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Ghimire P, Kinnersley B, Karami G, Arumugam P, Houlston R, Ashkan K, Modat M, Booth TC. Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies. Neurooncol Adv 2024; 6:vdae055. [PMID: 38680991 PMCID: PMC11046988 DOI: 10.1093/noajnl/vdae055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024] Open
Abstract
Background Immunotherapy is an effective "precision medicine" treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma. Methods A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered: CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis. Results Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed. Conclusions Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability.
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Affiliation(s)
- Prajwal Ghimire
- Department of Neurosurgery, Kings College Hospital NHS Foundation Trust, London, UK
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Ben Kinnersley
- Department of Oncology, University College London, London, UK
| | | | | | - Richard Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK
| | - Keyoumars Ashkan
- Department of Neurosurgery, Kings College Hospital NHS Foundation Trust, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
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Pan B, Luo Y, Ye D, Qiu J, Zhang X, Wu X, Yao Y, Wang X, Tang N. A modified immune cell infiltration score achieves ideal stratification for CD8 + T cell efficacy and immunotherapy benefit in hepatocellular carcinoma. Cancer Immunol Immunother 2023; 72:4103-4119. [PMID: 37755466 DOI: 10.1007/s00262-023-03546-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 09/09/2023] [Indexed: 09/28/2023]
Abstract
Immunotherapy, which aims to enhance the function of T cells, has emerged as a novel therapeutic approach for hepatocellular carcinoma (HCC). Nevertheless, the clinical utility of using flow cytometry to assess immune cell infiltration (ICI) is hindered by its cumbersome procedures, prompting the need for more accessible methods. Here, we acquired gene expression profiles and survival data of HCC from TCGA and GSE10186 datasets. The patients were categorized into two clusters of ICI, and a set of 11 characteristic genes responsible for the differentiation performance of these ICI clusters were identified. Subsequently, we successfully developed a modified ICI score (mICIS) by utilizing the expression levels of these genes. The efficacy of our mICIS was confirmed via mass cytometry, flow cytometry, and immunohistochemistry. Our research indicated that the favorable overall survival (OS) rate could be attributed to the improved function of anti-tumor leukocytes rather than their infiltration. Furthermore, we observed that the low score group exhibited lower expression levels of T-cell exhaustion-associated genes, which was confirmed in both HCC tissues from patients and mice, which demonstrated that the benefits of the low scores were due to enhanced active/cytotoxic CD8+ T cells and reduced exhausted CD8+ T cells. Additionally, our mICIS stratified the benefits derived from immunotherapies. Lastly, we observed a misalignment between CD8+ T-cell infiltration and function in HCC. In summary, our mICIS demonstrated proficiency in assessing the OS rate of HCC and offering significant stratified data pertaining to distinct responses to immunotherapy.
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Affiliation(s)
- Banglun Pan
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Yue Luo
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Dongjie Ye
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Jiacheng Qiu
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Xiaoxia Zhang
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Xiaoxuan Wu
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Yuxin Yao
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Xiaoqian Wang
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Nanhong Tang
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
- Cancer Center of Fujian Medical University, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
- Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, 350122, China.
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Wang W, Zhang Z, Li W, Wei D, Xu J, Qian Y, Cao S, Lei D. Characterization of the immune cell function landscape in head and neck squamous carcinoma to assist in prognosis prediction and immunotherapy. Aging (Albany NY) 2023; 15:12588-12617. [PMID: 37955651 PMCID: PMC10683602 DOI: 10.18632/aging.205201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/03/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND The malignant characteristics of cancer depend not only on intrinsic properties of cancer cells but also on the functions of infiltrating immune cells. In this study, we aimed to investigate the functional landscape of immune cells in head and neck squamous cell carcinoma (HNSCC). METHODS We employed single-sample gene set enrichment analysis to examine the immunophenotypes of HNSCC based on 29 immune cell functions (ICFs) in TCGA and GSE65858 datasets. We analyzed the clinical features, immune microenvironment, molecular profiles, and biological processes. Additionally, we developed and validated an ICF-based risk score for personalized prognosis prediction. We confirmed the value of the ICF score in our cohort using qRT-PCR and immunohistochemistry. Molecular docking was used to predict potential compounds for immunotherapy. RESULTS Three immunophenotypes (Immune-L, Immune-M, and Immune-H) were identified in 769 HNSCC samples. The characteristics of Immune-H were consistent with a "Hot" tumor, Immune-L was similar to a "Cold" tumor, and Immune-M exhibited intermediate features. The ICF risk score was associated with immune checkpoints, infiltrating immune cells, tumor mutation burden, and sensitivities to targeted/chemotherapeutic agents. Gene set variation analysis implicated the involvement of metabolic reprogramming pathways in the high-risk group. The combination of "Tumor Immune Dysfunction and Exclusion" and "Immunophenoscore" algorithms indicated that the low-risk group had a higher likelihood of benefiting from immunotherapy. Finally, we identified Eltrombopag and other compounds that may be beneficial for HNSCC immunotherapy. CONCLUSION Our study provides a novel perspective on the tumor microenvironment of HNSCC, aiding in the understanding of HNSCC heterogeneity and the development of personalized/precision medicine.
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Affiliation(s)
- Wenlun Wang
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, P.R. China
- NHC Key Laboratory of Otorhinolaryngology, Shandong University, Jinan, Shandong, P.R. China
- Key Laboratory for Experimental Teratology of the Ministry of Education and Department of Pathology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, P.R. China
| | - Zhouyi Zhang
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, P.R. China
- NHC Key Laboratory of Otorhinolaryngology, Shandong University, Jinan, Shandong, P.R. China
| | - Wenming Li
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, P.R. China
- NHC Key Laboratory of Otorhinolaryngology, Shandong University, Jinan, Shandong, P.R. China
| | - Dongmin Wei
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, P.R. China
- NHC Key Laboratory of Otorhinolaryngology, Shandong University, Jinan, Shandong, P.R. China
| | - Jianing Xu
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, P.R. China
- NHC Key Laboratory of Otorhinolaryngology, Shandong University, Jinan, Shandong, P.R. China
| | - Ye Qian
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, P.R. China
- NHC Key Laboratory of Otorhinolaryngology, Shandong University, Jinan, Shandong, P.R. China
| | - Shengda Cao
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, P.R. China
- NHC Key Laboratory of Otorhinolaryngology, Shandong University, Jinan, Shandong, P.R. China
| | - Dapeng Lei
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, P.R. China
- NHC Key Laboratory of Otorhinolaryngology, Shandong University, Jinan, Shandong, P.R. China
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Chen L, Gao W, Lin L, Sha C, Li T, Chen Q, Wei H, Yang M, Xing J, Zhang M, Zhao S, Xu W, Li Y, Long L, Zhu X. A methylation- and immune-related lncRNA signature to predict ovarian cancer outcome and uncover mechanisms of chemoresistance. J Ovarian Res 2023; 16:186. [PMID: 37674251 PMCID: PMC10483746 DOI: 10.1186/s13048-023-01260-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 08/13/2023] [Indexed: 09/08/2023] Open
Abstract
Tumor-associated lncRNAs regulated by epigenetic modification switches mediate immune escape and chemoresistance in ovarian cancer (OC). However, the underlying mechanisms and concrete targets have not been systematically elucidated. Here, we discovered that methylation modifications played a significant role in regulating immune cell infiltration and sensitizing OC to chemotherapy by modulating immune-related lncRNAs (irlncRNAs), which represent tumor immune status. Through deep analysis of the TCGA database, a prognostic risk model incorporating four methylation-related lncRNAs (mrlncRNAs) and irlncRNAs was constructed. Twenty-one mrlncRNA/irlncRNA pairs were identified that were significantly related to the overall survival (OS) of OC patients. Subsequently, we selected four lncRNAs to construct a risk signature predictive of OS and indicative of OC immune infiltration, and verified the robustness of the risk signature in an internal validation set. The risk score was an independent prognostic factor for OC prognosis, which was demonstrated via multifactorial Cox regression analysis and nomogram. Moreover, risk scores were negatively related to the expression of CD274, CTLA4, ICOS, LAG3, PDCD1, and PDCD1LG2 and negatively correlated with CD8+, CD4+, and Treg tumor-infiltrating immune cells. In addition, a high-risk score was associated with a higher IC50 value for cisplatin, which was associated with a significantly worse clinical outcome. Next, a competing endogenous RNA (ceRNA) network and a signaling pathway controlling the infiltration of CD8+ T cells were explored based on the lncRNA model, which suggested a potential therapeutic target for immunotherapy. Overall, this study constructed a prognostic model by pairing mrlncRNAs and irlncRNAs and revealed the critical role of the FTO/RP5-991G20.1/hsa-miR-1976/MEIS1 signaling pathway in regulating immune function and enhancing anticancer therapy.
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Affiliation(s)
- Lu Chen
- Reproductive Medicine Center, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang City, 212001, Jiangsu Province, China
- Department of Gynaecology and Obstetrics, Taixing People's Hospital, Taixing, Jiangsu, China
| | - Wujiang Gao
- Reproductive Medicine Center, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang City, 212001, Jiangsu Province, China
- Department of Gynaecology and Obstetrics, Yangzhou First People's Hospital, Yangzhou, Jiangsu, China
| | - Li Lin
- Reproductive Medicine Center, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang City, 212001, Jiangsu Province, China
| | - Chunli Sha
- Reproductive Medicine Center, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang City, 212001, Jiangsu Province, China
- Department of Gynaecology and Obstetrics, The First People's Hospital of Nantong City, Nantong, Jiangsu, China
| | - Taoqiong Li
- Reproductive Medicine Center, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang City, 212001, Jiangsu Province, China
| | - Qi Chen
- Reproductive Medicine Center, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang City, 212001, Jiangsu Province, China
| | - Hong Wei
- Reproductive Medicine Center, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang City, 212001, Jiangsu Province, China
| | - Meiling Yang
- Reproductive Medicine Center, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang City, 212001, Jiangsu Province, China
- Department of Gynaecology and Obstetrics, The First People's Hospital of Nantong City, Nantong, Jiangsu, China
| | - Jie Xing
- Reproductive Medicine Center, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang City, 212001, Jiangsu Province, China
| | - Mengxue Zhang
- Reproductive Medicine Center, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang City, 212001, Jiangsu Province, China
| | - Shijie Zhao
- Reproductive Medicine Center, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang City, 212001, Jiangsu Province, China
| | - Wenlin Xu
- Reproductive Medicine Center, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang City, 212001, Jiangsu Province, China.
| | - Yuefeng Li
- Medical school, Jiangsu University, No. 301, Xuefu Road, Zhenjiang City, 212031, Jiangsu Province, China.
| | - Lulu Long
- Oncology Department, Affiliated People's Hospital of jiangsu university, No. 8, Dianli Road, Zhenjiang City, 212001, Jiangsu Province, China.
| | - Xiaolan Zhu
- Reproductive Medicine Center, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang City, 212001, Jiangsu Province, China.
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5
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Wu X, Jin B, Liu X, Mao Y, Wan X, Du S. An immune-related biomarker index for predicting the effectiveness of immunotherapy and prognosis in hepatocellular carcinoma. J Cancer Res Clin Oncol 2023; 149:10319-10333. [PMID: 37273105 DOI: 10.1007/s00432-023-04899-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 05/20/2023] [Indexed: 06/06/2023]
Abstract
OBJECTIVE Currently, there are no recognized biomarkers for predicting the immunotherapy response and prognosis of hepatocellular carcinoma (HCC). This study aimed to establish an immune-related gene prognostic index (IRGPI) for HCC, and to investigate the clinical, immune, molecular, and microenvironmental characteristics of the IRGPI subgroups, as well as their impact on the effectiveness of immune checkpoint inhibitors (ICIs) therapy and patients' prognosis. METHODS We analyzed the LIHC dataset (n = 424) from the The Cancer Genome Atlas (TCGA) database and the GSE10140 dataset (n = 84) from the Gene Expression Omnibus (GEO) database using weighted gene co-expression network analysis (WGCNA) and univariate/multivariate Cox regression analysis to identify immune-related hub genes with prognostic significance. Subsequently, The IRGPI was then established with these special genes obtained, and the molecular, immune, and clinicopathological characteristics of the IRGPI subgroups, along with their predictive role in ICIs treatment and HCC prognosis, were investigated. RESULTS The IRGPI was composed of nine genes, namely CHGA, GAL, CCR3, MMP7, STC1, UCN, OXT, SOCS2, and GCG. The IRGPI-high group exhibited a worse prognosis in both the TCGA and GEO databases compared to the IRGPI-low group. The IRGPI-high group was primarily associated with adaptive immune response and cell-cell interaction pathways and exhibited a higher frequency of gene mutations (such as TP53 and CTNNB1), higher expression of PD-L1 and CTLA4, a higher proportion of macrophages M0 and follicular helper T cells, and a higher APC_co_inhibition and T_cell_co-inhibition immune score. Furthermore, the IRGPI-high group was associated with worse immune subtypes, clinicopathological characteristics, immunotherapy response, and clinical prognosis. CONCLUSION IRGPI is a biomarker with significant potential for predicting the immunotherapy response and prognosis of HCC patients, and is closely related to the immunosuppressive microenvironment and poorer clinicopathological characteristics.
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Affiliation(s)
- Xiang'an Wu
- Department of Liver Surgery, Peking Union Medical College Hospital, PUMC and Chinese Academy of Medical Sciences, Dongcheng, Beijing, 100730, China
| | - Bao Jin
- Department of Liver Surgery, Peking Union Medical College Hospital, PUMC and Chinese Academy of Medical Sciences, Dongcheng, Beijing, 100730, China
| | - Xiao Liu
- Department of Liver Surgery, Peking Union Medical College Hospital, PUMC and Chinese Academy of Medical Sciences, Dongcheng, Beijing, 100730, China
| | - Yilei Mao
- Department of Liver Surgery, Peking Union Medical College Hospital, PUMC and Chinese Academy of Medical Sciences, Dongcheng, Beijing, 100730, China
| | - Xueshuai Wan
- Department of Liver Surgery, Peking Union Medical College Hospital, PUMC and Chinese Academy of Medical Sciences, Dongcheng, Beijing, 100730, China.
| | - Shunda Du
- Department of Liver Surgery, Peking Union Medical College Hospital, PUMC and Chinese Academy of Medical Sciences, Dongcheng, Beijing, 100730, China.
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Chen J, Ye J, Lai R. A lipid metabolism-related gene signature reveals dynamic immune infiltration of the colorectal adenoma-carcinoma sequence. Lipids Health Dis 2023; 22:92. [PMID: 37403152 DOI: 10.1186/s12944-023-01866-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 06/29/2023] [Indexed: 07/06/2023] Open
Abstract
BACKGROUND Lipid metabolism-related genes (LMRGs) have been reported to be correlated with the immune infiltration of colorectal cancer (CRC). This study aimed to investigate the immune infiltration characteristics along the colorectal adenoma-carcinoma sequence (ACS) based on LMRGs. METHODS Gene expression data of colorectal adenoma and carcinoma samples were obtained from the public databases. The "limma" package was applied to determine the differentially expressed LMRGs. Unsupervised consensus clustering was used to cluster colorectal samples. The features of the tumor microenvironment were analyzed by the "ESTIMATE", "GSVA", and "TIDE" algorithms. RESULTS The expression of 149 differentially expressed LMRGs was defined as the LMRG signature. Based on this signature, the adenoma and carcinoma samples were divided into three clusters. Unexpectedly, these sequential clusters showed a directional relationship and collectively constituted the progressive course of colorectal ACS. Interestingly, the LMRG signature revealed that adenoma progression was accompanied by a progressive loss of immune infiltration and a stepwise establishment of a cold microenvironment, but carcinoma progression was characterized by a progressive gain of immune infiltration and a gradual establishment of a hot microenvironment. CONCLUSIONS The LMRG signature reveals dynamic immune infiltration along the colorectal ACS, which substantially changes the understanding of the tumor microenvironment of CRC carcinogenesis and provides novel insight into the role of lipid metabolism in this process.
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Affiliation(s)
- Jie Chen
- Department of Gastroenterology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China
- Molecular Imaging Center, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China
| | - Jianfang Ye
- Department of Endocrinology and Metabolism, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China
- Molecular Imaging Center, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China
| | - Renxu Lai
- Department of Gastroenterology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China.
- Molecular Imaging Center, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China.
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Wu Q, Tian R, He X, Liu J, Ou C, Li Y, Fu X. Machine learning-based integration develops an immune-related risk model for predicting prognosis of high-grade serous ovarian cancer and providing therapeutic strategies. Front Immunol 2023; 14:1164408. [PMID: 37090728 PMCID: PMC10113544 DOI: 10.3389/fimmu.2023.1164408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 03/28/2023] [Indexed: 04/08/2023] Open
Abstract
BackgroundHigh-grade serous ovarian cancer (HGSOC) is a highly lethal gynecological cancer that requires accurate prognostic models and personalized treatment strategies. The tumor microenvironment (TME) is crucial for disease progression and treatment. Machine learning-based integration is a powerful tool for identifying predictive biomarkers and developing prognostic models. Hence, an immune-related risk model developed using machine learning-based integration could improve prognostic prediction and guide personalized treatment for HGSOC.MethodsDuring the bioinformatic study in HGSOC, we performed (i) consensus clustering to identify immune subtypes based on signatures of immune and stromal cells, (ii) differentially expressed genes and univariate Cox regression analysis to derive TME- and prognosis-related genes, (iii) machine learning-based procedures constructed by ten independent machine learning algorithms to screen and construct a TME-related risk score (TMErisk), and (iv) evaluation of the effect of TMErisk on the deconstruction of TME, indication of genomic instability, and guidance of immunotherapy and chemotherapy.ResultsWe identified two different immune microenvironment phenotypes and a robust and clinically practicable prognostic scoring system. TMErisk demonstrated superior performance over most clinical features and other published signatures in predicting HGSOC prognosis across cohorts. The low TMErisk group with a notably favorable prognosis was characterized by BRCA1 mutation, activation of immunity, and a better immune response. Conversely, the high TMErisk group was significantly associated with C-X-C motif chemokine ligands deletion and carcinogenic activation pathways. Additionally, low TMErisk group patients were more responsive to eleven candidate agents.ConclusionOur study developed a novel immune-related risk model that predicts the prognosis of ovarian cancer patients using machine learning-based integration. Additionally, the study not only depicts the diversity of cell components in the TME of HGSOC but also guides the development of potential therapeutic techniques for addressing tumor immunosuppression and enhancing the response to cancer therapy.
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Affiliation(s)
- Qihui Wu
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
| | - Ruotong Tian
- Department of Pharmacology, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiaoyun He
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- Departments of Ultrasound Imaging, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jiaxin Liu
- Department of Pathology, School of Basic Medical Sciences, Central South University, Changsha, China
| | - Chunlin Ou
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Xiaodan Fu, ; ; Yimin Li, ; Chunlin Ou,
| | - Yimin Li
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- *Correspondence: Xiaodan Fu, ; ; Yimin Li, ; Chunlin Ou,
| | - Xiaodan Fu
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Xiaodan Fu, ; ; Yimin Li, ; Chunlin Ou,
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Hoffmann E, Gerwing M, Niland S, Niehoff R, Masthoff M, Geyer C, Wachsmuth L, Wilken E, Höltke C, Heindel WL, Hoerr V, Schinner R, Berger P, Vogl T, Eble JA, Maus B, Helfen A, Wildgruber M, Faber C. Profiling specific cell populations within the inflammatory tumor microenvironment by oscillating-gradient diffusion-weighted MRI. J Immunother Cancer 2023; 11:jitc-2022-006092. [PMID: 36918222 PMCID: PMC10016257 DOI: 10.1136/jitc-2022-006092] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND The inflammatory tumor microenvironment (TME) is formed by various immune cells, being closely associated with tumorigenesis. Especially, the interaction between tumor-infiltrating T-cells and macrophages has a crucial impact on tumor progression and metastatic spread. The purpose of this study was to investigate whether oscillating-gradient diffusion-weighted MRI (OGSE-DWI) enables a cell size-based discrimination between different cell populations of the TME. METHODS Sine-shaped OGSE-DWI was combined with the Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion (IMPULSED) approach to measure microscale diffusion distances, here relating to cell sizes. The accuracy of IMPULSED-derived cell radii was evaluated using in vitro spheroid models, consisting of either pure cancer cells, macrophages, or T-cells. Subsequently, in vivo experiments aimed to assess changes within the TME and its specific immune cell composition in syngeneic murine breast cancer models with divergent degrees of malignancy (4T1, 67NR) during tumor progression, clodronate liposome-mediated depletion of macrophages, and immune checkpoint inhibitor (ICI) treatment. Ex vivo analysis of IMPULSED-derived cell radii was conducted by immunohistochemical wheat germ agglutinin staining of cell membranes, while intratumoral immune cell composition was analyzed by CD3 and F4/80 co-staining. RESULTS OGSE-DWI detected mean cell radii of 8.8±1.3 µm for 4T1, 8.2±1.4 µm for 67NR, 13.0±1.7 for macrophage, and 3.8±1.8 µm for T-cell spheroids. While T-cell infiltration during progression of 4T1 tumors was observed by decreasing mean cell radii from 9.7±1.0 to 5.0±1.5 µm, increasing amount of intratumoral macrophages during progression of 67NR tumors resulted in increasing mean cell radii from 8.9±1.2 to 12.5±1.1 µm. After macrophage depletion, mean cell radii decreased from 6.3±1.7 to 4.4±0.5 µm. T-cell infiltration after ICI treatment was captured by decreasing mean cell radii in both tumor models, with more pronounced effects in the 67NR tumor model. CONCLUSIONS OGSE-DWI provides a versatile tool for non-invasive profiling of the inflammatory TME by assessing the dominating cell type T-cells or macrophages.
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Affiliation(s)
- Emily Hoffmann
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Mirjam Gerwing
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Stephan Niland
- Institute of Physiological Chemistry and Pathobiochemistry, University of Münster, Münster, Germany
| | - Rolf Niehoff
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Max Masthoff
- Clinic of Radiology, University of Münster, Münster, Germany
| | | | - Lydia Wachsmuth
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Enrica Wilken
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Carsten Höltke
- Clinic of Radiology, University of Münster, Münster, Germany
| | | | - Verena Hoerr
- Clinic of Radiology, University of Münster, Münster, Germany.,Department of Internal Medicine II, Heart Center Bonn, University Hospital Bonn, Bonn, Germany
| | - Regina Schinner
- Department of Radiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Philipp Berger
- Institute of Immunology, University of Münster, Münster, Germany
| | - Thomas Vogl
- Institute of Immunology, University of Münster, Münster, Germany
| | - Johannes A Eble
- Institute of Physiological Chemistry and Pathobiochemistry, University of Münster, Münster, Germany
| | - Bastian Maus
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Anne Helfen
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Moritz Wildgruber
- Clinic of Radiology, University of Münster, Münster, Germany.,Department of Radiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Cornelius Faber
- Clinic of Radiology, University of Münster, Münster, Germany
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Feng J, Yu Y, Yin W, Qian S. Development and verification of a 7-lncRNA prognostic model based on tumor immunity for patients with ovarian cancer. J Ovarian Res 2023; 16:31. [PMID: 36739404 PMCID: PMC9898952 DOI: 10.1186/s13048-023-01099-0] [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: 08/15/2022] [Accepted: 01/11/2023] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Both immune-reaction and lncRNAs play significant roles in the proliferation, invasion, and metastasis of ovarian cancer (OC). In this study, we aimed to construct an immune-related lncRNA risk model for patients with OC. METHOD Single sample GSEA (ssGSEA) algorithm was used to analyze the proportion of immune cells in The Cancer Genome Atlas (TCGA) and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells for OC patients. The stromal and immune scores were computed utilizing the ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) analyses were utilized to detect immune cluster-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression was conducted for lncRNA selection. The selected lncRNAs were used to construct a prognosis-related risk model, which was then validated in Gene Expression Omnibus (GEO) database and in vitro validation. RESULTS We identify two subtypes based on the ssGSEA analysis, high immunity cluster (immunity_H) and low immunity cluster (immunity_L). The proportion of patients in immunity_H cluster was significantly higher than that in immunity_L cluster. The ESTIMATE related scores are relative high in immunity_H group. Through WGCNA and LASSO analyses, we identified 141 immune cluster-related lncRNAs and found that these genes were mainly enriched in autophagy. A signature consisting of 7 lncRNAs, including AL391832.3, LINC00892, LINC02207, LINC02416, PSMB8.AS1, AC078788.1 and AC104971.3, were selected as the basis for classifying patients into high- and low-risk groups. Survival analysis and area under the ROC curve (AUC) of the signature pointed out that this risk model had high accuracy in predicting the prognosis of patients with OC. We also conducted the drug sensitive prediction and found that rapamycin outperformed in patient with high risk score. In vitro experiments also confirmed our prediction. CONCLUSIONS We identified 7 immune-related prognostic lncRNAs that effectively predicted survival in OC patients. These findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for these patients.
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Affiliation(s)
- Jing Feng
- grid.452270.60000 0004 0614 4777Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, Hebei Province 061000 China
| | - Yiping Yu
- grid.452270.60000 0004 0614 4777Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, Hebei Province 061000 China
| | - Wen Yin
- grid.452270.60000 0004 0614 4777Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, Hebei Province 061000 China
| | - Sumin Qian
- grid.452270.60000 0004 0614 4777Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, Hebei Province 061000 China
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Ding W, Li B, Zhang Y, He L, Su J. A neutrophil extracellular traps-associated lncRNA signature predicts the clinical outcomes in patients with lung adenocarcinoma. Front Genet 2022; 13:1047231. [PMID: 36419832 PMCID: PMC9676361 DOI: 10.3389/fgene.2022.1047231] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 10/25/2022] [Indexed: 10/24/2023] Open
Abstract
Backgrounds: Neutrophil extracellular traps (NETs) play an important role in the occurrence, metastasis, and immune escape of cancers. We aim to investigate Long non-coding RNAs (lncRNAs) that are correlated to NETs to find some potentially useful biomarkers for lung adenocarcinoma (LUAD), and to explore their correlations with immunotherapy and chemotherapy, as well as the tumor microenvironment. Methods: Based on the The Cancer Genome Atlas (TCGA) database, we identified the prognosis-related lncRNAs which are associated with NETs using cox regression. The patients were then separated into two clusters based on the expression of NETs-associated lncRNAs to perform tumor microenvironment analysis and immune-checkpoint analysis. Least absolute shrinkage and selection operator (LASSO) regression was then performed to establish a prognostic signature. Furthermore, nomogram analysis, tumor mutation burden analysis, immune infiltration analysis, as well as drug sensitivity analysis were performed to test the signature. Results: Using univariate cox regression, we found 10 NETs-associated lncRNAs that are associated with the outcomes of LUAD patients. Also, further analysis which separated the patients into 2 clusters showed that the 10 lncRNAs had significant correlations with the tumor microenvironment. Using LASSO regression, we finally constructed a signature to predict the outcomes of the patients based on 4 NETs-associated lncRNAs. The 4 NETs-associated lncRNAs were namely SIRLNT, AL365181.3, FAM83A-AS1, and AJ003147.2. Using Kaplan-Meier (K-M) analysis, we found that the risk model was strongly associated with the survival outcomes of the patients both in the training group and in the validation group 1 and 2 (p < 0.001, p = 0.026, and p < 0.01). Using receiver operating characteristic (ROC) curve, we tested the sensitivity combined with the specificity of the model and found that the risk model had a satisfactory level of 1-year, 3-year, and 5-year concordance index (C-index) (C = 0.661 in the training group, C = 0.679 in validation group 1, C = 0.692 in validation group 2). We also explored the immune microenvironment and immune checkpoint correlation of the risk model and found some significant results. Conclusion: We constructed a NETs-associated lncRNA signature to predict the outcome of patients with LUAD, which is associated with immunephenoscores and immune checkpoint-gene expression.
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Affiliation(s)
- Wencong Ding
- Department of Rheumatology, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, Guangdong, China
| | - Biyi Li
- Department of Emergency Foshan Hospital of Traditional Chinese Medicine, Foshan, Guangdong, China
| | - Yuan Zhang
- Department of Emergency, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, Guangdong, China
| | - Liu He
- Guangdong Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China
| | - Junwei Su
- Department of Emergency, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, Guangdong, China
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Identification of Tumor Microenvironment Scoring Scheme Based on Bioinformatics Analysis of Immune Cell Infiltration Pattern of Ovarian Cancer. JOURNAL OF ONCOLOGY 2022; 2022:7745675. [PMID: 36081665 PMCID: PMC9448528 DOI: 10.1155/2022/7745675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/20/2022] [Accepted: 07/26/2022] [Indexed: 11/22/2022]
Abstract
Background Tumor microenvironment (TME) is the crucial mediator of tumor progression, and the TME model based on immune cell infiltration to characterize ovarian cancer is considered to be a promising strategy. Methods Sample data of three ovarian cancer cohorts were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The scores of 22 kinds of immune cells were calculated based on CIBERSORT, and the TME clusters (TMECs) of ovarian cancer was determined by ConsensusClusterPlus. Genomic subtype was identified by non-negative matrix factorization (NMF). A TME scoring scheme was constructed using k-means algorithm and principal component analysis (PCA) to quantify the TME infiltration pattern of individuals. Results Four TME subtypes of ovarian cancer samples were defined: TMEC1, TMEC2, TMEC3, and TMEC4. There were also significant differences in overall survival (OS) among the four TMEC, and the OS of TMEC3 was the longest. The difference analysis of TMEC3 and the other three TMECs respectively identified the DEGs and took the intersection, and 585 DEGs were obtained. Two genomic subtypes were identified by NMF based on the expression of 585 genes, which were called GeneC1 and GeneC2. The TME scoring scheme constructed by k-means and PCA algorithm was used to calculate the TME score of ovarian cancer in TCGA. High-TME score was significantly correlated with shorter survival time, older age, lower immunoactivated molecules, and immune checkpoint gene expression. Conclusions This study highlighted the complexity and diversity of TME infiltration patterns in ovarian cancer and constructed a set of TME scoring scheme to reveal TME infiltration patterns and provided new insights into the landscape of TME.
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Zhao N, Xing Y, Hu Y, Chang H. Exploration of the Immunotyping Landscape and Immune Infiltration-Related Prognostic Markers in Ovarian Cancer Patients. Front Oncol 2022; 12:916251. [PMID: 35880167 PMCID: PMC9307664 DOI: 10.3389/fonc.2022.916251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 06/01/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundIncreasing evidence indicates that immune cell infiltration (ICI) affects the prognosis of multiple cancers. This study aims to explore the immunotypes and ICI-related biomarkers in ovarian cancer.MethodsThe ICI levels were quantified with the CIBERSORT and ESTIMATE algorithms. The unsupervised consensus clustering method determined immunotypes based on the ICI profiles. Characteristic genes were identified with the Boruta algorithm. Then, the ICI score, a novel prognostic marker, was generated with the principal component analysis of the characteristic genes. The relationships between the ICI scores and clinical features were revealed. Further, an ICI signature was integrated after the univariate Cox, lasso, and stepwise regression analyses. The accuracy and robustness of the model were tested by three independent cohorts. The roles of the model in the immunophenoscores (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and immunotherapy responses were also explored. Finally, risk genes (GBP1P1, TGFBI, PLA2G2D) and immune cell marker genes (CD11B, NOS2, CD206, CD8A) were tested by qRT-PCR in clinical tissues.ResultsThree immunotypes were identified, and ICI scores were generated based on the 75 characteristic genes. CD8 TCR pathways, chemokine-related pathways, and lymphocyte activation were critical to immunophenotyping. Higher ICI scores contributed to better prognoses. An independent prognostic factor, a three-gene signature, was integrated to calculate patients’ risk scores. Higher TIDE scores, lower ICI scores, lower IPS, lower immunotherapy responses, and worse prognoses were revealed in high-risk patients. Macrophage polarization and CD8 T cell infiltration were indicated to play potentially important roles in the development of ovarian cancer in the clinical validation cohort.ConclusionsOur study characterized the immunotyping landscape and provided novel immune infiltration-related prognostic markers in ovarian cancer.
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Affiliation(s)
- Na Zhao
- Department of Gynecology, Dongying People’s Hospital, Dongying, China
| | - Yujuan Xing
- Department of Gynecology, Dongying People’s Hospital, Dongying, China
| | - Yanfang Hu
- Department of Gynecology, Dongying People’s Hospital, Dongying, China
- *Correspondence: Yanfang Hu, ; Hao Chang,
| | - Hao Chang
- Department of Cancer Research, Hanyu Biomed Center Beijing, Beijing, China
- *Correspondence: Yanfang Hu, ; Hao Chang,
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Identification of an Immune Gene-Based Cisplatin Response Model and CD27 as a Therapeutic Target against Cisplatin Resistance for Ovarian Cancer. J Immunol Res 2022; 2022:4379216. [PMID: 35647204 PMCID: PMC9133897 DOI: 10.1155/2022/4379216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/28/2022] [Accepted: 05/03/2022] [Indexed: 11/17/2022] Open
Abstract
Objective. Evidence demonstrates that the immune microenvironment is extensively associated with chemotherapy response of ovarian cancer (OV). Herein, this study is aimed at establishing a cisplatin response prediction model for OV on the basis of immune genes. Methods. The expression profiles of cisplatin-sensitive and cisplatin-resistant OV specimens were integrated from multiple public datasets. The abundance scores of 22 immune cells were estimated with CIBERSORT algorithm. Differentially expressed immune genes (DEGs) were determined between cisplatin-sensitive and cisplatin-resistant groups. Thereafter, a cisplatin response model was constructed based on prognostic DEGs with logistic regression analysis. The prediction performance was validated in independent cohorts. The possible relationships between the model and immunotherapy were then assessed. Results. Treg scores were significantly decreased in cisplatin-resistant than cisplatin-sensitive OV specimens, with the opposite results for naïve B cells and activated dendritic cells. Fourteen prognostic DEGs were identified and used to develop a cisplatin-response model. The response scores, estimated by the model, showed favorable performance in discriminating cisplatin-response and nonresponse samples. The response scores also presented significantly negative correlations with three well-known cisplatin-resistant pathways and a positive correlation with the expression of CD274 (PD-L1). Moreover, the decreased CD27 expression was observed in cisplatin-resistant groups, and OV specimens with higher CD27 expressions were more sensitive to cisplatin treatment. Conclusion. Altogether, our findings proposed a cisplatin response prediction model and identified CD27 that might be involved in cisplatin resistance. Further investigations suggested that CD27 could be a promising immunotherapeutic target for cisplatin-resistant subset of OV.
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Natural Killer Cells: the Missing Link in Effective Treatment for High-Grade Serous Ovarian Carcinoma. Curr Treat Options Oncol 2022; 23:210-226. [PMID: 35192139 DOI: 10.1007/s11864-021-00929-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/17/2021] [Indexed: 12/22/2022]
Abstract
OPINION STATEMENT Ovarian cancer (OC), especially high-grade serous cancer (HGSC), is a highly heterogeneous malignancy with limited options for curative treatment and a high frequency of relapse. Interactions between OC and the immune system may permit immunoediting and immune escape, and current standard of care therapies can influence immune cell infiltration and function within the tumor microenvironment. Natural killer (NK) cells are involved in cancer immunosurveillance and immunoediting and can be activated by therapy, but deliberate approaches to maximize NK cell reactivity for treatment of HGSC are in their infancy. NK cells may be the ideal target for immunotherapy of HGSC. The diverse functions of NK cells, and their established roles in immunosurveillance, make them attractive candidates for more precise and effective HGSC treatment. NK cells' functional capabilities differ because of variation in receptor expression and genetics, with meaningful impacts on their anticancer activity. Studying HGSC:NK cell interactions will define the features that predict the best outcomes for patients with the disease, but the highly diverse nature of HGSC will likely require combination therapies or approaches to simultaneously target multiple, co-existing features of the tumor to avoid tumor escape and relapse. We expect that the ideal therapy will enable NK cell infiltration and activity, reverse immunosuppression within the tumor microenvironment, and enable effector functions against the diverse subpopulations that comprise HGSC.
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