WO2013147330A1 - Système de prédiction de pronostic du cancer gastrique avancé localement - Google Patents

Système de prédiction de pronostic du cancer gastrique avancé localement Download PDF

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WO2013147330A1
WO2013147330A1 PCT/KR2012/002193 KR2012002193W WO2013147330A1 WO 2013147330 A1 WO2013147330 A1 WO 2013147330A1 KR 2012002193 W KR2012002193 W KR 2012002193W WO 2013147330 A1 WO2013147330 A1 WO 2013147330A1
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mir
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허용민
서진석
노성훈
정재호
박은성
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Industry Academic Cooperation Foundation of Yonsei University
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/575—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/5753—Immunoassay; Biospecific binding assay; Materials therefor for cancer of the stomach or small intestine
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA

Definitions

  • the present invention relates to a novel prognostic prediction system capable of predicting the prognosis of locally advanced gastric cancer through comparative analysis of gene or protein aggregation expression.
  • Gastric cancer is clearly different from stage 1 to stage 4 according to the TNM staging system, unlike breast cancer and colorectal cancer (see FIG. 1). That is, in case of stage 1, the 5-year survival rate is 90% or more, and in stage 4, the difference is 20% or less. Therefore, the prognostic predictive power of the TNM staging system is very good [Ref., 7th edition of the AJCC cancer staging Manual: stomach. Ann Surg Oncol 2010; 17: 3077-3079.
  • gastric cancer can often be divided into early gastric cancer, locally advanced gastric cancer, locally advanced invasive gastric cancer, and metastatic gastric cancer. .
  • Oncologists do not qualify for labeling for specific cancers and chemotherapeutic agents characterized as "standards of care,” but have numerous treatment options available to them by combining numerous drugs that are effective against the cancer. have.
  • the best possibility for good treatment outcomes should specify the optimal cancer treatment available to the patient and this designation needs to be made as soon as possible after diagnosis.
  • it is important to determine the likelihood of patient response to "treatment basis” chemotherapy because chemotherapeutic agents such as anthracycline and taxanes have limited efficacy and are toxic.
  • identification of the most responsive or least responsive patients can, via smarter patient selection, increase the net benefits that these drugs must provide and reduce net mortality and toxicity.
  • RNA-based testing was not frequently used due to the problem of RNA degradation over time and the fact that fresh tissue samples for analysis were difficult to obtain from patients. Tissues immobilized and embedded in paraffin are more readily available, and methods for detecting RNA in fixed tissues have been established. However, these methods typically do not allow the study of large numbers of genes (DNA or RNA) from small amounts of material. Thus, traditionally, fixed tissues are rarely used except for immunohistochemical detection of proteins.
  • the present invention provides a method for predicting the clinical outcome (prognosis) of the N0 gastric cancer patient group, such as T1N0, T2N0, T3N0 or T4N0 gastric cancer patient group in the TNM stage.
  • the invention provides at least one RNA transcript selected from the group consisting of FZD1, GLI3, ANGPTL7, ABL1, SMARCD3, ILK, CAV1, VIP, HSPB7, TOP2A and FANCD2 in a biological sample comprising cancer cells from a subject; And determining the expression level of one or more miRNAs selected from the group consisting of hsa-miR-933, hsa-miR-184, hsa-miR-380 *, hsa-miR-190b, hsa-miR-27a * and hsa-miR-1201. Making; And
  • RS recurrence score
  • the present invention provides a method for predicting prognosis in a subject diagnosed as gastric cancer, the method including determining a prognosis according to the RS value.
  • the RS can be calculated according to the following equation (1):
  • HR n represents the hazard ratio of the nth RNA transcript or microRNA
  • normLogTransValue n means the value associated with the expression of the n-th RNA transcript or micro RNA.
  • RS can be obtained as follows:
  • Risk Score FZD1 ⁇ 4.302 + GLI3 ⁇ 4.073 + ANGPTL7 ⁇ 2.949 + ABL1 ⁇ 2.784 + SMARCD3 ⁇ 2.266 + ILK ⁇ 2.251 + CAV1 ⁇ 1.788 + VIP ⁇ 1.73 + HSPB7 ⁇ 1.535-TOP2A ⁇ 1.766-FANCD2 ⁇ 2.793 + miR933 ⁇ 5.256 + miR184 ⁇ 1.674 + miR380 * ⁇ 1.903-miR190b ⁇ 3.597-miR27a * ⁇ 1.7-miR1201 ⁇ 1.35
  • the present invention provides a useful method for predicting clinical outcome of the entire gastric cancer patient group irrespective of the TNM stage.
  • the invention is directed to a biological sample comprising cancer cells obtained from a subject.
  • the increase in expression of transcript X is determined to be an increase in the likelihood of a positive clinical outcome
  • the increase in expression of transcript Y is determined to be a decrease in the likelihood of a positive clinical outcome. Provide a way to predict.
  • the invention also relates to a biological sample comprising cancer cells obtained from a subject,
  • hsa-miR-1 HS_6, HS_111, HS_114, hsa-let-7c, HS_126, HS_90, hsa-miR-548d-5p, hsa-miR-189: 9.1, solexa-4793-177, HS_135, hsa- measuring the expression level of one or more miRNA (II) selected from the group consisting of miR-20b * and hsa-miR-658; And
  • Increased expression of miRNA (I) is judged to be an increase in the likelihood of positive clinical outcomes, and increased expression of miRNA (II) is determined to be a decrease in the likelihood of positive clinical outcomes. Provide a way to predict.
  • the invention also relates to a biological sample comprising cancer cells obtained from a subject,
  • the present invention provides a method for predicting prognosis in a subject diagnosed as gastric cancer, the method including determining a prognosis according to the RS value.
  • the RS may be calculated according to Equation 2:
  • HR n represents the hazard ratio of the nth functional protein
  • RPPAValue n means the value associated with the expression of the n th functional protein.
  • the present invention also provides a computer readable recording medium having recorded thereon a program for executing prognostic prediction of gastric cancer.
  • a medium useful for predicting clinical outcome of a stage N0 gastric cancer patient group during a TNM stage may be provided. for example,
  • RS recurrence score
  • a computer readable recording medium having recorded thereon a program for causing a computer to classify a patient having a higher RS than a setpoint is a high probability of relapse and a patient having a lower RS is set to a lower likelihood of relapse.
  • RS value using the expression level of the RNA transcript or miRNA can be obtained through the above equation.
  • a medium may be provided that is useful for predicting clinical outcomes of the entire gastric cancer patient population independent of TNM stages. for example,
  • a computer-readable recording medium having recorded thereon a program for causing a computer to classify a patient whose RS is greater than a setpoint is a high probability of recurrence and a patient smaller than the setpoint is a low likelihood of relapse.
  • the present invention creates a predictive model of overall survival rate and relapse-free survival rate for stage N0 gastric cancer patients in the TNM stage, and then determines the expression level of micro RNA, RNA transcript or protein that affects statistically significant survival. By producing a system to calculate prognostic indicators, the clinical results after resection by gastric cancer surgery can be predicted.
  • the present invention enables the analysis of the gene group according to the biological function of gastric cancer itself by using a gene aggregation system according to the biological function of the gene.
  • Figure 2 shows an example using a recurrence scoring method using micro RNA expression in gastric cancer stage 3a.
  • Figure 3 shows the results of survival analysis of Akt pS473 as a functional protein .
  • Figure 4 shows the number of deaths in the group with the good prognosis and the number of deaths in the poor prognosis when the prognostic index (prognostic index) using the protein expression level is 0.
  • FIG. 5 shows survival analysis results according to prognostic indicators (risk scoring system) (when scores are divided into + and ⁇ groups) in a T1NO, T2N0, T3N0, or T4N0 gastric cancer patient group.
  • FIG. 6 shows the number of deaths in the group with the good prognosis and the number of deaths in the group with the poor prognosis when the prognostic index is 0 based on the T1NO, T2N0, T3N0, or T4N0 gastric cancer patient groups. will be.
  • FIG. 7 illustrates a process of extracting a correlation between the expression level of microRNA and the expression level of RNA transcript in a T1NO, T2N0, T3N0, or T4N0 gastric cancer patient group.
  • the present invention was devised to develop a system for predicting clinical outcome after gastric resection for the entire gastric cancer patient group or the N0 patient group in the TNM stage, and is useful for predicting clinical outcome after surgical resection of gastric cancer patients.
  • MicroRNA or protein sets were devised to develop a system for predicting clinical outcome after gastric resection for the entire gastric cancer patient group or the N0 patient group in the TNM stage, and is useful for predicting clinical outcome after surgical resection of gastric cancer patients. , MicroRNA or protein sets.
  • the present invention provides a method for predicting clinical outcome after resection by surgery in a stage N0 patient group, such as T1NO, T2N0, T3N0, or T4N0 stage of advanced TCC stage.
  • a stage N0 patient group such as T1NO, T2N0, T3N0, or T4N0 stage of advanced TCC stage.
  • RNA transcripts selected from the group consisting of FZD1, GLI3, ANGPTL7, ABL1, SMARCD3, ILK, CAV1, VIP, HSPB7, TOP2A and FANCD2; And determining the expression level of one or more miRNAs selected from the group consisting of hsa-miR-933, hsa-miR-184, hsa-miR-380 *, hsa-miR-190b, hsa-miR-27a * and hsa-miR-1201. Making; And
  • RS recurrence score
  • the present invention provides a method for predicting prognosis in a subject diagnosed as gastric cancer, the method including determining a prognosis according to the RS value.
  • the RS can be calculated according to the following equation (1):
  • HR n represents the hazard ratio of the nth RNA transcript or microRNA
  • normLogTransValue n means the value associated with the expression of the n-th RNA transcript or micro RNA.
  • the term "Hazard Ratio" means a coefficient that reflects the contribution to cancer progression, relapse, or therapy response.
  • the risk factor can be derived by various statistical techniques.
  • the risk factor, HR value can be determined in various statistical models, for example in the Univariate Cox's proportional harzard model.
  • the HR value when the HR value is greater than or equal to 1, the HR value may be used as it is, and when the HR value is less than 1, the 1 / HR value may be used.
  • a value value associated with the expression of an RNA transcript or microRNA means a value associated with the expression of an individual gene, for example RNA transcript, micro RNA, protein.
  • the value can be determined, for example, using various known statistical means.
  • the value related to expression may be a value after quantile normalization after transforming p value measured by Univariate Cox's proportional harzard model into log2 function value.
  • the RS can be determined as follows:
  • Risk Score FZD1 ⁇ 4.302 + GLI3 ⁇ 4.073 + ANGPTL7 ⁇ 2.949 + ABL1 ⁇ 2.784 + SMARCD3 ⁇ 2.266 + ILK ⁇ 2.251 + CAV1 ⁇ 1.788 + VIP ⁇ 1.73 + HSPB7 ⁇ 1.535-TOP2A ⁇ 1.766-FANCD2 ⁇ 2.793 + miR933 ⁇ 5.256 + miR184 ⁇ 1.674 + miR380 * ⁇ 1.903-miR190b ⁇ 3.597-miR27a * ⁇ 1.7-miR1201 ⁇ 1.35
  • the method may be useful for predicting clinical outcome after surgery for surgical treatment of stage N0 gastric cancer patients in a TNM stage, such as stage T0N0, T2N0, T3N0 or T4N0 stage advanced gastric cancer.
  • the method may determine that the RS value is a positive prognosis in terms of overall survival (OS) or recurrence free survival (RFS), and the prognosis is a negative value.
  • OS overall survival
  • RFS recurrence free survival
  • a positive value indicates a low overall survival rate or a high incidence of deaths due to relapse during at least 3 years, 5 years, 8 years, and 10 years. Higher overall survival or abnormal incidence of death patients without relapse for at least 8 years or more than 10 years.
  • good prognosis can be expressed as an increase in the likelihood of a positive clinical outcome of a clinical outcome, and a bad prognosis can be expressed as a decrease in the likelihood of a positive clinical outcome of a clinical outcome.
  • the present invention provides a useful method for predicting clinical outcome after surgical resection of total gastric cancer regardless of TNM stage.
  • the invention is directed to a biological sample comprising cancer cells obtained from a subject.
  • the increase in expression of transcript X is determined to be an increase in the likelihood of a positive clinical outcome
  • the increase in expression of transcript Y is determined to be a decrease in the likelihood of a positive clinical outcome. Provide a way to predict.
  • the method may be a PCR based method or an array based method.
  • the expression level may be one that is normalized to the expression level of one or more RNA transcripts.
  • the clinical result may be expressed in terms of overall survival (OS) or recurrence free survival (RFS).
  • OS overall survival
  • RFS recurrence free survival
  • the method may comprise measuring the expression level of at least two RNA transcripts selected from RNA transcripts X and Y. More specifically, the prognosis can be predicted by measuring two or more expression levels selected from RNA transcripts X and Y and analyzing each increase in expression to determine the increase or decrease in the likelihood of a positive clinical outcome.
  • the method may comprise measuring the expression level of at least five RNA transcripts selected from RNA transcripts X and Y. More specifically, five or more expression levels selected from RNA transcripts X and Y can be measured and each expression increase analyzed to determine the increase or decrease in the likelihood of a positive clinical outcome to predict prognosis.
  • the method may comprise measuring the expression level of at least 10 RNA transcripts selected from RNA transcripts X and Y. More specifically, 10 or more expression levels selected from RNA transcripts X and Y can be measured and each expression increase analyzed to determine the increase or decrease in the likelihood of a positive clinical outcome to predict prognosis.
  • the method may comprise measuring the expression level of RNA transcript X and Y total RNA transcript. More specifically, the prognosis can be predicted by measuring the overall expression level of RNA transcripts X and Y and analyzing the increase in expression to determine the increase or decrease in the likelihood of a positive clinical outcome.
  • the invention also relates to a biological sample comprising cancer cells obtained from a subject,
  • hsa-miR-1 HS_6, HS_111, HS_114, hsa-let-7c, HS_126, HS_90, hsa-miR-548d-5p, hsa-miR-189: 9.1, solexa-4793-177, HS_135, hsa- measuring the expression level of one or more miRNA (II) selected from the group consisting of miR-20b * and hsa-miR-658; And
  • Increased expression of miRNA (I) is judged to be an increase in the likelihood of positive clinical outcomes, and increased expression of miRNA (II) is determined to be a decrease in the likelihood of positive clinical outcomes. Provide a way to predict.
  • the clinical result may be expressed in terms of overall survival (OS) or recurrence free survival (RFS).
  • OS overall survival
  • RFS recurrence free survival
  • the method may comprise measuring the expression level of two or more micro RNAs selected from micro RNA transcripts I and II. More specifically, two or more expression levels selected from micro RNA transcripts I and II can be measured and each expression increase analyzed to determine the increase or decrease in the likelihood of a positive clinical outcome to predict prognosis.
  • the method may comprise measuring the expression level of at least five micro RNAs selected from micro RNA transcripts I and II. More specifically, five or more expression levels selected from microRNA transcripts I and II can be measured and each expression increase analyzed to determine the increase or decrease in the likelihood of a positive clinical outcome to predict prognosis.
  • the method may comprise measuring the expression level of at least 10 microRNAs selected from micro RNA transcripts I and II. More specifically, 10 or more expression levels selected from micro RNA transcripts I and II can be measured and each expression increase analyzed to determine the increase or decrease in the likelihood of a positive clinical outcome to predict prognosis.
  • the method may comprise measuring the expression level of micro RNA throughout the micro RNA transcripts I and II. More specifically, the prognosis can be predicted by measuring the expression levels of the entire micro RNA transcripts I and II and analyzing the respective increase in expression to determine the increase or decrease in the likelihood of a positive clinical outcome.
  • the invention also relates to a biological sample comprising cancer cells obtained from a subject,
  • the present invention provides a method for predicting prognosis in a subject diagnosed as gastric cancer, the method including determining a prognosis according to the RS value.
  • the RS may be calculated according to Equation 2:
  • HR n represents the hazard ratio of the nth functional protein
  • RPPAValue n means the value associated with the expression of the n th functional protein.
  • Values associated with the expression of the risk factor and the functional protein can use the values measured as described above.
  • the method may be a bad prognosis if the RS value is greater than the set point in terms of overall survival (OS) or recurrence free survival (RFS), and the prognosis is good if the RS value is less than the set point. .
  • OS overall survival
  • RFS recurrence free survival
  • the invention also provides a computer readable recording medium having recorded thereon a program for executing a prediction of prognosis after resection by surgery of gastric cancer.
  • a medium useful for predicting clinical outcome after surgical resection of a stage N0 gastric cancer patient during a TNM staging can be provided.
  • a medium useful for predicting clinical outcome after surgical resection of a stage N0 gastric cancer patient during a TNM staging can be provided. For example, in nucleic acid samples obtained from patients
  • RNA transcripts selected from the group consisting of FZD1, GLI3, ANGPTL7, ABL1, SMARCD3, ILK, CAV1, VIP, HSPB7, TOP2A and FANCD2; And determining the expression level of one or more miRNAs selected from the group consisting of hsa-miR-933, hsa-miR-184, hsa-miR-380 *, hsa-miR-190b, hsa-miR-27a * and hsa-miR-1201. Making; And
  • RS recurrence score
  • a computer readable recording medium having recorded thereon a program for causing a computer to classify a patient having a higher RS than a setpoint is a high probability of relapse and a patient having a lower RS is set to a lower likelihood of relapse.
  • the RS may be calculated according to Equation 1.
  • the recording medium is regarded as a high probability of recurrence when the RS value is higher than the set point in terms of overall survival (OS) or recurrence free survival (RFS), and a low recurrence rate when the RS value is lower than the set point.
  • OS overall survival
  • RFS recurrence free survival
  • a low recurrence rate when the RS value is lower than the set point.
  • the set value is expressed as +/-, it may be determined that the recurrence is high when the RS is a positive value, and the recurrence is low when the value is ⁇ .
  • a medium that can be useful for predicting clinical outcome after gastric resection of the entire gastric cancer patient group irrespective of the TNM stage can be provided.
  • a medium that can be useful for predicting clinical outcome after gastric resection of the entire gastric cancer patient group irrespective of the TNM stage can be provided.
  • a computer-readable recording medium having recorded thereon a program for causing a computer to classify a patient whose RS is greater than a setpoint is a high probability of recurrence and a patient smaller than the setpoint is a low likelihood of relapse.
  • the RS may be calculated according to Equation 2.
  • the recording medium has a high probability of recurrence when the RS value is larger than the set point in terms of overall survival or recurrence free survival (RFS), and a low recurrence rate when the RS value is smaller than the set point. It may be. For example, when the set value is 0, if the RS value is greater than 0, recurrence is high, and if the RS value is less than 0, recurrence is low.
  • RFS overall survival or recurrence free survival
  • microarray refers to the regular placement of hybridizable array elements, preferably polynucleotide probes, on a substrate.
  • polynucleotide when used in the singular or plural, generally refers to any polyribonucleotide or polydeoxyribonucleotide, which may be unmodified RNA or DNA or modified RNA or DNA.
  • polynucleotides as defined herein include, but are not limited to, DNA comprising one- and two-stranded DNA, one- and two-stranded regions, one- and two-stranded RNA, one-and RNAs comprising two-stranded regions, single-stranded or more typically two-stranded, or hybrid molecules comprising DNA and RNA comprising one- and two-stranded regions.
  • polynucleotide refers to a three-stranded region comprising RNA or DNA or both RNA and DNA.
  • the strands in this region can be from the same molecule or from different molecules.
  • a zone may comprise all of one or more molecules, but more specifically includes only one zone of some of the molecules.
  • One of the molecules of the triple-helix region is an oligonucleotide.
  • polynucleotide specifically includes cDNA.
  • the term includes DNA (including cDNA) and RNA containing one or more modified bases.
  • a DNA or RNA having a backbone modified for stability or for other reasons is a “polynucleotide” as intended herein.
  • DNA or RNA comprising an unusual base such as inosine or a modified base such as tritium base is included within the term “polynucleotide” as defined herein.
  • polynucleotide refers to all chemically, enzymatically and / or metabolically modified forms of unmodified polynucleotides, as well as DNA and RNA characteristics of cells and viruses, including simple and complex cells. It includes a chemical form having a.
  • oligonucleotide refers to a relatively short polynucleotide, including but not limited to one-strand deoxyribonucleotide, one- or two-strand ribonucleotide, RNA: DNA hybrid and two-strand DNA. Oligonucleotides, such as single-stranded DNA probe oligonucleotides, are often synthesized by chemical methods using, for example, commercially available automated oligonucleotide synthesizers. However, oligonucleotides can be prepared by a variety of other methods, including in vitro recombinant DNA-mediated techniques, and by expression of DNA in cells and organisms.
  • differentially expressed gene As used herein, “differentially expressed gene”, “differential gene expression” and their synonyms used interchangeably refer to their expression in a disease, in particular stomach cancer, as compared to their expression in normal or control subjects. It refers to a gene that is activated at a higher or lower level among a subject suffering from a cancer such as. The term also includes genes whose expression is activated at higher or lower levels in different stages of the same disease. It will also be appreciated that differentially expressed genes may be activated or inhibited at the nucleic acid level or the protein level, or may undergo other splicing to result in different polypeptide products. Such differences can be demonstrated, for example, by changes in mRNA levels, surface expression, secretion or other distribution of the polypeptide.
  • Differential gene expression is a comparison of expression between two or more genes or their gene products, or a comparison of expression ratios between two or more genes or their gene products, or even two differently processed genes of the same gene. Comparison of products (these may differ between a normal subject and a disease, in particular a subject suffering from cancer, or between various stages of the same disease). Differential expression is, for example, a quantitative, as well as qualitative difference in the pattern of transient or cell expression in a gene or its expression product between normal and diseased cells, or between cells undergoing different disease events or disease stages. Include all of them.
  • “differential gene expression” is at least about 2 times, preferably at least about 4 times, between the expression of a given gene in normal and diseased subjects or at various stages of disease development in a diseased subject, More preferably at least about 6 times and most preferably at least about 10 times.
  • standardized with respect to a gene transcript or gene expression product refers to the level of the transcript or gene expression product relative to the average level of the transcript / product of the reference gene set, wherein the reference genes are throughout the patient, tissue or treatment. Selected based on their minimal variation (“housekeeping genes”), or reference genes are all of the genes tested. In the latter case, generally referred to as “global normalization", it is important that the total number of genes tested is relatively large, preferably greater than 50.
  • the term 'standardized' with respect to RNA transcripts refers to the level of transcription relative to the average of the levels of transcription of a set of reference genes. More specifically, the mean level of RNA transcript as measured by TaqMan® RT-PCR refers to the Ct value—mean Ct value of the reference gene transcript set.
  • expression threshold and “defined expression threshold” are used interchangeably and above this level the gene or gene product of that gene or gene product is used as a predictive marker for patient response or drug resistance. Say the level. Thresholds are typically defined experimentally from clinical studies. The expression threshold may be selected for maximum sensitivity (eg to detect all responders to one drug), or maximum selectivity (eg to select only responders for one drug), or minimum error.
  • gene amplification refers to the process by which multiple copies of a gene or gene fragment are formed in a particular cell or cell line. Replicated regions (extension of amplified DNA) are often referred to as "amplicons”. Often, the amount of messenger RNA (mRNA) produced, ie gene expression, is also increased in proportion to the number of copies made of a particular gene.
  • mRNA messenger RNA
  • prognosis is used herein to refer to the prediction of the likelihood of death by cancer or progression of neoplastic disease such as gastric cancer (including relapse, metastatic spread and drug resistance).
  • prediction is used herein to refer to the likelihood that a patient will survive for a certain period of time without cancer recurrence after surgical removal of the primary tumor.
  • the prediction method of the present invention can be used clinically to determine treatment by selecting the most appropriate treatment technique for any particular patient.
  • the predictive method of the present invention is an invaluable means in predicting whether a patient is likely to respond favorably to a treatment regimen, for example a surgical procedure, or whether the patient can survive long term after the end of the surgery.
  • prognostic indicator can be used interchangeably with "recurrence score.”
  • long term survival is used herein to refer to survival of at least 3 years, more preferably at least 5 or 8 years, most preferably at least 10 years after surgery or other treatment.
  • tumor refers to all neoplastic cell growth and proliferation (whether malignant or benign) and all cancerous and cancerous cells and tissues.
  • cancer and “cancerous” describe or refer to physiological conditions in mammals that are typically characterized by unregulated cell growth.
  • examples of cancer include gastric cancer, breast cancer, colon cancer, lung cancer, prostate cancer, hepatocellular cancer, gastric cancer, pancreatic cancer, cervical cancer, ovarian cancer, liver cancer, bladder cancer, cancer of the urethra, thyroid cancer, kidney cancer, carcinoma, melanoma, or brain cancer But not limited to these.
  • the “stringency” of the hybridization reaction is easily determined by one of ordinary skill in the art and is an experimental calculation that generally depends on probe length, wash temperature and salt concentration. In general, longer probes require higher temperatures for proper annealing, while shorter probes require lower temperatures. Hybridization generally depends on the ability of denatured DNA to reanneal when complementary strands are present in an environment below their melting temperature. The higher the degree of desired homology between the probe and the hybridizable sequence, the higher the relative temperature that can be used. As a result, higher relative temperatures tend to make the reaction conditions more stringent, while lower temperatures are less so. For further details and explanation of the stringency of the hybridization reaction, see Ausubel et al. , Current Protocols in Molecular Biology, Wiley Interscience Publishers, (1995).
  • “Strict conditions” or “high stringency conditions” as defined herein typically include (1) low ionic strength, for example, at 50 ° C. for 0.015 M sodium chloride / 0.0015 M sodium citrate / 0.1% sodium dodecyl sulfate wash and Using high temperatures; (2) denaturant at 42 ° C.
  • formamide for example 50% (v / v) formamide and 0.1% bovine serum albumin / 0.1% Ficoll / 0.1% polyvinylpyrrolidone / Using 750 mM sodium chloride, 75 mM sodium citrate with 50 mM sodium phosphate buffer, pH 6.5; Or (3) 50% formamide at 42 ° C., 5 ⁇ SSC (0.75M NaCl, 0.075 M sodium citrate), 50 mM sodium phosphate (pH 6.8), 0.1% sodium pyrophosphate, 5 ⁇ Denhardt's solution , Sonicated salmon sperm DNA (50 ⁇ g / ml), 0.1% SDS, and 10% dextran sulfate, 0.2 ⁇ SSC (sodium chloride / sodium citrate) and 50% formamide (at 55 ° C.) at 42 ° C. ), followeded by a high-stringency wash consisting of 0.1 x SSC containing EDTA at 55 ° C.
  • Modely stringent conditions may be the same as described in Sambrook et al ., Molecular Cloning: A Laboratory Manual, New York: Cold Spring Harbor Press, 1989, and less stringent washing solutions and hybridizations than those described above. The use of conditions (eg, temperature, ionic strength and% SDS). Examples of moderately stringent conditions include 20% formamide, 5 ⁇ SSC (150 mM NaCl, 15 mM trisodium citrate), 50 mM sodium phosphate (pH 7.6), 5 ⁇ denhardt solution, 10% dextran sulfate at 37 ° C.
  • conditions eg, temperature, ionic strength and% SDS
  • moderately stringent conditions include 20% formamide, 5 ⁇ SSC (150 mM NaCl, 15 mM trisodium citrate), 50 mM sodium phosphate (pH 7.6), 5 ⁇ denhardt solution, 10% dextran sulfate at 37 ° C.
  • Gene expression profiling methods include methods based on hybridization analysis of polynucleotides, methods based on sequencing polynucleotides, and methods based on proteomics.
  • the most commonly used methods known in the art for the quantification of mRNA expression in samples are Northern blotting and in situ hybridization (Parker & Barnes, Methods in Molecular Biology 106: 247 -283 (1999)]); RNAse protection assay (Hod, Biotechniques 13: 852-854 (1992)); And PCR-based methods such as reverse transcription polymerase chain reaction (RT-PCR) (Weis et al., Trends in Genetics 8: 263-264 (1992)).
  • RT-PCR reverse transcription polymerase chain reaction
  • antibodies can be used that can recognize two specific strands, including two DNA strands, two RNA strands, and two DNA-RNA hybrid strands or two DNA-protein strands.
  • Representative methods for sequencing-based gene expression analysis include gene expression analysis by serial analysis of gene expression (SAGE) and massively parallel signature sequencing (MPSS). .
  • RT-PCR Reverse Transcriptase PCR
  • RT-PCR One of the most sensitive and most flexible quantitative PCR-based gene expression profiling methods is RT-PCR, which compares mRNA levels in different sample populations in normal and tumor tissues with or without drug treatment. It can be used to characterize gene expression patterns, determine closely related mRNAs, and analyze RNA structure.
  • the first step is the isolation of mRNA from the target sample.
  • Starting materials are typically total RNA isolated from human tumors or tumor cell lines and corresponding normal tissue or cell lines, respectively.
  • RNA along with pooled DNA from a healthy donor, may be a tumor or tumor of various major tumors (breast, lung, colon, prostate, brain, liver, kidney, pancreas, spleen, thyroid, testes, ovaries, uterus, etc. Cell lines).
  • the source of mRNA is the primary tumor, the mRNA can be extracted, for example, from frozen or stored paraffin-embedded and immobilized (eg formalin-fixed) tissue samples.
  • RNA isolation can be performed according to the manufacturer's instructions using commercial kits, such as purification kits from Qiagen, buffer sets and proteases. For example, total RNA from cells in culture can be isolated using Qiagen RN easy mini-columns.
  • RNA isolation kits include the MasterPureTM Complete DNA and RNA Purification Kit (EPICENTRE, Madison, WI) and Paraffin Block RNA Isolation Kit (Ambion, Inc.) Ambion, Inc.). Complete RNA from tissue samples can be isolated using RNA Stat-60 (Tel-Test). RNA prepared from tumors can be isolated, for example, by cesium chloride density gradient centrifugation.
  • RNA cannot be used as a template for PCR
  • the first step in gene expression profiling by RT-PCR is reverse transcription of the RNA template into cDNA, followed by exponential amplification into its PCR reaction.
  • the two most commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney rat leukemia virus reverse transcriptase (MMLV-RT).
  • the reverse transcription step is typically first antigen-stimulated using specific primers, random hexamers, or oligo-dT primers, depending on the environment and goal of expression profiling.
  • the extracted RNA can be reverse-transcribed using the GeneAmp RNA PCR Kit (Perkin Elmer, California, USA) following the manufacturer's instructions.
  • the derived cDNA can then be used as a template in subsequent PCR reactions.
  • the PCR step can use a variety of thermostable DNA-dependent DNA polymerases, it typically uses Taq DNA polymerase, which has 5'-3 'nuclease activity, but has 3'-5' read protection. There is a lack of proofreading endonuclease activity.
  • Takman PCR typically utilizes a 5'-nuclease activity that hybridizes a hybridization probe bound to its target amplicon of Taq or Tth polymerase, but with any 5 'nuclease activity equivalent. Enzymes can be used. Two oligonucleotide primers are used to generate representative amplicons of the PCR reaction.
  • the third oligonucleotide or probe is designed to detect a nucleotide sequence located between two PCR primers.
  • the probe is non-extensible by Taq DNA polymerase enzyme and is labeled with a reporter fluorescent dye and a quencher fluorescent dye. Any laser-induced emission from the reporter dye is quenched by the quench dye when the two dyes are placed together as close as they are on the probe.
  • Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resulting probe fragments dissociate in solution and have no quenching effect of the second fluorophore on the signal from the released reporter dye.
  • One molecule of reporter dye is released from each of the synthesized new molecules, and detection of the unquenched reporter dye provides a basis for quantitative interpretation of the data.
  • TAKMAN RT-PCR is a commercially available instrument, for example ABI Prism 7700TM Sequence Detection SystemTM (Perkin-Elmer-Applied Biosystems, California, USA Foster City), or Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany).
  • the 5 'nuclease procedure is performed on a real time quantitative PCR device, such as the ABI Prism 7700TM Sequence Detection SystemTM.
  • the system consists of a thermocycler, laser, charge-coupled device (CCD), camera and computer.
  • the system amplifies the sample in a 96-well format on a thermocycler. During amplification, laser-induced fluorescence signals are collected in real time via fiber optic cables for all 96 wells.
  • the system includes software for running the device and for analyzing the data.
  • 5'-nuclease assay data is initially expressed as Ct, or threshold cycle.
  • Ct threshold cycle
  • the fluorescence value is recorded every cycle and represents the amount of product amplified to that point in the amplification reaction.
  • the point when the fluorescence signal is first recorded as statistically significant is the threshold cycle (Ct).
  • RT-PCR is generally performed using reference RNA, which is ideally expressed at some level between different tissues, and is not affected by experimental treatment.
  • the RNA most often used to normalize gene expression patterns is mRNA for the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPD) and ⁇ -actin (ACTB).
  • Real-time quantitative PCR measures PCR product accumulation via double-labeled fluorogenic probes (ie, tagman probes).
  • Real-time PCR is compatible with both quantitative competitive PCR (wherein internal competitors for each target sequence are used for standardization) and quantitative comparison PCR using standardized genes contained in the sample or housekeeping genes for RT-PCR. For further details, see, eg, Held et al., Genome Research 6: 986-994 (1996).
  • CDNA obtained after isolation and reverse transcription of RNA is synthesized from a synthetic DNA molecule (competitor) (this is a single Spiked to the targeting cDNA region at all positions except base) and used as internal standard.
  • the cDNA / competitor mixture is PCR amplified and post-PCR shrimp alkaline phosphatase (SAP) enzyme treatment is added to cause dephosphorylation of the remaining nucleotides.
  • SAP shrimp alkaline phosphatase
  • PCR products from competitors and cDNAs are primer stretched, which produces separate mass signals for competitor- and cDNA-derived PCR products. After purification, these products are metered onto a chip array that is already loaded with the components necessary for analysis using matrix-assisted laser desorption ionization flow time mass spectrometry (MALDI-TOF MS) analysis.
  • MALDI-TOF MS matrix-assisted laser desorption ionization flow time mass spectrometry
  • PCR-based techniques are described, for example, in parallax displays (Liang and Pardee, Science 257: 967-971 (1992)); Amplified fragment length polymorphism (iAFLP) (Kawamoto et al., Genome Res .
  • iAFLP Amplified fragment length polymorphism
  • BeadArray TM technology (Illumina, San Diego, CA) (Oliphant et al ., Discovery of Markers for Disease (Supplement to Biotechniques, June 2002) and Ferguson et al., Analytical Chemistry 72: 5618 (2000)])); Beads for gene expression detection using a commercially available Luminex 100 LabMAP system and multicolor-coded microspheres (Luminex Corp., Austin, Texas) for rapid assays for gene expression Arrays for Detection of Gene Expression (BADGE) (Yang et al., Genome Res . 11: 1888-1898 (2001)); And high coat expression profiling (HiCEP) analysis (Fukumura et al., Nucl. Acids. Res . 31 (16) e94 (2003)).
  • PCR amplified inserts of cDNA clones are applied onto the substrate in a dense array.
  • 10,000 or more nucleotide sequences are added to the substrate.
  • Microarrayed genes immobilized on the microchip with 10,000 elements each are suitable for hybridization under stringent conditions.
  • Fluorescently labeled cDNA probes can be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissue of interest.
  • Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array. After rigorous washing to remove non-specifically bound probes, the chip is scanned by in-focus laser microscopy or by another detection method such as a CCD camera.
  • Miniaturization scale hybridization provides convenient and rapid evaluation of expression patterns for large numbers of genes. This method has been shown to have the necessary sensitivity to detect rare transcripts (which are expressed in a few copies per cell) and to reproducibly detect at least approximately two-fold differences in expression (Schena et al., Proc. Natl. Acad. Sci . USA 93 (2): 106-149 (1996)].
  • Microarray analysis can be performed by commercially available equipment according to the manufacturer's protocol, for example using Affymetrix GenChip technology or Insight's microarray technology.
  • microarray methods for large-scale analysis of gene expression makes it possible to systematically study molecular markers of cancer classification and performance prediction in various tumor types.
  • Serial analysis of gene expression is a method that allows for simultaneous and quantitative analysis of large numbers of gene transcripts without the need to provide separate hybridization probes for each transcript.
  • a short sequence tag (about 10-14 bp) is generated that contains enough information to uniquely identify a transcript, with the tag being obtained from a unique location within each transcript.
  • Many transcripts are then linked together to form a long series of molecules (sequenced to represent the identity of multiple tags simultaneously).
  • the expression pattern of any transcript population can be quantitatively assessed by measuring the excess of an individual tag and identifying the gene corresponding to each tag. For more details, see, eg, Velculescu et al., Science 270: 484-487 (1995) and Velculescu et al., Cell 88: 243-51 (1997).
  • microbead libraries of DNA templates are constructed by in vitro cloning. This is followed by the assembly of planar arrays of template-containing microbeads in high density (typically 3 ⁇ 10 6 microbeads / cm) flow cells. The free end of the cloned template on each microbead is analyzed simultaneously using a fluorescence-based signature sequencing method that does not require DNA fragment separation. This method has been shown to provide hundreds and thousands of gene signature sequences from yeast cDNA libraries simultaneously and accurately in one operation.
  • Immunohistochemical methods are also suitable for detecting the expression of prognostic markers of the present invention.
  • expression is detected using antibodies or antisera, preferably polyclonal antisera and most preferably monoclonal antibodies specific for each marker.
  • Antibodies can be detected by direct labeling of the antibodies themselves, for example with radiolabels, fluorescent labels, hapten labels, such as biotin, or enzymes such as horse radish peroxidase or alkaline phosphatase.
  • an unlabeled primary antibody is used in combination with a labeled secondary antibody comprising an antiserum, polyclonal antiserum or monoclonal antibody specific for the primary antibody. Immunohistochemistry protocols and kits are known in the art and are commercially available.
  • proteome is defined as the entirety of a protein present in a sample (eg, tissue, organism or cell culture) at a particular time period.
  • Proteomics in particular involves the study of the overall change in protein expression in a sample (also called “expression proteomics”).
  • Proteomics typically include the following steps: (1) separation of individual proteins in a sample by 2-D gel electrophoresis (2-D PAGE); (2) identification of individual proteins recovered from the gel, such as mass spectrometry or N-terminal sequencing, and (3) analysis of data using bioinformatics.
  • Proteomics methods are valuable appendices to other methods of gene expression profiling and can be used alone or in combination with other methods to detect the product of prognostic markers of the present invention.
  • RNA isolation, purification, primer extension and amplification are described in various published magazine articles (eg, (TE Godfrey et al. J. Molec. Diagnostics 2: 84-91 [2000] and K. Specht et al., Am. J. Pathol . 158: 419-29 [2001]).
  • a representative method begins with cutting about 10 ⁇ m thick sections of paraffin-embedded tumor tissue samples. RNA is then extracted and proteins and DNA are removed.
  • RNA repair and / or amplification steps may be included if necessary, followed by RT-PCR after RNA is reverse transcribed using a gene specific promoter. Finally, the data is analyzed to determine the best treatment option (s) available to the patient based on the characteristic gene expression patterns identified in the observed tumor samples.
  • An important aspect of the present invention is the use of the measured expression of specific genes by gastric cancer tissue to provide prognostic information. For this purpose, it is essential to correct (standardize) the amount of RNA tested, variations in RNA quality used, and differences in other factors, such as machine and operator differences. Therefore, assays typically measure and incorporate the use of reference RNA, including those transcribed from known housekeeping genes such as GAPD and ACTB. Accurate methods for standardizing gene expression data are provided in "User Bulletin # 2" for the ABI PRISM 7700 Sequence Detection System (Applied Biosystems; 1997). Alternatively, normalization can be based on the mean or median signal (Ct) of the assayed genes or all of their many subsets (full normalization approach). In the studies described in the Examples below, a so-called central standardization strategy was used, which used a subset of screened genes selected based on lack of correlation with clinical outcome for standardization.
  • CDNA synthesized from RNA of a sample is prepared using a multiplex RT or TaqMan low-density array for a TaqMan array human microRNA panel (Applied Biosystems, Foster City, Calif.).
  • the operation that distinguishes cancer prognosis methods against the likelihood of recurring gastric cancer is characterized by 1) the unique set of test mRNAs (or corresponding gene expression products) used to measure recurrence, and 2) the specific data used to combine expression data. Weights, and 3) thresholds used to divide patients into groups with different levels of risk, such as low, medium, and high risk groups. This operation yields a numerical recurrence score (RS).
  • RS numerical recurrence score
  • test requires laboratory assays to determine the levels of specified mRNAs or expression products thereof, but is fixed or paraffin embedded tumor biopsies that are either fresh or frozen tissue or already collected and stored from patients. Test specimens are available in very small quantities. Thus, the test can be non-invasive. It is also compatible with several different methods of tumor tissue harvested, for example, via core biopsy or microneedle aspiration.
  • the cancer recurrence score (RS) is
  • (f) is determined by summing the values for each subset multiplied by the coefficients to obtain a recurrence score (RS), where the contribution of each subset that does not show a linear correlation with cancer recurrence is merely a predetermined threshold value.
  • Increased expression of the specified genes gives a negative value to subsets that reduce the risk of cancer recurrence and positive expression to the subsets where expression of the specified genes increases the risk of cancer recurrence.
  • RS is
  • RNA transcript selected from the group consisting of FZD1, GLI3, ANGPTL7, ABL1, SMARCD3, ILK, CAV1, VIP, HSPB7, TOP2A and FANCD2; And measuring the expression level of at least one miRNA selected from the group consisting of hsa-miR-933, hsa-miR-184, hsa-miR-380 *, hsa-miR-190b, hsa-miR-27a * and hsa-miR-1201 and;
  • HR n represents the hazard ratio of the nth RNA transcript or microRNA
  • normLogTransValue n means the value associated with the expression of the n-th RNA transcript or micro RNA.
  • the RS value is a positive value, it is a bad prognosis, and if the RS value is a -value, it is determined that it is a good prognosis.
  • RS is
  • HR n represents the hazard ratio of the nth functional protein
  • RPPAValue n means the value associated with the expression of the n th functional protein.
  • the prognosis is bad, and if the RS value is less than 0, the prognosis is determined to be good.
  • the ABI Prism 7900TM consists of a thermocycler, laser, charge-coupled device (CCD), camera and computer. The system amplifies the sample in a 384-well format on a thermocycler. During amplification, laser-induced fluorescence signals are collected in real time for all 384 wells and detected in the CCD.
  • the system includes software for running the device and for analyzing the data.
  • CT Threshold cycle
  • Table 1 lists a set of genes showing good prognosis with genes with Hazard ratio ⁇ 1.0 and p ⁇ 0.05, and Table 2 lists sets of genes showing bad prognosis with genes with Hazard ratio> 1.0 and p ⁇ 0.05. will be.
  • prognostic analysis was performed not only in the overall stage but also in the locally advanced gastric cancer group. Accordingly, a good prognosis group showing 85% or more of 5 year overall survival rate as well as 5 year no recurrence survival rate was selected by micro RNA group in locally advanced gastric cancer group.
  • the first and fourth stages were set as the training set, and the predictive model was made using the locally advanced gastric cancer based on the second and third stages as the test set.
  • training set is meant a subject sample from which statistically significant RNA transcripts and microRNAs were extracted.
  • the test set refers to a set for testing the accuracy of the extracted variable can actually determine whether the prognosis is good or bad. The reason for using this method is not only to be able to effectively predict prognosis in a specific sample group, but also to determine that it is effective in an independent sample. Based on the table, the following accuracy was obtained when leave one out cross validation (Reference, BRB-ArrayTools Version 4.2 User's Manual p74-81).
  • the accuracy of the prediction model is very high. It makes it possible to make prognostic predictions more clear, especially in locally advanced gastric cancer.
  • Tables 4 and 5 are lists of microRNAs that influence survival using the Univariate Cox's proportional harzard model.
  • the left column shows the names of the microRNAs, and the left column to the right, Cox p value, harzard ratio. , The degree of expression of the microRNA, and finally the fold difference between the maximum and minimum values.
  • a total of 27 microRNAs showed survival and statistical significance. Among them, the names of each of the 14 microRNAs affecting survival and survival analysis are shown.
  • Table 4 lists micro RNA sets that show good prognosis with genes with Hazard ratio ⁇ 1.0 and p ⁇ 0.05, and Table 5 shows micro RNA sets showing bad prognosis with genes with Hazard ratio> 1.0 and p ⁇ 0.05. It is listed.
  • Prognostic Index (HR 1 * normLogTransValue 1 + HR 2 * normLogTransValue 2 + ... + HR n * normLogTransValue n )
  • HR n represents the hazard ratio of the n-th micro RNA, in particular, when the hazard ratio is less than 1, it is replaced with -1 / hazard ratio.
  • normLogTransValue n means the value after quantile normalization after transformation to log2 of nth micro RNA.
  • the prognostic index is about -20 or less, the prognosis is good, and when -20 or more, the prognosis is poor.
  • Figure 2 shows an example using the recurrence scoring method in stage 3a of gastric cancer
  • the y-axis represents the prognostic indicator value
  • SDS-sample buffer (without) was extracted from total cellular proteome using Reverse phase protein array (RPPA) Lysis buffer from frozen tumor tissue. After denaturation using bromophenol blue) and serial dilution of 6-8 times, printing was carried out with a robot arrayer on a nitrocellulose coated glass slide.
  • RPPA Reverse phase protein array
  • the slides in which the proteins derived from the tumor freezing tissues are printed in high density can be used for the biological characteristics of the tumor cells such as tumor growth, cell death, survival and cell cycle transition and invasion, metastasis and cell neovascularization.
  • a specific antibody including phosphorylated protein specific antibodies
  • Table 6 shows a list of functional proteins that affect survival significantly statistically using the univariate cox's proportional hazard model among a total of 250 specific antibodies for detecting functional proteins.
  • the left column of Table 8 indicates the name of the functional protein, and shows the Cox p value (parametric), harzard ratio, and standard error of log intensities, from left to right.
  • Figure 3 shows the results of survival analysis in the case of Akt pS473 , the lower the expression of the phosphorylated protein has a better prognosis.
  • the phosphorylated protein may be referred to as a target biomarker having a feature that can simultaneously function as a target of a target therapeutic agent.
  • HR n represents the hazard ratio of the nth functional protein, and in particular, when the hazard ratio is less than 1, it is substituted with -1 / hazard ratio.
  • RPPAValue n is the value after transformation to log2 of the n th functional protein.
  • the prognostic index is greater than zero, the prognosis is bad, and if the prognostic index is less than zero, the prognosis is good.
  • the risk score system using the five markers shows the characteristics of functional proteins that can look at the prognosis as well as the availability of various targeted therapies.
  • the five targeted markers such as Akt pS473 , PAI, SMAD3, P70 S6K , and VEGFR2
  • the therapeutic drugs by Akt and VEGFR2 targets are already used in other cancers in the clinic.
  • the advantage is that it can be applied immediately. It can also be used as a prognostic model for locally advanced gastric cancer.
  • Table 7 lists a set of RNA transcripts showing good prognosis with genes with Hazard ratio ⁇ 1.0 and p ⁇ 0.05 and a set of RNA transcripts showing bad prognosis with genes> 1.0 and p ⁇ 0.05.
  • Table 8 lists the micro RNA set showing good prognosis with genes with Hazard ratio ⁇ 1.0 and p ⁇ 0.05 and the micro RNA set showing bad prognosis with genes> 1.0 and p ⁇ 0.05.
  • Risk Score FZD1 ⁇ 4.302 + GLI3 ⁇ 4.073 + ANGPTL7 ⁇ 2.949 + ABL1 ⁇ 2.784 + SMARCD3 ⁇ 2.266 + ILK ⁇ 2.251 + CAV1 ⁇ 1.788 + VIP ⁇ 1.73 + HSPB7 ⁇ 1.535-TOP2A ⁇ 1.766-FANCD2 ⁇ 2.793 + miR933 ⁇ 5.256 + miR184 ⁇ 1.674 + miR380 * ⁇ 1.903-miR190b ⁇ 3.597-miR27a * ⁇ 1.7-miR1201 ⁇ 1.35
  • RNA transcripts and microRNAs are a survival analysis result when the risk score in the N0 gastric cancer group is divided into negative cases and positive cases when using the prognostic indicators. Similar anatomical stages clearly show differences in survival rates. This means superiority of prognostic indicators using RNA transcripts and microRNAs.
  • FIG. 6 shows that the risk scoring system in N0 gastric cancer is separated into two groups based on 0, showing a 7% relapse survival rate in a good prognosis group and a 41% recurrence survival rate in a poor prognosis group. Show an ability to clearly distinguish
  • FIG. 7 illustrates a process in which a clear prognostic difference appears between clusters when hierachial clustering is performed using statistically correlated genes of microRNAs. This means the value of the combined use of microRNA and RNA transcripts.
  • biologically specific microRNAs can have the biological significance of such statistical methods as they have the ability to collectively inhibit specific group RNA transcripts.
  • the present invention can be used in the field of predicting gastric cancer recurrence prognosis.

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Abstract

La présente invention concerne un nouveau système de prédiction de pronostic capable de prédiction d'un pronostic d'un cancer gastrique avancé localement. Plus spécifiquement, la présente invention peut prédire un résultat clinique par un procédé d'analyse comparative des expressions d'un groupe de gènes ou de protéines après l'élimination chirurgicale d'un cancer gastrique.
PCT/KR2012/002193 2012-03-26 2012-03-26 Système de prédiction de pronostic du cancer gastrique avancé localement Ceased WO2013147330A1 (fr)

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CN114941031A (zh) * 2022-01-28 2022-08-26 中国医学科学院北京协和医院 早期胃癌预后差异基因与复发预测模型
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