WO2020080871A9 - 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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WO2020080871A9
WO2020080871A9 PCT/KR2019/013717 KR2019013717W WO2020080871A9 WO 2020080871 A9 WO2020080871 A9 WO 2020080871A9 KR 2019013717 W KR2019013717 W KR 2019013717W WO 2020080871 A9 WO2020080871 A9 WO 2020080871A9
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marker
biomarker composition
reactivity
drug
determining
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WO2020080871A2 (fr
WO2020080871A3 (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

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  • the present invention predicts a haplotype biomarker including functional information using cancer drug reactivity and gene expression information and copy number variation (CNV), so that cancer drug responsiveness can be reliably predicted and drug labeling.
  • NGS next generation sequencing
  • GDSC Genetic of Drug Sensitivity in Cancer
  • GDSC is an example of a publicly available database.
  • GDSC is a public database that experimentally measures drug toxicity information of 1,070 human cancer cells for 265 anticancer compounds.
  • GDSC cell line project used here was published at the following site (CCLP: COSMIC Cell Lines Project, http://cancer.sanger.ac.uk/cell_lines). These common resources are expected to be of great help in realizing genome-based precision cancer treatments.
  • Deep learning learning method is a field of technology that performs deep machine learning from a large amount of high-dimensional raw data.
  • PubChem (pubchem.ncbi.nlm.nih.gov) is operated by the National Technical Information Center (NCBI) in the United States, and provides information on about 100 million compounds, 200 million substances and bioassays. I have it (en.wikipedia.org/wiki/PubChem).
  • the Padell method can be expressed as 1,875 (1D and 2D 1,444, and 3D 431) features and 12 fingerprints (about 16,092 bits in total) in drugs.
  • QSAR quantitative structure activity relationship
  • drug development using drug cytotoxicity data drug development using drug cytotoxicity data
  • expression control of whole genome sequencing based on deep learning, structural variation, etc. are independently applied and utilized. Became.
  • CDRscan Cancer drug response scanning
  • IC50 drug-cell lines-toxicity
  • a Padell method or the like may be applied.
  • Genomic fingerprint or a set of mutation features
  • the second can be explained by the literature method of the full-length genome (or target protein) fingerprint (Genomic fingerprint, or a set of mutation features), and is the most standard deep learning method.
  • this method can be used in a clinical decision supporting system for accurate drug response prediction model and drug reuse/repositioning, screening of chemical substances, discovery of new anticancer drug candidates, and selection of patient-specific anticancer drugs. .
  • CDRscan cancer drug reaction scan
  • eQTL expression and Quantitative Trait Loci
  • pQTL protein and quantitative trait loci
  • cQTL Copy Number Variation and Quantitative Trait Loci
  • CNV copy number variation
  • GBLscan gene biomarker label scan
  • eQTL expression and Quantitative Trait Loci
  • cQTL Copy Number Variation and Quantitative Trait Loci
  • the definition of the drug type is the type of drug targets and functional similar functional types of the drug, or a gene group, drug pathway, or drug signaling in the category affected by the drug. All of the pathways (drug signaling pathways), etc. are described as one generic drug type (DrugClass).
  • the functional haplotype is different from the haplotype for several locuses that have been phasing in the gene, which is a commonly used haplotype, in the present invention, Dec.
  • Genome Reference Consortium Human 38 (GCA_000001405.15)
  • GCF_000001405.26 When doing so, information is read in the form of a genotype at each gene locus. These genotypes are expressed in the form of 1:homo, 2:hetero or 3: alt home, and such a vowel of the genotype can be called a relative halfrotype. And if a relative haplotype composed of a single or a combination of a specific locus is related to the expression of a specific gene, this is defined as a functional subprotype in the present invention.
  • the present invention has been devised to solve the conventional problems as described above, and the present invention is based on the technical background and social demands as described above, the reactivity of cancer drugs, the genetic characteristics and fingerprints of the genome to be reacted with functional information. It is to provide a system for predicting drug indication and sensitivity with high precision by converting to a haplotype, and a specific object of the present invention is to provide a known non-clinical cell line genome, their gene expression information and gene copy number variation, and in vivo It is to provide a predictive system that can reliably predict drug response-related quantitative trait loci (QTL) information-based linear regression modeling and deep learning machine learning.
  • QTL quantitative trait loci
  • a multivariate-based haplotype with functional information is generated by collecting quantitative trait positions (QTLs) having a high correlation with gene RNA expression and copy number variation (CNV).
  • QTLs quantitative trait positions
  • CNV copy number variation
  • the present invention comprises the steps of generating a collection of positions of quantitative trait positions (QTL) to be examined; Calculating a functional haplotype (FRH) and a drug response correlation; And, it consists of generating a functional haplotype-based drug response prediction model.
  • steps (A) and (B) include both gene expression and gene copy number mutations as common variables, and by using gene expression information, which is a common variable, the association with multiple or single mutations affecting drug response. Collect the mutations you have. This is called a collection of positions of quantitative trait positions (QTLs).
  • QTLs quantitative trait positions
  • a functional haplotype is created, and drug reactivity information is extracted from the same cell line, and these functional haplotypes are used as a functional haplotype, FRH (Functionally relevant haplotope). ), and FRH is expressed as a hash number.
  • the haplotype (FRH) was changed to a hash number, and the converted hash number was used to calculate drug reactivity information and Perform the Pairwise Pearson Test and repeat the above operation for all carcinomas.
  • a process related to creating a drug pathway-based drug response prediction model in a specific cancer is performed as follows.
  • the present invention calculates functional haplotypes by utilizing a plurality of mutations in a nucleotide sequence collected from the genome to be tested, and : Calculating a solution of the linear regression model; It includes a system for selecting highly sensitive cancer drugs among cancer drugs by using this solution.
  • the present invention comprises the steps of: (A) calculating a collection of positions of quantitative trait positions (QTLs) for mutations affecting a drug response; (B) generating a functional subflow type for the mutation contained in the quantitative trait location (QTL); (C) calculating a mutation and a drug response correlation based on the functional haplotype; And (D) predicting drug responsiveness based on the functional haplotype.
  • the calculation of the position collection of the quantitative trait positions (QTLs) in the (A) step includes the steps of: (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 variants (CNVs) included in the plurality of cell line genomes; (A3) calculating gene expression information on gene RNA expression or gene copy number variation that commonly affects the correlation in the (A1) and (A2) steps; And (A4) calculating a single or multiple mutations involved in the gene expression information, and calculating a quantitative trait location (QTL) for mutations affecting the drug response. It may be performed including the step of generating a collection of locations.
  • step (B) may be generated by extracting the haplotype for single or multiple mutations included in the quantitative trait location (QTL) from the genome-integrated DB with drug reactivity.
  • the functional haplotype includes information on the correlation between gene RNA expression (Exp) and drug responsiveness; It may also contain information on the correlation between gene loci and drug responsiveness.
  • the functional haplotype the correlation information between gene copy number variation (CNV) and drug responsiveness; It may also contain information on the correlation between gene loci and drug responsiveness.
  • the gene loci may be a collection of locuses selected based on a quantitative trait location (QTL).
  • QTL quantitative trait location
  • the genome integration DB may be a cell line integration DB composed of a genotype.
  • the drug reactivity may be expressed as an ln Ic50 value.
  • RNA expression may be classified into over-expression, normal and under-expr profession.
  • the CNV may be classified into amplification, normal, and deletion.
  • the calculation of the correlation in the (C) step includes the steps of: (C1) determining gene RNA expression (Exp) associated with gene loci; (C2) determining drug responsiveness to the gene loci by calculating drug responsiveness to the gene RNA expression (Exp) from the functional haplotype; And (C3) comparing and verifying the drug responsiveness calculated from the correlation information between the gene loci and drug responsiveness included in the functional haplotype and the drug responsiveness determined in the step (C2). It may also be performed including.
  • step (C1) may be performed by discriminating the cell line containing the gene loci and the gene RNA expression (Exp) information contained in the cell line.
  • step (D) when the difference in drug response to the difference in gene RNA expression is greater in underexpression than overexpression, it is determined that underexpression of gene RNA is sensitive to drug response; If the difference in drug response to the difference in gene RNA expression is less under-expression than over-expression, it may be determined that over-expression of gene RNA is sensitive to drug response.
  • the difference between the absolute value of the drug response to the difference in gene RNA expression of the overexpression group is the highest sensitivity of the drug response to the difference in gene RNA expression of the underexpression group. If it is greater than the difference value, it is determined that overexpression of the gene RNA is associated with the gene loci type; If the difference between the absolute maximum sensitivity of the drug response to the difference in gene RNA expression of the overexpressing group is less than the difference between the maximum sensitivity of the drug response to the difference in gene RNA expression of the underexpression group, the underexpression of the gene RNA is gene multiplex. It can also be determined to be related to the loci type.
  • the functional subflow type may be expressed as a hash number.
  • the correlation between the functional haplotype and the drug response may be calculated by performing a drug response information and a pairwise Pearson Test using a hash number.
  • the present invention includes a functional haplotyping system using drug responsiveness, gene expression information, and copy number variation to generate a functional haplotype by the method as described above.
  • the functional haplotype may be used for drug-responsive diagnosis, discovery of biomarkers for companion diagnosis to select patient groups for drugs, discovery of drug targets, or virtual clinical for drugs.
  • the present invention can derive the reactivity correlation of the pharmacological functional groups constituting the drug with the mutation information of the genome, when the mutation of the genome to be analyzed and the pharmacological functional groups of the drug are extracted, the degree of reactivity of the drug with respect to the genome is determined. There is an effect that can be predicted reliably.
  • the present invention can derive the reactivity correlation of the drug type with the mutation characteristic information of the genome, knowing the characteristic information and the drug type information about the mutation 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 of an unknown polymer compound (a substance for drug development) to a cell line or human body containing a specific genome before clinical trials, thereby significantly reducing the time and cost of new drug development.
  • an unknown polymer compound a substance for drug development
  • the degree of reactivity to genomes other than those that have been clinically identified can be predicted in advance, thus significantly reducing research costs and time for discovery of other uses and side effects for existing drugs. There is.
  • FIG. 1 is an exemplary view showing an example of quantitative trait location (QTL), genetic replication number variation, and RNA expression for predicting drug response according to the present invention.
  • QTL quantitative trait location
  • RNA expression for predicting drug response according to the present invention.
  • Figure 2 is an exemplary diagram showing a drug reactivity calculation schema using a genetic biomarker according to the present invention.
  • Figure 3 is an exemplary view showing the relationship between the genetic biomarker and drug responsiveness of breast cancer 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 between the cell line drug response value (IC50) and functional haplotype according to the present invention.
  • Figure 6 is an exemplary view showing the relationship between the quantitative trait position (QTL) and drug response according to the present invention.
  • FIG. 7 is an exemplary diagram showing a mutation, RNA expression, gene replication mutation and drug response network according to the present invention.
  • FIG. 8 is an exemplary view showing an example of mutation and correlation cell line, cancer, drug reaction, RNA expression and CNV amplification according to the present invention.
  • FIG. 9 is an exemplary diagram showing a calculation schema for a correlation between functional haplotype and drug reactivity according to the present invention.
  • Figure 10 is an exemplary diagram showing a functional haplotype and drug responsive network according to the present invention.
  • FIG. 11 is an exemplary view showing an example of functional haplotype and gene expression and gene replication mutation according to the present invention.
  • FIG. 12 is an exemplary view showing the definition of a functional ha flow type according to the present invention.
  • FIG. 13 is an exemplary view showing an example of predicting the FRH method and gene expression and drug responsiveness according to the present invention.
  • FIG 14 is an exemplary view showing an example in which the difference between the average IC50 value is 1.0 as an example of the FRH according to the present invention.
  • 15 is an exemplary view showing an example in which the difference between the average IC50 value is 1.5 as an example of the FRH according to the present invention.
  • 16 is an exemplary view showing an embodiment of functional haplotype and gene expression according to the present invention.
  • 17 is an exemplary view showing an example of generating a drug response prediction model based on a specific cancer drug pathway according to the present invention.
  • FIG. 18 is an exemplary view showing the relationship between functional haplotype and drug responsiveness R ⁇ 2 according to the present invention.
  • FIG. 19 is an exemplary view showing an example of FRH having a high correlation between the Afatinib drug and the FRH in the example shown in FIG. 18.
  • FIG. 20 is an exemplary diagram showing an example of a prediction model using FRH having a high correlation between Afatinib and the drug in FRH in the example shown in FIG. 18.
  • Fig. 21 is an exemplary view showing an example of predicting drug responsiveness of blood cancer using FRH according to the present invention.
  • FIG. 22 is an exemplary view showing an example of a usable system using the 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 the configuration of a biomarker expression structure according to the present invention.
  • 25 is a graph showing correlation (P-vlaue) values of major biomarkers for blood cancer drugs.
  • 26 is a graph showing correlation (P-vlaue) values for genes expressed by major biomarkers and blood cancer drugs.
  • 27 is a graph showing correlation (P-vlaue) values of major biomarkers for female cancer drugs.
  • 29 is a graph showing correlation (P-vlaue) values of major biomarkers for brain tumor drugs.
  • 30 is a graph showing correlations (P-vlaue) values for genes expressed by major biomarkers and brain tumor drugs.
  • 31 is a graph showing correlation (P-vlaue) values of major biomarkers for head and neck cancer drugs.
  • FIG. 32 is a graph showing correlation (P-vlaue) values for genes expressed by major biomarkers and drugs for head and neck cancer.
  • 33 is a graph showing correlation (P-vlaue) values of major biomarkers for lung cancer drugs.
  • 34 is a graph showing correlation (P-vlaue) values for genes expressed by major biomarkers and lung cancer drugs.
  • 35 is a graph showing correlation (P-vlaue) values of major biomarkers for sarcoma cancer drugs.
  • 36 is a graph showing correlation (P-vlaue) values for genes expressed by major biomarkers and sarcoma cancer drugs.
  • 39 is a graph showing correlation (P-vlaue) values of major biomarkers for EGRF drugs.
  • 40 is a graph showing correlation (P-vlaue) values of major biomarkers for ERK_MAPK drugs.
  • 41 is a graph showing correlation (P-vlaue) values of major biomarkers for IGFR drugs.
  • Fig. 42 is a graph showing correlation (P-vlaue) values of major biomarkers for MITOSIS drugs.
  • a preferred embodiment of the present invention is a biomarker composition consisting of a combination of markers containing mutations that control gene expression for determining reactivity to blood cancer drugs, 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), comprising a biomarker composition for determining blood cancer drug reactivity.
  • biomarker composition may further include any one or more markers among the markers shown 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 the reactivity to a blood cancer drug by detecting a biomarker composition consisting of a combination of markers containing a mutation that controls gene expression in order to determine the reactivity to a blood cancer drug, the
  • the biomarker composition includes 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).
  • the biomarker composition may further include any one or more markers among the markers shown in Table 21 below.
  • the present invention is a diagnostic chip for detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine reactivity to a blood cancer drug, wherein the biomarker composition to be detected 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).
  • the biomarker composition may further include any one or more markers among the markers described in Table 21 below.
  • a preferred embodiment of the present invention is a biomarker composition consisting of a combination of markers containing mutations that control gene expression for determining reactivity to female cancer drugs, 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 biomarker composition for determining the reactivity of female cancer drugs, wherein the biomarker composition is 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 consisting of a combination of markers containing a mutation that controls 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 markers among the markers listed in Table 22.
  • the present invention provides a diagnostic chip for detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine reactivity to female cancer drugs, wherein the biomarker composition, which is a detection target, 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 detection of a biomarker composition for determining female cancer drug reactivity, wherein the biomarker composition is shown in Table 22. It may be configured to further include any one or more markers among the described markers.
  • the biomarker composition in the biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine reactivity to brain tumor drugs, 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_G (marker 6), 12_40313960_G_T_C242_T_934 (marker 169_T (marker 7), 169_1817) and 2113C_934) It includes a biomarker composition for determining the reactivity of a brain tumor drug comprising, in this case, the biomarker composition may further include any one or more of the markers listed in Table 23.
  • the present invention is a method for determining the reactivity to a brain tumor drug by detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to a brain tumor drug, the bio Marker composition, 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 5), 16817_1 (12242_403), G_960_1 (12242_403), marker 6), ) And 2_113934259_C_T (marker 9), and a method for determining brain tumor drug reactivity using a biomarker composition, wherein the biomarker composition further includes any one or more markers among the markers listed in Table 23. It could be.
  • the present invention provides a diagnostic chip for detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine reactivity to a brain tumor drug, wherein the biomarker composition to be detected 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_259_1_C_T_7 (Marker 16933_C_T_7), 113 93 ), and a diagnostic chip for detecting a biomarker composition for determining brain tumor drug reactivity, wherein the biomarker composition may further include any one or more markers among the markers listed in Table 23. .
  • a preferred embodiment of the present invention is a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine reactivity to a head and neck cancer drug, wherein the biomarker composition is 14_21082261_T_G (marker 1) and a biomarker composition for determining the reactivity of a head and neck cancer drug comprising: 1) and 7_140753336_A_T (marker 2), wherein the biomarker composition further includes any one or more markers among the markers listed in Table 24 It can also be configured.
  • the present invention in the method for determining the reactivity to the head and neck cancer drug by detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to the head and neck cancer drug, the The biomarker composition includes a method for determining drug reactivity for head and neck cancer using a biomarker composition comprising 14_21082261_T_G (marker 1) and 7_140753336_A_T (marker 2), wherein the biomarker composition includes the markers shown in Table 24 It may be configured to further include any one or more markers.
  • the biomarker composition to be detected is 14_21082261_T_G (marker 1) and a diagnostic chip for detection of a biomarker composition for determining drug reactivity of head and neck cancer comprising 1) and 7_140753336_A_T (marker 2), wherein the biomarker composition includes at least one marker among the markers listed in Table 24 It may be configured to further include.
  • the biomarker composition in the biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to lung cancer drugs, 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 lung cancer drugs, wherein the biomarker composition is , It may be configured to further include any one or more markers among the markers listed in Table 25.
  • the present invention in the method for determining the reactivity to lung cancer drugs by detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression, to determine the reactivity to lung cancer drugs, 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), and ,
  • 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 consisting of a combination of markers containing mutations that control gene expression for determining reactivity to lung cancer drugs, the biomarker composition being a detection target, 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 detecting a biomarker composition for determining the reactivity of lung cancer drugs,
  • the biomarker composition may further include any one or more markers among the markers listed in Table 25.
  • the biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to the sarcoma cancer drug
  • 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) comprising a biomarker composition for determining the reactivity of a 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 in the method for determining the reactivity to the sarcoma cancer drug by detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to the sarcoma cancer drug, the The biomarker composition includes 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).
  • the biomarker composition may further include any one or more markers among the markers shown in Table 26.
  • the present invention is a diagnostic chip for detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to a sarcoma cancer drug, the biomarker composition being a detection target, 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), including a diagnostic chip for detection of a biomarker composition for determining the reactivity of sarcoma cancer drugs
  • the biomarker composition may further include any one or more markers among the markers shown in Table 26.
  • the biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to ABL drugs
  • the biomarker composition is 15_59223315_G_A (marker 1 ) And 13_30465849_T_C (marker 2) comprising a biomarker composition for determining ABL drug reactivity
  • the biomarker composition further includes any one or more markers among the markers listed in Table 27. May be.
  • the present invention is a method for determining the reactivity to ABL drugs by detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to ABL drugs, the biomarker
  • the composition includes a method for determining ABL drug reactivity using a biomarker composition comprising 15_59223315_G_A (marker 1) and 13_30465849_T_C (marker 2), wherein the biomarker composition is any one of the markers listed in Table 27 It may be configured to further include the above markers.
  • the present invention is a diagnostic chip for detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to an ABL drug, the biomarker composition as a detection target, 15_59223315_G_A (Marker 1) and 13_30465849_T_C (marker 2) comprising a diagnostic chip for detection of a biomarker composition for determining ABL drug reactivity, wherein the biomarker composition includes any one or more of the markers listed in Table 27. It may be configured to further include.
  • the biomarker composition in a biomarker composition consisting of a combination of markers containing mutations that control gene expression, in order to determine reactivity to CHROMATIN drugs, the biomarker composition is 2_72891611_A_C (marker 1 ) And a biomarker composition for determining CHROMATIN drug reactivity comprising 1_8013331_A_G (marker 2), wherein the biomarker composition further includes any one or more markers among the markers listed in Table 28. May be.
  • the present invention in the method for determining the reactivity to CHROMATIN drugs by detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to CHROMATIN drugs, the biomarker
  • the composition includes 2_72891611_A_C (marker 1) and 1_8013331_A_G (marker 2), and at this time, the biomarker composition may further include any one or more of the markers listed in Table 28.
  • the present invention provides a diagnostic chip for detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine reactivity to CHROMATIN drugs, wherein the biomarker composition to be detected is 2_72891611_A_C
  • a diagnostic chip for detection of a biomarker composition for determining CHROMATIN drug reactivity comprising (marker 1) and 1_8013331_A_G (marker 2), wherein the biomarker composition includes at least one of the markers listed in Table 28. It may be configured to further include a marker.
  • the biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to EGFR drugs
  • the biomarker composition is 9_111597344_C_A (marker 1 ) And 18_47115026_C_T (marker 2) comprising a biomarker composition for determining EGFR drug reactivity, wherein the biomarker composition further includes any one or more markers among the markers listed in Table 29. May be.
  • the present invention in the method for determining the reactivity to EGFR drugs by detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to the EGFR drug, the biomarker
  • the composition includes a method for determining EGFR drug reactivity using a biomarker composition comprising 9_111597344_C_A (marker 1) and 18_47115026_C_T (marker 2), wherein the biomarker composition is any one of the markers listed in Table 29. It may be configured to further include the above markers.
  • the present invention provides a diagnostic chip for detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine reactivity to EGFR drugs, wherein the biomarker composition to be detected is 9_111597344_C_A (Marker 1) and 18_47115026_C_T (marker 2) comprising a diagnostic chip for detection of a biomarker composition for determining EGFR drug reactivity, wherein the biomarker composition is any one or more of the markers listed in Table 29 It may be configured to further include a marker.
  • a preferred embodiment of the present invention is a biomarker composition comprising a combination of markers containing mutations that control gene expression in order to determine reactivity to ERK_MAPK drugs, wherein the biomarker composition is 5_142319698_T_G (marker 1 ) And 12_89350540_G_A (marker 1) comprising a biomarker composition for determining ERK_MAPK drug reactivity, wherein the biomarker composition further comprises any one or more markers among the markers listed in Table 30. May be.
  • the present invention detects a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to the ERK_MAPK drug, and in the method of determining the reactivity to the ERK_MAPK drug, the biomarker
  • the composition includes a method of determining ERK_MAPK drug reactivity 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 provides a diagnostic chip for detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to ERK_MAPK drugs, wherein the biomarker composition to be detected is 5_142319698_T_G
  • a diagnostic chip for detecting a biomarker composition for determining ERK_MAPK drug reactivity comprising (marker 1) and 12_89350540_G_A (marker 1), wherein the biomarker composition includes at least one of the markers listed in Table 30. It may be configured to further include a marker.
  • the biomarker composition consisting of a combination of markers containing mutations that control gene expression for determining reactivity to IGFR drugs
  • the biomarker composition is X_119618658_T_G (marker 1 ) And 1_16148437_A_C (marker 2), and a biomarker composition for determining IGFR drug reactivity
  • the biomarker composition further includes any one or more markers among the markers listed in Table 31. May be.
  • the present invention detects a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine reactivity to IGFR drugs, and in the method of determining reactivity to IGFR drugs, the biomarker
  • the composition includes a method for determining IGFR drug reactivity using a biomarker composition comprising X_119618658_T_G (marker 1) and 1_16148437_A_C (marker 2), wherein the biomarker composition is any one of the markers listed in Table 31 It may be configured to further include the above markers.
  • the present invention provides a diagnostic chip for detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine reactivity to IGFR drugs, wherein the biomarker composition to be detected is X_119618658_T_G (Marker 1) and 1_16148437_A_C (marker 2) comprising a diagnostic chip for detection of a biomarker composition for determining IGFR drug reactivity, 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 biomarker composition in a biomarker composition consisting of a combination of markers containing mutations that control gene expression, for determining reactivity to MITOSIS drugs, is 1_85581605_TA_T (marker 1 ) And 6_135201764_A_C (marker 2) comprising a biomarker composition for determining MITOSIS drug reactivity, wherein the biomarker composition further includes any one or more markers among the markers listed in Table 32. May be.
  • the present invention in the method for determining the reactivity to MITOSIS drugs by detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to the MITOSIS drug, the biomarker
  • the composition includes a method for determining MITOSIS drug reactivity 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.
  • the present invention is a diagnostic chip for detecting a biomarker composition consisting of a combination of markers containing mutations that control gene expression in order to determine the reactivity to MITOSIS drugs, the biomarker composition as a detection target, 1_85581605_TA_T (Marker 1) and 6_135201764_A_C (marker 2) comprising a diagnostic chip for detection of a biomarker composition for determining MITOSIS drug reactivity, wherein the biomarker composition is any one or more of the markers listed in Table 32 It may be configured to further include a marker.
  • FIG. 1 is an exemplary view showing an example of quantitative trait location (QTL), genetic replication number variation, and RNA expression for drug response prediction according to the present invention
  • FIG. 2 is a drug reactivity using the genetic biomarker according to the present invention.
  • An exemplary diagram showing a calculation schema FIG. 3 is an exemplary diagram showing the relationship between the genetic biomarker and drug reactivity of breast cancer according to the present invention
  • FIG. 4 is an NGS input and drug response and RNA expression network according to the present invention
  • 5 is an exemplary diagram showing a correlation between a cell line drug response value (IC50) and a functional haplotype according to the present invention
  • FIG. 6 is a quantitative trait location (QTL) according to the present invention.
  • Figure 7 is an exemplary diagram showing a mutation, RNA expression, gene replication variable and drug response network according to the present invention
  • Figure 8 is a mutation and correlation cell line according to the present invention
  • Cancer, drug reaction, RNA expression, and CNV amplification example is an exemplary diagram
  • Figure 9 is an exemplary diagram showing a calculation schema of the functional haplotype and drug responsiveness correlation according to the present invention
  • Figure 10 It is an exemplary diagram showing a functional haplotype and a drug-reactive network by the present invention
  • FIG. 11 is an exemplary diagram showing an example of functional haplotype and gene expression and gene replication mutation according to the present invention
  • FIG. 12 is a function according to the present invention It is an exemplary diagram showing the definition of a haplotype
  • FIG. 13 is an exemplary diagram showing an example of the FRH method according to the present invention and gene expression and drug reactivity prediction
  • FIG. 14 is an average IC50 value as an example of FRH according to the present invention Is an exemplary diagram showing an embodiment in which the difference of is 1.0
  • FIG. 15 is an exemplary diagram showing an embodiment in which the difference between the average IC50 value is 1.5 as an example of the FRH according to the present invention
  • FIG. 17 is an exemplary diagram showing an example of generation of a drug response prediction model based on a specific cancer drug pathway according to the present invention
  • FIG. 18 is the present invention Is an exemplary diagram showing the relationship between functional haplotype and drug responsiveness R ⁇ 2
  • FIG. 19 is In the example shown in 18, an exemplary diagram showing an example of FRH having a high correlation between the Afatinib drug and the FRH
  • FIG. 20 is a prediction model using the FRH having a high correlation between the Afatinib drug and the FRH in the example shown in FIG. 18
  • Fig. 21 is an exemplary view showing an example of predicting drug reactivity of blood cancer using FRH according to the present invention
  • FIG. 22 is an exemplary view showing an example of predicting drug reactivity of blood cancer using FRH according to the present invention, and Fig. 22 is usable using FRH according to the present invention. It is an exemplary diagram showing an example of the system, and FIG. 23 is an exemplary diagram showing an example of a method of manufacturing a cancer and drug companion diagnostic chip using the FRH biomarker according to the present invention, and FIG. 24 is It is an exemplary diagram explaining the configuration of the biomarker expression structure by.
  • the present invention basically calculates the quantitative trait location (QTL) for (A) gene RNA expression information and copy number variation information that are difficult to be clinically extracted from cancer patients, and (B) non-clinical cell line DB (GDSC and CCLE). ), using common data such as gene RNA expression information, copy number variation information, and drug responsiveness correlation experiment information, and then integrating the step (A) and step (B) to locate the quantitative trait (QTL).
  • QTL quantitative trait location
  • haplotype including functional information is calculated to predict drug reactivity information of cancer patients.
  • FIG. 1 shows examples of quantitative trait location (QTL), genetic replication variable, and RNA expression for predicting drug response. That is, in the example of eQTL experiment in 1, it is shown that mutation and expression are regulated by direct (cis) and indirect (trans) methods, and Drug-Gene Expression in 2 shows the correlation between drug and RNA expression. And Drug Cell viabillity in 3 shows the relationship between drug concentration and cell activity, and Drug-CNV in 4 shows the correlation between drug concentration and gene copy number variation (CNV). And Expression-CNV in 5 shows the correlation between gene expression and copy number variation (CNV).
  • QTL quantitative trait location
  • RNA expression for predicting drug response. That is, in the example of eQTL experiment in 1, it is shown that mutation and expression are regulated by direct (cis) and indirect (trans) methods, and Drug-Gene Expression in 2 shows the correlation between drug and RNA expression. And Drug Cell viabillity in 3 shows the relationship between drug concentration and cell activity, and Drug-CNV in 4 shows the correlation between drug concentration and gene copy number variation
  • Figure 1 shows that the concentration of the drug, RNA expression, and gene replication number variation (CNV) have a correlation, and in many cases, RNA expression and gene replication number variation (CNV) are the causes thereof. Shows that there is a correlation with single and multiple mutations.
  • the genetic biomarkers are composed of reproductive and somatic mutations, driver mutations, copy number mutations, functional haplotype mutations, epigenetic mutations such as methylation and gene expression.
  • Figure 3 shows an example of the relationship between the genetic biomarkers of breast cancer and drug responsiveness.
  • Figure 4 shows an example of the NGS input, drug reaction and RNA expression network.
  • the basic structure of the NGS input and drug response and RNA expression network is: A) predicting the amplification/reduction of gene RNA overexpression/low expression and gene copy number variation based on quantitative trait location (QTL) previously calculated from NGS input data, B) Using common drug responsiveness data in vitro, gene expression data, and correlation information, C) predicting quantitative trait location (QTL)-based drug response in clinical (in vivo).
  • the present invention can be described as a process of A) obtaining quantitative trait location (QTL) information, B) converting gene RNA expression information and drug responsiveness experimental data into a library (DB), and C) predicting drug responsiveness. This is because, in general, it is difficult or almost impossible to obtain drug response information and gene expression information of step B) for a patient in a short time.
  • FIGS. 1 to 4 show single and multiple mutation information related to genomic information and RNA expression information-based quantitative trait location (QTL) information from a patient, and a non-clinical cell line that is common drug responsiveness information. It is a method of making connections with experimental information and applying them to patients in the clinic (in vivo).
  • QTL quantitative trait location
  • FIG. 5 is an exemplary diagram illustrating the correlation between the cell line drug response value (IC50) and the functional haplotype, and FIG. 5 shows an example of finally calculating the correlation between the cell lines and drug responsiveness information and the functional haplotype by Pearson correlation.
  • IC50 cell line drug response value
  • FIG. 5 shows an example of finally calculating the correlation between the cell lines and drug responsiveness information and the functional haplotype by Pearson correlation.
  • IC50 cell line drug response value
  • FIG. 5 shows an example of finally calculating the correlation between the cell lines and drug responsiveness information and the functional haplotype by Pearson correlation.
  • 265 drugs are classified into 18 (Dn) drug pathways and applied, and the functional haplotype is already drug based on quantitative trait location (QTL) information in each cell line genome for 12 carcinomas (Cancer N).
  • QTL quantitative trait location
  • a schematic diagram of the Pearson correlation is shown by converting combinations (multivariates) of mutations related to the response into hash numbers.
  • Figure 6 shows an exemplary diagram of the relationship between the quantitative trait location (QTL) and drug response.
  • QTL quantitative trait location
  • 1 is eQTL
  • 2 is cQTL
  • 3 is dQTL
  • 4 is the correlation between gene RNA expression and gene replication mutation
  • 5 is the relationship between drug concentration and gene RNA expression
  • 6 shows the correlation between drug concentration and gene replication mutation.
  • 1 eQTL and 2 cQTL are used, and 3 to 6 show a state in which there is a correlation or as a result.
  • FIG. 7 is an exemplary diagram showing the network structure of mutation, RNA expression, gene replication mutation, and drug response, and is a schematic diagram of making networking in terms of the relationship between the contents shown in FIG. 6, and genotype (single mutation), It shows the relationship between the flow type (combination and multiple variants) and the conditional expressions with the drug response.
  • the functional haplotype refers to both a single mutation and a multiple mutation.
  • each mutation is collected by the method of quantitative trait location (QTL), with the correlation between gene RNA expression and gene copy number variation determined to be p-value ⁇ 10 ⁇ 5 or less.
  • QTL quantitative trait location
  • Figure 8 shows an example of mutation and correlation cell line, cancer, drug reaction, RNA expression and CNV amplification.
  • examples of correlated mutations and cell lines, cancer, drug response, RNA expression, and CNV amplification are finally selected quantitative trait site (QTL)-based drug responsiveness-mutation-RNA expression-CNV as one object.
  • QTL quantitative trait site
  • the mutation may be linked to the location of the chromosome, the mutation type, the amino acid mutation, information on whether or not it is used as a biomarker.
  • the present invention is largely composed of a first step and a second step.
  • the first step is a step of generating a collection of positions of quantitative trait positions (QTL), and the second step is a step of calculating a correlation between functional haplotype and drug response.
  • QTL quantitative trait positions
  • the first step includes 1) calculating the expression correlation between mutations (4 million in total) and genes (20,000), and 2) gene expression of drugs (265) and cell line genomes (1,000) ( 20,000 each), 3) collecting the same gene expression pattern associated with multiple or single mutations in 1) and 2), and 4) Haplotype (single and Multivariate) is extracted with drug reactivity.
  • the second step is 5) defining such a haplotype as a functional haplotype (FRH), which is a function and association haplotype, expressing the FRH as a hash number, and 6) a hash number and It is performed including the step of calculating the Pairwise Pearson Test, and these processes are repeated for all cancer species.
  • FRH functional haplotype
  • FIG. 10 shows a network of functional haplotypes and drug responsiveness, and shows a system for implementing the functional haplotyping method shown in FIG. 9.
  • FIG. 9 In addition, in the functional haplotype of FIG. 11 and the exemplary diagram of gene RNA expression and gene replication, the process of FIG. 9 will be described in detail from a functional point of view.
  • A) indicates that the relationship between drug reactivity information and gene RNA expression is expressed as the final product, loci and exp, and the relationship between drug reactivity information and gene replication variable (CNV) is expressed as loci and CNV. Is shown.
  • loci multiple mutations (loci) are described as a collection of locuses selected based on the quantitative trait location (QTL), and loci are represented by various types of loci-1, loci-2, ... loci-N, and each The loci type has a frequency in the entire population.
  • ln IC50 values (X, Y, ...Z), which are average drug reactivity information, are calculated.
  • RNA expression there are three different expression patterns, which can be expressed in over-expression, normal, and under-expr profession formats, and loci types and In the same way, frequencies in each type can be grouped, and the average Ln IC50 values (X', Y', ...Z') in each group are calculated.
  • CNV which is a copy number variant
  • ln IC50 value in terms of average drug reactivity information in each frequency group
  • the frequency values of all loci, gene-exp, and gene-CNV can be expressed in the form of weights on a scale of 0 to 1.
  • B) can be described as a weight multiplied by loci and Exp, and loci and CNV.
  • FIG. 12 shows the definition of a functional haplotype.
  • a relative-haplotype refers to a combination of locus giving significance in a functional haplotype (FRH) and eQTL. It refers to a haplotype relationship related to.
  • FRH Exp
  • FRH Lici
  • loci-3 has a QTL relationship with RNA expression over, and when RNA expression is over, the drug reactivity value can be predicted to be the same as the drug expression value of loci-3.
  • IC50) represents a value (max) in which the difference in absolute drug responsiveness is large compared to the drug responsiveness value in no change (normal)
  • IC50) ) Represents the value (max) with the largest difference in absolute drug reactivity compared with the drug reactivity value at no change in drug response (normal) in various haplotypes.
  • the haplotype defined in FIG. 12 can be described in more detail through the FRH method and an example of predicting gene expression and drug reactivity.
  • [1] and [2] of Fig. 13 represent the max difference (Normal vs. Over or Under) of the absolute value of the drug response to the difference in gene expression, and [3] is a functionally related haplotype for cell lines.
  • FIG. 14 is a table showing data of an example in which the difference between average IC50 values is '1.0' as an example of FRH.
  • LN IC50 average drug reactivity
  • FIG. 15 is a table showing data of an example in which the difference between average IC50 values is '1.5' as an example of FRH.
  • LN IC50 average drug reactivity
  • FIG. 16 shows functionally related haplotypes and gene expression examples.
  • FRH contains four types.
  • the mutation (locus position) is described as three different genotypes of 1: Ref Homo, 2: Hetero, and 3: Alt Homo.
  • the frequency of each type is the second column
  • '12' is 1 person
  • '21' is 1 person
  • '11' is 2 and '22' show the majority with 153.
  • FRH which is a combination of two locuses, overexpresses the EGFR gene in the '11' type, which is the cause, and is sensitive to drug reactivity. Therefore, it can be predicted that FRH is sensitive to Afatinib drugs in cell lines and patients with '11' type.
  • FIG. 17 shows a method of generating a drug response prediction model based on a specific cancer drug pathway.
  • the drug response prediction model based on a specific cancer drug pathway is 1) the correlation between the function-related haplotype (FRH) and each drug related to the specific drug pathway of a specific cancer. And 2) clustering the correlation information of the X-axis and Y-axis, a matrix consisting of FRH and drug-reactivity correlation, and 3) collecting the high significance (p-value) in the predictive model, and their interaction
  • the function is applied to the model, and 4) NGS data is input and the FRH associated with the drug reactivity is calculated to predict the drug reactivity.
  • the correlation R ⁇ 2 between functional haplotype and drug responsiveness shown in FIG. 18 describes an example of a clustering process of 2) correlation information in the method of generating the prediction model of FIG. 17.
  • the cancer type is hematologic cancer
  • Y EGFR pathway 9 drugs
  • X FRH haplotype
  • each pixel indicates the Pearson correlation between the drug response value and FRH, where * indicates significance (pval ⁇ 0.05), ** indicates significance (pval ⁇ 0.01).
  • the blue color shows positive correlation
  • the orange color shows negative correlation
  • FIG. 19 shows an example of FRH having a high correlation between Afatinib drug and FRH in the example of FIG. 18.
  • the results are shown for the drug-reactivity correlation with the four FRHs (LAMC2_2, JPH1_2, PRKCB_3, CML_1).
  • FIG. 20 is an example of a prediction model using FRH having a high correlation between the Afatinib drug and the FRH in the example of FIG. 18, and describes an example in which 3) the interaction function of the prediction model generation method of FIG. 17 is applied to the model. .
  • 21 FRHs with p-val ⁇ 0.01 or less were put as an interaction term, and a linear regression model was applied. In this case, the Pearson correlation is 0.89.
  • FIG. 21 shows predicted IC50 (real) values for various drugs (AP-24534, PAC-1, Obatoclax Mesylate, Bleomycin, etc.) having a functional haplotype 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.) having a functional haplotype of the genome of one patient with blood cancer.
  • some drugs are predicted to have high sensitivity, while others are predicted to have high resistance.
  • IC50(real) the average IC50(ave) value of the functional subtype group.
  • next-generation sequencing data when individual functional flow types based on next-generation sequencing data according to the present invention are collected, they can be utilized in various areas. In other words, it can be used for virtual and actual clinical tests, discovery of new drug targets, diagnosis of disease drug sensitivity, and biomarker discovery system for companion diagnosis.
  • biomarkers that are significant in diagnosing drug reactivity were discovered through the functional haplotype as described above.
  • the discovered biomarkers are DNA bases containing mutations, which are classified according to the type of cancer (CancerClass) and the biomarkers that affect the reactivity of the drug to the cancer, and the drug's action path and expression target.
  • CancerClass type of cancer
  • DrugClass drug type
  • biomarkers will be classified and organized by type.
  • TCGA cancer genome atlas belonging to the cancer type (CancerClass) division CancerClass TCGA CANCER_NAME
  • CancerClass TCGA CANCER_NAME
  • ALLLAMLLCML Acute lymphoblastic leukemia, Acute Myeloid Leukemia, Chronic Myelogenous Leukemia 2
  • Female cancer (breast, ovary)
  • BRCABLCAUCECOVCESC Bladder Urothelial Carcinoma, Breast invasive carcinoma, Ovarian serous cystadenocarcinoma, Cervical squamous cell carcinoma and endocervical adenocarcinoma, Uterine Corpus Endometrial Carcinoma 3
  • LGGNBGBM Glioblastoma multiforme
  • Neuroblastoma nb
  • 4 Head&neck cancer HNSCTHCA Head and Neck squamous cell carcinoma Thyroid carcinoma 5 Lung cancer LUADLUSCNSCLC Lung adenocarcino
  • Cancer Class described in [Table 1] above is classified by cancer occurrence site and cause, blood cancer (blood), female cancer (breast, ovary), brain tumor (brain), head and neck cancer (head&neck), lung cancer (lung), sarcoma. It is classified as a bone.
  • TCGA represents the detailed cancer types classified in'The cancer genome atlas', and information on each item is described in [Table 1].
  • biomarkers that affect reactivity to drugs were calculated for each cancer type through GBLscan using a functional haplotype.
  • biomarkers of blood see Table 2
  • 50 biomarkers of female cancer see Table 4
  • brain tumors see Table 6
  • 32 biomarkers of head & neck cancer see Table 8
  • 277 biomarkers of lung cancer see Table 10
  • biomarkers of sarcoma cancer bone
  • biomarkers discovered by the present invention for each cancer type may be a combination of single and multiple mutations, and the type of mutation is also in various forms such as single nucleotide polymorphism (SNV), Indel, gene copy number variation (CNV), and chromosomal rearrangement. It can be a variation.
  • SNV single nucleotide polymorphism
  • Indel gene copy number variation
  • CNV gene copy number variation
  • chromosomal rearrangement It can be a variation.
  • Gene_EXP displayed in the present invention represents the name of the mRNA expression gene of the biomarker, as shown in FIG. 24.
  • the biomarker is expressed in the form of ⁇ Mark 1_Mark 2_Mark 3_Mark 4 ⁇ , where Mark 1 represents a chromosome number containing a mutation, and has a value of 1 to 22 or any one of X and Y. .
  • Mark 2 indicates the starting position of the nucleotide sequence in which the mutation occurred on the corresponding chromosome
  • Mark 3 indicates the normal base before mutation and refers to the base at the corresponding position on the reference genome (GRCh/hg38 standard).
  • Mark 4 represents the mutated base.
  • Mark 3 refers to the base sequence from mark 2 to mark 4.
  • the biomarker specifically refers to a nucleotide sequence in which the position from the second position to the base length of the mark 3 in the mark 1 chromosome is composed of the base of mark 4.
  • the biomarker of SEQ ID NO: 1 in [Table 2] was expressed as ⁇ 14_30910985_T_G ⁇ , which means that the nucleotide sequence in which the 30910985th base of chromosome 14 is G is a biomarker, and that of sequence 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 base from nucleotide 113524119 to 113524123 of chromosome 13 is T (base at 4 digit means'deletion').
  • chromosome 113510211 is C
  • the 113495576 base of chromosome 13 is C
  • the 113503637 base of chromosome 13 is T
  • the nucleotide sequence of chromosome 13 113539563 is T is a biomarker.
  • the biomarker shown in SEQ ID NO: 29 in [Table 23] indicates that in the'SLC6A12' chromosome, the 190673067 th base of chromosome 2 is GT, and the 114323991 th base of ninth chromosome is G. it means. At this time, the 190673067 base of chromosome 2 means that G is substituted with GT.
  • nucleotide sequence of chromosomes 1 to 22 is as described above, the genome 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 FRHs associated with blood cancer drugs, blood cancer drugs associated therewith, and markers defining FRH.
  • FRH is expressed in the form of "FRH name_number of markers included", and "Var_Exp(pval)” refers to the correlation value (Pvlaue) that each marker has on the gene expression, and "Var_Drug(pval)” Means the correlation (P-vlaue) value of the corresponding marker on the reactivity of the drug, and the reactivity correlation value for each drug is divided by the classification factor ⁇ / ⁇ according to the order of the drugs summarized in [Table 2].
  • ⁇ Exp_Drug(pval) ⁇ The correlation (Pvlaue) value of drug responsiveness of the gene expression in question is organized according to the order of the drugs listed in [Table 2], and ⁇ Ave_VarDrug(pval) ⁇ is ⁇ Var_Drug(pval)" refers to the average value of the calculated values, and "Rank” refers to the number of blood cancer drugs that the FRH significantly affects.
  • FIG. 25 correlations (P-vlaue) values of major biomarkers for blood cancer drugs among the biomarkers listed in Table 2 are graphically displayed, and in FIG. 26, genes expressed by major biomarkers Correlation (P-vlaue) values for blood cancer drugs and blood cancer are displayed in a graph.
  • Table 4 lists FRHs related to female cancer (breast, ovary) drugs, female cancer drugs related thereto, and markers defining FRH.
  • FRH is expressed in the form of "FRH name_number of markers included", and "Var_Exp(pval)” refers to the correlation value (Pvlaue) that each marker has on the gene expression, and "Var_Drug(pval)” Means the correlation value (Pvlaue) that the corresponding marker has on the reactivity of the drug.
  • the reactivity correlation values for each drug are classified by the classification factor ⁇ / ⁇ .
  • ⁇ Exp_Drug(pval) ⁇ The correlation (Pvlaue) value of drug responsiveness of the gene expression in question is arranged according to the order of the drugs summarized in [Table 4], and ⁇ Ave_VarDrug(pval) ⁇ is ⁇ Var_Drug( pval)" refers to the average value of the calculated values, and "Rank” refers to the number of female cancer drugs that the FRH significantly affects.
  • Figure 27 shows the correlation (P-vlaue) values of major biomarkers for female cancer drugs among the biomarkers listed in Table 4 as a graph
  • Figure 28 shows genes expressed by major biomarkers. Correlation (P-vlaue) values for women and female cancer drugs are plotted.
  • Table 6 lists FRHs related to brain tumor drugs, brain tumor drugs related thereto, and markers defining FRH.
  • FRH is expressed in the form of "FRH name_number of markers included", and "Var_Exp(pval)” refers to the correlation value (Pvlaue) that each marker has on the gene expression, and "Var_Drug(pval)" Means the correlation value (Pvlaue) that the corresponding marker has on the reactivity of the drug, and the reactivity correlation value for each drug is classified by the classification factor ⁇ / ⁇ according to the order of the drugs arranged in [Table 6].
  • ⁇ Exp_Drug(pval) ⁇ The correlation (Pvlaue) value of drug responsiveness of the gene expression in question is arranged according to the order of the drugs summarized in [Table 6], and ⁇ Ave_VarDrug(pval) ⁇ is ⁇ Var_Drug( pval)” refers to the average value of the calculated values, and “Rank” refers to the number of brain tumor drugs that the FRH significantly affects.
  • Figure 29 shows the correlation (P-vlaue) values of major biomarkers for brain tumor drugs among the biomarkers listed in Table 6 as a graph
  • Figure 30 shows genes expressed by the major biomarkers. Correlation (P-vlaue) values for and brain tumor drugs are plotted as a graph.
  • Table 8 lists FRHs related to head & neck cancer drugs, head and neck cancer drugs related thereto, and markers defining FRH.
  • FRH is expressed in the form of "FRH name_number of markers included", and "Var_Exp(pval)” refers to the correlation value (Pvlaue) that each marker has on the gene expression, and "Var_Drug(pval)” Means the correlation value (Pvlaue) that the corresponding marker has on the reactivity of the drug.
  • the reactivity correlation values for each drug are classified by the classification factor ⁇ / ⁇ .
  • ⁇ Exp_Drug(pval) ⁇ The correlation (Pvlaue) value of the drug responsiveness of the gene expression in question is arranged according to the order of the drugs summarized in [Table 8], and ⁇ Ave_VarDrug(pval) ⁇ is ⁇ Var_Drug( pval)" refers to the average value of the calculated values, and "Rank” refers to the number of head and neck cancer drugs that the FRH significantly affects.
  • FIG. 31 correlations (P-vlaue) values of major biomarkers for head and neck cancer drugs among the biomarkers listed in Table 8 are graphed, and in FIG. 32, genes expressed by major biomarkers Correlation (P-vlaue) values for the patients and head and neck cancer drugs are displayed in a graph.
  • Reactive biomarkers for head and neck cancer drugs No. FRH Drug DrugType Var (marker)
  • Table 10 lists FRHs related to lung cancer drugs, lung cancer drugs related thereto, and markers defining FRH.
  • FRH is expressed in the form of "FRH name_number of markers included", and "Var_Exp(pval)” refers to the correlation value (Pvlaue) that each marker has on the gene expression, and "Var_Drug(pval)” Means the correlation value (Pvlaue) that the corresponding marker has on the reactivity of the drug.
  • the reactivity correlation values for each drug are classified by the classification factor ⁇ / ⁇ .
  • ⁇ Exp_Drug(pval) ⁇ The correlation (Pvlaue) value of the drug responsiveness of the gene expression in question is arranged according to the order of the drugs summarized in [Table 10], and ⁇ Ave_VarDrug(pval) ⁇ is ⁇ Var_Drug( pval)” refers to the average value of the calculated values, and “Rank” refers to the number of lung cancer drugs that the FRH significantly affects.
  • Figure 33 shows the correlation (P-vlaue) values of major biomarkers for lung cancer drugs among the biomarkers listed in Table 10 as a graph
  • Figure 34 shows genes expressed by the major biomarkers.
  • the correlation (P-vlaue) values for the and lung cancer drugs are shown in a graph.
  • Table 12 summarizes the FRHs associated with sarcoma cancer (bone) drugs, sarcoma cancer drugs associated therewith, and markers defining FRH.
  • FRH is expressed in the form of "FRH name_number of markers included", and "Var_Exp(pval)” refers to the correlation value (Pvlaue) that each marker has on the gene expression, and "Var_Drug(pval)” Means the correlation value (Pvlaue) that the corresponding marker has on the reactivity of the drug.
  • the reactivity correlation values for each drug are classified by the classification factor ⁇ / ⁇ .
  • ⁇ Exp_Drug(pval) ⁇ The correlation (Pvlaue) value of drug responsiveness of the gene expression in question is arranged according to the order of the drugs listed in [Table 12], and ⁇ Ave_VarDrug(pval) ⁇ is ⁇ Var_Drug( pval)” refers to the average value of the calculated values, and “Rank” refers to the number of sarcoma cancer drugs that the FRH significantly affects.
  • Figure 35 shows the correlation (P-vlaue) values of major biomarkers for sarcoma cancer drugs among the biomarkers listed in Table 12 as a graph
  • Figure 36 shows genes expressed by the major biomarkers. Correlation (P-vlaue) values for the sarcoma and sarcoma cancer drugs are plotted.
  • biomarkers that affect the reactivity to drugs for each drug type calculated through GBLscan using a functional haplotype in the present invention, will be described.
  • ABL biomarkers 108 see Table 15
  • CHROMATIN biomarkers 132 see Table 16
  • EGFR biomarkers 832 see Table 17
  • ERK_MAPK 42 biomarkers of see Table 18
  • 153 biomarkers of IGFR see Table 19
  • 88 biomarkers of MITOSIS see Table 20.
  • DrugClass DrugClass
  • DrugClass Drugs Pathway & signaling DrugClass Drugs Pathway & signaling(Target gene & function)
  • DrugClass DrugClass
  • PDGFR Proliferative kinase
  • ABL1 Proliferative kinase
  • PDGFR Proliferative kinase
  • ABL1 Proliferative kinase
  • PDGFR Proliferative kinase
  • ABL1,TEC LOK
  • LTK TRCB
  • 2 CHROMATIN Phenformin AICA_Ribonucleotide
  • Daporinad HDAC, RAR HDAC inhibitor Class I, IIa, IIb, IV
  • HDAC6, EGFR G9a(EHMT2), GLP(EHMT1), Q8TEK3 (DOT1L)
  • Table 15 shows the biomarkers associated with the ABL drug and the degree of reactivity (P-value) of the drug.
  • FRH is expressed in the form of "FRH name_number of markers included", and "Var_Drug(pval)" refers to the correlation (P-vlaue) value of the corresponding marker on the reactivity of the drug.
  • the reactivity correlation values for each drug are separated by the classification factor ⁇ / ⁇ , and ⁇ Var_Exp(pval) ⁇ means the correlation value (Pvlaue) that each marker has on the gene expression.
  • the reactivity correlation values for each drug are classified and organized by the classification factor ⁇ / ⁇ .
  • FRH is expressed in the form of "FRH name_number of markers included", and "Var_Drug(pval)" refers to the correlation (P-vlaue) value of the corresponding marker on the reactivity of the drug.
  • the reactivity correlation values for each drug are separated by the classification factor ⁇ / ⁇ , and ⁇ Var_Exp(pval) ⁇ means the correlation value (Pvlaue) that each marker has on the gene expression.
  • the reactivity correlation values for each drug are classified and organized by the classification factor ⁇ / ⁇ .
  • Table 17 shows the biomarkers associated with the EGRF drug and the degree of reactivity (P-value) of the drug by it as a table.
  • FRH is expressed in the form of "FRH name_number of markers included", and "Var_Drug(pval)" refers to the correlation (P-vlaue) value of the corresponding marker on the reactivity of the drug.
  • the reactivity correlation values for each drug are separated by the classification factor ⁇ / ⁇ , and ⁇ Var_Exp(pval) ⁇ means the correlation value (Pvlaue) that each marker has on the gene expression.
  • the reactivity correlation values for each drug are classified and organized by the classification factor ⁇ / ⁇ .
  • Table 18 shows the biomarkers associated with the ERK_MAPK drug and the degree of reactivity (P-value) of the drug.
  • FRH is expressed in the form of "FRH name_number of markers included", and "Var_Drug(pval)" refers to the correlation (P-vlaue) value of the corresponding marker on the reactivity of the drug.
  • the reactivity correlation values for each drug are separated by the classification factor ⁇ / ⁇ , and ⁇ Var_Exp(pval) ⁇ means the correlation value (Pvlaue) that each marker has on the gene expression ,
  • the reactivity correlation values for each drug are classified and organized by the classification factor ⁇ / ⁇ .
  • Table 19 shows the biomarkers associated with the IGFR drug and the degree of reactivity (P-value) of the drug.
  • FRH is expressed in the form of "FRH name_number of markers included", and "Var_Drug(pval)" refers to the correlation (P-vlaue) value of the corresponding marker on the reactivity of the drug.
  • the reactivity correlation values for each drug are separated by the classification factor ⁇ / ⁇ , and ⁇ Var_Exp(pval) ⁇ means the correlation value (Pvlaue) that each marker has on the gene expression.
  • the reactivity correlation values for each drug are classified and organized by the classification factor ⁇ / ⁇ .
  • Table 20 shows the biomarkers associated with the MITOSIS drug and the degree of reactivity (P-value) of the drug.
  • FRH is expressed in the form of "FRH name_number of markers included", and "Var_Drug(pval)" refers to the correlation (P-vlaue) value of the corresponding marker on the reactivity of the drug.
  • the reactivity correlation values for each drug are separated by the classification factor ⁇ / ⁇ , and ⁇ Var_Exp(pval) ⁇ means the correlation value (Pvlaue) that each marker has on the gene expression.
  • the reactivity correlation values for each drug are classified and organized by the classification factor ⁇ / ⁇ .
  • Figure 23 shows a method of manufacturing a test chip used for biomarker detection for co-diagnosis of drugs for the six cancer types summarized in [Table 1] and the six drug types summarized in [Table 14]. Has been.
  • a diagnostic chip for each biomarker for the cancer type and drug type specified in [Table 1] and [Table 14] is prepared, or the cancer type and drug type All biomarkers for or all of them were prepared in one chip, and for FRH markers related to drug types or cancer types from patients, the example shown in FIG. 18 (EGFR pathway, which is one of the drug types of hematologic cancer )
  • EGFR pathway which is one of the drug types of hematologic cancer
  • drug reactions can be predicted in the form of generating the Linear Regression prediction model of FIG. Accordingly, various cancer and cancer drug companion diagnostic chips can be manufactured and utilized as shown in FIG. 23 by using specific FRH biomarkers for 6 cancer types and 6 drug types 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 constituted by various combinations including the biomarkers disclosed in Table 2 in addition to the above-described markers.
  • each of the markers disclosed in [Table 4] was calculated to be significant, and among these markers, the marker 1_13248174_T_C (marker 1) associated with the expression of the IRX5 gene ), 16_85071785_A_G (marker2) and 9_34371054_A_C (marker3) were analyzed as the major marker combinations.
  • biomarker composition according to the present invention may be configured by various combinations including the biomarkers disclosed in [Table 4] in addition to the above-described markers.
  • biomarker composition according to the present invention may be constituted by various combinations including the biomarkers disclosed in Table 6 in addition to the above-described markers.
  • each of the markers disclosed in [Table 8] was calculated to be significant, and among these markers, a marker associated with RAI14 gene expression 14_21082261_T_G (marker 1) And 7_140753336_A_T (marker2) were analyzed as the major marker combination.
  • biomarker composition according to the present invention may be configured by various combinations including the biomarkers disclosed in Table 8 in addition to the above-described 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 constituted by various combinations including the biomarkers disclosed in Table 10 in addition to the above-described markers.
  • each of the markers disclosed in [Table 12] was calculated to be significant, and among these markers, a 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 major marker combinations.
  • biomarker composition according to the present invention may be constituted by various combinations including the biomarkers disclosed in Table 10 in addition to the above-described markers.
  • each of the markers disclosed in [Table 15] was calculated to be significant, among these markers, markers associated with MYO1E gene expression 15_59223315_G_A and HMGB1 gene expression Marker 13_30465849_T_C was analyzed as the main marker combination.
  • biomarker composition according to the present invention may be constituted by various combinations including the biomarkers disclosed in [Table 15] in addition to the above-described markers.
  • each of the markers disclosed in [Table 16] was calculated to be significant, among these markers, markers associated with SPR gene expression 2_72891611_A_C and ERRFI1 gene expression and The associated marker 1_8013331_A_G was analyzed as the main marker combination.
  • biomarker composition according to the present invention may be constituted by various combinations including the biomarkers disclosed in Table 16 in addition to the above-described markers.
  • each of the markers disclosed in [Table 17] was calculated to be significant, among these markers, markers associated with PTGR1 gene expression 9_111597344_C_A and HDHD2 gene expression Marker 18_47115026_C_T was analyzed as the main marker combination.
  • biomarker composition according to the present invention may be constituted by various combinations including the biomarkers disclosed in [Table 17] in addition to the above-described markers.
  • each of the markers disclosed in [Table 18] was calculated to be significant, among these markers, markers associated with SPRY4 gene expression 5_142319698_T_G and DUSP6 gene expression and The associated marker 12_89350540_G_A was analyzed as the main marker combination.
  • biomarker composition according to the present invention may be constituted by various combinations including the biomarkers disclosed in Table 18 in addition to the above-described markers.
  • each of the markers disclosed in [Table 19] was calculated to be significant, among these markers, markers associated with SEPT6 gene expression X_119618658_T_G and EPHA2 gene expression associated Marker 1_16148437_A_C was analyzed as the main marker combination.
  • biomarker composition according to the present invention may be constituted by various combinations including the biomarkers disclosed in Table 19 in addition to the above-described markers.
  • each of the markers disclosed in [Table 20] was calculated to be significant, among these markers, markers associated with CYR61 gene expression 1_85581605_TA_T and MYB gene expression and The associated marker 6_135201764_A_C was analyzed as the main marker combination.
  • biomarker composition according to the present invention may be configured by various combinations including the biomarkers disclosed in Table 20 in addition to the above-described markers.
  • the definition of the drug type is the type of drug targets and functional similar functional types of the drug, or a gene group, drug pathway, or drug signaling in the category affected by the drug.
  • drug signaling pathway etc. is a term that describes both as a generic drug type (DrugClass).
  • FRH is a term for functionally related relative haplotype
  • eQTL is a term for expression quantitative trait loci
  • IC50) is over/under )
  • IC50) is a term indicating the average value of the IC50 for the amplification/deletion cluster
  • IC50) is a given loci It is a term indicating the average value of IC50 for a type cluster
  • GDSC is a term for Genomics of Drug Sensitivity in Cancer
  • CCLP is a term for COSMIC Cell Lines Project, and the web page http://cancer.sanger.ac.uk/cell_lines From
  • CNV is a term representing copy number variation
  • GBLscan is a term representing Cancer Biomarker Labeling Scan
  • ISCT is a term representing In Silico Clinical Trial. It's a term.
  • the present invention provides a drug indication and response prediction system, which is a new linear regression model capable of reliably predicting drug responsiveness by binding analysis of specific gene mutation fingerprints related to diseases including cancer and molecular pharmacological functional groups of drugs, and
  • the method relates to GBLscan (Genetic Biomarker Scan), and according to the present invention, the degree of reactivity of the drug with the genome for which the pharmacological effect is unknown from the results of the reactivity of the drug to the genome collected from in vitro and in vivo clinical trials. There is an effect that can predict

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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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KR1020190096410A KR20200044677A (ko) 2018-10-18 2019-08-07 암 약물 반응성 판단을 위한 바이오 마커, 이를 이용한 암 약물 반응성 판단 방법 및 이를 위한 암 약물 반응성 진단칩
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KR1020190098165A KR20200054058A (ko) 2018-10-18 2019-08-12 암 약물 반응성 판단을 위한 바이오 마커 조성물, 바이오 마커 조성물을 이용한 암 약물 반응성 판단 방법 및 암 약물 반응성 판단을 위한 바이오 마커 조성물 검출용 진단칩
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KR1020190098492A KR20200054059A (ko) 2018-10-18 2019-08-13 암 약물 반응성 판단을 위한 바이오 마커 조성물, 바이오 마커 조성물을 이용한 암 약물 반응성 판단 방법 및 암 약물 반응성 판단을 위한 바이오 마커 조성물 검출용 진단칩

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