Zhongjian Chen1,2, Chenxi Yang1,2, Zhenying Guo1,2, Siyu Song1,2, Yun Gao1,2, Ding Wang1,2, Weimin Mao3,4, Junping Liu5,6. 1. The Cancer Research Institute, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Zhejiang, 310022, Hangzhou, China. 2. Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Zhejiang, 310022, Hangzhou, China. 3. The Cancer Research Institute, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Zhejiang, 310022, Hangzhou, China. maowm@zjcc.org.cn. 4. Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Zhejiang, 310022, Hangzhou, China. maowm@zjcc.org.cn. 5. The Cancer Research Institute, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Zhejiang, 310022, Hangzhou, China. liujunpingzjcc@163.com. 6. Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Zhejiang, 310022, Hangzhou, China. liujunpingzjcc@163.com.
Abstract
BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare and aggressive carcinoma located in pleural cavity. Due to lack of effective diagnostic biomarkers and therapeutic targets in MPM, the prognosis is extremely poor. Because of difficulties in sample extraction, and the high rate of misdiagnosis, MPM is rarely studied. Therefore, novel modeling methodology is crucially needed to facilitate MPM research. METHODS: A novel patient-derived xenograft (PDX) modeling strategy was designed, which included preliminary screening of patients with pleural thickening using computerized tomography (CT) scan, further reviewing history of disease and imaging by a senior sonographer as well as histopathological analysis by a senior pathologist, and PDX model construction using ultrasound-guided pleural biopsy from MPM patients. Gas chromatography-mass spectrometry-based metabolomics was further utilized for investigating circulating metabolic features of the PDX models. Univariate and multivariate analysis, and pathway analysis were performed to explore the differential metabolites, enriched metabolism pathways and potential metabolic targets. RESULTS: After screening using our strategy, 5 out of 116 patients were confirmed to be MPM, and their specimens were used for modeling. Two PDX models were established successfully. Metabolomics analysis revealed significant metabolic shifts in PDX models, such as dysregulations in amino acid metabolism, TCA cycle and glycolysis, and nucleotide metabolism. CONCLUSIONS: To sum up, we suggested a novel modeling strategy that may facilitate specimen availability for MM research, and by applying metabolomics in this model, several metabolic features were identified, whereas future studies with large sample size are needed.
BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare and aggressive carcinoma located in pleural cavity. Due to lack of effective diagnostic biomarkers and therapeutic targets in MPM, the prognosis is extremely poor. Because of difficulties in sample extraction, and the high rate of misdiagnosis, MPM is rarely studied. Therefore, novel modeling methodology is crucially needed to facilitate MPM research. METHODS: A novel patient-derived xenograft (PDX) modeling strategy was designed, which included preliminary screening of patients with pleural thickening using computerized tomography (CT) scan, further reviewing history of disease and imaging by a senior sonographer as well as histopathological analysis by a senior pathologist, and PDX model construction using ultrasound-guided pleural biopsy from MPM patients. Gas chromatography-mass spectrometry-based metabolomics was further utilized for investigating circulating metabolic features of the PDX models. Univariate and multivariate analysis, and pathway analysis were performed to explore the differential metabolites, enriched metabolism pathways and potential metabolic targets. RESULTS: After screening using our strategy, 5 out of 116 patients were confirmed to be MPM, and their specimens were used for modeling. Two PDX models were established successfully. Metabolomics analysis revealed significant metabolic shifts in PDX models, such as dysregulations in amino acid metabolism, TCA cycle and glycolysis, and nucleotide metabolism. CONCLUSIONS: To sum up, we suggested a novel modeling strategy that may facilitate specimen availability for MM research, and by applying metabolomics in this model, several metabolic features were identified, whereas future studies with large sample size are needed.
Authors: Xin Dong; Jun Guan; John C English; Julia Flint; John Yee; Kenneth Evans; Nevin Murray; Calum Macaulay; Raymond T Ng; Peter W Gout; Wan L Lam; Janessa Laskin; Victor Ling; Stephen Lam; Yuzhuo Wang Journal: Clin Cancer Res Date: 2010-02-23 Impact factor: 12.531
Authors: Michael P Kim; Douglas B Evans; Huamin Wang; James L Abbruzzese; Jason B Fleming; Gary E Gallick Journal: Nat Protoc Date: 2009-10-29 Impact factor: 13.491
Authors: Christopher Cao; David Tian; John Park; James Allan; Kristopher A Pataky; Tristan D Yan Journal: Lung Cancer Date: 2013-12-06 Impact factor: 5.705
Authors: Luca Maria Sconfienza; Giovanni Mauri; Francesco Grossi; Mauro Truini; Giovanni Serafini; Francesco Sardanelli; Carmelina Murolo Journal: Radiology Date: 2012-11-30 Impact factor: 11.105
Authors: Oluf Dimitri Røe; Endre Anderssen; Helmut Sandeck; Tone Christensen; Erik Larsson; Steinar Lundgren Journal: Lung Cancer Date: 2010-01 Impact factor: 5.705
Authors: Shu-Chun Chuang; Anouar Fanidi; Per Magne Ueland; Caroline Relton; Oivind Midttun; Stein Emil Vollset; Marc J Gunter; Michael J Seckl; Ruth C Travis; Nicholas Wareham; Antonia Trichopoulou; Pagona Lagiou; Dimitrios Trichopoulos; Petra H M Peeters; H Bas Bueno-de-Mesquita; Heiner Boeing; Angelika Wientzek; Tilman Kuehn; Rudolf Kaaks; Rosario Tumino; Claudia Agnoli; Domenico Palli; Alessio Naccarati; Eva Ardanaz Aicua; María-José Sánchez; José Ramón Quirós; María-Dolores Chirlaque; Antonio Agudo; Mikael Johansson; Kjell Grankvist; Marie-Christine Boutron-Ruault; Françoise Clavel-Chapelon; Guy Fagherazzi; Elisabete Weiderpass; Elio Riboli; Paul J Brennan; Paolo Vineis; Mattias Johansson Journal: Cancer Epidemiol Biomarkers Prev Date: 2013-12-19 Impact factor: 4.254