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Hakobyan A, Meyenberg M, Vardazaryan N, Hancock J, Vulliard L, Loizou JI, Menche J. Pan-cancer analysis of the interplay between mutational signatures and cellular signaling. iScience 2024; 27:109873. [PMID: 38783997 PMCID: PMC11112613 DOI: 10.1016/j.isci.2024.109873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 12/19/2023] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
Cancer is a multi-faceted disease with intricate relationships between mutagenic processes, alterations in cellular signaling, and the tissue microenvironment. To date, these processes have been largely studied in isolation. A systematic understanding of how they interact and influence each other is lacking. Here, we present a framework for systematically characterizing the interaction between pairs of mutational signatures and between signatures and signaling pathway alterations. We applied this framework to large-scale data from TCGA and PCAWG and identified multiple positive and negative interactions, both cross֊tissue and tissue֊specific, that provide new insights into the molecular routes observed in tumorigenesis and their respective drivers. This framework allows for a more fine-grained dissection of common and distinct etiology of mutational signatures. We further identified several interactions with both positive and negative impacts on patient survival, demonstrating their clinical relevance and potential for improving personalized cancer care.
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Affiliation(s)
- Anna Hakobyan
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT25.3, 1090 Vienna, Austria
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- University of Vienna, Center for Molecular Biology, Department of Structural and Computational Biology, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
| | - Mathilde Meyenberg
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT25.3, 1090 Vienna, Austria
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- University of Vienna, Center for Molecular Biology, Department of Structural and Computational Biology, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Center for Cancer Research, Comprehensive Cancer Center, Medical University of Vienna, Spitalgasse 23, BT86/E 01, 1090 Vienna, Austria
| | - Nelli Vardazaryan
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan, 0062 Yerevan, Armenia
| | - Joel Hancock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT25.3, 1090 Vienna, Austria
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- University of Vienna, Center for Molecular Biology, Department of Structural and Computational Biology, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
| | - Loan Vulliard
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT25.3, 1090 Vienna, Austria
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- University of Vienna, Center for Molecular Biology, Department of Structural and Computational Biology, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
| | - Joanna I. Loizou
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT25.3, 1090 Vienna, Austria
- Center for Cancer Research, Comprehensive Cancer Center, Medical University of Vienna, Spitalgasse 23, BT86/E 01, 1090 Vienna, Austria
| | - Jörg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT25.3, 1090 Vienna, Austria
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- University of Vienna, Center for Molecular Biology, Department of Structural and Computational Biology, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria
- Ludwig Boltzmann Institute for Network Medicine at the University of Vienna, Augasse 2-6, 1090 Vienna, Austria
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Budak B, Arga KY. Tumor Mutation Burden as a Cornerstone in Precision Oncology Landscapes: Effect of Panel Size and Uncertainty in Cutoffs. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:193-203. [PMID: 38657109 DOI: 10.1089/omi.2024.0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Tumor mutation burden (TMB) has profound implications for personalized cancer therapy, particularly immunotherapy. However, the size of the panel and the cutoff values for an accurate determination of TMB are still controversial. In this study, a pan-cancer analysis was performed on 22 cancer types from The Cancer Genome Atlas. The efficiency of gene panels of different sizes and the effect of cutoff values in accurate TMB determination was assessed on a large cohort using Whole Exome Sequencing data (n = 9929 patients) as the gold standard. Gene panels of four different sizes (i.e., 0.44-2.54 Mb) were selected for comparative analyses. The heterogeneity of TMB within and between cancer types is observed to be very high, and it becomes possible to obtain the exact TMB value as the size of the panel increases. In panels with limited size, it is particularly difficult to recognize patients with low TMB. In addition, the use of a general TMB cutoff can be quite misleading. The optimal cutoff value varies between 5 and 20, depending on the TMB distribution of the different tumor types. The use of comprehensive gene panels and the optimization of TMB cutoff values for different cancer types can make TMB a robust biomarker in precision oncology. Moreover, optimization of TMB can help accelerate translational medicine research, and by extension, delivery of personalized cancer care in the future.
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Affiliation(s)
- Betul Budak
- Department of Genetics and Bioengineering, Istanbul Bilgi University, Istanbul, Türkiye
- Department of Bioengineering, Marmara University, Istanbul, Türkiye
| | - Kazim Yalcin Arga
- Department of Bioengineering, Marmara University, Istanbul, Türkiye
- Genetic and Metabolic Diseases Research and Investigation Center, Marmara University, Istanbul, Türkiye
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Türkiye
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3
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Sun C, Luo T, Liu Z, Ge J, Shao L, Liu X, Li B, Zhang S, Qiu Q, Wei W, Wang S, Bian XW, Tian J. Tumor Mutation Burden-Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning. THE AMERICAN JOURNAL OF PATHOLOGY 2023; 193:2111-2121. [PMID: 37741452 DOI: 10.1016/j.ajpath.2023.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/08/2023] [Accepted: 08/25/2023] [Indexed: 09/25/2023]
Abstract
Tumor mutation burden (TMB) is a potential biomarker for evaluating the prognosis and response to immune checkpoint inhibitors, but its costly and time-consuming method of measurement limits its widespread application. This study aimed to identify the TMB-related histopathologic features from hematoxylin and eosin slides and explore their prognostic value in gliomas. TMB-related features were detected using a graph convolutional neural network from whole-slide images of patients from The Cancer Genome Atlas data set (619 patients), and the correlation between features and TMB was evaluated in an external validation set (237 patients). TMB-related features were used for predicting overall survival (OS) of patients to investigate whether these features have potential for prognostic prediction. Moreover, biological pathways underlying the prognostic value of the features were further explored. Histopathologic features derived from whole-slide images were significantly associated with patient TMB (P = 0.007 in the external validation set). TMB-related features showed excellent performance for OS prediction, and patients with lower-grade gliomas could be further stratified into different risk groups according to the features (P = 0.00013; hazard ratio, 4.004). Pathways involved in the cell cycle and execution of immune response were enriched in patients with higher OS risk. The TMB-related features could be used to estimate TMB and aid in prognostic risk stratification of patients with glioma with dysregulated biological pathways.
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Affiliation(s)
- Caixia Sun
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing; Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Tao Luo
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing
| | - Zhenyu Liu
- Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing
| | - Jia Ge
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing
| | - Lizhi Shao
- Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xiangyu Liu
- Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Bao Li
- Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Song Zhang
- Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Qi Qiu
- Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Wei Wei
- Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Shuo Wang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing; Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xiu-Wu Bian
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing.
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing; Chinese Academy of Sciences Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China.
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Wang HC, Moi SH, Chan LP, Wu CC, Du JS, Liu PL, Chou MC, Wu CW, Huang CJ, Hsiao HH, Pan MR, Chen LT. The role of the genomic mutation signature and tumor mutation burden on relapse risk prediction in head and neck squamous cell carcinoma after concurrent chemoradiotherapy. Exp Mol Med 2023:10.1038/s12276-023-00984-4. [PMID: 37121970 DOI: 10.1038/s12276-023-00984-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 01/07/2023] [Accepted: 01/30/2023] [Indexed: 05/02/2023] Open
Abstract
Personalized genetic profiling has focused on improving treatment efficacy and predicting risk stratification by identifying mutated genes and selecting targeted agents according to genetic testing. Therefore, we evaluated the role of genetic profiling and tumor mutation burden (TMB) using next-generation sequencing in patients with head and neck squamous cell carcinoma (HNSC). The relapse mutation signature (RMS) and chromatin remodeling mutation signature (CRMS) were explored to predict the risk of relapse in patients with HNSC treated with concurrent chemoradiotherapy (CCRT) with platinum-based chemotherapy. Patients in the high RMS and CRMS groups showed significantly shorter relapse-free survival than those in the low RMS and CRMS groups, respectively (p < 0.001 and p = 0.006). Multivariate Cox regression analysis showed that extranodal extension, CCRT response, and three somatic mutation profiles (TMB, RMS, and CRMS) were independent risk predictors for HNSC relapse. The predictive nomogram showed satisfactory performance in predicting relapse-free survival in patients with HNSC treated with CCRT.
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Affiliation(s)
- Hui-Ching Wang
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
- Department of Internal Medicine, Division of Hematology and Oncology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Sin-Hua Moi
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
- Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Leong-Perng Chan
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Chun-Chieh Wu
- Department of Pathology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Jeng-Shiun Du
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
- Department of Internal Medicine, Division of Hematology and Oncology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Pei-Lin Liu
- Department of Nursing, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Meng-Chun Chou
- Department of Nursing, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Che-Wei Wu
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Chih-Jen Huang
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
- Department of Radiation Oncology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Hui-Hua Hsiao
- Department of Internal Medicine, Division of Hematology and Oncology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Mei-Ren Pan
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan.
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan.
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, 807, Taiwan.
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan.
| | - Li-Tzong Chen
- Department of Internal Medicine, Division of Hematology and Oncology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan.
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan.
- National Institute of Cancer Research, National Health Research Institutes, Tainan, Taiwan.
- Center for Cancer Research, Kaohsiung Medical University, Kaohsiung, 807, Taiwan.
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5
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Li H, Lin D, Wang X, Feng Z, Zhang J, Wang K. The development of a novel signature based on the m6A RNA methylation regulator-related ceRNA network to predict prognosis and therapy response in sarcomas. Front Genet 2022; 13:894080. [PMID: 36313417 PMCID: PMC9597465 DOI: 10.3389/fgene.2022.894080] [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: 03/11/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Background: N6 methyladenosine (m6A)-related noncoding RNAs (including lncRNAs and miRNAs) are closely related to the development of cancer. However, the gene signature and prognostic value of m6A regulators and m6A-associated RNAs in regulating sarcoma (SARC) development and progression remain largely unexplored. Therefore, further research is required. Methods: We obtained expression data for RNA sequencing (RNA-seq) and miRNAs of SARC from The Cancer Genome Atlas (TCGA) datasets. Correlation analysis and two target gene prediction databases (miRTarBase and LncBase v.2) were used to deduce m6A-related miRNAs and lncRNAs, and Cytoscape software was used to construct ceRNA-regulating networks. Based on univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, an m6A-associated RNA risk signature (m6Ascore) model was established. Prognostic differences between subgroups were explored using Kaplan–Meier (KM) analysis. Risk score-related biological phenotypes were analyzed in terms of functional enrichment, tumor immune signature, and tumor mutation signature. Finally, potential immunotherapy features and drug sensitivity predictions for this model were also discussed. Results: A total of 16 miRNAs, 104 lncRNAs, and 11 mRNAs were incorporated into the ceRNA network. The risk score was obtained based on RP11-283I3.6, hsa-miR-455-3p, and CBLL1. Patients were divided into two risk groups using the risk score, with patients in the low-risk group having longer overall survival (OS) than those in the high-risk group. The receiver operating characteristic (ROC) curves indicated that risk characteristic performed well in predicting the prognosis of patients with SARC. In addition, lower m6Ascore was also positively correlated with the abundance of immune cells such as monocytes and mast cells activated, and several immune checkpoint genes were highly expressed in the low-m6Ascore group. According to our analysis, lower m6Ascore may lead to better immunotherapy response and OS outcomes. The risk signature was significantly associated with the chemosensitivity of SARC. Finally, a nomogram was constructed to predict the OS in patients with SARC. The concordance index (C-index) for the nomogram was 0.744 (95% CI: 0.707–0.784). The decision curve analysis (DCA), calibration plot, and ROC curve all showed that this nomogram had good predictive performance. Conclusion: This m6Ascore risk model based on m6A RNA methylation regulator-related RNAs may be promising for clinical prediction of prognosis and might contain potential biomarkers for treatment response prediction for SARC patients.
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Affiliation(s)
- Huling Li
- School of Public Health, Xinjiang Medical University, Urumqi, China
| | - Dandan Lin
- School of Public Health, Xinjiang Medical University, Urumqi, China
| | - Xiaoyan Wang
- School of Public Health, Xinjiang Medical University, Urumqi, China
| | - Zhiwei Feng
- School of Continuing Education, Xinjiang Medical University, Urumqi, China
| | - Jing Zhang
- School of Public Health, Xinjiang Medical University, Urumqi, China
| | - Kai Wang
- Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China
- *Correspondence: Kai Wang,
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Luo L, Wei Q, Xu C, Dong M, Zhao W. Immune landscape and risk prediction based on pyroptosis-related molecular subtypes in triple-negative breast cancer. Front Immunol 2022; 13:933703. [PMID: 36189269 PMCID: PMC9524227 DOI: 10.3389/fimmu.2022.933703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 09/12/2022] [Indexed: 11/30/2022] Open
Abstract
The survival outcome of triple-negative breast cancer (TNBC) remains poor, with difficulties still existing in prognosis assessment and patient stratification. Pyroptosis, a newly discovered form of programmed cell death, is involved in cancer pathogenesis and progression. The role of pyroptosis in the tumor microenvironment (TME) of TNBC has not been fully elucidated. In this study, we disclosed global alterations in 58 pyroptosis-related genes at somatic mutation and transcriptional levels in TNBC samples collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Based on the expression patterns of genes related to pyroptosis, we identified two molecular subtypes that harbored different TME characteristics and survival outcomes. Then, based on differentially expressed genes between two subtypes, we established a 12-gene score with robust efficacy in predicting short- and long-term overall survival of TNBC. Patients at low risk exhibited a significantly better prognosis, more antitumor immune cell infiltration, and higher expression of immune checkpoints including PD-1, PD-L1, CTLA-4, and LAG3. The comprehensive analysis of the immune landscape in TNBC indicated that alterations in pyroptosis-related genes were closely related to the formation of the immune microenvironment and the intensity of the anticancer response. The 12-gene score provided new information on the risk stratification and immunotherapy strategy for highly heterogeneous patients with TNBC.
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7
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Huang ZD, Liu ZZ, Liu YY, Fu YC, Lin LL, Hu C, Gu HY, Wei RX. Molecular Subtypes Based on Cell Differentiation Trajectories in Head and Neck Squamous Cell Carcinoma: Differential Prognosis and Immunotherapeutic Responses. Front Immunol 2022; 12:791621. [PMID: 35003112 PMCID: PMC8739483 DOI: 10.3389/fimmu.2021.791621] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 11/30/2021] [Indexed: 12/24/2022] Open
Abstract
Objective Head and neck squamous cell carcinoma (HNSCC) is one of the most common and lethal malignant tumors. We aimed to investigate the HNSCC cell differentiation trajectories and the corresponding clinical relevance. Methods Based on HNSCC cell differentiation-related genes (HDRGs) identified by single-cell sequencing analysis, the molecular subtypes and corresponding immunity, metabolism, and stemness characteristics of 866 HNSCC cases were comprehensively analyzed. Machine-learning strategies were used to develop a HNSCC cell differentiation score (HCDscore) in order to quantify the unique heterogeneity of individual samples. We also assessed the prognostic value and biological characteristics of HCDscore using the multi-omics data. Results HNSCCs were stratified into three distinct molecular subtypes based on HDRGs: active stroma (Cluster-A), active metabolism (Cluster-B), and active immune (Cluster-C) types. The three molecular subtypes had different characteristics in terms of biological phenotype, genome and epigenetics, prognosis, immunotherapy and chemotherapy responses. We then demonstrated the correlations between HCDscore and the immune microenvironment, subtypes, carcinogenic biological processes, genetic variation, and prognosis. The low-HCDscore group was characterized by activation of immunity, enhanced response to anti-PD-1/PD-L1 immunotherapy, and better survival compared to the high-HCDscore group. Finally, by integrating the HCDscore with prognostic clinicopathological characteristics, a nomogram with strong predictive performance and high accuracy was constructed. Conclusions This study revealed that the cell differentiation trajectories in HNSCC played a nonnegligible role in patient prognosis, biological characteristics, and immune responses. Evaluating cancer cell differentiation will help to develop more effective immunotherapy, metabolic therapy, and chemotherapy strategies.
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Affiliation(s)
- Zhen-Dong Huang
- Department of Spine and Orthopedic Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Department of Stomatology, Southern Medical University, Guangzhou, China
| | - Zi-Zhen Liu
- The Third Clinical School, Hubei University of Medicine, Shiyan, China
| | - Yan-Yi Liu
- Department of Stomatology, Southern Medical University, Guangzhou, China
| | - Yong-Cheng Fu
- The Third Clinical School, Hubei University of Medicine, Shiyan, China
| | - Lu-Lu Lin
- Department of Pathology and Pathophysiology, School of Basic Medicine, Wuhan University, Wuhan, China
| | - Chao Hu
- Department of Spine and Orthopedic Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Hui-Yun Gu
- Department of Spine and Orthopedic Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Ren-Xiong Wei
- Department of Spine and Orthopedic Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
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Tarantino P, Barroso-Sousa R, Garrido-Castro AC, McAllister SS, Guerriero JL, Mittendorf E, Curigliano G, Tolaney SM. Understanding resistance to immune checkpoint inhibitors in advanced breast cancer. Expert Rev Anticancer Ther 2021; 22:141-153. [PMID: 34919490 DOI: 10.1080/14737140.2022.2020650] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
INTRODUCTION The addition of immune checkpoint inhibitors (ICIs) to frontline chemotherapy has improved survival for patients with advanced triple-negative breast cancer (TNBC) expressing programmed death-ligand 1 (PD-L1). Nonetheless, most patients develop resistance, with outcomes remaining poor for this population. Moreover, unsatisfactory activity has been observed with ICIs in PD-L1-negative TNBC and in other breast cancer (BC) subtypes, warranting a deeper understanding of resistance to ICIs in BC. AREAS COVERED We discuss the immune landscape of distinct BC subtypes, review the clinical activity of immunotherapy in BC, and highlight strategies under development to overcome resistance to ICIs. EXPERT OPINION Activity and resistance to ICIs in BC are strongly related to the intrinsic immunophenotype of the tumor tissue. Several promising biomarkers reflecting the immunological state of BC are emerging, with only PD-L1 expression currently adopted into clinical practice. However, limitations make of PD-L1 a sub-optimal biomarker for patient selection, which require efforts to integrate this marker with other immunological features. Concomitantly, a wide variety of drug combinations designed to overcome immune-resistance are being evaluated, with some encouraging signals observed in early-phase trials. Combination strategies tailored to patient and tumor immunophenotype may allow to overcome resistance and fully exploit the potential of ICIs.
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Affiliation(s)
- Paolo Tarantino
- Division of New Drugs and Early Drug Development, European Institute of Oncology IRCCS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,Breast Oncology Program Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | | | - Ana C Garrido-Castro
- Breast Oncology Program Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Sandra S McAllister
- Harvard Medical School, Boston, MA, USA.,Hematology Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jennifer L Guerriero
- Breast Oncology Program Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Elizabeth Mittendorf
- Breast Oncology Program Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development, European Institute of Oncology IRCCS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Sara M Tolaney
- Breast Oncology Program Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
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