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Kaneko S, Takasawa K, Asada K, Shiraishi K, Ikawa N, Machino H, Shinkai N, Matsuda M, Masuda M, Adachi S, Takahashi S, Kobayashi K, Kouno N, Bolatkan A, Komatsu M, Yamada M, Miyake M, Watanabe H, Tateishi A, Mizuno T, Okubo Y, Mukai M, Yoshida T, Yoshida Y, Horinouchi H, Watanabe SI, Ohe Y, Yatabe Y, Saloura V, Kohno T, Hamamoto R. Mechanism of ERBB2 gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations. Mol Cancer 2024; 23:126. [PMID: 38862995 PMCID: PMC11165761 DOI: 10.1186/s12943-024-02035-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 05/30/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND In an extensive genomic analysis of lung adenocarcinomas (LUADs), driver mutations have been recognized as potential targets for molecular therapy. However, there remain cases where target genes are not identified. Super-enhancers and structural variants are frequently identified in several hundred loci per case. Despite this, most cancer research has approached the analysis of these data sets separately, without merging and comparing the data, and there are no examples of integrated analysis in LUAD. METHODS We performed an integrated analysis of super-enhancers and structural variants in a cohort of 174 LUAD cases that lacked clinically actionable genetic alterations. To achieve this, we conducted both WGS and H3K27Ac ChIP-seq analyses using samples with driver gene mutations and those without, allowing for a comprehensive investigation of the potential roles of super-enhancer in LUAD cases. RESULTS We demonstrate that most genes situated in these overlapped regions were associated with known and previously unknown driver genes and aberrant expression resulting from the formation of super-enhancers accompanied by genomic structural abnormalities. Hi-C and long-read sequencing data further corroborated this insight. When we employed CRISPR-Cas9 to induce structural abnormalities that mimicked cases with outlier ERBB2 gene expression, we observed an elevation in ERBB2 expression. These abnormalities are associated with a higher risk of recurrence after surgery, irrespective of the presence or absence of driver mutations. CONCLUSIONS Our findings suggest that aberrant gene expression linked to structural polymorphisms can significantly impact personalized cancer treatment by facilitating the identification of driver mutations and prognostic factors, contributing to a more comprehensive understanding of LUAD pathogenesis.
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Affiliation(s)
- Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan.
| | - Ken Takasawa
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Ken Asada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Kouya Shiraishi
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Noriko Ikawa
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
| | - Hidenori Machino
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Norio Shinkai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Maiko Matsuda
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Mari Masuda
- Department of Proteomics, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Shungo Adachi
- Department of Proteomics, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Satoshi Takahashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Nobuji Kouno
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Amina Bolatkan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Masaaki Komatsu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Mototaka Miyake
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Hirokazu Watanabe
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Akiko Tateishi
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Takaaki Mizuno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
- Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yu Okubo
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Masami Mukai
- Division of Medical Informatics, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Tatsuya Yoshida
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yukihiro Yoshida
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Hidehito Horinouchi
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Shun-Ichi Watanabe
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yuichiro Ohe
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yasushi Yatabe
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Vassiliki Saloura
- Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Takashi Kohno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan.
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Liu Y, Wu W, Cai C, Zhang H, Shen H, Han Y. Patient-derived xenograft models in cancer therapy: technologies and applications. Signal Transduct Target Ther 2023; 8:160. [PMID: 37045827 PMCID: PMC10097874 DOI: 10.1038/s41392-023-01419-2] [Citation(s) in RCA: 55] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/21/2023] [Indexed: 04/14/2023] Open
Abstract
Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.
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Affiliation(s)
- Yihan Liu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Wantao Wu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Changjing Cai
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Hao Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Hong Shen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China.
| | - Ying Han
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China.
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Ryu S, Hayashi Y, Yagishita S, Takahashi A, Yokoi A, Ohuchi M, Hamada A. Development of an analytical method to determine E7130 concentration in mouse plasma by micro-sampling using ultra-performance liquid chromatography-high resolution mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 2022; 1207:123366. [PMID: 35853391 DOI: 10.1016/j.jchromb.2022.123366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/16/2022] [Accepted: 07/10/2022] [Indexed: 11/19/2022]
Abstract
E7130 is a novel microtubule inhibitor and a promising tumor microenvironment ameliorator. Since the amount of the administration in preclinical study is very small due to the high potency of E7130, this study aimed to establish a sensitive analytical method to measure E7130 concentration in mouse plasma samples obtained via microsampling. A sensitive and validated method was developed based on ultra-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS). Chromatographic separation was achieved using a Waters ACQUITY UPLC BEH C18 1.7 µm (2.1 × 50 mm) column. Mobile phase A comprised 0.1% formic acid and 10 mM ammonium formate in water, and mobile phase B was methanol. A gradient elution was applied at a flow rate of 0.5 mL/min. The calibration curve drawn was linear in the 0.2-100 ng/mL E7130 concentration range for mouse plasma microsamples (10 µL). Analytical results demonstrated good precision (<6.7%) and accuracy (88.5%-100.0%) in E7130 quantitation, indicating that UHPLC-HRMS is a useful method for pharmacokinetic analysis and a valuable approach for the quantitation of hardly fragmented compounds.
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Affiliation(s)
- Shoraku Ryu
- Department of Pharmacology and Therapeutics, Fundamental Innovative Oncology Core, National Cancer Center Research Institute, Tokyo, Japan
| | - Yoshiharu Hayashi
- Division of Molecular Pharmacology, National Cancer Center Research Institute, Tokyo, Japan
| | - Shigehiro Yagishita
- Division of Molecular Pharmacology, National Cancer Center Research Institute, Tokyo, Japan
| | - Ako Takahashi
- Division of Molecular Pharmacology, National Cancer Center Research Institute, Tokyo, Japan
| | - Akira Yokoi
- Medicine Creation, Oncology Business Group, Eisai Co., Ltd., 1-3, Tokodai 5, Tsukuba, Ibaraki 300-2635, Japan
| | - Mayu Ohuchi
- Department of Pharmacology and Therapeutics, Fundamental Innovative Oncology Core, National Cancer Center Research Institute, Tokyo, Japan
| | - Akinobu Hamada
- Department of Pharmacology and Therapeutics, Fundamental Innovative Oncology Core, National Cancer Center Research Institute, Tokyo, Japan; Division of Molecular Pharmacology, National Cancer Center Research Institute, Tokyo, Japan.
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