An Optimized NGS Workflow Defines Genetically Based Prognostic Categories for Patients with Uveal Melanoma
Background: Despite advances in uveal melanoma (UM) diagnosis and treatment, about 50% of patients develop distant metastases, thereby displaying poor overall survival. Molecular profiling has identified several genetic alterations that can stratify patients with UM into different risk categories. H...
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2025-01-01
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| Online Access: | https://www.mdpi.com/2218-273X/15/1/146 |
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| author | Michele Massimino Elena Tirrò Stefania Stella Cristina Tomarchio Sebastiano Di Bella Silvia Rita Vitale Chiara Conti Marialuisa Puglisi Rosa Maria Di Crescenzo Silvia Varricchio Francesco Merolla Giuseppe Broggi Federica Martorana Alice Turdo Miriam Gaggianesi Livia Manzella Andrea Russo Giorgio Stassi Rosario Caltabiano Stefania Staibano Paolo Vigneri |
| author_facet | Michele Massimino Elena Tirrò Stefania Stella Cristina Tomarchio Sebastiano Di Bella Silvia Rita Vitale Chiara Conti Marialuisa Puglisi Rosa Maria Di Crescenzo Silvia Varricchio Francesco Merolla Giuseppe Broggi Federica Martorana Alice Turdo Miriam Gaggianesi Livia Manzella Andrea Russo Giorgio Stassi Rosario Caltabiano Stefania Staibano Paolo Vigneri |
| author_sort | Michele Massimino |
| collection | DOAJ |
| description | Background: Despite advances in uveal melanoma (UM) diagnosis and treatment, about 50% of patients develop distant metastases, thereby displaying poor overall survival. Molecular profiling has identified several genetic alterations that can stratify patients with UM into different risk categories. However, these genetic alterations are currently dispersed over multiple studies and several methodologies, emphasizing the need for a defined workflow that will allow standardized and reproducible molecular analyses. Methods: Following the findings published by “The Cancer Genome Atlas–UM” (TCGA-UM) study, we developed an NGS-based gene panel (called the UMpanel) that classifies mutation sets in four categories: initiating alterations (<i>CYSLTR2</i>, <i>GNA11</i>, <i>GNAQ</i> and <i>PLCB4</i>), prognostic alterations (<i>BAP1</i>, <i>EIF1AX</i>, <i>SF3B1</i> and <i>SRSF2</i>), emergent biomarkers (<i>CDKN2A</i>, <i>CENPE</i>, <i>FOXO1</i>, <i>HIF1A</i>, <i>RPL5</i> and <i>TP53</i>) and chromosomal abnormalities (imbalances in chromosomes 1, 3 and 8). Results: Employing commercial gene panels, reference mutated DNAs and Sanger sequencing, we performed a comparative analysis and found that our methodological approach successfully predicted survival with great specificity and sensitivity compared to the TCGA-UM cohort that was used as a validation group. Conclusions: Our results demonstrate that a reproducible NGS-based workflow translates into a reliable tool for the clinical stratification of patients with UM. |
| format | Article |
| id | doaj-art-fa8c1d1111ca4542b68f6f541905165b |
| institution | Kabale University |
| issn | 2218-273X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj-art-fa8c1d1111ca4542b68f6f541905165b2025-01-24T13:25:22ZengMDPI AGBiomolecules2218-273X2025-01-0115114610.3390/biom15010146An Optimized NGS Workflow Defines Genetically Based Prognostic Categories for Patients with Uveal MelanomaMichele Massimino0Elena Tirrò1Stefania Stella2Cristina Tomarchio3Sebastiano Di Bella4Silvia Rita Vitale5Chiara Conti6Marialuisa Puglisi7Rosa Maria Di Crescenzo8Silvia Varricchio9Francesco Merolla10Giuseppe Broggi11Federica Martorana12Alice Turdo13Miriam Gaggianesi14Livia Manzella15Andrea Russo16Giorgio Stassi17Rosario Caltabiano18Stefania Staibano19Paolo Vigneri20Department of General Surgery and Medical-Surgical Specialties, University of Catania, 95123 Catania, ItalyCenter of Experimental Oncology and Hematology, A.O.U. Policlinico “G. Rodolico-S. Marco”, 95123 Catania, ItalyCenter of Experimental Oncology and Hematology, A.O.U. Policlinico “G. Rodolico-S. Marco”, 95123 Catania, ItalyCenter of Experimental Oncology and Hematology, A.O.U. Policlinico “G. Rodolico-S. Marco”, 95123 Catania, ItalyDepartment of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, 90127 Palermo, ItalyCenter of Experimental Oncology and Hematology, A.O.U. Policlinico “G. Rodolico-S. Marco”, 95123 Catania, ItalyDepartment of Human Pathology “G. Barresi”, University of Messina, 98125 Messina, ItalyDepartment of Human Pathology “G. Barresi”, University of Messina, 98125 Messina, ItalyPathology Unit, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, ItalyPathology Unit, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, ItalyDepartment of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medical and Surgical Sciences and Advanced Technologies “G.F. Ingrassia”, Anatomic Pathology, University of Catania, 95123 Catania, ItalyDepartment of Clinical and Experimental Medicine, University of Catania, 95123 Catania, ItalyDepartment of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, 90127 Palermo, ItalyDepartment of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, 90127 Palermo, ItalyCenter of Experimental Oncology and Hematology, A.O.U. Policlinico “G. Rodolico-S. Marco”, 95123 Catania, ItalyDepartment of General Surgery and Medical-Surgical Specialties, University of Catania, 95123 Catania, ItalyDepartment of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, 90127 Palermo, ItalyDepartment of Medical and Surgical Sciences and Advanced Technologies “G.F. Ingrassia”, Anatomic Pathology, University of Catania, 95123 Catania, ItalyPathology Unit, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, ItalyDepartment of Clinical and Experimental Medicine, University of Catania, 95123 Catania, ItalyBackground: Despite advances in uveal melanoma (UM) diagnosis and treatment, about 50% of patients develop distant metastases, thereby displaying poor overall survival. Molecular profiling has identified several genetic alterations that can stratify patients with UM into different risk categories. However, these genetic alterations are currently dispersed over multiple studies and several methodologies, emphasizing the need for a defined workflow that will allow standardized and reproducible molecular analyses. Methods: Following the findings published by “The Cancer Genome Atlas–UM” (TCGA-UM) study, we developed an NGS-based gene panel (called the UMpanel) that classifies mutation sets in four categories: initiating alterations (<i>CYSLTR2</i>, <i>GNA11</i>, <i>GNAQ</i> and <i>PLCB4</i>), prognostic alterations (<i>BAP1</i>, <i>EIF1AX</i>, <i>SF3B1</i> and <i>SRSF2</i>), emergent biomarkers (<i>CDKN2A</i>, <i>CENPE</i>, <i>FOXO1</i>, <i>HIF1A</i>, <i>RPL5</i> and <i>TP53</i>) and chromosomal abnormalities (imbalances in chromosomes 1, 3 and 8). Results: Employing commercial gene panels, reference mutated DNAs and Sanger sequencing, we performed a comparative analysis and found that our methodological approach successfully predicted survival with great specificity and sensitivity compared to the TCGA-UM cohort that was used as a validation group. Conclusions: Our results demonstrate that a reproducible NGS-based workflow translates into a reliable tool for the clinical stratification of patients with UM.https://www.mdpi.com/2218-273X/15/1/146molecular profilingTCGAuveal melanomaNGS |
| spellingShingle | Michele Massimino Elena Tirrò Stefania Stella Cristina Tomarchio Sebastiano Di Bella Silvia Rita Vitale Chiara Conti Marialuisa Puglisi Rosa Maria Di Crescenzo Silvia Varricchio Francesco Merolla Giuseppe Broggi Federica Martorana Alice Turdo Miriam Gaggianesi Livia Manzella Andrea Russo Giorgio Stassi Rosario Caltabiano Stefania Staibano Paolo Vigneri An Optimized NGS Workflow Defines Genetically Based Prognostic Categories for Patients with Uveal Melanoma Biomolecules molecular profiling TCGA uveal melanoma NGS |
| title | An Optimized NGS Workflow Defines Genetically Based Prognostic Categories for Patients with Uveal Melanoma |
| title_full | An Optimized NGS Workflow Defines Genetically Based Prognostic Categories for Patients with Uveal Melanoma |
| title_fullStr | An Optimized NGS Workflow Defines Genetically Based Prognostic Categories for Patients with Uveal Melanoma |
| title_full_unstemmed | An Optimized NGS Workflow Defines Genetically Based Prognostic Categories for Patients with Uveal Melanoma |
| title_short | An Optimized NGS Workflow Defines Genetically Based Prognostic Categories for Patients with Uveal Melanoma |
| title_sort | optimized ngs workflow defines genetically based prognostic categories for patients with uveal melanoma |
| topic | molecular profiling TCGA uveal melanoma NGS |
| url | https://www.mdpi.com/2218-273X/15/1/146 |
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