Background: Best practice for cardiac resynchronization therapy (CRT) device optimization is not established. This study compared Tissue Doppler Imaging (TDI) to study left ventricular (LV) synchrony and left ventricular outflow tract velocity‐time integral (LVOT VTI) to assess hemodynamic performance. Methods: LVOT VTI and LV synchrony were tested in 50 patients at three interventricular (VV) delays (LV preactivation at −30 ms, simultaneous biventricular pacing, and right ventricular preactivation at +30 ms), selecting the highest VTI and the greatest degree of superposition of the displacement curves, respectively, as the optimum VV delay. Results: In 39 patients (81%), both techniques agreed (Kappa = 0.65, p < 0.0001) on the optimum VV delay. LV preactivation (VV − 30) was the interval most frequently chosen. Conclusions: Both TDI and LVOT VTI are useful CRT programming methods for VV optimization. The best hemodynamic response correlates with the best synchrony. In most patients, the optimum VV interval is LV preactivation. (PACE 2011; 34:984–990)
Abstract Motivation: Several computational methods have been developed to identify cancer drivers genes—genes responsible for cancer development upon specific alterations. These alterations can cause the loss of function (LoF) of the gene product, for instance, in tumor suppressors, or increase or change its activity or function, if it is an oncogene. Distinguishing between these two classes is important to understand tumorigenesis in patients and has implications for therapy decision making. Here, we assess the capacity of multiple gene features related to the pattern of genomic alterations across tumors to distinguish between activating and LoF cancer genes, and we present an automated approach to aid the classification of novel cancer drivers according to their role. Result: OncodriveROLE is a machine learning-based approach that classifies driver genes according to their role, using several properties related to the pattern of alterations across tumors. The method shows an accuracy of 0.93 and Matthew's correlation coefficient of 0.84 classifying genes in the Cancer Gene Census. The OncodriveROLE classifier, its results when applied to two lists of predicted cancer drivers and TCGA-derived mutation and copy number features used by the classifier are available at http://bg.upf.edu/oncodrive-role. Availability and implementation: The R implementation of the OncodriveROLE classifier is available at http://bg.upf.edu/oncodrive-role. Contact: abel.gonzalez@upf.edu or nuria.lopez@upf.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Objective: After positive experience with use of endocardial cryoablation our objective was to evaluate feasibility of liquid nitrogen cryocatheter in epicardial approach as treatment of nonmitral atrial fibrillation.Methods: From September 2002 to December 2003 10 patients with paroxysmal atrial fibrillation underwent epicardial pulmonary vein isolation with liquid nitrogen cryocatheter.The procedure was concominant to 6 aortic valve replacements, 3 coronary artery bypass grafting and 1 coronary artery bypass grafting and aortic valve replacement.During the ablation pulmonary veins were occluded with cryocatheter in order to reduce convection phenomenon.Results: Postoperatively and at discharge stable sinus rhythm was restored in 5 (50%) patients.In follow-up (100% patients) from 3 to 18 months (7,9 + 5,7 months) 8 (80%) patients were in stable sinus rhythm, 1 had paroxysmal atrial fibrillation, 1 required pacemaker implantation. Conclusion:The results show high effectiveness of epicardial cryoablation in follow-up, though poor initial resuks.It could be recommended method in centres being in possesion only of cryocatheter in treatment of nonmitral paroxysmal atrial fibrillation.Further clinical evaluation is necessary.
Genome studies of diffuse large B-cell lymphoma (DLBCL) have revealed a large number of somatic mutations and structural alterations. However, the clinical significance of these alterations is still not well defined. In this study, we have integrated the analysis of targeted next-generation sequencing of 106 genes and genomic copy number alterations (CNA) in 150 DLBCL. The clinically significant findings were validated in an independent cohort of 111 patients. Germinal center B-cell and activated B-cell DLBCL had a differential profile of mutations, altered pathogenic pathways and CNA. Mutations in genes of the NOTCH pathway and tumor suppressor genes (TP53/CDKN2A), but not individual genes, conferred an unfavorable prognosis, confirmed in the independent validation cohort. A gene expression profiling analysis showed that tumors with NOTCH pathway mutations had a significant modulation of downstream target genes, emphasizing the relevance of this pathway in DLBCL. An in silico drug discovery analysis recognized 69 (46%) cases carrying at least one genomic alteration considered a potential target of drug response according to early clinical trials or preclinical assays in DLBCL or other lymphomas. In conclusion, this study identifies relevant pathways and mutated genes in DLBCL and recognizes potential targets for new intervention strategies.
Abstract The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
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