WO2022097844A1 - Method for predicting survival prognosis of pancreatic cancer patients by using gene copy number variation information - Google Patents
Method for predicting survival prognosis of pancreatic cancer patients by using gene copy number variation information Download PDFInfo
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- WO2022097844A1 WO2022097844A1 PCT/KR2021/001162 KR2021001162W WO2022097844A1 WO 2022097844 A1 WO2022097844 A1 WO 2022097844A1 KR 2021001162 W KR2021001162 W KR 2021001162W WO 2022097844 A1 WO2022097844 A1 WO 2022097844A1
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- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
- C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
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- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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- C12Q2531/00—Reactions of nucleic acids characterised by
- C12Q2531/10—Reactions of nucleic acids characterised by the purpose being amplify/increase the copy number of target nucleic acid
- C12Q2531/107—Probe or oligonucleotide ligation
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- C12Q2537/00—Reactions characterised by the reaction format or use of a specific feature
- C12Q2537/10—Reactions characterised by the reaction format or use of a specific feature the purpose or use of
- C12Q2537/16—Assays for determining copy number or wherein the copy number is of special importance
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- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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- C12Q2563/00—Nucleic acid detection characterized by the use of physical, structural and functional properties
- C12Q2563/159—Microreactors, e.g. emulsion PCR or sequencing, droplet PCR, microcapsules, i.e. non-liquid containers with a range of different permeability's for different reaction components
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- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/118—Prognosis of disease development
Definitions
- the present invention relates to an information providing method for predicting the survival prognosis of a pancreatic cancer patient, specifically, a pancreatic cancer patient characterized by using a gene mutation specific to pancreatic cancer, in particular, a gene copy number variation (CNV). It relates to a method for providing information for predicting the survival prognosis of and its use.
- CNV gene copy number variation
- the pancreas is located behind the stomach and in the middle of the body and is about 20 cm long. It is surrounded by organs such as the stomach, duodenum, small intestine, large intestine, liver, gallbladder, and spleen. The total length is about 15 to 20 cm, and the weight is about 100 g, and it is divided into a head, a body, and a tail.
- the pancreas has an exocrine function that secretes digestive enzymes that break down carbohydrates, fats, and proteins in the ingested food, and an endocrine function that secretes hormones such as insulin and glucagon that control blood sugar.
- Pancreatic cancer is a mass (tumor mass) made up of cancer cells in the pancreas.
- pancreatic cancer There are several types of pancreatic cancer, and pancreatic ductal adenocarcinoma generated from pancreatic duct cells accounts for about 90% of pancreatic ductal adenocarcinoma.
- cystic cancer cystic adenocarcinoma
- endocrine tumors there are cystic cancer (cystic adenocarcinoma) and endocrine tumors.
- Pancreatic cancer is difficult to detect early because there are no specific early symptoms. Loss of appetite, weight loss, etc. appear, but it is not a characteristic symptom of pancreatic cancer, but may appear sufficiently in other diseases.
- pancreas is thin, about 2 cm thick, and is surrounded by only a capsule, and it is in close contact with the superior mesenteric artery that supplies oxygen to the small intestine and the portal vein that transports nutrients absorbed from the intestine to the liver, so cancer invasion occurs easily.
- it is characterized by early metastasis to the nerve bundles and lymph nodes in the back of the pancreas.
- pancreatic cancer cells grow rapidly.
- pancreatic cancer is the 14th most common cancer in the world, and its incidence is remarkably increasing, and it ranks fourth among the major causes of cancer deaths in the United States.
- the initial symptoms of pancreatic cancer are not specific, and clinical symptoms such as weakness, loss of appetite, and weight loss occur after systemic metastasis has already occurred, so regular diagnosis is necessary.
- Pancreatic cancer has a poor prognosis, with only 1 to 4% of patients showing a 5-year survival rate after surgery, and a median survival of 5 months. Since 80 to 90% of patients are found in a state in which curative resection, which is expected to be cured at the time of diagnosis, is not possible, treatment mainly relies on anticancer therapy.
- Anticancer drugs known to be effective in pancreatic cancer so far include 5-fluorouracil, gemcitabine, and tarceva, but their effectiveness is extremely low and the response rate is only around 15%. Therefore, there is an urgent need to develop more effective early diagnosis and treatment methods to improve the prognosis of pancreatic cancer patients.
- chromosome microarray various tests such as karyotyping, fluorescence allotropy, chromosome microarray, and NGS-based screening tests are performed to check for chromosomal abnormalities including DNA copy number mutations (CNVs) that appear due to a deficiency or duplication of a part of the chromosome.
- CNVs DNA copy number mutations
- Karyotyping has a lower resolution of about 5 Mb compared to other tests, and it is impossible to detect a chromosomal deletion/duplication of a smaller size.
- microdeletions/duplications Chromosomal deletions and duplications of less than 5 Mb are referred to as microdeletions/duplications, and the ratio of microdeletions/duplications among single-gene diseases corresponds to 15% of all mutations (Vissers LE, et al., Hum Mol). Genet. Vol. 15;14 Spec No. 2:R215-23., 2005).
- Fluorescence in situ hybridization is a test method that confirms the presence of a specific nucleotide sequence in a chromosome by attaching a fluorescent label to a probe complementary to the nucleotide sequence to be confirmed. Since it shows a resolution of 100kb-1Mb, it is possible to detect microdeletions/duplications, but there is a disadvantage that only known mutations can be detected because only the parts complementary to the probe sequence can be identified.
- microarray-based comparative genomic hybridization (aCGH) is being used as the most common test method to check chromosomal microdeletion/duplication (Russo CD, et al., Cancer Discov. Vol. 4(1), pp. 19-21, 2014).
- the size of the CNV detectable through the microarray is determined by the density of the probe, and it is possible to detect CNVs with a size of approximately 50 kb.
- chromosomal abnormalities due to chromosomal rearrangement such as translocation or inversion, cannot be detected.
- NGS Next-generation sequencing
- SNPs single nucleotide polymorphisms
- INDELs indels
- NGS is capable of detecting chromosomal abnormalities caused by chromosomal rearrangements that cannot be detected in probe-based microarrays and new previously unknown CNVs (Talkowski ME, et al., Am J Hum Genet. Vol. 88 (Talkowski ME, et al., Am J Hum Genet. Vol. 88) 4), pp. 469-81, 2011).
- it has the advantage of showing higher coverage and resolution than microarrays and detecting breakpoints where chromosomal abnormalities start due to the characteristic of fragmenting the chromosome into small pieces and analyzing the nucleotide sequence (Zhao M, et al., BMC Bioinformatics) (Vol. 14, Suppl 11:S1, 2013).
- nucleic acid copy number variation refers to a phenomenon in which a specific region of the genome is deleted or amplified.
- the copy number variation of may be as follows.
- nucleic acid fragment data of a person with a deletion mutation is aligned with a human reference chromosome
- a smaller amount of nucleic acid fragment is obtained (reduction in the number of copies) compared to a person without mutation in the mutation region, and by the same logic, a person with an amplification mutation is
- nucleic acid fragment data is aligned with a reference chromosome
- a larger amount of nucleic acid fragments are obtained (increased number of copies) compared to a person without mutation in the corresponding mutation region.
- Such nucleic acid copy number variation can affect the prognosis of pancreatic cancer in various ways, for example, an increase in the amount of gene expression due to an increase in the copy number of oncogenes, proto-oncogenes, etc., cancer suppressor genes (tumor suppressor gene) is known to have a good or bad effect on the prognosis of pancreatic cancer according to a decrease in the amount of gene expression due to a decrease in the copy number or a change in the gene expression amount due to a change in the copy number of other genes.
- cancer suppressor genes tumor suppressor gene
- pancreatic cancer-specific genetic mutations particularly gene copy number mutations
- the present inventors made intensive efforts to develop a method for predicting the survival prognosis of pancreatic cancer patients based on copy number mutation. As a result, it was found that the presence or absence of copy number mutation in a specific gene is closely related to the survival prognosis of pancreatic cancer patients. And it was confirmed that the prognosis of pancreatic cancer patients, especially the survival prognosis, can be accurately predicted by using this.
- An object of the present invention is to provide a method of providing information for predicting the survival prognosis of pancreatic cancer patients.
- Another object of the present invention is to provide an apparatus for providing information for predicting survival prognosis of pancreatic cancer patients.
- Another object of the present invention is to provide information for predicting survival prognosis of a pancreatic cancer patient by the above method, and to provide a computer-readable recording medium including instructions configured to be executed by a processor.
- Another object of the present invention is to provide a kit for amplifying a target nucleic acid used in the method.
- Another object of the present invention is to provide a method for predicting survival prognosis of pancreatic cancer patients.
- Another object of the present invention is to provide an apparatus for predicting the survival prognosis of a pancreatic cancer patient by the above method.
- Another object of the present invention is to predict the survival prognosis of a pancreatic cancer patient by the above method, and to provide a computer-readable recording medium including instructions configured to be executed by a processor.
- the present invention provides ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP2, DMRT1, DOCK5, DMRT1, ERICH1-AS1, FAM135B, FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00578INC00, LMLINC00INC00 LOC100128993, LINC02052, LRMP, LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1,
- the present invention also provides an information providing apparatus used in the information providing method for predicting the survival prognosis of the pancreatic cancer patient, and a computer readable recording medium including instructions for performing the information providing method.
- the present invention also provides ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNAS1, DLGAP2, DMRT1, DOCK135B, DPYSL2, ERICH1-DPYSL2 FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC00639, LMLN02, LOC10012899 LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS
- the present invention provides the use of the kit for amplifying the target nucleic acid to predict the survival prognosis of pancreatic cancer patients.
- the present invention relates to ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNAS1, DLGAP2, DMRT1, DOCK5, ERICH1-DPYSL2, ERICH1 , FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC05C00639, LMLNC02 LINC00639 , LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, R
- the present invention also provides an apparatus for predicting survival prognosis of a pancreatic cancer patient used in the method for predicting survival prognosis of a pancreatic cancer patient, and a computer-readable recording medium including instructions for performing the method.
- FIG. 1 is an overall flowchart of a method for providing information for predicting the survival prognosis of a pancreatic cancer patient according to the present invention.
- FIG. 3 is a result showing an amplification segment derived by GISTIC analysis according to the present invention.
- the lower X-axis value represents False Discovery Rate (FDR)-adjusted p value (Q value) value
- the upper X-axis is calculated in GISTIC analysis.
- the G-score value (the value calculated by calculating the frequency and intensity of CNV observed in 315 patients with pancreatic cancer) is shown, and the y-axis means the chromosome number.
- the lower X-axis value represents False Discovery Rate (FDR)-adjusted p value (Q value) value
- the upper X-axis is calculated in GISTIC analysis.
- the G-score value (the value calculated by calculating the frequency and intensity of CNV observed in 315 patients with pancreatic cancer) is shown, and the y-axis means the chromosome number.
- FIG. 5 is an example of a method of grouping genes by GISTIC analysis according to the present invention
- (A) shows the Z value of the gene for each sample
- (B) is a gene for each sample according to the standard of the present invention is the result of grouping
- K-M Kaplan-Meier
- FIG. 7 is a graph comparing p-value values derived from K-M survival analysis of GSS_All or GSS_TopN for each set according to an embodiment of the present invention.
- FIG. 8 is a Venn diagram of a TopN gene for each set according to an embodiment of the present invention.
- 11 is a result of analyzing the survival prognosis prediction performance of GSS_8 in pancreatic cancer patient data of the TCGA database according to an embodiment of the present invention.
- FIG. 12 is a summary of the prognostic prediction performance of GSS_79, GSS_10, and GSS_8 analyzed from pancreatic cancer patient data of the TCGA database according to an embodiment of the present invention.
- each process constituting the method may occur differently from the specified order unless a specific order is clearly described in context. That is, each process may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the reverse order.
- the present invention relates to a method for providing information for predicting the prognosis of pancreatic cancer patients, particularly survival prognosis, characterized by using a gene mutation specific for pancreatic cancer, in particular, a copy number variation (CNV) of the gene, and its use. .
- CNV copy number variation
- the present invention relates to ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNAS1, DLGAP2, DMRT1, DOCK5, ERICH1-DPYSL2, ERICH1 , FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC05C00639, LMLNC02 LINC00639 , LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, R
- the genes for detecting the gene copy number mutation are ABHD6, CASC1, FAM49B, KANK1, LINC00477 , MCPH1, SOX5 and at least one selected from the group consisting of TATDN1, preferably 2 or more, more preferably 5 or more, most preferably all 8 types,
- ACVR2B ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP2, DMRT1, DOCK5, DPYSL2, ERICH135B, FER1, FAML , GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00578, LINC00639, LMLN, LOC100128993, LINC02052, LRMP, AA LRRC6, LTFIN, NXMAP4, LTFIN, NXMAP4 , OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBT1, SGK223, SLC38A3, SMARCA2, SQLE, TBL1XR1, THSD
- the eight genes of ABHD6, CASC1, FAM49B, KANK1, LINC00477, MCPH1, SOX5 and TATDN1 may include, but are not limited to, KRAS and CDKN2A.
- the present invention in one aspect,
- step (1) If the number of genes whose copy number variation degree of the gene quantified in step (1) exceeds the cut-off value, it is determined that the survival prognosis of the pancreatic cancer patient is bad. step;
- It relates to a method of providing information for predicting survival prognosis of pancreatic cancer patients, including.
- the step (1) may be characterized in that it is performed by a method including the following steps, but is not limited thereto.
- the copy number variation may be detected using a Digital Droplet Polymerase Chain Reaction (ddPCR) or Multiplex Ligation-dependent Probe Amplification (MLPA) method.
- ddPCR Digital Droplet Polymerase Chain Reaction
- MLPA Multiplex Ligation-dependent Probe Amplification
- ddPCR is an experimental method that amplifies and quantifies the amount of target DNA by separating (about 20 ⁇ l) PCR reaction solution into (about 20,000) fine droplets. (amplified), 0 (not amplified) digital signals are recognized and counted, and the number of copies of the target DNA can be calculated through the Poisson distribution.
- step (1) may be characterized in that it is performed by a method including the following steps, but is not limited thereto.
- multiplex ligation-dependent probe amplification is a method that can check the presence or concentration of a target site by hybridizing a probe to a target site, ligation, and amplifying the product by PCR. It can be used to search for duplicate mutations.
- the step (1) may be characterized in that it is performed by a method including the following steps, but is not limited thereto.
- step (1-4) may be characterized in that it is performed by a method including the following steps, but is not limited thereto.
- step (b) after counting the read count for each gene section of the aligned target sample obtained in step (1-3), the value of the number of reads aligned in each gene section is calculated as the total number of reads in the sample calculating a normalized depth value of a target sample for each gene section by dividing by .
- Z gene (Normalized_Depth gene - Reference_Mean_Depth gene ) / Reference_SD gene
- any method known to those skilled in the art may be used as a method of correcting the depth bias by the GC amount.
- the read depth varies depending on the amount of GC in the bin when the read depth analysis is performed in units of bins. That is, as the amount of GC increases, the depth value shows a specific tendency.
- the GC amount is received as an input (independent variable) using the LOESS (Locally Estimated Scatterplot Smoothing) algorithm, and the representative depth is predicted (dependent). variable) to build a regression model.
- LOESS Lically Estimated Scatterplot Smoothing
- the depth value predicted through the built-up regression model can be considered as a depth bias according to the amount of GC, and the depth is corrected according to the amount of GC by subtracting this depth bias value from the depth value calculated for each bin (Equation 2)
- sequence information refers to nucleic acid fragments obtained by analyzing sequence information using various methods known in the art. Therefore, in the present specification, the terms “sequence information” and “lead” have the same meaning in that they are the result of obtaining sequence information through a sequencing process.
- bin is used synonymously with a certain section or section, and refers to a portion of the entire chromosome sequence having a specific size.
- the size of a certain section (bin) in the present invention may be characterized in that it is 10 to 100,000 kbp, preferably 50 to 50,000 kbp, more preferably 100 to 10,000 kbp, and most preferably 500 to 5,000 kbp. It is not limited.
- the term “reference sample” refers to a sample of a reference group that can be compared like a standard sequence database, and is a sample obtained from a group of people who do not currently have a specific disease or condition.
- the standard nucleotide sequence in the standard chromosomal sequence database of the reference sample may be a reference chromosome registered with a public health institution such as NCBI.
- biological sample refers to a sample obtained from a living body of an animal such as a human, preferably selected from blood, abdominal fluid, tissue, saliva, urine, hair, feces, spinal fluid, cerebrospinal fluid, and bile fluid. It may be characterized as one or more, but is not limited thereto.
- the DNA of the target sample obtained from the biological sample can be used without limitation as long as it is a fragment of nucleic acid extracted from the biological sample, preferably cell-free DNA, exosomal DNA, or a fragment of intracellular nucleic acid. may be, but is not limited thereto.
- the copy number of the gene The degree of variation can be quantified and determined based on the Z (Z gene ) value, and the normal range of the Z value is -1 to 1, preferably -1.5 to 1.5, more preferably -2 to 2
- the present invention is not limited thereto, and may be flexibly set according to the purpose or accuracy of the diagnosis.
- the reference value may be set to a value of 10% or more, preferably 20% or more, more preferably 30% or more, and most preferably 40% of all target genes, for example, In the case of detecting the degree of gene copy number variation for 40 genes, the cut-off may be set to 4 at 10%, preferably 8 at 20%, and more preferably at 12 at 30%.
- the present invention is not limited thereto.
- the cut-off is one at 10%, preferably two at 20%. , more preferably 30%, but may be set to three, but is not limited thereto.
- reads can be obtained by, but not limited to, massively parallel sequencing methods.
- the massively parallel sequencing method is preferably performed as a next-generation sequencing (NGS) method, but is not limited thereto.
- NGS next-generation sequencing
- next-generation sequencing method may be performed by any method known in the art using a next-generation sequencer.
- Next-generation sequencing includes any sequencing method that determines the nucleotide sequence of either an individual nucleic acid molecule or a clonally extended proxy for an individual nucleic acid molecule in a highly similar manner (e.g., 105 or more molecules are sequenced simultaneously do).
- the relative abundance of a nucleic acid species in a library can be estimated by counting the relative number of occurrences of its cognate sequence in data generated by sequencing experiments.
- Next-generation sequencing methods are known in the art and are described, for example, in Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference.
- next-generation sequencing is performed to determine the nucleotide sequence of an individual nucleic acid molecule (e.g., HeliScope Gene Sequencing system from Helicos BioSciences and Pacific Biosciences). PacBio RS system).
- sequencing e.g., mass-parallel short-read sequencing that yields more bases of sequence per sequencing unit (e.g., San Diego, CA) than other sequencing methods yielding fewer but longer reads.
- the Illumina Inc. Solexa sequencer method determines the nucleotide sequence of a cloned extended proxy for an individual nucleic acid molecule (e.g., Illumina, San Diego, CA).
- Solexa sequencer 454 Life Sciences (Branford, Conn.) and Ion Torrent).
- Other methods or machines for next-generation sequencing include, but are not limited to, 454 Life Sciences (Branford, Conn.), Applied Biosystems (Foster City, CA; SOLiD Sequencer), Helicos. Bioscience Corporation (Cambridge, MA) and emulsion and microfluidic sequencing techniques Nano Droplets (eg, GnuBio Drops).
- Platforms for next-generation sequencing include, but are not limited to, Roche/454's Genome Sequencer (GS) FLX System, Illumina/Solexa Genome Analyzer (GA). , Life/APG's Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator's G.007 system, Helicos BioSciences' HeliScope Gene Sequencing system and Pacific Biosciences' PacBio RS system.
- the alignment step is not limited thereto, but may be performed using the BWA algorithm and the Hg19 sequence.
- the BWA algorithm may include, but is not limited to, BWA-mem, BWA-ALN, BWA-SW or Bowtie2.
- the step of obtaining DNA sequence information (reads) of the target sample obtained from the biological sample according to step (1-1) comprises:
- nucleic acid purified by removing proteins, fats, and other residues from the isolated DNA using a salting-out method, a column chromatography method, or a beads method obtaining a;
- the step of checking the quality of the aligned sequence information (reads) according to step (1-3) is to select a sequence that satisfies the quality reference value of the mapping quality score It may be characterized in that it is carried out in a method comprising a step, but is not limited thereto.
- the quality reference value may vary depending on a desired standard, but is preferably 15-70 points, more preferably 50-70 points, and most preferably 60 points, but is limited thereto. it is not
- the information providing method for predicting the survival prognosis of a pancreatic cancer patient according to the present invention may include the following steps in one specific form, but is not limited thereto (see FIG. 1 ).
- the present invention is an information providing device used in a method for providing information for predicting survival prognosis of a pancreatic cancer patient according to the present invention, the device comprising:
- a gene copy number mutation detection unit for detecting a copy number mutation of a gene in which a pancreatic cancer-specific gene copy number mutation occurs according to the present invention described in Table 1 and the like;
- a survival prognosis determining unit that determines that the survival prognosis is poor when the number of genes whose degree of gene copy number mutation is out of the normal range exceeds a reference value
- It relates to an information providing device comprising a.
- the present invention is a computer-readable medium used for a method of providing information for predicting survival prognosis of a pancreatic cancer patient according to the present invention from another aspect, wherein the medium is provided by a processor providing information for predicting survival prognosis of a pancreatic cancer patient a command that is configured to be executed;
- It relates to a computer-readable medium comprising instructions configured to be executed by a processor comprising:
- the present invention provides a kit for amplifying a target nucleic acid used in a method for providing information for predicting survival prognosis of a pancreatic cancer patient according to the present invention, the kit comprising:
- a probe that specifically binds to a pancreatic cancer-specific gene according to the present invention described in Table 1 and the like Or it relates to a kit for amplifying a target nucleic acid comprising a primer for amplifying a pancreatic cancer-specific gene according to the present invention described in Table 1 and the like.
- the kit is a nucleic acid amplification reaction such as a buffer, DNA polymerase, DNA polymerase cofactor, and deoxyribonucleotide-5-triphosphate (dNTP) (eg, , polymerase chain reaction) may optionally include reagents necessary for carrying out.
- the kit of the present invention may also include various oligonucleotide molecules, reverse transcriptase, various buffers and reagents, and antibodies that inhibit DNA polymerase activity.
- the optimal amount of the reagent used in a specific reaction of the kit can be easily determined by a person skilled in the art after learning the description herein.
- the equipment of the present invention may be manufactured as a separate package or compartment comprising the aforementioned components.
- the kit may include a compartmentalized carrier means for holding a sample, a container for containing reagents, and a container for containing primers or probes.
- the carrier means is suitable for containing one or more containers, such as bottles and tubes, each container containing independent components for use in the method of the present invention.
- containers such as bottles and tubes
- each container containing independent components for use in the method of the present invention.
- one of ordinary skill in the art can readily dispense the required formulation in a container.
- the present invention provides ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP1, DMRT1, DOCK5, DPYSL2 AS1, FAM135B, FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00INC00578, LINCOC1001200INC00578 LINC02052, LRMP, LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF
- the present invention provides an information providing device used in a method for predicting survival prognosis of a pancreatic cancer patient according to the present invention, the device comprising:
- a gene copy number mutation detection unit for detecting a copy number mutation of a gene in which a pancreatic cancer-specific gene copy number mutation occurs according to the present invention described in Table 1 and the like;
- a calculation unit that quantifies the degree of copy number variation based on the detected gene copy number variation information, and calculates the number of genes whose quantified gene copy number variation is outside a normal range
- a survival prognosis determining unit that determines that the survival prognosis is poor when the number of genes whose degree of gene copy number mutation is out of the normal range exceeds the reference value
- It relates to a device for predicting survival prognosis of pancreatic cancer patients, comprising:
- the present invention is a computer-readable medium used in a method for predicting survival prognosis of a pancreatic cancer patient according to the present invention, wherein the medium includes instructions configured to be executed by a processor for predicting the survival prognosis of a pancreatic cancer patient,
- It relates to a computer-readable medium comprising instructions configured to be executed by a processor comprising:
- the present invention provides a kit for amplifying a target nucleic acid used in the method for predicting survival prognosis of a pancreatic cancer patient according to the present invention, the kit comprising:
- a probe that specifically binds to a pancreatic cancer-specific gene according to the present invention described in Table 1 and the like Or it relates to a kit for amplifying a target nucleic acid comprising a primer for amplifying a pancreatic cancer-specific gene according to the present invention described in Table 1 and the like.
- DNA from 315 pancreatic cancer patients was extracted and a library for full-length chromosomes was prepared.
- the completed library was subjected to sequencing on NextSeq equipment (illumina, USA), and sequence information data of an average of 18.4 million reads per sample was produced.
- the fastq file was aligned with the library sequence based on the Hg19 sequence of the reference chromosome using the BWA-mem algorithm. Sequencing data confirmed that Q30 satisfies 80% or more and Mapping quality satisfies 60.
- Z bin (Normalized_Depth bin - Reference_Mean_Depth bin ) / Reference_SD bin
- Circular Binary Segmentation (CBS) algorithm was applied to the calculated Z value for each bin to detect (segmentation) a region with a different copy number from the periphery of the entire genome region (see FIG. 2).
- A is an example of an amplification segment in which the number of copies is increased compared to the surrounding
- B is an example of a deletion segment in which the number of copies is decreased compared to the surrounding
- a continuous red line indicates one segment.
- FIGS. 3 and 4 show the amplification segment region repeatedly observed in 315 pancreatic cancer patients, and the blue figure on the right shows the deletion segment region.
- the lower x-axis value of FIGS. 3 and 4 represents the false discovery rate (FDR)-adjusted p value (Q value) value
- the upper X-axis is the G-score value calculated in the GISTIC analysis (observed in 315 patients with pancreatic cancer). CNV frequency and intensity), and the y-axis is the chromosome number.
- pancreatic cancer-specific CNV regions selected through GISTIC analysis, regions related to pancreatic cancer survival prognosis were secondarily selected in units of genes.
- the Z value of the gene unit was calculated for 2,272 genes included in the coordinate region first selected in step 2-1. That is, after counting the number of reads for each gene section of a reference sample without gene copy number variation, the read count value aligned in each gene section is divided by the total number of reads in the sample, and the GC content ( contents), calculates the depth average (Reference_Mean_Depth gene ) and standard deviation value (Reference_SD gene ) of the reference sample for each gene section, and then reads for each gene section of the sorted target sample After counting the number (read count), the value of the number of reads aligned in each gene section is divided by the total number of reads of the sample, and depth bias by GC content is corrected, 2,272 After calculating the normalized depth value of the target sample for each gene section, the Z (Z gene ) value for each normalized gene section of the aligned sequence information was calculated using Equation 1:
- Z gene (Normalized_Depth gene - Reference_Mean_Depth gene ) / Reference_SD gene
- the corresponding gene value of the sample is designated as group 1 (poor prognosis group), or ,
- the corresponding gene value of the sample was designated as group 1 (poor prognosis group), and when the above two conditions were not satisfied The corresponding gene value of the sample was assigned to group 0 (good prognosis group).
- Samples 1 to 315 are divided into 1 and 0 groups based on the Z value of gene Gene1 included in the GISTIC deletion region
- Sample_2 and Sample_4 satisfying Z ⁇ -2 are designated as group 1 and the remaining samples can be designated as group 0, and when the groups of Samples 1 to 315 are divided based on the Z value of gene Gene3 included in the GISTIC Amplification region, Sample_1 and Sample_4 satisfying Z > 2 are designated as group 1. and the remaining samples can be designated as group 0.
- GSS_All was calculated by adding the Z values of all genes that satisfy the raw p-value ⁇ 0.05. 40 in CV_3, 45 in CV_4, 38 in CV_4, and 47 in CV_5.
- GSS_All showed a statistically significant difference in survival prognosis between the two groups in 4 out of 5 CVs and 3 in GSS_TopN.
- a better p-value was found in GSS_TopN than in GSS_All.
- Example 3-1 the GSS_8 value was calculated using 8 genes that were commonly selected (intersection) in all five CVs in Example 3-1, and the prognostic performance was confirmed when the cutoff criterion was 1, as shown in FIG. Likewise, the GSS_8 value showed a statistically significant difference in survival prognosis. 12 is a summary of the prognostic prediction performance of GSS_79, GSS_10, and GS_8 in TCGA data.
- the method of providing information for predicting the survival prognosis of pancreatic cancer patients predicts the survival prognosis based on the copy number variation for the pancreatic cancer survival prognosis-specific gene. Not only can it be increased, but it is useful because it does not require whole-genome sequencing.
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Abstract
The present invention relates to a method of providing information for predicting the survival prognosis of pancreatic cancer patients. Specifically, the present invention relates to: a method of providing information for predicting the survival prognosis of pancreatic cancer patients, the method being characterized by the use of a gene variation specific for pancreatic cancer, in particular, a gene copy number variation (CNV); and a use thereof. The method of providing information for predicting the survival prognosis of pancreatic cancer patients according to the present invention is highly accurate due to predicting the survival prognosis on the basis of a copy number variation for a pancreatic cancer survival prognosis-specific gene and thus can increase utility related to the prediction of treatment effects and survival prognosis, and does not require whole-genome sequencing and thus is fast and therefore useful.
Description
본 발명은 췌장암 환자의 생존 예후를 예측하기 위한 정보제공 방법에 관한 것으로, 구체적으로는 췌장암에 특이적인 유전자 변이, 특히 유전자의 복제수 변이(CNV: copy number variation)를 이용하는 것을 특징으로 하는 췌장암 환자의 생존 예후를 예측하기 위한 정보제공 방법 및 그 용도에 관한 것이다.The present invention relates to an information providing method for predicting the survival prognosis of a pancreatic cancer patient, specifically, a pancreatic cancer patient characterized by using a gene mutation specific to pancreatic cancer, in particular, a gene copy number variation (CNV). It relates to a method for providing information for predicting the survival prognosis of and its use.
췌장은 위장의 뒤쪽, 몸의 가운데에 있으며 길이가 20cm 정도로 길다. 위, 십이지장, 소장, 대장, 간, 담낭, 비장 등의 장기에 둘러싸여 있다. 전체 길이는 약 15 내지 20 cm, 무게는 100g 정도이고, 두부(頭部), 체부(體部), 미부(尾部)로 구분된다. 췌장은 섭취한 음식물 중의 탄수화물, 지방, 단백질을 분해하는 소화 효소를 분비하는 외분비 기능과 혈당을 조절하는 인슐린과 글루카곤 등의 호르몬을 분비하는 내분비 기능을 갖는다.The pancreas is located behind the stomach and in the middle of the body and is about 20 cm long. It is surrounded by organs such as the stomach, duodenum, small intestine, large intestine, liver, gallbladder, and spleen. The total length is about 15 to 20 cm, and the weight is about 100 g, and it is divided into a head, a body, and a tail. The pancreas has an exocrine function that secretes digestive enzymes that break down carbohydrates, fats, and proteins in the ingested food, and an endocrine function that secretes hormones such as insulin and glucagon that control blood sugar.
췌장암(pancreatic cancer)이란 췌장에 생긴 암세포로 이루어진 종괴(종양덩어리)이다. 췌장암에는 여러 가지 종류가 있는데 췌관세포에서 발생한 췌관 선암종이 90% 정도를 차지하고 있어 일반적으로 췌장암이라고 하면 췌관 선암종을 말한다. 그 외에 낭종성암(낭선암), 내분비종양 등이 있다.Pancreatic cancer is a mass (tumor mass) made up of cancer cells in the pancreas. There are several types of pancreatic cancer, and pancreatic ductal adenocarcinoma generated from pancreatic duct cells accounts for about 90% of pancreatic ductal adenocarcinoma. In addition, there are cystic cancer (cystic adenocarcinoma) and endocrine tumors.
췌장암은 특별한 초기 증상이 없기 때문에, 조기발견하기 어렵다. 식욕이 떨어지거나, 체중감소 등이 나타나지만 췌장암의 특징적인 증상이 아니라 다른 질환에서도 충분히 나타날 수 있다.Pancreatic cancer is difficult to detect early because there are no specific early symptoms. Loss of appetite, weight loss, etc. appear, but it is not a characteristic symptom of pancreatic cancer, but may appear sufficiently in other diseases.
또한 췌장은 두께가 2 cm 정도로 얇으며 피막만으로 싸여 있는데다가 소장에 산소를 공급하는 상장간막 동맥과 장에서 흡수한 영양분을 간으로 운반하는 간문맥 등과 밀착되어 있어 암의 침윤이 쉽게 일어난다. 또한 췌장 후면의 신경 다발과 임파선에도 조기에 전이가 발생하는 특징이 있다. 특히 췌장 암세포는 성장 속도가 빠르다.In addition, the pancreas is thin, about 2 cm thick, and is surrounded by only a capsule, and it is in close contact with the superior mesenteric artery that supplies oxygen to the small intestine and the portal vein that transports nutrients absorbed from the intestine to the liver, so cancer invasion occurs easily. In addition, it is characterized by early metastasis to the nerve bundles and lymph nodes in the back of the pancreas. In particular, pancreatic cancer cells grow rapidly.
췌장암(Pancreatic cancer)은 세계에서 14번째로 다발하는 암으로서, 그 발병 빈도가 현저히 증가하고 있으며, 미국에서는 암으로 인한 사망의 주요 원인 중 4위에 해당한다. 췌장암은 초기 증상이 특이적이지 않으며, 이미 전신 전이가 일어난 후에 쇠약, 식욕감퇴, 체중감소 등의 임상증상이 발생하므로 정기적인 진단이 필요하다.Pancreatic cancer is the 14th most common cancer in the world, and its incidence is remarkably increasing, and it ranks fourth among the major causes of cancer deaths in the United States. The initial symptoms of pancreatic cancer are not specific, and clinical symptoms such as weakness, loss of appetite, and weight loss occur after systemic metastasis has already occurred, so regular diagnosis is necessary.
췌장암은 1 내지 4%의 환자만이 수술 후 5년의 생존율을 보이며, 중앙 생존기간이 5개월에 이를 정도로 예후가 불량하다. 80 내지 90%의 환자가 진단 시 완치를 기대하는 근치적 절제가 불가능한 상태에서 발견되기 때문에, 치료는 주로 항암 요법에 의존하고 있다. 현재까지 췌장암에 효과가 있다고 알려진 항암제는 5-플루오로유라실, 젬시타빈(gemcitabine), 타르세바(tarceva) 등이 있으나 효과가 지극히 저조하여 반응율이 15% 내외에 불과하다. 따라서 췌장암 환자의 예후를 향상시키기 위해 보다 효과적인 조기 진단법 및 치료법의 개발이 절실하다.Pancreatic cancer has a poor prognosis, with only 1 to 4% of patients showing a 5-year survival rate after surgery, and a median survival of 5 months. Since 80 to 90% of patients are found in a state in which curative resection, which is expected to be cured at the time of diagnosis, is not possible, treatment mainly relies on anticancer therapy. Anticancer drugs known to be effective in pancreatic cancer so far include 5-fluorouracil, gemcitabine, and tarceva, but their effectiveness is extremely low and the response rate is only around 15%. Therefore, there is an urgent need to develop more effective early diagnosis and treatment methods to improve the prognosis of pancreatic cancer patients.
한편, 염색체의 일부가 결핍 또는 중복되어 나타나는 DNA 복제수 변이(CNVs)를 포함한 염색체 이상을 확인하기 위해 핵형분석, 형광동소보합법, 염색체 마이크로어레이, NGS기반의 스크리닝 검사와 같이 다양한 검사가 이루어지고 있다(Capalbo A, et al., Hum Reprod. Vol. 32(3), pp. 492-498, 2017). 핵형분석은 다른 검사들에 비해 5Mb 정도의 낮은 해상도를 보이며 그보다 작은 크기의 염색체 결실/중복은 검출이 불가능하다. 5Mb 미만의 작은 크기의 염색체 결실 및 중복을 미세결실/중복이라고 하며, 단일유전자에 의한 질환 중 미세결실/중복에 의한 비율이 전체 변이의 15%에 해당한다(Vissers LE, et al., Hum Mol Genet. Vol. 15;14 Spec No. 2:R215-23., 2005). On the other hand, various tests such as karyotyping, fluorescence allotropy, chromosome microarray, and NGS-based screening tests are performed to check for chromosomal abnormalities including DNA copy number mutations (CNVs) that appear due to a deficiency or duplication of a part of the chromosome. (Capalbo A, et al., Hum Reprod. Vol. 32(3), pp. 492-498, 2017). Karyotyping has a lower resolution of about 5 Mb compared to other tests, and it is impossible to detect a chromosomal deletion/duplication of a smaller size. Chromosomal deletions and duplications of less than 5 Mb are referred to as microdeletions/duplications, and the ratio of microdeletions/duplications among single-gene diseases corresponds to 15% of all mutations (Vissers LE, et al., Hum Mol). Genet. Vol. 15;14 Spec No. 2:R215-23., 2005).
이러한 미세결실/중복을 검출하기 위하여 특정 염기서열에 상보적인 탐침자를 활용한 형광동소보합법(FISH)과 염색체 마이크로어레이 검사가 이루어지고 있다. 형광동소보합법은 확인하려는 염기서열에 상보적인 탐침자에 형광라벨을 붙여 염색체 내에 특정 염기서열의 여부를 확인하는 검사법이다. 100kb-1Mb의 해상도를 보이기 때문에 미세결실/중복의 검출이 가능하지만 탐침자 서열에 상보적인 부분만 확인이 가능하기 때문에 기존에 알려진 변이에 대해서만 검출이 가능하다는 단점이 있다. In order to detect such microdeletions/duplications, fluorescence in situ hybridization (FISH) using a probe complementary to a specific nucleotide sequence and a chromosome microarray test are being conducted. Fluorescence in situ hybridization is a test method that confirms the presence of a specific nucleotide sequence in a chromosome by attaching a fluorescent label to a probe complementary to the nucleotide sequence to be confirmed. Since it shows a resolution of 100kb-1Mb, it is possible to detect microdeletions/duplications, but there is a disadvantage that only known mutations can be detected because only the parts complementary to the probe sequence can be identified.
현재 염색체 미세결실/중복을 확인하는 가장 일반적인 검사법으로 마이크로어레이를 기반으로 하는 비교유전체혼성화법(aCGH)이 활용되고 있다(Russo CD, et al., Cancer Discov. Vol. 4(1), pp. 19-21, 2014). 마이크로어레이를 통해 검출 가능한 CNV의 크기는 탐침자의 밀도에 의해 결정되며 대략 50kb 크기의 CNV까지 검출이 가능하다. 하지만 전좌 또는 역위와 같이 염색체 재배열에 의한 염색체 이상은 검출이 불가능하다. Currently, microarray-based comparative genomic hybridization (aCGH) is being used as the most common test method to check chromosomal microdeletion/duplication (Russo CD, et al., Cancer Discov. Vol. 4(1), pp. 19-21, 2014). The size of the CNV detectable through the microarray is determined by the density of the probe, and it is possible to detect CNVs with a size of approximately 50 kb. However, chromosomal abnormalities due to chromosomal rearrangement, such as translocation or inversion, cannot be detected.
차세대염기서열분석법(NGS)은 염색체를 작은 조각으로 나누고 각 조각의 유전정보를 병렬적으로 분석하는 염기서열분석법이다. NGS는 유전자분석 기술이 발전하면서 상대적으로 검사의 소요시간과 비용이 적고 단일염기 다형성(SNP), 삽입-결실(INDELs)까지 검출 가능한 높은 해상도 때문에 신생아의 유전성 질환 선별검사로 활용되고 있다. 그러나 염색체를 작게 나누어 분석하는 NGS의 원리적 특성상 큰 규모의 염색체의 구조적 변이나 CNVs을 검출하는데 기술적 한계가 있다(Yohe S, Thyagarajan B., Arch Pathol Lab Med. Vol. 141(11), pp. 1544-1557, 2017.). Next-generation sequencing (NGS) is a sequencing method that divides chromosomes into small pieces and analyzes the genetic information of each piece in parallel. With the development of genetic analysis technology, NGS is being used as a screening test for genetic diseases in newborns because of its relatively short test time and cost, and its high resolution capable of detecting single nucleotide polymorphisms (SNPs) and indels (INDELs). However, due to the principle nature of NGS, which divides and analyzes chromosomes into small ones, there are technical limitations in detecting structural variations or CNVs of large-scale chromosomes (Yohe S, Thyagarajan B., Arch Pathol Lab Med. Vol. 141(11), pp. 1544-1557, 2017.).
하지만 NGS는 탐침자를 기반으로 하는 마이크로어레이에서 검출할 수 없는 염색체 재배열에 의한 염색체 이상과 기존에 알려지지 않은 새로운 CNV의 검출이 가능하다(Talkowski ME, et al., Am J Hum Genet. Vol. 88(4), pp. 469-81, 2011). 또한 염색체를 작게 조각 내어 염기서열을 분석하는 특성으로 마이크로어레이 보다 더 높은 coverage와 해상도를 보이고 염색체 이상이 시작되는 구획점(breakpoint) 검출이 가능하다는 장점이 있다(Zhao M, et al., BMC Bioinformatics. Vol. 14, Suppl 11:S1, 2013).However, NGS is capable of detecting chromosomal abnormalities caused by chromosomal rearrangements that cannot be detected in probe-based microarrays and new previously unknown CNVs (Talkowski ME, et al., Am J Hum Genet. Vol. 88 (Talkowski ME, et al., Am J Hum Genet. Vol. 88) 4), pp. 469-81, 2011). In addition, it has the advantage of showing higher coverage and resolution than microarrays and detecting breakpoints where chromosomal abnormalities start due to the characteristic of fragmenting the chromosome into small pieces and analyzing the nucleotide sequence (Zhao M, et al., BMC Bioinformatics) (Vol. 14, Suppl 11:S1, 2013).
한편, 핵산 복제수 변이(DNA Copy Number Variation, CNV)는 게놈의 특정 영역이 삭제(Deletion)되거나 증폭(Amplification)되는 현상을 의미하는데, 예를 들어, A-B-C-D-E-F-G 형태를 갖는 게놈이 있을 때 이 게놈에서의 복제 수 변이의 모습은 아래와 같을 수 있다.On the other hand, nucleic acid copy number variation (DNA Copy Number Variation, CNV) refers to a phenomenon in which a specific region of the genome is deleted or amplified. The copy number variation of may be as follows.
1. A-B-E-F-G (-C-D- 영역 Deletion)1. ABEFG (- C - D - Area Deletion)
2. A-B-C-D-D-D-D-D-E-F-G (-D- 영역 Amplification)2. ABCDDDDDDEFG ( -D -region Amplification)
Deletion 변이를 갖는 사람의 핵산 단편 데이터를 휴먼 참조 염색체에 정렬할 경우, 해당 변이 영역에서 변이가 없는 사람 대비 적은 양의 핵산 단편이 획득되고(복제수 감소), 같은 논리로 Amplification 변이를 갖는 사람의 핵산 단편 데이터를 참조 염색체에 정렬할 경우, 해당 변이 영역에서 변이가 없는 사람 대비 많은 양의 핵산 단편이 획득된다(복제수 증가).When nucleic acid fragment data of a person with a deletion mutation is aligned with a human reference chromosome, a smaller amount of nucleic acid fragment is obtained (reduction in the number of copies) compared to a person without mutation in the mutation region, and by the same logic, a person with an amplification mutation is When nucleic acid fragment data is aligned with a reference chromosome, a larger amount of nucleic acid fragments are obtained (increased number of copies) compared to a person without mutation in the corresponding mutation region.
이러한 핵산 복제수 변이는 다양한 방법으로 췌장암 예후에 영향을 미칠 수 있는데, 예를 들어 암 유발 유전자(oncogene), 원암 유전자(proto-oncogene) 등의 복제수 증가로 인한 유전자 발현양 증가, 암 억제 유전자(tumor suppressor gene)에서의 복제수 감소로 인한 유전자 발현양 감소 또는 기타 유전자들의 복제수 변이로 인한 유전자 발현양 변화 등에 따라 췌장암의 예후에 좋거나 나쁜 영향을 미치는 것으로 알려져 있다.Such nucleic acid copy number variation can affect the prognosis of pancreatic cancer in various ways, for example, an increase in the amount of gene expression due to an increase in the copy number of oncogenes, proto-oncogenes, etc., cancer suppressor genes (tumor suppressor gene) is known to have a good or bad effect on the prognosis of pancreatic cancer according to a decrease in the amount of gene expression due to a decrease in the copy number or a change in the gene expression amount due to a change in the copy number of other genes.
최근에는 LYRM1, KNTC1, IGF2BP2 및 CDC6 유전자의 발현 양상이 췌장암의 생존 예후와 관련이 있다는 사실이 보고되었다(Xiaokai Yan et al., Cancer Manag Res., Vol. 11, pp. 273-283, 2019).Recently, it was reported that the expression patterns of LYRM1, KNTC1, IGF2BP2 and CDC6 genes are related to the survival prognosis of pancreatic cancer (Xiaokai Yan et al., Cancer Manag Res., Vol. 11, pp. 273-283, 2019). .
하지만 아직까지 췌장암 특이적인 유전자 변이, 특히 유전자 복제수 변이를 기반으로 하는 높은 정확도와 민감도로 췌장암 환자의 생존 예후를 예측하는 방법은 알려져 있지 않은 상황으로, 이러한 기술에 대한 수요가 절실한 상황이다. However, a method for predicting the survival prognosis of pancreatic cancer patients with high accuracy and sensitivity based on pancreatic cancer-specific genetic mutations, particularly gene copy number mutations, is not known, and there is an urgent need for such a technology.
이러한 기술배경 하에, 본 발명자들은 복제수 변이 기반의 췌장암 환자의 생존 예후 예측 방법을 개발하기 위해 예의 노력한 결과, 특정 유전자에서의 복제수 변이 유무가 췌장암 환자의 생존 예후와 밀접하게 관련된다는 사실을 규명하고, 이를 이용함으로써 췌장암 환자의 예후, 특히 생존 예후를 정확히 예측할 수 있음을 확인하였다. Under this technical background, the present inventors made intensive efforts to develop a method for predicting the survival prognosis of pancreatic cancer patients based on copy number mutation. As a result, it was found that the presence or absence of copy number mutation in a specific gene is closely related to the survival prognosis of pancreatic cancer patients. And it was confirmed that the prognosis of pancreatic cancer patients, especially the survival prognosis, can be accurately predicted by using this.
발명의 요약Summary of the invention
본 발명의 목적은 췌장암 환자의 생존 예후 예측을 위한 정보의 제공 방법을 제공하는 것이다.An object of the present invention is to provide a method of providing information for predicting the survival prognosis of pancreatic cancer patients.
본 발명의 다른 목적은 췌장암 환자의 생존 예후 예측을 위한 정보의 제공 장치를 제공하는 것이다.Another object of the present invention is to provide an apparatus for providing information for predicting survival prognosis of pancreatic cancer patients.
본 발명의 또 다른 목적은 상기 방법으로 췌장암 환자의 생존 예후 예측을 위한 정보를 제공하며, 프로세서에 의해 실행되도록 구성되는 명령을 포함하는 컴퓨터 판독 가능한 기록매체를 제공하는 것이다.Another object of the present invention is to provide information for predicting survival prognosis of a pancreatic cancer patient by the above method, and to provide a computer-readable recording medium including instructions configured to be executed by a processor.
본 발명의 또 다른 목적은 상기 방법에 이용되는 표적 핵산 증폭용 키트를 제공하는 것이다.Another object of the present invention is to provide a kit for amplifying a target nucleic acid used in the method.
본 발명의 또 다른 목적은 췌장암 환자의 생존 예후 예측 방법을 제공하는 것이다.Another object of the present invention is to provide a method for predicting survival prognosis of pancreatic cancer patients.
본 발명의 또 다른 목적은 상기 방법으로 췌장암 환자의 생존 예후를 예측하는 장치를 제공하는 것이다.Another object of the present invention is to provide an apparatus for predicting the survival prognosis of a pancreatic cancer patient by the above method.
본 발명의 또 다른 목적은 상기 방법으로 췌장암 환자의 생존 예후를 예측하며, 프로세서에 의해 실행되도록 구성되는 명령을 포함하는 컴퓨터 판독 가능한 기록매체를 제공하는 것이다.Another object of the present invention is to predict the survival prognosis of a pancreatic cancer patient by the above method, and to provide a computer-readable recording medium including instructions configured to be executed by a processor.
상기 목적을 달성하기 위하여, 본 발명은 ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP2, DMRT1, DOCK5, DPYSL2, ERICH1-AS1, FAM135B, FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC00639, LMLN, LOC100128993, LINC02052, LRMP, LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBT1, SGK223, SLC38A3, SMARCA2, SOX5, SQLE, TATDN1, TBL1XR1, THSD7A, TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 및 ZNF583로 구성된 군에서 선택되는 1종 이상의 유전자의 복제수 변이(CNV : copy number variation) 정보를 이용, 구체적으로는 상기 유전자의 복제수 변이를 검출하는 단계를 포함하는 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보제공 방법을 제공한다. In order to achieve the above object, the present invention provides ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP2, DMRT1, DOCK5, DMRT1, ERICH1-AS1, FAM135B, FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00578INC00, LMLINC00INC00 LOC100128993, LINC02052, LRMP, LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SEROX5, SGKLC2, SFMBT3, SGKLC223 Copy number variation (CNV) information of one or more genes selected from the group consisting of SQLE, TATDN1, TBL1XR1, THSD7A, TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 and ZNF583 Use, specifically, provides an information providing method for predicting survival prognosis of pancreatic cancer patients, characterized in that it comprises the step of detecting a copy number mutation of the gene.
또한 본 발명은 상기 췌장암 환자의 생존 예후 예측을 위한 정보제공 방법에 이용되는 정보제공 장치 및 상기 정보제공 방법을 수행하기 위한 명령을 포함하는 컴퓨터 판독 가능한 기록매체를 제공한다. The present invention also provides an information providing apparatus used in the information providing method for predicting the survival prognosis of the pancreatic cancer patient, and a computer readable recording medium including instructions for performing the information providing method.
또한 본 발명은 ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP2, DMRT1, DOCK5, DPYSL2, ERICH1-AS1, FAM135B, FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC00639, LMLN, LOC100128993, LINC02052, LRMP, LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBT1, SGK223, SLC38A3, SMARCA2, SOX5, SQLE, TATDN1, TBL1XR1, THSD7A, TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 및 ZNF583로 구성된 군에서 선택되는 1종 이상의 유전자에 특이적으로 결합하는 프로브; 또는 상기 췌장암 특이적 유전자를 증폭하는 프라이머를 포함하는 표적 핵산 증폭용 키트를 제공한다.The present invention also provides ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNAS1, DLGAP2, DMRT1, DOCK135B, DPYSL2, ERICH1-DPYSL2 FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC00639, LMLN02, LOC10012899 LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBTAT1, SGK223, SLC38A3, SMARCA2, SOXDN5, SMARCA2 a probe that specifically binds to one or more genes selected from the group consisting of THSD7A, TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 and ZNF583; Or it provides a kit for amplifying a target nucleic acid comprising a primer for amplifying the pancreatic cancer-specific gene.
또한 본 발명은 상기 표적 핵산 증폭용 키트의 췌장암 환자의 생존 예후 예측 용도를 제공한다.In addition, the present invention provides the use of the kit for amplifying the target nucleic acid to predict the survival prognosis of pancreatic cancer patients.
또한, 본 발명은 ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP2, DMRT1, DOCK5, DPYSL2, ERICH1-AS1, FAM135B, FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC00639, LMLN, LOC100128993, LINC02052, LRMP, LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBT1, SGK223, SLC38A3, SMARCA2, SOX5, SQLE, TATDN1, TBL1XR1, THSD7A, TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 및 ZNF583로 구성된 군에서 선택되는 1종 이상의 유전자의 복제수 변이(CNV : copy number variation) 정보를 이용, 구체적으로는 상기 유전자의 복제수 변이를 검출하는 단계를 포함하는 것을 특징으로 하는 췌장암 환자의 생존 예후 예측 방법을 제공한다.In addition, the present invention relates to ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNAS1, DLGAP2, DMRT1, DOCK5, ERICH1-DPYSL2, ERICH1 , FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC05C00639, LMLNC02 LINC00639 , LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBTOX1, SGK223, SFMBTOX1, TBL1XR1, TBLARCA3, SMARCA5 , THSD7A, TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 and ZNF583 Using copy number variation (CNV) information of one or more genes selected from the group consisting of, specifically It provides a method for predicting survival prognosis of a pancreatic cancer patient, comprising the step of detecting a copy number mutation of the gene.
또한 본 발명은 상기 췌장암 환자의 생존 예후 예측 방법에 이용되는 췌장암 환자의 생존 예후 예측 장치 및 상기 방법을 수행하기 위한 명령을 포함하는 컴퓨터 판독 가능한 기록매체를 제공한다. The present invention also provides an apparatus for predicting survival prognosis of a pancreatic cancer patient used in the method for predicting survival prognosis of a pancreatic cancer patient, and a computer-readable recording medium including instructions for performing the method.
도 1은 본 발명의 췌장암 환자의 생존 예후를 예측하기 위한 정보 제공 방법의 전체 흐름도이다. 1 is an overall flowchart of a method for providing information for predicting the survival prognosis of a pancreatic cancer patient according to the present invention.
도 2는 본 발명에 따른 CBS 알고리즘을 적용하여 복제수 변이를 검출한 결과의 예시로서, A로 표기된 부분은 Amplification segment의 예시이고, B로 표기된 부분은 Deletion segment의 예시이며, 이어져 있는 붉은 선은 하나의 segment를 의미한다. 2 is an example of the result of detecting copy number variation by applying the CBS algorithm according to the present invention. It means one segment.
도 3은 본 발명에 따른 GISTIC 분석에 의해 도출한 Amplification segment를 나타낸 결과로서, 아래쪽 X 축 값은 False Discovery Rate (FDR) - adjusted p value (Q value) 값을 나타내고, 위쪽 X 축은 GISTIC 분석에서 계산된 G-score 값(췌장암 환자 315명에서 관찰되는 CNV의 빈도 및 세기를 계산한 값)을 나타내며, y 축은 염색체 번호를 의미한다. 3 is a result showing an amplification segment derived by GISTIC analysis according to the present invention. The lower X-axis value represents False Discovery Rate (FDR)-adjusted p value (Q value) value, and the upper X-axis is calculated in GISTIC analysis. The G-score value (the value calculated by calculating the frequency and intensity of CNV observed in 315 patients with pancreatic cancer) is shown, and the y-axis means the chromosome number.
도 4는 본 발명에 따른 GISTIC 분석에 의해 도출한 Deletion segment를 나타낸 결과로서, 아래쪽 X 축 값은 False Discovery Rate (FDR) - adjusted p value (Q value) 값을 나타내고, 위쪽 X 축은 GISTIC 분석에서 계산된 G-score 값(췌장암 환자 315명에서 관찰되는 CNV의 빈도 및 세기를 계산한 값)을 나타내며, y 축은 염색체 번호를 의미한다.4 is a result showing a deletion segment derived by GISTIC analysis according to the present invention. The lower X-axis value represents False Discovery Rate (FDR)-adjusted p value (Q value) value, and the upper X-axis is calculated in GISTIC analysis. The G-score value (the value calculated by calculating the frequency and intensity of CNV observed in 315 patients with pancreatic cancer) is shown, and the y-axis means the chromosome number.
도 5는 본 발명에 따른 GISTIC 분석에 의해 유전자를 그룹핑하는 방법의 일예시로서, (A)는 각 샘플 별 유전자의 Z값을 나타낸 것이며, (B)는 본 발명의 기준에 따라 각 샘플 별로 유전자를 그룹핑한 결과이다. 5 is an example of a method of grouping genes by GISTIC analysis according to the present invention, (A) shows the Z value of the gene for each sample, (B) is a gene for each sample according to the standard of the present invention is the result of grouping
도 6은 본 발명의 일 실시예에 따른 Kaplan-Meier(K-M) 분석에 의해 도출된 유전자의 개수를 나타낸 그래프이다.6 is a graph showing the number of genes derived by Kaplan-Meier (K-M) analysis according to an embodiment of the present invention.
도 7은 본 발명의 일 실시예에 따른 각 세트별 GSS_All 또는 GSS_TopN의 K-M 생존분석에서 도출한 p-value 값을 비교한 그래프이다.7 is a graph comparing p-value values derived from K-M survival analysis of GSS_All or GSS_TopN for each set according to an embodiment of the present invention.
도 8은 본 발명의 일 실시예에 따른 각 세트별 TopN 유전자의 벤다이어 그램이다. 8 is a Venn diagram of a TopN gene for each set according to an embodiment of the present invention.
도 9는 본 발명의 일 실시예에 따라 선별한 79개의 유전자를 사용하여 TCGA 데이터베이스에서 수득한 췌장암 환자 183명의 생존 예후를 GSS_79로 분석한 결과이다.9 is a result of GSS_79 analysis of the survival prognosis of 183 pancreatic cancer patients obtained from the TCGA database using 79 genes selected according to an embodiment of the present invention.
도 10은 본 발명의 일 실시예에 따른 TCGA 데이터베이스의 췌장암 환자데이터에서 GSS_10의 생존 예후 예측 성능을 분석한 결과이다.10 is a result of analyzing the survival prognosis prediction performance of GSS_10 in pancreatic cancer patient data of the TCGA database according to an embodiment of the present invention.
도 11은 본 발명의 일 실시예에 따른 TCGA 데이터베이스의 췌장암 환자데이터에서 GSS_8의 생존 예후 예측 성능을 분석한 결과이다.11 is a result of analyzing the survival prognosis prediction performance of GSS_8 in pancreatic cancer patient data of the TCGA database according to an embodiment of the present invention.
도 12는 본 발명의 일 실시예에 따른 TCGA 데이터베이스의 췌장암 환자 데이터에서 분석한 GSS_79, GSS_10 및 GSS_8의 예후 예측 성능을 정리한 것이다.12 is a summary of the prognostic prediction performance of GSS_79, GSS_10, and GSS_8 analyzed from pancreatic cancer patient data of the TCGA database according to an embodiment of the present invention.
발명의 상세한 설명 및 바람직한 구현예DETAILED DESCRIPTION OF THE INVENTION AND PREFERRED EMBODIMENTS
다른 식으로 정의되지 않는 한, 본 명세서에서 사용된 모든 기술적 및 과학적 용어들은 본 발명이 속하는 기술 분야에서 숙련된 전문가에 의해서 통상적으로 이해되는 것과 동일한 의미를 갖는다. 일반적으로 본 명세서에서 사용된 명명법 및 이하에 기술하는 실험 방법은 본 기술 분야에서 잘 알려져 있고 통상적으로 사용되는 것이다.Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In general, the nomenclature used herein and the experimental methods described below are well known and commonly used in the art.
본 명세서에서 사용되는 용어에서 단수의 표현은 문맥상 명백하게 다르게 해석되지 않는 한 복수의 표현을 포함하는 것으로 이해되어야 하고, "포함한다" 등의 용어는 설시된 특징, 개수, 단계, 동작, 구성요소, 부분품 또는 이들을 조합한 것이 존재함을 의미하는 것이지, 하나 또는 그 이상의 다른 특징들이나 개수, 단계 동작 구성요소, 부분품 또는 이들을 조합한 것들의 존재 또는 부가 가능성을 배제하지 않는 것으로 이해되어야 한다.In terms of terms used herein, the singular expression should be understood to include a plural expression unless the context clearly dictates otherwise, and terms such as "comprises" include the specified feature, number, step, operation, and element. , parts or combinations thereof are to be understood, but not to exclude the possibility of the presence or addition of one or more other features or numbers, step operation components, parts or combinations thereof.
또한 본 발명에 따른 방법을 수행함에 있어서, 상기 방법을 이루는 각 과정들은 문맥상 명백하게 특정 순서를 기재하지 않은 이상 명기된 순서와 다르게 일어날 수 있다. 즉, 각 과정들은 명기된 순서와 동일하게 일어날 수도 있고 실질적으로 동시에 수행될 수도 있으며 반대의 순서대로 수행될 수도 있다.Also, in performing the method according to the present invention, each process constituting the method may occur differently from the specified order unless a specific order is clearly described in context. That is, each process may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the reverse order.
본 발명은 췌장암에 특이적인 유전자 변이, 특히 유전자의 복제수 변이(CNV: copy number variation)를 이용하는 것을 특징으로 하는 췌장암 환자의 예후, 특히 생존 예후를 예측하기 위한 정보제공 방법 및 그 용도에 대한 것이다. The present invention relates to a method for providing information for predicting the prognosis of pancreatic cancer patients, particularly survival prognosis, characterized by using a gene mutation specific for pancreatic cancer, in particular, a copy number variation (CNV) of the gene, and its use. .
구체적으로 본 발명은 ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP2, DMRT1, DOCK5, DPYSL2, ERICH1-AS1, FAM135B, FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC00639, LMLN, LOC100128993, LINC02052, LRMP, LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBT1, SGK223, SLC38A3, SMARCA2, SOX5, SQLE, TATDN1, TBL1XR1, THSD7A, TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 및 ZNF5833로 구성된 군에서 선택되는 1종 이상, 바람직하게는 2종 이상, 더욱 바람직하게는 5종 이상, 가장 바람직하게는 8종 이상의 유전자의 복제수 변이를 검출하는 단계를 포함하는 것을 특징으로 하는 췌장암 환자의 예후, 특히 생존 예후 예측을 위한 정보제공 방법에 대한 것이다. Specifically, the present invention relates to ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNAS1, DLGAP2, DMRT1, DOCK5, ERICH1-DPYSL2, ERICH1 , FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC05C00639, LMLNC02 LINC00639 , LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBTOX1, SGK223, SFMBTOX1, TBL1XR1, TBLARCA3, SMARCA5 , THSD7A, TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 and at least one selected from the group consisting of ZNF5833, preferably 2 or more, more preferably 5 or more, most preferably is to an information providing method for predicting the prognosis of pancreatic cancer patients, in particular survival prognosis, comprising the step of detecting copy number mutations of 8 or more genes.
본 발명에 따른 유전자 복제수 변이를 통한 췌장암 환자의 예후, 특히 생존 예후 예측을 위한 정보제공 방법에 이용되는 유전자들의 구체적인 정보는 표 1에 기재된 바와 같다. Specific information on genes used in the information providing method for predicting the prognosis of pancreatic cancer patients through gene copy number mutation according to the present invention, in particular, survival prognosis is as shown in Table 1.
바람직하게는 본 발명에 따른 유전자 복제수 변이 검출을 통한 췌장암 환자의 예후, 특히 생존 예후 예측을 위한 정보제공 방법에 있어, 상기 유전자 복제수 변이 검출을 위한 유전자는 ABHD6, CASC1, FAM49B, KANK1, LINC00477, MCPH1, SOX5 및 TATDN1으로 구성된 군에서 선택되는 1종 이상, 바람직하게는 2종 이상, 더욱 바람직하게는 5종 이상, 가장 바람직하게는 8종 모두를 포함하는 것을 특징으로 하며,Preferably, in the method for providing information for predicting the prognosis, particularly survival prognosis, of a pancreatic cancer patient through the detection of the gene copy number mutation according to the present invention, the genes for detecting the gene copy number mutation are ABHD6, CASC1, FAM49B, KANK1, LINC00477 , MCPH1, SOX5 and at least one selected from the group consisting of TATDN1, preferably 2 or more, more preferably 5 or more, most preferably all 8 types,
추가적으로 ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP2, DMRT1, DOCK5, DPYSL2, ERICH1-AS1, FAM135B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00578, LINC00639, LMLN, LOC100128993, LINC02052, LRMP, LRRC6, LTF, MAP4, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBT1, SGK223, SLC38A3, SMARCA2, SQLE, TBL1XR1, THSD7A, TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 및 ZNF583으로 구성된 군에서 선택되는 1종 이상을 포함하는 것을 특징으로 한다.Additionally ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP2, DMRT1, DOCK5, DPYSL2, ERICH135B, FER1, FAML , GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00578, LINC00639, LMLN, LOC100128993, LINC02052, LRMP, AA LRRC6, LTFIN, NXMAP4, LTFIN, NXMAP4 , OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBT1, SGK223, SLC38A3, SMARCA2, SQLE, TBL1XR1, THSD7A, TMEM110, TMEM110-MUSTN1, TMEM65, ZNF5, TMEM65, NFEM196, ZNF5 And ZNF583 is characterized in that it comprises one or more selected from the group consisting of.
가장 바람직하게는 ABHD6, CASC1, FAM49B, KANK1, LINC00477, MCPH1, SOX5 및 TATDN1의 8종의 유전자와, KRAS 및 CDKN2A를 포함할 수 있지만, 이에 한정되는 것은 아니다. Most preferably, the eight genes of ABHD6, CASC1, FAM49B, KANK1, LINC00477, MCPH1, SOX5 and TATDN1 may include, but are not limited to, KRAS and CDKN2A.
본 발명은 일 관점에서, The present invention in one aspect,
(1) ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP2, DMRT1, DOCK5, DPYSL2, ERICH1-AS1, FAM135B, FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC00639, LMLN, LOC100128993, LINC02052, LRMP, LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBT1, SGK223, SLC38A3, SMARCA2, SOX5, SQLE, TATDN1, TBL1XR1, THSD7A, TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 및 ZNF583로 구성된 군에서 선택되는 1종 이상의 유전자의 복제수 변이(CNV: copy number variation)를 검출하고, 검출된 유전자의 복제수 변이 정도를 계량화하는 단계; 및 (1) ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNAS1, DLGAP2, DMRT1, DOCK5, FAMDPYSL2, EAMDPYSL2, EAMDPYSL2 , FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC00639, LMLN, LR LOC100128993, INC05, LOC100639, LMLN , LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBT1, SGK223, SLC38A3, THBLSD1 SMARCA2, TOXAT , TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 and ZNF583 Detecting copy number variation (CNV) of one or more genes selected from the group consisting of, and copying the detected gene quantifying the degree of number variation; and
(2) 상기 (1) 단계에서 계량화된 유전자의 복제수 변이 정도가 정상범위(normal range)를 벗어나는 유전자의 개수가 기준값(cut-off)을 초과할 경우 췌장암 환자의 생존 예후가 나쁜 것으로 판정하는 단계;(2) If the number of genes whose copy number variation degree of the gene quantified in step (1) exceeds the cut-off value, it is determined that the survival prognosis of the pancreatic cancer patient is bad. step;
를 포함하는 췌장암 환자의 생존 예후 예측을 위한 정보의 제공 방법에 관한 것이다.It relates to a method of providing information for predicting survival prognosis of pancreatic cancer patients, including.
바람직하게는 상기 (1) 단계는 다음의 단계를 포함하는 방법으로 수행되는 것을 특징으로 할 수 있지만, 이에 한정되는 것은 아니다. Preferably, the step (1) may be characterized in that it is performed by a method including the following steps, but is not limited thereto.
(1-1) 생체시료에서 수득된 대상 샘플의 DNA 서열정보(reads)를 획득하는 단계; (1-1) obtaining DNA sequence information (reads) of a target sample obtained from a biological sample;
(1-2) 상기 서열정보(reads)를 참조집단의 표준 염색체 서열 데이터베이스 (reference genome database)에 정렬(alignment)하는 단계; (1-2) aligning the sequence information (reads) to a reference genome database of a reference group;
(1-3) 상기 정렬된 서열정보(reads)의 퀄리티를 확인하는 단계; 및(1-3) checking the quality of the aligned sequence information (reads); and
(1-4) 상기 유전자의 복제수 변이를 검출하고, 복제수 변이의 정도를 계량화하는 단계(1-4) detecting the copy number variation of the gene and quantifying the degree of copy number variation
예를 들어, 상기 (1) 단계는 ddPCR (Digital Droplet Polymerase Chain Reaction) 또는 MLPA (Multiplex Ligation-dependent Probe Amplification) 방법을 이용하여 복제 수 변이를 검출할 수 있다.For example, in step (1), the copy number variation may be detected using a Digital Droplet Polymerase Chain Reaction (ddPCR) or Multiplex Ligation-dependent Probe Amplification (MLPA) method.
ddPCR은 (약 20μl의) PCR 반응 용액을 (약 20,000 개의) 미세 방울로 (droplet) 분리시켜 타겟 DNA를 증폭시키고 그 양을 정량 하는 실험 방법으로, 실험 과정 중 droplet 안에서 타겟 DNA의 증폭 여부를 1(증폭됨), 0(증폭 안됨) 의 디지털 신호로 인식하여 계수하고, 푸아송 분포를 통해 타겟 DNA의 복제 수를 계산할 수 있다. ddPCR is an experimental method that amplifies and quantifies the amount of target DNA by separating (about 20 μl) PCR reaction solution into (about 20,000) fine droplets. (amplified), 0 (not amplified) digital signals are recognized and counted, and the number of copies of the target DNA can be calculated through the Poisson distribution.
예를 들어 바람직하게는 상기 (1) 단계는 다음의 단계를 포함하는 방법으로 수행되는 것을 특징으로 할 수 있지만, 이에 한정되는 것은 아니다. For example, preferably, step (1) may be characterized in that it is performed by a method including the following steps, but is not limited thereto.
(1-1) 생체시료에서 수득된 대상 샘플의 DNA를 ddPCR로 증폭하는 단계; (1-1) amplifying the DNA of the target sample obtained from the biological sample by ddPCR;
(1-2) 상기 유전자의 증폭 여부를 계수하는 단계; 및 (1-2) counting whether the gene is amplified; and
(1-3) 푸아송 분포를 통해, 상기 유전자의 복제수 변이를 검출하고, 복제수 변이의 정도를 계량화하는 단계.(1-3) Detecting the copy number variation of the gene through the Poisson distribution, and quantifying the degree of copy number variation.
또한, MLPA(multiplex ligation-dependent probe Amplification)는 탐침자를 표적지에 교잡시킨 후, ligation 시키고, 그 산물을 PCR로 증폭시킴으로써 표적지의 존재 여부 또는 농도를 확인할 수 있는 방법으로, 여러 유전자들에 대한 결실 및 중복 돌연변이에 대한 탐색에 이용될 수 있다.In addition, multiplex ligation-dependent probe amplification (MLPA) is a method that can check the presence or concentration of a target site by hybridizing a probe to a target site, ligation, and amplifying the product by PCR. It can be used to search for duplicate mutations.
예를 들어 상기 (1) 단계는 다음의 단계를 포함하는 방법으로 수행되는 것을 특징으로 할 수 있지만, 이에 한정되는 것은 아니다. For example, the step (1) may be characterized in that it is performed by a method including the following steps, but is not limited thereto.
(1-1) 생체시료에서 수득된 대상 샘플의 DNA에 상기 유전자에 특이적으로 결합할 수 있는 탐침자를 처리하여 ligation 시키는 단계; (1-1) ligation by treating the DNA of the target sample obtained from the biological sample with a probe capable of specifically binding to the gene;
(1-2) 상기 유전자와 탐침자의 ligation 산물을 PCR로 증폭하는 단계; 및 (1-2) amplifying the ligation product of the gene and the probe by PCR; and
(1-3) 증폭산물을 분석하여, 상기 유전자의 복제수 변이를 검출하고, 복제수 변이의 정도를 계량화하는 단계.(1-3) analyzing the amplification product, detecting a copy number variation of the gene, and quantifying the degree of copy number variation.
또한, 상기 (1-4) 단계는 다음의 단계를 포함하는 방법으로 수행되는 것을 특징으로 할 수 있지만, 이에 한정되는 것은 아니다. In addition, the step (1-4) may be characterized in that it is performed by a method including the following steps, but is not limited thereto.
(a) 유전자 복제수 변이가 없는 참조 샘플의 각 유전자 구간별 리드 개수(read count)를 계수(counting)한 후, 각 유전자 구간에 정렬된 리드 개수 값을 샘플의 전체 리드 개수로 나누고, GC 함량(contents)에 의한 뎁스 바이어스(depth bias)를 보정하는 단계를 수행하여, 각 유전자 구간별 참조 샘플의 뎁스 평균(Reference_Mean_Depthgene)과 표준편차 값(Reference_SDgene)을 계산하는 단계;(a) After counting the number of reads for each gene section of a reference sample without gene copy number variation, the value of the number of reads aligned in each gene section is divided by the total number of reads in the sample, and the GC content calculating a depth average (Reference_Mean_Depthgene) and a standard deviation value (Reference_SDgene) of a reference sample for each gene section by performing a step of correcting a depth bias by (contents);
(b) 상기 (1-3) 단계에서 얻어진 정렬된 대상 샘플의 각 유전자 구간별 리드 개수(read count)를 계수(counting)한 후, 각 유전자 구간에 정렬된 리드 개수 값을 샘플의 전체 리드 개수로 나누고, GC 함량(contents)에 의한 뎁스 바이어스(depth bias)를 보정하는 단계를 수행하여, 각 유전자 구간별 대상 샘플의 표준화된 뎁스(normalized depth) 값을 계산하는 단계; 및(b) after counting the read count for each gene section of the aligned target sample obtained in step (1-3), the value of the number of reads aligned in each gene section is calculated as the total number of reads in the sample calculating a normalized depth value of a target sample for each gene section by dividing by . and
(c) 상기 (a) 단계에서 수득된 참조 샘플의 뎁스 평균(Reference_Mean_Depthgene)과 표준편차 값(Reference_SDgene)과 (b) 단계에서 수득된 대상 샘플의 표준화된 뎁스 값(normalized depth)에 기반하여 하기 수식 1을 사용하여 정렬된 서열정보의 정규화된 유전자 구간별 Z(Zgene)값을 계산하는 단계; (c) the depth average (Reference_Mean_Depthgene) and standard deviation value (Reference_SDgene) of the reference sample obtained in step (a) and the normalized depth value of the target sample obtained in step (b) based on the following formula calculating a Z (Zgene) value for each normalized gene section of the aligned sequence information using 1;
수식 1: Formula 1:
Zgene = (Normalized_Depthgene - Reference_Mean_Depthgene) / Reference_SDgene
Z gene = (Normalized_Depth gene - Reference_Mean_Depth gene ) / Reference_SD gene
본 발명에서 상기 GC 양에 의한 depth bias를 보정하는 방법은 통상의 기술자에게 알려진 모든 방법을 사용할 수 있다.In the present invention, any method known to those skilled in the art may be used as a method of correcting the depth bias by the GC amount.
본 발명에서 GC 양은 특정 영역 (gene, bin 등)을 구성하고 있는 염기 서열 A, T, G, C 중에서 G와 C 의 비율을 나타내는 값을 의미한다. 예를 들어, ATTCGCACATCCCGCACACT 라는 서열이 있을 때, 이 서열을 구성하는 전체 20 개의 염기 서열 중 A, T, G, C 염기의 개수는 각각 5, 4, 2, 9개이고, 이 중 G와 C 염기의 비율인 (2+9) / 20 = 55% 값을 이 서열의 GC양이다.In the present invention, the amount of GC means a value representing the ratio of G to C among the nucleotide sequences A, T, G, and C constituting a specific region (gene, bin, etc.). For example, when there is a sequence called ATTCGCACATCCCGCACACT, the number of A, T, G, and C bases among the total 20 base sequences constituting this sequence is 5, 4, 2, and 9, respectively, of which the G and C bases are The ratio (2+9) / 20 = 55% is the amount of GC in this sequence.
일반적으로 Bin 단위의 read depth 분석을 할 때 bin의 GC양에 따라 read depth 가 종속적으로 변하는 현상이 나타난다고 알려져 있다. 즉, GC 양이 증가함에 따라 Depth 값이 특정한 경향성을 나타내게 되는 것이다.In general, it is known that the read depth varies depending on the amount of GC in the bin when the read depth analysis is performed in units of bins. That is, as the amount of GC increases, the depth value shows a specific tendency.
이러한 GC 양에 따른 depth bias를 보정하기 위해 아래와 같은 방법을 적용할 수 있다. In order to correct the depth bias according to the amount of GC, the following method can be applied.
먼저, 분석 대상인 모든 bin들의 GC 양을 소수점 1자리까지 반올림하여 계산할 경우, 하나의 GC 양 값을 갖는 bin이 여러 개 존재하게 되는데, 이런 bin들의 median depth 값을 이 GC 양의 대표 depth 값으로 결정한다. First, if the GC amount of all bins to be analyzed is rounded to one decimal place and calculated, several bins with one GC amount value exist. The median depth value of these bins is determined as the representative depth value of this GC amount. do.
예를 들어, Bin1, Bin2, Bin3, Bin4, Bin5의 Depth 값이 각각 10, 20, 30, 40, 50 이고, GC양이 각각 31.5, 31.5, 31.5, 28.4, 28.4 였다면, 이 샘플에서 GC양이 31.5일 때의 대표 depth 값은 median(10, 20, 30) = 20 이 되고, GC양이 28.4 일 때의 대표 depth 값은 median(40, 50) = 45이다.For example, if the depth values of Bin1, Bin2, Bin3, Bin4, and Bin5 were 10, 20, 30, 40, and 50, respectively, and the GC amount was 31.5, 31.5, 31.5, 28.4, and 28.4, respectively, the GC amount in this sample was The representative depth value when 31.5 is median(10, 20, 30) = 20, and when the GC amount is 28.4, the representative depth value is median(40, 50) = 45.
상기 방법으로 한 샘플에서 나올 수 있는 모든 GC양 값의 대표 depth 값을 계산한 다음, LOESS (Locally Estimated Scatterplot Smoothing) 알고리즘을 이용해 GC 양을 인풋으로 받아(독립변수), 대표 Depth를 예측하는(종속변수) 회귀 모델을 구축한다. After calculating the representative depth value of all the GC amount values that can come out of one sample in the above method, the GC amount is received as an input (independent variable) using the LOESS (Locally Estimated Scatterplot Smoothing) algorithm, and the representative depth is predicted (dependent). variable) to build a regression model.
구축한 회귀 모델을 통해 예측된 depth 값을 GC 양에 따른 Depth bias라 생각할 수 있고, Bin별로 계산된 depth 값에서 이 depth bias 값을 빼주는 방법으로 GC 양에 따른 depth를 보정한다(수식 2)The depth value predicted through the built-up regression model can be considered as a depth bias according to the amount of GC, and the depth is corrected according to the amount of GC by subtracting this depth bias value from the depth value calculated for each bin (Equation 2)
수식 2: GC-corrected Depth = Depthbin - LOESS Predicted Depthbin
Equation 2: GC-corrected Depth = Depth bin - LOESS Predicted Depth bin
본 발명에 있어서 용어 "리드(reads)"는, 당업계에 알려진 다양한 방법을 이용하여 서열정보를 분석한 핵산 단편을 의미한다. 따라서, 본 명세서에서 용어 “서열정보” 및 “리드”는 시퀀싱 과정을 통해 서열정보를 수득한 결과물이라는 점에서 동일한 의미를 가진다.As used herein, the term "reads" refers to nucleic acid fragments obtained by analyzing sequence information using various methods known in the art. Therefore, in the present specification, the terms “sequence information” and “lead” have the same meaning in that they are the result of obtaining sequence information through a sequencing process.
본 발명에서 용어 “bin”은, 일정구간 또는 구간과 같은 의미로 사용되며, 특정 크기를 가지는 염색체 전체 서열의 일부를 의미한다.In the present invention, the term “bin” is used synonymously with a certain section or section, and refers to a portion of the entire chromosome sequence having a specific size.
본 발명에서의 일정 구간(bin)의 크기는 10 내지 100,000 kbp, 바람직하게는 50 내지 50,000 kbp, 더욱 바람직하게는 100 내지 10,000 kbp, 가장 바람직하게는 500 내지 5,000 kbp인 것을 특징으로 할 수 있지만 이에 한정되는 것은 아니다. The size of a certain section (bin) in the present invention may be characterized in that it is 10 to 100,000 kbp, preferably 50 to 50,000 kbp, more preferably 100 to 10,000 kbp, and most preferably 500 to 5,000 kbp. It is not limited.
본 발명에서 용어 ”참조샘플”은 표준 염기서열 데이터베이스와 같이 비교할 수 있는 기준(reference) 집단의 샘플로서, 현재 특정 질환 또는 병증이 없는 사람의 집단에서 수득된 샘플을 의미한다. 본 발명에 있어서, 상기 참조샘플의 표준 염색체 서열 데이터베이스에서 표준 염기서열은 NCBI 등의 공공보건기관에 등록되어 있는 참조 염색체일 수 있다. In the present invention, the term “reference sample” refers to a sample of a reference group that can be compared like a standard sequence database, and is a sample obtained from a group of people who do not currently have a specific disease or condition. In the present invention, the standard nucleotide sequence in the standard chromosomal sequence database of the reference sample may be a reference chromosome registered with a public health institution such as NCBI.
본 발명에서 용어 “생체시료”는 인간 등의 동물의 생체에서 수득된 시료를 의미하며, 바람직하게는 혈액, 복강액, 조직, 타액, 소변, 모발, 배변물, 척수액, 뇌수액 및 담액에서 선택된 1종 이상인 것을 특징으로 할 수 있지만 이에 한정되는 것은 아니다. As used herein, the term “biological sample” refers to a sample obtained from a living body of an animal such as a human, preferably selected from blood, abdominal fluid, tissue, saliva, urine, hair, feces, spinal fluid, cerebrospinal fluid, and bile fluid. It may be characterized as one or more, but is not limited thereto.
본 발명에서 상기 생체시료에서 수득된 대상 샘플의 DNA는 생체시료에서 추출한 핵산의 조각이면 제한없이 이용할 수 있으며, 바람직하게는 세포 유리 핵산(cell-free DNA), exosomal DNA, 또는 세포 내 핵산의 조각일 수 있으나, 이에 한정되는 것은 아니다.In the present invention, the DNA of the target sample obtained from the biological sample can be used without limitation as long as it is a fragment of nucleic acid extracted from the biological sample, preferably cell-free DNA, exosomal DNA, or a fragment of intracellular nucleic acid. may be, but is not limited thereto.
상기 계량화된 유전자의 복제수 변이 정도가 정상범위(normal range)를 벗어나는 유전자의 개수가 기준값(cut-off)을 초과할 경우 췌장암 환자의 생존 예후가 나쁜 것으로 판정하는 단계에 있어서, 유전자의 복제수 변이 정도는 상기 Z(Zgene)값을 기준으로 계량화되어 판정될 수 있으며, 상기 Z 값의 정상범위는 -1 내지 1, 바람직하게는 -1.5 내지 1.5, 더욱 바람직하게는 -2 내지 2일 수 있지만, 이에 한정되는 것은 아니며, 그 진단의 목적이나 정확도 등에 따라 유연하게 설정될 수 있다. In the step of determining that the survival prognosis of the pancreatic cancer patient is bad when the number of genes in which the quantified degree of variation in the copy number of the gene exceeds a cut-off value, the copy number of the gene The degree of variation can be quantified and determined based on the Z (Z gene ) value, and the normal range of the Z value is -1 to 1, preferably -1.5 to 1.5, more preferably -2 to 2 However, the present invention is not limited thereto, and may be flexibly set according to the purpose or accuracy of the diagnosis.
또한, 상기 기준값(cut-off)은 전체 대상 유전자의 10% 이상, 바람직하게는 20% 이상, 더욱 바람직하게는 30% 이상, 가장 바람직하게는 40%의 값으로 설정될 수 있는데, 예를 들어 40개의 유전자를 대상으로 유전자 복제수 변이 정도를 검출할 경우에는 기준값(cut-off)은 10%인 4개, 바람직하게는 20%인 8개, 더욱 바람직하게는 30% 인 12개로 설정될 수 있지만 이에 한정되는 것은 아니다.In addition, the reference value (cut-off) may be set to a value of 10% or more, preferably 20% or more, more preferably 30% or more, and most preferably 40% of all target genes, for example, In the case of detecting the degree of gene copy number variation for 40 genes, the cut-off may be set to 4 at 10%, preferably 8 at 20%, and more preferably at 12 at 30%. However, the present invention is not limited thereto.
특히, ABHD6, CASC1, FAM49B, KANK1, LINC00477, MCPH1, SOX5 및 TATDN1, KRAS 및 CDKN2A의 10개 유전자가 사용될 경우, 기준값(cut-off)은 10%인 1개, 바람직하게는 20%인 2개, 더욱 바람직하게는 30%인 3개로 설정될 수 있지만 이에 한정되는 것은 아니다In particular, when 10 genes of ABHD6, CASC1, FAM49B, KANK1, LINC00477, MCPH1, SOX5 and TATDN1, KRAS and CDKN2A are used, the cut-off is one at 10%, preferably two at 20%. , more preferably 30%, but may be set to three, but is not limited thereto.
본 발명에 있어서, 리드(reads)는 대규모 병렬 서열분석 방법으로 수득될 수 있지만 이에 한정되는 것은 아니다. 대규모 병렬 서열분석 방법은 차세대 유전자 서열검사(next-generation sequencing: NGS) 방법으로 수행되는 것이 바람직하지만 이에 한정되는 것은 아니다. In the present invention, reads can be obtained by, but not limited to, massively parallel sequencing methods. The massively parallel sequencing method is preferably performed as a next-generation sequencing (NGS) method, but is not limited thereto.
본 발명에서 차세대 유전자 서열검사(next-generation sequencing) 방법은 차세대 유전자서열검사기(next-generation sequencer)를 이용하여 당업계에 공지된 임의의 방법으로 수행될 수 있다. 차세대 시퀀싱은 개개의 핵산 분자 또는 고도로 유사한 방식으로 개개의 핵산 분자에 대해 클론으로 확장된 프록시 중 하나의 뉴클레오타이드 서열을 결정하는 임의의 시퀀싱 방법을 포함한다(예를 들어, 105개 이상의 분자가 동시에 시퀀싱된다). 일 실시형태에서, 라이브러리 내 핵산 종의 상대적 존재비는 시퀀싱 실험에 의해 만들어진 데이터에서 그것의 동족 서열의 상대적 발생 수를 계측함으로써 추정될 수 있다. 차세대 시퀀싱 방법은 당업계에 공지되어 있고, 예를 들어 본 명세서에 참조로서 포함된 문헌(Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46)에 기재된다.In the present invention, the next-generation sequencing method may be performed by any method known in the art using a next-generation sequencer. Next-generation sequencing includes any sequencing method that determines the nucleotide sequence of either an individual nucleic acid molecule or a clonally extended proxy for an individual nucleic acid molecule in a highly similar manner (e.g., 105 or more molecules are sequenced simultaneously do). In one embodiment, the relative abundance of a nucleic acid species in a library can be estimated by counting the relative number of occurrences of its cognate sequence in data generated by sequencing experiments. Next-generation sequencing methods are known in the art and are described, for example, in Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference.
일 실시형태에서, 차세대 시퀀싱은 개개의 핵산 분자의 뉴클레오타이드 서열을 결정하기 위해 한다(예를 들어, 헬리코스 바이오사이언스(Helicos BioSciences)의 헬리스코프 유전자 시퀀싱 시스템(HeliScope Gene Sequencing system) 및 퍼시픽바이오사이언스의 팩바이오 알에스 시스템(PacBio RS system)). 다른 실시형태에서, 시퀀싱, 예를 들어, 더 적지만 더 긴 리드를 만들어내는 다른 시퀀싱 방법보다 시퀀싱 단위 당 서열의 더 많은 염기를 만들어내는 대량병렬의 짧은-리드 시퀀싱(예를 들어, 캘리포니아주 샌디에고에 소재한 일루미나 인코포레이티드(Illumina Inc.) 솔렉사 시퀀서(Solexa sequencer)) 방법은 개개의 핵산 분자에 대해 클론으로 확장된 프록시의 뉴클레오타이드 서열을 결정한다(예를 들어, 캘리포니아주 샌디에고에 소재한 일루미나 인코포레이티드(Illumina Inc.) 솔렉사 시퀀서(Solexa sequencer); 454 라이프 사이언스(Life Sciences)(코네티컷주 브랜포드에 소재) 및 아이온 토렌트(Ion Torrent)). 차세대 시퀀싱을 위한 다른 방법 또는 기계는, 이하에 제한되는 것은 아니지만, 454 라이프 사이언스(Life Sciences)(코네티컷주 브랜포드에 소재), 어플라이드 바이오시스템스(캘리포니아주 포스터 시티에 소재; SOLiD 시퀀서), 헬리코스 바이오사이언스 코포레이션(매사추세츠주 캠브릿지에 소재) 및 에멀젼 및 마이크로 유동 시퀀싱 기법 나노 점적(예를 들어, 지누바이오(GnuBio) 점적)에 의해 제공된다.In one embodiment, next-generation sequencing is performed to determine the nucleotide sequence of an individual nucleic acid molecule (e.g., HeliScope Gene Sequencing system from Helicos BioSciences and Pacific Biosciences). PacBio RS system). In other embodiments, sequencing, e.g., mass-parallel short-read sequencing that yields more bases of sequence per sequencing unit (e.g., San Diego, CA) than other sequencing methods yielding fewer but longer reads. The Illumina Inc. Solexa sequencer method determines the nucleotide sequence of a cloned extended proxy for an individual nucleic acid molecule (e.g., Illumina, San Diego, CA). Illumina Inc. Solexa sequencer; 454 Life Sciences (Branford, Conn.) and Ion Torrent). Other methods or machines for next-generation sequencing include, but are not limited to, 454 Life Sciences (Branford, Conn.), Applied Biosystems (Foster City, CA; SOLiD Sequencer), Helicos. Bioscience Corporation (Cambridge, MA) and emulsion and microfluidic sequencing techniques Nano Droplets (eg, GnuBio Drops).
차세대 시퀀싱을 위한 플랫폼은, 이하에 제한되는 것은 아니지만, 로슈(Roche)/454의 게놈 시퀀서(Genome Sequencer: GS) FLX 시스템, 일루미나(Illumina)/솔렉사(Solexa) 게놈 분석기(Genome Analyzer: GA), 라이프(Life)/APG의 서포트 올리고(Support Oligonucleotide Ligation Detection: SOLiD) 시스템, 폴로네이터(Polonator)의 G.007 시스템, 헬리코스 바이오사이언스의 헬리스코프 유전자 시퀀싱 시스템(Helicos BioSciences' HeliScope Gene Sequencing system) 및 퍼시픽 바이오사이언스(Pacific Biosciences)의 팩바이오알에스(PacBio RS) 시스템을 포함한다.Platforms for next-generation sequencing include, but are not limited to, Roche/454's Genome Sequencer (GS) FLX System, Illumina/Solexa Genome Analyzer (GA). , Life/APG's Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator's G.007 system, Helicos BioSciences' HeliScope Gene Sequencing system and Pacific Biosciences' PacBio RS system.
본 발명에 있어서, 상기 정렬단계는 이에 제한되지는 않으나, BWA 알고리즘 및 Hg19 서열을 이용하여 수행되는 것일 수 있다. 본 발명에 있어서, 상기 BWA 알고리즘은 BWA-mem, BWA-ALN, BWA-SW 또는 Bowtie2 등이 포함될 수 있으나 이에 한정되는 것은 아니다.In the present invention, the alignment step is not limited thereto, but may be performed using the BWA algorithm and the Hg19 sequence. In the present invention, the BWA algorithm may include, but is not limited to, BWA-mem, BWA-ALN, BWA-SW or Bowtie2.
본 발명에 있어서, 상기 (1-1) 단계에 따른 생체시료에서 수득된 대상 샘플의 DNA 서열정보(reads)를 획득하는 단계는 In the present invention, the step of obtaining DNA sequence information (reads) of the target sample obtained from the biological sample according to step (1-1) comprises:
(i) 분리된 DNA에서 염석 방법(salting-out method), 컬럼크로마토그래피 방법(column chromatography method), 또는 비드 방법(beads method)을 사용하여 단백질, 지방, 및 기타 잔여물을 제거하고 정제된 핵산을 수득하는 단계; (i) a nucleic acid purified by removing proteins, fats, and other residues from the isolated DNA using a salting-out method, a column chromatography method, or a beads method obtaining a;
(ii) 상기 정제된 핵산에 대하여, 싱글-엔드 시퀀싱(single-end sequencing) 또는 페어-엔드 시퀀싱(pair-end sequencing) 라이브러리(library)를 제작하는 단계;(ii) preparing a single-end sequencing or pair-end sequencing library for the purified nucleic acid;
(iii) 상기 제작된 라이브러리를 차세대 유전자서열검사기(next-generation sequencer)에 반응시키는 단계; 및(iii) reacting the prepared library with a next-generation sequencer; and
(iv) 상기 차세대 유전자서열검사기에서 핵산의 서열정보(reads)를 획득하는 단계;(iv) obtaining sequence information (reads) of nucleic acids from the next-generation gene sequencing machine;
를 포함하여 수행될 수 있지만 이에 한정되는 것은 아니다. may be performed, including, but not limited to.
또한, 본 발명에 있어서, 상기 (1-3) 단계에 따른 상기 정렬된 서열정보(reads)의 퀄리티를 확인하는 단계는, 정렬 퀄리티 점수(mapping quality score)의 퀄리티 기준값을 만족하는 서열을 선별하는 단계를 포함하는 방법으로 수행되는 것을 특징으로 할 수 있지만 이에 한정되는 것은 아니다. In addition, in the present invention, the step of checking the quality of the aligned sequence information (reads) according to step (1-3) is to select a sequence that satisfies the quality reference value of the mapping quality score It may be characterized in that it is carried out in a method comprising a step, but is not limited thereto.
또한, 본 발명에서 상기 퀄리티 기준값은, 원하는 기준에 따라 달라질 수 있으나, 바람직하게는 15-70점, 더욱 바람직하게는 50~70점 일 수 있고, 가장 바람직하게는 60점일 수 있으나, 이에 한정되는 것은 아니다. In addition, in the present invention, the quality reference value may vary depending on a desired standard, but is preferably 15-70 points, more preferably 50-70 points, and most preferably 60 points, but is limited thereto. it is not
본 발명에 따른 췌장암 환자의 생존 예후 예측을 위한 정보제공 방법은 하나의 구체화된 형태로 다음과 같은 단계를 포함하여 이루어질 수 있지만 이에 한정되는 것은 아니다(도 1 참조). The information providing method for predicting the survival prognosis of a pancreatic cancer patient according to the present invention may include the following steps in one specific form, but is not limited thereto (see FIG. 1 ).
(1) 말초혈액의 혈장에서 세포유리핵산 (cell-free DNA, cfDNA) 추출(1) Extraction of cell-free DNA (cfDNA) from plasma of peripheral blood
(2) 대규모병렬서열분석(massive parallel sequencing) 방법으로 핵산 단편(reads) 데이터 확보(2) Securing nucleic acid fragment data by massive parallel sequencing method
(3) 상기 핵산 단편 데이터를 휴먼 참조 유전체에 정렬(3) Aligning the nucleic acid fragment data to a human reference genome
(4) 상기 정렬된 데이터에서 퀄리티를 확인(4) Check the quality in the sorted data
(5) 췌장암 관련 유전자 복제 수 변이 검출 (5) Detection of gene copy number mutations related to pancreatic cancer
(6) 변이 점수 도출(6) Derivation of mutation score
(7) 변이 점수가 정상범위 이상인 유전자 개수 계수(counting)(7) Counting the number of genes whose mutation score is above the normal range
(8) 췌장암 환자의 생존 예후 예측(8) Prediction of survival prognosis of pancreatic cancer patients
본 발명은 다른 관점에서, 본 발명에 따른 췌장암 환자의 생존 예후 예측을 위한 정보의 제공방법에 이용되는 정보제공 장치로서, 상기 장치는 In another aspect, the present invention is an information providing device used in a method for providing information for predicting survival prognosis of a pancreatic cancer patient according to the present invention, the device comprising:
(1) 표 1 등에 기재된 본 발명에 따른 췌장암 특이적 유전자 복제수 변이가 일어나는 유전자의 복제수 변이를 검출하는 유전자 복제수 변이 검출부; (1) a gene copy number mutation detection unit for detecting a copy number mutation of a gene in which a pancreatic cancer-specific gene copy number mutation occurs according to the present invention described in Table 1 and the like;
(2) 검출된 유전자 복제수 변이 정보를 기반으로 복제수 변이 정도를 계량화하고, 계량화된 유전자 복제수 변이 정도가 정상범위(normal range)를 벗어나는 유전자의 개수를 계산하는 계산부; 및(2) a calculation unit that quantifies the degree of copy number variation based on the detected gene copy number variation information, and calculates the number of genes whose quantified gene copy number variation is outside a normal range; and
(3) 유전자 복제수 변이 정도가 정상범위를 벗어나는 유전자의 개수가 기준값을 초과할 경우, 생존 예후가 나쁜 것으로 판정하는 생존 예후 판정부;(3) a survival prognosis determining unit that determines that the survival prognosis is poor when the number of genes whose degree of gene copy number mutation is out of the normal range exceeds a reference value;
를 포함하는 것을 특징으로 하는 정보 제공 장치에 관한 것이다. It relates to an information providing device comprising a.
본 발명은 또 다른 관점에서 본 발명에 따른 췌장암 환자의 생존 예후 예측을 위한 정보의 제공방법에 이용되는 컴퓨터 판독 가능한 매체로서, 상기 매체는 췌장암 환자의 생존 예후 예측을 위한 정보를 제공하는 프로세서에 의해 실행되도록 구성되는 명령을 포함하되, The present invention is a computer-readable medium used for a method of providing information for predicting survival prognosis of a pancreatic cancer patient according to the present invention from another aspect, wherein the medium is provided by a processor providing information for predicting survival prognosis of a pancreatic cancer patient a command that is configured to be executed;
(1) 표 1 등에 기재된 본 발명에 따른 췌장암 특이적 유전자 복제수 변이가 일어나는 유전자의 복제수 변이를 검출하는 단계;(1) detecting a copy number mutation of a gene in which a pancreatic cancer-specific gene copy number mutation occurs according to the present invention described in Table 1 and the like;
(2) 검출된 유전자 복제수 변이 정보를 기반으로 복제수 변이 정도를 계량화하고, 계량화된 유전자 복제수 변이 정도가 정상범위(normal range)를 벗어나는 유전자의 개수를 계산하는 단계; 및(2) quantifying the degree of copy number variation based on the detected gene copy number variation information, and calculating the number of genes whose quantified gene copy number variation is outside a normal range; and
(3) 유전자 복제수 변이 정도가 정상범위를 벗어나는 유전자의 개수가 기준값을 초과할 경우, 생존 예후가 나쁜 것으로 판정하는 단계;(3) determining that the survival prognosis is poor when the number of genes whose degree of gene copy number mutation is out of the normal range exceeds a reference value;
를 포함하는 프로세서에 의해 실행되도록 구성되는 명령을 포함하는 컴퓨터 판독 가능한 매체에 관한 것이다.It relates to a computer-readable medium comprising instructions configured to be executed by a processor comprising:
본 발명은 또 다른 관점에서, 본 발명에 따른 췌장암 환자의 생존 예후 예측을 위한 정보의 제공방법에 이용되는 표적 핵산 증폭용 키트로서, 상기 키트는In another aspect, the present invention provides a kit for amplifying a target nucleic acid used in a method for providing information for predicting survival prognosis of a pancreatic cancer patient according to the present invention, the kit comprising:
표 1 등에 기재된 본 발명에 따른 췌장암 특이적 유전자에 특이적으로 결합하는 프로브; 또는 표 1 등에 기재된 본 발명에 따른 췌장암 특이적 유전자를 증폭하는 프라이머를 포함하는 것을 특징으로 하는 표적 핵산 증폭용 키트에 관한 것이다.a probe that specifically binds to a pancreatic cancer-specific gene according to the present invention described in Table 1 and the like; Or it relates to a kit for amplifying a target nucleic acid comprising a primer for amplifying a pancreatic cancer-specific gene according to the present invention described in Table 1 and the like.
본 발명에 있어서, 상기 키트는 버퍼(buffer), DNA 중합효소(DNA polymerase), DNA 중합효소 조인자(DNA polymerase cofactor) 및 데옥시리보뉴클레오티드-5-트리포스페이트(dNTP)와 같은 핵산 증폭 반응(예컨대, 중합효소연쇄반응)을 실시하는데 필요한 시약을 선택적으로 포함할 수 있다. 선택적으로, 본 발명의 키트는 또한 다양한 올리고뉴클레오티드(oligonucleotide) 분자, 역전사효소(reverse transcriptase), 다양한 버퍼 및 시약, 및 DNA 중합효소 활성을 억제하는 항체를 포함할 수 있다. 또한, 상기 키트의 특정 반응에서 사용되는 시약의 최적량은, 본 명세서의 기재사항을 습득한 당업자에 의해서 용이하게 결정될 수 있다. 전형적으로, 본 발명의 장비는 앞서 언급된 구성 성분들을 포함하는 별도의 포장 또는 컴파트먼트(compartment)로 제작될 수 있다.In the present invention, the kit is a nucleic acid amplification reaction such as a buffer, DNA polymerase, DNA polymerase cofactor, and deoxyribonucleotide-5-triphosphate (dNTP) (eg, , polymerase chain reaction) may optionally include reagents necessary for carrying out. Optionally, the kit of the present invention may also include various oligonucleotide molecules, reverse transcriptase, various buffers and reagents, and antibodies that inhibit DNA polymerase activity. In addition, the optimal amount of the reagent used in a specific reaction of the kit can be easily determined by a person skilled in the art after learning the description herein. Typically, the equipment of the present invention may be manufactured as a separate package or compartment comprising the aforementioned components.
하나의 실시예에서, 상기 키트는 샘플을 담는 구획된 캐리어 수단, 시약을 포함하는 용기 및 프라이머 또는 프로브를 포함하는 용기를 포함할 수 있다. In one embodiment, the kit may include a compartmentalized carrier means for holding a sample, a container for containing reagents, and a container for containing primers or probes.
상기 캐리어 수단은 병, 튜브와 같은 하나 이상의 용기를 함유하기에 적합하고, 각 용기는 본 발명의 방법에 사용되는 독립적 구성요소들을 함유한다. 본 발명의 명세서에서, 당해 분야의 통상의 지식을 가진 자는 용기 중의 필요한 제제를 손쉽게 분배할 수 있다.The carrier means is suitable for containing one or more containers, such as bottles and tubes, each container containing independent components for use in the method of the present invention. In the context of the present invention, one of ordinary skill in the art can readily dispense the required formulation in a container.
본 발명은 또 다른 관점에서, ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP2, DMRT1, DOCK5, DPYSL2, ERICH1-AS1, FAM135B, FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC00639, LMLN, LOC100128993, LINC02052, LRMP, LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBT1, SGK223, SLC38A3, SMARCA2, SOX5, SQLE, TATDN1, TBL1XR1, THSD7A, TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 및 ZNF583로 구성된 군에서 선택되는 1종 이상의 유전자의 복제수 변이(CNV : copy number variation) 정보를 이용, 구체적으로는 상기 유전자의 복제수 변이를 검출하는 단계를 포함하는 것을 특징으로 하는 췌장암 환자의 생존 예후 예측 방법에 관한 것이다.In another aspect, the present invention provides ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP1, DMRT1, DOCK5, DPYSL2 AS1, FAM135B, FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00INC00578, LINCOC1001200INC00578 LINC02052, LRMP, LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBT1, SGK223, SFMOXLC38A3, SMK223 Using copy number variation (CNV) information of one or more genes selected from the group consisting of TATDN1, TBL1XR1, THSD7A, TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 and ZNF583; Specifically, it relates to a method for predicting survival prognosis of a pancreatic cancer patient, comprising the step of detecting a copy number mutation of the gene.
본 발명은 다른 관점에서, 본 발명에 따른 췌장암 환자의 생존 예후 예측방법에 이용되는 정보제공 장치로서, 상기 장치는 In another aspect, the present invention provides an information providing device used in a method for predicting survival prognosis of a pancreatic cancer patient according to the present invention, the device comprising:
(4) 표 1 등에 기재된 본 발명에 따른 췌장암 특이적 유전자 복제수 변이가 일어나는 유전자의 복제수 변이를 검출하는 유전자 복제수 변이 검출부; (4) a gene copy number mutation detection unit for detecting a copy number mutation of a gene in which a pancreatic cancer-specific gene copy number mutation occurs according to the present invention described in Table 1 and the like;
(5) 검출된 유전자 복제수 변이 정보를 기반으로 복제수 변이 정도를 계량화하고, 계량화된 유전자 복제수 변이 정도가 정상범위(normal range)를 벗어나는 유전자의 개수를 계산하는 계산부; 및(5) a calculation unit that quantifies the degree of copy number variation based on the detected gene copy number variation information, and calculates the number of genes whose quantified gene copy number variation is outside a normal range; and
(6) 유전자 복제수 변이 정도가 정상범위를 벗어나는 유전자의 개수가 기준값을 초과할 경우, 생존 예후가 나쁜 것으로 판정하는 생존 예후 판정부;(6) a survival prognosis determining unit that determines that the survival prognosis is poor when the number of genes whose degree of gene copy number mutation is out of the normal range exceeds the reference value;
를 포함하는 것을 특징으로 하는 췌장암 환자의 생존 예후 예측 장치에 관한 것이다. It relates to a device for predicting survival prognosis of pancreatic cancer patients, comprising:
본 발명은 또 다른 관점에서 본 발명에 따른 췌장암 환자의 생존 예후 예측방법에 이용되는 컴퓨터 판독 가능한 매체로서, 상기 매체는 췌장암 환자의 생존 예후를 예측하는 프로세서에 의해 실행되도록 구성되는 명령을 포함하되, In another aspect, the present invention is a computer-readable medium used in a method for predicting survival prognosis of a pancreatic cancer patient according to the present invention, wherein the medium includes instructions configured to be executed by a processor for predicting the survival prognosis of a pancreatic cancer patient,
(4) 표 1 등에 기재된 본 발명에 따른 췌장암 특이적 유전자 복제수 변이가 일어나는 유전자의 복제수 변이를 검출하는 단계;(4) detecting a copy number mutation of a gene in which a pancreatic cancer-specific gene copy number mutation occurs according to the present invention described in Table 1 and the like;
(5) 검출된 유전자 복제수 변이 정보를 기반으로 복제수 변이 정도를 계량화하고, 계량화된 유전자 복제수 변이 정도가 정상범위(normal range)를 벗어나는 유전자의 개수를 계산하는 단계; 및(5) quantifying the degree of copy number variation based on the detected gene copy number variation information, and calculating the number of genes whose quantified gene copy number variation is outside a normal range; and
(6) 유전자 복제수 변이 정도가 정상범위를 벗어나는 유전자의 개수가 기준값을 초과할 경우, 생존 예후가 나쁜 것으로 판정하는 단계;(6) determining that the survival prognosis is poor when the number of genes whose degree of gene copy number mutation is out of the normal range exceeds a reference value;
를 포함하는 프로세서에 의해 실행되도록 구성되는 명령을 포함하는 컴퓨터 판독 가능한 매체에 관한 것이다.It relates to a computer-readable medium comprising instructions configured to be executed by a processor comprising:
본 발명은 또 다른 관점에서, 본 발명에 따른 췌장암 환자의 생존 예후 예측 방법에 이용되는 표적 핵산 증폭용 키트로서, 상기 키트는In another aspect, the present invention provides a kit for amplifying a target nucleic acid used in the method for predicting survival prognosis of a pancreatic cancer patient according to the present invention, the kit comprising:
표 1 등에 기재된 본 발명에 따른 췌장암 특이적 유전자에 특이적으로 결합하는 프로브; 또는 표 1 등에 기재된 본 발명에 따른 췌장암 특이적 유전자를 증폭하는 프라이머를 포함하는 것을 특징으로 하는 표적 핵산 증폭용 키트에 관한 것이다.a probe that specifically binds to a pancreatic cancer-specific gene according to the present invention described in Table 1 and the like; Or it relates to a kit for amplifying a target nucleic acid comprising a primer for amplifying a pancreatic cancer-specific gene according to the present invention described in Table 1 and the like.
이하 본 발명을 실시예를 통하여 보다 상세하게 설명한다. 이하 본 발명을 실시예를 통하여 보다 상세하게 설명한다. 그러나, 이들 실시예는 본 발명을 예시적으로 설명하기 위한 것으로 본 발명의 범위가 이들 실시예에 한정되는 것은 아니다.Hereinafter, the present invention will be described in more detail through examples. Hereinafter, the present invention will be described in more detail through examples. However, these examples are for illustrative purposes only, and the scope of the present invention is not limited to these examples.
실시예Example
이하, 실시예를 통하여 본 발명을 더욱 상세히 설명하고자 한다. 이들 실시예는 오로지 본 발명을 예시하기 위한 것으로서, 본 발명의 범위가 이들 실시예에 의해 제한되는 것으로 해석되지는 않는 것은 당업계에서 통상의 지식을 가진 자에게 있어서 자명할 것이다.Hereinafter, the present invention will be described in more detail through examples. These examples are only for illustrating the present invention, and it will be apparent to those of ordinary skill in the art that the scope of the present invention is not to be construed as being limited by these examples.
실시예 1. 췌장암 환자에서의 복제수 변이 검출Example 1. Detection of copy number variation in pancreatic cancer patients
315명의 췌장암 환자의 DNA를 추출하고 전장 염색체에 대한 라이브러리를 제조하였다. 완성된 라이브러리는 NextSeq 장비에서(illumina, USA) 염기서열 분석을 수행하였으며, 샘플당 평균 18.4 million read의 서열정보 데이터를 생산하였다. DNA from 315 pancreatic cancer patients was extracted and a library for full-length chromosomes was prepared. The completed library was subjected to sequencing on NextSeq equipment (illumina, USA), and sequence information data of an average of 18.4 million reads per sample was produced.
차세대염기서열분석(NGS) 장비에서 Bcl 파일(염기서열정보 포함)을 fastq 형식으로 변환한 다음, fastq 파일을 BWA-mem 알고리즘을 사용하여 참조염색체 Hg19서열 기준으로 라이브러리 서열을 정렬하였다. 시퀀싱 데이터는 Q30이 80% 이상, Mapping quality가 60을 만족하는 것을 확인하였다. After converting the Bcl file (including sequencing information) into fastq format in the next-generation sequencing (NGS) equipment, the fastq file was aligned with the library sequence based on the Hg19 sequence of the reference chromosome using the BWA-mem algorithm. Sequencing data confirmed that Q30 satisfies 80% or more and Mapping quality satisfies 60.
염색체를 일정구간(1,000,000bp, bin)으로 나눈 다음, bin에 정렬되는 read 수를 카운트 한 뒤, 각 bin에 정렬된 read count 값을 샘플의 전체 read 수로 나눠 주고, GC contents에 의한 depth bias를 R language의 기본 통계 패키지인 stat package에 내장되어있는 loess 함수를 사용하여 보정하였다. After dividing the chromosome into a certain section (1,000,000bp, bin), count the number of reads aligned in the bin, divide the read count value aligned in each bin by the total number of reads in the sample, and calculate the depth bias by GC contents in R Corrected using the loess function built into the stat package, which is the language's basic statistics package.
상기 과정을 복제수 변이(CNV)가 없는 정상 샘플 군에서 진행하여 각 bin별 평균과 표준편차 값을 계산하고, 췌장암 환자의 샘플 군에서 상기 과정을 수행하여 bin별 normalized depth 값을 계산한 다음, 하기 수식 3을 이용하여 표준화된 Zbin 값을 수득하였다. The above procedure is performed in the normal sample group without copy number variation (CNV), the mean and standard deviation values for each bin are calculated, and the normalized depth value is calculated for each bin by performing the above procedure in the sample group of pancreatic cancer patients, A standardized Z bin value was obtained using Equation 3 below.
수식 3:Formula 3:
Zbin = (Normalized_Depthbin - Reference_Mean_Depthbin) / Reference_SDbin
Z bin = (Normalized_Depth bin - Reference_Mean_Depth bin ) / Reference_SD bin
계산한 bin별 Z 값에 Circular Binary Segmentation (CBS) 알고리즘을 적용하여 전체 게놈 영역 중 주변과 카피 수가 다른 영역을 검출(segmentation)하였다(도 2 참조). 도 2에 기재된 바와 같이, A는 주변보다 카피 수가 증가한, Amplification segment의 예이고, B는 주변보다 카피 수가 감소한, Deletion segment의 예이며, 이어져 있는 붉은 선은 하나의 segment를 나타낸다. Circular Binary Segmentation (CBS) algorithm was applied to the calculated Z value for each bin to detect (segmentation) a region with a different copy number from the periphery of the entire genome region (see FIG. 2). As shown in FIG. 2 , A is an example of an amplification segment in which the number of copies is increased compared to the surrounding, B is an example of a deletion segment in which the number of copies is decreased compared to the surrounding, and a continuous red line indicates one segment.
실시예 2. 췌장암 특이적 유전자 영역 선별Example 2. Pancreatic cancer-specific gene region selection
2-1. 췌장암 특이적 게놈 영역 1차 선별2-1. Primary screening of pancreatic cancer-specific genomic regions
췌장암 환자 315 명에서 얻어진 DNA 샘플일 이용하여, 상기 segment 분석을 수행하고, Performing the segment analysis using DNA samples obtained from 315 patients with pancreatic cancer,
실시예 1에서 수득한 복제수 변이 영역에 대하여, Genomic Identification of Significant Targets in Cancer (GISTIC) 알고리즘을 이용하여 315 명의 췌장암 환자에서 공통적으로 빈번하게 발생하는 Amplification, Deletion 영역을 1차 선별하였다. For the copy number mutation region obtained in Example 1, using the Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm, amplification and deletion regions commonly occurring in 315 pancreatic cancer patients were first selected.
그 결과, 도 3 및 도 4에 기재된 바와 같이, 총 9개의 Amplification 영역과 6개의 Deletion 영역을 선별하였다. As a result, as shown in FIGS. 3 and 4 , a total of 9 amplification regions and 6 deletion regions were selected.
도 3 및 도 4의 왼쪽의 붉은색 그림이 췌장암 환자 315 명에서 반복적으로 관찰되는 Amplification segment 영역을 나타내고, 오른쪽의 파란 그림은 Deletion segment 영역을 나타낸다. 또한, 도 3 및 도 4의 아래쪽 x 축 값은 False Discovery Rate (FDR) - adjusted p value (Q value) 값을 나타내고, 위쪽 X 축은 GISTIC 분석에서 계산된 G-score 값(췌장암 환자 315명에서 관찰되는 CNV의 빈도 및 세기를 계산한 값)이며, y 축은 염색체 번호이다.The red figure on the left of FIGS. 3 and 4 shows the amplification segment region repeatedly observed in 315 pancreatic cancer patients, and the blue figure on the right shows the deletion segment region. In addition, the lower x-axis value of FIGS. 3 and 4 represents the false discovery rate (FDR)-adjusted p value (Q value) value, and the upper X-axis is the G-score value calculated in the GISTIC analysis (observed in 315 patients with pancreatic cancer). CNV frequency and intensity), and the y-axis is the chromosome number.
도출된 각 영역의 좌표는 표 2와 같다.The derived coordinates of each area are shown in Table 2.
2-2. 췌장암 생존 예후와 관련이 있는 유전자 영역 2차 선별2-2. Secondary Screening of Gene Regions Relevant to Pancreatic Cancer Survival Prognosis
GISTIC 분석을 통해 선별된 췌장암 특이적 CNV 영역 중 췌장암 생존 예후와 관련 있는 영역을 유전자 단위로 2차 선별하였다. Among the pancreatic cancer-specific CNV regions selected through GISTIC analysis, regions related to pancreatic cancer survival prognosis were secondarily selected in units of genes.
구체적으로 상기 2-1 과정에서 1차 선별된 좌표 영역에 포함되는 유전자 2,272 개를 대상으로 유전자 단위의 Z 값을 계산하였다. 즉, 유전자 복제수 변이가 없는 참조 샘플의 각 유전자 구간별 리드 개수(read count)를 계수(counting)한 후, 각 유전자 구간에 정렬된 리드 개수 값을 샘플의 전체 리드 개수로 나누고, GC 함량(contents)에 의한 뎁스 바이어스(depth bias)를 보정하여, 각 유전자 구간별 참조 샘플의 뎁스 평균(Reference_Mean_Depthgene)과 표준편차 값(Reference_SDgene)을 계산한 다음, 정렬된 대상 샘플의 각 유전자 구간별 리드 개수(read count)를 계수(counting)한 후, 각 유전자 구간에 정렬된 리드 개수 값을 샘플의 전체 리드 개수로 나누고, GC 함량(contents)에 의한 뎁스 바이어스(depth bias)를 보정하여, 2,272개 유전자 구간별 대상 샘플의 표준화된 뎁스(normalized depth) 값을 계산한 다음, 하기 수식 1을 사용하여 정렬된 서열정보의 정규화된 유전자 구간별 Z(Zgene)값을 계산하였다:Specifically, the Z value of the gene unit was calculated for 2,272 genes included in the coordinate region first selected in step 2-1. That is, after counting the number of reads for each gene section of a reference sample without gene copy number variation, the read count value aligned in each gene section is divided by the total number of reads in the sample, and the GC content ( contents), calculates the depth average (Reference_Mean_Depth gene ) and standard deviation value (Reference_SD gene ) of the reference sample for each gene section, and then reads for each gene section of the sorted target sample After counting the number (read count), the value of the number of reads aligned in each gene section is divided by the total number of reads of the sample, and depth bias by GC content is corrected, 2,272 After calculating the normalized depth value of the target sample for each gene section, the Z (Z gene ) value for each normalized gene section of the aligned sequence information was calculated using Equation 1:
수식 1:Formula 1:
Zgene = (Normalized_Depthgene - Reference_Mean_Depthgene) / Reference_SDgene
Z gene = (Normalized_Depth gene - Reference_Mean_Depth gene ) / Reference_SD gene
그 뒤 도 5에 기재된 바와 같이, 각 샘플에서 계산된 유전자 단위의 Z 값이 GISTIC Amplification 영역에서 Z > 2를 만족할 때, 해당 샘플의 해당 유전자 값을 그룹 1로 지정하고(예후 나쁨 그룹), 또는, 각 샘플에서 계산된 유전자 단위의 Z 값이 GISTIC Deletion 영역에서 Z < -2를 만족할 때, 해당 샘플의 해당 유전자 값을 그룹 1로 지정하였으며(예후 나쁨 그룹), 상기 두 조건을 만족하지 않을 때 해당 샘플의 해당 유전자 값을 그룹 0으로 지정하였다(예후 좋음 그룹).Then, as described in FIG. 5, when the Z value of the gene unit calculated in each sample satisfies Z > 2 in the GISTIC Amplification region, the corresponding gene value of the sample is designated as group 1 (poor prognosis group), or , When the Z value of the gene unit calculated in each sample satisfies Z < -2 in the GISTIC Deletion region, the corresponding gene value of the sample was designated as group 1 (poor prognosis group), and when the above two conditions were not satisfied The corresponding gene value of the sample was assigned to group 0 (good prognosis group).
즉, 도 5에 기재된 바와 같이 GISTIC Deletion 영역에 포함되는 유전자 Gene1의 Z 값을 기준으로 Sample 1 ~ 315를 1과 0 그룹으로 나누어 보면, Z < -2 를 만족하는 Sample_2와 Sample_4가 그룹 1로 지정되고, 나머지 샘플들은 그룹 0으로 지정할 수 있으며, GISTIC Amplification 영역에 포함되는 유전자 Gene3의 Z 값을 기준으로 Sample 1 ~ 315의 그룹을 나누어 보면, Z > 2 를 만족하는 Sample_1 과 Sample_4 가 그룹 1로 지정되고, 나머지 샘플들은 그룹 0으로 지정할 수 있다.That is, as shown in FIG. 5, when Samples 1 to 315 are divided into 1 and 0 groups based on the Z value of gene Gene1 included in the GISTIC deletion region, Sample_2 and Sample_4 satisfying Z < -2 are designated as group 1 and the remaining samples can be designated as group 0, and when the groups of Samples 1 to 315 are divided based on the Z value of gene Gene3 included in the GISTIC Amplification region, Sample_1 and Sample_4 satisfying Z > 2 are designated as group 1. and the remaining samples can be designated as group 0.
그 뒤, GISTIC Peak Region에 포함되어 있는 전체 2,272개 유전자에서 췌장암 예후 나쁨 그룹 (그룹 1)과 췌장암 예후 좋음 그룹 (그룹 0) 사이의 생존 예후 차이를 비교하였다. 이 때, Overfitting을 방지하기 위하여, 둘 중 한 그룹에 포함되는 샘플의 숫자가 20명 미만일 경우 해당 유전자는 분석 대상에서 제외하였다. 아울러, 한정된 데이터에서 과적합 (Overfitting) 문제를 피하면서 유전자를 선별하고 GSS의 예후 예측 성능을 검증하기 위해 전체 315명 데이터를 5등분하여 Five Fold Cross Validation (5-F CV) 방법을 사용하였다.Then, the difference in survival prognosis between the poor prognosis group (group 1) and the good prognosis group (group 0) of pancreatic cancer was compared in all 2,272 genes included in the GISTIC Peak Region. At this time, in order to prevent overfitting, if the number of samples included in one of the two groups was less than 20, the corresponding gene was excluded from the analysis target. In addition, the Five Fold Cross Validation (5-F CV) method was used by dividing the data of a total of 315 people into 5 to select genes while avoiding the problem of overfitting in limited data and to verify the prognostic performance of GSS.
Kaplan-Meier 생존분석으로 두 그룹 사이에 생존 기간의 차이가 있는지 (그룹 1의 생존 기간이 그룹 0 의 생존 기간보다 짧은지) 통계적 유의성을 Log-rank test를 이용하여 확인하였다. 즉, log-rank test 결과로 계산되는 raw p-value 값을 기준으로 p-value < 0.05 조건을 만족하는 전체 유전자, 또는 그 중 상위 N 개의 유전자를 선별하였다. With Kaplan-Meier survival analysis, the statistical significance of whether there was a difference in the survival period between the two groups (the survival period of group 1 was shorter than the survival period of group 0) was confirmed using the log-rank test. That is, all genes satisfying the p-value <0.05 condition or the top N genes among them were selected based on the raw p-value calculated from the log-rank test result.
예를 들어, CV_1 에서는 2,272개 유전자 중 K-M 분석 raw p-value < 0.05 를 만족하는 유전자가 229개 확인하였고, 이 229개 유전자들에서 계산된 K-M p-value 값이 가장 작은 유전자부터 순서대로 2개, 3개, 4개 … 50개를 선별해 GSS_2부터 GSS_50을 모두 계산하고 (N = 2~50 모두 테스트), 예후 예측 성능을 확인해본 결과, Top 36개 유전자를 사용하여 GSS를 계산했을 때 (best N=36) 0, 1 그룹 사이의 생존 차이가 가장 크게 나뉘는 것을 확인하여, CV_1의 상위 N 값은 36인 것을 확인하였다. For example, in CV_1, 229 genes satisfying the K-M analysis raw p-value < 0.05 among 2,272 genes were identified, and the K-M p-value calculated from these 229 genes was calculated from the smallest gene to 2 in order. , three, four … Select 50 and calculate all GSS_2 to GSS_50 (N = 2 to 50 tests), and as a result of checking the prognostic performance, when GSS was calculated using the top 36 genes (best N=36) 0, It was confirmed that the difference in survival between groups 1 was the largest, and the upper N value of CV_1 was 36.
다섯 번의 CV(Cross Validation)에서 K-M 분석을 진행한 결과, raw p-value < 0.05 기준을 만족하는 유전자가 CV_1에서 229개, CV_2에서 269개, CV_3에서 301개, CV_4에서 213개, CV_5에서 246개 도출되는 것을 확인하였다(도 6).As a result of K-M analysis in five CVs (Cross Validation), there were 229 genes in CV_1, 269 genes in CV_2, 301 genes in CV_3, 213 genes in CV_4, and 246 genes in CV_5 satisfying the raw p-value < 0.05 criteria. It was confirmed that dogs were derived (FIG. 6).
또한, 각 CV 마다 raw p-value < 0.05 를 만족하는 모든 유전자들의 Z 값을 더해 GSS_All을 계산하고 췌장암 예후 나쁨 그룹과 췌장암 예후 좋은 그룹을 구분할 최적의 cutoff 값을 찾아본 결과, CV_1에서 45, CV_2에서 40, CV_3에서 45, CV_4에서 38, CV_5에서 47인 것을 확인하였고, 각 CV마다 raw p-value 값이 작은 순서대로 N 개 유전자들의 값을 더해 GSS_TopN을 계산하고 최적의 cutoff 값을 찾아본 결과, CV_1에서 N=36, cutoff=6, CV_2에서 N=35, cutoff=7, CV_3에서 N=15, cutoff=2, CV_4에서 N=43, cutoff=7, CV_5에서 N=33, cutoff=4 인 것을 확인하였다(표 3). In addition, for each CV, GSS_All was calculated by adding the Z values of all genes that satisfy the raw p-value < 0.05. 40 in CV_3, 45 in CV_4, 38 in CV_4, and 47 in CV_5. The result of calculating GSS_TopN by adding the values of N genes in the order of the smallest raw p-value for each CV and finding the optimal cutoff value , CV_1 to N=36, cutoff=6, CV_2 to N=35, cutoff=7, CV_3 to N=15, cutoff=2, CV_4 to N=43, cutoff=7, CV_5 to N=33, cutoff=4 was confirmed (Table 3).
최적의 cut-off는 예를 들어, CV_1에서는 GSS_TopN이 36인 것을 확인하였으므로, cut-off 값으로 1~35(N-1=36-1) 사이의 모든 정수 값을 설정해보며 0, 1 그룹 사이의 생존 차이 성능을 확인하고, 가장 큰 차이를 보이게 하는 cutoff 값을 선택하였다. 즉, GSS_Top36의 경우에 cutoff =1, 2, 3, … 35 이렇게 총 35개의 cut-off를 모두 적용해본 결과, cutoff = 6으로 선택하여 GSS_Top36 값이 0~6 사이의 값을 갖는 환자들을 예후 좋음(0) 그룹으로, 7~36 사이의 값을 갖는 환자들을 예후 나쁨(1) 그룹으로 구분하였을 때, 두 그룹 간 생존 기간의 차이가 가장 크게 나타나는 것을 확인하고, cut-off를 6으로 결정하였다.For optimal cut-off, for example, in CV_1, GSS_TopN was confirmed to be 36, so set all integer values between 1 and 35 (N-1=36-1) as cut-off values between 0 and 1 groups. The survival difference performance was checked, and the cutoff value showing the largest difference was selected. That is, in the case of GSS_Top36, cutoff = 1, 2, 3, ... 35 As a result of applying all 35 cut-offs in this way, cutoff = 6 was selected, and patients with GSS_Top36 values between 0 and 6 were classified as good (0) and patients with values between 7 and 36. When the patients were classified into the poor prognosis (1) group, it was confirmed that the difference in survival period between the two groups was the largest, and the cut-off was determined as 6.
실시예 3. 췌장암 특이적 유전자 영역과 생존 예후 예측 성능 확인Example 3. Confirmation of pancreatic cancer-specific gene region and survival prognosis prediction performance
3-1. GSS 기반 췌장암 생존 예후 예측3-1. GSS-Based Pancreatic Cancer Survival Prognosis Prediction
실시예 2에서 도출한 GSS 값을 표 3의 cutoff 기준으로 췌장암 생존 예후 좋음, 나쁨 그룹으로 나누어 두 그룹 간 비교한 K-M 생존분석 결과, 도 7에 기재된 바와 같이 Training 데이터에서는 GSS_All, GSS_TopN 모두 5번의 CV 전체에서 두 그룹 간 통계적으로 유의미한 생존 예후 차이가 나타났으며(raw p-value <0.05), GSS_All 보다 GSS_TopN 에서 더 좋은 p-value 값을 확인할 수 있었다.As a result of K-M survival analysis comparing the two groups by dividing the GSS values derived in Example 2 into good and poor pancreatic cancer survival prognosis groups based on the cutoff criteria in Table 3, as shown in FIG. Overall, there was a statistically significant difference in survival prognosis between the two groups (raw p-value <0.05), and a better p-value was confirmed in GSS_TopN than in GSS_All.
또한, Test 데이터에서는 GSS_All은 5번의 CV 중 4번, GSS_TopN은 3번에서 두 그룹 간 통계적으로 유의미한 생존 예후 차이를 보였다. Test 데이터에서도 대부분의 경우 GSS_All보다 GSS_TopN 에서 더 좋은 p-value 값을 확인할 수 있었다.Also, in the test data, GSS_All showed a statistically significant difference in survival prognosis between the two groups in 4 out of 5 CVs and 3 in GSS_TopN. In the test data, in most cases, a better p-value was found in GSS_TopN than in GSS_All.
도 8에 기재된 바와 같이, 다섯 번의 CV 과정에서 각각 선별된 Top N 유전자들의 포함 관계에서, 다섯 번의 CV에서 적어도 한번 이상 선별되었던(합집합) 유전자는 총 79개가 있었고(표 1 참조), 그 중 다섯 번의 CV 전체에서 공통적으로 선별되었던(교집합) 유전자는 KANK1, ABHD6, CASC1, TATDN1, SOX5, FAM49B, LINC00477 및 MCPH1로 총 8개인 것을 확인하였다.As shown in FIG. 8 , in the inclusion relationship of the Top N genes selected in each of the five CVs, there were a total of 79 genes that were selected (union) at least once in the five CVs (see Table 1), of which five Genes that were commonly selected (intersecting) in the entire CV of No. were identified as KANK1, ABHD6, CASC1, TATDN1, SOX5, FAM49B, LINC00477 and MCPH1 in total.
[표 1][Table 1]
3-2. GSS_79 기반 췌장암 생존 예후 예측 성능 검증3-2. GSS_79-based pancreatic cancer survival prognosis prediction performance verification
미국 국립 암 연구소 (National Cancer Institute, NCI) 에서 주도하는 The Cancer Genome Atlas (TCGA) Research Network에서 일반에 공개하고 있는 췌장암 환자 183명의 유전자 단위 복제 수 변이 및 생존 예후 데이터(https://www.cbioportal.org/study/summary?id=paad_tcga_pan_can_atlas_2018)를 외부 검증 (external validation) 데이터로 활용하여, 실시예 3-1에서 선별한 79개 유전자를 사용하여 GSS_79 값을 계산하고, cutoff 기준을 8로 하였을 때 예후 예측 성능을 확인한 결과, 도 9에 기재된 바와 같이, GSS_79 값은 통계적으로 유의미한 생존 예후 차이를 나타내는 것을 확인하였다.Gene-unit copy number mutation and survival prognostic data of 183 pancreatic cancer patients published to the public by The Cancer Genome Atlas (TCGA) Research Network led by the National Cancer Institute (NCI) (https://www.cbioportal) When .org/study/summary?id=paad_tcga_pan_can_atlas_2018) is used as external validation data, the GSS_79 value is calculated using the 79 genes selected in Example 3-1, and the cutoff criterion is 8 As a result of confirming the prognosis prediction performance, as shown in FIG. 9 , it was confirmed that the GSS_79 value represents a statistically significant difference in survival prognosis.
3-3. GSS_8+KRAS+CDKN2A 기반 췌장암 생존 예후 예측 성능 검증3-3. GSS_8+KRAS+CDKN2A-based pancreatic cancer survival prognosis prediction performance verification
기존 Bin 단위 분석에서 의미 있다고 판단된 KRAS와 CDKN2A는 Gene 단위 분석에서 일부 CV 에서만 통계적 기준을 통과하였다(KRAS: 3번, CDKN2A: 2번). 비록 일부 CV에서만 유의했던 유전자이지만, 기존 Bin 단위 분석에서 중요했던 유전자들이었기 때문에 위의 8개 유전자에 KRAS와 CDKN2A를 더해 10개 유전자로 GSS_10을 계산하고 cutoff 기준을 1로 하여 예후 예측 성능을 확인한 결과, 도 10에 기재된 바와 같이, GSS_10은 p-value = 0.059인 것을 확인하였다.KRAS and CDKN2A, which were judged to be meaningful in the existing bin unit analysis, passed the statistical criteria only in some CVs in the gene unit analysis (KRAS: 3 times, CDKN2A: 2 times). Although it was a gene that was significant only in some CVs, since they were important genes in the existing bin unit analysis, KRAS and CDKN2A were added to the above 8 genes, GSS_10 was calculated from 10 genes, and the prognosis prediction performance was confirmed by setting the cutoff criterion as 1. As a result, as described in FIG. 10 , it was confirmed that GSS_10 was p-value = 0.059.
3-4. GSS_8 기반 췌장암 생존 예후 예측3-4. GSS_8-based Pancreatic Cancer Survival Prognosis Prediction
실시예 3-1에서 다섯 번의 CV 전체에서 공통적으로 선별되었던(교집합) 유전자는 8개를 사용하여 GSS_8 값을 계산하고, cutoff 기준을 1로 하였을 때 예후 예측 성능을 확인한 결과, 도 11에 기재된 바와 같이, GSS_8 값은 통계적으로 유의미한 생존 예후 차이를 나타내었다. 도 12는 TCGA 데이터에서 GSS_79, GSS_10, GS_8의 예후 예측 성능을 정리한 것입니다. In Example 3-1, the GSS_8 value was calculated using 8 genes that were commonly selected (intersection) in all five CVs in Example 3-1, and the prognostic performance was confirmed when the cutoff criterion was 1, as shown in FIG. Likewise, the GSS_8 value showed a statistically significant difference in survival prognosis. 12 is a summary of the prognostic prediction performance of GSS_79, GSS_10, and GS_8 in TCGA data.
이상으로 본 발명 내용의 특정한 부분을 상세히 기술하였는 바, 당업계의 통상의 지식을 가진 자에게 있어서 이러한 구체적 기술은 단지 바람직한 실시 양태일 뿐이며, 이에 의해 본 발명의 범위가 제한되는 것이 아닌 점은 명백할 것이다. 따라서, 본 발명의 실질적인 범위는 첨부된 청구항들과 그것들의 등가물에 의하여 정의된다고 할 것이다.As a specific part of the present invention has been described in detail above, for those of ordinary skill in the art, it is clear that this specific description is only a preferred embodiment, and the scope of the present invention is not limited thereby. will be. Accordingly, it is intended that the substantial scope of the present invention be defined by the appended claims and their equivalents.
본 발명에 따른 췌장암 환자의 생존 예후 예측을 위한 정보의 제공 방법은 췌장암 생존 예후 특이적 유전자에 대한 복제수 변이를 기반으로 생존 예후를 예측하기 때문에 정확도가 높아 치료효과 및 생존 예후 예측과 관련된 활용도를 높일 수 있을 뿐만 아니라, 전장 유전체 시퀀싱이 필요 없으므로 속도가 빨라 유용하다. The method of providing information for predicting the survival prognosis of pancreatic cancer patients according to the present invention predicts the survival prognosis based on the copy number variation for the pancreatic cancer survival prognosis-specific gene. Not only can it be increased, but it is useful because it does not require whole-genome sequencing.
Claims (17)
- ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP2, DMRT1, DOCK5, DPYSL2, ERICH1-AS1, FAM135B, FAM49B, FER1L6, FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC00639, LMLN, LOC100128993, LINC02052, LRMP, LRRC6, LTF, MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBT1, SGK223, SLC38A3, SMARCA2, SOX5, SQLE, TATDN1, TBL1XR1, THSD7A, TMEM110, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 및 ZNF583로 구성된 군에서 선택되는 1종 이상의 유전자의 복제수 변이(CNV : copy number variation)를 검출하는 단계를 포함하는 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보제공 방법.ABHD6, ACVR2B, ADCY8, ARHGEF10, ATF6, ATP13A4, BCAT1, BCL2, BMP1, C8orf12, C9orf92, CASC1, CCBE1, CDCP1, CDKN2A, CSGALNACT1, DLGAP2, DMRT1, DOCK5, FLAMDPYSL2, FAMER1, FRICH6,BYSL2 FLNB, GATA4, GLDC, GLIS3, GSDMC, IFLTD1, ISPD-AS1, ITPR2, KANK1, KCNMB2, KHDRBS3, KIAA0196, KRAS, LARS2-AS1, LINC00477, LINC00578, LINC00639, LMLN, LRRC02 LMLN, LINC02, LINC6 MAP4, MCPH1, MFHAS1, NAALADL2, NIN, NXPH1, OPA1, PEBP4, PHF20L1, PHLPP1, PSD3, RASSF8, RPA3-AS1, SERPINB5, SFMBT1, SGK223, SLC38A3, SMARCA2, TMATOXDN1, SQLE, TBLXR38A3, SMARCA2, TMATOXDN1, SQLE, TMEM110-MUSTN1, TMEM196, TMEM65, TMEM71, ZFP30, ZNF569, ZNF577 and ZNF583 of at least one gene selected from the group consisting of copy number variation (CNV: copy number variation) pancreatic cancer comprising the step of detecting the A method of providing information for predicting patient survival prognosis.
- 제1항에 있어서, 상기 유전자는 ABHD6, CASC1, FAM49B, KANK1, LINC00477, MCPH1, SOX5 및 TATDN1으로 구성된 군에서 선택되는 1종 이상인 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보제공 방법.The method of claim 1, wherein the gene is at least one selected from the group consisting of ABHD6, CASC1, FAM49B, KANK1, LINC00477, MCPH1, SOX5 and TATDN1.
- 제2항에 있어서, 상기 유전자는 ABHD6, CASC1, FAM49B, KANK1, LINC00477, MCPH1, SOX5, TATDN1, KRAS 및 CDKN2A인 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보제공 방법.The method of claim 2, wherein the genes are ABHD6, CASC1, FAM49B, KANK1, LINC00477, MCPH1, SOX5, TATDN1, KRAS and CDKN2A.
- 제1항 내지 제3항 중 어느 한 항에 있어서, 다음 단계를 포함하는 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보제공 방법:[4] The method according to any one of claims 1 to 3, wherein the method for providing information for predicting survival prognosis of a pancreatic cancer patient comprising the following steps:(1) 상기 유전자의 복제수 변이를 검출하고, 검출된 유전자의 복제수 변이 정도를 계량화하는 단계; 및(1) detecting the copy number variation of the gene and quantifying the degree of copy number variation of the detected gene; and(2) 상기 (1) 단계에서 계량화된 유전자의 복제수 변이 정도가 정상범위(normal range)를 벗어나는 유전자의 개수가 기준값(cut-off)을 초과할 경우 췌장암 환자의 생존 예후가 나쁜 것으로 판정하는 단계.(2) If the number of genes whose copy number variation degree of the gene quantified in step (1) exceeds the cut-off value, it is determined that the survival prognosis of the pancreatic cancer patient is bad. step.
- 제4항에 있어서, 상기 (1) 단계는 다음의 단계를 포함하는 방법으로 수행되는 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보의 제공 방법:[Claim 5] The method according to claim 4, wherein step (1) is performed by a method comprising the following steps:(1-1) 생체시료에서 수득된 대상 샘플의 DNA 서열정보(reads)를 획득하는 단계; (1-1) obtaining DNA sequence information (reads) of a target sample obtained from a biological sample;(1-2) 상기 서열정보(reads)를 참조집단의 표준 염색체 서열 데이터베이스 (reference genome database)에 정렬(alignment)하는 단계; (1-2) aligning the sequence information (reads) to a reference genome database of a reference group;(1-3) 상기 정렬된 서열정보(reads)의 퀄리티를 확인하는 단계; 및(1-3) checking the quality of the aligned sequence information (reads); and(1-4) 상기 유전자의 복제수 변이를 검출하고, 복제수 변이의 정도를 계량화하는 단계(1-4) detecting the copy number variation of the gene and quantifying the degree of copy number variation
- 제5항에 있어서, 상기 (1-4) 단계는 다음의 단계를 포함하는 방법으로 수행되는 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보 제공 방법:[Claim 6] The method according to claim 5, wherein step (1-4) is performed by a method comprising the following steps:(a) 유전자 복제수 변이가 없는 참조 샘플의 각 구간별 리드 개수(read count)를 계수(counting)한 후, 각 유전자 구간에 정렬된 리드 개수 값을 샘플의 전체 리드 개수로 나누고, GC 함량(contents)에 의한 뎁스 바이어스(depth bias)를 보정하는 단계를 수행하여, 각 유전자 구간별 참조 샘플의 뎁스 평균(Reference_Mean_Depthgene)과 표준편차 값(Reference_SDgene)을 계산하는 단계;(a) After counting the number of reads in each section of the reference sample without gene copy number variation, the value of the number of reads aligned in each gene section is divided by the total number of reads in the sample, and the GC content ( contents), calculating a depth average (Reference_Mean_Depth gene ) and a standard deviation value (Reference_SD gene ) of a reference sample for each gene section by performing a step of correcting a depth bias;(b) 상기 (1-3) 단계에서 얻어진 정렬된 대상 샘플의 각 구간별 리드 개수(read count)를 계수(counting)한 후, 각 유전자 구간에 정렬된 리드 개수 값을 샘플의 전체 리드 개수로 나누고, GC 함량(contents)에 의한 뎁스 바이어스(depth bias)를 보정하는 단계를 수행하여, 각 유전자 구간별 대상 샘플의 표준화된 뎁스(normalized depth) 값을 계산하는 단계; 및(b) after counting the number of reads for each section of the aligned target sample obtained in step (1-3), the value of the number of reads aligned in each gene section is used as the total number of reads in the sample calculating a normalized depth value of a target sample for each gene section by dividing and correcting a depth bias by GC contents; and(c) 상기 (b) 단계에서 수득된 참조 샘플의 뎁스 평균(Reference_Mean_Depthgene)과 표준편차 값(Reference_SDgene)과 (c) 단계에서 수득된 대상 샘플의 표준화된 뎁스 값(normalized depth)에 기반하여 하기 수식 1을 사용하여 정렬된 서열정보의 정규화된 구간별 Z(Zgene)값을 계산하는 단계; (c) the depth average (Reference_Mean_Depth gene ) and standard deviation value (Reference_SD gene ) of the reference sample obtained in step (b) and the normalized depth value of the target sample obtained in step (c) Based on (normalized depth) calculating Z (Z gene ) values for each normalized section of the aligned sequence information using Equation 1 below;수식 1: Formula 1:Zgene = (Normalized_Depthgene - Reference_Mean_Depthgene) / Reference_SDgene Z gene = (Normalized_Depth gene - Reference_Mean_Depth gene ) / Reference_SD gene
- 제6항에 있어서, 상기 Z 값의 정상범위는 -2 내지 2인 것을 특징으로 하는 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보 제공 방법. [Claim 7] The method of claim 6, wherein the normal range of the Z value is -2 to 2.
- 제7항에 있어서, 상기 Z 값의 정상범위를 벗어나는 유전자의 개수의 기준값(cut-off)은 전체 유전자 개수의 10% 이상인 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보의 제공 방법.The method of claim 7, wherein the cut-off of the number of genes outside the normal range of the Z value is 10% or more of the total number of genes.
- 제4항에 있어서, 상기 (1) 단계는 ddPCR (Digital Droplet Polymerase Chain Reaction) 또는 MLPA (Multiplex Ligation-dependent Probe Amplification) 방법을 이용하여 복제 수 변이를 검출하는 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보의 제공 방법.According to claim 4, wherein in step (1), ddPCR (Digital Droplet Polymerase Chain Reaction) or MLPA (Multiplex Ligation-dependent Probe Amplification) method is used to detect the copy number mutation predicting survival prognosis of pancreatic cancer patients How to provide information for
- 제5항에 있어서, 상기 생체시료는 혈액, 복강액, 조직, 타액, 소변, 모발, 배변물, 척수액, 뇌수액 및 담액에서 선택된 1종 이상인 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보의 제공 방법.According to claim 5, wherein the biological sample is blood, abdominal fluid, tissue, saliva, urine, hair, feces, spinal fluid, brain fluid and bile fluid, characterized in that at least one selected from the pancreatic cancer patient survival prognosis prediction information How to provide.
- 제5항에 있어서, 상기 생체시료에서 수득된 대상 샘플의 DNA는 세포유리 핵산(cell-free DNA) 또는 엑소좀 핵산(exosomal DNA)인 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보의 제공 방법.According to claim 5, wherein the DNA of the target sample obtained from the biological sample is cell-free nucleic acid (cell-free DNA) or exosomal nucleic acid (exosomal DNA) provides information for predicting survival prognosis of pancreatic cancer patients Way.
- 제5항에 있어서, 상기 (1-1) 단계는 다음의 단계를 포함하는 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보의 제공 방법:[Claim 6] The method according to claim 5, wherein step (1-1) comprises the following steps:(i) 분리된 DNA에서 염석 방법(salting-out method), 컬럼크로마토그래피 방법(column chromatography method), 또는 비드 방법(beads method)을 사용하여 단백질, 지방, 및 기타 잔여물을 제거하고 정제된 핵산을 수득하는 단계; (i) a nucleic acid purified by removing proteins, fats, and other residues from the isolated DNA using a salting-out method, a column chromatography method, or a beads method obtaining a;(ii) 상기 정제된 핵산에 대하여, 싱글-엔드 시퀀싱(single-end sequencing) 또는 페어-엔드 시퀀싱(pair-end sequencing) 라이브러리(library)를 제작하는 단계;(ii) preparing a single-end sequencing or pair-end sequencing library for the purified nucleic acid;(iii) 상기 제작된 라이브러리를 차세대 유전자서열검사기(next-generation sequencer)에 반응시키는 단계; 및(iii) reacting the prepared library with a next-generation sequencer; and(iv) 상기 차세대 유전자서열검사기에서 핵산의 서열정보(reads)를 획득하는 단계.(iv) obtaining sequence information (reads) of nucleic acids in the next-generation gene sequencing machine.
- 제5항에 있어서, 상기 (1-3) 단계는 정렬 퀄리티 점수(mapping quality score)의 퀄리티 기준값을 만족하는 서열을 선별하는 단계를 포함하는 방법으로 수행되는 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보의 제공방법.[Claim 6] The survival prognosis prediction of a pancreatic cancer patient according to claim 5, wherein step (1-3) is performed by a method comprising selecting a sequence that satisfies a quality criterion value of a mapping quality score. How to provide information for
- 제13항에 있어서, 상기 퀄리티 기준값은, 상기 정렬 퀄리티 점수가 15 내지 70인 것을 특징으로 하는 췌장암 환자의 생존 예후 예측을 위한 정보의 제공방법.The method of claim 13, wherein the quality reference value is the alignment quality score of 15 to 70.
- 제1항 내지 제14항 중 어느 한 항에 따른 췌장암 환자의 생존 예후 예측을 위한 정보의 제공방법에 이용되는 정보제공 장치로서, 상기 장치는 An information providing device used in a method of providing information for predicting survival prognosis of a pancreatic cancer patient according to any one of claims 1 to 14, the device comprising:(1) 상기 유전자의 복제수 변이를 검출하는 복제수 변이 검출부; (1) a copy number mutation detection unit for detecting a copy number mutation of the gene;(2) 검출된 유전자 복제수 변이 정보를 기반으로 복제수 변이 정도를 계량화하고, 계량화된 유전자 복제수 변이 정도가 정상범위(normal range)를 벗어나는 유전자의 개수를 계산하는 계산부; 및(2) a calculation unit that quantifies the degree of copy number variation based on the detected gene copy number variation information, and calculates the number of genes whose quantified gene copy number variation is outside a normal range; and(3) 유전자 복제수 변이 정도가 정상범위를 벗어나는 유전자의 개수가 기준값을 초과할 경우, 생존 예후가 나쁜 것으로 판정하는 생존 예후 판정부;(3) a survival prognosis determining unit that determines that the survival prognosis is poor when the number of genes whose degree of gene copy number mutation is out of the normal range exceeds a reference value;를 포함하는 것을 특징으로 하는 정보 제공 장치.Information providing device comprising a.
- 제1항 내지 제14항 중 어느 한 항에 따른 췌장암 환자의 생존 예후 예측을 위한 정보의 제공방법에 이용되는 컴퓨터 판독 가능한 기록매체로서, 상기 매체는 췌장암 환자의 생존 예후 예측을 위한 정보를 제공하는 프로세서에 의해 실행되도록 구성되는 명령을 포함하되, A computer-readable recording medium used for a method of providing information for predicting the survival prognosis of a pancreatic cancer patient according to any one of claims 1 to 14, wherein the medium provides information for predicting the survival prognosis of a pancreatic cancer patient instructions configured to be executed by a processor;(1) 상기 유전자의 복제수 변이를 검출하는 단계;(1) detecting a copy number variation of the gene;(2) 검출된 유전자 복제수 변이 정보를 기반으로 복제수 변이 정도를 계량화하고, 계량화된 유전자 복제수 변이 정도가 정상범위(normal range)를 벗어나는 유전자의 개수를 계산하는 단계; 및(2) quantifying the degree of copy number variation based on the detected gene copy number variation information, and calculating the number of genes whose quantified gene copy number variation is outside a normal range; and(3) 유전자 복제수 변이 정도가 정상범위를 벗어나는 유전자의 개수가 기준값을 초과할 경우, 생존 예후가 나쁜 것으로 판정하는 단계;(3) determining that the survival prognosis is poor when the number of genes whose degree of gene copy number mutation is out of the normal range exceeds a reference value;를 포함하는 프로세서에 의해 실행되도록 구성되는 명령을 포함하는 컴퓨터 판독 가능한 기록매체.A computer-readable recording medium comprising instructions configured to be executed by a processor comprising a.
- 제1항 내지 제14항 중 어느 한 항에 따른 췌장암 환자의 생존 예후 예측을 위한 정보의 제공방법에 이용되는 표적 핵산 증폭용 키트로서, 상기 키트는A kit for amplifying a target nucleic acid used in the method of providing information for predicting the survival prognosis of a pancreatic cancer patient according to any one of claims 1 to 14, the kit comprising:상기 유전자에 특이적으로 결합하는 프로브; 또는 상기 유전자를 증폭하는 프라이머를 포함하는 것을 특징으로 하는 표적 핵산 증폭용 키트.a probe that specifically binds to the gene; Or a kit for amplifying a target nucleic acid comprising a primer for amplifying the gene.
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| KR101686146B1 (en) * | 2015-12-04 | 2016-12-13 | 주식회사 녹십자지놈 | Copy Number Variation Determination Method Using Sample comprising Nucleic Acid Mixture |
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| LU SHUANGSHUANG, AHMED TASQEEN, DU PAN, WANG YAOHE: "Genomic Variations in Pancreatic Cancer and Potential Opportunities for Development of New Approaches for Diagnosis and Treatment", INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, vol. 18, no. 6, 5 June 2017 (2017-06-05), pages 1201, XP055928780, DOI: 10.3390/ijms18061201 * |
| MOHAN SUMITRA, AYUB MAHMOOD, ROTHWELL DOMINIC G., GULATI SAKSHI, KILERCI BEDIRHAN, HOLLEBECQUE ANTOINE, SUN LEONG HUI, SMITH NIGEL: "Analysis of circulating cell-free DNA identifies KRAS copy number gain and mutation as a novel prognostic marker in Pancreatic cancer", SCIENTIFIC REPORTS, vol. 9, no. 1, 1 December 2019 (2019-12-01), pages 11610, XP055928784, DOI: 10.1038/s41598-019-47489-7 * |
| WANG YINGYI, YANG YUEMEI, WANG YANFENG, LI XIAOOU, XIAO YU, WANG WENZE: "High Cancer Susceptibility Candidate 8 Expression Is Associated With Poor Prognosis of Pancreatic Adenocarcinoma: Validated Analysis Based on Four Cancer Databases", FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY, vol. 8, XP055928778, DOI: 10.3389/fcell.2020.00392 * |
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