JP6977965B2 - Ovarian cancer histological differentiation method - Google Patents

Description

The present invention relates to a method for differentiating ovarian cancer histology.

Ovarian cancer is known to have a poor prognosis among gynecologic malignancies because it is often found after it has progressed with few initial symptoms. The basic treatment for ovarian cancer is surgical therapy, but since the main mode of progression is peritoneal dissemination, it is rarely completely cured by surgical therapy, and many cases are targeted for chemotherapy. Chemotherapy centered on platinum preparations was introduced in the 1980s, and the response rate to ovarian cancer clearly increased after the combined use of taxane preparations in the 1990s. Chemotherapy of platinum preparations (carboplatin) + taxane preparations (paclitaxel) The therapy has become the standard chemotherapy for ovarian cancer.
Ovarian cancer is broadly classified into three types: superficial epithelial / interstitial tumor, germ cell tumor, and sex cord-gonadal tumor. Of these, ovarian cancers of superficial epithelial and interstitial tumors include four main histological types: serous adenocarcinoma, clear cell adenocarcinoma, mucinous adenocarcinoma, and endometrioid adenocarcinoma. It is important to identify the histological type in the treatment of ovarian cancer, and treatment according to the histological type is desirable. In particular, among the above four histological types, clear cell adenocarcinoma is the most difficult disease to treat. Serous adenocarcinoma (about 80% in Europe and the United States, about 40% in Japan), which is the most common ovarian cancer, has a good response to chemotherapy and can be expected to have a therapeutic effect. On the other hand, the incidence of clear cell adenocarcinoma is low in Europe and the United States (several percent) but high in Japan (about 24%), and in advanced cases, patients with clear cell adenocarcinoma have a significantly worse prognosis than patients with serous adenocarcinoma. One of the main reasons for this is poor drug responsiveness to chemotherapy. For this reason, the development of a treatment method different from the standard treatment for clear cell adenocarcinoma is underway.
Usually, in clinical practice, the histopathological examination of surgical specimens is performed to identify the histological type of ovarian cancer. However, clear cell adenocarcinomas with papillary growth are very similar in shape to serous adenocarcinoma. Therefore, in such cases, it may be difficult to distinguish between serous adenocarcinoma and clear cell adenocarcinoma pathologically. In addition, both may be mixed, and it may be difficult to determine which one is strongly reflected by the character of the tumor. Considering personalized medicine for clear cell adenocarcinoma, it is necessary to diagnose both more accurately.
In recent years, in order to determine the prognosis and classification of ovarian cancer, analysis methods using gene markers focusing on gene expression and protein expression have been studied. For example, Patent Document 1 discloses a method for detecting a glycoprotein specific to cancer cells as a marker for differentiating epithelial ovarian cancer. In addition, Patent Document 2 discloses a method for diagnosing ovarian cancer that is resistant to chemotherapeutic intervention by measuring the expression level of a specific microRNA.
As described above, it has been desired to develop a method capable of differentiating the histological type of ovarian cancer without using a histopathological method. However, so far, there is no report on a genetic marker that can clearly distinguish between serous adenocarcinoma and clear cell adenocarcinoma in ovarian cancer.

Japanese Unexamined Patent Publication No. 2016-408270 Japanese Patent Publication No. 2014-530612

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

In order to solve the above problems, the present inventors obtained gene expression profiles for cancer tissues excised from a plurality of serous adenocarcinoma patients and clear cell adenocarcinoma patients by using a comprehensive gene expression analysis technique. However, we found 29 gene groups with large differences in expression levels between serous adenocarcinoma and clear cell adenocarcinoma. Then, by measuring the expression level of these genes in the sample of the subject, we developed a method that can easily and accurately distinguish between serous adenocarcinoma and clear cell adenocarcinoma among the histological types in ovarian cancer.
Therefore, in order to investigate which of the 29 genes should be used to accurately distinguish between serous adenocarcinoma and clear cell adenocarcinoma, the present inventors have selected genes among each sample of the 29 genes. Seven genes with little variation in expression levels were identified. Furthermore, the present inventors attempted to score gene expression for differences in gene expression levels between serous adenocarcinoma and clear cell adenocarcinoma, and further investigated which gene should be selected based on the scoring. rice field. As a result, the present inventors so as to satisfy the total value of the gene expression scores of the three genes (GLRX gene, GDF15 gene, and GABARAPL1 gene) having the highest gene expression scores among the above seven genes. By selecting genes from 29 species, we found that it is possible to distinguish between serous adenocarcinoma and clear cell adenocarcinoma with high accuracy, and completed the present invention. The present invention is based on this finding and provides the following.

また、本発明の鑑別方法は、一実施の形態において、
〔４〕上記〔１〕〜〔３〕のいずれかに記載の鑑別方法であって、
前記遺伝子セットに含まれる少なくとも一つの遺伝子が、PPP1R3B遺伝子、TOB1遺伝子、RBPMS遺伝子、GABARAPL1遺伝子、GLRX遺伝子、GSTM3遺伝子、および、GDF15遺伝子からなる群より選択されることを特徴とする。
また、本発明の鑑別方法は、一実施の形態において、
〔５〕上記〔１〕〜〔４〕のいずれかに記載の鑑別方法であって、
前記遺伝子セットに含まれる遺伝子の全てが、PPP1R3B遺伝子、TOB1遺伝子、RBPMS遺伝子、GABARAPL1遺伝子、GLRX遺伝子、GSTM3遺伝子、および、GDF15遺伝子からなる群より選択されることを特徴とする。
また、本発明の鑑別方法は、一実施の形態において、
〔６〕上記〔１〕〜〔５〕のいずれかに記載の鑑別方法であって、
前記遺伝子セットがGLRX遺伝子、GDF15遺伝子およびGABARAPL1遺伝子を含むことを特徴とする。
また、本発明の鑑別方法は、一実施の形態において、
〔７〕上記〔１〕〜〔６〕のいずれかに記載の鑑別方法であって、
前記遺伝子セットにおける各遺伝子の発現プロファイルと、漿液性腺癌患者または明細胞腺癌患者由来の試料における対応する遺伝子セットにおける各遺伝子の発現プロファイルとを比較することにより、前記被検試料の組織型を鑑別することを特徴とする。
また、本発明の鑑別方法は、一実施の形態において、
〔８〕上記〔１〕〜〔７〕のいずれかに記載の鑑別方法であって、
前記遺伝子セットにおける各遺伝子の発現プロファイルと、漿液性腺癌患者または明細胞腺癌患者由来の試料における対応する遺伝子セットにおける各遺伝子の発現プロファイルとを比較することにより、前記被検試料が、比較した組織型卵巣癌の遺伝子の発現プロファイルと同等の遺伝子の発現プロファイルを有する場合に比較した組織型の卵巣癌であると評価するか、または、比較した組織型卵巣癌の遺伝子の発現プロファイルと異なる遺伝子の発現プロファイルを有する場合に比較した組織型の卵巣癌ではないと評価することを特徴とする。
また、本発明の鑑別方法は、一実施の形態において、
〔９〕上記〔１〕〜〔８〕のいずれかに記載の鑑別方法であって、
前記遺伝子セットにおける各遺伝子の発現プロファイルをクラスタ分析することにより前記被検試料の組織型を鑑別することを特徴とする。
また、本発明の鑑別方法は、一実施の形態において、
〔１０〕上記〔１〕〜〔９〕のいずれかに記載の鑑別方法であって、
被検試料における前記遺伝子セットの各遺伝子の定量的な発現プロファイルを、所定の閾値と比較することにより組織型を鑑別することを特徴とする。 That is, the present invention
[1] A method for obtaining an expression profile of a gene in a test sample derived from a patient with ovarian cancer and distinguishing whether it is a histological 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, DNER 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, LRIG1 gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and at least 3 genes selected from the group consisting of IGF2 gene. A step of measuring the expression level of each gene in the included gene set.
Each gene was selected so that the total value of the gene expression scores of each gene included in the gene set was equal to or greater than the total value of the gene expression scores of the GLRX gene, GDF15 gene, and GABARAPL1 gene. It relates to a discrimination method including a step of being a thing.
Further, the discrimination method of the present invention is, in one embodiment,.
[2] The discrimination method according to the above [1].
The gene expression score is characterized by the magnitude of the difference in the expression level of a specific gene between a sample derived from a patient with serous adenocarcinoma and a sample derived from a patient with clear cell adenocarcinoma.
Further, the discrimination method of the present invention is, in one embodiment,.
[3] The discrimination method according to the above [2].
It is determined that at least three genes are selected so that the combination of genes contained in the gene set is equal to or greater than 7.006 in total by referring to the gene expression scores shown in the table below. It is a feature.

Further, the discrimination method of the present invention is, in one embodiment,.
[4] The discrimination method according to any one of the above [1] to [3].
It 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.
Further, the discrimination method of the present invention is, in one embodiment,.
[5] The discrimination method according to any one of the above [1] to [4].
All of the genes included in the gene set are selected from the group consisting of PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, and GDF15 gene.
Further, the discrimination method of the present invention is, in one embodiment,.
[6] The discrimination method according to any one of the above [1] to [5].
The gene set is characterized by containing the GLRX gene, the GDF15 gene and the GABARAPL1 gene.
Further, the discrimination method of the present invention is, in one embodiment,.
[7] The discrimination method according to any one of the above [1] to [6].
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 a sample derived from a serous adenocarcinoma patient or a clear cell adenocarcinoma patient, the histological type of the test sample can be determined. It is characterized by distinguishing.
Further, the discrimination method of the present invention is, in one embodiment,.
[8] The discrimination method according to any one of the above [1] to [7].
The test samples were 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 a sample derived from a patient with serous or clear cell adenocarcinoma. A gene that is evaluated as having a histological ovarian cancer compared to that having a gene expression profile equivalent to the gene expression profile of the histological ovarian cancer, or a gene different from the genetic expression profile of the compared histological ovarian cancer. It is characterized by evaluating that it is not a histological type of ovarian cancer as compared with the case of having an expression profile of.
Further, the discrimination method of the present invention is, in one embodiment,.
[9] The discrimination method according to any one of the above [1] to [8].
It is characterized in that the histological type of the test sample is differentiated by cluster analysis of the expression profile of each gene in the gene set.
Further, the discrimination method of the present invention is, in one embodiment,.
[10] The discrimination method according to any one of the above [1] to [9].
It is characterized in that the histological type is differentiated by comparing the quantitative expression profile of each gene of the gene set in the test sample with a predetermined threshold value.

Further, as another aspect, the present invention is a differential marker set for differentiating whether the histological type is serous adenocarcinoma or clear cell adenocarcinoma in a test sample derived from [11] ovarian cancer patient. ,
The differential marker set includes 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, DLX5 gene. , AOC1 gene, ANXA4 gene, FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIG1 gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and IGF2 gene. Contains at least a combination of three genes
Each gene is selected so that the total value of the gene expression scores of each gene included in the differential marker set is equal to or greater than the total value of the gene expression scores of the GLRX gene, the GDF15 gene, and the GABARAPL1 gene. Regarding the identification marker set.
Here, the differential marker set of the present invention is, in one embodiment,.
[12] The differential marker set according to the above [11].
It is characterized in that the expression level of mRNA of each gene contained in the gene set is measured.
In addition, the marker gene set of the present invention, in one embodiment,
[13] The differential marker set according to [11] above, wherein the differential marker set includes PPP1R3B gene, TOB1 gene, RBPMS gene, A4GALT gene, RIMKLB gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, LAMB1 gene, and DNER. 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, LRIG1 gene, LYPD1 gene, A differential marker set that is a combination of mRNAs encoded by at least three genes selected from the group consisting of the CRIP1 gene, the UQCRH gene, and the IGF2 gene.

Further, another aspect of the present invention is a kit for discriminating between [14] a test sample derived from an ovarian cancer patient and having a histological type of serous adenocarcinoma or clear cell adenocarcinoma.
The kit includes PP1R3B 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. At least selected from the group consisting of genes, ANXA4 gene, FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIG1 gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and IGF2 gene. Including means for measuring the expression level of each gene in a gene set containing three genes.
A kit in which each gene is selected so that the total value of the gene expression scores of each gene included in the gene set is equal to or greater than the total value of the gene expression scores of the GLRX gene, the GDF15 gene, and the GABARAPL1 gene. Regarding.
Here, the kit of the present invention, in one embodiment,
[15] At least one of the kits according to [14] above, wherein the means for measuring the expression level of each gene is selected from the group consisting of primers, probes, or labels thereof for each gene. It is characterized by being a means.
Further, the kit of the present invention, in one embodiment,
[16] The kit according to the above [15], which is for PCR, for microarrays, or for RNA sequencing.
Further, the present invention is another aspect of the present invention.
[17] A step of administering to the ovarian cancer patient an effective amount of an anticancer agent for serous adenocarcinoma or clear cell adenocarcinoma based on the result of the differentiation method according to any one of the above [1] to [10]. Including, regarding treatment methods.

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

FIG. 1 shows the results of cluster analysis by the Euclidean distance group averaging method based on microarray data on the 29 differential marker genes in 39 serous adenocarcinoma specimens and 18 clear cell adenocarcinoma specimens. FIG. 2 shows a rearrangement of microarray data on differential marker 29 genes in 39 serous adenocarcinoma specimens and 18 clear cell adenocarcinoma specimens based on the scoring system of Example 4 below. Specifically, the total value of the gene expression scores of the differential marker 29 genes in each sample derived from serous adenocarcinoma or clear cell adenocarcinoma was calculated and arranged in ascending order from the left. The lower graph of FIG. 2 shows the total value of the gene expression scores in each sample. FIG. 3 shows an ROC curve (upper row) and a sensitivity / specificity curve (lower row) prepared based on microarray data on differential marker 29 genes in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 4 shows a group scatter plot prepared based on microarray data on differential marker 29 genes in 39 specimens of serous adenocarcinoma and 18 specimens of clear cell adenocarcinoma. FIG. 5 is a hierarchy based on microarray data on 7 differential marker genes (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 results of a targeted cluster analysis (group averaging method based on Euclidean distance) are shown. FIG. 6 shows 7 differential marker genes (PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, GDF15 gene) in 39 serous adenocarcinoma and 18 clear cell adenocarcinoma based on the scoring system. Here is a rearrangement of the microarray data for. The lower graph of FIG. 6 shows the total value of the gene expression scores in each sample. FIG. 7 is prepared based on microarray data on 7 differential marker genes (PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, GDF15 gene) in 39 serous adenocarcinoma specimens and 18 clear cell adenocarcinoma specimens. The group scatter diagram is shown. FIG. 8 is prepared based on microarray data on 7 differential marker genes (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 row) and sensitivity / specificity curve (lower row) are shown. FIG. 9 shows a group scatter plot prepared based on microarray data on differential marker 3 genes (GLRX gene, GDF15 gene, GABARAPL1 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 10 shows the ROC curve (upper) and the sensitivity / specificity curve prepared based on the microarray data on the differential marker 3 genes (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 a group scatter plot prepared based on microarray data on differential marker 4 genes (LYPD1 gene, CRIP1 gene, LRIG1 gene, A4GALT gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 12 shows the ROC curve (upper) and sensitivity / sensitivity prepared based on the microarray data for the 4 differential marker genes (LYPD1 gene, CRIP1 gene, LRIG1 gene, A4GALT gene) in 39 serous adenocarcinoma and 18 clear cell adenocarcinoma. The specificity curve (lower) is shown. FIG. 13 shows a group scatter plot 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 the ROC curve (upper) and the sensitivity / specificity curve prepared based on the microarray data on the differential marker 3 genes (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 plot prepared based on microarray data on differential marker 3 genes (FXYD2 gene, GPX3 gene, TMEM101 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 16 shows the ROC curve (upper) and the sensitivity / specificity curve prepared based on the microarray data on the differential marker 3 genes (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 a group scatter plot prepared based on microarray data on differential marker 3 genes (DLX5 gene, UQCRH gene, ARG2 gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 18 shows the ROC curve (upper) and the sensitivity / specificity curve prepared based on the microarray data on the differential marker 3 genes (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 a group scatter plot prepared based on microarray data on 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 the ROC curve (upper) and the sensitivity / specificity curve prepared based on the microarray data on the differential marker 3 genes (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 a group scatter plot prepared based on microarray data on differential marker 3 genes (C12orf75 gene, DNER gene, RIMKLB gene) in 39 samples of serous adenocarcinoma and 18 samples of clear cell adenocarcinoma. FIG. 22 shows an ROC curve (upper) and a sensitivity / specificity curve prepared based on microarray data on 3 differential marker genes (C12orf75 gene, DNER gene, RIMKLB gene) in 39 serous adenocarcinoma specimens and 18 clear cell adenocarcinoma specimens. (Lower) is shown. FIG. 23A shows 59 serous adenocarcinoma and 25 clear cell adenocarcinoma by adding 19 serous adenocarcinoma and 7 clear cell adenocarcinoma for verification to 39 serous adenocarcinoma and 18 clear cell adenocarcinoma. The results of cluster analysis by the group averaging method based on the Euclidean distance are shown based on the microarray data of 29 differential marker genes in those samples. In addition, FIG. 23B shows a rearrangement of microarray data on the differential marker 29 genes in 59 samples of serous adenocarcinoma and 25 samples of clear cell adenocarcinoma based on the scoring system. FIG. 24A shows 59 serous adenocarcinoma and 25 clear cell adenocarcinoma by adding 19 serous adenocarcinoma and 7 clear cell adenocarcinoma for verification to 39 serous adenocarcinoma and 18 clear cell adenocarcinoma. Based on the microarray data of 7 differential marker genes (PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, GDF15 gene) in those samples, cluster analysis of group average method by Euclidean distance was performed. The results are shown. In addition, FIG. 24B shows the differential marker 7 genes (PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, in 59 samples of serous adenocarcinoma and 25 samples of clear cell adenocarcinoma based on the scoring system. The rearranged microarray data for the GDF15 gene) are shown. FIG. 25A shows 59 serous adenocarcinoma and 25 clear cell adenocarcinoma by adding 19 serous adenocarcinoma and 7 clear cell adenocarcinoma for verification to 39 serous adenocarcinoma and 18 clear cell adenocarcinoma. The results of cluster analysis by the group averaging method based on the Euclidean distance are shown based on the microarray data of the three differential marker genes (GLRX gene, GDF15 gene, GABARAPL1 gene) in those samples. In addition, FIG. 25B shows a rearrangement of microarray data related to the differential marker 3 genes (GLRX gene, GDF15 gene, GABARAPL1 gene) in the above 59 samples of serous adenocarcinoma and 25 samples of clear cell adenocarcinoma based on the scoring system. show.

1. 1. Differentiating markers that can distinguish between serous adenocarcinoma and clear cell adenocarcinoma 1-1. Overview The first aspect of the present invention is a differential marker that can distinguish between serous adenocarcinoma and clear cell adenocarcinoma. The differential marker of the present invention consists of at least 29 gene groups, and by measuring the expression level in the sample of the subject, any tissue of serous adenocarcinoma or clear cell adenocarcinoma among the histological types of ovarian cancer. It is possible to accurately distinguish whether it is type ovarian cancer.

1-2. Definition "Ovarian cancer" is an epithelial malignant tumor that develops in the ovary and is known to include histological types of serous adenocarcinoma, clear cell adenocarcinoma, mucinous adenocarcinoma, and endometrial adenocarcinoma.
"Serous adenocarcinoma" is a histological ovarian cancer that is suitable for anticancer drug treatment because it accounts for about 40% of all ovarian cancers, is the most frequent, and is highly sensitive to anticancer drugs. On the other hand, "clear cell adenocarcinoma" is a few percent in Europe and the United States, but it is known that it is a histological type with a particularly high incidence (about 24%) in Japanese, and it is known that anticancer drugs are difficult to work. ing.
As used herein, the term "differential gene group" refers to the PPP1R3B gene, TOB1 gene, RBPMS gene, A4GALT gene, RIMKLB gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, LAMB1 gene, DNER gene, ARG2 gene, TMEM101 gene, and GDF15 gene. , C12orf75 gene, DLX5 gene, AOC1 gene, ANXA4 gene, FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIG1 gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and IGF2 gene. Consists of.
In the present specification, the "differential marker" is a marker associated with a gene included in the "differential gene group", and the histological type of ovarian cancer classified into superficial epithelial / interstitial tumor is defined as serous adenocarcinoma. A biomarker that can be distinguished from cell adenocarcinoma. In the present invention, the biomarker is, in particular, a transcript (mRNA) of each gene contained in a group of differential genes or a cDNA thereof.
In the present specification, the "gene expression score" is a score determined for each gene included in the "differential gene group", and the expression of a gene between a sample derived from a serous adenocarcinoma patient and a sample derived from a clear cell adenocarcinoma patient. Indicates the magnitude of the level difference. More specifically, for a specific gene, the difference between the average value of the expression profile in multiple samples derived from the serous adenocarcinoma patient group and the average value of the expression profile in multiple samples derived from the clear cell adenocarcinoma patient group. It can be obtained as an absolute value.
As used herein, the term "gene expression level" refers to the expression profile, transcript amount, expression intensity or expression frequency of a differential gene. The expression level of the gene referred to here is not limited to the expression level of the wild-type gene of the differential gene, but may include the expression level of a mutant gene such as a point mutation gene. In addition, the transcript showing the expression of the differential gene may also include atypical transcripts (variants) such as splicing variants and fragments thereof. This is because the expression profile of the gene in the present invention can be constructed even with information based on a mutant gene, a transcript, or a fragment thereof. The expression level of the gene can be obtained as a measured value by measuring the transcript of the gene cluster constituting the differential marker, that is, the amount of mRNA or the like.
In a preferred embodiment, the measurement of the gene expression profile is the measurement of mRNA.
As used herein, the "measured value" is a value obtained by a method for measuring a gene expression level. The measured value may be an absolute value in which the amount of mRNA in the sample is expressed by a volume such as ng (nanogram) or μg (microgram), or is expressed by the absorbance with respect to the control value, the fluorescence intensity of the labeled molecule, or the like. It may be a relative value.
The measured value of the expression level of each gene depends on the measuring method, but can be calculated as a relative ratio to a common sample (hereinafter referred to as "common reference"), for example. The common reference for calculating the expression ratio may be any as long as it is the same in the measurement conditions between the samples to be compared. For example, it may be a specific cell line or a mixture of a plurality of cell lines. Alternatively, a commercially available universal reference, a known housekeeping gene or a combination thereof can be used as a common reference.
As used herein, the term "differential" means a sample derived from a subject with a history of ovarian cancer classified as superficial epithelial or interstitial tumor and belongs to either serous adenocarcinoma or clear cell adenocarcinoma histological type. It means to distinguish whether it belongs to or to which tissue type is likely to belong to.

1-3. Configuration The differential markers of this embodiment are 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, 29 types of DLX5 gene, AOC1 gene, ANXA4 gene, FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIG1 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 containing at least 3 kinds of genes is selected from these 29 kinds of "differential gene clusters", and the expression of each gene in the sample is measured.
In one preferred embodiment, 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. .. The seven genes listed above have less variation in gene expression levels among individuals than other genes included in the differential gene cluster. Therefore, by measuring the expression level of these genes, the histological type of ovarian cancer can be differentiated with higher accuracy. Thus, in a more preferred embodiment, all 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 embodiment is composed of a combination of genes contained in a group of differential genes of 3 or more and 29 or less. Usually, the more the types of genes constituting the differentiating marker of this embodiment, the higher the accuracy of differentiating between serous adenocarcinoma and clear cell adenocarcinoma.
In the present specification, the differential marker is a gene consisting of a base sequence represented by DNA (cDNA) in principle, but it is generated as a transcript when the gene is expressed, and mRNA consisting of an RNA sequence and its partial fragments are also markers. It can indirectly serve as a differential marker because it reflects the expression of a gene.
Here, the method of selecting at least three kinds of genes from the "differential gene group" can be selected based on the gene expression score of each gene. Specifically, the gene of each gene so that the total value of the gene expression scores in the selected combination of genes is equal to or higher than the total value of the gene expression scores of the GLRX gene, the GDF15 gene, and the GABARAPL1 gene. Select a combination by referring to the expression score. At this time, the total value of the gene expression scores of the combination of at least three genes selected from the "differential gene group" is equal to or equal to the total value of the gene expression scores of the GLRX gene, the GDF15 gene, and the GABARAPL1 gene. Not limited to more than that, 3 types, 4 types, 5 types, 6 types, 7 types, 8 types, 9 types, 10 types, 11 types, 12 types, 13 types, 14 types, 15 types, 16 types. Species, including all 17 gene combinations. The gene selection method is preferably selected from genes having a high gene expression score, but is not particularly limited to such a form.
Further, in a preferred embodiment, the total value of the gene expression scores in the combination of genes selected from the differential gene group is equal to or higher than the total value of the gene expression scores of the GLRX gene, the GDF15 gene, and the GABARAPL1 gene. be.
As described above, the gene expression score of a specific gene is the average value of the expression level in a plurality of samples derived from a serous adenocarcinoma patient group and a plurality of samples derived from a clear cell adenocarcinoma patient group for a specific gene. It can be obtained as an absolute value of the difference from the average value of the expression level of a specific gene in.

表２中、「gene name」は鑑別遺伝子名を、「symbol」は鑑別遺伝子の略称を、「Gene ID」は鑑別遺伝子のアクセッションナンバーを示す。
また、上記の表２中の遺伝子発現スコアを参照する際、鑑別遺伝子群より選択される遺伝子の組み合わせにおいて遺伝子発現スコアの合計値を、８以上、９以上、１０以上とすることにより、鑑別の精度がより高まるため好ましい。当該遺伝子スコアの合計値を１１〜５０以上（例えば、１１以上、１２以上、１３以上、１４以上、１５以上、２０以上、２５以上、３０以上、３５以上、４０以上、４５以上、５０以上）とするとさらに鑑別の精度が高まり好ましい。一方で、鑑別遺伝子群より選択される少なくとも３遺伝子以上の組み合わせについて、遺伝子発現スコアの合計値が、GLRX遺伝子、GDF15遺伝子、および、GABARAPL1遺伝子の遺伝子発現スコアの合計値より下回る場合、例えば６未満であった場合、臨床に耐えうる精度を担保できなかった。 Although not limited to the following, the gene expression scores of each gene included in the “differential gene cluster” in one embodiment are as shown 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 selecting a gene combination from the "differential gene group" with reference to the gene expression score in Table 2 below, from the "differential gene group" so that the total value of the gene expression scores is equal to or higher than 7.006. You can choose a combination of at least three genes. Here, the fact that the total value of the gene expression score is equivalent to 7.006 includes the total value of the gene expression score of 6.9 or more. In a preferred embodiment, the total gene expression score for the combination of genes selected from the differential gene cluster is 7.006 or higher.

In Table 2, "gene name" indicates the differential gene name, "symbol" indicates the abbreviation of the differential gene, and "Gene ID" indicates the accession number of the differential gene.
Further, when referring to the gene expression score in Table 2 above, the total value of the gene expression scores in the combination of genes selected from the differentiated gene group is set to 8 or more, 9 or more, or 10 or more for differentiation. This is preferable because the accuracy is higher. The total value of the gene score is 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). Then, the accuracy of discrimination is further improved, which is preferable. On the other hand, for combinations of at least 3 genes selected from the differential gene cluster, if the total gene expression score is lower than the total gene expression scores of the GLRX gene, GDF15 gene, and GABARAPL1 gene, for example, less than 6. In that case, it was not possible to guarantee clinically acceptable accuracy.

このように、PPP1R3B遺伝子、TOB1遺伝子、RBPMS遺伝子、A4GALT遺伝子、RIMKLB遺伝子、GABARAPL1遺伝子、GLRX遺伝子、GSTM3遺伝子、LAMB1遺伝子、DNER遺伝子、ARG2遺伝子、TMEM101遺伝子、GDF15遺伝子、C12orf75遺伝子、DLX5遺伝子、AOC1遺伝子、ANXA4遺伝子、FBLN1遺伝子、TSPAN1遺伝子、APOL1遺伝子、FXYD2遺伝子、GPX3遺伝子、DEFB1遺伝子、FLJ43606遺伝子、LRIG1遺伝子、LYPD1遺伝子、CRIP1遺伝子、UQCRH遺伝子、および、IGF2遺伝子は、それぞれ配列番号１〜２９に示される塩基配列を含む遺伝子である。 Table 3 below shows the correspondence between the full-length nucleotide sequences of 29 genes or the sequence numbers of the probes. In Table 3 below, A is the sequence number of the full-length base sequence of the differential gene, and B is the sequence number of the base sequence used for the probe.

Thus, 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, DLX5 gene, AOC1 The genes, ANXA4 gene, FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIG1 gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and IGF2 gene are SEQ ID NOs: 1-29, respectively. It is a gene containing the base sequence shown in.

Further, in the present specification, when a gene containing a base sequence represented by a specific sequence number is referred to, 70% or more (preferably 75% or more, 80% or more, 85% or more, more preferably 90% or more) of the base sequence is used. , 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more), and includes a gene having a base sequence and retaining the function of the target gene.
For example, in one embodiment, when the LRIG1 gene used in the present invention is specified as a gene containing the base sequence shown by SEQ ID NO: 25, it is 70% or more (preferably 75% or more) with the base sequence shown by 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). It contains a gene that retains the function of the LRIG1 gene. In addition, in this specification, "base identity" means when two base sequences are aligned and a gap is introduced as necessary so that the degree of base matching between the two base sequences is the highest. Refers to the ratio (%) of the same number of bases in the base sequence of the nucleotide to be compared with respect to the total number of bases of the gene.
Specific examples of genes containing such base sequences include genes consisting of base sequences containing degenerate codons encoding the same amino acid sequence, various variants (variants) of each gene, and mutant genes such as point mutation genes. , And ortholog 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 that hybridizes with a nucleotide fragment consisting of a base sequence complementary to the partial base sequence of the gene under stringent conditions. Nucleotides that retain enzymatic activity are also included. By "stringent condition" is meant a condition in which a non-specific hybrid is not formed. Generally, the lower the salt concentration and the higher the temperature, the higher the stringent condition. The low stringent condition is, for example, a condition of washing at 1 × SSC, 0.1% SDS, about 37 ° C in the washing after hybridization, and more severely, 0.5 × SSC, 0.1% SDS, 42 ° C to 50. It is a condition to wash at about ℃. More stringent high stringent conditions are those that are washed after hybridization, for example, at 50 ° C to 70 ° C, 55 ° C to 68 ° C, or 65 ° C to 68 ° C, with 0.1 × SSC and 0.1% SDS. be. Generally, high stringent conditions are preferred. The combination of SSC, SDS and temperature is only an example. Those skilled in the art can also determine the stringency of hybridization by appropriately combining other conditions such as the probe concentration, the probe base length, and the hybridization time in addition to the temperature of the SSC, SDS, and the like.

2. 2. Method for Differentiating Serous Adenocarcinoma from Clear Cell Adenocarcinoma 2-1. Overview Another aspect of the present invention is a method for differentiating serous adenocarcinoma from clear cell adenocarcinoma among the histological types of ovarian cancer of a subject. Since the method for differentiating the present invention can be used in combination with a histopathological examination or the like, in one embodiment, it can be paraphrased as a method for assisting the differentiating.
In the differentiation method of the present invention, an expression profile of a differential marker contained in a sample collected from a subject is obtained, and based on the expression profile, the histological type of ovarian cancer in the subject is serous adenocarcinoma or serous adenocarcinoma. , Is a method of distinguishing whether it is clear cell adenocarcinoma. The "expression profile of a differential marker" refers to information regarding the expression level of each gene constituting the differential marker. This specification specifically includes information on the expression levels of multiple differential genes. In general, the larger the expression profile of the gene to be acquired, the more accurate the differentiation becomes possible.

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

The "measurement step" is a step of measuring the expression level of the differential marker in the sample collected from the subject and obtaining the measured value. For the measurement of the expression of the differential marker, it is preferable to measure the expression level per unit amount of each differential marker.
As used herein, the term "subject" refers to a human individual who provides a sample and is subjected to an examination. The subject may be either an individual with a history of ovarian cancer or an individual with suspected ovarian cancer. The term "individual with a history of ovarian cancer" as used herein includes a patient currently suffering from ovarian cancer and a person having a history of ovarian cancer having previously suffered from ovarian cancer. In cases of clear cell adenocarcinoma showing papillary growth, it is histopathologically difficult to distinguish between serous adenocarcinoma and clear cell adenocarcinoma, so histopathologically serous adenocarcinoma or Patients diagnosed with any suspicion of clear cell adenocarcinoma or those with a history of ovarian cancer are suitable as subjects.
As used herein, the term "serous adenocarcinoma patient" refers to a patient suffering from serous adenocarcinoma among ovarian cancers. In particular, it may refer to a sample provided for comparison with a sample derived from a subject or a patient with clear cell adenocarcinoma. Further, as used herein, the term "clear cell adenocarcinoma patient" refers to a patient suffering from clear cell adenocarcinoma among ovarian cancers. In particular, it may refer to a sample provided for comparison with a sample derived from a subject or a patient with serous adenocarcinoma.
The "subject", "serous adenocarcinoma patient" and "clear cell adenocarcinoma patient" used in this embodiment are not particularly limited in physical conditions such as gender, age, height and weight, and the number of people is not particularly limited, but they are compared. It is preferable that the “serosal adenocarcinoma patient” and the “clear cell adenocarcinoma patient” have the same or similar physical conditions as the subject, such as age, height, and weight. In this specification, a group consisting of a plurality of "serous adenocarcinoma patients" or "clear cell adenocarcinoma patients" is referred to as a "serous adenocarcinoma patient group" or a "clear cell adenocarcinoma patient group".

As used herein, the term "sample" is taken from the subject and used in the discrimination method of this embodiment, and corresponds to, for example, a tissue, a cell, a body fluid, or a peritoneal lavage fluid. The "tissue" and "cell" referred to here may be derived from any part of the subject, but preferably a sample collected by biopsy or excised by surgery, more specifically, ovarian tissue or ovarian cells. be. Particularly preferred are ovarian cancer cells or ovarian tissue or ovarian cells suspected of having ovarian cancer collected by biopsy. In addition, these tissues or cells may be those embedded in paraffin after formalin fixation (FFPE: Formalin-Fixed Paraffin Embedded). The term "body fluid" as used herein refers to a liquid biological sample collected from a subject. Examples thereof include blood (including serum, plasma and interstitial fluid), cerebrospinal fluid (cerebrospinal fluid), urine, lymph, digestive fluid, ascites, pleural fluid, perineural fluid, and extracts of tissues or cells. .. Blood is preferred.

The sample may be obtained by biopsy or surgical removal by surgery if it is a tissue or cell. Further, if it is a body fluid, it may be carried out based on a method known in the art. For example, in the case of blood or lymph, a known blood collection method may be followed. The amount of sample required for the discrimination method of this embodiment is not particularly limited. If it is a tissue or cell, it is preferably at least 10 μg, preferably at least 0.1 mg, but a biopsy material may be used. Further, 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, and more preferably at least 10 mL may be sufficient. The sample can be prepared and processed as needed so that the differential marker can be measured. For example, if the sample is a tissue or a cell, homogenization treatment, cytolysis treatment, removal of impurities by centrifugation or filtration, addition of a protease inhibitor, or the like can be mentioned. Details of these processes can be found in Green & Sambrook, Molecular Cloning, 2012, Fourth Ed., Cold Spring Harbor Laboratory Press for reference.

As used herein, the term "unit amount" refers to an arbitrarily determined amount of sample. For example, volume (represented by μL, mL) and weight (represented by μg, mg, g) are applicable. Although the unit amount is not particularly specified, it is preferable that the unit amount measured by a series of discrimination methods is constant. More accurate discrimination is possible by keeping the unit amount of the sample derived from the subject to be measured in this step and the sample derived 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 keep the unit amount constant.

Hereinafter, a method for measuring a transcript of a gene will be specifically described. A method for measuring a transcript of a gene is known. Hereinafter, the description regarding the method for measuring a transcript or translation product of a gene in JP-A-2016-13081 will be referred to or cited. In the following, a method for measuring a transcription product or a translation product of a typical gene will be described, but the method is not limited to these methods, and a known measurement 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. Generally, for the measurement of a transcript of a gene, a method of measuring the expression level of the gene as an absolute value or a relative value by using a nucleotide containing all or a part of the base sequence of the above gene as a primer or a probe is adopted. ..
The primer or probe of this embodiment is usually composed of a natural nucleic acid such as DNA or RNA. DNA that is highly stable, easy to synthesize, and inexpensive is particularly preferred. Further, if necessary, a natural nucleic acid can be combined with a chemically modified nucleic acid or a pseudo-nucleic acid. Examples of the chemically modified nucleic acid and pseudo-nucleic acid include PNA (Peptide Nucleic Acid), LNA (Locked Nucleic Acid; registered trademark), methylphosphonate-type DNA, phosphorothioate-type DNA, 2'-O-methyl-type RNA and the like. In addition, the primer and probe may be a fluorescent substance and / or a quencher substance, a labeling substance such as a radioisotope (for example, 32P, 33P, 35S), or a modifying substance such as biotin or (streptavidin) avidin, or magnetic beads. It may be labeled or modified using. The labeling substance is not limited, and commercially available substances can be used. For example, as a fluorescent substance, FITC, Texas, Cy3, Cy5, Cy7, Cyanine3, Cyanine5, Cyanine7, FAM, HEX, VIC, fluorescamine and its derivatives, rhodamine and its derivatives and the like can be used. As long as it is a quencher substance, AMRA, DABCYL, BHQ-1, BHQ-2, BHQ-3 and the like can be used. The labeling position of the labeling substance in the primer and the probe may be appropriately determined according to the characteristics of the modifying substance and the purpose of use. In general, it is often modified to the 5'or 3'end. Further, one primer and probe molecule may be labeled with one or more labeling substances. Labeling of these substances on nucleotides can be performed by known methods.
The nucleotide used as the primer or probe 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 or longer to the full length of the gene, preferably 15 bases or longer to the full length of the gene, more preferably 30 bases or longer to the full length of the gene, and further preferably 50 bases or longer. The total length of the gene is from the base length or more, and if it is used for a microarray, it is 10 to 200 base lengths, preferably 20 to 150 base lengths, and more preferably 30 to 100 base lengths. 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 higher the specificity. Specific examples of the probe include 80 base nucleotides consisting of the base sequences shown in SEQ ID NOs: 30 to 58 in Table 2B. On the other hand, in the case of a primer, each of the forward primer and the reverse primer may be 10 to 50 bp, preferably 15 to 30 bp.
The preparation of the above-mentioned 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. It is also possible to provide sequence information to a nucleic acid synthesis contract manufacturer and perform contract manufacturing.
The measurement of the transcript of the differential gene may be performed by a 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" uses a nucleic acid fragment having a base sequence complementary to all or part of the base sequence of the target nucleic acid to be detected as a probe, and utilizes the base pairing between the nucleic acid and the probe. , A method for detecting and quantifying a target nucleic acid or a fragment thereof. In this 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 in order to eliminate unintended nucleic acids that hybridize non-specifically. The above-mentioned low salt concentration and high stringent conditions under high temperature are more preferable. As the hybridization method, several methods with different detection means are known, and for example, Northern blotting (Northern hybridization method), microarray method, surface plasmon resonance method, or quartz crystal microbalance method is preferable. ..
The "Northern blot method" is the most common method for analyzing gene expression. Total RNA or mRNA prepared from a sample is separated by electrophoresis on an agarose gel or polyacrylamide gel under denaturing conditions, and transferred to a filter ( This is a method of detecting a target nucleic acid using a probe having a base sequence specific to the target RNA after blotting). By labeling the probe with a suitable marker such as a fluorescent dye or radioactive isotope, for example, a chemiluminescent analyzer (eg, Light Capture; Atto), a scintillation counter, an imaging analyzer (eg, FUJIFILM). : It is also possible to quantify the target nucleic acid using a measuring device such as BAS series). The Northern Blotting method is a well-known and well-known technique in the art, and for example, the above-mentioned Green, MR and Sambrook, J. (2012) may be referred to.
In the "microarray method", a nucleic acid fragment complementary to all or part of the base sequence of a target nucleic acid is arranged as a probe at high density in a small spot on a substrate, and the target nucleic acid is contained in a solidified microarray or microchip. This is a method of reacting a sample and detecting a nucleic acid hybridized to a 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 or the like with a microplate reader or a scanner. The measured fluorescence intensity can be used to determine the amount of mRNA or cDNA or their abundance to reference mRNA (reference mRNA). The microarray method is also a well-known technique in the art. For example, the DNA microarray method (DNA microarray and latest PCR method (2000), supervised by Masaaki Muramatsu, Hiroyuki Nawa, Shujunsha) and the like may be referred to.
The "Surface Plasmon Resonance (SPR) 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 light irradiated to the metal thin film is changed. This is a method of detecting and quantifying adsorbents on the surface of a metal thin film with extremely high sensitivity by utilizing the phenomenon. In the present invention, for example, a probe having a sequence complementary to the base sequence of the target nucleic acid is immobilized on the surface of the metal thin film, the surface portion of the other metal thin film is blocked, and then the subject or a healthy body or a healthy body group is used. By circulating the collected sample on the surface of a metal thin film, a 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 the measured values before and after the sample is distributed. Detection and quantification by the surface plasmon resonance method can be performed by using, for example, an SPR sensor commercially available from Biacore. This technique is well known in the art. See, for example, Kazuhiro Nagata and Hiroshi Handa, Real-time Analytical Experimentals for Biological Interactions, Springer Fairlark Tokyo, Tokyo, 2000.
The "Quartz Crystal Microbalance (QCM) method" utilizes the phenomenon that when a substance is adsorbed on the surface of an electrode attached to a crystal oscillator, the resonance frequency of the crystal oscillator decreases according to its mass. This is a mass measurement method that quantitatively captures a very small amount of adsorbate according to the amount of change in the resonance frequency. Similar to the SPR method, the detection and quantification by this method also uses 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 the subject, or a healthy body or a healthy body. The target nucleic acid can be detected and quantified by base pairing with the target nucleic acid in the sample collected from the body group. This technology is well known in the art, for example, Christopher J. et al., 2005, Self-Assembled Monolayers of a Form of Nanotechnology, Chemical Review, 105: 1103-1169, Toyoe Morizumi, Takamichi Nakamoto, ( 1997) See Sensor Engineering, Shokodo.
The "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, the PCR method (including the RT-PCR method), the NASBA method, the ICAN method, and the LAMP® method (including the RT-LAMP method) can be mentioned. The PCR method is preferable. A quantitative nucleic acid amplification method such as a real-time RT-PCR method is used as a method for measuring a gene transcript using a nucleic acid amplification method. Further, as the real-time RT-PCR method, an intercalator method using SYBR (registered trademark) Green or the like, a Taqman (registered trademark) probe method, a digital PCR method, and a cycling probe method are known, and any of these methods can be used. There may be. All of these are known methods and are also described in appropriate protocols in the art, so you can refer to them.
The method of quantifying the transcript of a gene by the real-time RT-PCR method will be briefly described below with an example. The real-time RT-PCR method is a reaction system in which the amplification product of PCR is specifically fluorescently labeled using cDNA prepared by reverse transcription reaction from the mRNA in the sample, and the fluorescence intensity derived from the amplification product is detected. This is a nucleic acid quantification method in which PCR is performed using a functional temperature cycler device. The amount of amplification product of the target nucleic acid in the reaction is monitored in real time, and the result is subjected to regression analysis by computer. Methods for labeling the amplification product 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® PCR method uses a probe with a 5'end modified with a quencher and a 3'end modified with a fluorescent dye. Normally, the 5'end quencher substance suppresses the 3'end fluorescent dye, but when PCR is performed, the probe is degraded by the 5'→ 3'exonuclease activity of Taq polymerase. As a result, the suppression of the quencher substance is released, and the fluorescence is emitted. The amount of fluorescence reflects the amount of amplification product. Since the number of cycles (CT) when the amplification product reaches the detection limit and the initial template amount are inversely related, the real-time measurement method quantifies the initial template amount by measuring CT. By measuring CT using a known amount of templates in several stages and preparing a calibration curve, the absolute value of the initial template amount of an unknown sample can be calculated. 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 of real-time PCR are generally based on a known PCR method, the base length of the nucleic acid fragment to be amplified, the amount of nucleic acid for the template, the base length and Tm value of the primer to be used, and the optimum reaction of the nucleic acid polymerase to be used. Since it varies depending on the temperature, optimum pH, etc., it may be appropriately determined according to these conditions. As an example, the denaturation reaction is usually carried out at 94-95 ° C for 5 seconds to 5 minutes, the annealing reaction is carried out at 50 to 70 ° C for 10 seconds to 1 minute, and the extension reaction is carried out at 68 to 72 ° C for 30 seconds to 3 minutes. The elongation reaction can be repeated for about 15 to 40 cycles as one cycle. When using a kit commercially available from the manufacturer, in principle, the protocol attached to the kit may be followed.
The nucleic acid polymerase used in real-time PCR is a DNA polymerase, especially a heat resistant DNA polymerase. Various types 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 it comes with a buffer optimized for the activity of the attached DNA polymerase.

2-3. Differentiation method In the differentiation method of the present invention, whether the test sample belongs to serous adenocarcinoma or clear cell adenocarcinoma is differentiated based on the expression profile of the differential marker measured as described above. Differentiating markers are genes with significant differences in expression levels between serous or clear cell adenocarcinoma, and the expression profile of the combined gene set is derived from serous or clear cell adenocarcinoma patients. By comparing with the sample, it becomes possible to distinguish which histological type of ovarian cancer the test sample has.
That is, in the differentiation method of the present invention, the expression profile of each gene in the gene set selected from the differentiation gene group of the test sample and each gene in the corresponding gene set in the sample derived from the serous adenocarcinoma patient or the clear cell adenocarcinoma patient. By comparing with the expression profile of the test sample, the histological type of the test sample is differentiated.
As 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, a pre-measured one may be used, or a new one may be used. A sample derived from a patient with serous adenocarcinoma or a patient with clear cell adenocarcinoma may be used by measuring the expression level of each gene contained in the gene set.
Therefore, the differentiation method of the present invention further comprises, in one embodiment, measuring the expression level of each gene contained 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. The samples derived from the serous adenocarcinoma patient or the clear cell adenocarcinoma patient may be one sample each, or two or more samples may be used. The larger the number of samples, the more the individual difference for each sample can be averaged and the accuracy of discrimination is improved, which is preferable.

In one embodiment of the invention, the expression profile of each gene in the gene set of the test sample is compared with the expression profile of each gene in the corresponding gene set in a sample derived from a patient with serous adenocarcinoma or a patient with clear cell adenocarcinoma. When the test sample has a gene expression profile equivalent to the gene expression profile of the compared histological ovarian cancer, it is differentiated from the histological ovarian cancer compared to the case, or the compared tissue. It can be differentiated that it is not a histological type of ovarian cancer as compared with the case where the gene expression profile of the type ovarian cancer is different from the expression profile of the gene.
For example, when comparing the expression profiles of the test sample and the sample derived from a patient with serous adenocarcinoma, if it is determined that they have the same gene expression profiles as each other, the test sample is considered to be serous adenocarcinoma. Can be distinguished. In addition, when comparing the expression profiles of the test sample and the sample derived from the clear cell adenocarcinoma patient, if it is determined that they have the same gene expression profiles as each other, the test sample is clear cell adenocarcinoma. It can be identified as being present. On the other hand, when comparing the expression profiles of the test sample and the sample derived from the patient with serous adenocarcinoma, if it is determined that the test sample has different gene expression profiles, the test sample is not serous adenocarcinoma. Can be distinguished. In addition, when comparing the expression profiles of the test sample and the sample derived from a clear cell adenocarcinoma patient, if it is determined that the test sample has different gene expression profiles, the test sample is not clear cell adenocarcinoma. Can be distinguished from.
Here, "having an equivalent gene expression profile" means that the expression profile of each gene contained in the gene set is similar. Further, "having different gene expression profiles" means that the expression profiles of each gene contained in the gene set are not similar.
A known method can be adopted as a specific method for discriminating whether the expression profiles of each gene in the gene set are equivalent or different. For example, but not limited to, (i) a method of classifying a test sample into serous adenocarcinoma or clear cell adenocarcinoma based on hierarchical clustering analysis, (ii) a method of evaluating by comparison of gene expression levels, (iiii). ) A method of distinguishing whether the test sample belongs to serous adenocarcinoma or clear cell adenocarcinoma by setting a threshold value can be mentioned.

(I) Hierarchical clustering analysis In one embodiment, the differentiation method of the present invention can differentiate the histological type of a test sample by cluster analysis of 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 subjected to hierarchical cluster analysis in comparison with the expression profile of the corresponding gene set in the samples derived from patients with serous adenocarcinoma and clear cell adenocarcinoma. Can be done. When differentiating the histological type of a test sample by hierarchical clustering analysis, in addition to the expression profile of the gene set in the test sample, the expression profile of the gene set in the samples derived from patients with serous adenocarcinoma and clear cell adenocarcinoma is required. It becomes. The method of hierarchical cluster analysis is known, and in a preferred embodiment of the present invention, it is cluster analysis by the group averaging method based on the Euclidean distance. Known software can be used for cluster analysis, and, for example, ExpressionView Pro software (MicroDiagnostic) can be used as commercially available software, but is not limited to the following.
By performing hierarchical cluster analysis, it is possible to draw a hierarchical structure (dendrogram) consisting of a test sample, a sample derived from a serous adenocarcinoma patient, and a sample derived from a clear cell adenocarcinoma patient. It can be roughly divided into two clusters, one is a cluster and the other is a clear cell adenocarcinoma. This makes it possible to confirm which tissue type the test sample is classified into, and to distinguish which tissue type is likely to be.

(Ii) Method for evaluation by comparison of gene expression levels In one embodiment, the total value of the gene expression levels of each gene combination contained in the gene set in the test sample and the serous adenocarcinoma patient or The histological type of the test sample can be evaluated by comparing it with the total expression level of the gene in the sample derived from a clear cell adenocarcinoma patient.
Not limited to the following, but to explain one morphology, the total value of gene expression levels in the serous adenocarcinoma patient group and the clear cell adenocarcinoma patient group is plotted as a group scatter diagram, and the gene of the gene set in the test sample is plotted. See where the total expression level of is plotted. The plotted positions can be used to assess which histological ovarian cancer the test sample is likely to belong to.
When the total expression level of genes is used for differentiation, the obtained expression level values are inverted and used for the five genes LRIG-1, LYPD1, CRIP1, UQCRH, and IGF2. For example, when using the total expression level of each gene obtained by a method such as a microarray for the expression level of a gene, the expression level of 5 genes of LRIG-1, LYPD1, CRIP1, UQCRH, and IGF2 (for example, Multiply -1 by the Log2 ratio to the common reference) to obtain the inverted value, and calculate the total value of the inverted value and the expression levels of other genes.

(Iii) Method for Discriminating by Setting Thresholds Further, in one embodiment, the histological type can be discriminated by comparing the expression profile of the gene set in the test sample with a predetermined threshold value.
Here, the "predetermined threshold value" refers to a predetermined cutoff value based on the expression profile of the differential marker in the samples derived from the serous adenocarcinoma patient group and the clear cell adenocarcinoma patient group. The cutoff value can be set, for example, as follows, but the cutoff value is not limited to this. That is, the expression level of the differential marker gene is measured for the samples derived from the serous adenocarcinoma patient group and the clear cell adenocarcinoma patient group, and the total value of the gene expression level is calculated for each sample. Then, a predetermined cutoff value can be derived by creating an ROC curve from the total value of gene expression levels in a sample derived from a serous adenocarcinoma patient group or a clear cell adenocarcinoma patient group. By setting a cutoff value, it is possible to distinguish which histological type ovarian cancer is most likely to be, depending on whether or not the cutoff value is exceeded.
In the "ROC curve (Receiver Operating Characteristic curve)", the vertical axis is the true positive rate (TPF: True Positive Fraction), that is, the sensitivity, and the horizontal axis is the false positive rate (FPF: False Positive Fraction), that is. It is created by setting (1-specificity) and plotting by changing the threshold value of which value of the test result is judged to have a finding, that is, the cutoff point as an intermediary variable. Specificity is the rate at which a negative person is accurately judged to be negative.
The method of setting the cutoff value from the created ROC curve can basically be set to increase both sensitivity and specificity (to approach 1). For that purpose, the cutoff value may be set to a value that gives the point closest to the point (0,1) on the ROC curve. In the most preferred embodiment, the cutoff value is such that the sample derived from the serous adenocarcinoma patient group and the sample derived from the clear cell adenocarcinoma patient group can be clearly distinguished.
When a 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 levels of the gene for the combination of the threshold and the specific differential marker in the test sample. Just compare it with the value.

2-3. Effect According to the differentiation method of this embodiment, it is possible to distinguish whether the sample is serous adenocarcinoma or clear cell adenocarcinoma by examining the sample removed by biopsy or surgery. The differentiation method of this embodiment, which has a high accuracy rate, makes it possible to diagnose which histological type of ovarian cancer is, and has an advantage that it can be dealt with in consideration of the risk of recurrence and the judgment of the treatment method.
In addition, conventionally, the histological type of ovarian cancer has been determined based on pathological diagnosis, but it is very difficult to distinguish clear cell adenocarcinoma that has grown papillary from serous adenocarcinoma, and the diagnosis is made. Different judgments were made depending on the doctors who judge the results and the hospitals. However, the disease differentiation method of this embodiment can provide more accurate and objective data to assist in the diagnosis of serous adenocarcinoma or clear cell adenocarcinoma.

3. 3. Kit 3-1 for differentiating whether the histological type is serous adenocarcinoma or clear cell adenocarcinoma in a test sample derived from an ovarian cancer patient. Overview Another aspect of the present invention is a reagent (differentiation reagent) for differentiating whether the tissue type is serous adenocarcinoma or clear cell adenocarcinoma. By applying the differential reagent of this embodiment to, for example, a sample derived from a subject suffering from ovarian cancer, it is possible to discriminate whether the subject's ovarian cancer is serous adenocarcinoma or clear cell adenocarcinoma. Can be done.
3-1-1. Composition The differential reagent of this embodiment contains a probe or primer set for detecting a transcript of a group of differential genes constituting a differential marker, that is, mRNA or cDNA. These specific configurations are described in the column of measurement process. For example, when detecting a transcript of four different gene groups constituting a differential marker, the differential kit can include four probe groups capable of detecting the transcript of the corresponding gene.
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 is, for example, 3.5 mm × 5.5 mm, 18 mm × 18 mm, 22 mm × 75 mm, etc., which can be set in various ways according to the number of spots of the probe and the size of the spots. .. The probe is usually 0.1 μg to 0.5 μg of nucleotides per spot. As a method for immobilizing a nucleotide, a method using the charge of the nucleotide to electrostatically bond it to a solid phase carrier surface-treated with a polycation such as polylysine, poly-L-lysine, polyethyleneimine, or polyalkylamine, or amino. Examples thereof include a method in which a nucleotide having a functional group such as an amino group, an aldehyde group, an SH group or a biotin is covalently bonded to a solid phase surface into which a functional group such as a group, an aldehyde group or an epoxy group has been introduced.
3-2. Effect By applying the differential kit of this embodiment to subjects with a history of ovarian cancer, it is possible to determine whether the ovarian cancer belongs to the histological type of serous adenocarcinoma or clear cell adenocarcinoma. It can be identified objectively and accurately.
The detection kit of the present invention may include other reagents necessary for the detection of the differential marker, such as a buffer or a secondary antibody, and instructions used for detection and result discrimination.
4. Therapeutic method based on the differentiation result As another aspect, the present invention is based on the differentiation result for a subject who has differentiated whether it is serous adenocarcinoma or clear cell adenocarcinoma by the differentiation method described above. To provide a therapeutic method for administering an anticancer drug.
That is, an effective amount of an anticancer agent effective for serous adenocarcinoma is administered to a subject identified as having 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 has been identified as clear cell adenocarcinoma by the differentiation method of the present invention.
Antineoplastic agents effective for serous adenocarcinoma and clear cell adenocarcinoma (for example, paclitaxel, cisplatin, carboplatin, docetaxel), combinations thereof, administration methods, dosages, etc. are known, and those skilled in the art appropriately respond to histological types. Chemotherapy can be performed.

(Example 1. RNA preparation)
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 a freezing storage tube and frozen in liquid nitrogen. For frozen samples, total RNA was extracted using ISOGEN (Nippon Gene Co., Ltd., Tokyo, Japan). Samples that were able to obtain total RNA of 125 μg or more were subsequently purified with poly (A) + RNA using the MicroPoly (A) purist Kit (Ambion, Austin, TX, USA).
22 human cancer cell lines (A431, A549, AKI, HBL-100, HeLa, HepG2, HL60, IMR-32, Jurkat, K562, KP4, MKN7, NK-92, Raji, RD, Total RNA extracted from Saos-2, SK-N-MC, SW-13, T24, U251, U937 and Y79) or an equal mixture of purified poly (A) + RNA was used. Whether to use an equal amount of total RNA or an equal amount of purified poly (A) + RNA as a common reference was consistent with the method for preparing the sample to be analyzed.

(Example 2. Comprehensive gene expression analysis)
The DNA microarray used for gene expression profile acquisition using poly (A) + RNA (named "System 1") is a combination of 31,797 synthetic DNAs (80 mers) (MicroDiagnostic, Tokyo,) corresponding to human-derived transcripts. Japan) was used as an array on a slide glass with a custom layer. On the other hand, the DNA microarray for gene expression profile acquisition (named "System 2") using total RNA is a custom layer of 14,400 synthetic DNA (80 mers) (MicroDiagnostic) corresponding to human-derived transcripts. The array was used on a slide glass.
Specimen-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, CA, USA) and Cyanine 5-d UTP. (Perkin-Elmer Inc.) was used to synthesize labeled RNA. Similarly, for human common reference RNA, labeled cDNA was synthesized 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 a microarray was performed using a 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, CA, USA). In addition, the expression ratio (fluorescence intensity of Cyanine-5-labeled cDNA derived from the sample / Cyanine- derived from the common reference for humans) was increased by gradually adjusting the fluorescence intensity of the Cyanine-5-labeled cDNA derived from the sample to the fluorescence intensity of the Cyanine-3 labeled cDNA derived from the human common reference. Fluorescence intensity of 3-labeled cDNA) was calculated. Furthermore, using GenePix Pro 3.0 software (Axon Instruments, Inc.,), the calculated expression ratio was multiplied by the normalization factor for normalization. Next, the expression ratio was converted to Log2, and the converted value was named the Log2 ratio. The expression ratio was converted using Excel software (Microsoft, Bellevue, WA, USA) and MDI gene expression analysis software package (MicroDiagnostic).

(Example 3. Statistical analysis)
In order to narrow down the marker gene candidates that can distinguish between serous adenocarcinoma and clear cell adenocarcinoma, the following i) to iii) are used using the Log2 ratio data between the sample derived from serous adenocarcinoma and the sample derived from clear cell adenocarcinoma. Data processing was performed in the order of.
i) Genes with fluorescence intensity below the detection limit were excluded from 1 or more of 39 serous adenocarcinoma and 18 clear cell adenocarcinoma.
ii) The statistical difference between the serous adenocarcinoma group and the clear cell adenocarcinoma group was tested by Student's t-test (two-sided test), and the genes corresponding to ^{p <1.0 × 10 -5 judged to be significant were extracted.}
iii) For each gene, the average Log2 ratio in the serous adenocarcinoma group and the clear cell adenocarcinoma group was calculated. Twenty-nine genes with an absolute difference between the mean values of the serous adenocarcinoma group and the clear cell adenocarcinoma group of 2.0 or more were extracted as differential marker gene candidates. Using all the samples, the expression levels of the extracted 29 genes were measured and cluster analysis was performed. The results of the expression levels of 29 genes in all the samples are shown in Tables 4-9. The cluster analysis was performed by the Euclidean distance group averaging method using ExpressionView Pro software (MicroDiagnostic). The results of the cluster analysis are shown in FIG. As shown in FIG. 1, when a hierarchical cluster analysis was performed based on the expression profile of the extracted 29 genes, two clusters, a cluster mainly composed of serous adenocarcinoma group and a cluster mainly composed of clear cell adenocarcinoma group, were performed. Formed.

(Example 4. Construction of gene expression scoring system)
We attempted to construct a scoring system that distinguishes between serous adenocarcinoma and clear cell adenocarcinoma using 29 differential marker candidates. First, among the 29 differential marker candidate genes, 5 genes (LRIG-1, LYPD1, CRIP1, UQCRH, IGF2) with a low Log2 ratio in the clear cell adenocarcinoma group were multiplied by -1 and inverted to +. The total score was calculated for each sample by summing up the Log2 ratios of the 5 genes with inverted values and the remaining 24 genes. The scoring system was created by rearranging from the left in ascending order of score value (Fig. 2). In order to verify the diagnostic accuracy of the scoring system, the area under the curve (AUC) was calculated using the ROC (Receiver Operating Characteristic curve) curve (Fig. 3). In addition, a sensitivity / specificity curve was created and the optimum cutoff value was obtained (Fig. 3). In addition, a group scatter plot was created for each case (Fig. 4). The ROC curve and group scatter plot are created using StatFlex ver.6.0 software (Artech Co., Ltd., Osaka, Japan), and the optimum cutoff value is set to maximize the sum of sensitivity and specificity. It was set as a value (hereinafter, the setting of the optimum cutoff value was the same). As a result, in the group scatter chart, the average of the total values of the specimens belonging to each histological type ovarian cancer of the serous adenocarcinoma group or the clear cell adenocarcinoma group was obtained for each histological type. The value was 1.9487, while the average total value of clear cell adenocarcinoma was 74.9497, and the groups had a significant difference at ^{P = 2.1488 × 10 -10.}
As described above, there is a significant difference in the total expression profile of 29 genes, which are the differential marker candidate genes listed above, between serous adenocarcinoma and clear cell adenocarcinoma, and the expression in the combination of these genes is significant. By measuring and comparing the profiles, it is possible to distinguish which histological type the target ovarian cancer sample has.
In the ROC curve, the AUC was 0.9444, and in the sensitivity / specificity curve, the optimum cutoff value was set to 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, it was found that the 29 differential marker candidate genes are a group of genes 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 between serous adenocarcinoma and clear cell adenocarcinoma group, which belongs to histological ovarian cancer. Therefore, at least how many gene combinations can be used to distinguish between serous adenocarcinoma and clear cell adenocarcinoma with high sensitivity and specificity.
First, the gene expression scores of the 29 differential marker candidates were calculated as follows. _{That is, for the Log 2} ratio of each gene obtained in Example 2 above, an average value was calculated for each histological sample group 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 calculated for the calculated _{average value of the Log 2 ratio of each gene.} This difference (absolute value) was used as the "gene expression score" of each gene. The "gene expression score" of each gene is shown in Table 10 below.

Next, in order to identify a gene group that is more useful and has less variation from the 29 genes, the standard deviation of the serous adenocarcinoma group is less than 1, and the standard deviation of the clear cell adenocarcinoma group is less than 1.5. A group of genes (7 genes) was extracted (Table 11). As shown in FIG. 5, when a hierarchical cluster analysis was performed based on the expression profiles of the extracted 7 genes, two clusters, a cluster mainly composed of serous adenocarcinoma group and a cluster mainly composed of clear cell adenocarcinoma group, were performed. Formed. In addition, the score values of the 7 genes were calculated and sorted in descending order of the score values (Fig. 6). Furthermore, the total value of the Log ₂ ratios of the 7 genes was obtained, and the distribution was plotted as a group scatter plot (Fig. 7). When calculating the total value of the Log2 ratio, multiply the Log2 ratio of the five genes LRIG-1, LYPD1, CRIP1, UQCRH, and IGF2 by -1 to obtain the inverted value, and then calculate the inverted value and the Log2 ratio of the other genes. (Note that in the following examples as well, when calculating the total value of the Log2 ratio, the Log2 ratio of the five genes LRIG-1, LYPD1, CRIP1, UQCRH, and IGF2 is -1. The value inverted by multiplying by is used). When the average of the total values of the specimens belonging to each histological ovarian cancer in the serous adenocarcinoma group or the clear cell adenocarcinoma group was calculated for each histological type, the average value of the total values of serous adenocarcinoma was -1.6402, while the average value was -1.6402. The average total value of clear cell adenocarcinoma was 13.6458, and the groups had a significant difference at ^{P = 1.1607 × 10 -10.} In the ROC curve, the AUC was 0.9573, and in the sensitivity / specificity curve, the optimum cutoff value was set to 3.5717, at which time the sensitivity and specificity were 89.7% (Fig. 8).

Next, a plurality of genes having a high gene expression score were selected from the above 7 genes, and based on the data obtained in Example 4, a cutoff value having a sensitivity and specificity of 89% or more was obtained. The minimum number of gene combinations that can be set was determined.
As a result, it was found that the above conditions were satisfied by selecting three genes, GLRX gene, GDF15 gene and GABARAPL1 gene, in descending order of gene expression score from the above seven genes. For each sample belonging to the serous adenocarcinoma group and the clear cell adenocarcinoma group, _{the total value of the Log 2} ratios of the three genes GLRX gene, GDF15 gene and GABARAPL1 gene was obtained, and the distribution was plotted as a group scatter plot (Fig. 9). .. When the average of the total values of the specimens belonging to each histological ovarian cancer in the serous adenocarcinoma group or the clear cell adenocarcinoma group was calculated for each histological type, the average value of the total values for serous adenocarcinoma was -1.9373, while The average value of the total values of clear cell adenocarcinoma was 5.0687, and the groups had a significant difference at ^{P = 4.129 × 10 -10.}
In the ROC curve, the AUC was 0.9573, and in the sensitivity / specificity curve, the optimum cutoff value was set at 1.3432, at which time the sensitivity and specificity were 89.7% (Fig. 10).
Thus, there is a significant difference in the total expression levels of the 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. Therefore, it is possible to distinguish which histological type the target ovarian cancer sample has.

(Example 6. Examination of gene set A 2)
Next, a combination of other genes capable of distinguishing serous adenocarcinoma and clear cell adenocarcinoma with the same high accuracy as in Example 5 was examined.
The 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 29 differential marker candidates based on a value of 7.006 or higher, which is the total of the "gene expression scores" of the three genes, the GLRX gene, the GDF15 gene, and the GABARAPL1 gene.
Select the LYPD1 gene, CRIP1 gene, LRIG1 gene and A4GALT gene so that the total gene expression score is 7.006 or higher (total gene expression score is 8.637732). It was verified whether it could be distinguished from clear cell adenocarcinoma.
_{Specifically, the total value of the Log 2} ratios of the LYPD1 gene, CRIP1 gene, LRIG1 gene and A4GALT gene was obtained 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. 11). When the average of the total values of the specimens belonging to each histological ovarian cancer in the serous adenocarcinoma group or the clear cell adenocarcinoma group was calculated for each histological type, the average value of the total values of serous adenocarcinoma was -3.4530, while the average value was -3.4530. The average total value of clear cell adenocarcinoma was 5.1849, and the groups had a significant difference at ^{P = 6.128 × 10 -12.}
In the ROC curve, the AUC was 0.9501, and in the sensitivity / specificity curve, the optimum cutoff value was set to 2.2401. At this time, the sensitivity and specificity were 94.4% (Fig. 12).
In addition, a hierarchical cluster analysis was performed based on the expression profiles of the LYPD1 gene, CRIP1 gene, LRIG1 gene, and A4GALT gene. Two clusters were formed, and almost the same results as the hierarchical cluster analysis based on the expression profiles of 29 genes (Example 3) and 7 genes (Example 5) could be obtained.
Thus, there is a significant difference in the total expression levels of the LYPD1 gene, CRIP1 gene, LRIG1 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 histological type the target ovarian cancer sample has.

(Example 7. Examination of gene set B 3)
We also investigated combinations of other genes that can distinguish serous adenocarcinoma from clear cell adenocarcinoma with high accuracy.
Select the ANXA4 gene, LAMB1 gene and APOL1 gene so that the total gene expression score is 7.006 or higher (total gene expression score is 7.648277), and based on the measurement results of these gene expressions, serous adenocarcinoma and clear cell adenocarcinoma It was verified whether it could be distinguished from cancer.
_{Specifically, the total value of the Log 2} ratios of the ANXA4 gene, LAMB1 gene and APOL1 gene was obtained 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 of the total values of the specimens belonging to each histological ovarian cancer in the serous adenocarcinoma group or the clear cell adenocarcinoma group was calculated for each histological type, the average value of the total values of serous adenocarcinoma was 0.9036, while it was clear. The average total value of cell adenocarcinoma was 8.5519, and the groups had a significant difference at ^{P = 2.685 × 10 -10.}
In the ROC curve, the AUC was 0.9331, and in the sensitivity / specificity curve, the optimum cutoff value was set to 5.2992, at which time the sensitivity and specificity were 88.9% (Fig. 14).
In addition, when a hierarchical cluster analysis was performed based on the expression profiles of the ANXA4 gene, LAMB1 gene, and APOL1 gene, two clusters, a cluster mainly composed of serous adenocarcinoma group and a cluster mainly composed of clear cell adenocarcinoma group, were found. It was formed, and almost the same results as the hierarchical cluster analysis based on the expression profiles of 29 genes (Example 3) and 7 genes (Example 5) could be obtained.
Thus, there is a significant difference in the total expression levels of the ANXA4 gene, LAMB1 gene and APOL1 gene between serous adenocarcinoma and clear cell adenocarcinoma, and the expression level in the combination of these genes is measured. Therefore, it is possible to distinguish which histological type the target ovarian cancer sample has.

(Example 8. Examination of gene set C 4)
We also investigated combinations of other genes that can distinguish serous adenocarcinoma from clear cell adenocarcinoma with high accuracy.
Select the FXYD2 gene, GPX3 gene and TMEM101 gene so that the total gene expression score is 7.006 or higher (total gene expression score is 9.911373), and based on the measurement results of these gene expressions, serous adenocarcinoma and clear cell adenocarcinoma. It was verified whether it could be distinguished from cancer.
_{Specifically, the total value of the Log 2} ratios of the FXYD2 gene, GPX3 gene, and TMEM101 gene was obtained 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. 15). ). When the average of the total values of the specimens belonging to each histological ovarian cancer in the serous adenocarcinoma group or the clear cell adenocarcinoma group was calculated for each histological type, the average value of the total values of serous adenocarcinoma was 0.3106, while it was clear. The average total value of cell adenocarcinoma was 10.2220, and the groups had a significant difference at ^{P = 2.782 × 10 -8.}
In the ROC curve, the AUC was 0.9331, and in the sensitivity / specificity curve, the optimum cutoff value was set to 3.6064, and at this time, the sensitivity and specificity were 88.9% (Fig. 14).
In addition, when a hierarchical cluster analysis was performed based on the expression profiles of the FXYD2 gene, GPX3 gene, and TMEM101 gene, two clusters, a cluster mainly composed of serous adenocarcinoma group and a cluster mainly composed of clear cell adenocarcinoma group, were found. It was formed, and almost the same results as the hierarchical cluster analysis based on the expression profiles of 29 genes (Example 3) and 7 genes (Example 5) could be obtained.
Thus, there is a significant difference in the total expression levels of the FXYD2 gene, GPX3 gene and TMEM101 gene between serous adenocarcinoma and clear cell adenocarcinoma, and the expression level in the combination of these genes is measured. Therefore, it is possible to distinguish which histological type the target ovarian cancer sample has.

(Example 9. Examination of gene set D 5)
We also investigated combinations of other genes that can distinguish serous adenocarcinoma from clear cell adenocarcinoma with high accuracy.
Select the DLX5 gene, UQCRH gene, and ARG2 gene so that the total gene expression score is 7.006 or higher (total gene expression score is 7.541611), and based on the measurement results of these gene expressions, serous adenocarcinoma and clear cell adenocarcinoma. It was verified whether it could be distinguished from cancer.
_{Specifically, the total value of the Log 2} ratios of the DLX5 gene, UQCRH gene, and ARG2 gene was obtained 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 of the total values of the specimens belonging to each histological ovarian cancer in the serous adenocarcinoma group or the clear cell adenocarcinoma group was calculated for each histological type, the average value of the total values of serous adenocarcinoma was 0.26930, while it was clear. The average total value of cell adenocarcinoma was 7.8109, and the groups had a significant difference at ^{P = 3.901 × 10 -10.}
In the ROC curve, the AUC was 0.9388, and in the sensitivity / specificity curve, the optimum cutoff value was set to 4.0090, and at this time, the sensitivity and specificity were 88.9% (Fig. 18).
In addition, when a hierarchical cluster analysis was performed based on the expression profiles of the DLX5 gene, UQCRH gene, and ARG2 gene, two clusters, a cluster mainly composed of serous adenocarcinoma group and a cluster mainly composed of clear cell adenocarcinoma group, were found. It was formed, and almost the same results as the hierarchical cluster analysis based on the expression profiles of 29 genes (Example 3) and 7 genes (Example 5) could be obtained.
Thus, there is a significant difference in the total expression levels of the DLX5 gene, UQCRH gene, and ARG2 gene between serous adenocarcinoma and clear cell adenocarcinoma, and the expression level in the combination of these genes is measured. Therefore, it is possible to distinguish which histological type the target ovarian cancer sample has.

(Example 10. Examination of gene set E 6)
We also investigated combinations of other genes that can distinguish serous adenocarcinoma from clear cell adenocarcinoma with high accuracy.
Select the FBLN1 gene, AOC1 gene and TSPAN1 gene so that the total gene expression score is 7.006 or higher (total gene expression score is 8.076977), and based on the measurement results of these gene expressions, serous adenocarcinoma and clear cell adenocarcinoma. It was verified whether it could be distinguished from cancer.
_{Specifically, the total value of the Log 2} ratios of the FBL N1 gene, AOC1 gene, and TSPAN1 gene was obtained 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 of the total values of the specimens belonging to each histological ovarian cancer in the serous adenocarcinoma group or the clear cell adenocarcinoma group was calculated for each histological type, the average value of the total values of serous adenocarcinoma was -1.8339, while the average value was -1.8339. The average total value of clear cell adenocarcinoma was 6.0301, and the groups had a significant difference at ^{P = 3.829 × 10 -7.}
In the ROC curve, the AUC was 0.88178, and in the sensitivity / specificity curve, the optimum cutoff value was set to 0.3689, and at this time, the sensitivity and specificity were 82.1% (Fig. 20).
In addition, when a hierarchical cluster analysis was performed based on the expression profiles of the FBLN1 gene, AOC1 gene, and TSPAN1 gene, two clusters, a cluster mainly composed of serous adenocarcinoma group and a cluster mainly composed of clear cell adenocarcinoma group, were found. It was formed, and almost the same results as the hierarchical cluster analysis based on the expression profiles of 29 genes (Example 3) and 7 genes (Example 5) could be obtained.
Thus, there is a significant difference in the total expression levels of the FBLN1 gene, AOC1 gene and TSPAN1 gene between serous adenocarcinoma and clear cell adenocarcinoma, and the expression level in the combination of these genes is measured. Therefore, it is possible to distinguish which histological type the target ovarian cancer sample has.

(Example 11. Examination of gene set F 7)
We also investigated combinations of other genes that can distinguish serous adenocarcinoma from clear cell adenocarcinoma with high accuracy.
Select the C12orf75 gene, DNER gene and RIMKLB gene so that the total gene expression score is 7.006 or higher (total gene expression score is 8.127428), and based on the measurement results of these gene expressions, serous adenocarcinoma and clear cell adenocarcinoma. It was verified whether it could be distinguished from cancer.
_{Specifically, the total value of the Log 2} ratios of the C12orf75 gene, DNER gene, and RIMKLB gene was obtained 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. 21). ). When the average of the total values of the specimens belonging to each histological ovarian cancer in the serous adenocarcinoma group or the clear cell adenocarcinoma group was calculated for each histological type, the average value of the total values of serous adenocarcinoma was -1.3507, while the average value was -1.3507. The average value of the total values of clear cell adenocarcinoma was 6.7768, and the groups of each other had a significant difference at ^{P = 1.245 × 10 -8.}
In the ROC curve, the AUC was 0.8803, and in the sensitivity / specificity curve, the optimum cutoff value was set to 1.1226, at which time the sensitivity and specificity were 88.9% (Fig. 22).
In addition, when a hierarchical cluster analysis was performed based on the expression profiles of the C12orf75 gene, DNER gene, and RIMKLB gene, two clusters, a cluster mainly composed of serous adenocarcinoma group and a cluster mainly composed of clear cell adenocarcinoma group, were found. It was formed, and almost the same results as the hierarchical cluster analysis based on the expression profiles of 29 genes (Example 3) and 7 genes (Example 5) could be obtained.
Thus, there is a significant difference in the total expression levels of the 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. Therefore, it is possible to distinguish which histological type the target ovarian cancer sample has.

(Example 12. Additional test using additional sample)
In addition to the studies used in Example 1 and the like above, 19 samples of serous adenocarcinoma and 7 samples of clear cell adenocarcinoma for verification were prepared, and these samples were also prepared with high accuracy for serous adenocarcinoma and clear cell adenocarcinoma. It was verified whether it was possible to distinguish between.
Specifically, RNA preparation and comprehensive gene expression analysis were performed on additional verification samples in the same manner as in the methods described in Examples 1 and 2 above.
The microarray data on the differential marker 29 genes obtained for the additional sample was added to the microarray data of the sample used in Example 1 and the like above, and cluster analysis by the group averaging method based on the Euclidean distance was performed. The result is shown in FIG. 23A. Further, FIG. 23B shows a rearrangement of the microarray data based on the scoring system described in the fourth embodiment.
As shown in FIGS. 23A and 23B, serous adenocarcinoma and clear cell adenocarcinoma could be differentiated with 100% accuracy for the additional validation specimens.

(Example 13. Additional test using additional sample 2)
For the additional validation specimens of Example 12 (19 serous adenocarcinoma and 7 clear cell adenocarcinoma), 7 differential marker genes (PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, GDF15 gene) ) Was added to the microarray data of the sample used in Example 1 and the like described above, and cluster analysis by the group averaging method based on the Euclidean distance was performed. The results are shown in FIG. 24A. Further, FIG. 24B shows a rearrangement of the microarray data based on the scoring system.
As shown in FIGS. 24A and 24B, serous adenocarcinoma and clear cell adenocarcinoma could be differentiated with 100% accuracy for the additional validation specimens.

(Example 14. Additional test using additional sample 3)
For the additional verification specimens of Example 12 (19 specimens of serous adenocarcinoma and 7 specimens of clear cell adenocarcinoma), microarray data on 3 differential marker genes (GLRX gene, GDF15 gene, GABARAPL1 gene) were obtained in Example 1 above. In addition to the microarray data of the sample used, cluster analysis of the group averaging method by Euclidean distance was performed. The results are shown in FIG. 25A. Further, FIG. 25B shows the rearrangement of the microarray data based on the scoring system.
As shown in FIGS. 25A and 25B, serous adenocarcinoma and clear cell adenocarcinoma could be differentiated with 100% accuracy for the additional validation specimens.

Claims (11)

It is a method of obtaining an expression profile of a gene in a test sample derived from an ovarian cancer patient and assisting in differentiating whether it is a histological 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, DNER 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, LRIG1 gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and at least 3 genes selected from the group consisting of IGF2 gene. A step of measuring the expression level of each gene in the included gene set.
Each gene was selected so that the total value of the gene expression scores of each gene included in the gene set was equal to or greater than the total value of the gene expression scores of the GLRX gene, GDF15 gene, and GABARAPL1 gene. Monodea is, and
It looks including the step is the measurement of the expression level of mRNA for each gene measured in the expression level is included in the gene set,
A method for assisting differentiation , wherein the gene expression score is the magnitude of the difference in the expression level of a specific gene between a sample derived from a patient with serous adenocarcinoma and a sample derived from a patient with clear cell adenocarcinoma. A method for assisting the discrimination according to claim 1.
The gene expression score is the sum of the log2 values of the measured fluorescence intensities, and
Differentiation in which at least three genes are selected so that the combination of genes contained in the gene set is equal to or greater than 7.006 in total by referring to the gene expression scores shown in the table below. How to assist.

A method for assisting the discrimination according to claim 1 or 2.
A method for assisting differentiation, 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. A method for assisting the discrimination according to any one of claims 1 to 3.
A method for assisting differentiation, in which all of the genes included in the gene set are selected from the group consisting of PPP1R3B gene, TOB1 gene, RBPMS gene, GABARAPL1 gene, GLRX gene, GSTM3 gene, and GDF15 gene. A method for assisting the discrimination according to any one of claims 1 to 4.
A method of assisting differentiation, wherein the gene set comprises the GLRX gene, the GDF15 gene and the GABARAPL1 gene. A method for assisting the discrimination according to any one of claims 1 to 5.
A method of assisting differentiation comprising comparing the expression profile of each gene in the gene set with the expression profile of each gene in the corresponding gene set in a sample derived from a patient with serous or clear cell adenocarcinoma. A method for assisting the discrimination according to any one of claims 1 to 6.
A method for assisting differentiation, comprising a step of cluster analysis of the expression profile of each gene in the gene set. A method for assisting the discrimination according to any one of claims 1 to 7.
A method of assisting differentiation, comprising the step of comparing the quantitative expression profile of each gene of the gene set in a test sample with a predetermined threshold. In a test sample derived from a patient with ovarian cancer, in either serous adenocarcinoma or clear cell adenocarcinoma histological type
It is a kit for distinguishing whether it exists,
The kit includes PP1R3B 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. At least selected from the group consisting of genes, ANXA4 gene, FBLN1 gene, TSPAN1 gene, APOL1 gene, FXYD2 gene, GPX3 gene, DEFB1 gene, FLJ43606 gene, LRIG1 gene, LYPD1 gene, CRIP1 gene, UQCRH gene, and IGF2 gene. Including means for measuring the expression level of mRNA of each gene in a gene set containing three genes.
A kit in which each gene is selected so that the total value of the gene expression scores of each gene included in the gene set is equal to or greater than the total value of the gene expression scores of the GLRX gene, the GDF15 gene, and the GABARAPL1 gene. .. The kit according to claim 9.
A kit, wherein the means for measuring the expression level of each gene is at least one means selected from the group consisting of primers, probes, or labels thereof for each gene. The kit according to claim 10.
A kit for PCR, microarrays, or RNA sequencing.

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