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- Duran-Ferrer, Marti, et al.
(författare)
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The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome
- 2020
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Ingår i: NATURE CANCER. - : Springer Nature. - 2662-1347. ; 1:11, s. 1066-1081
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Tidskriftsartikel (refereegranskat)abstract
- We report a systematic analysis of the DNA methylation variability in 1,595 samples of normal cell subpopulations and 14 tumor subtypes spanning the entire human B-cell lineage. Differential methylation among tumor entities relates to differences in cellular origin and to de novo epigenetic alterations, which allowed us to build an accurate machine learning-based diagnostic algorithm. We identify extensive individual-specific methylation variability in silenced chromatin associated with the proliferative history of normal and neoplastic B cells. Mitotic activity generally leaves both hyper- and hypomethylation imprints, but some B-cell neoplasms preferentially gain or lose DNA methylation. We construct a DNA-methylation-based mitotic clock, called epiCMIT, whose lapse magnitude represents a strong independent prognostic variable in B-cell tumors and is associated with particular driver genetic alterations. Our findings reveal DNA methylation as a holistic tracer of B-cell tumor developmental history, with implications in differential diagnosis and the prediction of clinical outcome. Martin-Subero and colleagues analyze DNA methylation patterns in B-cell tumors and their normal cells of origin, and develop epiCMIT, a methylation-based mitotic clock with prognostic relevance.
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- Hageman, Isabel C., et al.
(författare)
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A Quality Assessment of the ARM-Net Registry Design and Data Collection
- 2023
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Ingår i: Journal of Pediatric Surgery. - : Elsevier BV. - 0022-3468. ; 58:10, s. 1921-1928
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Tidskriftsartikel (refereegranskat)abstract
- Background: Registries are important in rare disease research. The Anorectal Malformation Network (ARM-Net) registry is a well-established European patient registry collecting demographic, clinical, and functional outcome data. We assessed the quality of this registry through review of the structure, data elements, collected data, and user experience. Material and methods: Design and data elements were assessed for completeness, consistency, usefulness, accuracy, validity, and comparability. An intra- and inter-user variability study was conducted through monitoring and re-registration of patients. User experience was assessed via a questionnaire on registration, design of registry, and satisfaction. Results: We evaluated 119 data elements, of which 107 were utilized and comprised 42 string and 65 numeric elements. A minority (37.0%) of the 2278 included records had complete data, though this improved to 83.5% when follow-up elements were excluded. Intra-observer variability demonstrated 11.7% incongruence, while inter-observer variability was 14.7%. Users were predominantly pediatric surgeons and typically registered patients within 11–30 min. Users did not experience any significant difficulties with data entry and were generally satisfied with the registry, but preferred more longitudinal data and patient-reported outcomes. Conclusions: The ARM-Net registry presents one of the largest ARM cohorts. Although its collected data are valuable, they are susceptible to error and user variability. Continuous evaluations are required to maintain relevant and high-quality data and to achieve long-term sustainability. With the recommendations resulting from this study, we call for rare disease patient registries to take example and aim to continuously improve their data quality to enhance the small, but impactful, field of rare disease research. Level of Evidence: V.
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- Solanes, A, et al.
(författare)
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Combining MRI and clinical data to detect high relapse risk after the first episode of psychosis
- 2022
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Ingår i: Schizophrenia (Heidelberg, Germany). - : Springer Science and Business Media LLC. - 2754-6993. ; 8:1, s. 100-
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Tidskriftsartikel (refereegranskat)abstract
- Detecting patients at high relapse risk after the first episode of psychosis (HRR-FEP) could help the clinician adjust the preventive treatment. To develop a tool to detect patients at HRR using their baseline clinical and structural MRI, we followed 227 patients with FEP for 18–24 months and applied MRIPredict. We previously optimized the MRI-based machine-learning parameters (combining unmodulated and modulated gray and white matter and using voxel-based ensemble) in two independent datasets. Patients estimated to be at HRR-FEP showed a substantially increased risk of relapse (hazard ratio = 4.58, P < 0.05). Accuracy was poorer when we only used clinical or MRI data. We thus show the potential of combining clinical and MRI data to detect which individuals are more likely to relapse, who may benefit from increased frequency of visits, and which are unlikely, who may be currently receiving unnecessary prophylactic treatments. We also provide an updated version of the MRIPredict software.
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