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Wang Y, Li Y, Sheng Z, Deng W, Yuan H, Wang S, Liu Y. Advances of Patient-Derived Organoids in Personalized Radiotherapy. Front Oncol 2022; 12:888416. [PMID: 35574360 PMCID: PMC9102799 DOI: 10.3389/fonc.2022.888416] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 03/28/2022] [Indexed: 11/16/2022] Open
Abstract
Patient-derived organoids (PDO), based on the advanced three-dimensional (3D) culture technology, can provide more relevant physiological and pathological cancer models, which is especially beneficial for developing and optimizing cancer therapeutic strategies. Radiotherapy (RT) is a cornerstone of curative and palliative cancer treatment, which can be performed alone or integrated with surgery, chemotherapy, immunotherapy, or targeted therapy in clinical care. Among all cancer therapies, RT has great local control, safety and effectiveness, and is also cost-effective per life-year gained for patients. It has been reported that combing RT with chemotherapy or immunotherapy or radiosensitizer drugs may enhance treatment efficacy at faster rates and lower cost. However, very few FDA-approved combinations of RT with drugs or radiosensitizers exist due to the lack of accurate and relevant preclinical models. Meanwhile, radiation dose escalation may increase treatment efficacy and induce more toxicity of normal tissue as well, which has been studied by conducting various clinical trials, very expensive and time-consuming, often burdensome on patients and sometimes with controversial results. The surged PDO technology may help with the preclinical test of RT combination and radiation dose escalation to promote precision radiation oncology, where PDO can recapitulate individual patient’ tumor heterogeneity, retain characteristics of the original tumor, and predict treatment response. This review aims to introduce recent advances in the PDO technology and personalized radiotherapy, highlight the strengths and weaknesses of the PDO cancer models, and finally examine the existing RT-related PDO trials or applications to harness personalized and precision radiotherapy.
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Affiliation(s)
- Yuenan Wang
- Department of Radiation Oncology, Peking University Shenzhen Hospital, Shenzhen, China
- *Correspondence: Yuenan Wang, ; Yajie Liu, ; Shubin Wang,
| | - Ye Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zonghai Sheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Weiwei Deng
- Department of Mechanical and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Hongyan Yuan
- Department of Mechanical and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Shubin Wang
- Department of Medical Oncology, Peking University Shenzhen Hospital, Shenzhen, China
- *Correspondence: Yuenan Wang, ; Yajie Liu, ; Shubin Wang,
| | - Yajie Liu
- Department of Radiation Oncology, Peking University Shenzhen Hospital, Shenzhen, China
- *Correspondence: Yuenan Wang, ; Yajie Liu, ; Shubin Wang,
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Singh J, Hatcher S, Ku AA, Ding Z, Feng FY, Sharma RA, Pfister SX. Model Selection for the Preclinical Development of New Drug-Radiotherapy Combinations. Clin Oncol (R Coll Radiol) 2021; 33:694-704. [PMID: 34474951 DOI: 10.1016/j.clon.2021.08.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 07/13/2021] [Accepted: 08/11/2021] [Indexed: 12/12/2022]
Abstract
Radiotherapy plays an essential role in the treatment of more than half of all patients with cancer. In recent decades, advances in devices that deliver radiation and the development of treatment planning software have helped radiotherapy attain precise tumour targeting with minimal toxicity to surrounding tissues. Simultaneously, as more targeted drug therapies are being brought into the market, there has been significant interest in improving cure rates for cancer by adding drugs to radiotherapy to widen the therapeutic window, the difference between normal tissue toxicity and treatment efficacy. The development of new combination therapies will require judicious adaptation of preclinical models that are routinely used for traditional drug discovery. Here we highlight the strengths and weaknesses of each of these preclinical models and discuss how they can be used optimally to identify new and clinically beneficial drug-radiotherapy combinations.
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Affiliation(s)
- J Singh
- Global Translational Science, Varian, a Siemens Healthineers company, Palo Alto, California, USA
| | - S Hatcher
- Global Translational Science, Varian, a Siemens Healthineers company, Palo Alto, California, USA
| | - A A Ku
- Global Translational Science, Varian, a Siemens Healthineers company, Palo Alto, California, USA
| | - Z Ding
- Global Translational Science, Varian, a Siemens Healthineers company, Palo Alto, California, USA
| | - F Y Feng
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA; Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco, California, USA; Department of Radiation Oncology, University of California, San Francisco, California, USA; Department of Urology, University of California, San Francisco, California, USA
| | - R A Sharma
- Global Translational Science, Varian, a Siemens Healthineers company, Palo Alto, California, USA; UCL Cancer Institute, University College London, London, UK
| | - S X Pfister
- Global Translational Science, Varian, a Siemens Healthineers company, Palo Alto, California, USA.
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Zhao Z, He K, Zhang Y, Hua X, Feng M, Zhao Z, Sun Y, Jiang Y, Xia Q. XRCC2 repairs mitochondrial DNA damage and fuels malignant behavior in hepatocellular carcinoma. Cancer Lett 2021; 512:1-14. [PMID: 33964350 DOI: 10.1016/j.canlet.2021.04.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 01/03/2023]
Abstract
The effects of DNA damage repair (DDR) and mitochondrial dysfunction associated with HCC have been investigated, but the functional role of mitochondrial DDR in HCC remains elusive. We studied the DDR genes and identified XRCC2 as a potential prognostic marker for HCC. XRCC2 overexpression was detected in HCC cells and shown to promote the malignant behavior of cancer cells. XRCC2 depletion in HCC cells led to DNA damage accumulation at the replication site in the nucleus. Additionally, XRCC2-depleted HCC cells exhibited impaired mitochondrial respiration and reduced complex I (CI) activity as XRCC2 was responsible for elimination of mitochondrial DNA (mtDNA) damage and maintenance of mtDNA-encoded CI-related genes' transcription in a RAD51-dependent manner. We showed that tunicamycin (Tm)-activated sXBP1 bound to the TGTCAT domain and suppressed XRCC2 expression. In HCC patients, we observed a negative correlation between XBP1 and XRCC2 expression. Moreover, XRCC2 inhibition by Tm led to genomic and mtDNA damage, which impaired the transcription of mtDNA-encoded CI-related genes and prevented tumor proliferation in vivo. We described the role of XRCC2 in mtDNA damage repair and HCC progression while unveiling the potential anti-tumor effect of Tm.
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Affiliation(s)
- Zhenjun Zhao
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Kang He
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yu Zhang
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiangwei Hua
- Center of Organ Transplantation, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Mingxuan Feng
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhichong Zhao
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuan Sun
- SJTU-Yale Joint Centre for Biostatistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Yuhui Jiang
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Qiang Xia
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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