JPWO2020137076A1 - Methods for Predicting Cancer Sensitivity to PARP Inhibitors and Methods for Detecting Cancers with Homologous Recombination Deficiency - Google Patents

Abstract

An object of the present invention is to provide a method for predicting the susceptibility of a target cancer to a PARP inhibitor. According to the present invention, it is a method for predicting the susceptibility of cancer to a PARP inhibitor, which comprises a step of performing sequence analysis on a nucleic acid sample obtained from cancer cells to create a mutation catalog, and the present invention relates to the mutation catalog. The contribution of the BRCA signature outweighs the contribution of the Age signature to the mutation catalog, and the absence of cyclin E1 amplification provides a predictive method of indicating the presence of cancer susceptible to PARP inhibitors.

Description

Reference of related application

The present application enjoys the benefit of the priority of Japanese Patent Application No. 2018-248619 (filed on December 28, 2018), which is a prior application in Japan, and the entire disclosure thereof is hereby cited by reference. Be part of.

The present invention relates to a method for predicting the susceptibility of cancer to a PARP inhibitor and a method for detecting cancer having homologous recombination repair failure.

In recent years, molecular-targeted drugs targeting changes in genes and proteins that occur specifically in cancer cells have been developed. Since such molecular-targeted drugs target cancer cells and do not damage normal cells, they are attracting attention as anticancer agents with reduced side effects. In order to administer a molecular-targeted drug, it is necessary not only to diagnose the cancer type but also to detect gene mutations and protein changes at the molecular level.

Among the molecular-targeted drugs, olaparib, which is a poly (ADP-ribose) polymerase (PARP) inhibitor developed targeting homologous recombination deficiency (HRD), is 2014. It was approved by the US Food and Drug Administration (FDA) in 2014 for recurrent ovarian cancer with germline BRCA mutations. PARP is an enzyme involved in the repair of single-strand and double-strand breaks in DNA, and repair of double-strand breaks is performed by homologous recombination (HR). On the other hand, the BRCA1 / 2 gene plays an important role in HR in double-strand break repair of DNA. When a PARP inhibitor is allowed to act on cells having a BRCA1 / 2 mutation, DNA repair by homologous recombination is inhibited, resulting in cell death and an antitumor effect.

It has been reported that in serous ovarian cancer, germline BRCA1 / 2 mutations account for about 20%, whereas HRDs account for about 50% (Non-Patent Document 1). Since PARP inhibitors are agents that target HRD, it is desirable to administer them widely to patients with cancer having HRD, without limiting to subjects with germline BRCA1 / 2 mutations. However, until now, there has been no suitable biomarker for evaluating HRD in just proportion.

The Cancer Genome Atlas Research Network, Nature, 474: 609-615 (2011)

An object of the present invention is to provide a method for predicting the susceptibility of a target cancer to a PARP inhibitor and a method for detecting a cancer having homologous recombination repair failure.

The present inventors recently conducted a mutation signature analysis based on all exon sequence analysis for cases of ovarian serous cancer, and found that 77% of cases had a BRCA signature contribution to the mutation catalog that exceeded the Age signature contribution. I found. The present inventors also set a biomarker (MSBM) indicating the presence of cancer having homologous recombination repair failure, and performed total exon sequence analysis, non-negative matrix factorization (NMF), etc., and found that MSBM-positive cases. Was found to be 68%. We further performed MSBM determination using an oncogene panel test and found that MSBM positivity could be an indicator of homologous recombination repair failure and susceptibility to PARP inhibitors. Furthermore, the present inventors have found that the contribution of 30 mutation signatures to the mutation catalog can be calculated by deconstructSigs analysis, and MSBM determination can be performed based on the analysis result. The present invention is based on these findings.

（上記式中、ｎ＝９６であり、ｉは、図１に記載の９６種の置換分類のうちｉ番目の置換変異を意味し、Ｍｉは、前記変異カタログのｉ番目の置換変異の変異率または変異数であり、ＫＡｉは、Ａｇｅシグネチャーのｉ番目の置換変異の変異率である。）
により算出される、上記［１１］に記載の方法。
［１３］ＢＲＣＡシグネチャースコアが下記式（２）：

（上記式中、ｎ＝９６であり、ｉは、図１に記載の９６種の置換分類のうちｉ番目の置換変異を意味し、Ｍｉは、前記変異カタログのｉ番目の置換変異の変異率または変異数であり、ＫＢｉは、ＢＲＣＡシグネチャーのｉ番目の置換変異の変異率である。）
により算出される、上記［１１］に記載の方法。
［１４］前記工程（３）において、Ａｇｅシグネチャースコアと、ＢＲＣＡシグネチャースコアとの和に対するＢＲＣＡシグネチャースコアの比率Ａ（％）を算出することにより、ＢＲＣＡシグネチャーの寄与の優位性を評価する、上記［９］および［１１］〜［１３］のいずれかに記載の方法。
［１５］前記工程（３）において、ＣからＴへの置換以外の置換変異のスコア（ＢＲＣＡシグネチャースコア）と、ＣからＴへの置換変異のスコア（Ａｇｅシグネチャースコア）との和に対するＣからＴへの置換以外の置換変異のスコアの比率Ｂ（％）を算出することにより、ＢＲＣＡシグネチャーの寄与の優位性を評価する、上記［９］および［１１］〜［１３］のいずれかに記載の方法。
［１６］比率Ａ（％）が５０％を超える場合に、前記（ａ１）を満たすと判定する、上記［１４］に記載の方法。
［１７］比率Ｂ（％）が５０％を超える場合に、前記（ａ１）を満たすと判定する、上記［１５］に記載の方法。
［１８］下記工程（４）〜（６）：
（４）ＡｇｅシグネチャーおよびＢＲＣＡシグネチャーを少なくとも含む変異シグネチャーを準備すること、
（５）変異カタログに対する変異シグネチャーそれぞれの寄与度を評価すること、および
（６）Ａｇｅシグネチャーの寄与度に対するＢＲＣＡシグネチャーの寄与度の優位性を評価すること
をさらに含んでなる、上記［１］〜［８］のいずれかに記載の方法。
［１９］（７）前記変異シグネチャー全体に対するＢＲＣＡシグネチャーの寄与度を評価することをさらに含んでなる、上記［１８］に記載の方法。
［２０］前記変異シグネチャーが、ＣＯＳＭＩＣデータベースの３０種の変異シグネチャーの全部または一部を含む、上記［１８］に記載の方法。
［２１］前記工程（５）において、変異カタログに対する変異シグネチャーの寄与度をｄｅｃｏｎｓｔｒｕｃｔＳｉｇｓ解析により評価する、上記［１８］に記載の方法。
［２２］前記工程（６）において、Ａｇｅシグネチャーの寄与度と、ＢＲＣＡシグネチャーの寄与度との和に対するＢＲＣＡシグネチャーの寄与度の比率Ｃ（％）を算出することにより、ＢＲＣＡシグネチャーの寄与度の優位性を評価する、上記［１８］に記載の方法。
［２３］前記工程（６）において、Ａｇｅシグネチャーの寄与度と、ＢＲＣＡシグネチャーの寄与度との和に対するＢＲＣＡシグネチャーの寄与度の比率Ｃ（％）を算出することにより、ＢＲＣＡシグネチャーの寄与度の優位性を評価し、かつ、
前記工程（７）において、前記変異シグネチャーの寄与度の和（１）に対するＢＲＣＡシグネチャーの寄与度の比率Ｄ（％）を算出することにより、ＢＲＣＡシグネチャーの寄与度を評価する、上記［１９］に記載の方法。
［２４］比率Ｃ（％）が５０％を超える場合に、前記（ａ１）を満たすと判定する、上記［２２］に記載の方法。
［２５］比率Ｃ（％）が５０％を超え、かつ、比率Ｄ（％）がｋ％（ｋ＝２０〜３０）を超える場合に、前記（ａ１）を満たすと判定する、上記［２３］に記載の方法。
［２６］条件（Ａ）の（ａ１）および（ａ２）の両方が満たされる場合に、前記癌細胞がＰＡＲＰ阻害剤に対して感受性を有する癌細胞集団を含むと判定する工程を含む、上記［１］〜［２５］のいずれかに記載の方法。
［２７］対象の被験試料について網羅的メチル化解析を実施する工程をさらに含んでなり、（Ｂ）ＢＲＣＡ１のプロモーター領域のメチル化がＰＡＲＰ阻害剤に対して感受性を有する癌の存在を示す、上記［１］〜［２６］のいずれかに記載の方法。
［２８］相同組換修復不全（ＨＲＤ）を有する癌を検出する方法であって、
癌細胞から得られた核酸試料について配列解析を実施して変異カタログを作成する工程を含んでなり、（Ａ）（ａ１）変異カタログに対するＢＲＣＡシグネチャーの寄与が、変異カタログに対するＡｇｅシグネチャーの寄与を上回り、かつ（ａ２）サイクリンＥ１増幅が不存在であることが、相同組換修復不全を有する癌の存在を示す、検出方法。
［２９］対象の被験試料について網羅的メチル化解析を実施する工程をさらに含んでなり、（Ｂ）ＢＲＣＡ１のプロモーター領域のメチル化が相同組換修復不全を有する癌の存在を示す、上記［２８］に記載の検出方法。
［３０］ＰＡＲＰ阻害剤による癌患者の治療方法であって、上記［１］〜［２７］のいずれかに記載の方法を実施してＰＡＲＰ阻害剤に対して感受性を有する癌を有する癌患者を特定するか、あるいは上記［２８］または［２９］に記載の方法を実施して相同組換修復不全を有する癌を有する癌患者を特定し、次いで、該患者に有効量のＰＡＲＰ阻害剤を投与することを含んでなる、治療方法。
［３１］ＰＡＲＰ阻害剤を有効成分として含んでなる癌治療剤であって、前記治療が上記［３０］に記載の方法により行われることを特徴とする、癌治療剤。According to the present invention, the following inventions are provided.
[1] A method for predicting the susceptibility of cancer to PARP inhibitors.
It includes the steps of performing sequence analysis on nucleic acid samples obtained from cancer cells to create a mutation catalog, and (A) (a1) the contribution of the BRCA signature to the mutation catalog exceeds the contribution of the Age signature to the mutation catalog. And (a2) the absence of cyclin E1 amplification indicates the presence of cancers susceptible to PARP inhibitors, a predictive method.
[2] The method according to [1] above, wherein the cancer cells are cells obtained from a cancer patient.
[3] The method according to [1] or [2] above, wherein the cancer is a cancer that may exhibit homologous recombination repair abnormality.
[4] The method according to any one of the above [1] to [3], wherein the PARP inhibitor is olaparib, lucaparib, niraparib, veliparib or tarazoparib.
[5] The method according to any one of [1] to [4] above, wherein the sequence analysis includes a sequence determination procedure and a sequence data analysis procedure.
[6] The method according to any one of [1] to [5] above, wherein a somatic gene mutation is identified by the sequence analysis according to the 96 types of substitution classification shown in FIG. 1 and a mutation catalog is prepared.
[7] The method according to any one of [1] to [6] above, wherein the sequence analysis is performed by whole exon sequence analysis.
[8] The method according to any one of [1] to [6] above, wherein the sequence analysis is performed by an oncogene panel test.
[9] The following steps (1) to (3):
(1) Preparing Age signatures and BRCA signatures,
The above [1] to [] further include (2) evaluating the contribution of the Age signature and the BRCA signature to the mutation catalog, and (3) evaluating the superiority of the contribution of the BRCA signature to the contribution of the Age signature. 8] The method according to any one of.
[10] In the step (1), mutation signature analysis is performed on a plurality of cancer patients suffering from cancer predicting susceptibility, and the mutation rate of the substitution mutation of the Age signature and / or the BRCA signature is specified to identify the Age signature and / or the BRCA signature. The method according to [9] above, which prepares a BRCA signature.
[11] The method according to [9] above, wherein in the step (2), the contribution of the mutation catalog is evaluated by calculating the Age signature score and the BRCA signature score.
[12] The Age signature score is the following formula (1):

(In the above formula, n = 96, i means the i-th substitution mutation in the 96 substitution classifications shown in FIG. 1, and Mi is the mutation rate of the i-th substitution mutation in the mutation catalog. Or the number of mutations, KAi is the mutation rate of the i-th substitution mutation of the Age signature.)
The method according to the above [11], which is calculated according to the above.
[13] The BRCA signature score is the following formula (2):

(In the above formula, n = 96, i means the i-th substitution mutation in the 96 substitution classifications shown in FIG. 1, and Mi is the mutation rate of the i-th substitution mutation in the mutation catalog. Or the number of mutations, KBi is the mutation rate of the i-th substitution mutation in the BRCA signature.)
The method according to the above [11], which is calculated according to the above.
[14] In the step (3), the superiority of the contribution of the BRCA signature is evaluated by calculating the ratio A (%) of the BRCA signature score to the sum of the Age signature score and the BRCA signature score. 9] and the method according to any one of [11] to [13].
[15] In the step (3), C to T with respect to the sum of the score of the substitution mutation other than the substitution from C to T (BRCA signature score) and the score of the substitution mutation from C to T (Age signature score). The above-mentioned [9] and [11] to [13], wherein the superiority of the contribution of the BRCA signature is evaluated by calculating the ratio B (%) of the score of the substitution mutation other than the substitution with. Method.
[16] The method according to the above [14], wherein it is determined that the above (a1) is satisfied when the ratio A (%) exceeds 50%.
[17] The method according to the above [15], wherein it is determined that the above (a1) is satisfied when the ratio B (%) exceeds 50%.
[18] The following steps (4) to (6):
(4) Preparing a mutant signature containing at least an Age signature and a BRCA signature,
The above [1] to [1] to further include evaluating (5) the contribution of each mutation signature to the mutation catalog, and (6) evaluating the superiority of the BRCA signature's contribution to the age signature's contribution. The method according to any one of [8].
[19] (7) The method of [18] above, further comprising assessing the contribution of the BRCA signature to the entire mutation signature.
[20] The method of [18] above, wherein the mutation signature comprises all or part of the 30 mutation signatures in the COSMIC database.
[21] The method according to the above [18], wherein in the step (5), the contribution of the mutation signature to the mutation catalog is evaluated by the decodeSigs analysis.
[22] In the step (6), the superiority of the contribution of the BRCA signature is obtained by calculating the ratio C (%) of the contribution of the BRCA signature to the sum of the contribution of the Age signature and the contribution of the BRCA signature. The method according to [18] above, which evaluates sex.
[23] In the step (6), the superiority of the contribution of the BRCA signature is obtained by calculating the ratio C (%) of the contribution of the BRCA signature to the sum of the contribution of the Age signature and the contribution of the BRCA signature. Evaluate sex and
In the step (7), the contribution of the BRCA signature is evaluated by calculating the ratio D (%) of the contribution of the BRCA signature to the sum of the contributions of the mutation signature (1). The method described.
[24] The method according to the above [22], wherein it is determined that the above (a1) is satisfied when the ratio C (%) exceeds 50%.
[25] When the ratio C (%) exceeds 50% and the ratio D (%) exceeds k% (k = 20 to 30), it is determined that the above (a1) is satisfied. The method described in.
[26] The above-mentioned [26], which comprises a step of determining that the cancer cells include a cancer cell population susceptible to a PARP inhibitor when both (a1) and (a2) of the condition (A) are satisfied. 1] The method according to any one of [25].
[27] The above, further comprising performing a comprehensive methylation analysis on the test sample of interest, wherein (B) methylation of the promoter region of BRCA1 indicates the presence of cancer sensitive to PARP inhibitors. The method according to any one of [1] to [26].
[28] A method for detecting cancer having homologous recombination repair failure (HRD).
It includes the steps of performing sequence analysis on nucleic acid samples obtained from cancer cells to create a mutation catalog, and (A) (a1) the contribution of the BRCA signature to the mutation catalog exceeds the contribution of the Age signature to the mutation catalog. And (a2) the absence of cyclin E1 amplification indicates the presence of cancer with homologous recombination repair failure.
[29] Further including the step of performing a comprehensive methylation analysis on the test sample of interest, (B) methylation of the promoter region of BRCA1 indicates the presence of cancer having homologous recombination repair failure, as described above [28]. ] The detection method described in.
[30] A method for treating a cancer patient with a PARP inhibitor, wherein the method according to any one of the above [1] to [27] is carried out to obtain a cancer patient having cancer sensitive to the PARP inhibitor. Identify or perform the method according to [28] or [29] above to identify a cancer patient with cancer having homologous recombinant repair failure, followed by administration of an effective amount of PARP inhibitor to the patient. A method of treatment that involves doing.
[31] A cancer therapeutic agent comprising a PARP inhibitor as an active ingredient, wherein the treatment is performed by the method according to the above [30].

The prediction method of [1] to [27] and the detection method of [28] and [29] may be hereinafter referred to as "the method of the present invention".

According to the method of the present invention, it is advantageous in that cancer having homologous recombination repair failure can be detected in just proportion.

FIG. 1 is a diagram showing substitution classifications of 96 types of 6 classes of somatic gene substitution mutations in human cancer. Each class contains 16 trinucleotide sequences of 4 types of nucleotides 1 base before the mutated base and 4 types of nucleotides 1 base behind, and 6 classes of substitution mutations are classified into 96 types of trinucleotide sequences. FIG. 2 is a diagram showing a mutation signature analysis performed using a total exon sequence analysis on freshly frozen samples of 78 cases of ovarian serous cancer in Example 1 (1). (A) The relationship between the number of signatures by non-negative matrix factorization (NMF) and the stability and swing width is shown. (B) The Age signature and BRCA signature extracted by integrating the gene mutations (mutation catalog) of 78 cases of ovarian serous cancer and performing non-negative matrix factorization (NMF) are shown. Each signature is indicated by a 96 permutation classification. The horizontal axis shows the mutant type (96 substitution classifications, see FIG. 1), and the vertical axis shows the ratio of mutation of a specific mutant type. Mutation signatures are displayed based on the trinucleotide frequency of the human genome. FIG. 3 is a diagram showing the contribution of the mutation signature to the mutation catalog of each case by total exon sequence analysis for fresh frozen samples of 78 cases of ovarian serous cancer in Example 1 (2). Each bar graph shows each case, and each case is arranged in correspondence with (A) and (B). (A) The results of analysis of the contribution of the BRCA signature and the Age signature to the mutation catalog of each case by non-negative matrix factorization (NMF) are shown. The vertical axis represents the number of mutations. The ratio of each signature score to the sum of the BRCA signature score and the Age signature score was calculated for each case. (B) The figure which was sorted based on the ratio of the signature score when the number of mutations of each case in (A) was 100% is shown. 76.9% (60 of 78) had a BRCA signature predominantly contributed, with an average mutation of 95.7. FIG. 4 is a diagram showing the contribution of the mutation signature to the mutation catalog of each case in TCGA, ICGC and 78 cases of high-grade ovarian cancer in Example 1 (2). Each vertical line indicates each case, and each case is arranged in correspondence with (A) and (B). (A) The results of analysis of the contribution of BRCA signature and Age signature to somatic mutation in each case by non-negative matrix factorization (NMF) are shown. The vertical axis represents the number of mutations. The ratio of the contribution of each signature to the sum of the ratio of the contribution of the BRCA signature and the ratio of the contribution of the Age signature was calculated for each case. (B) The figure which was sorted based on the contribution ratio of each signature when the number of mutations of each case in (A) was 100% is shown. The number of cases in which the BRCA signature contributes predominantly is 67.5% in ICGC (54 out of 80 cases, average number of mutations 92.5) and 63.3% in TCGA (200 cases out of 316 cases, average number of mutations). Was 57.4). FIG. 5 shows, in Example 2 (2), total exon sequence analysis, non-negative matrix factorization (NMF), comprehensive methylation analysis and cyclin E1 amplification analysis were performed on fresh frozen samples of 78 cases of ovarian serous cancer. It is a figure which showed the result (signature which contributes dominantly, presence or absence of methylation of BRCA1 promoter region and presence or absence of cyclin E1 amplification). As a result of the judgment by MSBM, 68% of the cases were judged to be MSBM positive. Gray cells represent cases in which the contribution of the BRCA signature is predominant, cases with methylation of the BRCA1 promoter region, or cases in the absence of cyclin E1 amplification. FIG. 6 shows a mutation catalog of each case by oncogene panel test in Example 3. Case X was gBRCA1 / 2 mutation positive and MSBM positive. FIG. 7 shows a mutation catalog of each case by the oncogene panel test in Example 3. Case Y was gBRCA1 / 2 mutation negative and MSBM positive. FIG. 8 shows a mutation catalog of each case by oncogene panel test in Example 3. Case Z was gBRCA1 / 2 mutation negative and MSBM negative. FIG. 9 is a diagram showing a comparison between the results of NMF analysis in Example 1 (2) and the results of deconstructSigs analysis in Example 4 for 78 cases of TCGA, ICGC and high-grade ovarian cancer. (A) The results of analysis of the contribution of BRCA signature and Age signature to somatic mutation in each case by non-negative matrix factorization (NMF) are shown. The ratio of the contribution of each signature to the sum of the ratio of the contribution of the BRCA signature and the ratio of the contribution of the Age signature is calculated for each case, and is based on the ratio of the contribution of each signature when the number of mutations in each case is 100%. (See FIG. 4B). (B) The results of analyzing the contribution of the BRCA signature and the Age signature to the somatic mutation in each case by deconstructSigmas are shown. Since only the contributions of the BRCA signature and the Age signature are displayed, and the contributions of signatures other than these two types are not displayed, the total contribution is not 1 in each case. FIG. 10 is a scatter plot showing the contribution of the BRCA signature by NMF analysis in Example 1 (2) and deconstructSigmas analysis in Example 4 for 78 cases of TCGA, ICGC and high-grade ovarian cancer. FIG. 11 is a scatter plot showing the contribution of the BRCA signature by NMF analysis in Example 1 (2) and deconstructSigmas analysis in Example 4 for 78 cases of TCGA, ICGC and high-grade ovarian cancer. FIG. 12 is a scatter plot showing the contribution of the BRCA signature to 78 cases of high-grade ovarian cancer by NMF analysis in Example 1 (2) and deconstructSigmas analysis in Example 4.

Specific description of the invention

In the present invention, the "PARP inhibitor" refers to an agent that inhibits the function of poly (ADP-ribose) polymerase-1, and examples thereof include olaparib, lucaparib, niraparib, veliparib, and tarazoparib.

In the present invention, the cancer cell can be a cell obtained from a cancer patient. Although rare, germline mutations in the BRCA gene are widespread in solid tumors (Mandelker D et al., JAMA. 318: 825-835 (2017)). It can be a cancer to be excluded or a solid cancer. In a preferred embodiment of the invention, the cancer can be a cancer that may exhibit homologous recombination repair abnormalities, such cancers include, for example, ovarian cancers (particularly ovarian serous cancers, within the class). High-grade ovarian cancers such as membrane cancer, carcinosarcoma, mixed cancer, and undifferentiated cancer), breast cancer, prostate cancer, and pancreatic cancer.

In the present invention, "susceptibility" means a response to a standard dose of a drug or a standard treatment procedure.

In the present invention, the "Age Signature" is a mutation signature that correlates with aging in cancer cells. In the present invention, the "BRCA Signature" is a mutation signature that correlates with a homologous recombination repair abnormality due to factors including BRCA1 / 2 mutation and BRCA1 / 2 inactivation in cancer cells.

In the present invention, the "mutation signature" is a characteristic mutation pattern or profile extracted from somatic gene substitution mutations in a plurality of human cancers, and is a substitution classification of 96 types in 6 classes (C → A, C → G, Classify according to 16 types of base-substituted trinucleotides (see Fig. 1) for each of C → T, T → A, T → C, and T → G, and the ratio of mutations that contribute to a specific mutation type on the horizontal axis. Can be displayed on the vertical axis (Alexandrov LB et al., Nature, 500: 415-421 (2013)).

In the present invention, the "mutation catalog" refers to a somatic gene substitution mutation specified for a test nucleic acid sample in 6 classes and 96 types of substitution classification (C → A, C → G, C → T, T → A, T → C). , T → G are classified or arranged according to 16 kinds of base-substituted trinucleotides (see FIG. 1). The classified or organized substitution mutations can be expressed by the mutation rate (%) (ratio to all substitution mutations) or the number of mutations.

The method of the present invention includes a step of performing sequence analysis on a nucleic acid sample obtained from cancer cells. Sequence analysis includes DNA sequence analysis, RNA sequence analysis, whole exson sequence analysis, and whole genome sequence analysis. In the present invention, the susceptibility of a target cancer to a PARP inhibitor can be predicted, and a cancer having homologous recombination repair failure can be predicted. Can be used without particular limitation as long as it can be detected. Sequence analysis may also be performed by an oncogene panel test, and the oncogene panel tests that can be used in the present invention include Todai OncoPanel (University of Tokyo Hospital), MSK-IMPACT, Foundation One. Can be mentioned. If the present invention is approved by the regulatory affairs in relation to whole exon sequence analysis or whole genome sequence analysis, the present invention is advantageous because it is positioned as an in vitro diagnostic agent capable of accurately determining the sensitivity of a PARP inhibitor.

In the present invention, the sequence analysis may be composed of a sequence determination procedure and a sequence data analysis procedure. For example, a sequence determination procedure is performed on a nucleic acid sample, and sequence data is obtained based on the sequence data obtained by the sequence determination procedure. The analysis procedure can be carried out.

The sequencing procedure can include sequencing the nucleic acid sample obtained from the subject and obtaining sequence data for the nucleic acid sample. Sequencing can be performed using, for example, a next-generation sequencer.

The sequence data analysis procedure can include a step of analyzing the obtained sequence data to obtain a desired analysis result. The analysis of the sequence data can include an analysis for creating a mutation catalog, an analysis for determining the condition (A) and the condition (B), and a signature analysis.

The analysis for creating the mutation catalog can be performed, for example, as follows. That is, a somatic gene mutation is identified in the test nucleic acid sample by sequence data analysis, and the identified somatic gene mutation is classified or organized according to the 96 substitution classifications shown in FIG. 1 to create a mutation catalog of the test nucleic acid sample. can do. Here, identification of somatic cell gene mutations is performed by sequence data of normal tissues of cancer patients (for example, leukocytes in blood, surgical / biopsy tissue samples of non-tumor parts) and tumor tissues of cancer patients (for example, cancer cells). ) Can be compared with the sequence data. Comparison of such sequence data and identification of somatic gene mutations can be performed according to known algorithms (eg, Totoki et al., Nat Genet. 2014 Dec; 46 (12): 1267-73). Mutations identified include base substitutions, deletions, insertions and additions, copy count changes, inversions, translocations, and fusion genes.

In the analysis for determining the condition (a1) of the condition (A), it is determined whether the mutation catalog obtained for the case to be evaluated is similar to the Age signature or the BRCA signature, that is, the contribution of the Age signature. The degree of superiority of the BRCA signature's contribution to the above can be analyzed. Such an analysis can be carried out using a known algorithm for pattern recognition such as non-negative matrix factorization (NMF), or can be carried out as described in (1) to (3) below. ..

(1) Preparation of Age Signature and BRCA Signature In (1), the mutation rate of the substitution mutation of the Age signature and / or the BRCA signature is prepared according to the 96 substitution classifications shown in FIG. For example, mutation signature analysis was performed in advance on nucleic acid samples of a plurality of cancer patients suffering from cancer that predict susceptibility, and the mutation rates of substitution mutations of Age signature and / or BRCA signature were obtained for the 96 substitution classifications shown in FIG. Can be identified. Mutation signature analysis can be performed, for example, as described in Alexandrov LB et al., Nature, 500: 415-21 (2013), wherein the mutation signature analysis is performed by non-negative matrix factorization (NMF). be able to. Moreover, it is preferable to use the same method as the analysis method of the mutation catalog for the signature analysis. For example, when a mutation catalog is created by total exon sequence analysis, mutation signature analysis can also be performed by total exon sequence analysis.

In (1), instead of performing a mutation signature analysis, the mutation rates of substitution mutations of known Age signatures and BRCA signatures may be used (eg, Alexandrov LB et al., Nature, 500: 415-421). (See 2013). Here, the mutation rate can be rephrased as the weighting (coefficient) for each substitution classification that characterizes the Age signature and the BRCA signature. Further, the mutation rate obtained by the mutation signature analysis or the known mutation rate can be used as it is in (2) below, but a part or all of the mutation rate may be arbitrarily changed in order to carry out a better determination. You may.

(2) Evaluation of Contribution of Age Signature and BRCA Signature to Mutation Catalog In (2), the contribution of Age Signature and BRCA Signature to the mutation catalog of the test nucleic acid sample is evaluated. The contribution of the Age signature and the contribution of the BRCA signature can be analyzed by non-negative matrix factorization (NMF). Assessment of the contribution of the Age signature and the contribution of the BRCA signature can also be made by calculating the Age signature score and the BRCA signature score for the mutation catalog. Specifically, the Age signature score and the BRCA signature score are the mutation rates of the substitution mutations obtained in (1) above in the substitution mutation amounts (mutation rate or number) constituting the mutation catalog for all 96 types of substitution mutations. Can be calculated by multiplying by and calculating the product, and obtaining the total value of the products obtained for 96 types of substitution mutations.

（上記式中、ｎ＝９６（すなわちｉ＝１〜９６）であり、ｉは、図１に記載の９６種の置換分類のうちｉ番目の置換変異を意味し、Ｍｉは、前記変異カタログのｉ番目の置換変異の変異率または変異数であり、ＫＡｉは、Ａｇｅシグネチャーのｉ番目の置換変異の変異率である。）For example, the Age signature score can be calculated as follows.

(In the above formula, n = 96 (that is, i = 1 to 96), i means the i-th substitution mutation among the 96 substitution classifications shown in FIG. 1, and Mi is the mutation catalog. It is the mutation rate or the number of mutations of the i-th substitution mutation, and KAi is the mutation rate of the i-th substitution mutation of the Age signature.)

（上記式中、ｎ＝９６（すなわちｉ＝１〜９６）であり、ｉは、図１に記載の９６種の置換分類のうちｉ番目の置換変異を意味し、Ｍｉは、前記変異カタログのｉ番目の置換変異の変異率であり、ＫＢｉは、ＢＲＣＡシグネチャーのｉ番目の置換変異の変異率である。）The BRCA signature score can be calculated, for example, as follows.

(In the above formula, n = 96 (that is, i = 1 to 96), i means the i-th substitution mutation among the 96 substitution classifications shown in FIG. 1, and Mi is the mutation catalog. It is the mutation rate of the i-th substitution mutation, and KBi is the mutation rate of the i-th substitution mutation of the BRCA signature.)

(3) Evaluation of the superiority of the contribution of the BRCA signature to the contribution of the Age signature In (3), the superiority of the contribution of the BRCA signature to the contribution of the Age signature is evaluated. Specifically, which signature is superior to the mutation catalog is evaluated based on the Age signature score and the BRCA signature score calculated in (2) above. For example, the ratio A (%) of the BRCA signature score to the sum of the Age signature score and the BRCA signature score can be calculated, and the superiority can be evaluated based on the ratio A. When the obtained ratio A exceeds 50%, it can be determined that the contribution of the BRCA signature to the mutation catalog exceeds the contribution of the Age signature to the mutation catalog, that is, the condition (a1) is satisfied.

When sequence analysis is performed by whole exon sequence analysis or whole genome sequence analysis, the mutation rates of age signatures and BRCA signatures are described in Alexandrov LB et al., Nature, 500: 415-421 (2013). And can be determined based on the BRCA signature, or signature analysis may be performed to set the Age and BRCA signatures and the mutation rate may be determined based on it.

When sequence analysis is performed by cancer gene panel test, signature analysis can be performed to set Age and BRCA signatures and the mutation rate can be determined based on them, or other than C to T substitution mutations. Substitution mutations (corresponding to i = 1-32 and 49-96 substitution mutations in FIG. 1) reflect BRCA signatures and C to T substitution mutations (corresponding to i = 33-48 substitution mutations in FIG. 1). ) May reflect the Age signature, and the Age signature score and BRCA signature score may be calculated. In the latter case, the Age signature score is the above equation (1) (here, KAi = 1 when i = 33 to 48, KAi = 0 when i = 1-32 and 49 to 96). The BRCA signature score can be calculated by the above formula (2) (where KBi = 1 when i = 1-32 and 49 to 96, and KBi when i = 33 to 48). = 0). For example, a substitution mutation other than a C to T substitution with respect to the sum of a C to T substitution mutation score (BRCA signature score) and a C to T substitution mutation score (Age signature score). The score ratio B (%) can be calculated and the superiority can be evaluated based on the ratio B. When the obtained ratio B exceeds 50%, it can be determined that the contribution of the BRCA signature to the mutation catalog exceeds the contribution of the Age signature to the mutation catalog, that is, the condition (a1) is satisfied.

The analysis for determining the condition (a1) among the conditions (A) can also be carried out as follows (4) to (6).

(4) Preparation of mutation signature In (4), a plurality of mutation signatures including at least an Age signature and a BRCA signature are prepared according to the 96 substitution classifications shown in FIG. The lower limit of the number of mutation signatures prepared in (4) can be 10 or 20, and the upper limit of the number can be 70, 60, 50 or 40. These lower limit value and upper limit value can be arbitrarily combined, and the range of the above number can be, for example, 10 to 70 kinds or 20 to 40 kinds.

Mutation signatures, including at least Age and BRCA signatures, can utilize a set of known mutational signatures, for example, 30 species registered in the COSMIC (Catalogue of somatic mutations in cancer) database of the British Sanger Institute. All or part of the mutation signature (COSMIC version 2), SBS (Single Base Substitution) signature (67 types), and Doublet Base Substitution (COSMIC version 3) among the mutation signatures (COSMIC version 3) registered in the COSMIC database of the Institute. All or part of the DBS) signature (11 types) or the Small Insertion and Deletion (ID) signature (17 types) can be used.

(5) Evaluation of Contribution of Age Signature and BRCA Signature to Mutation Catalog In (5), the contribution of each mutation signature to the mutation catalog of the test nucleic acid sample is evaluated. For example, when 30 types of mutation signatures are prepared, the degree of contribution is evaluated by calculating the optimum weight (contribution) for each of the 30 types of mutation signatures so that the mutation profile of the mutation catalog can be reconstructed. be able to. The calculation of such contribution can be carried out using a known program, for example, by deconstructsSigs analysis (Rosenthal et al., Genome Biology, 17:31 (2016)).

(6) Evaluation of the superiority of the contribution of the BRCA signature to the contribution of the Age signature In (6), the superiority of the contribution of the BRCA signature to the contribution of the Age signature is evaluated. Specifically, which signature is superior to the mutation catalog is evaluated based on the contribution of the Age signature and the contribution of the BRCA signature calculated in (5) above. For example, the ratio C (%) of the contribution of the BRCA signature to the sum of the contribution of the Age signature and the contribution of the BRCA signature can be calculated, and the superiority can be evaluated based on the ratio. When the obtained ratio C exceeds 50%, it can be determined that the contribution of the BRCA signature to the mutation catalog exceeds the contribution of the Age signature to the mutation catalog, that is, the condition (a1) is satisfied. ..

In the analysis for determining the condition (a1) among the conditions (A), the following (7) can be further carried out in addition to the above (4) to (6).
(7) Evaluation of the contribution of the BRCA signature to the entire mutation signature In (7), the contribution of the BRCA signature to the entire mutation signature is evaluated. Specifically, based on the contribution of the BRCA signature calculated in (5) above, the ratio of the BRCA signature to the total prepared mutation signature is evaluated. For example, the ratio D (%) of the contribution of the BRCA signature to the sum (100%) of the contribution of all the prepared mutation signatures can be calculated, and the contribution of the BRCA signature can be evaluated based on the ratio D. .. If the ratio C obtained in (6) exceeds 50% and the ratio D obtained in (7) exceeds k% (k = 15-40, preferably 20-30), a mutation is made. When the contribution of the BRCA signature to the catalog exceeds the contribution of the Age signature to the mutation catalog, that is, it can be determined that the condition (a1) is satisfied.

In the analysis for determining the condition (a2) among the conditions (A), the number of chromosome copies can be analyzed using the probe (SNP) of the genomic region of cyclin E1 as an index. It can be determined that the condition (a2) is satisfied when the number of copies of the number of chromosomes of cyclin E1 is 2 or less.

In the analysis for determining the condition (B), a comprehensive methylation analysis can be performed to identify the methylation of the promoter region of BRCA1 in the test sample. Techniques for exhaustive methylation analysis are known and can be performed, for example, according to The Cancer Genome Atlas Research Network, Nature, 474: 609-615 (2011).

In the present invention, if the following condition (A) is satisfied (that is, if both conditions (a1) and (a2) are satisfied), the target cancer may be a cancer that is sensitive to a PARP inhibitor. It can be determined that the cancer is highly likely or has a homologous recombination repair failure.
Conditions (A): (a1) The contribution of the BRCA signature to the mutation catalog outweighs the contribution of the Age signature to the mutation catalog, and (a2) the absence of cyclin E1 amplification.

In the present specification, the feature or profile of the condition (A) is referred to as Mutational Signature-based BioMarker (MSBM), and when at least the condition (A) is satisfied, it is regarded as MSBM positive, and when the condition (A) is not satisfied, it is regarded as MSBM negative. , Each has something to say. When the condition (A) is satisfied, that is, when it is determined that both the condition (a1) and the condition (a2) are satisfied as a result of the sequence analysis, it can be determined to be MSBM positive. When the analysis for determining the condition (a1) is carried out by the steps (4) to (6), the step (7) is further carried out and the condition (A) is set from the viewpoint of accurate judgment. The following conditions can be met.
Conditions (A): (a1) The contribution of the BRCA signature to the mutation catalog exceeds the contribution of the Age signature to the mutation catalog, and the contribution of the BRCA signature to the entire mutation signature exceeds k% (k = 15-40). And (a2) cyclin E1 amplification is absent

In the present invention, the following condition (B) can be used as a supplement, and if the following condition (B) is satisfied, it is highly possible that the target cancer is a cancer sensitive to a PARP inhibitor. Alternatively, it can be determined that the cancer is likely to have homologous recombination repair failure.
Condition (B): Methylation of the promoter region of BRCA1

If condition (B) is satisfied, that is, if it is determined that the promoter region of BRCA1 is methylated as a result of comprehensive methylation analysis, MSBM is positive on condition that condition (A) is satisfied. Can be determined. In this sense, the profile of condition (B) may be referred to as MSBM in some cases.

In the present invention, the positive / negative determination of MSBM does not depend on the analysis result of the gBRCA mutation. Therefore, even if the subject is gBRCA mutation 1/2 negative, it can be determined to be MSBM positive if the condition (A) is satisfied.

The condition (a1) "superiority of BRCA signature over Age signature" is the ratio (%) of the total BRCA signature score to the sum of the total Age signature score obtained for the nucleic acid sample and the total BRCA signature score. It can be determined based on the above, and when the ratio exceeds 50%, it can be determined that the condition (a1) is satisfied.

The condition (a2) "absence of cyclin E1 amplification" can be determined to satisfy the condition (a2) when the number of copies of the number of chromosomes of cyclin E1 is 2 or less, and when the number of chromosomes exceeds 2. Can be determined not to satisfy the condition (a2).

In the method of the present invention, mutation signature analysis is performed in advance on nucleic acid samples of a plurality of cancer patients suffering from cancer that predict susceptibility, and the 96 substitution classifications shown in FIG. The mutation rate can be specified. That is, according to the method of the present invention, an Age signature and a BRCA signature can be set for each type of sequence analysis, and an optimum inspection method can be constructed. For example, a signature analysis is performed in a particular oncogene panel test to prepare an Age signature and a BRCA signature, and the Age signature and BRCA signature prepared in advance in the oncogene panel test are used to prepare a PARP inhibitor according to the method of the present invention. It is possible to accurately and easily predict the susceptibility of cancer to a cancer, and to accurately and easily detect a cancer having a homologous recombination repair failure (HRD). When performing signature analysis in the oncogene panel test, archived samples may be used.

According to the present invention, a method for treating a cancer patient with a PARP inhibitor, wherein the prediction method of the present invention is carried out to identify a cancer patient having cancer sensitive to the PARP inhibitor, or the present invention. A method of treatment is provided that comprises performing a method of detecting the above to identify a cancer patient with cancer having homologous recombination repair failure and then administering to the patient an effective amount of a PARP inhibitor. According to the present invention, there is also provided a cancer therapeutic agent containing a PARP inhibitor as an active ingredient, wherein the treatment is performed according to the procedure of the treatment method of the present invention. .. In the therapeutic method of the present invention, identifying a cancer patient having a cancer susceptible to a PARP inhibitor and identifying a cancer patient having a cancer having homologous recombination repair failure are the prediction methods of the present invention. And can be carried out according to the description regarding the detection method. The therapeutically effective amount of the PARP inhibitor can be the standard dose of the drug. For example, for olaparib, 600 mg (which may be gradually reduced due to adverse events) can be the effective daily dose (oral administration) for adults, for example, 300 mg at a time is twice a day (300 mg). × 2 times / day) (Depending on adverse events, the dose may be gradually reduced to 250 mg × 2 times / day, 200 mg × 2 times / day, etc.). According to the therapeutic method of the present invention and the therapeutic agent of the present invention, a PARP inhibitor can be administered to a cancer patient who has a high possibility of responding to the PARP inhibitor, and thus a cancer patient (particularly a cancer patient showing HRD). It is advantageous in that it can provide a treatment option suitable for the patient.

The present invention will be described in more detail based on the following examples, but the present invention is not limited to these examples.

Example 1: Mutation signature analysis by whole exon sequence analysis (1) Mutation signature analysis in high-grade ovarian cancer About freshly frozen samples of cancer cells and normal cells (blood) in 78 cases of high-grade ovarian cancer (ovarian serous cancer) , All exon sequence analysis was performed (Center for Advanced Science and Technology, University of Tokyo), and mutation signature analysis (Mutational signature) was performed according to the description of Alexandrov LB et al., Nature, 500: 415-21 (2013). The mutation catalog of each case in the whole exon sequence analysis was integrated, and clustering was performed using non-negative matrix factorization (NMF, Lee DD and Seung HS, Nature, 401 (6755): 1999), and the number of clusters. Stability and Reconstruction Error based on (number of mutation signatures) were calculated (Sawada et al., Gastroenterology, 150: 1171-1182 (2016), Rosales RA et al., Bioinformatics, 33: 8- 16 (2017), Patch et al., Nature, 521: 489-94 (2015)). Here, the higher the stability and the lower the swing width, the more stable the clustering is considered.

The results were as shown in FIG. From the result of FIG. 2A, the value of the swing width decreases as the number of signatures is increased, but the stability is 0.5 in the case of the number of signatures 3, but is close to 1.0 in the case of the number of signatures 2. The result was that the stability was remarkably higher when the number of signatures was 2. The larger the ratio of stability to swing width, the more stable it is, indicating the validity of dividing it into two signatures (Age signature and BRCA signature). In addition, from the results shown in FIG. 2B, characteristic patterns of gene mutations in each signature, such as C to T substitution in the Age signature and uniform base substitution mutation in the BRCA signature (Alexandrov LB et al., Nature, 500). : 415-421 (2013)) was confirmed.

(2) Judgment of signature that contributes predominantly to high-grade ovarian cancer using total exon sequence analysis For 78 cases of high-grade ovarian cancer (ovarian serous cancer), each case by total exon sequence analysis in (1) above The contributions of the BRCA and Age signatures were analyzed based on the non-negative matrix factorization (NMF). Specifically, the number of mutations in each base substitution of somatic gene mutations in each case was identified. Here, base substitutions can be classified into 6 classes (C → A, C → G, C → T, T → A, T → C and T → G), and 5'and 3'of each substituted base. Including the bases adjacent to, each class contains 16 trinucleotide sequences and can be classified into 96 base-substituted trinucleotides in total (Serena Nki-Zainal et al., Cell 149: 979-993 (2012). )). The number of mutated trinucleotide sequences (number of mutations) and mutation rate were identified according to 96 substitution classifications for each case, and a mutation catalog was created and scored. The scoring is performed by multiplying the mutation rate by the multiplication rate of the mutation rate of the mutated trinucleotide sequence in the Age signature or BRCA signature calculated in (1) above (see FIG. 2B) for each substitution classification. went. The sum of the products calculated in relation to the Age signature or BRCA signature was used as the Age signature score or BRCA signature score. For each case, the ratio (%) of the Age signature score or BRCA signature score to the sum of the Age signature score and the BRCA signature score was calculated. In addition, the ratios were calculated and sorted in the same manner for the mutation catalogs in the public data sets TCGA and ICGC.

The results were as shown in FIGS. 3 and 4. It was confirmed that 76.9% of the cases had a predominant BRCA signature contribution over the Age signature contribution. It was also confirmed that the group in which the contribution of the BRCA signature was dominant had a significantly larger number of mutations (p <0.0001) than the group in which the contribution of the Age signature was dominant (see FIG. 3). Non-negative matrix factorization (NMF) in TCGA or ICGC confirmed that 63.3% and 67.5% of cases were predominantly contributed by the BRCA signature (see FIG. 4).

Example 2: Setting of biomarkers (MSBM) in whole exon sequence analysis (1) Setting of MSBM In ovarian serous cancer, the amplification of cyclin E1 (Cyclin E1; CCNE1) is positively correlated with the Age signature (Etemadmoghadam, D). . et al., Proc Natl Acad Sci USA, 110: 9489-94 (2013), Patch AM et al., Nature, 521: 489-94 (2015)) are known. Therefore, the condition (A) is that "the contribution of the BRCA signature to the mutation catalog exceeds the contribution of the Age signature to the mutation catalog, and the amplification of cyclin E1 is absent", and the profile corresponding to the condition (A) is set. , A biomarker that predicts susceptibility to PARP inhibitors targeting homologous recombination repair abnormalities and a biomarker that detects homologous recombination repair abnormalities. In addition, it is known that methylation of the promoter region of BRCA1 causes BRCA1 inactivation and causes homologous recombination repair abnormality (The Cancer Genome Atlas Research Network, Nature, 474: 609-615 (2011), Moschetta M. et al., Ann Oncol, 8: 1449-55 (2016)). Therefore, the condition (B) was "having methylation of the promoter region of BRCA1".

Regarding the condition (A), further condition (a1): "the contribution of the BRCA signature to the mutation catalog exceeds the contribution of the Age signature to the mutation catalog" and condition (a2): "the amplification of cyclin E1 is absent". When both the condition (a1) and the condition (a2) are satisfied, the condition A is satisfied. Condition (a1) is calculated by total exon sequence analysis and non-negative matrix factorization (NMF), specifically, for a mutation catalog of each case, a BRCA signature score and an Age signature score according to the method described in Example 1 (2). Then, it was determined that the condition (a1) was satisfied when the ratio of the BRCA signature score to the sum of the BRCA signature score and the Age signature score exceeded 50%. As for the condition (a2), the number of chromosomal copies was analyzed by examining the probe (SNP) of the genomic region of cyclin E1, and it was determined that the condition (a2) was satisfied when the number of chromosomal copies of cyclin E1 was 2 or less (a2). Ohshima et al., Sci Rep, 7:641 (2017)).

For condition (B), a comprehensive methylation analysis (Ouchi K et al., Cancer Sci., 106: 1722-9 (2015)) was performed. Specifically, based on a methylation array (Infinium Human Methylation450 BeadChip, manufactured by Illumina), 6 to 12 methylation probes in the promoter region of BRCA1 were extracted, and clustering was performed based on the methylation value. It was divided into a hypermethylated group and a hypomethylated group. The hypermethylated group was determined to satisfy condition (B).

(2) Judgment by MSBM in ovarian serous cancer Judgment by MSBM was performed in 78 cases of high-grade ovarian cancer (ovarian serous cancer) according to the method and criteria described in (1) above. The results were as shown in FIG. Of the 78 cases of high-grade ovarian cancer (ovarian serous cancer), 68% were shown to be MSBM-positive.

Example 3: Setting of MSBM in oncogene panel test (1) Judgment of signature that contributes significantly to mutation catalog All exons of 78 cases of high-grade ovarian cancer (ovarian serous cancer) performed in Example 2 (2) Following MSBM determination by sequence analysis and non-negative matrix factorization (NMF), 7 cases were subjected to MSBM determination using an oncogene panel test. Specifically, in 7 cases of ovarian serous cancer (including serous peritoneal cancer), DNA was extracted from tumor tissue and blood collected from patients, and sequenced and sequenced using Todai OncoPanel (University of Tokyo Hospital). Data analysis was performed and a mutation catalog for each case was created. The mutation catalog was created based on the 96 substitution classifications shown in FIG. 1 and the mutation rate for each substitution mutation.

In addition, germline BRCA (germline BRCA) mutations are known databases such as ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) and COSMIC (http://www.sanger). The pathological significance was determined with reference to .ac.uk/genetics/CGP/cosmic/) and OncoKB (https://oncokb.org/#/). Specifically, when the database was determined to be "pathogenic" or "highly likely", it was considered to be germline BRCA mutation positive. In the case of VUS (meaning unknown), the germline BRCA mutation was positive.

(2) Results The mutation catalog of each case and the result of gBRCA mutation analysis were classified into X, Y and Z, and the results are shown in FIGS. 6 to 8. No cyclin E1 amplification was observed in any of the cases (data not shown).

Classification X
FIG. 6 shows the results of classification X. According to the analysis of gBRCA mutations, case X1 (uterine body ovarian cancer poorly differentiated endometrioid adenocarcinoma), case X2 (lymph node ovarian cancer lymph node metastasis, same patient as case X1) and case X3 (spleen ovarian cancer) (Dissemination to the spleen) was positive for gBRCA1 / 2 mutation. Therefore, the contribution of the BRCA signature to the mutation catalogs of cases X1, X2 and X3 was predicted to outweigh the contribution of the Age signature. In any of the mutation catalogs of case X1, case X2, and case X3, the score of the age signature when the substitution from C to T is the age signature (the above formula (1) (here, when i = 33 to 48). Is KAi = 1 and can be calculated according to KAi = 0 when i = 1-32 and 49 to 96). The same shall apply hereinafter in Example 3) and substitutions other than the substitution from C to T (from C). Scores of A, C to G, T to A, T to C and T to G, and so on) (KBi = when the above formula (2) (where i = 1-32 and 49 to 96) It can be calculated according to (KBi = 0 when i = 33 to 48). The same applies to the following in Example 3.) The score of the substitution other than the substitution from C to T exceeds 50%. Was confirmed. From the above results, it was suggested that substitutions other than C to T substitutions could be set as BRCA signatures. That is, in the examination of Todai OncoPanel, the contribution of the BRCA signature (corresponding to the substitution other than the C to T substitution) exceeds the contribution of the Age signature (corresponding to the substitution from C to T), which indicates homologous recombination repair failure. It is considered that it can be an index of cancer.

Further, according to the analysis of gBRCA mutation, cases X1, X2 and X3 were positive for gBRCA1 / 2 mutation. From these results, it can be said that the cases of classification X are "gBRCA1 / 2 mutation positive" and "MSBM positive". In case X3, administration of the PARP inhibitor olaparib was confirmed to maintain progression-free survival. From the above, it was confirmed that cancer having homologous recombination repair abnormality (HRD) can be detected by the judgment by MSBM, and the susceptibility to PARP inhibitor can be predicted.

Classification Y
FIG. 7 shows the results of case Y. In both case Y1 (ovarian cancer, metastatic breast cancer) and case Y2 (ovarian cancer, metastatic breast cancer), a BRCA signature (other than C to T substitution) was calculated in the same manner as the method described in Category X. It was confirmed that the BRCA signature score for the sum of the (corresponding to substitution) score and the Age signature (corresponding to C to T substitution) score exceeds 50%. Furthermore, the results of classification X and classification Y suggested that the analysis in the Todai OncoPanel may have a tendency for the BRCA signature to show a T-to-C substitution pattern. Thus, it was confirmed that the accumulation of future cases can determine the BRCA signature and Age signature in the Todai Onco Panel in more detail.

Further, according to the analysis of gBRCA mutation, cases Y1 and Y2 were negative for gBRCA1 / 2 mutation. From these results, it was confirmed that the cases of classification Y were "gBRCA1 / 2 mutation negative" and "MSBM positive". From the above, it was confirmed that even if the gBRCA1 / 2 mutation is negative, cancer having homologous recombination repair abnormality (HRD) can be detected by determination by MSBM, and the possibility of predicting susceptibility to PARP inhibitors can be predicted.

Classification Z
FIG. 8 shows the results of Case Z. In both case Z1 (ovarian cancer recurrence) and case Z2 (peritoneal cancer after bilateral adnexectomy), Age signature (corresponding to C to T replacement) score and BRCA signature (other than C to T replacement) It was confirmed that the Age signature score for the sum with the score exceeds 50%. Here, case Z1 was resistant to the platinum preparation, which is the main drug for ovarian cancer. The sensitivity of the platinum product has been shown to be high in cases with HRD and is known to correlate with the sensitivity of PARP inhibitors (De Picciotto N et al., Crit Rev Oncol Hematol., 101: 50). -9 (2016)) (The indication of the PARP inhibitor olaparib in Japan is limited to platinum-sensitive recurrence). Therefore, Case Z1 was considered to be a case with a high possibility of being hyposensitive to the PARP inhibitor olaparib. In addition, according to the analysis of gBRCA mutation, cases Z1 and Z2 were negative for gBRCA1 / 2 mutation. These are classified as an indicator of homologous recombination repair anomalies (HRDs) when substitutions other than C to T substitutions in the Todai OncoPanel reflect BRCA signatures and the contribution of the BRCA signature exceeds the contribution of the Age signature. Consistent with the discussion of X and classification Y. From these results, it was confirmed that the cases of classification Z were "gBRCA1 / 2 mutation negative" and "MSBM negative".

Example 4: Setting of MSBM using decreasingSigs and determination using it (1) DeconstructSigs analysis a All exon sequence analysis DNA is extracted from tumor tissue (FFPE) and normal tissue (peripheral blood) of a case, and exome (Exome). Exon sequences were concentrated using a capture kit (Agilent SureSelect Human All Exon V6 (S07604514), Illumina) and a library was prepared according to the attached protocol. Sequences were obtained with 151 base pair ends using NextSeq (Illumina). The acquired data was converted into a fastq file for each sample based on the index array added for each library, and stored in the storage in the computer used for the subsequent data processing. The fastq file is used when saving a base sequence such as DNA and its quality score together in one file, and is a format for saving the base sequence data output from a next-generation sequencer or the like.

B. Mapping to the reference genome The fastq file obtained in the above a is used as a human genome sequence using bwa-mem (algorithm for genome analysis implemented in the mapping software bwa (http://www.liheng.org)). (Human reference genome sequence: GRCh38) was mapped.

C. Mutation detection mapping result (bam file) is processed by the mutation detection program karkinos (obtained from https://github.com/genome-rcast/karkinos) by combining the tumor part and the normal part of the same case. , Created a list of somatic mutations.

D. Frequency data of 96 base substitution patterns From the obtained mutation list, 1 base substitution and 1 base before and after it were extracted, and the frequency was totaled for each base substitution pattern. Specifically, by aligning +/- strands of genomic DNA to strands in which the base before mutation is C or T (for example, CGT> CAT is counted as the same as ACG> ATG). , 96 types of substitution classifications and mutation rates for each substitution mutation were tabulated to create a mutation catalog.

E. Mutation signature estimation Using the mutation frequency of the 96 types of mutation patterns (that is, the mutation catalog) obtained in the above d as an input, and using deconstructSigns, which is a program for estimating the weight of mutation signatures, of the existing 30 types of signatures. The contribution was estimated. The set of 30 mutation signatures (version2, March 2015) is the 30 mutation signatures registered in the COSMIC database (https://cancer.sanger.ac.uk/cosmic/signatures_v2) of the Sanger Institute in the United Kingdom. (COSMIC version 2) was used. Of the 30 signatures, the contributions of the BRCA signature (Signature 3 of the COSMIC database) and the AGE signature (Signature 1 of the COSMIC database) were analyzed in detail.

genomestractSigs is a program that estimates the weights (contributions) of existing mutation signatures for each sample, and compares the mutation profile reconstructed from the calculated weights with the original input to determine the suitability of the estimation ( Rosenthal et al., Genome Biology, 17:31 (2016)). In this example, the frequency of 96 mutation patterns of individual tumor samples [T: 1x96] (in parentheses, the symbol indicating the matrix: matrix size (row x col); the same applies hereinafter) and the existing mutation signature matrix. With [S: 30x96] as an input, the contribution Wi (i = 1 to 30) of each signature was calculated. To determine the weight W that optimally recreates T, first select one of the existing mutation signatures, the initial signature Si that most closely resembles the mutation profile of the tumor sample of interest, and the Si-reconstructed tumor mutation profile. W was decided to be the only signature that contributes to. Next, by iteratively calculating while changing the weight matrix [W], a weight (contribution) matrix was determined so as to minimize the reconstruction error calculated as T- (SW).

(2) Comparison of Signature Contribution in NMF Analysis and Deconstructs Analysis Method A method: TCGA, ICGC and 78 cases of high-grade ovarian cancer were analyzed by the deconstructs described in (1) above. The results of the analysis by deconstrucsigs and the results of the analysis (NMF analysis) based on the non-negative matrix factorization (NMF) described in Example 1 (2) were compared.

B Results The results were as shown in FIG. From the results of FIG. 9, it was confirmed that the contributions of the Age signature and the BRCA signature tended to be the same as a whole in the NMF analysis and the deconstructsSigs analysis. On the other hand, for example, in the NMF analysis, when the contribution rate of the BRCA signature is separated by 0.5 (see the vertical line in FIG. 9), the BRCA signature is dominant in the NMF analysis (> 0.5), but in the deconstructs analysis. Cases in which the BRCA signature is not dominant (<0.2), and conversely, cases in which the BRCA signature is not dominant in the NMF analysis (<0.5) but the BRCA signature is not so low in the deconstructs analysis (> 0.3). , It was confirmed that there are a certain number of cases in which the tendency of signature contribution does not match between the NMF analysis and the deconstructsSigs analysis.

(3) Comparison of Contribution of BRCA Signature in NMF Analysis and Deconstructs Analysis Method A method The deconstrutSigs analysis described in (1) above was performed on 78 cases of TCGA, ICGC and high-grade ovarian cancer. The contribution of the BRCA signature (Signature 3 of the COSMIC database) by the deconstructs analysis and the contribution of the BRCA signature by the NMF analysis described in Example 1 (2) are shown in a scatter diagram, and the contribution of the BRCA signature is determined by the NMF analysis. The threshold of the BRCA signature (Signature 3 of the COSMIC database) of the decaySigs analysis corresponding to the threshold (0.5) was determined.

B Results The results were as shown in FIGS. 10 and 11. From the result of FIG. 10, the threshold value of the deconstructsSigs analysis corresponding to the threshold value (0.5) determined by the NMF analysis was 0.25. The results in FIG. 11 showed that 66% of cases were predominantly BRCA signatures when the NMF analysis threshold (0.5) was used. It was also shown that the BRCA signature contribution was 0.25 or greater in 54% of cases when the threshold (0.25) for deconstructs analysis was used.

(4) Setting of biomarker (MSBM) in defectSigs analysis A method The deconstructSigs analysis described in (1) above was performed on 78 cases of highly atypical ovarian cancer. Specifically, for the mutation catalog of each case, the contribution of 30 types of mutation signatures including the BRCA signature and the Age signature was calculated according to the method described in (1) above, and the contribution of the BRCA signature was 0.25 ( Conditions according to the method and criteria described in Example 2 (1), except that it is determined that the condition (a1) is satisfied when the contribution of the BRCA signature exceeds the contribution of the AGE signature. (A) (condition (a2): including "amplification of cyclin E1 is absent") was defined.

B Results As a result of deconstructsSigs analysis, it was shown that the contribution of the BRCA signature exceeded the contribution of the AGE signature in 73% (57/78) cases, and mutation in 73% (57/78) cases. The contribution of the BRCA signature to the overall signature was shown to be 0.25 (25%) or greater. In addition, 73% (57/78) of cases had a BRCA signature contribution of 0.25 (25%) or more to the total mutation signature, and the BRCA signature contribution exceeded the AGE signature contribution. there were. Further, in the case where the contribution of the BRCA signature to the entire mutation signature is 0.25 (25%) or more and the contribution of the BRCA signature exceeds the contribution of the AGE signature, the cyclin E1 amplification is not achieved. The presence was confirmed in 68% (53/78). That is, it was shown that 68% of 78 cases of high-grade ovarian cancer (ovarian serous cancer) were MSBM-positive.

Here, germline HRD mutations (including germline BRCA1 / 2 mutations, RAD51C mutations and RAD51D mutations) are highly atypical ovarian cancers (ovarian serous cancers) 78. Of the 23 cases present (Fig. 12), 18 were present (78%) in cases where the contribution of the BRCA signature to the overall mutation signature was 0.25 (25%) or higher (78%), and the contribution of the BRCA signature. There were 13 cases (57%) in cases where the degree exceeded the contribution of the AGE signature. From these results, it can be determined that the condition (a1) is satisfied when the contribution of the BRCA signature by the deconstartSigs analysis exceeds the contribution of the AGE signature, but for a more accurate determination, the contribution of the BRCA signature can be determined. Is 0.25 (25%) or more, and the contribution of the BRCA signature exceeds the contribution of the AGE signature, it can be determined that the condition (a1) is satisfied. From the above, it was shown that cancer with homologous recombination repair abnormality (HRD) can be detected by the determination by MSBM based on the deconstructsSigs analysis, and the susceptibility to PARP inhibitors can be predicted.

Claims (31)

A method of predicting cancer susceptibility to PARP inhibitors,
It includes the steps of performing sequence analysis on nucleic acid samples obtained from cancer cells to create a mutation catalog, and (A) (a1) the contribution of the BRCA signature to the mutation catalog exceeds the contribution of the Age signature to the mutation catalog. And (a2) the absence of cyclin E1 amplification indicates the presence of cancers susceptible to PARP inhibitors, a predictive method. The method of claim 1, wherein the cancer cells are cells obtained from a cancer patient. The method of claim 1 or 2, wherein the cancer is a cancer that may exhibit homologous recombination repair abnormalities. The method according to any one of claims 1 to 3, wherein the PARP inhibitor is olaparib, lucaparib, niraparib, veliparib or tarazoparib. The method according to any one of claims 1 to 4, wherein the sequence analysis includes a sequence determination procedure and a sequence data analysis procedure. The method according to any one of claims 1 to 5, wherein the somatic gene mutation is identified by the sequence analysis according to the 96 substitution classifications shown in FIG. 1 and a mutation catalog is prepared. The method according to any one of claims 1 to 6, wherein the sequence analysis is performed by whole exon sequence analysis. The method according to any one of claims 1 to 6, wherein the sequence analysis is performed by an oncogene panel test. The following steps (1) to (3):
(1) Preparing Age signatures and BRCA signatures,
2. The method according to any one item. In the step (1), mutation signature analysis is performed on a plurality of cancer patients suffering from cancer predicting susceptibility, and the mutation rate of the substitution mutation of the Age signature and / or the BRCA signature is specified to obtain the Age signature and the BRCA signature. The method according to claim 9, which is prepared. The method of claim 9, wherein in step (2), the mutation catalog is evaluated for contribution by calculating an Age signature score and a BRCA signature score. The Age signature score is the following formula (1):

(In the above formula, n = 96, i means the i-th substitution mutation in the 96 substitution classifications shown in FIG. 1, and Mi is the mutation rate of the i-th substitution mutation in the mutation catalog. Or the number of mutations, KAi is the mutation rate of the i-th substitution mutation of the Age signature.)
11. The method of claim 11. The BRCA signature score is the following formula (2):

(In the above formula, n = 96, i means the i-th substitution mutation in the 96 substitution classifications shown in FIG. 1, and Mi is the mutation rate of the i-th substitution mutation in the mutation catalog. Or the number of mutations, KBi is the mutation rate of the i-th substitution mutation in the BRCA signature.)
11. The method of claim 11. Claims 9 and 11 evaluate the superiority of the contribution of the BRCA signature by calculating the ratio A (%) of the BRCA signature score to the sum of the Age signature score and the BRCA signature score in the step (3). The method according to any one of 13 to 13. In the step (3), substitution of C to T with respect to the sum of the score of the substitution mutation other than the substitution from C to T (BRCA signature score) and the score of the substitution mutation from C to T (Age signature score). The method according to any one of claims 9 and 11-13, wherein the predominance of the contribution of the BRCA signature is evaluated by calculating the ratio B (%) of the scores of the substitution mutations other than. The method according to claim 14, wherein when the ratio A (%) exceeds 50%, it is determined that the above (a1) is satisfied. The method according to claim 15, wherein when the ratio B (%) exceeds 50%, it is determined that the above (a1) is satisfied. The following steps (4) to (6):
(4) Preparing a mutant signature containing at least an Age signature and a BRCA signature,
Claims 1 to 8 further include (5) assessing the contribution of each mutation signature to the mutation catalog, and (6) assessing the superiority of the BRCA signature's contribution to the age signature's contribution. The method according to any one of the above. (7) The method of claim 18, further comprising assessing the contribution of the BRCA signature to the entire mutation signature. The method of claim 18, wherein the mutation signature comprises all or part of the 30 mutation signatures (COSMIC version 2) of the COSMIC database. The method according to claim 18, wherein in the step (5), the contribution of the mutation signature to the mutation catalog is evaluated by deconstructSigs analysis. In the step (6), the superiority of the contribution of the BRCA signature is evaluated by calculating the ratio C (%) of the contribution of the BRCA signature to the sum of the contribution of the Age signature and the contribution of the BRCA signature. The method according to claim 18. In the step (6), the superiority of the contribution of the BRCA signature is evaluated by calculating the ratio C (%) of the contribution of the BRCA signature to the sum of the contribution of the Age signature and the contribution of the BRCA signature. And then
19 of claim 19, wherein in the step (7), the contribution of the BRCA signature is evaluated by calculating the ratio D (%) of the contribution of the BRCA signature to the sum of the contributions of the mutation signature (1). the method of. The method according to claim 22, wherein when the ratio C (%) exceeds 50%, it is determined that the above (a1) is satisfied. The method according to claim 23, wherein when the ratio C (%) exceeds 50% and the ratio D (%) exceeds k% (k = 15 to 40), it is determined that the above (a1) is satisfied. .. Claims 1 to 25 include a step of determining that the cancer cells include a cancer cell population susceptible to a PARP inhibitor when both (a1) and (a2) of the condition (A) are satisfied. The method according to any one of the above. Claims 1 to further include the step of performing a comprehensive methylation analysis on the test sample of interest, wherein (B) methylation of the promoter region of BRCA1 indicates the presence of cancer that is sensitive to PARP inhibitors. The method according to any one of 26. A method for detecting cancer with homologous recombination repair failure (HRD).
It includes the steps of performing sequence analysis on nucleic acid samples obtained from cancer cells to create a mutation catalog, and (A) (a1) the contribution of the BRCA signature to the mutation catalog exceeds the contribution of the Age signature to the mutation catalog. And (a2) the absence of cyclin E1 amplification indicates the presence of cancer with homologous recombination repair failure. 28. Detection method. A method for treating a cancer patient with a PARP inhibitor, wherein the method according to any one of claims 1 to 27 is carried out to identify a cancer patient having a cancer susceptible to the PARP inhibitor. Alternatively, it comprises performing the method according to claim 28 or 29 to identify a cancer patient with cancer having homologous recombination repair failure and then administering to the patient an effective amount of a PARP inhibitor. Method of treatment. A cancer therapeutic agent comprising a PARP inhibitor as an active ingredient, wherein the treatment is performed by the method according to claim 30.

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