WO2024096618A1 - Procédé de prédiction du risque de survenue d'un cancer - Google Patents

Procédé de prédiction du risque de survenue d'un cancer Download PDF

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WO2024096618A1
WO2024096618A1 PCT/KR2023/017396 KR2023017396W WO2024096618A1 WO 2024096618 A1 WO2024096618 A1 WO 2024096618A1 KR 2023017396 W KR2023017396 W KR 2023017396W WO 2024096618 A1 WO2024096618 A1 WO 2024096618A1
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cancer
prs
clause
equation
snp
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Korean (ko)
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하태웅
최혜미
정우성
한원식
신희철
이한별
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주식회사 디시젠
서울대학교 산학협력단
서울대학교병원
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Publication of WO2024096618A1 publication Critical patent/WO2024096618A1/fr

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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
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • 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
    • C12Q2537/165—Mathematical modelling, e.g. logarithm, ratio
    • 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
    • C12Q2600/156—Polymorphic or mutational markers

Definitions

  • the present invention relates to a method for predicting the risk of developing cancer and a composition thereof. More specifically, the present invention relates to a method of using a single nucleotide polymorphism (SNP) as a marker to predict the risk of cancer development and a composition containing an agent for detecting it.
  • SNP single nucleotide polymorphism
  • Cancer is one of the most common causes of death worldwide. Approximately 10 million new cases occur each year, accounting for approximately 12% of all deaths, making it the third most common cause of death. Among various types of cancer, the incidence of breast cancer in particular is increasing significantly in Asian countries.
  • the present invention seeks to provide a method to accurately predict the risk of developing cancer by selecting single nucleotide polymorphisms (SNPs) that have a significant correlation with Asians and using them as combination markers.
  • SNPs single nucleotide polymorphisms
  • One object of the present invention is to provide a method for predicting the risk of developing cancer.
  • Another object of the present invention is to provide a composition for predicting the risk of cancer occurrence.
  • Another object of the present invention is to provide a method of providing a diagnostic device for predicting the risk of cancer occurrence.
  • the present invention relates to a method for predicting the risk of developing cancer.
  • the method may include generating a plurality of single polygenic risk scores (Single PRS) for biological samples obtained from a subject.
  • Single PRS single polygenic risk scores
  • the “polygenic risk score (PRS)” is a concept representing the estimated effect of polygenic variation on an individual, and is one of the commonly used concepts in genetics. It is calculated as the weighted sum of trait-associated alleles. It reflects an individual's genetic predisposition to a specific characteristic and can be used as a predictor for that characteristic. In other words, it estimates how likely an individual is to have a specific trait based solely on genetic characteristics without considering environmental factors.
  • the PRS may be generated based on the presence or absence of a single nucleotide polymorphism (SNP) marker.
  • SNP single nucleotide polymorphism
  • the term “plurality of single PRS” means that there is not only one single PRS, but at least two or more single PRSs exist.
  • the “Single PRS” may be expressed as the following equation 1:
  • n in ⁇ n and xn is an index of each SNP and is an integer selected from numbers 1 to 243 listed in Table 1 below,
  • the ⁇ n is a weight coefficient indicating the association with cancer for each single nucleotide polymorphism (SNP),
  • the xn is any one selected from the group consisting of 0, 1, and 2 depending on the SNP type detected in the biological sample,
  • a plurality of ⁇ 1 to ⁇ n values may be the same or different from each other.
  • Single nucleotide polymorphism refers to a genetic change or mutation that shows a difference in one base sequence (A, T, G, C) in the DNA base sequence.
  • SNP single nucleotide polymorphism
  • the three DNA fragments contain differences at a single base, such as AAGT[A/A]AG, AAGT[A/G]AG, and AAGT[G/G]AG, then two alleles (C or T), and generally, almost all SNPs have two alleles.
  • SNPs can be assigned a minor allele frequency (MAF), which is the lowest allele frequency at a locus found in a particular population. Variations exist within human populations, and a single SNP allele that is common across geological or ethnic groups is very rare. Single bases may be changed (replaced), removed (deleted), or added (inserted) to the polynucleotide sequence. SNPs can cause changes in the translation frame.
  • MAF minor allele frequency
  • Single nucleotide polymorphisms may be included in the coding sequence of a gene, non-coding regions of a gene, or intergenic regions between genes.
  • SNPs in the coding sequence of a gene do not necessarily cause changes in the amino acid sequence of the target protein due to the degeneracy of the genetic code.
  • SNPs that form the same polypeptide sequence are called synonymous (also called silent mutations), and SNPs that form different polypeptide sequences are called non-synonymous.
  • Non-synonymous SNPs can be missense or nonsense, with missense changes resulting in different amino acids while nonsense changes forming non-mature stop codons.
  • SNPs located outside of protein-coding regions can cause gene silencing, transcription factor binding, or non-coding RNA sequences. Variability in human DNA sequences can affect the development of disease and how humans respond to pathogens, chemicals, drugs, vaccines and other agents. In addition, SNP is considered an important tool (key enabler) to realize the concept of personalized medicine. Above all, SNPs, which have recently been actively developed as markers, are most important in biomedical research that diagnoses diseases by comparing genomic regions between groups with and without the disease. SNPs are the most abundant variation in the human genome, and are estimated to exist at a rate of one SNP per 1.9 kb (Sachidanandam et al., 2001). SNPs are very stable genetic markers, sometimes directly affecting the phenotype, and are well suited to automated genotyping systems (Landegren et al., 1998; Isaksson et al., 2000).
  • the SNP may be at least one selected from the group consisting of numbers 1 to 243 shown in Table 1 below.
  • the SNP index refers to the number of each SNP listed in Table 1, and the rs ID (rs number) listed in Table 1 is the official SNP assigned to each unique SNP by NCBI (the National Center for Biotechnological Information). Corresponds to the identifier of (Reference SNP, rs). Based on the rs number, specific information on the corresponding SNP can be found in well-known databases NCBI (https:/www.ncbi.nlm.nih.gov), SNPedia (https:/www.snpedia.com), and GWAS (https:/www.ebi). You can check it at .ac.uk/gwas/home), etc.
  • NCBI https:/www.ncbi.nlm.nih.gov
  • SNPedia https:/www.snpedia.com
  • GWAS https:/www.ebi
  • Position ID in Table 1 is a position on the genome based on the human reference genome (GRCh37/hg19).
  • the first number (1) refers to the human chromosome number
  • the second number (7917076) refers to the SNP in the corresponding chromosome.
  • It refers to positional information
  • the third and fourth bases (G:A) may refer to the reference (the allele in the reference genome, REF) and Alt (any other allele found at that locus), respectively.
  • Equation 1 single PRS
  • the total number of terms ( ⁇ 1 ⁇ x1 + ⁇ 2 ⁇ x2 + ... + ⁇ n ⁇ xn) added in Equation 1 (single PRS) of the present invention may be 1 to 243, specifically 1 to 201, 1 to It may be 190 or 1 to 38, and most specifically may be 201, 190, or 38, respectively, but is not limited thereto.
  • xn may have a value selected from the group consisting of 0, 1, and 2, depending on the type of SNP detected in the biological sample. Specifically, xn can be classified as 0, 1, or 2 depending on whether the SNP genotype detected in the subject's biological sample is homozygous wild type, heterozygous mutant type, or homozygous mutant type.
  • homozygous wild type it means normal with no mutation detected in both strands of DNA
  • heterozygous mutant type it means that mutation was detected in one of the two strands
  • homozygous mutant type it means in both strands. This may mean that a mutation has been detected.
  • Equation 1 when the total number of terms in Equation 1 is 201, it can be designated as PRS 201 , and the combination of SNPs included in this case can be 201 SNPs, and each SNP is numbered 1 to 190, numbered It may be selected from the group consisting of numbers 228 to 243, and specifically may include all SNPs selected from the group consisting of numbers 1 to 190 and numbers 228 to 243, but is not limited thereto.
  • the ⁇ weight coefficient ( ⁇ n) is 0; Or it may be a rational number in the range of 0.000001 to 2.0000 or -2.0000 to -0.000001, which is its absolute value, a rational number in the range of ⁇ 0.000001 to 2.0000 ⁇ ; specifically, 0; or a rational number in the range of 0.000005 to 1.0 or -1.0 to -0.000005, and its absolute value may be a rational number in the range of ⁇ 0.000005 to 1.0 ⁇ ; most specifically, 0; Or, it may be a rational number in the range of 0.00001 to 0.3 or -0.3 to -0.00001, which is its absolute value, but is not limited thereto.
  • the SNP combinations used to calculate the PRS 201 are rs707475, rs616488, rs2992756, rs4233486, rs2151842, rs612683, rs7513707, rs12406858, rs637868, rs6686987, 14172, rs2785646, rs11117758, rs11118563, rs6743383, rs6725517, rs12472404 , rs13147907, rs6756513, rs6746250, rs10164550, rs10179592, rs10197246, rs4442975, rs11693806, rs3791977, rs6762558, rs3010266, 47, rs62255657, rs17838698, rs56387622, rs2886671, rs147250346, rs30
  • Equation 1 if the total number of terms in Equation 1 is 190, it can be designated as PRS 190.
  • the combination of SNPs included may be 190 SNPs, and each SNP is composed of numbers 1 to 190. It may be selected from the group, and specifically may include all SNPs selected from the group consisting of numbers 1 to 190, but is not limited thereto.
  • the ⁇ weight coefficient ( ⁇ n) in Equation 1 of the present invention may be a rational number in the range of 0.000005 to 2.0000 or -2.0000 to -0.000005, and its absolute value may be a rational number in the range of ⁇ 0.000005 to 2.0000 ⁇ , specifically 0.00001 It may be a rational number in the range of ⁇ 0.00001 to 1.0 ⁇ , which is its absolute value. Most specifically, it is a rational number in the range of 0.00005 to 0.3 or -0.3 to -0.00005, and its absolute value is It may be a rational number in the range of ⁇ 0.00005 to 0.3 ⁇ , but is not limited thereto.
  • the SNP combinations used to calculate PRS 190 are rs707475, rs616488, rs2992756, rs4233486, rs2151842, rs612683, rs7513707, rs12406858, rs637868, rs6686987, 14172, rs2785646, rs11117758, rs11118563, rs6743383, rs6725517, rs12472404 , rs13147907, rs6756513, rs6746250, rs10164550, rs10179592, rs10197246, rs4442975, rs11693806, rs3791977, rs6762558, rs3010266, 47, rs62255657, rs17838698, rs56387622, rs2886671, rs147250346, rs
  • the total number of terms in Equation 1 when the total number of terms in Equation 1 is 38, it can be designated as PRS 38.
  • the combination of SNPs included may be 38 SNPs, and each SNP is numbered from number 117 to number 191. It may be selected from the group consisting of number 227, and specifically may include all SNPs selected from the group consisting of number 117, numbers 191 to 227, but is not limited thereto.
  • the ⁇ weight coefficient ( ⁇ n) in Equation 1 of the present invention may be a rational number in the range of 0.0001 to 2.0000 or -2.0000 to -0.0001, and its absolute value may be a rational number in the range of ⁇ 0.0001 to 2.0000 ⁇ , specifically 0.005 It may be a rational number in the range of ⁇ 0.005 to 1.3 ⁇ , which is its absolute value, and most specifically, it is a rational number in the range of 0.025 to 0.3 or -0.3 to -0.025, and its absolute value is It may be a rational number in the range of ⁇ 0.025 to 0.3 ⁇ , but is not limited thereto.
  • the SNP combinations used to calculate PRS 38 are rs10816625, rs2056417, rs67087079, rs10931936, rs73006998, rs146548970, rs16901937, rs112776581, rs985434, 332693, rs4897114, rs35143743, rs7763637, rs851970, rs61176871, rs74366500, rs1333035 , rs10760444, rs2393886, rs2912778, rs1631281, rs587074, rs805515, rs1391721, rs61929345, rs77554484, rs112149573, rs12935019, 26, rs78406988, rs72628381, rs12456097, rs186812658, rs93
  • the SNP may be a marker suitable for predicting the risk of developing cancer, as confirmed in the examples of the present invention, and specifically, may be a marker suitable for predicting the risk of developing cancer in Asians, and most specifically, cancer in Koreans. It may be a marker suitable for predicting the risk of occurrence, but is not limited to this.
  • Asian refers to the Far East region where Mongolian people reside, including Korea, China, and Japan.
  • Asian may refer to a population whose ancestors are Asian, and specifically may refer to a population whose ancestors are Asian for at least 10 generations. More specifically, the Asian of the present invention may be Korean, but is limited thereto. It doesn't work.
  • the method may further include calculating a multiple polygenic risk score (multiple PRS) from a single measured PRS value.
  • the “multiple PRS” may be expressed by the following equation 2:
  • the PRSn1 and PRSn2 each mean an independent single PRS
  • n1 and n2 refer to the number of SNPs used to calculate each single PRS
  • ⁇ 0 is a rational number ranging from ⁇ 0.0001 to 7.00 ⁇ ;
  • ⁇ 1 is a rational number between 0.001 and 1.00
  • ⁇ 2 is a rational number between 0.001 and 1.00
  • the plurality of ⁇ 0, ⁇ 1, or ⁇ 2 values may be the same or different from each other.
  • the multiple PRS can be configured using a linear combination of several single PRS (Single PRS), and specifically, can be configured using a linear combination of three single PRS (Single PRS). , Most specifically, it can be configured using a linear combination of two single PRS (Single PRS), but is not limited to this.
  • the multiple PRS can be calculated using a linear combination of at least two single PRS (Single PRS) selected from the group consisting of PRS 1 , PRS 243 , and specifically PRS 38 , PRS 190 , and It can be calculated using a linear combination of at least two single PRSs selected from the group consisting of PRS 201 .
  • ⁇ 0, ⁇ 1 or ⁇ 2 in the multiple PRS calculation equation represented by Equation 2 is calculated by applying a commonly used logistic regression model, and is 10-fold crossed by regression to reflect the breast cancer incidence rate as the calculation result. Ten-fold cross-validation by regression was performed to derive the ranges of ⁇ 0, ⁇ 1, and ⁇ 2 when applying the combination of specific SNPs of the present invention.
  • the regression coefficient value ⁇ 1 multiplied by the single PRS (Single PRS) value may be a rational number of 0.001 to 1.00, and ⁇ 2 may be a rational number of 0.001 to 1.00.
  • the regression coefficient value ⁇ 1 multiplied by the single PRS value may be a rational number of 0.01 to 0.50, and ⁇ 2 may be a rational number of 0.01 to 0.70.
  • the regression coefficient value ⁇ 1 multiplied by the single PRS value may be a rational number of 0.05 to 0.30, and ⁇ 2 may be a rational number of 0.10 to 0.50, but are not limited thereto.
  • ⁇ 0 which is a correction coefficient that is added to the value of the single PRS multiplied by the regression coefficient, is 0.0001 to 7.00 or -7.00 to -0.0001, and its absolute value is ⁇ 0.0001 to 7.00 ⁇ It may be a rational number in the range, and specifically, the correction coefficient ⁇ 0 is 0.0005 to 6.00 or -6.00 to -0.0005, and its absolute value may be a rational number in the range ⁇ 0.0005 to 6.00 ⁇ , and most specifically, the correction coefficient ⁇ 0 is 0.001. It may be a rational number ranging from ⁇ 0.001 to 5.00 ⁇ , which is its absolute value, but is not limited thereto.
  • the values of coefficients ⁇ 0, ⁇ 1, or ⁇ 2 in Equation 2 may be the same or different from each other.
  • the SNP markers numbered 1 to 243 in Table 1 are single nucleotide polymorphism (SNP) markers associated with Asian-specific expression traits or cancer diseases, and are a combination of genetic mutations present in the Asian genome. It has technical significance in discovering a new SNP marker combination with high reproducibility and accuracy in predicting the risk of cancer in Asians by integrating .
  • SNP single nucleotide polymorphism
  • “prediction” refers to the act of determining the likelihood of developing cancer in a patient's lifetime by predicting in advance the possibility of developing a cancer disease, the course and results after the occurrence of a cancer disease. More specifically, cancer risk prediction refers to predicting the possibility of developing cancer before treatment of a cancer disease, or the risk of developing cancer may vary depending on the patient's physiological or environmental condition after treatment of a cancer disease, and the condition of such patient can be determined. It can be interpreted to mean all actions that predict the risk of cancer recurrence when comprehensively considered.
  • the subject is a patient who has developed breast cancer or is suspected of having breast cancer, and may mean a patient who needs or is expected to receive appropriate treatment for breast cancer, but is not limited thereto.
  • the biological sample refers to any sample that can confirm the patient's genetic information, and any sample obtained or derived from a patient who has or is suspected of having breast cancer or is in need of or expected to receive appropriate treatment for breast cancer.
  • a substance, biological fluid, tissue or cell such as whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, or plasma. and blood, sputum, tears, mucus, nasal washes, nasal aspirate, breath, urine, including serum.
  • Semen saliva, peritoneal washings, pelvic fluids, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid ), pancreatic fluid, lymph fluid, pleural fluid, nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, It may include, but is not limited to, organ secretions, cells, cell extract, or cerebrospinal fluid.
  • the cancer includes breast cancer, glioma, thyroid cancer, lung cancer, liver cancer, pancreatic cancer, head and neck cancer, stomach cancer, colon cancer, urothelial cancer, kidney cancer, prostate cancer, testicular cancer, cervical cancer, ovarian cancer, endometrial cancer, and melanoma.
  • fallopian tube cancer uterine cancer, blood cancer, bone cancer, skin cancer, brain cancer, vaginal cancer, endocrine cancer, parathyroid cancer, ureteral cancer, urethral cancer, bronchial cancer, bladder cancer, bone marrow cancer, acute lymphocytic or lymphoblastic leukemia, acute or chronic lymphoma.
  • Constitutive leukemia acute nonlymphocytic leukemia, brain tumor, cervical cancer, chronic myelogenous leukemia, intestinal cancer, T-zone lymphoma, esophageal cancer, gallbladder cancer, Ewing's sarcoma, tongue cancer, Hopkins lymphoma, Kaposi's sarcoma, mesothelioma, multiple myeloma It may be one or more selected from the group consisting of neuroblastoma, non-Hopkin lymphoma, osteosarcoma, neuroblastoma, mammary cancer, cervical cancer, penile cancer, retinoblastoma, skin cancer, and uterine cancer, and may specifically be breast cancer, but is now limited. That is not the case.
  • the method may additionally include the step of calculating a non-genetic risk factor score (NGRF score).
  • NGRF score non-genetic risk factor score
  • the non-genetic risk score may be obtained from at least one selected from the group consisting of age at menarche, family history, menopause, age at first full-term pregnancy, height, BMI, age at menopause, and pregnancy experience, but is not limited thereto.
  • the present invention relates to a composition for predicting the risk of developing cancer.
  • the composition may include an agent for detecting a single nucleotide polymorphism (SNP) from a biological sample obtained from a subject.
  • SNP single nucleotide polymorphism
  • the SNP may be at least one selected from the group consisting of numbers 1 to 243 in Table 1. More specifically, the SNPs are rs707475, rs616488, rs2992756, rs4233486, rs2151842, rs612683, rs7513707, rs12406858, rs637868, rs6686987, rs7514172, 6, rs11117758, rs11118563, rs6743383, rs6725517, rs12472404, rs13147907, rs6756513, rs6746250, rs10164550, rs10179592, rs10197246, rs4442975, rs11693806, rs3791977, rs6762558, rs3010266, rs552647, rs62255657, rs17838698, r
  • composition for predicting the risk of developing cancer of the present invention descriptions of SNPs, subjects, biological samples, cancer, etc. are the same as described in the method for predicting the risk of developing cancer, and are therefore omitted to avoid excessive complexity of the specification.
  • the agent for detecting the single nucleotide polymorphism (SNP) of the present invention consists of a sequence that is perfectly complementary to the sequence containing the SNP or is substantially complementary to the sequence that does not interfere with specific hybridization. It may include, but is not limited to, one or more types selected from the group consisting of primers, probes, and antisense nucleotides having identical sequences.
  • the “primer” is a fragment that recognizes the target gene sequence and includes forward and reverse primer pairs, but is preferably a primer pair that provides analysis results with specificity and sensitivity. High specificity can be granted when the nucleic acid sequence of the primer is a sequence that is inconsistent with the non-target sequence present in the sample, so that the primer amplifies only the target gene sequence containing the complementary primer binding site and does not cause non-specific amplification. .
  • the "probe” refers to a substance that can specifically bind to a target substance to be detected in a sample, and refers to a substance that can specifically confirm the presence of the target substance in the sample through the binding.
  • the type of probe is not limited as it is a material commonly used in the art, but is preferably PNA (peptide nucleic acid), LNA (locked nucleic acid), peptide, polypeptide, protein, RNA or DNA, and is most preferred. It is PNA.
  • the probe is a biomaterial that is derived from or similar to living organisms or includes those manufactured in vitro, such as enzymes, proteins, antibodies, microorganisms, animal and plant cells and organs, nerve cells, DNA, and It may be RNA, DNA includes cDNA, genomic DNA, and oligonucleotides, RNA includes genomic RNA, mRNA, and oligonucleotides, and examples of proteins may include antibodies, antigens, enzymes, peptides, etc.
  • LNA Locked nucleic acids
  • LNA nucleosides contain the common nucleic acid bases of DNA and RNA and can form base pairs according to the Watson-Crick base pairing rules. However, due to the 'locking' of the molecule due to the methylene bridge, LNA does not form the ideal shape in Watson-Crick bonding.
  • LNA When LNA is included in a DNA or RNA oligonucleotide, the LNA can pair with the complementary nucleotide chain more quickly and increase the stability of the double helix.
  • the "antisense” refers to a sequence of nucleotide bases in which an antisense oligomer hybridizes with a target sequence in RNA by Watson-Crick base pairing, typically allowing the formation of an mRNA and RNA oligomer heteroduplex within the target sequence, and It refers to an oligomer with an inter-subunit backbone. Oligomers may have exact or approximate sequence complementarity to the target sequence.
  • the present invention relates to a kit for predicting the risk of developing cancer.
  • the kit may include a composition for predicting the risk of developing cancer.
  • the kit can predict the risk of cancer by identifying SNPs, which are markers for predicting the risk of cancer.
  • the kit may be an NGS kit, RT-PCR kit, or DNA chip kit, but is not limited thereto as long as it corresponds to the type of kit typically used for SNP analysis.
  • the kit of the present invention may further include one or more other component compositions, solutions, or devices suitable for the analysis method.
  • the kit may further include essential elements necessary to perform a reverse transcription polymerase reaction.
  • the reverse transcription polymerase reaction kit contains a pair of primers specific for the gene encoding the marker protein.
  • the primer is a nucleotide having a sequence specific to the nucleic acid sequence of the gene, and may have a length of about 7 bp to 50 bp, more preferably about 10 bp to 30 bp. It may also include primers specific to the nucleic acid sequence of the control gene.
  • reverse transcription polymerase reaction kits include test tubes or other suitable containers, reaction buffers (pH and magnesium concentrations vary), deoxynucleotides (dNTPs), enzymes such as Taq-polymerase and reverse transcriptase, DNase, and the RNase inhibitor DEPC.
  • -Can include DEPC-water, sterilized water, etc.
  • a DNA chip kit may include a substrate to which a cDNA or oligonucleotide corresponding to a gene or a fragment thereof is attached, and reagents, agents, enzymes, etc. for producing a fluorescent label probe.
  • the substrate may also include cDNA or oligonucleotides corresponding to control genes or fragments thereof.
  • the present invention may include a microarray chip having a substrate on which a nucleic acid containing the SNP site is immobilized.
  • the single nucleotide polymorphism can be confirmed by sequence analysis, hybridization by microarray, allele-specific PCR (allele specific PCR), PCR-single strand conformation polymorphism (PCR-SSCP), and PCR-restriction fragment (PCR-RFLP). length polymorphism, PCR extension analysis, dynamic allele-specific hybridization (DASH), and TaqMan, but is not limited thereto.
  • the cancer risk prediction kit of the present invention can not only predict the possibility of developing cancer before the subject develops cancer, but can also be used to predict the possibility of cancer recurrence after the subject develops cancer.
  • it relates to a diagnostic device for predicting the risk of cancer occurrence.
  • the diagnostic device includes (a) a detection unit that detects a single nucleotide polymorphism (SNP) in a biological sample obtained from a subject; (b) a calculation unit for deriving a plurality of single PRSs obtained by multiplying and summing the SNPs detected in the detection unit by the weight coefficient value and calculating multiple PRSs from the plurality of single PRSs; and (c) an output unit that predicts and outputs the cancer risk of the subject using the multiple PRS obtained from the calculation unit.
  • SNP single nucleotide polymorphism
  • the SNP may be at least one selected from the group consisting of numbers 1 to 243 in Table 1.
  • the multiple PRS may be obtained by multiplying the single PRS (single PRS) value obtained by multiplying the regression coefficient (weight) value and summing the SNP detected in the detector by the regression coefficient and adding the correction coefficient.
  • the diagnostic device refers to equipment capable of diagnosing diseases in vitro based on substances produced in the human body such as blood, saliva, and urine, and includes, for example, a detection unit; operation unit; and an output unit, etc., and may be included without limitation as long as it is in a form that can analyze genes from the material.
  • Figure 1 is a diagram showing a research flow according to the present invention.
  • Figure 2 is a diagram confirming the density distribution of PRS 38 _ASN+PRS 190 _EB between a patient with breast cancer (case) and a control group without breast cancer according to the present invention.
  • Figure 3 is a diagram showing the results of estimating the absolute risk of breast cancer through PRS 38 _ASN+PRS 190 _EB for women of various percentiles and age categories according to the present invention.
  • the dotted line represents the average risk
  • Figure 3a is the lifetime absolute risk
  • Figure 3b is the 5-year absolute risk.
  • Figure 4 is a diagram showing the area under the curve (AUC) for various PRS models and NGRF predicting breast cancer risk according to the present invention.
  • AUC was compared between NGRF, PRS, and integrated (PRS+NGRF) models for women, with Figure 4a for those under 50 years of age and Figure 4B for those over 50 years of age.
  • Figure 5 is a diagram showing the results of predicting the absolute risk of developing breast cancer at the seven percentiles using the integrated model (PRS 38 _ASN+PRS 190 _EB+NGRF) according to the present invention.
  • the dotted line represents the average risk
  • Figure 5a is the lifetime absolute risk
  • Figure 5b is the 5-year absolute risk.
  • Figure 6 is a diagram showing the results of predicting the absolute risk of breast cancer at the 7th percentile for women of various ages by the multiple PRS model (PRS 38_ASN +PRS 190_EB ) according to the present invention.
  • the dotted line represents the average risk
  • Figure 5a is the lifetime absolute risk
  • Figure 5b is the 5-year absolute risk.
  • Figure 7 is a density plot of the absolute risk of breast cancer at age 80 according to the present invention, stratified by 7 PRS percentiles.
  • Figure 7a is a multiple PRS model
  • Figure 7b is an integrated model.
  • Figure 8 is a diagram showing the results of measuring absolute risk using various combinations of PRS and NGRF risk levels according to the present invention.
  • PRS was classified into three risk groups according to percentile distribution (0-20%: low, 20-80%: medium, 80-100%: high), and NGRF score was median (0-50%: low, 50%). It was classified into two groups: ⁇ 100%: high).
  • the dotted line represents the average risk
  • Figure 8a is the lifetime absolute risk
  • Figure 8b is the 5-year absolute risk.
  • step (a) generating a plurality of single polygenic risk scores (Single PRS) for biological samples obtained from a subject; and (b) calculating a multiple polygenic risk score (Multiple PRS) from the Single PRS value measured in step (a).
  • Single PRS single polygenic risk scores
  • Multiple PRS multiple polygenic risk score
  • the present invention relates to a composition for predicting the risk of developing cancer, including an agent for detecting a single nucleotide polymorphism (SNP) from a biological sample obtained from a subject.
  • SNP single nucleotide polymorphism
  • a detection unit that detects a single nucleotide polymorphism (SNP) in a biological sample obtained from a subject
  • a calculation unit that derives a plurality of single PRSs obtained by multiplying and adding the weight coefficient values to the SNPs detected in the detection unit and calculates multiple PRSs from the plurality of single PRSs
  • an output unit that predicts and outputs the cancer risk of the subject using the multiple PRS obtained in the calculation unit, wherein the SNP is at least one selected from the group consisting of numbers 1 to 243 shown in Table 1. It is about diagnostic equipment.
  • the present inventors conducted the study using two stages depending on the purpose of the study.
  • the absolute breast cancer risk was evaluated using the PRS (see Figure 1, left).
  • SNPs single nucleotide polymorphisms
  • PRS polygenic risk scores
  • NGRF non-genetic risk factor
  • HEXA Health Examinee
  • KARE Korean Association Resource
  • KoGES Korean Genome and Epidemiology Study
  • HEXA which started in 2004, recruited 173,357 participants aged 40 or older from 38 health examination centers and training hospitals in 8 regions of Korea. Among them, 58,697 participants who had genotype data and met sample quality control standards were selected for analysis. . Specimens with low genotype call rate ( ⁇ 97%), cryptic relatedness, or gender discrepancy were excluded. Women without a cancer diagnosis were selected for further analysis (see Figure 1).
  • Cases were defined as those diagnosed with breast cancer but not other types of cancer, and controls were defined as people without cancer at the time of the baseline and follow-up surveys.
  • HEXA participants were followed up using active and passive methods.
  • the first follow-up cohort of HEXA at a median of 4.6 ⁇ 1.5 years was designated HEXA 1st .
  • KARE which started in 2001, recruited 10,038 participants aged 40 to 69 years from two cities in Ansan and Anseong, South Korea. Of these, 5,493 participants were selected who had genotype data and met the sample quality criteria used for HEXA. .
  • the KARE cohort was used as a reference data set for risk factor distribution when constructing the absolute risk estimation model.
  • HEXA and KARE participants performed genotyping using K-CHIP (Korean Chip), which was designed by the Korea National Institute of Health (KNIH) based on the UK Biobank Axom Array and Affymetrix Genome-Wide. Genotyping of BCCC participants was performed using Human SNP Array 6.0. The Michigan imputation server was used for staging (via Eagle v2.4) and imputation (via minimac4) using 1000 genome phase 3 data as a reference panel. After imputation, SNPs with low imputation quality score (INFO ⁇ 0.3), minor allele frequency (MAF ⁇ 0.01), genotype call rates ( ⁇ 95%), and Hardy-Weinberg equilibrium (P ⁇ 10 -6 ) was excluded.
  • K-CHIP Korean Chip
  • KNIH Korean National Institute of Health
  • ⁇ is a coefficient representing the association between each SNP and breast cancer
  • k represents the number of SNPs used
  • n represents the SNP index.
  • PRS 38 _ASN indicates that the ⁇ weight of each PRS is inferred from Asian or European weights, respectively.
  • PRS EB applied ⁇ weights based on a combination of Asian and European weights using an empirical-Bayesian approach.
  • PRS META is the European- and Asian-weighted metadata reported in a previous study (Yang Y, Tao R, Shu The ⁇ weights generated by the analysis were used (see Tables 5 to 7 below).
  • PRS 190_EUR 313 190 European SNPs and European weights reported in Mavaddat et al.
  • PRS 190_ASN 313 190 European SNPs and Asian weights reported in Ho et al.
  • PRS 190_EB 313 190 European SNPs and EB (Empirical Bayes) weights reported in Ho et al.
  • PRS 201_EUR 330 201 European SNPs and European weights reported in Mavaddat et al. and Zhang et al.
  • PRS 201_ASN 330 201 European SNPs and Asian weights reported in Yang et al.
  • PRS 201_META 330 201 European SNPs and meta-analysis between European and Asian SNPs reported in Yang et al.
  • the PRSs are PRS 38_ASN, PRS 190_EUR, PRS 190_ASN, PRS 190_EB, PRS 201_EUR, PRS 201_ASN, and Marked as PRS 201_META .
  • ⁇ 0 , ⁇ 1 , and ⁇ 2 were obtained by applying a logistic regression model with the breast cancer incidence rate as the result.
  • n1 and n2 refer to the number of SNPs (e.g., PRS 38, PRS 190, PRS 201 , etc.) used to calculate each single PRS.
  • PRS was normalized according to each standardized deviation of the HEXA control, and 10-fold cross-validation by regression analysis was performed for several PRS models. The relative contribution of each PRS to various PRS models is shown in Table 9 below.
  • ⁇ 0 , ⁇ 1 , and ⁇ 2 are obtained by applying a logistic regression model with breast cancer incidence rate as the outcome.
  • w ⁇ 1 / ( ⁇ 1 + ⁇ 2 ) and (1- w ) represent the contribution of European PRS to the linear combination.
  • PRSs were normalized to the respective standard deviation of the HEXA control.
  • NGRF was additionally integrated to build an integrated risk prediction model.
  • the PRS+NGRF model was constructed separately using different relative risks (RR) and RF and a cutoff age (50 years).
  • Information on estrogen-dependent NGRF in HEXA and KARE was obtained from survey data, and BMI measured at enrollment (mean age, 53 ⁇ 8.37 years) was used.
  • Breast cancer-related NGRF and each RR were obtained through external research. For women under 50 years of age, age at menopause, family history of breast cancer, menopausal status, age at first pregnancy, height, and BMI were included.
  • Equation 3 was used in all prediction models including the NGRF score.
  • F k and w k are the value of factor k and its weight, respectively:
  • the predictive performance of PRS was measured as the area under the receiver operating characteristic curve (AUC) using logistic regression.
  • AUC receiver operating characteristic curve
  • PRS+NGRF the integrated model
  • E/O ratios expected to observed ratios
  • the lifetime absolute risk of breast cancer was estimated, and the integrated PRS+NGRF model was used to recalculate the lifetime and 5-year absolute breast cancer risks.
  • the absolute risk of breast cancer for women aged ⁇ during the ⁇ + ⁇ time interval was defined according to Equation 4:
  • Equation 4 it was assumed that the risk factor (RF) Z acts in a multiplicative manner with respect to the reference risk function ⁇ 0 (t). Competing risks arising from mortality from other causes were explained through the age-specific mortality function, m(t).
  • the lifetime absolute risk was evaluated as the risk between a certain age from 20 to up to 80 years, and the 5-year absolute risk was defined as the risk within the next 5 years for women who reached a certain age.
  • the iCARE tool includes relative risks (RRs) of risk factors (RFs) (RRs of RFs (Z), log-relative risks ( ⁇ ), breast cancer mortality, breast cancer incidence, and distribution of RFs in the population. Age-specific incidence rates of all cause mortality except for are required.
  • RR was obtained from an external study and the RF distribution was derived from KARE, which was used as the reference cohort.
  • Breast cancer incidence and mortality rates by age among Korean women in 2010 were obtained from Statistics Korea, and absolute risks were assessed using the Individualized Consistent Absolute Risk Estimation (iCARE) R package (version 1.18.0) in R 4.2.1. P ⁇ 0.05 was considered significant.
  • PRS was classified into three risk groups (0-20%: low, 20-80%: medium, 80-100%: high), and NGRF scores were distributed with a median distribution (0-50%: low, 50-100%: high). High) was used to classify them into two groups.
  • the underlying data can be found in the article and online supplementary materials, and raw data were generated from the Korean Genomic and Epidemiological Study. The incidence and mortality rates of breast cancer by age among Korean women were confirmed through the Korean Statistical Information Service (SCR_023565).
  • PRS verification was performed on 20,434 Korean women (see Figure 1).
  • the PRS+NRGF model was evaluated among 18,142 controls.
  • 153 cases of cancer were discovered during the follow-up period, and among the 153 new cases, 68 were women under 50 years of age and 85 were over 50 years of age.
  • PRS 38 _ASN + PRS 190 _EB AUC: 0.621. It was confirmed that PRS 38 _ASN + PRS 190 The contribution of PRS 38 _ASN to EB was confirmed to be approximately 30% (see Table 9 above).
  • Age Category OR (95% CI) a P -value N (Case/Control) 40-50 1.39 (1.16-1.67) 2.95E-04 1,128/6,255 50-60 1.54 (1.29-1.85) 2.42E-06 684/7,702 60-70 1.44 (1-20.080) 5.06E-02 259/3,746 a OR, 95% CI and P value were estimated using a logistic regression model adjusted for age and study.
  • Table 13 below shows the percentile correlation of PRS 38 _ASN + PRS 190 _EB stratified into 7 percentile groups.
  • the top 5% of women had a 2.5 times higher risk of breast cancer than the average risk group (35 to 65%), and the bottom 5% had a 0.61 times higher risk, and although the association was not statistically significant, the risk distribution was well differentiated between risk percentile groups. It was confirmed that this was the case.
  • the lifespan and 5-year absolute risk of PRS 38 _ASN + PRS 190 _EB are shown in Figures 3a and 3b.
  • the absolute lifetime risk for women in the top 5% of women at age 80 was 9.91%, and the absolute lifetime risk for women in the bottom 5% was 2.18% (average lifetime absolute risk: 4.89%).
  • PRS 38 _ASN + PRS 201 _META+NGRF had the highest predictive power for women under 50 years of age
  • PRS 38 _ASN + PRS 190 _EB+NGRF was the most predictive model for women over 50 years of age.
  • PRS 38 _ASN + PRS 190 _EB+ NGRF hereafter the integrated model.
  • the integrated model is a model that includes PRS 38 _ASN + PRS 190 _EB (hereinafter referred to as the multiple PRS model), which showed the highest accuracy in stage 1 to further estimate absolute breast cancer risk.
  • the lifetime and 5-year absolute risk of the integrated model are shown in Figures 5a and 5b.
  • the absolute lifetime risk of breast cancer varied from 2% to 10%, with an average of 5.06% (see Figure 5a).
  • the absolute risk at age 80 for the top 5% of women was 9.93%, while for the bottom 5% of women it was 2.22%.
  • the peak 5-year absolute risk for the top 5% of women was 1.47% at age 48 and decreased thereafter (see Figure 5b).
  • the first recommended age for breast cancer screening in Korea the 5-year absolute risk for the average risk group was 0.6%.
  • women in the top 5% reached this level of risk much earlier, at age 33, and these results may support the need for individualized screening strategies for women at high risk, especially those under 40 years of age.
  • the present inventors applied the newly discovered SNP marker combination to actual Asian women and compared the accuracy of predicting breast cancer prognosis with the accuracy of the SNP combination in previously known papers. Table 16 shows the results.
  • PRS using the discovered SNP combination of the present invention PRS of SNP combination discovered in existing published papers PRS OR AUC PRS OR AUC PRS 190_EUR 1.38 (1.24-1.53) 0.611 (0.599-0.623) PRS 287_EUR 1.49(1.34-1.67) 0.614 PRS 190_ASN 1.41 (1.27-1.56) 0.612 (0.600-0.624) PRS 287_ASN 1.43(1.28-1.58) 0.592 PRS 190_EB 1.41 (1.27-1.56) 0.616 (0.604-0.627) PRS 287_EB 1.50(1.35-1.67) 0.613 PRS 38_ASN +PRS 190_EUR 1.44 (1.3-1.59) 0.619 (0.607-0.631) PRS 46 +PRS 287_EUR 1.48(1.33-1.65) 0.611 PRS 38_ASN +PRS 190_ASN 1.44 (1.3-1.59) 0.615 (0.604-0.627) PRS 46 +PRS 287_ASN 1.42(
  • the present inventors have discovered the optimal SNP marker combination suitable for Asian women, and the technical significance is that they have confirmed superior diagnostic effect compared to existing known SNP set combinations. Furthermore, it is expected that the accuracy of predicting lifetime breast cancer occurrence probability and risk can be significantly improved by combining environmental factors (NGRF) in addition to the prognostic score value (PRS score).
  • NGRF environmental factors
  • PRS score prognostic score value
  • the present invention provides information on a SNP set useful for predicting the risk of developing cancer. According to the present invention, the cancer risk of Asians, especially Asians, among specific ancestry can be predicted more precisely.

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Abstract

La présente invention concerne un procédé de prédiction du risque de survenue d'un cancer et une composition associée. Il a été confirmé que la présente invention est nettement supérieure aux combinaisons connues de SNP en ce qui concerne la prédiction du risque de survenue d'un cancer chez les patientes asiatiques, et qu'elle devrait en outre permettre d'améliorer considérablement la précision de la prédiction de la probabilité de survenue d'un cancer du sein en la combinant avec des facteurs de risque environnementaux (NGRF).
PCT/KR2023/017396 2022-11-02 2023-11-02 Procédé de prédiction du risque de survenue d'un cancer WO2024096618A1 (fr)

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KR102351306B1 (ko) * 2021-09-06 2022-01-14 주식회사 바스젠바이오 질환 연관 유전자 변이 분석을 통한 질환별 위험 유전자 변이 정보 생성 장치 및 그 방법
KR102382707B1 (ko) * 2021-11-02 2022-04-08 주식회사 바스젠바이오 다유전자 위험점수를 이용한 시간 의존 연관성 기반의 질환 발병 정보 생성 장치 및 그 방법
WO2022117996A1 (fr) * 2020-12-01 2022-06-09 Genomics Plc Procédé et appareil mis en œuvre par ordinateur pour l'analyse de données génétiques
US20220205043A1 (en) * 2017-06-02 2022-06-30 Myriad Genetics, Inc. Detecting cancer risk

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20090127939A (ko) * 2007-03-26 2009-12-14 디코드 제네틱스 이에이치에프 유방암의 위험도 평가, 진단, 예후 및 치료용 마커인 염색체 ２ 및 염색체 １６ 상의 유전적 변이
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WO2022117996A1 (fr) * 2020-12-01 2022-06-09 Genomics Plc Procédé et appareil mis en œuvre par ordinateur pour l'analyse de données génétiques
KR102351306B1 (ko) * 2021-09-06 2022-01-14 주식회사 바스젠바이오 질환 연관 유전자 변이 분석을 통한 질환별 위험 유전자 변이 정보 생성 장치 및 그 방법
KR102382707B1 (ko) * 2021-11-02 2022-04-08 주식회사 바스젠바이오 다유전자 위험점수를 이용한 시간 의존 연관성 기반의 질환 발병 정보 생성 장치 및 그 방법

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