Phosphoproteomics Enables Molecular Subtyping and Nomination of Kinase Candidates for Individual Patients of Diffuse-Type Gastric Cancer
The diffuse-type gastric cancer (DGC) constitutes a subgroup of gastric cancer with poor prognosis and no effective molecular therapies. Here, we report a phosphoproteomic landscape of DGC derived from 83 tumors together with their nearby tissues. Based on phosphorylation, DGC could be classified in...
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| Vydáno v: | iScience Ročník 22; s. 44 - 57 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Elsevier Inc
20.12.2019
Elsevier |
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| ISSN: | 2589-0042, 2589-0042 |
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| Abstract | The diffuse-type gastric cancer (DGC) constitutes a subgroup of gastric cancer with poor prognosis and no effective molecular therapies. Here, we report a phosphoproteomic landscape of DGC derived from 83 tumors together with their nearby tissues. Based on phosphorylation, DGC could be classified into three molecular subtypes with distinct overall survival (OS) and chemosensitivity. We identified 16 kinases whose activities were associated with poor OS. These activated kinases covered several cancer hallmark pathways, with the MTOR signaling network being the most frequently activated. We proposed a patient-specific strategy based on the hierarchy of clinically actionable kinases for prioritization of kinases for further clinical evaluation. Our global data analysis indicates that in addition to finding activated kinase pathways in DGC, large-scale phosphoproteomics could be used to classify DGCs into subtypes that are associated with distinct clinical outcomes as well as nomination of kinase targets that may be inhibited for cancer treatments.
[Display omitted]
•A phosphoproteomic landscape of diffuse-type gastric cancer (DGC) was depicted•DGC could be classified into three subtypes based on phosphorylation data•A bioinformatics workflow was used to identify 16 kinases as potential drug targets•A patient-specific strategy for nomination of kinases was proposed
Biological Sciences; Cancer Systems Biology; Proteomics; Systems Biology |
|---|---|
| AbstractList | The diffuse-type gastric cancer (DGC) constitutes a subgroup of gastric cancer with poor prognosis and no effective molecular therapies. Here, we report a phosphoproteomic landscape of DGC derived from 83 tumors together with their nearby tissues. Based on phosphorylation, DGC could be classified into three molecular subtypes with distinct overall survival (OS) and chemosensitivity. We identified 16 kinases whose activities were associated with poor OS. These activated kinases covered several cancer hallmark pathways, with the MTOR signaling network being the most frequently activated. We proposed a patient-specific strategy based on the hierarchy of clinically actionable kinases for prioritization of kinases for further clinical evaluation. Our global data analysis indicates that in addition to finding activated kinase pathways in DGC, large-scale phosphoproteomics could be used to classify DGCs into subtypes that are associated with distinct clinical outcomes as well as nomination of kinase targets that may be inhibited for cancer treatments. : Biological Sciences; Cancer Systems Biology; Proteomics; Systems Biology Subject Areas: Biological Sciences, Cancer Systems Biology, Proteomics, Systems Biology The diffuse-type gastric cancer (DGC) constitutes a subgroup of gastric cancer with poor prognosis and no effective molecular therapies. Here, we report a phosphoproteomic landscape of DGC derived from 83 tumors together with their nearby tissues. Based on phosphorylation, DGC could be classified into three molecular subtypes with distinct overall survival (OS) and chemosensitivity. We identified 16 kinases whose activities were associated with poor OS. These activated kinases covered several cancer hallmark pathways, with the MTOR signaling network being the most frequently activated. We proposed a patient-specific strategy based on the hierarchy of clinically actionable kinases for prioritization of kinases for further clinical evaluation. Our global data analysis indicates that in addition to finding activated kinase pathways in DGC, large-scale phosphoproteomics could be used to classify DGCs into subtypes that are associated with distinct clinical outcomes as well as nomination of kinase targets that may be inhibited for cancer treatments. The diffuse-type gastric cancer (DGC) constitutes a subgroup of gastric cancer with poor prognosis and no effective molecular therapies. Here, we report a phosphoproteomic landscape of DGC derived from 83 tumors together with their nearby tissues. Based on phosphorylation, DGC could be classified into three molecular subtypes with distinct overall survival (OS) and chemosensitivity. We identified 16 kinases whose activities were associated with poor OS. These activated kinases covered several cancer hallmark pathways, with the MTOR signaling network being the most frequently activated. We proposed a patient-specific strategy based on the hierarchy of clinically actionable kinases for prioritization of kinases for further clinical evaluation. Our global data analysis indicates that in addition to finding activated kinase pathways in DGC, large-scale phosphoproteomics could be used to classify DGCs into subtypes that are associated with distinct clinical outcomes as well as nomination of kinase targets that may be inhibited for cancer treatments.The diffuse-type gastric cancer (DGC) constitutes a subgroup of gastric cancer with poor prognosis and no effective molecular therapies. Here, we report a phosphoproteomic landscape of DGC derived from 83 tumors together with their nearby tissues. Based on phosphorylation, DGC could be classified into three molecular subtypes with distinct overall survival (OS) and chemosensitivity. We identified 16 kinases whose activities were associated with poor OS. These activated kinases covered several cancer hallmark pathways, with the MTOR signaling network being the most frequently activated. We proposed a patient-specific strategy based on the hierarchy of clinically actionable kinases for prioritization of kinases for further clinical evaluation. Our global data analysis indicates that in addition to finding activated kinase pathways in DGC, large-scale phosphoproteomics could be used to classify DGCs into subtypes that are associated with distinct clinical outcomes as well as nomination of kinase targets that may be inhibited for cancer treatments. The diffuse-type gastric cancer (DGC) constitutes a subgroup of gastric cancer with poor prognosis and no effective molecular therapies. Here, we report a phosphoproteomic landscape of DGC derived from 83 tumors together with their nearby tissues. Based on phosphorylation, DGC could be classified into three molecular subtypes with distinct overall survival (OS) and chemosensitivity. We identified 16 kinases whose activities were associated with poor OS. These activated kinases covered several cancer hallmark pathways, with the MTOR signaling network being the most frequently activated. We proposed a patient-specific strategy based on the hierarchy of clinically actionable kinases for prioritization of kinases for further clinical evaluation. Our global data analysis indicates that in addition to finding activated kinase pathways in DGC, large-scale phosphoproteomics could be used to classify DGCs into subtypes that are associated with distinct clinical outcomes as well as nomination of kinase targets that may be inhibited for cancer treatments. • A phosphoproteomic landscape of diffuse-type gastric cancer (DGC) was depicted • DGC could be classified into three subtypes based on phosphorylation data • A bioinformatics workflow was used to identify 16 kinases as potential drug targets • A patient-specific strategy for nomination of kinases was proposed Biological Sciences; Cancer Systems Biology; Proteomics; Systems Biology The diffuse-type gastric cancer (DGC) constitutes a subgroup of gastric cancer with poor prognosis and no effective molecular therapies. Here, we report a phosphoproteomic landscape of DGC derived from 83 tumors together with their nearby tissues. Based on phosphorylation, DGC could be classified into three molecular subtypes with distinct overall survival (OS) and chemosensitivity. We identified 16 kinases whose activities were associated with poor OS. These activated kinases covered several cancer hallmark pathways, with the MTOR signaling network being the most frequently activated. We proposed a patient-specific strategy based on the hierarchy of clinically actionable kinases for prioritization of kinases for further clinical evaluation. Our global data analysis indicates that in addition to finding activated kinase pathways in DGC, large-scale phosphoproteomics could be used to classify DGCs into subtypes that are associated with distinct clinical outcomes as well as nomination of kinase targets that may be inhibited for cancer treatments. [Display omitted] •A phosphoproteomic landscape of diffuse-type gastric cancer (DGC) was depicted•DGC could be classified into three subtypes based on phosphorylation data•A bioinformatics workflow was used to identify 16 kinases as potential drug targets•A patient-specific strategy for nomination of kinases was proposed Biological Sciences; Cancer Systems Biology; Proteomics; Systems Biology |
| Author | He, Fuchu Tong, Mengsha Yu, Chunyu Ge, Sai Shi, Tieliu Zhu, Weimin Qin, Jun Ji, Jiafu Ding, Chen Shen, Lin Wang, Yi Li, Tingting Shi, Jinwen Liu, Mingwei Song, Lei Gao, Jing Xia, Xia Huang, Wenwen Zhan, Dongdong Liu, Wanlin Feng, Jinwen Shi, Wenhao |
| AuthorAffiliation | 2 Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China 1 State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China 7 Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China 6 Center for Bioinformatics, East China Normal University, Shanghai 200241, China 3 Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China 4 State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Fudan University, Shanghai 200433, China 5 |
| AuthorAffiliation_xml | – name: 5 State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, China – name: 2 Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China – name: 4 State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Fudan University, Shanghai 200433, China – name: 6 Center for Bioinformatics, East China Normal University, Shanghai 200241, China – name: 1 State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China – name: 7 Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China – name: 3 Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China |
| Author_xml | – sequence: 1 givenname: Mengsha surname: Tong fullname: Tong, Mengsha organization: State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China – sequence: 2 givenname: Chunyu surname: Yu fullname: Yu, Chunyu organization: Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China – sequence: 3 givenname: Jinwen surname: Shi fullname: Shi, Jinwen organization: State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China – sequence: 4 givenname: Wenwen surname: Huang fullname: Huang, Wenwen organization: Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China – sequence: 5 givenname: Sai surname: Ge fullname: Ge, Sai organization: Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China – sequence: 6 givenname: Mingwei surname: Liu fullname: Liu, Mingwei organization: State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China – sequence: 7 givenname: Lei surname: Song fullname: Song, Lei organization: State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China – sequence: 8 givenname: Dongdong surname: Zhan fullname: Zhan, Dongdong organization: Center for Bioinformatics, East China Normal University, Shanghai 200241, China – sequence: 9 givenname: Xia surname: Xia fullname: Xia, Xia organization: State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China – sequence: 10 givenname: Wanlin surname: Liu fullname: Liu, Wanlin organization: State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China – sequence: 11 givenname: Jinwen surname: Feng fullname: Feng, Jinwen organization: Center for Bioinformatics, East China Normal University, Shanghai 200241, China – sequence: 12 givenname: Wenhao surname: Shi fullname: Shi, Wenhao organization: State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China – sequence: 13 givenname: Jiafu surname: Ji fullname: Ji, Jiafu organization: Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China – sequence: 14 givenname: Jing surname: Gao fullname: Gao, Jing organization: Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China – sequence: 15 givenname: Tieliu surname: Shi fullname: Shi, Tieliu organization: Center for Bioinformatics, East China Normal University, Shanghai 200241, China – sequence: 16 givenname: Weimin surname: Zhu fullname: Zhu, Weimin organization: State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China – sequence: 17 givenname: Chen surname: Ding fullname: Ding, Chen organization: State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China – sequence: 18 givenname: Yi surname: Wang fullname: Wang, Yi organization: State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China – sequence: 19 givenname: Fuchu surname: He fullname: He, Fuchu email: hefc@nic.bmi.ac.cn organization: State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China – sequence: 20 givenname: Lin surname: Shen fullname: Shen, Lin email: linshenpku@163.com organization: Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China – sequence: 21 givenname: Tingting surname: Li fullname: Li, Tingting email: litt@hsc.pku.edu.cn organization: Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China – sequence: 22 givenname: Jun surname: Qin fullname: Qin, Jun email: jqin1965@126.com organization: State Key Laboratory of Proteomics, Joint Laboratory of Gastrointestinal Oncology, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Institute of Lifeomics, Beijing 102206, China |
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| Title | Phosphoproteomics Enables Molecular Subtyping and Nomination of Kinase Candidates for Individual Patients of Diffuse-Type Gastric Cancer |
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