WO2020080871A2 - Composition de biomarqueur de détermination de réactivité d'un médicament contre le cancer, méthode de détermination de réactivité d'un médicament contre le cancer à l'aide de la composition de biomarqueur, et puce de diagnostic de détection de composition de biomarqueur de détermination de réactivité d'un médicament contre le cancer - Google Patents

Composition de biomarqueur de détermination de réactivité d'un médicament contre le cancer, méthode de détermination de réactivité d'un médicament contre le cancer à l'aide de la composition de biomarqueur, et puce de diagnostic de détection de composition de biomarqueur de détermination de réactivité d'un médicament contre le cancer Download PDF

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WO2020080871A2
WO2020080871A2 PCT/KR2019/013717 KR2019013717W WO2020080871A2 WO 2020080871 A2 WO2020080871 A2 WO 2020080871A2 KR 2019013717 W KR2019013717 W KR 2019013717W WO 2020080871 A2 WO2020080871 A2 WO 2020080871A2
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marker
biomarker composition
drug
reactivity
responsiveness
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WO2020080871A3 (fr
WO2020080871A9 (fr
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정종선
이선호
홍종희
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(주)신테카바이오
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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

Definitions

  • GDSC cell line project used here was published at the following site (CCLP: COSMIC Cell Lines Project, http://cancer.sanger.ac.uk/cell_lines). It is expected that these common resources will help in realizing genome-based precision cancer treatment.
  • Deep learning learning method is a field of deep machine learning from a large amount of high-dimensional raw data.
  • the Padel method can be expressed in 1875 (1D and 2D 1,444, and 3D 431) features and 12 fingerprints (about 16,092 bits in total) in the drug.
  • QSAR Quality of service
  • drug development using drug cytotoxicity data deep learning-based expression regulation of whole genome sequencing, structural variation, etc. are independently applied and utilized. Became.
  • CDR drug Cancer drug response scanning
  • IC50 drug-cell lines-toxicity
  • the second can be described as a literature method for the genomic fingerprint (or a set of mutation features) of the full-length genome (or target protein), and is the most standard deep learning method.
  • CDRscan cancer drug reaction scan
  • GBLscan genetic biomarker label scan
  • eQTL expression and Quantitative Trait Loci
  • cQTL Copy Number Variation and Quantitative Trait Loci
  • the definition of the drug type is a type of drug targets and a functionally similar functional type of drug, or a gene group, drug pathway or drug signaling of a category in which the drug is affected.
  • Pathway drug signaling pathway
  • the functional subflow type is a human genome version of Dec.
  • Genome Reference Consortium Human 38 (GCA_000001405.15)
  • GCF_000001405.26 When done, information is read in genotype form at each gene locus. This type of genotype is expressed in the form of 1: homo, 2: hetero, or 3: alt home, and such a collection of genotypes can be called a relative halfrotype. And if the relative haplotype composed of a single or a combination of specific locus is related to the expression of a specific gene, it is defined in the present invention as a functional hypoprotype.
  • the present invention has been devised to solve the above-described conventional problems, and the present invention has the genetic properties and fingerprints of functional genomes of the target genomes that respond to cancer drug reactivity according to the technical background and social needs as described above.
  • a specific object of the present invention is known non-clinical cell line genomes, their gene expression information and gene duplication, and in vivo It is to provide a prediction system that can reliably predict the information-based linear regression modeling and deep learning machine learning with a collection of quantitative trait loci (QTL) related to drug response.
  • QTL quantitative trait loci
  • the GBLscan method collects quantitative trait positions (QTL) that have high correlation with gene RNA expression and copy number variation (CNV) to generate multivariate-based haplotypes with functional information and drugs It is to provide a method for predicting drug reactivity for a virtual clinical trial (In Silico Clinical Trial) that effectively filters sensitivity and reactivity.
  • QTL quantitative trait positions
  • CNV copy number variation
  • the present invention comprises the steps of generating a collection of positions of the quantitative target location (QTL) to be inspected; Calculating a functional response type (FRH) and a drug response correlation; And, it consists of the step of generating a functional haplotype-based drug response prediction model.
  • steps (A) and (B) include both gene expression and gene copy number variations as common variables, and these common variables, gene expression information, are used to correlate with multiple or single mutations that affect drug responses. Collect the mutations you have. This is called a collection of quantitative trait locations (QTLs).
  • QTLs quantitative trait locations
  • the Haflow type (FRH) is changed to a hash number, and the converted drug hash number is used to convert the drug reactivity information.
  • the Pairwise Pearson Test is performed and the above operation is repeated for all carcinomas.
  • a process related to generating a drug pathway-based drug response prediction model in a specific cancer is performed as follows.
  • the present invention to calculate the drug reactivity prediction model using the functional haflow type information as described above, using a number of variations in the base sequence collected from the genome to be tested to calculate the functional haflow type and : Calculating a solution of the linear regression model; A system for screening cancer drugs with high sensitivity among cancer drugs using such a solution is included.
  • the present invention comprises the steps of: (A) calculating the positional collection of quantitative trait positions (QTLs) for variations affecting the drug response; (B) generating a functional subflow type for the variation included in the quantitative trait position (QTL); (C) calculating a correlation between a mutation and a drug response based on the functional subflow type; And (D) predicting drug responsiveness based on the functional subflow type.
  • calculating the positional collection of the quantitative trait positions (QTL) in the step (A) includes: (A1) deriving a correlation between mutations and gene RNA expression from a plurality of cell lines; (A2) deriving a correlation between drugs and gene RNA expression and gene copy number variation (CNV) contained in a plurality of cell line genomes; (A3) calculating gene expression information for gene RNA expression or gene duplication mutation, which commonly affects correlation in steps (A1) and (A2); And (A4) calculating a single or multiple mutations involved in the gene expression information to calculate a quantitative trait position (QTL) for mutations affecting the drug response. It can be performed, including the step of generating a collection of locations.
  • the generation of the functional subflow type in the step (B) may be generated by extracting the subflow type for a single or multiple mutations included in the quantitative trait position (QTL) from the genome integration DB together with the drug reactivity.
  • the functional underflow type includes correlation information between gene RNA expression (Exp) and drug responsiveness; It may also be configured to include information on the correlation between gene multiplex (loci) and drug responsiveness.
  • the functional subflow type correlation information between the gene replication mutation (CNV) and drug response; It may also be configured to include information on the correlation between gene multiplex (loci) and drug responsiveness.
  • the gene multivariate may be a collection of locus selected based on quantitative trait location (QTL).
  • the dielectric integration DB may be a Cell Lines integrated DB composed of a genotype.
  • the drug reactivity may be expressed by an ln Ic50 value.
  • RNA expression may be divided into over-expression, normal, and under-expr profession.
  • CNV copy number variation
  • the correlation calculation of the step (C) comprises: (C1) determining the gene RNA expression (Exp) associated with the gene multivariate (loci); (C2) calculating drug reactivity to the gene RNA expression (Exp) from the functional subtype to determine drug reactivity to the gene multivariate (loci); And (C3) comparing and verifying the drug responsiveness calculated from the correlation information between the gene multivariate (loci) included in the functional subflow type and the drug responsiveness and the drug responsiveness determined in step (C2). It may be carried out.
  • the association of the step (C1) may be performed by determining a cell line containing the gene multivariate (loci) and gene RNA expression (Exp) information included in the cell line.
  • step (D) when the difference in drug response to the difference in gene RNA expression is greater than the overexpression, it is determined that the low expression of gene RNA is sensitive to the drug reaction; When the difference in drug response to the difference in gene RNA expression is less than the overexpression, it may be determined that the overexpression of the gene RNA is sensitive to the drug reaction.
  • the difference value of the absolute value of the absolute value of the drug response to the difference in gene RNA expression in the overexpression group is the highest value of the absolute value of the drug response sensitivity to the difference in the gene RNA expression of the low expression group. If it is greater than the difference value, it is determined that the overexpression of the gene RNA is associated with the gene multivariate (loci) type; If the difference value of the absolute value sensitivity of the drug response to the difference in gene RNA expression of the overexpression group is less than the difference value of the absolute value of the drug response absolute value of the difference of the gene RNA expression of the difference in gene RNA expression of the overexpression group, the low expression of gene RNA is gene multiplexed. It can also be determined to be associated with a loci type.
  • the functional underflow type may be expressed as a hash number.
  • the correlation between the functional hypoflow type and the drug reaction may be calculated by performing a drug-reactivity information and a pairwise correlation test (Pairwise Pearson Test) using a hash number.
  • the present invention includes a drug-reaction type using a drug responsiveness, gene expression information, and copy number variation to generate a functional subtype in the same manner as described above.
  • the functional subflow type may be used for diagnosis of responsiveness to drugs, discovery of biomarkers for companion diagnosis to select patient groups for drugs, discovery of drug targets, or virtual clinical trials for drugs.
  • the present invention can derive the reactivity correlation of the pharmacological functional group constituting the drug to the variation information of the genome, and thus, if the variation of the genome to be analyzed and the pharmacological functional group of the drug are extracted, the degree of reactivity of the drug to the genome is determined It has an effect that can be reliably predicted.
  • the present invention can derive a drug type reactivity correlation with the genome variation characteristic information, knowing the characteristic information and the drug type information about the variation of the genome to be analyzed, the degree of reactivity of the drug to the genome can be reliably determined. It has a predictable effect.
  • the present invention can predict the reactivity to a cell line or a human body containing a specific genome of an unknown polymer compound (substance to be developed for drug) before a clinical trial, and thus can significantly reduce the time and cost of developing a new drug.
  • the degree of reactivity to genomes other than those found in clinical trials can be predicted in advance, thereby significantly reducing the research cost and time for discovering other uses for drugs and finding side effects. There is.
  • Figure 1 is an exemplary diagram showing a quantitative trait location (QTL), genetic replication number variation and RNA expression examples for the drug response prediction according to the present invention.
  • Figure 2 is an exemplary view showing a drug response calculation scheme using a genetic biomarker according to the present invention.
  • Figure 3 is an exemplary view showing the relationship between the genetic biomarker of breast cancer and drug response according to the present invention.
  • Figure 4 is an exemplary diagram showing the NGS input and drug reaction and RNA expression network according to the present invention.
  • Figure 5 is an exemplary view showing the correlation of the cell line drug response value (IC50) and functional hypoflow type according to the present invention.
  • Figure 6 is an exemplary view showing a correlation between the quantitative trait location (QTL) and the drug response according to the present invention.
  • Figure 7 is an exemplary diagram showing a variation, RNA expression, gene replication number variation and drug response network according to the present invention.
  • FIG. 8 is an exemplary diagram showing an example of cell line, cancer, drug response, RNA expression and CNV amplification correlated with mutation according to the present invention.
  • FIG. 9 is an exemplary diagram showing a functional hypoflow type and a drug reactivity correlation calculation schema according to the present invention.
  • Figure 10 is an exemplary view showing a functional haplotype and drug-responsive network according to the present invention.
  • Figure 11 is an exemplary diagram showing an example of functional subflow type and gene expression and gene replication variation according to the present invention.
  • FIG. 12 is an exemplary view showing a functional underflow type definition according to the present invention.
  • FIG. 13 is an exemplary view showing an example of FRH method and gene expression and drug reactivity prediction according to the present invention.
  • FIG. 14 is an exemplary view showing an example in which the difference in average IC50 values is 1.0 as an example of FRH according to the present invention.
  • 15 is an exemplary view showing an example in which the difference in average IC50 values is 1.5 as an example of FRH according to the present invention.
  • 16 is an exemplary view showing a functional subflow type and gene expression embodiment according to the present invention.
  • 17 is an exemplary view showing a specific cancer drug pathway-based drug response prediction model generation example according to the present invention.
  • Figure 18 is an exemplary diagram showing the relationship between functional reactivity and drug reactivity R ⁇ 2 according to the present invention.
  • FIG. 19 is an exemplary view showing an embodiment of FRH having high FRH correlation with Afatinib drug in the example shown in FIG. 18.
  • FIG. 20 is an exemplary view showing an embodiment of a prediction model using FRH having a high FRH correlation with Afatinib drug in the example shown in FIG. 18.
  • 21 is an exemplary view showing an embodiment of predicting drug response of blood cancer using FRH according to the present invention.
  • FIG. 22 is an exemplary view showing an example of an available system using FRH according to the present invention.
  • FIG. 23 is an exemplary view showing an example of a method of manufacturing a cancer and drug companion diagnostic chip using the FRH biomarker according to the present invention.
  • FIG. 24 is an exemplary view illustrating a configuration of a biomarker expression structure according to the present invention.
  • FIG. 25 is a graph showing correlation values (P-vlaue) values of major biomarkers for blood cancer drugs.
  • 26 is a graph showing correlation values (P-vlaue) values for genes expressed by major biomarkers and blood cancer drugs.
  • 27 is a graph showing the correlation (P-vlaue) values for female cancer drugs of major biomarkers.
  • Figure 29 is a graph showing the correlation (P-vlaue) values for brain tumor drugs of major biomarkers.
  • 30 is a graph showing correlation values (P-vlaue) values for genes expressed by major biomarkers and brain tumor drugs.
  • Figure 31 is a graph showing the correlation (P-vlaue) values for the head and neck cancer drugs of the major biomarkers.
  • FIG. 32 is a graph showing correlation values (P-vlaue) values for genes expressed by major biomarkers and head and neck cancer drugs.
  • Figure 33 is a graph showing the correlation (P-vlaue) values for lung cancer drugs of major biomarkers.
  • FIG. 34 is a graph showing correlation values (P-vlaue) values for genes expressed by major biomarkers and lung cancer drugs.
  • 35 is a graph showing correlation values (P-vlaue) of sarcoma cancer drugs of major biomarkers.
  • 36 is a graph showing correlation values (P-vlaue) values for genes expressed by major biomarkers and sarcoma cancer drugs.
  • Figure 38 is a graph showing the correlation (P-vlaue) values for CHROMATIN drugs of major biomarkers.
  • 39 is a graph showing correlation values (P-vlaue) of EGRF drugs of major biomarkers.
  • Figure 40 is a graph showing the correlation (P-vlaue) values for the ERK_MAPK drugs of major biomarkers.
  • FIG. 41 is a graph showing the correlation (P-vlaue) values for IGFR drugs of major biomarkers.
  • Figure 42 is a graph showing the correlation (P-vlaue) values of the major biomarkers for MITOSIS drugs.
  • a biomarker composition composed of a combination of markers containing a mutation that modulates gene expression, wherein the biomarker composition is 13_37746458_AA_A (Marker 1) , 16_46710765_T_A (marker 2), 11_63382198_CA_C (marker 3), 14_68477626_CT_C (marker 4) and 7_151221500_CT_C (marker 5).
  • biomarker composition may further include any one or more markers among markers described in Table 21 below.
  • the blood cancer may include any one or more of ALL (Acute lymphoblastic leukemia), LAML (Acute Myeloid Leukemia) or LCML (Chronic Myelogenous Leukemia).
  • ALL acute lymphoblastic leukemia
  • LAML acute Myeloid Leukemia
  • LCML Choronic Myelogenous Leukemia
  • the present invention is a method for determining reactivity to a blood cancer drug by detecting a biomarker composition composed of a combination of markers containing mutations that regulate gene expression, in order to determine reactivity to a blood cancer drug, wherein Biomarker composition, 13_37746458_AA_A (marker 1), 16_46710765_T_A (marker 2), 11_63382198_CA_C (marker 3), 14_68477626_CT_C (marker 4) and 7_151221500_CT_C (marker 5) using a blood marker drug reactivity determination method comprising a biomarker composition It includes.
  • bio-marker composition may be configured to further include any one or more of the markers described in [Table 21].
  • the present invention in order to determine the responsiveness to blood cancer drugs, in the diagnostic chip for detecting a biomarker composition consisting of a combination of markers containing mutations that regulate gene expression, the biomarker composition, which is the target of detection, , 13_37746458_AA_A (marker 1), 16_46710765_T_A (marker 2), 11_63382198_CA_C (marker 3), 14_68477626_CT_C (marker 4) and 7_151221500_CT_C (marker 5) diagnostic chip for detecting biomarker drug reactivity
  • the biomarker composition may further include any one or more markers among the markers described in Table 21 below.
  • the biomarker composition in order to determine the responsiveness to female cancer drugs, in the biomarker composition consisting of a combination of markers containing a variation that modulates gene expression, the biomarker composition is 1_13248174_T_C (marker 1), 16_85071785_A_G (marker 2) and 9_34371054_A_C (marker 3) comprises a biomarker composition for determining the reactivity of female cancer drugs, wherein the biomarker composition, any one of the markers listed in Table 22 It may be configured to further include the above markers.
  • the present invention is a method for determining the reactivity to a female cancer drug by detecting a biomarker composition composed of a combination of markers containing a variation that modulates gene expression in order to determine the reactivity to a female cancer drug
  • the biomarker composition includes a method for determining female cancer drug reactivity using a biomarker composition comprising 1_13248174_T_C (marker 1), 16_85071785_A_G (marker 2), and 9_34371054_A_C (marker 3), wherein the biomarker composition is , It may be configured to further include any one or more of the markers listed in Table 22.
  • the present invention is a diagnostic chip for detecting a biomarker composition composed of a combination of markers containing a variation that modulates gene expression for determining reactivity to a female cancer drug, wherein the biomarker composition is 1_13248174_T_C (Marker 1), 16_85071785_A_G (marker 2) and 9_34371054_A_C (marker 3) comprising a diagnostic chip for detecting a biomarker composition for determining drug responsiveness to cancer, wherein the biomarker composition is shown in Table 22 It may be configured to further include any one or more of the markers described.
  • the biomarker composition in order to determine the reactivity to the brain tumor drug, in the biomarker composition consisting of a combination of markers containing a variation that modulates gene expression, is 11_12162199_T_G (Marker 1 ), 13_113159942_A_G (Marker 2), 5_181155466_T_G (Marker 3), 11_65547388_T_G (Marker 4), 10_11921140_T_G (Marker 5), 17_55774876_T_G (Marker 6), 12_40313960_G_T (Marker 7), 12_40313960_G_T (Marker 7), 16_8_T It includes a biomarker composition for determining brain tumor drug reactivity, including, wherein, the biomarker composition may be configured to further include any one or more of the markers listed in Table 23.
  • the present invention in order to determine the reactivity to a brain tumor drug, in a method of determining a biomarker composition composed of a combination of markers containing a variation that controls gene expression, to determine the reactivity to a brain tumor drug, the bio Marker compositions are: 11_12162199_T_G (marker 1), 13_113159942_A_G (marker 2), 5_181155466_T_G (marker 3), 11_65547388_T_G (marker 4), 10_11921140_T_G (marker 5), 17_55774876_T_G (marker 6), 16_8_13 ) And 2_113934259_C_T (marker 9), comprising a method for determining brain tumor drug reactivity using a biomarker composition, wherein the biomarker composition further comprises any one or more of the markers listed in Table 23. It may be.
  • the present invention is a diagnostic chip for detecting a biomarker composition composed of a combination of markers containing a variation that modulates gene expression for determining reactivity to a brain tumor drug, wherein the biomarker composition is 11_12162199_T_G ( Marker 1), 13_113159942_A_G (marker 2), 5_181155466_T_G (marker 3), 11_65547388_T_G (marker 4), 10_11921140_T_G (marker 5), 17_55774876_T_G (marker 6), 12_40313960_G_T (marker 7) )
  • the biomarker composition may further include any one or more of the markers listed in Table 23. .
  • the biomarker composition in order to determine the reactivity to the head and neck cancer drug, in the biomarker composition consisting of a combination of markers containing a variation that modulates gene expression, the biomarker composition is 14_21082261_T_G (marker 1) and 7_140753336_A_T (marker 2) comprises a biomarker composition for determining head and neck cancer drug reactivity, wherein the biomarker composition further comprises any one or more of the markers listed in Table 24 It may be configured.
  • the present invention is a method for determining reactivity to a head and neck cancer drug by detecting a biomarker composition composed of a combination of markers containing a variation that modulates gene expression in order to determine the reactivity to the head and neck cancer drug.
  • the biomarker composition includes a method for determining head and neck cancer drug reactivity using a biomarker composition comprising 14_21082261_T_G (marker 1) and 7_140753336_A_T (marker 2), wherein the biomarker composition is the markers shown in Table 24 It may be configured to further include any one or more markers.
  • the biomarker composition which is a detection target, is 14_21082261_T_G (marker 1) and 7_140753336_A_T (marker 2) comprises a diagnostic chip for detecting biomarker composition for determining head and neck cancer drug reactivity, wherein the biomarker composition is any one or more of the markers listed in Table 24 It may be configured to further include.
  • the biomarker composition in order to determine the reactivity to the lung cancer drug, in the biomarker composition consisting of a combination of markers containing a variation that modulates gene expression, is 2_217847583_C_T (Marker 1 ), 2_71424561_G_GT (marker 2), 2_217847883_G_A (marker 3), 2_217848003_A_G (marker 4) and 17_81206909_A_C (marker 5) comprising a biomarker composition for determining the reactivity of the lung cancer drug, wherein the biomarker composition , It may be configured to further include any one or more of the markers listed in Table 25.
  • the present invention in order to determine the reactivity to the lung cancer drug, by detecting a biomarker composition consisting of a combination of markers containing a variation that modulates gene expression, to determine the reactivity to the lung cancer drug, the biomarker
  • the composition includes a method for determining lung cancer drug reactivity using a biomarker composition comprising 2_217847583_C_T (marker 1), 2_71424561_G_GT (marker 2), 2_217847883_G_A (marker 3), 2_217848003_A_G (marker 4), and 17_81206909_A_C (marker 5).
  • the biomarker composition may further include any one or more markers among the markers listed in Table 25.
  • the present invention is a diagnostic chip for detecting a biomarker composition composed of a combination of markers containing a variation that modulates gene expression for determining reactivity to a lung cancer drug, wherein the biomarker composition is 2_217847583_C_T (Marker 1), 2_71424561_G_GT (marker 2), 2_217847883_G_A (marker 3), 2_217848003_A_G (marker 4) and 17_81206909_A_C (marker 5) comprising a diagnostic chip for biomarker composition detection for lung cancer drug reactivity determination
  • the bio-marker composition may be configured to further include any one or more of the markers listed in Table 25.
  • the biomarker composition in order to determine the reactivity to a sarcoma cancer drug, in the biomarker composition consisting of a combination of markers containing a variation that modulates gene expression, the biomarker composition is 1_26457907_T_G (marker 1), 22_22643666_C_G (marker 2), 22_21127217_C_G (marker 3), 5_141432375_A_C (marker 4) and 22_18847351_G_C (marker 5) comprises a biomarker composition for the determination of the reactivity of the sarcoma cancer drug, wherein the biomarker
  • the composition may further include any one or more of the markers listed in Table 26.
  • the present invention is a method for determining the reactivity to a sarcoma cancer drug by detecting a biomarker composition composed of a combination of markers containing a variation that modulates gene expression, in order to determine the reactivity to a sarcoma cancer drug.
  • Biomarker composition 1_26457907_T_G (marker 1), 22_22643666_C_G (marker 2), 22_21127217_C_G (marker 3), 5_141432375_A_C (marker 4) and 22_18847351_G_C (marker 5) comprising a biomarker composition for determining the reactivity of the sarcoma cancer drug
  • the biomarker composition may be configured to further include any one or more of the markers listed in Table 26.
  • the present invention in order to determine the reactivity to the CHROMATIN drug, by detecting a biomarker composition consisting of a combination of markers containing a variation that modulates gene expression, to determine the reactivity to the CHROMATIN drug, the biomarker
  • the composition comprises 2_72891611_A_C (marker 1) and 1_8013331_A_G (marker 2), wherein the biomarker composition may further include any one or more of the markers listed in Table 28.
  • the biomarker composition in the method of detecting the biomarker composition composed of a combination of markers containing mutations that regulate gene expression, to determine the reactivity to the ERK_MAPK drug, the biomarker The composition includes an ERK_MAPK drug reactivity determination method using a biomarker composition comprising 5_142319698_T_G (marker 1) and 12_89350540_G_A (marker 1), wherein the biomarker composition is any one of the markers listed in Table 30 It may be configured to further include the above markers.
  • the present invention is a diagnostic chip for detecting a biomarker composition composed of a combination of markers containing a variation that modulates gene expression, in order to determine responsiveness to an IGFR drug, wherein the biomarker composition is X_119618658_T_G (Marker 1) and 1_16148437_A_C (Marker 2) comprises a diagnostic chip for detecting biomarker compositions for IGFR drug reactivity determination, wherein the biomarker composition is any one or more of the markers listed in Table 31 It may be configured to further include a marker.
  • the present invention is a method for determining the reactivity to a MITOSIS drug by detecting a biomarker composition composed of a combination of markers containing a mutation that controls gene expression, in order to determine the reactivity to a MITOSIS drug.
  • the composition includes a method for determining the reactivity of MITOSIS drug using a biomarker composition comprising 1_85581605_TA_T (marker 1) and 6_135201764_A_C (marker 2), wherein the biomarker composition is any one of the markers listed in Table 32 It may be configured to further include the above markers.
  • 1 eQTL and 2 cQTL are used, and 3 to 6 show a state as a result or correlation.
  • FIG. 7 is an exemplary diagram showing a network structure of mutation, RNA expression, gene replication number variation, and drug response, and is a schematic diagram in which networking of the content shown in FIG. 6 is made with respect to each other, and genotype (single mutation), ha It shows the relationship between flow type (combination and multivariate) and conditional expressions of drug reactions.
  • the functional subflow type collectively refers to both single and multiple variants.
  • A) expresses the relationship between drug reactivity information and gene RNA expression as the final result of multiple facets (loci) and Exp, and the relationship between drug reactivity information and gene replication variable (CNV) is expressed as loci and CNV. Is showing.
  • loci multivariate
  • QTL quantitative trait location
  • loci, gene-exp, and gene-CNV values can be expressed on a scale of 0 to 1 in the form of weights.
  • B) can be described as multiplying the weight by loci and Exp, loci and CNV.
  • the functional subflow type is the genomic assembly version Dec. 2013 (GRCh38 / hg38): Genome Reference Consortium Human 38 (GCA_000001405.15), https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26
  • GCF_000001405.26 Based on the reference sequence, individual cell lines and individuals It is defined as a form with different polymorphism information (allele).
  • IC50) represents the absolute difference in sensitivity of the drug response to the gene expression difference (Normal vs. Over or Under), and FRH (Loci
  • loci-3 and [1] Exp of [2] It shows that it is connected to -over.
  • Figure 14 shows an example of the difference in the average IC50 value '1.0' as an example of the FRH in a table.
  • the average drug reactivity (LN IC50) is 1, it shows a high positive correlation (pval ⁇ 2.812 * 10 ⁇ -7).
  • FIG. 15 shows an example data in which the difference of the average IC50 value is '1.5' as an example of FRH in a table.
  • the average drug reactivity (LN IC50) is 1.5, it shows a much higher negative correlation (pval ⁇ 7.191 * 10 ⁇ -7).
  • FIG. 16 shows an example of functionally related haplotype and gene expression.
  • FRH includes four types. At this time, the variation (locus position) is explained by three different genotypes: 1: Ref Homo, 2: Hetero, and 3: Alt Homo.
  • the frequency of each type is the second column, one in '12', one in '21', and one in '11'. 2 and '22' show a majority with 153.
  • the functional flow type shown in FIG. 18 and the drug reactivity relationship R ⁇ 2 describe an example of the clustering process of 2) correlation information of the method for generating the predictive model of FIG. 17.
  • the cancer type is blood cancer
  • Y EGFR pathway 9 drugs
  • X FRH haplotype
  • each pixel represents the Pearson correlation between the drug response value and FRH, where * indicates significance (pval ⁇ 0.05), ** indicates significance (pval ⁇ 0.01).
  • FIG. 19 shows an example of FRH having high FRH correlation with Afatinib drug in the example of FIG. 18.
  • the results show the correlation between the four FRHs (LAMC2_2, JPH1_2, PRKCB_3, CML_1) and drug response.
  • FIG. 21 shows predicted IC50 (real) values for various drugs (AP-24534, PAC-1, Obatoclax Mesylate, Bleomycin, etc.) with the functional subtype of the genome of one patient with blood cancer.
  • drugs AP-24534, PAC-1, Obatoclax Mesylate, Bleomycin, etc.
  • FIG. 21 shows predicted IC50 (real) values for various drugs (AP-24534, PAC-1, Obatoclax Mesylate, Bleomycin, etc.) with the functional subtype of the genome of one patient with blood cancer.
  • some drugs were predicted to be highly sensitive, while others were predicted to be highly resistant.
  • IC50 (real) the average IC50 (ave) value of the functional Haflow type group.
  • the excavated biomarkers are DNA bases containing mutations, which are largely classified according to the type of cancer (CancerClass) and the biomarkers that affect the drug's reactivity to the cancer and the drug's pathway and expression target. It is divided into biomarkers that affect drug reactivity by drug class (DrugClass).
  • the cancer classes listed in [Table 1] above are classified by cancer occurrence sites and causes, including blood cancer, female cancer, brain tumor, brain and head and neck, lung cancer, and sarcoma. It is divided into bones.
  • the biomarkers that affect the reactivity to drugs for each cancer type were calculated through GBLscan using the functional Haflow type.
  • biomarkers of blood cancer see Table 2
  • 50 biomarkers of breast and ovary see Table 4
  • brain tumors by type of cancer class 894 biomarkers (see Table 6), 32 head and neck biomarkers (see Table 8), 277 lung lung biomarkers (see Table 10), biomarkers of sarcoma cancer (bone) 331 (see Table 12).
  • the biomarker may be a combination of a single mutation and a multiple mutation, and the forms of the mutations may also be of various forms such as a single nucleotide polymorphism (SNV), an indel, gene copy number variation (CNV), and a chromosomal rearrangement. It can be a mutation.
  • SNV single nucleotide polymorphism
  • CNV gene copy number variation
  • chromosomal rearrangement It can be a mutation.
  • the biomarker specifically refers to a base sequence consisting of the base of the marker 4, the position from the second position of the marker to the base length of the marker 3 in the marker 1 chromosome.
  • the biomarker of SEQ ID NO: 1 in [Table 2] was expressed as “14_30910985_T_G”, which means that the base sequence of the 30910985 th base of chromosome 14 is the biomarker, and the sequence of SEQ ID NO: 5 of [Table 2]
  • the biomarker was expressed as ⁇ 13_113524119_TGTCA_T, 13_113510211_T_C, 13_113495576_T_C, 13_113503637_G_T, 13_113539563_C_T '', which means that the bases from the 113524119 bases of chromosome 13 to 113524123 are T (the base of 4 digits is 'deleted', 13 bases)
  • the 190673067th base of chromosome 2 is GT, and the 114323991th base of ninth chromosome is G. it means.
  • the 190673067th base of chromosome 2 refers to G being substituted with GT.
  • the entire nucleotide sequence of chromosomes 1 to 22 is a genomic assembly version of Dec. 2013 (GRCh38 / hg38): Genome Reference Consortium Human 38 (GCA_000001405.15), https: //www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26 being based on the reference (reference) sequence.
  • Table 2 lists the FRHs associated with blood cancer drugs, and markers defining the blood cancer drugs and FRHs associated with them.
  • FIG. 25 correlation values (P-vlaue) values of major biomarkers among blood biomarkers among the biomarkers listed in Table 2 are displayed in a graph, and FIG. 26 shows genes expressed by the major biomarkers.
  • the correlation values (P-vlaue) values for the fields and blood cancer drugs are plotted.
  • Table 4 lists the FRHs associated with the female cancer drug, and the markers defining the female cancer drugs and the FRHs associated therewith.
  • FRH is expressed in the form of "FRH name_number of included markers", and "Var_Exp (pval)” means the correlation (Pvlaue) value of each marker on the corresponding gene expression, and "Var_Drug (pval)” Means the correlation (Pvlaue) value of the corresponding marker on the reactivity of the drug, and according to the order of drugs listed in [Table 4], the reactivity correlation value for each drug is divided by the classification factor ⁇ / ⁇ ⁇ Exp_Drug (pval) '', and the correlation (Pvlaue) value of the gene expression corresponding to drug reactivity is arranged according to the order of drugs listed in [Table 4], and "Ave_VarDrug (pval)” is "Var_Drug ( pval) ⁇ means the average value of the calculated values, and ⁇ Rank ⁇ means the number of female cancer drugs that the FRH significantly affects.
  • FIG. 27 correlation values (P-vlaue) values of female biomarkers among major biomarkers among the biomarkers shown in Table 4 are graphically displayed, and in FIG. 28, genes expressed by major biomarkers P-vlaue values for female and female cancer drugs are shown graphically.
  • Reactive biomarkers for female and ovary drugs No. FRH Drug DrugType Var (marker)
  • Table 6 lists the FRHs associated with brain tumor drugs and the markers defining the brain tumor drugs and FRHs associated with them.
  • FRH is expressed in the form of "FRH name_number of included markers", and "Var_Exp (pval)” means the correlation (Pvlaue) value of each marker on the corresponding gene expression, and "Var_Drug (pval)” Means the correlation (Pvlaue) value of the corresponding marker on the reactivity of the drug, and according to the order of drugs listed in [Table 6], the reactivity correlation value for each drug is divided into the classification factor ⁇ / ⁇ ⁇ Exp_Drug (pval) '', the gene expression has a correlation (Pvlaue) value for drug responsiveness according to the order of drugs listed in [Table 6], and "Ave_VarDrug (pval)” is "Var_Drug ( pval) ⁇ means the average value of the calculated values, and ⁇ Rank ⁇ means the number of brain tumor drugs that significantly affect the corresponding FRH.
  • FIG. 29 the correlation values (P-vlaue) values of brain biomarkers among major biomarkers among the biomarkers listed in Table 6 are graphically displayed, and in FIG. 30, genes expressed by the major biomarkers And P-vlaue values for brain tumor drugs are graphed.
  • FRH is expressed in the form of "FRH name_number of included markers", and "Var_Exp (pval)” means the correlation (Pvlaue) value of each marker on the corresponding gene expression, and "Var_Drug (pval)” Means the correlation (Pvlaue) value of the corresponding marker on the reactivity of the drug, and according to the order of the drugs listed in [Table 8], the reactivity correlation values for each drug are divided into the classification factor ⁇ / ⁇ ⁇ Exp_Drug (pval) '', the gene expression has a correlation (Pvlaue) value for drug reactivity according to the order of drugs listed in [Table 8], and "Ave_VarDrug (pval)” is "Var_Drug ( pval) ⁇ means the mean of the calculated values, and ⁇ Rank ⁇ means the number of head and neck cancer drugs that the FRH significantly affects.
  • FIG. 31 correlation values (P-vlaue) values of the head and neck cancer drugs of the major biomarkers among the biomarkers listed in Table 8 are graphically displayed, and FIG. 32 shows genes expressed by the main biomarkers. The correlation values (P-vlaue) values for both the head and neck cancer drugs are plotted.
  • Reactive biomarker for head & neck drugs No. FRH Drug DrugType Var (marker)
  • Table 10 lists the FRHs associated with the lung cancer drug and the markers defining the lung cancer drugs and FRHs associated with them.
  • FRH was expressed in the form of "FRH name_number of included markers", and "Var_Exp (pval)” means the correlation (Pvlaue) value of each marker on the corresponding gene expression, and "Var_Drug (pval)” Means the correlation (Pvlaue) value of the corresponding marker on the reactivity of the drug, and according to the order of drugs listed in [Table 10], the reactivity correlation value for each drug is divided into the classification factor ⁇ / ⁇ ⁇ Exp_Drug (pval) '', the gene expression has a correlation (Pvlaue) value for drug responsiveness according to the order of drugs listed in [Table 10], and "Ave_VarDrug (pval)” is "Var_Drug ( pval) ⁇ means the average of the calculated values, and ⁇ Rank ⁇ means the number of lung cancer drugs that the FRH significantly affects.
  • FIG. 33 correlation values (P-vlaue) values of lung biologic drugs of the major biomarkers among the biomarkers listed in Table 10 are graphically displayed, and in FIG. 34, genes expressed by the major biomarkers And P-vlaue values for lung cancer drugs are plotted.
  • Table 12 lists the FRHs associated with the sarcoma cancer drug, and the markers defining the sarcoma cancer drugs and FRHs associated therewith.
  • FRH is expressed in the form of "FRH name_number of included markers", and "Var_Exp (pval)” means the correlation (Pvlaue) value of each marker on the corresponding gene expression, and "Var_Drug (pval)” Means the correlation (Pvlaue) value of the corresponding marker on the reactivity of the drug, and according to the order of drugs listed in [Table 12], the reactivity correlation value for each drug is divided into the classification factor ⁇ / ⁇ ⁇ Exp_Drug (pval) '', the gene expression has a correlation (Pvlaue) value for drug reactivity according to the order of drugs listed in [Table 12], and "Ave_VarDrug (pval)” is "Var_Drug ( pval) ⁇ means the average value of the calculated values, and ⁇ Rank ⁇ means the number of sarcoma cancer drugs in which the corresponding FRH significantly affects.
  • biomarkers of ABL see Table 15
  • 132 biomarkers of CHROMATIN see Table 16
  • 832 biomarkers of EGFR see Table 17
  • ERK_MAPK There are 42 biomarkers (see Table 18), 153 biomarkers of IGFR (see Table 19), and 88 biomarkers of MITOSIS (see Table 20).
  • [Table 15] to [Table 20] below include biomarkers discovered by the present invention for each drug type.
  • the form and expression method of the biomarker are the same as the biomarker classified for each cancer type.
  • DrugClass targeting specific pathway & signaling division DrugClass Drugs Pathway & signaling
  • DrugClass Drugs Pathway & signaling Target gene & function
  • Table 15 shows the biomarkers associated with ABL drugs and the degree of reactivity (P-value) of the drugs.
  • FRH was expressed in the form of "FRH name_number of markers included", and "Var_Drug (pval)" refers to the value of the correlation (P-vlaue) of the marker on drug reactivity.
  • the correlation value for each drug is divided into the division factor ⁇ / ⁇ , and ⁇ Var_Exp (pval) ⁇ means the correlation (Pvlaue) value that each marker has on the corresponding gene expression.
  • the values of the reactivity correlation for each drug are classified as the classification factor ⁇ / ⁇ .
  • Table 16 shows the biomarkers associated with the CHROMATIN drug and the degree of reactivity (P-value) of the drug by the table.
  • FRH was expressed in the form of "FRH name_number of markers included", and "Var_Drug (pval)" refers to the correlation (P-vlaue) value of the marker on drug reactivity.
  • the correlation value for each drug is divided into the division factor ⁇ / ⁇ , and ⁇ Var_Exp (pval) ⁇ means the correlation (Pvlaue) value that each marker has on the corresponding gene expression.
  • the values of the reactivity correlation for each drug are classified as the classification factor ⁇ / ⁇ .
  • Table 17 shows the biomarkers associated with EGRF drugs and the degree of reactivity (P-value) of the drugs by the tables.
  • FRH was expressed in the form of "FRH name_number of markers included", and "Var_Drug (pval)" means the correlation (P-vlaue) value of the corresponding marker on drug reactivity.
  • the correlation value for each drug is divided into the division factor ⁇ / ⁇ , and ⁇ Var_Exp (pval) ⁇ means the correlation (Pvlaue) value that each marker has on the corresponding gene expression.
  • the values of the reactivity correlation for each drug are classified by the classification factor ⁇ / ⁇ .
  • correlation values (P-vlaue) values of EGRF drugs of major biomarkers among the biomarkers described in [Table 17] are displayed in a graph.
  • Table 18 shows the biomarkers associated with the ERK_MAPK drug and the degree of reactivity (P-value) of the drug by the table.
  • FRH was expressed in the form of "FRH name_number of markers included", and "Var_Drug (pval)" refers to the value of the correlation (P-vlaue) that the marker has on the reactivity of the drug.
  • the correlation value for each drug is divided into the division factor ⁇ / ⁇
  • ⁇ Var_Exp (pval) ⁇ means the correlation (Pvlaue) value that each marker has on the corresponding gene expression.
  • the values of the reactivity correlation for each drug are classified as the classification factor ⁇ / ⁇ .
  • Table 19 shows the biomarkers associated with IGFR drugs and the degree of reactivity (P-value) of the drugs.
  • FRH was expressed in the form of "FRH name_number of markers included", and "Var_Drug (pval)" refers to the value of the correlation (P-vlaue) that the marker has on the reactivity of the drug.
  • the correlation value for each drug is divided into the division factor ⁇ / ⁇ , and ⁇ Var_Exp (pval) ⁇ means the correlation (Pvlaue) value that each marker has on the corresponding gene expression.
  • the values of the reactivity correlation for each drug are classified as the classification factor ⁇ / ⁇ .
  • correlation values (P-vlaue) values of IGFR drugs of major biomarkers among the biomarkers described in [Table 19] are displayed in a graph.
  • Table 20 shows the biomarkers associated with the MITOSIS drug and the degree of reactivity (P-value) of the drug by the table.
  • FRH was expressed in the form of "FRH name_number of markers included", and "Var_Drug (pval)" refers to the correlation value (P-vlaue) value of the corresponding marker on drug reactivity.
  • the correlation value for each drug is divided into the division factor ⁇ / ⁇ , and ⁇ Var_Exp (pval) ⁇ means the correlation (Pvlaue) value that each marker has on the corresponding gene expression.
  • the values of the reactivity correlation for each drug are classified as the classification factor ⁇ / ⁇ .
  • correlation values (P-vlaue) values of the major biomarkers among the biomarkers described in [Table 20] for MITOSIS drugs are displayed in a graph.
  • Fig. 23 shows a method of manufacturing a test chip used for biomarker detection for the co-diagnosis of drugs for 6 cancer types summarized in [Table 1] and 6 drug types summarized in [Table 14]. It is done.
  • a diagnostic chip for each biomarker for the cancer type and drug type specified in [Table 1] and [Table 14] is produced, or the cancer type and drug type All of the biomarkers for or all of them are made of one chip, and for the FRH marker associated with the drug type or cancer type from the patient, the illustrated example of FIG. 18 (EGFR pathway, one of the drug types of blood cancer) )
  • the drug reactivity can be predicted by generating a linear regression prediction model of FIG. 20.
  • various cancer and cancer drug companion diagnostic chips can be manufactured and utilized as shown in FIG. 23 using FRH biomarkers specific to the six cancer types (CancerClass) and six drug types (DrugClass) summarized in this patent.
  • each of the markers disclosed in [Table 2] was calculated to be significant, and among these markers, markers associated with MYB gene expression 13_37746458_AA_A, 16_46710765_T_A, 1_63382198_CA_C , 14_68477626_CT_C and 7_151221500_CT_C were analyzed as major marker combinations.
  • biomarker composition according to the present invention may be configured by various combinations by further including the biomarkers disclosed in [Table 2] in addition to the aforementioned markers.
  • markers 1_13248174_T_C (marker 1) associated with IRX5 gene expression
  • 16_85071785_A_G (marker 2)
  • 9_34371054_A_C (marker 3) were analyzed as the major marker combinations.
  • biomarker composition according to the present invention may be configured by various combinations by further including the biomarkers disclosed in [Table 4] in addition to the aforementioned markers.
  • each marker disclosed in [Table 6] was calculated to be significant, and among these markers, markers associated with PTRF gene expression 11_12162199_T_G (Marker 1), 13_113159942_A_G (Marker 2), 5_181155466_T_G (Marker 3), 11_65547388_T_G (Marker 4), 10_11921140_T_G (Marker 5), 17_55774876_T_G (Marker 6), 12_40313960_G_T (Marker 7), 16_1817242_T_C (Marker 8) was analyzed.
  • biomarker composition according to the present invention may be configured by various combinations by further including the biomarkers disclosed in [Table 6] in addition to the aforementioned markers.
  • each marker disclosed in [Table 8] was calculated to be significant, and among these markers, markers related to RAI14 gene expression 14_21082261_T_G (Marker 1) And 7_140753336_A_T (Marker2) were analyzed as the main marker combination.
  • biomarker composition according to the present invention may be configured by various combinations by further including the biomarkers disclosed in [Table 8] in addition to the aforementioned markers.
  • markers associated with POLR3K gene expression 2_217847583_C_T (Marker 1), 2_71424561_G_GT (Marker 2), 2_217847883_G_A (Marker 3), 2_217848003_A_G (Marker 4) and 17_81206909_A_C (Marker 5) were analyzed as the major marker combinations.
  • biomarker composition according to the present invention may be configured by various combinations by further including the biomarkers disclosed in [Table 10] in addition to the aforementioned markers.
  • each of the markers disclosed in [Table 12] was calculated to be significant, and among these markers, marker 1_26457907_T_G (marker 1) associated with LUC7L gene expression , 22_22643666_C_G (marker 2), 22_21127217_C_G (marker 3), 5_141432375_A_C (marker 4) and 22_18847351_G_C (marker 5) were analyzed as the major marker combinations.
  • biomarker composition according to the present invention may be configured by various combinations by further including the biomarkers disclosed in [Table 10] in addition to the aforementioned markers.
  • each of the markers disclosed in [Table 15] was calculated to be significant, and among these markers, the markers 15_59223315_G_A and HMGB1 were associated with gene expression.
  • Marker 13_30465849_T_C was analyzed as the main marker combination.
  • biomarker composition according to the present invention may be configured by various combinations by further including the biomarkers disclosed in [Table 15] in addition to the aforementioned markers.
  • each marker disclosed in [Table 16] was calculated to be significant, and among these markers, markers 2_72891611_A_C and ERRFI1 associated with SPR gene expression Associated markers 1_8013331_A_G were analyzed as the major marker combination.
  • biomarker composition according to the present invention may be configured by various combinations by further including the biomarkers disclosed in [Table 16] in addition to the aforementioned markers.
  • each of the markers disclosed in [Table 17] was calculated to be significant, and among these markers were associated with marker 9_111597344_C_A and HDHD2 gene expression associated with PTGR1 gene expression.
  • Markers 18_47115026_C_T were analyzed for major marker combinations.
  • biomarker composition according to the present invention may be configured by various combinations by further including the biomarkers disclosed in [Table 17] in addition to the aforementioned markers.
  • biomarker composition according to the present invention may be configured by various combinations by further including the biomarkers disclosed in [Table 18] in addition to the aforementioned markers.
  • each of the markers disclosed in Table 19 was calculated to be significant, and among these markers, the markers X_119618658_T_G and EPHA2 associated with SEPT6 gene expression were associated Markers 1_16148437_A_C were analyzed as the main marker combination.
  • biomarker composition according to the present invention may be configured by various combinations by further including the biomarkers disclosed in [Table 19] in addition to the aforementioned markers.
  • each of the markers disclosed in [Table 20] was calculated to be significant, and among these markers, markers 1_85581605_TA_T and MYB associated with CYR61 gene expression and Associated marker 6_135201764_A_C was analyzed with the main marker combination.
  • biomarker composition according to the present invention may be configured by various combinations by further including the biomarkers disclosed in [Table 20] in addition to the aforementioned markers.
  • the definition of the drug type is a type of drug targets and a functionally similar functional type of drug, or a gene group, drug pathway, or drug signaling of a category in which the drug is affected.
  • Pathway drug signaling pathway
  • DrugClass generic drug type
  • FRH is a term for functionally related relative haplotype
  • eQTL is a term for expression quantitative trait loci for expression
  • IC50) is over / under )
  • the term indicates the average value of IC50 for the expressed cluster
  • IC50) is the term indicating the average value of IC50 for the amplification / deletion cluster
  • IC50) is the given loci
  • GDSC is the term for Genomics of Drug Sensitivity in Cancer
  • CCLP is the term for COSMIC Cell Lines Project
  • the web page is http://cancer.sanger.ac.uk/cell_lines
  • CNV is a term for copy number variation
  • GBLscan is a term for Cancer Biomarker Labeling Scan
  • the present invention is a novel linear regression model that can reliably predict drug responsiveness by combining analysis of disease-specific specific gene mutation fingerprints including cancer and molecular pharmacological functional groups of drugs, drug indications and response prediction systems, and
  • the method relates to GBLscan (Genetic Biomarker Scan), and according to the present invention, in the present invention, the degree of reactivity of the drug with a genome whose pharmacological effect is unknown from the results of drug reactivity to the genome collected from in vitro and in vivo clinical trials There is an effect that can be predicted.

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Abstract

La présente invention concerne une composition de biomarqueur de détermination de la réactivité d'un médicament contre un cancer découvert à l'aide d'un balayage de marquage de biomarqueur génétique (GBLscan), qui est un système et une méthode d'indication de médicament et de prédiction de réactivité de médicament, en tant que nouveau modèle d'apprentissage pouvant prédire de manière fiable la réactivité d'un médicament par application d'une analyse de couplage à un profil moléculaire du médicament après transformation d'empreintes de variation génétique spécifiques associées à des maladies comprenant le cancer dans un haplotype comportant des informations fonctionnelles, une méthode de détermination de réactivité d'un médicament contre le cancer à l'aide de la composition de biomarqueur, et une puce de diagnostic de détection d'une composition de biomarqueur de détermination de réactivité d'un médicament contre le cancer.
PCT/KR2019/013717 2018-10-18 2019-10-18 Composition de biomarqueur de détermination de réactivité d'un médicament contre le cancer, méthode de détermination de réactivité d'un médicament contre le cancer à l'aide de la composition de biomarqueur, et puce de diagnostic de détection de composition de biomarqueur de détermination de réactivité d'un médicament contre le cancer WO2020080871A2 (fr)

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