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- Andersson, Anna, et al.
(författare)
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The landscape of somatic mutations in infant MLL-rearranged acute lymphoblastic leukemias.
- 2015
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 47:4, s. 192-330
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Tidskriftsartikel (refereegranskat)abstract
- Infant acute lymphoblastic leukemia (ALL) with MLL rearrangements (MLL-R) represents a distinct leukemia with a poor prognosis. To define its mutational landscape, we performed whole-genome, exome, RNA and targeted DNA sequencing on 65 infants (47 MLL-R and 18 non-MLL-R cases) and 20 older children (MLL-R cases) with leukemia. Our data show that infant MLL-R ALL has one of the lowest frequencies of somatic mutations of any sequenced cancer, with the predominant leukemic clone carrying a mean of 1.3 non-silent mutations. Despite this paucity of mutations, we detected activating mutations in kinase-PI3K-RAS signaling pathway components in 47% of cases. Surprisingly, these mutations were often subclonal and were frequently lost at relapse. In contrast to infant cases, MLL-R leukemia in older children had more somatic mutations (mean of 6.5 mutations/case versus 1.3 mutations/case, P = 7.15 × 10(-5)) and had frequent mutations (45%) in epigenetic regulators, a category of genes that, with the exception of MLL, was rarely mutated in infant MLL-R ALL.
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| 2. |
- Parmar, Priya, et al.
(författare)
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The Stroke Riskometer (TM) App: Validation of a data collection tool and stroke risk predictor
- 2015
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Ingår i: International Journal of Stroke. - : SAGE Publications. - 1747-4949 .- 1747-4930. ; 10:2, s. 231-244
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundThe greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the mass' approach), the high risk' approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer(TM), has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods. Methods752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke Riskometer(TM)) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R-2 statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm. ResultsThe Stroke Riskometer(TM) performed well against the FSRS five-year AUROC for both males (FSRS=750% (95% CI 723%-776%), Stroke Riskometer(TM)=740(95% CI 713%-767%) and females [FSRS=703% (95% CI 679%-728%, Stroke Riskometer(TM)=715% (95% CI 690%-739%)], and better than QStroke [males - 597% (95% CI 573%-620%) and comparable to females=711% (95% CI 690%-731%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 051-056, D-statistic ranging from 001-012). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data (P<0006). ConclusionsThe Stroke Riskometer(TM) is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer(TM) will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors.
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