WO2022097844A1 - Procédé pour prédire le pronostic de survie de patients atteints de cancer pancréatique en utilisant les informations sur la variation du nombre de copies de gènes - Google Patents

Procédé pour prédire le pronostic de survie de patients atteints de cancer pancréatique en utilisant les informations sur la variation du nombre de copies de gènes Download PDF

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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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gene
copy number
pancreatic cancer
survival prognosis
value
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Korean (ko)
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공선영
한성식
우상명
김민경
기창석
조은해
이태림
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국립암센터
주식회사 녹십자지놈
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • 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
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • 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
    • 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
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • 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
    • 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
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • 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
    • 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
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • 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
    • 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

La présente invention concerne un procédé permettant de fournir des informations pour prédire le pronostic de survie de patients atteints de cancer pancréatique. Plus précisément, la présente invention concerne les éléments suivants : un procédé permettant de fournir des informations pour prédire le pronostic de survie de patients atteints de cancer pancréatique, le procédé étant caractérisé par l'utilisation d'une variation de gène spécifique au cancer pancréatique, plus particulièrement, une variation du nombre de copies de gène (CNV); et une utilisation dudit procédé. Le procédé permettant de fournir des informations pour prédire le pronostic de survie de patients atteints de cancer pancréatique selon la présente invention est très précis en raison de la prédiction du pronostic de survie sur la base d'une variation du nombre de copies pour un gène spécifique du pronostic de survie du cancer pancréatique et peut donc augmenter l'utilité liée à la prédiction des effets du traitement et du pronostic de survie, ne nécessite pas le séquençage du génome entier et est donc rapide et par conséquent utile.
PCT/KR2021/001162 2020-11-04 2021-01-28 Procédé pour prédire le pronostic de survie de patients atteints de cancer pancréatique en utilisant les informations sur la variation du nombre de copies de gènes WO2022097844A1 (fr)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101686146B1 (ko) * 2015-12-04 2016-12-13 주식회사 녹십자지놈 핵산의 혼합물을 포함하는 샘플에서 복제수 변이를 결정하는 방법
KR101815184B1 (ko) * 2009-10-26 2018-02-05 애보트 모레큘러 인크. 비-소세포 폐암의 예후를 측정하기 위한 진단 방법

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101815184B1 (ko) * 2009-10-26 2018-02-05 애보트 모레큘러 인크. 비-소세포 폐암의 예후를 측정하기 위한 진단 방법
KR101686146B1 (ko) * 2015-12-04 2016-12-13 주식회사 녹십자지놈 핵산의 혼합물을 포함하는 샘플에서 복제수 변이를 결정하는 방법

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
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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