KR102608064B1 - Marker for predicting response to erastin and use thereof - Google Patents
Marker for predicting response to erastin and use thereof Download PDFInfo
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- KR102608064B1 KR102608064B1 KR1020190143564A KR20190143564A KR102608064B1 KR 102608064 B1 KR102608064 B1 KR 102608064B1 KR 1020190143564 A KR1020190143564 A KR 1020190143564A KR 20190143564 A KR20190143564 A KR 20190143564A KR 102608064 B1 KR102608064 B1 KR 102608064B1
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Classifications
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- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
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- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
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- C12Q2600/00—Oligonucleotides characterized by their use
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- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
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- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis
Abstract
본 발명은 이라스틴에 대한 반응성 예측용 마커 및 이의 용도에 관한 것으로서, 보다 구체적으로는 항암제인 이라스틴에 대한 반응성을 예측할 수 있는 마커 유전자, 이의 발현수준을 측정하는 제제를 포함하는 반응성 예측용 조성물 및 키트, 및 상기 마커 유전자를 이용해 반응성을 예측하는 방법에 관한 것이다. 본 발명에 따른 유전자 바이오마커는 이라스틴 항암제에 대한 반응성을 예측하는데 유효한 마커임을 확인하였는바, 이라스틴 또는 이라스틴 유사체를 이용한 치료에서 상기 유전자들의 발현수준을 측정함으로써 암 환자, 특히 폐암 환자에서 이라스틴 항암제에 대한 반응성을 미리 예측할 수 있고, 이를 통해 반응성 유무에 따라 암 환자를 효율적으로 분류하여 적절하고 효과적인 치료법을 적용하는데 유용하게 활용될 것으로 기대된다. The present invention relates to a marker for predicting responsiveness to Irastine and its use, and more specifically, to a composition for predicting responsiveness comprising a marker gene capable of predicting responsiveness to Irastine, an anticancer drug, and an agent for measuring its expression level. and a kit, and a method for predicting reactivity using the marker gene. It has been confirmed that the genetic biomarker according to the present invention is an effective marker for predicting responsiveness to the anticancer drug Irastine. By measuring the expression levels of the genes in treatment using Irastine or Irastine analogues, it can be used in cancer patients, especially lung cancer patients. Responsiveness to Steen anticancer drugs can be predicted in advance, and this is expected to be useful in efficiently classifying cancer patients according to the presence or absence of reactivity and applying appropriate and effective treatments.
Description
본 발명은 이라스틴에 대한 반응성 예측용 마커 및 이의 용도에 관한 것으로서, 보다 구체적으로는 항암제인 이라스틴에 대한 반응성을 예측할 수 있는 마커 유전자, 이의 발현수준을 측정하는 제제를 포함하는 반응성 예측용 조성물 및 키트, 및 상기 마커 유전자를 이용해 반응성을 예측하는 방법에 관한 것이다. The present invention relates to a marker for predicting responsiveness to Irastine and its use, and more specifically, to a composition for predicting responsiveness comprising a marker gene capable of predicting responsiveness to Irastine, an anticancer drug, and an agent for measuring its expression level. and a kit, and a method for predicting reactivity using the marker gene.
이라스틴(Erastin)은 원래 RAS 발암유전자를 갖는 암세포에서 활성화된 RAS-RAF-MEK 신호전달 하에 산화적, 비-아폽토시스 세포사멸을 통한 합성 치사를 유도하는 저분자로 발견되었다. 이후, 이라스틴에 의한 세포사멸의 작용 기전이 비-아폽토시스 세포사멸의 독특한 철-의존적 형태인 페롭토시스(ferroptosis)인 것으로 확인되었다. 이라스틴은 시스틴(cystine) 유입을 통한 글루타티온(glutathione; GSH) 합성 과정에서 SLC7A11에 의해 암호화되는 시스틴/글루타메이트(cystine/glutamate)의 역수용체인 시스템 Xc-(XCT)를 억제함에 따라, 항산화 방어능의 손상에 의해 세포의 산화적 사멸이 유발된다. 신경퇴행성질환, 뇌졸중, 허혈성 손상 및 발암과 같은 다양한 병리학적 세포사멸에서 페롭토시스에 대한 새로운 연구가 진행됨에 따라, 저분자 억제제 또는 페롭토시스 유도제가 치료제로 개발되고 있으며, 새로운 항암제로서 이라스틴과 같은 페롭토시스 유도제(ferroptosis inducers; FIN)가 광범위하게 검증되어 왔다. 그럼에도 불구하고, 신경교종 환자들을 대상으로 한 다른 XCT 억제제인 설파살라진(sulfasalazine)의 임상시험 결과는 임상적 반응이 없는 것으로 보고되었다. Erastin was originally discovered as a small molecule that induces synthetic lethality through oxidative, non-apoptotic cell death under activated RAS-RAF-MEK signaling in cancer cells harboring the RAS oncogene. Later, it was confirmed that the mechanism of action of cell death caused by Ilastin was ferroptosis, a unique iron-dependent form of non-apoptotic cell death. Irastine inhibits system Damage causes oxidative death of cells. As new research on ferroptosis progresses in various pathological cell death such as neurodegenerative diseases, stroke, ischemic injury, and carcinogenesis, small molecule inhibitors or ferroptosis inducers are being developed as treatments, and new anticancer drugs such as irastin and Similar ferroptosis inducers (FINs) have been extensively tested. Nevertheless, clinical trials of sulfasalazine, another XCT inhibitor, in glioma patients reported no clinical response.
최근 연구에 따르면, 다양한 유형의 암세포에서 대사적 이질성 또는 중간엽 특성, 분화 상태와 같은 세포의 특성이 페롭토시스 세포사멸에 대한 감수성을 결정한다고 보고되었다(Nature. 2017 Jul 27;547(7664):453-457). 특히, 치료 저항성 중간엽 암세포에서 인지질 글루타티온 과산화효소 4(glutathione peroxidase 4; GPX4)에 의해 진행되는 지질 과산화효소 경로에 대한 높은 의존성이 GPX4 억제 또는 이라스틴에 의한 GSH 고갈에 의한 페롭토시스에 대하여 높은 취약성을 부여한다. 그러나, 페롭토시스에 대하여 GPX4의 높은 의존성을 나타내는 ‘약물 내성 지속성 암세포’는 이라스틴에 덜 민감하다. 이라스틴에 의해 유도되는 페롭토시스 세포사멸에 대한 GPX4 의존성은 세포 유형에 특이적인데, p53 상태, 전사인자, 신호전달 경로(예컨대, MAPK, ATM 또는 YAP), 인테그린 및 GSH 조절자와 같은 다양한 세포 및 분자적 기능이 여러 세포의 모델 시스템에서 이라스틴에 의한 페롭토시스의 취약성을 결정하는 요인으로 나타났다. 따라서 XCT 억제 또는 GPX4 억제에 의한 암세포의 페롭토시스에 대한 감수성은 세포/분자적 특성에 따라 다양하며 완전히 규명되지 않았다. 이와 관련하여, 이라스틴에 대한 감수성을 예측할 수 있는 독특한 세포/분자적 시그니처를 확립하는 것은 현재 임상 시험 중인 이라스틴 유사체를 이용한 항암치료에서 효능을 최대화하고 독성을 최소화하기 위해 매우 중요하다. According to recent studies, it has been reported that cell characteristics such as metabolic heterogeneity, mesenchymal characteristics, and differentiation state determine susceptibility to ferroptotic cell death in various types of cancer cells (Nature. 2017 Jul 27;547(7664) :453-457). In particular, the high dependence on the lipid peroxidase pathway mediated by phospholipid glutathione peroxidase 4 (GPX4) in treatment-resistant mesenchymal cancer cells results in a high risk of ferroptosis by GPX4 inhibition or GSH depletion by irastin. It confers vulnerability. However, ‘drug-resistant persistent cancer cells’, which show a high dependence on GPX4 for ferroptosis, are less sensitive to irastin. GPX4 dependence on ferroptotic cell death induced by irlastin is cell type specific, including p53 status, transcription factors, signaling pathways (e.g. MAPK, ATM or YAP), integrins and GSH regulators. Cellular and molecular functions have been shown to be determinants of susceptibility to ferroptosis by irastin in several cellular model systems. Therefore, the susceptibility of cancer cells to ferroptosis by XCT inhibition or GPX4 inhibition varies depending on cellular/molecular characteristics and has not been fully characterized. In this regard, establishing a unique cellular/molecular signature that can predict susceptibility to irastin is very important to maximize efficacy and minimize toxicity in anticancer treatments using irastin analogs currently in clinical trials.
약물 반응에서 게놈의 역할을 탐구하는 약물유전체학적 접근법은 약물 반응에 기여하는 분자적 시그니처를 체계적으로 확인함으로써 약물 MoA에 대한 우리의 이해를 향상시켰다. 특히, NCI-DREAM 약물 민감성 예측 챌린지에 대한 엄격한 평가에서, 유전자 발현 데이터는 다수의 오믹스 데이터 세트(예를 들어, 게놈, 프로테오믹스 및 후성적 프로파일링) 중에서 인간 유방암 세포의 약물 반응을 예측하는데 가장 효과적인 것으로 확인되었다. 정밀 종양학 분야에서, 전사체 프로파일링은 수 내지 수천 개의 배양된 인간 세포를 사용하여 치료 방안을 결정하기 위한 예측 유전자 시그니처를 스크리닝하는데 널리 이용되어왔다. Pharmacogenomic approaches, exploring the role of the genome in drug response, have improved our understanding of drug MoA by systematically identifying molecular signatures that contribute to drug response. In particular, in a rigorous evaluation of the NCI-DREAM drug susceptibility prediction challenge, gene expression data was the best among multiple omics data sets (e.g., genomics, proteomics, and epigenetic profiling) for predicting drug response in human breast cancer cells. It was confirmed to be effective. In the field of precision oncology, transcriptome profiling has been widely used to screen predictive gene signatures for determining treatment options using tens to thousands of cultured human cells.
본 발명은 대규모 약물유전체학 데이터 세트를 기반으로 이라스틴에 대한 감수성을 예측하기 위한 효율적인 모델을 확립하였다. 본 발명자들은 유전자 예측 변수에서 NRF2(Nuclear factor erythroid 2-related factor 2) 및 아릴 탄화수소 수용체(Aryl hydrocarbon receptor; AhR) 활성이 이라스틴에 매개 페롭토시스 세포 사멸에 대한 중요한 결정 인자임을 발견하였다. The present invention established an efficient model for predicting susceptibility to irastin based on a large-scale pharmacogenomics data set. In the genetic predictors, we found that Nuclear factor erythroid 2-related factor 2 (NRF2) and Aryl hydrocarbon receptor (AhR) activity were important determinants for irastin-mediated ferroptotic cell death.
본 발명자들은 전술한 바와 같이 약물유전체학 데이터세트를 기반으로 이라스틴에 대한 반응성을 효과적으로 예측할 수 있는 모델을 확립하였고, 결과적으로 NRF2 및 AhR 경로를 포함하는 핵 수용체 메타-경로에 관련된 43개 유전자를 유효한 예측 마커로 발굴하였는바, 이에 기초하여 본 발명을 완성하였다.As described above, the present inventors established a model that can effectively predict responsiveness to irastin based on a pharmacogenomics dataset, and as a result, 43 genes related to the nuclear receptor meta-pathway, including the NRF2 and AhR pathways, were effectively identified. It was discovered as a predictive marker, and based on this, the present invention was completed.
이에, 본 발명은 ACOX1, ALOX5AP, ANKRD1, BAAT, BHLHE40, CCL2, CYP1A1, DNAJC7, ESR1, GCLC, GPAM, GSTM1, HES1, HSPA1A, IL17B, JUND, MFGE8, NAV3, NCOA6, NQO1, PLTP, PMP2, PRDX1, PTGES3, PTGR1, PTPA, RXRA, S100P, SEC14L1, SERPINB2, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, SLC7A5, TGFBR2, TNF 및 TNS4로 이루어진 군으로부터 선택되는 하나 이상의 유전자, 또는 상기 유전자가 암호화하는 단백질을 포함하는, 이라스틴(Erastin)에 대한 반응성 예측용 마커 조성물을 제공하는 것을 목적으로 한다. Therefore, the present invention ACOX1, ALOX5AP, ANKRD1, BAAT, BHLHE40, CCL2, CYP1A1, DNAJC7, ESR1, GCLC, GPAM, GSTM1, HES1, HSPA1A, IL17B, JUND, MFGE8, NAV3, NCOA6, NQO1, PLTP, PMP2, PRDX1 , PTGES3, PTGR1, PTPA, RXRA, S100P, SEC14L1, SERPINB2, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, SLC7A5, TGFBR2, TNF and TNS4. gene, Alternatively, the object is to provide a marker composition for predicting reactivity to Erastin, which includes the protein encoded by the gene.
또한, 본 발명은 상기 유전자의 mRNA 또는 상기 유전자가 암호화하는 단백질 수준을 측정하는 제제를 포함하는 이라스틴(Erastin)에 대한 반응성 예측용 조성물, 상기 조성물을 포함하는 이라스틴(Erastin)에 대한 반응성 예측용 키트를 제공하는 것을 다른 목적으로 한다. In addition, the present invention provides a composition for predicting reactivity to Erastin containing an agent for measuring the level of mRNA of the gene or protein encoded by the gene, and predicting reactivity to Erastin containing the composition. Another purpose is to provide kits for use.
또한, 본 발명은 상기 유전자의 mRNA 또는 상기 유전자가 암호화하는 단백질 수준을 측정하는 단계를 포함하는, 이라스틴(Erastin)에 대한 반응성 예측을 위한 정보제공방법을 제공하는 것을 또 다른 목적으로 한다.Another object of the present invention is to provide a method for providing information for predicting responsiveness to Erastin, which includes measuring the level of the mRNA of the gene or the protein encoded by the gene.
그러나 본 발명이 이루고자 하는 기술적 과제는 이상에서 언급한 과제에 제한되지 않으며, 언급되지 않은 또 다른 과제들은 아래의 기재로부터 당업자에게 명확하게 이해될 수 있을 것이다. However, the technical problem to be achieved by the present invention is not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.
상기와 같은 본 발명의 목적을 달성하기 위하여, 본 발명은 ACOX1, ALOX5AP, ANKRD1, BAAT, BHLHE40, CCL2, CYP1A1, DNAJC7, ESR1, GCLC, GPAM, GSTM1, HES1, HSPA1A, IL17B, JUND, MFGE8, NAV3, NCOA6, NQO1, PLTP, PMP2, PRDX1, PTGES3, PTGR1, PTPA, RXRA, S100P, SEC14L1, SERPINB2, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, SLC7A5, TGFBR2, TNF 및 TNS4로 이루어진 군으로부터 선택되는 하나 이상의 유전자, 또는 상기 유전자가 암호화하는 단백질을 포함하는, 이라스틴(Erastin)에 대한 반응성 예측용 마커 조성물을 제공한다. In order to achieve the object of the present invention as described above, the present invention is ACOX1, ALOX5AP, ANKRD1, BAAT, BHLHE40, CCL2, CYP1A1, DNAJC7, ESR1, GCLC, GPAM, GSTM1, HES1, HSPA1A, IL17B, JUND, MFGE8, NAV3 , NCOA6, NQO1, PLTP, PMP2, PRDX1, PTGES3, PTGR1, PTPA, RXRA, S100P, SEC14L1, SERPINB2, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, SLC 7A5, TGFBR2, TNF and TNS4 Provided is a marker composition for predicting responsiveness to Erastin, comprising at least one gene selected from the group consisting of, or a protein encoded by the gene.
본 발명의 일구현예로, 상기 GSTM1, HSPA1A, PRDX1, PTGR1, RXRA, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, TGFBR2, GCLC 및 NQO1은 NRF2(Nuclear factor erythroid 2-related factor 2) 경로에 관련된 유전자인 것일 수 있다. In one embodiment of the present invention, GSTM1, HSPA1A, PRDX1, PTGR1, RXRA, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, TGFBR2, GCLC and NQO1 are NRF2 (Nuclear factor erythroid 2- related factor 2) It may be a gene related to the pathway.
본 발명의 다른 구현예로, 상기 CYP1A1, ESR1, HES1, IL17B, JUND, PTGES3, SERPINB2, SLC7A5, TNF, GCLC 및 NQO1은 AhR(Aryl hydrocarbon receptor) 경로에 관련된 유전자인 것일 수 있다. In another embodiment of the present invention, CYP1A1, ESR1, HES1, IL17B, JUND, PTGES3, SERPINB2, SLC7A5, TNF, GCLC, and NQO1 may be genes related to the AhR (Aryl hydrocarbon receptor) pathway.
또한, 본 발명은 ACOX1, ALOX5AP, ANKRD1, BAAT, BHLHE40, CCL2, CYP1A1, DNAJC7, ESR1, GCLC, GPAM, GSTM1, HES1, HSPA1A, IL17B, JUND, MFGE8, NAV3, NCOA6, NQO1, PLTP, PMP2, PRDX1, PTGES3, PTGR1, PTPA, RXRA, S100P, SEC14L1, SERPINB2, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, SLC7A5, TGFBR2, TNF 및 TNS4로 이루어진 군으로부터 선택되는 하나 이상의 유전자의 mRNA, 또는 상기 유전자가 암호화하는 단백질 수준을 측정하는 제제를 포함하는, 이라스틴(Erastin)에 대한 반응성 예측용 조성물을 제공한다.In addition, the present invention provides ACOX1, ALOX5AP, ANKRD1, BAAT, BHLHE40, CCL2, CYP1A1, DNAJC7, ESR1, GCLC, GPAM, GSTM1, HES1, HSPA1A, IL17B, JUND, MFGE8, NAV3, NCOA6, NQO1, PLTP, PMP2, PRDX1 , PTGES3, PTGR1, PTPA, RXRA, S100P, SEC14L1, SERPINB2, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, SLC7A5, TGFBR2, TNF and TNS4. genetic Provided is a composition for predicting responsiveness to Erastin, which includes an agent for measuring the level of mRNA or protein encoded by the gene.
본 발명의 일구현예로, 상기 유전자의 mRNA 수준을 측정하는 제제는 유전자의 mRNA에 상보적으로 결합하는 센스 및 안티센스 프라이머, 또는 프로브일 수 있다. In one embodiment of the present invention, the agent for measuring the mRNA level of the gene may be sense and antisense primers or probes that bind complementary to the mRNA of the gene.
본 발명의 다른 구현예로, 상기 단백질 수준을 측정하는 제제는 상기 유전자가 암호화하는 단백질에 특이적으로 결합하는 항체일 수 있다. In another embodiment of the present invention, the agent for measuring the protein level may be an antibody that specifically binds to the protein encoded by the gene.
또한, 본 발명은 상기 조성물을 포함하는, 이라스틴(Erastin)에 대한 반응성 예측용 키트를 제공한다. Additionally, the present invention provides a kit for predicting reactivity to Erastin, comprising the composition.
또한, 본 발명은 암 환자유래의 생물학적 시료에서 ACOX1, ALOX5AP, ANKRD1, BAAT, BHLHE40, CCL2, CYP1A1, DNAJC7, ESR1, GCLC, GPAM, GSTM1, HES1, HSPA1A, IL17B, JUND, MFGE8, NAV3, NCOA6, NQO1, PLTP, PMP2, PRDX1, PTGES3, PTGR1, PTPA, RXRA, S100P, SEC14L1, SERPINB2, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, SLC7A5, TGFBR2, TNF 및 TNS4로 이루어진 군으로부터 선택되는 하나 이상의 유전자의 mRNA, 또는 상기 유전자가 암호화하는 단백질 발현수준을 측정하는 단계를 포함하는, 이라스틴(Erastin)에 대한 반응성 예측을 위한 정보제공방법을 제공한다. In addition, the present invention relates to ACOX1, ALOX5AP, ANKRD1, BAAT, BHLHE40, CCL2, CYP1A1, DNAJC7, ESR1, GCLC, GPAM, GSTM1, HES1, HSPA1A, IL17B, JUND, MFGE8, NAV3, NCOA6, NQO1, PLTP, PMP2, PRDX1, PTGES3, PTGR1, PTPA, RXRA, S100P, SEC14L1, SERPINB2, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, SLC7A5, TGF Group consisting of BR2, TNF and TNS4 Provides a method of providing information for predicting responsiveness to Erastin, which includes measuring the expression level of the mRNA of one or more genes selected from, or the expression level of the protein encoded by the gene.
본 발명의 다른 구현예로, 상기 mRNA의 발현수준은 in situ 교잡법(in situ hybridization), 중합효소연쇄반응(PCR), 역전사 중합효소연쇄반응(RT-PCR), 실시간 중합효소연쇄반응(Real-time PCR), RNase 보호 분석법(RNase protection assay; RPA), 마이크로어레이(microarray), 및 노던 블롯팅(northern blotting)으로 이루어진 군으로부터 선택되는 1종 이상의 방법을 통해 측정될 수 있다. In another embodiment of the present invention, the expression level of the mRNA is determined by in situ hybridization, polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), and real-time polymerase chain reaction (Real-time polymerase chain reaction). -time PCR), RNase protection assay (RPA), microarray, and northern blotting.
본 발명의 또 다른 구현예로, 상기 단백질의 발현수준은 웨스턴 블롯팅(western blotting), 방사선면역분석법(radioimmunoassay; RIA), 방사 면역 확산법(radioimmunodiffusion), 효소면역분석법(ELISA), 면역침강법(immunoprecipitation), 유세포분석법(flow cytometry), 면역형광염색법(immunofluorescence), 오우크테로니(ouchterlony), 보체 고정 분석법(complement fixation assay), 및 단백질 칩(protein chip)으로 이루어진 군으로부터 선택되는 1종 이상의 방법을 통해 측정될 수 있다. In another embodiment of the present invention, the expression level of the protein is determined by Western blotting, radioimmunoassay (RIA), radioimmunodiffusion, enzyme-linked immunosorbent assay (ELISA), and immunoprecipitation ( One or more types selected from the group consisting of immunoprecipitation, flow cytometry, immunofluorescence, ouchterlony, complement fixation assay, and protein chip. It can be measured through a method.
본 발명에 따른 유전자 바이오마커는 이라스틴(Erastin) 항암제에 대한 반응성을 예측하는데 유효한 마커임을 확인하였는바, 이라스틴 또는 이라스틴 유사체를 이용한 치료에서 상기 유전자들의 발현수준을 측정함으로써 암 환자, 특히 폐암 환자에서 이라스틴 항암제에 대한 반응성을 미리 예측할 수 있고, 이를 통해 반응성 유무에 따라 암 환자를 효율적으로 분류하여 적절하고 효과적인 치료법을 적용하는데 유용하게 활용될 것으로 기대된다. The genetic biomarker according to the present invention has been confirmed to be an effective marker for predicting responsiveness to the anticancer drug Erastin. By measuring the expression levels of the genes in treatment using Erastin or Erastin analogues, cancer patients, especially lung cancer, can be identified. It is expected that the patient's responsiveness to the anti-cancer drug Ilastine can be predicted in advance, and this will be useful in efficiently classifying cancer patients according to the presence or absence of responsiveness and applying appropriate and effective treatment.
도 1은 이라스틴 감수성과 NRF2 경로의 높은 상관관계를 확인한 결과를 나타낸 것으로서, 도 1a는 123개 폐암 세포주에서 NOX4, GPX4, 및 ZEB1 유전자의 발현수준(logTPM)과 이라스틴에 대한 감수성(AUC) 간의 상관관계를 분석한 결과이고, 도 1b는 598개 비-혈액암 세포주에서 KRAS 및 HRAS의 돌연변이 상태(야생형(Wt) 또는 돌연변이(Mut))에 따른 이라스틴에 대한 감수성(AUC) 분포를 분석한 결과이며, 도 1c는 세포의 치료-저항성 EMT 시그니처와 CTRP 데이터의 543개 화합물에 대한 감수성 간의 상관관계를 분석한 결과이고, 도 1d는 이라스틴에 대한 감수성을 기준으로 랭크된 6개의 폐암 세포주의 분포를 나타낸 것이며, 도 1e는 이라스틴 약물 반응과 관련된 신호전달 경로의 상관관계 정도를 나타낸 결과이고, 도 1f는 6개의 NRF2 표적 유전자(NQO1, GCLC, GCLM, SLC7A11, ME1 및 SRXN1)의 유전자 발현 정도와 이라스틴 감수성 간의 상관관계를 분석한 결과이다.
도 2는 이라스틴 감수성 예측을 위한 통합된 NRF2/AhR 경로 관련 유전자 시그니처를 확인한 결과로서, 도 2a는 18개 유전자 각각에 기반한 ssGSEA, 선형회귀 및 elastic net 회귀 분석을 실시한 후 이라스틴 감수성에 대한 예측 성능을 비교한 결과이고, 도 2b는 모든 598개 암세포에 대하여 핵 수용체 메타-경로에 관련된 312개 유전자의 최종 모델을 제작하고 분석하여 도출된 43개 유전자의 결과를 나타낸 것이며, 도 2c는 핵 수용체 메타-경로와 관련된 43개 유전자에 기반한 elastic net 모델의 예측인자 가운데 NRF2 경로 및 AhR 경로에 관련된 유전자와 나머지 유전자들을 분류하여 도시한 것이고, 도 2d는 각각 이라스틴과 페롭토시스 유도체(FINs; RSL3, ML210, 및 ML162) 약물에 대한 반응을 보여주는 대표적인 기능성 유전자 시그니처를 벤다이어그램으로 도시한 것이며, 도 2e는 NRF2/AhR 모델을 기반으로 하여 CTRP에서의 543개 화합물에 대한 상관관계를 분석하여 이라스틴에 대한 반응성을 예측한 결과이고, 도 2f는 NRF2/AhR 모델과 이전 연구에서 얻은 3개의 EMT 점수를 사용한 이라스틴 감수성 예측 성능을 보여주는 ROC 곡선 결과이며, 도 2g는 A549 및 TD 세포의 예측 점수 및 CCLE에서 얻은 모든 암세포주의 전사체 데이터의 점수 분포를 나타낸 결과이다. Figure 1 shows the results confirming the high correlation between irastin sensitivity and the NRF2 pathway. Figure 1a shows the expression levels (logTPM) and sensitivity to irastin (AUC) of NOX4, GPX4, and ZEB1 genes in 123 lung cancer cell lines. This is the result of analyzing the correlation between the cells, and Figure 1b analyzes the distribution of susceptibility (AUC) to Irastin according to the mutation status of KRAS and HRAS (wild type (Wt) or mutation (Mut)) in 598 non-hematological cancer cell lines. Figure 1c shows the results of analyzing the correlation between the treatment-resistance EMT signature of cells and susceptibility to 543 compounds in CTRP data, and Figure 1d shows six lung cancer cell lines ranked based on sensitivity to irastin. shows the distribution of , and Figure 1e is a result showing the degree of correlation of the signaling pathway related to the drug response to Irastine, and Figure 1f is the gene of six NRF2 target genes (NQO1, GCLC, GCLM, SLC7A11, ME1, and SRXN1). This is the result of analyzing the correlation between expression level and irastin sensitivity.
Figure 2 shows the results of confirming the integrated NRF2/AhR pathway-related gene signature for predicting irastin susceptibility. Figure 2a shows the prediction of irastin susceptibility after performing ssGSEA, linear regression, and elastic net regression analysis based on each of the 18 genes. This is the result of comparing performance, and Figure 2b shows the results of 43 genes derived by creating and analyzing the final model of 312 genes related to the nuclear receptor meta-pathway for all 598 cancer cells, and Figure 2c shows the results of the nuclear receptor meta-pathway. Among the predictors of the elastic net model based on 43 genes related to the meta-pathway, genes related to the NRF2 pathway and AhR pathway and the remaining genes are classified and shown, and Figure 2d shows the classification of irastin and ferroptosis derivatives (FINs; RSL3), respectively. , ML210, and ML162) Representative functional gene signatures showing response to drugs are shown in a Venn diagram, and Figure 2e shows the correlation between 543 compounds in CTRP based on the NRF2/AhR model and the This is the result of predicting reactivity to , and Figure 2f is the ROC curve result showing the prediction performance of irastin susceptibility using the NRF2/AhR model and the three EMT scores obtained in the previous study, and Figure 2g is the prediction score of A549 and TD cells and This result shows the score distribution of transcriptome data of all cancer cell lines obtained from CCLE.
본 발명자들은 약물유전체학 데이터세트를 기반으로 이라스틴에 대한 반응성을 효과적으로 예측할 수 있는 모델을 확립하고, 결과적으로 NRF2 및 AhR 경로를 포함하는 핵 수용체 메타-경로에 관련된 43개 유전자를 유효한 예측 마커로 발굴하였는바, 이에 기초하여 본 발명을 완성하였다.The present inventors established a model that can effectively predict responsiveness to irastin based on a pharmacogenomics dataset, and ultimately identified 43 genes related to the nuclear receptor meta-pathway, including the NRF2 and AhR pathways, as valid predictive markers. Based on this, the present invention was completed.
이에, 본 발명은 ACOX1(acyl-CoA oxidase 1; GenBank 접근(accession) 번호: NM_001185039, NM_004035, NM_007292), ALOX5AP(arachidonate 5-lipoxygenase activating protein; NM_001204406, NM_001629), ANKRD1(ankyrin repeat domain 1; NM_014391), BAAT(bile acid-CoA:amino acid N-acyltransferase; NM_001127610, NM_001701), BHLHE40(basic helix-loop-helix family member e40; NM_003670), CCL2(C-C motif chemokine ligand 2; NM_002982), CYP1A1(cytochrome P450 family 1 subfamily A member 1; NM_000499, NM_001319216, NM_001319217), DNAJC7(DnaJ heat shock protein family (Hsp40) member C7; NM_001144766, NM_003315), ESR1(estrogen receptor 1; NM_000125, NM_001122740, NM_001122741, NM_001122742, NM_001291230, NM_001291241, NM_001328100), GCLC(glutamate-cysteine ligase catalytic subunit; NM_001197115, NM_001498), GPAM(glycerol-3-phosphate acyltransferase, mitochondrial; NM_001244949, NM_020918), GSTM1(glutathione S-transferase mu 1; NM_000561, NM_146421), HES1(hes family bHLH transcription factor 1; NM_005524), HSPA1A(heat shock protein family A (Hsp70) member 1A; NM_005345), IL17B(interleukin 17B; NM_001317987, NM_014443), JUND(JunD proto-oncogene, AP-1 transcription factor subunit; NM_001286968, NM_005354), MFGE8(milk fat globule-EGF factor 8 protein; NM_001114614, NM_001310319, NM_001310320, NM_001310321, NM_005928), NAV3(neuron navigator 3; NM_001024383, NM_014903), NCOA6(nuclear receptor coactivator 6; NM_001242539, NM_001318240, NM_014071), NQO1(NAD(P)H quinone dehydrogenase 1; NM_000903, NM_001025433, NM_001025434, NM_001286137), PLTP(phospholipid transfer protein; NM_001242920, NM_001242921, NM_006227, NM_182676), PMP2(peripheral myelin protein 2; NM_001348381, NM_002677), PRDX1(peroxiredoxin 1; NM_001202431, NM_002574, NM_181696, NM_181697), PTGES3(prostaglandin E synthase 3; NM_001282601, NM_001282602, NM_001282603, NM_001282604, NM_001282605, NM_006601), PTGR1(prostaglandin reductase 1; NM_001146108, NM_001146109, NM_012212), PTPA(protein phosphatase 2 phosphatase activator; NM_001193397, NM_001271832, NM_021131, NM_178000, NM_178001, NM_178002, NM_178003), RXRA(retinoid X receptor alpha; NM_001291920, NM_001291921, NM_002957), S100P(S100 calcium binding protein P; NM_005980), SEC14L1(SEC14 like lipid binding 1; NM_001039573, NM_001143998, NM_001143999, NM_001144001, NM_001204408, NM_001204410, NM_003003), SERPINB2(serpin family B member 2; NM_001143818, NM_002575), SLC2A12(solute carrier family 2 member 12; NM_145176), SLC2A13(solute carrier family 2 member 13; NM_052885), SLC39A12(solute carrier family 39 member 12; NM_001145195, NM_001282733, NM_001282734, NM_152725), SLC39A3(solute carrier family 39 member 3; NM_144564, NM_213568), SLC39A7(solute carrier family 39 member 7; NM_001077516, NM_001288777, NM_006979), SLC5A4(solute carrier family 5 member 4; NM_014227), SLC6A18(solute carrier family 6 member 18; NM_182632), SLC6A7(solute carrier family 6 member 7; NM_014228), SLC7A11(solute carrier family 7 member 11; NM_014331), SLC7A5(solute carrier family 7 member 5; NM_003486), TGFBR2(transforming growth factor beta receptor 2; NM_001024847, NM_003242), TNF(tumor necrosis factor; NM_000594) 및 TNS4(tensin 4; NM_032865)로 이루어진 군으로부터 선택되는 하나 이상의 유전자, 또는 상기 유전자가 암호화하는 단백질을 포함하는, 이라스틴(Erastin)에 대한 반응성 예측용 마커 조성물을 제공한다. Accordingly, the present invention relates to ACOX1 (acyl-CoA oxidase 1; GenBank accession numbers: NM_001185039, NM_004035, NM_007292), ALOX5AP (arachidonate 5-lipoxygenase activating protein; NM_001204406, NM_001629), and ANKRD1 (ankyrin repeat domain 1; NM_014391) , BAAT (bile acid-CoA:amino acid N-acyltransferase; NM_001127610, NM_001701), BHLHE40 (basic helix-loop-helix family member e40; NM_003670), CCL2 (C-C motif chemokine ligand 2; NM_002982), CYP1A1 (cytochrome P450 family) 1 subfamily A member 1; NM_000499, NM_001319216, NM_001319217), DNAJC7 (DnaJ heat shock protein family (Hsp40) member C7; NM_001144766, NM_003315), ESR1 (estrogen receptor 1; NM_000125, NM_001122) 740, NM_001122741, NM_001122742, NM_001291230, NM_001291241, NM_001328100 ), GCLC (glutamate-cysteine ligase catalytic subunit; NM_001197115, NM_001498), GPAM (glycerol-3-phosphate acyltransferase, mitochondrial; NM_001244949, NM_020918), GSTM1 (glutathione S-transferase mu 1; NM_000561, NM_1 46421), HES1(hes family bHLH transcription factor 1; NM_005524), HSPA1A (heat shock protein family A (Hsp70) member 1A; NM_005345), IL17B (interleukin 17B; NM_001317987, NM_014443), JUND (JunD proto-oncogene, AP-1 transcription factor subunit; NM_001286968, NM_005354), MFGE8 (milk fat globule-EGF factor 8 protein; NM_00111 4614, NM_001310319, NM_001310320, NM_001310321 , NM_005928), NAV3 (neuron navigator 3; NM_001024383, NM_014903), NCOA6 (nuclear receptor coactivator 6; NM_001242539, NM_001318240, NM_014071), NQO1 (NAD(P)H quinone dehydrogenase 1; NM _000903, NM_001025433, NM_001025434, NM_001286137), PLTP (phospholipid transfer protein; NM_001242920, NM_001242921, NM_006227, NM_182676), PMP2 (peripheral myelin protein 2; NM_001348381, NM_002677), PRDX1 (peroxiredoxin 1; NM_001202431, NM_ 002574, NM_181696, NM_181697), PTGES3 (prostaglandin E synthase 3; NM_001282601, NM_001282602 , NM_001282603, NM_001282604, NM_001282605, NM_006601), PTGR1 (prostaglandin reductase 1; NM_001146108, NM_001146109, NM_012212), PTPA (protein phosphatase 2 phosphatase activator; NM_001193397, NM_001271832, NM_021131, NM_178000, NM_178001, NM_178002, NM_178003), RXRA (retinoid receptor alpha; NM_001291920, NM_001291921, NM_002957), S100P (S100 calcium binding protein P; NM_005980), SEC14L1 (SEC14 like lipid binding 1; NM_001039573, NM_001143998, NM_001143999, NM_0 01144001, NM_001204408, NM_001204410, NM_003003), SERPINB2 (serpin family B member 2; NM_001143818 , NM_002575), SLC2A12(solute carrier family 2 member 12; NM_145176), SLC2A13(solute carrier family 2 member 13; NM_052885), SLC39A12(solute carrier family 39 member 12; NM_001145195, NM_001282733, NM_00 1282734, NM_152725), SLC39A3 (solute carrier family 39 member 3; NM_144564, NM_213568), SLC39A7 (solute carrier family 39 member 7; NM_001077516, NM_001288777, NM_006979), SLC5A4 (solute carrier family 5 member 4; NM_014227), SLC6A18 (solute carrier family) 6 member 18; NM_182632), SLC6A7 (Solute Carrier Family 6 MEMBER 7; NM_014228), SLC7A11 (Solute Carrier Family 7 MEMBER 11; NM_014331), SLC7A5 (Solute Carrier Family 7 ME_00 3486), TGFBR2 (Transforming Growth Factor Beta Receptor 2; NM_001024847, NM_003242), TNF (tumor necrosis factor; Provided is a marker composition for predicting responsiveness to Erastin, comprising at least one gene selected from the group consisting of NM_000594) and TNS4 (tensin 4; NM_032865), or a protein encoded by the gene.
상기 “이라스틴(Erastin)”은 과산화지질의 축적에 의해 특징지어지는 철 의존성 세포사멸인 페롭토시스(ferroptosis)라는 비-아폽토시스 세포사멸을 유발하는 저분자 화합물로, 하기의 화학식으로 표시될 수 있다. 상기 화합물은 VDAC2(Voltage-dependent anion-selective channel protein 2) 및 VDAC3(Voltage-dependent anion-selective channel protein 3)에 결합하고 이를 억제하며 기능적으로 시스틴-글루타메이트 역수용체(cystine-glutamate antiporter)인 시스템 Xc-를 저해한다고 알려져 있다. 이라스틴이 처리된 세포는 시스테인이 고갈되어 항산화제인 글루타티온을 합성할 수 없게 되며 결국 초과적인 지질 과산화 및 세포사멸에 이르게 된다.The “Erastin” is a low-molecular-weight compound that induces non-apoptotic cell death called ferroptosis, an iron-dependent cell death characterized by the accumulation of lipid peroxides, and can be represented by the following chemical formula: . The compound binds to and inhibits VDAC2 (Voltage-dependent anion-selective channel protein 2) and VDAC3 (Voltage-dependent anion-selective channel protein 3) and is functionally a cystine-glutamate antiporter system - It is known to inhibit. Cells treated with Irastine are depleted of cysteine and are unable to synthesize the antioxidant glutathione, ultimately leading to excessive lipid peroxidation and cell death.
[화학식][Chemical formula]
본 발명자들은 구체적인 실시예를 통해 본 발명에 따른 상기 43개 유전자에 대하여 이라스틴에 대한 유효한 반응성 예측 효과를 입증하였다.Through specific examples, the present inventors demonstrated an effective predictive effect of responsiveness to irastin for the 43 genes according to the present invention.
본 발명에 있어서, 상기 핵 수용체 메타-경로에 관련된 43개 유전자 가운데 GSTM1, HSPA1A, PRDX1, PTGR1, RXRA, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, TGFBR2, GCLC 및 NQO1은 NRF2(Nuclear factor erythroid 2-related factor 2) 경로에 관련된 유전자이고, CYP1A1, ESR1, HES1, IL17B, JUND, PTGES3, SERPINB2, SLC7A5, TNF, GCLC 및 NQO1은 AhR(Aryl hydrocarbon receptor) 경로에 관련된 유전자이며, GCLC 및 NQO1은 상기 두 가지 경로에 공통적으로 관련된 유전자일 수 있다. In the present invention, among the 43 genes related to the nuclear receptor meta-pathway, GSTM1, HSPA1A, PRDX1, PTGR1, RXRA, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, TGFBR2, GCLC and NQO1 is a gene related to the NRF2 (Nuclear factor erythroid 2-related factor 2) pathway, and CYP1A1, ESR1, HES1, IL17B, JUND, PTGES3, SERPINB2, SLC7A5, TNF, GCLC, and NQO1 are genes related to the AhR (Aryl hydrocarbon receptor) pathway. , and GCLC and NQO1 may be genes commonly related to the two pathways.
이에, 본 발명의 다른 양태로서, 본 발명은 ACOX1, ALOX5AP, ANKRD1, BAAT, BHLHE40, CCL2, CYP1A1, DNAJC7, ESR1, GCLC, GPAM, GSTM1, HES1, HSPA1A, IL17B, JUND, MFGE8, NAV3, NCOA6, NQO1, PLTP, PMP2, PRDX1, PTGES3, PTGR1, PTPA, RXRA, S100P, SEC14L1, SERPINB2, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, SLC7A5, TGFBR2, TNF 및 TNS4로 이루어진 군으로부터 선택되는 하나 이상의 유전자의 mRNA, 또는 상기 유전자가 암호화하는 단백질 수준을 측정하는 제제를 포함하는, 이라스틴(Erastin)에 대한 반응성 예측용 조성물 및 상기 조성물을 포함하는 이라스틴(Erastin)에 대한 반응성 예측용 키트를 제공한다. Accordingly, in another aspect of the present invention, the present invention provides ACOX1, ALOX5AP, ANKRD1, BAAT, BHLHE40, CCL2, CYP1A1, DNAJC7, ESR1, GCLC, GPAM, GSTM1, HES1, HSPA1A, IL17B, JUND, MFGE8, NAV3, NCOA6, NQO1, PLTP, PMP2, PRDX1, PTGES3, PTGR1, PTPA, RXRA, S100P, SEC14L1, SERPINB2, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, SLC7A5, TGF Group consisting of BR2, TNF and TNS4 A composition for predicting reactivity to Erastin, comprising an agent for measuring the level of mRNA of one or more genes selected from, or the protein level encoded by the gene, and reactivity to Erastin comprising the composition A prediction kit is provided.
상기 유전자의 mRNA 수준을 측정하는 제제는 유전자의 mRNA에 상보적으로 결합하는 센스 및 안티센스 프라이머, 또는 프로브일 수 있다.Agents for measuring the mRNA level of the gene may be sense and antisense primers or probes that bind complementary to the mRNA of the gene.
본 발명에서 사용되는 용어, “프라이머(primer)”란 DNA 합성의 기시점이 되는 짧은 유전자 서열로써, 진단, DNA 시퀀싱 등에 이용할 목적으로 합성된 올리고뉴클레오티드를 의미한다. 상기 프라이머들은 통상적으로 15 내지 30 염기쌍의 길이로 합성하여 사용할 수 있으나, 사용 목적에 따라 달라질 수 있으며, 공지된 방법으로 메틸화, 캡화 등으로 변형시킬 수 있다.The term “primer” used in the present invention refers to a short gene sequence that serves as the starting point for DNA synthesis and an oligonucleotide synthesized for use in diagnosis, DNA sequencing, etc. The primers can generally be synthesized and used in a length of 15 to 30 base pairs, but may vary depending on the purpose of use, and can be modified by methylation, capping, etc. by known methods.
본 발명에서 사용되는 용어, “프로브(probe)”란 효소 화학적인 분리정제 또는 합성과정을 거쳐 제작된 수 염기 내지 수백 염기길이의 mRNA와 특이적으로 결합할 수 있는 핵산을 의미한다. 방사성 동위원소, 효소, 또는 형광체 등을 표지하여 mRNA의 존재 유무를 확인할 수 있으며, 공지된 방법으로 디자인하고 변형시켜 사용할 수 있다.As used in the present invention, the term “probe” refers to a nucleic acid that can specifically bind to mRNA of a few bases to hundreds of bases in length, produced through enzyme-chemical separation purification or synthesis. The presence or absence of mRNA can be confirmed by labeling it with a radioactive isotope, enzyme, or fluorescent substance, and it can be designed and modified by known methods.
상기 단백질 수준을 측정하는 제제는 유전자가 암호화하는 단백질에 특이적으로 결합하는 항체일 수 있으나, 이에 제한되는 것은 아니다. The agent for measuring the protein level may be an antibody that specifically binds to the protein encoded by the gene, but is not limited thereto.
본 발명에서 사용되는 용어, “항체”는 면역학적으로 특정 항원과 반응성을 갖는 면역글로불린 분자를 포함하며, 단클론(monoclonal) 항체 및 다클론(polyclonal) 항체를 모두 포함한다. 또한, 상기 항체는 키메라성 항체(예를 들면, 인간화 뮤린 항체) 및 이종결합항체(예를 들면, 양특이성 항체)와 같은 유전공학에 의해 생산된 형태를 포함한다. As used in the present invention, the term “antibody” includes immunoglobulin molecules that are immunologically reactive with a specific antigen, and includes both monoclonal antibodies and polyclonal antibodies. Additionally, the antibodies include forms produced by genetic engineering, such as chimeric antibodies (eg, humanized murine antibodies) and heterologous antibodies (eg, bispecific antibodies).
본 발명의 항암제 반응성 예측용 키트는 분석 방법에 적합한 한 종류 또는 그 이상의 다른 구성성분 조성물, 용액 또는 장치로 구성될 수 있다.The kit for predicting anticancer drug reactivity of the present invention may be composed of one or more different component compositions, solutions, or devices suitable for the analysis method.
본 발명의 또 다른 양태로서, 본 발명은 암 환자유래의 생물학적 시료에서 ACOX1, ALOX5AP, ANKRD1, BAAT, BHLHE40, CCL2, CYP1A1, DNAJC7, ESR1, GCLC, GPAM, GSTM1, HES1, HSPA1A, IL17B, JUND, MFGE8, NAV3, NCOA6, NQO1, PLTP, PMP2, PRDX1, PTGES3, PTGR1, PTPA, RXRA, S100P, SEC14L1, SERPINB2, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, SLC7A5, TGFBR2, TNF 및 TNS4로 이루어진 군으로부터 선택되는 하나 이상의 유전자의 mRNA, 또는 상기 유전자가 암호화하는 단백질 발현수준을 측정하는 단계를 포함하는, 이라스틴(Erastin)에 대한 반응성 예측을 위한 정보제공방법을 제공한다. In another aspect of the present invention, ACOX1, ALOX5AP, ANKRD1, BAAT, BHLHE40, CCL2, CYP1A1, DNAJC7, ESR1, GCLC, GPAM, GSTM1, HES1, HSPA1A, IL17B, JUND, MFGE8, NAV3, NCOA6, NQO1, PLTP, PMP2, PRDX1, PTGES3, PTGR1, PTPA, RXRA, S100P, SEC14L1, SERPINB2, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC 7A11, SLC7A5, TGFBR2, Provided is a method of providing information for predicting responsiveness to Erastin, which includes measuring the expression level of the mRNA of one or more genes selected from the group consisting of TNF and TNS4, or the expression level of the protein encoded by the gene.
본 발명에 있어서, 암 환자유래의 생물학적 시료에서 상기 유전자들의 mRNA 또는 상기 유전자가 암호화하는 단백질의 발현이 증가된 경우 이라스틴 항암제에 대한 반응성이 있음을 예측할 수 있다.In the present invention, if the expression of the mRNA of the above genes or the protein encoded by the above genes is increased in a biological sample derived from a cancer patient, it can be predicted that there is responsiveness to the anticancer drug Ilastine.
본 발명에서 사용되는 용어, "예측"은 본원에서 대상 환자가 약물 또는 약물 세트에 대해 유리하게 또는 불리하게 반응할 가능성을 지칭하는데 사용된다. 한 실시양태에서, 예측은 이러한 반응의 정도에 관한 것이다. 예컨대, 예측은 환자가 처치 후, 예를 들어 특정한 치료제의 처치 및/또는 초발성 종양의 수술적 제거 및/또는 특정 기간 동안의 화학요법 후에 암 재발 없이 생존할 지의 여부 및/또는 그러할 확률에 관한 것이다. 본 발명의 예측은 암 환자에 대한 가장 적절한 치료 방식을 선택함으로써 치료를 결정하는데 임상적으로 사용될 수 있다. 본 발명의 예측은 환자가 치료 처치, 예컨대 주어진 치료적 처치, 예를 들어 주어진 치료제 또는 조합물의 투여, 수술적 개입, 화학요법 등에 유리하게 반응할 것인지 또는 치료적 처치 후에 환자의 장기 생존이 가능한 지의 여부를 예측하는데 있어서 유용한 도구이다.As used herein, the term “prediction” is used herein to refer to the likelihood that a subject patient will respond favorably or unfavorably to a drug or set of drugs. In one embodiment, the prediction relates to the extent of this response. For example, the prediction relates to whether and/or the probability that a patient will survive without cancer recurrence after treatment, such as treatment with a particular therapeutic agent and/or surgical removal of a primary tumor and/or chemotherapy for a particular period of time. . The predictions of the present invention can be used clinically to make treatment decisions by selecting the most appropriate treatment modality for cancer patients. The prediction of the present invention is to determine whether a patient will respond favorably to a given therapeutic treatment, such as administration of a given therapeutic agent or combination, surgical intervention, chemotherapy, etc., or whether long-term survival of the patient is possible after the therapeutic treatment. It is a useful tool in predicting whether
본 발명에 있어서, 상기 생물학적 시료는 세포, 조직, 혈액, 혈장 또는 혈청일 수 있으며, 본 발명에 따른 이라스틴(Erastin)에 대한 반응성 예측용 바이오마커를 검출할 수 있는 환자유래 시료라면 이에 제한되지 않는다.In the present invention, the biological sample may be a cell, tissue, blood, plasma or serum, and is not limited thereto as long as it is a patient-derived sample capable of detecting a biomarker for predicting reactivity to Erastin according to the present invention. No.
상기 유전자의 mRNA의 발현수준은 in situ 교잡법(in situ hybridization), 중합효소연쇄반응(PCR), 역전사 중합효소연쇄반응(RT-PCR), 실시간 중합효소연쇄반응(Real-time PCR), RNase 보호 분석법(RNase protection assay; RPA), 마이크로어레이(microarray), 및 노던 블롯팅(northern blotting)으로 이루어진 군으로부터 선택되는 1종 이상의 방법을 통해 측정될 수 있으나, 이에 제한되는 것은 아니다. The expression level of the mRNA of the gene was determined by in situ hybridization, polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), real-time polymerase chain reaction (Real-time PCR), and RNase. It may be measured using one or more methods selected from the group consisting of RNase protection assay (RPA), microarray, and northern blotting, but is not limited thereto.
상기 단백질의 발현수준은 웨스턴 블롯팅(western blotting), 방사선면역분석법(radioimmunoassay; RIA), 방사 면역 확산법(radioimmunodiffusion), 효소면역분석법 (ELISA), 면역침강법(immunoprecipitation), 유세포분석법 (flow cytometry), 면역형광염색법(immunofluorescence), 오우크테로니(ouchterlony), 보체 고정 분석법(complement fixation assay), 및 단백질 칩(protein chip)으로 이루어진 군으로부터 선택되는 1종 이상의 방법을 통해 측정될 수 있으나, 이에 제한되는 것은 아니다.The expression level of the protein was determined by Western blotting, radioimmunoassay (RIA), radioimmunodiffusion, enzyme-linked immunosorbent assay (ELISA), immunoprecipitation, and flow cytometry. It may be measured using one or more methods selected from the group consisting of immunofluorescence, ouchterlony, complement fixation assay, and protein chip. It is not limited.
이하, 본 발명의 이해를 돕기 위하여 바람직한 실시예를 제시한다. 그러나 하기의 실시예는 본 발명을 보다 쉽게 이해하기 위하여 제공되는 것일 뿐, 하기 실시예에 의해 본 발명의 내용이 한정되는 것은 아니다.Below, preferred embodiments are presented to aid understanding of the present invention. However, the following examples are provided only to make the present invention easier to understand, and the content of the present invention is not limited by the following examples.
[실시예][Example]
실시예 1. 실험준비 및 실험방법Example 1. Experimental preparation and experimental method
1-1. RNA 서열분석 및 데이터 처리1-1. RNA sequencing and data processing
Trizol 시약을 이용하여 제조사의 프로토콜에 따라 총 RNA를 분리하였고, 라이브러리 구축에는 TruSeq Stranded mRNA Library Prep Kit(Illumina, San Diego, CA)를 이용하였다. 간단하게, 가닥-특이적 프로토콜 단계는 다음과 같다: 제1가닥 cDNA 합성; dTTP 대신 dUTP를 사용하여 제2가닥 합성; 말단 수리, A-tailing 및 어댑터 접합; PCR 증폭. 다음으로, 제조사의 권장 프로토콜에 따라 Illumina NextSeq 500에서 페어-리드 시퀀싱 (2 X 75bp)의 76 사이클 동안 각 라이브러리를 8 pM로 희석하였다.Total RNA was isolated using Trizol reagent according to the manufacturer's protocol, and the TruSeq Stranded mRNA Library Prep Kit (Illumina, San Diego, CA) was used to construct the library. Briefly, strand-specific protocol steps are as follows: first-strand cDNA synthesis; Second strand synthesis using dUTP instead of dTTP; End repair, A-tailing and adapter joining; PCR amplification. Next, each library was diluted to 8 pM for 76 cycles of pair-read sequencing (2
Raw FASTQ 파일의 서열분석 품질은 FastQC(https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)를 이용해 확인하였고, BBDuk(http://jgi.doe.gov/data-and-tools/bb-tools/)을 이용하여 품질이 낮은 리드 및 리드의 어댑터 서열을 제거하였다. 이후 검수된 리드는 STAR aligner(v2.6.0a)를 사용하여 GRCh37 게놈 레퍼런스(build 38)에 정렬시켰으며, 리드 수는 Gencode v19 주석 GTF 파일과 함께 HTSeq를 사용하여 얻었다. FASTQ 파일 및 전처리 데이터는 Gene Expression Omnibus(GEO : GSE135402)를 통해 이용 가능하다. 한편, A549와 TD 세포 사이에 차등적으로 발현되는 유전자는 DESeq2를 사용하여 확인하였다. The sequencing quality of the raw FASTQ file was confirmed using FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/), and BBDuk (http://jgi.doe.gov/data-and- Tools/bb-tools/) were used to remove low-quality reads and their adapter sequences. The reads were then aligned to the GRCh37 genome reference (build 38) using STAR aligner (v2.6.0a), and the number of reads was obtained using HTSeq with Gencode v19 annotated GTF file. FASTQ files and preprocessing data are available through Gene Expression Omnibus (GEO: GSE135402). Meanwhile, genes differentially expressed between A549 and TD cells were identified using DESeq2.
1-2. 암세포주의 이라스틴 감수성 및 전사체 데이터 분석1-2. Irastin sensitivity and transcriptome data analysis of cancer cell lines
이라스틴에 대한 886개 암세포주의 약물 반응 데이터는 CTD2 데이터 포털 (https://ocg.cancer.gov/programs/ctd2/data-portal)에서 다운로드하였다. 세포 생존율(cell viability) 값은 성장 억제(growth inhibition)로 전환하고 0~100% 범위로 조정하였다. 조정된 성장 억제 값을 4-매개변수 로지스틱 회귀법을 통해 맞추고 저품질 프로파일(적합성 < 0.7)은 제거하였다. 커브 아래 면적 값인 AUC는 주어진 농도 범위에서 0% 성장 억제로 가정된 최대 AUC에 의해 0-1의 범위로 정규화하였다. 또한 이라스틴 감수성에 대해 스크리닝된 암세포주의 기저수준의 유전자 발현 프로파일은 BAM 파일 형식으로 Genomics Data Common(GDC, https://gdc.cancer.gov/)에서 다운로드하였다. 리드 수는 Gencode v19 주석 GTF 파일과 함께 HTSeq를 사용하여 얻었다. 비인간 및 혈액학적 조직 유형의 세포주를 제외하고, 598개 암세포주의 유전자 발현 프로파일을 분석에 사용하였다.Drug response data of 886 cancer cell lines to irastin were downloaded from the CTD2 data portal (https://ocg.cancer.gov/programs/ctd2/data-portal). Cell viability values were converted to growth inhibition and adjusted to the range of 0 to 100%. Adjusted growth inhibition values were fitted using 4-parameter logistic regression and low-quality profiles (fit < 0.7) were removed. AUC, the area under the curve value, was normalized to a range of 0-1 by the maximum AUC assumed to be 0% growth inhibition in a given concentration range. Additionally, the basal level gene expression profiles of cancer cell lines screened for irastin sensitivity were downloaded from Genomics Data Common (GDC, https://gdc.cancer.gov/) in BAM file format. Read counts were obtained using HTSeq with Gencode v19 annotated GTF files. Excluding cell lines from non-human and hematological tissue types, gene expression profiles of 598 cancer cell lines were used for analysis.
1-3. Elastic net 회귀 모델링1-3. Elastic net regression modeling
세포주의 유전자 발현에 기초한 이라스틴에 대한 반응성(AUC)을 설명하는 예측 모델을 도출하기 위해 Elastic net 회귀를 적용하였다. 보다 구체적으로, 나머지 샘플(예컨대, 597개 세포)에 의해 훈련된 모델을 시험하는데 단일 샘플(예컨대, 세포주)을 사용하는 LOOCV(nested leave-one-out cross-validation) 프로세스로 상기 예측 모델을 평가하였으며, 모든 샘플이 테스트 데이터세트로 사용될 때까지 상기 프로세스를 반복하였다. 각 테스트에서, 최적화된 매개변수(알파, 람다)는 테스트 데이터에 대해 10회 반복하여 5배 교차 검증에 대한 평균 오차를 최소화하는 것으로 결정되었다. 그런 다음 각 테스트 세트에서 독립적으로 예측 된 반응 값을 Pearson 상관계수를 사용하여 측정된 AUC 값과 비교해 예측 성능을 추정했다. 전체적 절차는 R의 glmnet 및 caret 패키지를 이용하여 구현하였다.Elastic net regression was applied to derive a prediction model that describes the responsiveness (AUC) to irastin based on the gene expression of the cell line. More specifically, evaluating the prediction model with a nested leave-one-out cross-validation (LOOCV) process that uses a single sample (e.g., a cell line) to test the model trained by the remaining samples (e.g., 597 cells). The above process was repeated until all samples were used as the test dataset. For each test, the optimized parameters (alpha, lambda) were determined to minimize the average error over 5-fold cross-validation over 10 iterations on the test data. Prediction performance was then estimated by comparing the independently predicted response values in each test set to the measured AUC values using the Pearson correlation coefficient. The overall procedure was implemented using R's glmnet and caret packages.
실시예 2. 이라스틴 감수성과 NRF2 경로의 높은 상관관계 확인Example 2. Confirmation of high correlation between irastin susceptibility and NRF2 pathway
본 발명자들은 이라스틴에 의해 유도되는 선택적 페롭토시스와 관련된 원인 유전자(예컨대, NOX4, GPX4 및 ZEB1)를 조사하고자 하였다. 이를 위해, 먼저 CTRP 및 CCLE 데이터베이스에서 이라스틴에 대한 감수성을 갖는 123개 폐암 세포주의 mRNA 발현 프로파일을 얻어 이들의 상관관계를 조사하였으나, 도 1a에서 볼 수 있는 바와 같이 어떠한 상관관계도 발견하지 못하였다. 따라서, 본 발명자들은 단일 유전자의 발현수준이 이라스틴에 의해 유도되는 페롭토시스에 대한 감수성을 설명하기에 충분하지 않다는 결론을 내렸다. 이에, 단일 유전자와 이라스틴에 대한 감수성의 낮은 상관관계를 고려하여, 수백 개의 암세포주를 이용해 CCLE 및 CTRP 데이터베이스의 전사체 및 이라스틴 반응에 대한 데이터세트로부터 이라스틴에 대한 감수성과 관련된 주요 분자 시그니처를 체계적으로 조사하였다. 그 결과 중요하게도, 도 1b에 나타낸 바와 같이 RAS 돌연변이와 이라스틴 감수성 사이의 일관된 연관성이 관찰되지 않았으며, 오히려 HRAS 돌연변이 세포주는 이라스틴에 대한 중간 저항성을 나타냈다(t-test P <0.05). 이러한 결과로부터 RAS의 돌연변이 상태는 이라스틴 감수성을 예측하기에 충분하지 않음을 알 수 있었다. The present inventors sought to investigate the causal genes (eg, NOX4, GPX4, and ZEB1) associated with selective ferroptosis induced by irastin. To this end, we first obtained the mRNA expression profiles of 123 lung cancer cell lines with sensitivity to irastin from the CTRP and CCLE databases and examined their correlation, but as shown in Figure 1a, no correlation was found. . Therefore, we concluded that the expression level of a single gene is not sufficient to explain susceptibility to ferroptosis induced by irastin. Therefore, considering the low correlation between single genes and susceptibility to irastin, we used hundreds of cancer cell lines to identify key molecular signatures associated with susceptibility to irastin from datasets on transcriptome and irastin response in the CCLE and CTRP databases. was systematically investigated. Importantly, as shown in Figure 1B, no consistent association between RAS mutations and irastin sensitivity was observed; rather, HRAS mutant cell lines showed intermediate resistance to irastin (t-test P <0.05). These results showed that the mutation status of RAS was not sufficient to predict irastin sensitivity.
종래 연구 보고에 따르면, GPX4 억제제에 대해 높은 감수성을 갖는 치료 저항성 중간엽 암세포는 이라스틴에도 민감하다고 보고되어 있다. 그러나, 세포주의 중간엽 점수와 약물 감수성에 대한 AUC 측정 값 사이의 상관관계 분석 결과, 도 1c에 나타낸 바와 같이 이라스틴이 치료-저항성 암세포에 대해 중간 정도의 효과를 나타내는 것을 확인하였다. 유사한 계통에서, 세포 유형에 특이적 방식으로 이라스틴 및 GPX4 억제제 간의 일치하지 않는 것이 있었다. 이러한 결과는 돌연변이 상태 또는 치료 저항성 EMT 시그니처 외에 이라스틴에 대한 감수성을 결정하는 인자가 존재한다는 것을 암시하는 것이다.According to previous research reports, treatment-resistant mesenchymal cancer cells with high sensitivity to GPX4 inhibitors are also reported to be sensitive to Ilastin. However, as a result of correlation analysis between the mesenchymal score of the cell line and the AUC measurement value for drug sensitivity, it was confirmed that Irastin showed a moderate effect on treatment-resistant cancer cells, as shown in Figure 1c. In similar lineages, there was a discrepancy between irastin and GPX4 inhibitors in a cell type specific manner. These results suggest that factors other than mutational status or treatment resistance EMT signature exist that determine susceptibility to irastin.
상기 이라스틴에 대한 감수성을 결정하는 인자를 조사하기 전에, 본 발명자들은 먼저 TD 모델의 발현 프로파일이 CCLE에서 이라스틴-감수성 세포의 발현 프로파일과 유사한지 여부를 조사하였다. 이를 위해, 도 1d에서 볼 수 있는 바와 같이 사전에 테스트한 6개 폐암 세포주를 기반으로 세포를 이라스틴 S 및 R 그룹으로 분류하기 위해 컷오프(AUC = 0.7)를 대략적으로 정의하였다. 다음으로, 생물학적 신호전달 경로 정보를 이용하여 598개 비- 혈액암 세포에 대해 ssGSEA(single-sample gene set enrichment analysis)를 수행하였고, 각 세포주에 대하여 445개 PES(pathway enrichment score)를 산출한 후 이들 세포주의 PES를 이라스틴에 대한 감수성(AUC)과 연관시켜 분석하였다. 그 결과, 도 1e에 나타낸 바와 같이 이라스틴에 대한 반응이 페롭토시스 내성을 유발하는 항산화 반응의 마스터 조절자인 NRF2(Nuclear factor erythroid 2-related factor 2)와 높은 상관관계가 있는 것을 발견하였다. 한편, EMT 관련 또는 치료-저항성 관련 유전자는 이라스틴에 대한 반응성과 관련이 없는 것으로 나타났다. 이와 일치하게, 도 1f에서 볼 수 있는 바와 같이 항산화 활성에 관여하는 6가지 전형적인 NRF2 표적 유전자(SLC7A11, NQO1, GCLC, GCLM, ME1 및 SRXN1)의 발현은 폐암뿐만 아니라 모든 암세포에서 이라스틴에 대한 감수성과 유의한 상관관계가 있는 것으로 나타났다. 이러한 결과는 NRF2 관련 유전자의 발현으로부터 이라스틴에 대한 감수성을 예측할 수 있음을 시사한다.Before investigating the factors that determine sensitivity to Irastin, the present inventors first investigated whether the expression profile of the TD model was similar to that of Irastin-sensitive cells in CCLE. For this purpose, a cutoff (AUC = 0.7) was roughly defined to classify cells into Ilastin S and R groups based on six previously tested lung cancer cell lines, as shown in Figure 1D. Next, single-sample gene set enrichment analysis (ssGSEA) was performed on 598 non-blood cancer cells using biological signaling pathway information, and 445 PES (pathway enrichment scores) were calculated for each cell line. The PES of these cell lines was analyzed in relation to sensitivity to irastin (AUC). As a result, as shown in Figure 1e, it was found that the response to irastin was highly correlated with NRF2 (Nuclear factor erythroid 2-related factor 2), a master regulator of the antioxidant response that causes ferroptosis resistance. Meanwhile, EMT-related or therapy-resistance-related genes were not found to be related to responsiveness to Irastine. Consistent with this, as shown in Figure 1f, the expression of six typical NRF2 target genes (SLC7A11, NQO1, GCLC, GCLM, ME1, and SRXN1) involved in antioxidant activity showed sensitivity to irastin not only in lung cancer but also in all cancer cells. It was found that there was a significant correlation with . These results suggest that sensitivity to irastin can be predicted from the expression of NRF2-related genes.
실시예 3. 이라스틴 감수성 예측을 위한 통합된 NRF2/AhR 경로 관련 유전자 시그니처 확인Example 3. Confirmation of integrated NRF2/AhR pathway-related gene signature for predicting irastin susceptibility
NRF2 관련 유전자들의 밀접한 상관관계에 비추어, 본 발명자들은 통합된 유전자 발현이 이라스틴 감수성을 가장 잘 나타내는 최적의 유전자 시그니처임을 추가적으로 확인하고자 하였다. 이를 위해, 관심 있는 유전자 즉, 상기 도 1e에서 확인된 11개의 양의 상관관계와 5개의 음의 상관관계, 및 추가적으로 두 개의 EMT 시그니처를 포함한 18개 유전자 세트의 발현을 이용하여 598개 CCLE 세포주에 대하여, 게놈 규모 데이터에서 특징 선별에 널리 사용되는 벌점 모델(penalized model)인 elastic net 회귀 분석을 적용하였다. 주어진 유전자 세트에 대해, 이라스틴에 대한 개별 세포주의 반응은 일회성 교차 검증 체계를 통해 예측하였고, 예측 성능을 평가하기 위해 피어슨의 상관 계수 (r)를 사용하여 측정된 AUC 값과 비교하였다. 그 결과, 도 2a에 나타낸 바와 같이 전반적으로 elastic net은 일반적인 선형 회귀 및 ssGSEA를 능가했으며, 이는 이라스틴에 대한 감수성은 몇 가지 지정된 신호전달 경로 관련 유전자 세트로 예측될 수 있음을 제시하는 것이다. 특히, 핵 수용체 메타-경로(Nuclear Receptor Meta-Pathway) 유전자에 기반한 elastic net 모델은 최고 성능을 나타했으며(r = 0.456), 이는 놀랍게도 도 2a의 붉은색 선으로 나타내어진 총 18,965개의 유전자(r = 0.429)에 기초한 모델보다 더 높게 나타난 결과였다. 따라서, 본 발명자들은 핵 수용체 메타-경로에 초점을 맞추고 상기 경로에 관련된 312개 유전자의 발현으로 모든 598개 암세포에 대한 최종 모델을 제작하였다. 결과 모델은 도 2b에서 볼 수 있는 바와 같이 0이 아닌 계수를 나타내는 43개 유전자를 포함했으며, 대부분 다른 상위 경로에 관여했다. 그 중에서도, 도 2c에서 볼 수 있는 바와 같이 NRF2와 아릴 탄화수소 수용체(AhR) 경로가 가장 큰 비중을 차지하였다. 그 외에 다른 페롭토시스 유도제(FIN)인 GPX4 억제제(RSL3, ML162 및 ML210)에 대한 감수성과 세포주 PES의 상관 분석결과, 도 2d에 나타낸 바와 같이 NRF2와 산화-관련 유전자가 일반적으로 FIN에 대한 저항과 관련이 있는 것으로 나타났다. 그러나 AhR 경로는 이라스틴에 대한 저항성과 관련된 유일한 인자임을 알 수 있었다. 흥미롭게도, 게놈 전체의 CRISPR-Cas9 스크리닝 데이터 세트(DepMap 19Q2)는 GPX4 또는 종래 연구결과와 일치하게 모든 FINs에 감수성을 갖는 세포에서 셀렌단백질(selenoproteins)(SEPSECS, EEFSEC 및 SEPHS2)을 암호화하는 유전자의 높은 의존성을 밝혀냈으나, 이와 반대로 본 발명자들은 AHR(AhR을 암호화하는 유전자)의 소실이 이라스틴 저항성 세포주에서 취약성을 설명하며, 반면에 NFE2L2(NRF2를 암호화하는 유전자)는 이라스틴 및 GPX4 억제제 둘 다의 원인임을 확인하였다. 이러한 결과는 AhR(AHR의해 암호화됨) 경로가 이라스틴 저항성과 독특한 상관관계가 있음을 의미한다. In light of the close correlation of NRF2-related genes, the present inventors sought to further confirm that integrated gene expression is the optimal gene signature that best represents irastin sensitivity. To this end, expression of a set of 18 genes of interest, i.e. 11 positive and 5 negative correlations identified in Figure 1e above, and additionally two EMT signatures was used to analyze 598 CCLE cell lines. In this regard, elastic net regression analysis, a penalized model widely used for feature selection in genome-scale data, was applied. For a given set of genes, the response of individual cell lines to irastin was predicted through a one-time cross-validation scheme and compared to the measured AUC values using Pearson's correlation coefficient (r) to assess prediction performance. As a result, as shown in Figure 2a, elastic net overall outperformed general linear regression and ssGSEA, suggesting that susceptibility to irastin can be predicted by several designated signaling pathway-related gene sets. In particular, the elastic net model based on Nuclear Receptor Meta-Pathway genes showed the best performance (r = 0.456), which, surprisingly, included a total of 18,965 genes (r = 0.456), indicated by the red line in Figure 2a. The result was higher than the model based on 0.429). Therefore, we focused on the nuclear receptor meta-pathway and created a final model for all 598 cancer cells with the expression of 312 genes involved in this pathway. The resulting model contained 43 genes with non-zero coefficients, most of which were involved in different upstream pathways, as seen in Figure 2B. Among them, as can be seen in Figure 2c, NRF2 and the aryl hydrocarbon receptor (AhR) pathway accounted for the largest proportion. In addition, as a result of correlation analysis of the cell line PES with the sensitivity to GPX4 inhibitors (RSL3, ML162, and ML210), which are other ferroptosis inducers (FIN), NRF2 and oxidation-related genes are generally resistant to FIN, as shown in Figure 2d. It was found to be related to . However, the AhR pathway was found to be the only factor associated with resistance to irastin. Interestingly, a genome-wide CRISPR-Cas9 screening data set (DepMap 19Q2) identified a higher abundance of genes encoding selenoproteins (SEPSECS, EEFSEC and SEPHS2) in cells susceptible to GPX4 or all FINs, consistent with previous findings. We found a high dependence, but in contrast, we found that loss of AHR (the gene encoding AhR) accounts for vulnerability in Irastin-resistant cell lines, whereas NFE2L2 (the gene encoding NRF2) is sensitive to both Irastin and GPX4 inhibitors. It was confirmed that it was the cause. These results imply that the AhR (encoded by AHR) pathway has a unique correlation with irastin resistance.
도 1c에 개시된 EMT 시그니처를 기반으로 한 예측과 달리, NRF2/AhR-enriched 모델(이하, NRF2/AhR 모델)을 기반으로 한 이라스틴에 대한 반응 예측 결과 도 2e에 나타낸 바와 같이 CTRP에서의 543개 화합물 가운데 FINs이 이라스틴에 대한 감수성과 가장 크게 연관되어 있는 것으로 나타났다. 단, 페롭토시스를 유발하는 것으로 밝혀진 STATIN(Fluvastatin, Lovastatin 및 Simvastatin)은 아니었다. 실제로, 도 2f에서 볼 수 있는 바와 같이 이라스틴에 대한 감수성을 예측하기 위한 본 발명에 따른 모델은 이전 연구에서 FIN을 최적의 치료 옵션으로 도출하는 데 사용된 3개의 독립적인 EMT 시그니처를 능가했다. 더욱이, 도 2g에 나타낸 바와 같이 본 발명자들은 A549와 TD의 유전자 발현 데이터에 NRF2/AhR 모델을 적용하고, 각 세포주에서 이라스틴에 대한 감수성을 정확히 예측하였다. 이는 본 발명에 따른 NRF2/AhR 모델이 다른 코호트에서 생성된 전사체 데이터에 보편적으로 적용 가능하며, 효과적으로 이라스틴에 대한 감수성을 예측할 수 있음을 의미하는 것이다. In contrast to the prediction based on the EMT signature disclosed in Figure 1c, the response prediction result to Irastine based on the NRF2/AhR-enriched model (hereinafter referred to as NRF2/AhR model) 543 in CTRP as shown in Figure 2e. Among the compounds, FINs appeared to be most significantly associated with susceptibility to irastin. However, it was not STATIN (Fluvastatin, Lovastatin, and Simvastatin), which has been shown to induce ferroptosis. In fact, as can be seen in Figure 2f, our model for predicting susceptibility to irastin outperformed the three independent EMT signatures used in previous studies to derive FIN as the optimal treatment option. Moreover, as shown in Figure 2g, the present inventors applied the NRF2/AhR model to the gene expression data of A549 and TD and accurately predicted the sensitivity to irastin in each cell line. This means that the NRF2/AhR model according to the present invention is universally applicable to transcriptome data generated in different cohorts and can effectively predict susceptibility to irastin.
상기 진술한 본 발명의 설명은 예시를 위한 것이며, 본 발명이 속하는 기술분야의 통상의 지식을 가진 자는 본 발명의 기술적 사상이나 필수적인 특징을 변경하지 않고서 다른 구체적인 형태로 쉽게 변형이 가능하다는 것을 이해할 수 있을 것이다. 그러므로 이상에서 기술한 실시예들은 모든 면에서 예시적인 것이며 한정적이 아닌 것으로 이해해야만 한다. The description of the present invention stated above is for illustrative purposes, and a person skilled in the art to which the present invention pertains can understand that it can be easily modified into other specific forms without changing the technical idea or essential features of the present invention. There will be. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive.
Claims (14)
ACOX1 (acyl-CoA oxidase 1; GenBank accession numbers: NM_001185039, NM_004035, NM_007292), ALOX5AP (arachidonate 5-lipoxygenase activating protein; NM_001204406, NM_001629), ANKRD1 (ankyrin repeat domain 1; NM_01 4391), BAAT (bile acid -CoA:amino acid N-acyltransferase; NM_001127610, NM_001701), BHLHE40 (basic helix-loop-helix family member e40; NM_003670), CCL2 (CC motif chemokine ligand 2; NM_002982), CYP1A1 (cytochrome P450 family 1 subfamily A member 1) ; NM_000499, NM_001319216, NM_001319217), DNAJC7 (DnaJ heat shock protein family (Hsp40) member C7; NM_001144766, NM_003315), ESR1 (estrogen receptor 1; NM_000125, NM_001122740, NM_0 01122741, NM_001122742, NM_001291230, NM_001291241, NM_001328100), GCLC(glutamate -cysteine ligase catalytic subunit; NM_001197115, NM_001498), GPAM (glycerol-3-phosphate acyltransferase, mitochondrial; NM_001244949, NM_020918), GSTM1 (glutathione S-transferase mu 1; NM_000561, NM_146421), HES 1(hes family bHLH transcription factor 1; NM_005524), HSPA1A (heat shock protein family A (Hsp70) member 1A; NM_005345), IL17B (interleukin 17B; NM_001317987, NM_014443), JUND (JunD proto-oncogene, AP-1 transcription factor subunit; NM_001286968, NM_005354), MFGE8 (milk fat globule-EGF factor 8 protein; NM_00111 4614, NM_001310319, NM_001310320, NM_001310321 , NM_005928), NAV3 (neuron navigator 3; NM_001024383, NM_014903), NCOA6 (nuclear receptor coactivator 6; NM_001242539, NM_001318240, NM_014071), NQO1 (NAD(P)H quinone dehydrogenase 1; NM _000903, NM_001025433, NM_001025434, NM_001286137), PLTP (phospholipid transfer protein; NM_001242920, NM_001242921, NM_006227, NM_182676), PMP2 (peripheral myelin protein 2; NM_001348381, NM_002677), PRDX1 (peroxiredoxin 1; NM_001202431, NM_ 002574, NM_181696, NM_181697), PTGES3 (prostaglandin E synthase 3; NM_001282601, NM_001282602 , NM_001282603, NM_001282604, NM_001282605, NM_006601), PTGR1 (prostaglandin reductase 1; NM_001146108, NM_001146109, NM_012212), PTPA (protein phosphatase 2 phosphatase activator; NM_001193397, NM_001271832, NM_021131, NM_178000, NM_178001, NM_178002, NM_178003), RXRA (retinoid receptor alpha; NM_001291920, NM_001291921, NM_002957), S100P (S100 calcium binding protein P; NM_005980), SEC14L1 (SEC14 like lipid binding 1; NM_001039573, NM_001143998, NM_001143999, NM_0 01144001, NM_001204408, NM_001204410, NM_003003), SERPINB2 (serpin family B member 2; NM_001143818 , NM_002575), SLC2A12(solute carrier family 2 member 12; NM_145176), SLC2A13(solute carrier family 2 member 13; NM_052885), SLC39A12(solute carrier family 39 member 12; NM_001145195, NM_001282733, NM_00 1282734, NM_152725), SLC39A3 (solute carrier family 39 member 3; NM_144564, NM_213568), SLC39A7 (solute carrier family 39 member 7; NM_001077516, NM_001288777, NM_006979), SLC5A4 (solute carrier family 5 member 4; NM_014227), SLC6A18 (solute carrier family) 6 member 18; NM_182632), SLC6A7 (Solute Carrier Family 6 Member 7; NM_014228), SLC7A11 (Solute Carrier Family 7 MEMBER 11; NM_014331), SLC7A5 (Solute Carrier Family 7 ME_00 3486), TGFBR2 (Transforming Growth Factor Beta Receptor 2; NM_001024847, NM_003242), TNF (tumor necrosis factor; A marker composition for predicting responsiveness to Erastin, comprising genes of (NM_000594) and TNS4 (tensin 4; NM_032865), or proteins encoded by the genes.
상기 GSTM1, HSPA1A, PRDX1, PTGR1, RXRA, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, TGFBR2, GCLC 및 NQO1은 NRF2(Nuclear factor erythroid 2-related factor 2) 경로에 관련된 유전자인 것을 특징으로 하는, 마커 조성물.
According to paragraph 1,
The GSTM1, HSPA1A, PRDX1, PTGR1, RXRA, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, TGFBR2, GCLC and NQO1 are involved in the NRF2 (Nuclear factor erythroid 2-related factor 2) pathway. genes involved A marker composition, characterized in that.
상기 CYP1A1, ESR1, HES1, IL17B, JUND, PTGES3, SERPINB2, SLC7A5, TNF, GCLC 및 NQO1은 AhR(Aryl hydrocarbon receptor) 경로에 관련된 유전자인 것을 특징으로 하는, 마커 조성물.
According to paragraph 1,
A marker composition, wherein CYP1A1, ESR1, HES1, IL17B, JUND, PTGES3, SERPINB2, SLC7A5, TNF, GCLC and NQO1 are genes related to the AhR (Aryl hydrocarbon receptor) pathway.
ACOX1 (acyl-CoA oxidase 1; GenBank accession numbers: NM_001185039, NM_004035, NM_007292), ALOX5AP (arachidonate 5-lipoxygenase activating protein; NM_001204406, NM_001629), ANKRD1 (ankyrin repeat domain 1; NM_01 4391), BAAT (bile acid -CoA:amino acid N-acyltransferase; NM_001127610, NM_001701), BHLHE40 (basic helix-loop-helix family member e40; NM_003670), CCL2 (CC motif chemokine ligand 2; NM_002982), CYP1A1 (cytochrome P450 family 1 subfamily A member 1) ; NM_000499, NM_001319216, NM_001319217), DNAJC7 (DnaJ heat shock protein family (Hsp40) member C7; NM_001144766, NM_003315), ESR1 (estrogen receptor 1; NM_000125, NM_001122740, NM_0 01122741, NM_001122742, NM_001291230, NM_001291241, NM_001328100), GCLC(glutamate -cysteine ligase catalytic subunit; NM_001197115, NM_001498), GPAM (glycerol-3-phosphate acyltransferase, mitochondrial; NM_001244949, NM_020918), GSTM1 (glutathione S-transferase mu 1; NM_000561, NM_146421), HES 1(hes family bHLH transcription factor 1; NM_005524), HSPA1A (heat shock protein family A (Hsp70) member 1A; NM_005345), IL17B (interleukin 17B; NM_001317987, NM_014443), JUND (JunD proto-oncogene, AP-1 transcription factor subunit; NM_001286968, NM_005354), MFGE8 (milk fat globule-EGF factor 8 protein; NM_00111 4614, NM_001310319, NM_001310320, NM_001310321 , NM_005928), NAV3 (neuron navigator 3; NM_001024383, NM_014903), NCOA6 (nuclear receptor coactivator 6; NM_001242539, NM_001318240, NM_014071), NQO1 (NAD(P)H quinone dehydrogenase 1; NM _000903, NM_001025433, NM_001025434, NM_001286137), PLTP (phospholipid transfer protein; NM_001242920, NM_001242921, NM_006227, NM_182676), PMP2 (peripheral myelin protein 2; NM_001348381, NM_002677), PRDX1 (peroxiredoxin 1; NM_001202431, NM_ 002574, NM_181696, NM_181697), PTGES3 (prostaglandin E synthase 3; NM_001282601, NM_001282602 , NM_001282603, NM_001282604, NM_001282605, NM_006601), PTGR1 (prostaglandin reductase 1; NM_001146108, NM_001146109, NM_012212), PTPA (protein phosphatase 2 phosphatase activator; NM_001193397, NM_001271832, NM_021131, NM_178000, NM_178001, NM_178002, NM_178003), RXRA (retinoid receptor alpha; NM_001291920, NM_001291921, NM_002957), S100P (S100 calcium binding protein P; NM_005980), SEC14L1 (SEC14 like lipid binding 1; NM_001039573, NM_001143998, NM_001143999, NM_0 01144001, NM_001204408, NM_001204410, NM_003003), SERPINB2 (serpin family B member 2; NM_001143818 , NM_002575), SLC2A12(solute carrier family 2 member 12; NM_145176), SLC2A13(solute carrier family 2 member 13; NM_052885), SLC39A12(solute carrier family 39 member 12; NM_001145195, NM_001282733, NM_00 1282734, NM_152725), SLC39A3 (solute carrier family 39 member 3; NM_144564, NM_213568), SLC39A7 (solute carrier family 39 member 7; NM_001077516, NM_001288777, NM_006979), SLC5A4 (solute carrier family 5 member 4; NM_014227), SLC6A18 (solute carrier family) 6 member 18; NM_182632), SLC6A7 (Solute Carrier Family 6 Member 7; NM_014228), SLC7A11 (Solute Carrier Family 7 MEMBER 11; NM_014331), SLC7A5 (Solute Carrier Family 7 ME_00 3486), TGFBR2 (Transforming Growth Factor Beta Receptor 2; NM_001024847, NM_003242), TNF (tumor necrosis factor; A composition for predicting responsiveness to Erastin, comprising an agent for measuring the mRNA level of the genes of (NM_000594) and TNS4 (tensin 4; NM_032865), or the protein encoded by the genes.
상기 유전자의 mRNA 수준을 측정하는 제제는 유전자의 mRNA에 상보적으로 결합하는 센스 및 안티센스 프라이머, 또는 프로브인 것을 특징으로 하는, 조성물.
According to paragraph 4,
A composition, characterized in that the agent for measuring the mRNA level of the gene is sense and antisense primers, or probes that bind complementary to the mRNA of the gene.
상기 단백질 수준을 측정하는 제제는 상기 유전자가 암호화하는 단백질에 특이적으로 결합하는 항체인 것을 특징으로 하는, 조성물.
According to paragraph 4,
A composition, characterized in that the agent for measuring the protein level is an antibody that specifically binds to the protein encoded by the gene.
상기 GSTM1, HSPA1A, PRDX1, PTGR1, RXRA, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, TGFBR2, GCLC 및 NQO1은 NRF2(Nuclear factor erythroid 2-related factor 2) 경로에 관련된 유전자인 것을 특징으로 하는, 조성물.
According to paragraph 4,
The GSTM1, HSPA1A, PRDX1, PTGR1, RXRA, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, TGFBR2, GCLC and NQO1 are involved in the NRF2 (Nuclear factor erythroid 2-related factor 2) pathway. genes involved A composition characterized in that:
상기 CYP1A1, ESR1, HES1, IL17B, JUND, PTGES3, SERPINB2, SLC7A5, TNF, GCLC 및 NQO1은 AhR(Aryl hydrocarbon receptor) 경로에 관련된 유전자인 것을 특징으로 하는, 조성물.
According to paragraph 4,
The composition, wherein CYP1A1, ESR1, HES1, IL17B, JUND, PTGES3, SERPINB2, SLC7A5, TNF, GCLC and NQO1 are genes related to the AhR (Aryl hydrocarbon receptor) pathway.
A kit for predicting reactivity to Erastin, comprising the composition of claim 4.
In biological samples from cancer patients, ACOX1 (acyl-CoA oxidase 1; GenBank accession numbers: NM_001185039, NM_004035, NM_007292), ALOX5AP (arachidonate 5-lipoxygenase activating protein; NM_001204406, NM_001629), and ANKRD1 (ankyrin repeat domain) 1; NM_014391), BAAT (bile acid-CoA:amino acid N-acyltransferase; NM_001127610, NM_001701), BHLHE40 (basic helix-loop-helix family member e40; NM_003670), CCL2 (CC motif chemokine ligand 2; NM_002982), CYP1A1 (cytochrome P450 family 1 subfamily A member 1; NM_000499, NM_001319216, NM_001319217), DNAJC7 (DnaJ heat shock protein family (Hsp40) member C7; NM_001144766, NM_003315), ESR1 (estrogen receptor 1; NM_000125, NM_0 01122740, NM_001122741, NM_001122742, NM_001291230, NM_001291241 , NM_001328100), GCLC (glutamate-cysteine ligase catalytic subunit; NM_001197115, NM_001498), GPAM (glycerol-3-phosphate acyltransferase, mitochondrial; NM_001244949, NM_020918), GSTM1 (glutathione S-transferase mu 1; NM_000561, NM_146421), HES1 ( hes family bHLH transcription factor 1; NM_005524), HSPA1A (heat shock protein family A (Hsp70) member 1A; NM_005345), IL17B (interleukin 17B; NM_001317987, NM_014443), JUND (JunD proto-oncogene, AP-1 transcription factor subunit; NM_001286968, NM_005354), MFGE8 (milk fat globule-EGF factor 8 protein; NM_00111 4614, NM_001310319, NM_001310320, NM_001310321 , NM_005928), NAV3 (neuron navigator 3; NM_001024383, NM_014903), NCOA6 (nuclear receptor coactivator 6; NM_001242539, NM_001318240, NM_014071), NQO1 (NAD(P)H quinone dehydrogenase 1; NM _000903, NM_001025433, NM_001025434, NM_001286137), PLTP (phospholipid transfer protein; NM_001242920, NM_001242921, NM_006227, NM_182676), PMP2 (peripheral myelin protein 2; NM_001348381, NM_002677), PRDX1 (peroxiredoxin 1; NM_001202431, NM_ 002574, NM_181696, NM_181697), PTGES3 (prostaglandin E synthase 3; NM_001282601, NM_001282602 , NM_001282603, NM_001282604, NM_001282605, NM_006601), PTGR1 (prostaglandin reductase 1; NM_001146108, NM_001146109, NM_012212), PTPA (protein phosphatase 2 phosphatase activator; NM_001193397, NM_001271832, NM_021131, NM_178000, NM_178001, NM_178002, NM_178003), RXRA (retinoid receptor alpha; NM_001291920, NM_001291921, NM_002957), S100P (S100 calcium binding protein P; NM_005980), SEC14L1 (SEC14 like lipid binding 1; NM_001039573, NM_001143998, NM_001143999, NM_0 01144001, NM_001204408, NM_001204410, NM_003003), SERPINB2 (serpin family B member 2; NM_001143818 , NM_002575), SLC2A12(solute carrier family 2 member 12; NM_145176), SLC2A13(solute carrier family 2 member 13; NM_052885), SLC39A12(solute carrier family 39 member 12; NM_001145195, NM_001282733, NM_00 1282734, NM_152725), SLC39A3 (solute carrier family 39 member 3; NM_144564, NM_213568), SLC39A7 (solute carrier family 39 member 7; NM_001077516, NM_001288777, NM_006979), SLC5A4 (solute carrier family 5 member 4; NM_014227), SLC6A18 (solute carrier family) 6 member 18; NM_182632), SLC6A7 (Solute Carrier Family 6 Member 7; NM_014228), SLC7A11 (Solute Carrier Family 7 MEMBER 11; NM_014331), SLC7A5 (Solute Carrier Family 7 ME_00 3486), TGFBR2 (Transforming Growth Factor Beta Receptor 2; NM_001024847, NM_003242), TNF (tumor necrosis factor; NM_000594) and TNS4 (tensin 4; NM_032865) A method of providing information for predicting responsiveness to Erastin, comprising measuring the mRNA expression level of the genes or the protein encoded by the genes.
상기 GSTM1, HSPA1A, PRDX1, PTGR1, RXRA, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, TGFBR2, GCLC 및 NQO1은 NRF2(Nuclear factor erythroid 2-related factor 2) 경로에 관련된 유전자인 것을 특징으로 하는, 정보제공방법.
According to clause 10,
The GSTM1, HSPA1A, PRDX1, PTGR1, RXRA, SLC2A12, SLC2A13, SLC39A12, SLC39A3, SLC39A7, SLC5A4, SLC6A18, SLC6A7, SLC7A11, TGFBR2, GCLC and NQO1 are involved in the NRF2 (Nuclear factor erythroid 2-related factor 2) pathway. genes involved An information provision method, characterized in that:
상기 CYP1A1, ESR1, HES1, IL17B, JUND, PTGES3, SERPINB2, SLC7A5, TNF, GCLC 및 NQO1은 AhR(Aryl hydrocarbon receptor) 경로에 관련된 유전자인 것을 특징으로 하는, 정보제공방법.
According to clause 10,
A method of providing information, wherein CYP1A1, ESR1, HES1, IL17B, JUND, PTGES3, SERPINB2, SLC7A5, TNF, GCLC and NQO1 are genes related to the AhR (Aryl hydrocarbon receptor) pathway.
상기 mRNA의 발현수준은 in situ 교잡법(in situ hybridization), 중합효소연쇄반응(PCR), 역전사 중합효소연쇄반응(RT-PCR), 실시간 중합효소연쇄반응(Real-time PCR), RNase 보호 분석법(RNase protection assay; RPA), 마이크로어레이(microarray), 및 노던 블롯팅(northern blotting)으로 이루어진 군으로부터 선택되는 1종 이상의 방법을 통해 측정되는 것을 특징으로 하는, 정보제공방법.
According to clause 10,
The expression level of the mRNA was determined by in situ hybridization, polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), real-time polymerase chain reaction (Real-time PCR), and RNase protection assay. A method of providing information, characterized in that it is measured through one or more methods selected from the group consisting of (RNase protection assay (RPA)), microarray, and northern blotting.
상기 단백질의 발현수준은 웨스턴 블롯팅(western blotting), 방사선면역분석법(radioimmunoassay; RIA), 방사 면역 확산법(radioimmunodiffusion), 효소면역분석법(ELISA), 면역침강법(immunoprecipitation), 유세포분석법(flow cytometry), 면역형광염색법(immunofluorescence), 오우크테로니(ouchterlony), 보체 고정 분석법(complement fixation assay), 및 단백질 칩(protein chip)으로 이루어진 군으로부터 선택되는 1종 이상의 방법을 통해 측정되는 것을 특징으로 하는, 정보제공방법.According to clause 10,
The expression level of the protein was determined by Western blotting, radioimmunoassay (RIA), radioimmunodiffusion, enzyme-linked immunosorbent assay (ELISA), immunoprecipitation, and flow cytometry. , characterized in that it is measured through one or more methods selected from the group consisting of immunofluorescence, ouchterlony, complement fixation assay, and protein chip. , Method of providing information.
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