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Lu Y, Travnickova J, Badonyi M, Rambow F, Coates A, Khan Z, Marques J, Murphy LC, Garcia-Martinez P, Marais R, Louphrasitthiphol P, Chan AHY, Schofield CJ, von Kriegsheim A, Marsh JA, Pavet V, Sansom OJ, Illingworth RS, Patton EE. ALDH1A3-acetaldehyde metabolism potentiates transcriptional heterogeneity in melanoma. Cell Rep 2024; 43:114406. [PMID: 38963759 DOI: 10.1016/j.celrep.2024.114406] [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: 09/26/2023] [Revised: 05/08/2024] [Accepted: 06/11/2024] [Indexed: 07/06/2024] Open
Abstract
Cancer cellular heterogeneity and therapy resistance arise substantially from metabolic and transcriptional adaptations, but how these are interconnected is poorly understood. Here, we show that, in melanoma, the cancer stem cell marker aldehyde dehydrogenase 1A3 (ALDH1A3) forms an enzymatic partnership with acetyl-coenzyme A (CoA) synthetase 2 (ACSS2) in the nucleus to couple high glucose metabolic flux with acetyl-histone H3 modification of neural crest (NC) lineage and glucose metabolism genes. Importantly, we show that acetaldehyde is a metabolite source for acetyl-histone H3 modification in an ALDH1A3-dependent manner, providing a physiologic function for this highly volatile and toxic metabolite. In a zebrafish melanoma residual disease model, an ALDH1-high subpopulation emerges following BRAF inhibitor treatment, and targeting these with an ALDH1 suicide inhibitor, nifuroxazide, delays or prevents BRAF inhibitor drug-resistant relapse. Our work reveals that the ALDH1A3-ACSS2 couple directly coordinates nuclear acetaldehyde-acetyl-CoA metabolism with specific chromatin-based gene regulation and represents a potential therapeutic vulnerability in melanoma.
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Affiliation(s)
- Yuting Lu
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK; Edinburgh Cancer Research, CRUK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Jana Travnickova
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK; Edinburgh Cancer Research, CRUK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Mihaly Badonyi
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Florian Rambow
- Department of Applied Computational Cancer Research, Institute for AI in Medicine (IKIM), University Hospital Essen, 45131 Essen, Germany; University of Duisburg-Essen, 45141 Essen, Germany
| | - Andrea Coates
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK; Edinburgh Cancer Research, CRUK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Zaid Khan
- Edinburgh Cancer Research, CRUK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Jair Marques
- Edinburgh Cancer Research, CRUK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Laura C Murphy
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Pablo Garcia-Martinez
- Insitute of Genetics and Cancer, The Univeristy of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Richard Marais
- Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park SK10 4TG, UK; Oncodrug Ltd, Alderley Park, Macclesfield SK10 4TG, UK
| | - Pakavarin Louphrasitthiphol
- Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Headington, Oxford OX3 7DQ, UK
| | - Alex H Y Chan
- Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, Chemistry Research Laboratory, University of Oxford, 12 Mansfield Road, Oxford OX1 5JJ, UK
| | - Christopher J Schofield
- Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, Chemistry Research Laboratory, University of Oxford, 12 Mansfield Road, Oxford OX1 5JJ, UK
| | - Alex von Kriegsheim
- Edinburgh Cancer Research, CRUK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Joseph A Marsh
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Valeria Pavet
- Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park SK10 4TG, UK; Cancer Research UK Beatson Institute, CRUK Scotland Centre, Garscube Estate, Switchback Road, Bearsden Glasgow G61 1BD, UK
| | - Owen J Sansom
- Cancer Research UK Beatson Institute, CRUK Scotland Centre, Garscube Estate, Switchback Road, Bearsden Glasgow G61 1BD, UK; School of Cancer Sciences, University of Glasgow, Glasgow G12 0ZD, UK
| | - Robert S Illingworth
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, The University of Edinburgh, Edinburgh BioQuarter, Edinburgh EH16 4UU, UK
| | - E Elizabeth Patton
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK; Edinburgh Cancer Research, CRUK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XR, UK.
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Xie J, Zhang P, Ma C, Tang Q, Zhou X, Xu X, Zhang M, Zhao S, Zhou L, Qi M. Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism. Exp Dermatol 2024; 33:e15119. [PMID: 38881438 DOI: 10.1111/exd.15119] [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: 02/02/2024] [Revised: 05/07/2024] [Accepted: 05/29/2024] [Indexed: 06/18/2024]
Abstract
This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE22153, and GSE65904 cohorts from GEO database were processed and harmonized to mitigate batch effects. Lactate metabolism scores were assigned to individual cells using the 'AUCell' package. Weighted Co-expression Network Analysis (WGCNA) was employed to identify gene modules correlated with lactate metabolism. Machine learning algorithms were applied to construct a prognostic model, and its performance was evaluated in multiple cohorts. Immune correlation, mutation analysis, and enrichment analysis were conducted to further characterize the prognostic model's biological implications. Finally, the function of key gene NDUFS7 was verified by cell experiments. Machine learning resulted in an optimal prognostic model, demonstrating significant prognostic value across various cohorts. In the different cohorts, the high-risk group showed a poor prognosis. Immune analysis indicated differences in immune cell infiltration and checkpoint gene expression between risk groups. Mutation analysis identified genes with high mutation loads in SKCM. Enrichment analysis unveiled enriched pathways and biological processes in high-risk SKCM patients. NDUFS7 was found to be a hub gene in the protein-protein interaction network. After the expression of NDUFS7 was reduced by siRNA knockdown, CCK-8, colony formation, transwell and wound healing tests showed that the activity, proliferation and migration of A375 and WM115 cell lines were significantly decreased. This study offers insights into the prognostic significance of lactate metabolism-related genes in SKCM.
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Affiliation(s)
- Jiaheng Xie
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Pengpeng Zhang
- Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Chenfeng Ma
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, Jiangsu, China
| | - Qikai Tang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, Jiangsu, China
| | - Xinxin Zhou
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Xiaolong Xu
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Min Zhang
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Songyun Zhao
- Department of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Liping Zhou
- Emergency Department of Xiangya Hospital, Central South University, Changsha, China
| | - Min Qi
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
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Schmidt M, Avagyan S, Reiche K, Binder H, Loeffler-Wirth H. A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections. Curr Issues Mol Biol 2024; 46:4701-4720. [PMID: 38785552 PMCID: PMC11119626 DOI: 10.3390/cimb46050284] [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: 03/25/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.
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Affiliation(s)
- Maria Schmidt
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
| | - Susanna Avagyan
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Kristin Reiche
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Perlickstrasse 1, 04103 Leipzig, Germany
- Institute for Clinical Immunology, University Hospital of Leipzig, 04103 Leipzig, Germany
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
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Mao F, Wan N. Creating a multifaceted prognostic model for cutaneous melanoma: the convergence of single-cell and bulk sequencing with machine learning. Front Cell Dev Biol 2024; 12:1401945. [PMID: 38770150 PMCID: PMC11102988 DOI: 10.3389/fcell.2024.1401945] [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: 03/16/2024] [Accepted: 04/15/2024] [Indexed: 05/22/2024] Open
Abstract
Background Cutaneous melanoma is a highly heterogeneous cancer, and understanding the role of inflammation-related genes in its progression is crucial. Methods The cohorts used include the TCGA cohort from TCGA database, and GSE115978, GSE19234, GSE22153 cohort, and GSE65904 cohort from GEO database. Weighted Gene Coexpression Network Analysis (WGCNA) identified key inflammatory modules. Machine learning techniques were employed to construct prognostic models, which were validated across multiple cohorts, including the TCGA cohort, GSE19234, GSE22153, and GSE65904. Immune cell infiltration, tumor mutation load, and immunotherapy response were assessed. The hub gene STAT1 was validated through cellular experiments. Results Single-cell analysis revealed heterogeneity in inflammation-related genes, with NK cells, T cells, and macrophages showing elevated inflammation-related scores. WGCNA identified a module highly associated with inflammation. Machine learning yielded a CoxBoost + GBM prognostic model. The model effectively stratified patients into high-risk and low-risk groups in multiple cohorts. A nomogram and Receiver Operating Characteristic (ROC) curves confirmed the model's accuracy. Low-risk patients exhibited increased immune cell infiltration, higher Tumor Mutational Burden (TMB), and potentially better immunotherapy response. Cellular experiments validated the functional role of STAT1 in melanoma progression. Conclusion Inflammation-related genes play a critical role in cutaneous melanoma progression. The developed prognostic model, nomogram, and validation experiments highlight the potential clinical relevance of these genes and provide a basis for further investigation into personalized treatment strategies for melanoma patients.
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Affiliation(s)
- Fei Mao
- Department of Urology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huai’an, China
| | - Neng Wan
- Department of Plastic Surgery, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huai’an, China
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Xie J, Zhang P, Xu X, Zhou X, Zhao S, Zhang M, Qi M. PANoptosis-related signature in melanoma: Transcriptomic mapping and clinical prognostication. ENVIRONMENTAL TOXICOLOGY 2024; 39:2545-2559. [PMID: 38189554 DOI: 10.1002/tox.24126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 12/19/2023] [Accepted: 12/25/2023] [Indexed: 01/09/2024]
Abstract
Programmed cell death plays a pivotal role in maintaining tissue homeostasis, and recent advancements in cell biology have uncovered PANoptosis-a novel paradigm integrating pyroptosis, apoptosis, and necroptosis. This study investigates the implications of PANoptosis in melanoma, a formidable skin cancer known for its metastatic potential and resistance to conventional therapies. Leveraging bulk and single-cell transcriptome analyses, machine learning modeling, and immune correlation assessments, we unveil the molecular intricacies of PANoptosis in melanoma. Single-cell sequencing identifies diverse cell types involved in PANoptosis, while bulk transcriptome analysis reveals key gene sets correlated with PANoptosis. Machine learning algorithms construct a robust prognostic model, demonstrating consistent predictive power across diverse cohorts. Patients with different cohorts can be divided into high-risk and low-risk groups according to this PANoptosis score, with the high-risk group having a significantly worse prognosis. Immune correlation analyses unveil a link between PANoptosis and immunotherapy response, with potential therapeutic implications. Mutation analysis and enrichment studies provide insights into the mutational landscape associated with PANoptosis. Finally, we used cell experiments to verify the expression and function of key gene PARVA, showing that PARVA was highly expressed in melanoma cell lines, and after PARVA is knocked down, cell invasion, migration, and colony formation ability were significantly decreased. This study advances our understanding of PANoptosis in melanoma, offering a comprehensive framework for targeted therapeutic interventions and personalized medicine strategies in combating this aggressive malignancy.
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Affiliation(s)
- Jiaheng Xie
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Pengpeng Zhang
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Xiaolong Xu
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Xinxin Zhou
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Songyun Zhao
- Department of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Min Zhang
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Min Qi
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
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6
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Xie J, Wu D, Zhang P, Zhao S, Qi M. Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms. Exp Dermatol 2024; 33:e15070. [PMID: 38570935 DOI: 10.1111/exd.15070] [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: 01/25/2024] [Revised: 03/14/2024] [Accepted: 03/20/2024] [Indexed: 04/05/2024]
Abstract
Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.
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Affiliation(s)
- Jiaheng Xie
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Dan Wu
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Pengpeng Zhang
- Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Songyun Zhao
- Department of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Min Qi
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
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Maher NG, Vergara IA, Long GV, Scolyer RA. Prognostic and predictive biomarkers in melanoma. Pathology 2024; 56:259-273. [PMID: 38245478 DOI: 10.1016/j.pathol.2023.11.004] [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: 10/10/2023] [Accepted: 11/20/2023] [Indexed: 01/22/2024]
Abstract
Biomarkers help to inform the clinical management of patients with melanoma. For patients with clinically localised primary melanoma, biomarkers can help to predict post-surgical outcome (including via the use of risk prediction tools), better select patients for sentinel lymph node biopsy, and tailor catch-all follow-up protocols to the individual. Systemic drug treatments, including immune checkpoint inhibitor (ICI) therapies and BRAF-targeted therapies, have radically improved the prognosis of metastatic (stage III and IV) cutaneous melanoma patients, and also shown benefit in the earlier setting of stage IIB/C primary melanoma. Unfortunately, a response is far from guaranteed. Here, we review clinically relevant, established, and emerging, prognostic, and predictive pathological biomarkers that refine clinical decision-making in primary and metastatic melanoma patients. Gene expression profile assays and nomograms are emerging tools for prognostication and sentinel lymph node risk prediction in primary melanoma patients. Biomarkers incorporated into clinical practice guidelines include BRAF V600 mutations for the use of targeted therapies in metastatic cutaneous melanoma, and the HLA-A∗02:01 allele for the use of a bispecific fusion protein in metastatic uveal melanoma. Several predictive biomarkers have been proposed for ICI therapies but have not been incorporated into Australian clinical practice guidelines. Further research, validation, and assessment of clinical utility is required before more prognostic and predictive biomarkers are fluidly integrated into routine care.
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Affiliation(s)
- Nigel G Maher
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Ismael A Vergara
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Georgina V Long
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; Royal North Shore and Mater Hospitals, Sydney, NSW, Australia
| | - Richard A Scolyer
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
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Leng S, Nie G, Yi C, Xu Y, Zhang L, Zhu L. Machine learning-derived identification of tumor-infiltrating immune cell-related signature for improving prognosis and immunotherapy responses in patients with skin cutaneous melanoma. Cancer Cell Int 2023; 23:214. [PMID: 37752452 PMCID: PMC10521465 DOI: 10.1186/s12935-023-03048-9] [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: 06/29/2023] [Accepted: 08/31/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND Immunoblockade therapy based on the PD-1 checkpoint has greatly improved the survival rate of patients with skin cutaneous melanoma (SKCM). However, existing anti-PD-1 therapeutic efficacy prediction markers often exhibit a poor situation of poor reliability in identifying potential beneficiary patients in clinical applications, and an ideal biomarker for precision medicine is urgently needed. METHODS 10 multicenter cohorts including 4 SKCM cohorts and 6 immunotherapy cohorts were selected. Through the analysis of WGCNA, survival analysis, consensus clustering, we screened 36 prognostic genes. Then, ten machine learning algorithms were used to construct a machine learning-derived immune signature (MLDIS). Finally, the independent data sets (GSE22153, GSE54467, GSE59455, and in-house cohort) were used as the verification set, and the ROC index standard was used to evaluate the model. RESULTS Based on computing framework, we found that patients with high MLDIS had poor overall survival and has good prediction performance in all cohorts and in-house cohort. It is worth noting that MLDIS performs better in each data set than almost all models which from 51 prognostic signatures for SKCM. Meanwhile, high MLDIS have a positive prognostic impact on patients treated with anti-PD-1 immunotherapy by driving changes in the level of infiltration of immune cells in the tumor microenvironment. Additionally, patients suffering from SKCM with high MLDIS were more sensitive to immunotherapy. CONCLUSIONS Our study identified that MLDIS could provide new insights into the prognosis of SKCM and predict the immunotherapy response in patients with SKCM.
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Affiliation(s)
- Shaolong Leng
- Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
| | - Gang Nie
- Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
| | - Changhong Yi
- Department of Interventional Radiology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Yunsheng Xu
- Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
| | - Lvya Zhang
- Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
| | - Linyu Zhu
- Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
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Kuras M. Exploring the Complex and Multifaceted Interplay between Melanoma Cells and the Tumor Microenvironment. Int J Mol Sci 2023; 24:14403. [PMID: 37762707 PMCID: PMC10531837 DOI: 10.3390/ijms241814403] [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: 08/29/2023] [Revised: 09/17/2023] [Accepted: 09/20/2023] [Indexed: 09/29/2023] Open
Abstract
Malignant melanoma is a very aggressive skin cancer, characterized by a heterogeneous nature and high metastatic potential. The incidence of melanoma is continuously increasing worldwide, and it is one of the most common cancers in young adults. In the past twenty years, our understanding of melanoma biology has increased profoundly, and disease management for patients with disseminated disease has improved due to the emergence of immunotherapy and targeted therapy. However, a significant fraction of patients relapse or do not respond adequately to treatment. This can partly be explained by the complex signaling between the tumor and its microenvironment, giving rise to melanoma phenotypes with different patterns of disease progression. This review focuses on the key aspects and complex relationship between pathogenesis, genetic abnormalities, tumor microenvironment, cellular plasticity, and metabolic reprogramming in melanoma. By acquiring a deeper understanding of the multifaceted features of melanomagenesis, we can reach a point of more individualized and patient-centered disease management and reduced costs of ineffective treatments.
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Affiliation(s)
- Magdalena Kuras
- Department of Biomedical Engineering, Lund University, 221 00 Lund, Sweden;
- Section for Clinical Chemistry, Department of Translational Medicine, Lund University, 205 02 Malmö, Sweden
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10
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Azimi A, Fernandez-Peñas P. Molecular Classifiers in Skin Cancers: Challenges and Promises. Cancers (Basel) 2023; 15:4463. [PMID: 37760432 PMCID: PMC10526380 DOI: 10.3390/cancers15184463] [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: 04/23/2023] [Revised: 08/29/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
Skin cancers are common and heterogenous malignancies affecting up to two in three Australians before age 70. Despite recent developments in diagnosis and therapeutic strategies, the mortality rate and costs associated with managing patients with skin cancers remain high. The lack of well-defined clinical and histopathological features makes their diagnosis and classification difficult in some cases and the prognostication difficult in most skin cancers. Recent advancements in large-scale "omics" studies, including genomics, transcriptomics, proteomics, metabolomics and imaging-omics, have provided invaluable information about the molecular and visual landscape of skin cancers. On many occasions, it has refined tumor classification and has improved prognostication and therapeutic stratification, leading to improved patient outcomes. Therefore, this paper reviews the recent advancements in omics approaches and appraises their limitations and potential for better classification and stratification of skin cancers.
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Affiliation(s)
- Ali Azimi
- Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Department of Dermatology, Westmead Hospital, Westmead, NSW 2145, Australia
- Centre for Cancer Research, The Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW 2145, Australia
| | - Pablo Fernandez-Peñas
- Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Department of Dermatology, Westmead Hospital, Westmead, NSW 2145, Australia
- Centre for Cancer Research, The Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW 2145, Australia
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11
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Li C, Zhang B, Schaafsma E, Reuben A, Wang L, Turk MJ, Zhang J, Cheng C. TimiGP: Inferring cell-cell interactions and prognostic associations in the tumor immune microenvironment through gene pairs. Cell Rep Med 2023; 4:101121. [PMID: 37467716 PMCID: PMC10394258 DOI: 10.1016/j.xcrm.2023.101121] [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: 12/02/2022] [Revised: 04/11/2023] [Accepted: 06/21/2023] [Indexed: 07/21/2023]
Abstract
Determining the prognostic association of different immune cell types in the tumor microenvironment is critical for understanding cancer biology and developing new therapeutic strategies. However, this is challenging in certain cancer types, where the abundance of different immune subsets is highly correlated. In this study, we develop a computational method named TimiGP to overcome this challenge. Based on bulk gene expression and survival data, TimiGP infers cell-cell interactions that reveal the association between immune cell relative abundance and prognosis. As demonstrated in metastatic melanoma, TimiGP prioritizes immune cells critical in prognosis based on the identified cell-cell interactions. Highly consistent results are obtained by TimiGP when applied to seven independent melanoma datasets and when different cell-type marker sets are used as inputs. Additionally, TimiGP can leverage single-cell RNA sequencing data to delineate the tumor immune microenvironment at high resolutions across a wide range of cancer types.
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Affiliation(s)
- Chenyang Li
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, TX 77030, USA
| | - Baoyi Zhang
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77030, USA
| | - Evelien Schaafsma
- Department of Microbiology and Immunology, Dartmouth College, Hanover, NH 03755, USA
| | - Alexandre Reuben
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, TX 77030, USA; Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, TX 77030, USA
| | - Mary Jo Turk
- Department of Microbiology and Immunology, The Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA; Norris Cotton Cancer Center, The Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| | - Jianjun Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, TX 77030, USA; Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Lung Cancer Genomics Program, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Lung Cancer Interception Program, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Chao Cheng
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA; Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA; The Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX 77030, USA.
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Identification of Pyroptosis-Relevant Signature in Tumor Immune Microenvironment and Prognosis in Skin Cutaneous Melanoma Using Network Analysis. Stem Cells Int 2023; 2023:3827999. [PMID: 36818162 PMCID: PMC9931490 DOI: 10.1155/2023/3827999] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/19/2022] [Accepted: 11/25/2022] [Indexed: 02/10/2023] Open
Abstract
Background Pyroptosis is closely related to the programmed death of cancer cells as well as the tumor immune microenvironment (TIME) via the host-tumor crosstalk. However, the role of pyroptosis-related genes as prognosis and TIME-related biomarkers in skin cutaneous melanoma (SKCM) patients remains unknown. Methods We evaluated the expression profiles, copy number variations, and somatic mutations (CNVs) of 27 genes obtained from MSigDB database regulating pyroptosis among TCGA-SKCM patients. Thereafter, we conducted single-sample gene set enrichment analysis (ssGSEA) for evaluating pyroptosis-associated expression patterns among cases and for exploring the associations with clinicopathological factors and prognostic outcome. In addition, a prognostic pyroptosis-related signature (PPRS) model was constructed by performing Cox regression, weighted gene coexpression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) analysis to score SKCM patients. On the other hand, we plotted the ROC and survival curves for model evaluation and verified the robustness of the model through external test sets (GSE22153, GSE54467, and GSE65904). Meanwhile, we examined the relations of clinical characteristics, oncogene mutations, biological processes (BPs), tumor stemness, immune infiltration degrees, immune checkpoints (ICs), and treatment response with PPRS via multiple methods, including immunophenoscore (IPS) analysis, gene set variation analysis (GSVA), ESTIMATE, and CIBERSORT. Finally, we constructed a nomogram incorporating PPRS and clinical characteristics to improve risk evaluation of SKCM. Results Many pyroptosis-regulated genes showed abnormal expression within SKCM. TP53, TP63, IL1B, IL18, IRF2, CASP5, CHMP4C, CHMP7, CASP1, and GSDME were detected with somatic mutations, among which, a majority displayed CNVs at high frequencies. Pyroptosis-associated profiles established based on pyroptosis-regulated genes showed markedly negative relation to low stage and superior prognostic outcome. Blue module was found to be highly positively correlated with pyroptosis. Later, this study established PPRS based on the expression of 8 PAGs (namely, GBP2, HPDL, FCGR2A, IFITM1, HAPLN3, CCL8, TRIM34, and GRIPAP1), which was highly associated with OS, oncogene mutations, tumor stemness, immune infiltration degrees, IC levels, treatment responses, and multiple biological processes (including cell cycle and immunoinflammatory response) in training and test set samples. Conclusions Based on our observations, analyzing modification patterns associated with pyroptosis among diverse cancer samples via PPRS is important, which can provide more insights into TIME infiltration features and facilitate immunotherapeutic development as well as prognosis prediction.
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Suresh S, Rabbie R, Garg M, Lumaquin D, Huang TH, Montal E, Ma Y, Cruz NM, Tang X, Nsengimana J, Newton-Bishop J, Hunter MV, Zhu Y, Chen K, de Stanchina E, Adams DJ, White RM. Identifying the Transcriptional Drivers of Metastasis Embedded within Localized Melanoma. Cancer Discov 2023; 13:194-215. [PMID: 36259947 PMCID: PMC9827116 DOI: 10.1158/2159-8290.cd-22-0427] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/25/2022] [Accepted: 10/14/2022] [Indexed: 01/16/2023]
Abstract
In melanoma, predicting which tumors will ultimately metastasize guides treatment decisions. Transcriptional signatures of primary tumors have been utilized to predict metastasis, but which among these are driver or passenger events remains unclear. We used data from the adjuvant AVAST-M trial to identify a predictive gene signature in localized tumors that ultimately metastasized. Using a zebrafish model of primary melanoma, we interrogated the top genes from the AVAST-M signature in vivo. This identified GRAMD1B, a cholesterol transfer protein, as a bona fide metastasis suppressor, with a majority of knockout animals rapidly developing metastasis. Mechanistically, excess free cholesterol or its metabolite 27-hydroxycholesterol promotes invasiveness via activation of an AP-1 program, which is associated with increased metastasis in humans. Our data demonstrate that the transcriptional seeds of metastasis are embedded within localized tumors, suggesting that early targeting of these programs can be used to prevent metastatic relapse. SIGNIFICANCE We analyzed human melanoma transcriptomics data to identify a gene signature predictive of metastasis. To rapidly test clinical signatures, we built a genetic metastasis platform in adult zebrafish and identified GRAMD1B as a suppressor of melanoma metastasis. GRAMD1B-associated cholesterol overload activates an AP-1 program to promote melanoma invasion. This article is highlighted in the In This Issue feature, p. 1.
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Affiliation(s)
- Shruthy Suresh
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Roy Rabbie
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Manik Garg
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingdom
| | - Dianne Lumaquin
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, New York
| | - Ting-Hsiang Huang
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Emily Montal
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yilun Ma
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, New York
| | - Nelly M Cruz
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Xinran Tang
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
- Biochemistry and Structural Biology, Cellular and Developmental Biology and Molecular Biology Ph.D. Program, Weill Cornell Graduate School of Medical Sciences, New York, New York
| | - Jérémie Nsengimana
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences Newcastle University, Newcastle upon Tyne, United Kingdom
| | | | - Miranda V. Hunter
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yuxin Zhu
- Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kevin Chen
- Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Elisa de Stanchina
- Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - David J. Adams
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Richard M. White
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
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ACAP1 Deficiency Predicts Inferior Immunotherapy Response in Solid Tumors. Cancers (Basel) 2022; 14:cancers14235951. [PMID: 36497434 PMCID: PMC9740925 DOI: 10.3390/cancers14235951] [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: 09/14/2022] [Revised: 11/20/2022] [Accepted: 11/29/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND ACAP1 plays a key role in endocytic recycling, which is essential for the normal function of lymphocytes. However, the expression and function of ACAP1 in lymphocytes have rarely been studied. METHODS Large-scale genomic data, including multiple bulk RNA-sequencing datasets, single-cell sequencing datasets, and immunotherapy cohorts, were exploited to comprehensively characterize ACAP1 expression, regulation, and function. Gene set enrichment analysis (GSEA) was used to uncover the pathways associated with ACAP1 expression. Eight algorithms, including TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, xCELL, MCPCOUNTER, EPIC, and TIDE, were applied to estimate the infiltrating level of immune cells. Western blotting, qPCR, and ChIP-PCR were used to validate the findings from bioinformatic analyses. A T-cell co-culture killing assay was used to investigate the function of ACAP1 in lymphocytes. RESULTS ACAP1 was highly expressed in immune-related tissues and cells and minimally in other tissues. Moreover, single-cell sequencing analysis in tumor samples revealed that ACAP1 is expressed primarily in tumor-infiltrating lymphocytes (TILs), including T, B, and NK cells. ACAP1 expression is negatively regulated by promoter DNA methylation, with its promoter hypo-methylated in immune cells but hyper-methylated in other cells. Furthermore, SPI1 binds to the ACAP1 promoter and positively regulates its expression in immune cells. ACAP1 levels positively correlate with the infiltrating levels of TILs, especially CD8+ T cells, across a broad range of solid cancer types. ACAP1 deficiency is associated with poor prognosis and immunotherapeutic response in multiple cancer types treated with checkpoint blockade therapy (ICT). Functionally, the depletion of ACAP1 by RNA interference significantly impairs the T cell-mediated killing of tumor cells. CONCLUSIONS Our study demonstrates that ACAP1 is essential for the normal function of TILs, and its deficiency indicates an immunologically "cold" status of tumors that are resistant to ICT.
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15
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Mall R, Bynigeri RR, Karki R, Malireddi RKS, Sharma B, Kanneganti TD. Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology. NAR Cancer 2022; 4:zcac033. [PMID: 36329783 PMCID: PMC9623737 DOI: 10.1093/narcan/zcac033] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 10/03/2022] [Accepted: 10/28/2022] [Indexed: 11/24/2022] Open
Abstract
Resistance to programmed cell death (PCD) is a hallmark of cancer. While some PCD components are prognostic in cancer, the roles of many molecules can be masked by redundancies and crosstalks between PCD pathways, impeding the development of targeted therapeutics. Recent studies characterizing these redundancies have identified PANoptosis, a unique innate immune-mediated inflammatory PCD pathway that integrates components from other PCD pathways. Here, we designed a systematic computational framework to determine the pancancer clinical significance of PANoptosis and identify targetable biomarkers. We found that high expression of PANoptosis genes was detrimental in low grade glioma (LGG) and kidney renal cell carcinoma (KIRC). ZBP1, ADAR, CASP2, CASP3, CASP4, CASP8 and GSDMD expression consistently had negative effects on prognosis in LGG across multiple survival models, while AIM2, CASP3, CASP4 and TNFRSF10 expression had negative effects for KIRC. Conversely, high expression of PANoptosis genes was beneficial in skin cutaneous melanoma (SKCM), with ZBP1, NLRP1, CASP8 and GSDMD expression consistently having positive prognostic effects. As a therapeutic proof-of-concept, we treated melanoma cells with combination therapy that activates ZBP1 and showed that this treatment induced PANoptosis. Overall, through our systematic framework, we identified and validated key innate immune biomarkers from PANoptosis which can be targeted to improve patient outcomes in cancers.
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Affiliation(s)
- Raghvendra Mall
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Ratnakar R Bynigeri
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Rajendra Karki
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | | | - Bhesh Raj Sharma
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
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Zhu J, Chang R, Wei B, Fu Y, Chen X, Liu H, Zhou W. Photothermal Nano-Vaccine Promoting Antigen Presentation and Dendritic Cells Infiltration for Enhanced Immunotherapy of Melanoma via Transdermal Microneedles Delivery. Research (Wash D C) 2022; 2022:9816272. [PMID: 36157510 PMCID: PMC9484834 DOI: 10.34133/2022/9816272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/09/2022] [Indexed: 11/06/2022] Open
Abstract
Immunotherapy has demonstrated the potential to cure melanoma, while the current response rate is still unsatisfactory in clinics. Extensive evidence indicates the correlation between the efficacy and pre-existing T-cell in tumors, whereas the baseline T-cell infiltration is lacking in low-response melanoma patients. Herein, we demonstrated the critical contribution of dendritic cells (DCs) on melanoma survival and baseline T-cell level, as well as the efficacy of immunotherapy. Capitalized on this fact, we developed a photothermal nano-vaccine to simultaneously promote tumor antigens presentation and DCs infiltration for enhanced immunotherapy. The nano-vaccine was composed of polyserotonin (PST) core and tannic acid (TA)/Mn2+ coordination-based metal-organic-framework (MOF) shell for β-catenin silencing DNAzyme loading, which was further integrated into dissolving microneedles to allow noninvasive and transdermal administration at melanoma skin. The nano-vaccine could rapidly penetrate skin upon microneedles insertion and exert a synergistically amplified photothermal effect to induce immunogenic cell death (ICD). The MOF shell then dissociated and released Mn2+ as a cofactor to self-activate DNAzyme for β-catenin suppression, which in turn caused a persistent CCL4 excretion to promote the infiltration of DCs into the tumor. Meanwhile, the liberated PST core could effectively capture and facilitate tumor antigens presentation to DCs. As a result, potent antitumor efficacies were achieved for both primary and distal tumors without any extra treatment, indicating the great promise of such a nano-vaccine for on-demand personalized immunotherapy of melanoma.
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Affiliation(s)
- Jiaojiao Zhu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, China
| | - Ruimin Chang
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Benliang Wei
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
| | - Yao Fu
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
| | - Hong Liu
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
| | - Wenhu Zhou
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, China
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17
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Zhu Z, Li G, Li Z, Wu Y, Yang Y, Wang M, Zhang H, Qu H, Song Z, He Y. Core immune cell infiltration signatures identify molecular subtypes and promote precise checkpoint immunotherapy in cutaneous melanoma. Front Immunol 2022; 13:914612. [PMID: 36072600 PMCID: PMC9441634 DOI: 10.3389/fimmu.2022.914612] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 07/27/2022] [Indexed: 11/25/2022] Open
Abstract
Yutao Wang, China Medical University, ChinaThe tumor microenvironment (TME) has been shown to impact the prognosis of tumors in patients including cutaneous melanoma (CM); however, not all components of TME are important. Given the aforementioned situation, the functional immune cell contents correlated with CM patient prognosis are needed to optimize present predictive models and reflect the overall situation of TME. We developed a novel risk score named core tumor-infiltrating immune cell score (cTICscore), which showed certain advantages over existing biomarkers or TME-related signatures in predicting the prognosis of CM patients. Furthermore, we explored a new gene signature named cTILscore−related module gene score (cTMGs), based on four identified TME-associated genes (GCH1, GZMA, PSMB8, and PLAAT4) showing a close correlation with the cTICscore, which was generated by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis to facilitate clinical application. Patients with low cTMGs had significantly better overall survival (OS, P = 0.002,< 0.001, = 0.002, and = 0.03, respectively) in the training and validating CM datasets. In addition, the area under the curve values used to predict the immune response in four CM cohorts were 0.723, 0.723, 0.754, and 0.792, respectively, and that in one gastric cohort was 0.764. Therefore, the four-gene signature, based on cTICscore, might improve prognostic information, serving as a predictive tool for CM patients receiving immunotherapy.cutaneous melanoma, tumor microenvironment, prognosis, immunotherapy, cTICscore
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Affiliation(s)
- Zheng Zhu
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Guoyin Li
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an, China
| | - Zhenning Li
- Department of Oromaxillofacial-Head and Neck Surgery, Liaoning Province Key Laboratory of Oral Disease, School and Hospital of Stomatology, China Medical University, Shenyang, China
| | - Yinghua Wu
- School of Medicine, Central South University, Changsha, China
| | - Yan Yang
- Department of Public Health, Southwest Medical University, Luzhou, China
| | - Mingyang Wang
- Department of Ophthalmology, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Huihua Zhang
- Department of Plastic Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, China
| | - Hui Qu
- Department of Plastic Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, China
| | - Zewen Song
- Department of Oncology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Yuanmin He
- Department of Dermatology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- *Correspondence: Yuanmin He,
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18
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Zou Z, Zhao W, Liang J, Chen M, Yu F. Identification of Core Genes and Prognostic Models of Laryngeal Cancer by Autophagy Related Biomarkers. J BIOMATER TISS ENG 2022. [DOI: 10.1166/jbt.2022.3071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Background: The aims of our article were to identify the core genes of the autophagy-related genes (ARGs) which abnormally expressed in laryngeal cancer (LC) and constructed a risk prognostic models with these genes. Methods: In this study, we identified genes with abnormally
expressed in LC, and they were mainly involved in some cancer-related gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG). Statistical analysis were conducted to identify the powerful independent prognostic factors associated with clinical factors and survival.
Results: A total of 35 DEGs were identified in our research. The risk prediction model was constructed with three potential prognostic genes (VEGFA, SPNS1 and CCL2) of autophagy by lasso regression analysis that can successfully predict the prognosis in LC. We applied ROC curve to evaluate
the effectiveness of the risk prognostic model, and found that AUC was 0.693 below the curve. Risk prediction model was only related to survival status (P < 0.01), and was not related to clinicopathological factors. Furthermore, the genes (VEGFA and CCL2) were considered as core
genes not only because they were the highly connected genes but also they were the composed genes of risk prognostic model. Conclusions: Taken together, ARGs were considered as important roles in the progression of LC and the prognostic model can help to identification of new targets
to guide the diagnosis and therapy.
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Affiliation(s)
- Zirou Zou
- Department of Otolaryngology, Head and Neck Surgery, Guangzhou Red Cross Hospital, Guangzhou, Guangdong, 510220, China
| | - Wenmin Zhao
- Department of Otolaryngology, Head and Neck Surgery, Guangzhou Red Cross Hospital, Guangzhou, Guangdong, 510220, China
| | - Jiajian Liang
- Department of Otolaryngology, Head and Neck Surgery, Guangzhou Red Cross Hospital, Guangzhou, Guangdong, 510220, China
| | - Mingtao Chen
- Department of Otolaryngology, Head and Neck Surgery, Guangzhou Red Cross Hospital, Guangzhou, Guangdong, 510220, China
| | - Feng Yu
- Department of Otolaryngology, Head and Neck Surgery, Guangzhou Red Cross Hospital, Guangzhou, Guangdong, 510220, China
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Conway K, Tsai YS, Edmiston SN, Parker JS, Parrish EA, Hao H, Kuan PF, Scott GA, Frank JS, Googe P, Ollila DW, Thomas NE. Characterization of the CpG Island Hypermethylated Phenotype Subclass in Primary Melanomas. J Invest Dermatol 2022; 142:1869-1881.e10. [PMID: 34843679 PMCID: PMC9135958 DOI: 10.1016/j.jid.2021.11.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/28/2021] [Accepted: 11/08/2021] [Indexed: 12/26/2022]
Abstract
Cutaneous melanoma can be lethal even if detected at an early stage. Epigenetic profiling may facilitate the identification of aggressive primary melanomas with unfavorable outcomes. We performed clustering of whole-genome methylation data to identify subclasses that were then assessed for survival, clinical features, methylation patterns, and biological pathways. Among 89 cutaneous primary invasive melanomas, we identified three methylation subclasses exhibiting low methylation, intermediate methylation, or hypermethylation of CpG islands, known as the CpG island methylator phenotype (CIMP). CIMP melanomas occurred as early as tumor stage 1b and, compared with low-methylation melanomas, were associated with age at diagnosis ≥65 years, lentigo maligna melanoma histologic subtype, presence of ulceration, higher American Joint Committee on Cancer stage and tumor stage, and lower tumor-infiltrating lymphocyte grade (all P < 0.05). Patients with CIMP melanomas had worse melanoma-specific survival (hazard ratio = 11.84; confidence interval = 4.65‒30.20) than those with low-methylation melanomas, adjusted for age, sex, American Joint Committee on Cancer stage, and tumor-infiltrating lymphocyte grade. Genes hypermethylated in CIMP compared with those in low-methylation melanomas included PTEN, VDR, PD-L1, TET2, and gene sets related to development/differentiation, the extracellular matrix, and immunity. CIMP melanomas exhibited hypermethylation of genes important in melanoma progression and tumor immunity, and although present in some early melanomas, CIMP was associated with worse survival independent of known prognostic factors.
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Affiliation(s)
- Kathleen Conway
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Department of Dermatology, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Lineberger Comprehensive Cancer Center, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
| | - Yihsuan S Tsai
- Lineberger Comprehensive Cancer Center, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Sharon N Edmiston
- Lineberger Comprehensive Cancer Center, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Joel S Parker
- Lineberger Comprehensive Cancer Center, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Department of Genetics, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Eloise A Parrish
- Lineberger Comprehensive Cancer Center, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Honglin Hao
- Department of Genetics, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Pei Fen Kuan
- Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York, USA
| | - Glynis A Scott
- Department of Dermatology, University of Rochester Medical Center, Rochester, New York, USA; Department of Pathology & Laboratory Medicine, University of Rochester Medical Center, Rochester, New York, USA
| | - Jill S Frank
- Department of Surgery, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Paul Googe
- Department of Dermatology, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Department of Pathology and Lab Medicine, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - David W Ollila
- Lineberger Comprehensive Cancer Center, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Department of Surgery, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Nancy E Thomas
- Department of Dermatology, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Lineberger Comprehensive Cancer Center, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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20
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Bakr MN, Takahashi H, Kikuchi Y. Analysis of Melanoma Gene Expression Signatures at the Single-Cell Level Uncovers 45-Gene Signature Related to Prognosis. Biomedicines 2022; 10:biomedicines10071478. [PMID: 35884783 PMCID: PMC9313451 DOI: 10.3390/biomedicines10071478] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/12/2022] [Accepted: 06/19/2022] [Indexed: 11/16/2022] Open
Abstract
Since the current melanoma clinicopathological staging system remains restricted to predicting survival outcomes, establishing precise prognostic targets is needed. Here, we used gene expression signature (GES) classification and Cox regression analyses to biologically characterize melanoma cells at the single-cell level and construct a prognosis-related gene signature for melanoma. By analyzing publicly available scRNA-seq data, we identified six distinct GESs (named: “Anti-apoptosis”, “Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, “Extracellular structure organization”, and “Epithelial-Mesenchymal Transition (EMT)”). We verified these GESs in the bulk RNA-seq data of patients with skin cutaneous melanoma (SKCM) from The Cancer Genome Atlas (TCGA). Four GESs (“Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, and “Extracellular structure organization”) were significantly correlated with prognosis (p = 1.08 × 10−5, p = 0.042, p = 0.001, and p = 0.031, respectively). We identified a prognostic signature of melanoma composed of 45 genes (MPS_45). MPS_45 was validated in TCGA-SKCM (HR = 1.82, p = 9.08 × 10−6) and three other melanoma datasets (GSE65904: HR = 1.73, p = 0.006; GSE19234: HR = 3.83, p = 0.002; and GSE53118: HR = 1.85, p = 0.037). MPS_45 was independently associated with survival (p = 0.002) and was proved to have a high potential for predicting prognosis in melanoma patients.
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Affiliation(s)
- Mohamed Nabil Bakr
- Department of Biological Science, Graduate School of Science, Hiroshima University, Kagamiyama 1-3-1, Higashi-Hiroshima, Hiroshima 739-8526, Japan;
- National Institute of Oceanography and Fisheries (NIOF), Cairo 11516, Egypt
| | - Haruko Takahashi
- Department of Biological Science, Graduate School of Science, Hiroshima University, Kagamiyama 1-3-1, Higashi-Hiroshima, Hiroshima 739-8526, Japan;
- Graduate School of Integrated Sciences for Life, Hiroshima University, Kagamiyama 1-3-1, Higashi-Hiroshima, Hiroshima 739-8526, Japan
- Correspondence: (H.T.); (Y.K.); Tel.: +81-82-424-7440 (Y.K.)
| | - Yutaka Kikuchi
- Department of Biological Science, Graduate School of Science, Hiroshima University, Kagamiyama 1-3-1, Higashi-Hiroshima, Hiroshima 739-8526, Japan;
- Graduate School of Integrated Sciences for Life, Hiroshima University, Kagamiyama 1-3-1, Higashi-Hiroshima, Hiroshima 739-8526, Japan
- Correspondence: (H.T.); (Y.K.); Tel.: +81-82-424-7440 (Y.K.)
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21
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lv Y, Yuan CH, Han LY, Huang GR, Ju LC, Chen LH, Han HY, Zhang C, Zeng LH. The Overexpression of SLC25A13 Predicts Poor Prognosis and Is Correlated with Immune Cell Infiltration in Patients with Skin Cutaneous Melanoma. DISEASE MARKERS 2022; 2022:4091978. [PMID: 35607442 PMCID: PMC9124094 DOI: 10.1155/2022/4091978] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/07/2022] [Accepted: 04/09/2022] [Indexed: 11/29/2022]
Abstract
Purpose Skin cutaneous melanoma (SKCM) is one of the most malignant and aggressive cancers with poor prognosis due to its rapid progression towards metastasis. Thus, finding clinically relevant biomarkers for early diagnosis, prognosis, and therapy prediction is essential. This study focused on the identification of SLC25A13 as a novel biomarker for SKCM and is aimed at investigating the biological functions of solute carrier family 25 member 13 (SLC25A13) in the development of SKCM. Methods GEPIA was used to analyze the diagnostic and prognostic values of SLC25A13 in SKCM using the TCGA dataset. PrognoScan was used to validate the prognostic value of SLC25A13 and its coexpressed genes in SKCM. TISIDB was established to reveal the relationship between the expression of SLC25A13 and immune infiltration in SKCM. The protein expression of SLC25A13 in SKCM was evaluated by the Human Protein Atlas. The signaling pathways and biological functions of SLC25A13 in SKCM were analyzed by LinkOmics. Metascape was applied to analyze the functional enrichment analysis of SLC25A13. Protein-protein interaction analysis of SLC25A13 was performed by GeneMANIA. Results The mRNA and protein levels of SLC25A13 in the SKCM were much higher than those in the normal tissue. Furthermore, the overexpression of SLC25A13 predicts worse outcomes of SKCM patients. Moreover, the SLC25A13 expression was negatively correlated with the immune infiltration level of SKCM. The overexpression of SLC25A13 coexpressed genes, such as ACLY and AFG3L2, and SCL25A13 interacting genes also predicted the unfavorable prognosis of SKCM patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of SLC25A13 coexpressed genes showed that these genes are enriched in ATPase activity, cell cycle, mTOR, and VEGFA-VEGFR2 signaling pathways, which were relevant to tumor development and angiogenesis. Gene set enrichment analysis (GSEA) demonstrated that the SLC25A13 expression was related to infiltrating immune cells in SKCM. Conclusion Our findings revealed that SLC25A13 might be a potential prognostic and therapeutic biomarker for SKCM.
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Affiliation(s)
- Yue lv
- Department of Nursing, School of Medicine, Zhejiang University City College, Hangzhou, Zhejiang, China 310015
| | - Chun-hui Yuan
- Department of Pharmacology, School of Medicine, Zhejiang University City College, Hangzhou, Zhejiang, China 310015
| | - Lu-yao Han
- Department of Nursing, School of Medicine, Zhejiang University City College, Hangzhou, Zhejiang, China 310015
| | - Gao-ru Huang
- Department of Nursing, School of Medicine, Zhejiang University City College, Hangzhou, Zhejiang, China 310015
| | - Ling-ce Ju
- Department of Nursing, School of Medicine, Zhejiang University City College, Hangzhou, Zhejiang, China 310015
| | - Ling-hui Chen
- Thyroid Surgery Department, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China 310003
| | - Hai-ying Han
- Department of Nursing, School of Medicine, Zhejiang University City College, Hangzhou, Zhejiang, China 310015
| | - Chong Zhang
- Department of Nursing, School of Medicine, Zhejiang University City College, Hangzhou, Zhejiang, China 310015
- Department of Pharmacology, School of Medicine, Zhejiang University City College, Hangzhou, Zhejiang, China 310015
| | - Ling-hui Zeng
- Department of Nursing, School of Medicine, Zhejiang University City College, Hangzhou, Zhejiang, China 310015
- Department of Pharmacology, School of Medicine, Zhejiang University City College, Hangzhou, Zhejiang, China 310015
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22
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Kenny C, Dilshat R, Seberg HE, Van Otterloo E, Bonde G, Helverson A, Franke CM, Steingrímsson E, Cornell RA. TFAP2 paralogs facilitate chromatin access for MITF at pigmentation and cell proliferation genes. PLoS Genet 2022; 18:e1010207. [PMID: 35580127 PMCID: PMC9159589 DOI: 10.1371/journal.pgen.1010207] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 06/01/2022] [Accepted: 04/19/2022] [Indexed: 12/13/2022] Open
Abstract
In developing melanocytes and in melanoma cells, multiple paralogs of the Activating-enhancer-binding Protein 2 family of transcription factors (TFAP2) contribute to expression of genes encoding pigmentation regulators, but their interaction with Microphthalmia transcription factor (MITF), a master regulator of these cells, is unclear. Supporting the model that TFAP2 facilitates MITF's ability to activate expression of pigmentation genes, single-cell seq analysis of zebrafish embryos revealed that pigmentation genes are only expressed in the subset of mitfa-expressing cells that also express tfap2 paralogs. To test this model in SK-MEL-28 melanoma cells we deleted the two TFAP2 paralogs with highest expression, TFAP2A and TFAP2C, creating TFAP2 knockout (TFAP2-KO) cells. We then assessed gene expression, chromatin accessibility, binding of TFAP2A and of MITF, and the chromatin marks H3K27Ac and H3K27Me3 which are characteristic of active enhancers and silenced chromatin, respectively. Integrated analyses of these datasets indicate TFAP2 paralogs directly activate enhancers near genes enriched for roles in pigmentation and proliferation, and directly repress enhancers near genes enriched for roles in cell adhesion. Consistently, compared to WT cells, TFAP2-KO cells proliferate less and adhere to one another more. TFAP2 paralogs and MITF co-operatively activate a subset of enhancers, with the former necessary for MITF binding and chromatin accessibility. By contrast, TFAP2 paralogs and MITF do not appear to co-operatively inhibit enhancers. These studies reveal a mechanism by which TFAP2 profoundly influences the set of genes activated by MITF, and thereby the phenotype of pigment cells and melanoma cells.
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Affiliation(s)
- Colin Kenny
- Department of Anatomy and Cell Biology, College of Medicine, University of Iowa, Iowa City, Iowa, United States of America
| | - Ramile Dilshat
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Hannah E. Seberg
- Department of Anatomy and Cell Biology, College of Medicine, University of Iowa, Iowa City, Iowa, United States of America
| | - Eric Van Otterloo
- Department of Anatomy and Cell Biology, College of Medicine, University of Iowa, Iowa City, Iowa, United States of America
| | - Gregory Bonde
- Department of Anatomy and Cell Biology, College of Medicine, University of Iowa, Iowa City, Iowa, United States of America
| | - Annika Helverson
- Department of Anatomy and Cell Biology, College of Medicine, University of Iowa, Iowa City, Iowa, United States of America
| | - Christopher M. Franke
- Department of Surgery, College of Medicine, University of Iowa, Iowa City, Iowa, United States of America
| | - Eiríkur Steingrímsson
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Robert A. Cornell
- Department of Anatomy and Cell Biology, College of Medicine, University of Iowa, Iowa City, Iowa, United States of America
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Xu L, Zhang Y, Liu T, Wang L, Zhao Z, Zhang X, Li X, Wu W, Yu S. Melanoma Molecular Subtypes and Development of Prognostic and Immunotherapy-Related Genetic Characteristics by Ferroptosis Gene Analysis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2992939. [PMID: 35516454 PMCID: PMC9064509 DOI: 10.1155/2022/2992939] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/14/2022] [Accepted: 02/19/2022] [Indexed: 12/03/2022]
Abstract
The dissimilarity is a major problem in clinical therapy of skin cutaneous melanoma (SKCM). Objective and reproducible classification systems may help decode SKCM heterogeneity. ConsensusClusterPlus was used to establish a stable immune molecular classification based on ferroptosis-related genes that had been acquired from FerrDb. Moreover, the prognosis, somatic mutations, immune microenvironment characteristics, functional enrichment, and clinical responsiveness to the immune checkpoint blockade of different subtypes in two independent melanin datasets were compared. Kaplan-Meier curves, univariate, multivariate, least absolute contraction, and selection operator (LASSO) Cox regression analysis were used to develop a molecular model for predicting survival, which was verified by a nomogram on the basis of independent prognostic indicators. Two molecular subtypes (C1 and C2) for SKCM were first identified according to ferroptosis-related genes; C1 showed a poor prognosis, with lower infiltration degree of immune cells and TIED score and higher homologous recombination defects, fraction altered, the number of segments, and copy number amplification and deletion. These characteristics of C2 were the opposite of C1. A ferroptosis-related prognosis risk score (FPRS) model was constructed using 6 of 463 genes with differential expression between C1 and C2. This model splits patients into low- and high-risk cohorts. There were significant differences in the infiltration and proportion of immune cells, immune checkpoint gene expression, responsiveness to immune checkpoint therapy, and sensitivity to chemotherapeutic medications between low- and high-risk cohorts. This model was an independent prognostic marker for SKCM and has a high AUC. In summary, we have identified two subtypes of SKCM with different molecular and immune characteristics on the basis of ferroptosis-related genes and further developed and verified an FPRS model, which might independently serve as a prognostic marker for SKCM.
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Affiliation(s)
- Libin Xu
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Yu Zhang
- Department of Immunology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ting Liu
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Luqiang Wang
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Zhenguo Zhao
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Xinxin Zhang
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Xiaoyang Li
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Wence Wu
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Shengji Yu
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
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24
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Liu C, Liu Y, Yu Y, Zhao Y, Yu A. Comprehensive analysis of ferroptosis-related genes and prognosis of cutaneous melanoma. BMC Med Genomics 2022; 15:39. [PMID: 35232428 PMCID: PMC8886785 DOI: 10.1186/s12920-022-01194-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 02/24/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Cutaneous Melanoma (CM) is a malignant disease with increasing incidence and high mortality. Ferroptosis is a new kind of cell death and related to tumor blood and lymphatic metastasis. This study aims at using bioinformatics technology to construct a prognostic signature and identify ferroptosis-related biomarkers to improve the prognosis and treatment of cutaneous melanoma. METHODS We used bioinformatics tools to analyze RNA sequencing expression data with clinical information from multiple databases, utilized varieties of statistical methods to construct a ferroptosis-related prognostic signature of cutaneous melanoma and screened out specific genes with independent prognostic ability. RESULTS We obtained 22 ferroptosis-related (P < 0.05) prognostic DEGs in the uniCox regression analysis, among which 10 high-expressed genes (ATG5, CHAC1, FANCD2, FBXL5, HMOX2, HSPB1, NQO1, PEBP1, PRNP, SLC3A2) were screened out by LASSO regression analysis to establish a predictive model. Meanwhile, the ferroptosis-related signature and the nomogram we drew performed an excellent performance based on Kaplan-Meier (K-M), Receiver operating characteristic (ROC) and calibration curves. Univariate and multivariable cox analyses displayed that our model was greater than other prognostic features. GO and KEGG analyses revealed that 10-biomarker signature was mainly related to epidermis differentiation and immunity. ssGSEA analysis indicated that the immune status between the two risk groups was highly different. Besides, we found that two genes (CP, ZEB1) had independent prognostic ability and can be applied for drug research. Both genes were highly related to immunity. GSEA illustrated that ZEB1 may be involved in cellular functions such as proliferation, apoptosis, and migration, while CP was closely connected to immune cell related functions. CONCLUSION The present study suggested a 10-biomarker signature can be clinically used to predict the prognosis of cutaneous melanoma, which was better than conventional factors. CP and ZEB1 were independent prognostic genes and can be applied to guide treatment. In addition, ZEB1 mutation was highly related to overall survival in cutaneous melanoma, while CP may be associated with tumor progression. Our study comprehensively analyzed the relationship between iron metabolism, ferroptosis-related genes, and the prognosis of cutaneous melanoma, provided new insight for molecular mechanisms and treatment of ferroptosis and cutaneous melanoma.
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Affiliation(s)
- Changjiang Liu
- Department of Orthopedics, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Yuhang Liu
- Department of Orthopedics, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Yifeng Yu
- Department of Orthopedics, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Yong Zhao
- Department of Orthopedics, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Aixi Yu
- Department of Orthopedics, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China.
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25
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Newell F, Pires da Silva I, Johansson PA, Menzies AM, Wilmott JS, Addala V, Carlino MS, Rizos H, Nones K, Edwards JJ, Lakis V, Kazakoff SH, Mukhopadhyay P, Ferguson PM, Leonard C, Koufariotis LT, Wood S, Blank CU, Thompson JF, Spillane AJ, Saw RPM, Shannon KF, Pearson JV, Mann GJ, Hayward NK, Scolyer RA, Waddell N, Long GV. Multiomic profiling of checkpoint inhibitor-treated melanoma: Identifying predictors of response and resistance, and markers of biological discordance. Cancer Cell 2022; 40:88-102.e7. [PMID: 34951955 DOI: 10.1016/j.ccell.2021.11.012] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 09/15/2021] [Accepted: 11/29/2021] [Indexed: 02/06/2023]
Abstract
We concurrently examine the whole genome, transcriptome, methylome, and immune cell infiltrates in baseline tumors from 77 patients with advanced cutaneous melanoma treated with anti-PD-1 with or without anti-CTLA-4. We show that high tumor mutation burden (TMB), neoantigen load, expression of IFNγ-related genes, programmed death ligand expression, low PSMB8 methylation (therefore high expression), and T cells in the tumor microenvironment are associated with response to immunotherapy. No specific mutation correlates with therapy response. A multivariable model combining the TMB and IFNγ-related gene expression robustly predicts response (89% sensitivity, 53% specificity, area under the curve [AUC], 0.84); tumors with high TMB and a high IFNγ signature show the best response to immunotherapy. This model validates in an independent cohort (80% sensitivity, 59% specificity, AUC, 0.79). Except for a JAK3 loss-of-function mutation, for patients who did not respond as predicted there is no obvious biological mechanism that clearly explained their outlier status, consistent with intratumor and intertumor heterogeneity in response to immunotherapy.
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Affiliation(s)
- Felicity Newell
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Ines Pires da Silva
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; Cancer Centre, Blacktown Hospital, Sydney, NSW 2148, Australia
| | - Peter A Johansson
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Alexander M Menzies
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; Department of Medical Oncology, Royal North Shore Hospital, Sydney, NSW 2065, Australia; Mater Hospital, Sydney, NSW 2060, Australia
| | - James S Wilmott
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
| | - Venkateswar Addala
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia; Faculty of Medicine, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Matteo S Carlino
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; Centre for Cancer Research, Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW 2145, Australia; Department of Medical Oncology, Westmead Hospital, Sydney, NSW 2145, Australia
| | - Helen Rizos
- Faculty of Medicine, Health and Human Sciences, Macquarie University, North Ryde, NSW 2109, Australia
| | - Katia Nones
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Jarem J Edwards
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
| | - Vanessa Lakis
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Stephen H Kazakoff
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | | | - Peter M Ferguson
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Camperdown, NSW 2050, Australia
| | - Conrad Leonard
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | | | - Scott Wood
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Christian U Blank
- Department of Molecular Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Molecular Oncology and Immunology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - John F Thompson
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; Mater Hospital, Sydney, NSW 2060, Australia; Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
| | - Andrew J Spillane
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; Mater Hospital, Sydney, NSW 2060, Australia
| | - Robyn P M Saw
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; Mater Hospital, Sydney, NSW 2060, Australia; Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
| | - Kerwin F Shannon
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Mater Hospital, Sydney, NSW 2060, Australia; Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
| | - John V Pearson
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Graham J Mann
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Centre for Cancer Research, Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW 2145, Australia; John Curtin School of Medical Research, Australian National University, ACT 2601, Australia
| | - Nicholas K Hayward
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Richard A Scolyer
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Camperdown, NSW 2050, Australia
| | - Nicola Waddell
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia; Faculty of Medicine, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Georgina V Long
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; Department of Medical Oncology, Royal North Shore Hospital, Sydney, NSW 2065, Australia; Mater Hospital, Sydney, NSW 2060, Australia.
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26
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Yang M, Johnsson P, Bräutigam L, Yang XR, Thrane K, Gao J, Tobin NP, Zhou Y, Yu R, Nagy N, Engström PG, Tuominen R, Eriksson H, Lundeberg J, Tucker MA, Goldstein AM, Egyhazi-Brage S, Zhao J, Cao Y, Höiom V. Novel loss-of-function variant in DENND5A impedes melanosomal cargo transport and predisposes to familial cutaneous melanoma. Genet Med 2022; 24:157-169. [PMID: 34906508 PMCID: PMC10617683 DOI: 10.1016/j.gim.2021.09.003] [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: 05/05/2021] [Revised: 08/05/2021] [Accepted: 09/10/2021] [Indexed: 11/18/2022] Open
Abstract
PURPOSE More than half of the familial cutaneous melanomas have unknown genetic predisposition. This study aims at characterizing a novel melanoma susceptibility gene. METHODS We performed exome and targeted sequencing in melanoma-prone families without any known melanoma susceptibility genes. We analyzed the expression of candidate gene DENND5A in melanoma samples in relation to pigmentation and UV signature. Functional studies were carried out using microscopic approaches and zebrafish model. RESULTS We identified a novel DENND5A truncating variant that segregated with melanoma in a Swedish family and 2 additional rare DENND5A variants, 1 of which segregated with the disease in an American family. We found that DENND5A is significantly enriched in pigmented melanoma tissue. Our functional studies show that loss of DENND5A function leads to decrease in melanin content in vitro and pigmentation defects in vivo. Mechanistically, harboring the truncating variant or being suppressed leads to DENND5A losing its interaction with SNX1 and its ability to transport the SNX1-associated vesicles from melanosomes. Consequently, untethered SNX1-premelanosome protein and redundant tyrosinase are redirected to lysosomal degradation by default, causing decrease in melanin content. CONCLUSION Our findings provide evidence of a physiological role of DENND5A in the skin context and link its variants to melanoma susceptibility.
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Affiliation(s)
- Muyi Yang
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Per Johnsson
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden; Ludwig Institute for Cancer Research, Stockholm, Sweden
| | - Lars Bräutigam
- Comparative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Xiaohong R Yang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, MD
| | - Kim Thrane
- Department of Gene Technology, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jiwei Gao
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Nicholas P Tobin
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Yitian Zhou
- Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Rong Yu
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Noemi Nagy
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Pär G Engström
- Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, SciLifeLab, Stockholm University, Stockholm, Sweden
| | - Rainer Tuominen
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Hanna Eriksson
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Karolinska University Hospital, Stockholm, Sweden
| | - Joakim Lundeberg
- Department of Gene Technology, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Margaret A Tucker
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, MD
| | - Alisa M Goldstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, MD
| | | | - Jian Zhao
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
| | - Yihai Cao
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Veronica Höiom
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Karolinska University Hospital, Stockholm, Sweden.
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Falkenius J, Keskitalo J, Kanter L, Johansson H, Höiom V, Hansson J, Egyhazi Brage S. A biomarker panel predicts recurrence-free survival in ulcerated primary cutaneous melanoma. Acta Oncol 2022; 61:14-21. [PMID: 34694198 DOI: 10.1080/0284186x.2021.1989719] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND Ulceration is an independent adverse prognostic factor in cutaneous malignant melanoma (CMM). There is, however, a need for additional prognostic markers to identify patients with ulcerated stage I-II CMM who have a high-risk for recurrence. The aim of this study was to examine the prognostic impact of BRAF mutation, proliferation and presence of tumour infiltrating lymphocytes (TILs) in primary ulcerated CMM. MATERIAL AND METHODS We have used a consecutive cohort consisting of 71 primary ulcerated CMM (T1b-T4b). BRAF mutation was detected using Cobas test and pyrosequencing. Protein expression of the proliferation marker Ki67 was analysed using immunohistochemistry. Presence of TILs was evaluated in representative hematoxylin-eosin stained formalin-fixed paraffin-embedded tumour sections. RESULTS Proportion of BRAF mutated alleles, proliferation and presence of TILs all had a statistically significant impact on recurrence free survival in univariate analyses (HR 2.44, 95% CI 1.23-4.84, p = 0.011; HR 2.66, 95% CI 1.32-5.35, p = 0.006 respectively HR 0.48, 95% CI 0.24-0.98, p = 0.045). A trend test found a statistically significant decrease in the proportion of recurrence by including the three favourable factors (BRAF wildtype/low proportion of BRAF mutated alleles, low proliferation and high presence of TILs) (p = 0.0004). When at least two out of three factors were present there was a statistically significant association with longer recurrence free survival in the multivariate analysis (HR 0.30, 95% CI 0.15-0.61, p = 0.001) when adjusted for Breslow thickness, an established independent prognostic marker for CMM. CONCLUSION Thus, this panel of markers could be an interesting novel concept for predicting the clinical outcome in patients with high-risk stage I-II ulcerated CMM.
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Affiliation(s)
- Johan Falkenius
- Department of Oncology-Pathology, Karolinska Institutet, Bioclinicum, Karolinska University Hospital, Stockholm, Sweden
| | - Johanna Keskitalo
- Department of Oncology-Pathology, Karolinska Institutet, Bioclinicum, Karolinska University Hospital, Stockholm, Sweden
| | - Lena Kanter
- Department of Oncology-Pathology, Karolinska Institutet, Bioclinicum, Karolinska University Hospital, Stockholm, Sweden
| | - Hemming Johansson
- Department of Oncology-Pathology, Karolinska Institutet, Bioclinicum, Karolinska University Hospital, Stockholm, Sweden
| | - Veronica Höiom
- Department of Oncology-Pathology, Karolinska Institutet, Bioclinicum, Karolinska University Hospital, Stockholm, Sweden
| | - Johan Hansson
- Department of Oncology-Pathology, Karolinska Institutet, Bioclinicum, Karolinska University Hospital, Stockholm, Sweden
| | - Suzanne Egyhazi Brage
- Department of Oncology-Pathology, Karolinska Institutet, Bioclinicum, Karolinska University Hospital, Stockholm, Sweden
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Wu L, Hu X, Dai H, Chen K, Liu B. Identification of an m6A Regulators-Mediated Prognosis Signature For Survival Prediction and Its Relevance to Immune Infiltration in Melanoma. Front Cell Dev Biol 2021; 9:718912. [PMID: 34900983 PMCID: PMC8656227 DOI: 10.3389/fcell.2021.718912] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 11/08/2021] [Indexed: 12/15/2022] Open
Abstract
Despite robust evidence for the role of m6A in cancer development and progression, its association with immune infiltration and survival outcomes in melanoma remains obscure. Here, we aimed to develop an m6A-related risk signature to improve prognostic and immunotherapy responder prediction performance in the context of melanoma. We comprehensively analyzed the m6A cluster and immune infiltration phenotypes of public datasets. The TCGA (n = 457) and eleven independent melanoma cohorts (n = 758) were used as the training and validation datasets, respectively. We identified two m6A clusters (m6A-clusterA and m6A-clusterB) based on the expression pattern of m6A regulators via unsupervised consensus clustering. IGF2BP1 (7.49%), KIAA1429 (7.06%), and YTHDC1 (4.28%) were the three most frequently mutated genes. There was a correlation between driver genes mutation statuses and the expression of m6A regulators. A significant difference in tumor-associated immune infiltration between two m6A clusters was detected. Compared with m6A-clusterA, the m6A-clusterB was characterized by a lower immune score and immune cell infiltration but higher mRNA expression-based stemness index (mRNAsi). An m6A-related risk signature consisting of 12 genes was determined via Cox regression analysis and divided the patients into low- and high-risk groups (IL6ST, MBNL1, NXT2, EIF2A, CSGALNACT1, C11orf58, CD14, SPI1, NCCRP1, BOK, CD74, PAEP). A nomogram was developed for the prediction of the survival rate. Compared with the high-risk group, the low-risk group was characterized by high expression of immune checkpoints and immunophenoscore (IPS), activation of immune-related pathways, and more enriched in immune cell infiltrations. The low-risk group had a favorable prognosis and contained the potential beneficiaries of the immune checkpoint blockade therapy and verified by the IMvigor210 cohort (n = 298). The m6A-related signature we have determined in melanoma highlights the relationships between m6A regulators and immune cell infiltration. The established risk signature was identified as a promising clinical biomarker of melanoma.
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Affiliation(s)
- Liuxing Wu
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Xin Hu
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Hongji Dai
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ben Liu
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
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29
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Cham J, Shavit A, Ebrahimi A, Viray M, Gibbs P, Bhangoo MS. Malignant Melanoma With Neuroendocrine Differentiation: A Case Report and Literature Review. Front Oncol 2021; 11:763992. [PMID: 34926265 PMCID: PMC8671631 DOI: 10.3389/fonc.2021.763992] [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: 08/24/2021] [Accepted: 11/15/2021] [Indexed: 11/29/2022] Open
Abstract
Background Melanoma has a wide range of histologic variants and cytomorphologic features that make its diagnosis challenging. Melanoma can also rarely have neuroendocrine markers adding further diagnostic uncertainty particularly given that unrelated tumor types, such as prostate cancer, can also display focal neuroendocrine differentiations. Case presentation Our patient is a 74-year-old Caucasian man found to have a lung mass. Initial biopsy revealed typical microscopic morphology and neuroendocrine differentiation consistent with small cell carcinoma. Despite standard chemoradiation treatment, the patient continued to progress with new metastasis in the brain, liver and bone. Subsequent chest wall biopsy revealed golden-brown pigment associated with melanin. Further tumor immunohistochemistry revealed extensive neuroendocrine differentiation with CD56, synaptophysin, and INSM1, as well as strong immunoreactivity for melanocyte markers including SOX10, S100, PRAME, and MITF, consistent with metastatic melanoma with neuroendocrine differentiation. Genomic testing revealed increased tumor mutational burden and alterations in NF1, BRAF, CDKN2A/B, TERT. The patient was transitioned to checkpoint inhibitor therapy with nivolumab and ipilimumab and had resolution of his intracranial mass and decrease in size of other metastatic lesions. Conclusion Often the combination of anatomic findings such as a lung mass, typical microscopic morphology, and confirmation of neuroendocrine differentiation correctly identifies a patient with small cell carcinoma. However, in a patient who fails to respond to treatment, a broader immunohistochemical workup along with molecular testing with additional tissue may be warranted.
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Affiliation(s)
- Jason Cham
- Department of Internal Medicine, Scripps Clinic/Scripps Green Hospital, La Jolla, CA, United States
- *Correspondence: Jason Cham,
| | - Ayal Shavit
- Department of Internal Medicine, Scripps Clinic/Scripps Green Hospital, La Jolla, CA, United States
| | - Aren Ebrahimi
- Department of Internal Medicine, Scripps Clinic/Scripps Green Hospital, La Jolla, CA, United States
| | - Miguel Viray
- Department of Pathology, Scripps Clinic/Scripps Memorial Hospital, Encinitas, Encinitas, CA, United States
| | - Paul Gibbs
- Department of Pathology, Scripps Clinic/Scripps Memorial Hospital, Encinitas, Encinitas, CA, United States
| | - Munveer S. Bhangoo
- Department of Hematology and Oncology, Scripps Clinic/Scripps Green Hospital, La Jolla, CA, United States
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30
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Yan M, Hu J, Ping Y, Xu L, Liao G, Jiang Z, Pang B, Sun S, Zhang Y, Xiao Y, Li X. Single-Cell Transcriptomic Analysis Reveals a Tumor-Reactive T Cell Signature Associated With Clinical Outcome and Immunotherapy Response In Melanoma. Front Immunol 2021; 12:758288. [PMID: 34804045 PMCID: PMC8602834 DOI: 10.3389/fimmu.2021.758288] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 10/19/2021] [Indexed: 12/19/2022] Open
Abstract
The infiltration of tumor-reactive T cells in the tumor site is associated with better survival and immunotherapy response. However, tumor-reactive T cells were often represented by the infiltration of total CD8+ T cells, which was confounded by the presence of bystander T cells. To identify tumor-reactive T cells at the cancer lesion, we performed integration analyses of three scRNA-seq data sets of T cells in melanoma. Extensive heterogeneous functional states of T cells were revealed in the tumor microenvironment. Among these states, we identified a subset of tumor-reactive T cells which specifically expressed tumor-reactive markers and T cell activation signature, and were strongly enriched for larger T cell receptor (TCR) clones. We further identified and validated a tumor-reactive T cell signature (TRS) to evaluate the tumor reactivity of T cells in tumor patients. Patients with high TRS scores have strong immune activity and high mutation burden in the TCGA-SKCM cohort. We also demonstrated a significant association of the TRS with the clinical outcomes of melanoma patients, with higher TRS scores representing better survival, which was validated in four external independent cohorts. Furthermore, the TRS scores exhibited greater performance on prognosis prediction than infiltration levels of CD8+ T cells and previously published prognosis-related signatures. Finally, we observed the capability of TRS to predict immunotherapy response in melanoma. Together, based on integrated analysis of single-cell RNA-sequencing, we developed and validated a tumor-reactive-related signature that demonstrated significant association with clinical outcomes and response to immunotherapy.
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Affiliation(s)
- Min Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jing Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yanyan Ping
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Liwen Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Gaoming Liao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zedong Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Bo Pang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shangqin Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Key Laboratory of High Throughput Omics Big Data for Cold Region's Major Diseases in Heilongjiang Province, Harbin Medical University, Harbin, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Key Laboratory of High Throughput Omics Big Data for Cold Region's Major Diseases in Heilongjiang Province, Harbin Medical University, Harbin, China
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Tjokrowidjaja A, Browne L, Soudy H. External validation of the American Joint Committee on Cancer melanoma staging system eighth edition using the surveillance, epidemiology, and end results program. Asia Pac J Clin Oncol 2021; 18:e280-e288. [PMID: 34811927 DOI: 10.1111/ajco.13689] [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] [Received: 01/23/2021] [Accepted: 09/23/2021] [Indexed: 12/24/2022]
Abstract
AIM The American Joint Committee on Cancer (AJCC) melanoma staging system eighth edition (AJCC-8) was recently released to provide accurate staging reflecting advances in the treatment of melanoma. Using population registry data, this study independently validates and compares the prognostic performance of AJCC-8 to the seventh edition (AJCC-7). METHODS We extracted patient-, tumor-related, and survival data from the SEER-18 registry between 2010 and 2015. To assess overall survival (OS) and cancer-specific survival (CSS) for AJCC-7 and AJCC-8, we performed Kaplan-Meier analysis and computed cumulative hazard functions using Nelson-Aalen function. RESULTS Of 126,408 individuals, 59,989 (47%) and 60,411 (48%) had available data for pathological and clinical-stage OS analysis, respectively. The 3-year OS for AJCC-7 among pathologically staged patients was: stage IA 97%, stage IB 95%, stage IIA 87%, stage IIB 76%, stage IIC 57%, stage IIIA 86%, stage IIIB 69%, stage IIIC 50%, and stage IV 24%. The 3-year OS for AJCC-8 patients was similar but was 56% for stage IIIC and 30% for stage IIID. Stage IV individuals with an elevated LDH had worse OS and CSS at all measured time-points up to 60 months compared to those with a normal LDH. CONCLUSION The discriminatory ability of AJCC-8 and AJCC-7 appear comparable. Changes in AJCC-8 identified stage IIID as a poor prognostic subgroup among stage III patients and elevated LDH in stage IV. However, patients with advanced T-stage, node-negative tumors experienced worse survival compared to those with earlier T-stage, node-positive tumors, and the results of ongoing trials should inform adjuvant therapy in this subset of patients.
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Affiliation(s)
- Angelina Tjokrowidjaja
- Department of Medical Oncology, St. George Hospital, Kogarah, New South Wales, Australia.,Department of Medical Oncology, Sutherland Hospital, Kogarah, New South Wales, Australia.,National Health and Medical Research Council Clinical Trials Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Lois Browne
- Department of Radiation Oncology, St. George Hospital, Kogarah, New South Wales, Australia
| | - Hussein Soudy
- Department of Medical Oncology, St. George Hospital, Kogarah, New South Wales, Australia.,Department of Medical Oncology, Sutherland Hospital, Kogarah, New South Wales, Australia.,School of Medicine, University of New South Wales, Kensington, New South Wales, Australia.,Faculty of Medicine, Cairo University, Cairo, Egypt
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32
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Ayton SG, Pavlicova M, Robles-Espinoza CD, Tamez Peña JG, Treviño V. Multiomics subtyping for clinically prognostic cancer subtypes and personalized therapy: A systematic review and meta-analysis. Genet Med 2021; 24:15-25. [PMID: 34906494 DOI: 10.1016/j.gim.2021.09.006] [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] [Received: 05/20/2021] [Revised: 05/20/2021] [Accepted: 09/10/2021] [Indexed: 12/14/2022] Open
Abstract
PURPOSE Multiomics cancer subtyping is becoming increasingly popular for directing state-of-the-art therapeutics. However, these methods have never been systematically assessed for their ability to capture cancer prognosis for identified subtypes, which is essential to effectively treat patients. METHODS We systematically searched PubMed, The Cancer Genome Atlas, and Pan-Cancer Atlas for multiomics cancer subtyping studies from 2010 through 2019. Studies comprising at least 50 patients and examining survival were included. Pooled Cox and logistic mixed-effects models were used to compare the ability of multiomics subtyping methods to identify clinically prognostic subtypes, and a structural equation model was used to examine causal paths underlying subtyping method and mortality. RESULTS A total of 31 studies comprising 10,848 unique patients across 32 cancers were analyzed. Latent-variable subtyping was significantly associated with overall survival (adjusted hazard ratio, 2.81; 95% CI, 1.16-6.83; P = .023) and vital status (1 year adjusted odds ratio, 4.71; 95% CI, 1.34-16.49; P = .015; 5 year adjusted odds ratio, 7.69; 95% CI, 1.83-32.29; P = .005); latent-variable-identified subtypes had greater associations with mortality across models (adjusted hazard ratio, 1.19; 95% CI, 1.01-1.42; P = .050). Our structural equation model confirmed the path from subtyping method through multiomics subtype (βˆ = 0.66; P = .048) on survival (βˆ = 0.37; P = .008). CONCLUSION Multiomics methods have different abilities to define clinically prognostic cancer subtypes, which should be considered before administration of personalized therapy; preliminary evidence suggests that latent-variable methods better identify clinically prognostic biomarkers and subtypes.
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Affiliation(s)
- Sarah G Ayton
- Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, Mexico
| | - Martina Pavlicova
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY
| | - Carla Daniela Robles-Espinoza
- Laboratorio Internacional de Investigación sobre el Genoma Humano (LIIGH), Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - José G Tamez Peña
- Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, Mexico
| | - Víctor Treviño
- Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, Mexico.
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Kramer ET, Godoy PM, Kaufman CK. TRANSCRIPTIONAL PROFILE AND CHROMATIN ACCESSIBILITY IN ZEBRAFISH MELANOCYTES AND MELANOMA TUMORS. G3-GENES GENOMES GENETICS 2021; 12:6428538. [PMID: 34791221 PMCID: PMC8727958 DOI: 10.1093/g3journal/jkab379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 10/02/2021] [Indexed: 11/14/2022]
Abstract
Transcriptional and epigenetic characterization of melanocytes and melanoma cells isolated from their in vivo context promises to unveil key differences between these developmentally related normal and cancer cell populations. We therefore engineered an enhanced Danio rerio (zebrafish) melanoma model with fluorescently labeled melanocytes to allow for isolation of normal (wild type) and premalignant (BRAFV600E-mutant) populations for comparison to fully transformed BRAFV600E-mutant, p53 loss-of-function melanoma cells. Using fluorescence-activated cell sorting to isolate these populations, we performed high-quality RNA- and ATAC-seq on sorted zebrafish melanocytes vs. melanoma cells, which we provide as a resource here. Melanocytes had consistent transcriptional and accessibility profiles, as did melanoma cells. Comparing melanocytes and melanoma, we note 4128 differentially expressed genes and 56,936 differentially accessible regions with overall gene expression profiles analogous to human melanocytes and the pigmentation melanoma subtype. Combining the RNA- and ATAC-seq data surprisingly revealed that increased chromatin accessibility did not always correspond with increased gene expression, suggesting that though there is widespread dysregulation in chromatin accessibility in melanoma, there is a potentially more refined gene expression program driving cancerous melanoma. These data serve as a resource to identify candidate regulators of the normal vs. diseased states in a genetically controlled in vivo context.
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Affiliation(s)
- Eva T Kramer
- Division of Medical Oncology, Department of Medicine and Department of Developmental Biology, Washington University in Saint Louis, St. Louis, MO 63110 USA
| | - Paula M Godoy
- Division of Medical Oncology, Department of Medicine and Department of Developmental Biology, Washington University in Saint Louis, St. Louis, MO 63110 USA
| | - Charles K Kaufman
- Division of Medical Oncology, Department of Medicine and Department of Developmental Biology, Washington University in Saint Louis, St. Louis, MO 63110 USA
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Ma EZ, Hoegler KM, Zhou AE. Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review. Genes (Basel) 2021; 12:1751. [PMID: 34828357 PMCID: PMC8621295 DOI: 10.3390/genes12111751] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/19/2021] [Accepted: 10/28/2021] [Indexed: 12/20/2022] Open
Abstract
Over 100,000 people are diagnosed with cutaneous melanoma each year in the United States. Despite recent advancements in metastatic melanoma treatment, such as immunotherapy, there are still over 7000 melanoma-related deaths each year. Melanoma is a highly heterogenous disease, and many underlying genetic drivers have been identified since the introduction of next-generation sequencing. Despite clinical staging guidelines, the prognosis of metastatic melanoma is variable and difficult to predict. Bioinformatic and machine learning analyses relying on genetic, clinical, and histopathologic inputs have been increasingly used to risk stratify melanoma patients with high accuracy. This literature review summarizes the key genetic drivers of melanoma and recent applications of bioinformatic and machine learning models in the risk stratification of melanoma patients. A robustly validated risk stratification tool can potentially guide the physician management of melanoma patients and ultimately improve patient outcomes.
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Affiliation(s)
| | | | - Albert E. Zhou
- Department of Dermatology, University of Maryland School of Medicine, Baltimore, MD 21230, USA; (E.Z.M.); (K.M.H.)
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Ge Y, Che X, Gao X, Zhao S, Su J. Combination of radiotherapy and targeted therapy for melanoma brain metastases: a systematic review. Melanoma Res 2021; 31:413-420. [PMID: 34406985 DOI: 10.1097/cmr.0000000000000761] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Radiotherapy is a mainstay of efficient treatment of brain metastases from solid tumors. Immunotherapy has improved the survival of metastatic cancer patients across many tumor types. However, targeted therapy is a feasible alternative for patients unable to continue immunotherapy or with poor outcomes of immunotherapy. The combination of radiotherapy and targeted therapy for the treatment of brain metastases has a strong theoretical underpinning, but data on the efficacy and safety of this combination is still limited. A systematic search of PubMed, Embase, Web of Science and the Cochrane library database was conducted. Eleven studies were included for a total of 316 patients. Median OS was about 6.2-17.8 months from radiotherapy. Weighted survival and local control at 1 and 2 years were correlated (50.1 and 17.8%, 90.7 and 14.7% at 1 and 2 year, respectively). Radiotherapy given before or concurrently to targeted therapy provided the best effect on the outcome. For patients with brain metastases from cutaneous melanoma, the addition of concurrent targeted therapy to brain radiotherapy can increase survival and provide long-term control.
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Affiliation(s)
- Yi Ge
- Department of Dermatology, Xiangya Hospital, Central South University
- Hunan Engineering Research Center of Skin Health and Disease
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Central South University, Changsha, China
| | - Xuanlin Che
- Department of Dermatology, Xiangya Hospital, Central South University
- Hunan Engineering Research Center of Skin Health and Disease
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Central South University, Changsha, China
| | - Xin Gao
- Department of Dermatology, Xiangya Hospital, Central South University
- Hunan Engineering Research Center of Skin Health and Disease
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Central South University, Changsha, China
| | - Shuang Zhao
- Department of Dermatology, Xiangya Hospital, Central South University
- Hunan Engineering Research Center of Skin Health and Disease
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Central South University, Changsha, China
| | - Juan Su
- Department of Dermatology, Xiangya Hospital, Central South University
- Hunan Engineering Research Center of Skin Health and Disease
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Central South University, Changsha, China
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Lv L, Wei Q, Wang Z, Zhao Y, Chen N, Yi Q. Clinical and Molecular Correlates of NLRC5 Expression in Patients With Melanoma. Front Bioeng Biotechnol 2021; 9:690186. [PMID: 34307322 PMCID: PMC8299757 DOI: 10.3389/fbioe.2021.690186] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/14/2021] [Indexed: 11/13/2022] Open
Abstract
NLRC5 is an important regulator in antigen presentation and inflammation, and its dysregulation promotes tumor progression. In melanoma, the impact of NLRC5 expression on molecular phenotype, clinical characteristics, and tumor features is largely unknown. In the present study, public datasets from the Cancer Cell Line Encyclopedia (CCLE), Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and cBioPortal were used to address these issues. We identify that NLRC5 is expressed in both immune cells and melanoma cells in melanoma samples and its expression is regulated by SPI1 and DNA methylation. NLRC5 expression is closely associated with Breslow thickness, Clark level, recurrence, pathologic T stage, and ulceration status in melanoma. Truncating/splice mutations rather than missense mutations in NLRC5 could compromise the expression of downstream genes. Low expression of NLRC5 is associated with poor prognosis, low activity of immune-related signatures, low infiltrating level of immune cells, and low cytotoxic score in melanoma. Additionally, NLRC5 expression correlates with immunotherapy efficacy in melanoma. In summary, these findings suggest that NLRC5 acts as a tumor suppressor in melanoma via modulating the tumor immune microenvironment. Targeting the NLRC5 related pathway might improve efficacy of immunotherapy for melanoma patients.
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Affiliation(s)
- Lei Lv
- Anhui Cancer Hospital, West Branch of the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Qinqin Wei
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Zhiwen Wang
- Anhui Cancer Hospital, West Branch of the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yujia Zhao
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Ni Chen
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Qiyi Yi
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
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Krijgsman O, Kemper K, Boshuizen J, Vredevoogd DW, Rozeman EA, Ibanez Molero S, de Bruijn B, Cornelissen-Steijger P, Shahrabi A, Del Castillo Velasco-Herrera M, Song JY, Ligtenberg MA, Kluin RJC, Kuilman T, Ross-Macdonald P, Haanen JBAG, Adams DJ, Blank CU, Peeper DS. Predictive Immune-Checkpoint Blockade Classifiers Identify Tumors Responding to Inhibition of PD-1 and/or CTLA-4. Clin Cancer Res 2021; 27:5389-5400. [PMID: 34230026 DOI: 10.1158/1078-0432.ccr-20-4218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/01/2020] [Accepted: 06/25/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE Combining anti-PD-1 + anti-CTLA-4 immune-checkpoint blockade (ICB) shows improved patient benefit, but it is associated with severe immune-related adverse events and exceedingly high cost. Therefore, there is a dire need to predict which patients respond to monotherapy and which require combination ICB treatment. EXPERIMENTAL DESIGN In patient-derived melanoma xenografts (PDX), human tumor microenvironment (TME) cells were swiftly replaced by murine cells upon transplantation. Using our XenofilteR deconvolution algorithm we curated human tumor cell RNA reads, which were subsequently subtracted in silico from bulk (tumor cell + TME) patients' melanoma RNA. This produced a purely tumor cell-intrinsic signature ("InTumor") and a signature comprising tumor cell-extrinsic RNA reads ("ExTumor"). RESULTS We show that whereas the InTumor signature predicts response to anti-PD-1, the ExTumor predicts anti-CTLA-4 benefit. In PDX, InTumorLO, but not InTumorHI, tumors are effectively eliminated by cytotoxic T cells. When used in conjunction, the InTumor and ExTumor signatures identify not only patients who have a substantially higher chance of responding to combination treatment than to either monotherapy, but also those who are likely to benefit little from anti-CTLA-4 on top of anti-PD-1. CONCLUSIONS These signatures may be exploited to distinguish melanoma patients who need combination ICB blockade from those who likely benefit from either monotherapy.
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Affiliation(s)
- Oscar Krijgsman
- Department of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Kristel Kemper
- Department of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Julia Boshuizen
- Department of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - David W Vredevoogd
- Department of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Elisa A Rozeman
- Medical Oncology Department, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Sofia Ibanez Molero
- Department of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Beaunelle de Bruijn
- Department of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Paulien Cornelissen-Steijger
- Department of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Aida Shahrabi
- Department of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | | | - Ji-Ying Song
- Animal Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Maarten A Ligtenberg
- Department of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Roelof J C Kluin
- Genomics Core Facility, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Thomas Kuilman
- Department of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | | | - John B A G Haanen
- Medical Oncology Department, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - David J Adams
- Experimental Cancer Genetics, The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | - Christian U Blank
- Medical Oncology Department, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Daniel S Peeper
- Department of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands.
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Hakobyan S, Loeffler-Wirth H, Arakelyan A, Binder H, Kunz M. A Transcriptome-Wide Isoform Landscape of Melanocytic Nevi and Primary Melanomas Identifies Gene Isoforms Associated with Malignancy. Int J Mol Sci 2021; 22:ijms22137165. [PMID: 34281234 PMCID: PMC8268681 DOI: 10.3390/ijms22137165] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/22/2021] [Accepted: 06/28/2021] [Indexed: 12/12/2022] Open
Abstract
Genetic splice variants have become of central interest in recent years, as they play an important role in different cancers. Little is known about splice variants in melanoma. Here, we analyzed a genome-wide transcriptomic dataset of benign melanocytic nevi and primary melanomas (n = 80) for the expression of specific splice variants. Using kallisto, a map for differentially expressed splice variants in melanoma vs. benign melanocytic nevi was generated. Among the top genes with differentially expressed splice variants were Ras-related in brain 6B (RAB6B), a member of the RAS family of GTPases, Macrophage Scavenger Receptor 1 (MSR1), Collagen Type XI Alpha 2 Chain (COLL11A2), and LY6/PLAUR Domain Containing 1 (LYPD1). The Gene Ontology terms of differentially expressed splice variants showed no enrichment for functional gene sets of melanoma vs. nevus lesions, but between type 1 (pigmentation type) and type 2 (immune response type) melanocytic lesions. A number of genes such as Checkpoint Kinase 1 (CHEK1) showed an association of mutational patterns and occurrence of splice variants in melanoma. Moreover, mutations in genes of the splicing machinery were common in both benign nevi and melanomas, suggesting a common mechanism starting early in melanoma development. Mutations in some of these genes of the splicing machinery, such as Serine and Arginine Rich Splicing Factor A3 and B3 (SF3A3, SF3B3), were significantly enriched in melanomas as compared to benign nevi. Taken together, a map of splice variants in melanoma is presented that shows a multitude of differentially expressed splice genes between benign nevi and primary melanomas. The underlying mechanisms may involve mutations in genes of the splicing machinery.
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Affiliation(s)
- Siras Hakobyan
- Institute of Molecular Biology NAS RA, Yerevan 0014, Armenia; (S.H.); (A.A.)
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (H.L.-W.); (H.B.)
| | - Arsen Arakelyan
- Institute of Molecular Biology NAS RA, Yerevan 0014, Armenia; (S.H.); (A.A.)
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (H.L.-W.); (H.B.)
| | - Manfred Kunz
- Department of Dermatology, Venereology and Allergology, University of Leipzig Medical Center, Philipp-Rosenthal-Str. 23, 04103 Leipzig, Germany
- Correspondence: ; Tel.: +49-341-9718610; Fax: +49-341-9718609
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Li Y, Qi J, Yang J. RTP4 is a novel prognosis-related hub gene in cutaneous melanoma. Hereditas 2021; 158:22. [PMID: 34154655 PMCID: PMC8215788 DOI: 10.1186/s41065-021-00183-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 05/11/2021] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE Melanoma accounts for 80% of skin cancer deaths. The pathogenesis of melanoma is regulated by gene networks. Thus, we aimed here to identify gene networks and hub genes associated with melanoma and to further identify their underlying mechanisms. METHODS GTEx (normal skin) and TCGA (melanoma tumor) RNA-seq datasets were employed for this purpose. We conducted weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes associated with melanoma. Log-rank analysis and multivariate Cox model analysis were performed to identify prognosis genes, which were validated using two independent melanoma datasets. We also evaluated the correlation between prognostic gene and immune cell infiltration. RESULTS The blue module was the most relevant for melanoma and was thus considered the key module. Intersecting genes were identified between this module and differentially expressed genes (DEGs). Finally, 72 genes were identified and verified as hub genes using the Oncomine database. Log-rank analysis and multivariate Cox model analysis identified 13 genes that were associated with the prognosis of the metastatic melanoma group, and RTP4 was validated as a prognostic gene using two independent melanoma datasets. RTP4 was not previously associated with melanoma. When we evaluated the correlation between prognostic gene and immune cell infiltration, we discovered that RTP4 was associated with immune cell infiltration. Further, RTP4 was significantly associated with genes encoding components of immune checkpoints (PDCD1, TIM-3, and LAG3). CONCLUSIONS RTP4 is a novel prognosis-related hub gene in cutaneous melanoma. The novel gene RTP4 identified here will facilitate the exploration of the molecular mechanism of the pathogenesis and progression of melanoma and the discovery of potential new target for drug therapy.
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Affiliation(s)
- Yiqi Li
- School of Basic Medical Sciences, Dali University, Dali, 671000, Yunnan, China
- Institute of Translational Medicine for Metabolic Diseases, Dali University, Dali, 671000, Yunnan, China
| | - Jue Qi
- Department of Dermatology, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, Yunnan, China
| | - Jiankang Yang
- School of Basic Medical Sciences, Dali University, Dali, 671000, Yunnan, China.
- Institute of Translational Medicine for Metabolic Diseases, Dali University, Dali, 671000, Yunnan, China.
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40
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Bagaev A, Kotlov N, Nomie K, Svekolkin V, Gafurov A, Isaeva O, Osokin N, Kozlov I, Frenkel F, Gancharova O, Almog N, Tsiper M, Ataullakhanov R, Fowler N. Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell 2021; 39:845-865.e7. [PMID: 34019806 DOI: 10.1016/j.ccell.2021.04.014] [Citation(s) in RCA: 478] [Impact Index Per Article: 159.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 09/14/2020] [Accepted: 04/23/2021] [Indexed: 12/18/2022]
Abstract
The clinical use of molecular targeted therapy is rapidly evolving but has primarily focused on genomic alterations. Transcriptomic analysis offers an opportunity to dissect the complexity of tumors, including the tumor microenvironment (TME), a crucial mediator of cancer progression and therapeutic outcome. TME classification by transcriptomic analysis of >10,000 cancer patients identifies four distinct TME subtypes conserved across 20 different cancers. The TME subtypes correlate with patient response to immunotherapy in multiple cancers, with patients possessing immune-favorable TME subtypes benefiting the most from immunotherapy. Thus, the TME subtypes act as a generalized immunotherapy biomarker across many cancer types due to the inclusion of malignant and microenvironment components. A visual tool integrating transcriptomic and genomic data provides a global tumor portrait, describing the tumor framework, mutational load, immune composition, anti-tumor immunity, and immunosuppressive escape mechanisms. Integrative analyses plus visualization may aid in biomarker discovery and the personalization of therapeutic regimens.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | - Nathan Fowler
- BostonGene, Waltham, MA 02453, USA; Department of Lymphoma and Myeloma, Unit 0429, the University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.
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41
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Zhao Y, Dong Y, Sun Y, Cheng C. AutoEncoder-Based Computational Framework for Tumor Microenvironment Decomposition and Biomarker Identification in Metastatic Melanoma. Front Genet 2021; 12:665065. [PMID: 34122516 PMCID: PMC8191580 DOI: 10.3389/fgene.2021.665065] [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: 02/07/2021] [Accepted: 04/12/2021] [Indexed: 11/13/2022] Open
Abstract
Melanoma is one of the most aggressive cancer types whose prognosis is determined by both the tumor cell-intrinsic and -extrinsic features as well as their interactions. In this study, we performed systematic and unbiased analysis using The Cancer Genome Atlas (TCGA) melanoma RNA-seq data and identified two gene signatures that captured the intrinsic and extrinsic features, respectively. Specifically, we selected genes that best reflected the expression signals from tumor cells and immune infiltrate cells. Then, we applied an AutoEncoder-based method to decompose the expression of these genes into a small number of representative nodes. Many of these nodes were found to be significantly associated with patient prognosis. From them, we selected two most prognostic nodes and defined a tumor-intrinsic (TI) signature and a tumor-extrinsic (TE) signature. Pathway analysis confirmed that the TE signature recapitulated cytotoxic immune cell related pathways while the TI signature reflected MYC pathway activity. We leveraged these two signatures to investigate six independent melanoma microarray datasets and found that they were able to predict the prognosis of patients under standard care. Furthermore, we showed that the TE signature was also positively associated with patients' response to immunotherapies, including tumor vaccine therapy and checkpoint blockade immunotherapy. This study developed a novel computational framework to capture the tumor-intrinsic and -extrinsic features and identified robust prognostic and predictive biomarkers in melanoma.
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Affiliation(s)
- Yanding Zhao
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States.,Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, United States
| | - Yadong Dong
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States.,Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, United States
| | - Yongqi Sun
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Chao Cheng
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States.,Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, United States
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42
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Oba J, Woodman SE. The genetic and epigenetic basis of distinct melanoma types. J Dermatol 2021; 48:925-939. [PMID: 34008215 DOI: 10.1111/1346-8138.15957] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 04/14/2021] [Indexed: 12/12/2022]
Abstract
Melanoma represents the deadliest skin cancer. Recent therapeutic developments, including targeted and immune therapies have revolutionized clinical management and improved patient outcome. This progress was achieved by rigorous molecular and functional studies followed by robust clinical trials. The identification of key genomic alterations and gene expression profiles have propelled the understanding of distinct characteristics within melanoma subtypes. The aim of this review is to summarize and highlight the main genetic and epigenetic findings of melanomas and highlight their pathological and therapeutic importance.
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Affiliation(s)
- Junna Oba
- Genomics Unit, Keio Cancer Center, Keio University School of Medicine, Tokyo, Japan
| | - Scott E Woodman
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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43
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Nacer DF, Liljedahl H, Karlsson A, Lindgren D, Staaf J. Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor. Brief Bioinform 2021; 22:6272790. [PMID: 33971670 PMCID: PMC8574611 DOI: 10.1093/bib/bbab154] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/17/2021] [Accepted: 04/02/2021] [Indexed: 12/24/2022] Open
Abstract
Gene-expression profiling can be used to classify human tumors into molecular subtypes or risk groups, representing potential future clinical tools for treatment prediction and prognostication. However, it is less well-known how prognostic gene signatures derived in one malignancy perform in a pan-cancer context. In this study, a gene-rule-based single sample predictor (SSP) called classifier for lung adenocarcinoma molecular subtypes (CLAMS) associated with proliferation was tested in almost 15 000 samples from 32 cancer types to classify samples into better or worse prognosis. Of the 14 malignancies that presented both CLAMS classes in sufficient numbers, survival outcomes were significantly different for breast, brain, kidney and liver cancer. Patients with samples classified as better prognosis by CLAMS were generally of lower tumor grade and disease stage, and had improved prognosis according to other type-specific classifications (e.g. PAM50 for breast cancer). In all, 99.1% of non-lung cancer cases classified as better outcome by CLAMS were comprised within the range of proliferation scores of lung adenocarcinoma cases with a predicted better prognosis by CLAMS. This finding demonstrates the potential of tuning SSPs to identify specific levels of for instance tumor proliferation or other transcriptional programs through predictor training. Together, pan-cancer studies such as this may take us one step closer to understanding how gene-expression-based SSPs act, which gene-expression programs might be important in different malignancies, and how to derive tools useful for prognostication that are efficient across organs.
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44
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L Antigen Family Member 3 Serves as a Prognostic Biomarker for the Clinical Outcome and Immune Infiltration in Skin Cutaneous Melanoma. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6648182. [PMID: 33829062 PMCID: PMC8000545 DOI: 10.1155/2021/6648182] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 02/26/2021] [Accepted: 03/04/2021] [Indexed: 12/31/2022]
Abstract
L Antigen Family Member 3 (LAGE3) is an important RNA modification-related protein. Whereas few studies have interrogated the LAGE3 protein, there is limited data on its role in tumors. Here, we analyzed and profiled the LAGE3 protein in skin cutaneous melanoma (CM) using TCGA, GTEx, or GEO databases. Our data showed an upregulation of LAGE3 in melanoma cell lines compared to normal skin cell lines. Besides, the Kaplan–Meier curves and Cox proportional hazard model revealed that LAGE3 was an independent survival indicator for CM, especially in metastatic CM. Moreover, LAGE3 was negatively associated with multiple immune cell infiltration levels in CM, especially CD8+ T cells in metastatic CM. Taken together, our study suggests that LAGE3 could be a potential prognostic biomarker and might be a potential target for the development of novel CM treatment strategies.
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45
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Wong HSC, Chang WC. Single-cell melanoma transcriptomes depicting functional versatility and clinical implications of STIM1 in the tumor microenvironment. Am J Cancer Res 2021; 11:5092-5106. [PMID: 33859736 PMCID: PMC8039943 DOI: 10.7150/thno.54134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/06/2020] [Indexed: 12/31/2022] Open
Abstract
Rationale: Previous studies have implicated the functions of stromal interaction molecule 1 (STIM1) in immunity and malignancy, however, the specificity and effects of STIM1 expression in malignant and non-malignant cells in the tumor microenvironment are unclear. Methods: In the current study, we posed two central questions: (1) does STIM1 expression elicit different cellular programs in cell types within the melanoma tumor microenvironment (2) whether the expression of STIM1 and STIM1-coexpressed genes (SCGs) serve as prognostic indicators of patient's outcomes? To answer these questions, we dissected cell-specific STIM1-associated cellular programs in diverse cell types within the melanoma tumor microenvironment by measuring cell-type specificity of STIM1 expression and SCGs. Results: A distinct set of SCGs was highly affected in malignant melanoma cells, but not in the other cell types, suggesting the existence of malignant-cell-specific cellular programs reflected by STIM1 expression. In contrast to malignant cells, STIM1 expression appeared to trigger universal and non-specific biological functions in non-malignant cell types, as exemplified by the transcriptomes of macrophages and CD4+ T regulatory cells. Results from bioinformatic analyses indicated that SCGs in malignant cells may alter cell-cell interactions through cytokine/chemokine signaling and/or orchestrate immune infiltration into the tumor. Moreover, a prognostic association between SCGs in CD4+ T regulatory cells and patient's outcomes was identified. However, we didn't find any correlation between SCGs and responsiveness of immunotherapy. Conclusions: Overall, our results provide an integrated biological framework for understanding the functional and clinical consequences of cell-specific STIM1 expression in melanoma.
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46
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Netanely D, Leibou S, Parikh R, Stern N, Vaknine H, Brenner R, Amar S, Factor RH, Perluk T, Frand J, Nizri E, Hershkovitz D, Zemser-Werner V, Levy C, Shamir R. Classification of node-positive melanomas into prognostic subgroups using keratin, immune, and melanogenesis expression patterns. Oncogene 2021; 40:1792-1805. [PMID: 33564068 PMCID: PMC7946641 DOI: 10.1038/s41388-021-01665-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 11/08/2020] [Accepted: 01/18/2021] [Indexed: 01/30/2023]
Abstract
Cutaneous melanoma tumors are heterogeneous and show diverse responses to treatment. Identification of robust molecular biomarkers for classifying melanoma tumors into clinically distinct and homogenous subtypes is crucial for improving the diagnosis and treatment of the disease. In this study, we present a classification of melanoma tumors into four subtypes with different survival profiles based on three distinct gene expression signatures: keratin, immune, and melanogenesis. The melanogenesis expression pattern includes several genes that are characteristic of the melanosome organelle and correlates with worse survival, suggesting the involvement of melanosomes in melanoma aggression. We experimentally validated the secretion of melanosomes into surrounding tissues by melanoma tumors, which potentially affects the lethality of metastasis. We propose a simple molecular decision tree classifier for predicting a tumor's subtype based on representative genes from the three identified signatures. Key predictor genes were experimentally validated on melanoma samples taken from patients with varying survival outcomes. Our three-pattern approach for classifying melanoma tumors can contribute to advancing the understanding of melanoma variability and promote accurate diagnosis, prognostication, and treatment.
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Affiliation(s)
- Dvir Netanely
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Stav Leibou
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Roma Parikh
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Neta Stern
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Hananya Vaknine
- Department of Oncology, Edith Wolfson Medical Center, Holon, Israel
| | - Ronen Brenner
- Department of Oncology, Edith Wolfson Medical Center, Holon, Israel
| | - Sarah Amar
- Department of Oncology, Edith Wolfson Medical Center, Holon, Israel
| | - Rivi Haiat Factor
- Department of Plastic and Reconstructive Surgery, Edith Wolfson Medical Center, Holon, Israel
| | - Tomer Perluk
- Department of Plastic and Reconstructive Surgery, Edith Wolfson Medical Center, Holon, Israel
| | - Jacob Frand
- Department of Plastic and Reconstructive Surgery, Edith Wolfson Medical Center, Holon, Israel
| | - Eran Nizri
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Surgery A, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Dov Hershkovitz
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Institute of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | | | - Carmit Levy
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ron Shamir
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
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47
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Garg M, Couturier DL, Nsengimana J, Fonseca NA, Wongchenko M, Yan Y, Lauss M, Jönsson GB, Newton-Bishop J, Parkinson C, Middleton MR, Bishop DT, McDonald S, Stefanos N, Tadross J, Vergara IA, Lo S, Newell F, Wilmott JS, Thompson JF, Long GV, Scolyer RA, Corrie P, Adams DJ, Brazma A, Rabbie R. Tumour gene expression signature in primary melanoma predicts long-term outcomes. Nat Commun 2021; 12:1137. [PMID: 33602918 PMCID: PMC7893180 DOI: 10.1038/s41467-021-21207-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 01/15/2021] [Indexed: 02/08/2023] Open
Abstract
Adjuvant systemic therapies are now routinely used following resection of stage III melanoma, however accurate prognostic information is needed to better stratify patients. We use differential expression analyses of primary tumours from 204 RNA-sequenced melanomas within a large adjuvant trial, identifying a 121 metastasis-associated gene signature. This signature strongly associated with progression-free (HR = 1.63, p = 5.24 × 10-5) and overall survival (HR = 1.61, p = 1.67 × 10-4), was validated in 175 regional lymph nodes metastasis as well as two externally ascertained datasets. The machine learning classification models trained using the signature genes performed significantly better in predicting metastases than models trained with clinical covariates (pAUROC = 7.03 × 10-4), or published prognostic signatures (pAUROC < 0.05). The signature score negatively correlated with measures of immune cell infiltration (ρ = -0.75, p < 2.2 × 10-16), with a higher score representing reduced lymphocyte infiltration and a higher 5-year risk of death in stage II melanoma. Our expression signature identifies melanoma patients at higher risk of metastases and warrants further evaluation in adjuvant clinical trials.
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Affiliation(s)
- Manik Garg
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, UK
| | - Dominique-Laurent Couturier
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, UK
| | - Jérémie Nsengimana
- University of Leeds School of Medicine, Leeds, United Kingdom
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Nuno A Fonseca
- CIBIO/InBIO-Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Rua Padre Armando Quintas, 4485-601, Vairão, Portugal
| | - Matthew Wongchenko
- Oncology Biomarker Development, Genentech Inc., 1 DNA Way, South San Francisco, CA, 94080, USA
| | - Yibing Yan
- Oncology Biomarker Development, Genentech Inc., 1 DNA Way, South San Francisco, CA, 94080, USA
| | - Martin Lauss
- Lund University Cancer Center, Lund University, Lund, Sweden
| | - Göran B Jönsson
- Lund University Cancer Center, Lund University, Lund, Sweden
| | | | - Christine Parkinson
- Cambridge Cancer Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Mark R Middleton
- Oxford NIHR Biomedical Research Centre and Department of Oncology, University of Oxford, Oxford, UK
| | | | - Sarah McDonald
- Department of Pathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Nikki Stefanos
- Department of Pathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - John Tadross
- Department of Pathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Ismael A Vergara
- Melanoma Institute Australia, The University of Sydney, North Sydney, NSW, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Serigne Lo
- Melanoma Institute Australia, The University of Sydney, North Sydney, NSW, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Felicity Newell
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - James S Wilmott
- Melanoma Institute Australia, The University of Sydney, North Sydney, NSW, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - John F Thompson
- Melanoma Institute Australia, The University of Sydney, North Sydney, NSW, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- Discipline of Surgery, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Georgina V Long
- Melanoma Institute Australia, The University of Sydney, North Sydney, NSW, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- Royal North Shore and Mater Hospitals, Sydney, Australia
| | - Richard A Scolyer
- Melanoma Institute Australia, The University of Sydney, North Sydney, NSW, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and New South Wales Health Pathology, Sydney, NSW, Australia
| | - Pippa Corrie
- Cambridge Cancer Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - David J Adams
- Experimental Cancer Genetics, The Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, UK
| | - Roy Rabbie
- Cambridge Cancer Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
- Experimental Cancer Genetics, The Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK.
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48
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McGray AJR, Bernard D, Hallett R, Kelly R, Jha M, Gregory C, Bassett JD, Hassell JA, Pare G, Wan Y, Bramson JL. Combined vaccination and immunostimulatory antibodies provides durable cure of murine melanoma and induces transcriptional changes associated with positive outcome in human melanoma patients. Oncoimmunology 2021; 1:419-431. [PMID: 22754760 PMCID: PMC3382903 DOI: 10.4161/onci.19534] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
We have developed a recombinant adenovirus vaccine encoding dopachrome tautomerase (rHuAd5-hDCT) that produces robust DCT-specific immunity, but only provides modest suppression of murine melanoma. In the current study, an agonist antibody against 4-1BB was shown to enhance rHuAd5-hDCT efficacy and evoke tumor regression, but most tumors ultimately relapsed. The vaccine triggered upregulation of the immune inhibitory PD-1 signaling pathway and PD-1 blockade dramatically enhanced the rHuAd5-hDCT + anti-4-1BB strategy, resulting in complete regression of growing tumors in > 70% of recipients. The impact of the combined anti-4-1BB/anti-PD-1 treatment did not manifest as a dramatic enhancement in either the magnitude or functionality of DCT-specific tumor infiltrating lymphocytes relative to either treatment alone. Rather, a synergistic enhancement in intratumoral cytokine expression was observed, suggesting that the benefit of the combined therapy was a local event within the tumor. Global transcriptional analysis revealed immunological changes within the tumor following the curative vaccination, which extended beyond the T cell compartment. We identified an immune signature of 85 genes associated with clearance of murine melanoma that correlated with improved survival outcome in two independent cohorts of human melanoma patients. Our data reinforce the concept that successful vaccination must overcome local hurdles in the tumor microenvironment that are not manifest within the periphery. Further, tumor rejection following vaccination involves more than simply T cells. Finally, the association of our immune signature with positive survival outcome in human melanoma patients suggests that similar vaccination strategies may be promising for melanoma treatment.
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Affiliation(s)
- A J Robert McGray
- Department of Pathology and Molecular Medicine; McMaster University; Hamilton, ON Canada
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49
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Dilshat R, Fock V, Kenny C, Gerritsen I, Lasseur RMJ, Travnickova J, Eichhoff OM, Cerny P, Möller K, Sigurbjörnsdóttir S, Kirty K, Einarsdottir BÓ, Cheng PF, Levesque M, Cornell RA, Patton EE, Larue L, de Tayrac M, Magnúsdóttir E, Ögmundsdóttir MH, Steingrimsson E. MITF reprograms the extracellular matrix and focal adhesion in melanoma. eLife 2021; 10:63093. [PMID: 33438577 PMCID: PMC7857731 DOI: 10.7554/elife.63093] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 01/11/2021] [Indexed: 12/20/2022] Open
Abstract
The microphthalmia-associated transcription factor (MITF) is a critical regulator of melanocyte development and differentiation. It also plays an important role in melanoma where it has been described as a molecular rheostat that, depending on activity levels, allows reversible switching between different cellular states. Here, we show that MITF directly represses the expression of genes associated with the extracellular matrix (ECM) and focal adhesion pathways in human melanoma cells as well as of regulators of epithelial-to-mesenchymal transition (EMT) such as CDH2, thus affecting cell morphology and cell-matrix interactions. Importantly, we show that these effects of MITF are reversible, as expected from the rheostat model. The number of focal adhesion points increased upon MITF knockdown, a feature observed in drug-resistant melanomas. Cells lacking MITF are similar to the cells of minimal residual disease observed in both human and zebrafish melanomas. Our results suggest that MITF plays a critical role as a repressor of gene expression and is actively involved in shaping the microenvironment of melanoma cells in a cell-autonomous manner.
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Affiliation(s)
- Ramile Dilshat
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Valerie Fock
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Colin Kenny
- Department of Anatomy and Cell biology, Carver College of Medicine, University of Iowa, Iowa City, United States
| | - Ilse Gerritsen
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Romain Maurice Jacques Lasseur
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Jana Travnickova
- MRC Institute of Genetics and Molecular Medicine, MRC Human Genetics Unit, University of Edinburgh, Edinburgh, United Kingdom
| | - Ossia M Eichhoff
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
| | - Philipp Cerny
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Katrin Möller
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Sara Sigurbjörnsdóttir
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Kritika Kirty
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Berglind Ósk Einarsdottir
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Phil F Cheng
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
| | - Mitchell Levesque
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
| | - Robert A Cornell
- Department of Anatomy and Cell biology, Carver College of Medicine, University of Iowa, Iowa City, United States
| | - E Elizabeth Patton
- MRC Institute of Genetics and Molecular Medicine, MRC Human Genetics Unit, University of Edinburgh, Edinburgh, United Kingdom
| | - Lionel Larue
- Institut Curie, CNRS UMR3347, INSERM U1021, Centre Universitaire, Orsay, France
| | - Marie de Tayrac
- Service de Génétique Moléculaire et Génomique, CHU, Rennes, France.,Univ Rennes1, CNRS, IGDR (Institut de Génétique et Développement de Rennes), Rennes, France
| | - Erna Magnúsdóttir
- Department of Anatomy, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Margrét Helga Ögmundsdóttir
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Eirikur Steingrimsson
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
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50
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Tsubaki M, Genno S, Takeda T, Matsuda T, Kimura N, Yamashita Y, Morii Y, Shimomura K, Nishida S. Rhosin Suppressed Tumor Cell Metastasis through Inhibition of Rho/YAP Pathway and Expression of RHAMM and CXCR4 in Melanoma and Breast Cancer Cells. Biomedicines 2021; 9:biomedicines9010035. [PMID: 33406809 PMCID: PMC7824767 DOI: 10.3390/biomedicines9010035] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 12/29/2020] [Indexed: 11/24/2022] Open
Abstract
The high mortality rate of cancer is strongly correlated with the development of distant metastases at secondary sites. Although Rho GTPases, such as RhoA, RhoB, RhoC, and RhoE, promote tumor metastasis, the main roles of Rho GTPases remain unidentified. It is also unclear whether rhosin, a Rho inhibitor, acts by suppressing metastasis by a downstream inhibition of Rho. In this study, we investigated this mechanism of metastasis in highly metastatic melanoma and breast cancer cells, and the mechanism of inhibition of metastasis by rhosin. We found that rhosin suppressed the RhoA and RhoC activation, the nuclear localization of YAP, but did not affect ERK1/2, Akt, or NF-κB activation in the highly metastatic cell lines B16BL6 and 4T1. High expression of YAP was associated with poor overall and recurrence-free survival in patients with breast cancer or melanoma. Treatment with rhosin inhibited lung metastasis in vivo. Moreover, rhosin inhibited tumor cell adhesion to the extracellular matrix via suppression of RHAMM expression, and inhibited SDF-1-induced cell migration and invasion by decreasing CXCR4 expression in B16BL6 and 4T1 cells. These results suggest that the inhibition of RhoA/C-YAP pathway by rhosin could be an extremely useful therapeutic approach in patients with melanoma and breast cancer.
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Affiliation(s)
- Masanobu Tsubaki
- Division of Pharmacotherapy, Faculty of Pharmacy, Kindai University, Kowakae, Higashi-Osaka 577-8502, Japan; (M.T.); (S.G.); (T.T.); (T.M.); (N.K.); (Y.Y.); (Y.M.)
| | - Shuuji Genno
- Division of Pharmacotherapy, Faculty of Pharmacy, Kindai University, Kowakae, Higashi-Osaka 577-8502, Japan; (M.T.); (S.G.); (T.T.); (T.M.); (N.K.); (Y.Y.); (Y.M.)
| | - Tomoya Takeda
- Division of Pharmacotherapy, Faculty of Pharmacy, Kindai University, Kowakae, Higashi-Osaka 577-8502, Japan; (M.T.); (S.G.); (T.T.); (T.M.); (N.K.); (Y.Y.); (Y.M.)
| | - Takuya Matsuda
- Division of Pharmacotherapy, Faculty of Pharmacy, Kindai University, Kowakae, Higashi-Osaka 577-8502, Japan; (M.T.); (S.G.); (T.T.); (T.M.); (N.K.); (Y.Y.); (Y.M.)
| | - Naoto Kimura
- Division of Pharmacotherapy, Faculty of Pharmacy, Kindai University, Kowakae, Higashi-Osaka 577-8502, Japan; (M.T.); (S.G.); (T.T.); (T.M.); (N.K.); (Y.Y.); (Y.M.)
| | - Yuuma Yamashita
- Division of Pharmacotherapy, Faculty of Pharmacy, Kindai University, Kowakae, Higashi-Osaka 577-8502, Japan; (M.T.); (S.G.); (T.T.); (T.M.); (N.K.); (Y.Y.); (Y.M.)
| | - Yuusuke Morii
- Division of Pharmacotherapy, Faculty of Pharmacy, Kindai University, Kowakae, Higashi-Osaka 577-8502, Japan; (M.T.); (S.G.); (T.T.); (T.M.); (N.K.); (Y.Y.); (Y.M.)
- Department of Phamacy, Municipal Ikeda Hospital, Ikeda, Osaka 563-0025, Japan;
| | - Kazunori Shimomura
- Department of Phamacy, Municipal Ikeda Hospital, Ikeda, Osaka 563-0025, Japan;
| | - Shozo Nishida
- Division of Pharmacotherapy, Faculty of Pharmacy, Kindai University, Kowakae, Higashi-Osaka 577-8502, Japan; (M.T.); (S.G.); (T.T.); (T.M.); (N.K.); (Y.Y.); (Y.M.)
- Correspondence: ; Tel.: +81-6-6721-2332
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