JP2019076068A - Ovarian cancer tissue type discrimination method - Google Patents

Abstract

To provide methods capable of discriminating serous egg adenocarcinoma and clear cell adenocarcinoma in ovarian cancer.SOLUTION: Provided is a method of discriminating between serous adenocarcinoma and clear cell adenocarcinoma tissue types based on a gene expression profile obtained in a test sample derived from an ovarian cancer patient, the method comprising a step of measuring the expression level of each gene in a gene set comprising at least three genes selected from a discrimination gene group, each gene being selected so that the total value of the gene expression score of each gene comprised in the gene set is equivalent to or more than the total value of the gene expression score of GLRX gene, GDF15 gene, and GABARAPL1 gene.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an ovarian cancer tissue typing method.

Among the gynecologic malignancies, ovarian cancer is known to have a poor prognosis, as ovarian cancer is often found after the early onset of symptoms and progression. Although the basic treatment for ovarian cancer is surgical treatment, the primary mode of development is peritoneal dissemination, and surgery is less likely to be completely cured, and many cases are targeted for chemotherapy. Chemotherapy centering on platinum preparations has been introduced since the 1980's, and the response rate to ovarian cancer has clearly increased since the 1990's combined with taxane preparations, and the chemistry of platinum preparations (carboplatin) + taxane preparations (paclitaxel) The therapy has become the standard of care in ovarian cancer.
Ovarian cancer is roughly classified into three: superficial epithelial / interstitial tumor, germ cell tumor, and sexual stroma / interstitial tumor. Among these, ovarian cancer of the surface epithelial and interstitial tumors mainly includes four tissue types, serous adenocarcinoma, clear cell adenocarcinoma, mucinous adenocarcinoma, and endometrioid adenocarcinoma, and these histotypes are respectively characterized Because their characteristics are different, identification of tissue type is important in the treatment of ovarian cancer, and treatment along the tissue type is desirable. In particular, among the above four tissue types, clear cell adenocarcinoma is the most difficult disease to treat. The most frequent serous adenocarcinoma (about 80% in Europe and the United States, about 40% in Japan) among ovarian cancer has a good response to chemotherapy and can be expected to have therapeutic effects. On the other hand, although the frequency of onset of clear cell adenocarcinoma is low in Europe and the United States (several percent) and high in Japan (approximately 24%), patients with advanced cell carcinoma have a significantly worse prognosis than serous adenocarcinoma patients. One of the main reasons is that drug responsiveness to chemotherapy is low. For this reason, clear cell adenocarcinoma is being developed as a treatment different from standard treatment.
Usually, in the clinical site, the histotype of ovarian cancer is identified by histopathological examination of surgical specimens. However, of the clear cell adenocarcinoma cases showing papillary growth, the shape is very similar to serous adenocarcinoma. Therefore, in such a case, it may be difficult to histopathologically distinguish between serous adenocarcinoma and clear cell adenocarcinoma. In addition, there may be a case where both are mixed, and it may be difficult to determine which of the characteristics of the tumor strongly reflects. When considering personalized medicine for clear cell adenocarcinoma, it is necessary to diagnose both more accurately.
In recent years, analysis methods using gene markers focused on gene expression and protein expression have been studied in order to make a prognosis or classification of ovarian cancer. For example, Patent Document 1 discloses a method of detecting a glycoprotein specific to cancer cells as an epithelial ovarian cancer differential marker. In addition, Patent Document 2 discloses a method of diagnosing ovarian cancer resistant to a chemotherapy intervention by measuring the expression level of a specific microRNA.
Thus, the development of a method capable of differentiating the histotype of ovarian cancer has been desired without using a histopathological method. However, to date, there has been no report on a gene marker that can clearly distinguish between serous adenocarcinoma and clear cell adenocarcinoma in ovarian cancer.

JP, 2016-048270, A Japanese Patent Publication No. 2014-530612 gazette

  The present invention is to provide a method capable of differentiating serous adenocarcinoma and clear cell adenocarcinoma in ovarian cancer.

In order to solve the above problems, the present inventors obtain gene expression profiles using comprehensive gene expression analysis techniques for cancerous tissues removed from multiple serous adenocarcinoma patients and clear cell adenocarcinoma patients. We found 29 gene groups with large differences in expression levels between serous and clear cell adenocarcinomas. Then, by measuring the expression level of these genes in the sample of the subject, a method was developed which can easily and accurately distinguish serous adenocarcinoma and clear cell adenocarcinoma among tissue types in ovarian cancer.
Therefore, in order to investigate which of 29 kinds of genes can be used to distinguish serous adenocarcinoma and clear cell adenocarcinoma with high accuracy, the present inventors examined the gene among each sample among 29 kinds of genes. Seven genes with less variation in expression levels were identified. Furthermore, the present inventors attempted to score gene expression for differences in expression levels of genes between serous adenocarcinoma and clear cell adenocarcinoma, and repeatedly examined which gene should be selected by the scoring. The As a result, the present inventors have satisfied the total value of the gene expression score of three types of genes (GLRX gene, GDF15 gene, and GABARAPL1 gene) having high gene expression score among the above seven types of genes. By selecting genes from 29 types, it has been found that serous adenocarcinoma and clear cell adenocarcinoma can be differentiated with high accuracy, and the present invention has been completed. The present invention is based on the findings and provides the following.

That is, the present invention
[1] A method for obtaining an expression profile of a gene in a test sample derived from an ovarian cancer patient, and identifying which tissue type of serous adenocarcinoma or clear cell adenocarcinoma based on the expression profile,
PPP1R3B gene, TOB1 gene, RBPMS gene, A4GALT gene, RIMKLB gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, LAMB1 gene, LNER gene, ARG2 gene, TMEM101 gene, GDF15 gene, C12orf75 gene, DLX5 gene, AOC1 gene, ANXA4 gene , At least three genes selected from the group consisting of: FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIGI gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and IGF2 gene Measuring the expression level of each gene in the set of genes comprising
Each gene was selected such that the total value of the gene expression score of each gene contained in the gene set is equal to or greater than the total value of the gene expression scores of the GLRX gene, GDF15 gene, and GABARAPL1 gene The present invention relates to a discrimination method including a process which is
In one embodiment of the identification method of the present invention,
[2] The discrimination method according to [1] above,
The gene expression score is characterized in that the magnitude of the difference in the expression level of a specific gene between a sample from a serous adenocarcinoma patient and a sample from a clear cell adenocarcinoma patient.
In one embodiment of the identification method of the present invention,
[3] The discrimination method according to [2] above,
The combination of the genes included in the gene set is that at least three genes have been selected such that the total is equal to 7.006 or more than 7.006 with reference to the gene expression score described in the following table. It features.
In one embodiment of the identification method of the present invention,
[4] The discrimination method according to any one of [1] to [3] above,
The method is characterized in that at least one gene included in the gene set is selected from the group consisting of PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, and GDF15 gene.
In one embodiment of the identification method of the present invention,
[5] The discrimination method according to any one of [1] to [4] above,
All of the genes included in the gene set are characterized in that they are selected from the group consisting of the PPP1 R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, and GDF15 gene.
In one embodiment of the identification method of the present invention,
[6] The discrimination method according to any one of [1] to [5] above,
The gene set comprises the GLRX gene, the GDF15 gene and the GABARAPL1 gene.
In one embodiment of the identification method of the present invention,
[7] The discrimination method according to any one of [1] to [6] above,
The tissue type of the test sample is determined by comparing the expression profile of each gene in the gene set with the expression profile of each gene in the corresponding gene set in samples from serous adenocarcinoma patients or clear cell adenocarcinoma patients. It is characterized by discriminating.
In one embodiment of the identification method of the present invention,
[8] The discrimination method according to any one of [1] to [7] above,
The test sample was compared by comparing the expression profile of each gene in the gene set with the expression profile of each gene in the corresponding gene set in samples from serous adenocarcinoma patients or clear cell adenocarcinoma patients. If it has an expression profile of a gene equivalent to the expression profile of a gene of histologic ovarian cancer, it is evaluated as a comparative ovarian cancer of a histologic type or a gene different from the expression profile of a gene of a histological type ovarian cancer compared It is characterized in that it is not evaluated as a histologic type of ovarian cancer as compared with the case of having an expression profile of
In one embodiment of the identification method of the present invention,
[9] The identification method according to any one of the above [1] to [8],
The present invention is characterized in that the tissue type of the test sample is identified by cluster analysis of expression profiles of each gene in the gene set.
In one embodiment of the identification method of the present invention,
[10] The discrimination method according to any one of [1] to [9] above,
A tissue type is identified by comparing a quantitative expression profile of each gene of the gene set in a test sample with a predetermined threshold.

In addition, the present invention is, in another aspect, [11] a differential marker set for discriminating whether it is a serous adenocarcinoma or clear cell adenocarcinoma tissue type in a test sample derived from an ovarian cancer patient, ,
The differential marker set includes the PPP1R3B gene, TOB1 gene, RBPMS gene, A4GALT gene, RIMKLB gene, GABARAPL1 gene, GABARXL gene, GLRX gene, GSTM3 gene, LAMB1 gene, LNER gene, ARG2 gene, TMEM101 gene, GDF15 gene, C12orf75 gene, DLX5 gene , AOC1 gene, ANXA4 gene, FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIGI gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and IGF2 gene selected from the group Containing a combination of at least three
Each gene is selected such that the total value of the gene expression score of each gene contained in the differential marker set is equal to or more than the total value of the gene expression score of the GLRX gene, the GDF15 gene, and the GABARAPL1 gene. It relates to a differential marker set.
Here, the discrimination marker set of the present invention is, in one embodiment,
[12] The discrimination marker set according to [11] above,
It is characterized in that the expression level of mRNA of each gene contained in the gene set is measured.
Moreover, in one embodiment, the marker gene set of the present invention
[13] The differential marker set according to [11], which is a PPP1 R3 B gene, TOB 1 gene, RBPMS gene, A4GALT gene, RIMKLB gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, LAMB1 gene, DNER Gene, ARG2 gene, TMEM101 gene, GDF15 gene, C12orf75 gene, DLX5 gene, AOC1 gene, ANXA4 gene, FBXN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIGI gene, LYPD1 gene A differential marker set, which is a combination of mRNAs encoded by at least three genes selected from the group consisting of a CRIP1 gene, a UQCRH gene, and an IGF2 gene.

In another embodiment, the present invention relates to [14] a kit for differentiating a serous adenocarcinoma or a clear cell adenocarcinoma tissue type in a test sample derived from an ovarian cancer patient,
The kit includes the PP1R3B gene, TOB1 gene, RBPMS gene, A4GALT gene, RIMKLB gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, LAMB1 gene, LNER gene, ARG2 gene, TMEM101 gene, GDF15 gene, C12orf75 gene, DLX5 gene, AOC1 Gene, ANXA4 gene, FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIGI gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and at least an IGF2 gene selected from the group consisting of Including means for measuring the expression level of each gene in a gene set comprising three genes,
A kit, wherein each gene is selected such that the total value of gene expression scores of each gene contained in the gene set is equal to or more than the total value of gene expression scores of GLRX gene, GDF15 gene, and GABARAPL1 gene About.
Here, the kit of the present invention, in one embodiment,
[15] The kit according to the above-mentioned [14], wherein the means for measuring the expression level of each gene is at least one selected from the group consisting of a primer for each gene, a probe, or a labeled product thereof. It is characterized in that it is a means.
Moreover, the kit of the present invention is, in one embodiment,
[16] The kit according to [15] above, which is for PCR, microarray or RNA sequencing.
Also, as another aspect of the present invention,
[17] A step of administering an effective amount of an anticancer agent against serous adenocarcinoma or clear cell adenocarcinoma to the ovarian cancer patient based on the result of the discrimination method according to any one of the above [1] to [10] It relates to the treatment method including.

  According to the method of the present invention, it is possible to distinguish whether a sample derived from an ovarian cancer patient is serous adenocarcinoma or clear cell adenocarcinoma.

FIG. 1 shows the result of cluster analysis of group average method by Euclidean distance based on microarray data of differential marker 29 gene in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 2 shows the rearranged microarray data of differential marker 29 gene in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma based on the scoring system of Example 4 below. Specifically, the sum value of the gene expression score of the differential marker 29 gene in each serous adenocarcinoma-derived or clear cell adenocarcinoma-derived specimen was calculated, and arranged in the order of the specimen with the smallest sum from the left. The graph at the bottom of FIG. 2 shows the sum value of the gene expression score in each sample. FIG. 3 shows ROC curves (upper) and sensitivity / specificity curves (lower) generated based on microarray data of differential marker 29 genes in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 4 shows group scatter plots generated based on microarray data for differential marker 29 gene in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 5 is based on the microarray data of differential marker 7 gene (PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, GDF15 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma It shows the results of dynamic cluster analysis (group averaging with Euclidean distance). FIG. 6 shows the differential marker 7 gene (PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, GDF15 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma based on the scoring system. Shows rearranged microarray data on The graph at the bottom of FIG. 6 shows the sum value of the gene expression score in each sample. FIG. 7 is generated based on microarray data of differential marker 7 gene (PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, GDF15 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma Shows a group scatter plot. FIG. 8 is generated based on microarray data of differential marker 7 gene (PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, GDF15 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma The ROC curve (upper) and the sensitivity / specificity curve (lower) are shown. FIG. 9 shows group scatter plots prepared based on microarray data of differential marker 3 genes (GLRX gene, GDF15 gene, and GABARAPL1 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 10 shows ROC curves (upper row) and sensitivity / specificity curves generated based on microarray data of differential marker 3 gene (GLRX gene, GDF15 gene, GABARAPL1 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma (Lower) is shown. FIG. 11 shows group scatter plots prepared based on microarray data of differential marker 4 genes (LYPD1 gene, CRIP1 gene, LRIGI gene, A4GALT gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 12 shows ROC curves (upper row) and sensitivities that were generated based on microarray data of differential marker 4 genes (LYPD1 gene, CRIP1 gene, LRIGI gene, A4GALT gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma The specificity curve (bottom) is shown. FIG. 13 shows a group scatter diagram prepared based on microarray data on differential marker 3 genes (ANXA4 gene, LAMB1 gene, APOL1 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 14 shows ROC curves (upper row) and sensitivity / specificity curves generated based on microarray data of differential marker 3 gene (ANXA4 gene, LAMB1 gene, APOL1 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma (Lower) is shown. FIG. 15 shows a group scatter diagram prepared based on microarray data of differential marker 3 gene (FXYD2 gene, GPX3 gene, TMEM101 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 16 shows ROC curves (upper) and sensitivity / specificity curves generated based on microarray data of differential marker 3 gene (FXYD2 gene, GPX3 gene, TMEM101 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma (Lower) is shown. FIG. 17 shows group scatter plots generated based on microarray data of differential marker 3 gene (DLX5 gene, UQCRH gene, ARG2 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 18 shows ROC curves (upper row) and sensitivity / specificity curves generated based on microarray data of differential marker 3 gene (DLX5 gene, UQCRH gene, ARG2 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma (Lower) is shown. FIG. 19 shows group scatter plots prepared based on microarray data of differential marker 3 genes (FBLN1 gene, AOC1 gene, TSPAN1 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 20 shows ROC curves (upper) and sensitivity / specificity curves generated based on microarray data of differential marker 3 gene (FBLN1 gene, AOC1 gene, TSPAN1 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma (Lower) is shown. FIG. 21 shows group scatter plots prepared based on microarray data of differential marker 3 gene (C12 or f75 gene, DNER gene, RIMKLB gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 22 shows ROC curves (upper row) and sensitivity / specificity curves generated based on microarray data of differential marker 3 gene (C12 or f75 gene, DNER gene, RIMKLB gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma (Lower) is shown. FIG. 23A shows that 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma were further added with 19 samples of serous adenocarcinoma and 7 samples of clear cell adenocarcinoma for verification, and 59 samples of serous adenocarcinoma and 25 clear cell adenocarcinoma The result of having performed the cluster analysis of the group average method by Euclidean distance as a sample and based on the microarray data regarding the differential marker 29 gene in those samples is shown. FIG. 23B also shows rearranged microarray data of differential marker 29 gene in the above-mentioned 59 samples of serous adenocarcinoma and 25 samples of clear cell adenocarcinoma based on the scoring system. FIG. 24A shows that 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma were further added with 19 samples of serous adenocarcinoma and 7 samples of clear cell adenocarcinoma for verification, and 59 samples of serous adenocarcinoma and 25 clear cell adenocarcinoma As a sample, based on microarray data on differential marker 7 genes (PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, GDF15 gene) in those samples, cluster analysis by group average method by Euclidean distance Show the results. FIG. 24B shows the differential marker 7 gene (PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene) in the above 59 samples of serous adenocarcinoma and 25 samples of clear cell adenocarcinoma based on the scoring system. The rearranged data of the microarray data for GDF15 gene is shown. FIG. 25A shows that 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma were further added with 19 samples of serous adenocarcinoma and 7 samples of clear cell adenocarcinoma for verification, and 59 samples of serous adenocarcinoma and 25 clear cell adenocarcinomas. As a sample, the result of having performed the cluster analysis of the group average method by Euclidean distance based on the microarray data regarding the differential marker 3 gene (GLRX gene, GDF15 gene, GABARAPL1 gene) in those samples is shown. Further, FIG. 25B shows a rearrangement of microarray data of differential marker 3 genes (GLRX gene, GDF15 gene, and GABARAPL1 gene) in the above-mentioned 59 samples of serous adenocarcinoma and 25 samples of clear cell adenocarcinoma based on the scoring system. Show.

1. Differential markers that can distinguish between serous adenocarcinoma and clear cell adenocarcinoma 1-1. SUMMARY A first aspect of the present invention is a differential marker that can distinguish serous adenocarcinoma and clear cell adenocarcinoma. The differential marker of the present invention consists of at least 29 gene groups, and by measuring its expression level in a sample of a subject, any tissue of serous adenocarcinoma or clear cell adenocarcinoma among histologic types of ovarian cancer. It can be accurately distinguished whether it is ovarian cancer.

1-2. Definitions "Ovarian cancer" is an epithelial malignancy that develops in the ovary and is known to include histologic types of serous adenocarcinoma, clear cell adenocarcinoma, mucinous adenocarcinoma, endometrioid adenocarcinoma.
"Serous adenocarcinoma" accounts for about 40% of all ovarian cancers and is the most frequent, and is highly sensitive to anticancer drugs, so it is a tissue-type ovarian cancer suitable for anticancer drug treatment. On the other hand, “bright cell adenocarcinoma” is a few% in Europe and the US, but is known to be a tissue type with a particularly high incidence (about 24%) in Japanese, and it is known that anticancer agents are less effective ing.
In the present specification, the “differentiation gene group” refers to the PPP1 R3B gene, TOB1 gene, RBPMS gene, A4GALT gene, RIMKLB gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, LAMB1 gene, DNER gene, ARG2 gene, TMEM101 gene, GDF15 gene , C12orf75 gene, DLX5 gene, AOC1 gene, ANXA4 gene, FBLN1 gene, TSPAN1 gene, TAPOL1 gene, APY1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIGI gene, LYPD1 gene, CRIP1 gene, UQCRH gene and IGF2 gene It consists of
In the present specification, the “differentiation marker” is a marker associated with the genes included in the “differentiation gene group”, and serous adenocarcinoma and clear cancer cell types of ovarian cancer classified into superficial epithelial / interstitial tumors. It refers to a biomarker capable of differentiating from cell adenocarcinoma. In the present invention, the biomarker is particularly a transcript (mRNA) of each gene included in the differential gene group or its cDNA.
In the present specification, the "gene expression score" is a score determined for each gene included in the "differentiation gene group", and expression of a gene between a sample from a serous adenocarcinoma patient and a sample from a clear cell adenocarcinoma patient Indicates the magnitude of the level difference. More specifically, for a particular gene, the difference between the average of the expression profile in multiple samples from the serous adenocarcinoma patient group and the average of the expression profile in multiple samples from the clear cell adenocarcinoma patient group It can be determined as an absolute value.
As used herein, "gene expression level" refers to the expression profile of the differential gene, transcript amount, expression intensity or expression frequency. The expression level of the gene referred to herein may include not only the expression level of the wild type gene of the differential gene but also the expression level of a mutant gene such as a point mutation gene. In addition, transcripts showing differential gene expression may also include variant transcripts (variants) such as splice variants and fragments thereof. This is because even if the information is based on a mutant gene, a transcript or a fragment thereof, it is possible to construct an expression profile of the gene in the present invention. The expression level of the gene can be obtained as a measurement value by measuring the amount of transcripts of the gene group constituting the differential marker, ie, the amount of mRNA.
In a preferred embodiment, the measurement of the expression profile of a gene is the measurement of mRNA.
In the present specification, the "measurement value" is a value obtained by the method of measuring the gene expression level. The measured value may be an absolute value expressing the amount of mRNA or the like in the sample in a volume such as ng (nanogram) or μg (microgram), etc. It may be a relative value.
Although the measured value of the expression level of each gene depends on the measuring method, it can be calculated, for example, as a relative ratio to a common sample (hereinafter referred to as "common reference"). The common reference in calculating the expression ratio may be any common reference as long as the measurement conditions between samples to be compared are the same. For example, it may be a specific cell line or a mixture of multiple cell lines. Alternatively, a commercially available universal reference, a known housekeeping gene or a combination thereof can be used as a common reference.
In the present specification, "differentiation" refers to a sample derived from a subject with a history of ovarian cancer that has been classified as superficial epithelial / interstitial tumor, which belongs to any tissue type of serous adenocarcinoma or clear cell adenocarcinoma. To identify which tissue type it is likely to belong to.

1-3. Configuration The differential marker of this embodiment is PPP1R3B gene, TOB1 gene, RBPMS gene, A4GALT gene, RIMKLB gene, GABARAPL1 gene, GLRX gene, GLRX gene, GSTM3 gene, LAMB1 gene, DNER gene, ARG2 gene, TMEM101 gene, GDF15 gene, C12orf75 gene, 29 kinds of DLX5 gene, AOC1 gene, ANXA4 gene, FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIGI gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and IGF2 gene It is selected from the differential gene group. When differentiating a sample derived from an ovarian cancer patient, a gene set including at least three genes is selected from these 29 "differentiation gene groups", and the expression of each gene in the sample is measured.
In a preferred embodiment, at least one gene contained in the gene set is selected from the group consisting of the PPP1 R3 B gene, TOB 1 gene, RBPMS gene, GABA RAPL1 gene, GLRX gene, GSTM 3 gene, and GDF 15 gene. . The seven genes listed above have less variation in the expression level of genes among individuals as compared to other genes included in the differential gene group. Thus, by measuring the expression levels of these genes, it is possible to more accurately distinguish the ovarian cancer tissue types. Thus, in a more preferred embodiment, all the genes included in the gene set are selected from the group consisting of the PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, and GDF15 gene. .

As described above, the differential marker of this aspect is composed of a combination of genes included in the differential gene group of 3 or more and 29 or less. In general, in the differential marker of this embodiment, as the number of types of genes constituting the differential marker increases, the differential accuracy as to which of the serous adenocarcinoma and the clear cell adenocarcinoma tissue types is higher.
In the present specification, a differential marker is a gene consisting essentially of a nucleotide sequence shown in DNA (cDNA), but it is generated as a transcript when the gene is expressed, and mRNA consisting of an RNA sequence and partial fragments thereof are also markers It can be a differential marker indirectly to reflect the expression of a certain gene.
Here, the method of selecting at least three types of genes from the “differentiation gene group” can be selected based on the gene expression score of each gene. Specifically, the gene of each gene is selected such that the total value of gene expression scores in the combination of selected genes is equal to or greater than the total value of gene expression scores of GLRX gene, GDF15 gene, and GABARAPL1 gene. The combination is selected with reference to the expression score. At this time, the combination of at least three kinds of genes selected from the “differentiation gene group” has a total value of its gene expression score equal to the total value of gene expression scores of the GLRX gene, GDF15 gene and GABARAPL1 gene As long as it is more than that, it is not limited, and three, four, five, six, seven, eight, nine, ten, eleven, twelve, twelve, thirteen, fourteen, fifteen, sixteen Species, including all 17 gene combinations. The gene selection method is preferably selected from genes with high gene expression scores, but is not particularly limited to such a form.
In a preferred embodiment, the total value of gene expression scores in combinations of genes selected from the differential gene group is equal to or greater than the total value of gene expression scores of GLRX gene, GDF15 gene, and GABARAPL1 gene. is there.
In addition, the gene expression score of a specific gene is, as described above, an average value of expression levels in a plurality of samples derived from serous adenocarcinoma patients and a plurality of samples derived from clear cell adenocarcinoma patients with respect to a specific gene. It can be determined as an absolute value of the difference between the expression level of a specific gene and the average value of the gene.

Although not limited to the following, in one embodiment, the gene expression score of each gene included in the “differentiation gene group” is as described in Table 2 below in one embodiment. At this time, the total value of the gene expression scores of the GLRX gene, the GDF15 gene, and the GABARAPL1 gene is 7.006. That is, when a combination of genes is selected from the “differentiation gene group” with reference to the gene expression scores in Table 2 below, the “differentiation gene group” is selected so that the total value of the gene expression score is equal to or higher than 7.006. Combinations of at least three genes can be selected. Here, that the total value of the gene expression score is equal to 7.006 means that the total value of the gene expression score is 6.9 or more. In a preferred embodiment, the total value of gene expression scores in combinations of genes selected from the differential gene group is 7.006 or more.
In Table 2, "gene name" indicates the identification gene name, "symbol" indicates the abbreviation of identification gene, and "Gene ID" indicates the accession number of the identification gene.
In addition, when referring to the gene expression score in Table 2 above, the total value of the gene expression score in the combination of genes selected from the differential gene group is 8 or more, 9 or more, or 10 or more. It is preferable because the accuracy is further enhanced. 11 to 50 or more (for example, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 20 or more, 25 or more, 30 or more, 35 or more, 40 or more, 45 or more, 50 or more) If so, the accuracy of the discrimination is further enhanced, which is preferable. On the other hand, for a combination of at least three or more genes selected from the differential gene group, for example, less than 6 if the total value of gene expression scores is lower than the total value of gene expression scores of GLRX gene, GDF15 gene, and GABARAPL1 gene If that were the case, it was not possible to guarantee the accuracy that could be tolerated by the clinic.

Table 3 below shows the correspondence of the full length nucleotide sequence of 29 genes or the sequence numbers of the probes. In Table 3 below, A indicates the sequence number of the full-length nucleotide sequence of the differential gene, and B indicates the sequence number of the nucleotide sequence used for the probe.
Thus, the PPP1R3B gene, TOB1 gene, RBPMS gene, A4GALT gene, RIMKLB gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, LAMB1 gene, LNER gene, ARG2 gene, TMEM101 gene, GDF15 gene, C12orf75 gene, DLX5 gene, AOC1 Gene, ANXA4 gene, FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIGI gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and IGF2 gene are SEQ ID NOS: 1 to 29, respectively. It is a gene containing the nucleotide sequence shown in

Further, in the present specification, when referring to a gene containing a base sequence represented by a specific SEQ ID NO, 70% or more (preferably 75% or more, 80% or more, 85% or more, more preferably 90% or more) 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more), a gene consisting of a base sequence having a base identity, and including a gene retaining the function of the target gene.
For example, in one embodiment, when the LRI G1 gene used in the present invention is specified as a gene containing the base sequence shown in SEQ ID NO: 25, 70% or more (preferably 75% or more) with the base sequence shown in SEQ ID NO: 25 80% or more, 85% or more, more preferably 90% or more, 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more). And a gene that retains the function of the LRI G1 gene. In the present specification, “base identity” refers to alignment of two base sequences, and introducing gaps as necessary to maximize the degree of base identity between both base sequences. The ratio (%) of the number of identical bases in the nucleotide sequence of the compared nucleotides to the total number of bases of the gene.
As a specific example of a gene containing such a nucleotide sequence, a gene consisting of a nucleotide sequence containing a degenerate codon encoding the same amino acid sequence, a mutant gene such as various variants (variants) of each gene, and a point mutation gene And orthologous genes of other species such as chimpanzees.

  Further, in the present specification, a gene containing a base sequence represented by a specific SEQ ID NO is a nucleic acid which hybridizes with a nucleotide fragment consisting of a base sequence complementary to a partial base sequence of the gene under stringent conditions. Also included are nucleotides that retain enzymatic activity. "Stringent conditions" means conditions under which nonspecific hybrids are not formed. Generally, the lower the salt concentration and the higher the temperature, the more stringent the conditions. The low stringency conditions are, for example, the conditions for washing with 1 × SSC, 0.1% SDS, about 37 ° C. in the washing after hybridization, more strictly 0.5 × SSC, 0.1% SDS, 42 ° C. to 50 It is the conditions to wash at about ° C. Further stringent high stringent conditions are, for example, conditions of washing with 0.1 × SSC and 0.1% SDS at 50 ° C. to 70 ° C., 55 ° C. to 68 ° C., or 65 ° C. to 68 ° C. in the washing after hybridization. is there. In general, high stringency conditions are preferred. The combination of SSC, SDS and temperature is only exemplary. Those skilled in the art can determine the stringency of hybridization by appropriately combining the aforementioned SSC, SDS, and temperature, and other conditions such as probe concentration, probe base length, hybridization time and the like as appropriate.

2. Method of differentiating between serous adenocarcinoma and clear cell adenocarcinoma 2-1. Overview Another aspect of the present invention is a method for differentiating serous adenocarcinoma from clear cell adenocarcinoma among tissue types in ovarian cancer of a subject. In addition, since the method of discrimination | determination of this invention can be used together with a histopathological examination etc., in one Embodiment, it can also be paraphrased as the method of assisting discrimination.
The differentiation method of the present invention obtains the expression profile of the differentiation marker contained in the sample collected from the subject, and the histologic type of ovarian cancer in the subject is serous adenocarcinoma based on the expression profile, or And clear cell adenocarcinoma. The “expression profile of differential marker” refers to information on the expression level of each gene that constitutes the differential marker. In particular, the present specification includes information on the expression levels of multiple differential genes. In general, the more expression profiles of the gene to be obtained, the more accurate discrimination becomes possible.

2-2. Measurement Method The differentiation method of the present invention includes, as an essential step, a step of measuring the expression levels of at least three genes contained in the differentiation gene group. The measurement method will be specifically described below.

The “measuring step” is a step of measuring the expression level of a differential marker in a sample collected from a subject to obtain the measured value. It is preferable that the measurement of the expression of a differential marker measures the expression level per unit amount in each differential marker.
As used herein, "subject" refers to a human individual who provides a sample and is subjected to a test. The subject may be either an individual with an ovarian cancer history or an individual suspected of having ovarian cancer. The "individual with a history of ovarian cancer" as used herein includes a patient who is currently suffering from ovarian cancer and an ovarian cancer history who has suffered from ovarian cancer in the past. In the case of clear cell adenocarcinoma showing papillary growth, it is difficult to distinguish between serous adenocarcinoma and clear cell adenocarcinoma histopathologically, so it is histopathologically serous adenocarcinoma or A patient diagnosed with any suspected clear cell adenocarcinoma or a person with a history of ovarian cancer is suitable as a subject.
As used herein, "serous adenocarcinoma patient" refers to a patient suffering from serous adenocarcinoma among ovarian cancer. It may refer to one that provides a sample, particularly for comparison with a sample from a subject or a patient with clear cell adenocarcinoma. Also, in the present specification, “a clear cell adenocarcinoma patient” refers to a patient suffering from clear cell adenocarcinoma among ovarian cancer. It may refer to one that provides a sample, particularly for comparison with a sample from a subject or serous adenocarcinoma patient.
The “subject”, “serous adenocarcinoma patient” and “bright cell adenocarcinoma patient” used in the present embodiment are not particularly limited in physical condition such as gender, age, height, weight, etc. The “serous adenocarcinoma patients” and “clear cell adenocarcinoma patients” preferably have the same or similar physical conditions as the subject, such as age, height, and weight. In the present specification, a group consisting of a plurality of “serous adenocarcinoma patients” or “clear cell adenocarcinoma patients” is referred to as “serous adenocarcinoma patient group” or “clear cell adenocarcinoma patient group”.

  In the present specification, the "sample" is collected from the subject and subjected to the discrimination method of the present embodiment, and corresponds to, for example, a tissue, a cell, a body fluid or a peritoneal lavage. The "tissue" and "cell" as used herein may be derived from any part of a subject, but preferably are specimens collected by biopsy or surgically resected, more specifically ovarian tissue or ovarian cells. is there. Particularly preferred are ovarian cancer cells collected by biopsy or ovarian tissue or cells suspected of having ovarian cancer. These tissues or cells may be formalin-fixed and then embedded in paraffin (FFPE: Formalin-Fixed Paraffin Embedded). In addition, "body fluid" as used herein refers to a liquid biological sample collected from a subject. For example, blood (including serum, plasma and interstitial fluid), spinal fluid (cerebrospinal fluid), urine, lymph fluid, digestive fluid, ascites fluid, pleural fluid, nerve root fluid, extract of each tissue or cell, etc. may be mentioned. . Preferably it is blood.

  The collection of the sample may be obtained by biopsy or surgical removal by surgery if it is a tissue or a cell. Moreover, if it is a body fluid, it may be performed based on the well-known method of the said field. For example, in the case of blood or lymph fluid, a known blood collection method may be used. The amount of sample required in the discrimination method of this embodiment is not particularly limited. If it is a tissue or a cell, at least 10 μg, preferably at least 0.1 mg is desirable, but it may be a biopsy material. In the case of a body fluid such as blood or lymph, a volume of at least 0.1 mL, preferably at least 1 mL, more preferably at least 10 mL may be sufficient. The sample can be prepared and processed as needed so that measurement of a differential marker is possible. For example, when the sample is a tissue or a cell, homogenization treatment or cell lysis treatment, removal of contaminants by centrifugation or filtration, addition of a protease inhibitor, etc. may be mentioned. The details of these treatments are described in detail in Green & Sambrook, Molecular Cloning, 2012, Fourth Ed., Cold Spring Harbor Laboratory Press, and can be referred to.

  In the present specification, "unit amount" refers to an amount of a sample which is arbitrarily determined. For example, volumes (expressed in μL, mL) and weights (expressed in μg, mg, g) correspond. Although the unit amount is not particularly specified, it is preferable that the unit amount measured by a series of discrimination methods be constant. More accurate discrimination can be achieved by making the unit amount of the sample from the subject to be subjected to measurement in this step and the sample from the serous adenocarcinoma patient or the clear cell adenocarcinoma patient to be compared constant. In particular, when measuring the expression level of the differential marker as an absolute value, it is necessary to make the unit amount constant.

  Hereinafter, the method for measuring the transcript of a gene will be specifically described. In addition, the measuring method of the transcription product of a gene is known. Below, the description regarding the measuring method of the transcription product or translation product of the gene of Unexamined-Japanese-Patent No. 2016-13081 is described with reference or citation. In addition, although the measuring method of the transcription product or translation product of a typical gene is demonstrated below, it is not limited to these methods, A well-known measuring method can be used.

The measurement of the transcript of the differential gene may be a measurement of the amount of mRNA or a measurement of the amount of cDNA obtained by reverse transcription from mRNA. In general, a method of measuring the expression level of a gene as an absolute value or a relative value is employed to measure a transcript of a gene, using a nucleotide containing all or part of the base sequence of the gene as a primer or a probe. .
The primers or probes of this embodiment are usually composed of natural nucleic acids such as DNA and RNA. Particularly preferred is DNA which has high stability and is easy to synthesize and inexpensive. In addition, natural nucleic acid and chemically modified nucleic acid or pseudo-nucleic acid can also be combined as needed. Examples of the chemically modified nucleic acid and the pseudo-nucleic acid include PNA (Peptide Nucleic Acid), LNA (Locked Nucleic Acid; registered trademark), methyl phosphonate type DNA, phosphorothioate type DNA, 2'-O-methyl type RNA and the like. In addition, primers and probes may be fluorescent and / or quencher substances, or labeling substances such as radioactive isotopes (for example, 32P, 33P, 35S), or modifiers such as biotin or (strept) avidin, or magnetic beads. It may be used to label or modify. The labeling substance is not limited, and commercially available ones can be used. For example, in the case of a fluorescent substance, FITC, Texas, Cy3, Cy5, Cy7, Cyanine 3, Cyanine 5, Cyanine 7, FAM, HEX, VIC, fluorescein and its derivatives, rhodamine and its derivatives, etc. can be used. As the quencher substance, AMRA, DABCYL, BHQ-1, BHQ-2, BHQ-3 or the like can be used. The labeling position of the labeling substance in the primer and the probe may be appropriately determined depending on the characteristics of the modifying substance and the purpose of use. In general, it is often modified at the 5 'or 3' end. In addition, one primer and probe molecule may be labeled with one or more labeling substances. Labeling of these substances to nucleotides can be carried out by known methods.
The nucleotides used as primers or probes may be either the sense strand of each gene constituting the differential marker or the nucleotide consisting of the antisense strand.
The base length of the primer or probe is not particularly limited. In the case of a probe, if it is used in the hybridization method described later, it is at least 10 bases long to full gene length, preferably 15 bases long to full gene length, more preferably 30 bases long to full gene length, further preferably 50 If it is a base length or more to the entire gene length and it is used for a microarray, it is 10 to 200 bases long, preferably 20 to 150 bases long, more preferably 30 to 100 bases long. Generally, the longer the probe, the higher the hybridization efficiency and the higher the sensitivity. On the other hand, the shorter the probe, the lower the sensitivity but conversely, the specificity increases. A specific example of the probe is a 80-base nucleotide consisting of the base sequence shown in SEQ ID NOS: 30 to 58 in B of Table 2. On the other hand, in the case of primers, each of the forward and reverse primers may be 10 to 50 bp, preferably 15 to 30 bp.
The preparation of the above-described primers or probes is known to those skilled in the art and can be prepared, for example, according to the method described in Green & Sambrook, Molecular Cloning (2012) described above. In addition, it is also possible to provide sequence information to a contract manufacturer of nucleic acid synthesis, and to contract manufacturing.
The measurement of the transcript of the differential gene may be any known nucleic acid detection / quantification method, and is not particularly limited. For example, a hybridization method or a nucleic acid amplification method can be mentioned.
The “hybridization method” refers to using a nucleic acid fragment having a base sequence complementary to all or part of the base sequence of a target nucleic acid to be detected as a probe, and using base pairing between the nucleic acid and the probe A method of detecting and quantifying a target nucleic acid or a fragment thereof. In the present embodiment, the target nucleic acid corresponds to mRNA or cDNA of each gene constituting a differential marker, or a fragment thereof. In general, the hybridization method is preferably performed under stringent conditions to exclude non-target nucleic acids that hybridize nonspecifically. The above-mentioned low salt concentration and high stringent conditions under high temperature are more preferable. Several different detection means are known as hybridization methods, for example, Northern blot method (Northern hybridization method), microarray method, surface plasmon resonance method or quartz crystal microbalance method is suitable. .
"Northern blotting" is the most common method of analyzing gene expression, in which total RNA or mRNA prepared from the sample is separated by electrophoresis on agarose gel or polyacrylamide gel under denaturing conditions and transferred to a filter ( It is a method of detecting a target nucleic acid using a probe having a base sequence specific to a target RNA after blotting). By labeling the probe with a suitable marker such as a fluorescent dye or radioactive isotope, for example, a chemilumi (chemiluminescence) imaging analyzer (for example, light capture; Atto Corporation), a scintillation counter, an imaging analyzer (for example, FUJIFILM Corporation) : It is also possible to quantify a target nucleic acid using a measuring device such as BAS series). Northern blotting is a well-known technique well known in the art, and may be referred to, for example, the aforementioned Green, MR and Sambrook, J. (2012).
The “microarray method” is a method in which a nucleic acid fragment complementary to all or part of the base sequence of a target nucleic acid is arranged in a small spot at high density as a probe on a substrate and contains the target nucleic acid in a immobilized microarray or microchip This is a method of reacting a sample and detecting the nucleic acid hybridized to the substrate spot by fluorescence or the like. The target nucleic acid may be either RNA, such as mRNA, or DNA, such as cDNA. Detection and quantification can be achieved by detecting and measuring fluorescence based on hybridization of a target nucleic acid and the like with a microplate reader or a scanner. By means of the measured fluorescence intensity, it is possible to determine the amount of mRNA or the amount of cDNA or their abundance relative to the reference mRNA (reference mRNA). Microarray methods are also well known in the art. For example, reference may be made to DNA microarray method (DNA microarray and latest PCR method (2000) Masaaki Muramatsu, supervised by Hiroyuki Nana, Shujunsha) and the like.
The "SPR (Surface Plasmon Resonance) method" is a surface plasmon resonance in which the reflected light intensity is significantly attenuated at a specific incident angle (resonance angle) when the incident angle of the laser beam irradiated to the metal thin film is changed. It is a method of detecting and quantifying the adsorbate on the metal thin film surface with extremely high sensitivity using the phenomenon. In the present invention, for example, after a probe having a sequence complementary to the base sequence of the target nucleic acid is immobilized on the metal thin film surface and the other metal thin film surface portion is blocked, from a subject or a healthy subject or healthy subject group By collecting the collected sample on the surface of the metal thin film, base pairing between the target nucleic acid and the probe can be formed, and the target nucleic acid can be detected and quantified from the difference in measurement value before and after the sample distribution. Detection and quantification by surface plasmon resonance can be performed, for example, using an SPR sensor commercially available from Biacore. This technology is well known in the art. For example, it may be referred to Kazuhiro Nagata and Handa Hiroshi, real-time analysis method of interaction between biological materials, Springer-Falark Tokyo, Tokyo, 2000.
The “Quartz Crystal Microbalance (QCM) method” uses the phenomenon that the resonance frequency of a quartz resonator decreases according to its mass when a substance is adsorbed on the electrode surface attached to the quartz resonator. This is a mass measurement method that quantitatively captures an extremely small amount of adsorbate by the amount of change in resonance frequency. Similarly to the SPR method, detection and quantification by this method also utilize a commercially available QCM sensor, for example, a probe having a sequence complementary to the base sequence of the target nucleic acid immobilized on the electrode surface and a subject, a healthy subject or a healthy subject The target nucleic acid can be detected and quantified by base pairing with the target nucleic acid in a sample collected from a body group. This technology is well known in the art, and for example, Christopher J. et al., 2005, Self-Assembled Monolayers of a Form of Nanotechnology, Chemical Review, 105: 1103-1169, Toyozumi Morizumi, Takamoto Nakamoto, 1997) See Sensor Engineering, Shohodo.
"Nucleic acid amplification method" refers to a method of amplifying a specific region of a target nucleic acid by a nucleic acid polymerase using a forward / reverse primer. For example, PCR method (including RT-PCR method), NASBA method, ICAN method, LAMP (registered trademark) method (including RT-LAMP method) can be mentioned. Preferably, it is a PCR method. A quantitative nucleic acid amplification method such as real time RT-PCR method is used for the method of measuring the transcript of a gene using the nucleic acid amplification method. As real-time RT-PCR methods, intercalator methods using SYBR (registered trademark) Green, etc., Taqman (registered trademark) probe methods, digital PCR methods, and cycling probe methods are known. It may be. These are all known methods, and are described in appropriate protocols in the art, so they may be referred to.
The method for quantifying the transcript of a gene by real-time RT-PCR will be briefly described by way of example below. Real-time RT-PCR detects the fluorescence intensity derived from amplification products in a reaction system in which the amplification product of PCR is specifically fluorescently labeled using cDNA prepared by reverse transcription from mRNA in a sample as a template It is a nucleic acid quantification method which performs PCR using a temperature cycler apparatus equipped with a function. The amount of amplification product of the target nucleic acid in the reaction is monitored in real time, and the result is regressed by computer. Methods of labeling amplification products include a method using a fluorescently labeled probe (for example, TaqMan (registered trademark) PCR method) and an intercalator method using a reagent that specifically binds to double-stranded DNA. The TaqMan (registered trademark) PCR method uses a probe modified at the 5 'end with a quencher substance and at the 3' end with a fluorescent dye. Usually, the quencher substance at the 5 'end suppresses the fluorescent dye at the 3' end, but when PCR is performed, the probe is degraded by the 5 'to 3' exonuclease activity of Taq polymerase, As a result, the suppression of the quencher substance is released, so that fluorescence is emitted. The amount of fluorescence reflects the amount of amplification product. Since the cycle number (CT) when the amplification product reaches the detection limit and the initial template quantity are in an inverse correlation, the initial template quantity is quantified by measuring CT in the real-time measurement method. By measuring CT using several stages of known amounts of template and preparing a calibration curve, it is possible to calculate the absolute value of the initial template amount of an unknown sample. As the reverse transcriptase used in RT-PCR, for example, M-MLV RTase, ExScript RTase (TaKaRa), Super Script II RT (Thermo Fisher Scientific) and the like can be used.
The reaction conditions for real-time PCR are generally based on the known PCR method, the base length of the nucleic acid fragment to be amplified and the amount of template nucleic acid, and the base length and Tm value of the primer used, optimum reaction of the nucleic acid polymerase used Since it fluctuates depending on temperature, optimum pH and the like, it may be appropriately determined according to these conditions. As an example, usually, denaturation reaction is carried out at 94 to 95 ° C. for 5 seconds to 5 minutes, annealing reaction is carried out at 50 to 70 ° C. for 10 seconds to 1 minute, elongation reaction is carried out at 68 to 72 ° C. for 30 seconds to 3 minutes, The extension reaction can be repeated about 15 to 40 cycles as one cycle. When using a kit commercially available from the manufacturer, in principle, the procedure may be performed in accordance with the protocol attached to the kit.
The nucleic acid polymerase used in real time PCR is a DNA polymerase, in particular a heat resistant DNA polymerase. A variety of such nucleic acid polymerases are commercially available, and they can also be used. For example, Taq DNA polymerase attached to the Applied Biosystems TaqMan MicroRNA Assays Kit (Thermo Fisher Scientific) can be mentioned. In particular, such a commercially available kit is useful because a buffer optimized for the activity of the attached DNA polymerase is attached.

2-3. Identification Method The identification method of the present invention identifies whether the test sample belongs to serous adenocarcinoma or clear cell adenocarcinoma based on the expression profile of the differentiation marker measured as described above. The differential marker is a gene having a significant difference in expression level between serous adenocarcinoma or clear cell adenocarcinoma, and the expression profile of a set of genes combining them is derived from serous adenocarcinoma patients or clear cell adenocarcinoma patients Comparison with the sample makes it possible to distinguish which tissue type of ovarian cancer the test sample is.
That is, the differentiation method of the present invention comprises the expression profile of each gene in the gene set selected from the differential gene group of the test sample, and each gene in the corresponding gene set in the sample from serous adenocarcinoma patient or clear cell adenocarcinoma patient The tissue type of the test sample is identified by comparison with the expression profile of
The expression profile of each gene contained in the gene set of the sample derived from the serous adenocarcinoma patient or the clear cell adenocarcinoma patient to be compared with the test sample may be previously measured, or may be newly added. What measured the expression level of each gene contained in a gene set about the sample from a serous adenocarcinoma patient or a clear cell adenocarcinoma patient may be used.
Therefore, in one embodiment, the differentiation method of the present invention further includes the step of measuring the expression level of each gene included in the gene set in a sample derived from a serous adenocarcinoma patient or a clear cell adenocarcinoma patient. The expression profile obtained by this step can be compared with the expression profile of the test sample. Samples from serous adenocarcinoma patients or clear cell adenocarcinoma patients may be one sample each or two or more samples may be used. The larger the number of samples, the more individual differences among samples can be averaged, and this is preferable because the accuracy of discrimination is enhanced.

In one embodiment of the present invention, the expression profile of each gene in the test sample gene set is compared with the expression profile of each gene in the corresponding gene set in samples from serous or clear cell adenocarcinoma patients. When the test sample has a gene expression profile comparable to that of the compared tissue type ovarian cancer genes, the test sample is differentiated as a tissue type of ovarian cancer compared to the tissue type compared, or It can be distinguished that it is not a histologic type of ovarian cancer compared to the case where it has an expression profile of a gene different from that of the gene of the ovarian cancer.
For example, when the expression profiles of the test sample and the serous adenocarcinoma patient-derived sample are compared, if it is judged that they have expression profiles of genes equivalent to each other, the test sample is serous adenocarcinoma It can be discriminated. In addition, when the expression profiles of the test sample and the clear cell adenocarcinoma patient-derived sample are compared, if it is judged that they have expression profiles of genes equivalent to each other, the test sample is a clear cell adenocarcinoma. It can be distinguished that there is. On the other hand, when the expression profiles of the test sample and the serous adenocarcinoma patient-derived sample are compared, if it is judged that they have different gene expression profiles, the test sample is not serous adenocarcinoma. It can be discriminated. In addition, when the expression profiles of the test sample and the clear cell adenocarcinoma patient-derived sample are compared, if it is judged that they have different gene expression profiles, the test sample is not clear cell adenocarcinoma. And it can be distinguished.
Here, "having the expression profile of equivalent genes" means that the expression profiles of the respective genes included in the gene set are similar. Also, "having expression profiles of different genes" means that the expression profiles of each gene included in the gene set are not similar.
A known method can be adopted as a specific method of discriminating whether the expression profile of each gene in the gene set is the same or different. For example, although not limited to the following, (i) a method of classifying a test sample into serous or clear cell adenocarcinoma based on hierarchical clustering analysis, (ii) a method of evaluating by comparing expression levels of genes, (iii A method of determining whether the test sample belongs to serous adenocarcinoma or clear cell adenocarcinoma according to the setting of the threshold.

(I) Hierarchical Clustering Analysis In one embodiment, the discrimination method of the present invention can discriminate the tissue type of a test sample by performing cluster analysis on the expression profile of each gene in the gene set of the test sample.
More specifically, the expression profile of the gene set measured in the test sample should be compared with the expression profile of the corresponding gene set in samples from serous adenocarcinoma patients and clear cell adenocarcinoma patients to perform hierarchical cluster analysis Can. In order to distinguish the tissue types of test samples by hierarchical clustering analysis, in addition to the expression profiles of gene sets in test samples, expression profiles of gene sets in samples from serous adenocarcinoma patients and clear cell adenocarcinoma patients are required. It becomes. The method of hierarchical cluster analysis is known, and in a preferred embodiment of the present invention, is cluster analysis by Euclidean distance group average method. Known software can be used for cluster analysis, and for example, ExpressionView Pro software (MicroDiagnostic) can be used as commercially available software, although not limited thereto.
By performing hierarchical cluster analysis, a hierarchical structure (dendrogram) consisting of test samples, samples from serous adenocarcinoma patients, and samples from clear cell adenocarcinoma patients can be drawn, and serous adenocarcinoma Clusters and clear cell adenocarcinoma can be broadly divided into two clusters. This makes it possible to confirm which tissue type cluster the test sample is classified into, and to discriminate which tissue type is likely to be a tissue type.

(Ii) Method of Evaluating by Comparison of Expression Levels of Genes In one embodiment, the total value of expression levels of genes of each combination of genes included in the gene set in the test sample, serous adenocarcinoma patient or The tissue type of the test sample can be evaluated by comparing it with the total value of the expression level of the gene in samples from clear cell adenocarcinoma patients.
Although not limited to the following, when one form is described, the total value of gene expression levels in serous adenocarcinoma patients and clear cell adenocarcinoma patients is plotted as a group scatter plot, and the genes of the gene set in the test sample are plotted. Check where the total expression level of is plotted. The plotted position can be used to evaluate which tissue type ovarian cancer the test sample is likely to belong to.
In addition, when using the total value of the expression level of a gene for discrimination, the value of the obtained expression level is inverted and used about five genes of LRIG-1, LYPD1, CRIP1, UQCRH, and IGF2. For example, when using the total value of the expression level of each gene obtained by a method such as microarray for the expression level of genes, the expression levels of 5 genes LLR-1, LYPD1, CRIP1, UQCRH, IGF2 (for example, The Log2 ratio to the common reference) is multiplied by -1 to obtain an inverted value, and the total value of the inverted value and the expression amount of another gene is calculated.

(Iii) Method of Differentiation by Setting of Threshold In one embodiment, the tissue type can be distinguished by comparing the expression profile of the gene set in the test sample with a predetermined threshold.
Here, the "predetermined threshold value" refers to a predetermined cutoff value based on the expression profile of the differential marker in samples from serous adenocarcinoma patients and clear cell adenocarcinoma patients. The setting of the cutoff value can be set, for example, as follows, but is not limited thereto. That is, the expression levels of differential marker genes are measured for samples from serous adenocarcinoma patients and clear cell adenocarcinoma patients, and the sum of gene expression levels is calculated for each sample. Then, a predetermined cutoff value can be derived by creating an ROC curve from the sum of expression levels of genes in samples from serous adenocarcinoma patients or clear cell adenocarcinoma patients. By setting the cut-off value, it is possible to distinguish which tissue-type ovarian cancer is highly likely to be by whether the cut-off value is exceeded or not.
The “ROC curve (Receiver Operating Characteristic curve)” has a vertical axis representing True Positive Fraction (TPF), ie sensitivity, and a horizontal axis representing False Positive Fraction (FPF), ie (1-Specificity), it is created by changing and plotting as a parameter the threshold of which value of the test result is to be judged as having a finding, that is, the cutoff point. Specificity is the rate at which a negative person is correctly judged negative.
Basically, the method of setting the cutoff value from the created ROC curve can be set to increase both sensitivity and specificity (to approach 1). For that purpose, the cutoff value may be set to a value giving the point closest to the point (0, 1) on the ROC curve. In the most preferred embodiment, the sample derived from the serous adenocarcinoma patient group and the sample derived from the clear cell adenocarcinoma patient group have a clearly distinguishable cutoff value.
When the predetermined threshold is set as described above, the comparison between the threshold and the expression profile of the gene set in the sample derived from the subject is the sum of the expression level of the gene for the combination of the threshold and the specific differential marker in the test sample. Compare it with the value.

2-3. Effects According to the discrimination method of this embodiment, it is possible to discriminate whether the specimen is serous adenocarcinoma or clear cell adenocarcinoma by examining a specimen removed by biopsy or surgery. The discrimination method according to this embodiment having a high accuracy diagnosis rate makes it possible to diagnose which tissue-type ovarian cancer it is, and has an advantage that it can be dealt with in consideration of the recurrence risk and the judgment of the treatment.
In addition, conventionally, ovarian cancer has been determined histotype based on pathological diagnosis, but it is very difficult to distinguish particularly papillary-like clear cell adenocarcinoma from serous adenocarcinoma, Different judgments occurred depending on the difference between doctors who judged the results and between hospitals. However, according to the morbidity determination method of the present embodiment, more accurate objective data can be provided to assist in the diagnosis of serous adenocarcinoma or clear cell adenocarcinoma.

3. A kit 3-1 for identifying whether a serous adenocarcinoma or a clear cell adenocarcinoma tissue type is present in a test sample derived from an ovarian cancer patient. Overview Another aspect of the present invention is a reagent (differentiation reagent) for discriminating whether it is a serous adenocarcinoma or a clear cell adenocarcinoma tissue type. The discrimination reagent of this embodiment is applied to, for example, a sample derived from a subject suffering from ovarian cancer to discriminate whether the ovarian cancer in the subject is serous adenocarcinoma or clear cell adenocarcinoma. Can.
3-1-1. Configuration The differential reagent of the present embodiment includes a probe or primer set for detecting a transcript of differential gene cluster constituting the differential marker, that is, mRNA or cDNA. The specific configuration of these is described in the section of the measurement process. For example, in the case of detecting transcripts of four differential gene groups constituting differential markers, the differential kit can include four probe groups capable of detecting the transcripts of corresponding genes.
When the discrimination reagent of this embodiment is a probe as described above, the discrimination reagent can also be provided in the form of a DNA microarray or DNA microchip in which each probe is immobilized on a substrate. The material of the substrate on which each probe is fixed is not limited, but a glass plate, a quartz plate, a silicon wafer or the like is usually used. The size of the substrate may be, for example, 3.5 mm × 5.5 mm, 18 mm × 18 mm, 22 mm × 75 mm, etc., which can be set variously according to the number of spots of the probe, the size of the spots, etc. . The probe is usually used at 0.1 μg to 0.5 μg of nucleotide per spot. A method for immobilizing nucleotides includes a method of electrostatically bonding to a solid support surface-treated with a polycation such as polylysine, poly-L-lysine, polyethyleneimine, polyalkylamine or the like by utilizing the charge of the nucleotide, or amino The method of making the solid phase surface which introduce | transduced functional groups, such as a group, an aldehyde group, an epoxy group, the nucleotide which introduce | transduced functional groups, such as an amino group, an aldehyde group, SH group, biotin, couple | bonds together by covalent bond is mentioned.
3-2. Efficacy By applying the discrimination kit according to this embodiment to a subject having a history of ovarian cancer, whether the ovarian cancer belongs to any of serous adenocarcinoma or clear cell adenocarcinoma tissue types, whether it is ovarian cancer or not It can be discriminated objectively and accurately.
The detection kit of the present invention may contain other reagents necessary for detection of a differential marker, such as a buffer, a secondary antibody, and instructions for detection and result discrimination.
4. Therapeutic method based on discrimination result As another aspect, the present invention is based on the discrimination result for a subject who has discriminated whether it is serous adenocarcinoma or clear cell adenocarcinoma by the above-mentioned discrimination method. Provide a therapeutic method of administering an anti-cancer agent.
That is, an effective amount of an anticancer agent effective for serous adenocarcinoma is administered to a subject who is determined to be serous adenocarcinoma by the differentiation method of the present invention. On the other hand, an effective amount of an anticancer agent effective for clear cell adenocarcinoma is administered to a subject who is determined to be clear cell adenocarcinoma by the differentiation method of the present invention.
Anticancer agents effective for serous adenocarcinoma and clear cell adenocarcinoma (for example, paclitaxel, cisplatin, carboplatin, docetaxel), combinations thereof, administration methods, dosages and the like are known, and those skilled in the art appropriately correspond to the tissue types. Chemotherapy can be performed.

Example 1 Preparation of RNA
A part of ovarian cancer was surgically collected from patients suffering from serous adenocarcinoma (39 samples) or clear cell adenocarcinoma (18 samples) among ovarian cancer patients. The collected surgical specimens were placed in freezing storage tubes and frozen with liquid nitrogen. The frozen sample extracted total RNA using ISOGEN (Nippon Gene Co., Ltd., Tokyo, Japan). The sample from which 125 μg or more of total RNA could be obtained was subsequently purified with poly (A) + RNA using MicroPoly (A) purist Kit (Ambion, Austin, TX, USA).
The human common reference RNA comprises 22 types of human cancer cell lines (A431, A549, AKI, HBL-100, HeLa, HepG2, HL60, IMR-32, Jurkat, K562, KP4, MKN7, NK-92, Raji, RD, A mixture of equal amounts of total RNA or purified poly (A) + RNA extracted from Saos-2, SK-N-MC, SW-13, T24, U251, U937 and Y79) was used. The mixture of equal amounts of total RNA or the mixture of equal amounts of purified poly (A) + RNAs was used as a common reference, in accordance with the method of preparing the sample to be analyzed.

Example 2 Comprehensive Gene Expression Analysis
The DNA microarray used for gene expression profile acquisition (designated as “system 1”) using poly (A) + RNA is 31,797 kinds of synthetic DNA (80 mers) corresponding to human-derived transcripts (MicroDiagnostic, Tokyo, Japan) was arrayed on a slide glass with a custom arrayer. On the other hand, a DNA microarray for gene expression profile acquisition (named as “system 2”) using total RNA is a custom-aperture of 14,400 kinds of synthetic DNA (80 mers) (MicroDiagnostic) corresponding to human-derived transcripts. Arrayed on a glass slide.
Sample-derived RNA is from 2 μg poly (A) + RNA for System 1 and 5 μg total RNA for System 2 SuperScript II (Invitrogen Life Technologies, Carlsbad, Calif., USA) and Cyanine 5-dUTP Labeled cDNA was synthesized using (Perkin-Elmer Inc.). Similarly, human common reference RNA synthesized labeled cDNA from 2 μg of poly (A) + RNA or 5 μg of total RNA using SupreScript II and Cyanine 3-dUTP (Perkin-Elmer Inc.).
Hybridization with the microarray was performed using Labeling and Hybridization kit (MicroDiagonostic).
The fluorescence intensity after hybridization with the microarray was measured using a GenePix 4000B Scanner (Axon Instruments, Inc., Union City, Calif., USA). In addition, the fluorescence intensity of the sample-derived Cyanine-5 labeled cDNA is reduced by the fluorescence intensity of the human common reference-derived Cyanine-3 labeled cDNA (the fluorescence intensity of the sample-derived Cyanine-5 labeled cDNA / human common reference-derived Cyanine- The fluorescence intensity of 3 labeled cDNAs was calculated. Furthermore, normalization was performed using GenePix Pro 3.0 software (Axon Instruments, Inc.,) by multiplying the calculated expression ratio by the normalization factor. Next, the expression ratio was converted to Log2, and the converted value was named Log2 ratio. The conversion of expression ratio was performed using Excel software (Microsoft, Bellevue, WA, USA) and MDI gene expression analysis software package (MicroDiagnostic).

Example 3 Statistical Analysis
In order to narrow down marker gene candidates capable of differentiating between serous adenocarcinoma and clear cell adenocarcinoma, using data of Log 2 ratio between specimens from serous adenocarcinoma and samples from clear cell adenocarcinoma, the following i) to iii) Data processing was performed in the order of
i) Genes whose fluorescence intensity is below the detection limit in at least one sample out of 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma
ii) The statistical difference between serous adenocarcinoma group and clear cell adenocarcinoma group was tested by Student's t-test (two-sided test), and the gene corresponding to p <1.0 × 10 ^-5 judged to be significant was extracted
iii) For each gene, the mean value of Log 2 ratio in serous adenocarcinoma group and clear cell adenocarcinoma group was calculated. The absolute value of the difference of the average value of the serous adenocarcinoma group and the clear cell adenocarcinoma group extracted the 2.0 gene or more 29 genes as a differential marker gene candidate. All samples were used to measure the expression level of the extracted 29 genes, and cluster analysis was performed. In addition, the result of the expression level of 29 genes in all the samples is shown to Tables 4-9. Moreover, cluster analysis was performed by group average method by Euclidean distance using ExpressionView Pro software (MicroDiagnostic). The results of cluster analysis are described in FIG. As shown in FIG. 1, when hierarchical cluster analysis was performed based on the expression profile of the extracted 29 genes, two clusters mainly composed of serous adenocarcinoma group and clusters mainly composed of clear cell adenocarcinoma group Formed.

Example 4 Construction of gene expression scoring system
We tried to construct a scoring system to distinguish serous adenocarcinoma from clear cell adenocarcinoma using differential marker candidate 29 gene. First, among the differential marker candidate 29 genes, 5 genes (LRIG-1, LYPD1, CRIP1, UQCRH, IGF2) having a low Log2 ratio in the clear cell adenocarcinoma group were inverted by 1 and multiplied by +. The Log 2 ratio of 5 genes whose values were inverted and the remaining 24 genes was summed up to calculate the total score value for each sample. The score system was rearranged from the left in ascending order of score values (Fig. 2). In order to verify the diagnostic accuracy of the scoring system, an ROC (Receiver Operating Characteristic curve) curve was used to determine the area under the ROC (AUC: area under the curve) (FIG. 3). In addition, sensitivity / specificity curves were created, and the optimal cutoff value was determined (Fig. 3). In addition, group scatter plots were created for each case (Figure 4). ROC curves and group scatter plots are generated using StatFlex ver. 6.0 software (Artech Co., Ltd., Osaka, Japan), and the setting of the optimum cutoff value is the maximum of the sum of sensitivity and specificity. (Hereinafter, the setting of the optimal cutoff value was the same.) As a result, in the group scatter diagram, when the average value of the total value of the samples belonging to the tissue type ovarian cancer in each of the serous adenocarcinoma group and the clear cell adenocarcinoma group was determined for each tissue type The value was 1.9487, while the mean value of the total value of clear cell adenocarcinoma was 74.9497, and the groups of each other had a significant difference at P = 2.1488 × 10 ⁻¹⁰ .
Thus, in serous adenocarcinoma and clear cell adenocarcinoma, there is a significant difference in the total value of the expression profiles of 29 genes, which are the differential marker candidate genes listed above, and the expression in the combination of these genes By measuring and comparing the profiles, it is possible to distinguish which tissue type the target ovarian cancer sample is.
Further, in the ROC curve, AUC was 0.9444, and in the sensitivity / specificity curve, the optimum cutoff value was determined to be 19.2648, at which time the sensitivity and specificity were 89.7% (FIG. 3).

Example 5 Examination of Gene Set 1
In the above example, the differential marker candidate 29 gene was found to be a gene group having a significant difference in expression between serous adenocarcinoma and clear cell adenocarcinoma. Therefore, by using any of these genes, it is possible to distinguish which of the serous adenocarcinoma and the clear cell adenocarcinoma group belong to the tissue type ovarian cancer. Therefore, it was examined at least how many combinations of genes could be used to distinguish serous adenocarcinoma and clear cell adenocarcinoma with high sensitivity and specificity.
First, the gene expression score was calculated as follows about the differential marker candidate 29 genes. That is, with regard to the Log ₂ ratio of each gene obtained in Example 2 above, an average value was calculated for each of the tissue type sample groups of the serous adenocarcinoma group or the clear cell adenocarcinoma group. Next, the difference (absolute value) between the serous adenocarcinoma group and the clear cell adenocarcinoma group was determined for the calculated mean value of Log ₂ ratio of each gene. This difference (absolute value) was taken as the "gene expression score" of each gene. The “gene expression score” of each gene is shown in Table 10 below.

Next, the standard deviation of serous adenocarcinoma group is less than 1 and the standard deviation of clear cell adenocarcinoma group is less than 1.5 in order to identify a gene group that is more useful for identification and less variation among the 29 genes. One gene group (7 genes) was extracted (Table 11). As shown in FIG. 5, when hierarchical cluster analysis was performed based on the expression profile of the extracted seven genes, two clusters consisting mainly of serous adenocarcinoma group and clusters mainly consisting of clear cell adenocarcinoma group Formed. In addition, score values of 7 genes were calculated and sorted in descending order of score values (FIG. 6). Furthermore, the sum of Log ₂ ratios of 7 genes was determined, and the distribution was drawn as a group scatter plot (FIG. 7). When calculating the total value of Log2 ratio, Log2 ratio of 5 genes of LRIG-1, LYPD1, CRIP1, UQCRH, and IGF2 is multiplied by -1 to obtain an inverted value, and the inverted value and Log2 ratio of other genes In addition, when calculating the total value of Log2 ratio similarly in the following examples as well, LIG-1, LYPD1, CRIP1, UQCRH, and Log2 ratio of 5 genes of IGF-1 are -1. And the inverted value is used). When the average value of the total value of specimens belonging to the respective tissue-type ovarian cancers in the serous adenocarcinoma group or the clear cell adenocarcinoma group is determined for each tissue type, the average value of the total value of serous adenocarcinoma is -1.6402, while The average value of the total value of clear cell adenocarcinoma was 13.6458, and each other group had a significant difference at P = 1.1607 × 10 ⁻¹⁰ . Further, in the ROC curve, AUC was 0.9573, and in the sensitivity / specificity curve, the optimum cutoff value was determined to 3.5717, at which time the sensitivity and specificity were 89.7% (FIG. 8).

Next, a plurality of genes are selected from those having high gene expression scores among the 7 genes, and based on the data obtained in Example 4, a cutoff value at which the sensitivity and specificity become 89% or more We asked for the combination of the minimum number of genes that can be set.
As a result, it was found that the above conditions were satisfied by selecting three genes of GLRX gene, GDF15 gene and GABARAPL1 gene in descending order of gene expression score from the seven genes. The total value of Log ₂ ratio of three genes of GLRX gene, GDF15 gene and GABARAPL1 gene was determined for each specimen belonging to serous adenocarcinoma group and clear cell adenocarcinoma group, and the distribution was plotted as a group scatter chart (FIG. 9) . When the average value of the total value of specimens belonging to each type of ovarian cancer of serous adenocarcinoma group or clear cell adenocarcinoma group was determined for each tissue type, the average value of total value of serous adenocarcinoma is -1.9373, The average value of the total value of clear cell adenocarcinomas was 5.0687, and each other group had a significant difference at P = 4.129 × 10 ⁻¹⁰ .
Further, in the ROC curve, AUC was 0.9573, and in the sensitivity / specificity curve, the optimum cutoff value was fixed at 1.3432, at which time the sensitivity and specificity were 89.7% (FIG. 10).
Thus, there is a significant difference in the sum of expression levels of GLRX gene, GDF15 gene and GABARAPL1 gene between serous adenocarcinoma and clear cell adenocarcinoma, and the expression level in the combination of these genes is measured. This makes it possible to distinguish which tissue type the target ovarian cancer sample is.

Example 6 Examination of Gene Set A 2
Next, combinations of other genes capable of differentiating serous adenocarcinoma and clear cell adenocarcinoma with high accuracy as in Example 5 were examined.
Selection of the combination of a plurality of genes was performed based on the “gene expression score” described in Example 5 above. Specifically, a plurality of genes were selected from the differential marker candidate 29 genes based on a value of 7.006 or more, which is the sum of "gene expression scores" of three genes of GLRX gene, GDF15 gene and GABARAPL1 gene.
The LYPD1 gene, CRIP1 gene, LRIGI gene and A4GALT gene were selected so that the sum of the gene expression score is at least 7.006 (the total gene expression score is 8.637832), and from the measurement results of these gene expression, serous adenocarcinoma It was verified whether it could be differentiated from clear cell adenocarcinoma.
Specifically, for each sample belonging to the serous adenocarcinoma group and the clear cell adenocarcinoma group, the total value of Log ₂ ratio of LYPD1 gene, CRIP1 gene, LRIGI gene and A4GALT gene was determined, and the distribution was drawn as a group scatter plot (Figure 11). When the average value of the total value of specimens belonging to each of the tissue-type ovarian cancers in the serous adenocarcinoma group or the clear cell adenocarcinoma group is determined for each tissue type, the average value of the total value of serous adenocarcinoma is -3.4530, while The average value of the total value of clear cell adenocarcinoma was 5.1849, and each group had a significant difference at P = 6.128 × 10 ⁻¹² .
Further, in the ROC curve, AUC was 0.9501, and in the sensitivity / specificity curve, the optimum cutoff value was fixed at 2.2401, at which time the sensitivity and specificity were 94.4% (FIG. 12).
In addition, when hierarchical cluster analysis was performed based on the expression profiles of the LYPD1 gene, CRIP1 gene, LRIGI gene and A4GALT gene, 2 clusters mainly composed of serous adenocarcinoma group and 2 clusters mainly composed of clear cell adenocarcinoma group Two clusters were formed, and almost the same results as hierarchical cluster analysis based on expression profiles of 29 genes (Example 3) and 7 genes (Example 5) could be obtained.
Thus, there is a significant difference in the sum of expression levels of the LYPD1 gene, CRIP1 gene, LRIGI gene and A4GALT gene between serous adenocarcinoma and clear cell adenocarcinoma, and the expression level in the combination of these genes By measuring, it is possible to distinguish which tissue type the target ovarian cancer sample is.

Example 7 Examination of Gene Set B 3
In addition, we examined combinations of other genes that can distinguish serous adenocarcinoma and clear cell adenocarcinoma with high accuracy.
The ANXA4 gene, LAMB1 gene and APOL1 gene were selected so that the sum of gene expression scores is at least 7.006 (the total gene expression score is 7.648277), and from the measurement results of these gene expression, serous adenocarcinoma and clear cell glands It was verified whether it could be differentiated from cancer.
Specifically, the sum of the Log ₂ ratio of the ANXA4 gene, the LAMB1 gene and the APOL1 gene was determined for each sample belonging to the serous adenocarcinoma group and the clear cell adenocarcinoma group, and the distribution was plotted as a group scatter plot (FIG. 13) ). When the average value of the total value of specimens belonging to the histologic type ovarian cancer in the serous adenocarcinoma group or the clear cell adenocarcinoma group is determined for each tissue type, the average value of the serous adenocarcinoma is 0.9036, while The average value of the total value of cell adenocarcinomas was 8.5519, and each group had a significant difference at P = 2.685 × 10 ⁻¹⁰ .
Further, in the ROC curve, AUC was 0.9331, and in the sensitivity / specificity curve, the optimum cutoff value was determined to 5.2992, at which time the sensitivity and specificity were 88.9% (FIG. 14).
In addition, hierarchical cluster analysis was performed based on the expression profiles of the ANXA4, LAMB1 and APOL1 genes, and two clusters consisting of a serous adenocarcinoma group and a clear cell adenocarcinoma group were used. It was possible to obtain almost the same results as hierarchical cluster analysis based on the expression profiles of 29 genes (Example 3) and 7 genes (Example 5).
Thus, there is a significant difference in the sum of the expression levels of the ANXA4, LAMB1 and APOL1 genes between serous adenocarcinoma and clear cell adenocarcinoma, and the expression level in the combination of these genes is measured. This makes it possible to distinguish which tissue type the target ovarian cancer sample is.

Example 8 Examination of Gene Set C 4
In addition, we examined combinations of other genes that can distinguish serous adenocarcinoma and clear cell adenocarcinoma with high accuracy.
The FXYD2 gene, GPX3 gene and TMEM101 gene were selected so that the sum of gene expression scores is at least 7.006 (the sum of gene expression scores is 9.911373), and from the measurement results of these gene expression, serous adenocarcinoma and clear cell glands It was verified whether it could be differentiated from cancer.
Specifically, for each sample belonging to the serous adenocarcinoma group and the clear cell adenocarcinoma group, the total value of Log ₂ ratio of FXYD2 gene, GPX3 gene and TMEM101 gene was determined, and the distribution was drawn as a group scatter plot (FIG. 15) ). When the average value of the total value of specimens belonging to each of the tissue-type ovarian cancers in the serous adenocarcinoma group or the clear cell adenocarcinoma group is determined for each tissue type, the average value of the serous adenocarcinoma is 0.3106, while The mean value of the total value of cell adenocarcinomas was 10.2220, and the groups of each other had a significant difference at P = 2.782 × 10 ⁻⁸ .
Further, in the ROC curve, AUC was 0.9331, and in the sensitivity / specificity curve, the optimal cutoff value was determined to be 3.6064, at which time the sensitivity and specificity were 88.9% (FIG. 14).
In addition, when hierarchical cluster analysis was performed based on the expression profiles of the FXYD2, GPX3 and TMEM101 genes, two clusters consisting of a serous adenocarcinoma group and a clear cell adenocarcinoma group were used. It was possible to obtain almost the same results as hierarchical cluster analysis based on the expression profiles of 29 genes (Example 3) and 7 genes (Example 5).
Thus, there is a significant difference in the sum of the expression levels of the FXYD2, GPX3 and TMEM101 genes between serous adenocarcinoma and clear cell adenocarcinoma, and the expression level of the combination of these genes is measured. This makes it possible to distinguish which tissue type the target ovarian cancer sample is.

Example 9 Examination of Gene Set D 5
In addition, we examined combinations of other genes that can distinguish serous adenocarcinoma and clear cell adenocarcinoma with high accuracy.
The DLX5 gene, the UQCRH gene and the ARG2 gene were selected so that the sum of the gene expression scores is at least 7.006 (the sum of the gene expression scores is 7.541611), and from the measurement results of these gene expression, serous adenocarcinoma and clear cell glands It was verified whether it could be differentiated from cancer.
Specifically, the sum of the Log ₂ ratio of the DLX5 gene, the UQCRH gene, and the ARG2 gene was determined for each sample belonging to the serous adenocarcinoma group and the clear cell adenocarcinoma group, and the distribution was plotted as a group scatter plot (FIG. 17). ). When the average value of the total value of specimens belonging to the respective tissue-type ovarian cancers in the serous adenocarcinoma group or the clear cell adenocarcinoma group is determined for each tissue type, the average value of the total value of the serous adenocarcinoma is 0.26930. The average value of the total value of cell adenocarcinomas was 7.8109, and the groups had a significant difference at P = 3.901 × 10 ⁻¹⁰ .
Also, in the ROC curve, AUC was 0.9388, and in the sensitivity / specificity curve, the optimal cutoff value was determined to 4.0090, at which time the sensitivity and specificity were 88.9% (FIG. 18).
In addition, hierarchical cluster analysis was performed based on the expression profiles of the DLX5 gene, the UQCRH gene, and the ARG2 gene. As a result, two clusters consisting mainly of serous adenocarcinoma group and clusters consisting mainly of clear cell adenocarcinoma group It was possible to obtain almost the same results as hierarchical cluster analysis based on the expression profiles of 29 genes (Example 3) and 7 genes (Example 5).
Thus, there is a significant difference in the sum of the expression levels of the DLX5 gene, the UQCRH gene and the ARG2 gene between serous adenocarcinoma and clear cell adenocarcinoma, and the expression level in the combination of these genes is measured. This makes it possible to distinguish which tissue type the target ovarian cancer sample is.

Example 10 Examination of Gene Set E 6
In addition, we examined combinations of other genes that can distinguish serous adenocarcinoma and clear cell adenocarcinoma with high accuracy.
The FBLN1 gene, AOC1 gene and TSPAN1 gene were selected so that the sum of the gene expression score is at least 7.006 (the sum of the gene expression score is 8.076977), and from the measurement results of these gene expression, serous adenocarcinoma and clear cell gland It was verified whether it could be differentiated from cancer.
Specifically, the total value of the Log ₂ ratio of the FBLN1 gene, the AOC1 gene, and the TSPAN1 gene was determined for each sample belonging to the serous adenocarcinoma group and the clear cell adenocarcinoma group, and the distribution was plotted as a group scatter plot (FIG. 19) ). When the average value of the total value of specimens belonging to the respective tissue-type ovarian cancers in the serous adenocarcinoma group or the clear cell adenocarcinoma group is determined for each tissue type, the average value of the total value of serous adenocarcinoma is -1.8339, while The average value of the total value of clear cell adenocarcinoma was 6.0301, and each other group had a significant difference at P = 3.829 × 10 ⁻⁷ .
Further, in the ROC curve, AUC was 0.88178, and in the sensitivity / specificity curve, the optimum cutoff value was determined to be 0.3689, at which time the sensitivity and specificity were 82.1% (FIG. 20).
In addition, when hierarchical cluster analysis was performed based on the expression profiles of the FBLN1 gene, AOC1 gene and TSPAN1 gene, two clusters consisting of a serous adenocarcinoma group and a clear cell adenocarcinoma group were used. It was possible to obtain almost the same results as hierarchical cluster analysis based on the expression profiles of 29 genes (Example 3) and 7 genes (Example 5).
Thus, there is a significant difference in the sum of the expression levels of the FBLN1, AOC1 and TSPAN1 genes between serous adenocarcinoma and clear cell adenocarcinoma, and the expression level of the combination of these genes is measured. This makes it possible to distinguish which tissue type the target ovarian cancer sample is.

Example 11 Examination of Gene Set F 7
In addition, we examined combinations of other genes that can distinguish serous adenocarcinoma and clear cell adenocarcinoma with high accuracy.
C12orf75 gene, DNER gene and RIMKLB gene were selected so that the sum of gene expression score is at least 7.006 (the sum of gene expression score is 8.127428), and from the measurement results of these gene expression, serous adenocarcinoma and clear cell gland It was verified whether it could be differentiated from cancer.
Specifically, for each sample belonging to the serous adenocarcinoma group and the clear cell adenocarcinoma group, the total value of Log ₂ ratio of C12orf75 gene, DNER gene and RIMKLB gene was determined, and the distribution was drawn as a group scatter plot (FIG. 21) ). When the average value of the total value of specimens belonging to each of the tissue-type ovarian cancers in the serous adenocarcinoma group or the clear cell adenocarcinoma group is determined for each tissue type, the average value of the total value of serous adenocarcinoma is -1.3507, while The average value of the total value of clear cell adenocarcinoma was 6.7768, and the groups had a significant difference at P = 1.245 × 10 ⁻⁸ .
Further, in the ROC curve, AUC was 0.8803, and in the sensitivity / specificity curve, the optimum cutoff value was fixed at 1.1226, at which time the sensitivity and specificity were 88.9% (FIG. 22).
In addition, when hierarchical cluster analysis was performed based on the expression profiles of C12orf75 gene, DNER gene and RIMKLB gene, two clusters consisting mainly of serous adenocarcinoma group and clusters mainly consisting of clear cell adenocarcinoma group It was possible to obtain almost the same results as hierarchical cluster analysis based on the expression profiles of 29 genes (Example 3) and 7 genes (Example 5).
Thus, there is a significant difference in the sum of expression levels of C12orf75 gene, DNER gene and RIMKLB gene between serous adenocarcinoma and clear cell adenocarcinoma, and the expression level in the combination of these genes is measured This makes it possible to distinguish which tissue type the target ovarian cancer sample is.

(Example 12: Additional test using additional sample)
Apart from the examination used in the above Example 1, etc., 19 samples of serous adenocarcinoma and 7 samples of clear cell adenocarcinoma were prepared for verification, and serous adenocarcinoma and clear cell adenocarcinoma with high accuracy also for these samples. It was verified whether it was possible to distinguish
Specifically, RNA preparation and comprehensive gene expression analysis were performed on additional verification samples in the same manner as the methods described in Examples 1 and 2 above.
The microarray data of the differential marker 29 gene obtained for the additional sample was added to the microarray data of the sample used in the above-mentioned Example 1 and the like to perform cluster analysis of group average method by Euclidean distance. The results are shown in FIG. 23A. Moreover, what rearranged microarray data based on the scoring system as described in the said Example 4 is shown to FIG. 23B.
As shown in FIGS. 23A and B, serous and clear cell adenocarcinoma could be differentiated with 100% accuracy for the additional validation samples.

(Example 13: Additional test 2 using additional sample)
The differential marker 7 gene (PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, GDF15 gene) for the additional verification samples of Example 12 (19 samples of serous adenocarcinoma and 7 samples of clear cell adenocarcinoma) The cluster data of the group averaging method by Euclidean distance was carried out by adding the microarray data of (1) to the microarray data of the sample used in the above-mentioned Example 1 and the like. The results are shown in FIG. 24A. Moreover, what rearranged microarray data based on a scoring system is shown to FIG. 24B.
As shown in FIGS. 24A and B, serous and clear cell adenocarcinoma could be differentiated with 100% accuracy for the additional validation samples.

(Example 14 additional test 3 using additional samples)
The microarray data of the differential marker 3 gene (GLRX gene, GDF15 gene, GABARAPL1 gene) for the additional verification samples of the example 12 (19 samples of serous adenocarcinoma and 7 samples of clear cell adenocarcinoma) are shown in the above example 1 etc. In addition to the microarray data of the samples used, cluster analysis of group average method by Euclidean distance was performed. The results are shown in FIG. 25A. Moreover, what rearranged microarray data based on a scoring system is shown to FIG. 25B.
As shown in FIGS. 25A and B, serous and clear cell adenocarcinoma could be differentiated with 100% accuracy for the additional validation specimens.

Claims (16)

A method of obtaining an expression profile of a gene in a test sample derived from an ovarian cancer patient, and discriminating whether it is a serous adenocarcinoma or clear cell adenocarcinoma tissue type based on the expression profile,
PPP1R3B gene, TOB1 gene, RBPMS gene, A4GALT gene, RIMKLB gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, LAMB1 gene, LNER gene, ARG2 gene, TMEM101 gene, GDF15 gene, C12orf75 gene, DLX5 gene, AOC1 gene, ANXA4 gene , At least three genes selected from the group consisting of: FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIGI gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and IGF2 gene Measuring the expression level of each gene in the set of genes comprising
Each gene was selected such that the total value of the gene expression score of each gene contained in the gene set is equal to or greater than the total value of the gene expression scores of the GLRX gene, GDF15 gene, and GABARAPL1 gene The identification method including the process which is a thing. The identification method according to claim 1, wherein
The differentiation method, wherein the gene expression score is the magnitude of the difference in expression level of a specific gene between a sample from a serous adenocarcinoma patient and a sample from a clear cell adenocarcinoma patient. The identification method according to claim 2, wherein
The combination of the genes contained in the said gene set is a thing by which at least 3 genes were selected so that a sum might be equal to 7.006 or more with reference to the gene expression score described in the following table. Method.
It is a discrimination method according to any one of claims 1 to 3,
The identification method, wherein at least one gene contained in the gene set is selected from the group consisting of PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, and GDF15 gene. It is a discrimination method as described in any one of Claims 1-4, Comprising:
The identification method, wherein all of the genes contained in the gene set are selected from the group consisting of PPP1 R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, and GDF15 gene. The identification method according to any one of claims 1 to 5, wherein
The identification method, wherein the gene set comprises a GLRX gene, a GDF15 gene and a GABARAPL1 gene. It is a discrimination method according to any one of claims 1 to 6,
The tissue type of the test sample is determined by comparing the expression profile of each gene in the gene set with the expression profile of each gene in the corresponding gene set in samples from serous adenocarcinoma patients or clear cell adenocarcinoma patients. Differentiating method. It is a discrimination method according to any one of claims 1 to 7,
The test sample was compared by comparing the expression profile of each gene in the gene set with the expression profile of each gene in the corresponding gene set in samples from serous adenocarcinoma patients or clear cell adenocarcinoma patients. If it has an expression profile of a gene equivalent to the expression profile of a gene of histologic ovarian cancer, it is evaluated as a comparative ovarian cancer of a histologic type or a gene different from the expression profile of a gene of a histological type ovarian cancer compared It is evaluated that it is not an ovarian cancer of the tissue type compared with the case of having an expression profile of It is a discrimination method as described in any one of Claims 1-8, Comprising:
The identification method which identifies the tissue type of the said test sample by carrying out cluster analysis of the expression profile of each gene in the said gene set. The identification method according to any one of claims 1 to 9, wherein
A differentiation method of discriminating tissue types by comparing a quantitative expression profile of each gene of the gene set in a test sample with a predetermined threshold value. It is a differential marker set for discriminating whether it is a serous adenocarcinoma or a clear cell adenocarcinoma tissue type in a test sample derived from an ovarian cancer patient,
The differential marker set includes the PPP1R3B gene, TOB1 gene, RBPMS gene, A4GALT gene, RIMKLB gene, GABARAPL1 gene, GABARXL gene, GLRX gene, GSTM3 gene, LAMB1 gene, LNER gene, ARG2 gene, TMEM101 gene, GDF15 gene, C12orf75 gene, DLX5 gene , AOC1 gene, ANXA4 gene, FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIGI gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and IGF2 gene selected from the group Containing a combination of at least three
Each gene is selected such that the total value of the gene expression score of each gene contained in the differential marker set is equal to or more than the total value of the gene expression score of the GLRX gene, the GDF15 gene, and the GABARAPL1 gene. Differentiation marker set. A differential marker set according to claim 11, wherein
A differential marker set in which the expression level of mRNA of each gene contained in the gene set is measured. The differential marker set according to claim 11, wherein the differential marker set is a PPP1R3B gene, TOB1 gene, RBPMS gene, A4GALT gene, RIMKLB gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, LAMB1 gene, DNER gene, ARG2 gene , TMEM101 gene, GDF15 gene, C12orf75 gene, DLC5 gene, AOC1 gene, ANXA4 gene, FBLN1 gene, TSPAN1 gene, TOPOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIG1 gene, LYPD1 gene, CRIPI gene, UQCRH A differential marker set, which is a combination of a gene and mRNA encoded by at least three genes selected from the group consisting of the IGF2 gene. A kit for determining whether a serous adenocarcinoma or clear cell adenocarcinoma tissue type is present in a test sample derived from an ovarian cancer patient,
The kit includes the PP1R3B gene, TOB1 gene, RBPMS gene, A4GALT gene, RIMKLB gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, LAMB1 gene, LNER gene, ARG2 gene, TMEM101 gene, GDF15 gene, C12orf75 gene, DLX5 gene, AOC1 Gene, ANXA4 gene, FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIGI gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and at least an IGF2 gene selected from the group consisting of Including means for measuring the expression level of each gene in a gene set comprising three genes,
A kit, wherein each gene is selected such that the total value of gene expression scores of each gene contained in the gene set is equal to or more than the total value of gene expression scores of GLRX gene, GDF15 gene, and GABARAPL1 gene . The kit according to claim 14, wherein
The kit, wherein the means for measuring the expression level of each gene is at least one means selected from the group consisting of primers for each gene, a probe, or labels thereof. A kit according to claim 15, wherein
A kit for PCR, microarray, or RNA sequencing.