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Qin Y, Huo M, Liu X, Li SC. Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy. Front Immunol 2024; 15:1368749. [PMID: 38524135 PMCID: PMC10957591 DOI: 10.3389/fimmu.2024.1368749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 02/27/2024] [Indexed: 03/26/2024] Open
Abstract
Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.
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Affiliation(s)
- Yurong Qin
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
| | - Miaozhe Huo
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
| | - Xingwu Liu
- School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
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Jafari A, Farahani M, Abdollahpour-Alitappeh M, Manzari-Tavakoli A, Yazdani M, Rezaei-Tavirani M. Unveiling diagnostic and therapeutic strategies for cervical cancer: biomarker discovery through proteomics approaches and exploring the role of cervical cancer stem cells. Front Oncol 2024; 13:1277772. [PMID: 38328436 PMCID: PMC10847843 DOI: 10.3389/fonc.2023.1277772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 12/27/2023] [Indexed: 02/09/2024] Open
Abstract
Cervical cancer (CC) is a major global health problem and leading cause of cancer deaths among women worldwide. Early detection through screening programs has reduced mortality; however, screening compliance remains low. Identifying non-invasive biomarkers through proteomics for diagnosis and monitoring response to treatment could improve patient outcomes. Here we review recent proteomics studies which have uncovered biomarkers and potential drug targets for CC. Additionally, we explore into the role of cervical cancer stem cells and their potential implications in driving CC progression and therapy resistance. Although challenges remain, proteomics has the potential to revolutionize the field of cervical cancer research and improve patient outcomes.
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Affiliation(s)
- Ameneh Jafari
- Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Masoumeh Farahani
- Skin Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Asma Manzari-Tavakoli
- Department of Biology, Faculty of Science, Rayan Center for Neuroscience and Behavior, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mohsen Yazdani
- Laboratory of Bioinformatics and Drug Design, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Lee KH, Choi GH, Yun J, Choi J, Goh MJ, Sinn DH, Jin YJ, Kim MA, Yu SJ, Jang S, Lee SK, Jang JW, Lee JS, Kim DY, Cho YY, Kim HJ, Kim S, Kim JH, Kim N, Kim KM. Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study. NPJ Digit Med 2024; 7:2. [PMID: 38182886 PMCID: PMC10770025 DOI: 10.1038/s41746-023-00976-8] [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: 03/31/2023] [Accepted: 11/29/2023] [Indexed: 01/07/2024] Open
Abstract
The treatment decisions for patients with hepatocellular carcinoma are determined by a wide range of factors, and there is a significant difference between the recommendations of widely used staging systems and the actual initial treatment choices. Herein, we propose a machine learning-based clinical decision support system suitable for use in multi-center settings. We collected data from nine institutions in South Korea for training and validation datasets. The internal and external datasets included 935 and 1750 patients, respectively. We developed a model with 20 clinical variables consisting of two stages: the first stage which recommends initial treatment using an ensemble voting machine, and the second stage, which predicts post-treatment survival using a random survival forest algorithm. We derived the first and second treatment options from the results with the highest and the second-highest probabilities given by the ensemble model and predicted their post-treatment survival. When only the first treatment option was accepted, the mean accuracy of treatment recommendation in the internal and external datasets was 67.27% and 55.34%, respectively. The accuracy increased to 87.27% and 86.06%, respectively, when the second option was included as the correct answer. Harrell's C index, integrated time-dependent AUC curve, and integrated Brier score of survival prediction in the internal and external datasets were 0.8381 and 0.7767, 91.89 and 86.48, 0.12, and 0.14, respectively. The proposed system can assist physicians by providing data-driven predictions for reference from other larger institutions or other physicians within the same institution when making treatment decisions.
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Affiliation(s)
- Kyung Hwa Lee
- Department of Radiation Oncology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Gwang Hyeon Choi
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University, Seongnam, Republic of Korea
| | - Jihye Yun
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jonggi Choi
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Myung Ji Goh
- Department of Internal Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Dong Hyun Sinn
- Department of Internal Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Young Joo Jin
- Department of Internal Medicine, Inha University Hospital, Incheon, Republic of Korea
| | - Minseok Albert Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul National University, Seoul, Republic of Korea
| | - Su Jong Yu
- Department of Internal Medicine, Seoul National University Hospital, Seoul National University, Seoul, Republic of Korea
| | - Sangmi Jang
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University, Seongnam, Republic of Korea
- Department of Internal Medicine, Inha University Hospital, Incheon, Republic of Korea
| | - Soon Kyu Lee
- Department of Internal Medicine, Seoul St. Mary's Hospital, Seoul, Republic of Korea
- Department of Internal Medicine, Incheon St. Mary's Hospital, Incheon, Republic of Korea
| | - Jeong Won Jang
- Department of Internal Medicine, Seoul St. Mary's Hospital, Seoul, Republic of Korea
| | - Jae Seung Lee
- Department of Internal Medicine, Seoul Severance Hospital, Seoul, Republic of Korea
| | - Do Young Kim
- Department of Internal Medicine, Seoul Severance Hospital, Seoul, Republic of Korea
| | - Young Youn Cho
- Department of Internal Medicine, Chung-Ang University Hospital, Seoul, Republic of Korea
| | - Hyung Joon Kim
- Department of Internal Medicine, Chung-Ang University Hospital, Seoul, Republic of Korea
| | - Sehwa Kim
- Department of Internal Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Bundang Jesaeng General Hospital, Seongnam, Republic of Korea
| | - Ji Hoon Kim
- Department of Internal Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Kang Mo Kim
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Shen X, Wu J, Su J, Yao Z, Huang W, Zhang L, Jiang Y, Yu W, Li Z. Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework. Front Genet 2023; 14:1004481. [PMID: 37007970 PMCID: PMC10064216 DOI: 10.3389/fgene.2023.1004481] [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: 07/27/2022] [Accepted: 02/14/2023] [Indexed: 03/09/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high morbidity and fatality rate. Traditional diagnostic methods for HCC are primarily based on clinical presentation, imaging features, and histopathology. With the rapid development of artificial intelligence (AI), which is increasingly used in the diagnosis, treatment, and prognosis prediction of HCC, an automated approach to HCC status classification is promising. AI integrates labeled clinical data, trains on new data of the same type, and performs interpretation tasks. Several studies have shown that AI techniques can help clinicians and radiologists be more efficient and reduce the misdiagnosis rate. However, the coverage of AI technologies leads to difficulty in which the type of AI technology is preferred to choose for a given problem and situation. Solving this concern, it can significantly reduce the time required to determine the required healthcare approach and provide more precise and personalized solutions for different problems. In our review of research work, we summarize existing research works, compare and classify the main results of these according to the specified data, information, knowledge, wisdom (DIKW) framework.
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Affiliation(s)
- Xiaomin Shen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Jinxin Wu
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Junwei Su
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Zhenyu Yao
- School of Computer Science, King's College London, London, United Kingdom
| | - Wei Huang
- Department of Gastroenterology II, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Li Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Yiheng Jiang
- Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Wei Yu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Zhao Li
- School of Computer Science, Zhejiang University, Hangzhou, China
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Asim MN, Fazeel A, Ibrahim MA, Dengel A, Ahmed S. MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses. Front Med (Lausanne) 2022; 9:1025887. [PMID: 36465911 PMCID: PMC9709337 DOI: 10.3389/fmed.2022.1025887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/17/2022] [Indexed: 09/19/2023] Open
Abstract
Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.
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Affiliation(s)
- Muhammad Nabeel Asim
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
| | - Ahtisham Fazeel
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
| | - Muhammad Ali Ibrahim
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
| | - Andreas Dengel
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
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