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Li R, Xu S, Li Y, Tang Z, Feng D, Cai J, Ma S. Incorporating prior information in gene expression network-based cancer heterogeneity analysis. Biostatistics 2024:kxae028. [PMID: 39074174 DOI: 10.1093/biostatistics/kxae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 07/04/2024] [Accepted: 07/08/2024] [Indexed: 07/31/2024] Open
Abstract
Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informative for cancer heterogeneity analysis than some simpler measures. Gene interconnections can be classified as "direct" and "indirect," where the latter can be caused by shared genomic regulators (such as transcription factors, microRNAs, and other regulatory molecules) and other mechanisms. It has been suggested that incorporating the regulators of gene expressions in network analysis and focusing on the direct interconnections can lead to a deeper understanding of the more essential gene interconnections. Such analysis can be seriously challenged by the large number of parameters (jointly caused by network analysis, incorporation of regulators, and heterogeneity) and often weak signals. To effectively tackle this problem, we propose incorporating prior information contained in the published literature. A key challenge is that such prior information can be partial or even wrong. We develop a two-step procedure that can flexibly accommodate different levels of prior information quality. Simulation demonstrates the effectiveness of the proposed approach and its superiority over relevant competitors. In the analysis of a breast cancer dataset, findings different from the alternatives are made, and the identified sample subgroups have important clinical differences.
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Affiliation(s)
- Rong Li
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06511, CT, United States
| | - Shaodong Xu
- Center for Applied Statistics and School of Statistics, Renmin University of China, 59 Zhongguancun Street, 100872, Beijing, China
| | - Yang Li
- Center for Applied Statistics and School of Statistics, Renmin University of China, 59 Zhongguancun Street, 100872, Beijing, China
| | - Zuojian Tang
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., 900 Ridgebury Road, Ridgefield, 06877, CT, United States
| | - Di Feng
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., 900 Ridgebury Road, Ridgefield, 06877, CT, United States
| | - James Cai
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., 900 Ridgebury Road, Ridgefield, 06877, CT, United States
| | - Shuangge Ma
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06511, CT, United States
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Xu R, Chen R, Tu C, Gong X, Liu Z, Mei L, Ren X, Li Z. 3D Models of Sarcomas: The Next-generation Tool for Personalized Medicine. PHENOMICS (CHAM, SWITZERLAND) 2024; 4:171-186. [PMID: 38884054 PMCID: PMC11169319 DOI: 10.1007/s43657-023-00111-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 05/16/2023] [Accepted: 05/23/2023] [Indexed: 06/18/2024]
Abstract
Sarcoma is a complex and heterogeneous cancer that has been difficult to study in vitro. While two-dimensional (2D) cell cultures and mouse models have been the dominant research tools, three-dimensional (3D) culture systems such as organoids have emerged as promising alternatives. In this review, we discuss recent developments in sarcoma organoid culture, with a focus on their potential as tools for drug screening and biobanking. We also highlight the ways in which sarcoma organoids have been used to investigate the mechanisms of gene regulation, drug resistance, metastasis, and immune interactions. Sarcoma organoids have shown to retain characteristics of in vivo biology within an in vitro system, making them a more representative model for sarcoma research. Our review suggests that sarcoma organoids offer a potential path forward for translational research in this field and may provide a platform for developing personalized therapies for sarcoma patients.
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Affiliation(s)
- Ruiling Xu
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, No. 139 Renmin Road, Changsha, 410011 Hunan China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, No. 139 Renmin Road, Changsha, 410011 Hunan China
| | - Ruiqi Chen
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, No. 139 Renmin Road, Changsha, 410011 Hunan China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, No. 139 Renmin Road, Changsha, 410011 Hunan China
| | - Chao Tu
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, No. 139 Renmin Road, Changsha, 410011 Hunan China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, No. 139 Renmin Road, Changsha, 410011 Hunan China
| | - Xiaofeng Gong
- College of Life Science, Fudan University, Shanghai, 200433 China
| | - Zhongyue Liu
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, No. 139 Renmin Road, Changsha, 410011 Hunan China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, No. 139 Renmin Road, Changsha, 410011 Hunan China
| | - Lin Mei
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, No. 139 Renmin Road, Changsha, 410011 Hunan China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, No. 139 Renmin Road, Changsha, 410011 Hunan China
| | - Xiaolei Ren
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, No. 139 Renmin Road, Changsha, 410011 Hunan China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, No. 139 Renmin Road, Changsha, 410011 Hunan China
| | - Zhihong Li
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, No. 139 Renmin Road, Changsha, 410011 Hunan China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, No. 139 Renmin Road, Changsha, 410011 Hunan China
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Sanchez-Fdez A, Sharma AK, Tiriac H, Sicklick JK. Patient-Derived Sarcoma Organoids Offer a Novel Platform for Personalized Precision Medicine. Ann Surg Oncol 2022; 29:7239-7241. [PMID: 35831519 PMCID: PMC10173699 DOI: 10.1245/s10434-022-12152-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 06/27/2022] [Indexed: 11/18/2022]
Affiliation(s)
- Adrian Sanchez-Fdez
- Division of Surgical Oncology, Department of Surgery, University of California San Diego, San Diego, CA, USA
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
| | - Ashwyn K Sharma
- Division of Surgical Oncology, Department of Surgery, University of California San Diego, San Diego, CA, USA
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
| | - Herve Tiriac
- Division of Surgical Oncology, Department of Surgery, University of California San Diego, San Diego, CA, USA
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
| | - Jason K Sicklick
- Division of Surgical Oncology, Department of Surgery, University of California San Diego, San Diego, CA, USA.
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.
- Department of Pharmacology, University of California San Diego, San Diego, CA, USA.
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Cheng C, Feng X, Li X, Wu M. Robust analysis of cancer heterogeneity for high-dimensional data. Stat Med 2022; 41:5448-5462. [PMID: 36117143 DOI: 10.1002/sim.9578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 06/04/2022] [Accepted: 09/05/2022] [Indexed: 11/06/2022]
Abstract
Cancer heterogeneity plays an important role in the understanding of tumor etiology, progression, and response to treatment. To accommodate heterogeneity, cancer subgroup analysis has been extensively conducted. However, most of the existing studies share the limitation that they cannot accommodate heavy-tailed or contaminated outcomes and also high dimensional covariates, both of which are not uncommon in biomedical research. In this study, we propose a robust subgroup identification approach based on M-estimators together with concave and pairwise fusion penalties, which advances from existing studies by effectively accommodating high-dimensional data containing some outliers. The penalties are applied on both latent heterogeneity factors and covariates, where the estimation is expected to achieve subgroup identification and variable selection simultaneously, with the number of subgroups being apriori unknown. We innovatively develop an algorithm based on parallel computing strategy, with a significant advantage of capable of processing large-scale data. The convergence property of the proposed algorithm, oracle property of the penalized M-estimators, and selection consistency of the proposed BIC criterion are carefully established. Simulation and analysis of TCGA breast cancer data demonstrate that the proposed approach is promising to efficiently identify underlying subgroups in high-dimensional data.
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Affiliation(s)
- Chao Cheng
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Xingdong Feng
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Xiaoguang Li
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Mengyun Wu
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
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