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Huang X, Ren Q, Yang L, Cui D, Ma C, Zheng Y, Wu J. Immunogenic chemotherapy: great potential for improving response rates. Front Oncol 2023; 13:1308681. [PMID: 38125944 PMCID: PMC10732354 DOI: 10.3389/fonc.2023.1308681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/15/2023] [Indexed: 12/23/2023] Open
Abstract
The activation of anti-tumor immunity is critical in treating cancers. Recent studies indicate that several chemotherapy agents can stimulate anti-tumor immunity by inducing immunogenic cell death and durably eradicate tumors. This suggests that immunogenic chemotherapy holds great potential for improving response rates. However, chemotherapy in practice has only had limited success in inducing long-term survival or cure of cancers when used either alone or in combination with immunotherapy. We think that this is because the importance of dose, schedule, and tumor model dependence of chemotherapy-activated anti-tumor immunity is under-appreciated. Here, we review immune modulation function of representative chemotherapy agents and propose a model of immunogenic chemotherapy-induced long-lasting responses that rely on synergetic interaction between killing tumor cells and inducing anti-tumor immunity. We comb through several chemotherapy treatment schedules, and identify the needs for chemotherapy dose and schedule optimization and combination therapy with immunotherapy when chemotherapy dosage or immune responsiveness is too low. We further review tumor cell intrinsic factors that affect the optimal chemotherapy dose and schedule. Lastly, we review the biomarkers indicating responsiveness to chemotherapy and/or immunotherapy treatments. A deep understanding of how chemotherapy activates anti-tumor immunity and how to monitor its responsiveness can lead to the development of more effective chemotherapy or chemo-immunotherapy, thereby improving the efficacy of cancer treatment.
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Affiliation(s)
- Xiaojun Huang
- Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Qinghuan Ren
- Alberta Institute, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Leixiang Yang
- Cancer Center, The Key Laboratory of Tumor Molecular Diagnosis and Individualized Medicine of Zhejiang Province, Center for Reproductive Medicine, Department of Genetic and Genomic Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Di Cui
- Cancer Center, The Key Laboratory of Tumor Molecular Diagnosis and Individualized Medicine of Zhejiang Province, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Chenyang Ma
- Department of Internal Medicine of Traditional Chinese Medicine, The Second People’s Hospital of Xiaoshan District, Hangzhou, Zhejiang, China
| | - Yueliang Zheng
- Cancer Center, Emergency and Critical Care Center, Department of Emergency Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Junjie Wu
- Cancer Center, The Key Laboratory of Tumor Molecular Diagnosis and Individualized Medicine of Zhejiang Province, Center for Reproductive Medicine, Department of Genetic and Genomic Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
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Smieja J. Mathematical Modeling Support for Lung Cancer Therapy-A Short Review. Int J Mol Sci 2023; 24:14516. [PMID: 37833963 PMCID: PMC10572824 DOI: 10.3390/ijms241914516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/01/2023] [Accepted: 09/12/2023] [Indexed: 10/15/2023] Open
Abstract
The paper presents a review of models that can be used to describe dynamics of lung cancer growth and its response to treatment at both cell population and intracellular processes levels. To address the latter, models of signaling pathways associated with cellular responses to treatment are overviewed. First, treatment options for lung cancer are discussed, and main signaling pathways and regulatory networks are briefly reviewed. Then, approaches used to model specific therapies are discussed. Following that, models of intracellular processes that are crucial in responses to therapies are presented. The paper is concluded with a discussion of the applicability of the presented approaches in the context of lung cancer.
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Affiliation(s)
- Jaroslaw Smieja
- Department of Systems Biology and Engineering, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
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