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Sökning: WFRF:(Boye A)

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  • Stacchiotti, S., et al. (författare)
  • Epithelioid hemangioendothelioma, an ultra-rare cancer : a consensus paper from the community of experts
  • 2021
  • Ingår i: ESMO Open. - : Elsevier BV. - 2059-7029. ; 6:3
  • Forskningsöversikt (refereegranskat)abstract
    • Epithelioid hemangioendothelioma (EHE) is an ultra-rare, translocated, vascular sarcoma. EHE clinical behavior is variable, ranging from that of a low-grade malignancy to that of a high-grade sarcoma and it is marked by a high propensity for systemic involvement. No active systemic agents are currently approved specifically for EHE, which is typically refractory to the antitumor drugs used in sarcomas. The degree of uncertainty in selecting the most appropriate therapy for EHE patients and the lack of guidelines on the clinical management of the disease make the adoption of new treatments inconsistent across the world, resulting in suboptimal outcomes for many EHE patients. To address the shortcoming, a global consensus meeting was organized in December 2020 under the umbrella of the European Society for Medical Oncology (ESMO) involving >80 experts from several disciplines from Europe, North America and Asia, together with a patient representative from the EHE Group, a global, disease-specific patient advocacy group, and Sarcoma Patient EuroNet (SPAEN). The meeting was aimed at defining, by consensus, evidence-based best practices for the optimal approach to primary and metastatic EHE. The consensus achieved during that meeting is the subject of the present publication.
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  • Elvsashagen, T, et al. (författare)
  • The genetic architecture of human brainstem structures and their involvement in common brain disorders
  • 2020
  • Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1, s. 4016-
  • Tidskriftsartikel (refereegranskat)abstract
    • Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson’s disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.
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  • Hindi, N., et al. (författare)
  • Efficacy of immune checkpoint inhibitors in alveolar soft-part sarcoma : results from a retrospective worldwide registry
  • 2023
  • Ingår i: ESMO Open. - 2059-7029. ; 8:6
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Conventional cytotoxic drugs are not effective in alveolar soft-part sarcoma (ASPS). Immune checkpoint (programmed cell death protein 1/programmed death-ligand 1) inhibitors (ICIs) are promising drugs in ASPS. A worldwide registry explored the efficacy of ICI in ASPS. Materials and methods: Data from adult patients diagnosed with ASPS and treated with ICI for advanced disease in expert sarcoma centers from Europe, Australia and North America were retrospectively collected, including demographics and data related to treatments and outcome. Results: Seventy-six ASPS patients, with a median age at diagnosis of 25 years (range 3-61 years), were registered. All patients received ICI for metastatic disease. Immunotherapy regimens consisted of monotherapy in 38 patients (50%) and combination in 38 (50%) (23 with a tyrosine kinase inhibitor). Among the 68 assessable patients, there were 3 complete responses and 34 partial responses, translating into an overall response rate of 54.4%. After a median follow-up of 36 months [95% confidence interval (CI) 32-40 months] since the start of immunotherapy, 45 (59%) patients have progressed on ICI, with a median progression-free survival (PFS) of 16.3 months (95% CI 8-25 months). Receiving ICI in first line (P = 0.042) and achieving an objective response (P = 0.043) correlated with a better PFS. Median estimated overall survival (OS) from ICI initiation has not been reached. The 12-month and 24-month OS rates were 94% and 81%, respectively. Conclusions: This registry constitutes the largest available series of ASPS treated with ICI. Our results suggest that the ICI treatment provides long-lasting disease control and prolonged OS in patients with advanced ASPS, an ultra-rare entity with limited active therapeutic options.
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  • Dalmas, T., et al. (författare)
  • Introduction
  • 2014
  • Ingår i: Proceedings 2014 Workshop on Dialogue in Motion, DM 2014. - : Association for Computational Linguistics (ACL).
  • Konferensbidrag (refereegranskat)
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8.
  • Høland, Maren, et al. (författare)
  • Transcriptomic subtyping of malignant peripheral nerve sheath tumours highlights immune signatures, genomic profiles, patient survival and therapeutic targets
  • 2023
  • Ingår i: EBioMedicine. - 2352-3964. ; 97
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Malignant peripheral nerve sheath tumour (MPNST) is an aggressive orphan disease commonly affecting adolescents or young adults. Current knowledge of molecular tumour biology has been insufficient for development of rational treatment strategies. We aimed to discover molecular subtypes of potential clinical relevance. Methods: Fresh frozen samples of MPNSTs (n = 94) and benign neurofibromas (n = 28) from 115 patients in a European multicentre study were analysed by DNA copy number and/or transcriptomic profiling. Unsupervised transcriptomic subtyping was performed and the subtypes characterized for genomic aberrations, clinicopathological associations and patient survival. Findings: MPNSTs were classified into two transcriptomic subtypes defined primarily by immune signatures and proliferative processes. “Immune active” MPNSTs (44%) had sustained immune signals relative to neurofibromas, were more frequently low-grade (P = 0.01) and had favourable prognostic associations in a multivariable model of disease-specific survival with clinicopathological factors (hazard ratio 0.25, P = 0.003). “Immune deficient” MPNSTs were more aggressive and characterized by proliferative signatures, high genomic complexity, aberrant TP53 and PRC2 loss, as well as high relative expression of several potential actionable targets (EGFR, ERBB2, EZH2, KIF11, PLK1, RRM2). Integrated gene-wise analyses suggested a DNA copy number-basis for proliferative transcriptomic signatures in particular, and the tumour copy number burden further stratified the transcriptomic subtypes according to patient prognosis (P < 0.01). Interpretation: Approximately half of MPNSTs belong to an “immune deficient” transcriptomic subtype associated with an aggressive disease course, PRC2 loss and expression of several potential therapeutic targets, providing a rationale for molecularly-guided intervention trials. Funding: Research grants from non-profit organizations, as stated in the Acknowledgements.
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9.
  • McWhinney, Sean R, et al. (författare)
  • Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.
  • 2024
  • Ingår i: Human brain mapping. - 1097-0193. ; 45:8
  • Tidskriftsartikel (refereegranskat)abstract
    • Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.
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