JP2019213473A - Discrimination method of prognostic risk after pancreatic tumor extraction operation - Google Patents
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Abstract
Description
本発明は、例えば膵腫瘍の摘出術後の予後における再発等の悪性度予測又はリスク予測法に関する。 The present invention relates to a method for predicting the degree of malignancy or the risk of recurrence in the prognosis after extirpation of a pancreatic tumor, for example.
日本における癌死は年間37万人を超えており、死因の第1位である。また、癌の罹患数も年々増加しており、2017年には100万人を超えると予測されている。その中で、膵癌罹患数は39,800人であり、全体における割合は約4%で第7位の希少性の高い癌である。加えて、同年の膵癌での死亡者数はほぼ同数であり、年齢調整率を加味した場合でも、男女共に死亡者数が減少傾向でない癌である。5年相対生存率を他の癌と比較すると、膵癌は7.7%と極めて低く、難治性癌の代表といえる(2017年,国立がん研究センター試算)。 Cancer deaths in Japan exceed 370,000 a year and are the leading cause of death. In addition, the number of cancer cases is increasing year by year, and is forecast to exceed 1 million in 2017. Among them, the number of pancreatic cancer cases is 39,800, about 4% of the total, which is the seventh most rare cancer. In addition, the number of deaths from pancreatic cancer in the same year is almost the same, and even when the age adjustment rate is added, the number of deaths is not decreasing for both men and women. Compared with other cancers, the 5-year relative survival rate is extremely low at 7.7% for pancreatic cancer, and it can be said that pancreatic cancer is a representative of refractory cancer (2017, calculated by National Cancer Center).
膵癌は症状がほとんどないため発見が遅れ、手術ができるのは、患者全体の20〜30%程度しかない。また、切除手術ができたとしても、5年生存率は20%程度と極めて低く、消化器系の癌の中では最も低い数字となっている。しかしながら、腫瘍径が1cm以下で発見された場合の5年生存率は、80.4%にも及ぶことが近年報告されている。 Because pancreatic cancer has few symptoms, its discovery is delayed, and only about 20 to 30% of patients can undergo surgery. Even if resection surgery is possible, the 5-year survival rate is extremely low at about 20%, the lowest among cancers of the digestive system. However, it has recently been reported that the 5-year survival rate when a tumor is found less than 1 cm in diameter is as high as 80.4%.
膵癌の予後を改善するには、手術が可能な段階での早期発見、早期治療を目指すことが何よりも重要となる。膵癌は、腫瘍マーカー検査(CA19-9、CEA)でもはっきりとした上昇が見られず、上昇する割合は半分にも満たない状況となっている。加えて、超音波検査やCT検査による腫瘍径1cm以下の抽出率は決して高いとは言えず、超音波検査で17〜70%、造影CT検査で35〜75%とされている。 In order to improve the prognosis of pancreatic cancer, it is most important to aim for early detection and early treatment at the stage where surgery is possible. For pancreatic cancer, no clear increase was observed in tumor marker tests (CA19-9, CEA), and the rate of increase was less than half. In addition, the rate of extraction of tumors with a diameter of 1 cm or less by ultrasonography or CT examination is far from high, and is 17-70% by ultrasonography and 35-75% by contrast-enhanced CT examination.
従って、低侵襲性な体液検体や術後の摘出組織検体等の臨床検体を用いた、膵癌及び膵管内乳頭粘液性腫瘍(IPMN)の「早期診断法」又は「悪性度を規定する方法」の確立が切望されている。 Therefore, using clinical specimens such as minimally invasive body fluid specimens and post-operative excised tissue specimens, the `` early diagnosis method '' or `` method for defining the degree of malignancy '' of pancreatic cancer and intraductal papillary mucinous neoplasm (IPMN) The establishment is eagerly awaited.
近年、膵癌を含む種々の癌において、糖タンパク質であるムチン分子ファミリーの異常発現が確認され、増殖・浸潤転移・抗癌剤耐性・免疫機構回避等癌進展の各プロセスにおいて、癌の悪性度に関与することが報告されている(非特許文献1)。一方、本発明者等は、以下のように、早くからムチンに注目して研究を進め、様々な組織におけるムチン発現と悪性度との関係や、その発現機構を世界に先駆けて明らかにしてきた:
(1) 膵腫瘍の生物学的悪性度と一連のムチン抗原発現について詳細な分析を行い、前癌病変(PanIN)、膵癌(PDAC)、膵管内乳頭粘液性腫瘍(IPMN)におけるMUC1、MUC2、MUC4のムチン発現と悪性度との関連を世界に先駆けて明らかにしてきた。加えて、MUC1の細胞質内ドメインに対する新規抗体を開発した(特許文献1);
(2) 各ムチン遺伝子が各々のプロモーター領域のメチル化により、対象遺伝子の発現が制御されていることを見出した(非特許文献2);
(3) 従来のDNAメチル化検出限界を超える0.1%の解像度を有する新規高感度メチル化解析法の開発を行い報告した(特許文献2及び非特許文献3);
(4) 実際の膵液検体・組織検体を用いた新規メチル化解析法による解析において、その悪性度・予後とDNAメチル化の相関を明らかにし(非特許文献4及び5)、膵腫瘍の病型診断方法(特許文献3)として報告している。
In recent years, abnormal expression of the mucin family of glycoproteins has been confirmed in various cancers including pancreatic cancer, and is involved in cancer malignancy in each process of cancer progression such as proliferation, invasive metastasis, resistance to anticancer drugs, and evasion of the immune mechanism. (Non-Patent Document 1). On the other hand, the present inventors have been conducting research on mucin from an early stage, and have clarified the relationship between mucin expression and malignancy in various tissues and the mechanism of the expression as follows, as follows:
(1) A detailed analysis was performed on the biological grade of pancreatic tumors and the expression of a series of mucin antigens.Pre-malignant lesions (PanIN), pancreatic cancer (PDAC), MUC1, MUC2 in intraductal papillary mucinous neoplasms (IPMN), The relationship between MUC4 mucin expression and malignancy has been clarified for the first time in the world. In addition, a novel antibody against the MUC1 cytoplasmic domain has been developed (Patent Document 1);
(2) Each mucin gene was found to control the expression of the target gene by methylation of each promoter region (Non-Patent Document 2);
(3) Development and report of a novel high-sensitivity methylation analysis method with a resolution of 0.1% exceeding the conventional DNA methylation detection limit (Patent Document 2 and Non-Patent Document 3);
(4) In the analysis by a novel methylation analysis method using actual pancreatic juice samples and tissue samples, the correlation between the malignancy and prognosis and DNA methylation was clarified (Non-patent Documents 4 and 5), and the type of pancreatic tumor This is reported as a diagnostic method (Patent Document 3).
本発明は、上述の実情に鑑み、膵腫瘍の早期診断又は膵腫瘍の悪性度の規定に有効な膵腫瘍の予後判別方法を提供することを目的とする。 The present invention has been made in view of the above circumstances, and has as its object to provide a method for determining the prognosis of a pancreatic tumor, which is effective for early diagnosis of pancreatic tumor or definition of malignancy of pancreatic tumor.
上記課題を解決するため鋭意研究を行った結果、検体試料中の癌遺伝子及び/又は癌抑制遺伝子のメチル化状況を解析し、機械学習により膵腫瘍の予後を予測できることを見出し、本発明を完成するに至った。 As a result of intensive studies to solve the above problems, the inventors have found that the methylation status of oncogenes and / or tumor suppressor genes in a specimen sample can be analyzed and that the prognosis of pancreatic tumor can be predicted by machine learning, and the present invention has been completed. I came to.
すなわち、本発明は、以下を包含する。
(1)被験者由来の検体試料中の癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域のメチル化状況を測定する第1工程と、前記被験者の膵腫瘍の予後を判別するために、腫瘍組織由来の検体試料であること又は非腫瘍組織由来の検体試料であることが既知の複数の検体試料中の前記癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域のメチル化状況を測定することで得られた測定値を教師とした機械学習モデルの判別式に、第1工程で得られた癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域のメチル化状況の測定値を代入する第2工程とを含む、膵腫瘍の予後判別方法。
(2)機械学習モデルが、サポートベクターマシーンモデル、ニューラルネットワークモデル、ランダムフォレストモデル及びディープラーニングモデルから成る群より選択される、(1)記載の方法。
(3)腫瘍組織及び非腫瘍組織が膵臓組織である、(1)又は(2)記載の方法。
(4)癌遺伝子及び/又は癌抑制遺伝子が、MUC1遺伝子、MUC2遺伝子及びMUC4遺伝子から成る群より選択される1以上のムチンコアタンパク質をコードする遺伝子を含む遺伝子セットである、(1)〜(3)のいずれか1記載の方法。
(5)癌遺伝子及び/又は癌抑制遺伝子が、MUC1遺伝子、MUC2遺伝子及びMUC4遺伝子を含む遺伝子セットである、(1)〜(3)のいずれか1記載の方法。
(6)癌遺伝子及び/又は癌抑制遺伝子が、MUC1遺伝子、MUC2遺伝子及びMUC4遺伝子から成る遺伝子セットである、(1)〜(3)のいずれか1記載の方法。
(7)MUC1遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域が、配列番号1に示される塩基配列から成る領域である、(4)〜(6)のいずれか1記載の方法。
(8)MUC2遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域が、配列番号2に示される塩基配列から成る領域である、(4)〜(7)のいずれか1記載の方法。
(9)MUC4遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域が、配列番号3に示される塩基配列から成る領域である、(4)〜(8)のいずれか1記載の方法。
(10)膵腫瘍が膵癌又は膵管内乳頭粘液性腫瘍である、(1)〜(9)のいずれか1記載の方法。
That is, the present invention includes the following.
(1) a first step of measuring the methylation status of a 5′-side untranslated region or a region containing a 5′-side untranslated region and a translated region of a cancer gene and / or a tumor suppressor gene in a specimen sample derived from a subject; In order to determine the prognosis of the subject's pancreatic tumor, the oncogene and / or cancer in a plurality of sample samples known to be a sample sample derived from tumor tissue or a sample sample derived from non-tumor tissue The discriminant of the machine learning model using the measured value obtained by measuring the methylation status of the region including the 5′-side untranslated region or the 5′-side untranslated region and the translated region of the suppressor gene as a teacher, A second step of substituting the measured value of the methylation status of the 5'-side untranslated region or the region containing the 5'-side untranslated region and the translated region of the oncogene and / or tumor suppressor gene obtained in one step. Methods for determining prognosis of pancreatic tumors.
(2) The method according to (1), wherein the machine learning model is selected from the group consisting of a support vector machine model, a neural network model, a random forest model, and a deep learning model.
(3) The method according to (1) or (2), wherein the tumor tissue and the non-tumor tissue are pancreatic tissues.
(4) (1) to (1), wherein the oncogene and / or the tumor suppressor gene is a gene set including genes encoding one or more mucin core proteins selected from the group consisting of MUC1, MUC2, and MUC4 genes. The method according to any one of 3).
(5) The method according to any one of (1) to (3), wherein the oncogene and / or the tumor suppressor gene is a gene set including the MUC1, MUC2, and MUC4 genes.
(6) The method according to any one of (1) to (3), wherein the oncogene and / or the tumor suppressor gene is a gene set consisting of the MUC1, MUC2, and MUC4 genes.
(7) The (4) to (6) of (6), wherein the 5′-side untranslated region of the MUC1 gene or the region containing the 5′-side untranslated region and the translated region is a region consisting of the nucleotide sequence shown in SEQ ID NO: 1. A method according to any one of the preceding claims.
(8) (4) to (7), wherein the 5′-side untranslated region of the MUC2 gene or the region including the 5′-side untranslated region and the translated region is a region comprising the base sequence shown in SEQ ID NO: 2. A method according to any one of the preceding claims.
(9) The region of (4) to (8), wherein the 5′-side untranslated region of the MUC4 gene or the region including the 5′-side untranslated region and the translated region is a region comprising the base sequence shown in SEQ ID NO: 3. A method according to any one of the preceding claims.
(10) The method according to any one of (1) to (9), wherein the pancreatic tumor is pancreatic cancer or intraductal papillary mucinous tumor.
本発明によれば、膵腫瘍の予後を早期に診断・予測することができる。 According to the present invention, the prognosis of a pancreatic tumor can be diagnosed and predicted at an early stage.
以下、本発明を詳細に説明する。
本発明に係る膵腫瘍の予後判別方法(以下、「本方法」と称する)は、被験者由来の検体試料中の癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域のメチル化状況を測定する第1工程と、当該被験者の膵腫瘍の予後を判別するために、腫瘍組織由来の検体試料であること又は非腫瘍組織由来の検体試料であることが既知の複数の検体試料中の癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域のメチル化状況を測定することで得られた測定値を教師とした機械学習モデルの判別式に、第1工程で得られた癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域のメチル化状況の測定値を代入する第2工程とを含む。換言すれば、本方法は、膵腫瘍の予後を判別するための評価方法、又は膵腫瘍の予後を判別するための情報を収集する方法とすることができる。
Hereinafter, the present invention will be described in detail.
The method for determining the prognosis of a pancreatic tumor according to the present invention (hereinafter, referred to as `` the method '') is a 5′-side untranslated region or 5′-side untranslated region of a cancer gene and / or a tumor suppressor gene in a specimen sample derived from a subject. A first step of measuring the methylation status of the region including the region and the translation region, and a sample sample derived from a tumor tissue or a sample sample derived from a non-tumor tissue in order to determine the prognosis of pancreatic tumor in the subject By measuring the methylation status of a region containing a 5′-side untranslated region or a 5′-side untranslated region and a translated region of a cancer gene and / or a tumor suppressor gene in a plurality of sample samples known to be The discriminant of the machine learning model using the obtained measured value as a teacher, the 5 ′ untranslated region or the 5 ′ untranslated region and the translated region of the oncogene and / or tumor suppressor gene obtained in the first step. And substituting the measured value of the methylation status of the region including In other words, the present method can be an evaluation method for determining the prognosis of a pancreatic tumor, or a method of collecting information for determining the prognosis of a pancreatic tumor.
膵腫瘍としては、例えば膵癌及び膵管内乳頭粘液性腫瘍(IPMN)が挙げられる。
被験者由来の検体試料としては、例えば膵腫瘍の摘出術後組織検体、病理診断余剰検体、細胞診余剰検体、血液検体、体液検体等の臨床検体が挙げられる。
Pancreatic tumors include, for example, pancreatic cancer and intraductal papillary mucinous neoplasm (IPMN).
The specimen sample derived from the subject includes, for example, clinical specimens such as a tissue specimen after excision of a pancreatic tumor, an excess specimen for pathological diagnosis, an extra specimen for cytology, a blood specimen, and a body fluid specimen.
また、腫瘍組織(好ましくは、膵臓組織(すなわち、膵腫瘍組織))由来の検体試料であることが既知の検体試料としては、例えば、腫瘍の摘出術後の組織検体より熟練の病理医がマクロで腫瘍部を切除したサンプル、針生検により採取された腫瘍組織・細胞、ホルマリン固定パラフィン包埋(FFPE)組織を薄切しレーザーマイクロダイセクションを用いて腫瘍部のみを回収した検体、膵腫瘍患者由来の胆汁や膵液・血液などの体液検体等が挙げられる。 Further, as a specimen sample known to be a specimen sample derived from a tumor tissue (preferably, a pancreatic tissue (that is, pancreatic tumor tissue)), for example, a skilled pathologist may be able to obtain a macroscopic specimen from a tissue specimen after tumor excision. Pancreatic tumor patient, a sample from which the tumor was excised in step 2, tumor tissue / cells collected by needle biopsy, and formalin-fixed paraffin-embedded (FFPE) tissue sliced and only the tumor was collected using laser microdissection Examples include bile and bodily fluid samples such as pancreatic juice and blood.
さらに、非腫瘍組織(好ましくは、非腫瘍の膵臓組織)由来の検体試料であることが既知の検体試料としては、例えば腫瘍の摘出術後の組織検体より熟練の病理医がマクロで非腫瘍部を切除したサンプル、針生検により採取された非腫瘍組織・細胞、ホルマリン固定パラフィン包埋(FFPE)組織を薄切しレーザーマイクロダイセクションを用いて非腫瘍部のみを回収した検体、非腫瘍患者由来の胆汁や膵液・血液などの体液検体等が挙げられる。 Furthermore, as a specimen sample known to be a non-tumor tissue (preferably, a non-tumor pancreatic tissue), for example, a skilled pathologist may be able to obtain a macroscopic sample from a non-tumor site from a tissue sample after tumor excision. Excised sample, non-tumor tissue / cells collected by needle biopsy, formalin-fixed paraffin-embedded (FFPE) tissue sliced, and non-tumor sample collected using laser microdissection, from non-tumor patients Bile and body fluid samples such as pancreatic juice and blood.
癌遺伝子としては、例えばムチンコアタンパク質(MUC)をコードする遺伝子(又はムチン遺伝子とも称される)、Gsαタンパク質をコードする遺伝子、Rabファミリータンパク質をコードする遺伝子、癌幹細胞に関与するCD44をコードする遺伝子、免疫チェックポイントタンパク質PD-1のリガンドをコードする遺伝子、及びこれらの組合せが挙げられる。特に、MUCをコードする遺伝子としては、例えばMUC1遺伝子、MUC2遺伝子、MUC4遺伝子及びこれらのうち2種又は3種全ての組合せが挙げられる。 As oncogenes, for example, a gene encoding a mucin core protein (MUC) (also referred to as a mucin gene), a gene encoding a Gsα protein, a gene encoding a Rab family protein, encoding CD44 involved in cancer stem cells Genes, genes encoding ligands for the immune checkpoint protein PD-1, and combinations thereof. In particular, examples of the gene encoding MUC include the MUC1 gene, the MUC2 gene, the MUC4 gene, and a combination of two or all three of them.
一方、癌抑制遺伝子としては、例えば、転写因子であるP53やBRCAファミリーをコードする遺伝子、サイクリン依存性キナーゼ阻害であるP16をコードする遺伝子、SMADファミリータンパク質をコードする遺伝子の組合せが挙げられる。加えて、細胞接着に関与するカドヘリンやカテニンをコードする遺伝子、及びこれらの組合せが挙げられる。 On the other hand, examples of the tumor suppressor gene include a combination of genes encoding transcription factors P53 and BRCA family, a gene encoding P16 which is cyclin-dependent kinase inhibition, and a gene encoding SMAD family protein. In addition, genes encoding cadherin and catenin involved in cell adhesion, and combinations thereof.
メチル化度検出の標的配列(標的領域)である癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域は、癌遺伝子及び/又は癌抑制遺伝子の5'側上流に位置する非翻訳領域(UTR)であり、特に癌遺伝子及び/又は癌抑制遺伝子のプロモーター領域を含む領域である。さらに、癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域と翻訳領域とを含む領域は、プロモーター領域等の5'非翻訳領域と当該5'非翻訳領域に隣接する翻訳領域(コーディング領域)とを含む領域である。 The target sequence for the detection of the degree of methylation (target region) is an oncogene and / or a 5′-side untranslated region of a tumor suppressor gene, an untranslated region located on the 5′-side upstream of the oncogene and / or tumor suppressor gene ( UTR), particularly a region containing a promoter region of an oncogene and / or a tumor suppressor gene. Furthermore, a region containing the 5′-side untranslated region and the translated region of the oncogene and / or the tumor suppressor gene is a 5′-untranslated region such as a promoter region and a translated region adjacent to the 5′-untranslated region (coding region). ).
癌遺伝子及び/又は癌抑制遺伝子のプロモーター領域又は当該プロモーター領域と翻訳領域とを含む領域に存在する1以上(1又は複数)のCpG部位を、メチル化度を検出すべき標的配列とすることができる。ここで、CpG部位とは、5'-シトシン-グアニン-3'(5'-CG-3')のジヌクレオチドを意味する。2以上のCpG部位を標的配列とする場合には、各CpG部位を個別に、又は2以上のCpG部位を含む領域を標的配列としてもよい。 One or more (one or more) CpG sites present in a promoter region of an oncogene and / or a tumor suppressor gene or a region containing the promoter region and the translation region may be a target sequence for which the degree of methylation is to be detected. it can. Here, the CpG site means a 5′-cytosine-guanine-3 ′ (5′-CG-3 ′) dinucleotide. When two or more CpG sites are used as the target sequence, each CpG site may be used individually, or a region containing two or more CpG sites may be used as the target sequence.
図1〜3は、それぞれ被メチル化によってヒトMUC1、MUC2及びMUC4遺伝子の発現を調節するプロモーター領域の配列及び隣接する翻訳領域の配列を示す。図1〜3において、(TIS)は転写開始部位(transcription initiation site)を示し、四角で囲こまれたCGは被メチル部位(CpG部位)を示し、太い四角で囲まれたCGは、ヒトMUC遺伝子の発現に関与する被メチル化部位(CpG部位)を示す。また、図1B、図2B及び図3Bにおいて、イタリックのTは、Bisulfite処理により非メチル化シトシンから変換されたウラシルをチミンで表示したものである。なお、転写開始部位(TIS)以降の塩基配列は、翻訳領域(コーディング領域)である。 FIGS. 1 to 3 show the sequences of the promoter region and the sequences of the adjacent translation regions that regulate the expression of human MUC1, MUC2, and MUC4 genes by methylation, respectively. In FIGS. 1 to 3, (TIS) indicates a transcription initiation site (transcription initiation site), CG surrounded by a square indicates a methylated site (CpG site), CG surrounded by a thick square is human MUC The methylated sites (CpG sites) involved in gene expression are shown. In FIG. 1B, FIG. 2B, and FIG. 3B, italic T indicates uracil converted from unmethylated cytosine by bisulfite treatment in thymine. The base sequence after the transcription start site (TIS) is a translation region (coding region).
図1Aは、被メチル化によってヒトMUC1遺伝子の発現を調節するプロモーター領域の配列及び隣接する翻訳領域の配列(配列番号4)を示す。図1Bは、当該配列のBisulfite処理後のDNA配列(配列番号5)を示し、非メチル化シトシンから変換されたウラシルをチミンで表示する。図1において、下記の参考例で説明する第2プライマーセットに相当するPrimer 1-3とPrimer 1-4との間の領域(配列番号1)は、CpG部位(又はCpGアイランド)172-181を有する(Norishige Yamada, Yukari Nishida, Hideaki Tsutsumida, Tomofumi Hamada, Masamichi Goto, Michiyo Higashi, Mitsuharu Nomoto and Suguru Yonezawa (2008) MUC1 expression is regulated by DNA methylation and histone H3-K9 modification in cancer cells. Cancer Res., 68(8): 2708-16と同様に、CpG部位の番号は、ヒトMUC1遺伝子の推定プロモーター上流(当該遺伝子の転写開始点より上流2,753bp)より順にナンバリングされている)。 FIG. 1A shows the sequence of a promoter region that regulates the expression of the human MUC1 gene by methylation and the sequence of an adjacent translation region (SEQ ID NO: 4). FIG. 1B shows the DNA sequence of the sequence after Bisulfite treatment (SEQ ID NO: 5), and uracil converted from unmethylated cytosine is indicated by thymine. In FIG. 1, the region between Primer 1-3 and Primer 1-4 corresponding to the second primer set described in the following Reference Example (SEQ ID NO: 1) has a CpG site (or CpG island) 172-181. (Norishige Yamada, Yukari Nishida, Hideaki Tsutsumida, Tomofumi Hamada, Masamichi Goto, Michiyo Higashi, Mitsuharu Nomoto and Suguru Yonezawa (2008) MUC1 expression is regulated by DNA methylation and histone H3-K9 modification in cancer cells.Cancer Res., 68 (8): As in 2708-16, the numbers of CpG sites are numbered sequentially from the putative promoter upstream of the human MUC1 gene (2,753 bp upstream from the transcription start point of the gene).
図2Aは、被メチル化によってヒトMUC2遺伝子の発現を調節するプロモーター領域の配列及び隣接する翻訳領域の配列(配列番号6)を示す。図2Bは、当該配列のBisulfite処理後のDNA配列(配列番号7)を示し、非メチル化シトシンから変換されたウラシルをチミンで表示する。図2において、下記の参考例で説明する第2プライマーセットに相当するPrimer 2-3とPrimer 2-4との間の領域(配列番号2)は、CpG部位37-43を有する(Norishige Yamada, Tomofumi Hamada, Masamichi Goto, Hideaki Tsutsumida, Michiyo Higashi, Mitsuharu Nomoto and Suguru Yonezawa (2006) MUC2 expression is regulated by histone H3 modification and DNA methylation in pancreatic cancer. Int. J. Cancer, 119(8): 1850-7と同様に、CpG部位の番号は、ヒトMUC2遺伝子の推定プロモーター上流(当該遺伝子の転写開始点より上流1,989bp)より順にナンバリングされている)。 FIG. 2A shows the sequence of the promoter region that regulates the expression of the human MUC2 gene by methylation and the sequence of the adjacent translation region (SEQ ID NO: 6). FIG. 2B shows the DNA sequence of the sequence after Bisulfite treatment (SEQ ID NO: 7), and uracil converted from unmethylated cytosine is indicated by thymine. In FIG. 2, the region between Primer 2-3 and Primer 2-4 corresponding to the second primer set described in the following Reference Example (SEQ ID NO: 2) has a CpG site 37-43 (Norishige Yamada, Tomofumi Hamada, Masamichi Goto, Hideaki Tsutsumida, Michiyo Higashi, Mitsuharu Nomoto and Suguru Yonezawa (2006) MUC2 expression is regulated by histone H3 modification and DNA methylation in pancreatic cancer.Int.J. Cancer, 119 (8): 1850-7 Similarly, the numbers of the CpG sites are numbered sequentially from the putative promoter upstream of the human MUC2 gene (1,989 bp upstream from the transcription start point of the gene).
図3Aは、被メチル化によってヒトMUC4遺伝子の発現を調節するプロモーター領域の配列及び隣接する翻訳領域の配列(配列番号8)を示す。図3Bは、当該配列のBisulfite処理後のDNA配列(配列番号9)を示し、非メチル化シトシンから変換されたウラシルをチミンで表示する。図3において、下記の参考例で説明する第2プライマーセットに相当するPrimer 4-3とPrimer 4-4との間の領域(配列番号3)は、CpG部位108-118を有する(Norishige Yamada, Yukari Nishida, Hideaki Tsutsumida, Masamichi Goto, Michiyo Higashi, Mitsuharu Nomoto and Suguru Yonezawa (2009) Promoter CpG methylation in cancer cells contributes to regulation of MUC4. Br. J. Cancer, 100 (2): 344-51と同様に、CpG部位の番号は、ヒトMUC4遺伝子の推定プロモーター上流(当該遺伝子の転写開始点より上流3,622bp)より順にナンバリングされている)。 FIG. 3A shows the sequence of the promoter region that regulates the expression of the human MUC4 gene by methylation and the sequence of the adjacent translation region (SEQ ID NO: 8). FIG. 3B shows the DNA sequence of the sequence after Bisulfite treatment (SEQ ID NO: 9), and uracil converted from unmethylated cytosine is indicated by thymine. In FIG. 3, the region between Primer 4-3 and Primer 4-4 corresponding to the second primer set described in the following Reference Example (SEQ ID NO: 3) has CpG sites 108-118 (Norishige Yamada, As with Yukari Nishida, Hideaki Tsutsumida, Masamichi Goto, Michiyo Higashi, Mitsuharu Nomoto and Suguru Yonezawa (2009) Promoter CpG methylation in cancer cells contributes to regulation of MUC4.Br.J.Cancer, 100 (2): 344-51. The numbers of CpG sites are numbered sequentially from the putative promoter upstream of the human MUC4 gene (3,622 bp upstream from the transcription start point of the gene).
本方法では、例えば癌遺伝子であるヒトMUC1、MUC2及びMUC4遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域として、上述のそれぞれ第2プライマーセットのプライマー間の配列(すなわち、配列番号1〜3に示される塩基配列)に存在する1以上(1又は複数)のCpG部位(特に、ヒトMUC遺伝子の発現に関与する被メチル化部位)を、メチル化度を検出すべき標的配列とすることができ、特に、ヒトMUC遺伝子の発現に関与する被メチル化部位(CpG部位)を全て含む、配列番号1〜3に示される塩基配列又は当該塩基配列を含む領域を、メチル化度を検出すべき標的配列とすることが好ましい。 In the present method, for example, the human MUC1, which is an oncogene, the MUC2 and MUC4 genes as the 5′-side untranslated region or the 5′-side untranslated region and the translated region, the sequence between the primers of the second primer set described above (that is, One or more (one or more) CpG sites present in the nucleotide sequences shown in SEQ ID NOs: 1 to 3 (particularly, methylation sites involved in the expression of the human MUC gene) are targets for which the degree of methylation is to be detected. It can be a sequence, in particular, including all methylated sites involved in the expression of the human MUC gene (CpG site), the nucleotide sequence shown in SEQ ID NOs: 1 to 3 or a region containing the nucleotide sequence, methylation Preferably, the target sequence is to be detected.
以下、本方法の各工程を説明する。
1.被験者由来の検体試料中の癌遺伝子及び/又は癌抑制遺伝子のメチル化状況を測定する第1工程
本工程では、被験者由来の検体試料において、癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域のメチル化度を検出する。メチル化度の検出(又は測定)は、従来において知られるメチル化を検出する方法であってよく、例えば特定領域におけるメチル化DNAの検出では、重亜硫酸塩(Bisulfite)処理による塩基置換反応を行い、DNAの配列を決定する。当該塩基置換反応では、非メチル化シトシンが重亜硫酸ナトリウムと反応して、ウラシルへと変換される。メチル化シトシンは重亜硫酸ナトリウムと反応しないので、原理上全てのシトシンのメチル化状態を塩基の違いとして検出できる。従来におけるメチル化の検出方法としては、例えばPCRを使用したメチル化特異的PCR(MSP)法、定量的PCRを使用したReal-time MSP法、TAクローニングを使用したBisulfite-sequencing法、質量分析を使用したMassARRAY法、核酸取り込み時のピロリン酸放出をモニターするパイロシークエンス法、次世代シークエンサーによるアンプリコン解析等が挙げられる。さらに、重亜硫酸塩反応を必要としないICON-prove法やPacbioを用いたロングシークエンス法も開発されている。さらに、DNAメチル化パターン又は連続性を検出できるMSE(Methylation Specific Electrophoresis)法がある。MSE法は、(a)DNAを重亜硫酸塩処理に供する工程、(b)標的領域の外側の領域に対応する第1プライマーセットを用いて重亜硫酸塩処理後のDNAを第1のPCRに供する工程、(c)標的領域に対応する第2プライマーセットを用いて第1のPCR後の増幅DNAを第2のPCRに供する工程、(d)第2のPCR後の増幅DNAを変性剤濃度勾配ゲル電気泳動(DGGE)に供する工程を含む方法である(特許第5765586号公報及び特許第5866739号公報)。MSE法や次世代シークエンサー、Pacbioによる解析は、従来法であるBisulfite-sequencing法と比較すると大幅な時間短縮を行うことができる。
Hereinafter, each step of the method will be described.
1. First Step of Measuring Methylation Status of Cancer Gene and / or Tumor Suppressor Gene in Sample Sample Derived from Subject In this step, in the sample sample derived from the subject, the 5 ′ untranslated cancer gene and / or tumor suppressor gene The degree of methylation of the region or the region containing the 5 ′ untranslated region and the translated region is detected. Detection (or measurement) of the degree of methylation may be a conventionally known method for detecting methylation, for example, in the detection of methylated DNA in a specific region, a bisulfite (Bisulfite) by performing a base substitution reaction , Determine the sequence of the DNA. In the base substitution reaction, unmethylated cytosine reacts with sodium bisulfite to be converted to uracil. Since methylated cytosine does not react with sodium bisulfite, in principle, the methylation status of all cytosines can be detected as a difference in base. Conventional methods for detecting methylation include, for example, methylation-specific PCR (MSP) using PCR, Real-time MSP using quantitative PCR, Bisulfite-sequencing using TA cloning, and mass spectrometry. Examples include the MassARRAY method used, a pyrosequencing method for monitoring pyrophosphate release during nucleic acid uptake, and amplicon analysis using a next-generation sequencer. Further, an ICON-prove method which does not require a bisulfite reaction and a long sequence method using Pacbio have been developed. Furthermore, there is an MSE (Methylation Specific Electrophoresis) method capable of detecting a DNA methylation pattern or continuity. In the MSE method, (a) a step of subjecting DNA to bisulfite treatment, (b) subjecting the DNA after bisulfite treatment to first PCR using a first primer set corresponding to a region outside the target region Step, (c) subjecting the amplified DNA after the first PCR to the second PCR using the second primer set corresponding to the target region, (d) denaturant concentration gradient of the amplified DNA after the second PCR This is a method including a step of subjecting to gel electrophoresis (DGGE) (Japanese Patent No. 5765586 and Japanese Patent No. 5866739). Analysis using the MSE method, next-generation sequencer, and Pacbio can significantly reduce the time compared to the conventional method, Bisulfite-sequencing.
例えばMSE法におけるDGGE後のゲルの写真におけるバンドの発光強度をImage J等の画像処理ソフトウェアにより数値化し、統計解析ソフトウェア(例えば、統計解析ソフト「R」)により統計処理を行う。具体的には、癌遺伝子及び/又は癌抑制遺伝子のメチル化状況の測定値は、メチル化陽性を示す合成遺伝子の測定値を100、メチル化陰性を示す合成遺伝子の測定値を0とし測定間の誤差を二点補正する。 For example, the emission intensity of the band in the photograph of the gel after DGGE in the MSE method is digitized by image processing software such as Image J, and statistical processing is performed by statistical analysis software (for example, statistical analysis software “R”). Specifically, the measured value of the methylation status of the oncogene and / or the tumor suppressor gene is defined as 100 for the measured value of the synthetic gene showing methylation positive and 0 for the measured value of the synthetic gene showing methylation negative. Is corrected by two points.
2.被験者の膵腫瘍の予後を判別するために、腫瘍組織由来の検体試料であること又は非腫瘍組織由来の検体試料であることが既知の複数の検体試料中の癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域のメチル化状況を測定することで得られた測定値を教師とした機械学習モデルの判別式に、第1工程で得られた癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域のメチル化状況の測定値を代入する第2工程
本工程では、先ず、第1工程と同様に、腫瘍組織由来の検体試料であること又は非腫瘍組織由来の検体試料であることが既知の複数の検体試料中の癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域のメチル化状況を測定し、当該測定値を教師サンプルとした機械学習モデルの判別式を作成する。
2. To determine the prognosis of the pancreatic tumor of the subject, the oncogene and / or cancer suppressor gene in a plurality of sample samples that are known to be a sample sample derived from tumor tissue or a sample sample derived from non-tumor tissue In the first step, the discriminant of the machine learning model using the measured value obtained by measuring the methylation status of the 5′-side untranslated region or the region including the 5′-side untranslated region and the translated region as a teacher, In the second step of substituting the measured value of the methylation status of the obtained oncogene and / or the region containing the 5'-side untranslated region or the 5'-side untranslated region and the translated region of the tumor suppressor gene, In the same manner as in the first step, the 5′-side of the oncogene and / or the tumor suppressor gene in a plurality of sample samples known to be a sample sample derived from a tumor tissue or a sample sample derived from a non-tumor tissue. Methylation of the region containing the translation region or the 5 'untranslated region and the translation region The situation is measured, and a discriminant of a machine learning model using the measured value as a teacher sample is created.
機械学習モデルとしては、例えばサポートベクターマシーン(以下、「SVM」と称する)モデル、ニューラルネットワーク(以下、「NNET」と称する)モデル、ランダムフォレストモデル、ディープラーニングモデル等が挙げられる。 Examples of the machine learning model include a support vector machine (hereinafter, referred to as “SVM”) model, a neural network (hereinafter, referred to as “NNET”) model, a random forest model, a deep learning model, and the like.
SVMは超平面と呼ばれる境界面を決定し、該境界面を用いてデータを分類する判別式を決定する。NNETは人の神経回路を模倣し人工ニューラルネットワークを構築し非線形判別分析を実行する。ランダムフォレストは決定木を弱学習器とする集団学習アルゴリズムであり、トレーニングデータによって学習した多数の決定木を使用する。ディープラーニングは4層以上の深層ニューラルネットを用いる。これらの機械学習モデルを用いて、分類すべき群分け情報が既知のデータセットの特定のデータ項目を説明変数、分類すべき群分けを目的変数として、該データセットを既知の群分けに正しく分類する判別式を構築する。出力形式としては、予後不良もしくは予後良好の2値判別を行う。もしくはその割合・確率を算出する。 The SVM determines a boundary plane called a hyperplane, and determines a discriminant for classifying data using the boundary plane. NNET imitates human neural circuits, constructs artificial neural networks, and performs nonlinear discriminant analysis. The random forest is a group learning algorithm using a decision tree as a weak learner, and uses a large number of decision trees learned based on training data. Deep learning uses a deep neural network of four or more layers. Using these machine learning models, a specific data item of a data set with known grouping information to be classified is used as an explanatory variable, and the grouping to be classified is used as an objective variable, and the data set is correctly classified into a known grouping. Construct a discriminant to perform As an output format, binary determination of poor prognosis or good prognosis is performed. Alternatively, the ratio / probability is calculated.
以下、実施例を用いて本発明をより詳細に説明するが、本発明の技術的範囲はこれら実施例に限定されるものではない。 Hereinafter, the present invention will be described in more detail with reference to Examples, but the technical scope of the present invention is not limited to these Examples.
〔参考例〕ムチン遺伝子のメチル化解析
以下の実施例では、特許第5866739号公報の記載に準じて、組織検体試料において、MUC1、MUC2及びMUC4遺伝子プロモーター又は当該プロモーターと隣接する翻訳領域上の発現に関与する被メチル化部位(CpG部位)のメチル化解析をMSE法にて行った。
(Reference Example) Methylation analysis of mucin gene In the following examples, in accordance with the description of Patent No. 5686739, in a tissue sample, MUC1, MUC2 and MUC4 gene promoter or expression on the translation region adjacent to the promoter. The methylation analysis of the methylated site (CpG site) involved in the DNA was performed by the MSE method.
1.MSE法を用いたMUC1遺伝子プロモーターの発現に関与する被メチル化部位(CpG部位)のメチル化解析
MSE法を使用して、ヒトMUC1遺伝子プロモーターの発現に関与する被メチル化部位(CpG部位)を解析した。図1Aは、被メチル化によってヒトMUC1遺伝子の発現を調節するプロモーター領域の配列及び隣接する翻訳領域の配列(配列番号4)を示す。図1Bは、当該配列のBisulfite処理後のDNA配列(配列番号5)を示し、非メチル化シトシンから変換されたウラシルをチミンで表示する。図1において、下記で説明するPrimer 1-3とPrimer 1-4との間の領域(配列番号1)は、CpG部位172-181を有し、当該CpG部位を含む領域を標的領域とする。なお、論文(Norishige Yamada, Yukari Nishida, Hideaki Tsutsumida, Tomofumi Hamada, Masamichi Goto, Michiyo Higashi, Mitsuharu Nomoto and Suguru Yonezawa (2008) MUC1 expression is regulated by DNA methylation and histone H3-K9 modification in cancer cells. Cancer Res., 68(8): 2708-16)と同様に、CpG部位の番号は、ヒトMUC1遺伝子の推定プロモーター上流(当該遺伝子の転写開始点より上流2,753bp)より順にナンバリングされている。
1. MSE analysis of methylated sites (CpG sites) involved in MUC1 gene promoter expression using MSE method
Using the MSE method, the methylated site (CpG site) involved in the expression of the human MUC1 gene promoter was analyzed. FIG. 1A shows the sequence of a promoter region that regulates the expression of the human MUC1 gene by methylation and the sequence of an adjacent translation region (SEQ ID NO: 4). FIG. 1B shows the DNA sequence of the sequence after Bisulfite treatment (SEQ ID NO: 5), and uracil converted from unmethylated cytosine is indicated by thymine. In FIG. 1, a region (SEQ ID NO: 1) between Primer 1-3 and Primer 1-4 described below has CpG sites 172-181, and a region including the CpG site is used as a target region. The paper (Norishige Yamada, Yukari Nishida, Hideaki Tsutsumida, Tomofumi Hamada, Masamichi Goto, Michiyo Higashi, Mitsuharu Nomoto and Suguru Yonezawa (2008) MUC1 expression is regulated by DNA methylation and histone H3-K9 modification in cancer cells.Cancer Res. , 68 (8): 2708-16), the numbers of the CpG sites are numbered sequentially from the putative promoter upstream of the human MUC1 gene (2,753 bp upstream from the transcription start point of the gene).
組織検体試料からDNAを、DNeasy Blood & Tissue Kit(QIAGEN社製)を使用して抽出した。
次いで、EpiTect Bisulfite Kits(QIAGEN社製)を使用して、抽出したDNAをBisulfite処理に供した。
DNA was extracted from the tissue sample using the DNeasy Blood & Tissue Kit (QIAGEN).
Next, the extracted DNA was subjected to Bisulfite treatment using EpiTect Bisulfite Kits (manufactured by QIAGEN).
Bisulfite処理後のDNAを、以下のプライマーを使用したPCRに供した。
プライマーセット(小文字の塩基配列はGC clampである):
Primer 1-1: 5'-AAAGGGGGAGGTTAGTTGGA-3'(配列番号10);
Primer 1-2: 5'-AAACAACCCACTCCCCACCT-3'(配列番号11);
Primer 1-3:
5'-cgcccgccgcgcgcggcgggcggggcgggggcacggggggAAGAGGTAGGAGGTAGGGGA-3'(配列番号12);
Primer 1-4: 5'-AAAACAAAACAAATTCAAAC-3'(配列番号13)。
The DNA after the bisulfite treatment was subjected to PCR using the following primers.
Primer set (base sequence in lower case is GC clamp):
Primer 1-1: 5'-AAAGGGGGAGGTTAGTTGGA-3 '(SEQ ID NO: 10);
Primer 1-2: 5'-AAACAACCCACTCCCCACCT-3 '(SEQ ID NO: 11);
Primer 1-3:
5'-cgcccgccgcgcgcggcgggcggggcgggggcacggggggAAGAGGTAGGAGGTAGGGGA-3 '(SEQ ID NO: 12);
Primer 1-4: 5'-AAAACAAAACAAATTCAAAC-3 '(SEQ ID NO: 13).
各PCRは、図1に示すように、1st PCRは上記Primer 1-1とPrimer 1-2を用いて、2nd PCR(nested PCR)は上記Primer 1-3とPrimer 1-4を用いて行った。ポリメラーゼは、AmpliTaq Gold(登録商標)Fast PCR Master Mix(Applied Biosystem社製)を使用した。PCR条件及び温度設定を下記表1に示す。 Each PCR, as shown in FIG. 1, 1 st PCR by using the Primer 1-1 and Primer 1-2, 2 nd PCR (nested PCR) by using the Primer 1-3 and Primer 1-4 went. As the polymerase, AmpliTaq Gold (registered trademark) Fast PCR Master Mix (manufactured by Applied Biosystem) was used. The PCR conditions and temperature settings are shown in Table 1 below.
次いで、下記表2に示すDGGEゲル条件下の変性剤濃度勾配ゲルを使用し、2nd PCR後の反応液をDGGEに供した。なお、電気泳動条件は、泳動槽温度:60℃、定電圧:230V、泳動時間:300分であった。電気泳動槽は、Dcodeシステム(BIO-RAD社製)を使用した。 Then, using a denaturing gradient gel DGGE gel under the conditions shown in Table 2 below, was subjected to the reaction solution after 2 nd PCR to DGGE. The electrophoresis conditions were as follows: electrophoresis bath temperature: 60 ° C., constant voltage: 230 V, electrophoresis time: 300 minutes. The electrophoresis tank used was a Dcode system (manufactured by BIO-RAD).
2.MSE法を用いたMUC2遺伝子プロモーターの発現に関与する被メチル化部位(CpG部位)のメチル化解析
MSE法を使用して、ヒトMUC2遺伝子プロモーターの発現に関与する被メチル化部位(CpG部位)を解析した。図2Aは、被メチル化によってヒトMUC2遺伝子の発現を調節するプロモーター領域の配列及び隣接する翻訳領域の配列(配列番号6)を示す。図2Bは、当該配列のBisulfite処理後のDNA配列(配列番号7)を示し、非メチル化シトシンから変換されたウラシルをチミンで表示する。図2において、下記で説明するPrimer 2-3とPrimer 2-4との間の領域(配列番号2)は、CpG部位37-43を有し、当該CpG部位を含む領域を標的領域とする。なお、論文(Norishige Yamada, Tomofumi Hamada, Masamichi Goto, Hideaki Tsutsumida, Michiyo Higashi, Mitsuharu Nomoto and Suguru Yonezawa (2006) MUC2 expression is regulated by histone H3 modification and DNA methylation in pancreatic cancer. Int. J. Cancer, 119(8): 1850-7)と同様に、CpG部位の番号は、ヒトMUC2遺伝子の推定プロモーター上流(当該遺伝子の転写開始点より上流1,989bp)より順にナンバリングされている。
2. MSE analysis of methylated sites (CpG sites) involved in MUC2 gene promoter expression using MSE method
Using the MSE method, the methylated site (CpG site) involved in the expression of the human MUC2 gene promoter was analyzed. FIG. 2A shows the sequence of the promoter region that regulates the expression of the human MUC2 gene by methylation and the sequence of the adjacent translation region (SEQ ID NO: 6). FIG. 2B shows the DNA sequence of the sequence after Bisulfite treatment (SEQ ID NO: 7), and uracil converted from unmethylated cytosine is indicated by thymine. In FIG. 2, a region between Primer 2-3 and Primer 2-4 (SEQ ID NO: 2) described below has a CpG site 37-43, and a region including the CpG site is set as a target region. Thesis (Norishige Yamada, Tomofumi Hamada, Masamichi Goto, Hideaki Tsutsumida, Michiyo Higashi, Mitsuharu Nomoto and Suguru Yonezawa (2006) MUC2 expression is regulated by histone H3 modification and DNA methylation in pancreatic cancer.Int.J. Cancer, 119 ( 8): As in 1850-7), the numbers of CpG sites are numbered in order from the putative promoter upstream of the human MUC2 gene (1,989 bp upstream from the transcription start point of the gene).
上記第1節と同様にして、組織検体試料からDNAをDNeasy Blood & Tissue Kit(QIAGEN社製)を使用して抽出した後、EpiTect Bisulfite Kits(QIAGEN社製)を使用して、抽出したDNAをBisulfite処理に供した。 DNA was extracted from the tissue sample using the DNeasy Blood & Tissue Kit (manufactured by QIAGEN) in the same manner as in Section 1 above, and the extracted DNA was extracted using the EpiTect Bisulfite Kits (manufactured by QIAGEN). It was subjected to Bisulfite treatment.
Bisulfite処理後のDNAを、以下のプライマーを使用したPCRに供した。
プライマーセット(小文字の塩基配列はGC clampである):
Primer 2-1: 5'-TTTGGGGTTAGGTTTGGAAG-3'(配列番号14);
Primer 2-2: 5'-ACCTTCTTCAAAATAAAACAACC-3'(配列番号15);
Primer 2-3:
5'-cgcccgccgcgcgcggcgggcggggcgggggcacggggggTTTTAGAGTTTGGGTTTTAG-3'(配列番号16);
Primer 2-4: 5'-TAACCTAAATACCAACACACA-3'(配列番号17)。
The DNA after the bisulfite treatment was subjected to PCR using the following primers.
Primer set (base sequence in lower case is GC clamp):
Primer 2-1: 5'-TTTGGGGTTAGGTTTGGAAG-3 '(SEQ ID NO: 14);
Primer 2-2: 5'-ACCTTCTTCAAAATAAAACAACC-3 '(SEQ ID NO: 15);
Primer 2-3:
5'-cgcccgccgcgcgcggcgggcggggcgggggcacggggggTTTTAGAGTTTGGGTTTTAG-3 '(SEQ ID NO: 16);
Primer 2-4: 5'-TAACCTAAATACCAACACACA-3 '(SEQ ID NO: 17).
各PCRは図2に示すように、1st PCRは上記Primer 2-1とPrimer 2-2を用いて、2nd PCR(nested PCR)は上記Primer 2-3とPrimer 2-4を用いて行った。ポリメラーゼは、AmpliTaq Gold(登録商標)Fast PCR Master Mix(Applied Biosystem社製)を使用した。PCR条件及び温度設定を下記表3に示す。 Each PCR, as shown in FIG. 2, 1 st PCR by using the Primer 2-1 and Primer 2-2, 2 nd PCR (nested PCR) is performed using the Primer 2-3 and Primer 2-4 Was. As the polymerase, AmpliTaq Gold (registered trademark) Fast PCR Master Mix (manufactured by Applied Biosystem) was used. Table 3 shows the PCR conditions and temperature settings.
次いで、下記表4に示すDGGEゲル条件下の変性剤濃度勾配ゲルを使用し、2nd PCR後の反応液をDGGEに供した。なお、電気泳動条件及び電気泳動槽は、上記第1節と同様であった。 Then, using a denaturing gradient gel DGGE gel under the conditions shown in Table 4 below, was subjected to the reaction solution after 2 nd PCR to DGGE. The electrophoresis conditions and the electrophoresis tank were the same as those in the first section.
3.MSE法を用いたMUC4遺伝子プロモーターの発現に関与する被メチル化部位(CpG部位)のメチル化解析
MSE法を使用して、ヒトMUC4遺伝子プロモーターの発現に関与する被メチル化部位(CpG部位)を解析した。図3Aは、被メチル化によってヒトMUC4遺伝子の発現を調節するプロモーター領域の配列及び隣接する翻訳領域の配列(配列番号8)を示す。図3Bは、当該配列のBisulfite処理後のDNA配列(配列番号9)を示し、非メチル化シトシンから変換されたウラシルをチミンで表示する。図3において、下記で説明するPrimer 4-3とPrimer 4-4との間の領域(配列番号3)は、CpG部位108-118を有し、当該CpG部位を含む領域を標的領域とする。なお、論文(Norishige Yamada, Yukari Nishida, Hideaki Tsutsumida, Masamichi Goto, Michiyo Higashi, Mitsuharu Nomoto and Suguru Yonezawa (2009) Promoter CpG methylation in cancer cells contributes to regulation of MUC4. Br. J. Cancer, 100 (2): 344-51)と同様に、CpG部位の番号は、ヒトMUC4遺伝子の推定プロモーター上流(当該遺伝子の転写開始点より上流3,622bp)より順にナンバリングされている。
3. MSE analysis of methylated sites (CpG sites) involved in MUC4 gene promoter expression using MSE method
Using the MSE method, methylated sites (CpG sites) involved in the expression of the human MUC4 gene promoter were analyzed. FIG. 3A shows the sequence of the promoter region that regulates the expression of the human MUC4 gene by methylation and the sequence of the adjacent translation region (SEQ ID NO: 8). FIG. 3B shows the DNA sequence of the sequence after Bisulfite treatment (SEQ ID NO: 9), and uracil converted from unmethylated cytosine is indicated by thymine. In FIG. 3, a region (SEQ ID NO: 3) between Primer 4-3 and Primer 4-4 described below has CpG sites 108 to 118, and a region including the CpG site is used as a target region. Thesis (Norishige Yamada, Yukari Nishida, Hideaki Tsutsumida, Masamichi Goto, Michiyo Higashi, Mitsuharu Nomoto and Suguru Yonezawa (2009) Promoter CpG methylation in cancer cells contributes to regulation of MUC4.Br.J. Cancer, 100 (2): Similarly to (344-51), the numbers of CpG sites are numbered sequentially from the putative promoter upstream of the human MUC4 gene (3,622 bp upstream from the transcription start point of the gene).
上記第1節と同様にして、組織検体試料からDNAをDNeasy Blood & Tissue Kit(QIAGEN社製)を使用して抽出した後、EpiTect Bisulfite Kits(QIAGEN社製)を使用して、抽出したDNAをBisulfite処理に供した。 DNA was extracted from the tissue sample using the DNeasy Blood & Tissue Kit (manufactured by QIAGEN) in the same manner as in Section 1 above, and the extracted DNA was extracted using the EpiTect Bisulfite Kits (manufactured by QIAGEN). It was subjected to Bisulfite treatment.
Bisulfite処理後のDNAを、以下のプライマーを使用したPCRに供した。
プライマーセット(小文字の塩基配列はGC clampである):
Primer 4-1: 5'-TAGTGGGGTGGGGTTGA-3'(配列番号18);
Primer 4-2: 5'-AAACACCCAAAAAACCC-3'(配列番号19);
Primer 4-3:
5'-cgcccgccgcgcgcggcgggcggggcgggggcacggggggAGGAGAGAAAAGGGTGATTA-3'(配列番号20);
Primer 4-4: 5'-ACCCAAAAAACCCTCCTCCA-3'(配列番号21)。
The DNA after the bisulfite treatment was subjected to PCR using the following primers.
Primer set (base sequence in lower case is GC clamp):
Primer 4-1: 5'-TAGTGGGGTGGGGTTGA-3 '(SEQ ID NO: 18);
Primer 4-2: 5'-AAACACCCAAAAAACCC-3 '(SEQ ID NO: 19);
Primer 4-3:
5'-cgcccgccgcgcgcggcgggcggggcgggggcacggggggAGGAGAGAAAAGGGTGATTA-3 '(SEQ ID NO: 20);
Primer 4-4: 5'-ACCCAAAAAACCCTCCTCCA-3 '(SEQ ID NO: 21).
各PCRは図3に示すように、1st PCRは上記Primer 4-1とPrimer 4-2を用いて、2nd PCR(nested PCR)は上記Primer 4-3とPrimer 4-4を用いて行った。ポリメラーゼは、AmpliTaq Gold(登録商標)Fast PCR Master Mix(Applied Biosystem社製)を使用した。PCR条件及び温度設定を下記表5に示す。 Each PCR, as shown in FIG. 3, 1 st PCR by using the Primer 4-1 and Primer 4-2, 2 nd PCR (nested PCR) is performed using the Primer 4-3 and Primer 4-4 Was. As the polymerase, AmpliTaq Gold (registered trademark) Fast PCR Master Mix (manufactured by Applied Biosystem) was used. Table 5 below shows the PCR conditions and temperature settings.
次いで、下記表6に示すDGGEゲル条件下の変性剤濃度勾配ゲルを使用し、2nd PCR後の反応液をDGGEに供した。なお、電気泳動条件及び電気泳動槽は、上記第1節と同様であった。 Then, using a denaturing gradient gel DGGE gel under the conditions shown in Table 6, it was subjected to the reaction solution after 2 nd PCR to DGGE. The electrophoresis conditions and the electrophoresis tank were the same as those in the first section.
4.MSE法のゲルの写真におけるバンドの発光強度に基づく統計解析
MSE法のDGGE後のゲルの写真におけるバンドの発光強度をImage Jにより数値化し、統計解析ソフト「R」により統計処理を行った。
4. Statistical analysis based on emission intensity of bands in MSE gel photo
The emission intensity of the band in the photograph of the gel after DGGE in the MSE method was quantified by Image J, and statistical processing was performed by statistical analysis software “R”.
〔実施例1〕SVMモデル構築
1.材料及び方法
癌部84検体と、対応する症例を含む非癌部135検体におけるムチン遺伝子(MUC1、MUC2及びMUC4遺伝子から成る遺伝子セット)のDNAメチル化度を解析し、その予後情報(不・良の閾値を5カ月とし二値に分類した)を加えて訓練データとし、予後の不・良を二値判別するモデルを構築した。判別モデルは交差検定によりパラメーターを調整した。
[Example 1] Construction of SVM model Materials and Methods The degree of DNA methylation of the mucin gene (gene set consisting of MUC1, MUC2 and MUC4 genes) in 84 cancerous parts and 135 non-cancerous parts including the corresponding cases was analyzed, and the prognostic information (unsatisfactory / non-good) was obtained. The threshold was set to 5 months and the data was classified into binary), and the data were used as training data. The parameters of the discriminant model were adjusted by cross-validation.
2.結果及び考察
図4に示すように、早期ステージにおいて、モデルにより不良と判別された群は有意に予後不良であり、そのハザードレシオは腫瘍部において14.54、非腫瘍部において9.86と非常に高い角度で有意に予後不良群の鑑別が可能であった。同様に、進行ステージにおいてもモデルにより不良と判別された群は有意に予後不良であり、そのハザードレシオは腫瘍部において13.97、非腫瘍部において10.44と高精度に予後不良群のスクリーニングが可能であった。
2. Results and Discussion As shown in FIG. 4, in the early stage, the group determined to be poor by the model had a significantly poor prognosis, and the hazard ratio was 14.54 in the tumor part and 9.86 in the non-tumor part at a very high angle. The group with significantly poor prognosis could be distinguished. Similarly, at the advanced stage, the group that was determined to be poor by the model had a significantly poor prognosis, and the hazard ratio was 13.97 in the tumor area and 10.44 in the non-tumor area. Was.
〔実施例2〕SVMモデルによるリスク判別
1.材料及び方法
膵管内乳頭粘液産生腫瘍(IPMN)部20検体と、対応する症例を含む非腫瘍部29検体におけるムチン遺伝子のメチル化解析結果を、実施例1において構築したSVMモデルを用いて予後の良・不の二値判別を行った。
[Embodiment 2] Risk discrimination by SVM model Materials and Methods The results of methylation analysis of mucin gene in 20 samples of intrapancreatic papillary mucus-producing tumor (IPMN) and 29 non-tumor sites including the corresponding cases were analyzed for prognosis using the SVM model constructed in Example 1. A good / bad binary decision was made.
2.結果及び考察
実施例1において構築したSVMモデルによるテスト検体群として20例のIPMN:膵管内乳頭粘液性腫瘍(非癌)を解析し、リスク予測を行った。図5に示すように、モデルにより不良と判別された群は有意に予後不良であり、テスト群においても有意に予後不良群のスクリーニングが可能であった。
2. Results and Discussion Twenty cases of IPMN: intraductal papillary mucinous neoplasm (non-cancer) were analyzed as a test sample group by the SVM model constructed in Example 1, and risk prediction was performed. As shown in FIG. 5, the group determined to be poor by the model had a significantly poor prognosis, and the test group was also able to screen the significantly poor prognostic group.
〔実施例3〕NNETモデルによるリスク判別
1.材料及び方法
ランダムに膵癌検体25症例、非癌部検体25症例を選択し、ムチン遺伝子(MUC1、MUC2及びMUC4遺伝子から成る遺伝子セット)のDNAメチル化度を解析し、その予後情報(不・良の閾値を5カ月とし二値に分類した)を用いて予後の不・良を二値判別する判別器をニューラルネットワークにより構築した。判別モデルは交差検定によりパラメーターを調整した。
[Embodiment 3] Risk discrimination by NNET model Materials and Methods Twenty-five pancreatic cancer specimens and 25 non-cancerous specimens were randomly selected, analyzed for the degree of DNA methylation of the mucin gene (gene set consisting of MUC1, MUC2 and MUC4 genes), and analyzed for prognostic information A threshold value of 5 months was classified into binary values), and a discriminator for binarizing the poor or good prognosis using a neural network was constructed. The parameters of the discriminant model were adjusted by cross-validation.
2.結果及び考察
テスト検体群として膵癌検体81症例、非癌部検体140症例を解析し、予後リスク予測を行った。図6に示すように、単一モデルによる判別で、モデルにより良好と判別された群は有意に予後良好であり、そのハザードレシオは腫瘍部において0.363、非腫瘍部において0.356であり、腫瘍部・非腫瘍部ともハイリスク群のスクリーニングを行うことが可能であった。
2. Results and Discussion 81 test cases of pancreatic cancer samples and 140 cases of non-cancerous samples were analyzed as test sample groups to predict prognostic risk. As shown in FIG. 6, the group determined to be good by the model in the discrimination by the single model has significantly better prognosis, and the hazard ratio is 0.363 in the tumor part, 0.356 in the non-tumor part, and 0.356 in the non-tumor part. High-risk groups could be screened for both non-tumor sites.
Claims (10)
前記被験者の膵腫瘍の予後を判別するために、腫瘍組織由来の検体試料であること又は非腫瘍組織由来の検体試料であることが既知の複数の検体試料中の前記癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域のメチル化状況を測定することで得られた測定値を教師とした機械学習モデルの判別式に、第1工程で得られた癌遺伝子及び/又は癌抑制遺伝子の5'側非翻訳領域又は5'側非翻訳領域と翻訳領域とを含む領域のメチル化状況の測定値を代入する第2工程と、
を含む、膵腫瘍の予後判別方法。 A first step of measuring the methylation status of a region containing a 5′-side untranslated region or a 5′-side untranslated region and a translated region of a cancer gene and / or a tumor suppressor gene in a specimen sample derived from a subject,
In order to determine the prognosis of the pancreatic tumor of the subject, the oncogene and / or cancer suppression in a plurality of sample samples known to be a sample sample derived from tumor tissue or a sample sample derived from non-tumor tissue The discriminant of the machine learning model using the measurement value obtained by measuring the methylation status of the 5′-side untranslated region of the gene or the region including the 5′-side untranslated region and the translated region as a teacher, A second step of substituting the measured value of the methylation status of the region containing the 5′-side untranslated region or the 5′-side untranslated region and the translated region of the oncogene and / or the tumor suppressor gene obtained in the step,
A method for determining the prognosis of a pancreatic tumor, comprising:
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