to the first main aspect of the present invention described above, the identified mutation is accumulated in the database as a somatic mutation in two or more cases. Can have been.

  Further, according to another embodiment of the present invention, in the method of the first main aspect of the present invention described above, the tumor can be pancreatic cancer. In this case, it is preferable that the mutation is a mutation of the TP53 gene, and 10 or more cases of somatic mutation are accumulated in the database. In this case, the normal cells can also include intraductal papillary mucinous tumor cells of the pancreas.

  Further, according to still another embodiment of the present invention, in the method of the first main aspect of the present invention described above, the blood can be used as a plasma component.

  According to a second main aspect of the present invention, there is provided a system for carrying out the method according to the above-mentioned first main aspect, and in particular, among mutations detected in a subject, A mutation discriminating system for highly accurately discriminating a generated mutation from a mutation generated in a normal cell, comprising means for identifying a mutation in DNA of a subject, and the identified mutation as a cancer-specific mutation Is a means for collating with an accumulated database, wherein the identified mutation is accumulated in the database as a mutation specific to the tumor in a predetermined threshold number of cases or more, the tumor cell A system is provided which comprises said matching means for determining a mutation of origin.

  In addition, the features of the present invention other than those described above, and the remarkable operation / effect will be clear to those skilled in the art by referring to the following embodiment section and drawings.

FIG. 1 is a reaction scheme showing the attachment of barcode tags for barcode sequences in one embodiment of the present invention. FIG. 2 is a scatter plot showing the distribution of the number of filtered variants and the number of variants excluded by the filtering in the sample data according to an embodiment of the present invention.

An embodiment and an example according to the present invention will be described below with reference to the drawings.
As described above, the present invention is a method for highly accurately discriminating a mutation occurring in a tumor cell from a mutation occurring in a normal cell among mutations detected in a subject, which is a sudden mutation in a DNA of a subject. A step of identifying a mutation and a step of matching the identified mutation with a database in which cancer-specific mutations are accumulated, wherein the identified mutation is stored in the database as a mutation specific to the tumor. When the number of cases of a predetermined threshold value or more is accumulated, it is determined that the mutation is derived from the tumor cell, and the collating step is included.

  As used herein, the term “tumor cell-generated mutation” refers to a mutation in circulating tumor DNA (ctDNA), which is free DNA derived from a tumor floating in the blood of a subject. By using a liquid biopsy based on ctDNA having a mutation unique to a particular cancer, it is possible to detect the cancer before it is diagnosed by a method such as imaging, and it is possible to judge the success of the treatment. It will be possible. In addition, it is generally known that, among DNAs existing in blood, ctDNA is present in a very small amount as compared with normal cell DNA.

  As used herein, the term “mutation produced in normal cells” refers to a mutation in cell-free DNA (cfDNA) released from cells into plasma by the death of normal cells of a subject.

  In one embodiment of the present invention, the “database in which cancer-specific mutations are accumulated” is obtained from a cancer tissue-derived base sequence in which unique mutations existing in various cancer tissues are captured in base units. Therefore, a database that comprehensively accumulates information on somatic mutations related to cancer such as single nucleotide polymorphism, copy number mutation, structural polymorphism, etc. may be used. For example, as the “database in which cancer-specific mutations are accumulated”, Catalog Of Somatic Mutations In Cancer (COSMIC), The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and the like can be used, It is not limited to this.

  Further, in the present specification, the "predetermined threshold number of cases", when determining whether the identified mutation is a tumor cell-derived mutation or a normal cell-derived mutation using the above database, It refers to the number of cases that is the threshold for the determination. For example, the threshold number of cases is 2 cases, 3 cases, 4 cases, 5 cases, 6 cases, 7 cases, 8 cases, 9 cases, 10 cases or more. It can be set as appropriate.

  For example, in the case where the tumor is pancreatic cancer, it can be determined that the mutation is derived from a tumor cell when the identified mutation is accumulated in the database as two or more cancer tissue mutations. The number of cases can be changed depending on the mutation and the gene in which the mutation occurs. In the case of the TP53 gene, the number of registered mutations is large, and there are many mutations in normal cells or errors in determining the nucleotide sequence. Therefore, when the identified mutation is the TP53 gene, the threshold number of cases can be changed. When 10 or more mutations are accumulated in the database, it can be determined to be a mutation derived from a tumor cell. As described above, in the present specification, the “predetermined threshold number of cases” can be appropriately changed depending on the type of cancer, the type of identified mutation, the type of gene in which the mutation has occurred, and the like.

  In one embodiment of the present invention, the predetermined threshold number of cases for determining such a tumor cell-derived mutation can also be referred to as a CV78 filter. By processing with this filter, mutants with a number of cases exceeding the set threshold value are selected and can be considered as somatic mutations specific to the tumor tissue, while other mutations that are less than that number of cases are selected. The body can be excluded. For example, in the CV78 filter, the threshold number of cases is set to 10 in the case of mutation of the TP53 gene, and 2 in the case of mutation of other genes. It is also possible to set a threshold number of cases to.

  Further, in one embodiment of the present invention, when the tumor is pancreatic cancer, intraductal papillary mucinous tumor of the pancreas (IPMN) cells may be included as normal cells. By doing so, IPMN and pancreatic cancer can also be distinguished based on whether the identified mutation is a pancreatic cancer cell-derived mutation or an IPMN cell-derived mutation.

  In the present specification, the “subject DNA” may be DNA obtained from a subject, and the cell or tissue from which it is derived is not particularly limited. In one embodiment of the present invention, the “subject DNA” can be cell-free DNA derived from the blood of the subject, and in this case, it is preferable to use the plasma component of the subject.

  The mutation identification method as described above is preferable as the number of accumulated samples in the database in which cancer-specific mutations are accumulated is larger, and the accuracy of the identification method according to the present invention is considered to be higher. Further, in the present invention, it is possible to adopt a configuration in which the data relating to such accumulated samples can be stored in any database. That is, the present invention can also provide a database that stores such data, and an analysis device or system that reads and executes the data and a program necessary for comparative analysis. According to such an analysis device or system, information on mutations to be identified is accumulated for each target subject, and the accumulated information is extracted as needed to accumulate cancer-specific mutations. Among the mutations detected from the subject, the mutations occurring in the tumor cells and the mutations occurring in the normal cells can be discriminated with high accuracy at any time by collating with the database obtained.

  In addition, such a system is configured by connecting an external storage device such as a RAM, a ROM, a HDD, and a magnetic disk and an input / output interface (I / F) to a CPU incorporated in a computer system via a system bus. be able to. An input device such as a keyboard and a mouse, an output device such as a display, and a communication device such as a modem are connected to the input / output I / F. The external storage device includes a ctDNA amount information DB, an image diagnosis information DB, and a program storage unit, all of which are fixed storage areas secured in the storage device.

  Further, such a system can have a system for measuring and analyzing the amount of DNA and a system for identifying mutations, and such a system includes a nucleic acid collected from a subject by a blood collection tube for molecular diagnosis or the like. A nucleic acid analyzer for performing nucleic acid analysis based on a biological sample can be included, and each can be electronically connected by a communication network such as a dedicated line or a public line.

  Hereinafter, the present invention will be described in more detail with reference to examples, but the present invention is not limited to these examples.

(Experimental method and materials)
The experimental methods and materials used in the present invention will be described below. Although the following experimental methods are used in the present embodiment, similar results can be obtained by using other experimental methods.

Subjects and Samples Blood samples of pancreatic cancer patients and IPMN (intraductal papillary mucinous tumor of the pancreas) patients were sampled between January 2012 and February 2016 at Osaka Prefectural Adult Center. Plasma preparation and DNA extraction were performed by means well known in the art. Tissue samples were obtained using endoscopy, ultrasound guidance, and fine needle aspiration. Written consent was obtained from all patients and the study was approved by the Ethics Committee of the Osaka Cancer Center.

Adapters and Primers for Amplifying Target Regions Target regions of genes associated with pancreatic cancer are shown in Table 1. A 30-base long adapter sequence containing a primer sequence for ion torrent sequencing was bonded to 5 bases as a solid index, 12 bases as a molecular index, and 20 bases as a 3'side spacer.

Library construction using barcode strands Two separate reaction mixtures were prepared per plasma sample with two gene specific primers. About 1 ml of cell-free DNA derived from whole blood was treated with 50 mM Tris-HCl, pH 8.0, 10 mM MgCl ₂ , 10 mM dithiothreitol, 1 mM ATP, 0.4 mM dNTP, 2.4 units of T4 DNA polymerase (Takara Bio, Kusatu, Japan), 7.5 units of T4 polynucleotide kinase (NEB, Ipswich, MA, USA), and 0.5 units of KOD DNA polymerase (Toyobo, Osaka, Japan) in a 15 μl solution at 25 ° C. The ends were repaired by incubating for 30 minutes, then 20 minutes at 75 ° C. Ligation of adapters tagged with a 12 nucleotide barcode sequence was performed in 20 μl of end repair solution with 0.5 μl of 10 × T4 DNA ligase buffer (NEB), 40 pmol of adapter and 2000 units of T4 DNA ligase. Was added and incubated at 25 ° C. for 15 minutes. The ligation product was purified twice with 1.2 volumes of AMPure XP beads (Beckman Coulter, Brea, CA, USA). Purified beads were added to 20 μl of linear amplification solution containing 1 × Q5 reaction buffer (NEB), 0.2 mM dNTPs, 6 μM gene-specific primer mix, and 0.4 units of Q5 Hot Start High Fidelity DNA Polymerase (NEB). Mixed. After removing the AMPure XP beads, amplification was performed as follows. 15 cycles of 98 ° C for 30 seconds for denaturation, then 98 ° C for 10 seconds, and 65 ° C for 2 minutes. Subsequently, 1.2 μL of 100 μM T_PCR_A was added to the reaction mixture and amplified at 98 ° C. for 10 seconds, 65 ° C. for 30 seconds, and 72 ° C. for 30 seconds in 15 cycles. The amplified product was purified once with 1.2 volumes of AMPure XP and recovered with 20 μL of 0.1 × TE. 3 μl of purified product was added to 1 × High Fidelity PCR buffer (Thermo Fisher Scientific, Waltham, MA, USA), 0.2 mM dNTPs, 2 mM MgSO ₄ , 0.5 μM T_PCR_A, 0.5 μM nested primer mix, and 0.4. A unit of Platinum Taq DNA polymerase, High Fidelity (Thermo Fisher Scientific) was added to two tubes of PCR amplification solution (20 μL each). The thermal cycle was performed as follows. Denaturation at 95 ° C. for 2 minutes and 95 ° C. for 15 seconds, 63 ° C. for 1 minute for 25 or 30 cycles. The amplification product was purified with 1.2 volumes of AMPure XP beads. Product concentrations were measured using the Qubit dsDNA HS Assay Kit or the Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific).

Massively parallel sequencing was performed using an Ion Torrent Proton sequencer (Thermo Fisher Scientific) according to the sequencing and data analysis protocols. The raw signal was converted to base call using the Torrent Suite (Thermo Fisher Scientific) and the FASTQ file of sequence reads was extracted.

FASTQ format reads were classified using a 5-base index for individual assignment. The sequence between the 5 base index and the spacer sequence was used as a molecular barcode tag. If the spacer and subsequent sequence were greater than 50 bases in length, the reads were aligned to the target region using BWA-MEM. Reads with short mapping ends (less than 40 bases) were discarded. Reads with the same barcode sequence were grouped together and detection and removal of error barcode tags was performed as described in US Pat. A consensus sequence for reads with the same barcode was performed using VarScan. When 85% or more of reads had the same base at a position, it was set as a consensus base. For the detection of variants, a Poisson distribution model was applied to calculate the sequencing error. Each target region was evaluated for each presence of the mutant, and P = 10 ⁻⁴ was set as the detection threshold. Each base position of codons 12 and 13 of the KRAS gene was evaluated at a specific threshold. This analysis did not consider common SNP sites and error prone sites. The version of the human reference genome is GRCh37 / hg19.

Results Pancreatic Cancer Sequencing Target regions of pancreatic cancer-related genes of KRAS, TP53, SMAD4, CTNNB1, CDKN2A, GNAS, HRAS, and NRAS were sequenced by the method described above. The total size of the target area was 2.8 kb. The barcode-tagged adapter was directly attached to the undigested end of cfDNA. For library construction, the target region was amplified with a mixture of adapters and gene-specific primers after a linear amplification step using only gene-specific primers. The reaction scheme is shown in FIG. This library was sequenced on an Ion Torrent sequencer. Sequence reads were grouped using molecular barcodes. After removing the error sequences, high quality sequence data was used to construct a consensus sequence for each read group. The average number of molecules sequenced was 900 bases per target region.

Construction of filters to remove non-tumor specific variants The first data set was obtained from a cohort of 12 healthy subjects and 57 patients with pancreatic cancer. The results of mutant detection are summarized in the upper half of Table 2. Twelve mutants were found in the healthy volunteer samples (Table 3).

  Variants present in less than 1% of reads are unaffected by analysis of conventional applications of next-gen sequencers such as whole genome or exome sequencing, but pose a significant problem in the detection of ctDNA. In this example, 5 out of 12 healthy individuals were determined to be mutant positive (Table 2). In other words, there are 5 individuals who were judged to be positive for the cancer-specific mutation even in healthy people, and it is directly determined from the results of sequencing that the cancer-specific mutation is positive. Is not suitable for diagnosing mutations in. Therefore, we set up a mutant filter.

  All cancer-specific mutation data are stored in a public database called the Cancer Somatic Mutation Catalog (COSMIC). Recent large-scale efforts to characterize the cancer genome, such as the International Cancer Genome Consortium and the Cancer Genome Atlas, are presumed to identify most mutations attributable to the primary tumor. However, 10 of the 12 mutants identified in healthy individuals were not registered with COSMIC. The present inventors presupposed the following two things in order to cope with this. (1) COSMIC covers all somatic mutations present in cancer tissues. (2) Infrequent entries in COSMIC can result from artificial factors such as DNA damage and PCR / sequencing errors. Therefore, TP53 variants were excluded, and variants not cataloged in COSMIC (version 78) and single entry variants were excluded. For TP53, many somatic mutations were cataloged in its coding region, so more stringent criteria were applied and variants with less than 10 entries were excluded. Such a bioinformatics process was named CV78 filter. Validation of the variants by the CV78 filter ruled out all variants present in healthy individuals (Table 2). Filtered variants were defined as those selected by the CV78 filter (Table 2). Insertion / deletion errors were rare in the experiments of this example and were therefore excluded from the analysis (one deletion and one insertion).

  Both plasma and tumor samples were available for 10 patients with pancreatic cancer. The variants identified by sequencing are shown in Table 4.

  Of the 35 variants identified, 6 were only detected in plasma samples. Validation of these variants with a CV78 filter ruled out all 6 variants. That is, it can be seen that the CV78 filter can discriminate between mutants existing in normal cells and mutants existing in tumor cells.

Discrimination between Tubular Papillary Mucinous Tumor (IPMN) and Pancreatic Cancer The entire process of first identifying variants by sequencing and then filtering variants for cancer specificity is validated in a second independent sample set. did. This sample set includes plasma samples from 20 IPMN patients and 86 pancreatic cancer patients. Plasma samples from patients with pancreatic cancer included in the second dataset were obtained later than those of patients included in the first dataset, except for plasma samples paired with tissue samples. All samples in the second set were assayed and analyzed only after construction of the CV78 filters.

  IPMN is a neoplasm that grows in the pancreatic duct and is therefore unlikely to release cfDNA in the bloodstream. KRAS mutations are rarely detected in the plasma of benign neoplastic patients. The distinction between IPMN and pancreatic cancer is believed to have substantial clinical benefit, as a significant proportion of IPMN cases progress to pancreatic cancer.

  Ten of the 20 IPMN patients were mutant positive before CV78 filtering, but only one patient was mutant positive after CV78 filtering (Table 2). On the other hand, 32 out of 86 patients with pancreatic cancer were also mutant positive after CV78 filter treatment.

Other Features of Mutations Identified by Barcode Sequences Mutations identified by barcode sequences were classified according to their presence in a particular gene (Table 5).

  Common to both the first and second sample sets, treatment with the CV78 filter is often considered a mutation in the case of a KRAS mutation. This is due to the mutation hotspots present at codons 12 and 13. There was no significant difference in recovery rate (rate selected as mutant) between the first and second sample sets.

  Since the results for the first and second sample sets were not significantly different, subsequent analyzes were performed combining data from both sample sets. In the filtered mutants, G> T / C> A transversion mutations and C> T / G> A transition mutations were found at a rate of 23.3% and 62.8%, respectively (Table 6). ). In the mutants excluded by the CV78 filter, G> T / C> A transversion mutations and C> T / G> A transition mutations were found at rates of 32.2% and 40.6%, respectively. (Table 6). These transversion and transition mutations accounted for the majority of mutations in both datasets.

  All samples were plotted on a scatter plot with the number of molecules sequenced on the x-axis and the number of mutant molecules on the y-axis (Figure 2). The distribution of filtered mutants (mutants left over by filtering) and excluded mutants differed from each other. The filtered mutant molecules were widely distributed at respective rates from> 10% to <1% of the sequenced molecules. On the other hand, the excluded mutants occupy almost no more than 10% of the sequenced molecules, the ratio being around 1%.

  In addition, it goes without saying that the present invention can be variously modified, and is not limited to the above-described one embodiment, and can be variously modified without changing the gist of the invention.

Claims (8)

Among the mutations detected from the subject, a method for highly accurately distinguishing between mutations occurring in tumor cells and mutations occurring in normal cells,
Identifying a mutation in the subject's DNA,
The step of matching the identified mutation with a database in which cancer-specific mutations are accumulated, wherein the identified mutation is a predetermined threshold number of cases in the database as the tumor-specific mutation or The above-mentioned collating step of determining that the mutation is derived from the tumor cell when the above-mentioned accumulations have been made.   The method according to claim 1, wherein the DNA is cell-free DNA derived from blood.   The method according to claim 1, wherein the identified mutation is accumulated in the database as a somatic mutation in two or more cases.   The method of claim 1, wherein the tumor is pancreatic cancer.   The method according to claim 4, wherein the identified mutation is a mutation in the TP53 gene, and 10 or more cases are accumulated in the database as a somatic mutation.   The method of claim 4, wherein the normal cells comprise intraductal papillary mucinous tumor cells of the pancreas.   The method of claim 1, wherein the blood is a plasma component. Among mutations detected from a subject, a mutation discrimination system for highly accurately discriminating a mutation generated in a tumor cell and a mutation generated in a normal cell,
Means for identifying mutations in the subject's DNA,
A means for collating the identified mutation with a database in which cancer-specific mutations are accumulated, wherein the identified mutation is a predetermined threshold number of cases in the database as the tumor-specific mutation or The above-mentioned means for collating, which determines that the mutation is derived from the tumor cell when accumulated above.

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