JP2003304884A - Anti-cancer drug suitability prediction method - Google Patents
Anti-cancer drug suitability prediction methodInfo
- Publication number
- JP2003304884A JP2003304884A JP2003031049A JP2003031049A JP2003304884A JP 2003304884 A JP2003304884 A JP 2003304884A JP 2003031049 A JP2003031049 A JP 2003031049A JP 2003031049 A JP2003031049 A JP 2003031049A JP 2003304884 A JP2003304884 A JP 2003304884A
- Authority
- JP
- Japan
- Prior art keywords
- gene
- anticancer drug
- anticancer
- marker gene
- sample
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
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Abstract
(57)【要約】
【課題】 培養細胞株を用いて抗がん剤適合性の指標と
なりうる遺伝子を特定し、該遺伝子により未知検体の抗
がん剤適合性を予測すること。
【解決手段】 培養がん細胞株の抗がん剤感受性と該細
胞のインタクトな状態における遺伝子発現プロファイル
に基づき抗がん剤適合性マーカー遺伝子を特定する。さ
らに検体中のがん細胞における前記遺伝子の発現量と各
抗がん剤に対する該遺伝子の感受性を比較することによ
り、該検体の抗がん剤適合性を予測する。(57) [Summary] [PROBLEMS] To identify a gene that can be an index of anticancer drug suitability using a cultured cell line, and predict an anticancer drug suitability of an unknown sample using the gene. SOLUTION: An anticancer drug-compatible marker gene is identified based on the sensitivity of a cultured cancer cell line to an anticancer drug and a gene expression profile of the cell in an intact state. Further, by comparing the expression level of the gene in cancer cells in the sample with the sensitivity of the gene to each anticancer agent, the suitability of the sample for the anticancer agent is predicted.
Description
【０００１】[0001]
【発明の属する技術分野】本発明は、抗がん剤の適合性
予測方法に関する。より詳しくは、培養がん細胞株を用
いて特定される抗がん剤適合性マーカー遺伝子と該遺伝
子を利用した未知検体の抗がん剤適合性予測方法に関す
る。TECHNICAL FIELD The present invention relates to a method for predicting compatibility of anticancer agents. More specifically, the present invention relates to an anticancer drug compatibility marker gene identified by using a cultured cancer cell line and an anticancer drug compatibility prediction method for an unknown sample using the gene.
【０００２】[0002]
【従来の技術】がんは、複数の要因が複雑に関連して発
症する遺伝子の病気である。がんの病態はその種類、発
症部位或いは進行度等により様々であり、こうした多様
性と患者個人の応答性の違いが、抗がん剤による治療を
困難なものとしている。実際、固形腫瘍の多くは抗がん
剤治療が奏功しないにもかかわらず、患者の多くは骨髄
抑制や下痢などの強い副作用に苦しんでいるのが現状で
ある。このことから、投薬前に抗がん剤の有効性を予測
する方法、すなわち抗がん剤感受性診断法の開発が求め
られている。2. Description of the Related Art Cancer is a genetic disease in which multiple factors are involved in a complicated manner. The disease state of cancer varies depending on its type, onset site, degree of progression, and the like, and such diversity and difference in individual patient responsiveness make it difficult to treat with an anticancer drug. In fact, despite the fact that many solid tumors do not respond to anticancer drug treatment, most patients suffer from strong side effects such as myelosuppression and diarrhea. Therefore, there is a demand for development of a method for predicting the efficacy of an anticancer drug before administration, that is, an anticancer drug sensitivity diagnostic method.
【０００３】これまでに、抗がん剤の適合性を予測する
方法としては、患者のがん細胞と抗がん剤を試験管内で
接触させて増殖の抑制をみる、単純な「抗がん剤感受性
テスト」が試みられてきた。しかし、この検査の正診率
は約５０％程度にすぎないといわれており、有効な抗が
ん剤感受性診断法となってはいない。As a method for predicting the suitability of an anti-cancer drug, a simple “anti-cancer drug” has been used, in which cancer cells of a patient are brought into contact with an anti-cancer drug in vitro to check growth inhibition. "Drug sensitivity test" has been tried. However, it is said that the accuracy rate of this test is only about 50%, and it is not an effective anticancer drug sensitivity diagnostic method.
【０００４】がん細胞の抗がん剤感受性は、個々のがん
細胞における遺伝子発現の違いを反映しているものと考
えられる。実際、Ｐ-糖タンパク質をコードするMDR-1遺
伝子や、がん抑制遺伝子p53など、がん細胞の抗がん剤
有効性に大きく影響することが知られている遺伝子群が
多数報告されている。しかしながら、これまでに報告さ
れた遺伝子群ではがん細胞の抗がん剤有効性を系統的に
説明することはできない。昨今、ヒトゲノム解析が進
み、cDNAマイクロアレイ法などによるゲノムワイドな遺
伝子発現解析が可能になった。このような手法を用いて
個々のがんの病態や抗がん剤感受性の違いを遺伝子レベ
ルで解析し、患者毎に至適化した「オーダーメード医
療」を実現しようという動きが高まっている。The sensitivity of cancer cells to anticancer agents is considered to reflect the difference in gene expression in individual cancer cells. In fact, many gene groups that are known to greatly affect the efficacy of anticancer agents in cancer cells, such as the MDR-1 gene encoding P-glycoprotein and the tumor suppressor gene p53, have been reported. . However, the gene groups reported so far cannot systematically explain the efficacy of anticancer agents in cancer cells. Recently, the human genome analysis has advanced, and genome-wide gene expression analysis using the cDNA microarray method or the like has become possible. There is an increasing trend to analyze the pathological condition of individual cancers and differences in anticancer drug sensitivities at the gene level using such a method, and to realize “custom-made medicine” optimized for each patient.
【０００５】ＮＣＩのWeinsteinらは、さまざまなヒト
培養がん細胞株における抗がん剤感受性と遺伝子発現プ
ロファイルのデータベース化を進めており（Scherf, U.
etal.:Nat. Genet., 24 236-244, 2000）、このデータ
ベースを用いて抗がん剤感受性と遺伝子発現の相関解析
を行っている。またKiharaらは、特定の抗がん剤投与後
の予後が良い患者群と悪い患者群における遺伝子発現プ
ロファイルの違いを比較することで、抗がん剤感受性に
関連する遺伝子の探索を行っている（Kihara, C. et a
l.: Cancer Res. 61, 6474-6479, 2001）。Weinstein et al. Of NCI are proceeding with the creation of a database of anticancer drug sensitivity and gene expression profiles in various human cultured cancer cell lines (Scherf, U.
et al.:Nat. Genet., 24 236-244, 2000), using this database, we are conducting correlation analysis between anticancer drug sensitivity and gene expression. Kihara et al. Are also searching for genes related to anticancer drug sensitivity by comparing the differences in gene expression profiles between patients with good prognosis and those with poor prognosis after administration of a specific anticancer drug. (Kihara, C. et a
l .: Cancer Res. 61, 6474-6479, 2001).
【０００６】これらの方法では、いずれも膨大な遺伝子
を網羅的に解析するためにマイクロアレイが用いられ、
データはそれぞれの基準によってコンピューターで解析
処理されている。重要なことは、統計学的に意味のある
結果を得るための十分なデータ量の確保と、それに応じ
た適切な解析手段の選択である。In each of these methods, a microarray is used to comprehensively analyze a huge amount of genes,
The data are analyzed by a computer according to each standard. What is important is to secure a sufficient amount of data to obtain statistically meaningful results and to select an appropriate analysis method accordingly.
【０００７】しかしながら、現状では以下のような問題
点がある。がん細胞株を用いたWeinsteinらの報告で
は、抗がん剤感受性と遺伝子発現の相関の具体的なクラ
イテリアを示しておらず、また、抗がん剤適合性マーカ
ー遺伝子の抽出も試みていない。また、臨床検体を使用
した場合、がん細胞が本来持っている抗がん剤感受性に
加えて患者固有の抗がん剤の体内動態や肝代謝酵素群に
よる抗がん剤不活化などの要因も考慮しなければなら
ず、がん細胞自身が持っている抗がん剤感受性のデータ
を正確に得るのが難しい。このことから、遺伝子発現解
析ができても、がん細胞固有の抗がん剤感受性に関与す
る遺伝子を正確に同定する方法は未だ確立されていな
い。However, at present, there are the following problems. Weinstein et al.'S report using cancer cell lines does not show specific criteria for the correlation between anticancer drug sensitivity and gene expression, and does not attempt to extract marker genes compatible with anticancer drugs. . In addition, when clinical samples are used, in addition to the anti-cancer drug sensitivity inherent in cancer cells, factors such as the disposition of patient-specific anti-cancer drugs and inactivation of anti-cancer drugs by hepatic metabolic enzymes Therefore, it is difficult to obtain accurate data on the sensitivity of anticancer drugs possessed by cancer cells themselves. For this reason, even if gene expression analysis can be performed, a method for accurately identifying a gene involved in anticancer drug susceptibility peculiar to cancer cells has not yet been established.
【０００８】[0008]
【発明が解決しようとする課題】本発明は、培養がん細
胞株を用いて抗がん剤適合性マーカーとなりうる遺伝子
群を特定し、該遺伝子を利用して未知のがん細胞の抗が
ん剤感受性を予測する方法を確立し、患者の抗がん剤適
合性を診断する「オーダーメード医療」に役立てること
を課題とする。DISCLOSURE OF THE INVENTION The present invention uses a cultured cancer cell line to identify a group of genes that can serve as markers for compatibility with anticancer drugs, and utilizes the genes to detect the resistance of unknown cancer cells. The challenge is to establish a method for predicting drug sensitivity and to use it in "custom-made medicine" for diagnosing patient compatibility with anticancer drugs.
【０００９】[0009]
【課題を解決するための手段】本発明者らは、上記課題
を解決するため鋭意検討した結果、種々の細胞株の抗が
ん剤に対する応答性の違いは、その細胞の平常時（抗が
ん剤等を投与しない、インタクトな状態）における遺伝
子発現の違いに深く関与することに着目した。そして、
種々のがん細胞株の抗がん剤感受性と、該細胞のインタ
クトな状態における遺伝子発現プロファイルの関係を網
羅的に解析し、抗がん剤適合性の指標となりうる遺伝子
の特定に成功した。さらに、検体中のがん細胞における
該遺伝子の発現プロファイルを調べることにより、該検
体の抗がん剤適合性を予測できることを見出し本発明を
完成させた。Means for Solving the Problems As a result of diligent studies to solve the above problems, the present inventors have found that the difference in the responsiveness of various cell lines to an anticancer agent is the We paid attention to the fact that it is deeply involved in the difference in gene expression in an intact state where no drug or the like is administered. And
We have comprehensively analyzed the relationship between the anticancer drug susceptibility of various cancer cell lines and the gene expression profile in the intact state of the cells, and succeeded in identifying the gene that can be an index of anticancer drug compatibility. Furthermore, they have completed the present invention by finding that the suitability of an anti-cancer agent for the sample can be predicted by examining the expression profile of the gene in cancer cells in the sample.
【００１０】すなわち、本発明は以下の（１）〜（１
３）を提供するものである。
（１）以下の工程で選択される、抗がん剤適合性マーカ
ー遺伝子。
1) 少なくとも２０種以上のがん細胞株のそれぞれを対
象として、抗がん剤による５０％増殖阻害濃度（モル/
L）の常用対数値の絶対値Ｘiを求める；
2) インタクトな状態において、上記各細胞株における
遺伝子Ｇの発現量をコントロールに対する発現量比で表
し、その底を２とした対数値Ｙiを求める；
3) 対象がん細胞株における上記Ｘi及びＹiの値から、
ＹをＸに回帰させたときの回帰直線の傾きａ、ならびに
Ｘiの最大値Ｘmax及び最小値Ｘminを求める；
4) 少なくとも１の抗がん剤に対して、Ｐ＜０．０５、
かつ｜ａ（Ｘmax-Ｘmin）｜＞１.５の要件を満たすと
き、上記遺伝子Ｇを抗がん剤適合性マーカ遺伝子として
選択する。
（２）作用メカニズムの異なる２種以上の抗がん剤に対
して、前記工程４）の要件を満たす、上記（１）記載の
抗がん剤適合性マーカー遺伝子。
（３）作用メカニズムが共通する特定の抗がん剤に対し
てのみ、前記工程４）の要件を満たす、上記（１）記載
の抗がん剤適合性マーカー遺伝子。
（４）配列番号１〜４に示されるいずれか１の塩基配列
で特定される、抗がん剤適合性マーカー遺伝子。
（５）上記（１）〜（４）のいずれか１項に記載の遺伝
子と特異的にハイブリダイズし、該遺伝子を検出するた
めの１００〜１５００塩基の連続したポリヌクレオチ
ド。
（６）前記ポリヌクレオチドが、繰り返し配列を含ま
ず、前記抗がん剤適合性マーカー遺伝子のｍＲＮＡ
３'ＵＴＲを含む領域に特異的にハイブリダイズするも
のである、上記（５）記載のポリヌクレオチド。
（７）上記（５）又は（６）記載のポリヌクレオチドを
増幅するためのオリゴヌクレオチドプライマー。
（８）上記（５）又は（６）記載のポリヌクレオチドを
固定した、抗がん剤適合性を予測するための固相化試
料。
（９）検体中のがん細胞における上記（１）〜（４）の
いずれか１項に記載の抗がん剤適合性マーカー遺伝子の
発現量により、該検体の抗がん剤適合性を予測する方
法。
（１０）以下の工程を含む、上記（９）記載の方法。
1) 検体中のがん細胞よりmRNAを抽出し、サンプルとす
る；
2) 上記サンプルにおける、上記（１）〜（４）のいず
れか１項に記載の抗がん剤適合性マーカー遺伝子のコン
トロールに対する相対的発現量を求める；
3) 上記サンプルにおける抗がん剤適合性マーカー遺伝
子の相対的発現量と、がん細胞株における遺伝子発現量
と抗がん剤感受性の相関を利用して、上記検体の抗がん
剤適合性を予測する；
（１１）前記抗がん剤適合性マーカー遺伝子の相対的発
現量をｃＤＮＡマイクロアレイ又はRT-PCR法を用いて解
析することを特徴とする、上記（９）又は（１０）記載
の方法。
（１２）以下の１）〜３）のいずれか１つ以上を含む、
抗がん剤適合性を予測するためのキット。
１）上記（５）又は（６）記載のポリヌクレオチド
２）上記（７）記載のオリゴヌクオチドプライマー
３）上記（８）記載の固相化試料
（１３） 以下の工程を含む、抗がん剤適合性マーカー
遺伝子の選択方法。
1) 少なくとも２０種以上のがん細胞株のそれぞれを対
象として、抗がん剤による５０％増殖阻害濃度（モル/
L）の常用対数値の絶対値Ｘiを求める；
2) インタクトな状態において、上記各細胞株における
遺伝子Ｇの発現量をコントロールに対する発現量比で表
し、その底を２とした対数値Ｙiを求める；
3) 対象がん細胞株における上記Ｘi及びＹiの値から、
ＹをＸに回帰させたときの回帰直線の傾きａ、ならびに
Ｘiの最大値Ｘmax及び最小値Ｘminを求める；
4) 少なくとも１の抗がん剤に対して、Ｐ＜０．０５、
かつ｜ａ（Ｘmax-Ｘmin）｜＞１.５の要件を満たすと
き、上記遺伝子Ｇを抗がん剤適合性マーカ遺伝子として
選択する。That is, the present invention provides the following (1) to (1
3) is provided. (1) An anticancer drug compatibility marker gene selected in the following steps. 1) 50% growth inhibitory concentration (mol / mol) of anti-cancer drug for each of at least 20 types of cancer cell lines
L) Obtain the absolute value Xi of the common logarithmic value; 2) In an intact state, express the expression level of gene G in each of the above cell lines by the expression level ratio with respect to the control, and calculate the logarithmic value Yi with the base as 2. 3) From the values of Xi and Yi in the target cancer cell line,
The slope a of the regression line when Y is regressed to X, and the maximum value Xmax and the minimum value Xmin of Xi are obtained; 4) P <0.05 for at least one anticancer agent,
And when the requirement of | a (Xmax-Xmin) |> 1.5 is satisfied, the gene G is selected as an anticancer drug compatibility marker gene. (2) The anticancer drug compatibility marker gene according to (1) above, which satisfies the requirement of the above step 4) for two or more anticancer drugs having different action mechanisms. (3) The anticancer drug compatibility marker gene according to (1) above, which satisfies the requirement of the above step 4) only for specific anticancer drugs having a common action mechanism. (4) An anticancer drug compatibility marker gene specified by any one of the nucleotide sequences shown in SEQ ID NOs: 1 to 4. (5) A continuous polynucleotide of 100 to 1500 bases which specifically hybridizes with the gene according to any one of (1) to (4) above and is used for detecting the gene. (6) The polynucleotide does not include a repetitive sequence, and the mRNA of the anticancer drug compatibility marker gene
The polynucleotide according to (5) above, which specifically hybridizes to a region containing 3'UTR. (7) An oligonucleotide primer for amplifying the polynucleotide according to (5) or (6) above. (8) A solid-phased sample for predicting compatibility with an anticancer agent, to which the polynucleotide according to (5) or (6) above is immobilized. (9) The anticancer drug compatibility of the sample is predicted by the expression level of the anticancer drug compatibility marker gene according to any one of (1) to (4) above in the cancer cells in the sample. how to. (10) The method according to (9) above, which comprises the following steps. 1) mRNA is extracted from cancer cells in a sample and used as a sample; 2) Control of the marker gene for anticancer drug compatibility according to any one of (1) to (4) above in the sample 3) Using the correlation between the relative expression level of the anticancer drug compatibility marker gene in the above sample and the gene expression level in the cancer cell line and the anticancer drug sensitivity, (11) The relative expression level of the anticancer agent compatibility marker gene is analyzed by using a cDNA microarray or RT-PCR method. The method according to 9) or (10). (12) includes any one or more of the following 1) to 3),
A kit for predicting compatibility with anticancer drugs. 1) Polynucleotide according to (5) or (6) 2) Oligonucleotide primer according to (7) 3) Solid-phased sample according to (8) (13) Anti-cancer comprising the following steps Method for selecting drug compatibility marker gene. 1) 50% growth inhibitory concentration (mol / mol) of anti-cancer drug for each of at least 20 types of cancer cell lines
L) Obtain the absolute value Xi of the common logarithmic value; 2) In an intact state, express the expression level of gene G in each of the above cell lines by the expression level ratio with respect to the control, and calculate the logarithmic value Yi with the base as 2. 3) From the values of Xi and Yi in the target cancer cell line,
The slope a of the regression line when Y is regressed to X, and the maximum value Xmax and the minimum value Xmin of Xi are obtained; 4) P <0.05 for at least one anticancer agent,
And when the requirement of | a (Xmax-Xmin) |> 1.5 is satisfied, the gene G is selected as an anticancer drug compatibility marker gene.
【００１１】[0011]
【発明の実施の形態】以下、本発明について詳細に説明
する。
１．抗がん剤適合性マーカー遺伝子
「抗がん剤適合性マーカー遺伝子」とは、特定の抗がん
剤がヒトがん患者に対して有効か否か（適合性）を予測
する“指標”として使用しうる遺伝子をいう。該抗がん
剤適合性マーカー遺伝子は、以下のようにして選択され
る。BEST MODE FOR CARRYING OUT THE INVENTION The present invention will be described in detail below. 1. Anti-cancer drug compatibility marker gene "Anti-cancer drug compatibility marker gene" is used as an "index" to predict whether or not a specific anti-cancer drug is effective for human cancer patients (compatibility). Refers to genes that can be used. The anticancer drug compatibility marker gene is selected as follows.
【００１２】(1) がん細胞株における抗がん剤の５０％
増殖阻害濃度
まず、少なくとも２０種以上のがん細胞株のそれぞれを
対象として、抗がん剤による５０％増殖阻害濃度（モル
/L）の常用対数値の絶対値Ｘiを求める；上記工程で用
いられる「がん細胞株」は、ヒト由来の確立された培養
がん細胞株であって、その部位やがんの種類は限定され
ず、任意のものを用いることができる。例えば、乳がん
細胞株としてはHBC-4、BSY-1、HBC-5、MCF-7及びMDA-MB
231、神経腫細胞株としてはU251、SF-268、SF-295、SF-
539、SNB-75及びSNB-78、大腸がん細胞株としてはHCC29
98、KM-12、HT-29、WiDr、HCT-15及びHCT-116、肺がん
細胞株としてはNCI-H23、NCI-H226、NCI-H522、NCI-H48
3、A549、DMS273及びDMS114、メラノーマ細胞株として
はLOX-IMVI、卵巣がん細胞株としては、OVCAR-3、OVCAR
-4、OVCAR-5、OVCAR-8及びSK-OV-3、前立腺がん細胞株
としてはRXF-631L及びACHN、胃がん細胞株としてはSt-
4、MKN1、MKN7、MKN28、MKN45及びMKN74を用いることが
できる。(1) 50% of anticancer agents in cancer cell lines
Growth inhibition concentration First, 50% growth inhibition concentration (molar
/ L) to obtain the absolute value Xi of the common logarithmic value; the “cancer cell line” used in the above step is an established cultured cancer cell line of human origin, and its site and cancer type are There is no limitation, and any one can be used. For example, breast cancer cell lines include HBC-4, BSY-1, HBC-5, MCF-7 and MDA-MB
231, as neuroma cell line U251, SF-268, SF-295, SF-
539, SNB-75 and SNB-78, HCC29 as colon cancer cell line
98, KM-12, HT-29, WiDr, HCT-15 and HCT-116, NCI-H23, NCI-H226, NCI-H522, NCI-H48 as lung cancer cell lines
3, A549, DMS273 and DMS114, LOX-IMVI as melanoma cell line, OVCAR-3, OVCAR as ovarian cancer cell line
-4, OVCAR-5, OVCAR-8 and SK-OV-3, RXF-631L and ACHN as prostate cancer cell lines, St- as gastric cancer cell line
4, MKN1, MKN7, MKN28, MKN45 and MKN74 can be used.
【００１３】上記工程では、少なくとも２０種類以上、
好ましくは３０種類以上、より好ましくは３５種類以上
の前記細胞株を対象として、５０％増殖阻害濃度及び後
述する遺伝子の発現量を求めなければならない。対象細
胞株が少ないと信頼性の高いデータを得ることができな
いからである。In the above process, at least 20 kinds or more,
It is necessary to determine the 50% growth inhibitory concentration and the expression level of the gene described below, preferably for 30 or more types, and more preferably for 35 or more types of the cell lines. This is because reliable data cannot be obtained when the number of target cell lines is small.
【００１４】上記工程で用いられる「抗がん剤」は、が
んの治療用としてがん細胞増殖阻害効果が確認されてい
る化合物又は組成物であれば特に限定されず、市販の抗
がん剤のほか開発中の抗がん剤を用いてもよい。該抗が
ん剤は後の解析のためには、その作用メカニズムが確認
されているものが好ましい。例えば、Toremifene、Tamo
xifen、PSC833、L-Asparaginase、6-Thioguanine、6-Me
rcaptopurine、Vindecine、Docetaxel、Paclitaxel、Vi
ncristine、Vinblastine、Actinomycin-D、Tomudex、Me
thotrexate、10-EdAM、Cladribine、Gemcitabine、Enoc
itabine、Cytarabine、SN-38、Irinotecan、Camptothec
in、Neocarzinostatin、Etoposide、Mitomycin-C、Mito
xantrone、Peplomycin、Bleomycin、Thiotepa、Amsacri
ne、Carboquone、Melphalan、Pirarubicin、Epirubici
n、Doxorubicin、Daunorubicin、Genistein、Elliptici
ne、Oxaliplatin、Aclarubicin、Carmofur、5-Fluorour
acil、Tegafur、Doxifluridine、Cisplatin、Carboplat
in、Nitrogen mustard、Interferon-gamma、Interferon
-beta、Interferon-alpha、Estramustine、Dacarbazin
e、Busulfan、ICRF-154等を用いることができる。The "anticancer agent" used in the above step is not particularly limited as long as it is a compound or composition which has been confirmed to have a cancer cell growth inhibitory effect for the treatment of cancer, and is a commercially available anticancer agent. In addition to the drug, an anticancer drug under development may be used. The anticancer agent is preferably one whose mechanism of action has been confirmed for later analysis. For example, Toremifene, Tamo
xifen, PSC833, L-Asparaginase, 6-Thioguanine, 6-Me
rcaptopurine, Vindecine, Docetaxel, Paclitaxel, Vi
ncristine, Vinblastine, Actinomycin-D, Tomudex, Me
thotrexate, 10-EdAM, Cladribine, Gemcitabine, Enoc
itabine, Cytarabine, SN-38, Irinotecan, Camptothec
in, Neocarzinostatin, Etoposide, Mitomycin-C, Mito
xantrone, Peplomycin, Bleomycin, Thiotepa, Amsacri
ne, Carboquone, Melphalan, Pirarubicin, Epirubici
n, Doxorubicin, Daunorubicin, Genistein, Elliptici
ne, Oxaliplatin, Aclarubicin, Carmofur, 5-Fluorour
acil, Tegafur, Doxifluridine, Cisplatin, Carboplat
in, Nitrogen mustard, Interferon-gamma, Interferon
-beta, Interferon-alpha, Estramustine, Dacarbazin
e, Busulfan, ICRF-154, etc. can be used.
【００１５】上記工程では、任意の抗がん剤から１の抗
がん剤を選んで、対象とするがん細胞株に対する５０％
増殖阻害濃度 [GI50] を求め、その常用対数値の絶対
値：|log10[GI50]|をＸiとする。ここで、「５０％増殖
阻害濃度」とは、in vitroで任意の細胞に抗がん剤を接
触させたとき、該細胞の増殖を５０％阻害するのに必要
とされる抗がん剤のモル濃度（モル/Ｌ）をいい、該細
胞の「抗がん剤感受性」を示すものである。増殖阻害活
性は、公知の方法、例えば、スルフォローダミンＢ(SR
B)アッセイ、MTTアッセイ等を用いて評価することがで
きるが、特にスルフォローダミンＢアッセイを用いるこ
とが簡便で好ましい。In the above step, one anticancer agent is selected from arbitrary anticancer agents, and 50% of the target cancer cell line is selected.
The growth inhibitory concentration [GI 50 ] is determined, and the absolute value of its common logarithmic value: | log 10 [GI 50 ] | is defined as Xi. Here, the “50% growth inhibitory concentration” means the anticancer agent required to inhibit the growth of the cells by 50% when any cell is contacted with the anticancer agent in vitro. It refers to the molar concentration (mol / L) and indicates the "anticancer drug sensitivity" of the cells. The growth inhibitory activity can be measured by a known method, for example, sulforhodamine B (SR
B) Assay, MTT assay and the like can be used for evaluation, but it is particularly preferable to use the sulforhodamine B assay because it is convenient.
【００１６】スルフォローダミンＢアッセイによる５０
％増殖阻害濃度の測定は、公知の方法 (Monks, A. et a
l., J Natl Cancer Inst. 83, 757-766, 1991) にした
がい、例えば以下のように実施することができる。ま
ず、各がん細胞株をプレートに播種し、24時間後抗がん
剤を10倍希釈系列で5点（10XM、10X+1M、10X+2M、10X+3
M、10X+4M）とって培地に加える。48時間インキュベー
トした後、各サンプルにスルフォローダミンＢを加えて
その吸光度を測定し、セルカルチャーに含まれる総蛋白
量の変化として細胞増殖活性を測定する。ついで、薬剤
を加えずに同じ期間インキュベートしたサンプルを１０
０％、薬剤を処理する前のサンプルを０％として、細胞
増殖を５０％阻害するのに必要な薬剤濃度を算出する。50 by Sulfoledamine B Assay
The measurement of the% growth inhibitory concentration is carried out by a known method (Monks, A. et a
l., J Natl Cancer Inst. 83, 757-766, 1991), for example, as follows. First, each cancer cell line was seeded on a plate, and 24 hours later, the anticancer drug was diluted to 5 points (10 X M, 10 X + 1 M, 10 X + 2 M, 10 X + 3 M) in a 10-fold dilution series.
M, 10 X + 4 M) and add to the medium. After incubating for 48 hours, sulforhodamine B is added to each sample, the absorbance is measured, and the cell growth activity is measured as the change in the total amount of protein contained in the cell culture. Then 10 samples were incubated for the same period without drug.
The concentration of the drug required to inhibit cell growth by 50% is calculated by setting 0% as the sample before treatment with the drug as 0%.
【００１７】(2) インタクトな状態における遺伝子の発
現量比
つぎに、インタクトな状態において、上記各細胞株にお
ける遺伝子Ｇの発現量をコントロールに対する発現量比
で表し、その底を２とした対数値Ｙiを求める；上記工
程で用いられる遺伝子Ｇは、ヒト由来の遺伝子の中から
任意に選んで特定された遺伝子であって、全長遺伝子の
みならずその部分断片も含まれる。なお、本明細書中に
おいて遺伝子という用語には、DNAのみならずそのmRNA
やcDNAも含むものとする。(2) Ratio of expression level of gene in intact state Next, in the intact state, the expression level of gene G in each of the above cell lines is expressed as a ratio of expression level to control, and the logarithm of which the base is 2 Yi is determined; the gene G used in the above step is a gene specified by arbitrarily selecting it from human genes, and includes not only the full-length gene but also its partial fragment. In the present specification, the term gene includes not only DNA but also its mRNA.
And cDNA are also included.
【００１８】上記工程では、インタクトな状態におい
て、遺伝子Ｇの各細胞株における発現量を適当なコント
ロールに対する発現量比として求める。ここで、「イン
タクトな状態」とは、細胞に抗がん剤等を接触させてい
ない無傷の状態、すなわち平常状態を意味するものとす
る。また、コントロールは測定に供される各サンプル量
のバラツキを是正するための内部標準であって、特に限
定されるものではないが、解析対象とする全て又は一部
のがん細胞株由来mRNAの混合物を用いることが好まし
い。In the above step, the expression level of gene G in each cell line in an intact state is determined as an expression level ratio with respect to an appropriate control. Here, the “intact state” means an intact state in which an anticancer drug or the like is not brought into contact with cells, that is, a normal state. Further, the control is an internal standard for correcting the variation in the amount of each sample to be measured, and is not particularly limited, but all or some of the cancer cell line-derived mRNAs to be analyzed are Preference is given to using mixtures.
【００１９】また、「遺伝子の発現量」は遺伝子の発現
を定量的に示す値であればよく、例えば測定しようとす
るmRNAやcDNAを適当な標識を用いてラベルしたときの、
シグナル強度として求めることができる。すなわち、遺
伝子を蛍光ラベルにより検出すれば蛍光強度として、放
射ラベルにより検出すれば放射活性として求めることが
できる。The "gene expression level" may be any value that quantitatively indicates the expression of a gene, for example, when the mRNA or cDNA to be measured is labeled with an appropriate label,
It can be determined as the signal intensity. That is, the fluorescence intensity can be obtained by detecting a gene with a fluorescent label, and the radioactivity can be obtained by detecting with a radiolabel.
【００２０】遺伝子の発現量を解析する方法としては、
例えば、DNAチップ、cDNAマイクロアレイ、及びメンブ
レンフィルターから選ばれる固相化試料を用いた核酸ハ
イブリダイゼーション法、RT-PCR法、リアルタイムPCR
法、サブトラクション法、ディファレンシャル・ディス
プレイ法、ディファレンシャル・ハイブリダイゼーショ
ン法、ならびにクロスハイブリダイゼーション法等を挙
げることができる。As a method for analyzing the expression level of a gene,
For example, a nucleic acid hybridization method using a solid-phased sample selected from a DNA chip, a cDNA microarray, and a membrane filter, RT-PCR method, real-time PCR
Method, subtraction method, differential display method, differential hybridization method, cross-hybridization method and the like.
【００２１】(3) cDNAマイクロアレイによる遺伝子の発
現量比の検出
上記工程の好ましい態様として、遺伝子の発現量比は、
cDNAマイクロアレイを用いて検出することができる。cD
NAマイクロアレイは数千〜数万の遺伝子の発現を、定性
的かつ定量的に、一度で検出することが可能なため本工
程の実施に好適である。(3) Detection of Gene Expression Ratio by cDNA Microarray As a preferred embodiment of the above step, the gene expression ratio is
It can be detected using a cDNA microarray. cD
The NA microarray is suitable for carrying out this step because it can qualitatively and quantitatively detect the expression of thousands of genes to tens of thousands at a time.
【００２２】前記cDNAマイクロアレイは、検出対象であ
るヒト遺伝子がスポットされているものであれば特に限
定されず、既成のものを用いてもよい。cDNAマイクロア
レイはまた、公知の方法に基づいて作製することもでき
る。cDNAマイクロアレイの作製は、例えばヒト組織から
抽出したmRNA或いは市販のヒトmRNAからRT-PCR法により
cDNAを調製し、ついでこのcDNAのうち特異性の高い部分
（例えば、3’側の反復配列を含まないＵＴＲ領域）をP
CR増幅する。この作業を繰り返して目的とする全ての遺
伝子のcDNAを調製したら、これらを市販のスポッター
（例えば、Amersham社製など）を用いてスライドグラス
上にスポッティングすればよい。The cDNA microarray is not particularly limited as long as the human gene to be detected is spotted, and an existing one may be used. The cDNA microarray can also be prepared based on a known method. The cDNA microarray can be prepared, for example, by RT-PCR from mRNA extracted from human tissue or commercially available human mRNA.
Prepare cDNA, and then insert a highly specific portion of this cDNA (eg, the UTR region that does not contain the 3'repeated sequence) into P
CR amplification. After this operation is repeated to prepare the cDNAs of all the genes of interest, these may be spotted on a slide glass using a commercially available spotter (for example, manufactured by Amersham).
【００２３】cDNAマイクロアレイによる遺伝子発現量の
検出は、サンプルmRNA（或いはcDNA）を適当な試薬でラ
ベルし、これをアレイ上のcDNAとハイブリダイズさせた
ときのシグナル強度として測定することができる。遺伝
子の発現量は、通常アレイ上にスポットされたcDNA量の
バラツキを考慮し、適当なコントロールとの比較値、す
なわち発現量比として求める。該コントロールとして
は、解析対象とする全て又は一部のがん細胞株由来mRNA
の混合物を用いることが好ましい。The detection of the gene expression level by the cDNA microarray can be measured by labeling the sample mRNA (or cDNA) with an appropriate reagent and measuring the signal intensity when the sample mRNA (or cDNA) is hybridized with the cDNA on the array. The expression level of a gene is usually determined as a comparison value with an appropriate control, that is, an expression level ratio, in consideration of variations in the amount of cDNA spotted on the array. As the control, mRNA derived from all or part of the cancer cell line to be analyzed
It is preferable to use a mixture of
【００２４】以下にcDNAマイクロアレイを用いた遺伝子
発現量の測定例について説明する。対象とする細胞株よ
りmRNAを抽出し、Cy5標識dCTP存在下逆転写してCy5標識
cDNAを調製する。つぎに、解析対象とする全ての細胞株
由来のmRNAを混合し、同様の方法でCy3標識cDNAを調製
する。Cy5標識cDNA（サンプル）とCy3標識cDNA（コント
ロール）を等量ずつ混合し、アレイ上のcDNAとハイブリ
ダイズさせる。得られた蛍光強度を適当なスキャナーで
読み取り、数値化すれば、この値はコントロールに対す
るサンプルの遺伝子発現量比に相当する。なお、標識用
色素はサンプルにCy3を、コントロールにCy5を利用して
もよいし、その他の適当な標識試薬を用いてもよい。ま
た、スキャナーで読み取った蛍光強度は必要に応じて、
誤差の調整や各試料毎のばらつきの正規化を行ってもよ
い。正規化は、ハウスキーピング遺伝子等各サンプルで
共通に発現している遺伝子を基準に行うことができる。
さらに、信頼性限界ラインを特定して、相関性の低いデ
ータを除いてもよい。求めるＹiは、上記のようにして
検出された遺伝子の発現量比（Cy3/Cy5）の２を底とす
る対数値；log2(Cy3/Cy5) として算出する。An example of measuring the gene expression level using a cDNA microarray will be described below. Extract mRNA from the target cell line and reverse-transcribe it in the presence of Cy5-labeled dCTP to Cy5-label
Prepare cDNA. Next, mRNA from all cell lines to be analyzed is mixed, and Cy3-labeled cDNA is prepared by the same method. Cy5-labeled cDNA (sample) and Cy3-labeled cDNA (control) are mixed in equal amounts and hybridized with the cDNA on the array. When the obtained fluorescence intensity is read by an appropriate scanner and digitized, this value corresponds to the gene expression level ratio of the sample to the control. As the labeling dye, Cy3 may be used for the sample, Cy5 may be used for the control, or other appropriate labeling reagent may be used. In addition, the fluorescence intensity read by the scanner is
The error may be adjusted or the variation of each sample may be normalized. Normalization can be performed on the basis of genes that are commonly expressed in each sample, such as housekeeping genes.
Further, a confidence limit line may be specified to exclude data with low correlation. The Yi to be calculated is calculated as a logarithmic value whose base is 2 of the expression level ratio (Cy3 / Cy5) of the genes detected as described above; log 2 (Cy3 / Cy5).
【００２５】(4) 回帰直線の傾きa、Ｘmax及びＸmin
すべての対象がん細胞株における上記Ｘi及びＹiの値か
ら、遺伝子発現量（Ｙ）を細胞株の抗がん剤感受性
（Ｘ）に回帰させたときの「回帰直線の傾きａ」、なら
びにＸｉの最大値Ｘmax及び最小値Ｘminを求める。回帰
直線の傾きａは、例えば最小二乗法により求めることが
好ましい。(4) The slopes a, Xmax and Xmin of the regression line, from the values of Xi and Yi in all the target cancer cell lines, the gene expression level (Y) is determined as the anticancer drug sensitivity (X) of the cell lines. The "slope a of the regression line" when regressing, and the maximum value Xmax and minimum value Xmin of Xi are obtained. The slope a of the regression line is preferably obtained by, for example, the least square method.
【００２６】(5) 検定と抗がん剤適合性マーカーの選択
最後に、少なくとも１の抗がん剤に対して、Ｐ＜０．０
５、かつ|ａ(Ｘmax-Ｘmin)|＞１．５の要件を満たすと
き、前記遺伝子Ｇを抗がん剤適合性マーカー遺伝子とし
て選択する。前記「Ｐ値」は、例えばピアソン相関係数
とデータ数（対象となる全細胞株数）からＪＩＳ相関係
数検定表に基づいて求めることができる。なお、ピアソ
ン相関係数は、ピアソンの積率相関係数を意味し、次式
であらわされる値である。(5) Assay and Selection of Anticancer Drug Compatibility Marker Finally, P <0.0 for at least one anticancer drug.
5, and when the requirement of | a (Xmax-Xmin) |> 1.5 is satisfied, the gene G is selected as an anticancer drug compatibility marker gene. The “P value” can be obtained, for example, from the Pearson correlation coefficient and the number of data (the total number of target cell lines) based on the JIS correlation coefficient test table. The Pearson correlation coefficient means a Pearson product moment correlation coefficient, and is a value expressed by the following equation.
【００２７】[0027]
【数１】 i：は任意の整数[Equation 1] i: is any integer
【００２８】Ｐ値が有意水準（本発明においては、０．
０５とする）よりも小さく、感受性細胞と耐性細胞にお
ける発現量の差がある程度大きければ（本発明では|ａ
(Ｘmax-Ｘmin)|で評価するものとし、この値が１．５よ
り大とする）、抗がん剤感受性は細胞株毎にある程度の
差を有し、かつ該抗がん剤感受性と遺伝子Ｇの発現量は
有意な相関を示すと判定する。つまり、上記工程で選択
された遺伝子は、特定の抗がん剤感受性と有意な相関を
もって発現し、細胞が抗がん剤感受性を有するか否かを
予測する指標：「抗がん剤適合性マーカー」となりう
る。The P value is a significant level (in the present invention, 0.
If the difference in expression level between sensitive cells and resistant cells is large to some extent (in the present invention, | a
(Xmax-Xmin) |, and this value is greater than 1.5), and the anticancer drug sensitivity has a certain degree of difference between cell lines, and the anticancer drug sensitivity and gene It is determined that the expression level of G shows a significant correlation. That is, the gene selected in the above step is expressed with a significant correlation with a specific anticancer drug sensitivity, and an index for predicting whether or not a cell has an anticancer drug sensitivity: "anticancer drug compatibility" It can be a “marker”.
【００２９】(6) 抗がん剤適合性マーカーの具体例
以上のようにして選択された抗がん剤適合性マーカー遺
伝子としては、例えば表１に記載した1067の遺伝子を挙
げることができる。前記抗がん剤適合性マーカー遺伝子
には、
種々の抗がん剤に対して広く相関を示すもの、すなわ
ち、作用メカニズムの異なる多種の抗がん剤に対して、
Ｐ＜０．０５、かつ|ａ(Ｘmax-Ｘmin)|＞１．５の要件
を満たすマーカー遺伝子、及び
作用メカニズムが共通する特定の抗がん剤に対して選
択的相関を示すもの、すなわち、作用メカニズムが共通
する特定の抗がん剤に対して、Ｐ＜０．０５、かつ|ａ
(Ｘmax-Ｘmin)|＞１．５の要件を満たすマーカー遺伝
子、などがある。(6) Specific Examples of Anticancer Drug Compatibility Marker Examples of the anticancer drug compatibility marker gene selected as described above include the 1067 genes shown in Table 1. The anti-cancer drug compatibility marker gene, those showing a wide correlation to various anti-cancer agents, that is, to various anti-cancer agents of different action mechanism,
A marker gene satisfying the requirements of P <0.05 and | a (Xmax-Xmin) |> 1.5, and one showing a selective correlation with a specific anticancer drug having a common mechanism of action, that is, P <0.05, and | a for specific anticancer drugs with a common mechanism of action
There are marker genes that meet the requirement of (Xmax-Xmin) |> 1.5.
【００３０】前記に含まれる遺伝子としては、例えば
表１に記載された遺伝子のうち、GenBank Accession N
o.; M16985, U16954, L08246, AF070598, L41887, X965
86, U18300, Z30094, AF016370, AI077599, M13519, U4
2360, AF029082, L19605, AB011140, AA292973, AB0066
22, U17989, U02031, AI028438, X16832, AF028824, D4
5906, U63717, U26648, M74091, U48734, AA255699, AF
042384, AA514818. AF029082, X16832, D45906, U9087
8, AB011140, U48734 等が挙げられる。より具体的に
は、アルドースレダクターゼ:AKR1B1遺伝子（GenBank A
ccession No.J04795：配列番号１）、損傷DNA結合タン
パク:DDB2遺伝子（GenBank Accession No.U18300：配列
番号２）、LIMドメインキナーゼ:LIMK2遺伝子（GenBank
Accession No.D45906：配列番号３）、及びカテプシン
Ｈ:CTSH遺伝子（GenBank Accession No.X16832：配列番
号４）を挙げることができる。Examples of the genes included in the above include GenBank Accession N among the genes listed in Table 1.
o .; M16985, U16954, L08246, AF070598, L41887, X965
86, U18300, Z30094, AF016370, AI077599, M13519, U4
2360, AF029082, L19605, AB011140, AA292973, AB0066
22, U17989, U02031, AI028438, X16832, AF028824, D4
5906, U63717, U26648, M74091, U48734, AA255699, AF
042384, AA514818.AF029082, X16832, D45906, U9087
8, AB011140, U48734 and the like. More specifically, the aldose reductase: AKR1B1 gene (GenBank A
ccession No.J04795: SEQ ID NO: 1), damaged DNA binding protein: DDB2 gene (GenBank Accession No. U18300: SEQ ID NO: 2), LIM domain kinase: LIMK2 gene (GenBank
Accession No. D45906: SEQ ID NO: 3), and cathepsin H: CTSH gene (GenBank Accession No. X16832: SEQ ID NO: 4).
【００３１】また、に含まれる遺伝子としては、例え
ば、アントラサイクリン系抗生物質にのみ特異的に正の
感受性を示す遺伝子群；GenBank Accession No.; X0334
2, D38305, AA632225, U28946、アントラサイクリン系
抗生物質にのみ特異的に負の感受性を示す遺伝子群；Ge
nBank Accession No.; U51224, Z29630, M63967, L3698
3, X63679、またトポ１阻害剤に選択的に相関を示す遺
伝子群；GenBank Accession No. ; X74929, Y00503, AA
489569, X03212, J00269 等を挙げることができる。As the genes contained in, for example, a group of genes specifically showing positive sensitivity to anthracycline antibiotics; GenBank Accession No .; X0334
2, D38305, AA632225, U28946, a group of genes specifically showing negative sensitivity to anthracycline antibiotics; Ge
nBank Accession No .; U51224, Z29630, M63967, L3698
3, X63679, and gene groups that selectively correlate with topo 1 inhibitors; GenBank Accession No .; X74929, Y00503, AA
489569, X03212, J00269 and the like can be mentioned.
【００３２】これら各群に含まれるマーカー遺伝子は、
抗がん剤の適合性予測方法において、それぞれ異なった
利用方法が期待できる。例えばに含まれるマーカー遺
伝子群は、未知検体が概して抗がん剤に適合性を有する
かを広く予測したい場合に用いられ、に含まれるマー
カー遺伝子群は、未知検体がどのような種類の抗がん剤
に対して適合性を有するかどうかを判断したい場合に用
いられる。The marker genes contained in each of these groups are
Different usages can be expected in the anticancer drug compatibility prediction method. For example, the marker gene group included in is used when it is generally desired to predict whether or not an unknown sample is compatible with an anticancer drug, and the marker gene group included in It is used when it is desired to judge whether or not it is compatible with a drug.
【００３３】２．抗がん剤適合性マーカー遺伝子検出用
ポリヌクレオチド
本発明にかかる抗がん剤適合性マーカー遺伝子検出用ポ
リヌクレオチドとは、前記抗がん剤適合性マーカー遺伝
子と特異的にハイブリダイズし、該遺伝子を検出するた
めに用いられるポリヌクレオチドである。かかるポリヌ
クレオチドは、前記抗がん剤適合性マーカー遺伝子のう
ち特異性の高い領域に相補的な配列として設計すること
ができ、１００〜１５００塩基長が好ましく、２００〜
１１００塩基長であることがより好ましい。2. Polynucleotide for detecting anticancer drug compatibility marker gene The polynucleotide for detecting anticancer drug compatibility marker gene according to the present invention specifically hybridizes with the anticancer drug compatibility marker gene, and the gene Is a polynucleotide used to detect Such a polynucleotide can be designed as a sequence complementary to a highly specific region of the anticancer drug compatibility marker gene, and preferably has a nucleotide length of 100 to 1500 and preferably 200 to
More preferably, it is 1100 bases long.
【００３４】特に、前記ポリヌクレオチドは検出対象で
ある遺伝子のmRNAの3'側ＵＴＲを含む領域に相補的な配
列として設計することが望ましい。3'側ＵＴＲ領域はポ
リＡ配列から近いため、mRNAを抽出する際に壊れにくい
ばかりでなく、遺伝子の特異性が高い領域だからであ
る。なお、前記ポリヌクレオチドは、抗がん剤適合性マ
ーカー遺伝子を鋳型として、後述するプライマーを用い
たPCR法により取得することができる。In particular, it is desirable that the polynucleotide is designed as a sequence complementary to the region containing the 3'UTR of the mRNA of the gene to be detected. This is because the 3'-side UTR region is close to the poly A sequence and is not only difficult to break when mRNA is extracted, but also has a high gene specificity. The polynucleotide can be obtained by a PCR method using an anticancer drug compatibility marker gene as a template and a primer described later.
【００３５】３．抗がん剤適合性マーカー遺伝子検出用
ポリヌクレオチドの増幅用プライマー
本発明にかかるプライマーは、本発明の抗がん剤適合性
マーカー遺伝子を鋳型として、前記抗がん剤適合性マー
カー遺伝子検出用ポリヌクレオチドを特異的にPCR増幅
するためのオリゴヌクレオチドである。3. Primer for amplification of polynucleotide for detecting anticancer drug compatibility marker gene The primer according to the present invention is a polynucleotide for detecting the anticancer drug compatibility marker gene using the anticancer drug compatibility marker gene of the present invention as a template. An oligonucleotide for PCR amplification of nucleotides.
【００３６】前記プライマーは、本発明の抗がん剤適合
性マーカー遺伝子が選択されれば、その特異性の高い領
域に基づいて、市販のプライマー設計ソフトを用いるな
どして、容易に設計することができる。該プライマー
は、本発明の抗がん剤適合性マーカー遺伝子のmRNAの3'
側ＵＴＲを含む領域を特異的に増幅する１５〜２５塩基
長のオリゴヌクレオチドとして設計することが好まし
い。When the anticancer drug compatibility marker gene of the present invention is selected, the primer can be easily designed based on the highly specific region thereof by using commercially available primer designing software. You can The primer is 3'of mRNA of the anticancer drug compatibility marker gene of the present invention.
It is preferably designed as an oligonucleotide having a length of 15 to 25 bases that specifically amplifies the region containing the side UTR.
【００３７】４．抗がん剤適合性マーカー遺伝子検出用
固相化試料（cDNAマイクロアレイ等）
本発明にかかる抗がん剤適合性マーカー遺伝子検出用固
体相化試料は、前記抗がん剤適合性マーカー遺伝子検出
用ポリヌクレオチドを公知の方法に基づき、固相上に固
定することにより作製することができる。固相化試料と
しては、DNAチップ、cDNAマイクロアレイ、メンブレン
フィルター等が挙げられる。固相としては、ガラススラ
イド、金属板、ガラスキャピラリー等が挙げられる。固
定方法は、cDNAマイクロアレイのようにあらかじめ合成
したポリヌクレオチドを市販のスポッター（例えば、Am
ersham社製など）を用いてガラススライド等の固相上に
固定する方法であっても、DNAチップのように固相上で
ポリヌクレオチドを合成する方法であってもよい。前記
固相化試料は、後述する本発明の抗がん剤適合性予測方
法に用いることができる。4. Solid phase sample for detection of anti-cancer agent compatibility marker gene (cDNA microarray etc.) The solid phase sample for detection of anti-cancer agent compatibility marker gene according to the present invention is used for the detection of anti-cancer agent compatibility marker gene It can be prepared by immobilizing a polynucleotide on a solid phase based on a known method. Examples of the solid-phased sample include a DNA chip, a cDNA microarray, a membrane filter and the like. Examples of the solid phase include glass slides, metal plates, glass capillaries and the like. The immobilization method is to use a commercially available spotter (for example, Am
(manufactured by ersham, etc.) or immobilized on a solid phase such as a glass slide, or a method for synthesizing a polynucleotide on a solid phase such as a DNA chip. The immobilized sample can be used in the anticancer agent compatibility prediction method of the present invention described below.
【００３８】５．抗がん剤適合性予測方法
本発明の抗がん剤適合性マーカー遺伝子は、細胞の抗が
ん剤感受性と有意な相関をもって発現し、抗がん剤適合
性の指標として使用しうる遺伝子である。したがって、
検体中のがん細胞におけるこれらマーカー遺伝子の発現
量により、該検体の抗がん剤適合性を予測することがで
きる。ここで「検体」とは、未知のがん細胞や、がん患
者から採取されたがん細胞等を意味する。5. Anticancer agent compatibility prediction method The anticancer agent compatibility marker gene of the present invention is a gene that is expressed with a significant correlation with the anticancer agent sensitivity of cells and can be used as an index of anticancer agent compatibility. is there. Therefore,
Based on the expression levels of these marker genes in the cancer cells in the sample, the compatibility of the sample with anticancer drug can be predicted. Here, the “specimen” means an unknown cancer cell, a cancer cell collected from a cancer patient, or the like.
【００３９】(1) 抗がん剤適合性予測方法の具体的工程
被験者の抗がん剤適合性予測は、例えば以下の工程によ
り実施することができる。
1) 検体中のがん細胞よりmRNAを抽出し、サンプルとす
る；
2) 上記サンプルにおける、請求項１記載の抗がん剤適
合性マーカー遺伝子のコントロールに対する相対的発現
量を求める；
3) 上記サンプルにおける抗がん剤適合性マーカー遺伝
子の相対的発現量と、がん細胞株における遺伝子発現量
と抗がん剤感受性の相関を利用して、上記検体の抗がん
剤適合性を予測する；工程１において、検体中のがん細
胞からのmRNAの抽出は、公知の方法に基づき、例えばQi
agen社製RNeasy RNA purification kit などを用いて実
施することができる。抽出されたmRNAは、必要であれば
T7 RNAポリメラーゼを用いた増幅法により増幅して用い
てもよい。(1) Specific Steps of Anticancer Agent Compatibility Prediction Method The anticancer agent compatibility prediction of a subject can be carried out, for example, by the following steps. 1) mRNA is extracted from cancer cells in a sample and used as a sample; 2) Relative expression level of the anticancer drug compatibility marker gene of claim 1 in the sample is determined; 3) Above Predict the anticancer drug suitability of the above sample by using the relative expression level of the anticancer drug compatibility marker gene in the sample and the correlation between the gene expression level in the cancer cell line and the anticancer drug sensitivity In the step 1, mRNA extraction from the cancer cells in the sample is performed according to a known method such as Qi
It can be carried out using RNeasy RNA purification kit manufactured by agen. The extracted mRNA can be
It may be used after being amplified by an amplification method using T7 RNA polymerase.
【００４０】工程２において、抗がん剤適合性マーカー
遺伝子の発現量は、コントロールに対する相対的発現量
として求める。コントロールは測定に供されるサンプル
量のバラツキを是正するための内部標準であって、特に
限定されるものではないが、抗がん剤適合性マーカー遺
伝子の選択において、対象とした全て又は一部のがん細
胞株由来のmRNAの混合物を用いることが好ましい。In step 2, the expression level of the anticancer drug compatibility marker gene is determined as a relative expression level with respect to the control. The control is an internal standard for correcting the variation in the amount of sample used for measurement, and is not particularly limited, but all or part of the target in the selection of anticancer drug compatibility marker gene It is preferable to use a mixture of mRNAs derived from the above cancer cell lines.
【００４１】遺伝子の相対的発現量の検出は、特に限定
されず、例えばRT-PCR法(Taqmanなどのreal time含
む)、Northern blot法、ATAC-PCR法、オリゴアレイ（ア
フィメトリクス社製等）、DNAアレイ、cDNAマイクロア
レイ、及びメンブレンフィルター等の固相化試料を用い
た核酸ハイブリダイゼーション法、サブトラクション
法、ディファレンシャル・ディスプレイ法、ディファレ
ンシャル・ハイブリダイゼーション法、ならびにクロス
ハイブリダイゼーション法など、任意の遺伝子発現定量
法を用いることができる。Detection of the relative expression level of the gene is not particularly limited, and examples thereof include RT-PCR method (including real time such as Taqman), Northern blot method, ATAC-PCR method, oligo array (Affymetrix, etc.), Arbitrary gene expression quantification method such as nucleic acid hybridization method, subtraction method, differential display method, differential hybridization method, and cross hybridization method using solid-phased samples such as DNA array, cDNA microarray, and membrane filter Can be used.
【００４２】なかでも、cDNAマイクロアレイを用いて検
出する方法は多数の遺伝子の発現プロファイルを一度に
解析する場合に簡便で好ましい。該cDNAマイクロアレイ
は、本発明の抗がん剤適合性マーカー遺伝子を検出でき
るものであればよく、例えば、前項４．に記載した本発
明のマイクロアレイを用いることができる。cDNAマイク
ロアレイによる抗がん剤適合性マーカー遺伝子の相対的
発現量は、１．の(3)に記載した方法にしたがって検出
することができる。すなわち、サンプルmRNAをCy5標識c
DNAとし、コントロールmRNAをCy3標識cDNAとし、各々等
量ずつ混合してcDNAマイクロアレイにハイブリダイズさ
せ、蛍光強度を読み取ればよい。Among them, the method of detecting using a cDNA microarray is preferable because it is convenient when analyzing the expression profiles of many genes at once. The cDNA microarray may be any as long as it can detect the anticancer drug compatibility marker gene of the present invention. The microarray of the present invention described in 1. can be used. The relative expression level of the anticancer drug compatibility marker gene by the cDNA microarray was 1. It can be detected according to the method described in (3). That is, the sample mRNA was labeled with Cy5 c
Fluorescence intensity may be read by using DNA as control DNA and Cy3 labeled cDNA as control mRNA, mixing equal amounts of each and hybridizing to a cDNA microarray.
【００４３】また、RT-PCR法やその１つであるリアルタ
イムPCR（TaqMan PCR）法は微量なDNAを高感度かつ定量
的に検出できるという点で好ましい。RT-PCRでは、検出
すべき遺伝子、例えばAKR1遺伝子（配列番号１）のmRNA
を逆転写し、これを該遺伝子を特異的に増幅するプライ
マー（例えば、配列番号５及び配列番号６に示される塩
基配列からなるプライマー）によりPCR増幅する。増幅
産物は、電気泳動等により分離し、定量することができ
る。リアルタイムPCR（TaqMan PCR）法は、5’端を蛍光
色素（レポーター）で、3’端を蛍光色素（クエンチャ
ー）で標識した、目的遺伝子の特定領域にハイブリダイ
ズするオリゴヌクレオチドプローブを用いて検出を行
う。該プローブは、通常の状態ではクエンチャーによっ
てレポーターの蛍光が抑制されている。この蛍光プロー
ブを目的遺伝子に完全にハイブリダイズさせた状態で、
その外側からTaq DNAポリメラーゼを用いてPCRを行う。
TaqDNAポリメラーゼによる伸長反応が進むと、そのエキ
ソヌクレアーゼ活性により蛍光プローブが5’端から加
水分解され、レポーター色素が遊離し、蛍光を発する。
リアルタイムPCR法は、この蛍光強度をリアルタイムで
モニタリングすることにより、鋳型DNAの初期量を正確
に定量することができる。The RT-PCR method and the real-time PCR (TaqMan PCR) method, which is one of the RT-PCR methods, are preferable in that a trace amount of DNA can be detected with high sensitivity and quantitatively. In RT-PCR, the gene to be detected, eg mRNA of AKR1 gene (SEQ ID NO: 1)
Is reverse-transcribed, and this is PCR-amplified with a primer that specifically amplifies the gene (for example, a primer consisting of the nucleotide sequences shown in SEQ ID NO: 5 and SEQ ID NO: 6). Amplification products can be separated and quantified by electrophoresis or the like. The real-time PCR (TaqMan PCR) method uses an oligonucleotide probe that hybridizes to a specific region of the gene of interest, labeled with a fluorescent dye (reporter) at the 5'end and a fluorescent dye (quencher) at the 3'end. I do. In the normal state of the probe, the fluorescence of the reporter is suppressed by the quencher. With this fluorescent probe completely hybridized to the target gene,
PCR is performed using Taq DNA polymerase from the outside.
When the extension reaction by Taq DNA polymerase proceeds, the fluorescent probe is hydrolyzed from the 5 ′ end by its exonuclease activity, the reporter dye is released, and fluorescence is emitted.
The real-time PCR method can accurately quantify the initial amount of template DNA by monitoring the fluorescence intensity in real time.
【００４４】工程３において、未知検体の抗がん剤適合
性は、前記抗がん剤適合性マーカー遺伝子の相対的発現
量と、がん細胞株における遺伝子発現量と抗がん剤感受
性の相関を利用して予測する。ここで、「がん細胞株に
おける遺伝子発現量と抗がん剤感受性の相関」とは、
１．に記載した、培養がん細胞株における遺伝子発現量
（Ｙ）と該細胞株の抗がん剤感受性＝５０％増殖阻害濃
度（Ｘ）との相関を意味する。In step 3, the anticancer drug compatibility of the unknown sample is determined by the correlation between the relative expression level of the anticancer drug compatibility marker gene and the gene expression level in the cancer cell line and the anticancer drug sensitivity. Use to predict. Here, "correlation between gene expression level in cancer cell line and anticancer drug sensitivity" means
1. It means the correlation between the gene expression level (Y) in the cultured cancer cell line and the anti-cancer agent sensitivity = 50% growth inhibitory concentration (X) of the cell line described in 1.
【００４５】(2)検体の抗がん剤適合性予測
検体中のがん細胞において相対的発現量が高い遺伝子群
が特定の抗がん剤Ｇに対して相関が高い遺伝子群と一致
する率が高ければ、該検体はその抗がん剤Ｇに対して適
合性であることが予測できる。一方、検体中のがん細胞
において相対的発現量が高い遺伝子群が、特定の抗がん
剤Ｇに対して相関が高い遺伝子群と全く一致しなけれ
ば、該検体はその抗がん剤Ｇに対して適合性が低いこと
が予測できる。(2) Prediction of anticancer drug suitability of a sample The rate at which a gene group having a high relative expression level in cancer cells in a sample matches a gene group having a high correlation with a specific anticancer drug G Is high, it can be predicted that the sample is compatible with the anticancer drug G. On the other hand, if the gene group having a high relative expression level in the cancer cells in the sample does not match the gene group having a high correlation with the specific anticancer agent G, the sample has the anticancer agent G. Can be expected to be poorly compatible with.
【００４６】上記の例は最も単純な予測方法であり、抗
がん剤適合性マーカー遺伝子群の同定に用いたがん細胞
株において学習された遺伝子発現と抗がん剤感受性の相
関から、検体中のがん細胞の抗がん剤適合性マーカー遺
伝子群の発現プロファイルをコンピューターを用いて比
較解析することにより、多数の抗がん剤適合性マーカー
遺伝子を指標としたより信頼性の高い適合性予測をする
こともできる。The above-mentioned example is the simplest prediction method, and the correlation between the gene expression learned in the cancer cell line used for the identification of the anticancer drug compatibility marker gene group and the anticancer drug sensitivity is used to determine the specimen. Computer-based comparative analysis of the expression profiles of anti-cancer drug compatibility marker genes in cancer cells to obtain more reliable compatibility using a number of anti-cancer drug compatibility marker genes as indicators You can also make predictions.
【００４７】すなわち、本発明の抗がん剤適合性マーカ
ー遺伝子の「遺伝子発現と抗がん剤感受性の相関」にか
かるデータ、或いはこれを記録したコンピューターで読
み取り可能な記録媒体は、抗がん剤適合性予測方法の有
用なツールとして利用できる。That is, the data relating to the “correlation between gene expression and anticancer drug sensitivity” of the anticancer drug compatibility marker gene of the present invention, or a computer-readable recording medium recording this data is an anticancer drug. It can be used as a useful tool for drug compatibility prediction methods.
【００４８】６．抗がん剤適合性予測用キット
本発明はまた、抗がん剤の適合性を予測するためのキッ
トを提供する。該キットは、既に説明した以下の１）〜
３）の少なくとも１つ以上を含む。
１）抗がん剤適合性マーカー遺伝子検出用ポリヌクレオ
チド
２）抗がん剤適合性マーカー遺伝子検出用ポリヌクレオ
チドの増幅用プライマー
３）抗がん剤適合性マーカー遺伝子検出用固相化試料
上記ポリヌクレオチドやオリゴヌクレオチドプライマー
は放射性物質、蛍光色素、酵素等で標識されていてもよ
いし、適当なリンカーが付加されていてもよい。また、
キットには、上記構成要素のほか、本発明の抗がん剤適
合性予測方法を実施するために必要な他の試薬、酵素、
反応液等を含んでいても良い。6. Anticancer Agent Compatibility Prediction Kit The present invention also provides a kit for predicting the compatibility of anticancer agents. The kit includes the following 1) to
At least one or more of 3) is included. 1) Polynucleotide for detecting anticancer drug compatibility marker gene 2) Primer for amplifying polynucleotide for detecting anticancer drug compatibility marker gene 3) Solid phase sample for detecting anticancer drug compatibility marker gene The nucleotide or oligonucleotide primer may be labeled with a radioactive substance, a fluorescent dye, an enzyme or the like, or an appropriate linker may be added. Also,
In addition to the above-mentioned components, the kit contains other reagents, enzymes, necessary for carrying out the anticancer drug compatibility prediction method of the present invention.
It may contain a reaction solution or the like.
【００４９】[0049]
【実施例】以下、実施例を用いて本発明について詳細に
説明するが、本発明の範囲はかかる実施例に限定される
ものではない。The present invention will be described in detail below with reference to examples, but the scope of the present invention is not limited to these examples.
【００５０】実施例１：ヒトがん細胞株における抗がん
剤感受性プロファイル
＜被験試料＞
がん細胞株（39種）
乳がん：HBC-4、BSY-1、HBC-5、MCF-7、MDA-MB231、
神経腫：U251、SF-268、SF-295、SF-539、SNB-75、SNB-
78、
大腸がん：HCC2998、KM-12、HT-29、WiDr、HCT-15、HCT
-116、
肺がん：NCI-H23、NCI-H226、NCI-H522、NCI-H483、A54
9、DMS273、DMS114、
メラノーマ：LOX-IMVI、
卵巣がん：OVCAR-3、OVCAR-4、OVCAR-5、OVCAR-8、SK-O
V-3、
前立腺がん：RXF-631L、ACHN、
胃がん：St-4、MKN1、MKN7、MKN28、MKN45、MKN74、
抗がん剤（55種）
Toremifene、Tamoxifen、PSC833、L-Asparaginase、6-T
hioguanine、6-Mercaptopurine、Vindecine、Docetaxe
l、Paclitaxel、Vincristine、Vinblastine、Actinomyc
in-D、Tomudex、Methotrexate、10-EdAM、Cladribine、
Gemcitabine、Enocitabine、Cytarabine、SN-38、Irino
tecan、Camptothecin、Neocarzinostatin、Etoposide、
Mitomycin-C、Mitoxantrone、Peplomycin、Bleomycin、
Thiotepa、Amsacrine、Carboquone、Melphalan、Piraru
bicin、Epirubicin、Doxorubicin、Daunorubicin、Geni
stein、Ellipticine、Oxaliplatin、Aclarubicin、Carm
ofur、5-Fluorouracil、Tegafur、Doxifluridine、Cisp
latin、Carboplatin、Nitrogen mustard、Interferon-g
amma、Interferon-beta、Interferon-alpha、Estramust
ine、Dacarbazine、Busulfan、ICRF-154Example 1: Anticancer drug sensitivity profile in human cancer cell line <Test sample> Cancer cell line (39 types) Breast cancer: HBC-4, BSY-1, HBC-5, MCF-7, MDA -MB231, Neuroma: U251, SF-268, SF-295, SF-539, SNB-75, SNB-
78, Colorectal cancer: HCC2998, KM-12, HT-29, WiDr, HCT-15, HCT
-116, Lung cancer: NCI-H23, NCI-H226, NCI-H522, NCI-H483, A54
9, DMS273, DMS114, melanoma: LOX-IMVI, ovarian cancer: OVCAR-3, OVCAR-4, OVCAR-5, OVCAR-8, SK-O
V-3, Prostate cancer: RXF-631L, ACHN, Gastric cancer: St-4, MKN1, MKN7, MKN28, MKN45, MKN74, Anticancer drug (55 types) Toremifene, Tamoxifen, PSC833, L-Asparaginase, 6- T
hioguanine, 6-Mercaptopurine, Vindecine, Docetaxe
l, Paclitaxel, Vincristine, Vinblastine, Actinomyc
in-D, Tomudex, Methotrexate, 10-EdAM, Cladribine,
Gemcitabine, Enocitabine, Cytarabine, SN-38, Irino
tecan, Camptothecin, Neocarzinostatin, Etoposide,
Mitomycin-C, Mitoxantrone, Peplomycin, Bleomycin,
Thiotepa, Amsacrine, Carboquone, Melphalan, Piraru
bicin, Epirubicin, Doxorubicin, Daunorubicin, Geni
stein, Ellipticine, Oxaliplatin, Aclarubicin, Carm
ofur, 5-Fluorouracil, Tegafur, Doxifluridine, Cisp
latin, Carboplatin, Nitrogen mustard, Interferon-g
amma, Interferon-beta, Interferon-alpha, Estramust
ine, Dacarbazine, Busulfan, ICRF-154
【００５１】＜試験方法＞
１．増殖阻害活性（抗がん剤感受性）の測定
各がん細胞株（39種）を96ウェルプレートに播種し、翌
日抗がん剤を10倍希釈系列で5点とり培地に加えた。48
時間インキュベート後、各サンプルにおける増殖阻害活
性をスルフォローダミンＢアッセイにより、セルカルチ
ャーに含まれる総蛋白量の変化として測定した。図１に
試験の概略とスルフォローダミンＢアッセイの写真を示
す。得られた測定値より、公知の方法(Monks, A. et a
l., J Natl Cancer Inst. 83, 757-766, 1991)に基づき
50％増殖阻害に必要な薬剤濃度（GI50）を求める。これ
を繰り返し、39種類の対象がん細胞株全てに対する増殖
阻害活性を求めた。<Test method> 1. Measurement of growth inhibitory activity (anticancer drug sensitivity) Each cancer cell line (39 kinds) was seeded on a 96-well plate, and the next day, the anticancer drug was added to the medium at 5 points in a 10-fold dilution series. 48
After incubation for a period of time, the growth inhibitory activity in each sample was measured by the sulforudamin B assay as a change in the total amount of protein contained in the cell culture. FIG. 1 shows an outline of the test and a photograph of the sulforhodamine B assay. From the measured values obtained, a known method (Monks, A. et a
l., J Natl Cancer Inst. 83, 757-766, 1991).
Determine the drug concentration required for 50% growth inhibition (GI 50 ). This was repeated to determine the growth inhibitory activity against all 39 target cancer cell lines.
【００５２】＜結果＞図２に、乳がん細胞株5系につい
て、トポ１阻害剤カンプトテシンを処理した際の増殖阻
害を示す。MCF7とHBC4は10-6M以下で50％阻害され、カ
ンプトテシンに感受性であると考えられた。また、その
他の3系は50％阻害に10-5M程度を必要とし、カンプトテ
シンに耐性であると考えられた。また、各細胞株におけ
る抗がん剤感受性は大きく異なっていた。このようなが
ん細胞の性質は、個々の細胞株おける遺伝子発現の違い
を反映しているものと考えられた。<Results> FIG. 2 shows the growth inhibition when the topo 1 inhibitor camptothecin was treated in the breast cancer cell line 5 line. MCF7 and HBC4 were inhibited by 50% at 10-6 M or less, and were considered to be sensitive to camptothecin. In addition, the other three systems required about 10-5M for 50% inhibition, and were considered to be resistant to camptothecin. In addition, the anticancer drug sensitivities of the cell lines differed greatly. Such properties of cancer cells were considered to reflect differences in gene expression in individual cell lines.
【００５３】実施例２：抗がん剤感受性遺伝子の発現プ
ロファイル
実施例１で、さまざまな抗がん剤への感受性が明らかに
された39種類のがん細胞株における遺伝子発現プロファ
イル（遺伝子発現の総体的特徴）を解析し、これまでに
蓄積された薬剤感受性データとの統合データベース化を
はかることにより、抗がん剤感受性に統計学的に有意な
相関を示す遺伝子群を抽出することを試みた。まず、各
抗がん剤感受性が明らかにされた39種類のがん細胞株に
おける遺伝子発現プロファイルを、cDNAマイクロアレイ
を用いて解析した。なお、cDNAマイクロアレイは、約90
00種類のヒトcDNAがスポットされたものを作製して用い
た。Example 2 Expression Profile of Anticancer Drug Sensitivity Gene Gene expression profiles in 39 types of cancer cell lines whose sensitivity to various anticancer drugs was clarified in Example 1 (gene expression We tried to extract a group of genes showing a statistically significant correlation with anticancer drug susceptibility by analyzing the overall characteristics) and creating an integrated database with the drug sensitivity data accumulated so far. It was First, the gene expression profiles in 39 types of cancer cell lines whose susceptibility to each anticancer drug was revealed were analyzed using a cDNA microarray. It should be noted that the cDNA microarray has about 90
00 spots of human cDNA were prepared and used.
【００５４】１．cDNAマイクロアレイによるmRNA発現プ
ロファイルの解析
1) 試料の調製
各細胞株からmRNAを抽出し、Cy5でラベルしたdCTP存在
下逆転写し、Cy5標識cDNAを作製した。コントロールと
して、39種類の細胞株から抽出したmRNAを等量混合し、
Cy3でラベルしたdCTP存在下逆転写し、Cy3標識cDNAを作
製した。最後に上記のmRNA（cDNA）サンプルを等量ずつ
混合し、マイクロアレイ アプライ用試料とした。
2) mRNA発現量比の測定
調製した試料をマイクロアレイにハイブリダイズさせ、
スキャナー（Gen IIIarray scanner：Amersham社製）で
蛍光強度を読み取った。得られた画像イメージはソフト
ウェア“Array Vision”（Imaging research社製）を用
いて数値化した。つぎに、ハウスキーピング遺伝子の発
現を基に各試料毎のばらつきを正規化し、さらに信頼性
限界ラインを特定して、相関性の低いデータを除いた。
こうしてコントロールに対するサンプルmRNAにおける各
遺伝子の相対的発現量が求められた。図３にcDNAマイク
ロアレイを用いたmRNA発現量の検出方法の概略を示す。
3) mRNA発現プロファイルの解析
特定の抗がん剤及び特定の遺伝子について、実施例１で
求めた抗がん剤の各細胞に対するGI50の常用対数値の絶
対値：|log10[GI50]| を横軸に、各細胞における遺伝子
の発現量比（Cy5/Cy3）の底を２とした対数値：log2(Cy
5/Cy3) を縦軸にとり、39細胞についてそれぞれＸＹ平
面上にプロットした。そして、Ｘの最大値Ｘmax最小値
Ｘminを求めた。つぎに39点の回帰直線を引き、最小二
乗法により回帰直線の傾きaとa(Ｘmax-Ｘmin)、ピアソ
ン相関係数ｒを求め、Ｐ＜０．０５（すなわち相関係数
0.32より大）、｜a(Ｘmax-Ｘmin)｜＞１．５の要件を満
たす遺伝子を有意な相関がある遺伝子とした。1. Analysis of mRNA expression profile by cDNA microarray 1) Preparation of sample mRNA was extracted from each cell line and reverse-transcribed in the presence of Cy5-labeled dCTP to prepare Cy5-labeled cDNA. As a control, equal amounts of mRNA extracted from 39 cell lines were mixed,
Reverse transcription was carried out in the presence of Cy3 labeled dCTP to prepare Cy3 labeled cDNA. Finally, the above mRNA (cDNA) samples were mixed in equal amounts to prepare a sample for microarray application. 2) Measurement of mRNA expression ratio Hybridize the prepared sample to a microarray,
The fluorescence intensity was read with a scanner (Gen III array scanner: manufactured by Amersham). The obtained image image was digitized using software "Array Vision" (manufactured by Imaging research). Next, the variation for each sample was normalized based on the expression of the housekeeping gene, and the reliability limit line was specified to remove data with low correlation.
In this way, the relative expression level of each gene in the sample mRNA with respect to the control was determined. FIG. 3 shows the outline of the method for detecting the mRNA expression level using a cDNA microarray. 3) Analysis of mRNA expression profile For a specific anticancer drug and a specific gene, the absolute value of the common logarithmic value of GI 50 for each cell of the anticancer drug determined in Example 1 is: | log 10 [GI 50 ]. | Is the horizontal axis and the logarithmic value with the base of the gene expression ratio (Cy5 / Cy3) in each cell as 2 is: log 2 (Cy
5 / Cy3) was plotted on the vertical axis, and 39 cells were plotted on the XY plane. Then, the maximum value Xmax and the minimum value Xmin of X were obtained. Next, a 39-point regression line is drawn and the slopes a and a (Xmax-Xmin) of the regression line and the Pearson correlation coefficient r are obtained by the least squares method, and P <0.05 (that is, the correlation coefficient
Genes satisfying the requirement of | a (Xmax-Xmin) |> 1.5 were determined to have a significant correlation.
【００５５】図４に、LIMK2遺伝子（LIM domain kinase
2 gene, GenBank Accession No. D45906）を例にした
相関解析を示す。LIMK2遺伝子の発現量が高いほどエト
ポシドに耐性になる、すなわち、薬剤耐性因子候補と言
うことができる。逆に、相関係数が正のものは薬剤感受
性因子候補ということができる。FIG. 4 shows the LIMK2 gene (LIM domain kinase).
2 gene, GenBank Accession No. D45906) is shown as an example. The higher the expression level of the LIMK2 gene, the more resistant it is to etoposide, that is, it can be said to be a drug resistance factor candidate. On the contrary, those having a positive correlation coefficient can be said to be drug sensitivity factor candidates.
【００５６】以上の相関解析を9000遺伝子×55薬剤＝５
０万回行い、少なくとも１の薬剤について有意な相関を
示した遺伝子のみを選択したところ、アレイ上の9000種
類の遺伝子のうち以下に示す1067種類の遺伝子が選択さ
れた。表１に選択された遺伝子とそのGenBank Accessio
n No.及びUniGene Code(2001年3月)を示す。Based on the above correlation analysis, 9000 genes × 55 drugs = 5
When the gene which showed significant correlation with at least one drug was selected for 0,000 times, 1067 kinds of genes shown below were selected from the 9,000 kinds of genes on the array. Selected genes in Table 1 and their GenBank Accessio
n No. and UniGene Code (March 2001) are shown.
【００５７】[0057]
【表１】 [Table 1]
【００５８】２．クラスター解析
選択された1067の遺伝子について、55薬剤との相関をよ
り直接的に示すためにピアソン相関係数を基にした２次
元クラスター解析を行った（図５）。さらに、アントラ
サイクリン系薬剤（Epirubicin, Daunorubicin, Doxoru
bicin）とトポ１阻害剤（SN-38, Irinotecan, Camptoth
ecin）について、これらに有意な相関を示す遺伝子群
（各薬剤群3剤中2剤以上で有意な相関を示す遺伝子群）
を抽出し、それらの遺伝子の55剤に対するピアソン相関
係数を基にした２次元クラスター解析を行った（図６、
図７）。図中、赤色は正の相関、緑色は負の相関を示
す。2. Cluster analysis For the selected 1067 genes, two-dimensional cluster analysis based on the Pearson correlation coefficient was performed in order to more directly show the correlation with 55 drugs (Fig. 5). In addition, anthracyclines (Epirubicin, Daunorubicin, Doxoru
bicin) and topo 1 inhibitor (SN-38, Irinotecan, Camptoth
ecin), a gene group that shows a significant correlation with these (a gene group that shows a significant correlation with 2 or more out of 3 drugs in each drug group)
Were extracted and subjected to a two-dimensional cluster analysis based on the Pearson correlation coefficient of 55 genes of those genes (Fig. 6,
(Fig. 7). In the figure, red indicates a positive correlation and green indicates a negative correlation.
【００５９】＜結果＞
アントラサイクリン系薬剤のクラスター解析（図６）：
正の相関を示す遺伝子群（感受性遺伝子群）では、表中
のDDB2（GenBank Accession No.U18300：配列番号２）
を含むクラスターに含まれる遺伝子群（GenBankAccessi
on No.; M16985, U16954, L08246, AF070598, L41887,
X96586, U18300, Z30094, AF016370, AI077599, M1351
9, U42360）は、アントラサイクリンに限らず、トポ１
阻害剤やブレオマイシンなどさまざまな抗がん剤に相関
を示すのに対し、TOB1（GenBank Accession No.D38305)
などを含むクラスターに含まれる遺伝子群（GenBank Ac
cession No.; X03342, D38305, AA632225, U28946）は
アントラサイクリン系抗生物質にのみ特異的に相関を示
した。<Results> Cluster analysis of anthracycline drugs (FIG. 6):
In the gene group showing a positive correlation (susceptibility gene group), DDB2 (GenBank Accession No. U18300: SEQ ID NO: 2) in the table
Of genes included in the cluster containing (GenBankAccessi
on No .; M16985, U16954, L08246, AF070598, L41887,
X96586, U18300, Z30094, AF016370, AI077599, M1351
9, U42360) is not limited to anthracyclines, but topo 1
While showing a correlation with various anticancer agents such as inhibitors and bleomycin, TOB1 (GenBank Accession No.D38305)
Gene groups included in the cluster including (GenBank Ac
cession No .; X03342, D38305, AA632225, U28946) showed a specific correlation only with anthracycline antibiotics.
【００６０】一方、負の相関を示す遺伝子群（耐性遺伝
子群）も、カテプシンＨ（GenBankAccession No.X1683
2：配列番号４）やサイクリンＣ（GenBank Accession N
o. M74091）を含むクラスターに含まれる遺伝子群（Gen
Bank Accession No.; AF029082, L19605, AB011140, AA
292973, AB006622, U17989, U02031, AI028438, X1683
2, AF028824, D45906（配列番号３）, U63717, U26648,
M74091, U48734, AA255699, AF042384, AA514818）は
さまざまなタイプの抗がん剤に共通して相関を示すのに
対し、アルデヒドデヒドロゲナーゼ５（GenBank Access
ion No.; M63967）などの遺伝子群（GenBank Accession
No.; U51224, Z29630, M63967, L36983,X63679）はア
ントラサイクリン系抗生物質にのみ特異的な相関を示し
た。On the other hand, a group of genes showing negative correlation (group of resistance genes) was also cathepsin H (GenBank Accession No. X1683).
2: SEQ ID NO: 4) and cyclin C (GenBank Accession N)
o. M74091) gene group (Gen
Bank Accession No .; AF029082, L19605, AB011140, AA
292973, AB006622, U17989, U02031, AI028438, X1683
2, AF028824, D45906 (SEQ ID NO: 3), U63717, U26648,
M74091, U48734, AA255699, AF042384, AA514818) are commonly associated with various types of anticancer agents, whereas aldehyde dehydrogenase 5 (GenBank Access
ion No .; M63967) and other gene groups (GenBank Accession
No .; U51224, Z29630, M63967, L36983, X63679) showed a specific correlation only with anthracycline antibiotics.
【００６１】トポ１阻害剤のクラスター解析（図７）：
正の相関を示す遺伝子群は、トポ阻害剤に選択的な相関
を示す遺伝子群は抽出されなかったが、負の相関を示す
耐性遺伝子候補群は、カテプシンＨ（GenBankAccession
No. X16832：配列番号４）や14-3-3proteinなど多くの
抗がん剤に相関を示す遺伝子群（GenBank Accession N
o. ; AF029082, X16832, D45906, U90878, AB011140, U
48734）と、ケラチン群のようにトポ１阻害剤に選択的
に相関を示す遺伝子群（GenBank Accession No. ; X749
29, Y00503, AA489569, X03212,J00269）に分類され
た。Cluster analysis of Topo 1 inhibitors (FIG. 7):
Genes showing a positive correlation were not extracted from genes showing a selective correlation with topo inhibitors, but resistance gene candidates showing a negative correlation were cathepsin H (GenBank Accession).
No. X16832: Gene group (GenBank Accession N) showing correlation with many anticancer agents such as SEQ ID NO: 4) and 14-3-3 protein
o.; AF029082, X16832, D45906, U90878, AB011140, U
48734) and a gene group (GenBank Accession No .; X749) that selectively correlates with a topo-1 inhibitor like the keratin group.
29, Y00503, AA489569, X03212, J00269).
【００６２】＜結論＞相関係数のクラスター解析の結
果、抽出された1067の遺伝子は、さまざまな作用機序の
抗がん剤に相関を示す遺伝子群と、ある種の作用機序の
抗がん剤に特異的に相関を示す遺伝子群に分類された。
一方、抗がん剤の側からみると、作用メカニズムの似た
薬剤では有意な相関を示す遺伝子群が共通していること
が確認された。<Conclusion> As a result of the cluster analysis of the correlation coefficient, the extracted 1067 genes have a group of genes showing a correlation with anti-cancer agents having various action mechanisms, and an anti-cancer agent having a certain action mechanism. They were categorized into genes that have a specific correlation with drugs.
On the other hand, from the viewpoint of anticancer drugs, it was confirmed that drugs having a similar mechanism of action have a common gene group showing a significant correlation.
【００６３】実施例３：抗がん剤適合性マーカー遺伝子
のバリデーション
実施例１及び２で、39種のがん細胞株を用いて選択され
た抗がん剤適合性マーカー遺伝子のバリデーションを行
った。バリデーションの対象としては、実施例２でさま
ざまなタイプの抗がん剤に共通して相関を示すことが確
認されたマーカー遺伝子から下表に示す４つを選択し
た。このうちAKR1B1とDDB2は種々の抗がん剤と正の相関
を、LIMK2とCTSHは負の相関を示すマーカー遺伝子であ
る。Example 3: Validation of anticancer drug compatibility marker gene In Examples 1 and 2, the anticancer drug compatibility marker gene selected using 39 types of cancer cell lines was validated. . As a subject of validation, four markers shown in the following table were selected from the marker genes confirmed to show a common correlation with various types of anticancer agents in Example 2. Of these, AKR1B1 and DDB2 are marker genes that have a positive correlation with various anticancer agents, and LIMK2 and CTSH have a negative correlation.
【００６４】[0064]
【表２】 [Table 2]
【００６５】抗がん剤としては、表３に示すように、実
施例１で用いた55種の薬剤から選択した42の薬剤と新た
な23種の薬剤の計65種の薬剤を用いた。細胞株として
は、実施例１で用いた細胞株に加えて、新たに12種の胃
がん細胞株（GCIY、GT3TKB、HGC27、AZ521、Ist-4、NUG
C-3、NUGC-3/5FU、HCS-42、AGS、KWS-1、OKIBA、及びAO
TO）を用いた。各細胞におけるマーカー遺伝子の発現量
はRT-PCRを用いて解析し、各マーカー遺伝子の発現パタ
ーンと抗がん剤感受性との関係をピアソン相関係数によ
り評価した。As the anti-cancer agents, as shown in Table 3, a total of 65 kinds of agents, 42 kinds of medicines selected from 55 kinds of medicines used in Example 1 and 23 kinds of new medicines were used. As the cell line, in addition to the cell line used in Example 1, 12 kinds of gastric cancer cell lines (GCIY, GT3TKB, HGC27, AZ521, Ist-4, NUG) were newly added.
C-3, NUGC-3 / 5FU, HCS-42, AGS, KWS-1, OKIBA, and AO
TO) was used. The expression level of the marker gene in each cell was analyzed using RT-PCR, and the relationship between the expression pattern of each marker gene and the anticancer drug sensitivity was evaluated by the Pearson correlation coefficient.
【００６６】(1)新規薬剤を対象とした遺伝子発現パタ
ーンとの相関解析
新規23薬剤を含む65種の抗がん剤を対象として、AKR1B
1、DDB2、LIMK2、及びCTSHの4遺伝子の発現パターンと
抗がん剤感受性との相関解析を行った。解析は、実施例
２で用いた３９種のヒトがん細胞におけるcDNAマイクロ
アレイによる遺伝子発現データを用いて、実施例２と同
様の方法で解析した。結果を表３、及び図８に示す。(1) Correlation analysis with gene expression patterns targeting new drugs AKR1B targeting 65 anticancer drugs including 23 new drugs
Correlation analysis was performed between the expression patterns of 1, DDB2, LIMK2, and CTSH genes and anticancer drug susceptibility. The analysis was performed in the same manner as in Example 2 by using the gene expression data by the cDNA microarray in the 39 types of human cancer cells used in Example 2. The results are shown in Table 3 and FIG.
【００６７】[0067]
【表３】
表中、ｒは薬剤と遺伝子のピアソン相関係数、▲は負の
相関、***P<0.001, **P<0.01, *P<0.05を示す。[Table 3] In the table, r indicates the Pearson correlation coefficient between drug and gene, ▲ indicates negative correlation, *** P <0.001, ** P <0.01, * P <0.05.
【００６８】４つのマーカー遺伝子の発現レベルは、AK
R1B1が大腸がん由来の細胞株でやや低い発現量を示した
ことを除いて、細胞の起源組織の違いにあまり影響を受
けなかった（図８）。AKR1B1とDDB2は、それぞれ65薬剤
中23及び27薬剤と正の相関関係を示し、さらに今回の研
究で新たに調べた23薬剤についてはともに8薬剤と正の
相関関係を示した。他方、CTSH とLIMK2 はそれぞれ、6
5薬剤中27及び28薬剤と負の相関関係を示し、さらに今
回の研究で新たに調べた23薬剤についてはそれぞれ8及
び10薬剤と負の相関関係を示した。すなわち、４つのマ
ーカー遺伝子は、新たな23薬剤についても、多くの抗が
ん剤と相関を示すことが確認された。The expression levels of the four marker genes are AK
It was not significantly affected by the difference in the tissue of origin of the cells, except that R1B1 showed a slightly lower expression level in the colon cancer-derived cell line (FIG. 8). AKR1B1 and DDB2 were positively correlated with 23 and 27 out of 65 drugs, respectively, and the 23 newly investigated drugs in this study were both positively correlated with 8 drugs. On the other hand, CTSH and LIMK2 each have 6
Negative correlations were found with 27 and 28 of the 5 drugs, and with the newly investigated 23 drugs in this study, negative correlations with 8 and 10 drugs, respectively. That is, it was confirmed that the four marker genes showed correlation with many anticancer agents even for the 23 new drugs.
【００６９】(2)RT-PCRによるcDNA マイクロアレイ遺伝
子発現データの確認
cDNA マイクロアレイによる遺伝子発現データを確認す
るために、39細胞株中11の細胞株（6つの胃がん細胞株S
t-4、MKN1、MKN7、MKN28、MKN45及びMKN74：ならびに、
5つの大腸がん細胞株：HCC2998、KM-12、HT-29、WiDr、
HCT-15及びHCT-116）について、RT-PCRにより前述の４
つのマーカー遺伝子の発現解析を行った。(2) Confirmation of cDNA microarray gene expression data by RT-PCR To confirm the gene expression data by cDNA microarray, 11 out of 39 cell lines (6 gastric cancer cell lines S
t-4, MKN1, MKN7, MKN28, MKN45 and MKN74: and
5 colorectal cancer cell lines: HCC2998, KM-12, HT-29, WiDr,
For HCT-15 and HCT-116), the above-mentioned 4 by RT-PCR
Expression analysis of one marker gene was performed.
【００７０】まず各細胞株は誘導期まで単層培養し、PB
Sで2回洗浄後、TRIzol 試薬(Life Technologies, Inc.
製)を用いて全RNAを抽出し、DNase I (Boehringer Mann
heim製)によりゲノムDNAを除去した。この全RNA 1μgを
Superscript II reverse transcriptaseにより逆転写
し、RNA PCR core kit (PE biosystems製)を利用してPC
R反応を行った。用いたPCRプライマーの配列は以下に示
すとおりである。
AKR1B1:
Forward primer:5'-ctggactacctggacctctacct-3'（配列
番号５）
Reverse primer:5'-tttgaggcaaagagaagtctt-3'(配列番
号６)
DDB2:
Forward primer:5'-ctagtagccgaatggtggtca-3'(配列番
号７)
Reverse primer:5'-attggccatatcaaaagagcac-3'(配列番
号８)
LIMK2:
Forward primer:5'-tgtttacctgctcactggctcta-3'(配列
番号９)
Reverse primer:5'-tagtctgatcaatggcccagttc-3'(配列
番号１０)
CTSH:
Forward primer:5'-tactggtccctacccaccttc-3'(配列番
号１１)
Reverse primer:5'-ggaggtgctcactcaatgtttat-3'(配列
番号１２)
遺伝子の検出は、PCR産物をアガロースゲル電気泳動に
かけ、エチジウムブロマイドで染色し、各バンドをNIH
イメージソフトウェアを用いて定量することにより実施
した。RT-PCRは2回以上繰り返し、再現性を確認した。First, each cell line was cultured in a monolayer until the induction period, and then PB
After washing twice with S, TRIzol reagent (Life Technologies, Inc.
Total RNA was extracted using a DNAase I (Boehringer Mann
Genomic DNA was removed by heim). 1 μg of this total RNA
Reverse transcript by Superscript II reverse transcriptase and use RNA PCR core kit (made by PE biosystems) for PC
R reaction was performed. The sequences of the PCR primers used are as shown below. AKR1B1: Forward primer: 5'-ctggactacctggacctctacct-3 '(SEQ ID NO: 5) Reverse primer: 5'-tttgaggcaaagagaagtctt-3' (SEQ ID NO: 6) DDB2: Forward primer: 5'-ctagtagccgaatggtggtca-3 '(SEQ ID NO: 7) Reverse primer: 5'-attggccatatcaaaagagcac-3 '(SEQ ID NO: 8) LIMK2: Forward primer: 5'-tgtttacctgctcactggctcta-3' (SEQ ID NO: 9) Reverse primer: 5'-tagtctgatcaatggcccagttc-3 '(SEQ ID NO: 10) CTSH: Forward primer : 5'-tactggtccctacccaccttc-3 '(SEQ ID NO: 11) Reverse primer: 5'-ggaggtgctcactcaatgtttat-3' (SEQ ID NO: 12) Gene is detected by subjecting the PCR product to agarose gel electrophoresis, staining with ethidium bromide, and measuring each band. NIH
It was performed by quantification using image software. RT-PCR was repeated twice or more to confirm reproducibility.
【００７１】図９に示すよう、cDNAマイクロアレイで測
定された発現データは、RT-PCRの結果と非常によく一致
した（AKR1B1: r=0.74, P<0.01, DDB2: r=0.91, P<0.00
1, CTSH: r=0.85, P<0.001, LIMK2: r=0.58, P<0.1）。
同様の結果は、他の組織由来の細胞でも確認された。As shown in FIG. 9, the expression data measured by cDNA microarray were in very good agreement with the results of RT-PCR (AKR1B1: r = 0.74, P <0.01, DDB2: r = 0.91, P <0.00.
1, CTSH: r = 0.85, P <0.001, LIMK2: r = 0.58, P <0.1).
Similar results were confirmed in cells derived from other tissues.
【００７２】（3）新規細胞株を対象としたRT-PCR法に
よる解析
マーカー遺伝子と抗がん剤感受性との関係の普遍性を確
認するために、実施例１及び前項図９で用いた胃がん細
胞株6種（St-4、MKN1、MKN7、MKN28、MKN45、及びMKN7
4）に、新たに12種の胃がん細胞株（GCIY、GT3TKB、HGC
27、AZ521、Ist-4、NUGC-3、NUGC-3/5FU、HCS-42、AG
S、KWS-1、OKIBA、及びAOTO）を加え、計18種の胃がん
細胞株について、(1)の表３に示した65種の抗がん剤に
対する感受性を、実施例１と同様の方法で解析した。ま
た、(2)と同様にRT-PCR法により18種の胃がん細胞株に
おける前記４遺伝子の発現解析を行った（図４）。これ
らのデータを用い、(1)の表３でそれぞれの遺伝子と有
意な相関を示した抗がん剤について、18種の胃がん細胞
株においても相関を示すかを検討した（表４）。(3) Analysis of novel cell lines by RT-PCR method In order to confirm the universality of the relationship between marker genes and anticancer drug sensitivity, gastric cancer used in Example 1 and FIG. 9 above. Six cell lines (St-4, MKN1, MKN7, MKN28, MKN45, and MKN7
4) newly added 12 types of gastric cancer cell lines (GCIY, GT3TKB, HGC).
27, AZ521, Ist-4, NUGC-3, NUGC-3 / 5FU, HCS-42, AG
S, KWS-1, OKIBA, and AOTO) were added, and the sensitivity to the 65 anticancer agents shown in Table 3 of (1) for 18 kinds of gastric cancer cell lines was measured in the same manner as in Example 1. Analyzed in. Further, the expression analysis of the 4 genes in 18 kinds of gastric cancer cell lines was carried out by the RT-PCR method as in (2) (FIG. 4). Using these data, it was examined whether the anticancer agents that showed a significant correlation with each gene in Table 3 of (1) also showed a correlation in 18 kinds of gastric cancer cell lines (Table 4).
【００７３】[0073]
【表４】
表中、▲は負の相関、***P<0.001, **P<0.01, *P<0.05,
aP<0.1を示す。[Table 4] In the table, ▲ is a negative correlation, *** P <0.001, ** P <0.01, * P <0.05,
a Indicates P <0.1.
【００７４】AKR1B1は、(1)の表３に示したように、39
種の細胞株を用いた相関解析で65の薬剤中23の薬剤につ
いて、有意な相関を示した(P<0.05)。AKR1B1は、18種の
胃がん細胞株においても、前記23薬剤中20の薬剤に対し
て有意な相関(P<0.05)を示した（表２(a)）。つまり、3
9の細胞株で観察されたAKR1B1と抗がん剤と同様の関係
が、18の胃がん細胞株でも確認された。また、CTSHは39
種の細胞株における相関解析で、65の薬剤中27の薬剤と
有意な負の相関(P<0.05)を示した。同様の関係は、18種
の胃がん細胞株においても、27薬剤中15の薬剤について
確認された（表２(b)）。DDB2は、39種の細胞株を用い
た相関解析で27の薬剤に対して有意な正の相関を示し、
そのうち20の薬剤について、有意ではないが、胃がん細
胞株においても正の相関が認められた（表２(c)）。LIM
K2は39種の細胞株を用いた相関解析で有意な負の相関を
示した27の薬剤中26の薬剤と負の相関を示し、このうち
6の薬剤に対して有意な負の相関(P<0.05)が認められた
（（表２(d)）。いずれの遺伝子についても、2つの解析
間で、39種の細胞株を用いた相関解析で有意な正の相関
を示した薬剤が18種の胃がん細胞株では有意な負の相関
を示す（あるいはその逆）といったような矛盾した結果
は認められなかった。As shown in Table 3 of (1), AKR1B1 contains 39
Correlation analysis using cell lines of different species showed significant correlation for 23 of the 65 drugs (P <0.05). AKR1B1 also showed a significant correlation (P <0.05) with 20 of the 23 drugs in 18 types of gastric cancer cell lines (Table 2 (a)). That is, 3
A similar relationship between AKR1B1 and anticancer drugs observed in 9 cell lines was confirmed in 18 gastric cancer cell lines. Also, CTSH is 39
Correlation analysis in cell lines of the species showed a significant negative correlation (P <0.05) with 27 of the 65 drugs. A similar relationship was confirmed in 15 of 27 gastric cancer cell lines (Table 2 (b)). DDB2 shows a significant positive correlation for 27 drugs in correlation analysis using 39 cell lines,
For 20 of them, a positive correlation was observed in the gastric cancer cell line, although it was not significant (Table 2 (c)). LIM
K2 showed a significant negative correlation in the correlation analysis using 39 cell lines, and showed a negative correlation with 26 out of 27 drugs.
A significant negative correlation (P <0.05) was observed for 6 drugs ((Table 2 (d)). Correlation between 39 genes for two genes for each gene. No contradictory results were found in which drugs that showed a significant positive correlation in the analysis showed a significant negative correlation in 18 gastric cancer cell lines (or vice versa).
【００７５】以上のとおり、39種のがん細胞株で抗がん
剤感受性について有意な関係が認められたAKR1B1、DDB
2、LIMK2、及びCTSHの4遺伝子は、18種の胃がん細胞株
でも同様の関係が認められた。すなわち、本発明の方法
は抗がん剤適合性マーカー遺伝子の絞込みに有用であ
り、この方法によって選択された遺伝子は、抗がん剤適
合性マーカー遺伝子として普遍的に使用しうることが確
認された。As described above, AKR1B1 and DDB were found to have a significant relationship with anticancer drug sensitivity in 39 types of cancer cell lines.
Similar relationships were observed in 18 gastric cancer cell lines with 4 genes of 2, LIMK2, and CTSH. That is, the method of the present invention is useful for narrowing down anticancer drug compatibility marker genes, and it was confirmed that the genes selected by this method can be universally used as anticancer drug compatibility marker genes. It was
【００７６】[0076]
【発明の効果】本発明によれば、患者のがん細胞におけ
る遺伝子発現量の測定に基づいて、抗がん剤の適合性を
予測することが可能となり、より有効かつ安全な抗がん
剤治療が可能となる。INDUSTRIAL APPLICABILITY According to the present invention, the suitability of an anticancer drug can be predicted based on the measurement of gene expression levels in cancer cells of patients, and a more effective and safe anticancer drug can be predicted. Treatment is possible.
【００７７】[0077]
【配列表】 SEQUENCE LISTING <110> Japanese Foundation for Cancer Research <120> Method for predicting anticancer drug compatibility <130> P03-0035 <140> <141> <150> JP2002-035717 <151> 2002-02-13 <160> 12 <170> PatentIn Ver. 2.1 <210> 1 <211> 1416 <212> DNA <213> Homo sapiens <220> <223> Inventor: Yamori, Takao; Dan, Shingo; Nakamura, Yusuke <400> 1 acgggctatt taaaggtacg cgccgcggcc aaggccgcac cgtactgggc gggggtctgg 60 ggagcgcagc agccatggca agccgtctcc tgctcaacaa cggcgccaag atgcccatcc 120 tggggttggg tacctggaag tcccctccag ggcaggtgac tgaggccgtg aaggtggcca 180 ttgacgtcgg gtaccgccac atcgactgtg cccatgtgta ccagaatgag aatgaggtgg 240 gggtggccat tcaggagaag ctcagggagc aggtggtgaa gcgtgaggag ctcttcatcg 300 tcagcaagct gtggtgcacg taccatgaga agggcctggt gaaaggagcc tgccagaaga 360 cactcagcga cctgaagctg gactacctgg acctctacct tattcactgg ccgactggct 420 ttaagcctgg gaaggaattt ttcccattgg atgagtcggg caatgtggtt cccagtgaca 480 ccaacattct ggacacgtgg gcggccatgg aagagctggt ggatgaaggg ctggtgaaag 540 ctattggcat ctccaacttc aaccatctcc aggtggagat gatcttaaac aaacctggct 600 tgaagtataa gcctgcagtt aaccagattg agtgccaccc atatctcact caggagaagt 660 taatccagta ctgccagtcc aaaggcatcg tggtgaccgc ctacagcccc ctcggctctc 720 ctgacaggcc ctgggccaag cccgaggacc cttctctcct ggaggatccc aggatcaagg 780 cgatcgcagc caagcacaat aaaactacag cccaggtcct gatccggttc cccatgcaga 840 ggaacttggt ggtgatcccc aagtctgtga caccagaacg cattgctgag aactttaagg 900 tctttgactt tgaactgagc agccaggata tgaccacctt actcagctac aacaggaact 960 ggagggtctg tgccttgttg agctgtacct cccacaagga ttaccccttc catgaagagt 1020 tttgaagctg tggttgcctg ctcgtcccca agtgacctat acctgtgttt cttgcctcat 1080 ttttttcctt gcaaatgtag tatggcctgt gtcactcagc agtgggacag caacctgtag 1140 agtggccagc gagggcgtgt ctagcttgat gttggatctc aagagccctg tcagtagagt 1200 agaagtctct tccagtttgc tttgcccttc tttctaccct gctggggaaa gtacaacctg 1260 aatacccttt tctgaccaaa gagaagcaaa atctaccagg tcaaaatagt gccactaacg 1320 gttgagtttt gactgcttgg aactggaatc ctttcagcaa gacttctctt tgcctcaaat 1380 aaaaagtgct tttgtgagaa aaaaaaaaaa aaaaaa 1416 <210> 2 <211> 1820 <212> DNA <213> Homo sapiens <400> 2 gagctccaag ctggtttgaa caagccctgg gcatgtttgg cgggaagttg gcttagctcg 60 gctacctgtg gccccgcagt tttgtagtcc ccgccttgtt tctccccaga ggcctctcaa 120 tcctccctcc atgatcttcg catagagcac agtacccctt cacacggagg acgcgatggc 180 tcccaagaaa cgcccagaaa cccagaagac ctccgagatt gtattacgcc ccaggaacaa 240 gaggagcagg agtcccctgg agctggagcc cgaggccaag aagctctgtg cgaagggctc 300 cggtcctagc agaagatgtg actcagactg cctctgggtg gggctggctg gcccacagat 360 cctgccacca tgccgcagca tcgtcaggac cctccaccag cataagctgg gcagagcttc 420 ctggccatct gtccagcagg ggctccagca gtcctttttg cacactctgg attcttaccg 480 gatattacaa aaggctgccc cctttgacag gagggctaca tccttggcgt ggcacccaac 540 tcaccccagc accgtggctg tgggttccaa agggggagat atcatgctct ggaattttgg 600 catcaaggac aaacccacct tcatcaaagg gattggagct ggagggagca tcactgggct 660 gaagtttaac cctctcaata ccaaccagtt ttacgcctcc tcaatggagg gaacaactag 720 gctgcaagac tttaaaggca acattctacg agtttttgcc agctcagaca ccatcaacat 780 ctggttttgt agcctggatg tgtctgctag tagccgaatg gtggtcacag gagacaacgt 840 ggggaacgtg atcctgctga acatggacgg caaagagctt tggaatctca gaatgcacaa 900 aaagaaagtg acgcatgtgg ccctgaaccc atgctgtgat tggttcctgg ccacagcctc 960 cgtagatcaa acagtgaaaa tttgggacct gcgccaggtt agagggaaag ccagcttcct 1020 ctactcgctg ccgcacaggc atcctgtcaa cgcagcttgt ttcagtcccg atggagcccg 1080 gctcctgacc acggaccaga agagcgagat ccgagtttac tctgcttccc agtgggactg 1140 ccccctgggc ctgatcccgc accctcaccg tcacttccag cacctcacac ccatcaaggc 1200 agcctggcat cctcgctaca acctcattgt tgtgggccga tacccagatc ctaatttcaa 1260 aagttgtacc ccttatgaat tgaggacgat cgacgtgttc gatggaaact cagggaagat 1320 gatgtgtcag ctctatgacc cagaatcttc tggcatcagt tcgcttaatg aattcaatcc 1380 catgggggac acgctggcct ctgcaatggg ttaccacatt ctcatctgga gccaggagga 1440 agccaggaca cggaagtgag agacactaaa gaaggtgtgg gccagacaag gccttggagc 1500 ccacacatgg gatcaagtcc tgcaagcaga ggtggtgatt tgttaaaggg ccaaaagtat 1560 ccaaggttag ggttggagca ggggtgctgg gacctggggc actgtgggac tgggacactt 1620 ttatgttaat gctctggact tgcctccaga gactgctcca gagttggtga cacagctgtc 1680 ccaagggccc ctctgtatct agcctggaac caaggttatc ttggaactaa atgacttttc 1740 tcctctcagt gggtggtagc agagggatca agcagttatt tgatttgtgc tcttttgata 1800 tggccaataa aaccataccg 1820 <210> 3 <211> 3668 <212> DNA <213> Homo sapiens <400> 3 gtggtcttcc cgcgcctgag gcggcggcgg caggagctga ggggagttgt agggaactga 60 ggggagctgc tgtgtccccc gcctcctcct ccccatttcc gggctcccgg gaccatgtcc 120 gcgctggcgg gtgaagatgt ctggaggtgt ccaggctgtg gggaccacat tgctccaagc 180 cagatatggt acaggactgt caacgaaacc tggcacggct cttgcttccg gtgttcagaa 240 tgccaggatt ccctcaccaa ctggtactat gagaaggatg ggaagctcta ctgccccaag 300 gactactggg ggaagtttgg ggagttctgt catgggtgct ccctgctgat gacagggcct 360 tttatggtgg ctggggagtt caagtaccac ccagagtgct ttgcctgtat gagctgcaag 420 gtgatcattg aggatgggga tgcatatgca ctggtgcagc atgccaccct ctactgtggg 480 aagtgccaca atgaggtggt gctggcaccc atgtttgaga gactctccac agagtctgtt 540 caggagcagc tgccctactc tgtcacgctc atctccatgc cggccaccac tgaaggcagg 600 cggggcttct ccgtgtccgt ggagagtgcc tgctccaact acgccaccac tgtgcaagtg 660 aaagaggtca accggatgca catcagtccc aacaatcgaa acgccatcca ccctggggac 720 cgcatcctgg agatcaatgg gacccccgtc cgcacacttc gagtggagga ggtggaggat 780 gcaattagcc agacgagcca gacacttcag ctgttgattg aacatgaccc cgtctcccaa 840 cgcctggacc agctgcggct ggaggcccgg ctcgctcctc acatgcagaa tgccggacac 900 ccccacgccc tcagcaccct ggacaccaag gagaatctgg aggggacact gaggagacgt 960 tccctaaggc gcagtaacag tatctccaag tcccctggcc ccagctcccc aaaggagccc 1020 ctgctgttca gccgtgacat cagccgctca gaatcccttc gttgttccag cagctattca 1080 cagcagatct tccggccctg tgacctaatc catggggagg tcctggggaa gggcttcttt 1140 gggcaggcta tcaaggtgac acacaaagcc acgggcaaag tgatggtcat gaaagagtta 1200 attcgatgtg atgaggagac ccagaaaact tttctgactg aggtgaaagt gatgcgcagc 1260 ctggaccacc ccaatgtgct caagttcatt ggtgtgctgt acaaggataa gaagctgaac 1320 ctgctgacag agtacattga ggggggcaca ctgaaggact ttctgcgcag tatggatccg 1380 ttcccctggc agcagaaggt caggtttgcc aaaggaatcg cctccggaat ggcctatttg 1440 cactctatgt gcatcatcca ccgggatctg aactcgcaca actgcctcat caagttggac 1500 aagactgtgg tggtggcaga ctttgggctg tcacggctca tagtggaaga gaggaaaagg 1560 gcccccatgg agaaggccac caccaagaaa cgcaccttgc gcaagaacga ccgcaagaag 1620 cgctacacgg tggtgggaaa cccctactgg atggcccctg agatgctgaa cggaaagagc 1680 tatgatgaga cggtggatat cttctccttt gggatcgttc tctgtgagat cattgggcag 1740 gtgtatgcag atcctgactg ccttccccga acactggact ttggcctcaa cgtgaagctt 1800 ttctgggaga agtttgttcc cacagattgt cccccggcct tcttcccgct ggccgccatc 1860 tgctgcagac tggagcctga gagcagacca gcattctcga aattggagga ctcctttgag 1920 gccctctccc tgtacctggg ggagctgggc atcccgctgc ctgcagagct ggaggagttg 1980 gaccacactg tgagcatgca gtacggcctg acccgggact cacctcccta gccctggccc 2040 agccccctgc aggggggtgt tctacagcca gcattgcccc tctgtgcccc attcctgctg 2100 tgagcagggc cgtccgggct tcctgtggat tggcggaatg tttagaagca gaacaagcca 2160 ttcctattac ctccccagga ggcaagtggg cgcagcacca gggaaatgta tctccacagg 2220 ttctggggcc tagttactgt ctgtaaatcc aatacttgcc tgaaagctgt gaagaagaaa 2280 aaaacccctg gcctttgggc caggaggaat ctgttactcg aatccaccca ggaactccct 2340 ggcagtggat tgtgggaggc tcttgcttac actaatcagc gtgacctgga cctgctgggc 2400 aggatcccag ggtgaacctg cctgtgaact ctgaagtcac tagtccagct gggtgcagga 2460 ggacttcaag tgtgtggacg aaagaaagac tgatggctca aagggtgtga aaaagtcagt 2520 gatgctcccc ctttctactc cagatcctgt ccttcctgga gcaaggttga gggagtaggt 2580 tttgaagagt cccttaatat gtggtggaac aggccaggag ttagagaaag ggctggcttc 2640 tgtttacctg ctcactggct ctagccagcc cagggaccac atcaatgtga gaggaagcct 2700 ccacctcatg ttttcaaact taatactgga gactggctga gaacttacgg acaacatcct 2760 ttctgtctga aacaaacagt cacaagcaca ggaagaggct gggggactag aaagaggccc 2820 tgccctctag aaagctcaga tcttggcttc tgttactcat actcgggtgg gctccttagt 2880 cagatgccta aaacattttg cctaaagctc gatgggttct ggaggacagt gtggcttgtc 2940 acaggcctag agtctgaggg aggggagtgg gagtctcagc aatctcttgg tcttggcttc 3000 atggcaacca ctgctcaccc ttcaacatgc ctggtttagg cagcagcttg ggctgggaag 3060 aggtggtggc agagtctcaa agctgagatg ctgagagaga tagctccctg agctgggcca 3120 tctgacttct acctcccatg tttgctctcc caactcatta gctcctgggc agcatcctcc 3180 tgagccacat gtgcaggtac tggaaaacct ccatcttggc tcccagagct ctaggaactc 3240 ttcatcacaa ctagatttgc ctcttctaag tgtctatgag cttgcaccat atttaataaa 3300 ttgggaatgg gtttggggta ttaatgcaat gtgtggtggt tgtattggag cagggggaat 3360 tgataaagga gagtggttgc tgttaatatt atcttatcta ttgggtggta tgtgaaatat 3420 tgtacataga cctgatgagt tgtgggacca gatgtcatct ctggtcagag tttacttgct 3480 atatagactg tacttatgtg tgaagtttgc aagcttgctt tagggctgag ccctggactc 3540 ccagcagcag cacagttcag cattgtgtgg ctggttgttt cctggctgtc cccagcaagt 3600 gtaggagtgg tgggcctgaa ctgggccatt gatcagacta aataaattaa gcagttaaca 3660 taactggc 3668 <210> 4 <211> 1008 <212> DNA <213> Homo sapiens <400> 4 atgtgggcca cgctgccgct gctctgcgcc ggggcctggc tcctgggagt ccccgtctgc 60 ggtgccgccg aactgtgcgt gaactcctta gagaagtttc acttcaagtc atggatgtct 120 aagcaccgta agacctacag tacggaggag taccaccaca ggctgcagac gtttgccagc 180 aactggagga agataaacgc ccacaacaat gggaaccaca catttaaaat ggcactgaac 240 caattttcag acatgagctt tgctgaaata aaacacaagt atctctggtc agagcctcag 300 aattgctcag ccaccaaaag taactacctt cgaggtactg gtccctaccc accttccgtg 360 gactggcgga aaaaaggaaa ttttgtctca cctgtgaaaa atcagggtgc ctgcggcagt 420 tgctggactt tctccaccac tggggccctg gagtctgcaa tcgccatcgc aaccggaaag 480 atgctgtcct tggcggaaca gcagctggtg gactgcgccc aggacttcaa taattacggc 540 tgccaagggg gtctccccag ccaggctttc gagtatatcc tgtacaacaa ggggatcatg 600 ggtgaagaca cctaccccta ccagggcaag gatggttatt gcaagttcca acctggaaag 660 gccatcggct ttgtcaagga tgtagccaac atcacaatct atgacgagga agcgatggtg 720 gaggctgtgg ccctctacaa ccctgtgagc tttgcctttg aggtgactca ggacttcatg 780 atgtatagaa cgggcatcta ctccagtact tcctgccata aaactccaga taaagtaaac 840 catgcagtac tggctgttgg gtatggagaa aaaaatggga tcccttactg gatcgtgaaa 900 aactcttggg gtccccagtg gggaatgaac gggtacttcc tcatcgagcg cggaaagaac 960 atgtgtggcc tggctgcctg cgcctcctac cccatccctc tggtgtga 1008 <210> 5 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence:primer <400> 5 ctggactacc tggacctcta cct 23 <210> 6 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence:primer <400> 6 tttgaggcaa agagaagtct t 21 <210> 7 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence:primer <400> 7 ctagtagccg aatggtggtc a 21 <210> 8 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence:primer <400> 8 attggccata tcaaaagagc ac 22 <210> 9 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence:primer <400> 9 tgtttacctg ctcactggct cta 23 <210> 10 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence:primer <400> 10 tagtctgatc aatggcccag ttc 23 <210> 11 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence:primer <400> 11 tactggtccc tacccacctt c 21 <210> 12 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence:primer <400> 12 ggaggtgctc actcaatgtt tat 23[Sequence list] SEQUENCE LISTING <110> Japanese Foundation for Cancer Research <120> Method for predicting anticancer drug compatibility <130> P03-0035 <140> <141> <150> JP2002-035717 <151> 2002-02-13 <160> 12 <170> PatentIn Ver. 2.1 <210> 1 <211> 1416 <212> DNA <213> Homo sapiens <220> <223> Inventor: Yamori, Takao; Dan, Shingo; Nakamura, Yusuke <400> 1 acgggctatt taaaggtacg cgccgcggcc aaggccgcac cgtactgggc gggggtctgg 60 ggagcgcagc agccatggca agccgtctcc tgctcaacaa cggcgccaag atgcccatcc 120 tggggttggg tacctggaag tcccctccag ggcaggtgac tgaggccgtg aaggtggcca 180 ttgacgtcgg gtaccgccac atcgactgtg cccatgtgta ccagaatgag aatgaggtgg 240 gggtggccat tcaggagaag ctcagggagc aggtggtgaa gcgtgaggag ctcttcatcg 300 tcagcaagct gtggtgcacg taccatgaga agggcctggt gaaaggagcc tgccagaaga 360 cactcagcga cctgaagctg gactacctgg acctctacct tattcactgg ccgactggct 420 ttaagcctgg gaaggaattt ttcccattgg atgagtcggg caatgtggtt cccagtgaca 480 ccaacattct ggacacgtgg gcggccatgg aagagctggt ggatgaaggg ctggtgaaag 540 ctattggcat ctccaacttc aaccatctcc aggtggagat gatcttaaac aaacctggct 600 tgaagtataa gcctgcagtt aaccagattg agtgccaccc atatctcact caggagaagt 660 taatccagta ctgccagtcc aaaggcatcg tggtgaccgc ctacagcccc ctcggctctc 720 ctgacaggcc ctgggccaag cccgaggacc cttctctcct ggaggatccc aggatcaagg 780 cgatcgcagc caagcacaat aaaactacag cccaggtcct gatccggttc cccatgcaga 840 ggaacttggt ggtgatcccc aagtctgtga caccagaacg cattgctgag aactttaagg 900 tctttgactt tgaactgagc agccaggata tgaccacctt actcagctac aacaggaact 960 ggagggtctg tgccttgttg agctgtacct cccacaagga ttaccccttc catgaagagt 1020 tttgaagctg tggttgcctg ctcgtcccca agtgacctat acctgtgttt cttgcctcat 1080 ttttttcctt gcaaatgtag tatggcctgt gtcactcagc agtgggacag caacctgtag 1140 agtggccagc gagggcgtgt ctagcttgat gttggatctc aagagccctg tcagtagagt 1200 agaagtctct tccagtttgc tttgcccttc tttctaccct gctggggaaa gtacaacctg 1260 aatacccttt tctgaccaaa gagaagcaaa atctaccagg tcaaaatagt gccactaacg 1320 gttgagtttt gactgcttgg aactggaatc ctttcagcaa gacttctctt tgcctcaaat 1380 aaaaagtgct tttgtgagaa aaaaaaaaaa aaaaaa 1416 <210> 2 <211> 1820 <212> DNA <213> Homo sapiens <400> 2 gagctccaag ctggtttgaa caagccctgg gcatgtttgg cgggaagttg gcttagctcg 60 gctacctgtg gccccgcagt tttgtagtcc ccgccttgtt tctccccaga ggcctctcaa 120 tcctccctcc atgatcttcg catagagcac agtacccctt cacacggagg acgcgatggc 180 tcccaagaaa cgcccagaaa cccagaagac ctccgagatt gtattacgcc ccaggaacaa 240 gaggagcagg agtcccctgg agctggagcc cgaggccaag aagctctgtg cgaagggctc 300 cggtcctagc agaagatgtg actcagactg cctctgggtg gggctggctg gcccacagat 360 cctgccacca tgccgcagca tcgtcaggac cctccaccag cataagctgg gcagagcttc 420 ctggccatct gtccagcagg ggctccagca gtcctttttg cacactctgg attcttaccg 480 gatattacaa aaggctgccc cctttgacag gagggctaca tccttggcgt ggcacccaac 540 tcaccccagc accgtggctg tgggttccaa agggggagat atcatgctct ggaattttgg 600 catcaaggac aaacccacct tcatcaaagg gattggagct ggagggagca tcactgggct 660 gaagtttaac cctctcaata ccaaccagtt ttacgcctcc tcaatggagg gaacaactag 720 gctgcaagac tttaaaggca acattctacg agtttttgcc agctcagaca ccatcaacat 780 ctggttttgt agcctggatg tgtctgctag tagccgaatg gtggtcacag gagacaacgt 840 ggggaacgtg atcctgctga acatggacgg caaagagctt tggaatctca gaatgcacaa 900 aaagaaagtg acgcatgtgg ccctgaaccc atgctgtgat tggttcctgg ccacagcctc 960 cgtagatcaa acagtgaaaa tttgggacct gcgccaggtt agagggaaag ccagcttcct 1020 ctactcgctg ccgcacaggc atcctgtcaa cgcagcttgt ttcagtcccg atggagcccg 1080 gctcctgacc acggaccaga agagcgagat ccgagtttac tctgcttccc agtgggactg 1140 ccccctgggc ctgatcccgc accctcaccg tcacttccag cacctcacac ccatcaaggc 1200 agcctggcat cctcgctaca acctcattgt tgtgggccga tacccagatc ctaatttcaa 1260 aagttgtacc ccttatgaat tgaggacgat cgacgtgttc gatggaaact cagggaagat 1320 gatgtgtcag ctctatgacc cagaatcttc tggcatcagt tcgcttaatg aattcaatcc 1380 catgggggac acgctggcct ctgcaatggg ttaccacatt ctcatctgga gccaggagga 1440 agccaggaca cggaagtgag agacactaaa gaaggtgtgg gccagacaag gccttggagc 1500 ccacacatgg gatcaagtcc tgcaagcaga ggtggtgatt tgttaaaggg ccaaaagtat 1560 ccaaggttag ggttggagca ggggtgctgg gacctggggc actgtgggac tgggacactt 1620 ttatgttaat gctctggact tgcctccaga gactgctcca gagttggtga cacagctgtc 1680 ccaagggccc ctctgtatct agcctggaac caaggttatc ttggaactaa atgacttttc 1740 tcctctcagt gggtggtagc agagggatca agcagttatt tgatttgtgc tcttttgata 1800 tggccaataa aaccataccg 1820 <210> 3 <211> 3668 <212> DNA <213> Homo sapiens <400> 3 gtggtcttcc cgcgcctgag gcggcggcgg caggagctga ggggagttgt agggaactga 60 ggggagctgc tgtgtccccc gcctcctcct ccccatttcc gggctcccgg gaccatgtcc 120 gcgctggcgg gtgaagatgt ctggaggtgt ccaggctgtg gggaccacat tgctccaagc 180 cagatatggt acaggactgt caacgaaacc tggcacggct cttgcttccg gtgttcagaa 240 tgccaggatt ccctcaccaa ctggtactat gagaaggatg ggaagctcta ctgccccaag 300 gactactggg ggaagtttgg ggagttctgt catgggtgct ccctgctgat gacagggcct 360 tttatggtgg ctggggagtt caagtaccac ccagagtgct ttgcctgtat gagctgcaag 420 gtgatcattg aggatgggga tgcatatgca ctggtgcagc atgccaccct ctactgtggg 480 aagtgccaca atgaggtggt gctggcaccc atgtttgaga gactctccac agagtctgtt 540 caggagcagc tgccctactc tgtcacgctc atctccatgc cggccaccac tgaaggcagg 600 cggggcttct ccgtgtccgt ggagagtgcc tgctccaact acgccaccac tgtgcaagtg 660 aaagaggtca accggatgca catcagtccc aacaatcgaa acgccatcca ccctggggac 720 cgcatcctgg agatcaatgg gacccccgtc cgcacacttc gagtggagga ggtggaggat 780 gcaattagcc agacgagcca gacacttcag ctgttgattg aacatgaccc cgtctcccaa 840 cgcctggacc agctgcggct ggaggcccgg ctcgctcctc acatgcagaa tgccggacac 900 ccccacgccc tcagcaccct ggacaccaag gagaatctgg aggggacact gaggagacgt 960 tccctaaggc gcagtaacag tatctccaag tcccctggcc ccagctcccc aaaggagccc 1020 ctgctgttca gccgtgacat cagccgctca gaatcccttc gttgttccag cagctattca 1080 cagcagatct tccggccctg tgacctaatc catggggagg tcctggggaa gggcttcttt 1140 gggcaggcta tcaaggtgac acacaaagcc acgggcaaag tgatggtcat gaaagagtta 1200 attcgatgtg atgaggagac ccagaaaact tttctgactg aggtgaaagt gatgcgcagc 1260 ctggaccacc ccaatgtgct caagttcatt ggtgtgctgt acaaggataa gaagctgaac 1320 ctgctgacag agtacattga ggggggcaca ctgaaggact ttctgcgcag tatggatccg 1380 ttcccctggc agcagaaggt caggtttgcc aaaggaatcg cctccggaat ggcctatttg 1440 cactctatgt gcatcatcca ccgggatctg aactcgcaca actgcctcat caagttggac 1500 aagactgtgg tggtggcaga ctttgggctg tcacggctca tagtggaaga gaggaaaagg 1560 gcccccatgg agaaggccac caccaagaaa cgcaccttgc gcaagaacga ccgcaagaag 1620 cgctacacgg tggtgggaaa cccctactgg atggcccctg agatgctgaa cggaaagagc 1680 tatgatgaga cggtggatat cttctccttt gggatcgttc tctgtgagat cattgggcag 1740 gtgtatgcag atcctgactg ccttccccga acactggact ttggcctcaa cgtgaagctt 1800 ttctgggaga agtttgttcc cacagattgt cccccggcct tcttcccgct ggccgccatc 1860 tgctgcagac tggagcctga gagcagacca gcattctcga aattggagga ctcctttgag 1920 gccctctccc tgtacctggg ggagctgggc atcccgctgc ctgcagagct ggaggagttg 1980 gaccacactg tgagcatgca gtacggcctg acccgggact cacctcccta gccctggccc 2040 agccccctgc aggggggtgt tctacagcca gcattgcccc tctgtgcccc attcctgctg 2100 tgagcagggc cgtccgggct tcctgtggat tggcggaatg tttagaagca gaacaagcca 2160 ttcctattac ctccccagga ggcaagtggg cgcagcacca gggaaatgta tctccacagg 2220 ttctggggcc tagttactgt ctgtaaatcc aatacttgcc tgaaagctgt gaagaagaaa 2280 aaaacccctg gcctttgggc caggaggaat ctgttactcg aatccaccca ggaactccct 2340 ggcagtggat tgtgggaggc tcttgcttac actaatcagc gtgacctgga cctgctgggc 2400 aggatcccag ggtgaacctg cctgtgaact ctgaagtcac tagtccagct gggtgcagga 2460 ggacttcaag tgtgtggacg aaagaaagac tgatggctca aagggtgtga aaaagtcagt 2520 gatgctcccc ctttctactc cagatcctgt ccttcctgga gcaaggttga gggagtaggt 2580 tttgaagagt cccttaatat gtggtggaac aggccaggag ttagagaaag ggctggcttc 2640 tgtttacctg ctcactggct ctagccagcc cagggaccac atcaatgtga gaggaagcct 2700 ccacctcatg ttttcaaact taatactgga gactggctga gaacttacgg acaacatcct 2760 ttctgtctga aacaaacagt cacaagcaca ggaagaggct gggggactag aaagaggccc 2820 tgccctctag aaagctcaga tcttggcttc tgttactcat actcgggtgg gctccttagt 2880 cagatgccta aaacattttg cctaaagctc gatgggttct ggaggacagt gtggcttgtc 2940 acaggcctag agtctgaggg aggggagtgg gagtctcagc aatctcttgg tcttggcttc 3000 atggcaacca ctgctcaccc ttcaacatgc ctggtttagg cagcagcttg ggctgggaag 3060 aggtggtggc agagtctcaa agctgagatg ctgagagaga tagctccctg agctgggcca 3120 tctgacttct acctcccatg tttgctctcc caactcatta gctcctgggc agcatcctcc 3180 tgagccacat gtgcaggtac tggaaaacct ccatcttggc tcccagagct ctaggaactc 3240 ttcatcacaa ctagatttgc ctcttctaag tgtctatgag cttgcaccat atttaataaa 3300 ttgggaatgg gtttggggta ttaatgcaat gtgtggtggt tgtattggag cagggggaat 3360 tgataaagga gagtggttgc tgttaatatt atcttatcta ttgggtggta tgtgaaatat 3420 tgtacataga cctgatgagt tgtgggacca gatgtcatct ctggtcagag tttacttgct 3480 atatagactg tacttatgtg tgaagtttgc aagcttgctt tagggctgag ccctggactc 3540 ccagcagcag cacagttcag cattgtgtgg ctggttgttt cctggctgtc cccagcaagt 3600 gtaggagtgg tgggcctgaa ctgggccatt gatcagacta aataaattaa gcagttaaca 3660 taactggc 3668 <210> 4 <211> 1008 <212> DNA <213> Homo sapiens <400> 4 atgtgggcca cgctgccgct gctctgcgcc ggggcctggc tcctgggagt ccccgtctgc 60 ggtgccgccg aactgtgcgt gaactcctta gagaagtttc acttcaagtc atggatgtct 120 aagcaccgta agacctacag tacggaggag taccaccaca ggctgcagac gtttgccagc 180 aactggagga agataaacgc ccacaacaat gggaaccaca catttaaaat ggcactgaac 240 caattttcag acatgagctt tgctgaaata aaacacaagt atctctggtc agagcctcag 300 aattgctcag ccaccaaaag taactacctt cgaggtactg gtccctaccc accttccgtg 360 gactggcgga aaaaaggaaa ttttgtctca cctgtgaaaa atcagggtgc ctgcggcagt 420 tgctggactt tctccaccac tggggccctg gagtctgcaa tcgccatcgc aaccggaaag 480 atgctgtcct tggcggaaca gcagctggtg gactgcgccc aggacttcaa taattacggc 540 tgccaagggg gtctccccag ccaggctttc gagtatatcc tgtacaacaa ggggatcatg 600 ggtgaagaca cctaccccta ccagggcaag gatggttatt gcaagttcca acctggaaag 660 gccatcggct ttgtcaagga tgtagccaac atcacaatct atgacgagga agcgatggtg 720 gaggctgtgg ccctctacaa ccctgtgagc tttgcctttg aggtgactca ggacttcatg 780 atgtatagaa cgggcatcta ctccagtact tcctgccata aaactccaga taaagtaaac 840 catgcagtac tggctgttgg gtatggagaa aaaaatggga tcccttactg gatcgtgaaa 900 aactcttggg gtccccagtg gggaatgaac gggtacttcc tcatcgagcg cggaaagaac 960 atgtgtggcc tggctgcctg cgcctcctac cccatccctc tggtgtga 1008 <210> 5 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: primer <400> 5 ctggactacc tggacctcta cct 23 <210> 6 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: primer <400> 6 tttgaggcaa agagaagtct t 21 <210> 7 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: primer <400> 7 ctagtagccg aatggtggtc a 21 <210> 8 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: primer <400> 8 attggccata tcaaaagagc ac 22 <210> 9 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: primer <400> 9 tgtttacctg ctcactggct cta 23 <210> 10 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: primer <400> 10 tagtctgatc aatggcccag ttc 23 <210> 11 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: primer <400> 11 tactggtccc tacccacctt c 21 <210> 12 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: primer <400> 12 ggaggtgctc actcaatgtt tat 23
【００７８】[0078]
配列番号５−人工配列の説明：プライマー 配列番号６−人工配列の説明：プライマー 配列番号７−人工配列の説明：プライマー 配列番号８−人工配列の説明：プライマー 配列番号９−人工配列の説明：プライマー 配列番号１０−人工配列の説明：プライマー 配列番号１１−人工配列の説明：プライマー 配列番号１２−人工配列の説明：プライマー SEQ ID NO: 5-Description of artificial sequence: primer SEQ ID NO: 6-Description of Artificial Sequence: Primer SEQ ID NO: 7-Description of Artificial Sequence: Primer SEQ ID NO: 8-Description of Artificial Sequence: Primer SEQ ID NO: 9-Description of Artificial Sequence: Primer SEQ ID NO: 10-Description of Artificial Sequence: Primer SEQ ID NO: 11-Description of Artificial Sequence: Primer SEQ ID NO: 12-Description of Artificial Sequence: Primer
【図１】図１は、増殖阻害活性測定の概略とスルフォロ
ーダミンＢアッセイの写真を示す。FIG. 1 shows an outline of measurement of growth inhibitory activity and a photograph of a sulforudamin B assay.
【図２】図２は、乳がん細胞株5系について、トポ１阻
害剤カンプトテシンを処理した際の増殖阻害を示す[FIG. 2] FIG. 2 shows the growth inhibition when the topo-1 inhibitor camptothecin was treated in the breast cancer cell line 5 line.
【図３】図３は、cDNAマイクロアレイを用いた遺伝子発
現量の解析方法（概略）を示す。FIG. 3 shows a method (schematic) for analyzing gene expression levels using a cDNA microarray.
【図４】図４は、抗がん剤の増殖阻害活性とLIMK2遺伝
子発現の相関を示す。FIG. 4 shows the correlation between the growth inhibitory activity of anticancer agents and LIMK2 gene expression.
【図５】図５は、各抗がん剤と抗がん剤適合性マーカー
遺伝子のピアソン相関係数に基づくクラスター解析結果
を示す。FIG. 5 shows the results of cluster analysis based on the Pearson correlation coefficient between each anticancer agent and the anticancer agent compatibility marker gene.
【図６】図６は、アントラサイクリン系抗生物質と抗が
ん剤適合性マーカー遺伝子のピアソン相関係数に基づく
クラスター解析結果を示す。FIG. 6 shows the results of cluster analysis based on the Pearson correlation coefficient between anthracycline antibiotics and anticancer drug compatibility marker genes.
【図７】図７は、トポI阻害剤と抗がん剤適合性マーカ
ー遺伝子のピアソン相関係数に基づくクラスター解析結
果を示す。FIG. 7 shows the results of cluster analysis based on the Pearson correlation coefficient between the topo I inhibitor and the anticancer drug compatibility marker gene.
【図８】図８は、cDNAマイクロアレイによるAKR1B1、DD
B2、LIMK2、及びCTSH遺伝子の発現パターンを示すグラ
フである。図中、各発現レベルは、39細胞株の平均発現
レベルが"0"となるように調整されている。FIG. 8: AKR1B1, DD by cDNA microarray
It is a graph which shows the expression pattern of B2, LIMK2, and CTSH gene. In the figure, each expression level is adjusted so that the average expression level of 39 cell lines is "0".
【図９】図９は、6種の胃がん細胞株と5種の大腸がん細
胞株におけるcDNA マイクロアレイとRT-PCRで測定した
遺伝子発現レベルの関係を示す。図中、▲は胃がん細胞
株の結果、□は結腸がん細胞株の結果を示す。FIG. 9 shows the relationship between gene expression levels measured by cDNA microarray and RT-PCR in 6 types of gastric cancer cell lines and 5 types of colon cancer cell lines. In the figure, ▲ indicates the result of gastric cancer cell line, and □ indicates the result of colon cancer cell line.
【図１０】図１０は、18種の胃がん細胞株におけるAKR1
B1、DDB2、CTSH、及びLIMK2 のRT-PCRによる発現解析の
結果を示す。FIG. 10: AKR1 in 18 gastric cancer cell lines
The results of RT-PCR expression analysis of B1, DDB2, CTSH, and LIMK2 are shown.
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Claims (13)
性マーカー遺伝子。 1) 少なくとも２０種以上のがん細胞株のそれぞれを対
象として、抗がん剤による５０％増殖阻害濃度（モル/
L）の常用対数値の絶対値Ｘiを求める； 2) インタクトな状態において、上記各細胞株における
遺伝子Ｇの発現量をコントロールに対する発現量比で表
し、その底を２とした対数値Ｙiを求める； 3) 対象がん細胞株における上記Ｘi及びＹiの値から、
ＹをＸに回帰させたときの回帰直線の傾きａ、ならびに
Ｘiの最大値Ｘmax及び最小値Ｘminを求める； 4) 少なくとも１の抗がん剤に対して、Ｐ＜０．０５、
かつ｜ａ（Ｘmax-Ｘmin）｜＞１.５の要件を満たすと
き、上記遺伝子Ｇを抗がん剤適合性マーカ遺伝子として
選択する。1. An anticancer drug compatibility marker gene selected in the following steps. 1) 50% growth inhibitory concentration (mol / mol) of anti-cancer drug for each of at least 20 types of cancer cell lines
L) Obtain the absolute value Xi of the common logarithmic value; 2) In an intact state, express the expression level of gene G in each of the above cell lines by the expression level ratio with respect to the control, and calculate the logarithmic value Yi with the base as 2. 3) From the values of Xi and Yi in the target cancer cell line,
The slope a of the regression line when Y is regressed to X, and the maximum value Xmax and the minimum value Xmin of Xi are obtained; 4) P <0.05 for at least one anticancer agent,
And when the requirement of | a (Xmax-Xmin) |> 1.5 is satisfied, the gene G is selected as an anticancer drug compatibility marker gene.
ん剤に対して、前記工程４）の要件を満たす、請求項１
記載の抗がん剤適合性マーカー遺伝子。2. The requirement of the step 4) is satisfied for two or more anticancer agents having different action mechanisms.
The described anticancer drug compatibility marker gene.
剤に対してのみ、前記工程４）の要件を満たす、請求項
１記載の抗がん剤適合性マーカー遺伝子。3. The anticancer drug compatibility marker gene according to claim 1, which satisfies the requirement of the step 4) only for specific anticancer drugs having a common action mechanism.
塩基配列で特定される、抗がん剤適合性マーカー遺伝
子。4. An anticancer drug compatibility marker gene specified by any one of the nucleotide sequences shown in SEQ ID NOs: 1 to 4.
がん剤適合性マーカー遺伝子と特異的にハイブリダイズ
し、該遺伝子を検出するための１００〜１５００塩基の
連続したポリヌクレオチド。5. A continuous polynucleotide of 100 to 1500 bases for specifically hybridizing with the anticancer drug compatibility marker gene according to any one of claims 1 to 4 and detecting the gene. .
を含まず、前記抗がん剤適合性マーカー遺伝子のｍＲＮ
Ａ ３'ＵＴＲを含む領域に特異的にハイブリダイズす
るものである、請求項５記載のポリヌクレオチド。6. The mRN of the anticancer drug compatibility marker gene, wherein the polynucleotide does not contain a repetitive sequence.
The polynucleotide according to claim 5, which specifically hybridizes to a region containing A 3'UTR.
を増幅するためのオリゴヌクレオチドプライマー。7. An oligonucleotide primer for amplifying the polynucleotide according to claim 5 or 6.
を固定した、抗がん剤適合性を予測するための固相化試
料。8. A solid-phased sample, on which the polynucleotide according to claim 5 or 6 is immobilized, for predicting compatibility with an anticancer agent.
のいずれか１項に記載の抗がん剤適合性マーカー遺伝子
の発現量により、該検体の抗がん剤適合性を予測する方
法。9. The method according to claims 1 to 4 in cancer cells in a sample.
A method for predicting the compatibility of an anticancer drug of the sample with the expression level of the anticancer drug compatibility marker gene according to any one of 1.
法。 1) 検体中のがん細胞よりmRNAを抽出し、サンプルとす
る； 2) 上記サンプルにおける、請求項１〜４のいずれか１
項に記載の抗がん剤適合性マーカー遺伝子のコントロー
ルに対する相対的発現量を求める； 3) 上記サンプルにおける抗がん剤適合性マーカー遺伝
子の相対的発現量と、がん細胞株における遺伝子発現量
と抗がん剤感受性の相関を利用して、上記検体の抗がん
剤適合性を予測する；10. The method according to claim 9, comprising the following steps. 1) Extract mRNA as a sample from cancer cells in a sample; 2) Any one of claims 1 to 4 in the above sample
The relative expression level of the anticancer drug compatibility marker gene described in the paragraph is calculated with respect to the control; 3) The relative expression level of the anticancer drug compatibility marker gene in the above sample and the gene expression level in the cancer cell line And anticancer drug sensitivity correlation is used to predict the anticancer drug suitability of the above sample;
相対的発現量をｃＤＮＡマイクロアレイ又はＲＴ−ＰＣ
Ｒを用いて解析することを特徴とする、請求項９又は１
０記載の方法。11. The relative expression level of the anticancer drug compatibility marker gene is determined by cDNA microarray or RT-PC.
The analysis is performed by using R, 10.
The method described in 0.
を含む、抗がん剤適合性を予測するためのキット。 １）請求項５又は６記載のポリヌクレオチド ２）請求項７記載のオリゴヌクオチドプライマー ３）請求項８記載の固相化試料12. A kit for predicting compatibility with an anticancer agent, which comprises any one or more of the following 1) to 3). 1) The polynucleotide according to claim 5 or 6, 2) the oligonucleotide primer according to claim 7, and 3) the solid-phased sample according to claim 8.
ーカー遺伝子の選択方法。 1) 少なくとも２０種以上のがん細胞株のそれぞれを対
象として、抗がん剤による５０％増殖阻害濃度（モル/
L）の常用対数値の絶対値Ｘiを求める； 2) インタクトな状態において、上記各細胞株における
遺伝子Ｇの発現量をコントロールに対する発現量比で表
し、その底を２とした対数値Ｙiを求める； 3) 対象がん細胞株における上記Ｘi及びＹiの値から、
ＹをＸに回帰させたときの回帰直線の傾きａ、ならびに
Ｘiの最大値Ｘmax及び最小値Ｘminを求める； 4) 少なくとも１の抗がん剤に対して、Ｐ＜０．０５、
かつ｜ａ（Ｘmax-Ｘmin）｜＞１.５の要件を満たすと
き、上記遺伝子Ｇを抗がん剤適合性マーカ遺伝子として
選択する。13. A method for selecting an anticancer drug compatibility marker gene, which comprises the following steps. 1) 50% growth inhibitory concentration (mol / mol) of anti-cancer drug for each of at least 20 types of cancer cell lines
L) Obtain the absolute value Xi of the common logarithmic value; 2) In an intact state, express the expression level of gene G in each of the above cell lines by the expression level ratio with respect to the control, and calculate the logarithmic value Yi where the base is 2. 3) From the values of Xi and Yi in the target cancer cell line,
The slope a of the regression line when Y is regressed to X, and the maximum value Xmax and the minimum value Xmin of Xi are determined; 4) P <0.05 for at least one anticancer agent,
And when the requirement of | a (Xmax-Xmin) |> 1.5 is satisfied, the gene G is selected as an anticancer drug compatibility marker gene.
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| US7682785B2 (en) | 2005-06-30 | 2010-03-23 | Sysmex Corporation | Method for predicting effectiveness of chemotherapy using anticancer agent |
| US7700346B2 (en) | 2005-09-14 | 2010-04-20 | Sysmex Corporation | Tissue characteristic determination apparatus |
| US7957910B2 (en) | 2005-01-31 | 2011-06-07 | Sysmex Corporation | Method for predicting effectiveness of chemotherapy |
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| JP2001245666A (en) * | 2000-03-06 | 2001-09-11 | Kyowa Hakko Kogyo Co Ltd | Novel polypeptide |
| JP2002504687A (en) * | 1998-02-18 | 2002-02-12 | セライト リミテッド | Cancer Treatment |
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| JP2002504687A (en) * | 1998-02-18 | 2002-02-12 | セライト リミテッド | Cancer Treatment |
| JP2001245666A (en) * | 2000-03-06 | 2001-09-11 | Kyowa Hakko Kogyo Co Ltd | Novel polypeptide |
Cited By (3)
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| US7957910B2 (en) | 2005-01-31 | 2011-06-07 | Sysmex Corporation | Method for predicting effectiveness of chemotherapy |
| US7682785B2 (en) | 2005-06-30 | 2010-03-23 | Sysmex Corporation | Method for predicting effectiveness of chemotherapy using anticancer agent |
| US7700346B2 (en) | 2005-09-14 | 2010-04-20 | Sysmex Corporation | Tissue characteristic determination apparatus |
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