JP2010131006A - Neuroglioma prognosis prediction method and kit usable therefore - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method for predicting postoperative prognosis of a neuroglioma patient. <P>SOLUTION: The method for predicting postoperative prognosis of a neuroglioma patient comprises (a) a step for measuring an expression amount of an optional gene group in Fig.1 (not shown) of tumor tissues or tumor cells derived from the neuroglioma patient, (b) a step for standardizing the measured expression amount and calculating postoperative prognosis score from the standardized expression amount, and (c) a step for determining the prognosis to be good when the calculated postoperative prognosis score is ≤0, or the prognosis to be bad when the calculated postoperative prognosis score is ≥0. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a method for predicting the postoperative prognosis of a glioma patient.

  In cancer, which is one of the three major adult diseases, a brain tumor, which is a type of cancer, is one of the tumors that are likely to become malignant. These brain tumors are classified into two types: primary brain tumors that arise from the brain tissue itself, and metastatic brain tumors in which cancers of other organs have metastasized to the brain.

  Among the primary brain tumors, the most likely tumor is glioma, and malignant glioma is considered to be one of the malignant tumors with the poorest prognosis. Glioma is characterized by infiltration into the brain parenchyma, making it very difficult to remove the tumor completely. Therefore, radiation therapy and chemotherapy are often added after surgery, but the 5-year survival rate is 38.6%, which is about half of the 5-year survival rate (75.7%) of all primary brain tumors. In particular, if it is limited to the most malignant glioblastoma (grade IV), its 5-year survival rate will be 10% or less.

  Conventionally, in order to predict the postoperative prognosis of such glioma, prognosis-related molecules are classified and the prognosis is diagnosed pathologically, but clinical efficacy is confirmed compared with other prognostic factors The technique to do is not established. Note that Patent Document 1 is known as a method for predicting the prognosis of glioma.

Japanese Patent Application No. 2008-545929

  This invention is made | formed in view of such a condition, and it aims at providing the method of estimating the postoperative prognosis of a glioma patient.

  The present inventors have accomplished the present invention as a result of intensive studies to solve the above problems. Specifically, the present inventors measured 152 cases of glioma patients and the expression level of the 3456 gene by an adapter-added competitive PCR method. Then, it was found that feature extraction by principal component analysis is effective for this data matrix, and a prognostic method comprising 58 genes can be established by supervised PCA using Cox regression.

That is, according to the first main aspect of the present invention,
A method for predicting the postoperative prognosis of a glioma patient, comprising: (a) measuring the expression level of any gene group in Table 1 in a tumor tissue or tumor cell derived from the glioma patient; (B) standardizing the measured expression level and calculating a postoperative prognostic prediction score from the standardized expression level; and (c) a good prognosis when the calculated postoperative prognostic prediction score is 0 or less. And determining a poor prognosis if the calculated postoperative prognostic score is greater than or equal to zero.

  According to such a configuration, since the postoperative prognostic prediction score is calculated from the expression level of a predetermined gene, the postoperative prognosis of the glioma patient is calculated based on the expression level of the gene in the tissue or cell derived from the patient. Can be predicted from the original.

  According to one embodiment of the present invention, in this method, the postoperative prognosis prediction score is calculated by the following formula I.

  According to another embodiment of the present invention, this method uses any 10 or more genes in Table 1.

  According to another embodiment, the method uses any 20 or more genes in Table 1.

  According to yet another embodiment, the method uses any 30 or more genes in Table 1.

  According to yet another embodiment, the method uses any of the 40 or more genes in Table 1.

  According to yet another embodiment, the method uses any 50 or more genes in Table 1.

  According to another embodiment, the method uses all 58 genes in Table 1.

  According to another embodiment, the method uses all 30 of the genes shown in Table 3.

  In addition, according to a second main aspect of the present invention, there is provided a kit used for predicting the postoperative prognosis of a glioma patient, comprising at least a part of the nucleotide sequence of any gene group in Table 1. A kit comprising a polynucleotide or an oligonucleotide is provided.

  According to such a configuration, a kit for performing the above-described method for predicting the postoperative prognosis of a glioma patient is provided.

  According to one embodiment of the present invention, in this kit, the polynucleotide or oligonucleotide is labeled.

  According to another embodiment, the kit has any 10 or more gene sets in Table 1.

  According to yet another embodiment, the kit has any 30 or more gene sets in Table 1.

  According to yet another embodiment, the kit has all 58 gene sets of the genes in Table 1.

  According to yet another embodiment, the kit has all 30 gene sets of the genes in Table 3.

  It should be noted that the features of the present invention other than those described above and the remarkable actions and effects will become clear to those skilled in the art with reference to the following embodiments and drawings of the present invention.

FIG. 1 is a flowchart showing the operation of each component of the method according to the present invention.

FIG. 2 is a graph showing the results of Kaplan-Meieranalysis.

FIG. 3 is a graph showing the results of Kaplan-Meieranalysis using MGH (Massachettes General Hospital) and MDA (MDAnderson) data sets.

FIG. 4 is a graph showing the Kaplan-Meieranalysis results when 30 genes shown in Table 3 were used.

Hereinafter, an embodiment and an example of the present invention will be described with reference to the accompanying drawings.
As described above, the present invention is a method for predicting the postoperative prognosis of a glioma patient, and (a) any gene in Table 1 in the tumor tissue or tumor cell derived from the glioma patient Measuring the expression level of the group; (b) standardizing the measured expression level and calculating a postoperative prognostic prediction score from the standardized expression level; and (c) the calculated postoperative prognostic prediction score. A prognosis is determined to be good when the value is 0 or less, and a poor prognosis is determined when the calculated postoperative prognosis prediction score is 0 or more. That is, the gist of the present invention is to predict the postoperative prognosis of the glioma patient from the tumor tissue or tumor cell derived from the glioma patient through a predetermined process.

  Specifically, the present invention is performed according to the flowchart shown in FIG.

  FIG. 1 is a flowchart showing the operation of each component of the method according to the present invention. In addition, each code | symbol S1-S7 in a figure respond | corresponds to each step S1-step S7 in the following description.

  First, in step S1, tumor tissue or tumor cells are collected from a glioma patient. The tumor tissue or tumor cell can be collected by a conventionally known method, and the number, size, and the like of the collected sample are not particularly limited.

  Thereafter, the expression level of the gene shown in Table 1 below in the collected tumor tissue or tumor cell is measured by a conventionally known gene expression level measurement method (step S2). Next, in step S3, the measured gene expression level is standardized. In step 4, a PC1 score indicating a postoperative prognosis prediction score is calculated using the standardized gene expression level and the regression coefficient CoxBeta. The CoxBeta for each gene is shown in Table 1.

  If the PC1 score calculated in Step 4 is 0 or less, the postoperative prognosis of the glioma patient is determined to be good. If the PC1 score is 0 or higher, the postoperative prognosis of the glioma patient is poor. (Step S5 to Step S7).

  Note that the method for predicting the postoperative prognosis of a glioma patient as described above can also be executed by a PC or the like having normal arithmetic processing capability. For example, the above-described PC1 score can be output using an arithmetic processing unit having the above-described calculation of Formula I as a software program.

  In addition, for example, the above-mentioned PC1 score is automatically input when the above-mentioned standardized gene expression level is input to a PC incorporating a software program or the like that can execute the above-described calculation of Formula I, or when expression analysis and standardization are completed The PC may output it by receiving the expression level. Furthermore, the PC may automatically classify the PC1 score output as described above into a good prognosis or a poor prognosis based on the numerical value. In this case, a configuration may be provided in which which case the output PC1 score is associated with is stored.

  Hereinafter, examples according to the present embodiment will be described.

  In this example, a case of a phase 2 clinical study (the KNOG study) led by Kyoto University Neurosurgery was used. In 152 cases, the expression level of the 3456 gene was measured by an adapter-added competitive PCR method. Feature extraction by principal component analysis is effective for this data matrix, and a prognostic system consisting of 58 genes was developed by supervised PCA using Cox regression (hereinafter referred to as 58-gene profile).

  In this system, the PC1 score was calculated from the standardized expression level of 58 genes and classified into a good prognosis group and a poor prognosis group with 0 as a threshold value. Each gene and the PC1 score calculation method are shown in Table 1.

  Table 1 shows CoxBeta indicating regression coefficients, CoxP, GS number, Gene Symbol, RefSeq ID, and description indicating univariate analysis values.

  FIG. 2 is a graph showing the results of Kaplan-Meieranalysis (A for all gliomas, B for grade IV only). The vertical axis represents progression-free survival. From the graph of B, it turned out that it is a classification method reflecting prognosis rather than the conventional grade classification. Although FIG. 2 shows a learning data set (110 cases), similar results were obtained with the test data set (42 cases).

  The Cox regression analysis was then compared to other powerful prognostic factors: surgical removal, age, and grade classification. For PFS, the 58-gene profile was the only strong prognostic factor. With regard to overall survival, the surgical excision degree, age, and 58-gene profile were significant in the univariate analysis, but the surgical excision degree and 58-gene profile were independent prognostic factors in the multivariate analysis. Details are shown in Table 2.

  Next, the effectiveness of the 58-gene profile was examined using the expression data of other groups already published. Both MGH (Massachettes General Hospital) and MDA (MDAnderson) data sets showed a higher correlation with prognosis than grade classification (FIG. 3).

  Although there have been papers on molecular classification of prognosis of glioma in the past, the present invention is the first to establish clinical efficacy compared with other prognostic factors. This result strongly shows that the classification method based on gene expression may be a better diagnostic method than the conventional pathological classification.

  Similarly, using 30 genes arbitrarily selected from 58 genes listed in Table 1, the PC1 score is calculated from the standardized expression level, and is classified into a good prognosis group and a poor prognosis group with 0 as a threshold value. did. The selected 30 genes are shown in Table 3.

  FIG. 4 shows the result of Kaplan-Meieranalysis using 30 gene groups described in Table 3. Similar to FIG. 2, the vertical axis represents progression-free survival. FIG. 4 shows the results of predicting the postoperative prognosis of 37 cases. In the molecular classification performed using the method according to the present invention, the p value is 0.003, which is 0.0404 of the conventional tissue classification. It can be seen that it shows a lower value.

  This means that even if the method according to the present invention is performed using 30 genes arbitrarily selected from the gene group described in Table 1, the same result as that obtained when 58 genes are used can be obtained. Is shown.

(Modifications, explanation of terms)
In the present invention, “gene expression” means that the gene itself produces a gene product such as RNA or protein. Therefore, the expression level of the gene can be determined by measuring the amount of mRNA transcribed from the gene or the protein translated from the mRNA. In the method according to the present invention, the expression level of a gene in a tumor tissue or tumor cell can be measured by any method / means known to those skilled in the art.

  In addition, the gene group used in the method of the present invention may be one to several amino acids in a known amino acid sequence as long as it has substantially the same biological function as each gene product specified by the amino acid sequence. It may be a gene product having a mutated amino acid sequence in which the amino acid is substituted, inserted, or deleted.

  For example, DNA microarray (or DNA chip), oligonucleotide microarray, northern hybridization, in situ hybridization, reverse transcription polymerase chain reaction (RT-PCR) or the like can be used for measuring the amount of expressed mRNA. However, it is not limited to these measurement means, and any method may be used as long as it is a method available to those skilled in the art.

  On the other hand, methods such as Western blotting, ELISA assay, immunohistochemical staining, or yeast two hybrid can be used for measuring the amount of protein.

  The kit according to the present invention can have an appropriate configuration according to the method / means for measuring the expression level of the gene and / or the protein encoded by the gene in tumor tissue or tumor cells. For example, the length of a polynucleotide or oligonucleotide consisting of at least a part of the base sequence of any gene in Table 1 included in the kit of the present invention may be several tens of base pairs. Based on information readily available from various databases such as Genbank, those skilled in the art can appropriately select, design and prepare.

  Further, they can be used in the form of a DNA chip or a probe in Northern blotting, a primer in PCR, or the like. Furthermore, if necessary, the polynucleotide or oligonucleotide may be labeled with an appropriate labeling substance such as a radioactive substance, a fluorescent substance, or a dye.

  The kit includes other elements or components known to those skilled in the art, such as various reagents, enzymes, buffers, reaction plates (containers), and the like, depending on the configuration and purpose of use.

  In addition, the present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the scope of the invention.

Claims (15)

A method for predicting the postoperative prognosis of a glioma patient comprising:
(A) measuring the expression level of any gene group in Table 1 in the tumor tissue or tumor cell derived from the glioma patient;
(B) standardizing the measured expression level and calculating a postoperative prognosis prediction score from the standardized expression level;
And (c) determining that the prognosis is good when the calculated postoperative prognostic score is 0 or less, and determining that the prognosis is poor when the calculated postoperative prognostic score is 0 or more. how to. The method of claim 1, wherein
The postoperative prognostic score is calculated according to the following formula I.

  The method according to claim 1, characterized in that any 10 or more genes in Table 1 are used.   The method according to claim 1, characterized in that any 20 or more genes in Table 1 are used.   The method according to claim 1, characterized in that any 30 or more genes in Table 1 are used.   The method according to claim 1, characterized in that any 40 or more genes in Table 1 are used.   The method according to claim 1, characterized in that any 50 or more genes in Table 1 are used.   The method according to claim 1, characterized in that all 58 of the genes shown in Table 1 are used.   The method according to claim 1, wherein all 30 genes shown in Table 3 are used. A kit used to predict the postoperative prognosis of a glioma patient,
A kit comprising a polynucleotide or oligonucleotide consisting of at least a part of the base sequence of any gene group in Table 1.   The kit according to claim 10, wherein the polynucleotide or oligonucleotide is labeled.   11. Kit according to claim 10, characterized in that it has any 10 or more gene sets in Table 1.   11. Kit according to claim 10, characterized in that it has any 30 or more gene sets in Table 1.   11. Kit according to claim 10, characterized in that it has a set of all 58 genes in Table 1.   11. Kit according to claim 10, characterized in that it has a set of all 30 genes in Table 3.

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