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Yang G, Yu XR, Weisenberger DJ, Lu T, Liang G. A Multi-Omics Overview of Colorectal Cancer to Address Mechanisms of Disease, Metastasis, Patient Disparities and Outcomes. Cancers (Basel) 2023; 15:cancers15112934. [PMID: 37296894 DOI: 10.3390/cancers15112934] [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: 04/09/2023] [Revised: 05/16/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
Human colorectal cancer (CRC) is one of the most common malignancies in men and women across the globe, albeit CRC incidence and mortality shows a substantial racial and ethnic disparity, with the highest burden in African American patients. Even with effective screening tools such as colonoscopy and diagnostic detection assays, CRC remains a substantial health burden. In addition, primary tumors located in the proximal (right) or distal (left) sides of the colorectum have been shown to be unique tumor types that require unique treatment schema. Distal metastases in the liver and other organ systems are the major causes of mortality in CRC patients. Characterizing genomic, epigenomic, transcriptomic and proteomic (multi-omics) alterations has led to a better understanding of primary tumor biology, resulting in targeted therapeutic advancements. In this regard, molecular-based CRC subgroups have been developed that show correlations with patient outcomes. Molecular characterization of CRC metastases has highlighted similarities and differences between metastases and primary tumors; however, our understanding as to how to improve patient outcomes based on metastasis biology is lagging and remains a major obstacle to improving CRC patient outcomes. In this review, we will summarize the multi-omics features of primary CRC tumors and their metastases across racial and ethnic groups, the differences in proximal and distal tumor biology, molecular-based CRC subgroups, treatment strategies and challenges for improving patient outcomes.
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Affiliation(s)
- Guang Yang
- School of Sciences, China Pharmaceutical University, Nanjing 211121, China
- China Grand Enterprises, Beijing 100101, China
| | - Xi Richard Yu
- China Grand Enterprises, Beijing 100101, China
- Huadong Medicine Co., Ltd., Hangzhou 310011, China
| | - Daniel J Weisenberger
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Tao Lu
- School of Sciences, China Pharmaceutical University, Nanjing 211121, China
- State Key Laboratory of Natural Sciences, China Pharmaceutical University, Nanjing 211121, China
| | - Gangning Liang
- USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
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Li M, Gong J, Bao Y, Huang D, Peng J, Tong T. Special issue "The advance of solid tumor research in China": Prognosis prediction for stage II colorectal cancer by fusing CT radiomics and deep-learning features of primary lesions and peripheral lymph nodes. Int J Cancer 2022; 152:31-41. [PMID: 35484979 DOI: 10.1002/ijc.34053] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/10/2022] [Accepted: 04/21/2022] [Indexed: 11/11/2022]
Abstract
Currently, the prognosis assessment of stage II colorectal cancer (CRC) remains a difficult clinical problem; therefore, more accurate prognostic predictors must be developed. In this study, we developed a prognostic prediction model for stage II CRC by fusing radiomics and deep-learning (DL) features of primary lesions and peripheral lymph nodes (LNs) in computed tomography (CT) scans. First, two CT radiomics models were built using primary lesion and LN image features. Subsequently, an information fusion method was used to build a fusion radiomics model by combining the tumor and LN image features. Furthermore, a transfer learning method was applied to build a deep convolutional neural network (CNN) model. Finally, the prediction scores generated by the radiomics and CNN models were fused to improve the prognosis prediction performance. The disease-free survival (DFS) and overall survival (OS) prediction areas under the curves (AUCs) generated by the fusion model improved to 0.76±0.08 and 0.91±0.05, respectively. These were significantly higher than the AUCs generated by the models using the individual CT radiomics and deep image features. Applying the survival analysis method, the DFS and OS fusion models yielded concordance index (C-index) values of 0.73 and 0.9, respectively. Hence, the combined model exhibited good predictive efficacy; therefore, it could be used for the accurate assessment of the prognosis of stage II CRC patients. Moreover, it could be used to screen out high-risk patients with poor prognoses, and assist in the formulation of clinical treatment decisions in a timely manner to achieve precision medicine. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Menglei Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Yichao Bao
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Dan Huang
- Department of Pathology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Junjie Peng
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
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