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Zhao C, Tan T, Zhang E, Wang T, Gong H, Jia Q, Liu T, Yang X, Zhao J, Wu Z, Wei H, Xiao J, Yang C. A chronicle review of new techniques that facilitate the understanding and development of optimal individualized therapeutic strategies for chordoma. Front Oncol 2022; 12:1029670. [PMID: 36465398 PMCID: PMC9708744 DOI: 10.3389/fonc.2022.1029670] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 10/19/2022] [Indexed: 09/01/2023] Open
Abstract
Chordoma is a rare malignant bone tumor that mainly occurs in the sacrum and the clivus/skull base. Surgical resection is the treatment of choice for chordoma, but the local recurrence rate is high with unsatisfactory prognosis. Compared with other common tumors, there is not much research and individualized treatment for chordoma, partly due to the rarity of the disease and the lack of appropriate disease models, which delay the discovery of therapeutic strategies. Recent advances in modern techniques have enabled gaining a better understanding of a number of rare diseases, including chordoma. Since the beginning of the 21st century, various chordoma cell lines and animal models have been reported, which have partially revealed the intrinsic mechanisms of tumor initiation and progression with the use of next-generation sequencing (NGS) techniques. In this study, we performed a systematic overview of the chordoma models and related sequencing studies in a chronological manner, from the first patient-derived chordoma cell line (U-CH1) to diverse preclinical models such as the patient-derived organoid-based xenograft (PDX) and patient-derived organoid (PDO) models. The use of modern sequencing techniques has discovered mutations and expression signatures that are considered potential treatment targets, such as the expression of Brachyury and overactivated receptor tyrosine kinases (RTKs). Moreover, computational and bioinformatics techniques have made drug repositioning/repurposing and individualized high-throughput drug screening available. These advantages facilitate the research and development of comprehensive and personalized treatment strategies for indicated patients and will dramatically improve their prognoses in the near feature.
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Affiliation(s)
- Chenglong Zhao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Tao Tan
- Department of Orthopedics, 905 Hospital of People’s Liberation Army Navy, Shanghai, China
| | - E. Zhang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Ting Wang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Haiyi Gong
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Qi Jia
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Tielong Liu
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Xinghai Yang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Jian Zhao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Zhipeng Wu
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Haifeng Wei
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Jianru Xiao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Cheng Yang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
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Mellert K, Seeling C, Möller P, Barth TFE. [Chordoma-An update]. PATHOLOGIE (HEIDELBERG, GERMANY) 2022; 43:50-55. [PMID: 36175666 DOI: 10.1007/s00292-022-01118-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/31/2022] [Indexed: 06/16/2023]
Abstract
Chordomas are rare malignant tumors of the axial skeleton with notochordal differentiation. From a morphological point of view, chordomas display a broad spectrum ranging from the classical, conventional form not otherwise specified (NOS) to forms with hepatoid or renal carcinoma-like differentiation or even poorly or dedifferentiated variants. The detection of brachyury is highly characteristic, though not exclusive. The morphological differential diagnosis from a benign notochordal tumor (BNCT) requires integration of imaging since BNCT is limited to the vertebral bodies and is not osteolytic. Targeted therapy is a current research focus and cell lines as in vitro models are a precondition for the establishment and validation of this approach.
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Affiliation(s)
- K Mellert
- Institut für Pathologie, Universitätsklinikum Ulm, Albert-Einstein-Allee 11, 89081, Ulm, Deutschland
| | - C Seeling
- Institut für Pathologie, Universitätsklinikum Ulm, Albert-Einstein-Allee 11, 89081, Ulm, Deutschland
- Klinik für Innere Medizin III, Universitätsklinikum Ulm, Ulm, Deutschland
| | - P Möller
- Institut für Pathologie, Universitätsklinikum Ulm, Albert-Einstein-Allee 11, 89081, Ulm, Deutschland
| | - T F E Barth
- Institut für Pathologie, Universitätsklinikum Ulm, Albert-Einstein-Allee 11, 89081, Ulm, Deutschland.
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Seeling C, Lechel A, Svinarenko M, Möller P, Barth TFE, Mellert K. Molecular features and vulnerabilities of recurrent chordomas. J Exp Clin Cancer Res 2021; 40:244. [PMID: 34330313 PMCID: PMC8325178 DOI: 10.1186/s13046-021-02037-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 07/06/2021] [Indexed: 11/25/2022] Open
Abstract
Background Tumor recurrence is one of the major challenges in clinical management of chordoma. Despite R0-resection, approximately 50% of chordomas recur within ten years after initial surgery. The underlying molecular processes are poorly understood resulting in the lack of associated therapeutic options. This is not least due to the absence of appropriate cell culture models of this orphan disease. Methods The intra-personal progression model cell lines U-CH11 and U-CH11R were compared using array comparative genomic hybridization, expression arrays, RNA-seq, and immunocytochemistry. Cell line origin was confirmed by short tandem repeat analysis. Inter-personal cell culture models (n = 6) were examined to validate whether the new model is representative. Cell viability after HOX/PBX complex inhibition with small peptides was determined by MTS assays. Results Using whole genome microarray analyses, striking differences in gene expression between primary and recurrent chordomas were identified. These expression differences were confirmed in the world’s first intra-personal model of chordoma relapse consisting of cell lines established from a primary (U-CH11) and the corresponding recurrent tumor (U-CH11R). Array comparative genomic hybridization and RNA-sequencing analyses revealed profound genetic similarities between both cell lines pointing to transcriptomic reprogramming as a key mechanism of chordoma progression. Network analysis of the recurrence specific genes highlighted HOX/PBX signaling as a common dysregulated event. Hence, HOX/PBX complexes were used as so far unknown therapeutic targets in recurrent chordomas. Treating chordoma cell lines with the complex formation inhibiting peptide HXR9 induced cFOS mediated apoptosis in all chordoma cell lines tested. This effect was significantly stronger in cell lines established from chordoma relapses. Conclusion Clearly differing gene expression patterns and vulnerabilities to HOX/PBX complex inhibition in highly therapy resistant chordoma relapses were identified using the first intra-personal loco-regional and further inter-personal chordoma progression models. For the first time, HOX/PBX interference was used to induce cell death in chordoma and might serve as the basic concept of an upcoming targeted therapy for chordomas of all progression stages. Supplementary Information The online version contains supplementary material available at 10.1186/s13046-021-02037-y.
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Affiliation(s)
- Carolin Seeling
- Institute of Pathology, University Hospital Ulm, Albert-Einstein-Allee 11, 89081, Ulm, Germany
| | - André Lechel
- Department of Internal Medicine I, University Hospital Ulm, 89081, Ulm, Germany
| | - Michael Svinarenko
- Department of Internal Medicine I, University Hospital Ulm, 89081, Ulm, Germany
| | - Peter Möller
- Institute of Pathology, University Hospital Ulm, Albert-Einstein-Allee 11, 89081, Ulm, Germany.
| | - Thomas F E Barth
- Institute of Pathology, University Hospital Ulm, Albert-Einstein-Allee 11, 89081, Ulm, Germany
| | - Kevin Mellert
- Institute of Pathology, University Hospital Ulm, Albert-Einstein-Allee 11, 89081, Ulm, Germany
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Wei W, Wang K, Liu Z, Tian K, Wang L, Du J, Ma J, Wang S, Li L, Zhao R, Cui L, Wu Z, Tian J. Radiomic signature: A novel magnetic resonance imaging-based prognostic biomarker in patients with skull base chordoma. Radiother Oncol 2019; 141:239-246. [PMID: 31668985 DOI: 10.1016/j.radonc.2019.10.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 08/12/2019] [Accepted: 10/01/2019] [Indexed: 12/31/2022]
Abstract
BACKGROUND AND PURPOSE We used radiomic analysis to establish a radiomic signature based on anatomical magnetic resonance imaging (MRI) sequences and explore its effectiveness as a novel prognostic biomarker for skull base chordoma (SBC). MATERIALS AND METHODS In this retrospective study, radiomic analysis was performed using preoperative axial T1 FLAIR, T2-weighted, and enhanced T1 FLAIR from a single hospital. The primary clinical endpoint was progression-free survival. A total of 1860 3-D radiomic features were extracted from manually segmented region of interest. Pearson correlation coefficient was used for feature dimensional reduction and a ridge regression-based Cox proportional hazards model was used to determine a radiomic signature. Afterwards, radiomic signature and nine other potential prognostic factors, including age, gender, histological subtype, dural invasion, blood supply, adjuvant radiotherapy, extent of resection, preoperative KPS, and postoperative KPS were analyzed to build a radiomic nomogram and a clinical model. Finally, we compared the nomogram with each prognostic factor/model by DeLong's test. RESULTS A total of 148 SBC patients were enrolled, including 64 with disease progression. The median follow-up time was 52 months (range 4-122 months). The Harrell's concordance index of the radiomic signature was 0.745 (95% CI, 0.709-0.781) for the validation cohort, and its discrimination accuracy in predicting progression risk at 5 years in the same cohort was 82.4% (95% CI, 72.6-89.7%). CONCLUSIONS The radiomics is a low-cost, non-invasive method to predict SBC prognosis preoperatively. Radiomic signature is a potential prognostic biomarker that may allow the individualized evaluation of patients with SBC.
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Affiliation(s)
- Wei Wei
- School of Electronics and Information, Xi'an Polytechnic University, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
| | - Ke Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China; China National Clinical Research Center for Neurological Diseases, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Kaibing Tian
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China; China National Clinical Research Center for Neurological Diseases, China
| | - Liang Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China; China National Clinical Research Center for Neurological Diseases, China
| | - Jiang Du
- Department of Neuropathology, Beijing Neurosurgical Institute, China
| | - Junpeng Ma
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China; China National Clinical Research Center for Neurological Diseases, China
| | - Shuo Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
| | - Longfei Li
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Rui Zhao
- School of Electronics and Information, Xi'an Polytechnic University, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Luo Cui
- School of Electronics and Information, Xi'an Polytechnic University, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Zhen Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China; China National Clinical Research Center for Neurological Diseases, China.
| | - Jie Tian
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China.
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