Dynamic Survival Risk Prognostic Model and Genomic Landscape for Atypical Teratoid/Rhabdoid Tumors: A Population-Based, Real-World Study
Background: An atypical teratoid/rhabdoid tumor (AT/RT) is an uncommon and aggressive pediatric central nervous system neoplasm. However, a universal clinical consensus or reliable prognostic evaluation system for this malignancy is lacking. Our study aimed to develop a risk model based on comprehen...
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MDPI AG
2024-03-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/16/5/1059 |
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| author | Sihao Chen Yi He Jiao Liu Ruixin Wu Menglei Wang Aishun Jin |
| author_facet | Sihao Chen Yi He Jiao Liu Ruixin Wu Menglei Wang Aishun Jin |
| author_sort | Sihao Chen |
| collection | DOAJ |
| description | Background: An atypical teratoid/rhabdoid tumor (AT/RT) is an uncommon and aggressive pediatric central nervous system neoplasm. However, a universal clinical consensus or reliable prognostic evaluation system for this malignancy is lacking. Our study aimed to develop a risk model based on comprehensive clinical data to assist in clinical decision-making. Methods: We conducted a retrospective study by examining data from the Surveillance, Epidemiology, and End Results (SEER) repository, spanning 2000 to 2019. The external validation cohort was sourced from the Children’s Hospital Affiliated to Chongqing Medical University, China. To discern independent factors affecting overall survival (OS) and cancer-specific survival (CSS), we applied Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression analyses. Based on these factors, we structured nomogram survival predictions and initiated a dynamic online risk-evaluation system. To contrast survival outcomes among diverse treatments, we used propensity score matching (PSM) methodology. Molecular data with the most common mutations in AT/RT were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. Results: The annual incidence of AT/RT showed an increasing trend (APC, 2.86%; 95% CI:0.75–5.01). Our prognostic study included 316 SEER database participants and 27 external validation patients. The entire group had a median OS of 18 months (range 11.5 to 24 months) and median CSS of 21 months (range 11.7 to 29.2). Evaluations involving C-statistics, DCA, and ROC analysis underscored the distinctive capabilities of our prediction model. An analysis via PSM highlighted that individuals undergoing triple therapy (integrating surgery, radiotherapy, and chemotherapy) had discernibly enhanced OS and CSS. The most common mutations of AT/RT identified in the COSMIC database were SMARCB1, BRAF, SMARCA4, NF2, and NRAS. Conclusions: In this study, we devised a predictive model that effectively gauges the prognosis of AT/RT and briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges. |
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| language | English |
| last_indexed | 2024-04-25T00:33:32Z |
| publishDate | 2024-03-01 |
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| series | Cancers |
| spelling | doaj.art-9139f3f0edf5428e82f3a5cec85aadfe2024-03-12T16:41:22ZengMDPI AGCancers2072-66942024-03-01165105910.3390/cancers16051059Dynamic Survival Risk Prognostic Model and Genomic Landscape for Atypical Teratoid/Rhabdoid Tumors: A Population-Based, Real-World StudySihao Chen0Yi He1Jiao Liu2Ruixin Wu3Menglei Wang4Aishun Jin5Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing 400010, ChinaDepartment of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing 400010, ChinaChildren’s Hospital of Chongqing Medical University, Chongqing 400010, ChinaDepartment of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing 400010, ChinaDepartment of Pediatrics, Women and Children’s Hospital of Chongqing Medical University, Chongqing 400010, ChinaDepartment of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing 400010, ChinaBackground: An atypical teratoid/rhabdoid tumor (AT/RT) is an uncommon and aggressive pediatric central nervous system neoplasm. However, a universal clinical consensus or reliable prognostic evaluation system for this malignancy is lacking. Our study aimed to develop a risk model based on comprehensive clinical data to assist in clinical decision-making. Methods: We conducted a retrospective study by examining data from the Surveillance, Epidemiology, and End Results (SEER) repository, spanning 2000 to 2019. The external validation cohort was sourced from the Children’s Hospital Affiliated to Chongqing Medical University, China. To discern independent factors affecting overall survival (OS) and cancer-specific survival (CSS), we applied Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression analyses. Based on these factors, we structured nomogram survival predictions and initiated a dynamic online risk-evaluation system. To contrast survival outcomes among diverse treatments, we used propensity score matching (PSM) methodology. Molecular data with the most common mutations in AT/RT were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. Results: The annual incidence of AT/RT showed an increasing trend (APC, 2.86%; 95% CI:0.75–5.01). Our prognostic study included 316 SEER database participants and 27 external validation patients. The entire group had a median OS of 18 months (range 11.5 to 24 months) and median CSS of 21 months (range 11.7 to 29.2). Evaluations involving C-statistics, DCA, and ROC analysis underscored the distinctive capabilities of our prediction model. An analysis via PSM highlighted that individuals undergoing triple therapy (integrating surgery, radiotherapy, and chemotherapy) had discernibly enhanced OS and CSS. The most common mutations of AT/RT identified in the COSMIC database were SMARCB1, BRAF, SMARCA4, NF2, and NRAS. Conclusions: In this study, we devised a predictive model that effectively gauges the prognosis of AT/RT and briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges.https://www.mdpi.com/2072-6694/16/5/1059atypical teratoid/rhabdoid tumorLASSORandom ForestSEERCOSMICrisk prognostic model |
| spellingShingle | Sihao Chen Yi He Jiao Liu Ruixin Wu Menglei Wang Aishun Jin Dynamic Survival Risk Prognostic Model and Genomic Landscape for Atypical Teratoid/Rhabdoid Tumors: A Population-Based, Real-World Study Cancers atypical teratoid/rhabdoid tumor LASSO Random Forest SEER COSMIC risk prognostic model |
| title | Dynamic Survival Risk Prognostic Model and Genomic Landscape for Atypical Teratoid/Rhabdoid Tumors: A Population-Based, Real-World Study |
| title_full | Dynamic Survival Risk Prognostic Model and Genomic Landscape for Atypical Teratoid/Rhabdoid Tumors: A Population-Based, Real-World Study |
| title_fullStr | Dynamic Survival Risk Prognostic Model and Genomic Landscape for Atypical Teratoid/Rhabdoid Tumors: A Population-Based, Real-World Study |
| title_full_unstemmed | Dynamic Survival Risk Prognostic Model and Genomic Landscape for Atypical Teratoid/Rhabdoid Tumors: A Population-Based, Real-World Study |
| title_short | Dynamic Survival Risk Prognostic Model and Genomic Landscape for Atypical Teratoid/Rhabdoid Tumors: A Population-Based, Real-World Study |
| title_sort | dynamic survival risk prognostic model and genomic landscape for atypical teratoid rhabdoid tumors a population based real world study |
| topic | atypical teratoid/rhabdoid tumor LASSO Random Forest SEER COSMIC risk prognostic model |
| url | https://www.mdpi.com/2072-6694/16/5/1059 |
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